Musings on Markets

Oil, War and the Global Economy: The Market's Narrative in March 2026

2026-04-01T17:33:00.003-04:00

Markets play an expectations game, and in March 2026, we saw the process play out, with all of its upsides and downsides. The month started with a war in the Middle East, which quickly percolated into soaring oil prices and dropping stock prices, but the overwhelming factor was uncertainty about almost every dimension of the war - how long it would last, what permanent changes to oil prices would emerge as a consequence and how global governments and economies would respond to these changes. As we reach the end of the month, rather than getting answers, we face more questions, and not surprisingly, markets are volatile, not just on a day-to-day basis, but in intraday trading, driven as much by rumors and conjecture, as by facts. In keeping with my view that it is during periods of maximal uncertainty that you need perspective and to back to basics, I will focus my attention on market behavior in March, and what we can learn from that behavior, as a precursor for the months to come.

The Market Narrative in March

We live in an age of commentary, as self-proclaimed experts offer prognostications, half-baked or otherwise, about what is to come, and the Iran war, with its mix of politics, economics and religion baked in,, has drawn a large and extremely diverse set of expert forecasts. Given the strong priors (about Iran and Trump) that many of these experts bring to the game, it should not be surprising that their views about how the war will play out and the effect on markets is driven by those priors. It is up to markets to reconcile these contradictory perspectives, and come to consensus, and I will try to extract from market behavior what the market narrative is, leading into April 2026, with the recognition that it could be wrong and change overnight in good and bad ways. That said, over the last decade, I have learned that the market is far better at making sense of complexity and uncertainty than experts are, and it behooves us therefore to listen to what it is saying.

The Oil Price Shock

As with almost every event in the middle east, the effects of the Iran War played out first in oil prices, and oil has been the lead player in March, surging and volatile, but with disparate impacts even within that market. In the graph below, I look at spot prices on Brent Crude and West Texas Intermediate (WTI) during March:

Both Brent and WTI crude oil saw prices increase in March, but with the price of Brent rising 49.9% and WTI rising 48.6% during March, the difference between the two almost doubled during the second half of the month. That divergence reflects the two-fold effect of the war on oil supply, with the first being the shuttering of oil production in the Gulf States and the second being the effecting throttling of ship traffic through the Strait of Hormuz, a key passageway for Middle Eastern oil to Asia and Europe. While both factors push up oil prices, oil and gas production in the US, the largest oil producer in 2025 (producing 13.58 million barrels or 16% of the total), was less affected by the Hormuz closing and supply chain issues, explaining the increasing price divergence mid-month.

There was another tea leaf to read, and it came from watching oil futures prices. In the graph below, I compare the spot prices to Brent crude to June and December futures contracts prices:

While spot and futures prices have both risen in March, the latter have gone up less, indicating that, at least for the moment, the market sees the interruptions in oil supply as more temporary than permanent, though the market does see a lasting impact even in that optimistic scenario, with December futures up almost 25% over the pre-war level.

Inflation, Interest Rates and the Economy

The creation of OPEC and the oil price embargo in the 1970s and the subsequent inflation spiral in the 1970s is now part of market legend, and the interlude in 2022, when the Russian invasion of Ukraine, and the subsequent sanctioning of Russian oil, caused a spike in inflation rates, has made investors wary. While the effects on gasoline prices are in the news, it is one item in the inflation basket, and it is unclear still how much higher oil prices will affect inflation for the rest of the year and perhaps into next year. While we wait for the actual inflation numbers to come out, markets don't have that luxury and the early and perhaps best indicator of market expectations on inflation are showing up in interest rates. The graph below looks at 3-month and 10-year US treasuries over the course of March 2026:

The 3-month treasury bill rate has barely budged over the month, moving from 3.67% on February 27, 2026 to 3.70% on March 31, 2026, but the ten-year bond rate saw a much bigger increase from 3.97% on February 27, 2026, to 4.30% on March 31, 2026. The biggest increases in rates are in the intermediate maturities, with the 2-year and 5-year rates rising by 0.41% over the course of the month. If you view interest rates, as I do, as driven by expected inflation and expected real growth, the most plausible reading is that the market sees an increase in inflation that is persistent. If you are a Fed-watcher, though, your reading may be that the rise in oil prices has tied the hands of the Fed, lowering the likelihood that the Fed Funds rate will be cut in the coming months, but that would leave you with a puzzle to resolve. Since the Fed Funds rate, an overnight bank borrowing rate, has its biggest impact on the short end of the maturity spectrum, how do you explain the fact that short term rates have not changed much?

The increase in interest rates is not just specific to the US, with rises in rates across other currencies, as you can see in this graph of ten-year Euro, Yen and Yuan rates:

The Japanese Yen and Euro rates are up significantly over the month, but the Yuan rate has seen no change in March 2026. Staying with the market narrative, this indicates higher inflation across countries and currencies.

While there are many who are speculating on what higher inflation and oil prices will do to the economy, and investment banks and data services (See Moody's, Goldman Sachs) have been rushing to update their forecasts for the US economy, the market has not been in as much of a rush to make the judgment. The economy was showing signs of fatigue coming into March 2026, with anemic growth and employment numbers, and it is possible that the oil price shock will tip it over into a recession.

The Price of Risk

The heightened uncertainty generated by war and its consequences has played its way out not just in oil prices and treasury rates, but in the prices that investors charge for risk. In a month where the clash between greed and risk took front stage, with the balance shifting often on a minute-by-minute basis during the trading day, we also see increases in the price that investors charge for taking risk in both equity and bond markets. In the equity market, that price of risk is the equity risk premium, a topic that I talked about extensively in this post and paper, with the argument that a good measure of this risk premium will be forward-looking and dynamic. My implied equity risk premium estimates tried to capture the changes in equity risk premiums on a daily basis, and completing the assessments for the entire month, here is what the equity risk premiums looked like in March 2026:

The surprise here is not that the equity risk premium rose over the course of the month, expected given what was happening in the Middle East, but that it rose so modestly. In fact, over the course of March, the implied equity risk premium for the S&P 500 rose from 4.37% on February 27, 2026, to 4.77% at close of trading on March 31, 2026, an increase of 0.40% for the month.

In the bond market, the price of risk is the bond default spread, and in the graph below, I look at default spreads for seven bond ratings classes from AAA to C (& below):

Here again, the spreads increased over the month, but only modestly, even at the lowest ratings classes. Thus, the BBB default spread over the 10-year treasury rose only 0.08% during the month, from 1.07% on February 27, 2026, to 1.15%on March 31, 2026, and the high yield spread (for CCC and below) increased from 9.50% at the start of March 2026 to 10.10% at the end of the month.

The third proxy for risk is the volatility index (the VIX) for US equities, and that measure rose during the course of March 2026:

During March 2026, the VIX rose from19.86 at the start of the month to 25.25 by the end of the month, an increase much smaller than the increases we saw in March 2020 (COVID) or in the first week of April 2025 (Tariff week).

With the caveat that this is still mid-narrative, the bottom line from the movement in all of these risk measures is that while the market had a bad month, much of the marking down in equity values can be attributed to real concerns about higher inflation and economic damage, and is not the result of panic selling, at least in the aggregate. To back this up, I took a look at two collectibles - gold, which has a history of holding its value or even increasing during crises and panics in financial markets, and bitcoin, which has not had that history so far, but is marketed by its advocates as a potential hedge:

Gold was down 10.42% during March 2026, uncommon for a crisis month, but bitcoin was up 3.30% during the month, and it is entirely in keeping with bitcoin investors marching to their own music, though it will be interesting to see how this dynamic plays out, as this repricing continues.

Effect across Geographies

The war is in the Middle East, but there is no place to hide from its effects. To see how the war has played out in different regions, I looked at the change in aggregate market cap, in US dollar terms, in March 2026:

You may be surprised to see Africa & the Middle East and Eastern Europe & Russia show up as the best performing markets, with about 2% decreases in market capitalization, but it reflects the dual impact of the war. While it has wreaked havoc across the Middle East, the higher oil prices that it has brought with it are providing upside for oil producers that offsets some of the damage.

Since these dollar returns reflect local market performance as well as the strength/weaknesses of their currencies against the dollar, I looked at the US dollar's performance in March 2026:

I know that I am piling on at this stage, but I do compute equity risk premiums for other countries twice a year, once at the start and once mid-year. Given how much March has shaken up the status quo, I will make an exception and re-estimate equity risk premiums, by country, updating both my mature market premium (which I estimate from the S&P 500) as well as the country ratings, default spreads and country equity risk premiums for other countries.

Download spreadsheet with country ERPs

It is worth noting that these equity risk premiums are computed based upon sovereign ratings, which are slow to change, as the world convulses. That has been an issue with my ERP computations for Russia and Ukraine, since 2022, with the rating for the former withdrawn and the rating for the latter frozen at Ca (Moody's); I have use a country risk score from PRS for the last two years to update Russia's equity risk premium, an have done the same for the Ukraine in this update. You can see the same issues now, with the war in Iran rocking the boat, and at least for the Middle East, there is reason to believe that the ratings may understate country risk. While none of the countries in the war zone have seen their sovereign rating change (yet), these countries have market estimates of sovereign default risk in the form of sovereign CDS spreads, I looked at the movement in those spreads during the course of the month:

Not surprisingly, market measures of default risk are more sensitive to war effects, and have risen for much of the Middle East, as worries have mounted, with bigger increases in Qatar, the UAE and Turkey than in Saudi Arabia and Kuwait. The United States has also seen a surge in its sovereign CDS spread, and the global sovereign CDS spreads have risen about 12% in the first quarter of 2026. Using these sovereign CDS spreads as measures of default spreads for this part of the world may yield more realistic equity risk premiums.

What now?

I noted at the start of this post that the uncertainties that manifested during March 2026 about the direction, duration and effects of war are still unresolved and perhaps even grown as we start April. As investors try to navigate their way through this period, here are the questions that you will need to answer to decide where you fall in the continuum between complacency to full-blown panic:

In the complacency scenario, the war ends quickly (in days or weeks, rather than months), the damaged infrastructure is repaired quickly and the new regime in Iran is viewed favorably by the rest of the world, allowing the sanctions on the country to be removed, it is likely that oil prices will drop, perhaps even to below pre-war levels, as Russian and Iranian oil is freely bought and sold. In the full-scale panic scenario, the war continues for months, with lasting damage to infrastructure and supply chains and Iran's new government stays sanctioned, oil prices are likely to stay high and perhaps even go higher, the global economy will be kneecapped and parts of the Middle East (Dubai and Abu Dhabi) that had created a business and tourist friendly setting will struggle to find their balance.

In either case, the war has shaken up the status quo, and I see lasting consequences that go well beyond oil. The capital flows from the oil rich countries which has flowed generously to everything from AI start ups to Premier League clubs will shrink, creating down-market effects. That money, and the funds that were set aside to build vanity projects, from ski resorts in the deserts to state-of-the-art cities will be redirected to building pipelines and securing the flow of oil. Global politics has also been roiled, and even if the war ends quickly, there is damage that has been done to partnerships and security agreements that cannot be undone.

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Finding your investing lodestar: In Search of an Investment Philosophy

2026-03-24T13:23:00.001-04:00

When uncertainty roils markets, as is the case right now, it is natural for investors to get knocked off balance, a when off-balance, to make investment decisions that they often regret later. It is during those times that it helps to have a core set of beliefs about markets, and an investment philosophy that reflects those beliefs. You may not be able to mend the damage to your portfolio, but it will help you find balance again and make sense of the noise around you. As an investor, my investment philosophy has been a work-in-progress, but I have had an interest in how the investors around me develop their philosophies, and why differences persist. That interest was precipitated by a seminar class that I organized for NYU Stern MBAs in the late 1990s, where successful investors with very different market perspectives and investing styles presented their points of view, and students struggled to reconcile their different and contradictory points of view. In the aftermath of the class, I started working on a book and a class on investment philosophies, where the end game was not to find the "best" philosophy, but to provide a framework for investors to find the philosophy that best fits them. The first edition of the book came out almost two decades ago, followed by a second edition in 2012. In conjunction with the second edition of the book, I created a free online version of the class on my webpage in the same year, and NYU created a certificate class about six years for the class. While my core thinking on investment philosophies has nto changed, markets and the economy have, and both the book and the class have been in need of an update. I spent the last few months working on that update, and the third edition should be available at book stores in the coming week, and in conjunction, I have an updated (free) online version of the class on my webpage and on YouTube

The Origins

In the late 1990s, I was approached by the Stern School of Business with a request to serve as the organizer for a class on investing, where MBA students would spend a session a week, for a semester, hearing from successful investors of all stripes, and discuss what they learned from that talk in a second session each week. Over the course of the semester, the class had fourteen speakers, and because of our New York location, it drew from a range of investing types. Thus, students heard from a well-known value investor one week, the manager of one of the best-regarded growth mutual funds the next, a high-profile technical analyst in the third, and so on. The speakers approached investing in very different ways and had different perspectives on financial markets and how to exploit market mistakes, but they all had been successful as investors.

As I led the discussion of each speaker's market views and investment practices each week, I noticed students in my class developing whiplash, as they instinctively try to incorporate the views and practices of each speaker into their thinking. As the weeks went on, that became a problem, since other than investment success, the speakers shared little in common, and their views about markets were sometimes contradictory. By the end of the class, there was a fairly large subset of the students who ended up more confused by what they had heard during the semester, rather than enlightened. As I reviewed the class, before handing it off to someone else, I took an inventory of what I had seen not just in the class, but in investing in general, and came to the following general judgments about investing:

There are very few active investors, who win consistently over time: Active investing is one of the most difficult games to win at, and one reason is that you match the average investor, effortlessly and almost costlessly, by investing in index funds. Active investing has the unenviable task of trying to be better than average, and by enough to cover the costs (research, data, personnel, transactions) associated with being active. Just as illustration of how much of a mountain this is to climb, take a look at the percentage of active institutional investors who beat their respective indices over the last decade:

While there some active money managers who "beat the market" over a year, two years or even five, very few are able to hold on to these excess returns as you lengthen their active investing stint. Like gamblers in a casino, who strike it lucky early, but stay gambing too long, they often leave with none of their gains, or worse. Before I get a blowback, I am fully aware that that there are investing legends (Warren Buffett, Jim Simon and George Soros, to name just three), but the very fact that we can name them suggests that they are the exceptions, not the rule.
Even with those successful few, it is very difficult to separate luck from skill: Much as investment books and classes claim otherwise, investing results are affected by so many forces that are out of your control that disentangling how much of your final returns can be attributed to skill and how much to luck is very difficult to do.
These successful investors have widely different pathways to delivering success: If you were to make a list of the investors who have had the most success in markets in the last century, I would wager that you would be looking at a very diverse group, not just in terms of how they succeeded, but also in terms of personality. The three investors I named as legends - Buffett, Simon and Soros - obviously had very different views on markets, and how to exploit market mistakes, but even with investors who are often viewed as being from the same grouping, differences remain. Buffett may have learned his early lessons from Ben Graham, but the Graham and Buffett approaches to value investing are varied, with the former more focused on screening for cheap stocks and the latter more interested in finding companies with solid moats and great management.
Imitating successful investors does not seem to provide much payoff: The practices of successful investors have been probed and investigated by other investors and journalists, and some of them have dozens of books that claim to tell you the secret of their success. Warren Buffett is perhaps the winner in this race, with not only a multitude of books that track his investing life but also his annual letters to Berkshire shareholders which laid out his investing perspective in detail. That said, the investors who tried to follow in his footsteps, often imitating every aspect of his approach, have, for the most part, not been able to match his success.

My takeaways from these assessments are two fold. The first is that there can be no one dominant investment philosophy that is the best for all investors, and any claims to the contrary, whether it be for value investing or market timing or trading, are disingenuous. The second is that there is a right investment philosophy for each individual that reflects that individual's views and beliefs about markets and characteristics as a person.

The Core Idea

The recognition that each investor needs an investment philosophy that is tailor-made to his or her beliefs and personality became the starting point for my creating a class, and writing a book, about the topic. Before I describe what I try to do in the book, I should start with a definition of what I mean by an investment philosophy, and perhaps the best way to do that is by describing what it is not. First, an investment philosophy is much richer and more complete than an investment strategy, with the latter often coming out of the former. Thus, applying a screen to find stocks that trade at low multiples of earnings (low PE ratios or low multiple of EBITDA) is an investment strategy, but the investment philosophy that gives rise to that strategy is one that is built on markets under pricing companies with low growth or boring businesses, perhaps because investors are dazzled by growth and drawn to the excitement of newer businesses. Second, an investment philosophy is not an investment slogan. "Buy low, sell high" is an investment slogan, and a meaningless one at that, since that is the end game of almost every investment philosophy.

If you have been investing for a while, and have never stopped and asked yourself what your investment philosophy is, it is understandable. In fact, you may wonder why you should constrain yourself to an investment philosophy instead of looking for bargains wherever you can find them. The problem with not having a core philosophy is that is exposes you, as an investor, to a whole host of consequences, most of which are negative:

Chasing winners: If you don't have an investment philosophy, it is almost a given that you will find yourself drawn to whatever strategies worked best in the recent past. Your portfolio will suffer from whiplash as you chase last year's winners, whether that be the Mag Seven or technology stocks or small cap stocks, and while your turnover and transactions costs rise, you will have little to show in terms of returns.
Scam target: Greed is universal, and that leads us to look for ways to make lots of money with very little risk. Without an investment philosophy constraining you, you will be an easy mark for investment scams, drawn in with promises of upside with little or no downside.
Empty investing cupboards: If you do find an investment strategy that works at delivering returns, it is worth remembering that the clock is ticking, and that imitation and market corrections will cause that strategy to stop working, sooner rather than later. If that is all you brought to the market, your investing cupboard will be empty and you will find yourself running to stay in place. The advantage of having a coherent, well thought through investment philosophy is that you can go back to it and mine it for other strategies that may exploit the same market mistakes. Thus, if your investment philosophy is that markets undervalue boring, low-growth companies, and low PE ratios are no longer doing the trick (of finding cheap stocks), you may look for other screens (low volatility) that find you boring companies that are mispriced.

Simply put, every investor needs an investment philosophy to guide him or her in the difficult task of trying to delivering success.

Rather than create a laundry list of philosophies, I will use the investment process as the vehicle to describe how and where the different investment philosophies emerge from, as well as diverge:

Using this process, the choices in investment philosophies emerge:

1. Active investing versus Passive indexing: If, as we noted in the last section, doing nothing can deliver returns approximating the average, and nine out of ten investors who try to beat the average fail, there is no shame in adopting a passive indexing philosophy, where your allocation across asset classes is determined by your risk aversion and need for liquidity, and index funds fill out the rest of the dance card. It is human nature, though, to seek to be better than average, and it is perhaps that desire that drives many into active investing choices, and there are multiple pathways that they can adopt.

2. Investing versus Trading: The second divide in investing philosophies comes from the difference between value, which is driven by cashflows, growth and risk, and price, determined by demand and supply. Investing requires assessing the value of an asset, buying if the price is lower than that value and selling if it is higher, and waiting for the gap to close. Trading, on the other hand, is about gauging market mood and momentum, buying if you expect those forces to drive the price up and selling otherwise.

Within each of these groupings (investing or trading), there are sub-groupings. Trading can take different tacks, depending on where you think that market mistakes lie. The first, price traders, use the information on prices and trading volume to detect shifts in mood and momentum, with charts and technical indicators as tools, to try and generate profits. The second group, information traders, trades around information releases, such as earnings reports, acquisition announcements or even insider trades, with some trading ahead of the news, some at the time the news is announced and some in the aftermath, all trying to take advantage of what they see as market mistakes in reacting to that information. The third group, arbitrageurs, focused on finding the same or related assets trading on different markets, looking for mispricing across these markets, and locking in that mispricing as excess returns.

Investors, for instance, can be drawn to value or growth, and while that difference is often stated in terms of pricing multiples, with value investors buying low priced stocks (low PE, low price to book etc) and growth investors drawn to higher growth and high priced companies, I prefer to think of the differences in terms of where each group thinks it can find bargains. Using my financial balance sheet construct, where I divide the value of a firm into the value of investments already made (assets-in-place) and investments anticipated in the future (growth assets), value investors view their odds of finding market mistakes to be greater with assets-in-place, whereas growth investors feel that their odds are better in finding misvalued growth assets:

Within value and growth investing, there are further sub-divides. Value investing can span the spectrum from passive screening, where you screen for stocks that have specific characteristics (low PE, high growth, high ROE) and label them as cheap, to more activist poses, where investors with deep pockets (individual activist, private equity funds) not only take positions in companies that they believe are under or over valued, but also push for change at these companies. Growth investing has its own version of activist investing, in the form of venture capital, invested in young, growth companies, where in addition to supplying capital for growth, venture capitalists take an active role in how these companies evolve over time and exit the marketplace (IPOs, sale to another company).

3. Market Timing vs Stock/Asset Picking: In market timing, your focus is less on individual stocks or assets and more on deciding whether a market (equities, bonds, real estate etc.) is under or over priced. Returning to the investment process, your focus is on allocating your portfolio across asset classes, based on your market views, underweighting "expensive" asset classes and overweighting "cheap" ones. In stock/asset picking, you take the market as a given and try to find the best individual investments within each investment class for you - the cheapest stocks, bonds and real estate that you can find. There is an ironic contradiction in making this choice. It is undeniable that a successful market timer will make far more money than a good stock picker, but it is also true that it is much more difficult to be a successful market timer than it is to be a good stock picker. The picture below captures the choices in terms of investment philosophy, framed in terms of where they enter the investment process:

Even if you feel that you have an investment philosophy in place, I think being aware of how others approach markets and keeping an open mind, where you borrow parts of other philosophies and incorporate them into yours will make you a better investor.

Finding an Investment Philosophy

Looking at the menu of investment philosophies, from passive indexing to arbitrage, my end game in my book and for the class on investment philosophies was not to advance a single philosophy or even compare them, but to provide as unbiased and complete a picture, as I could, of the data backing each philosophy and more importantly, the personal characteristics that you would need to succeed with that philosophy.

Step 1: Views on Market Mistakes and Corrections

The first step in finding your investment philosophy is with a view of where (and why) markets make mistakes, and how they correct them. Even the firmest believer in efficient markets will concede that markets not only make mistakes, but sometimes make big ones, but the divergence between them and active investors lies in the nature of these mistakes. In an efficient market, market mistakes will be random, and since there is no systematic pattern to them, there is no pathway for active investors to find these mistakes, even with access to data and powerful tools. Active investors, in contrast, believe that there are systematic patterns that you can use to find these mistakes, and to exploit them for profits, with traders believing that those patterns are in the pricing and volume data and investors hewing more to fundamentals. That said, active investors can and will disagree about the types of market mistakes, with some buying into the notion that markets learn slowly, whereas others believe that markets overreact, and it is healthy for investors to have these disagreements.

Step 2: Pick an investment philosophy that reflects market views

Your views on market mistakes and corrections should guide you in your choice of investment philosophies. Thus, if you believe that markets overreact to news, good or bad, you may decide to become a contrarian, either trading (by buying after bad news and selling after good) or by investing (by buying companies with solid fundamentals whose stock prices have dropped by far more than they should have). Conversely, if you believe that it is momentum, not fundamentals, that is the biggest drivers of stock price movements, you may choose to ride that wave, based on charts and technical indicators. Superimposing time horizon onto the types of mistakes that markets make, you can create a matrix of investment philosophies:

Do you have to pick a single philosophy? Not necessarily! You can meld two or even more than two philosophies together, as long as you meet two conditions. The first is that the melded philosophies have to share a core belief about market mistakes. Thus, if you believe that market s overreact, you can be a contrarian value investor, buying companies that have been beaten up in markets but have intact fundamentals, and timing your purchases right after bad news releases, when markets overreact. The second is that you have to identify which of the philosophies is your dominant one, and which one is secondary, allowing you break ties where the two push you in different directions. Staying with the melded contrarian philosophy, and assuming that the contrarian value philosophy is your dominant one, you will choose to not to buy a stock that is down 15% after a bad earnings report, if it is still trading closer to its highs than lows.

Step 3: Check for viable strategies

Investment philosophies are a critical component, but to make money on a philosophy, no matter how well thought through, you need to devise investment strategies that can generate profits for you. In coming up with these strategies, you will confront the two realities that cause many strategies that look good on paper to fail: transactions costs and taxes.

On the transactions cost front, the brokerage trading cost is just a small part of the overall cost, with two other costs that can often be much larger. The first is the bid-ask spread, small for large, very liquid stocks, but much larger for smaller and less liquid investments. The second is price impact, again non-existent if you are a small investor buying or selling shares in a large market-cap company, but substantial if you are a large investor trading on an obscure stock.
On the tax front, some strategies will create more tax costs than others, partly because of how investment income is taxed (dividends create immediate tax consequences but capital gains require trading to incur tax liabilities) and partly because of how much trading your strategy will require of you, with higher turnover generally creating more tax liability.

If you are planning on being an active investor, there is one final skill set that you will need to acquire, and that is the capacity to test whether a strategy can beat the market. The volatility in returns can sometimes create illusions, where a strategy looks like it is delivering excess returns, but those returns are almost entirely due to statistical noise.

Step 4: Check for personal fit

Investment philosophies, and the strategies that emanate from them, come with different demands in terms of time horizon, with some requiring holding on to investments for many years and others requiring trading in minutes, different risk exposure and divergent tax consequences. Investors who choose to adopt these philosophies have to reflect on whether they are good matches, on the following fronts:

Capital to invest: If you are just starting on your investment journey, and have only a small amount of capital to invest, your choices in terms of investment strategies narrow. You will definitely not be able to be an activist investor, since you will have no weight (in terms of money invested or shares held) to throw around, and you may lack the wealth to buy illiquid, small companies, if that is where you think market mistakes are most often found, since you will not be able to spread your bets. The good news is that you continue to build up your capital, your investment choices will widen, and you can modify your investment strategies accordingly. At the other end of the spectrum, and this is perhaps more the case if you are managing other people's money, you can have so much capital to invest that some investment strategies become infeasible. For instance, if you are planning on investing in illiquid, small cap stocks, having billions of dollars to invest will increase your transactions costs (by increasing price impact when you trade).
Time horizon: Many investors, when asked the question about time horizon, claim to have long time horizons, often because they believe that it is the answer that "good" investors give. The truth is that for most investors, time horizon is as much determined by external factors, such as age, health and liquidity needs, as it is by internal motivations. If you have to pay tuition for your children or expect to have substantial hospital bills in the near future, your time horizon just became shorter, and that has to be factored into your choice of investment strategies.
Risk exposure: As with time horizon, the willingness to take risk is partly a function of your personal makeup and partly determined by your life standing. If you have accumulated wealth and have a job with a stable (or rising) income that more than covers your expenses, you are better positioned to take risks than if you are on the verge of retirement, and are investing money that you will be needing soon to cover your post-retirement cash needs.
Personal qualities: Your personality and characteristics also come into play in your choice of investment philosophy and strategies. If you are, by nature, impatient, it is unlikely that you will be able to sustain a strategy of buying undervalued companies and waiting for a long time for mistakes to correct. Similarly, if you are easily swayed by peer pressure and what the rest of the world is thinking and doing, it is difficult to be invested in contrarian causes, short-term or long-term. Finally, if your strategy requires special skills to be put into motion, you will have to either have or acquire those skills; a strategy built around finding undervalued companies will require that you know how to value companies and one built around analyzing large and complex datasets looking for mispricing needs statistical and data analysis knowhow.

When investor characteristics and investment philosophy needs are mismatched, there are two negative consequences. The first is that, lacking staying power, investors will abandon strategies well before they should, simply because they are uncomfortable with how they are playing out. The second is that a mismatch creates an emotional cost, where investors struggle with their portfolios and fail what I call the sleep test, where their portfolio's gyrations keep them awake at night.

Step 5: Keep the feedback loop open

If you have found an investment philosophy that maps on to your market beliefs, found viable strategies that reflect that philosophy and matched it to your personal makeup, you have reached steady state, but only for the moment. That is because almost every part of this process is subject to change, some because of outside forces, and some because of personal changes.

Economic setting: Over time, economic settings and structures change, and investment philosophies have to adapt or even be abandoned. For instance, I have argued that technology and disruption have created winner-take-all businesses in the twenty first century, and if you buy into that argument, an investment philosophy (and strategies) built around small cap companies will no longer deliver the payoff it did in the twentieth century.
Market lessons: Your views on market mistakes come from looking at data and your own experiences in the market, and as a consequence, they should be revisited as markets change. Just in this century, markets have been tested by crises (the financial crisis of 2008, the COVID meltdown in 2020 and the tariff announcements last April, just to name three), and it is becoming increasingly obvious that assets across classes (stocks, real estate etc) and geographies are moving far more in sync with each other than they did in the last century. That reality has to be integrated into your market views and the investment philosophy/strategies that you use.
Trading microstructure: It is undeniable that access to information and trading on most assets has become easier over the last few decades. That is good, but it does come with a cost. Investment philosophies built around the assumption that most investors, especially retail and individual, would not be able to access data or trade easily, may need tweaking, adapting or even abandonment.
Personal changes: It won't come as no secret to you, but you will get older, the amount of capital you have to invest will change, your health and family obligations will shift, and you may even become more or less patient or more or less susceptible to peer pressure. Those factors will all feed into your investment philosophy.

The investing world does not lend itself to absolutes. One of the red flags in investors (retail or institutional) is certitude about their investment choices and views, and an unwillingness to even consider alternatives, a sign that they will be unable to change as the world changes around them.

Book, Class, both or neither?

I like writing, not so much for its commercial potential, but because it allows to get my thoughts in order. I wrote the first edition of my investment philosophies book in ___, and it followed a structure that I have stayed true to, in subsequent editions. I start the book, with a description of what an investment philosophy is and how it first into the investment process, moving on a foundational section, where I look at risk measures, how to read accounting statements and do intrinsic valuation, how transactions costs and taxes drain returns, and at how to test investment strategies that claim to beat the market. In chapters 7 through 12, I spend each chapter looking at a broad investment philosophy (and related strategies), examining evidence for and against each one in the data before outlining what you (as an investor) need to bring to the table to succeed with each one. I close the book, by providing the sobering counter evidence to active investing, where I look at how difficult it to win at that game and the promise and peril of alternative investments (gold, cryptos, fine art, real estate).

If you have one of my earlier editions, is it worth upgrading? If you have the first edition, I do believe it is time, but if you do have the second edition and are budget-constrained, you can hold off. You can find the book online at Amazon and Barnes and Noble, with the latter offering a 25% discount, starting today (March 24).

In parallel, I developed a class that had the same content, and while the NYU certificate version of the class will cost you, I have had a free online version on my webpage, which I created in 2012. That class was in need of an update, and as I finished up the third edition of the book, I created a new version of this class, with forty two sessions covering the same material as the book. Again, if you have taken the earlier version of the class, you may find the material repetitive, but I hope that the updated data and the add ons allow for a richer experience. If you have never taken this class, and online learning works for you, it is designed for investors, individual as well as institutional, and requires little in terms of technical knowledge, and I hope that give it a shot.

The Investing End Game

We all share the same end game in investing, which is to generate the highest returns on the capital we invest, though there are wide variations in how much risk we are willing to take and how long we will wait before cashing out. That is the definition of investment success, but given that there are so many forces that are out of our control, you can do everything right and still fail to meet your objectives, leaving you frustrated and questioning yourself. It is for that reason that a better endgame is to seek out investment serenity, where you end up with an investment path that you are comfortable with, and accept the results that emerge, good or bad.

I have spent this entire post talking about investment philosophies, and in case you have not noticed, I have not shown my hand, on my investment philosophy. I have never believed in hiding behind vague and opaque generalities, and my investment philosophy is built around three principles:

Intrinsic value matters: I believe that every asset (anything that generates cash flows) has a intrinsic value, and that with imagination and a willingness to make mistakes, you can estimate that intrinsic value for any company, from start-ups to companies on the verge of default. I believe that much of what passes for valuation in practice is pricing, where people using pricing metrics (such as PE ratios or EV to EBITDA multiples) to make pricing judgments, and that a good valuation requires understanding business models, telling stories and converting these stories into valuation inputs and value estimates.
Markets are for the most part right, but make mistakes during periods of uncertainty and change: I never cease to marvel at markets, where millions of individuals with disparate views and information reach consensus on a price. In an age where we have turned over our choices on what movies to watch to Rotten Tomatoes, and which restaurant to eat at to Yelp!, it is worth remembering that markets were the original fount for crowd wisdom. That said, it is also true that markets have provided us with illustrations of crowd madness, where the collective wisdom is hopelessly wrong, and I believe that this is often the case when investors face significant uncertainty, as is the case when companies transition from one stage of the life cycle to another, entire industry groups are faced with the threat of disruption and markets are put into upheaval by crises.
Do no harm: While I seek out investments to make that will beat the market, I am cognizant of the reality that I am not entitled to rewards, just because I put in the work, and that luck and chance still can wreak havoc on my best-laid plans. In particular, I have learned, through experience, that my biggest mistakes come from overreach and overactivity, and I have built that learning into my investment philosophy by:

Spreading my bets: I have written before about the concentration versus diversification argument, and that what you choose to do as an investor will be a reflection of how much confidence you have in your investment choices, or “conviction”, in investing parlance. I must confess that I don’t share the conviction that concentrated investors bring to the game, and not only spread my portfolio over three dozen stocks, but also follow rigid rules on not letting any single investment exceed 15% of my portfolio.
Acting rarely: I don’t trade often, and when I do, I follow the old adage of measuring twice (or three times) before cutting (trading). It helps that I don’t track the market or my portfolio holdings all day, almost never watch the financial news and am not easily swayed by investment sales pitches.
Staying away from my weaknesses: I steer away from active market timing and sector bets for a simple reason. I am not good at either, and what I might gain from an occasional win will be wiped out by what I lose in the long term.
Being aware of my blind spots: I try to be self-aware, though I don’t always succeed. I know that I am thrown off my game plan by taxes (I don’t like playing them, and that sometimes gets in the way of doing what I should be doing) and I sometimes fall in love with company narratives, because I want them to be true.

This is my philosophy, it reflects my strengths and personality, and it works for me. I sleep well at night and I have no regrets, but I am lucky since I have an clientele of one (or perhaps two) to satisfy. My hope, with both my book and class, is that it provides you with the choices and material for you to find an investment philosophy that works for you and that it delivers the returns you hope to earn, and even if it does not, lets you sleep well at night!

YouTube Video

Investment Philosophies Book

Webpage for book
Bookseller links

Investment Philosophies Class

The Price of Risk: An Equity Risk Premium Monologue!

2026-03-15T19:05:00.001-04:00

I start my valuation classes with a question of whether valuation is an art or a science, and I argue that it is neither; it does not have the precision that characterizes a science and unlike an art, it does come with principles that constrain you on what you can and cannot do. I describe valuation as a craft, where you learn as you value companies, and in the process, there are times where you question how it is practiced, and try to find ways to do it better. I have learned my share of lessons in the four decades that I have practiced valuation, and I have often abandoned standard practices, in the hope of developing better ones. There is no input in valuation where I have found myself questioning existing practices more than in estimating the price of risk in equity markets, i.e., the equity risk premium, and I have wrestled with ways of coming up with alternatives. That endeavor was pushed into high gear by the 2008 market crisis, when I started to pay more attention to how markets price risk, what causes that price of risk to change over time and the limitations in the ways that we estimate that price of risk in financial analysis.

Status Quo and Standard Practice

Leading into 2008, I had long been skeptical about how we approached the estimation of equity risk premiums, essential ingredients in hurdle rates in corporate finance and discount rates in valuation. It was (and still remains) standard practice to look at historical data, almost entirely from the US, on what stocks had earned over treasuries, and use that historical equity risk premium as the best estimate of the equity risk premium for the future, That approach would have yielded an equity risk premiums of between 5.5% to 14.5%, at the start of 2026, depending on the time period used, the way we compute averages and what we use as the riskfree rate.

These historical equity risk premiums are not only backward-looking and very noisy (see the standard errors), but they allow bias to easily creep in, through the choice of equity risk premiums, with bullish (bearish) analysts picking lower (higher) numbers. Disconcertingly, they also move in the wrong direction, falling during crises (as historical returns get updates) and rising during good times.

A Forward-Looking Alternative

To counter the problems that I saw with historical risk premiums, I started estimating forward-looking equity risk premiums, by essentially backing out from stock prices and expected cash flows, the expected return (internal rate of returns) that markets were pricing into stocks.

That approach yields forward-looking equity risk premiums, and while there is estimation error in the expected earnings growth and payout numbers, it yields vastly more precise estimates that are also model-agnostic. Using this approach, the equity risk premium at the start of 2026 was 4.23% (over the US treasury bond rate):

Note that this estimation is model-agnostic, and is simply a measure of what markets are pricing in, given expected cash flows at the moment.

ERP Estimation during Crises

Unlike historical equity risk premiums, these implied premiums are sensitive to market gauges of fear and greed, and change, as those change. In fact, I computed the ERP, by day, during the 2008 market crisis, and you can see the shifts during that 14-week period below:

Note that the crisis started with the equity risk premiums at 4.2% on September 12, 2008m but almost doubled over the next two months, as stocks went into free fall. To me, these implied equity risk premiums made far more intuitive sense, rising as market fears about banks and the economy rose.

I have continued with the practice of estimating equity risk premiums, by day, during market crises (real or perceived). Here, for instance, is my assessment of the UK market in 2016 in the weeks leading up to the Brexit vote, the market reaction to COVID and the global economic shutdown in 2020, and how the tariffs roiled markets last year. In fact, as we wrestle with an war and oil price induced market shock in March 2026, I started my daily estimates for the ERP on March 1 and will report on how that price has changed over the last two weeks, in the next section.

Equity Risk Premiums - Lessons Learned

The process of estimating implied equity risk premiums on a continuing basis is driven less by intellectual curiosity and more by my need for these numbers, when I value companies. That process has taught me three lessons about equity risk premiums, and I have responded by altering my practices.

1. The equity risk premium is a dynamic and shifting number, and a good estimate of the premium should reflect this volatility. Using an equity risk premium that is different from the implied equity risk premium makes every valuation a joint judgment on what you think about the company and what you think about the market. Put simply, sticking with a 4% equity risk premium during a crisis, when the implied risk premium has surged to 6% will lead you to find most companies to be undervalued, almost entirely because you think that the market is undervalued (not the company). In my view, a company valuation should be market-neutral, and the only way you can get there is by using a current implied equity premium.

My response: Rather than compute the implied equity risk premium at the start of every year, and using that premium over the course of the year, I shifted to computing the equity risk premium for the S&P 500 at the start of every month, in September 2008. I report those numbers on my entry page to my website (damodaran.com) and use them to value companies during the course of the month. You can find these monthly equity risk premium estimates by going to this link.

2. The implied equity risk premium is a consolidated metric for market pricing, and every debate or discussion about whether the market is under or over priced can be reframed as a debate about whether the implied equity risk premium is too low (over pricing), just right (fairly priced) or too high (under pricing). Since the implied ERP incorporates the level of interest rates, expected growth and cash payout, it is a more complete assessment of the market than looking at dividend yields and earnings yields (or variants of PE ratios), two widely used proxies for market pricing. In this post, I took an extended look at how these different measures of equity risk premiums measure up, in terms of predicting future equity returns.

My response: I have been open about my discomfort with timing markets, but when I am asked what I think of the overall market (Is it too high? Is it a bubble?), I first measure the current equity risk premium and then assess it against history. I used this technique to assess US equities at the start of this year in a post, with the accompanying graph:

My conclusion, at the start of 2026, was that while stocks were richly priced using almost every conventional metric (high PE ratios, low dividend yields), the implied equity risk premium was in line with what US stocks have generated over the last 65 years. That said, I did note that 2025 was a tumultuous year, with tariffs making the news and the post-war dollar-centric global economic system starting to fray, and argued that the market seems to be too sanguine about catastrophic risk. Almost on cue, two weeks ago, bombs started falling in the Middle East, and US equities and bonds have been struggling to price in the effects of higher oil prices. In keeping with my practice of estimating equity risk daily, during troubled times, I did compute the implied ERP for the S&P 500 every day, during the last two weeks (Feb 27- March 13):

Oil is up to over a hundred dollars a barrel and the S&P 500 is down, but so far, the market is not behaving as if it is in crisis mode. The equity risk premium, which started March at 4.37% has risen, but only to 4.51%, over the two weeks. In fact, it is the ten-year US treasury bond that has had the bigger surge, up from 3.97% at close of trading, on February 27, to 4.28% at close of trading, on March 13, indicating inflation fears are trumping other market concerns right now. All of this could change next week or the week after, and I will continue to track the equity risk premiums, by day, until the market settles in.

3. The equity risk premium is an essential ingredient into almost every part of financial analysis, incorporated into hurdle rates in corporate finance, discount rates in valuation and in expected returns on equity in financial planning. Given this centrality, I was surprised how little attention it has received from both academics and practitioners, when I looked for references. There is very little usable academic research on equity risk premiums specifically, though there is a great deal on asset pricing and risk. As for practitioners, they have, for the most part, relied on historical risk premiums, and often obtain these premiums from services that summarize the historical data. When I took my first finance class, the historical risk premiums came from data from Ibbotson Associates, that contained annual return data on stocks, bonds and bills. That data was acquired by Duff and Phelps, where it became part of a voluminous book on cost of capital, but much of what that book had to say about equity risk premiums reflected slicing and dicing the historical data, hoping to get further insights, and for the most part failing, because of the noisiness in the data. The US historical data is now in the hands of Kroll, but there is little of value that be extracted by doing deeper and deeper mining expeditions on historical return data. In fact, if you are a fan of historical equity risk premiums (I am not, as you can guess), my suggestion would be to use the Credit Suisse Yearbook, which looks at historical equity risk premiums in 20 markets over more than a hundred years, and does not suffer from the selection bias of focusing on just US data.

My response: I am a practitioner and I decided, for my own understanding, to pull together everything I knew about equity risk premiums into a paper that I wrote in early 2009, and shared online that year. Practitioners seemed to find it useful, and I have updated that paper every year since, at the start of the year. It has grown over time, as I have sought to pull together new findings on equity risk premiums and incorporate changes in markets, and my seventeenth annual update is now ready. I have to confess that at this point, much of the change is data-driven, with tables and graphs updated to include the most recent year's data, but I hope you still find it useful. The paper resides on the social science research network (SSRN), an Elsevier-run platform for working papers in the social sciences. Unlike most of the other papers on that platform, I have no interest is ever publishing this paper, but you are welcome to download not just the paper, but all of the data that goes with the paper.

Equity Risk Premiums - The 2026 Edition

If you do get a chance to download the paper, I should warn you ahead of time that it long (153 pages), unexciting and entirely directed at practitioners. It is modular, though, and it is broadly broken down into the following sections:

1. The Determinants of Equity Risk Premiums: Given that equity risk premiums represent the price of risk in the market, it should come as no surprise that almost everything that happens in the market, political or economic, affect its level. The picture below summarizes the determinants, and you can find more details in the paper:

As you can see, all of these variables can and will change over time, explaining why the ERP should be a volatile number.

2. Historical Equity Risk Premiums (and spin offs): I spend a section of the paper discussing historical equity risk premiums, examining the statistical properties that make it a faulty approach, and why a belief in mean reversion has made it the status quo. While most of the historical equity risk premiums that you see reported in practice come from the US and are based upon the Ibbotson data going back to 1926, I also look at historical data that goes back further (to 1871) as well as historical premiums in the rest of the world. The historical data on returns in the US has also been mined by services to extract premiums that have been earned by subsets of stocks, and since these premiums often get used by practitioners, I look at the efficacy of these premiums. I specifically look at the small cap premium, a widely used add on in valuation, and not that not only has it been noisy over the entire time period (1926-2025), but that it has disappeared since 1981:

The fact that the small cap premium endures in practice is a testimonial to how once bad practices become embedded in valuation, they never leave.

3. Equity Risk Premiums, by country: While I do have a companion paper that explores country risk in detail, that I update in the middle of the year, I describe my process for estimating equity risk premiums, by country, starting with a mature market premium, and then adding on additional premiums, based on country default risk spreads (based on ratings and sovereign CDS spreads).

4. Implied Equity Risk Premiums and Alternatives: In this section, I start with a description of an intrinsic value model for the market, and use that model to illustrate what you would need to assume for the dividends yield or earnings yield to become reasonable proxies for the equity risk premiums; for the latter, for instance, you have to assume either that there is no earnings growth or that if there is growth, it is value neutral. I then use the full version of the model, allowing for higher growth and cash payout that includes buybacks, to derive my implied equity risk premium estimates. I also look at how my implied equity risk premium estimates relate to other risk proxies (default spreads on bonds, VIX etc.) and how they change over time, as the riskfree rate changes.

5. Efficacy of ERP Estimates: The test of whether an equity risk premium estimate is a good one is in the data, since equity risk premiums measure expectations of what investors hope to earn on equities in future periods. In the last section of the paper, I examine the predictive efficacy of alternative measures of equity risk premiums, by looking at their correlation with actual stock market returns in the next year, the next five years and the next ten years:

Since a good ERP estimate should have a large positive correlation with actual returns on stocks in future years, the current implied premium does best for the five-year and ten-year return, and the historical risk premium does worst, with actual returns increasing (decreasing) when it decreases (increases). In bad news for market timers, none of the equity risk premium approaches does well at forecasting next year's actual return, and even at the longer time periods, there is significant error in predictions.

Paper

Equity Risk Premiums (ERP): Determinants, Implications and Estimates - The 2026 Edition

Data

Spreadsheets

Spreadsheet to compute implied ERP - S&P 500 on February 27, 2026

AI Scenarios: From Doomsday Destruction to Do-Nothing Bots!

2026-03-04T17:10:00.003-05:00

When Chat GPT made its debut on November 30, 2022, it unleashed the hype of AI, and in the three years since, AI has taken on an outsized role not just in markets, but also in our lives. For much of the time, the AI story has been told by its advocates and its salespeople, and the companies in the AI ecosystem have benefited. Not surprisingly, given that its narrators benefit from this growth, that story has emphasized the positive, with dazzling AI use cases and optimistic extrapolation of the productivity gains from its adoption. In the last few months, we have seen cracks emerge in the AI story, with investors wondering when, and in what form, the immense investments in AI architecture will pay off, and how if they pay off, the businesses that they disrupt will fare. That disquiet has played out as negative market reactions to new AI investments at Meta and Amazon, a markdown in software company market capitalizations and in a sell off last week, in response, at least partially, to an AI scenario assessment from Citrini Research, a publisher of macro and stock research. Given that I know very little about the technology of AI, and that my macroeconomic knowhow is pedestrian, my intent in this post is less about promoting my favored AI scenario, and more about providing a framework for you to develop your own.

The Citrini AI Assessment - Report and Responses

The Citrini AI assessment came out on February 22, 2026, and it starts with a preface stating that it is presenting a scenario, not a prediction. I do have issues with that opening, but I will come to them later, but the report itself laid out a story for AI that unfolds with a dark end game for the economy, where by June 30, 2028, the AI disruption has unsettled businesses and displaced workers, with unemployment rates rising above 10% and the market down almost 40% in response. There have been other AI doomsayers, but many of those doomsday scenarios are built around the storyline that AI will not live up to its promise, and the pain comes from having over invested trillions of dollars in building its architecture. In contrast, the Citrini AI story is built on the expectation that not only does AI work well at doing tasks currently performed by white collar professionals, across a range of firms, but its adoption happens very quickly. The pain in the Citrini story comes from that disruption creating substantial job losses, and especially so among higher-earning workers, and the resulting loss of income driving these job losers to cut back on consumption. The ripple effects play out across businesses, with default risks and spreads rising, private credit collapsing and the market and economy pricing in the pain.

I do think that there are major flaws in the steps leading to the economic implosion in the Citrini assessment, but credit should be given where it is due. I have always been troubled by how much we have worshiped at the altar of disruption in this century, putting the founders of disruptors on pedestals and preaching disruption's virtue. In keeping with Joseph Schumpeter's description of capitalism as built around creative destruction, I do believe that a vibrant and dynamic economy needs a shake-up and challenging of the status quo, but disruption comes with costs to the businesses that are disrupted, and to the people who work in them. There is much to celebrate, as consumers, in terms of choice and price from the growth of online retail, but that does not take away from the devastation that has been wreaked on brick-and-mortar retail and its constituent parts. Ride sharing has brought car service from its nineteenth century ways into the twenty first century, but at the expense of yellow cabs and conventional car service businesses. The reason that many AI advocates took issue with the Citrini report was precisely because it bought into their sales pitch of how AI bots can not only do what lawyers, bankers, software engineers and consultants do, but also do them better, and then asked the question of "what then?.

The Citrini AI scenario must have hit some targets, because in the days since, we have been flooded with scenarios countering Citrini and arriving at different outcomes. While I was not surprised to see Goldman Sachs, Moody's and JP Morgan jump in with their AI scenarios, with more benign outcomes for the economy, where the job loss and income effects from AI are modest and temporary, I was surprised to see Citadel wade into the argument, with a direct rebuttal to Citrini, which sees a much more positive end game from AI disruption, and is built around three pillars. The first is the current data on jobs and layoffs in the businesses most directly targeted by AI, such as software, where they note that while jobs have been shed, the job losses have been modest, and AI adoption trends don’t see breakouts consistent with the speedy disruption predicted by Citrini. The second is history, where they look at disruptions in the past (PCs, the internet) and note that none of them have been speedy or have created the job losses or economic collapses predicted in the doomsday scenario. The third is grounded in macroeconomics, where they point to the inconsistency of assuming that a large positive productive shock, from AI’s success, will play out out as large negative shock to the economy and market in which it happens.

Completing the AI story

The problem with all of these AI scenarios is that they are rooted in the weakest of responses to uncertainty, which is to either pick a scenario and to describe it in detail, without establishing, at least in qualitative terms, how likely that scenario is, in the first place, or to list out a whole host of scenarios, without making judgments on likelihood on eany of them. It is entirely possible that what Citrini was presenting was a "worst-case" scenario (I read through the report and could not get a sense of if this was so, and the subsequent responses from Citrini have only muddied the waters), a "low likelihood" scenario or the "likely scenario" of how AI will unfold. If it is a likely scenario, and you buy into the pitch, the investment and personal consequences will be dramatic, since it is entirely possible that, if you are a white-collar worker, you may have lost your job by June 2028, and your savings, if invested in stocks, would have taken a beating. If it is a "low likelihood" scenario, and you are exposed, because of your job, age and portfolio composition, you should consider buying protection, but if it is a worst-case scenario, it is almost entirely useless, except for shock value.

Point Estimates and Probabilities

For much of its history, financial analysis has been built around point estimates, where you identify key drivers, estimate the effects on your bottom line (earnings, cash flows) and make your best judgments. Thus, when valuing a company, you estimate the earnings growth on base year earning, how much you will reinvest of those earnings to grow to get to cash flows, and discount those cash flows back at a risk-adjusted rate to get to value. The problem with point estimates, where almost everything is uncertain is that you will be wrong 100% of the time, though you may still make money, if you are wrong in the right direction.

Financial analysts and economics have been slow in adopting and using probabilistic approaches, where point estimates are replaced by distributions, and a single judgment on outcome by a distribution of outcomes. One reason, at least early on, was that economists and financial analysts often did not have rich enough data or powerful enough tools to use decision trees, simulations or scenario analysis in making their macroeconomic and investment judgments, but that is no longer true. Another reason may be that many in this group are uncomfortable with statistical distributions or probability estimates and stay away from using them, because of that discomfort. The third reason, at least for a subset of analysts, is a concern that being open about estimates and the errors in those estimates, which is visible to all in probabilistic approaches, will be viewed as a sign of weakness or lack of conviction on their part. I have a short paper on using probabilistic approaches, where I look not only at when you may want to use which approach (I look at decision trees, simulations and scenario analysis) but also have a short review of statistical distributions, if you are interested.

Since Citrini specifically titled their AI thought piece as a scenario, I will stick with scenario analysis in this post. In its most sloppy form, and one that has been around for decades, scenario analysis has taken the form of best case - base case - worst case scenarios, an almost useless exercise, since there are almost no risky investments that are going to pass muster under the worst case scenario, no matter how good they are, or are going to fail under the best case scenario, no matter how bad they are. A scenario analysis, done right, should look at scenarios that cover all possible outcomes on an investment or decision, and for completion, need probabilities attached to these scenarios, which can then be used by a decision maker to estimate expected values. That will be almost impossible to do if you are trying to work out future pathways to AI, since it is so early in the process and so little is known about outcomes.

There is an alternate path for scenario analysis that is less information-intensive and thus more feasible, and it draws on the 3P test that I use when valuing companies, where my company valuation narrative has to start with the possible test (it can happen) to being plausible (which requires more backing) and then on to the probable (where you can estimate a likelihood). In the context of scenario analysis, this would require that you categorize scenarios into their the three groupings:

The discussion around where AI is going would become much healthier if scenario proponents were required to state where their proposed scenarios fall in this spectrum. Citrini, for instance, could have saved itself from some of the backlash, if the writer of the AI doomsday report had specified that it was a possible, but not quite plausible scenario.

The AI Disruption - Gaming the Outcomes

In the last week, I have seen at least a dozen scenarios touted by individuals and entities, many of whom I respect, and I must confess that I am whipsawed. If, like me, you are drowning in these scenarios, with very different results and outcomes, the only way to retain your sanity and to take ownership of this process is for you to develop a framework where you can not only put each of these scenarios to the 3P test, but also to develop your own assessment of how AI will play out for businesses, investors and the economy.

1. The Disruption - Form and Speed

The first set of questions that you need to address in the AI story relate to how the AI disruption will evolve, both in form and timing, and to then trace out the aftereffects.

AI Disruption Magnitude - Worker Displacement versus Productivity-enhancing Tools: If you listen to some of AI’s lead players, AI will have the capacity to replace workers across multiple businesses, as it develops strengths that go beyond the purely mechanical. One reason that the AI effect on unemployment is so large in the Citrini doomsday scenario is because AI’s reach in the scenario is not just restricted to replacing programmers in software but extends to replacing white collar workers in other technology businesses, financial intermediaries, banking and consulting. In contrast, Citadel’s more benign AI reading comes from AI displacing workers in a smaller subset of businesses, while providing tools in others. At the other end of the spectrum, there are still some who believe that when all is said and done, AI will provide tools to workers that may save them time, but will not be powerful or dependable enough to replace them.
AI Disruption Speed: Here again, there is disagreement, with some AI optimists believing that its disruption of regular businesses is imminent, whether displacing workers or in giving them tools. Others believe that AI adoption will take time, partly because the tools need work and partly because businesses and workers are slow to adapt to change. The Federal Reserve in St. Louis has created a tracker of AI adoption rates across users, and while it does not capture the depth of the AI adoption, it does provide a measure of how much familiarity and comfort that users are acquiring, with AI tools.

With the caveats about survey data in place, there are interesting trends in these surveys. First, the use of Gen AI tools in non-work settings has grown more than its usage at work, an indication perhaps of how personal devices (phones, in particular) have changed technology adoption rates. Second, the time that AI has saved people, at least so far, has been modest, ranging from less than 1% in the accommodation and food businesses to about 4% in information and management of companies. Overall, this graph suggests that AI usage is neither as explosively fast growing nor as much of a time-saver, as its proponents suggest that it is. The pushback, though, is that these are surveys of the general population, and that there are data points indicating that the disruption effects are more substantial including the substantial write down in market capitalizations of software companies and layoffs at tech companies. The announcement by Block, the fintech company founded by Jack Dorsey, that it would it be letting go of almost 40% of its workforce, for instance, and blaming AI's rise for the action, was viewed as an indicator of AI's disruption potential. That is a noisy signal, though, since many tech companies have bloated work forces, and AI gives them easy cover, when correcting past mistakes.

It is true that there is no crystal ball that you can use to gauge the magnitude and speed of AI disruption, but every AI scenario that you see starts with a judgment on one or both.

2. The Disruption Aftershocks

Disruptions create aftershocks, some positive and some negative, and while we often avert our gaze and attention from the latter, a full assessment requires considering both. With AI, the positive effects take the form of higher productivity, as it either allows people to do their jobs more efficiently (with AI tools) or actually replaces people and does their jobs instead, in effect allowing for more output with less labor. Relating back to the different pathways that AI disruption can take, both in form and in form and speed, I would hypothesize that these disruption benefits will be a function of how AI disruption plays out.

Proposition 1: The disruption benefits from AI disruption will be greater from people displacement than from AI productivity tools
Proposition 2: The productivity effects from AI disruption will decrease, at least in economic value terms, the longer it takes for the AI disruption to unfold.

The negative effects of AI, in economic terms, will come from the immediate displacement of people, if AI replaces labor, or from the decrease in employees needed to get tasks done, if AI tools make existing employees more efficient. Here again, I would hypothesize that these disruption costs will be function of how the disruption plays out.

Proposition 3: The disruption costs from AI disruption will be greater from people displacement than from tools, as those laid off lose income and spending power.
Proposition 4: The productivity costs from AI disruption will decrease, at least in economic value terms, the longer it takes for the AI disruption to unfold, since time will allow new entrants into labor markets to adjust to a disrupted business world.

Intuitively, the longer it takes AI to find roots in business, the more time it gives workers time to adjust, retrain or move on. As you can see, the scenarios where AI displaces existing employees and happens quickly are the ones with the biggest benefits and the biggest costs, and the scenarios where AI supplies tools to existing employees and happens slowly has the least benefits and costs. Building on this theme, I see the net effect of AI disruption playing out as follows:

If AI disruption displaces existing workforces, across many businesses, and happens quickly, the net effect is likely to be negative, at least in the near term, since the economy will not only have to absorb major layoffs quickly, but also because those laid off will be higher-earning white collar workers. While that maps on to the Citrini doomsday scenario, there is still much to debate about which industries will see the most job displacement and how quickly these workers will find other jobs. There is also a discussion that should follow, even in this negative net-benefit scenario, of how quickly the economy (and workers) will adapt, and if and whether net benefits will turn positive in the long term. If AI job displacement is on a limited scale, and/or takes time to unfold, both the benefits and the costs of the AI disruption become smaller, but the net benefit is more likely to be positive, in the short and long term. Finally, the AI disruption takes the form of tools that make workers more efficient, but not efficient enough to reduce workforces, both the benefits and costs of AI become much smaller. In fact, if these tools take a long time to craft and displace little or no labor you get the AI disruption fizzle, with very small benefits and costs.

3. The 3P Test

Staying true to my earlier assertion that scenarios without probability estimates are not useful, I will try to put the various AI scenarios that I mapped out in the last section on the 3P continuum.

Let me start with the two possible, but not quite plausible scenarios. The first is the a speedy, massive AI disruption, where AI displaces worker across most businesses, and does so quickly, as visualized by Citrini. It can happen, but given the history of disruption, the limits of AI technology and inertia in the process, it is implausible. At the other extreme, it is possible that AI provides tools to workers that improve productivity marginally, with many ending up being more distractions than tools for productivity, effectively emptying its destructive potential, but that too strikes me as implausible, given what we are seeing in terms of AI capabilities. The most plausible scenarios are ones where AI displaces workers in some industries, such as software and some financial intermediaries, and provides tools that help workers to varying degrees in other businesses. As for probable, I think that disruption will reduce workforces in a subset of businesses, that its tools will include some game changers and that it will take longer to unfold, at least when it comes to monetization, than its advocates think.

My justification for why AI disruption will take time is based on a mix of factors. The first is that my (limited) knowledge and experience with AI products is that while they sometimes work magically well and quickly, they do have kinks, coming partly from being unable to separate good data from bad, and partly from their imperfect attempt to be imitate humans. The second is history, where no disruption has ever unfolded without delays and drawbacks; remember that the dot com disruption almost lost its moorings during the market bust in 2001. The third is human nature, where much as employees and managers claim to want to move on to new and better options, they remain attached to old technology and products; typewriters and mimeographs took a while to disappear after PCs stormed the workplace and flip phones persisted well into the smartphone era.

There are two reasons why I do think that AI disruption is still going to be significant, in the long term. The first is that some of those making the argument that AI will not displace jobs in the long term are assuming that AI in it more advanced form will look like ChatGPT on steroids or be primarily mechanical in its applications. Even my limited exposure to AI's advanced tools suggests that they have far greater capabilities, and their capacity to mimic human intuition and thought processes is unsettling. The second is the blanket assumption that workers in most white collar jobs will not be easily replaced because they bring training, brainpower and experience into those jobs that will be difficult to replicate. Many white collar workers are bright people with specialized knowledge, but the businesses that hire them put them in straight jackets, pushing mechanics over intuition and rule-driven thinking over principle-driven assessments. In short, it is the nature of the jobs that we have created in many white collar settings that makes them vulnerable to disruption, not the intelligence or training of the people holding those jobs.

It is worth noting that in my probable scenario, AI will unfold at different rates in different businesses, and if I were pushed to distinguish between the businesses that will be targeted most (and soonest) from the businesses where it will take more time, and have less impact, I would look at four factors:

There may be some confirmation and hindsight bias in this table, but there is a good reason why software, a relatively young industry, with young companies and employees, has been one of the first targets for AI disruption. it is profitable, its products and services are logical and rule-based and much of it has no regulatory or system protection. Within software, though, i would expect software that requires more user interface to be more resilient to AI disruption than software that operates in the background. This table, though, can help determine which white collar jobs will be most exposed to AI disruption, and which least, and perhaps also explain why blanket statements about job displacement in banking, consulting and law are overwrought. With banking and law, a substantial portion of the work done is to meet legal or regulatory requirements, not fill operating needs. I have written about the inanity and uselessness of fairness opinions in M&A, where bankers opine on whether an acquiring company is paying a "fair" value for a target, but this practice persists because these fairness opinions provide cover against lawsuits that ensue when deals fall apart. My guess is that the Delaware courts are not quite ready for an AI fairness opinion bot to take the stand and defend a deal, even if the quality of its work is better than a human banker. With consulting, where cookbook solutions are more the norm than the exception, it is worth remembering the clients pay consulting fees not for the advice, but so that they have someone else to blame, when things go wrong, and there too, an AI bot will not have the same outsourcing power as an army of bankers with Harvard MBAs from McKinsey.

4. Cui Bono?

Most of the AI scenarios yield net benefits, and even in the most damaging scenarios, where the AI disruption benefits are overwhelmed by its costs, at least in the short term, you could argue for net positive benefits in the long term. That is good news, but it should taken with a grain of salt, since the distribution of these net benefits across businesses and society will be unequal, and it is possible that the net benefits accrue to a few businesses (and individuals), leaving the rest (businesses and individuals) with net costs.

The interests of the AI companies and the rest the economy/market will diverge on AI disruption, with the former benefiting if the disruption is across many businesses and happens quickly, and the latter benefiting from a slower disruption restricted to a few businesses. This will be the case even if AI tools add to productivity, since the lower costs that companies acquiring these tools will have as a consequence, may not translate into higher profits, especially if their competitors can pay and acquire the same tools.
The last few major disruptions, starting with the internet, moving on the China and then the smartphone, have all tilted the playing field in many businesses towards larger companies, making businesses more winner-take-all. It is likely that the AI disruption will play out in similar ways, with the winners winning big, and lots of companies losing out.
At the individual level, it is not just plausible, but also likely, that a strong AI disruption will make wealth and income inequality worse, with founders of AI businesses joining the ranks of the deca-billionaires and centi-billionaires.

There is one final cost that may not be explicit in economic terms, at least immediately, but one that has to enter the discussions, As AI threatens to displace workers in white collar businesses, it is worth remembering that a job is not just an income-generator, but also a source of self esteem and worth. When software engineers, who pride themselves on their coding skills, bankers, who have spent decades becoming excel ninjas, and consultants, who have found inventive ways of packaging cookbook solutions and presenting them as new and inventive, find that AI can do what they have spent a lifetime perfecting almost effortlessly, the psychic damage will be significant. The fact that blue collar workers lost their jobs to the internet and China disruptions faced a similar predicament and were largely ignored also means that there may be more than a hint of schadenfreude in society's response to white collar job losses.

The AI Personal Threat

If you are looking at these side costs and threat to jobs that will come from the AI disruption, and wondering whether we should opt out, by regulating or restricting its reach, I am afraid that the choice is out of our hands. The genie is out of the bottle, and the only pathway that you have, if you operate in a space where AI is ubiquitous, is prepare for a reality where AI tools can automate and do much of what you do on a daily basis, but where you have to create a niche or moat that still makes you necessary.

Just about two years ago, I wrote about an AI entity called the Damodaran Bot, that was being developed by Vasant Dhar, my colleague at NYU, and noted that having made all that material that I have developed in my lifetime (classes, books, writing, models, videos) publicly available, I was completely exposed to AI disruption. I have watched that bot develop, with quirks and occasional hiccups, to a point where it can replicate much of what I do almost effortlessly. At the time, though, I did write about what I could do to keep the moat at bay, including the following:

Generalist vs Specialists: I am a dabbler, an expert in nothing and interested in lots of different things, and I do think that gives me an advantage over a bot that is trained to focus on a topic and drill down. The specialist advantages stem from mastering the vast content in a discipline, but those advantages are diluted with AI entities that can also see that content, but the generalist advantage of using multi-disciplinary thinking with be more difficult for AI to replicate.
Left and Right Brain: I value companies, and early in my valuation life, I decided that financial modeling was not the right path to value businesses, and that good valuations bridge stories and numbers. If the legend of the right and left brains holds, where the left brain controls logic and numbers and the right brain drives your imagination, a bot will have a tougher time replicating what you do, if you use both sides. That said, I have seen the Damodaran Bot get much better at story telling in the two years that I have watched it, and I need to up my game.
Reasoning muscle: When faced with questions in the days before the internet, you often had no choice but to reason your way to answers. That may have been time consuming, and your answers might even have been wrong, but each time you did this, you strengthened your reasoning muscles. As we move into a period, where the answer to every question is online, on Google Search and ChatGPT, we are losing the need to exercise those reasoning muscles, and exposing ourselves to being outsourced by our bots.
An idle mind: I am not a voracious reader nor a listener to podcasts, and since I don't have much real work to occupy me, I also have plenty of vacant time, with nothing to do. I use that time to daydream and ponder about questions that capture my imagination, including why someone would pay billions of dollars for a sports franchise (like the Washington Commanders), how to deal with the risk of lava from a volcano hitting a spa and ruining its valuation and how streaming has broken the entertainment business. None of these posts include deep insights, but my guess is that the Damodaran bot would have trouble keeping up with my wandering mind.

With the admission that is may not be enough, and that my bot may soon be able write my books and posts, teach my classes and analyze/present data better than I can, I think that you should all be acting as if a bot with your name is looking over your shoulder and trying to learn what you do, and think about what you can do to keep that bot at bay.

There is always the possibility that you are arming yourself for a disruption that fizzles, but I will draw on Pascal's wager to explain why you should prepare for an AI imitator or bot, even if you don't believe that it is imminent:

Pascal, a French mathematician, used the wager to explain why be believed in God, even if he was doubtful of a heavenly presence, because the expected value from believing in God exceeded the expected cost from not believing. In the context of AI, acting as if an AI presence and competitor is present will make you better at whatever you do, as a teacher, banker, consultant or software engineer, and that will persist, no matter what AI's impact is ultimately. Good luck!

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Data Links

Federal Reserve in St. Louis AI Adoption Tracker

AI Scenarios

Data Update 8 for 2026: Time for Harvesting - Dividends and Buybacks

2026-02-24T18:45:00.003-05:00

In the data update posts this year, I have wended my way from the macro (equities collectives, the bond market and other asset classes) to the micro, starting with hurdle rates and returns in posts five and six and the debt/equity choice in my seventh post. In this post, I will look at the decision by businesses on how much cash to return to their owners, and in what form (dividends or buybacks), and how that decision played out globally in 2025. I will argue that dividend policy, more than any other aspect of corporate finance, is dysfunctional both for the firms that choose to return the cash and the investors who receive that cash. It is also telling that there are many who seem to view the very act of returning cash as a sign of failure on the part of firms that do so, even though it is the end game for every successful business.

The Dividend Decision

I start my corporate finance classes with a description of three core decisions that every firm has to make in the course of business, starting with the investment decision, where you try to invest in projects and investments that earn more than your hurdle rate, moving on to the financing decision, where you decide on the mix of debt and equity to use in funding those investments, and ending with the dividend decision, where firms decide how much cash to return to their owners. In the case of privately owned businesses, this cash can be withdrawn by the owners from the business, but in publicly listed companies, it takes the form of dividends or buybacks. In keeping with the notion that these are the cashflows to equity investors, and that those cash flows should represent what is left after (residual) after all other needs have been met, dividends should reflect that status and, at least in principle, be set after investing and financing decisions have been made:

That utopian view of residual cash being returned to shareholders is put to the test by two real-world realities that often govern corporate dividend policy:

Inertia: In many companies, dividend policy is set on auto pilot, with dividends this year set equal to dividends in the last year. It is for that reason that the word I would use to describe dividend policy, at least when it comes to conventional dividends, is 'sticky', and you can can see that stickiness at play at US companies, if you track the percentage of companies that increase dividends, decrease dividends or leave them unchanged every year.

In every single year, from 1988 to 2025, the percentage of companies that pay the same dividends that they did in the previous year outnumbers companies that change dividends, and when dividends are changed, they are more likely to be increased than decreased.
Me-tooism: In most companies, managers look to peer group dividend policy for guidance on how much, if any, to pay in dividends. Thus, if you are a bank or a utility, it is likely that you will pay high dividends, because everyone else in the sector does so, whereas technology companies will pay no or low dividends, because that is industry practice. While there are good reasons why some industry groups pay more dividends than others, including more predictable earnings and lower growth (and investment needs), hewing to the peer group implies that there will be outliers in each group (fast-growing banks or a mature technology companies) that will be trapped into dividend policies that don't suit them.

When maintaining or increasing dividends become the end game for a business, you unleash dividend monsters, where investing and financing decisions are skewed to meet dividend needs. Thus, a firm may turn away good investments or borrow much more than it should because it feels the need to sustain dividends.

I have long argued that dividends, in their sticky form, are unsuitable as cash returns to shareholders, but for much of the last century, they remained the primary or often only way to return cash to shareholders. While buying back stock has always been an option available to US companies, its use as a systematic way of returning cash picked up in the 1980s, and in the years since, stock buybacks have become the dominant approach to returning cash for US companies:

As you can see, in the last decade, more than 60% of cash returned to shareholders took the form of buybacks. The primary reason, in my view, is that buybacks, unlike dividends, are flexible, with companies often reversing buybacks, if macro circumstances change, as was the case in 2008 and 2020. There are other reasons that have been offered for the explosive growth in buybacks, but none of them are as significant. There are some who have argued it is stock-based compensation for managers that is pushing them away from dividends to stock buybacks, but that rationale makes more sense for stock options, where stock prices mater, than for restricted stock. In fact, even as more companies shift to restricted stock as their stock compensation mechanism, buybacks have continued to climb, and they are just as high at companies that have no or very low stock based compensation as at companies with high stock-based compensation. Investor taxes are alway in the mix, since investors are often taxed at different rates on dividends and capital gains, but changes in tax law in the last two decades have reduced, if not eliminated, the tax disadvantages associated with dividends, cutting against this argument.

I know that there are many investors, especially in the value investing camp, and quite a few economists, who believe that the shift away from dividends to buybacks is unhealthy, albeit for different reasons. I will return to many of the myths that revolve around buybacks later in this post.

A Rational Cash Return Policy

If you were designing a sensible cash return policy, it has to start with an assessment of how much cash there is available for a firm to return. Since that "potential dividend" should be the cash left over after taxes are paid, reinvestment has been made and debt repaid, it can be computed fairly simply from the statement of cash flows, as free cashflow to equity:

Note that free cash flow to equity starts with equity earnings, converts those earnings to cash flows by adding back depreciation and other non-cash charges, and then netting out capital expenditures and changes in working capital, with increases (decreases) in working capital reducing (increasing) cash flows. It is completed by incorporating the cash flows from debt, with debt issuances representing cash inflows to equity investors and debt repayments becoming cash outflows. Can free cash flows to equity be negative? Absolutely, and it can happen either because you are a money-losing company, too deep in the hole to dig yourself out, or even a money-making companies, with large reinvestment needs? Obviously, paying out dividends or buying back stock when your free cash flows to equity is violating the simple rule that if you are in a hole, you need to stop digging.

If your free cash flow to equity is positive, you can choose to return it to shareholders, either in the form of dividends or buybacks, but you are not obligated to do so. In fact, if you have positive free cashflows to equity and you choose to return none or only a portion of that cash flow, the difference accumulates into a cash balance. If you choose to return more than your free cashflow to equity, you will either have to deplete an existing cash balance, or if you run out of cash, go out and raise fresh capital.

A company that systematically holds back on cash that it could have returned will, over time, accumulate a large cash balance, but that, by itself, may not trigger a shareholder response, if shareholders trust the company's managers with their cash. After all, cash invested in liquid and riskless investments, like treasury bills and commercial paper, is a neutral (zero NPV) investment, and leaves shareholders unaffected. If you don't trust management to be disciplined, though, you may punish a company for holding too much cash, effectively apply a "lack-of-trust" discount to the cash. The picture below provides a framework for thinking through the cash return decision, and how it will play out in markets.

As you look at the interplay between earnings, investment needs and potential dividends, you can already see why you should expect cash return policies to change over a company's life cycle:

The cash returns you see in this graph should largely map on to common sense, with start-ups and very young companies, often money-losing and requiring substantial reinvestment to grow, having negative free cash flow to equity (thus requiring equity infusions). Young growth companies are usually self-funding because internal cash flows may rise to cover reinvestment, but these cash flows are not enough to pay dividends. Mature growth companies have enough cash to return, but stick with buybacks, because they value flexibility. Mature stable companies represent the sweet spot for dividend paying, since they have little in reinvestment needs and large predictable earnings and cash flows. As with everything else in the aging process, companies that refuse to act their age, i.e., young companies that choose to pay dividends or buy back stock or mature companies that insist on holding on to cash, damage themselves and their shareholders.

Dividends in 2025

I will start the assessment of how much companies returned to shareholders in 2025 by looking at conventional dividends paid by companies, using two metrics. The first metric is the dividend payout ratio, where I divide dividends paid by net income, but only if net income is positive; if net income is negative, and dividends get paid, the payout ratio is not meaningful:

As you can see the median payout ratio is about 35% (59%) for US (global) companies, but in both samples, most companies do not pay dividends. There is a sizable subset of companies (12% of US and 14% of global companies) that pay out more than 100% of earnings as dividends, with multiple reasons for that oversized number including a bad earnings year, a desire to increase financial leverage and partial liquidation plans all coming into play.

The second metric is the dividend yield, computed by dividing dividends paid by market capitalization, or dividends per share by the market price per share. In the graph below, I look at the distribution of dividend yields across companies in the graph below, in 2025:

Again looking at only dividend paying firms in the US and global samples, the median dividend yield was 1.10% for the former and 2.43% for the latter, with major divergences across sub-regions; note that the percent of dividend paying firms in the United States has dropped below 30% and even globally, less than half of firms pay dividends. The dividend yield ties into the cost of equity discussion that I initiated in my fifth data update, where I described the cost of equity as the rate of return that investors expect to make on their equity investments. In the United States, for instance, that expected return was about 8.50% at the start of 2026, which would indicate that if you are an equity investor, it is price appreciation that you are dependent on, for the bulk of your equity return.

The dividend yield for equities has declined over time, with the drop off being most noticeable in the United States. The graph below looks at the dividend yield on the S&P 500 from 1960 to 2025, and how that number has become a smaller and smaller portion of the overall expected return on stocks (which I compute with the implied equity return approach):

In 1960, about half of your expected return on stocks came from dividends and that statistic has trended downwards for the last few decades, and in 2025, it represented less than 15% of the total return on stocks.

As a final part of this analysis, I looked at dividend yields and payout ratios, broken down by sector, for both US and global companies:

As you can see, the sectors with the highest percentage of firms paying dividends are financials, real estate and utilities, for both US and global companies, and consumer product companies join in that group, for global companies. In terms of payout ratios, the same three sectors dominate, with energy and real estate returning more than 200% of net income as dividends, in 2025, and posting dividend yields in excess of 6%. Technology companies and communication services have the lowest percent of dividend paying companies and the lowest dividend yields and payout ratios.

The drop in dividend yields over time for the market, the decline in dividend paying firms and the concentration of dividend paying firms in some sectors has put old time value investing to the test. Ben Graham's strategy of principal protection was built around buying large dividend paying firms and holding on for the long term and it has hit a wall. Any investing strategy built around dividends will result in a portfolio composed of mature and declining firms, and even if you accept that reality, those firms are increasingly concentrated in real estate, banking and utilities.

Buybacks - Myths and Realities

As buybacks have soared in the United States, misconceptions and myths about buybacks have also surged, with some myths used to back up the argument that buybacks are unhealthy and should therefore be banned and others presented as the basis for buybacks as good, representing cannot-lose strategies to beat the market. I will start with the myths that are used to argue against buybacks first, before moving on to those that are used to justify it:

1. Myths in favor of the argument that buybacks are bad and should be restricted or stopped

Myth 1.1: Buybacks are a US phenomenon

Reality 1.1: Buybacks are becoming a global phenomenon

When US firms first started buying back stock in the 1980s, it is true that is was almost entirely or primarily a phenomenon restricted to the US, with large parts of the world restricting or banning the use of buybacks to prevent price manipulation by companies. That is no longer the case, and companies around the world have taken to buybacks, as a flexible alternative to dividends, have adopted the practice. In 2025, I looked at dividends and buybacks from companies around the world:

Companies in the United States are still in the lead in the buyback race, buying back $1.153 trillion in stock in 2025, close to 60% of overall cash returned. Canada, the UK, and Japan are not far behind with more than 35% of cash returned taking the form of buybacks, and the EU and environs, often the slowest to adapt to change, saw almost 29% of cash returned in buybacks. For a variety of reasons, including poor corporate governance and regulatory restrictions, Africa & the Middle East, Eastern Europe and much of south and southeast Asia return relatively little in buybacks.

Myth 1.2: Buybacks are wasteful and reduce corporate investment

Reality 1.2: Buybacks redirect corporate investment from mature companies to growth businesses

The argument that buybacks are wasteful often come from using a firm as a self-contained economic unit, and noting that money used on buybacks cannot be reinvested back into the firm. That is absolutely true, but the cash that goes into buybacks goes to investors and mostly goes back into the market, as investments in other companies. While there are clearly exceptions, where companies that should be investing back into their businesses use that cash to buyback stock, the companies that are the biggest buyers of their own stock are mature firms with insufficient investment opportunities and the companies that have the cash redirected into them need that cash to fund their growth. You can see this play out, when you look at stock buybacks broken down, by age decile (based upon corporate age) for US and global companies:

As you can see, younger companies are not only less likely to buy back stock, but also return less cash in dividends and buybacks, at least as a percent of market capitalization than older companies. Using the life cycle perspective, this suggests that cash is rotating out of older, more mature businesses into younger businesses. I would argue that the difference between geographies where buybacks are rare and geographies where buybacks are common is not in how much corporate investment there is, but in where that investment is directed, with the former investing investing back into declining businesses and the latter funding higher growth and newer businesses.

Myth 1.3: Buybacks are funded with debt are are making companies too highly levered

Reality 1.3: Buybacks are primarily funded with free cash flows to equity and even as buybacks have surged, debt ratios have decreased.

I am not a great believer in case studies precisely because anecdotal evidence is spun into backing priors and preconception.s There are, of course, firms that have dug themselves into a hole by buying back immense amounts of stock, and funding those buybacks with debt, but the aggregate debt ratios for US non-financial service firms, with debt to capital ratios measured against both book and market, have declined over the last four decades, even as buybacks have surged.

If your response is that not all companies buy back stock, and that debt ratios has risen at companies that buy back stock, a comparison of debt ratios (debt to EBITDA and debt to capital) for US firms that bought back stock in 2025 versus those that do not dispels that argument:

If firms are borrowing money to fund buybacks, it is clearly not showing up in the statistics, since companies that bought back stock had much lower debt loads than the companies that did not, a simplistic comparison, but one that carries heft.

Myth 1.4: Buybacks are value-destroying because companies tend to buy back their own stock when prices are too high

Reality 1.4: Buybacks, at any price, can neither add nor destroy value. They can just transfer value

Warren Buffett was late to the buyback party, but when he initiated buybacks at Berkshire Hathaway, he introduced a constraint, which is that he would do buybacks only if he believed that the company's stock price was less than intrinsic value. He, of course, had the credibility to make this assertion, but most companies don't impose this constraint and there is evidence that they often buy back their shares when stock prices are higher than they are lower. That does seem like value destruction, but a cash return can neither add nor destroy value, but it can transfer wealth. In the case of stock buybacks at too high a price, wealth is transferred from those who remain loyal shareholders in the firm to those who sell their shares. While there is hand wringing about this, you have a choice, as a shareholder, in a buyback, to sell or hold on, and if you believe that the buyback is at too high a price, you should sell your shares back.

2. Myths in favor of the argument that buybacks are good and generate excess returns for investors

Myth 2.1: Buybacks are value-adding because companies that buy back their own stock when prices are lower than fair value are taking positive net present value investments.

Reality 2.1: Buybacks, at any price, can neither add nor destroy value. They can just transfer value.

This is the inverse of the argument that buybacks are value destroying and they are both grounded in a misclassification of buybacks as projects, rather than cash return, competing with investment projects for the company's dollars. The truth again is that a stock that buys back stock at lower than fair value is transferring wealth from those who sell back to those who remain, and here again, if you are on the wrong side of wealth transfer, it was your choice to sell back that made you the loser.

Myth 2.2: Buybacks are almost always good for stock prices, since there are fewer shares outstanding after buybacks, and that should increase the price per share.

Reality 2.2: A buyback can increase, do nothing or decrease value per share, depending on the price at which it is done and its effects on leverage.

Buybacks reduce share count (the denominator) but the cash that leaves the firm also reduces fir value (the numerator). A fair-value buyback will create offsetting effects, leaving value per share unchanged, though there can be a secondary effect on value, if the buyback, by reducing equity, changes the debt to capital mix and cost of capital for a company:

It is true that empirical evidence backs up the notion that stock prices benefit from buybacks, but that may be from the selection bias of under levered firms with large cash balances being the biggest players in the stock buyback game.

In general, almost all of these myths come out of treating buybacks as something new and different, rather than a variant on dividends. In general, companies that should not be paying dividends, either because they lack the cash or the future is uncertain, should not be buying back stock either.

Dividend Dysfunction

At the start of this post, I noted that dividend policy is dysfunctional at many firms, driven by inertia (we've always paid dividends or we've never paid dividend before) and the desire to hew to peer group policies. As a result, there are many companies around the world that adopt dividend policies that, at least of the face of it, take explaining including:

Money-losing companies that pay dividends: While there are some companies that offer justifications grounded in worries about sending bad signals or hopes of a bounce back in earnings, many get stuck with dividend policies, because of inertia or peer group pressure, that can drive them into ruin.
Money-making companies that refuse to pay dividends: Here again, there can be good reasons for holding back including concerns about whether you can sustain earning and expectations that you will need to invest more in the future, but in some cases, it can unwillingness to initiate dividends in an industry where no one else pays dividends.
Negative FCFE companies that return cash (dividends or buybacks): In addition to hopes for a bounce back in FCFE, companies may continue to return cash, even with negative FCFE, because they are trying to increase debt ratios or shrink their businesses over time.
Positive FCFE companies that return no cash: Companies that have positive FCFE that don't return cash may hold back that cash because of the desire to reduce debt ratios or because they ahve investment plans.

The graph below lists out the number of companies in each group, broken down by geography:

Across the globe in 2025, almost 18% of money-losing companies paid dividends, as did about 70% of money-making companies. With FCFE as your indicator, about 37% of companies that returned cash (in dividends and buybacks) in 2025, had negative FCFE, as did 66% of companies with positive FCFE.

Conclusion

There are a whole host of misalignments between what companies return to their shareholders, either as dividends or in buybacks, and what they can, as potential dividends. That suggests to me, and perhaps I am wrong, that investment strategies that are built around cash return, whether they be dividends or buybacks, are likely to go off the tracks. Furthermore, any strategy that is built entirely around dividends, as is the case with strategies where you load up on high dividend yield stocks or buy a handful of heavy dividend payers, such as the Dogs of the Dow, misses the essence of equity investing. A stock is not a bond, where dividends replace coupons, and you get some price appreciation on top, and treating it as such will only create disappointment.

YouTube Video

Data links

Dividend statistics, by industry (US and Global)
Buyback statistics, by industry (US and Global)
Dividends and Buybacks - History for US firms

Spreadsheets

Buyback stock price calculator

Data Update Posts for 2026

Data Update 7 for 2026: Debt and Taxes

2026-02-20T11:48:00.004-05:00

In my fifth data update, I examined hurdle rates in 2025, and in my sixth data update, I looked at the profitability and return metrics for firms. Both hurdle rates and profitability metricsmcan be affected by how much debt companies choose to have in their financing structure, and it enters explicitly into my cost of capital calculations, both through the costs of equity/debt and the mix of the two, and into my accounting return calculations, for net margin and return on equity. In this session, I start with an examination of the trade off that all businesses face when it comes to choosing between debt and equity to fund their operations, and then look the debt choices that companies made in 2025. As with every other one of my data updates this year, AI enters this conversation not only because of the huge investments that are being made into AI architecture, but also because a non-trivial portion of this investment is coming from debt, with private credit as a key contributor.

Debt versus Equity: Choices and Tradeoff

The discussion of the tradeoffs that businesses face on whether to borrow money (debt) or use owner's funds (equity) has to start with a clear distinction between what it is that sets them apart. While that distinction may seem trivial, since accountants do break financing down into debt and equity on accounting balance sheets, accountants are not always consistent in their categorization, and I think that understanding what sets debt apart from equity can help catch these inconsistencies. There are three dimensions where debt and equity deviate:

Nature of claim: Debt gives its holders a contractual claim on the cash flows, insofar as the terms of interest and principal payments are laid down contractually at the time of the borrowing. Note that these contractual claims cover both fixed rate debt, where the interest payments are fixed over the lifetime of the debt, and floating rate debt, where the interest payments will change over time, but in ways that are specified by the bond/loan agreements. Equity gives its holders a residual claim, i.e,, a claim on cash flows, if any, that are left over after other claim holders have been paid.
Priority of claim: This follows from the first distinction, but debt holders get first claim on the cashflows, when the firm is in operation, and on liquidation proceeds, if the firm ever goes bankrupt. It is this priority of claims that should generally make debt safer than equity in almost every enterprise that employs both.
Legal consequences: A company that fails to pay dividends to its equity investors, no matter how deeply set their expectations of receiving these dividends, may see its stock price drop, but it cannot be held legally accountable for the failure. A company that fails to make its contractual obligations on debt can not only be sued, but can be pushed into bankruptcy, effectively ending its business life.

There are three other distinctions, which do not always hold, but are usually true:

Tax Treatment: In much of the world, the tax code is tilted in favor of debt, with interest payments being tax deductible and cash flows to equity (dividends or buybacks) coming out of after-tax cash flows, but there are three caveats. The first is that the tax savings from debt kick in only when a company is generating a taxable profit, though laws on tax loss carry-forwards can allow even money-losing firms to get tax benefits, albeit with a delay. The second is that there are parts of the world, such as the Middle East, where the tax code explicitly bars interest tax deductions, though companies find work arounds sometimes to get the benefits. The third is that there are a few countries that try to even the playing field by either giving a tax deduction to companies for some payments to equity investors (interest on capital as a tax deduction in Brazil) or to investors directly by allowing them credits for corporate taxes paid, when they receive dividends.
Role in management: In most businesses, equity investors are given supremacy when it comes to managing the company, exercising that power through either direct ownership or corporate governance mechanisms (such as boards of directors). Again, there are exceptions, as is the case where lenders are given seats on boards of directors or veto power over major operating decisions, but these exceptions are usually triggered when companies violate covenants in loan agreements.
Maturity: Debt usually has a finite maturity, though as we saw with the Google hundred-year bond issuance just a few weeks ago, that maturity may be well beyond the lifetime of the buyers of the bond. Equity, in contrast, is, at least on paper, an instrument with no finite due date, and may have cash flows that last into perpetuity.

The figure below captures the differences between debt and equity in the context of a financial balance sheet:

With these distinctions in place, and given that businesses have a choice of using either debt or equity to fund their operations, let us look at the trade off, starting with what the fictional (but often used) reasons for using one source of funding over the other:

One of the most common (bad) reasons that I hear business owners and CFOs of even large companies give for borrowing money is that debt is cheaper than equity. On the face of it, that is of course true, but it is an illusion, at least without the tax benefits kicking in. If the debt is fairly priced, i.e., you are being charged an interest rate that reflects your default risk, borrowing money will make your equity more risky and leave your cost of capital unchanged (if you have no default risk) or raise it (if you have default risk). Intuitively, your cost of capital is designed to capture the risk in your operations, and playing games on the financing side cannot change your operational risk. Among risk-takers, a common reason for using debt is that it will increase your return on equity, and while that again is technically true, it will also raise your cost of equity and magnify the impact of both your successes and your failures. Thus, if you want to borrow money to magnify the payoff to you, as an equity investor, from a successful trade or investment, you should do so, but dispense with the illusion that this is a free lunch. Those who avoid debt have their own share of illusions, starting with the argument that the interest payments on borrowed money will lower net income. That is true, but since you have less equity invested, you may still come out as a beneficiary. They also argue that debt will increase default risk, and lower their bond ratings, but of which are likely to happen, but the objective in business is not to maximize bond ratings, but to increase value; a BBB-rated firm that borrows money and gets tax advantages can be worth more than the same firm with a AAA rating and no debt.

So what are the real trade offs? The first and biggest benefit of debt is its tax treatment, with the tax benefits adding to firm value. Note, and this is said with no moral or ethical judgment attached to it, that this increase in value is coming from taxpayers and not from your operations becoming more valuable. A secondary benefit may come from imposing discipline on managers in public companies, with the need to make interest payments operating as a restraint on a headlong rush into poorly performing investments. On the other side of the ledger, the biggest concern you should have when you borrow money is that it increases the risk of bankruptcy, which if it happens, truncates business life, and even it does not, concerns about it happening can alter how customers, suppliers and investors interact with a business. The other cost that you face when you borrow money is that equity investors and lenders have very different interests, with equity seeking upside and lenders worrying about downside, and the costs of that conflict of interests plays out in covenants and restrictions on operating activity. The figure below summarizes these real trade offs.

The tax benefits versus bankruptcy cost trade off on debt is a simple and very powerful explainer of how much companies should borrow, but in the real world, there are companies that sometimes override the tradeoff and choose to borrow far more or far less than you would expect them to, and they are not necessarily being irrational. Here are three reasons why companies may choose a sub-optimal financing mix:

Shields against bankruptcy: If the biggest restraint on borrowing more is the fear of default, anything that reduces or eliminates that fear will cause companies to borrow more money. That default protection can come from governments acting as implicit or explicit guarantors of corporate debt, as was the case with Korean companies in the 1990s, or from seeing other companies in trouble being bailed out by the government, because they were too big to fail.
Control versus Value: While businesses have the option of using either equity or debt to fund operations, raising fresh equity usually requires giving up ownership of the business to venture capitalists (at a private business) or to other public market investors (for public companies). For founders and family groups that value control over almost everything else, this can result in firms borrowing money, even though the fundamentals do not support the action. This can explain why Middle Eastern firms, many of which get no tax benefit from debt, may choose to borrow money to fund operations, usually with higher costs of capital, as well as the existence of venture debt, an almost absurd notion from a corporate finance standpoint, since you are lending to start-ups and young money-losing companies with unformed business models and
Subsidized debt: If a business has access to debt with below-market interest rates, given default risk, it may make sense to borrow money at these subsidized rates. These debt subsidies are often granted to companies that are seen as delivering on a social purpose (green energy in the last decade) or a political/security interests (defense and infrastructure businesses), and you should therefore not be surprised if they all carry too much debt.

On the other side of the ledger, there are three reasons why companies may borrow less than they should:

Restrictive covenants: In markets where debt comes primarily from bankers, it is possible that the covenants that come with this debt are so onerous that businesses will choose to leave tax benefits on the table in order to preserve operating flexibility; this may explain why technology companies, even those with large and stable cash flows, often choose not to borrow money or if they have to, go directly to bond markets.
Overpriced equity: Financial markets make mistakes, and sometimes those mistakes may work in your favor as a company with your stock price soaring well above what you think is justifiable, given your fundamentals. In that case, you may choose to use equity, even if you have debt capacity, using your own overpriced shares as currency in funding acquisitions.
Regulatory constraints: In some countries and/or sectors, there may be regulatory restrictions on borrowing that cap how much debt you can take on, even though you have the capacity to carry more in debt. Those restrictions can take the form of limits on book debt ratios or on how much interest expense is tax deductible, as a function of revenues or EBITDA.

The picture below captures these frictional considerations:

In sum, the choices between debt and equity play out differently at different companies, depending not only on the characteristics of the company (tax rate, default risk etc.) but also on the management team making that decision on whether to borrow money. If you are an optimizer, by nature, you may this discussion too diffuse, since it points you in a direction (more or less debt) and not to a specific debt mix, but that is easily remedied, if you use the cost of capital as your optimizing tool to find the mix of debt and equity that minimizes your cost of capital.

Download optimizer spreadsheet

Rather than try your patience by belaboring that process, I can point you in the direction of how that is done in my corporate finance class sessions, and with this tool.

Debt and Equity in 2025

With this tradeoff on debt and equity in mind, let's turn to the data, and in particular, I plan to focus on the choices that companies made globally, on the financing question, in 2025. I will start by looking at the two forces that should have the greatest relevance in this decision, the tax benefits of debt and the default risk, and then look at the mixes of financing across sectors, industries and regions.

The Tax Landscape

Any discussion of taxes has to start with reality checks. The first is that governments need tax revenues, to fund their spending, and corporations and businesses are a target, partly because they affect taxpayers (and voters) indirectly, rather than directly (as is the case with income and sales taxes). The second is that businesses do not like to pay taxes, and try to minimize the taxes they pay, mostly through legal means, with accountants, transfer pricing specialists and tax lawyers abetting, though they sometimes step over the line into tax evasion. When measuring the tax burden that businesses face, we have to distinguish between three measures of tax rates:

Marginal Tax Rates: The marginal tax rate reflects the tax rate you face on the last dollar of your taxable income, and thus comes from the statutory tax code of the domicile that the business operates in. While there are a few companies that try to report these tax rates, you are more likely to uncover them by going into the tax code. Fortunately, the leading accounting firms keep updated estimates of these marginal tax rates in the public domain, as do some tax watchdogs, and I used The Tax Foundation for this year's updates across countries, and the numbers are in the picture below:
Download corporate tax rates, by country
While your eye may be drawn to differences in corporate tax rates, across countries, these differences have narrowed, as the countries with the largest economies (and taxable business) are converging around a marginal tax rate of 25%. There are regional differences, with Latin America and Africa home to some of the highest corporate tax rates, and Eastern Europe and Russia home to some of the lowest. Clearly, there are exceptions within each region, with Ireland the leading outlier in Europe, with a marginal tax rate of 12%, and Paraguay in Latin America, with a marginal tax rate of 10%.
Effective tax rates: The effective tax rate is an accounting measure, reflecting the taxes paid and taxable income line items in the income statement, which follows accrual accounting principles. The effective and marginal tax rates can deviate for many reason, including corporate income earned in other countries, tax deferral strategies and even differences between tax and reporting books. I estimated effective tax rates for the companies in my database, and report the averages, by sub-region of the world, in the table below:
Corporate Marginal and Effective Tax Rates, by Country
In the aggregate, the effective tax rates were lower than the marginal tax rates in about 60% of the companies in my sample, and the difference is a rough proxy for the effectiveness of a tax system, with marginal tax rates running close to or behind effective tax rates in more effective tax regimes. By that measure, India has the least effective tax code among the regions, with an effective tax rate of 22.33% and a marginal tax rate of 30%, followed by the United States and Japan, though the caveat would foreign sales in lower tax locales, in each of these cases. The tax rate statistics, broken down by industry, for global companies, is at this link, if you are interested.
Cash tax rates: The cash tax rates also come from accounting statements, with the information in the statement of cash flows used to convert accrual taxes paid to cash taxes paid, and are reflective of what companies actually pay to governments during the course of the year. In 2025, the average cash tax rate across companies with taxable income was 25.86% (21.02%) for global (US) firms, about 1% higher than the effective tax rate in both cases.

For the debt question, it is the marginal tax rate that is most relevant, at least for computing tax benefits, since interest expenses save you taxes at the margin; interest expenses get deducted to get to taxable income, and it is the last dollars of taxable income that thus get protected from paying taxes.

The Default/Distress Landscape

In a world where companies never default, and you still get tax benefits from borrowing, companies push towards higher and higher debt ratios. In the real world, default acts as a brake on debt, with higher default risk translating into lower debt ratios. While default risk is company-specific, the exposure for default risk, across all companies, will vary over time, largely as a function of how well the economy is doing. The ratings agencies (Moody's, S&P and Fitch) track defaults on a year-to-year basis, and in 2025, they all recorded a drop in default rates across the globe, with US companies driving much of the decline. S&P, in its review of 2025 default and distress, reported that a drop in corporate defaults from 145 in 2024 to 117 to 2025, with the US share of defaults declining from 67% to 62%. To provide historical context, I looked at corporate default rates on loans (using data from FRED) on a quarterly basis going back to 1986:

Corporate loan default rates

While the low defaults in 2025 were a positive sign for lenders, especially given the economic turmoil created by tariffs and trade wars, there were some worrying trends as well. In May 2025, Moody's estimate of the probability of default at US companies spiked to 9.2%, its highest value since the 2008 crisis. On the bond ratings front, you had more ratings downgrades than upgrades during the year, and almost $60 billion in corporate bonds slipped below investment grade during the year. Breaking down all rated companies, by S&P ratings class, and by region, at the end of 2025:

Source: S&P Cap IQ

The US has the highest percentage of listed companies with bond ratings, but even in the US, only 11.43% of companies carry that rating, and that percentage is far lower in other parts of the world. Among rated companies, the US has the highest percentage of below investment-grade ratings, suggesting that in much of the rest of the world, there is a self-selection that occurs, where only companies that believe that they will get high ratings are willing to go through the ratings process. Finally, at the start of 2026, there are only AAA rated-companies left in the world, at least according to S&P, in Johnson & Johnson and Microsoft. Looking at 2025, through the lens of default, the numbers look comforting, at least on the surface, with the number of defaults decreasing, but there was disquiet below, as bond buyers wrestled with the consequences of a changing economic world order, and worries about another crisis lurking in the wings.

Debt Burden in 2025

With the background data on tax rates and default risk in place, I will turn to measuring the debt in publicly traded firms, in 2025, and differences in debt burdens across companies, sectors and regions. That mission requires clarity on how to measure debt burdens, and the picture below offers the choices:

Broadly speaking, debt burden metrics can capture debt comfort, i.e., the buffer that businesses have built in to meet their debt obligations and debt level, where you look at debt as a percent of overall funding. In the former group, there are two proxies that you can use to gauge the borrowing buffer - the interest coverage ratio, measuring how much companies have as operating income, relative to their interest expenses, and the debt as a multiple of EBITDA, capturing how many years it will take a company to pay off its debt, if current EBITDA is sustained. In the latter, I will look at debt as a percent of capital invested, using both accounting measures of capital invested (book value) and market value measures.

1. Debt comfort

When companies borrow money, the contractual claims from that debt usually take two forms. The first is interest expenses, and ongoing claim that gives you tax benefits but has to be covered out of income generated each year, and the second is repayment of principal, which comes due at maturity. The interest coverage ratio focuses entirely on the former, and interest payments are scaled to how much a company generates in operating income:

Interest coverage ratio = Earnings before interest and taxes/ Interest expenses

This ratio is simple, with high values associated with less default risk and more safety, at least from a lending perspective. It is still powerful, and it remains the financial ratio that best explains differences in bond ratings across non-financial service companies, and I use it to estimate synthetic bond ratings for firms in my corporate financial analysis.

The problem with interest coverage ratios is that they ignore the other contractual obligation that emerges from debt, which is principal payments due, and the ratio that is most often used to measure that exposure scales total debt at a firm to its earnings before interest, taxes and depreciation:

Debt to EBITDA = Total Debt/ EBITDA

With this ratio, lower values are associated with less default risk and more safety, because a firm, at least if it wanted to, could pay off its debt in fewer years with its operating cash flows.

In the table below, I look at interest coverage ratios and debt to EBITDA values, by sector, for US and global companies, using the same approach I employed in my last update and reporting a ratio based on aggregated values as well as the distribution of the ratio across companies:

As you can see, with both the US and global groupings, technology companies have the largest safety buffers when it comes to debt, with very high interest coverage ratios and low debt to EBITDA, whereas real estate and utilities have the least buffers, with low interest coverage ratios and high debt to EBITDA. As always, the contrast between the aggregated and median values indicate that larger companies, not surprisingly, operate with stronger buffers than smaller companies in almost every sector grouping. Finally, the debt comfort numbers are not computed for financial service companies, for the same reasons that we did not compute costs of and returns on capital for these firms - debt to a bank is raw material and not capital.

2. Debt level

If you go back to the financial balance sheet structure that I started this post with, the debt measure that emerges is one that scales it to the equity invested in the firm (debt to equity) and to the capital invested (debt to capital). These measures have resonance in corporate finance in valuation, because they become drivers of the costs of equity and debt and ingredients in the cost of capital.That said, you can measure this ratio using book value debt to capital (or equity), where you stay with the values of debt and equity reported on accounting balance sheets or with market value debt to capital (and equity ratios), where you use market values for debt and equity. At the risk of sounding dogmatic, book value debt ratios should never come into play in financial analysis and it is market value ratios that matter for two reasons. The first relates back to all of the criticisms I had of accounting invested capital in the context of computing account returns - it is dated and skewed by accounting contradictions and actions. The second is that it is unrelated to what you are trying to measure in a cost of capital, which is what it would cost you to acquire the firm today, where it is market price that determines how much you have to pay, not book value. That said, there remain a fairly large subset of analysts and firms who swear allegiance to book value for a variety of reasons, most of which have no basis in reality. I report book and market debt to capital ratios for all publicly traded firms, broken down by sector for global and US companies:

As you can see, companies look significantly more debt-laden with book value numbers than with market value, and in sectors like technology, where accountants fail to bring the biggest assets on to the books, the difference is even starker. The results in this table reinforce the findings in the debt comfort table, with technology companies carrying very little debt (3-5% in market cap terms) and utilities and real estate carrying the highest. I also reported, on the aggregated numbers, the gross and net debt ratios, with the latter netting cash holdings from debt.

AI Investing and Debt

In every data update post that I have written so far this year, AI has become a component of the discussion, reflecting the outsized role it played not just in market pricing during 2025, but also in business decisions made during the year. To see the connection between AI and debt, I will start with AI investing side, where hundreds of billions were spent by companies building AI infrastructure and large language models (LLMs) during 2025, with plans to spend more in the years to come. A sizable portion of this AI capital expenditure have come from big tech companies, with Meta, Alphabet, Amazon, Oracle and Microsoft all making large bets on the future of AI, and the extent of their investment is visible in the graph below, where I look at capital expenditures and cash acquisitions at these firms (with Broadcom added to the mix) from 2015 to 2025:

The shift at these firms from capital-light to capital-intensive models over this period has been staggering, with the collective investment in 2025 alone hitting $400 billion, with guidance suggesting that they are only getting started. It is worth noting that while big tech has garnered the AI cap ex headline, there are a whole host of other companies that are investing in AI architecture, which include real estate, data centers and power, and many of these companies are still not publicly listed. Going back to investment first principles, you can debate whether these companies can expect to generate positive net present value from their AI investments, and I have argued in earlier posts that it is very likely that they are collectively over investing, with over confidence and a fear of being left behind driving their both corporate investments and investor pricing, in keeping what you would expect when there is a big market delusion.

This big market delusion is a feature, not a bug, and we have seen it play out with dot com stocks in the 1990s, online advertising companies about ten years and even with cannabis stocks in the early years of their listing. The belief that the AI market will be huge, and have two or three big winners, is driving an investing frenzy not just at the big tech companies, but also in smaller start-ups and young firms, but the the market is not big enough to accommodate the expectations across all of these firms, and that will inevitably lead to a correction and clean up.

The AI investing boom enters the financing storyline, which is the focus for this post, because it needs immense amounts of capital. For many of the big tech companies, much of that capital has come from their existing businesses which are cash machines, although the AI cap ex will deplete the free cash flows available to return to shareholders. That said, though, the ramping up of capital investment has been so dramatic that even the cash-rich bit tech companies have turned to debt, as you can see in the graph below:

In 2025, the big tech companies collectively borrowed $160 billion, but given their cashflows and market capitalization, that debt does not put them at risk. For many of the smaller and lower-profile companies investing in this space, where internal cashflows are insufficient, there is a need for external capital, with some coming from equity and a significant portion coming from debt. It is in the context of the debt that I have to pick up on another storyline, which is the rise of private credit as an alternative to banks and the corporate bond market.

As you look at the explosive growth of private credit in this graph, it is worth emphasizing that private credit has been available as an option for borrowers for as long as borrowing has been around, but its usage explode in the last two decades. As AI has increasingly taken a starring role in markets, evidence is accumulating that more private debt is being directed to financing the AI investment boom,. With more than $200 billion in private debt going to AI firms in 2025, AI-related debt is rising as a percent of private credit portfolios.

As private credit has grown as an option, core questions remain of what it brings to a market as differentiating features that allow it to supplant more traditional lending alternatives, i.e. banking and the corporate bond market. Here are some of the reasons offered by private credit advocates for why it may be a preferred choice for entities, in general, and for those investing in AI architecture, in particular:

Better default risk assessments: One of the arguments that private credit lenders make is that they have the technical know-how to use data, that banks and bond markets have been more averse to using or have been constrained from using, to get better assessments of default risk. Those assessments, assuming that they are right, allows private credit to lend to entities at rates that are lower than they would be charged, with conventional risk assessments. In principle, that is a solid rationale, but I am unclear about what data it is that traditional lenders are not utilizing that private credit can use, but it is possible that technology and access to the internals of borrowing entities may provide an edge. In fact, the only way to gauge whether this argument of better credit assessment holds up is with a credit shock, where defaults spike across the board.
Cashflows-based versus Asset-based lending: A second argument is that traditional lenders, and especially banks, are focused too much on the value of the assets that they are lending against and too little on the cash flows. It is true that bank lending in particular is too focused on asset value, but that focus would provide an opening for private credit in AI, only if AI data centers and architecture investments are poised to start delivering large and positive cash flows soon, and banks are holding back on lending them money. I am hard pressed to think of too many AI investments that have these near-term payoffs.
Speedier and more Flexible/Customized Responses: IThis may be the biggest selling point for private credit in the AI investment world, where the investing entities are not just spending billions on AI architecture, but are in a hurry to do so. The regulatory and institutional constraints built into bank lending will stretch the process out in time, and issuing bonds, even if it were an option, comes with its own delay components. In addition, the debt for AI investments may need far more customization than what banks and bond markets can offer, or are allowed to offer, giving private credit an advantage. The problem with speed and customization being the biggest sales pitches for private credit is that it can go with taking short cuts on due diligence and adding terms to loans that cut against prudence, and those can be fatal to lending businesses.

Clearly, these reasons for the presence of private debt have merit, but only to a subset of borrowers, mostly smaller and private, and without a long borrowing history, and for a subset of projects. None that these reasons resonate for the larger tech companies, which have options to borrow money quickly and at fair market rates both from banks and the bond market, and Google's recent hundred year bond issue is an indication of how much slack bond markets are willing to concede to these firms. When a private credit fund lends Meta for an AI investment, as Blue Owl did in this transaction, the skeptic in me sees either a below-market-rate loan or one with terms that no prudent lender would accept in a loan, and neither is a sustainable lending strategy in the long term.

The coming together of the two storylines on AI and private credit comes with a risk that may extend well beyond the players in these spaces. If you agree with my contention that companies are collectively over investing in AI, driven by the big market delusion, there will be a time when that delusion dissipates and markets will have to correct. In an all or mostly-equity driven space, the pain will be borne by shareholders or owners of these companies, but while painful to them, its ripple effects will be limited. When debt enters the picture, as it has in the AI investment space, the effects of a correction will no longer be isolated to equity investors in these companies, and as private credit gets repriced (from the marking of debt down to reflect higher default risk), the pain to the rest of the economy increases. In effect, we will have a banking crisis created primarily by non-banking lenders behaving badly. We saw some of this start to happen in the last year, as the glow came off the AI rose, and S&P noted the stresses that it put on private credit players. Private credit has had a good run, in terms of delivering returns to investors in it, but it has, in my opinion, the relentless selling of it as an alternative investment class has made it much too big. A shakeout is overdue, which will separate the sloppy lenders from the good ones, and perhaps shrink private credit to healthier levels.

YouTube Video

Data links

Marginal and Effective tax rates, by country (January 2026)
Debt comfort ratios, by industry (US and Global)
Debt load ratios, by industry (US and Global)

Spreadsheet

Capital structure optimizer

Paper on Big Market Delusion

The Big Market Delusion (with Brad Cornell)

Data Update Posts for 2026

Data Update 6 for 2026: In Search of Profitability!

2026-02-16T15:12:00.003-05:00

Crass and mercantile though this may sound, the end game for a business is to make money, and a business that fails this simple test cannot survive for long, no matter how noble its social mission, how great its products and how much it is loved by its customers and employees. In this post, I start with a defense of this mercantile objective, and argue that attempts to expand it to incorporate social good leave both businesses and societies worse off. I look at business profitability, first in absolute terms in 2025, and then relative to revenues, examining why profit margins vary across businesses and sectors. I then raise the ante and argue that making money is too low a standard to hold companies to, since the capital invested in these companies can generate returns elsewhere, opening the door to bringing in the opportunity costs (costs of equity and capital) that I introduced in my last post.

The Business End Game

In 1970, Milton Friedman argued in a New York Times article that the social responsibility of a business is to deliver (and increase) profits. That view has come under attack in recent decades, but even in the immediate aftermath of the article’s appearance, there was some push back. Some came from people who argued that Friedman was missing details, with a few noting that it is cashflows, not earnings, that businesses should focus on, and others arguing that it is profits over the long term, not just immediate profits, that should be the focus of a business. My guess is that Professor Friedman would have agreed on both fronts, arguing that he was talking about economic, not accounting, profits, and that there was nothing in his mission statement that foreclosed a focus on long term profits.

In the decades since, there has been a more fundamental critique of the Friedman business end game, coming from those who believe that his view is far too cramped and narrow a vision for a business, and that businesses have obligations to society and the planet that need to be incorporated into decision-making. Initially, these critics argued for imposing social and environmental constraints on the profitability objective, and while Friedman may have taken issue with some of these constraints, arguing that that is what laws and regulations should be doing, he would (probably) have gone along with most of them, given real world frictions. Later, though, these critics decided to go for the jugular, arguing that the business objective itself be reframed to include these broader responsibilities, with some arguing for stakeholder wealth maximization, where businesses seek to maximize value to their different stakeholders (employees, lenders, customers). That idea gained traction among some academics, many of whom never grappled with putting this objective into practice in real businesses, and among some CEOs, who realized that being accountable to everyone effectively meant being accountable to no one, but I am not a fan. About two decades ago, stakeholder wealth maximization was supplemented by ESG, an acronym that quickly got buy-in from the establishment. In 2020, when I first looked at ESG, it was at the height of its allure, with investment managers (led by Blackrock), consultants (with McKinsey up front) and academics, all pushing for its adoption. Given the broad buy in, I expected to see clear and conclusive evidence that ESG was not just good for investors and businesses, but also for society, and I was disappointed on every front. The alpha that was attributed to ESG in investing was accidental, coming almost entirely from its overload on tech stocks in its early years, the evidence that ESG helped businesses deliver higher growth and profits was laughably weak, and on almost every societal dimension that ESG was supposed to make the world a better place, it had failed. Even on risk, the one dimension where a rational argument can be mounted for companies following the ESG rulebook, its impact was hazy, with no discernible effects on costs of capital and only anecdotal (and mostly ex-post) evidence for protecting against reputational and catastrophic risks. In the last five years, ESG has fallen out of favor, largely undone by its own internal inconsistencies, but the gravy train that lived off its largesse has moved on, and taken much of what filled the ESG space, repackaged it, and renamed it sustainability. While advocates for sustainability try to create distance between ESG and sustainability, in my (biased) view, much of that discussion is akin to painting lipstick on a pig and then debating what shade of lipstick suits the pig best, rather than attempting to create real change.

It is with intent, therefor, that I named these three forces - stakeholder wealth maximization, ESG and sustainability - the theocratic trifecta in a post that I wrote three years ago, and argued that they failed for the same reasons.

First, by rooting themselves in virtue rather than in business sense, they rendered a disservice to their own cause. After all, once you decide that you are on the side of goodness, any critics of what you do, no matter how well merited their criticism might be, are quickly consigned to the badness heap, and not just ignored, but also reviled for lacking moral fibre. The problem, of course, is that if an action makes business sense (increases profitability and value), you would not need a virtue brigade to push for that action in the first place. Second, by leaving the definitions of their central ideas (stakeholder wealth, ESG and sustainability) amorphous, they made it easier to sell to investors and companies, but at the expense of consistency and focus. In my 2022 post on ESG, where the Russian invasion of Ukraine had forced its defenders to morph in the face of evidence that that world was more dependent on fossil fuels and defense companies than they had been willing to concede in earlier years, I noted the loss of credibility that comes from shifting definitions of goodness. Third, and most critically, in their zeal to push these concepts to a wider audience and get more people to buy in, they sold a lie, i.e., that you can be good (whatever that definition of good may be) without sacrifice. I have no idea whether ESG and sustainability salespeople meant what they said when they argued that investors could earn higher returns, by adding ESG constraints to their portfolios, and that companies could become more profitable, if they incorporated environmental and social considerations into decision making, but my categorization of people in these spaces as either useful idiots or feckless knaves stems from a refusal to face up to the inherent trade offs.

After decades of pushback from critics of the Friedman business end game, I, for one, believe that Milton Friedman was right, and that we would all be better off to follow up and ask the question of what can be done, given that businesses are profit-seekers, to advance social good and curb externalities. I don't believe that the disclosure route, which seems to have become the fallback for some seeking better business behavior, will accomplish much, and it may do more harm than good. While laws and regulations can provide a partial fix, they are blunt instruments, and in a setting where businesses can move easily across borders, they may not be effective. Ultimately, we (as consumers and voter) get the businesses we deserve, and if after paying lip service to social causes, we buy products and vote for governments hat undercut those causes, no acronym or word salad will repair the breach.

Profitability in Businesses

I meant to have a short lead-in on why profitability matters at businesses, but as you can see from the previous section, I did get side tracked, but the underlying message is that making money is central to business success and survival, and that measuring profitability is therefore a necessary part of assessing business success and value.

Economic versus Accounting Profits

The Friedman view on the business endgame may have been driven by a vision of economic profits, but in the real world, we are dependent on accounting measures of profits, which are, at best, imperfect substitutes for economic profits. The table below looks at an accounting income statement, highlighting the many measures of profits - gross, operating and net - that you will find in it:

Each profit measure has utility, with gross profits reflecting unit economics, the difference between gross and operating profits capturing economies of scale and the difference between operating and net profits being driven by taxes and choices that businesses make on debt and non-operating assets. In 2025, looking at the aggregate values (in millions of US $) for these line items across sectors, here are the numbers, for both global firms and just the US subset:

In the aggregate, global firms generated $6.2 trillion in net income and $7.7 trillion in operating income on revenues of $72.4 trillion, in 2025; during the same year, US firms generated $2.2 trillion in net income and $2.9 trillion in operating income on revenues of $22.7 trillion in revenues. Across sectors, and looking at revenues, industrials carried the most weight for the global sample, but health care generated the most revenues across the US sub-sample.

Profits scaled to Revenues - Profit Margins

The problem with dollar profits is that comparisons across companies, industries or sectors are skewed by scale differences, and one simple scalar for earnings is revenues, yielding variants of profit margins. While you are undoubtedly familiar with these margin variants, their real use in analysis is in providing insight into business models

I am not a believer in financial ratio analysis, but I do believe that the income statements for companies, especially examined over time, give us insight into their business models and can help frame valuation narratives. In the table below, I look at differences in margins across sectors in 2025, again looking across global firms, and just US firms:

I have estimated margins, by sector, using the aggregated dollar values for profits and revenues from the previous table, and also reported the cross sectional distribution of company-level margins. Comparing the aggregated margin with the median margin across the sector should give you a sense of how top-heavy the sector is in terms of profitability. In technology, which has the highest weighted operating margin (24.7%) of across sectors, the median operating margin is only 3.41% (-0.30%) across global (US) technology firms; the bigger tech companies are money machines in a sector that still contains a lot of younger and smaller money-losing firms. Note that the margins are not computed for financial service firms, since revenues are often unreported (and mostly meaningless) and gross and operating profits don't have the same measurement value as they do for non-financial service firms.

Industry Margins and the AI Threat

Breaking down sectors into industries provides more granular detail, and there is a link at the bottom of this post that reports the margin statistics, by industry group. At the risk of stating the obvious, there are large disparities on margins across industries, reflecting differences in unit economics, economies of scale and leverage, as can be seen in this table that lists the industry groupings with the highest and lowest aggregated operating margins among US firms:

At one end of the spectrum, you have industry groups like basic chemicals, which has an aggregated (median) gross margin of 9.31%, making the margin hill much steeper to climb, since operating margins and net margins will be lower. At the other end of the spectrum, in addition to tobacco and railroads (surprised, right?), you have system and application software, delivering an aggregated gross margin of 71.72%, operating margin of 33.21% and net margins of 25.49%, capturing the strong unit economics that characterize the business.

While high margins are a desirable feature for a business, these same high margins can make a business vulnerable to disruption, and the AI sell off that we have seen play out in the last few months in software reflects the concerns that investors have of AI putting significant downward pressure on software margins. If your pushback is that the drop off in revenues and margins has not happened yet, and that it is unfair to software firms to mark their market pricing down preemptively, this is exactly what markets are supposed to do, and these software companies benefited earlier in their lives, when market prices were marked up well ahead of the run-up in margins. You live by the sword (expectations of growth and high margins), you die by it (expectations that growth rates will hit a cliff and margins will decline)!

Time Trends in Profits

I have tracked profit margins for companies for a long time (about three decades) in my datasets, and there is clear evidences that they have trended upwards during the period. In the graph below, I look at the net profit margins for the S&P 500 in the aggregate in this century (from 2000-2025):

As you can see, net profit margins have climbed over the last two decades for US companies, with a number of stories competing for why.

The most cynical explanation is that this increase in margins is all sleight-of-hand, where accountants are pushing through changes, aided and abetted by accounting rule-writers, to make companies look more profitable. As someone who has taken issue with the gaming of earnings that you often see at companies, I am disinclined to take this criticism seriously, since many of the changes in accounting rules (such as the expensing of stock-based compensation and R&D) should push earnings down, and accountants have more power to move income across periods than they do to increase the level of income.
A second explanation is that the macroeconomic environment makes it easier for companies to deliver profits, and this explanation had resonance when interest rates were at historic lows in the last decade. As rates have risen back to more normal levels and the economy limps along, I am skeptical of the reasoning in this explanation.
A third explanation, and this one has been eagerly adopted by many on the political left, is that that this reflects the increase in bargaining power for capital, relative especially to labor, implying that the increase in profits are coming primarily at the expense of worker wages. While there are certainly pockets of the economy where this is true, the margins for most manufacturing and service businesses, which have the highest employee count and wage costs, have stagnated or decreased over the last 20 years, indicating that neither capital nor labor has benefited at least in these sectors.
The fourth, and in my view the most salient rationale for margin increases, is that the composition of the market has changed, as technology companies supplant old-economy companies, bringing superior unit economics and economies of scale to play. Put simple, a market that gets the largest portion of its value from tech companies will deliver much higher margins that one that gets much of its value from manufacturing and service businesses.

Should we concerned that margins may compress in the future? Of course, and we always should, but that compression, if it happens, will depend almost entirely on how the economy performs and the effects of disruption, if it is coming, for tech companies.

Value Creation in Business

If we define the threshold for business success as generating profits, we are setting the bar too low for a simple reason. Starting a business requires capital, and that capital can earn a return elsewhere on investment of equivalent risk. If those words sounds familiar, it is because I used them in my last post on hurdle rates to describe the costs of equity and capital. Thus, value creation requires a business to generate a return on its equity (capital) that exceeds its cost of equity (capital). That is a simple proposition, and a powerful one, but the measurement challenge we face is in determining the returns that companies generate, and for better or worse, we are dependent on accounting measures of these returns. A good way to see what an accounting return is measuring or at least trying to measure is to look at returns on equity and invested capital in a financial balance sheet:

While accounting returns are widely used in practice, as a gauge of investment quality, they can be skewed not just by accounting inconsistencies but efforts by accountants to do the "right thing" (like writing off bad investments. I have laid out my concerns in exhaustive and incredibly boring detail in this paper on accounting returns, which is dated, but still relevant. I summarize the factors that can cause accounting returns on equity and capital to deviate from reality in the picture below:

With those concerns about accounting returns in place, I computed the accounting returns on equity and invested capital for all of the companies in my global sample (48.156 firms) and my US sample (5994 firms), and the following table reports the statistics for both groups, by sector:

Again, I report the accounting returns computed based on aggregated values first, and then the distributional statistics (first quartile, median, third quartile) for the company-level accounting returns. As with profit margins, you can see that even in sectors where the aggregated accounting returns are high (such as technology and communication services), the median value reflects the reality that most companies in these sectors struggle to deliver double-digit returns.

Turning back to our value creation metric, where we compare accounting returns to costs of equity and capital, you have to be consistent, comparing equity returns to equity costs and capital returns to capital costs:

The excess return is a numeric, but as with all numbers in business, it is worth looking behind the number at its drivers, i.e., why do some business deliver returns that consistently outstrip their costs of equity and capital, whereas others struggle? The most powerful explainer of excess returns is not qualitative, since the capacity to generate excess returns comes from barriers to entry and competitive advantages. In the language of value investing, it is the width (strength of competitive advantages) and depth (sustainability of competitive advantage) of moats that determine whether a company can earn more than its cost of equity or capital:

If you are interested in this topic, and it is a fascinating one, Michael Mauboussin brings his erudition and knowledge into play in this Morgan Stanley thought piece from October 2024.

Since I have estimates of costs of equity and capital for each of my firms (see my last data update for details), I compute excess returns, by sector, for my global and US samples:

Given what you saw in the last table, with accounting returns, you should not be surprised to learn that only 29% (28%) of global firms earn returns on equity (capital) that exceed their costs of equity (capital). In fact, if you raise the threshold and look at companies that generate 5% or more as excess returns, the numbers drop off to 19% (17%) for equity (capital) excess returns. Most companies have trouble earning their costs of equity and capital, but if you look at the aggregated values, there are multiple sectors in the US (technology, consumer goods and communication services) that earn double digit excess returns, pointing again to larger companies within these sectors being able to set themselves apart from the rest.

If your concern is that the global statistics are being skewed by regional differences, I compute the excess return statistics broken down by region:

As you can see, there is not a single geography where more than 50% of firms earn more than their required returns, with Japan ranking highest in percentages and Canada and Australia the lowest. Here again, the aggregated values tell a different story, with US companies collectively delivering excess returns of 8.44% on equity and 1.81% on capital, suggesting again that large US companies carry the weight of value creation in the market.

Given how much time we spend in finance examining investments and developing decision rules (NPV>0, IRR>Hurdle rate) that are supposed to protect businesses from taking "bad" investments, you may be surprised at the prevalence of value destroying investments. Some of the failure at businesses to deliver returns on capital that exceed the cost of capital may reflect imperfections in our accounting return measures, since it is based upon earnings in the most recent year, and that may bias us against young and growing companies building up to scale. In my book on corporate life cycle, I highlight how accounting returns shift as companies go from youth to decline:

To see if this is a factor in our global findings on excess returns, I break companies down by age into deciles and compute excess returns across these groupings:

The table broadly reflects what you should expect to see, with a corporate life cycle, as the percent of companies that beat their cost of capital increase as companies age, but the aggregated excess returns peak in middle age (the middle of the life cycle), more pronounced with US than global firms.

A Profitability Wrap Up

Looking at the data, and there is a danger here that I am overreaching, it seems to me that over the last four decades, moats have crumbled, partly as a result of global competition and partly because of disruption (which upends businesses, turning good businesses to bad ones), and the business landscape has tilted more decisively to larger firms, as more and more businesses become winner(s)-take-all. It is in this context that I take a more jaundiced view of what AI will do for company profitability and value. I believe that, as a disruptor, it will cause downward pressure on margins at most firms, and increase the advantages that larger firms have in each business. How do I reconcile this view with the happy talk of AI as a tool that will make companies more productive, and that the resulting lower costs will make them more profitable? Unless the AI tools that you are talking about are exclusive to these companies, in the sense that competitors cannot buy the same or equivalent tools, these AI tools will lower costs across the board, and competition will then kick in on the pricing front, lowering profitability. If that sounds like a reach, I would recommend a revisit of the US retail sector over the last three decades, as online retail, initially viewed as a boon by brick-and-mortar retail firms, ended up destroying most of them and reducing the margins for retail collectively. As consumers, we will benefit, but as investors or employees in the disrupted companies, we will pay a price that outweigh the benefits, for a sizable number of us. I do think that the AI disruption will be more akin to a slow-motion car wreck, in terms of its effect on overall profitability, and that the margin slippage will occur over time, but it will damaging. Time will tell!

YouTube Video

Datasets

Profit margins, by industry (US and Global)
Accounting returns and excess returns, by industry (US and Global)

Paper on Accounting Returns (Long and Boring)

Return on Capital, Return on Invested Capital and Return on Equity: Measurement and Implications

Data Update Posts for 2026

Data Update 5 for 2026: Risk and Hurdle Rates

2026-02-05T13:45:00.005-05:00

In my first four posts, I looked at markets - equity, debt and collectibles - in the aggregate performed in 2025. In this post, I turn my attention to divergences in risk across companies, looking at alternative measures of risk, some based on prices and others at earnings, and how these differences play out in hurdle rates, a necessary ingredient for businesses trying to determine whether and how much to invest in individual projects and for investors making that same judgment, when looking at companies.

Risk: Definition and Measures

For a concept tas central to investing and corporate finance as risk is, it is astonishing how much divergence there is across even finance experts and academics on what it is, and consequently on how to measure it. I have heard some describe risk as uncertainty, essentially substituting one fuzzy word for another, others as the threat of grevious loss and still and still others as the possibility of negative outcomes. If you have taken a finance class, and I confess to having a part in this, you may define risk as volatility or standard deviation, or even bring Greek alphabets into play. My favorite definition of risk and one that I start my corporate finance class with is that Chinese symbol for crisis or big risk (and I am sure that I have mangled the symbols, since I have been corrected a dozen times in the past):

As someone who can neither read nor speak Chinese, I am reliant on friends who know the language, and I have been told that the first of the two symbols is the one for danger and the second is a symbol for opportunity. In effect, by bunding together danger and opportunity, the risk measure captures how risk both attracts (to get to opportunity) and repels (with the threat of danger). That duality explains why an investment or business strategy generally cannot be built around the objective of just minimizing risk, since that effectively will remove access to opportunities or recklessly chasing after opportunities, ignoring dangers

With that definition of risk in place, I will start the discussion of risk measures by examining the choices that we face in making the measurement:

Upside versus Downside: If you start with a generic definition of risk as receiving an outcome that is different from what your expectation, it is worth recognizing that some of these outcomes will be positive (better than expected) and some will be negative (worse than expected), and that it is the latter than investors and businesses dislike. Thus, there are some who argue that risk measures should focus on just downside outcomes, not all unexpected outcome.
Price-based versus accounting-based: Risk measures that are based upon data can be built on market prices, for publicly traded firms, or on accounting data, especially earnings. Price-based measures have the advantage of constant updating, giving you more data, but are sometimes contaminated by the noise and volatility that come from trading. Accounting measures yield more stability, but since they are updated infrequently, and accounting smooths changes over time, they can offer stale or distorted values.
Total versus Non-diversifiable: The risk in an investment, whether a project or a business, can come from many different sources, but some of the risks are more investment-specific whereas others are market-wide:

To the question of why we should care, the presence of many investments in a portfolio implies that risks that are investment-specific will average out, decreasing or even disappearing as portfolios get larger, whereas market risks remain intact. This insight, which earned Harry Markowitz a Nobel prize, gave birth to modern portfolio theory and is at the heart of most risk and return models in finance.

I have my preferences on how best to measure risk, I would like to keep an open mind and start by laying out the choices we face on risk-measures:

As you can see, the risk measure you choose will be a function of whether you (as an investor or business) believe that the marginal investors, i.e., the investors who own the most shares in your business and trade those share, are diversified or not, and what you believe about financial markets and accounting data.

Risk across Companies in 2025

My sample includes 48,156 publicly traded firms and given that these companies trade across different geographies and are in different businesses, it should come as no surprise that there are wide variations in risk across these companies. In this section, I will start with accounting-based measures, with the caveat that accounting standards vary across the world, though IFRS and GAAP have created significant convergence.

Accounting Measures

While there are a variety of accounting metrics that you can use to measure risk, the most logical one to focus on is earnings, but you have many choices. You could use net income or earnings per share, which will reflect not only the riskiness of the business operate in, but also the amount of debt you have chosen to take on, or you can used operating income, more reflective of just market risk. Within each of these metrics, you can measure risk as volatility (in earnings) or in more simplistic terms, on whether you have positive or negative income. For those investors and businesses to whom, it is debt that is the risk trigger, you can look at measures of that debt burden:

Let’s start with volatility in earnings, where we have two estimation choices that we must make, before we get started. The first is history, and I compute the standard deviations in operating and net income using ten years of earnings data, for each firm, a compromise between a number too high (where I lose too many firms in my sample) and too low (where I lack enough data). The second is that earnings standard deviations in earnings will reflect the level of earnings, with higher earnings companies having higher standard deviations. To control for this, I divide the standard deviation of earnings by the average earnings over the ten years, yielding coefficients of variation in earnings. The following table summarizes the distributional values for this metric, across sectors:

It should come as no surprise that utilities have the least volatile operating earnings and have the lowest coefficient of variation on that metric, and that energy and technology haver the most volatile operating income. On a net income basis, financials and utilities have the lowest volatility in earnings, , and energy and communication services have the highest net income volatility.

If you use the frequency of loss-making, as a risk proxy, the table below captures differences on that metric across sectors on this dimension:

Utilities are again the least risky sector, with a lower percentage of money losers than any other sector, and health care and technology firms have a higher percent of money losers than other sectors.

While there are some who use debt loads as proxies for company risk, and we will come back and look at differences across sectors and industries in a later post, it is a narrow measure, since a young, risky, high growth company with no debt would be classified as low-risk, if it is not debt-laden.

Price-based Measures

All of the stocks in our sample are publicly traded, and consequently, you can use market prices to measure risk. That said, liquidity is a wild card, high in some markets and low in others, and that can cause distortions in the comparison.

1. High and Low Prices: One of the simplest measures of price volatility is the range of prices, with wider divergences between high and low prices at more risky companies and smaller ones at safer companies:

HiLo Risk Measure = (High Price – Low Price)/ (High Price + Low Price)

I computed this statistic for each company in my sample, and then the averages across companies in each industry, and it should be lower (higher) for safer (riskier) stocks. Using my global data, this is what this statistic looks like, across sectors:

Utilities again come in as safest, using this risk metric, tied with real estate, and health care has the widest price ranges of the companies in my sample.

2. Standard deviation in price changes: This is a standard statistical construct, and measures volatility in a stock, though it does not distinguish between upside and downside volatility. Based upon the company-specific standard deviations, again averaged out across sectors, here is what the numbers looked like in 2025:

Financials and utilities are the two safest sectors, and technology and health care are the riskiest, if you measure risk with standard deviation.

3. Betas: If you buy into the notion that the investors setting prices are diversified, and thus care only about risk that cannot be diversified away, you will focus only on the portion of the standard deviation in a stock that comes from the market, and betas, notwithstanding the misinterpretations and misreading, are trying to measure that non-diversifiable portion of standard deviation and scale around one. Again, looking across industries, I look at the distribution of betas, by sector:

If you are interested in a less broad categorization, you can check out betas by industry at the end of this post.

As you review the sector rankings using the varied risk measures, you can see why the heated debates about which risk measure to use is often overdone, since they, for the most part, rank the sectors similarly, with the sectors having less earnings volatility and fewer money-losers also having less volatility in stock price, smaller price ranges and lower betas.

Hurdle Rates
Even as we wrestle with choosing between price and accounting-based measures, it is worth remembering that the end game here is not the risk measure itself, and that risk measures are a means to an end, which is estimating hurdle rates. Hurdle rates come into play for both businesses and investors, setting thresholds that they can use to determine whether to invest or not:

There are some investors and businesses who believe that hurdle rates come from their guts, numbers that reflect personal risk aversion and past experiences, but hurdle rates are opportunity costs, reflecting returns that investors (businesses) can earn in the market on investments of equivalent risk.

In the context of a business, which raises money from debt and equity, you can look at hurdle rates through the eyes of the capital providers – a cost of equity, capturing what equity investor believers expect to make on other equity investments of equivalent risk, and a cost of debt, looking at what lenders can earn on lending to others with similar default risk:

That is what all risk and return models try to do, albeit with different degrees of fidelity to the principle. In fact, my use of an implied equity risk premium in the estimation of the cost of equity is designed to advance this cause, since it is model-agnostic and reflects what investors are pricing stocks to earn, on an annual basis. Thus, when you use the beta in the capital asset pricing model to derive the cost of equity, you should be computing the return you can earn elsewhere in the market on other investments with the same beta, making the cost of equity the hurdle rate for equity investments in a project or company. The cost of capital, which incorporate the cost of borrowing into its construct, is also a hurdle rate, albeit to both debt and equity providers:

As to the question of which of these hurdle rates you should use as a business, the answer lies in consistence. If you are looking at equity returns (return on equity or an internal rate of return based on equity cash flows alone), you should be measuring up against just the cost of equity. Alternatively, with returns on invested capital or an internal rate of return based upon cashflows to the business (pre-debt), it is the cost of capital that comes into play.

I compute the costs of equity and capital for all 48,156 firms in my sample, and in doing so, and in the interests of consistency and ease, I make some simplifying assumptions:

Once I have the costs of equity and capital for each firm, I compute industry averages, both for global firms, and by region (US, Japan, Europe, Emerging Markets, with India and China as sub-categories). You can find the links to the data at the end of this post, but there is another perspective that you can bring to the cost of capital discussion, based upon where a company falls in the company life cycle:

Intuitively, you would expect more uncertainty about business prospects with younger firms, than older ones, especially on the estimation front. That said, it is an open question of whether this uncertainty will translate into higher costs of equity and capital, since it depends on who the marginal investors in these firms are, and whether the risk is diversifiable (and not affect cost of equity) or non-diversifiable. To answer these questions, I classify firms into ten deciles, based on their corporate age, and compute costs of capital:

As you can see, there is no discernible pattern on costs of equity, as you go across the age classes. However, as firms age, they do borrow more, partly because their capacity to generate earnings increase, and that does have some impact on the cost of capital, especially with the oldest firms in the market.
In corporate finance and valuation, an undervalued skill is having perspective, a sense of what comprises typical, and what is a high or a low value. It is for that reason that I also compute a histogram of costs of capital of all publicly traded firms at the start of 2026:

This table is one on my most-used, for many reasons. First, when doing my own valuations, especially for young firms or for firms where the cost of capital is in flux, it gives me the input to us. Thus, if I am valuing a small, AI firm that has just gone public and has global operations, in US dollars, I will start the valuation with a cost of capital of 11.66% and move that cost of capital over time towards 8.65%, as its gets larger and more established. Second, I do see (and must review or grade) other people’s valuations more than I do my own, and this table operates as a plausibility check; a valuation of a publicly traded US company that has a dollar cost of capital of 14% goes on my suspect list, since that is well above the 90th percentile for US firms. Third, the table operates as a reminder that any analysts where the bulk of the time is spent estimating and finessing the cost of capital is time ill-spent, since the 80% of all US (global) companies have costs of capital between 5.26% (6.28%) and 9.88% (11.66%).

For those working in different currencies, the inflation differential approach that I described and used in the last post can be used to convert the entire table. Thus, if you use the expected inflation rates of 2.24% and 4.00% for the United States and India, from the IMF forecasts, you can 1.76% to each of the numbers to each dollar cost of capital that you see in the table or as an industry average.

Conclusion

To run a business or invest in one, you need hurdle rates, and that is what costs of equi6y and debt measure. While models and equations may be how you get these numbers, it is always worth going back to first principles, whenever you face questions on what to do. Thus, recognizing that the cost of capital is an opportunity cost, i.e., the rate of return you can earn elsewhere in the market, on investments of equivalent risk, should be a prompt to use betas that reflect the risk in investments, rather than the entities making the investment, and updated costs of borrowing for the cost of debt. As we enter 2026, we are now in our fourth year with US dollar riskfree rates around 4%, and companies and investors seem to have become acclimatized to the resulting costs of capital, and the shock of seeing dollar riskfree rates surge in 2022, pushing up costs of capital across the board seem to have faded.

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Datasets

Earnings variability, by industry (Global in 2025)
Money making and losing percentages, by industry (Global in 2025)
Pricing risk measures, by industry (Global in 2025)
Betas by industry group (US, Global, Japan, Europe, Emerging Markets, India & China)
Cost of capital by industry group (US, Global, Japan, Europe, Emerging Markets, India & China)

Data Update Posts for 2026

Data Update 4 for 2026: The Global Perspective!

2026-02-01T23:18:00.008-05:00

If you have read my first three data updates in 2026, I won’t blame you if you skip this one, because you found them long and boring. I won't take issue with you either if you viewed them as too US-focused, because I did spend my second data update, looking at US equities, and my third, examining US treasuries and the US dollar. In this post, I widen my data analysis to look at the rest of the world, starting with a journey through global equity markets in 2025, moving on to creating a snapshot of country risk at the start of 2025 and finishing by looking at interest rate differences across currencies. Along the way, I will argue for a larger narrative, underlying this global perspective. I am not a political or a macroeconomic analyst, but I attribute much of what we have seen in terms of global politics and economics in the last four decades, first to the rise of globalization as an almost unstoppable force, shaping immigration and economic policies in much of the world, and then, in most recent years, to a backlash against the same forces. That backlash has not only upended the political order in the developed world, with both Europe and the United States seeing changes in power structure, but also brought nationalist parties to power in many emerging market countries. From investing and business perspectives, we saw the effects play out strongly in 2025, and I don't think that this genie is going back into the bottle.

Global Equties in 2025

In my second data update, I noted that US equities had a good year in 2025, delivering a return of 17.72% for the year, but the US dollar weakened in 2025, down a bit more than 7% during the year. I started my exploration of global equities by looking at the returns in local currency terms of equity indices in different parts of the world:

In each region, I have highlighted the best performing index (in green) and worst performing one (in red), and you can see the disparities in market performance, even within regions. One of the problems with comparing returns across currencies is that they are distorted by the effects of inflation that also vary widely across currencies. While I will look at inflation differences in more detail later in this post, one way to make the returns comparable is to recompute them in a common currency. To this end, I compute the dollar returns, in aggregate dollar market capitalization terms, in 2025:

As I mentioned in my second data update, India was the worst performing sub-region of the world, up only 3.31% in 2025, and those returns reflect not just a relatively below-average year in local currency terms, with the Sensex up 8.55% for the year, but a weaker currency, with the rupee depreciating against the dollar. It is only one year and while I will need read too much into it, my argument earlier last year that the India story has legs, but that the path to delivering it will be rockier than many of its advocates seem to thing. For much of the rest of the world, the dollar returns are higher than local currency returns, because of currency appreciation against the dollar.

Zeroing in on the aggregate market capitalization across the world at the start of 2026, I first created a pie chart (on the left) breaking market capitalization by region, and as you can see, US equities, in spite of a weaker dollar, accounted for 47% of global market capitalization.

Evaluating just the change in market capitalization during 2025, in the second pie (not he right), you can see the reason for the slippage in the US hare, with the US punching in below its weight (38% of the change) and Europe and China weighing in, with larger shares.

To close this section, I will unwrite an epitaph for international diversification that many US investors, wealth advisors and market experts were starting to etch in stone even a year ago. For much of the twenty first century, an investor invested entirely in US stocks would have outperformed one who followed the textbook advice to diversify globally. While that may look sound conclusive, the truth is that two decades is not a long time period in stock market history and that you can have extended market runs that look permanent, even when they are not. It is true that as multinationals displace domestic companies, the payoff to international diversification has become smaller over time; buying the S&P 500 would have bought your exposure to the global economy, since the companies in the index, while incorporated in the US, get almost 60% of their revenues in the rest of the world. However, the underperformance of the US, relative to the rest of the world, in 2025 should be a reminder that international diversification still belongs in the toolkit for a prudent investor. That lesson cuts across the globe, and suggests that much as politicians and countries may want to delink from each others, investors don't have that choice.

Country risk in 2025

If you have been a reader of my posts, I do have a bit of an obsession with country risk,, i.e., why the risk of investing and doing business varies across countries, and what causes that risk to change. My defense for that is that I teach corporate finance and valuation, and to do either, I need answers to these country risk questions, and while you may not like the short cuts and approximations I use along the way, I will take you along on my January 2026 journey:

The place to start any discussion of country risk is with an examination of the factors that feed into that risk, and I will use a matrix that you may have seen in my prior posts on country risk:

While I do take a deeper and more detailed look at these factors in a mid-year update that I do every year (links to paper and my July 2025 blog post), the forces that cause differences in country risk span politics and economics, and include:

Political Structure: From an investing and business standpoint, the choice between democracy and autocracy is nuanced, with the former creating more continuous uncertainty, as changes in government bring more policy change , and the latter creating more policy stability in the near term, albeit with a greater likelihood for wrenching and potentially catastrophic uncertainties over time.
War and Violence: Investing and business become more hazardous, both physically and economically, if you invest in a more violent setting, and war, terrorism and access to weapons can create differences across countries.
Corruption: Corruption affects businesses directly, operating as implicit taxes on businesses that are exposed to it, and indirectly, by undercutting trust and the willingness to follow rules. While differences in corruption across countries are often attributed to cultural factors, a significant component of corruption comes from structures that are designed to encourage and reward it.
Legal and Property rights: Investors and businesses are dependent on contracts and legal agreements to operate, but protection for property rights. Legal systems that are capricious in how they enforce contractual and ownership rights, or delay judgments to make them effectively useless, create risks for businesses and investors.

There are many reasons to expect differences across countries, on these dimensions, there is a different perspective that can also help. As some of you may know, I look at businesses through the lens of a corporate life cycle, where as businesses age, their characteristics and challenges change as well. That life cycle structure can be used to explain differences across countries, where the age is less tied to how long a country has been in being and more to do with its economy.

Young economies have higher growth potential, but that higher economic growth comes with more risk (more volatile economies) and require more robust governance to deliver on their promise. As economies age, they face a period of lower growth, albeit with more economic stability, and governance matters less, effectively become mature (middle aged) economies. There is a final phase, where a country’s economy hits walls, and growth can stagnate or even become negative, driven partly by a loss of competitive edge and partly by aging populations. In each of these phases, countries often overreach, with young countries aspiring for the stability of middle age, while trying to grow at double-digit rates, and mature companies, seeking to rediscover high growth. Without treading too much on political terrain, it may be worth thinking about the Trump actions in 2025 as driven, at least partially, by nostalgia for a different time, when the United States was the dominant economic power, with a combination of solid economic growth and stability that few economies, almost unmatched in history.

With that philosophical discourse in country risk out of the way, let’s turn to the brass tacks of measuring country risk, starting with one of the most accessible and widely available one, which are ratings that agencies such as S&P, Moody’s and Fitch (among others) attach to sovereigns. The following is the heatmap of sovereign ratings (from Moody’s) at the start of 2026:

While Moody’s rates more than 140 countries, there remain a few (called frontier markets) that have no ratings, but in terms of the color map, I have included those countries with the lowest rated, because they share many of the same risk characteristics. There are three key features of these ratings that are worth emphasizing:

The sovereign ratings are focused almost entirely on default risk, and while the chance that a country will default is correlated with the core risks (violence, political structure, legal system and corruption) that I mentioned up front, there are countries on this list where they diverge. I believe that this is especially the case in the Middle East, where there are countries, like Saudi Arabia, that have low or no default risk, but remain exposed to large political risks.
The sovereign ratings have their share of biases, for or against regions, but their bigger sin is that they are slow to react. If you look at the list, you will see countries like Argentina and Venezuela that have seen significant changes in governance and politics in the last year, but where the ratings have not changed or barely changed. That will probably change in 2026, but this delayed response will mean that the sovereign ratings for some countries, at least, will not be good reflections of country risk, at the moment.
There were a few ratings changes in 2025, mostly at the margin, but the one that got the most attention was the ratings downgrade for the US that I highlighted at the time it happened. While markets, for the most part, took that ratings downgrade in stride, it did create waves in the process that I use to estimate riskfree rates and equity risk premiums, by country, as you will see later in this post.

The reason that so much of how we deal with country risk rests on sovereign ratings is not because ratings agencies have special insights, but because sovereign ratings, unlike other (often more comprehensive) measures of country risk, like country risk scores (from PRS or the Economist, to name two), can be converted into default spreads that conveniently feed into financial analysis. At the start of 2026, here are my estimates of default spreads for each sovereign rating:

As I noted earlier though, using sovereign ratings to get default spreads comes with the limitations that these ratings may not reflect current conditions, when change is rapid, and that is where the sovereign CDS market has created an alternative. For the 80 countries where sovereign CDS exist, you can get a market-determined number for the default spread, and here are the numbers at the start of 2026:

Note that these spreads, while noisy and reflective of market mood, reflect the world we live in, and both Argentina and Venezuela, which used to be uninsurable, have both seen improvement on these market-driven numbers, albeit from impossible to insure to really costly to insure.

As a final step in my country risk exploration, I repeat a process that I have used to estimate equity risk premiums, by country, every six months for close to three decades. That process starts with estimating an equity risk premium for the S&P 500, and then uses the country default spreads (based upon the ratings) to estimate equity risk premiums for countries:

It is undeniable that the ratings downgrade for the US has created some change in this process. Instead of using the S&P 500’s implied equity risk premium as my estimate of the mature market premium, which was my pathway until May 2025, I now remove the default spread (0.23%) for the US from that premium to get to a mature market equity risk premium (4.23%). To get to country risk premiums for individual countries, I scale up the ratings-based default spreads for the relative riskiness of equities, and add these country risk premiums to the mature market premium:

Download equity risk premiums, by country

Note that I bring the frontier countries into the mix, by using country risk scores for these countries to estimate country and equity risk premiums.

The Currency Effect

While it remains true that country risk and currency volatility/devaluation often go together, one of my concerns with mixing up the two up is that you end up double counting or miscounting risk. To understand the divide between country and currency risk, I start with a look at government bond rates in different currencies, with the caveat that there only about forty governments that issue bonds in their local currencies and that some or many of these government bonds are lightly traded, making their rates unreliable.

In many finance classes and textbooks, you are often taught (as I was) to use the government bond rate as the riskfree rate, on the facile assumption that governments should not default on these bonds, since they can print more currency and cover their debt obligations. The problem with that logic is that it is at odds with the reality that governments can, and often do, default on local currency bonds, choosing that option over devaluation. That also means that the government bond rates can include a default risk component, and to get to a riskfree rate, that default risk needs to be removed from the government bond rate. In the picture above, that is what I do, using the ratings-based default spread). After this clean-up, you can see that riskfree rates vary widely across currencies, from very low in some currencies (Swiss Franc, Japanese yen and the Thai Baht), slightly higher for others (US dollar, Euros) and very high on a few (Turkish Lira, Zambian kwacha).

In my third data update, I estimated an intrinsic riskfree rate for the US dollar, by adding inflation and real GDP growth. Extending that lesson to other currencies, the primary reason for differences in these riskfree rates, across currencies, is expected inflation, with higher(lower) interest rates in higher (lower) inflation currencies. While inflation measures are imperfect and expected inflation estimates are often flawed, I use the IMF’s estimates of inflation to build a global inflation heat map:

The logic that I used to argue that it is unlikely that you will see US treasury bond rates drop much below 4%, at least as long as inflation runs hot (2.5-3%), not only applies for other currencies, but yields a roadmap for estimating riskfree rates in those currencies (including those without a government bond in the local currency). To illustrate, I will try to estimate an Egyptian pound riskfree rate at the start of 2026:

Riskfree rate in local currency = Riskfree rate in US dollars + (Expected inflation rate in local currency – Expected inflation in US $)

Thus, the riskfree rate in Egyptian pounds, using the expected inflation rates of 7.78% for Egypt and 2.24% for the United States is 9.49%:

Riskfree rate in US dollars = US T.Bond rate - US default spread = 4.18% -0.23% = 3.95%

Riskfree rate in EGP (1/1/26) = Riskfree rate in US $ + (Expected inflation in Egypt – Expected inflation in US) = 3.95% + (7.78% - 2.24%) = 9.49%

Note that the riskfree rate in US $ is 3.95%, obtained by cleansing the US 10-year treasury rate on January 1, 2026 (4.18%) of US default risk (0.23%). The estimate for a riskfree rate is an approximation is an approximation, since inflation rates compound, and that compounded version is below:

Riskfree rate in EGP = (1+ US $ Riskfree Rate) × (1 + Expected inflation rate in EGP)/ (1+ Expected inflation rate in US $) -1 = 1.0395 × (1.0778/ 1.0224) -1 = .0958 or 9.58%

I have used IMF inflation rates to get riskfree rates in almost all global currencies in this link, but I don’t blame you, if you are skeptical about the expected inflation numbers. From a financial analysis and valuation perspective, I have good news and it is that it does not matter if you are wrong on inflation, if you are consistently so (in both your earnings and cash flows as well as your discount rates).

Put simply, the effects of expected inflation in valuation cancel out, and that is that the basis of what I would term “the currency invariance theorem”, where the value of a project or company should not change, if you change the currency in which you do your analysis. A project that has a positive NPV, when the analysis is done in US $, should continue to have the same positive NPV, if you redo the analysis in EGP, and a company that is overvalued, when the valuation is in US $, will remain overvalued, if you revalue it in EGP. The currency you chose to do an analysis is cannot alter the underlying value but that does not mean that changes in inflation cannot change the values of businesses, since that effect will depend on how well a company can pass inflation through to its customers (with pricing power), and I examined that relationship in 2022, after inflation had a resurgence in the United States after a decade of being low and boring.

Thus, high inflation in Turkish lira has undoubtedly wreaked havoc the value of some Turkish companies, but given that damage, my point is that revaluing these companies in Euros will not undo that damage.

The Bottom Line

As globalization gets a blowback, and in the midst of turmoil from tariffs, we got a reminder of how, much as we may want to go back to simpler times where the rest of the world did not intrude into our lives, we are all connected in good and bad ways. Thus, you may disagree with me on how to measure country risk and to bring into your analysis and investments, but it is undeniable that risk varies across countries and that we must incorporate that risk into our decision making. I hope that this post expose the layers in the process from the drivers of country risk to how these drivers play out as differences in country ratings, default spreads and equity risk premiums, while illustrating th how country risk can change over time, and sometimes in short periods.

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Data Update 3 for 2026: The Trust Deficit - Bonds, Currencies, Gold and Bitcoin!

2026-01-28T11:45:00.004-05:00

In my last post, I talked about the disconnect between the bad news stories that we were reading and the solid performance of US equities during 2025. In this one, I want to focus specifically on four news stories from last year - the US announcement of punitive tariffs on the rest of the world, the downgrade of the US, the longest shutdown in US government history and unprecedented challenges to the Fed's perceived independence - and examine how they played out in the rest of the market. I will start with a look at US treasuries, which should have been in the eye of the storm in all of the stories, move on to to currencies, with a focus on the US dollar, then to gold & silver, and close off with a riff on bitcoin. As I look at these diverse markets, with very different outcomes in 2025, I will argue that a loss of trust in institutions (governments, central banks, regulatory authorities) was the thread that best explains their performance.

The Trust Narrative

We often underestimate how much of the global economy and financial markets are built on trust - in central banks to preserve the buying power in currencies, in governments and businesses to honor their contractual commitments, in legal systems to enforce them and in norms restraining behavior. That trust can be tenuous, and when violated, not only can the consequences can be catastrophic, but regaining lost trust can be a long, arduous process. In fact, one of the divides between developed and emerging markets for much of the last century was on the trust dimension, with the implicit assumption that emerging countries were less trustworthy than developed countries. That distinction has been muddied in the twenty first century, as crises and political developments have undercut trust in institutions across the board.

I would argue that 2025 was a particularly testing year, as developments in the United States, a dominant player in the global economy and markets, shook trust, and that loss of trust reverberated across its trading partners and global investors.

The first of the developments was on the tariff front, where decades of progress towards reducing barriers to trade and establishing predictability was upended on Liberation day (on March 31, 2025), where the US imposed what seemed like arbitrary tariffs on countries, but made those tariffs punitively large. In the immediate aftermath, equity markets around the world went into free fall, and I wrote a post in April 2025 about the tariff effect.
Just two weeks later, on April 16, 2025, Moody's, which had been the lone holdout among the ratings agencies in preserving a Aaa rating for the US, lowered its rating, albeit marginally to Aa1, reducing the number of Aaa rated countries in the world to eight. That rating, though not a complete surprise, still had shock value, and created ripple effects for appraisers and analysts, and I made my assessment in a post in May 2025.
On October 1, 2025, the US government went into shutdown mode, as congress balked at increasing the debt limit for the country and on the terms for a new budget, and unlike previous shutdowns, which lasted a few days, this one stretched into weeks, before an agreement was reached to reopen the government on November 12, 2025.
In the final months of the year, the independence of the Federal Reserve became a subject of discussion as news stories and pronouncements on social media suggested that the administration was seeking to put its imprint on monetary policy, through its nominees.

Depending on your political persuasion, you may have been one side of the debate or the other about each of these developments, but each of them chipped away at trust in the US government and its institutions.

While Donald Trump is the easy answer to why trust is slipping, the truth is that in each case, the slippage has been occurring over much longer. The push towards uninhibited global trade started running out of steam a decade or more ago, as the costs created political backlash. The Moody's ratings downgrade followed similar actions by S&P, in 2011, and Fitch, in 2023, partly in reaction to government deficit/borrowing and partly to political dysfunction. The Fed's much-vaunted independence has always been built more on norms rather that legal strictures, and administrations through the decades have managed to nudge central banks to adopt their preferred paths, and especially so in the aftermath of the pandemic.

The Bond Market

The effect of a loss of trust should be visible most clearly and immediately in the bond market, since bond buyers, of US treasuries, are doing so on the expectation that the US government will not default and that the Fed will do its utmost to preserve the dollar's buying power (and keep inflation low). Since the shocks from the news stories listed in the section above have the potential to alter both default risk and expected inflation, I looked at the movement of US treasuries over the course of 2025:

US Treasury Data

As you can see, there was little movement in 20-year and 30-year treasuries over the course of the year, but rates dropped, and neither the Moody's downgrade nor the government shutdown had much effect, and the rise in rates around the downgrade (in April) were more in response to tariffs and preceded the downgrade announcement. In fact, in the face of all of the bad news, the ten-year treasury rate dropped by 39 basis points (from 4.58% to 4.19%) during the year, and short term treasuries dropped even more, effectively altering the slope of the yield curve. To capture that effect, I looked at the evolution of the difference between rates across different maturities over the course of the year:

US Treasury Data

During 2025, the spread between the 10-year and 30-year treasury doubled, the spread between the 10-year and 2-year increased by seven basis points, but at the short end of the maturity spectrum, the spread between the two year and three month treasuries decreased. The net effect was a much more upward sloping yield curve at the end of 2025 than at its start, and while I do not attribute the power to to the yield curve as a prognosticator of future economy growth that some do, it is still marginally a positive sign for the US economy.

To gauge how the news stories played out on the perception of US government default, I looked at the sovereign CDS spreads for the US, market-set numbers capturing the cost of buying insurance against US government default, in 2025:

After a blip in April, where the sovereign CDS spreads increased from 0.4% to just over 0.5% in April 2025, spreads have dropped back to levels lower than they were at the start of the year.

To get a sense of how expectation of inflation changed over the course of the year, I turned again to a market-based number from the treasury market, where the difference between the US ten-year treasury bond rate and the ten-year US treasury TIPs rate (a real rate) operates as a measure of expected inflation:

In 2025, these estimates suggest that the expected inflation barely budged, ending the year lower than it was at the start. That would have put the market at odds with experts, who forecasted a surge in inflation especially after the tariffs were announced, but would have put it in sync with actual inflation reported during the rest of the year.

On the final question of why the Fed independence fight has not created more turmoil in markets, I start with a different perspective from most, since I believe that the role of Fed in setting interest rates is vastly overstated. As I note in that post, the Fed's much publicized forays into changing the Fed Funds rate has some effect on the short term treasuries, but long term treasuries are driven less by the Fed’s actions (or inaction) and more by expected inflation and real growth. I capture that relationship every year by estimating an intrinsic ten-year riskfree rate, obtained by summing together actual inflation for the year and real GDP growth and comparing it to the ten-year treasury bond rate:

Download intrinsic riskfree data

Over the seventy years of data in this graph, it is clear that the big movements in treasury rates are captured in the intrinsic risk free rate, with higher inflation in the 1970s coinciding with the rise in the treasury rate, and the sustained low rates of the last decade largely in sync with the low inflation and anemic growth during the period. As you can see , after a stint (2021-25) where the intrinsic risk free rate was well above the ten-year treasury rate, largely because of higher inflation, the treasury rate of 4.18%, at the start of 2026, is within reach of the intrinsic rate of 5.10%, obtained by adding inflation and real growth in 2025. That said, though, I do think that the reason that treasury rates stayed well below the intrinsic risk free rate during this period is because markets believed that the Fed would use its powers to try to get inflation under control, even at the expense of a slowing economy (or a recession). It is this belief that will be put at risk if the Fed becomes viewed as an extension of the government, increasing the risks of inflation spiraling out of control, creating a cycle where higher inflation causes higher interest rates, and attempts by central banks to lower these rates actually feed into even higher inflation. It is in the best interests of governments and politicians to let central banks be independent and set rates, because it will lead to better economic outcomes and lower interest rates, while giving politicians cover for unpleasant choices that have to be made to deliver these results.

I complete the assessment of the bond market in 2025 by looking at corporate bonds, and especially at the default spreads of corporate bonds in different ratings classes during the course of the year:

There seems to be a divergence in how the year played out in the corporate bond market, with the higher rated bonds all seeing flat or lower spreads, but bonds below investment grade (below BBB) seeing an increase in spreads.

The Currency Market

Just as bond markets are driven by trust that governments will not default, unless it has run out of options, and that central banks will protect a currency’s buying power, currency markets are swayed by the same concerns. Here, a split emerged between the bond and currency markets. While bond markets, for the most part, took the news stories of the year in stride, the dollar was clearly knocked off balance, and it weakened over the course of the year, as can be seen in the graph below;

The trade-weighted dollar, a broad index of the dollar against multiple currencies, was down 7.24% for the year, but the dollar lost more value against developed market currencies than against emerging market currencies; it was down 8.19% against the former and 6.34% against the latter.

Gold and Silver

When investors lose trust in governments and central banks, it should come as not surprise that their money leaves financial asset markets and goes into collectibles, and in a post in October 2025, I looked at how this played out specifically in the gold market. In 2025, Gold had one of its best years ever, rising 65% during the year, and silver, the other widely held precious metal, had an even bigger year, rising 148% during the year:

The surge in precious metal prices in 2025 was unusual, at least on one dimension. Gold and silver prices tend to rise during periods of unexpectedly high inflation (1970s) or during intense crises, but at least in 2025, neither seemed to be at play. As we noted earlier, inflation came in much tamer than expected, and equity and equity and bond markets, after a brief meltdown in April, showed no signs of trauma. In fact, if you scale gold price to the CPI, the basis for the golden rule, where the argument that gold rises at roughly the inflation rate over time, gold price performance in 2025 broke the indicator, as the ratio of gold price to the CPI exploded well above historic norms.

It is worth noting that a loss of trust in the US government and, by extension, in the US dollar, have translated into increases in gold holdings at central banks, but that increase, while contributing to gold's allure, cannot explain its price rise during the year. If the rise in gold prices was a surprise, the rise in silver prices was even more so, and in 2025, silver prices rose enough to bring the ratio of gold to silver prices to below the long term median value:

It seems like the market is pulling in different directions on the trust question, with stocks and bonds largely underplaying them, the currency markets indicating some worry and gold and silver suggesting much bigger consequence to the loss of trust. That does not surprise me since the market is not a monolith, and while the broad investor base might have adopted the response of "What, me worry?", there is a significant segment of investors that see catastrophic risks emerging, and piling into precious metals.

Bitcoin

I have written off and on about bitcoin over the last fifteen years, and have generally straddled the middle, with both sides of the divide (bitcoin optimists and bitcoin doomsayers) taking issue with me. I have argued that bitcoin can be viewed either as a a central-bank free currency, designed by the paranoid for the paranoid, or millennial gold (a collectible), and that we would know better as we saw how it performed in response to macro developments. In many ways, 2025 provided us with a test, which should, if nothing else, advance our understanding of the endgame for bitcoin. In a year where the dollar was weakened as a global currency and central banking independence was questions, you would have expected to see bitcoin do well, both because of its status as a currency without a central bank and as a collectible. The actual price path for bitcoin, in US dollars and Euros, is captured below:

After setbacks in the first third of the year, bitcoin's price surged upwards in the middle of the year, making those who had built their narratives around it to look good. In my post on bitcoin on July, I focused on the suggestion that other companies should follow the Microstrategy path and put their cash balances into bitcoin, and argued that it was not a good idea. The months following have vindicated that view, as both bitcoin and Microstrategy have seen pricing collapses, and bitcoin ended the year down 6.4% in US dollar terms and 17.4% in Euro terms.

It remains too early in bitcoin's life to pass final judgment, but if the story for bitcoin is that it will draw in investors who have lost trust in governments and central banks, it is clear that gold and silver were the draws, at least in 2025, not bitcoin. As a final assessment of how the different asset classes moved in relation to each other, I looked at weekly returns in 2025 in six markets - bitcoin, gold, silver, large US stocks, small US stocks and the ten-year treasury bond - and computed correlations across the assets:

There are only a few co-movements which are large enough to be statistically significant. The first is that bitcoin is much more highly correlated with US equities than it is with its collectible counterparts, suggesting that it draws in risk seekers, not the risk averse. The second is that notwithstanding the fact that US treasuries did very little over the course of the year, on a week-to-week basis, their movements affected stock prices. At least in 2025, higher interest rates (translating into negative bond returns) were accompanied by higher stock prices, casting doubt on the notion that the stock market is being held afloat by Fed activity or inactivity.

Conclusion

The big news stories of the year, from the ratings downgrade to the government shutdown to the soap opera of who would lead the Fed all fed into a storyline of fraying trust in US institutions. While that trust deficit should have led to rising interest rates and a tough year for bonds, actual bond market performance, like equities in the prior post, suggested that markets were not swayed. That clearly does not mean that no one cared, since a subset of investors were concerned enough about the trust issue to push the dollar down and put gold and silver prices on stratospheric upward paths. Bitcoin remained the outlier, moving more with stocks and bonds, albeit without their upside (at least this year) and less with collectibles.

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Data Update 2 for 2026: Equities get tested, and pass again!

2026-01-23T11:26:00.003-05:00

It was a disquieting year , as political and economic news stories shook the foundations of the post-war economic order, built around global trade and the US dollar. In fact, if you had been read just the news all through the year, and were shielded from financial markets, and been asked what stocks did during the year, you would have guessed, based on the news, that they had a bad year. You would have been wrong, though, as equity markets proved resilient (yet again) and delivered another solid year of returns for investors. In this post, I will focus on US equities, starting with the indices, and then deconstructing the data to see the differences in the cross section. As has been my practice for the last few years, I will also use this post to update the equity risk premium for the S&P 500, my composite indicator for whether the market is richly priced or not, and estimate a value for the index, with a "reasonable" equity risk premium.

Back from the Brink: US Equities in 2025

At the start of 2025, the consensus view was that stocks were primed to do well, helped by what investors perceived would be a business-friendly administration and a Federal Reserve, ready to cut rates. In keeping with Robert Burn's phrase that the best-laid plans of mice and men go awry, the year did not measure up to those expectations at least in terms of policy and rate changes, but stocks still managed to find a way through. Let's start with a look at the S&P 500 and the NASDAQ, day-to-day through the year:

The first few weeks of 2025 saw of continuation of the momentum built up after the 2024 elections and stock prices continued upwards, but February and March saw a drawdown in stock prices as talk of tariffs and trade wars heated up before culminating in a dramatic sell off in early April, after liberation day, when breadth and magnitude of the tariffs blindsided markets. The sell off was brutal and short, and stocks hit their low point for the year on April 11, 2025. Over the next few months, stocks mounted a comeback, before leveling off at the end of September and coasting for the rest of the year. Early in the year, the S&P 500 held its value better than the NASDAQ, generating talk of a long-awaited tech sell off, but as stocks recovered in the subsequent months, the NASDAQ ended up moving ahead the S&P 500.

Across the entire year, the S&P 500 rose from 5881.6 to 6845.5, delivering price appreciation of 16.39% for the year. The dividends on the companies in the index for the year, based upon dividends in the first three quarters of 2025 and estimates for dividends in the last quarter amounted added a yield of 1.34%.

The S&P 500's return in 2025 of 17.72% was a solid year, but to provide perspective on how it measures up to history, I looked at annual returns from US stocks from 1928 to 2025, and computed distributional statistics:

Download data

While 2025's annual returns put it in the right in the middle of the distribution, close to the median and ranked 45th of the 98 years of US equity returns from 1928-2026, it represented a third consecutive year when the annual stock return exceeded the median returns, the longest streak since the mid 1990s; US equities between 2023 and 2025, a period where many market timers were suggesting not just caution but staying out the market, returned 85.32% to investors.

Deconstructing US Stock Price Performance

While stocks had a good year overall, the spoils were dividend unequally, as if often the case, across industries and sectors. To take a closer look at where the best and worst performance was in 2025, I started by looking at a breakdown by sector, where I computed the returns based on the change in aggregate market capitalization in 2025:

I have tracked the performance of each sector, by quarter, and across the year a measured the returns. The best performing sector in percentage returns was communication services (which includes Alphabet and Meta), up 30.63% for the year, followed by technology, which continued it sustained run of success by delivering 23.65% as an annual return; on a dollar value basis, it was not close with technology companies posting an increase of $4.17 trillion in market cap during the year. The worst performing sectors were consumer staples and real estate where the returns were about 2% for the year.

The problem with sector categorizations is the they are overly broad and include very diverse industry groupings, and to overcome that problem, I looked at returns by industry, with a breakdown into 95 industry groups. While you can find the full list at the end of this post, I ranked the industry returns in 2025, from best to worst, and extract the ten best and worst performing industry groups:

Download industry returns in 2025

The surge in gold and silver prices in 2025 carried precious metals companies to the top of the list, with a return of 169.2% for the year, and other energy and mining companies also made the best performer list, with a scattering of technology standouts. The worst performing businesses were primarily old economy, with chemicals, consumer product companies and food processing all struggling during the year.

One of the major changes that we have seen in cross sectional differences in the twenty first century has been the fading or even disappearance of two well documented phenomena from the twentieth century, the first being the small cap premium, where small market cap companies delivered much higher risk-adjusted returns that large market cap companies, and the value premium, where low price to book stocks beat high price to book stocks in the return game. I focused in how these categorizations behaved in 2025, and we did see small cap stocks and low price to book stocks return, at least in part, to favor:

If you are small cap or a value investor, though, I would not be celebrating the return on these premia, but I do think that we will start to see a return to balance, where the groupings will trade off winning in some years for losing in others.

As a final assessment, I did look at the seven stocks that have not only carried the market for the last few years, the Mag Seven, but have been the source of much hand wringing about how markets are becoming top-heavy and concentrated. I started by looking at the individual companies, and how they performed in 2025:

While the Mag Seven saw their collective market capitalization increase by 22.36%, Apple and Amazon lagged with single digit increases, and Nvidia (up 37.8%) and Alphabet (up 62.7%) for the year. Increasingly, the Mag Seven are diverging in their price paths, and that should be expected since they operate in very different businesses and have very different management running them. To examine how much the Mag Seven have carried the market, I tracked the market cap of the Mag Seven against the rest of US equity (close to 6000 companies) from 2014 through the four quarters of 2025.

The aggregate market cap of the Mag Seven has increased from 11% of the US equity market (composed of close to 6000 stocks) in 2014 to 30.89% of the market at the end of 2025, with the $3.9 billion in market cap added in 2025 accounting for 39.3% of the overall increase in market capitalization of all US equities during the year. While this Mag Seven party will undoubtedly end at some point, it did not happen in 2025.

US Equities: Too high, too low or just right?

This post, at least so far, has been a post mortem of the year that was, but investing is always about the future, and the question that we all face as investors, is where stocks will go this year. In my unscientific assessment of stock market opinion, from experts and market timers, there seems to a decided tilt towards bearishness at the start of 2026, for a variety of reasons. There are some who note that having had three good years in a run, stocks will take breather. Others point to history and note that stocks generally don't do well in the second years of presidential terms. The most common metric that bearish investors point to, though, is the PE ratio for stocks at the start of 2026 is pushing towards historic highs, as can be seen in the graph below, where I look at three variants on the PE ratio - a trailing PE, where I divide the index by earnings in the most recent 12 months, a normalized PE, where I divide the index by the average earnings over the last ten years and a Shiller PE, where I average inflation-adjusted earnings over the last ten years:

Download historical PE ratios for US equities

Using every PE ratio measure, it is undeniable that the PE ratio for the S&P 500, at the start of 2026, is much higher than it has been at any extended period in history, perhaps with the exception with the late 1990s. While this may sound like a slam dunk argument for US stocks being over priced, it is worth remembering that this indicator would have suggested staying out of US equities for much of the last decade. The problem with the PE pricing metric is that it is noisy and an unreliable indicator, and before you use it to build a case that equity investors in the US have become irrational, you may want to consider reasons why US stocks have benefited able to fight the gravitational forces of mean reversion.

1. Robust Earnings Growth & Earnings Resilience: In this century, US stocks have increased more than four-fold, with the S&P 500 rising from 1320.28 at the end of 2000 to 6845.5 at the end of 2025, but it is also worth noting that US companies have also had a solid run in earnings, with earnings increasing about 356% during that same time period.

It is also notable that not only did earnings register strong growth over this period, there were only three years in this century when earnings declined - 2001 (dot com bust), 2009 (2008 crisis) and 2020 (Covid). US companies have become more resilient in terms of delivering earnings through recessions and other crises, pointing to perhaps less risk in equities. I will return in a later post to examine why that may be, with some of the answers rooted in changes in US equity market composition and some in management behavior.

2. Healthy cash returns: In conjunction with delivering earnings growth, US companies have also been returning large amounts of cash to their shareholders, albeit more in buybacks than in conventional dividends. In 2025, the companies in the S&P 500 alone returned more than a trillion dollars in cash flows in buybacks, and in the graph below, I look at how the augmented cash yield (composed of dividends and buybacks) has largely sustained the market:

While the dividend payout ratio, computed using only dividends, has been on a downward trend all through this century, adding buyback to dividends and computing a cash yield ratios yields values that are comparable to what dividend yields used to be, before the buyback era.

In sum, you can see why both bulls and bears retreat to their favored arguments, and there is no obvious tie breaker. The level of stock prices (PE ratios) should be a concern, but you cannot dismiss the benefits of growing and resilient earnings, and substantial cash return. To break the tie, in a very self serving away, I will revert to my favored metric for the US equity market, the implied equity risk premium, which in addition to looking at stock price levels, the growth in earnings and the cash return, also brings in the level of rates. The implied equity risk premium, as I compute it, is the based upon the index level and the expected cashflows (from dividends and buybacks, augmented by earnings growth), and very simply, is an internal rate of return for stocks. Netting out the riskfree rate yields an equity risk premium. The table below contains the computation of the implied ERP at the start of 2026:

Download spreadsheet

Given the index level on January 1, 2026, of 6845.5, and the expected cash flows that I computed on that date (using the dividends and buybacks in the trailing 12 months as my starting point, and growing them at the same rate as earnings), I obtain an expected return on stocks of 8.41%. Subtracting out the US T. Bond rate (dollar riskfree rate) of 4.18% (3.95%) on that day yields an equity risk premium of 4.23% (4.46%) for the US. I want to emphasize again that this estimate is entirely a market-driven number and is model-agnostic.

If you are wondering how estimating this numbers lets you make a judgment on whether US stocks are over priced, all you need to reframe the equity risk premium by asking whether the current ERP is, in your view, too high, too low or just right.

If you believe that the market is pricing in too low an ERP, given the risks that are on the horizon, you are contending the stocks are over priced.
If your view is that the current ERP is too high, that is equivalent to arguing that stocks today are under priced.
If you are not a market timer, you are in effect arguing that the current ERP is, in fact, the right ERP for the market.

To illustrate this point, I have estimated the value of the index at equity risk premiums ranging from 2% to 6%:

With a 2% equity risk premium, you get an astounding value of 14834 for the S&P 500, which would make the index undervalued by 53%. At the other end of the spectrum, with a 6% equity risk premium, the index should trade at 4790, translating into an overvaluation of 43%. So, is the ERP of 4.23% (I will revert to this number, since my historical numbers did use the US treasury bond rate as the riskfree rate) at the start of 2026 a high, low or just-right number? Rather than make that judgment for you, I have computed the implied ERP for the S&P 500 going back to 1960:

Download historical implied ERP

There is something in this graph that almost every investor group can take comfort in, If you are market neutral, you will take comfort from the fact that the current ERP is almost exactly equal to the average for the 1960-2025 period. If you are bearish you will point to the fact that the ERP now is lower than it has been in the post-2008 period, backing up your case that an adjustment is overdue. I am leery of the bubble word, especially used in the context of this market, since unlike the end of 1999, when the ERP got as low as 2.05%, the current ERP is more in the middle of the historic range.

The Bottom Line

US equities had a good year in 2025, and there are signs of excess in at some parts of the market, especially related to AI. That said, the capacity of US companies to continue to deliver earnings and return cash flows even in the face of a tsunami of bad news continues to sustain the market. I am, at my core, a non market-timer, but I have held back on putting idle cash back into US equities in the last year, preferring to keep that cash in treasury bills. It is entirely possible that the market will continue to prove the naysayers wrong and post another strong year, but much as it may pain equity investors, the healthiest development for the market would be for it to deliver a return roughly equal to its expected return (8-9%) and clean up on pricing overreach along the way. For the bears, this may also be the year when the bad news stories of last year, including tariffs and political whiplash, will finally start to hit the bottom line, reducing aggregate earnings and cash flows, but waiting on the sidelines for this to happen has not been a good strategy for the last decade.

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Implied ERP computation for the S&P 500 on January 1, 2026

Data Update Posts for 2026

Data Update 1 for 2026: The Push and Pull of Data!

2026-01-09T17:36:00.002-05:00

In my musings on valuation, I have long described myself as more of a number cruncher than a storyteller, but it is because I love numbers for their own sake, rather than a fondness for abstract mathematics. It is that love for numbers that has led me at the beginning of each year since the 1990s to take publicly available data on individual companies, both from their financial statements and from the markets that they are listed and traded on, and try to make sense of that data for a variety of reasons - to gain perspective, to use in my corporate financial analysis and valuations and to separate information from disinformation . As my access to data has improved, what started as a handful of datasets in my first data update in 1994 has expanded to cover a much wider array of statistics than I had initially envisioned, and my 2026 data updates are now ready. If you are interested in what they contain, please read on.

The Push and Pull of Data

After a year during which we heard more talk about data and data centers than ever before in history, usually in the context of how AI will change our lives, it is worth considering the draw that data has aways had on not just businesses but on individuals, as well as the dangers with the proliferation of data and the trust we put on that data.

In a world where we feel adrift and uncertain, the appeal of data is clear. It gives us a sense of control, even if it is only in passing, and provides us with mechanisms for making decisions in the face of uncertainty.

Signal in the noise: Anyone who has to price/value a stock or assess a project at a firm has to make estimates in the face of contradictions, both in viewpoints and in numbers. The entire point of good data analysis is to find the signals in the noise, allowing for reasoned judgments, albeit with the recognition that you will make mistakes.
Coping mechanism for uncertainty: Investors and businesses, when faced with uncertainty, often respond in unhealthy ways, with denial and paralysis as common responses. Here again, data can help in two ways, first by helping you picture the range of possible outcomes and second by bringing in tools (simulations, data visualizations) for incorporating uncertainty into your decision-making.
Prescription against tunnel vision: It is easy to get bogged down in details, when faced with having to make investment decisions, and lose perspective. One of the advantages of looking at data differences over time and across firms is that it can help you elevate and regain perspective, separating the stuff that matters a lot from that which matters little.
Shield from disinformation: At the risk of getting backlash, I find that people make up stuff and present it as fact. While it is easy to blame social media, which has provided a megaphone for these fabulists, I read and hear statements in the media, ostensibly from experts, politicians and regulators, that cause me to do double takes since they are not just wrong, but easily provable as wrong, with the data.

While data clearly has benefits, as a data-user, I do know that it comes with costs and consequences, and it behooves us all to be aware of them.

False precision: It is undeniable that attaching a number to something that worries you, whether it be your health or your finances, can provide a sense of comfort, but there is the danger with treating estimates as facts. In one of my upcoming posts, for instance, I will look at the historical equity risk premium, measured by looking at what stocks have earned, on an annual basis, over treasury bonds for the last century. The estimate that I will provide is 7.03% (the average over the entire period), but that number comes with a standard error of 2.05%, resulting in a range from a little less than 4% (7.03% - 2 × 2.05%) to greater than 11%. This estimation error plays out over and over again in almost every number that we use in corporate finance and valuation, and while there is little that can be done about it, its presence should animate how we use the data.
The Role of Bias: I have long argued that we are all biased, albeit in varying degrees and in different directions, and that bias will find its way into the choices we make. With data, this can play out consciously, where we use data estimates that feed into our biases and avoid estimates that work in the opposite direction, but more dangerously, they can also play out subconsciously, in the choices we make. While it is true that practitioners are more exposed to bias, because their rewards and compensation are often tied to the output of their research, the notion that academics are somehow objective because their work is peer-reviewed is laughable, since their incentive systems create their own biases.
Lazy mean reversion: In a series of posts that I wrote about value investing, at least as practiced by many of its old-time practitioners, I argued that it was built around mean reversion, the assumption that the world (and markets) will revert back to historic norms. Thus, you buy low PBV stocks, assuming (and hoping) that those PBV ratios will revert to market averages, and argue that the market is overpriced because the PE ratio today is much higher than it has been historically. That strategy is attractive to those who use it, because mean reversion works much of the time, but it is breaks down when markets go through structural shifts that cause permanent departures from the past.
The data did it: As we put data on a pedestal, treating the numbers from emerge from it as the truth, there is also the danger that some analysts who use it view themselves as purely data engineers. While they make recommendations based upon the data, they also refuse to take ownership for their own prescriptions, arguing that it is the data that is responsible.

As the data that we collect and have access to gets richer and deeper, and the tools that we have to analyze that data become more powerful, there are some who see a utopian world where this data access and analysis leads to better decisions and policy as output. Having watched this data revolution play out in investing and markets, I am not so sure, at least in the investing space. Many analysts now complain that they have too much data, not too little, and struggle with data overload. At the same time, a version of Gresham's law seems to be kicking in, where bad data (or misinformation) often drives out good data, leading to worse decisions and policy choices. My advice, gingerly offered, is that as you access data, it is caveat emptor, and that you should do the following with any data (including my own):

(a) Consider the biases and priors of the data provider.

(b) Not use data that comes from black boxes, where providers refuse to detail how they arrived at numbers.

Data Coverage

As I mentioned at the start of this post, I started my data estimation for purely selfish reasons, which is that I needed those estimates for my corporate financial analyses and valuations. While my sharing of the data may seem altruistic, the truth is that there is little that is proprietary or special about my data analysis, and almost anyone with the time and access to data can do the same.

Data Sources

At the risk of stating the obvious, you cannot do data analysis without having access to raw data. In 1993, when I did my first estimates, I subscribed to Value Line and bought their company-specific data, which about 2000 US companies and included a subset of items on financial statements, on a compact disc. I used Value Line's industry categorizations to compute industry averages on a few dozen items, and presented them in a few datasets, which I shared with my students. In 2025, my access to data has widened, especially because my NYU affiliation gives me access S&P Capital IQ and a Bloomberg terminal, which I supplement with subscriptions (mostly free) to online data. It is worth noting that these almost all the data from these providers is in the public domain, either in the form of company filings for disclosure or in government macroeconomic data, and the primary benefit (and it is a big one) is easy access.

As my data access has improved, I have added variables to my datasets, but the data items that I report reflect my corporate finance and valuation needs. The figure below provides a partial listing of some of these variables:

As you can see from browsing this list, much of the data that I report is at the micro level, and the only macro data that I report is on variables that I need in valuation, such as default spreads and equity risk premiums. In computing these variables, I have tried to stay consistent with my own thinking and teaching and transparent about my usage. As an illustration for consistency, I have argued for three decades that lease commitments should be treated as debt and that R&D expenditures are capital, not operating, expenses, and my calculations have always reflected those views, even if they were at odds with the accounting rules. In 2019, the accounting rules caught up with my views on lease debt, and while the numbers that I report on debt ratios and invested capital are now closer to the accounting numbers, I continue to do my own computations of lease debt and report on divergences with accounting estimates. With R&D, I remain at odds with accountants, and I report on the affected numbers (like margins and accounting return) with and without my adjustments. On the transparency front, you can find the details of how I computed each variable at this link, and it is entirely possible that you may not agree with my computation, it is in the open.

There are a few final computational details that are worth emphasizing, and especially so if you plan to use this data in your analyses:

With the micro data, I report on industry values rather than on individual companies, for two reasons. The first is that my raw data providers are understandably protective of their company-level data and have a dim view of my entry into that space. The second is that if you want company-level data for an individual company or even a subset, that data is, for the most part, already available in the financial filings of the company. Put simply, you don't need Capital IQ or Bloomberg to get to the annual reports of an individual company.
For global statistics, where companies in different countries are included within each industry, and report their financials in different currencies, I download the data converted into US dollars. Thus, numbers that are in absolute value (like total market capitalization) are in US dollars, but most of the statistics that I report are ratios or fractions, where currency is not an issue, at least for measurement. Thus, the PE ratio that I report would be the same for any company in my sample, whether I compute it in US dollar or Chilean pesos, and the same can be said about accounting ratios (margins, accounting returns).
While computing industry averages may seem like a trivial computational challenge, there are two problems you face in large datasets of diverse companies. The first is that there will be individual companies where the data is missing or not available, as is the case with PE ratios for companies with negative earnings. The second is that the companies within a group can vary in size with very small and large companies in the mix. Consequently, a simple average will be a flawed measure for an industry statistic, since it weighs the very small and the very large companies equally, and while a size-weighted average may seem like a fix, the companies with missing data will remain a problem. My solution, and you may not like it, it to compute aggregated values of variable, and use these aggregated values to compute the representative statistics. Thus, my estimate the PE ratio for an industry grouping is obtained by dividing the total market capitalization of all companies in the grouping by the total net income of all companies (including money losers) in the grouping.

Since my data is now global, I also report on these variables not only across all companies globally in each industry group, but for regional sub-groupings:

I will admit that this breakdown may look quirky, but it reflects the history of my data updates. The reason Japan gets its own grouping is because when I started my data grouping two decades ago, it was a much larger part of both the global economy and markets. The emerging markets grouping has become larger and more unwieldy over time, as some of the countries in this group had or have acquired developed market status and as China and India have grown as economies and markets, I have started reporting statistics for them separately, in addition to including them in the emerging markets grouping. Europe, as a region, has become more dispersed in its risk characteristics, with parts of Southern Europe showing the volatility more typical of emerging markets.

Data Universe

In the first part of this post, I noted how bias can skew data analysis, and one of the biggest sources of bias is sampling, where you pick a subset of companies and draw the wrong conclusions about companies. Thus, using only the companies in the S&P 500 or companies that market capitalizations that exceed a billion in your sample in computing industry averages will yield results that reflect what large companies are doing or are priced at, and not the entire market. To reduce this sampling bias, I include all publicly traded companies that have a market price that exceeds zero in my sample, yielding a total sample size of 48,156 companies in my data universe. Note that there will be some sampling bias still left insofar as unlisted and privately owned businesses are not included, but since disclosure requirements for these businesses are much spottier, it is unlikely that we will have datasets that include these ignored companies in the sample in the near future.

In terms of geography, the companies in my sample span the globe, and I will add to my earlier note on regional breakdowns, by looking at the number of firms listed and market capitalizations of companies in each sub-region:

Current data link

As you can see, the United States, with 5994 firms and a total market capitalization of $69.8 trillion, continues to have a dominant share of the global market. While US stocks had a good year, up almost 16.8% in the aggregate, the US share of the global market dipped slightly from the 48.7% at the end of 2024 to 46.8% at the end of 2025. The best performing sub-region in 2025 was China, up almost 32.5% in US dollar terms, and the worst, again in US dollar terms, was India, up only 3.31%. Global equities added $26.3 trillion in market capitalization in 2025, up 21.46% for the year.

While I do report averages by industry group, for 95 industry groupings, these are part of broader sectors, and in the table below, you can see the breakdown of the overall sample by sector:

Current data link

Across all global companies, technology is now the largest share of the market, commanding almost 22% of overall market capitalization, followed by financial services with 17.51% and industrials with 12.76%. There is wide divergence across sectors, in terms of market performance in 2025, with technology delivering the highest (20.73%) and real estate and utilities the lowest. There is clearly much more that can be on both the regional and sector analyses that can enrich this analysis, but that will have to wait until the next posts

Usage

My data is open access and freely available, and it is not my place to tell you how to use it. That said, it behooves me to talk about both the users that this data is directed at, as well as the uses that it is best suited for.

For practitioners, not academic researchers: The data that I report is for practitioners in corporate finance, investing and valuation, rather than academic researchers. Thus, all of the data is on the current data link is data as of the start of January 2026, and can be used in assessments and analysis today. If you are doctoral student or researcher, you will be better served going to the raw data or having access to a full data service, but if you lack that access, and want to download and use my industry averages over time, you can use the archived data that I have, with the caveat being that not all data items have long histories and my raw data sources have changed over time.
Starting point, not ending point: If you do decide to use any of my data, please do recognize that it is the starting point for your analysis, not a magic bullet. Thus, if you are pricing a steel company in Thailand, you can start with the EV/EBITDA multiple that I report for emerging market steel companies, but you should adjust that multiple for the characteristics of the company being analyzed.
Take ownership: If you do use my data, whether it be on equity risk premiums or pricing ratios, please try to understand how I compute these numbers (from my classes or writing) and take ownership of the resulting analysis.

If you use my data, and acknowledge me as a source, I thank you, but you do not need to explicitly ask me for permission. The data is in the public domain to be used, not for show, and I am glad that you were able to find a use for it.

The Damodaran Bot!

In 2024, I talked about the Damodaran Bot, an AI entity that had read or watched everything that I have put online (classes, books, writing, spreadsheets) and talked about what I could do to stay ahead of its reach. I argued that AI bots will not only match, but be better than I am, at mechanical and rule-based tasks, and that my best pathways to creating a differential advantage was in finding aspects of my work that required multi-disciplinary (numbers plus narrative) and generalist thinking, with intuition and imagination playing a key role. As I looked at the process that I went through to put my datasets together, I realized that there was no aspect of it that a bot cannot do better and faster than I can, and I plan to work on involving my bot more in my data update next year, with the end game of having it take over almost the entire process.

I do think that there is a message here for businesses that are built around collecting and processing data, and charging high prices for that service. Unless they can find other differentials, they are exposed to disruption, with AI doing much of what they do. More generally, to the extent that a great deal of quant investing has been built around smart numbers people working with large datasets to eke out excess returns, it will become more challenging, not less so, with AI in the mix.

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Data Update Posts for 2026

Trillion Dollar Market Caps: Fairy Tale Pricing or Business Marvels?

2025-12-03T13:14:00.006-05:00

Stock markets have always rewarded winners with large capitalizations, and with each new threshold, the questions begin anew of whether animal spirits or fundamentals are driving the numbers. A few weeks ago, Nvidia seemed unstoppable as its market capitalization crested $5 trillion, and while markets have turned skeptical since, the core questions have not gone away, and the answers come from two extremes. At one end are the "realists”, who view themselves as rational, above the fray and entirely data-driven, who argue that there is no business model that can support a value this high, and that Nvidia is overvalued. At the other end are the “AI true believers”, who believe that if the market the company is going after is big enough, and they see AI as such a market, the upper bounds on value are released, the sky is the limit. As someone who entered the Nvidia sweepstakes early (in 2018) and has held it through much of its magical run, while expressing reservations about its pricing running ahead of its value, especially in the last three years, I will try to thread the needle (unsuccessfully, I am sure) in this post. In fact, rather than try to convince you that the company is under or overvalued, which is really your judgment to make, I will offer a simple model to reverse engineer from any given market capitalization, the revenues and profitability thresholds you have to meet, and allow you to come to your own conclusions.

A History of Market Cap Thresholds

In 1901, US Steel was created when Andrew Carnegie and J.P. Morgan consolidated much of the US steel business, with an eye to monopolizing the steel business, and the company became the first global firm with a market capitalization of a billion dollars, a small number in today's terms, but a number that was three times larger than the Federal budget in that year. The twentieth century was a good one for the US economy and US stocks, and the thresholds for highest market cap rose along the way:

Note the long stretch between Microsoft hitting the half-a-trillion dollar market cap in 1999, as the dot com boom peaked, and Apple doubling that threshold in 2018. Note also the quickening of the pace, as Apple hit the $2 trillion and $3 trillion market capitalization thresholds in the next four years, and Nvidia continued the streak hitting $4 trillion in 2024 and $5 trillion in 2025.

The table does provide a starting point to discussing multiple themes about how the US economy and US equities have evolved over the last century. You can see the shift away from the smokestack economy to technology , in the companies hitting the thresholds, with US Steel and GM firmly in the old economy mode, Microsoft, Apple, and Nvidia representing the new economy, and GE, with its large financial service arm, operating as a bridge. Having been in markets for all of the thresholds breached since 1981, the debate about whether the company breaking through has risen too much in too short a time period has been a recurring one.

Substance: To get a measure of operating substance, I looked at the revenues and net income in the year leading into the year in which each company broke through the threshold. As you can see, US Steel had revenues of $0.56 billion and net income of $0.13 billion in 1901, the year in which its market cap exceeded $1 billion. GM, at the time its market cap breached $10 billion, had revenues of $9.83 billion, on which it generated net income of $0.81 billion; if PE ratios are your pricing metric of choice, that would have translated into a PE ratio of 12.35. Between 2018 and 2022, as Apple's market cap tripled from $1 trillion to $3 trillion, its annual revenues increased by 72%, and its net profits almost doubled. Finally, coming to Nvidia, the surge in market cap to $4 trillion in 2024 and $5 trillion in 2025 has come on revenues and net income that are about a quarter of the size of Apple's revenues and net income.
Life cycle: Every company that climbed to the top of the market cap tables and hit a market cap threshold historically has had single-digit revenue growth in the year leading up, with two exceptions: Microsoft in 1999, which was coming off a 28% revenue growth rate in 1998, and Nvidia in both 2024 and 2025 coming off even higher growth rates. Using this revenue growth rate in conjunction with the ages of the companies involved, I think it is fair to conclude that there has been a shift across time, with the mature companies (older, lower growth) that were at the top of the list for much of the twentieth century to much younger companies with growth potential rising to the top in this one.
Investment returns: Looking at the returns in the years after these companies hit their market cap thresholds, the results are mixed. While buying Apple in 2018, 2020, or 2022 would have yielded winning returns, at least over the next year or two, buying Microsoft in 1999 would not. In some of these cases, extending the time horizon would have made a difference, for the positive with Microsoft and for the negative with GE.

From a rational perspective, you could argue that these thresholds (billion, half a billion, trillion, etc.) are arbitrary and that there is nothing gained by focusing on them, but in a post that I wrote in September 2018 on Apple and Microsoft becoming trillion-dollar companies, I argued that crossing these arbitrary thresholds can draw attention to the numbers, with the effects cutting both ways, drawing in investors who regret missing out on the rising market cap in the periods before (a positive) and causing existing investors to take a closer look at what they are getting in return (perhaps a negative).

Market Caps: Pathways to Intrinsic Value Break Even

Debates about whether a company is worth what it is trading for, whether it be a billion, ten billion, a hundred billion, or a trillion, devolve into shouting matches of "he said, she said", with each side staking out divergent perspectives on value and name-calling the other. Having been on the receiving end of some of that abuse, I decided to take a different pathway to examining this question. Rather than wonder whether Nvidia is worth five trillion or Eli Lilly is worth a trillion, I framed the question in terms of how much Nvidia or Eli Lilly would have to generate in revenues to justify their market capitalizations. The reason for my focus on revenues is simple since it is relatively unaffected by accounting games and can be compared to the total market size to gain perspective.

The tool that I plan to use to arrive at this breakeven revenue is intrinsic valuation, and I chose not to use the acronym "DCF" deliberately. A discounted cash flow valuation (DCF) sounds like an abstraction, with models driving discount rates and financial modeling driving cash flows. To me, a DCF is just a tool that allows you to assess how much you would pay for a business or the equity in the business, given its capacity to generate cash flows for its owners. Since it is easy to get lost in the labyrinth of estimates over time, I will simplify my DCF by doing two things. First, since our discussion is about market capitalization, i.e., the market's estimate of the value of equity, I will stay with an equity version of the model, where I focus on the cash flows that equity investors can get from the business and discount these cash flows back at a rate of return that they would demand for investing in that equity. In its most general form, this is what an equity valuation yields:

To simplify the assessment further, I structured this model to value equity in a mature company, i.e., one growing at or below the nominal growth rate of the economy in the very long term and again for simplicity, assumed that it could do this forever. The value of equity in this mature, long-lasting firm can be written as follows:

To put this model into use, let's take the $5 trillion dollar market capitalization that Nvidia commanded a few weeks ago and assign the following general inputs:

Cost of equity: Every month, I estimate the implied cost of equity for the S&P 500, and that number is model-agnostic and driven by what investors are willing to pay for stocks, given their fears and hopes. At the start of November 2025, that number was about 8%, with higher required returns (9-12%) for riskier stocks and lower expected returns (6-7%) for safer stocks.
Inflation rate: While inflation has come down from its 2022 highs, it has stayed stubbornly above 2%, which the Fed claims as its target, and it seems more realistic to assume that it will stay at 2.5%, which is consistent with the riskfree rate being about 4%.
Stable growth rate (nominal growth rate in the economy): This is a number that is in flux, as economists worry about recessions and economic growth, but since this is a long-term number that incorporates expected inflation, it seems reasonable to assume an expected nominal growth of 4% for the economy (about 1.5% real growth).

The net profit margin for Nvidia in the most recent twelve months has been 53.01%, an exceptionally high number, and the return on equity it has earned, on average over the last five years, is about 64.44%. I know that these numbers will come under pressure over time, as competition for AI chips picks up, and Nvidia's biggest customers (and chip maker) push for their share of the spoils, but even if you assume that Nvidia can maintain these margins, the revenue that Nvidia would have to deliver to justify its value is $483.38 billion.

Since Nvidia is still growing and you may need to wait, as equity investors, to get your cash flows, this breakeven number will get larger, the longer you have to wait and the lower the cash yield that equity investors receive during the growth period. In fact, with Nvidia, if you assume that it will take five years for them to grow to steady state, and that equity investors will receive a cash yield (cash flow as a percent of market cap) of 2% a year, the estimated breakeven revenue increases to $677.97 billion. The table below maps out the effects of waiting on breakeven revenues for a range of cash yield:

Download breakeven revenue spreadsheet

If, as seems reasonable, you assume that net margins and return on equity will decrease over time, the revenues you would need to break even will expand:

Download breakeven revenue spreadsheet

In fact, if you are a low-margin company, with net margins of 5% (as is the case with even the very best-run discount retailers) and a more modest return on equity of 10%, you will need revenues of $8 trillion or more to be able to get to a market capitalization of $5 trillion.

This framework can be used to compute breakeven revenues at other firms, and in the table below, we do so for the twelve largest market cap companies in the world, at their market capitalizations on November 20, 2025:

Note that, for simplicity, I have used a 2% cash yield and 4% growth rate in perpetuity for all of these firms, and that the breakeven revenues reflect current net margins and returns on equity at each of these firms, but with that said, there is still value in looking at differences. To allow for this comparison, I forecast out breakeven revenues five years from now, and estimated the growth that each company would need over the five years to justify its current market cap. Not surprisingly, Aramco can get to its breakeven revenues in year 5 with almost no growth (0.59% growth rate) but Tesla needs to deliver revenue growth of 86.4% to break even. Broadcom, another company that has benefited from the market's zeal for AI, has the next highest cliff to climb in terms of revenue growth. In fact, for all of the Mag Seven stocks, growth has to 15% or higher to breakeven, a challenge given their scale and size. In dollar value terms, three companies will need to get to breakeven revenues that exceed one trillion by year 5 to breakeven, Apple, Amazon and Tesla, but the first two are already more than a third of the way to their breakeven targets, but Tesla has a long, long way to go.

From Breakeven Revenues to Investment Action

While some are more comfortable replacing conventional intrinsic valuation, where you estimate value and compare it to price, with a breakeven assessment, the truth is that the two approaches are born out of the same intent.

The Economics of Breakeven Revenues

The model that I used to compute breakeven revenues is a vastly simplified version of a full equity valuation model, but even in its simplified form, you can see the drivers of breakeven revenues.

Market Capitalization: Since we work back from market capitalization to estimate breakeven revenues, the larger the market capitalization, holding all else constant, the greater the breakeven revenues will be. Using just Nvidia as an example, the company has seen its market capitalization rise from less than $400 billion in 2021, to $1 trillion in 2023, $2 trillion and $3 trillion thresholds in 2024 and crossed the $4 trillion and $ 5 trillion market cap levels in 2025. As the market cap has risen, the breakeven revenues have increased from $200 billion at the $1 trillion mark to $600 billion at the current market cap.
Operating Profitability: There are two profitability metrics in the drivers, with net margins determining how much of the revenues a company can convert to profits and the return on equity driving the reinvestment needed to sustain growth. Higher profitability will allow a company to deliver a higher market capitalization, at any given level of revenues. One reason manufacturing firms like Tesla will need higher breakeven revenues than software firms is that the unit economics are not as favorable.
Interest rates and equity risk premiums: The level of interest rates and equity risk premiums determine the cost of equity for all company, with higher values for the latter pushing up the costs of equity for riskier companies higher, relative to safer companies.
Operating and leverage risk: The riskiness in a business will push its cost of equity higher, and a higher debt load (relative to market cap) will have the same effect. A higher cost of equity will raise the breakeven revenues needed to deliver the same market capitalization.

In sum, while the breakeven revenue that you need to justify a given market cap always increases as the market cap increases, its level and rate of rise will be governed by business economics.

The 3Ps: Possible, Plausible, and Probable

Replacing a conventional intrinsic valuation with a breakeven revenue analysis still leaves open the final investment question of whether that breakeven revenue is a number that you are comfortable with, as an investor. To address this question, I will draw on a structure that I use for intrinsic valuation, where I put my assessment through what I call the 3P test.

It is possible that once you compute the breakeven revenues for a firm and measure it up against reality that it is impossible, i.e., a fairy tale. The most obvious case is when the breakeven revenues that you compute for your firm exceeds the total market for the products or services that it provides. If there is a lesson that tech companies learned in the last decade, it was in making the total addressable market (TAM) for their market into almost an art form, adding zeros and converting billion dollar markets into trillion dollar TAMs. If you pass the "it is possible" test, you enter the plausibility zone, and nuance and business economics enter the picture more fully. Thus, assuming that a luxury retailer with sky-high margins and small revenues, by staying with a niche market, can increase its revenues ten-fold, while keeping margins intact, is implausible, as is a net margin of 40% in stable growth for a company with gross margins that are barely above that number. Finally, assuming that revenues can multiply over time, without reinvesting in acquisitions or projects to deliver those revenues are also pushing the boundaries of what is plausible. Once breakeven revenues pass the possible and plausible tests, you should be on more familiar ground as you look at the entire story line for the company, and assess whether the combination of growth, profitability and reinvestment that you are assuming with your story has a reasonable probability of being delivered.

To apply these tests, consider Nvidia and Tesla. Nvidia needs about $590 billion in revenues by 2030 to break even at its current market capitalization of $4.3 trillion, requiring a growth rate in revenues of about 26% for the next five years. While that is a reach, it is both possible and plausible, with continued growth in the AI chip market and a dominant market share for Nvidia providing the pathway. It is on the probable test that you run into headwinds, since competition is heating up, and that will put pressure on both growth and margins. The problem for Tesla is that if the net margin stays low (at 5.31%), the revenues needed to breakeven exceed $2.2 trillion, and even with robotics and automated driving thrown into the business mix, you are pushing the limits of possibility. A Tesla optimist, though, would argue that these new businesses, when they arrive, will bring much higher net margins, which, in turn, will push down breakeven revenues and bring it into plausible territory.

The Aggregated 3P Test - Big Market Delusion

We tend to ask the 3P question at the company level with the companies that we choose to invest in (and like), but as we construct what look like plausible and probable stories for these companies, and invest in them accordingly, there are other investors are asking the same questions about the companies that they invest in, many of which compete in the same business as yours. That may sound unexceptional to you, but when the market that these companies are competing in is very large and still in formation, you can end up with what I described almost a decade ago as the big market delusion. In a paper on the topic, I used the dot.com boom, the cannabis stock surge and online advertising as case studies to explain how this behavior is a feature of big markets

The AI storyline clearly fits the big market delusion. There is talk of a "huge" market for AI products and services, with little to show as tangible evidence of that market’s existence right now, and that potential has drawn massive investments in AI architecture from tech companies. Along the way investors have also fallen under the spell of the big market, and have pushed up the market capitalizations of almost every company in the space. Using the language of breakeven revenues, investors in each of these companies is attributing large breakeven revenues to their chosen companies, but the delusion comes from the reality that if you aggregated these breakeven revenues across companies, the market is not big enough to sustain all of them. In short, each company passed the possible and plausible test, but in the aggregate, you are chasing an impossible target.

While the big market delusion is at play in every aspect of AI, one segment where it is most visible right now is in the Large Language Models (LLM) space, where high profile players like ChatGPT, Gemini, Grok and Claude are vying for users, and their creators are being rewarded with nosebleed pricing. OpenAI, while still unlisted, has used the early lead that ChatGPT gave it in the LLM race to attract investments from a host of big tech companies (including Nvidia and Amazon) and venture capitalists, with the most recent investors pricing it at $500 billion, an astonishing number, given that the company reported revenues of only $13 billion in the most recent twelve months. Anthropic, the creator of Claude, has seen its pricing jump in the most recent funding round (from Microsoft and Nvidia in November 2025) to $350 billion, fifty times its revenues of $7 billion in the last twelve months. Elon Musk's owners stake in xAI, Grok's originator, was estimated to be worth $230 billion in November 2025, again an immense multiple of its revenues of $3.2 billion (if you include combined revenues with X). Expanding the list to the large tech companies, it is undeniable that some of Alphabet's massive rise in market capitalization in 2025 is because of its ownership of Gemini, and that Meta (with Llama) and Amazon (with Nova) have also seen bumps in market capitalization. Finally, while Deepseek is no longer making headlines, it is also in the space, competing for business. In the aggregate, LLM ownership is being priced at $1.5 trillion or more, and the collective revenues, even generously defined, are less than $100 billion. It is entirely plausible that a big market exists for LLMs, and that one or even two of the players in this space will be winners, but in the aggregate, the market is overreaching.

The Management Effect

The mechanics of the breakeven revenue process may make it seem like managers are bystanders in the process and that investing can be on autopilot, but they are not. In fact, when market capitalizations rise, and breakeven revenues run well ahead of current revenues, I would argue that management matters more than ever. Going back to the breakeven revenues that we computed for the twelve largest market cap companies in the world, I would make the case that management matters much less (if at all) in Aramco and Berkshire Hathway, where breakeven revenues are close to current revenues, and the investments needed to deliver those revenues have already been made, that at the companies that still have steep climbs ahead of them to get to breakeven revenues.

In this context, I will reemphasize a concern that I raised at the height of Meta's metaverse investing fiasco, which is that investors at many tech companies, including most on the large cap list, have given up their corporate governance rights, often voluntarily (through the acceptance of shares with different voting rights), to founders and top management in these companies. When traditional corporate governance mechanisms break down, and top managers have unchecked power, there is an increased risk of overreach. That concern is multiplied in the LLM space, where Sam Altman (at OpenAI) and Elon Musk (at xAI) are more emperors than CEOs.

The Investing Bottomline

I started this post with mentions of market cap thresholds being breached, as the market pricing pushes up into the trillions for some of the biggest stock market winners. But what are the implications for investors?

Highly priced ≠ Overpriced: If you are an investor who considers any highly priced company to be overvalued, I hope that this post leads you to reconsider. By reframing a pricing in terms of breakeven revenues, profitability and reinvestment, it allows you to consider whether a stock, even if priced at $4 trillion, may still be a good buy.
The 3P test: Once you compute the operating metrics you need to breakeven on an investment in a highly priced company, passing those metrics through the 3P test (Is it possible? Is it plausible? Is it probable?) allows you to examine each company on its merits and potential, rather than use a broad brush or a rule of thumb (based on PE ratios or revenue multiples).
Room to disagree: I have never understood why, even if you believe strongly that a stock is over or under priced, that you need to evangelize that belief or contest people with alternate views. I think that the pathway that you would need (in terms of revenue growth and profitability) to justify Nvidia's and OpenAI's current pricing is improbable, but that is just my view, and it is entirely possible that you have an alternate perspective, leading to the conclusion that they are undervalued.
Reality checks: No matter what your view, optimistic or pessimistic, you have to be open to changing your mind, as you are faced with data. Thus, if you have priced a company to deliver 20% growth in revenues over the next five years (to break even) and actual revenues growth comes in at 10%, you have to be willing to revisit your story, admit that you were wrong, and adapt.

If you came into this post, expecting a definitive answer on whether Nvidia is overpriced, you are probably disappointed, but I hope that you use the breakeven spreadsheet to good effect to make up your own mind.

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Spreadsheets

Breakeven Revenue, given market capitalization

A Golden Year (2025): Gold's Price Surge - The Signal in the Noise!

2025-11-06T16:23:00.002-05:00

I grew up in India in a time where if you had wealth, your investment options were limited. A stock market with sparse listings, accompanied by a lack of trust in financial assets, led investors to put their wealth into tangible assets. Real estate was the most common choice but gold was a strong competitor, though investments in the latter often took the form of jewelry and ornaments. As financial markets have gained dominance across the globe, especially so in the last four decades, gold has retreated to the background, with lagging returns in most years. In 2025, as stock and bond markets climbed walls of worry almost nonchalantly to reach new highs, gold has also been a surprisingly big winner, building on a recovery that started in 2022 to crest $4000 an ounce in October 2025. For long term proponents of investing in gold, this has been vindication, but even for investors who have never held gold in their portfolios, there is a message from the gold's rise that they ignore at their own peril. I must confess that I have never felt the draw of gold, and have never held it in my portfolio, but I have always been fascinated by the hold that gold has on some investors, and the reasons for its longevity. In this post, I will start by first positioning gold in the investment continuum and then examining its price movements, both in 2025 and with a longer term perspective, to get a handle on the drivers of these movements, before looking at how gold may fit in investment portfolios.

Gold: Commodity, Currency or Collectible?

I have argued that all investments can be classified into one of four groups - assets, with expected cashflows, either contractual (fixed income) or residual, commodities, which derive their value from use as inputs into production of other products or services, currencies, used as mediums of exchange and stores of value, and collectibles, held for their scarcity and enduring demand. This categorization matters because it provides a starting point for discussions of how to attach prices to each:

With assets, you can estimate value based on expected cash flows and risk. but you also price them based upon demand and supply. With commodities like oil or iron ore, you may be able to estimate value, based upon aggregated demand and supply, but it is far more likely that pricing will dominate. With currencies and collectibles, the absence of expected cash flows makes pricing the only option, making mood and momentumkey variables determining pricing direction.

To assess gold as an investment, we need to first start by classifying it and while it is not an asset, it can or has been a currency, a commodity and a collectible at different points in history and in different forms.

It is an inefficient currency, and while there are undoubtedly transactions where gold coins have been used as tender, difficulties associated with checking authenticity, security and breaking down into small units have limited its use through history.
It can be used as a commodity, as is the case when it is used to make jewelry or statues (or in tooth fillings), but even when used in this context, it is often held more for its value as a collectible than for aesthetic reasons.
It is as a collectible that gold has stood out, with governments, banks and individuals attaching value to it over time.

Thus, it is safe to say that it is gold's role as a collectible that has driven its pricing over time. To the question of "so what", there are implications that follow almost immediately, and that will animate our discussion of gold's performance in 2025:

Since gold, absent cash flows, cannot be valued, arguing whether gold is under or over valued is a pointless one, just as it is for bitcoin. In fact, if your investment philosophy is strictly tethered to finding investments that are under valued by the market, gold will not have a place in your portfolio, explaining Warren Buffett's long standing aversion to it, as an investment. It is worth noting that Berkshire Hathaway did invest in Barrick Gold, but an investment in a gold mining company has expected cash flows and is thus an asset.
Gold is priced every day, and that pricing process is driven by demand and supply, and while we will outline macro variables that can affect one or both, it is ultimately a process where mood and momentum will carry the day.

Without doubt, gold is one of the longest standing collectibles, predating and outliving its competitors. So, what is it that explains gold's durability as a collectible? The following factors come into play, and in the process of assessing them, we can get some insight into gold's enduring standing:

Scarcity: The supply of gold is not fixed, since more gold can be extracted, but it is finite. At the start of 2025, there were approximately 244,000 metric tons of gold in the world, held in a variety of forms (jewelry, gold bars & coins etc.). While gold production in 2024 amounted added 3,000 tons to this quantity, it is estimated that that there about 60,000 metric tons of gold that are still in reserves. That puts it in a sweet spot between elements like platinum that are too scarce (about 10,000 metric tons) to be widely held, and more difficult to extract, and elements that are too plentiful to hold their value.
Durability: For a collectible to hold its value, it has to be durable, and one of the reasons that gold acquired its collectible status is because it is chemically stable, malleable and does not oxidize or corrode (when it comes into contact with acids and other agents).
Desirability: There is something about gold that exerts a hold on human beings. From the Greek myth of Midas, the king whose touch turned everything to gold, to the legend of El Dorado, a city made of gold, that led the Spanish to cross the ocean to seek it out in South America, gold has driven narratives and altered history.

Clearly, gold is not the only collectible, but almost every collectible, starting with other precious metals, moving to fine art and even Pokemon cards can be assessed on these three dimensions.

Gold: A Pricing Perspective

Gold has a long history in investing, and the best to way to understand where we are right now is to look back at that history. As you look back at up and down years, we can start to make sense of the fundamentals that drive gold prices, as well as the noise added by sentiment and momentum to the pricing process.

A Usage History

Gold has been viewed as precious by civilizations going back millennia, with evidence of usage in the form of coins going back to the Lydian civilization, located in Turkey in 600 BC, with the Greeks and the Romans following. In South America, where gold was abundant, it was more likely to have ceremonial or spiritual value, crafted into ornaments, ritual objects and artifacts, and it was only after the Spanish conquistadors arrived that gold acquired monetary status. In Asia, gold coins can be traced back to the Qin dynasty in China in 2500 BC, and to India and South East Asia.

It is worth noting that for centuries, the issuers (governments and kingdom) of fiat currencies tied them to gold to get skeptical populaces to hold them. In the eighteenth century, this linkage was formalized in the gold standard, where paper currency issuance was backed by holdings in gold, with paper money convertible into gold. England adopted a de facto bimetallic (silver and gold) standard in the early 1700s, but a miscalculation by Isaac Newton on the silver/gold ratio, where silver was overpriced relative to gold, made it a gold standard. While England did not formally adopt the gold standard until 1818, the United States, at its birth as a country, and eager to have its new currency (the dollar) be accepted, followed England’s model, with a brief break during the civil war in the 1860s.

In the second half of the nineteenth century, the gold standard became the base for most major currencies, but two events in the early twentieth century put it to the test. During the First World War, governments in need of money to fund their armies found their hands tied by the constraints of gold, and many were forced to abandon convertibility and the gold standard. The United States stayed with the gold standard into the Great Depression, with some economists blaming the Fed’s actions trying to defend it for worsening the economic collapse. In the face of crisis, individuals rushed to convert dollars to gold, leading to the halting of convertibility and an effective end to a true Gold Standard. After the Second World War, the United States emerged as the economic superpower, and with the Bretton-Woods agreement, the US dollar took the place of gold at the center of the global monetary system, with the dollar convertible to gold at a fixed price. That system held until the early seventies, but broke down as the dollar deflated, and in 1971, it was officially abandoned. While central banks continue to hold gold, the gold standard is now dead, though there are some who seek a return to the system, with its enforced discipline and rigidity.

A Pricing History

As we noted at the start of this post, gold has had quite a run in 2025, as you can see in the chart below, where we traces it daily price movements during the year:

Through October 24, 2025, gold prices are up 57% for the year, posting significant increases every quarter of the year. To provide perspective on how this year measures up against history, we looked at the percentage change in gold prices every year going back to 1963.

Gold has had its ups and downs over time, with a surge in prices in the late 1970s, with the very best and very worst years in terms of returns occurring within two years of each other; gold prices were up 133% in 1979 and down 32.15% in 1981. Inflation was the culprit, and while we will take a closer look at it in the next section, we also computed the gold price in inflation-adjusted terms in the graph, and on October 24, 2025, that inflation-adjusted price also hit an all time high, using year-end prices. In the graph, you will notice that the gold price was stagnant before 1971, largely because of the convertibility of US dollars into gold. After the Bretton Woods agreement established the US dollar as the international reserve currency, the United States agreed to back it up by agreeing to convert US dollars at $35 an ounce, andgold prices (at least in dollar terms) stayed tethered to that price. In 1971, the United States abandoned that backing, and gold prices have been set by demand and supply since.

Drivers of gold prices

There is a route that can be used to estimate the "fundamental" value of a commodity by gauging the demand for the commodity (based on its uses) and the supply. While that may work, at least in principle, for industrial commodities, it is tough to put into practice with precious metals in general, and gold because the demand is not driven primarily by practical uses. While gold does not have an intrinsic value, there are at least three factors historically that have influenced the price of gold- inflation, fear of crises and real interest rates.

1. Inflation

If as is commonly argued, gold is an alternative to paper currency, the price of gold will be determined by how much trust individuals have in paper currency. Thus, it is widely believed that if the value of paper currency is debased by inflation, gold will gain in value. To see if the widely held view of gold as a hedge against inflation has a basis, we looked at changes in gold prices and the inflation rate each year from 1963-2024 in the figure below:

The co-movement of gold and inflation is strongest in the 1970s, a decade where the US economy was plagued by high inflation and the correlation between gold prices and the inflation rate is brought home, when you regress returns on gold against the inflation rate for the entire period:

% Change in Gold price = -0.06 + 3.92 (Inflation rate) R squared = 18.8%

While this regression does back the conventional view of gold as an inflation hedge, there are two potential weak spots.

The first is that the R-squared is only 19%, suggesting that factors other than inflation have a significant effect on gold prices.
The second is that removing the 1970s essentially removes much of the significance from this regression. In fact, while the large move in gold prices in the 1970s can be explained by unexpectedly high inflation during the decade, the rise of gold prices between 2001 and 2012 cannot be attributed to inflation.

To get a cleaner look at the interaction between gold and inflation, we looked at the percentage change in gold prices, by decade, and contrasted it with the returns on stocks, bills, bonds and real estate in the table below:

Gold has three standout decades - 1971-1980, 2001-2010 and the last five years (2021-2025), with unexpectedly high inflation being the driver of returns in the first and third instances. Gold's surge in the 2001 to 2010 time period can be attributed partially to the 2008 crisis, but gold had several good years leading into the crisis. If there is one finding that we can glean from this data, gold is more a hedge against extreme (and unexpected) movements in inflation and does not really provide much protection against smaller inflation changes.

2. Fear of Crises

Through the centuries, gold has been the safe haven for investors fleeing a crisis. Thus, as investor fears ebb and flow, gold prices should go up and down. To test this effect, we used two forward-looking measures of investor fears – the default spread on a Baa-rated bond and the implied equity risk premium (which is a forward looking premium, computed based upon stock prices and expected cash flows). As investor fears increase, you should expect to see these risk premiums in both the equity and the bond market increase, and gold to rise in concurrence. The figure below summarizes the risk premiums in financial markets (bond default spreads and equity risk premiums) and gold returns each year from 1963 to 2024:

While the relationship is harder to decipher than the one with inflation, higher equity risk premiums correlate with higher gold prices, but only barely. Again, regressing annual returns on gold against these two measures separately, we get:

% Change in Gold Price = -0.13 + 5.21 (ERP) R squared = 5.02%

% Change in Gold Price = 0.13 -1.32 (Baa Rate - T.Bond Rate) R squared = 0.20%

These regressions suggest little or no relationship between bond default spreads and gold prices, but a modest positive relationship, albeit one with substantial noise, between gold prices and equity risk premiums. Thus, gold prices seem to move more with fear in the equity markets than with concerns in the bond market, with every 1% increase in the equity risk premium translating into an increase of 5.21% in gold prices. As with inflation, though, gold's protective role in crises seems to be greatest during potentially catastrophic economic events, giving it the patina as a crisis hedge.

3. Real interest rates

One of the costs of holding gold is that while you hold it, you lose the return you could have made investing it in a financial asset, dividends on stocks and coupons on bonds. The magnitude of this opportunity cost is captured by the real interest rate, with higher real interest rates translating into much higher opportunity costs and thus lower prices for gold. The real interest rate can be measured directly used the inflation indexed treasury bond (TIPs) rate or indirectly by netting out the expected inflation from a nominal risk free (or close to risk free) rate. The figure below summarizes real interest rates and gold price changes on a year-by-year basis from 1963 to 2023:

Note that the TIPs rate is available only for the two decades and that the real interest rate is computed as the difference between the ten-year US treasury bond rate in that year and the realized inflation rate (rather than the expected inflation rate). Regressing changes in gold prices against the real interest rate yields the following:

% Change in Gold price = 0.18 – 4.69 (T.Bond Rate - Inflation Rate) R Squared =21.9%

High real interest rates are negative for gold prices and low real interest rates, or negative real interest rates, push gold prices higher.

The Bottom line

Gold is often touted as a hedge against inflation and crises, but the evidence from history is nuanced. With inflation, it is a better hedge against unexpected inflation than expected inflation, and even with unexpected inflation, only for increases that put inflation above normal bounds. In short, it is a hedge against hyper inflation. With crises as well, the evidence is mixed, since gold prices are, for the most part, unaffected by movements in equity and bond risk measures that fall within historical bounds, but increase during risk events that are uncommon and potentially catastrophic. Investors who add gold to their portfolios because of the protection it offers should recognize it more akin to buying insurance against extreme events, and more useful if the bulk of their wealth is in financial assets.

Is gold expensive, correctly priced or cheap?

Knowing that gold prices move with inflation, equity risk premiums and real interest rates is useful, but it still does not help us answer the fundamental question of whether gold prices today are too high or low. Can you price gold against other investments or itself? The answer is yes, though the results are often noisy.

A. Against inflation

In companion papers, Erb and Harvey examined the relationship between gold prices and inflation. In these papers, the price of gold is related to the CPI index and a ratio of gold prices to the CPI index is computed. In the first of these papers, they argued that in the very long term, gold prices increase at roughly the inflation rate, but in the second, they do question that hypothesis. We try to replicate their findings and we use the US Department of Labor CPI index for all items (and all urban consumers) set to a base of 100 in 1982-84, but with data going back to 1947. The level of the index in December 2023 was 308.742. Dividing the gold price of $4118/oz on October 24, 2025, by the CPI index level of 324.80, on that day, yields a value of 17.81. To get a measure of whether that number is high or low, we computed it every year going back to 1963 in the figure below

The median value is 2.93 for the 1963-2024 period and 3.77 for the 1971-2024 period. Thus, based purely on the comparison of the current measure of the Gold/CPI ratio to the historical medians does miss the fact that lower interest rates and inflation in the last decade may be skewing the statistics. Consequently, we regressed the Gold/CPI index against equity risk premiums and real interest rates and while real interest rates seem to have little effect on the Gold/CPI ratio, there is strong evidence that it moves with the ERP, increasing (decreasing) as the ERP increases (decreases):

Gold Price/ CPI = -1.79 + 123.56 (ERP) R Squared = 47.7%

The implied equity risk premium for the S&P 500 at the start of October 2025 was 4.03%, and plugging that value into the gold/CPI regression yields the following:

Gold/CPI (given ERP of 4.03% on 10/24/25) = -1.79+ 123.56 (.0403) = 3.19

Put simply, gold looks overpriced in October 2025, even after correcting for changing equity risk premiums.

B. Against other precious metals

There is another way that you can frame the relative value of gold and that is against other precious metals. For instance, you can price gold, relative to silver, and make a judgment on whether it is cheap or expensive (on a relative basis). At the end of October 2025, the gold price was $4118/oz and the silver price was $47.80/oz, yielding a ratio of 84.73 for gold to silver prices (4118/47.80). To get a measure of where this number stands in a historical context, we looked at the ratio of gold prices to silver prices from 1963 to 2025 in the figure below

The median value of 57.09 over the 1963-2024 period would suggest that gold is overpriced, relative to silver. Given that gold and silver move together more often than they move in opposite directions, we are not sure that this relationship can be mined to address the question of whether gold is fairly priced today, but it can still be the basis for trading across precious metals.

The Bottom Line

The historical data yields two conclusions, albeit at odds with each other. If you believe that history is your best guide for the future and that mean reversion will win out, it is undeniable that gold is overpriced against almost every metric it is usually priced against. In fact, you could argue that the rise of gold prices in the last decade is unprecedented since it has not been accompanied by raging inflation or by big market crises (though there have been economic crises). The counter is that using historical data as a guide, gold has been overpriced over the last decade, a period over which its price has increased almost four fold, from $1060/oz at the end of 2015 to $4,118 on October 24, 2025. When an investment stays overpriced for that long, it is legitimate to question whether the pricing metric is flawed, and whether there a structural shift has occurred that has shifted the distribution. In the case of gold, priced on demand and supply, that shift has to be almost entirely on the demand side, since the stock of gold has continued to expand at a slow, but steady pace, during the period, and here are some of the possible reasons:

More pathways to buying/holding gold: For centuries, extending into the last century, the only way to invest in gold was to hold it in its physical form, with all of the limitations on making fractional investments and the added transactions/storage costs. The rise of Gold ETFs has reduced or removed both constraints allowing more investors entree into the gold market.
Mistrust of central banks: Investments in financial assets (stocks and bonds) are a reflection of the trust investors have in central banks and governments, working to preserve the buying power of the currencies that they issue. In the aftermath of central banking activism in the post-2008 period, that trust in central banks and governments has depleted, at least for a segment of the population, leading to a shift on their part to gold (and bitcoin).
Slippage of the US dollar: In the aftermath of Bretton Woods, the world adopted the US dollar as a global base currency, with a tether remaining to gold. During that period, central banks held gold, as backup for their currencies, though individuals were restricted or denied the ability to convert currency to gold. Even after the US removed its last formal connection to the gold standard in 1971, the strength of the dollar and the centrality of the US economy allowed investors to use the US dollar as a safe haven currency, as a substitute for gold. It is undeniable that the US economy and dollar have been under stress for the last decade or more, with the ratings downgrade for the US being only a manifestation of these stresses. With no other global currency ready (yet) to take the place of the dollar, you can argue that gold is once again asserting its role as safe haven, and that the rise in its price reflects that status.
The Trump effect: While the first two factors have been in play for decades, this year has seen unusual turmoil, as tariff threats and economic wars threaten to unravel an economic world order that has governed markets and economies for much of the last century. While there are some who will welcome that development, it is not clear what the replacement will be, and the possibility of a catastrophic outcome is perhaps greater than it was a year or two ago, and this too is a positive for gold prices.

For much of the last century, investors who held gold in their portfolios tended to be a subset of the market, older and more concerned about catastrophes than the rest of us, but it is undeniable that this group now is both larger and drawing in some who would have historically pushed it away.

Investment Consequences

With that long lead in, every investor is faced with the question of whether gold fits into their investment portfolios, and the reason for holding it. There are four pathways that an investor can follow with gold, and without any judgment attached, here they are, with the trade offs involved:

Gold as a core investment: There are some investors who have built their portfolios, with gold as a central component, representing a significant portion of their holdings.

The trade off: Looking at the last forty years of returns on different investment classes, you can see why making an argument for holding gold as your core investment is so difficult to justify. Gold, with its annual compounded return of 5.35% between 1984 and 2024, would have significantly underperformed an investment in US stocks, that earned a compounded return of 11.38%, a difference that translates into a significant shortfall in ending portfolio value for gold investors; investing in US stocks in 1984 would have generated almost ten times as high an ending value in 2024, as investing an equivalent amount in gold in 1984.

In fact, gold has also been a more risky investment, on a stand alone basis, than stocks with a higher standard deviation in annual returns. Does that make gold investors irrational? Not necessarily, because they may define risk in terms of best case and worst case outcomes, and while stock prices, at least in their perspective, have no lower bound, gold has a lower bound value, at least based on history.
The draw: For investors who have a deep attachment to gold combined with a distrust of financial assets, governments and central banks, the net effect of holding a portfolio dominated by gold is that it improves their odds of passing the sleep test, i.e., they don't lose sleep wondering how their portfolios are doing. In short, they are willing to accept lower compounded annual returns over the long term in return for the security of holding an investment that they view as timeless.
The choices: Gold's standing comes from its long history as a collectible, but it is not the only collectible. Through time, investors have also put their money in precious gems and other metals (silver, platinum), art and collectibles. In fact, some of the rise in cryptos (currencies, tokens and assets) can be attributed to a subset of (mostly younger) investors, who share the distrust of governments and central banks with gold investors, deciding to use bitcoin as an alternative to gold.

Gold as insurance: For investors with the bulk of their portfolios in financial assets (stocks and bonds), gold holdings can help insure their portfolios, at least partially, against inflation and market/economic crises.

The trade off: As we noted earlier in the post, gold has been only a weak hedge against inflation and market crises that fall within normal bounds, but has done much better as a edge against hyperinflation and catastrophic market/economic risks. Adding gold to a financial asset dominated portfolio can provide insurance against the latter, but only if held in large enough quantity to make a difference; given the history of stock and gold returns, a gold holding that is 5% of your portfolio will not be enough and you will need a holding closer to 15-20%.
The draw: All investors should be concerned about catastrophic risks, but it is undeniable that this concern varies across investors, with older and more risk averse investors more inclined to have that concern. It is also true that worries about catastrophes vary over time, increasing across all investors in troubled times.
The choices: The rise of derivatives markets has increased the choices for investors to buy protection against hyperinflation and catastrophes. Thus, you can use ETFs and options to hedge your portfolio against market collapses, if that is your concern, or shift your investments to other currencies and countries, if your worry is about hyperinflation in the domestic currency.

Gold as a trade: In trading, the key to winning is timing, buying when prices are low and selling when they are high, and there are some who make their money on gold by timing its ups and downs well.

The trade off: Getting the timing right in trading is easier said that done. While the peaks and bottoms of gold prices are easy to pinpoint in hindsight, it is worth remembering that many investors who became rich riding the gold price boom from 1977-1979 lost it all in next five years. The traders who bought gold in 2022 are riding high, at the moment, after a three-year surge in gold prices, but they too may be looking at disappointment, if they do not cash out at the right time.
The draw: Trading is a pricing game, and since price is determined more by mood and momentum, success in gold trading comes down to detecting momentum shifts before they occur, and trading on that basis. For some gold traders, this capacity may come from examining charts on gold prices and volume, and for others, it may be in reading the macroeconomic tealeaves, especially on inflation.
The choices: If trading is your game, the market is ripe with targets, ranging from cryptos in the collective space to meme stocks, and many of these alternatives offer a bigger payoff to trading, since they are more volatile than gold and in some cases, offer more liquidity.

Gold as a signal: There are many investors who have no desire to own or or are averse to holding gold in their portfolios, but use gold prices as signals of either hyperinflation or economic catastrophes to structure their portfolios.

The trade off: The allure of gold as a signal of inflation and market crises comes from history, where gold prices have tended to rise during periods of high inflation and economic uncertainty. Much of the relationship, though, is contemporaneous, i.e,, gold prices rise in periods when inflation is high and risks surge, and there is only weak evidence of gold prices being a leading indicator of future changes.
The draw: Since portfolios composed primarily or entirely of financial assets are badly damaged by unexpected inflation or a market meltdown, having a predictor, even a flawed one, that can give advance warning has big payoffs. In particular, if gold prices rising is a signal that inflation will be higher than expected in the future, you could alter your asset allocation, shifting money from stocks and long terms bonds to short term bills and commercial paper, or even your asset selection, moving money from companies that have little pricing power and significant operating risk to companies with substantial pricing power and predictable earnings streams.
The choices: Here again, markets offer other choices, with futures markets and forward contracts specifically targeted at predicting inflation or economic and market shocks. Thus, you could use inflation futures to protect against hyper inflation and volatility indicators (like the VIX) to hedge against market crises.

The Bottom Line

Gold has had a good run this year, and I will not begrudge those who got into it early. Some undoubtedly just got lucky to be at the right place at the right time, but some were prescient in detecting a shift in the market vibe, especially in 2025. The truth is that the market for gold has been and always will be a niche market, drawing a subset of investors, but that niche shrinks and expands over time. When the world is stable and times are good, the niche is composed almost entirely of true believers, a mix of conspiracy theorists and doomsday cultists who believe that fiat currencies are more paper than money and that financial asset markets are designed to enrich insiders. In scarier times, the niche expands, drawing in investors who normally invest in stocks and bonds, but decide, either because of distrust of central banks or perceived market bubbles, that they need the safety of gold. While I do not have a ledger listing everyone holding gold in October 2025, I will wager that it includes names that you would normally expect to see in the list. After all, if Jamie Dimon and Ray Dalio actually mean what they say about markets being a bubble, would it not make sense for them to hold gold?

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Datasets

A “Fairly Highly Valued” Market: A Fed Chair Opines on Stocks, but should we listen?

2025-10-06T10:02:00.001-04:00

In December 1996, Alan Greenspan used the words "irrational exuberance" to describe the stock market at the time, and those words not only became the title of Robert Shiller's cautionary book on market bubbles, but also the beginnings of the belief that central bankers had the wisdom to be market timers and the power to bend the economy to their views. I think that Greenspan's words seem prophetic, only with the benefit of hindsight, and I believe that central bankers have neither the power nor the tools to move the economy in significant ways. I was reminded of that episode when I read that Jerome Powell, the current Fed chair, had described the market as "fairly highly valued". In market strategy speak, these are words that are at war with each other, since markets can either be “fairly valued” or “highly valued”, but not both, but I don't blame Powell for being evasive. For much of this year, and especially since April, the question that market observers and investors have faced is whether stocks, especially in the United States, are pushing into “bubble” territory and headed for a correction. As someone who buys into the notion that market timing is the impossible dream, you may find it surprising that I think that Powell is right in his assessment that stocks are richly priced, but that said, I will try to explain why making the leap into concluding that stocks are in a bubble, and acting on that conclusion are much more difficult to do.

Financial Markets in 2025

It has, to put it mildly, been an interesting year for stocks, as economic headwinds and shocks have mounted, with tariffs, wars and politics all adding to the mix. After a first quarter, where it looked like financial markets would succumb to the pressure of bad news, stock markets have come roaring back, surprising market experts and economists. As a precursor to answering the question of whether stocks are "fairly highly valued" today, let’s take a look at how we got to where we are on September 30, 2025.

Resilient Equities

We will start with US equities, and while that may seem parochial, it is worth remembering that they represented more than 50% of the total market capitalization of all traded stocks in the world at the start of 2025. In the figure below, we look at the S&P 500 and the NASDAQ, with the former standing in as a rough proxy for large US market cap stocks and the latter for technology companies:

As you can see, US equities were down in the first quarter, but the standardized values indicate that it was much worse for technology companies than for the rest of the market, with the NASDAQ down 21.3% through April 8, the market bottom, while the S&P 500 was down 14.3%. On April 8, the consensus wisdom was that the long-awaited correction was upon us, and that tech stocks would take more of a beating over the rest of the year. The market, of course, decided to upend expectations, as tech came roaring back in the second and third quarters, carrying the market with it. In fact, through the first three quarters, the NASDAQ has reclaimed the lead, up 17.3% so far this year, whereas the S&P 500 is up 13.7%.

We take a closer and more detailed look at all publicly traded US equities, in the table below, where we break out the year-to-date performance, by sector:

The two best performing sectors in the first three quarters of 2025 have been technology (up $3.93 trillion and 22.4% YTD) and communication services (up $1.29 trillion and 22.3% YTD). There are five sectors which lagged the market, with consumer staples and health care effectively flat for the year, and energy consumer discretionary and real estate up only 4-6% for the year. Financial, industrials and materials, for the most part, matched the overall market in terms of percentage change, and the overall value of US equities increased by $8.3 trillion (13.76%) in the first nine months of 2025. If you puzzled by the outperformance of communication services, it is worth noting that Alphabet and Meta, both of which derive large portions of their revenues from online advertising, are categorized by S&P as communication service companies. These two companies are part of the Mag Seven. and the companies in this grouping have been the engine driving US equities for much of the last decade, leading to talk of a top-heavy market. To assess their contribution to market performance, we looked at the aggregate market cap of the seven companies, relative to all 5748 traded US equities in 2023, 2024 and 2025 (YTD):

The aggregate market capitalization of the Mag Seven, as a percent of market cap of all traded US companies, has risen from 17.5% at the end of 2022 to 24.6% at the end of 2023 to 29.3% at the end of 2024. Focusing just on 2025, the Mag Seven took a step back in the first quarter, dropping to 26.3% of overall market cap on March 31, 2025, but has made a decisive comeback since, with an increase in market cap of $2.8 trillion in the first nine months of 2025, accounting for 52.4% of the overall increase in market capitalization this year. In fact, the Mag Seven now command 30.35% of the total market capitalization for US equities, a higher percent than at the start of the year. Over the last three years, the Mag Seven alone have accounted for more than half of the increase in market capitalization of all US equities, each year.

There are other dimensions on which you can slice and dice US equities, and we did a quick run through some of them, by breaking US companies into groupings, based upon characteristics, and examining performance in each one:

Small cap versus Large cap: For much of the large century, small cap stocks (especially those in the bottom decile of market capitalization) delivered higher returns than large cap stocks. As I argued in a post from a decade ago, the small cap premium has not just disappeared since the 1980s, but been replaced with a large cap premium. Looking at returns in 2025, broken down by market capitalization at the start of the year, here is what we see:
As you can see, this has been a good year for small cap stocks, with the bottom half of the market seeing a much bigger increase, in percent terms, in market cap than the top half of the market, with much of the outperformance coming in the third quarter.
Value versus growth: Another enduring finding from the last century is that low price to book stocks delivered higher returns, after adjusting for risk, than high price to book stocks. While this is often categorized as a value effect, it works only if you accept price to book as a proxy for value, but even that effect has largely been absent in this century. Breaking down stocks based upon price to book ratios at the start of 2025, here is what we get:
While it is too early to celebrate the return of value, in 2025, low price to book stocks have done better than high price to book stocks, but all of the outperformance came in the first quarter of the year.
Momentum: Momentum has been a stronger force in markets than either market cap or value, and unlike those two, momentum has not just maintained its edge, but strengthened it over the last few years. Using the price change in 2024 as a proxy for momentum, we broke companies down into deciles and looked at returns in 2025:
After lagging in the first quarter, momentum stocks have made a comeback, with the top half of momentum stocks now leading the bottom half for the year to date in percent change in market capitalization.

In sum, it has been a good year, so far, for US equities, but the gains have been unevenly distributed across the market, and while the first quarter represented a break from the momentum and tech driven market of 2023 and 2024, the second and third quarters saw a return of those forces.

Directionless Treasuries

While interest rates are always a driver of stock prices, they have played less of a role in driving equity markets in 2025 than in prior years. To see why, take a look at US treasury rates, across maturities, in 2025:

Rates have for the most part are close to where they were at the start of the year, with very little intra-year volatility notwithstanding economic stories about inflation and real growth suggesting bigger moves. The battle between the Trump administration and the Federal Reserve has received a great deal of press attention, but the Fed's inaction for much of the year and lowering of the Fed Funds rate in September seem to have had little or no impact on treasury rates.

On May 16, 2025, Moody's lowered the ratings for the United States from Aaa to Aa1, joining Fitch and S&P, but again the effect on treasury rates was transient. If you are wondering why this did not translate into an increase in default spreads (and rates), the likely answer is that markets were not surprised by the downgrade, and the best evidence for this is in the 5-year US sovereign CDS spread, a market-set number for default risk (spreads):

As you can see there was a spike in the US sovereign CDS spread this year, but it happened in response to liberation day on March 31, when President Trump announced punishing tariffs on the rest of the world. The Moody's downgrade had little impact on the spread, and even the tariff effect had fully faded by September 30, 2025, with spreads back to where they were at the start of the year (and for much of the last few years).

Extending the assessment of default spreads to the corporate market, there has been relatively little movement in corporate default spreads in 2025:

Source: FRED

As you can see, the most striking part of the story is that so little has changed over the course of 2025, notwithstanding the spike in spreads in the first week of April, when the tariffs were announced. The Moody's rating and the talk of a recession seem to have done little to supercharge the fear factor, and by extension the spreads. In fact, the only rating that has seen a significant move is in the CCC and below grouping, where spreads are now higher than they were at the start of the year, but still much lower than they were at the end of the first quarter of 2025.

The Rest of the Story

The economic shocks that hit the US markets, and which US equities and debt shrugged off, for the most part, also reverberated in the rest of the world. The broadest measure of relative performance between US and global equities is the divergence between the S&P 500, a proxy for US equity performance, and the MSCI World index, a stand-in for large cap international stocks, and the results are below:

In the first nine months of 2025, the MSCI global equity index is up 16.6%, about 2.3% more than the S&P 500 over the same period. However, all of this underperformance occurred in the first quarter of 2025, and the S&P 500 has found its winning ways again in the second and third quarters.

The MSCI index does obscure differences across regions and is titled towards large cap stocks. Consequently, we looked at all publicly traded equities, broken down by regions, with the values in US dollars, and the results so far in 2025 are in the table below:

Global equities were up, in aggregate dollar market capitalization, by 16.8%, and while US equities have underperformed in the first nine months of 2025, with a 13,8% return, they have rediscovered their mojo in the second and third quarters. The worst performing regions of the world are India, down 3.15%, in US dollar terms, this year, and Africa and the Middle East, up only 2.13%. It is too early to spin stories for why these regions underperformed, but in my data update post from the start of 2025, I pointed to India as the most highly priced market in the world, and this year may reflect a cleaning up. The rest of the world ran ahead of the United States, with some of the additional return coming from a weaker US dollar; the local currency returns in these regions were lower than the returns you see in the table.

US Equities: Overpriced or Underpriced?

None of the discussion above answers the question that we started this post with, which is whether US equities are overpriced. To make that assessment, there are a variety of metrics that are used, and while all of them are flawed, they vary in terms of what they leave out of the assessment, and the assumptions that underlie them.

At one end of the spectrum, the simplest and most incomplete metric is based purely on price history, with markets that have had extended good runs being viewed as overpriced. A modification is to bring earnings into the assessment, with prices moving disproportionately more than earnings (resulting in higher or lower PE ratios) considered a signal of market mispricing. The third adaptation allows for the returns you can make on alternative investments, in the form of interest rates on treasuries, to make a judgment on market pricing. The final and fullest variant considers growth in the assessment, bringing in both its good side (that it increases earnings in future periods) and its bad side (that it needs a portion of earnings to be reinvested), to make a pricing judgment, but even that variant ignores disruptions that alter market dynamics and risk taking.

1. Rising stock prices

For some investors, an extended stretch of rising stock prices is, by itself, sufficient reason to conclude that if stocks are doing so well, they must be over priced. This concern will get deeper as the market run gets longer (in terms of time) and steeper (in terms of price rise). Using that framework, you can see why talk of a stock market bubble has built up over the last decade, as stocks keep climbing walls of worry and hitting new highs. We have had a remarkable bull run in US equities over the last 15 years, with the S&P 500 up over 500% over that period:

Download spreadsheet with historical returns on stocks

In short, the annual return (18.74%) that equity investors have earned over the last fifteen years is significantly higher than the annual return (9.94%) on US equities over the last century. For some, this run-up alone is enough to decide that equities are overpriced and incomplete though this analysis is, you can see its draw for many investors.

2. The Earnings Effect

Looking at rising stock prices as an indicator of overpricing ignores the reality that markets can sometimes be up strongly, not because of speculation or over pricing, but because of rising earnings. That is the reason that many investors look at market pricing scaled to earnings, or PE ratios, and the graph below captures three variants of the PE ratio - the trailing PE, where you scale market pricing to earnings in the last twelve months, a normalized PE, where you scale the market pricing to average earnings over a longer time period (a decade) and a CAPE or Shiller PE, where you first adjust earnings for inflation and then normalize:

Download historical PE ratios for US equities

All three versions of the PE ratio tell the same story, and in September 2025, all three stood close to all time highs, with the spike at the peak of the dot com boom being the only exception.

3. The Investing Alternatives

Stocks that trade at higher multiples of earnings are obviously more expensive than when then trade at lower, but to make a judgment on whether they are overpriced, you still have to compare them to what you can make on alternative investments. For investors in financial assets, those alternative investments are bonds (if you are investing long term) or commercial paper/treasury bills (if you are investing short term). Logically, if these alternatives are yielding low returns, you should be willing to pay a much higher multiples of earnings for risky assets (like stocks). One way in which we can bring in this choice is by flipping the PE ratio (to get the earning to price ratio or earnings yield) and comparing that earning yield to the ten-year treasury bond rate:

Between 2011 and 2020, for instance, the earnings yield was 5.46% but that was much higher than the 10-year treasury bond rate, which averaged 2.15% over that decade. In 2021, the earnings yield dropped to 4.33%, close to a historical low, butt with the treasury bond rate at 1.51%, you could argue that equity investors had nowhere else to go. As treasury bond rates climbed back towards 4% in 2022, stock prices dropped and the earnings yield climbed to 5.72%. In the last three years (2023-25), treasury rates have stayed higher (4% or more), but earnings yields have dropped. In fact, the earnings yield of 4% in September 2025 was 0.16% below the ten-year treasury bond rate, triggering bearish warnings from analysts who use the difference between the earnings yield and the ten-year bond rate as their market timing metric.

4. The Rest of the Story - Cash flows, Growth and Risk

The earnings yield, in conjunction with the treasury bond rate, is widely used as a market timing tool, but it has two, perhaps fatal, flaws.

The first is that it treats stocks as if they were glorified bonds, treating the earnings yield like a coupon, and misses the reason that investors are drawn to equities, which is the potential for growth. Incorporating growth into the analysis has two effects, with the first being that you need reinvestment to grow, and that reinvestment comes out of earnings, and the second being the upside of increasing earnings over time.
The second is that the earnings yield/ treasury bond rate differential has had a spotty record timing the market, missing much of the great bull market of the 1980s and 1990s, and clearly not providing much predictive power in the last two years.

There is an approach that you can use to incorporate the growth and cash flow effects into your market analysis. It is to estimate an intrinsic value for the market, where you incorporate the growth and reinvestment effects into expected cash flows, and discount them at a required return that incorporates what you can earn on a riskfree (or close to riskfree) investment and a risk premium for investing in equities.

As you can see, the intrinsic value equation can be used in one of two ways to assess the market. One is to back out an internal rate of return, i.e., a discount rate that yields a present value equal to the market index; netting out the treasury bond rate from this yields an implied equity risk premium for the market. The other is choose an equity risk premium that you believe is reasonable and to value the market.

I estimate an implied equity risk premium for the S&P 500 at the start of every month, and use it as my barometer of the market, a receptacle of market hopes and fears, falling in good times and rising during crises. By my computation, the expected return on the index at the end of September 2025 was 8.17%, and with the ten-year treasury rate of 4.16% netted from it yields an implied equity risk premium of 4.01%. The question of whether the market is over or underpriced can be reframed as one about whether the equity risk premium is too low (indicating an overpriced market) or too high (underpriced market). In the figure below, I put the September ERP into perspective by comparing it to implied equity risk premiums for the S&P 500 going back in time:

Download historical implied equity risk premiums for S&P 500

As is often the case with historical comparisons, there is something here for every side of the debate. For those who believe that the market is overpriced, the obvious comparison is to equity risk premiums since the 2008 crisis, and the conclusion would be that the Sept 2025 premium of 4.01% is too low (and stock prices are too high). For those who are more sanguine about the market, the comparison would be to the dot-com boom days, when the implied equity risk premium dipped to 2%, to conclude that this market is not in a bubble.

An alternate way to assess market pricing is to assume an equity risk premium and estimate the value of the index using that premium. Thus, if we assume that the average premium (4.25%) from 1960 to 2024 is a fair premium to the market, and revalue the index, here is what we would get as its value:

Download intrinsic value estimator

With an implied equity risk premium of 4.25%, and a riskfree rate of 4.16%, we get an expected return on stocks of 8.41%, and using analyst estimates of growth in earnings and cash payout ratios that adjust over time to sustainable levels, we arrive at a value for the index of about 5940, 12.6% lower than the index value on September 30, 2025.

The Market Timing Challenge

It is undeniable that this market is richly priced on every metric, from PE ratios to the earnings yield, net of treasuries, to intrinsic value measures like the equity risk premium, thus providing backing for Powell's assessment of equities as “fairly highly valued”. If you trust in mean reversion to historical averages, it seems reasonable to conclude that stocks are in fact overpriced, and due for a correction. In this section, we will examine why, even if you come to this conclusion, it is difficult to convert it into action.

Using lawyerly language, let's stipulate that markets are overpriced today, though that overpricing can cover a range of views from the market being a bubble to the markets just being expensive. There are five responses that you can have to this judgment, ranging from least aggressive to most aggressive on the market timing front:

Do nothing: The essence of being a non-market timer is that you do not alter any aspect of your portfolio to reflect your market views. Thus, if your preferred allocation mix is 60% in stocks and 40% in your bonds, you stay with that mix, and you not only hold on to your existing investments but you continue to add to them in the same way that you have always done.
Hold on to/ build cash holdings: For the most part, you match what you would have done in the do nothing response in terms of overall asset allocation mix and holdings, but you not only put your portfolio additions into cash (treasury bills, money market funds) but when you act, it will be more likely to be selling existing holdings (that you view as over valued) than buying new ones. For many equity mutual fund managers, this statistic (liquid assets and cash as a percent of assets under management) is a rough proxy of how bullish or bearish they are about the overall market.
Change asset allocation mix: In this response, you revisit your preferred asset allocation mix, which was set based on your age, cash needs and risk aversion, and alter it to reflect your market timing views. Thus, if you believe that stocks are overpriced, but you view bonds as fairly or even under priced, you will decrease your allocation to the former, and increase your allocation to the latter. If you are constrained to be an all-equity investor, an alternate version will be to reallocate your money from overpriced geographies to underpriced geographies, if the latter exist.
Buy protection: With the growth of options and futures markets, you now have ways of protecting your portfolio, without making wrenching changes to your asset allocation or holdings mix. You can buy puts on the index or sell index futures, if you think equities are overpriced, and benefit from the fact that the profits from these positions will offset the losses on your portfolio, if there is a correction.
Make leveraged bets of market correction: The most aggressive way to take advantage of market timing is to make leveraged bets on market corrections, using either derivatives markets (puts or futures) or selling short on either all of the stocks in an index, or a subset of the most overpriced.

In making this choices, you do have to consider three real world concerns. The first is taxes, with any strategies that requires significant disruptions to existing portfolios, such as changing asset allocation mixes or selling overvalued stocks in the portfolio, creating larger tax bills. The second is transactions costs, which will also be higher for any strategy that is built around more aggressive. The third is timing, which is that even if you are right about the overpricing, being right too early may wipe out the benefits. Speaking of Alan Greenspan's warnings about the dot com bubble, it is worth remembering that his "irrational exuberance" comments were made in 1996, and that the market correction occurred in 2001, and any investor who sold equities right after the comments were made would have underperformed an investor who held on to equities and took the hit from the correction.

Let's assume that you remove taxes and transactions costs from the assessment to give market timing the best possible pathway to success. To test whether market timing works, you have to create a market timing strategy around your metric of choice, with three steps fleshed out:

Choose your pricing metric: As noted in the last section, this can be the percentage increase in stock prices over a recent period, the current or normalized pricing ratio (PE, PBV, EV to EBITDA) or the equity risk premium/intrinsic value for the index.
Create your action rule: The action rule specifies the threshold for the chosen metric, where you will act on your market timing. You could, for instance, decide that you will increase your equity exposure if the PE ratio is more than 25% below the median value for the market's PE ratios over the previous 25, 50 or 100 years, and reduce your equity exposure if the PE ratio is more than 25% higher than the median value over the period. Note that the trade off on setting the threshold is that setting it to a larger value (say 50%) will mean that you time the market less.
Choose your market timing response: You specify how much you will increase or decrease your equity exposure in response to the market timing signal. Thus, if you have base asset allocation mix of 60% equities, 40% bonds, you can decide that if your threshold (from step 2) is breached, and the PE ratio drops (increases) by more than 25% below the median, your equity exposure will increase (decrease) to 80% (40%) and your bond exposure reduced (increased) to 20% (60%). The more aggressive you are as a market timer, the greater will be the shift away from your base mix. Thus, you could sell all equities (0% equities, 100% bonds) if the market is overpriced and hold only equities (100% equities, 0% bonds) if the market is underpriced.

To illustrate, let's use the Shiller PE, pick a 25% threshold for market cheapness and alter your asset allocation mix, which would normally be 60% equity/40% debt to 80% equities/20% debt if the Shiller PE drops 25% below the median (computed over the prior 50 years) and 40% equities/ 60% bonds if it rises 25% above the median.

Note that the test can easily be varied, using a different metric, different thresholds and different timing responses.

To avoid being accused of cherry picking the data or deviating from the standard measures of the Shiller PE, I downloaded the raw data on stock returns, bonds and the CAPE each year from 1871-2025 from Shiller's own webpage. To compute the payoff to market timing, I looked at the annual returns to an non-market-timing investor who stayed with a 60% equity/40% bond mix over time and compared it to the returns of a market timer, using the threshold/action strategy described above:

Download CAPE backtesting spreadsheet

Over the last century, this market timing strategy would have reduced your annual returns 0.04% each year, and that is before transactions costs and taxes. If you break this up into two half-centuries, any of the market timing gains were from 1924-1974, and they were mild, and trying to time the market would have reduced your annual returns by 0.41% a year, on average between 1975 and 2024.

To evaluate whether the payoff would have been different with alternate thresholds, we considered both a much lower threshold (10%) and a much higher one (50%), with the former leading to more market timing actions. We also looked at a more aggressive response, where the equity portion was increased to 100% (instead of 80%) if the market was underpriced and reduced to 0% (instead of 40%) if the market was overpriced. The results are in below:

Download CAPE backtesting spreadsheet

As you can see in this table, there is not a single market timing combination (threshold and action) that would have added to annual returns over the last fifty years. I completely understand that there are other combinations that may work, and you are welcome to download the spreadsheet and try for yourselves, changing the threshold levels for actions and the action itself. You may very well find a combination that adds value but the fact that you have try this hard is indicative of why market timing is a reach. It is also possible that making these timing judgments only once a year may be getting in the way of them working, but I did use the monthly data that Shiller has accessible, and in my experimenting, there was little that I could see in terms of added value.

Conclusion

The decision on whether to time markets is a personal one, and while I have concluded it does not work for me, it would be presumptuous to claim that it will not work for you. If you decide that market timing is part of your investment philosophy, though, there are three lessons that I hope that this post has highlighted. The first is that the more incomplete your market timing metrics are, the greater the chance that you will chasing a correction that never happens. It is the reason that you should be skeptical about arguments built around just pricing, PE ratios or earnings yields (relative to treasury bond rates), and even with more complete metrics, you should be scanning the horizon for fundamental changes in the economy and markets that may explain the deviation. The second is that the proof that a metric will work for you will not come from statistical measures (correlations and regressions), but from creating and back-testing an actionable strategy (of buying or selling traded instruments) based on the metric. The third is that even if you do all of this due diligence, market timing is noisy and flawed, and paraphrasing another widely used expressions, markets can stay mispriced for longer than you can stay solvent.

YouTube Video

Datasets

Spreadsheets

The Imitation Game: Defending against AI's Dark Side!

2025-08-05T15:25:00.000-04:00

A few weeks ago, I started receiving a stream of message about an Instagram post that I was allegedly starring in, where after offering my views on Palantir's valuation, I was soliciting investors to invest with me (or with an investment entity that had ties to me). I was not surprised, since I have lived with imitations for years, but I was bemused, since I don't have an Instagram account and have not posted on Facebook more than once or twice in a decade. In the last few days, those warnings have been joined by others, who have noted that there is now a video that looks and sounds like me, adding to the sales pitch with promises of super-normal returns if they reach out, and presumably send their money in. (Please don't go looking for these scams online, since the very act of clicking on them can expose you to their reach.)

I would like to think that readers of my books or posts, or students in my classes, know me well enough to be able to tell that these are fakes, and while this is not the first time I have been targeted, it is clear that AI has upped the ante, in terms of creating posts and videos that look authentic. In response, I cycled through a series of emotions, starting with surprise that there are some out there who think that using my name alone will draw in investors, moving on to anger at the targeting of vulnerable investors and ending with frustration at the social media platforms that allow these fakes to exist. As a teacher, though, curiosity beat out all of these emotions, and I thought that the best thing that I can do, in addition to the fruitless exercise of notifying the social media companies about the fakes, is to talk about what these AI imitators got right, what they were off target on and what they got wrong in trying to create these fakes of me. Put simply, I plan to grade my AI imitator, as I would any student in my class, recognizing that being objective in this exercise will be tough to do. In the lead-in, though, I have to bore you with details of my professional life and thought process, since that is the key to creating a general framework that you will be able to use to detect AI imitations, since the game will only get more sophisticated in the years to come.

An Easy Target?

In a post last year, I talked about a bot in my name, that was in development phase at NYU, and while officially sanctioned, it did open up existential challenges for me. In discussing that bot, I noted that this bot had accessed everything that I had ever written, talked about or valued in my lifetime, and that I had facilitated its path by making that access easy. I will explain my rationale for the open access, and provide you with the links if you want to get to them, hoping to pre-empt those who will try to charge you for that content.

My Open Access Policy

I have said this before, but there is no harm in saying it again, but I am a teacher, first and foremost, and almost every choice I make in my profession life reflects that mindset. A teacher, like an actor or singer, craves an audience, and the larger and more enthusiastic that audience, the better. When I started teaching in 1986, my audience was restricted to those in my physical classroom at NYU's business school, and my initial attempts at expanding that audience were very limited. I had video recorders set up to record my lectures, made three copies of each lecture tape, and put them on the shelves at NYU's library for patrons to check out and watch. The internet, for all of its sins, changed the game for me, allowing me to share not only class materials (slides, exams) but also my lecture videos, in online formats. Though my early attempts to make these conversions were primitive, the technology for recording classes and putting them online has made a quantum leap. In spring 2025, every one of my NYU classes was recorded by cameras that are built into classroom, the conversions to online videos happened in minutes, right after the class is done, and YouTube has been a game changer, in allowing access to anyone with an internet connection anywhere in the world.

As the internet has expanded its reach, and social media platforms have joined the mix, I have also shared the other components that go into my classes more widely, starting with the data on industry averages that I need and use in my own valuations, the spreadsheets that contain these valuations and blog posts on markets and companies and any other tools that I use in my own analyses. While I am happy to receive compliments for the sharing and praise for being unselfish, the truth is that my sharing is driven less by altruism (I am no Mother Theresa!) and more by two other forces. The first is that, as I noted in my post on country equity risk premiums last week, there much of what I know or write about is pedestrian, and holding it in secret seems silly. The second is that, while I am not easily outraged, I am driven to outrage by business consultants and experts who state the obvious (replacing words you know with buzzwords and acronyms), while making outrageous claims of what they can deliver and charging their customers absurd amounts for their advice and appraisals. If I can save even a few of these customers from making these payments, I consider it to be a win.

My Sharing Spots

Everything that I have ever written, worked on or taught is somewhere online, almost always with no protective shields (no passwords or subscriptions), and there are four places where you can find them:

Webpage: The oldest platform for my content remains my webpage, damodaran.com, and while it can be creaky, and difficult to navigate, it contains the links to my writing, teaching, data, spreadsheets and other tools.

Teaching: I teach two classes at Stern, corporate finance and valuation, and have four other classes - a lead-into-valuation accounting class, a made-for-finance statistics class, a class on investment philosophies and one on corporate life cycles, and I described these classes in a post on teaching at the start of 2025. You can find them all by going to the teaching link on my webpage, https://people.stern.nyu.edu/adamodar/New_Home_Page/teaching.html including my regular classes (class material, lecture notes, exams and quizzes and webcasts of the classes) in real time, as well as archived versions from previous semesters. In addition, the online classes are at the same link, with material, post- class tests and webcasts of sessions for each class. This is also the place where you can find links to seminars that I teach in the rest of the world, with slides and materials that I used for those classes (though I have been tardy about updating these).
Data: At the start of every year for the last three decades, I have shared my analysis of data on publicly traded companies, breaking down the data into corporate finance and valuation categories. This link, https://people.stern.nyu.edu/adamodar/New_Home_Page/data.html, will take you to the entry page, and you can then either access the most recent data (from the start of 2025, since I update only once a year, for most datasets) or archived data (from previous years). My raw data comes from a variety of sources, and in the interests of not stepping on the toes of my data providers, my data usually reflects industry averages, rather than company-specific data, but it does include regional breakdowns: US, Europe, Emerging Markets (with India and China broken out individually, Australia & Canada & New Zealand) and Japan.
Spreadsheets: I am not an Excel ninja, and while my spreadsheet-building skills are adequate, my capacity to make them look polished is limited. I do share the spreadsheets that I use in my classes and work here, with my most-used (by me) spreadsheet being one that I use to value most companies at this link, with a webcast explaining its usage.
Books: I have written eleven books and co-edited one, spread out across corporate finance, valuation and investing, and you can find them all listed here. Many of these books are in their third or fourth editions, but with each one, you should find a webpage that contains supplementary material for that book or edition (slides, answers to questions at the end of each chapter, data, spreadsheets backing the examples). This is the only section of the spreadsheet where you may encounter a gatekeeper, asking you for a password, and only if you seek access to instructor material. If you are wondering what is behind the gate, it is only the powerpoint slides, with my notes on each slide, but the pdf versions of these slides should be somewhere on the same page, without need for a password.
Papers: I don't much care much for academic research, but I do like to write about topics that interest or confound me, and you can find these papers at this link. My two most widely downloaded papers are updates I do each year on the equity risk premium (in March) and country risk premiums (in July). Much of the material in these papers has made its way into one or more of my books, and thus, if you find the books unaffordable, you can get that material here for free.

Blog posts: I will confess that when I write my first blog post on September 17, 2008, I had no idea what a blog was, what I was doing with it, and whether it would last through the following week. In the years since, this blog has become my first go-to, when I have doubts or questions about something, and I am trying to resolve those doubts for myself. In short, my blog has becoming my therapy spot, in times of uncertainty, and I have had no qualms about admitting to these doubts. During 2020, as COVID made us question almost everything we know about markets and the economy, for instance, I posted on where I was in the uncertainty spectrum every week from February 14, 2020 (when the virus became a global problem, not one restricted to China and cruise ships) to November 2020, when the vaccine appeared. You can get all of those posts in one paper, if you click on this link. While my original blog was on Google, in the last two years, I have replicated these posts on Substack (you need to be a subscriber, but it is free) and on LinkedIn. If you are on the latter, you are welcome to follow me, but I have hit my connections limit (I did not even know there was one, until I hit it) and am unable to add connections.
YouTube: For the last decade, I have posted my class videos on YouTube, grouping them into playlists for each class. You can start with the link to my YouTube channel here, but if you are interested in taking a class, my suggestion is that you click on the playlists and pick on the one that corresponds to the class. Here, for instance, are my links to my Spring 2025 MBA valuation class and my Spring 2025 Corporate Finance class. Starting about a decade, I have also accompanied every one of my blog posts with a YouTube video, that contains the same material, and you can find those posts in its own (very long) playlist.
X (Twitter): Some of you have strong feelings about X, with some of those feelings reflecting your political leanings and others driven by the sometimes toxic posting on the platform. I have been a user of the platform since April 2009, and I have used it as a bulletin board, to alert people to content being posted elsewhere. In fact, outside of these "alert" posts, I almost never post on X, and steer away as far away as I can from debates and discussions on the platform, since a version of Gresham's law seems to kick in, where the worst and least informed posters hijack the debate and take it in directions that you do not want it to go.

I cannot think of a single item of content that I have produced in the last decade that is not on one of these platforms, making my professional life an open book, and thus also accessible to any AI entity. The Damodaran bot that I wrote about last year has access to all of this material, and while I signed off on that and one other variant, there are multiple unauthorized versions that have been works-in-progress.

The Commonalities

My content has taken many forms including posts, videos, data and spreadsheets, and is on multiple platforms, but there are a few common features that they share:

Low tech: I am decidedly low tech, and it shows in my sharing. My website looks like it was designed two decades ago, because it was, and contains none of the bells and whistles that make for a modern website. My blog remains on Google blogger, notwithstanding everything I have been told about how using WordPress would make it more attractive/adaptable, and my posts are neither short nor punchy. Every week, I get people reaching out to me to tell me that my YouTube videos are far too long and verbose, and that I would get more people watching with shorter videos and catchier descriptions, and much as I appreciate their offers to help, I have not taken them up on it., In addition, I shoot almost every one of my videos in my office, sometimes with my dog in the background, and often with ambient noise and mistakes embedded, making them definitely unpolished. On twitter, I have only recently taken to stringing tweets together and I have never used the long text version that some professional twitter users have mastered. In my defense, I could always claim that I am too old to learn new tricks, but the truth is that I did not start any of my sharing as a means to acquiring a larger social media following, and it may very well be true that keeping my presence low-tech operates as a screener, repelling mismatched users.
Process over product: In my writing and teaching, I am often taken to task for not getting to the bottom line (Is the stock cheap or expensive? Should I buy or sell?) quickly, and spending so much time on the why and how, as opposed to the what. Much as my verbosity may frustrate you, it reflects what I think my job is as a teacher, which is to be transparent about process, i.e., explain how I reasoned my way to getting an answer than giving you my answer.
Pragmatism over Purity: Though I am often criticized for being an “academic”, I am a terrible one, and if there were an academic fraternity, I would be shunned. I view much of an academic research as navel gazing, and almost everything I write and teach is for practitioners. Consequently, I am quick to adapt and modify models to make them fit both reality and the available data, and make assumptions that would make a purist blanch.
No stock picks or investment advice: In all my years of writing about and valuing markets and individual stocks, I have tried my best to steer away from making stock picks or offering investment advice. That may sound odd, since so much of what I do relates to valuation, and the essence of valuation is that you act on your estimates of value, but here is how I explain the contradiction. I value stocks (like Meta or Nvidia or Amazon or Mercado Libre) and I act (buy or sell) those stocks, based on my valuations, but it is neither my place nor my role to try to get other people to do the same. That said, I will share my story and valuation spreadsheet with you, and if you want to adapt that story/spreadsheet to make it your own, I am at peace with that choice, even if it is different from mine. The essence of good investing is taking ownership of your investment actions, and it is antithetical to that view of the world for me or anyone else to be telling you what to buy or sell.
No commercial entanglements: If you do explore my content on any of the platforms it is available on, you will notice that they are free, both in terms of what you pay and how you access them. In fact, none of them are monetized, and if you do see ads on my YouTube videos, it is Google that is collecting the revenue, not me. One reason for this practice is that I am lazy, and monetizing any of these platforms requires jumping through hoops and catering to advertisers that I neither have the time nor the inclination to do. The other is that I believe (though this may be more hope than truth) that one of the reasons that people read what I write or listen to me is because, much as they may disagree with me, I am perceived as (relatively) unbiased. I fear that formalizing a link with any commercial entity (bank, consultant, investor), whether as advisor, consultant or as director, opens the door to the perception of bias. The one exception to the "no commercial entranglements' clause is for my teaching engagements, with the NYU Certificate program and for the handful of valuation seminars I teach in person in the rest of the world. I am grateful that NYU has allowed me to share my class recordings with the world, and I will not begrudge them whatever they make on my certificate classes, though I do offer the same content for free online, on my webpage. I am also indebted to the people and organizations that manage the logistics of my seminars in the rest of the world, and if I can make their life easier by posting about these seminars, I will do so.

The Imitation Game

Given that my end game in sharing is to give access to people who want to use my material, I have generally taken a lax view of others borrowing my slides, data, spreadsheets or even webcasts, for their own purposes.

For the most part, I categorize this borrowing as good neighbor sharing, where just as I would lend a neighbor a key cooking ingredient to save them the trouble of a trip to the grocery store, I am at peace with someone using my material to help in their teaching, save time on a valuation or a corporate finance project, prepare for an interview, or even burnish their credentials. An acknowledgement, when this happens, is much appreciated, but I don't take it personally when none is forthcoming.
There are less benign copycat versions of the imitation game - selectively using data from my site to back up arguments, misreading or misinterpreting what I have said and reproducing large portions of my writing without acknowledgement. To be honest, if made aware of these transgressions, I have gently nudged the culprits, but I don't have a legal hammer to follow up.
The most malignant variations of this game are scams, where the scammers use my content or name to separate people from their money - the education companies that used my YouTube videos and charge for classes, the data sites that copy my data or spreadsheets and sell them to people, and the valuation/investment sites that try to get people to invest money, with my name as a draw. Until now, I have tried, as best as I can, to let people know that they are being victimized, but for the most part, these scams have been so badly designed that they have tended to collapse under the weight of their own contradictions.

It is clear to me that AI is now going to change this game, and that I will have to think about new ways to counter its insidious reach. To get a measure of what the current AI scams that are making the rounds get right and wrong, I did take the time to take a closer look at both the Instagram post and the fake video that are making the rounds.

What they get right: The Instagram post, which is in shown below, uses language that clearly is drawn from my posts and an image that is clearly mine.

Not only does this post reflect the way I write, but it also picked Nvidia and Palantir as the two firms to highlight, the first a company that I own and have valued on my blog, and the second a company that I have been talking about as one that I am interested in owning, at the right price, giving it a patina of authenticity. The video looks and sounds like me, which should be no surprise since it had thousands of hours of YouTube videos to use as raw data. Using a yiddish word that I picked up in my days in New York, I have the give the scammers credit for chutzpah, on this front,, but I will take a notch off the grade, for the video's slickness, since my videos have much more of a homemade feel to them.
What they struggled with most: The scam does mention that Palantir is "overhyped", a word that I use rarely, and while it talks about the company’s valuation, it is cagey about what that value is and there is little of substance to back up the claim. Palantir is a fascinating company, but to value it, you need a story of a data/software firm, with two channels for value creation, one of which looks at the government as a customer (a lower-margin, stickier and lower growth business) and the other at its commercial market (higher margin, more volatile and higher growth). Each of the stories has shades of grey, with the potential for overlap and conflict, but this is not a company where you can extrapolate the past, slap numbers on revenue growth and profitability, and arrive at a value. This post not only does not provide any tangible backing for its words in terms of value, but it does not even try. If these scammers had truly wanted to pull this off, they could have made their AI bot take my class, construct a plausible Palantir story, put it into my valuation spreadsheet and provide it as a link.
What they get wrong: To get a sense of what this post gets wrong, you should revisit the earlier part of the post where I talk about my sharing philosophy, and with as much distance as I can muster, here are the false notes in this scam. First, this scam pushes people to join an investment club, where I will presumably guide them on what to buy or sell. Given that my view of clubs is very much that of Groucho Marx, which is that I would not be belong to any club which would admit me as a member, the notion of telling people which stocks to buy cuts against every grain of my being. Second, there is a part of this scam where I purportedly promise investors who decide to partake that they will generate returns of 60% or higher, and as someone who has chronicled that not only do most active investors not keep up with the market, and argued that anyone who promises to deliver substantially more than the market in the long term is either a liar or fraud, this is clearly not me.

In sum, there is good news and bad news in this grading assessment. The good news is that this AI scam gets my language and look right, but it is sloppily done in terms of content and capturing who I am as a person. The bad news is that it if this scammer was less lazy and more willing to put in some work, even with the current state of AI, it would have been easy to bring up the grades on content and message. I will wager that the Damodaran Bot that I mentioned earlier on in this post that is being developed at NYU Stern would have created a post that would have been much more difficult for you to detect as fake, making it a Frankenstein monster perhaps in the making. The worse news is that AI technology is evolving, and it will get better on every one of these fronts at imitating others, and you should prepare yourself for a deluge of investment scams.

An AI Protective Shield

I did think long about writing this post, wondering whether it would make a difference. After all, if you are a frequent reader of this blog or have read this post all the way down to this point, it is unlikely that you were fooled by the Instagram post or video. It remains an uncomfortable truth that the people most exposed to these scams are the ones who have read little or none of what I have written, and I wish there were a way that I could pass on the following suggestions on how they can protect themselves against the other fakes and scams that will undoubtedly be directed at them.

"Looks & sounds like" not good enough: Having seen the flood of fake AI videos in the news and on social media, I hope that you have concluded that “looks and sounds Iike” is no longer good enough to meet the authenticity test. This remains AI’s strongest suit, especially in the hands of the garden variety scammer, and you should prepare yourself for more fake videos, with political figures, investing luminaries and experts targeted.
Steer away from arrogance & hype: I have always been skeptical of the notion that there is “smart” money, composed of investors who know more than the rest of us and are able to beat the market consistently, and for long periods. For the most part, when you see a group of investors (hedge funds, private equity) beating the market, luck is more of a contributor as skill, and success is fleeting. In a talk on the topic, I argued that investors should steer away from arrogance and bombast, and towards humility, when it comes to who they trust with their money, and that applies in spades in the world of AI scams. Since most scammers don’t understand the subtlety of this idea, screening investment sales pitches for outlandish claims alone will eliminate most scams.
Do your homework: If you decide to invest with someone, based upon a virtual meet or sales pitch, you should do your homework and that goes well beyond asking for their track records in terms of performance. In my class on investment philosophies, I talk about how great investors through the ages have had very different views of markets and ways of making money, but each one has had an investment philosophy that is unique, consistent and well thought through. It is malpractice to invest with anyone, no matter what their reputation for earning high returns, without understanding that person’s investment philosophy, and this understanding will also give you a template for spotting fakes using that person’s name.
Avoid ROMO & FOMO: In my investing classes, I talk about the damage that ROMO (regret over missing out) and FOMO (fear of missing out) can do to investor psyches and portfolio.

With ROMO (regret over missing out), where you look back in time and regret not buying Facebook at its IPO price in 2012 or selling your bitcoin in November 2013, when it hit $1000, you expose yourself to two emotions. The first is jealousy, especially at those who did buy Facebook at its IPO or have held on to their bitcoin to see its price hit six digits. The second is that you start buying into conspiracy theories, where you convince yourself that these winners (at least in the rear view mirror) were able to win, because the game was fixed in their favor. Both make you susceptible to chasing after past winners, and easy prey for vendors of conspiracies.
With FOMO (fear of missing out), your overwhelming concern is that you will miss the next big multi-bagger, an investment that will increase five or ten fold over the next year or two. The emotion that is triggered is greed, leading you to overreach in your investing, cycling through your investments, as most of them fall short of your unrealistic expectations, and searching for the next “big thing”, making you susceptible to anyone offering a pathway to get there.

Much as we think of scammers as the criminals and the scammed as the victims, the truth is that scams are more akin to tangos, where each side needs the other. The scammer’s techniques work because they trigger the emotions (fear, greed) of the scammed, to respond, and AI will only make this easier to do. Looking to regulators or the government to protection will do little more than offer false comfort, and the best defense is “caveat emptor” or “buyer beware”.

YouTube Video

Links

Webpage: https://pages.stern.nyu.edu/~adamodar/New_Home_Page/home.htm

Blog:

(1) Google: https://aswathdamodaran.blogspot.com

(2) Substack: https://aswathdamodaran.substack.com

(3) LinkedIn: https://www.linkedin.com/in/aswathdamodaran/

YouTube

https://www.youtube.com/channel/UCLvnJL8htRR1T9cbSccaoVw X: https://x.com/aswathdamodaran?lang=en

Country Risk 2025: The Story behind the Numbers!

2025-07-31T12:18:00.003-04:00

At the start of July, I updated my estimates of equity risk premiums for countries, in an semiannual ritual that goes back almost three decades. As with some of my other data updates, I have mixed feelings about publishing these numbers. On the one hand, I have no qualms about sharing these estimates, which I use when I value companies, because there is no secret sauce or special insight embedded in them. On the other, I worry about people using these premiums in their valuations, without understanding the choices and assumptions that I had to make to get to them. Country risk, in particular, has many components to it, and while you have to ultimately capture them in numbers, I wanted to use this post to draw attention to the many layers of risk that separate countries. I hope, and especially if you are a user of my risk premiums, that you read this post, and if you do have the time and the stomach, a more detailed and much longer update that I write every year.

Country Risk - Dimensions

When assessing business risk from operating in a country, you will be affected by uncertainty that arises from almost every source, with concerns about political structure (democracies have very different risk profiles than authoritarian regimes), exposure to violence (affecting both costs and revenues), corruption (which operates an implicit tax) and legal systems (enforcing ownership rights) all playing out in business risk.

I will start with political structure, where the facile answer is that it less risky to operate a business in a democracy than in an authoritarian regime, but where the often unpalatable truth is that each structure brings its own risks. With democracies, the risk is that newly elected governments can revisit, modify or discard policies that a previous government have adopted, requiring businesses to adapt and change to continuous changes in policy. In contrast, an authoritarian government can provide long term policy continuity, with the catch being that changes in the government, though infrequent, can create wrenching policy shifts that businesses have to learn to live with. Keeping the contrast between the continuous risk of operating in a democracy and the discontinuous risk in an authoritarian structure in mind, take a look at this picture of how the world looked in terms of democracy leading into 2025:

Source: Economist Intelligence Unit (EIU)

It is worth noting that there are judgment calls that the Economist made in measuring democracy that you and I might disagree with, but not only is a large proportion of the world under authoritarian rule, but the trend lines on this dimension also have been towards more authoritarianism in the last decade.

On the second dimension, exposure to violence, the effects on business are manifold. In addition to the threat that violence can affect operations, its presence shows up as higher operating costs (providing security for employees and factories) and as insurance costs (if the risks can be insured). To measure exposure to violence, from both internal and external sources, I draw on measures developed and updated by the Institute of Economics & Peace across countries in 2024:

Institute of Economics & Peace

The Russia-Ukraine war has caused risk to flare up in the surrounding states and the Middle East and central Africa continue to be risk cauldrons, but at least according to the Institute's measures, the parts of the world that are least exposed to violence are in Northern Europe, Australia and Canada. Again, there are judgments that are made in computing these scores that will lead you to disagree with specific country measures (according the Peace Institute, the United States and Brazil have higher exposures to violence than Argentina and Chile, and India has more exposure to violence than China), but the bottom line is that there are significant differences in exposure to violence across the world.

Corruption is a concern for everyone, but for businesses, it manifests in two ways. First, it puts more honest business operators at a disadvantage in a corrupt environment, since they are less willing to break the rules and go along with corrupt practices than their less scrupulous competitors. Second, even for those businesses that are willing to play the corruption game, it creates costs that I would liken to an implicit tax that reduces profits, cash flows and value. The measure of corruption that I use comes from Transparency International, and leading into July 2025, and the heat map below captures corruption scores (with higher scores indicating less corruption), as well as the ten most and least corrupt countries in the world:

Transparency International

As you can see from the map, there are vast swaths of the world where businesses have to deal with corruption in almost every aspect of business, and while some may attribute this to cultural factors, I have long argued that corruption almost inevitably follows in bureaucratic settings, where you need licenses and approvals for even the most trivial of actions, and the bureaucrats (who make the licensing decisions) are paid a pittance relative to the businesses that they regulate.

As a final component, I look at legal systems, especially when it comes to enforcing contractual agreements and property rights, central to running successful businesses. Here, I used estimates from the IPRI, a non-profit institution that measures the quality of legal systems around the world. In their latest rankings from 2024, here is how countries measured up in 2024:

Property Rights Alliance

In making these assessments, you have to consider not just the laws in place but also the timeliness with which these laws get enforced, since a legal system where justice is delayed for years or even decades is almost as bad as one that is capricious and biased.

Country Risk - Measures

The simplest and most longstanding measure of country risk takes the form of sovereign ratings, with the same agencies that rate companies (S&P, Moody's and Fitch) also rating countries, with the ratings ranging from Aaa (safest) to D (in default). The number of countries with sovereign ratings available on them has surged in the last few decades; Moody’s rated 13 countries in 1985, but that number increased to 143 in 2025, with the figure below listing the number of rated countries over time:

Note that that the number of Aaa rated countries stayed at eleven, even while more countries were rated, and has dropped from fifteen just a decade ago, with the UK and France losing their Aaa ratings during that period. In May 2025, Moody's downgraded the United States, bringing them in line with the other ratings agencies; S&P downgraded the US in 2011 and Fitch in 2023. The heat map below captures sovereign ratings across the world in July 2025:

Moody's

While sovereign ratings are useful risk measures, they do come with caveats. First, their focus on default risk can lead them to be misleading measures of overall country risk, especially in countries that have political risk issues but not much default risk; the Middle East, for instance, has high sovereign ratings. Second, the ratings agencies have blind spots, and some have critiqued these agencies for overrating European countries and underrating Asian, African and Latin American countries. Third, ratings agencies are often slow to react to events on the ground, and ratings changes, when they do occur, often lag changes in default risk.

If you are leery about trusting ratings agencies, I understand your distrust, and there is an alternative measure of sovereign default risk, at least for about half of all countries, and that is the sovereign credit default swap (CDS) market, which investors can buy protection against country default. These market-determined numbers will reflect events on the ground almost instantaneously, albeit with more volatility than ratings. At the end of June 2025, there were about 80 countries with sovereign CDS available on them, and the figure below captures the values:

The sovereign CDS spreads are more timely, but as with all market-set numbers, they are subject to mood and momentum swings, and I find using them in conjunction with ratings gives me a better sense of sovereign default risk.

If default risk seems like to provide too narrow a focus on countr risk, you can consider using country risk scores, which at least in principle, incorporate other components of country risk. There are many services that estimate country risk scores, including the Economist and the World Bank, but I have long used Political Risk Services (PRS) for my scores.. The PRS country risk scores go from low to high, with the low scores indicative of more country risk, and the table below captures the world (at least according to PRS):

Political Risk Services (PRS)

There are some puzzling numbers here, with the United States coming in as riskier than Vietnam and Libya, but that is one reason why country risk scores have never acquired traction. They vary across services, often reflecting judgments and choices made by each service, and there is no easy way to convert these scores into usable numbers in business and valuation or compare them across services.

Country Risk - Equity Risk Premiums

My interest in country risk stems almost entirely from my work in corporate finance and valuation, since this risk finds its way into the costs of equity and capital that are critical ingredients in both disciplines. To estimate the cost of equity for an investment in a risky country. I will not claim that the approaches I use to compute equity risk premiums for countries are either original or brilliant, but they do have the benefit of consistency, since I have used them every year (with an update at the start of the year and mid-year) since the 1990s.

The process starts with my estimate of the implied equity risk premium for the S&P 500, and I make this choice not for parochial reasons but because getting the raw data that you need for the implied equity risk premium is easiest to get for the S&P 500, the most widely tracked index in the world. In particular, the process requires data on dividends and stock buybacks on the stocks in the index, as well as expected growth in these cash flows over time, and involves finding the discount rate (internal rate of return) that makes the present value of cash flows equal to the level of the index. On June 30, 2025, this assessment generated an expected return of 8.45% for the index:

Download ERP spreadsheet

Until May 2025, I just subtracted the US 10-year treasury bond rate from this expected return, to get to an implied equity risk premium for the index, with the rationale that the US T.Bond rate is the riskfree rate in US dollars. The Moody’s downgrade of the US from Aaa to Aa1 has thrown a wrench into the process, since it implies that the T.Bond rate has some default risk associated with it, and thus incorporates a default spread. To remove that risk, I net out the default spread associated with Aa1 rating from the treasury rate to arrive at a riskfree rate in dollars and an equity risk premium based on that:

Riskfree rate in US dollars = T.Bond rate minus Default Spread for Aa1 rating

= 4.24% - 0.27% = 3.97%

Implied equity risk premium for US = Expected return on S&P 500 minus US $ riskfree rate

= 8.45% - 3.97% = 4.48%

Note that this approach to estimating equity risk premiums is model agnostic and reflects what investors are demanding in the market, rather than making a judgment on whether the premium is right or what it should be (which I leave to market timers).

To get the equity risk premiums for other countries, I need a base premium for a mature market, i.e., one that has no additional country risk, and here again, the US downgrade has thrown a twist into the process. Rather than use the US equity risk premium as my estimate of the mature market premium, my practice in every update through the start of 2025, I adjusted that premium (4.48%) down to take out the US default spread (0.27%), to arrive at the mature market premium of 4.21%. That then becomes the equity risk premium for the eleven countries that continue to have Aaa ratings, but for all other countries, I estimate default spreads based upon their sovereign ratings. As a final adjustment, I scale these default spreads upwards to incorporate the higher risk of equities, and these become the country risk premiums, which when added to the mature market premium, yields equity risk premiums by country. The process is described below:

The results from following this process are captured in the picture below, where I create both a heat map based on the equity risk premiums, and report on the ratings, country risk premiums and equity risk premiums, by country:

Download equity risk premium, by country

If you compare the equity risk premium heat map with the heat maps on the other dimensions of country risk (political and legal structures, exposure to violence and corruption), you will notice the congruence. The parts of the world that are most exposed to corruption and violence, and have capricious legal systems, tend to have higher equity risk premiums. The effects of the US ratings downgrade also manifest in the table, with the US now having a higher equity risk premium than its Aaa counterparts in Northern Europe, Australia and Canada.

A User's Guide

My estimates of equity risk premiums, by country, are available for download, and I am flattered that there are analysts that have found use for these number. One reason may be that they are free, but I do have concerns sometimes that they are misused, and the fault is mine for not clarifying how they should be used. In this section, I will lay out steps in using these equity risk premiums in corporate finance and valuation practice, and if I have still left areas of grey, please let me know.

Step 1: Start with an understanding of what the equity risk premium measures

The starting point for most finance classes is with the recognition that investors are collectively risk averse, and will demand higher expected returns on investments with more risk. The equity risk premium is a measure of the “extra” return that investors need to make, over and above the riskfree rate, to compensate for the higher risk that they are exposed to, on equities collectively. In the context of country risk, it implies that investments in riskier countries will need to earn higher returns to beat benchmarks than in safer countries. Using the numbers from July 2025, this would imply that investors need to earn 7.46% more than the riskfree rate to invest in an average-risk investment in India, and 10.87% more than the riskfree rate to invest in an average risk investment in Turkey.

It is also worth recognizing how equity risk premiums play out investing and valuation. Increasing the equity risk premium will raise the rate of return you need to make on an investment, and by doing so, reduce its value. That is why equity risk premiums and stock prices move inversely, with the ERP rising as stock prices drop (all other thins being held constant) and falling as stock prices increase.

Step 2: Pick your currency of analysis (and estimate a riskfree rate)

I start my discussions of currency in valuation by positing that currency is a choice, and that not only can you assess any project or value any company in any currency, but also that your assessment of project worth or company value should not be affected by that choice. Defining the equity risk premium as the extra return that investors need to make, over and above the risk free rate, may leave you puzzled about what riskfree rate to use, and while the easy answer is that it should be the riskfree rate in the currency you chose to do the analysis in, it is worth emphasizing that this riskfree rate is not always the government bond rate, and especially so, if the government does not have Aaa rating and faces default risk. In that case, you will need to adjust the government bond rate (just as I did with the US dollar) for the default spread, to prevent double counting risk.

Staying with the example of an Indian investment, the expected return on an average-risk investment in Indian rupees would be computed as follows:

Indian government bond rate on July 1, 2025 = 6.32%

Default spread for India, based on rating on July 1, 2025 = 2.16%

Indian rupee risk free rate on July 1, 2025 = 6.32% - 2.16% = 4.16%

ERP for India on July 1, 2025 = 7.46%

Expected return on average Indian equity in rupees on July 1, 2025 = 4.16% + 7..46% = 11.62%

Note also that if using the Indian government bond rate as the riskfree rate in rupees, you would effectively be double counting Indian country risk, once in the government bond rate and once again in the equity risk premium.

I know that the ERP is in dollar terms, and adding it to a rupee riskfree rate may seem inconsistent, but it will work well for riskfree rates that are reasonably close to the US dollar risk free rate. For currencies, like the Brazilian real or Turkish lira, it is more prudent to do your calculations entirely in US dollars, and convert using the differential inflation rate:

US dollar riskfree rate on July 1, 2025 = 3.97%

ERP for Turkey on July 1, 2025 = 10.87%

Expected return on average Turkish equity in US $ on July 1, 2025 = 3.97% + 10.87% = 14.84%

Expected inflation rate in US dollars = 2.5%; Expected inflation rate in Turkish lira = 20%

Expected return on average Turkish equity Turkish lira on July 1, 2025 = 1.1484 *(1.20/1.025) -1 = 34.45%

Note that this process scales up the equity risk premium to a higher number for high-inflation currencies.

Step 3: Estimate the equity risk premium or premiums that come into play based on operations

Many analysts use the equity risk premiums for a country when valuing companies that are incorporated in that country, but I think that is too narrow a perspective. In my view, the exposure to country risk comes from where a company operates, not where it is incorporated, opening the door for bringing in country risk from emerging markets into the cost of equity for multinationals that may be incorporated in mature markets. I use revenue weights, based on geography, for most companies, but I am open to using production weights, for natural resource companies, and even a mix of the two.

In corporate finance, where you need equity risk premiums to estimate costs of equity and capital in project assessment, the location of the project will determine which country’s equity risk premiums come into play. When Amazon decides to invest in a Brazilian online retail project, it is the equity risk premium for Brazil that should be incorporated, with the choice of currency for analysis determining the riskfree rate.

Step 4: Estimate project-specific or company-specific risk measures and costs

The riskfree rate and equity-risk premiums are market-wide numbers, driven by macro forces. To complete this process, you need two company-specific numbers:

Not all companies or projects are average risk, for equity investors in them, and for companies that are riskier or safer than average, you need a measure of this relative risk. At the risk of provoking those who may be triggered by portfolio theory or the CAPM, the beta is one such measure, but as I have argued elsewhere, I am completely at home with alternative measures of relative equity risk. The cost of equity is calculated as follows:

Cost of equity = Riskfree rate + Beta × Equity Risk Premium

The beta (relative risk measure) measures the risk of the business that the company/project is in, and for a diversified investor, captures only risk that cannot be diversified away. While we are often taught to use regressions against market indices to get these betas, using industry-average or bottom-up betas yields much better estimates for projects and companies.

For the cost of debt, you need to estimate the default spread that the company will face. If the company has a bond rating, you can use this rating to estimate the default spread, and if it is not, you can use the company's financials to assess a synthetic rating.

Cost of debt =Riskfree Rate + Default spread

Harking back to the discussion of riskfree rates, a company in a country with sovereign default risk will often bear a double burden, carrying default spreads for both itself and the country.

The currency choice made in step two will hold, with the riskfree rate in both the cost of equity and debt being the long-term default free rate in that currency (and not always the government bond rate).

Step 5: Ensure that your cash flows are currency consistent

The currency choice made in step 2 determines not only the discount rates that you will be using but also the expected cash flows, with expected inflation driving both inputs. Thus, if you analyze a Turkish project in lira, where the expected inflation rate is 20%, you should expect to see costs of equity and capital that exceed 25%, but you should also see growth rates in the cash flows to be inflated the same expected inflation. If you assess the same project in Euros, where the expected inflation is 2%, you should expect to see much lower discount rates, high county risk notwithstanding, but the expected growth in cash flows will also be muted, because of the low inflation.

There is nothing in this process that is original or path-breaking, but it does yield a systematic and consistent process for estimating discount rates, the D in DCF. It works for me, because I am a pragmatist, with a valuation mission to complete, but you should feel free to adapt and modify it to meet your concerns.

YouTube Video

Paper

Country Risk Determinants: Determinants, Measures and Implications - The 2025 Edition

Datasets

Equity Risk Premiums, by country - July 2025

Country Risk Links

Spreadsheets

Implied Equity Risk Premium for S&P 500 on July 1, 2025

To Bitcoin or not to Bitcoin? A Corporate Cash Question!

2025-07-18T12:35:00.000-04:00

In this post, I will bring together two disparate and very different topics that I have written about in the past. The first is the role that cash holdings play in a business, an extension of the dividend policy question, with an examination of why businesses often should not pay out what they have available to shareholders. In my classes and writing on corporate finance, I look at the motives for businesses retaining cash, as well as how much cash is too much cash. The second is bitcoin, which can be viewed as either a currency or a collectible, and in a series of posts, I argued that bitcoin can only be priced, not valued, making debates about whether to buy or not to buy entirely a function of perception. In fact, I have steered away from saying much about bitcoin in recent years, though I did mention it in my post on alternative investments as a collectible (like gold) that can be added to the choice mix. While there may be little that seemingly connects the two topics (cash and bitcoin), I was drawn to write this post because of a debate that seems to be heating up on whether companies should put some or a large portion of their cash balances into bitcoin, with the success of MicroStrategy, a high-profile beneficiary of this action, driving some of this push. I believe that it is a terrible idea for most companies, and before Bitcoin believers get riled up, my reasoning has absolutely nothing to do with what I think of bitcoin as an investment and more to do with how little I trust corporate managers to time trades right. That said, I do see a small subset of companies, where the holding bitcoin strategy makes sense, as long as there are guardrails on disclosure and governance.

Cash in a Going Concern

In a world where businesses can raise capital (equity or debt) at fair prices and in a timely manner, there is little need to hold cash, but that is not the world we live in. For a variety of reasons, some internal and some external, companies are often unable or unwilling to raise capital from markets, and with that constraint in place, it is logical to hold cash to meet unforeseen needs. In this section, I will start by laying out the role that cash holdings play in any business, and examine how much cash is held by companies, broken down by groupings (regional, size, industry).

A Financial Balance Sheet

To understand the place of cash in a business, I will start with a financial balance sheet, a structure for breaking down a business, public or private:

On the asset side of the balance sheet, you start with the operating business or businesses that a company is in, with a bifurcation of value into value from investments already made (assets-in-place) and value from investments that the company expects to make in the future (growth assets). The second asset grouping, non-operating assets, includes a range of investments that a company may make, sometimes to augment its core businesses (strategic investments), and sometimes as side investments, and thus include minority holdings in other companies (cross holdings) and even investments in financial assets. Sometimes, as is the case with family group companies, these cross holdings may be a reflection of the company's history as part of the group, with investments in other group companies for either capital or corporate control reasons. The third grouping is for cash and marketable securities, and this is meant specifically for investments that share two common characteristics - they are riskless or close to riskless insofar as holding their value over time and they are liquid in the sense that they can be converted to cash quickly and with no penalty. For most companies, this has meant investing cash in short-term bonds or bills, issued by either governments (assuming that they have little default risk) or by large, safe companies (in the form of commercial paper issued by highly rated firms).

Note that there are two sources of capital for any business, debt or equity, and in assessing how levered a firm is, investors look at the proportion of the capital that comes from each:

Debt to Equity = Debt/ Equity
Debt to Capital = Debt/ (Debt + Equity)

In fact, there are many analysts and investors who estimate these debt ratios, using net debt, where they net the cash holdings of a company against the debt, with the rationale, merited or not, that cash can be used to pay down debt.

Net Debt to Equity = (Debt-Cash)/ Equity
Debt to Capital = (Debt-Cash)/ (Debt + Equity)

All of these ratios can be computed using accounting book value numbers for debt and equity or with market value numbers for both.

The Motives for holding Cash

In my introductory finance classes, there was little discussion of cash holdings in companies, outside of the sessions on working capital. In those sessions, cash was introduced as a lubricant for businesses, necessary for day-to-day operations. Thus, a retail store that had scores of cash customers, it was argued, needed to hold more cash, often in the form of currency, to meet its transactional needs, than a company with corporate suppliers and business customers, with predictable patterns in operations. In fact, there were rules of thumb that were developed on how much cash a company needed to have for its operations. As the world shifts away from cash to digital and online payments, this need for cash has decreased, but clearly not disappeared. The one carve out is the financial services sector, where the nature of the business (banking, trading, brokerage) requires companies in the sector to hold cash and marketable securities as part of their operating businesses.

If the only reason for holding cash was to cover operating needs, there would be no way to justify the tens of billions of dollars that many companies hold; Apple alone has often had cash balances that exceeded $200 billion, and the other tech giants are not far behind. For some companies, at least, the rationale for holding far more cash than justified by their operating needs is that it can operate as a shock absorber, something that they can fall back on during periods of crisis or to cover unexpected expenses. That is the reason that cyclical and commodity firms have often offered for holding large cash balances (as a percent of their overall firm value), since a recession or a commodity price downturn can quickly turn profits to losses.

Using the corporate life cycle structure can also provide insight into how the motives for holding cash can change as a company ages.

For start-ups, that are either pre-revenue or have very low revenues, cash is needed to keep the business operating, since employees have to be paid and expenses covered. Young firms that are money-losing and with large negative cash flows, hold cash to cover future cash flow needs and to fend off the risk of failure. In effect, these firms are using cash as life preservers, where they can make it through periods where external capital (venture capital, in particular) dries up, without having to sell their growth potential at bargain basement prices. As firms start to make money, and enter high growth, cash has use as a business scalar, for firms that want to scale up quickly. In mature growth, cash acquires optionality, useful in allowing the business to find new markets for its products or product extensions. Mature firms sometimes hold cash as youth serum, hoping that it can be used to make once-in-a-lifetime investments that may take them back to their growth days, and for declining firms, cash becomes a liquidation manager, allowing for the orderly repayment of debt and sale of assets.

There is a final rationale for holding cash that is rooted in corporate governance and the control and power that comes from holding cash. I have long argued that absent pressure from shareholders, managers at most publicly traded firms would choose to return very little of the cash that they generate, since that cash balance not only makes them more sought after (by bankers and consultants who are endlessly inventive about uses that the cash can be put to) but also gives them the power to build corporate empires and create personal legacies.

Corporate Cash Holdings

Given the multitude of reasons for holding cash, it should come as no surprise that publicly traded companies around the world have significant cash balances. Leading into July 2025, for instance, global non-financial-service firms held almost $11.4 trillion in cash and marketable securities; financial service firms held even more in cash and marketable securities, but those holdings, as we noted earlier, can represent their business needs. Using our earlier breakdown of the asset side of the balance sheet into cash, non-operating and operating assets, this is what non-financial service firms in the aggregate looked like in book value terms (global and just US firms):

Note that cash is about 11% of the book value of total assets, in the aggregate, for global firms, and about 9% of the book value of total assets, for US firms. Global firms do hold a higher percentage of their value in non-operating assets, but US firms are more active on the acquisition front, explaining why goodwill (which is triggered almost entirely by acquisitions) is greater at US firms.

The typical publicly traded firm holds a large cash balance, but there are significant differences in cash holdings, by sector. In the table below, I look at cash as a percent of total assets, a book value measure, as well as cash as a percent of firm value, computed by aggregating market values:

As you can see, technology firms, which presumably face more uncertainty about their future hold far more cash as a percent of book value, but the value that the market attaches to their growth brings down cash as a percent of firm value. Utilities, regulated and often stable businesses, tend to hold the least cash, both in book and market terms.

Breaking down the sample by region, I look at cash holdings, as a percent of total assets and firms, across the globe:

The differences across the globe can be explained by a mix of market access, with countries in parts of the world where it can be difficult to access capital (Latin America, Eastern Europe, Africa) holding more cash. In addition, and corporate governance, with cash holdings being greater in parts of the world (China, Russia) where shareholders have less power over managers.

Given the earlier discussion of how the motives for holding cash can vary across the life cycle, I broke the sample down by age decile, with age measured from the year of founding, and looked at cash holdings, by decile:

The results are mixed, with cash holdings as a percent of total assets being higher for the younger half of the sample (the top five deciles) than for the older half, but the is no discernible pattern, when cash is measured as a percent of firm value (market). Put simple, companies across the life cycle hold cash, though with different motives, with the youngest firms holding on to cash as lifesavers (and for survival) and the older firms keeping cash in the hopes that they can use it to rediscover their youth.

The Magic of Bitcoin

I have been teaching and working with investments now for four decades, and there has been no investment that has received as much attention from both investors and the financial press, relative to its actual value, as has bitcoin. Some of the draw has come from its connections to the digital age, but much of it has come from its rapid rise in price that has made many rich, with intermittent collapses that have made just as many poor. I am a novice when it comes to crypto, and while I have been open about the fact that it is not my investment preference, I understand its draw, especially for younger investors.

The Short, Eventful History of Bitcoin

The origin story for Bitcoin matters since it helps us understand both its appeal and its structure. It was born in November 2008, two months into one of the worst financial crises of the last century, with banks and governments viewed as largely responsible for the mess. Not surprisingly, Bitcoin was built on the presumption that you cannot trust these institutions, and its biggest innovation was the blockchain, designed as a way of crowd-checking transactions and preserving transaction integrity. I have long described Bitcoin as a currency designed by the paranoid for the paranoid, and I have never meant that as a critique, since in the untrustworthy world that we live in, paranoia is a justifiable posture.

From its humble beginnings, where only a few (mostly tech geeks) were aware of its existence, Bitcoin has accumulated evangelists, who argue that it is the currency of the future, and speculators who have used its wild price swings to make and lose tens of millions of dollars. In the chart below, I look at the price of bitcoin over the last decade, as its price has increased from less than $400 in September 2014 to more than $110,000 in June 2025:

Along the way, Bitcoin has also found some acceptance as a currency, first for illegal activities (drugs on the Silk Road) and then as the currency for countries with failed fiat currencies (like El Salvador), but even Bitcoin advocates will agree that its use in transactions (as the medium of exchange) has not kept pace with its growth as a speculative trade.

Pricing Bitcoin

In a post in 2017, I divided investments into four groups - assets that generate cash flows (stocks, bonds, private businesses), commodities that can be used to produce other goods (oil, iron ore etc), currencies that act as mediums of exchange and stores of value and collectibles that are priced based on demand and supply:

You may disagree with my categorization, and there are shades of gray, where an investment can be in more than one grouping. Gold, for instance, is both a collectible of long standing and a commodity that has specific uses, but the former dominates the latter, when it comes to pricing. In the same vein, crypto has a diverse array of players, with a few meeting the asset test and some (like ethereum) having commodity features. The contrast between the different investment classes also allows for a contrast between investing, where you buy (sell) an investment if it is under (over) valued, and trading, where you buy (sell) an investment if you expect its price to go up (down). The former is a choice, though not a requirement, with an asset (stocks, bonds or private businesses), though there may be others who still trade that asset. With currencies and collectibles, you can only trade, making judgments on price direction, which, in turn, requires assessments of mood and momentum, rather than fundamentals.

With bitcoin, this classification allows us to cut through the many distractions that pop up during discussions of its pricing level, since it can be framed either as a currency or a collectible, and thus only priced, not valued. Seventeen years into its existence, Bitcoin has struggled on the currency front, and while there are pockets where it has gained acceptance, its design makes it inefficient and its volatility has impeded its adoption as a medium of exchange. As a collectible, Bitcoin starts with the advantage of scarcity, restricted as it is to 21 million units, but it has not quite measured up, at least so far, when it comes to holding its value (or increasing it) when financial assets are in meltdown mode. In every crisis since 2008, Bitcoin has behaved more like risky stock, falling far more than the average stock, when stocks are down, and rising more, when they recover. I noted this in my posts looking at the performance of investments in both the first quarter of 2020, when COVID laid waste to markets, and in 2022, when inflation ravaged stock and bond markets. That said, it is still early in its life, and it is entirely possible that it may change its behavior as it matures and draws in a wider investor base. The bottom line is that discussions of whether Bitcoin is cheap or expensive are often pointless and sometimes frustrating, since it depends almost entirely on your perspective on how the demand for Bitcoin will shift over time. If you believe that its appeal will fade, and that it will be displaced by other collectibles, perhaps even in the crypto space, you will be in the short selling camp. If you are convinced that its appeal will not just endure but also reach fresh segments of the market, you are on solid ground in assuming that its price will continue to rise. It behooves both groups to admit that neither has a monopoly on the truth, and this is a disagreement about trading and not an argument about fundamentals.

The MicroStrategy Story

It is undeniable that one company, MicroStrategy, has done more to advance the corporate holding of Bitcoin than any other, and that has come from four factors;

A stock market winner: The company's stock price has surged over the last decade, making it one of the best performing stocks on the US exchanges:
It is worth noting that almost all of the outperformance has occurred in this decade, with the winnings concentrated into the last two years.
With the rise (increasingly) tied to Bitcoin: Almost all of MicroStrategy’s outperformance has come from its holdings of bitcoin, and not come from improvements in business operations. That comes through in the graph below, where I look at the prices of MicroStrategy and Bitcoin since 2014:

Note that MicroStrategy’s stock price has gone from being slightly negatively correlated with Bitcoin’s price between 2014-2018 to tracking Bitcoin in more recent years.
And disconnected from operations: In 2014, MicroStrategy was viewed and priced as a software/services tech company, albeit a small one with promise. In the last decade, its operating numbers have stagnated, with both revenues and gross profits declining, but during the same period, its enterprise value has soared from $1 billion in 2014 to more than more than $100 billion in July 2025:

It is clear now that anyone investing in MicroStrategy at its current market cap (>$100 billion) is making a bitcoin play.
With a high-profile "bitcoin evangelist" as CEO: MicroStrategy’s CEO, Michael Saylor, has been a vocal and highly visible promoter of bitcoin, and has converted many of his shareholders into fellow-evangelists and convinced at least some of them that he is prescient in detecting price movements. In recent years, he has been public in his plans to issue increasing amounts of stock and using the proceeds to buy more bitcoin.

In sum, MicroStrategy is now less a software company and more a Bitcoin SPAC or closed-end fund, where investors are trusting Saylor to make the right trading judgments on when to buy (and sell) bitcoin, and hoping to benefit from the profits.

The “Put your cash in bitcoin” movement

For investors in other publicly traded companies that have struggled delivering value in their operating businesses, MicroStrategy’s success with its bitcoin holdings seems to indicate a lost opportunity, and one that can be remedied by jumping on the bandwagon now. In recent months, even high profile companies, like Microsoft, have seen shareholder proposals pushing them to abandon their conventional practice of holding cash in liquid and close-to-riskless investments and buying Bitcoin instead. Microsoft’s shareholders soundly rejected the proposal, and I will start by arguing that they were right, and that for most companies, investing cash in bitcoin does not make sense, but in the second part, I will carve out the exceptions to this rule.

The General Principle: No to Bitcoin

As a general rule, I think it is not only a bad idea for most companies to invest their cash in bitcoin, but I would go further and also argue that they should banned from doing so. Let me hasten to add that I would make this assertion even if I was bullish on Bitcoin, and my argument would apply just as strongly to companies considering moving their cash into gold, Picassos or sports franchises, for five reasons:

Bitcoin does not meet the cash motives: Earlier in this post, I noted the reasons why a company holds cash, and, in particular, as a shock absorber, steadying a firm through bad times. Replacing low-volatility cash with high-volatility bitcoin would undercut this objective, analogous to replacing your shock absorbers with pogo sticks. In fact, given the history of moving with stock prices, the value of bitcoin on a company's balance sheet will dip at exactly the times where you would need it most for stability. The argument that bitcoin would have made a lot higher returns for companies than holding cash is a non-starter, since companies should hold cash for safety.
Bitcoin can step on your operating business narrative: I have long argued that successful businesses are built around narratives that incorporate their competitive advantages. When companies that are in good businesses put their cash in bitcoin, they risk muddying the waters on two fronts. First, it creates confusion about why a company with a solid business narrative from which it can derive value would seek to make money on a side game. Second, the ebbs and flows of bitcoin can affect financial statements, making it more difficult to connect operating results to story lines.
Managers as traders? When companies are given the license to move their cash into bitcoin or other non-operating investments, you are trusting managers to get the timing right, in terms of when to buy and sell these investments. That trust is misplaced, since top managers (CEOs and CFOs) are for the most part terrible traders, often buying at the market highs and selling at lows.
Leave it to shareholders: Even if you are unconvinced by the first three reasons, and you are a bitcoin advocate or enthusiast, you will be better served pushing companies that you are a shareholder in, to return their cash to you, to invest in bitcoin, gold or any other investment at your chosen time. Put simply, if you believe that Bitcoin is the place to put your money, why would you trust corporate managers to do it for you?
License for abuse: I am a skeptic when it comes to corporate governance, believing that managerial interests are often at odds with what's good for shareholders. Giving managers the permission to trade crypto tokens, bitcoin or other collectibles can open the door for self dealing and worse.

While I am a fan of letting shareholders determine the limits on what managers can or cannot do, I believe that the SEC (and other stock market regulators around the world) may need to become more explicit in their rules on what companies can (and cannot) do with cash.

The Carveouts

I do believe that there are cases when you, as a shareholder, may be at peace with the company not only investing cash in bitcoin, but doing so actively and aggressively. Here are four of my carveouts to the general rule on bitcoin:

The Bitcoin Savant: In my earlier description of MicroStrategy, I argued that shareholders in MicroStrategy have not only gained immensely from its bitcoin holdings, but also trust Michael Saylor to trade bitcoin for them. In short, the perception, rightly or wrongly, is that Saylor is a bitcoin savant, understanding the mood and momentum swings better than the rest of us. Generalizing, if a company has a leader (usually a CEO or CFO) who is viewed as someone who is good at gauging bitcoin price direction, it is possible that shareholders in the company may be willing to grant him or her the license to trade bitcoin on their behalf. This is, of course, not unique to bitcoin, and you can argue that investors in Berkshire Hathaway have paid a premium for its stock, and allowed it leeway to hold and deploy immense amounts of cash because they trusted Warren Buffett to make the right investment judgments.
The Bitcoin Business: For some companies, holding bitcoin may be part and parcel of their business operations, less a substitute for cash and more akin to inventory. PayPal and Coinbase, both of which hold large amounts of bitcoin, would fall into this carveout, since both companies have business that requires that holding.
The Bitcoin Escape Artist: As some of you may be aware, I noted that Mercado Libre, a Latin American online retail firm, is on my buy list, and it is a company with a fairly substantial bitcoin holding. While part of that holding may relate to the operating needs of their fintech business, it is worth noting that Mercado Libre is an Argentine company, and the Argentine peso has been a perilous currency to hold on to, making bitcoin a viable option for cash holdings. Generalizing, companies in countries with failed currencies may conclude that holding their cash in bitcoin is less risky than holding it in the fiat currencies of the locations they operate in.
The Bitcoin Meme: There is a final grouping of companies that I would put in the meme stock category, with AMC and Gamestop heading that list. These companies have operating business models that have broken down or have declining value, but they have become, by design or through accident, trading plays, where the price bears no resemblance to operating fundamentals and is instead driven by mood and momentum. If that is the case, it may make sense for these companies to throw in the towel on operating businesses entirely and instead make themselves even more into trading vehicles by moving into bitcoin, with the increased volatility adding to their "meme" allure.

Even with these exceptions, though, I think that you need guardrails before signing off on opening the door to letting companies hold bitcoin.

Shareholder buy-in: If you are a publicly traded company considering investing some or much of the company's cash in bitcoin, it behooves you to get shareholder approval for that move, since it is shareholder cash that is being deployed.
Transparency about Bitcoin transactions/holdings: Once a company invests in bitcoin, it is imperative that there be full and clear disclosure not only on those holdings but also on trading (buying and selling) that occurs. After all, if it is a company's claim that it can time its bitcoin trades better than the average investor, it should reveal the prices at which it bought and sold its bitcoin.
Clear mark-to-market rules: If a company invests its cash in bitcoin, I will assume that the value of that bitcoin will be volatile, and accounting rules have to clearly specify how that bitcoin gets marked to market, and where the profits and losses from that marking to market will show up in the financial statements.

As bitcoin prices rise to all time highs, there is the danger that regulators and rule-writers will be lax in their rule-writing, opening the door to corporate scandals in the future.

Cui Bono?

Bitcoin advocates have been aggressively pushing both institutional investors and companies to include Bitcoin in their investment choices, and it is true that at least first sight, they will benefit from that inclusion. Expanding the demand for bitcoin, an investment with a fixed supply, will drive the price upwards, and existing bitcoin holders will benefit. In fact, much of the rise of bitcoin since the Trump election in November 2024 can be attributed to the perception that this administration will ease the way for companies and investors to join in the crypto bonanza.

For bitcoin holders, increasing institutional and corporate buy-in to bitcoin may seem like an unmixed blessing, but there will be costs that, in the long run, may lead at least some of them to regret this push:

Different investor base: Drawing in institutional investors and companies into the bitcoin market will not only change its characteristics, but put traders who may know how to play the market now at a disadvantage, as it shifts dynamics.
Here today, gone tomorrow? Bitcoin may be in vogue now, but what will the consequences be if it halves in price over the next six months? Institutions and companies are notoriously ”sheep like” in their behavior, and what is in vogue today may be abandoned tomorrow. If you believe that bitcoin is volatile now, adding these investors to the mix will put that volatility on steroids.
Change asset characteristics: Every investment class that has been securitized and brought into institutional investing has started behaving like a financial asset, moving more with stocks and bonds than it has historically. This happened with real estate in the 1980s and 1990s, with mortgage backed securities and other tradable versions of real estate, making it far more correlated with stock and bonds, and less of a stand alone asset.

If the end game for bitcoin is to make it millennial gold, an alternative or worthy add-on to financial assets, the better course would be steer away from establishment buy-in and build it up with an alternative investor base, driven by different forces and motives than stock and bond markets.

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The (Uncertain) Payoff from Alternative Investments: Many a slip between the cup and the lip?

2025-06-17T11:32:00.003-04:00

It is true that most investing lessons are directed at those who invest only in stocks and bonds, and mostly with long-only strategies. It is also true that in the process, we are ignoring vast swaths of the investment universe, from other asset classes (real estate, collectibles, cryptos) to private holdings (VC, PE) to strategies that short stocks or use derivatives (hedge funds). These ignored investment classes are what fall under the rubric of alternative investments, and while many of these choices have been with us for as long as we have had financial markets, they were accessible to only a small subset of investors for much of that period. In the last two decades, alternative investments have entered the mainstream, first with choices directed at institutional investors, but more recently, in offerings for individual investors. Without giving too much away, the sales pitch for adding alternative investments to a portfolio composed primarily of stocks and bonds is that the melding will create a better risk-return tradeoff, with higher returns for any given risk level, albeit with two different rationales. The first is that they have low correlations with financial assets (stocks and bonds), allowing for diversification benefits and the second is investments in some of these alternative asset groupings have the potential to earn excess returns or alphas. While the sales pitch has worked, at least at the institutional level, in getting buy-in on adding alternative investments, the net benefits from doing so have been modest at best and negative at worst, raising questions about whether there need to be more guardrails on getting individual investors into the alternative asset universe.

The Alternative Investment Universe

The use of the word "alternative" in the alternative investing pitch is premised on the belief that much of investing advice is aimed at long-only investors allocating their portfolios between traded stocks, bonds and cash (close to riskless and liquid investments). In that standard investment model, investors choose a stock-bond mix, for investing, and use cash as a buffer to bring in not only liquidity needs and risk preferences, but also views on stock and bond markets (being over or under priced):

The mix of stocks and bonds is determined both by risk preferences, with more risk taking associated with a higher allocation to stocks, and market timing playing into more invested in stocks (if stocks are viewed as under priced) or more into bonds (if stocks are over priced and bond are viewed as neutral investments).

This framework accommodates a range of choices, from the purely mechanical (like the much touted 60% stocks/40% bonds mix) to more flexible, where allocations can vary across time and be a function of market conditions. This general framework allows for variants, including different view on markets (from those who believe that markets are efficient to stock pickers and market timers) as well as investors with very different time horizons and risk levels. However, there are clearly large segments of investing that are left out of this mix from private businesses (since they are not listed and traded) to short selling (where you can have negative portfolio weights not just on individual investments but on entire markets) to asset classes that are not traded. In fact, the best way to structure the alternative investing universe if by looking at alternatives through the lens of these missing pieces.

1. Long-Short

In principle, there is little difference between being long on an investment and holding a short position, with the only real difference being in the sequencing of cash flows, with the former requiring a negative cash flow at the time of the action (buying the stock or an asset) and a positive cash flow in a subsequent period (when it is sold), and the latter reversing the process, with the positive cash flow occurring initially (when you sell a stock or an asset that you do not own yet) and the negative cash flow later. That said, they represent actions that you would take with diametrically opposite views of the same stock (asset), with being long (going short) making sense on assets where you expect prices to go up (down). In practice, though, regulators and a subset of investors seem to view short selling more negatively, often not just attaching loaded terms like "speculation" to describe it, but also adding restrictions of how and when it can be done.

Many institutional investors, including most mutual, pension and endowment funds, are restricted from taking short positions on investments, with exceptions sometimes carved out for hedging. For close to a century, at least in the United States, hedge funds have been given the freedom to short assets, and while they do not always use that power to benefit, it is undeniable that having that power allows them to create return distributions (in terms of expected returns, volatility and other distributional parameters) that are different from those faced by long-only investors. Within the hedge fund universe, there are diverse strategies that not only augment long-only strategies (value, growth) but also invest across multiple markets (stocks, bonds and convertibles) and geographies.

The opening up of derivatives markets has allowed some investors to create investment positions and or structured products that use options, futures, swaps and forwards to create cash flow and return profiles that diverge from stock and bond market returns.

2. Public-Private

While much of our attention is spent on publicly traded stocks and bonds, there is a large segment of the economy that is composed of private businesses that are not listed or traded. In fact, there are economies, especially in emerging markets, where the bulk of economic activity occurs in the private business space, with only a small subset of businesses meeting the public listing/trading threshold. Many of these private businesses are owned and funded by their owners, but a significant proportion do need outside equity capital, and historically, there have been two providers:

For young private businesses, and especially those that aspire to become bigger and eventually go public, it is venture capital that fills the void, covering the spectrum from angel financing for idea businesses to growth capital for firms further along in their evolution. From its beginnings in the 1950s, venture capital has grown bigger and carries more heft, especially as technology companies have come to dominate the market in the twenty first century.
For more established private businesses, some of which need capital to grow and some of which have owners who want to cash out, the capital has come from private equity investors. Again, while private equity has been part of markets for a century or more, it has become more formalized and spread its reach in the last four decades, with the capacity to raise tens of billions of dollars to back up deal making.

On the debt front, the public debt and bank debt market is supplemented by private credit, where investors pool funds to lend to private businesses, with negotiated rates and terms. again a process that has been around a while, but one that has also become formalized and a much larger source of funds. Advocates for private credit investing argue that it can be value-adding partly because of the borrower composition (often cut off from other sources of credit, either because of their size or default history) and partly because private credit providers can be more discerning of true default risk. Even as venture capital, private equity and private credit have expanded as capital sources, they remained out of reach for both institutional and individual investors until a couple of decades ago, but are now integral parts of the alternative investing universe.

3. Asset classes

Public equity and debt, at least in the United States, cover a wide spectrum of the economy, and by extension, multiple asset classes and businesses, but there are big investment classes that are either underrepresented in public markets or missing.

Real estate: For much of the twentieth century, real estate remained outside the purview of public markets, with a segmented investor base and illiquid investments, requiring localized knowledge. That started to change with the creation of real estate investment trusts, which securitized a small segment of the market, creating liquidity and standardized units for public market investors. The securitization process gained stream in the 1980s with the advent of mortgage-backed securities. Thus, real estate now has a presence in public markets, but that presence is far smaller than it should be, given the value of real estate in the economy.
Collectibles: The collectible asset class spans an array of investment, most of which generate little or no cash flows, but derive their pricing from scarcity and enduring demand. The first and perhaps the longest standing collectible is gold, a draw for investors during inflationary period or when they lose faith in fiat currencies and governments. The second is art, ranging from paintings from the masters to digital art (non-fungible tokens or NFTs), that presumably offers owners not just financial returns but emotional dividends. At the risk of raising the ire of crypto-enthusiasts, I would argue that much of the crypto space (and especially bitcoin) also fall into this grouping, with a combination of scarcity and trading demand determining pricing.

Institutional and individual investors have dabbled with adding these asset classes to their portfolios, but the lack of liquidity and standardization and the need for expert assessments (especially on fine art) have limited those attempts.

The Sales Pitch for Alternatives

The strongest pitch for adding alternative investments to a portfolio dominated by publicly traded stocks and bonds comes from a basic building block for portfolio theory, which is that adding investments that have low correlation to the existing holdings in a portfolio can create better risk/return tradeoffs for investors. That pitch has been supplemented in the last two decades with arguments that alternative investments also offer a greater chance of finding market mistakes and inefficiencies, partly because they are more likely to persist in these markets, and partly because of superior management skills on the part of alternative investment managers, particularly hedge funds and private equity.

The Correlation Argument

Much of portfolio theory as we know it is built on the insight that combining two investments that are not perfectly correlated with each other can yield mixes that deliver higher returns for any given level of risk than holding either of the investments individually. That argument has both a statistical basis, with the covariance between the two investments operating as the mechanism for the risk reduction, and an economic basis that the idiosyncratic movements in each investment can offset to create a less risky combination.

In that vein, the argument for adding alternative investments to a portfolio composed primarily of stocks and bonds rests on a correlation matrix of stocks and bonds with alternative investments (hedge funds, private equity, private credit, fine art, gold and collectibles):

Guggenheim Investments

While the correlations in this matrix are non-stationary (with the numbers changing both with time periods used and the indices that stand in for the asset classes) and have a variety of measurement issues that I will highlight later in this post, it is undeniable that they at least offer a chance of diversification that may not be available in a long-only stock/bond portfolio.

Using historical correlations as the basis, advocates for alternative investments are able to create portfolios, at least on paper, that beat stock/bond combinations on a risk/return tradeoff, as can be see in this graph:

EquityMultiple Investment Partners, Green Street Advisors, and JPMorgan Asset Management

Note that the comparison is to a portfolio composed 60% of stocks and 40% of bonds, a widely used mix among portfolio managers, and in each of the cases, adding alternative investments to that portfolio results in a mix that yields higher returns with lower risk.

The Alternative Alpha Argument

The correlation-based argument for adding alternative investments to a portfolio is neither new nor controversial, since it is built on core portfolio theory arguments for diversification. For some advocates of alternative investments, though, that captures only a portion of the advantage of adding alternative investments. They argue that the investment classes from alternative investments draw on, which include non-traded real estate, collectibles and private businesses (young and old), are also the classes where market mistakes are more likely to persist, because of their illiquidity and opacity, and that alternative asset managers have the localized knowledge and intellectual capacity to find and take advantage of these mistakes. The payoff from doing so takes the form of "excess returns" which will supplement the benefits that flow from just diversification.

This alpha argument is often heard most frequently with those advocating for adding hedge funds, venture capital and private equity to conventional portfolios, where the perception of superior investment management persists, but is that perception backed up by the numbers? In the graph below, I reproduce a study that looks at looked at 20-year annualized returns, from 2003 to 2022, on many alternative asset classes:

Opto Insights

Given the differences in risk across alternative investment classes, the median returns themselves do not tell us much about whether they earn excess returns, but two facts come through nevertheless. The first is that the variation across managers within investment classes is significant in both private equity and venture capital. The second, and this is not visible in this graph, is that persistence in outperformance is more common in venture capital and private equity than it is in public market investors, with winners more likely to continue winning and losers dropping out. I expanded on some of the reasons for this persistence, at least in venture capital, in a post from some years ago.

The bottom line is that there is some basis for the argument that as investment classes, hedge funds, private equity and venture capital, generate excess returns, albeit modest, relative to other investors, but it is unclear whether these excess returns are just compensation for the illiquidity and opacity that go with the investments that they have to make. In addition, given the skewed payoffs, where there are a few big and persistent winners, the median hedge fund, private equity investor or venture capitalist may be no better at generating alpha than the average mutual fund manager.

The Rise of Alternative Investing

No matter what you think of the alternative investing sales pitch, it is undeniable that it has worked, at least at the institutional investor level, for some of its adopted, especially in the last two decades. In the graph below, for instance, you can track the rise of alternative investments in pension fund holdings in this graph (from KKR):

Source: KKR

That move towards alternatives is not just restricted to pension funds, as other allocators have joined the mix:

Source: KKR

Some of the early movers into alternative asset classes were lauded and used as role models by others in the space. David Swensen, at Yale, for instance, burnished a well-deserved reputation as a pioneer in investment management by moving Yale's endowment into private equity and hedge funds earlier than other Ivy League schools, allowing Yale to outpace them in the returns race for much of this century:

As other fund managers have followed Yale into the space, that surge has been good for private equity and hedge fund managers, who have seen their ranks grow (both in terms of numbers and dollar value under management) over time.

Where's the beef?

As funds have increased their allocations to alternative investments, drawn by the perceived gains on paper and the success of early adopters, it is becoming increasingly clear that the results from the move have been underwhelming. In short, the actual effects on returns and risk from adding alternative investments to portfolios are not matching up to the promise, leading to questions of why and where the leakage is occurring.

The Questionable Benefits of Alternative Investing

In theory and principle, adding investments from groupings of investments that are less correlated with stocks and bonds should yield benefits for investors, and at least in the aggregate, over long time periods that may hold. Cambridge Associates, in their annual review of endowments, presents this graph of returns and standard deviations, as a function of how much each endowment allocated to private investments over a ten-year period (from 2012-2022):

Cambridge Associates

With the subset of endowments that Cambridge examined, both annual returns and Sharpe ratios were higher at funds that invested more in private investments (which incorporates much of the alternative investment space). Those results, though, have been challenged by others looking at a broader group of funds. In an article in CFA magazine, Nicolas Rabener looked at the two arguments for adding hedge funds to a portfolio, i.e., that they increase Sharpe ratios and reduce drawdowns in fund value during market downturns, and found both absent in practice:

Nicolas Ramener, CFA Institute

With hedge funds, admittedly just one component of alternative investing, Rabener finds that notwithstanding the low correlations that some hedge fund strategies have with a conventional equity/bond portfolio, there is no noticeable improvement in Sharpe ratios or decrease in drawdowns from adding them to the portfolio.

Richard Ennis, a long-time critic of alternative investing, has a series of papers that question the benefits to funds from adding them to the mix.

Richard Ennis, SSRN

In the Ennis sample, the excess returns become more negative as the allocation to alternative investments is increased, undercutting a key sales pitch for the allocation. While alternative investing advocates will take issue with the Ennis findings, on empirical and statistical bases, even long-term beneficiaries from alternative investing seem to have become more skeptical about its benefits over time. In a 2018 paper, Fragkiskos, Ryan and Markov noted that among Ivy League endowments, properly adjusting for risk causes any benefits in terms of Sharpe ratios, from adding alternative investments to the mix, to disappear. In perhaps the most telling sign that the bloom is off the alternative investing rose, Yale's endowment announced its intent to sell of billions of dollars of private equity holdings in June 2025, after years of under performance on its holdings in that investment class.

Correlations: Real and Perceived

At the start of this post, I noted that a key sales pitch for alternative investments is their low correlation with stock/bond markets, and to the extent that this historical correlations seem to back this pitch, it may be surprising that the actual results don't measure up to what is promised. There are two reasons why these historical correlations may be understated for most private investment classes:

Pricing lags; Unlike publicly traded equities and bonds, where there are observable market prices from current transactions, most private assets are not liquid and the pricing is based upon appraisals. In theory, these appraisers are supposed to mark-to-market, but in practice, the pricing that they attach to private assets lag market changes. Thus, when markets are going up or down quickly, private equity and venture capital can look like they are going up or down less than public equity markets, but that is because of the lagged prices.
Market crises: While correlations between investment classes are often based upon long periods, and across up and down markets, the truth is that investors care most about risk (and correlations) during market crises, and many investment classes that exhibit low correlation during sideways or stable markets can have lose that feature and move in lock step with public markets during crisis. That was the case during the banking crisis in the last quarter of 2008 and during the COVID meltdown in the first quarter of 2020, when funds with large private investment allocations felt the same drawdown and pain as funds without that exposure.

In my view, this understatement of correlation is most acute in private equity and venture capital, which are after all equity investments in businesses, albeit private, instead of public. It is less likely to be the case for truly differentiated investment classes, such as gold, collectibles and real estate, but even here, correlations with public markets have risen, as they have become more widely held by funds. With hedge funds, it is possible to construct strategies that should have lower correlation with public markets, but some of these strategies can have catastrophic breakdowns (with the potential for wipeout) during market crises.

Illiquidity and Opacity (lack of transparency)

Even the strongest advocates for alternative investments accept that they are less liquid than public market investments, but argue that for investors with long time horizons and clearly defined cash flow needs (like pension and endowment funds), that illiquidity should not be a deal breaker. The problem with this argument is that much as investors like to believe that they control their time horizons and cash needs, they do not, and find their need for liquidity rising during acute market crises or panics. The other problem with illiquidity is that it manifests in transactions costs, manifesting both in terms of bid-ask spreads and in price impact that drains from returns.

The other aspect of the private investment market that is mentioned but then glossed over is that many of its vehicles tend to be opaque in terms of governance structure and reporting. Investors, including many large institutional players, that invest in hedge funds, private equity and venture capital are often on the outside looking in, as deals get structured and gains get apportioned. Again, that absence of transparency may be ignored in good times, but could make bad times worse.

Disappearing Alphas

When alternative investing first became accessible to institutional investors, the presumption was that market-beating opportunities abounded in private markets, and that hedge fund, private equity and venture capital managers brought superior abilities to the investment game. That may have been true then, but that perception has faded for many reasons. First, as the number of funds and money under management in these investment vehicles has increased, the capacity to make easy money has also faded, and in my view, the average venture capital, private equity or hedge fund manager is now no better or worse than the average mutual fund manager. Second, the investment game has also become more difficult to win, as the investment world has become flatter, with many of the advantages that fund managers used to extract excess returns dissipating over time. Third, the entry of passive investment vehicles like exchange traded funds (ETFS) that can spot and replicate active investors who are beating the market has meant that excess returns, even if present, do not last for long.

With hedge funds, the fading of excess returns over time has been chronicled. Sullivan looked at hedge funds between 1994 and 2019 and noted that even by 2009, the alpha had dropped to zero or below:

Sullivan, Hedge fund alpha: Cycle or Sunset

In a companion paper, Sullivan also noted another phenomenon undercutting the benefits of adding hedge funds to a public market portfolio, which is that correlations between hedge fund returns and public market returns have risen over time from 0.65 in the 1990s to 0.87 in the last decade.

With private investment funds, the results are similar, when performance is compared over time. A paper looking at private equity returns over time concluded that private equity returns, which ran well above public market returns between 1998 and 2007, have started to resemble public market returns in most recent years.

Ilmanen, Chandra and McQuinn

The positive notes in both hedge funds and private equity, as we noted in an earlier section on venture capital, is that while the typical manager in each group has converged to the average, the best managers in these groups have shown more staying power than in public markets. Put simple, the hope is that you can invest your money with these superior managers, and ride their success to earn more than you would have earned elsewhere, but there is a catch even with that scenario, which we will explore next.

The Cost Effect

Let's assume that even with fading alphas and higher correlations with public markets, some hedge funds and private market investors still provide benefits to funds invested primarily in public markets. Those benefits, though, still come with significant costs, since the managers of these alternative investment vehicles charge far more for their services than their equivalents in public markets. In general, the fees for alternative investments are composed of a management fee, specified as a percent of assets under management, and a performance fee, where the alternative investment manager gets a percent of returns earned over and above a specified benchmark. In the two-and-twenty model that many hedge and PE fund models used to adhere to, the fund managers collect 2% of the assets under management and 20% of returns in excess of the benchmark. Both numbers have been under downward pressure in recent years, as alternative investing has spread:

Even with the decline, though, these costs represent a significant drag on performance, and the chances of gaining a net benefit from adding an alternative investing class to a fund drop towards zero very quickly.

An Epitaph for Alternative Investing?

It is clear, looking at the trend lines, that the days of easy money for those selling alternative investments as well as those buying these investments have wound down. Even savvy institutional investors, who have been long-term believers in the benefits of alternative investing, are questioning whether private equity, hedge funds and venture capital have become too big and are too costly to be value-adding. As institutional investors become less willing to jump into the alternative investing fray, it looks like individual investors are now being targeted for the alternative investing sales pitch, and as with all things investing, I would suggest that buyer beware, and that investors, institutions and individual, keep the following in mind, when listening to alternative investing pitches:

Be picky about alternatives: Given that the alpha pitch (that hedge fund and private equity managers deliver excess returns) has lost its heft, it is correlations that should guide investor choices on alternative investments. That will reduce the attractiveness of private equity and venture capital, as investment vehicles, and increase the draw of some hedge funds, gold and many collectibles. As for cryptos, the jury is still out, since bitcoin, the highest profile component, has behaved more like risky equity, rising and falling with the market, than a traditional collectible.
Avoid high-cost and exotic vehicles: Investing is a tough enough game to win, without costs, and adding high cost vehicles makes it even more difficult. At the risk of drawing the ire of some, I would argue that any endowment or pension fund managers who pay two-and-twenty to a hedge fund, no matter how great its track record, first needs their heads examined and then summarily fired. On a related noted, alternative investments that are based upon strategies that are so complex that neither the seller nor buyer has an intuitive sense of what exactly they are trying to do should be avoided.
Be realistic about time horizon and liquidity needs: As noted many times through this post, alternative investing, no matter how well structured and practiced, will come with less liquidity and transparency than public investing, making it a better choice for investors with longer time horizons and well-specified cash needs. On this front, individual investors need to be honest with themselves about how susceptible they are to panic attacks and peer-group pressure, and institutional investors have to recognize that their time horizons are determined by their clients, and not by their own preferences.
Be wary of correlation matrices and historical alphas: The alternative investing sales pitch is juiced by correlation matrices (indicating that the alternative investing vehicle in question does not move with public markets) and historic alphas (showing that vehicle delivering market beating risk/return tradeoffs and Sharpe ratios). If there is one takeaway from this post, I hope that it is that historical correlations, especially when you have non-traded investments at play, are untrustworthy and that alphas fade over time, and more so when the vehicles that delivered them are sold relentlessly.

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Sovereign Ratings, Default Risk and Markets: The Moody's Downgrade Aftermath!

2025-06-02T14:31:00.003-04:00

I was on a family vacation in August 2011 when I received an email from a journalist asking me what I thought about the S&P ratings downgrade for the US. Since I stay blissfully unaware of most news stories and things related to markets when I am on the beach, I had to look up what he was talking about, and it was S&P's decision to downgrade the United States, which had always enjoyed AAA, the highest sovereign rating that can be granted to a country, to AA+, reflecting their concerns about both the fiscal challenges faced by the country, with mounting trade and budget deficits, as well as the willingness of its political institutions to flirt with the possibility of default. For more than a decade, S&P remained the outlier, but in 2023, Fitch joined it by also downgrading the US from AAA to AA+, citing the same reasons. That left Moody's, the third of the major sovereign ratings agencies, as the only one that persisted with a Aaa (Moody's equivalent of AAA) for the US, but that changed on May 16, 2025, when it too downgraded the US from Aaa (negative) to Aa1 (stable). Since the ratings downgrade happened after close of trading on a Friday, there was concern that markets would wake up on the following Monday (May 19) to a wave of selling, and while that did not materialize, the rest of the week was a down week for both stocks and US treasury bonds, especially at the longest end of the maturity spectrum. Rather than rehash the arguments about US debt and political dysfunction, which I am sure that you had read elsewhere, I thought I would take this moment to talk about sovereign default risk, how ratings agencies rate sovereigns, the biases and errors in sovereign ratings and their predictive power, and use that discussion as a launching pad to talk about how the US ratings downgrade will affect equity and bond valuations not just in the US, but around the world.

Sovereign Defaults: A History

Through time, governments have often been dependent on debt to finance themselves, some in the local currency and much in a foreign currency. A large proportion of sovereign defaults have occurred with foreign currency sovereign borrowing, as the borrowing country finds itself short of the foreign currency to meet its obligations. However, those defaults, and especially so in recent years, have been supplemented by countries that have chosen to default on local currency borrowings. I use the word "chosen" because most countries have the capacity to avoid default on local currency debt, being able to print money in that currency to pay off debt, but chose not to do so, because they feared the consequences of the inflation that would follow more than the consequences of default.

BoC/BoE Sovereign Default Database

While the number of sovereign defaults has ebbed and flowed over time, there are two points worth making about the data. The first is that, over time, sovereign defaults, especially on foreign currency debt, have shifted from bank debt to sovereign bonds, with three times as many sovereign defaults on bonds than on bank loans in 2023. The second is that local currency defaults are persistent over time, and while less frequent than foreign currency defaults, remain a significant proportion of total defaults.

The consequences of sovereign default have been both economic and political. Besides the obvious implication that lenders to that government lose some or a great deal of what is owed to them, there are other consequences. Researchers who have examined the aftermath of default have come to the following conclusions about the short-term and long-term effects of defaulting on debt:

Default has a negative impact on the economy, with real GDP dropping between 0.5% and 2%, but the bulk of the decline is in the first year after the default and seems to be short lived.
Default does affect a country’s long-term sovereign rating and borrowing costs. One study of credit ratings in 1995 found that the ratings for countries that had defaulted at least once since 1970 were one to two notches lower than otherwise similar countries that had not defaulted. In the same vein, defaulting countries have borrowing costs that are about 0.5 to 1% higher than countries that have not defaulted. Here again, though, the effects of default dissipate over time.
Sovereign default can cause trade retaliation. One study indicates a drop of 8% in bilateral trade after default, with the effects lasting for up to 15 years, and another one that uses industry level data finds that export-oriented industries are particularly hurt by sovereign default.
Sovereign default can make banking systems more fragile. A study of 149 countries between 1975 and 2000 indicates that the probability of a banking crisis is 14% in countries that have defaulted, an eleven percentage-point increase over non-defaulting countries.
Sovereign default also increases the likelihood of political change. While none of the studies focus on defaults per se, there are several that have examined the after-effects of sharp devaluations, which often accompany default. A study of devaluations between 1971 and 2003 finds a 45% increase in the probability of change in the top leader (prime minister or president) in the country and a 64% increase in the probability of change in the finance executive (minister of finance or head of central bank).

In summary, default is costly, and countries do not (and should not) take the possibility of default lightly. Default is particularly expensive when it leads to banking crises and currency devaluations; the former has a longstanding impact on the capacity of firms to fund their investments whereas the latter create political and institutional instability that lasts for long periods.

Sovereign Ratings: Measures and Process

Since few of us have the resources or the time to dedicate to understanding small and unfamiliar countries, it is no surprise that third parties have stepped into the breach, with their assessments of sovereign default risk. Of these third-party assessors, bond ratings agencies came in with the biggest advantages:

They have been assessing default risk in corporations for a hundred years or more and presumably can transfer some of their skills to assessing sovereign risk.
Bond investors who are familiar with the ratings measures, from investing in corporate bonds, find it easy to extend their use to assessing sovereign bonds. Thus, a AAA rated country is viewed as close to riskless whereas a C rated country is very risky.

Moody’s, Standard and Poor’s and Fitch’s have been rating corporate bond offerings since the early part of the twentieth century. Moody’s has been rating corporate bonds since 1919 and started rating government bonds in the 1920s, when that market was an active one. By 1929, Moody’s provided ratings for almost fifty central governments. With the Great Depression and the Second World War, investments in government bonds abated and with it, the interest in government bond ratings. In the 1970s, the business picked up again slowly. As recently as the early 1980s, only about thirteen governments, mostly in developed and mature markets, had ratings, with most of them commanding the highest level (Aaa). The decade from 1985 to 1994 added 34 countries to the sovereign rating list, with many of them having speculative or lower ratings and by 2024, Moody's alone was rating 143 countries, covering 75% of all emerging market countries and almost every developed market.

Not only have ratings agencies become more active in adding countries to their ratings list, but they have also expanded their coverage of countries with more default risk/ lower ratings. In fact, the number of Aaa rated countries was the same in 1985, when there were thirteen rated countries, as in 2025, when there were 143 rated countries. In the last two decades, at least five sovereigns, including Japan, the UK, France and now the US, have lost their Aaa ratings. In addition to more countries being rated, the ratings themselves have become richer. Moody’s and S&P now provide two ratings for each country – a local currency rating (for domestic currency debt/ bonds) and a foreign currency rating (for government borrowings in a foreign currency).

In assessing these sovereign ratings, ratings agencies draw on a multitude of data, quantitative and qualitative. Moody's describes its sovereign ratings process in the picture below:

The process is broad enough to cover both political and economic factors, while preserving wiggle room for the ratings agencies to make subjective judgments on default that can lead to different ratings for two countries with similar economic and political profiles. The heat map below provides the sovereign ratings, from Moody's, for all rated countries the start of 2025:

Moody's sovereign ratings

Note that the greyed out countries are unrated, with Russia being the most significant example; the ratings agencies withdrew their rating for Russia in 2022 and not reinstated it yet. There were only a handful of Aaa rated countries, concentrated in North America (United States and Canada), Northern Europe (Germany, Scandinavia), Australia & New Zealand and Singapore (the only Aaa-rated Asian country. In 2025, there have been a eight sovereign ratings changes, four upgrades and four downgrades, with the US downgrade from Aaa to Aa1 as the highest profile change

With the US downgrade, the list of Aaa-rated countries has become shorter, and as Canada and Germany struggle with budget imbalances, the likelihood is that more countries will drop off the list.

Sovereign Ratings: Performance and Alternatives

If sovereign ratings are designed to measure exposure to default risk, how well do they do? The answer depends on how you evaluate their performance. The ratings agencies provide tables that list defaults by rating that back the proposition that sovereign ratings and default are highly correlated. A Moody's update of default rates by sovereign ratings classes, between 1983 and 2024, yielded the following:

Default rates rise as sovereign ratings decline, with a default rate of 24% for speculative grade sovereign debt (Baa2 and below) as opposed to 1.8% for investment grade (Aaa to Baa1) sovereign debt.

That said, there are aspects of sovereign ratings that should give pause to anyone considering using them as their proxy for sovereign default, they do come with caveats and limitations:

Ratings are upward biased: Ratings agencies have been accused by some of being far too optimistic in their assessments of both corporate and sovereign ratings. While the conflict of interest of having issuers pay for the rating is offered as the rationale for the upward bias in corporate ratings, that argument does not hold up when it comes to sovereign ratings, since not only are the revenues small, relative to reputation loss, but a proportion of sovereigns are rated for no fees.
There is herd behavior: When one ratings agency lowers or raises a sovereign rating, other ratings agencies seem to follow suit. This herd behavior reduces the value of having three separate ratings agencies, since their assessments of sovereign risk are no longer independent.
Too little, too late: To price sovereign bonds (or set interest rates on sovereign loans), investors (banks) need assessments of default risk that are updated and timely. It has long been argued that ratings agencies take too long to change ratings, and that these changes happen too late to protect investors from a crisis.
Vicious Cycle: Once a market is in crisis, there is the perception that ratings agencies sometimes overreact and lower ratings too much, thus creating a feedback effect that makes the crisis worse. This is especially true for small countries that are mostly dependent on foreign capital for their funds.
Regional biases: There are many, especially in Asia and Latin America, that believe that the ratings agencies are too lax in assessing default risk for North America and Europe, overrating countries in those regions, while being too stringent in their assessments of default in Asia, Latin America and Africa, underrating countries in those regions.

In sum, the evidence suggests that while sovereign ratings are good measures of country default risk, changes in ratings often lag changes on the ground, making them less useful to lenders and investors.

If the key limitation of sovereign ratings is that they are not timely assessors of country default risk, that failure is alleviated by the development of the sovereign CDS market, a market where investors can buy insurance against country default risk by paying an (annualized) price. While that market still has issues in terms of counterparty risk and legal questions about what comprises default, it has expanded in the last two decades, and at the start of 2025, there were about 80 countries with sovereign CDS available on them. The heat map below provides a picture of sovereign (10-year) CDS spreads on January 1, 2025:

As you can see, even at the start of 2025, the market was drawing a distinction between the safest Aaa-rated countries (Scandinavia, Switzerland, Australia and New Zealand), all with sovereign CDS spreads of 0.20% or below, and more risky Aaa-rated countries (US, Germany, Canada). During 2025, the market shocks from tariff and trade wars have had an effect, with sovereign CDS spreads increasing, especially in April. The US, which started 2025 with a sovereign CDS spread of 0.41%, saw a widening of the spread to 0.62% in late April, before dropping back a bit in May, with the Moody's downgrade having almost no effect on the US sovereign CDS spread.

The US Downgrade: Lead-in and Aftermath

With that background on sovereign default and ratings, let's take a look at the story of the moment, which is the Moody's downgrade of the US from Aaa to Aa1. In the weeks since, we have not seen a major upheaval in markets, and the question that we face as investors and analysts is whether anything of consequence has changed as a result of the downgrade.

The Lead-in

As I noted at the start of this post, Moody's was the last of the big three sovereign ratings agencies giving the United States a Aaa rating, with S&P (in 2011) and Fitch (in 2023) having already downgraded the US. In fact, the two reasons that both ratings agencies provided at the time of their downgrades were rising government debt and politically dysfunction were also the reasons that Moody's noted in their downgrade. On the debt front, one of the measures that ratings agencies use to assess a country's financial standing is its debt to GDP ratio, and it is undeniable that this statistic has trended upwards for the United States:

The ramping up of US debt since 2008 is reflected in total federal debt rising from 80% of GDP in 2008 to more than 120% in 2024. While some of the surge in debt can be attributed to the exigencies caused by crises (the 2008 banking crisis and the 2020 COVID bailouts), the troubling truth is that the debt has outlasted the crises and blaming the crises for the debt levels today is disingenuous.

The problem with the debt-to-GDP measure of sovereign fiscal standing is that it is an imperfect indicator, as can be seen in this list of countries that scored highest and lowest on this measure in 2023:

IMF

Many of the countries with the highest debt to GDP ratios would be classified as safe and some have Aaa ratings, whereas very few of the countries on the lowest debt to GDP list would qualify as safe. Even if it it the high debt to GDP ratio for the US that triggered the Moody's downgrade, the question is why Moody's chose to do this in 2025 rather than a year or two or even a decade ago, and the answer to that lies, I think, in the political component. A sovereign default has both economic and political roots, since a government that is intent on preserving its credit standing will often find ways to pay its debt and avoid default. For decades now, the US has enjoyed special status with markets and institutions (like ratings agencies), built as much on its institutional stability (legal and regulatory) as it was on its economic power. The Moody's downgrade seems to me a signal that those days might be winding down, and that the United States, like the rest of the world, will face more accountability for lack of discipline in its fiscal and monetary policy.

Market Reaction

The ratings downgrade was after close of trading on Friday, May 16, and there was concern about how it would play out in markets, when they opened on Monday, May 19. US equities were actually up on that day, though they lost ground in the subsequent days:

If equity markets were relatively unscathed in the two weeks after the downgrade, what about bond markets, and specially, the US treasury market? After all, an issuer downgrade for any bond is bad news, and rates should be expected to rise to reflect higher default risk:

While rates did go up in the the first few days after the downgrade, the effect was muddled by the passage of a reconciliation bill in the house that potentially could add to the deficit in future years. In fact, by the May 29, 2025, almost all of the downgrade effect had faded, with rates close to where they were at the start of the year.

You may be surprised that markets did not react more negatively to the ratings downgrade, but I am not for three reasons:

Lack of surprise effect: While the timing of the Moody's downgrade was unexpected, the downgrade itself was not surprising for two reasons. First, since S&P and Fitch had already downgraded the US, Moody's was the outlier in giving the US a Aaa rating, and it was only a matter of time before it joined the other two agencies. Second, in addition to reporting a sovereign rating, Moody's discloses when it puts a country on a watch for a ratings changes, with positive (negative) indicating the possibility of a ratings upgrade (downgrade). Moody's changed its outlook for the US to negative in November 2023, and while the rating remained unchanged until May 2025, it was clearly considering the downgrade in the months leading up to it.
Magnitude of private capital: The immediate effect of a sovereign ratings downgrade is on government borrowing, and while the US does borrow vast amounts, private capital (in the form of equity and debt) is a far bigger source of financing and funding for the economy.
Ratings change: The ratings downgrade ws more of a blow to pride than to finances, since the default risk (and default spread) difference between an Aaa rating and a Aa1 rating is small. Austria and Finland, for instance, had Aa1 ratings in May 2025, and their ten-year bonds, denominated in Euros, traded at a spread of about 0.15- 0.20% over the German ten-year Euro bond; Germany had a Aaa rating.

Consequences for valuation and investment analysis

While the immediate economic and financial consequences of a downgrade from Aaa to Aa1 will be small, there are implications for analysts around the world. In particular, analysts will have to take steps when working with US dollars that they may already be taking already when working with most other currencies in estimating basic inputs into financial analysis.

Let's start with the riskfree rate, a basic building block for estimating costs of equity and capital, which are inputs into intrinsic valuation. In principle, the riskfree rate is what you will earn on a guaranteed investment in a currency, and any risk premiums, either for investing in equity (equity risk premium) or in fixed income securities (default spreads), are added to the riskfree rate. It is standard practice in many textbooks and classrooms to use the government bond rate as the risk free rate, but that is built on the presumption that governments cannot default (at least on bonds issued in the local currency). Using a Aaa (AAA) rating as a (lazy) proxy for default-free, that is the rationale we used to justify government bond rates as riskfree rates at the start of 2025, in Australian, Singapore and Canadian dollars, the Euro (Germany). Swiss francs and Danish krone. As we noted in the first section, the assumption that governments don't default is violated in practice, since some countries choose to default on local currency bonds, rather than face up to inflation. If that is the case, the government bond rate is no longer truly a riskfree rate, and getting to a riskfree rate will require netting out a default spread from the government bond rate:

Risk free rate = Government Bond rate − Default spread for the government

The default spread can be estimated either from the sovereign bond rating (with a look up table) or a sovereign CDS spread, and we used that process to get riskfree in rates in a host of currencies, where local currency government bonds had default risk, at the start of 2025:

Thus, to get a riskfree rate in Indian rupees, Brazilian reals or Turkish lira, we start with government bonds in these currencies and net out the default spreads for the countries in question. We do this to ensure that we don't double count country risk by first using the government bond (which includes default risk) as a riskfree rate and then using a larger equity risk premium to allow for the same country risk.

Now that the US is no longer Aaa rated, we have to follow a similar process to get a riskfree rate in US dollars:

US 10-year treasury bond rate on May 30, 2025 = 4.41%
Default spread based on Aa1 rating on May 30, 2025 = 0.40%
Riskfree rate in US dollars on May 30, 2025 = US 10-year treasury rate - Aa1 default spread = 4.41% - 0.40% = 4.01%

This adjustment yields a riskfree rate of 4.01% in US dollars, and it is also built on the presumption that the default spread manifested after the Moody's downgrade on May 16, when the more realistic reading is that US treasury markets have been carrying a default spread embedded in them for years, and that we are not making it explicit.

The ratings downgrade for the US will also affect the equity risk premium computations that I use to estimate the cost of equity for companies. As some of you who track my equity risk premiums by country know, I estimate an equity risk premium for the S&P 500, and at least until the start of this year, I used that as a premium for all mature markets (with a AAA (Aaa) rating as the indicator of maturity). Thus, countries like Canada, Germany, Australia and Singapore were all assigned the same premium as that attributed to the S&P 500. For countries with ratings below Aaa, I added an "extra country risk premium" computed based upon the default spreads that went with the country ratings:

With the ratings downgrade, I will have to modify this process in three ways. The first is that when computing the equity risk premium for the S& P 500, I will have to net out the adjusted riskfree rate in US dollars rather than the US treasury rate, yielding a higher equity risk premium for the US. Second, for Aaa rated countries, to the extent that they are safer than the US will have to be assigned an equity risk premium lower than the US, with the adjustment downward reflecting the Aa1 rating for the US. The third is that for all other countries, the country risk premium will be computed based upon the the their default spreads and the equity risk premium estimated for Aaa rated countries (rather than the US equity risk premium):

How will the cost of equity for a firm with all of its revenues in the United States be affected as a consequence? Let's take three companies, one below-average risk, one average-risk and one above average risk, and compute their costs of equity on May 30, 2025, with and without the downgrade factored in:

As you can see, the expected return on the S&P 500 as of May 30, 2025, reflecting the index level then and the expected cash flows, is 8.64%. Incorporating the effects of the downgrade changes the composition of that expected return, resulting in a lower riskfree rate (4.01% instead of 4.41%) and a higher equity risk premium (4.63% instead of 4.23%). Thus, while the expected return for the average stock remains at 8.64%, the expected return increases slightly for riskier stocks and decreases slightly for safer stocks, but the effects are so small that investors will hardly notice. If there is a lesson for analysts here, it is that the downgrade's effects on the discount rates (costs of equity and capital) are minimal, and that staying with the conventional approach (of using the ten-year US treasury bond rate as the riskfree rate and using that rate to compute the equity risk premium) will continue to work.

Conclusion

The Moody's ratings downgrade of the US made the news, and much was made of it during the weekend that followed. The financial and economic consequences, at least so far, have been inconsequential, with equity and bond markets shrugging off the downgrade, perhaps because the surprise factor was minimal. The downgrade also has had only a minimal impact on costs of equity and capital for US companies, and while that may change, the changes will come from macroeconomic news or from crises. For the most part, analysts should be able to continue to work with the US treasury rate as a riskfree rate and forward-looking equity risk premiums, as they did before the downgrade. With all of that said, though, the Moody's action does carry symbolic weight, another indicator that US exceptionalism, which allowed the US to take economic and fiscal actions that would have brought blowback for other countries, especially in emerging markets, is coming to an end. That is healthy, in the long term, for both the United States and the rest of the world, but it will come with short term pain.

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The Greed & Fear Tango: The Markets in April 2025!

2025-05-03T10:30:00.008-04:00

I started the month on a trip to Latin America, just as the tariff story hit my newsfeed and the market reacted with a sell off that knocked more than $9 trillion in market cap for global equities in the next two days. The month was off to a bad start, and tariffs remained the lead story for much of the month, contributing to both its biggest down days (with stories of trade war escalation) and to the biggest up days (with news of relief from the fight). To add to the volatility, there was talk midway through the month of replacing Jerome Powell as the Fed Chair, and assorted news adding to uncertainty about the direction of the economy. An observer reading just the news stories and asked to guess what the market did during the month would probably have bet on stocks falling steeply, but he or she would have lost that bet, because markets managed to surprise us all again, ending the month almost where they began.

Equities: Storm Clouds gather (and dissipate)!

It would be an understatement to describe equity markets in April 2025 as volatile, with the equity indices going through stomach wrenching up and down movements intraday and across days, as investors struggled to price in a world of tariffs, trade wars and policy uncertainty.

The journey that the indices went through during the course of the month has been extraordinary. Each of the indices lost close to 10% in the first two days of the month, went deeper into the hole in the second week of the month, but by the end of the month, they had each found their way back to almost where they started the month at, with the S&P 500, NASDAQ and the MSCI world index all within 1% of their start-of-the-month levels.

As I noted in my post right after the first couple of days of this month, where I framed the market crisis around tariffs, the indices sometimes obscure markets shifts that are occurring under the surface, and that looking at all publicly traded stocks on an aggregated basis provides a more complete picture. I will start by looking at the regional breakdown of what the month delivered in terms of change in market cap, in both dollar and percentage terms:

The crisis may have been birthed in the United States, but as has been the case with market crises in this century, it has spread across the world, with disparate impacts. There are truly no standouts in either direction, with China being the worst performing region, in terms of percentage change in dollar value, down 3.69%, and India and Latin America tied for best performing, up 3.57%. These are dollar returns, and since the US dollar came under selling pressure during the course of the month, the local currency returns were worse, especially in markets, like the EU, where the Euro gained about 5% in the courser of the month.

At the start of the month, as has been the case for much of the last decade, the focus was on technology, partly because of its large weight in overall equity value at the start of 2025, and partly because of the punishment meted out to tech stocks during the first quarter of the year. Focusing just on US equities, technology companies, which accounted for 29.4% of the overall market capitalization of all US companies at the start of 2025, lost $2.34 trillion (about 13.19%) in market capitalization in the first quarter of 2025. In the first few days of April, that trend continued as technology initially led the rout, losing an additional $1.78 trillion, but by the end of April, tech had made at least a partial comeback:

As you can see, technology ended the month as the second best performing sector, up 1.67% for the month, and in spite of the handwringing about their poor performance, their share of the market cap pie has barely changed after the first four months of 2025. While the first quarter continues to weigh the sector down, as was the case in 2022, the obituaries written for technology investing may have been premature.

Staying in the weeds, I also looked at the push and pull of growth versus value, by breaking US equities down into deciles based on earnings to price ratios and assessing their performance leading into April and in April 2025 alone:

As you can see, while there is no clearly discernible pattern across deciles of US stocks based upon earnings to price ratios, breaking down US stocks into a top and bottom half, based upon the ratio, yields the conclusion that while high PE stocks had a bad start to the year, losing 10.9% of their value in the first quarter, they made a comeback in April, up 1.74% for the year, while low PE stocks were down 2.22% for the month. That pattern of a reversal in April 2025 of trends that had been forming in the first quarter of 2025 shows upon in other proxies for the value versus growth tussle:

Looking at companies broken down by market capitalization into deciles, you find that larger cap companies outperformed small cap stocks during April,
Breaking down stocks based on dividends, dividend paying stocks and companies buying back stock underperformed non-cash returning stocks, indicating that there was no flight to safety in April.

Finally, I classified companies based upon their stock price performance in 2024 to see if what we are seeing in 2025 is just a correction of overreach in 2024. After all, if that is the case, we should see the stocks that have performed the best in 2024 be the ones that have taken the most punishment this year:

As you can see, momentum returned in force in April, with the best performing stocks in 2024 up 0.76% during the month, while the worst performing stocks of 2024 were down 5.31% for the month. In fact, the year-to-date numbers for 2025 indicate that momentum remains in the driver's seat, extending a long period of outperformance.

In sum, the market stresses in April 2025 seems to have pushed the market back into its 2024 ways, after a first quarter that promised reversal, as technology, growth and momentum all made a comeback in the last three weeks of April. The performance of the Mag Seven, which represent a combination of all three forces (large, high growth and technology), in April provides a tangible measure of this shift:

The Mag Seven have had a bad year to date, losing $2.6 trillion in market capitalization, but they made a comeback from the depths to finish April at about the same market cap that they had at the start of the month, recovering almost all of the $1.55 trillion that they lost in the first week of the month.

In short, not only did equities recover in the last three weeks of April 2025, but there seems have been a shift in sentiment back the forces that have borne markets upwards for the last few years, with technology, growth and momentum returning as market drivers. Of course, three weeks is a short time, but this is a trend worth watching for the rest of this year.

The Rest of the Market: Swirling Winds?

As equities careened through April 2025 between panic and delirium, the other asset classes were surprisingly staid, at least on the surface, starting with the US treasuries. Unlike other crises, where US treasuries saws funds flow in, pushing down yields and pushing up prices, treasury rates remained relatively stable through much of April:

Not only did rates remain almost unchanged across the maturity spectrum, but they were stable on a week-to-week basis. The yield curve, downward sloping for much of the last two years, is now u-shaped, with 3-month rates and 2-year rates higher than 5-year rates, before reverting back to higher longer term (10-year and 30-year rates). Coming from the camp that we read too much economic significance into yield curve slopes and dynamics, I am reluctant to draw big conclusions, but some of this can be attributed to expectations of higher inflation in the near term. There is another force at play in this crisis that has not been as visible in past ones, at least in the US treasury market, and that is concerns about the trustworthiness of the US government Though this is still an early indicator, that can be seen in the sovereign CDS market, where investors pay for insurance against default risk, and where the US CDS spread has risen in April:

The sovereign CDS spread for the US has risen about 38% during the course of this month, and the interesting part is that much of that rise happened in the last three weeks of the month, and during the first week, when equities were collapsing. The rise in perceptions of US default risk is more likely to have been precipitated by the threat to fire Jerome Powell, and by extension to the independence of the Fed as an institution. While that threat was withdrawn, the sovereign CDS spread has stayed high, and it will be worth watching whether it will come back down or whether some permanent damage has been done to US treasuries as a safe haven. As some of you who follow my thinking on riskfree rates may know, I argue that the riskfree rate in a currency is not necessarily the government bond rate in that currency, and that the default spread has to netted out from the government bond rate two get to a riskfree rate, if the sovereign in question is not viewed as default-free. Building on that principle, I may soon have to revisit my practice of using the US treasury rate as the riskfree rate in US dollars and net out a default spread for the US from that rate to get to a riskfree rate.

During April 2025, commodity prices were also on the move, and in the graph below, I look at oil prices as well as an overall commodity index during the month:

In the first third of the month, oil prices, in particular, and commodity prices, in general, joined equities, as they moved down, but in the last part of the month, they delinked, and stayed down, even as stock prices bounced back up. To the extent that the demand for commodities is driven by real economic growth, that would suggest that at least in the near term, the tariffs that precipitated the crisis will slow down global economies and reduce demand for commodities.

The concerns about central banking independence that triggered the surge in the US sovereign CDS spread also played out in currency markets, where the US dollar, already weakened in the first quarter, continued its decline in April. In the graph below, I look at the dollar-euro exchange rate and an index measuring the strength of the dollar against multiples currencies.

The dollar continued its decline in April, down about 3% against a broad basket of currencies, and more than 5% against the Euro.

Finally, I looked at two other investment classes - gold and bitcoin - for the same reasons that I brought them into the discussion at the start of April. They are collectibles, i.e., investments that investors are drawn to during crisis periods or when they lose faith in paper currencies and governments:

Download data

Gold had a good month in April, up about 5.3%, and hitting $3.500 towards the end of the month, but Bitcoin did even better rising almost 14.12% during the course of the month. That said, the fact that financial asset markets (equity and bond) recovered over the second part of the month made this a month where collectibles were not put to their test as crisis investments, and the rise in both can be attributed more to the loss of trust that has driven the sovereign CDS spread up and the US dollar down.

Risk and Co-movement

Early in April, I argued that the one number that would track the balance between greed and fear in markets would be the price of risk in markets, and I resolved to estimate that price every day, through April, for both equity and bond markets. With equity markets, the price of risk is the equity risk premium, and at least in my estimation process, it is a forward-looking number determined by the level of stock prices and expected cash flows. In the table below, I report on my estimates of the equity risk premium for the S&P 500 every trading day in April, in conjunction with the VIX, and equity volatility index that should be correlated:

Download day-to-day data

After rising above 5% in the first third of the month, the equity risk premium decreased in fits and starts over the rest of the month to end at almost the same value (4.58%) as at the start of the month (4.59%). In parallel, the VIX soared in the first few days of the month to peak at 52.33 on April 8, and then decreased over the rest of the month to a level (24.70) close to where it was at the start of the month (22.28).

In the bond market, the price of risk takes the form of default spreads, and these spreads followed a similar path to the equity risk measures:

Download data

The default spread on high yield bonds surged, rising by more than 1% between the start of the month and April 7, before declining, but unlike the equity risk measures, the bond default spreads did end the month at levels higher than at the start, indicating at least at this point that near term concerns about the economy and the ensuing default risk have not subsided.

As a final exercise, I looked at the correlation in price changes across investment classes - stocks, treasuries, investment-grade and high-yield corporate bonds, commodities, gold and bitcoin:

Download data

With the caveat that this is just 22 trading days in one month, it does yield some preliminary results about co-movements. First, stock and treasury bond prices moved together much of the month, not something that you would expect during a crisis, when bond prices gain as stock prices fall. Second, while both gold and bitcoin prices moved with stocks, gold prices movements were more closely tied to stock price movements, at least during the month. In sum, the movement across asset markets affirms our conclusion from looking at company-level data that this was more a month of asset reprising than panic selling or buying.

In sum, if I were to summarize what the data is pointing me towards, here are the general conclusions that I would draw, albeit with a small sample:

The market movements through much of the month were less driven by panic and more by investors trying to reprice companies to reflect a world with more trade barriers and tariffs and political turmoil.
While equities, in the aggregate, ended the month roughly where they started the month, a shift in sentiment seemed to occur in the last three weeks of the month, as technology, growth and momentum, three forces that seemed to be in retreat in the first quarter of 2025, made a come back.
With US treasuries, there was little movement on the rates, but under the surface, there were shifts that could be tectonic in the long term. There was clearly a drop in trust in the US government and its institutions, which played out in rising sovereign CDS spreads and a declining dollar, and trust once lost can be difficult to gain back.
The investment classes that are most vulnerable to the real economy, i.e.. commodities and higher yield corporate bonds, were down for the month, indicating a slowing down of global economic growth.

In the coming months, we will see whether the last three weeks of April were an aberration or the start of something bigger.

Lessons Learned

Every market meltdown carries pain to investors, but that pain is often spread unevenly across these investors, with the variation driven as much as by what they held coming into the crisis, as it is by how they behaved in response to the sell off. I am not sure April 2025 falls into the crisis column, but it did feel like one early in the month, and as I look back at the month, I come back to three market characteristics that stood out.

Market resilience: In the last five years, markets have repeatedly not only got the big trends right, but they have also shown far more resilience than any expert group. I would wager that if you had given a group of macro economists or market strategists just the news stories that came out during the course of the month and asked them to guess how they would play out in market reaction, almost none of them would have guessed the actual outcome (of flat markets). At the time of COVID, I argued that one reason for market resilience is that market influence has become diffuse, with social media and alternative sources of information supplementing and often replacing the traditional influencers - the financial press, media and investment talking heads, and market movements are less driven by large portfolio managers exhibiting herd behavior and more by disparate groups of traders, with different motives, models and patterns.
Market power: A key reason for the turnaround in markets during April was the administration's decision to walk back, reverse or delay actions that the market reacted to strongly and negatively. The "liberation day" tariffs that triggered the initial sell off have largely been put on hold or suspended, and the talk about replacing the Fed Chair was walked back quickly the week after it was made. In short, an administration that has been impervious to Wall Street journal editorials, warnings from economists and counter threats from other governments has been willing to bend to market selling pressure.
Market unpredictability: As markets rose and fell during the course of the month, the debate about the value added by active investing kicked into full gear. I heard quite a few advocates of active investing argue that it was during times like this (volatility and crisis) that the "sage counsel" and "timely decisions" of wealth or fund managers would protect investors on the downside. I would suggest the opposite, and am willing to bet that the extent of damage that April did to investor portfolios was directly proportional to how much time they spent watching CNBC and listening to (or reading) what market experts told them to do.

On a personal note, I stuck to my resolution early in the crisis to use it to stay true to my investment philosophy. As someone who stinks at market timing, I made no attempt to buy and sell the market through the month, perhaps leaving a great deal of money on the table, or more likely, saving myself just as much from getting the timing wrong. In the middle of April, I talked about three strands of contrarian investing, and in that post, I put myself in the opportunistic contrarian camp. I did use the mid-month sell off to add BYD, a stock that I like, to my portfolio, when its price dipped below my limit price ($80). Palantir and Mercado Libre (my two other limit buys) came close but not low enough to break through my limits, but I am willing to wait, revisiting my valuations along the way.

I do have some portfolio maintenance work that I need to do in the coming weeks, especially on the six of the seven Mag Seven stocks that remain in my portfolio (Tesla is out of my portfolio and Nvidia is at a quarter of my original holding). As these companies report their first quarter earnings, I plan to revisit my valuations from last year, when in the face of mild to moderate over valuation, I chose to maintain my holdings. As in prior years, I will post my assessments of value and my hold/sell judgments, but that has to wait because I do have more immediate priorities. First, as a teacher, with the semester end approaching, I have a stack of grading that has to get done. Second, as a father, I am looking forward to my daughter having her first child next week, and the market and my portfolio take a distant second place to getting acquainted with my new granddaughter.

YouTube Video

My Posts (from April 2025)

Data Links

Day-to-day numbers (ERP, default spreads and asset prices)

Buy the Dip: The Draw and Dangers of Contrarian Investing!

2025-04-20T21:48:00.003-04:00

When markets are in free fall, there is a great deal of advice that is meted out to investors, and one is to just buy the dip, i.e., buy beaten down stocks, in the hope that they will recover, or the entire market, if it is down. "Buying the dip" falls into a broad group of investment strategies that can be classified as "contrarian", where investors act in contrast to what the rest of the market is doing at the time, buying (selling) when the vast majority are selling (buying) , and it has been around through all of market history. There are strands of research in both behavioral finance and empirical studies that back up contrarian strategies, but as with everything to do with investing, it comes with caveats and constraints. In this post, I will posit that contrarian investing can take different forms, each based on different assumptions about market behavior, and present the evidence that we have on the successes and failures of each one. I will argue that even if you are swayed intellectually by the arguments for going against the crowd, it may not work for you, if you are not psychologically attuned to the stresses and demands that contrarian strategies bring with them.

Contrarianism - The Different Strands

All contrarian investing is built around a common theme of buying an investment, when its price goes down significantly, but there are wide variations in how it is practiced. In the first, knee-jerk contrarianism, you use a bludgeon, buying either individual companies or the entire market when they are down, on the expectation that you will benefit from an inevitable recovery in prices. In the second, technical contrarianism, you buy beaten-up stocks or the entire market, but only if charting or technical indicators support the decision. In the third, constrained contrarianism, you buy the stocks that are down, but only if they pass your screens for qualify and safety. In the fourth, opportunistic contrarianism, you use a price markdown as an opportunity to buy companies that you have always wanted to hold, but had not been able to buy because they were priced too high.

1. Knee-jerk Contrarianism

The simplest and most direct version of contrarian investing is to buy any traded asset where the price is down substantially from its highs, with the asset sometimes being an individual company, sometimes a sector and sometimes the entire market. Implicit in this strategy is an absolute belief in mean reversion, i.e., that what goes down will almost always go back up, and that buying at the beaten down price and being willing to wait will therefore pay off.

The evidence for this strategy comes from many sources. For the market, it is often built on papers (or books) that look at the historical data on what equity markets have delivered as returns over long periods, relative to what you would have made investing elsewhere. Using data for the United States, a market with the longest and most reliable historical records, you can see the substantial payoff to investing in equities:

Download historical data

No matter what time period you use for your time horizon, stocks deliver the highest returns, of all asset classes, and there some who look at this record and conclude that "stocks always win in the long term", with the implication that you should stay fully invested in stocks, even through the worst downturns, if you have a reasonably long time horizon. These returns to buying stocks become greater, when you buy them when they are cheaper, measured either through pricing metrics (low PE ratios) or after corrections. There are two problems with the conclusion. The first is that there is selection bias, where using historical data from the United States, one of the most successful equity markets of the last century, to draw general conclusions about the risk and returns of investing in equities will lead you to underestimate equity risk and overestimate equity returns. The second is that, even with US equities, an investor who bought stocks just before a major downturn would have to wait a long time before being made whole again. Thus, investors who put their money in stocks in 1929, just ahead of the Great Depression, would not have recovered until 1954.

With individual stocks, the strongest backing for buying the dip comes from studies of "loser" stocks, i.e., stocks that have gone down the most over a prior period. In a widely cited paper from 1985, DeBondt and Thaler classified stocks based upon stock price performance in the prior three years into winner and loser portfolios, with the top fifty performers going into the "winner" portfolio, and the bottom fifty into the "losers portfolio", and estimated the returns you could have made on each group in the following thirty six months:

DeBondt and Thaler (1985)

As you can see, the loser portfolio dramatically outperforms the winner portfolio, delivering about 30% more on a cumulative basis than the winner portfolio in the thirty six months after the portfolios are created, which DeBondt and Thaler argued was evidence that markets overreact. About a decade later, Jegadeesh and Titman revisited the study, with more granular data on time horizons, and found that the results were reversed, if you shorten the holding period, with winner stocks continuing to win over the first year after portfolio creation.

Jegadeesh and Titman (1993)

The reversal eventually kicks in after a year, but over the entire time period, the winner portfolio still outperforms the loser portfolio, on a cumulative basis. Jegadeesh and Titman also noted a skew in the loser portfolio towards smaller market cap and lower-priced stocks, with higher transactions costs (from bid-ask spreads and price impact). As other studies have added to the mix, the consensus on winner versus loser stocks is that there is no consensus, with evidence for both momentum, with winner stocks continuing to win, and for reversal, with loser stocks outperforming, depending on time horizon, and questions about whether these excess returns are large enough to cover the transactions costs involved.

Setting aside the mixed evidence for the moment, the biggest danger in knee-jerk contrarian investing at the market level is that buying the dip in the market is akin to catching a falling knife, since that initial market drop can be a prelude to a much larger sell off, and to the extent that there was an economic or fundamental reason for the sell off (a banking crisis, a severe recession), there may be no near term bounceback. With individual stocks, that danger gets multiplied, with investors buying stocks that are being sold off to for legitimate reasons (a broken business model, dysfunctional management, financial distress) and waiting for a market correction that never comes.

To examine the kinds of companies that you would invest in, with a knee-jerk contrarian investing strategy , I looked at all US stocks with a market capitalization exceeding a billion dollars on December 31, 2024, and found the companies that were the biggest losers, on a percent basis, between March 28 and April 18 of 2025:

You will note that technology and biotechnology firms are disproportionately represented on the list, but that is the by-product of a bludgeon approach.

2. Technical Contrarianism

In technical contrarianism, you start with the same basis as knee-jerk contrarianism, by looking at stocks and markets that have dropped significantly, but with an added requirement that the price has to meet a charting or technical indicator constraint before becoming a buy. While there are many who consign technical analysis to voodoo investing, I believe that charting patterns and technical indicators can provide signals of shifts in mood and momentum that drive price movements, at least in the near term. Thus, you can view technical contrarianism as buying stocks or markets when they are down, but only if the charts and technical indicators point to a shift in market mood.

One of the problems with testing technical contrarianism, to see if it works, is that even among technical analysts, there seems to be no consensus as to the best indicator to use, but broadly speaking, these indicators can be based on either price and/or volume movements. They range in sophistication from simple measures like relative strength (where you look at percentage price changes over a period) and moving averages to complex ones that combine price and volume. In recent decades, investors have added pricing in other markets to the mix, with the VIX (a traded volatility index) as well as the relative pricing of puts and calls in the options market being used in market timing. In sum, all of these indicators are directed at measuring fear in the market, with a "market capitulation" viewed as a sign that the market has bottomed out.

With market timing indicators, there is research backing up the use of VIX and trading volume as predictors of market movements, though with substantial error.

Source: S&P

As the VIX rises, the expected return on stocks in future periods goes up, albeit with much higher volatility around these expected returns. It is ironic that some of the best defenses of technical analysis have been offered by academics, especially in their studies of price momentum and reversal. Lo, Wang, and Mamaysky present a fairly convincing defense of technical analysis from the perspective of financial economists. They use daily returns of stocks on the New York Stock Exchange and NASDAQ from 1962 and 1996 and employ sophisticated computational techniques (rather than human visualization) to look for pricing patterns. They find that the most common patterns in stocks are double tops and bottoms, followed by the widely used head and shoulders pattern. In other words, they find evidence that some of the most common patterns used by technical analysts exist in prices. Lest this be cause for too much celebration among chartists, they also point out that these patterns offer only marginal incremental returns (an academic code word for really small) and offer the caveat that these returns may not survive transaction costs.

3. Constrained Contrarianism

If you are in the old-time value investing camp, your approach to contrarian investing will reflect that worldview, where you will buy stocks that have dropped in value, but only if they meet the other criteria that you have for good companies. In short, you will start with a list of beaten up stocks, and then screen them for high profitability, strong moats and low risk, hoping to separate companies that are cheap from those that deserve to be cheap.

As a constrained contrarian, you are hoping to avoid value traps, every value investor's nightmare , where a company looks cheap on a pricing basis (low PE, low price to book) and proceeds to become even cheaper after you buy it. The evidence on whether screening helps avoid value traps comes largely from studies of the interplay between proxies of value (such as low price to book ratios) and proxies for quality, including measures for both operating/capital efficiency (margins and returns on capital) and low risk (low debt ratios and volatility). Proponents of quality screens note that while value proxies alone no longer seem to deliver excess returns, incorporating quality screens seems to preserve these excess returns. Research Affiliates, an investment advisory service, looked at returns to pure value screens versus value plus quality screens and presents the following evidence on how screening for quality improves returns:

Research Affiliates Study

The evidence is supportive of the hypothesis that adding quality screens improves returns, and does so more for stocks that look cheap (low price to book) than for expensive stocks. That said, the evidence is underwhelming in terms of payoff, at least on an annual return basis, though the payoff is greater, if you factor in volatility and estimate Sharpe ratios (scaling annual return to volatility).

While much of the research on quality has been built around value and small cap investing, the findings can be extrapolated to contrarian investing, with the lesson being that rather than buy the biggest losers, you should be buying the losers that pass screening tests for high profitability (high returns on equity or capital) and low risk (low debt ratios and volatility). That may provide a modicum of protection, but the problem with these screens is that they are based upon historical data and do not capture structural changes in the economy or disruption in the industry, both of which have not yet found their way into the fundamentals that are in your screens.

To provide just an illustration of constrained contrarianism, I again returned to the universe of about 6,000 publicly traded US stocks on April 18, 2025, and after removing firms with market capitalizations less than $100 million (with the rationale that these companies will have more liquidity risk and transactions costs), I screened first for stocks that lost more than 20% of their market capitalization between March 28 and April 18, and then added three value screens:

A PE ratio less than 15, putting the stock in the bottom quintile of US stocks as of December 31, 2024
A dividend yield that exceeded 1%, a paltry number by historical norms, but ensuring that the company was dividend-paying in 2024, a year in which 60% of US stocks paid no dividends
A net debt/EBITDA ratio of less than two, dropping it into the bottom quintile of US companies in terms of debt load

The six companies that made it through the screens are below:

I am sure that if you are a value investor, you will disagree about both the screens that I used as well as my cut offs, but you are welcome to experiment with your own screens to find bargains.

4. Opportunistic Contrarianism

In a fourth variant of contrarian investing, you use a market meltdown as an opportunity to buy companies that you have always wanted to own but could not because they were over priced before the price drop, but look under priced after. The best place to start an assessment of opportunistic investing is with my post on why good companies are not always good investments, with the first being determined by all of the considerations that go into separating great businesses from bad businesses, including growth and profitability, and the second by the price you have to pay to buy them. In that post, I had a picture drawing the contrast between good companies and good investments:

Put simply, most great companies are neutral or even bad investments, because the market prices them to be great. A year ago, when I valued the Mag Seven stocks, I argued that these were, for the most part, great businesses, with a combination of growth at scale, high profitability and deep moats, but that at the prices that they were trading they were not great investments.

I also argued that even great companies have their market travails, where for periods of time, investors lose faith in them and drive their prices down not just to value, but below. It happened to Microsoft in 2014, Apple in 2017, Nvidia in 2018, Tesla at multiple times in the last decade, and to Facebook, at the height of the Metaverse fiasco. While those corrections were caused by company-specific news stories and issues, the same process can play out, when you have significant market markdowns, as we have had over the last few weeks.

The process of opportunistic contrarianism starts well before a market correction, with the identification of companies that you believe are good or great businesses:

At the time that you first value them, you are likely to find them to be over valued, which will undoubtedly be frustration. You may be tempted to play with the numbers to make these companies look undervalued, but a better path is to put them on your list of companies you would like to own, and leave them there. During a market crisis, and especially when investors are marking down the prices of everything, without discriminating between good and bad companies, you should revisit that list, with a caveat that you cannot compare the post-correction price to your pre-crisis valuation of your company. Instead, you will have to revalue the company, with adjustments to expected cash flows and risk premiums, given the crisis, and if that value exceeds the price, you should buy the stock.

Contrarian Investing: The Psychological Tests!

In the abstract, it is easy to understand the appeal of contrarian investing. Both behavioral and empirical research identify the existence of herd behavior in crowds, and point to tipping points where crowd wisdom becomes crowd madness. A rational decision-maker in the midst of animal spirits may feel that he or she has an advantage in this setting, and rightly so. That said, buying when the rest of the market is selling takes a mindset, a time horizon and a stronger stomach than most of us do not have.

The Mindset: Investing against the market will not come easily to those who are easily swayed by peer pressure, since they will have to buy, just as other investors (the peer group) will be selling, and often in companies that the market has turned against. There are some who march to their own drummers, willing to take a path that is different from the rest, and these are better suited to being contrarians.
The Time Horizon: To be a contrarian, you don't always need a long time horizon, since corrections can sometimes happen quickly, but you have to be willing to wait for a long period, if that is what is necessary for the correction. Relatively few investors have this capacity, since it is determined as much by your circumstances (age, health and cash needs) as it is by your personality.
The Stomach: Even if your buy decision is based on the best thought-through contrarian investing strategies, it is likely that in the aftermath of that decision, momentum will continue to push prices down, testing your faith. Without a strong stomach, you will capitulate, and while your decision may have been right in the long term, your investment will not reflect that success.

As you can see, the decision on whether to be a contrarian is not just one that you can make based upon the evidence and theory, but will depend on who you are as a person, and your makeup.

I have the luxury of a long time horizon and the luck of a strong stomach, for both food and market surprises. I am not easily swayed by peer pressure, but I am not immune from it either. I know that buying stocks in the face of market selling will not come easily, and that is the reason that I initiated limit buys on three companies that I have wanted to have in my portfolio, BYD, the Chinese electric car maker, Mercado Libre, the Latin American online retail/fintech firm, and Palantir, a company that I believe is closest to delivering on thee promise of AI products and services. The limit buy kicked in on BYD on April 7, when it briefly dipped below $80, my limit price, and while Palantir and Mercado Libre have a way to go before they hit my price limits, the crisis is young and the order is good until canceled!

YouTube Video

Anatomy of a Market Crisis: Tariffs, Markets and the Economy!

2025-04-07T17:10:00.001-04:00

I was boarding a plane for a trip to Latin America late in the evening last Wednesday (April 2), and as is my practice, I was checking the score on the Yankee game, when I read the tariff news announcement. Coming after a few days where the market seemed to have found its bearings (at least partially), it was clear from the initial reactions across the world that the breadth and the magnitude of the tariffs had caught most by surprise, and that a market markdown was coming. Not surprisingly, the markets opened down on Thursday and spent the next two days in that mode, with US equity indices declining almost 10% by close of trading on Friday. Luckily for me, I was too busy on both Thursday and Friday with speaking events, since as the speaker, I did not have the luxury (or the pain) of checking markets all day long. In my second venue, which was Buenos Aires, I quipped that while Argentina was trying its best to make its way back from chaos towards stability, the rest of the world was looking a lot more like Argentina, in terms of uncertainty. On Saturday, on a long flight back to New York, I wrestled with the confusion, denial and panic that come with a market meltdown, and tried to make sense of what had happened, and more importantly of what is coming. That thinking is still a work-in-progress but as in prior crises, I find that putting even unfinished thoughts down on paper (or in a post) is healthy, and perhaps a critical component to finding your way back to serenity.

The Tariffs and Markets

Since talk of tariffs has filled the airwaves for most of this year, you may wonder why markets reacted so strongly to the announcement on Wednesday. One reason might have been that investors and businesses were not expecting the tariff hit to be as wide and as deep as they turned out to be.

Note that while Canada and Mexico were not on the Wednesday list of tariff targets that was released on Wednesday, they have been targeted separately, and that the remaining countries that do not show up on this map (Russia and North Korea, for instance) are under sanctions that prevent them from trading in the first place.

Another reason for the market reaction was that the basis for the tariff estimates, which have now been widely shared, are not easily fixable, since they are not based on tariffs imposed by other countries, but on the magnitude of the trade deficit of the United States with these countries. Thus, any country with which the US runs a significant trade deficit faces a large tariff, and smaller countries are more exposed than larger ones since the trade deficit is computed on a percentage basis, from exports and imports related to that country. Thus, the easy out, where other countries offer to reduce or even remove their tariffs may have no or little effect on the tariffs, to the extent that the trade deficit may have little to do with tariffs.

Equities

The extent of the market hit can be seen by looking at the major US equity indices, the Dow, the S&P 500 and the NASDAQ, all of which shed significant portions of their value on Thursday and Friday:

Looking beyond these indices and across the globe, the negative reaction has been global, as can be seen in the returns to equity across sub-regions, with all returns denominated in US dollars:

The worst hit regions of the world is Small Asia, which is Asia not counting India, China and Japan, which saw equity values in the aggregate decline by 12.61% in the last week. US equities had the biggest decline in dollar value terms, losing $5.3 trillion in value last week, a 9.24% decline in value from the Friday close on March 28, 2025. China and India have held up the best in the last week, perhaps because both countries have large enough domestic markets to sustain them through a trade war. It is also a factory that with time differences, these markets both closed before the Friday beatdown on Wall Street unfolded, and the open on Monday may give a better indication of the true reaction. Breaking down just US equities, by sector, we can see the damage across sectors:

The technology sector lost the most in value last week, both in dollar terms, shedding almost $1.8 trillion (and 11.6%) in equity value, and consumer staples and utilities held up the best, dropping 2.30% and 4.40% respectively. In percentage terms, energy stocks have lost the most in value, with market capitalizations dropping by 14.2%, dragged down by declining oil prices.

Staying with US equities, and breaking down companies, based upon their market capitalizations coming into 2025, we can again see write downs in equity value across the spectrum from last week's sell off:

As you can see, it looks like there is little to distinguish across the market cap spectrum, as the pain was widely distributed across the market cap classes, with small and large companies losing roughly the same percent of value. To the extent that market crisis usually cause a flight to safety, I looked at US stocks, broken down by decile into earnings yield (Earnings to price ratios), over the last week:

The lowest earnings to price ratio (highest PE) stocks, in the aggregate, lost 10.91% of their market capitalization last week, compared to the 8.08% decline in market cap at the highest earnings to price (lowest PE ratio) companies, providing some basis for the flight to safety hypothesis. Staying with the safety theme, I looked at US companies, broken down by debt burden (measured as debt to EBITDA):

On this dimension, the numbers actually push against the flight to safety hypothesis, since the companies with the least debt performed worse than those with the most debt. Finally, I looked at whether dividend paying and cash returning companies were better protected in the sell off, by looking at dividend paying (buying back stock) companies versus non-dividend paying (not buying back stock) companies:

While dividend paying stocks did drop by less than non-dividend paying stocks, companies buying back stock underperformed those that did not buy back stock in 2024.

If you came into last week, believing that stocks were over priced, you would expect the correction to be worse at companies that have been bid up the most, and to test this, I classified US stocks based upon percentage stock price performance in 2024:

While the worst performers from last year came into the week down only 1.83% through March 28, whereas the best performers from 2024 were down 6.46% over the same period, there was little to distinguish between the two groups last week.

Finally, I looked at the Mag Seven stocks, since they have, in large part, carried US equities for much of the last two years;

Collectively, the Mag Seven came into last last week, already down 14.79% for the year (2025), but their losses last week, which massive in dollar value terms ($1.55 trillion) were close in percentage terms to the losses in the rest of the market.

Other Markets

As equity markets reacted to the tariff announcement, other markets followed. US treasury rates, which had entered the week down from the start of the year, continued to decline during the course of the week:

While the 3-month treasury bill rate remained fairly close to what it was at the start of the week, the rates at the longer end, from 2-year to 30-year all saw drops during the week, perhaps reflecting a search for safety on the part of investors. The drops, at least so far, have been modest and much smaller than what you would expect from a market sell off, where US equities dropped by $5.3 trillion.

Looking past financial markets, I focused on three diverse markets - the oil market as a stand-in for commodity markets overall, the gold market, representing the time-tested collectible, and Bitcoin, which is perhaps the millennial version of gold:

Oil prices dropped last week, especially as financial asset markets melted down on Thursday and Friday, while both gold and bitcoin held their own last week. For bitcoin advocates, that is good news, since in other market crises since its creation, it has behaved more like risky stock than a collectible. Of course, it I still early in this crisis, and the true tests will come in the next few weeks.

Summing up

In sum, the data seems to point more to a mark down in equity values than to panic selling, at least based upon the small sample of two days from last week. There was undoubtedly some panic selling on Friday, but the flight to safety, whether it be in moving into treasuries or high dividend paying stocks, was muted.

The Crisis Cycle

Each crisis is unique both in its origins and in how it plays out, but there is still value in looking across crises, to see how they unfold, what causes them to crest, and how and why they recede. In this section, I will present a crisis cycle, which almost every crisis works its way through, with big differences in how quickly, and with how much damage. The crisis cycle starts with a trigger event, which can be economic, political or financial, though there are often smaller events ahead of is occurrence that point to its coming. The immediate effect is in markets, where investors respond with the only instrument the they control, which is the prices they pay for assets, which they mark down to reflect at least their initial response to the crisis. In the language of risk, they are demanding higher prices for risk, translating into higher risk premiums. In conjunction, they often move their money to safer assets, with treasuries and collectibles historically benefiting from the fund flows. In the days and weeks that follow, there are aftershocks from the trigger event, both on the news and the market fronts, and while these aftershocks can sometimes be positive for markets, the net effect is usually negative. The effects find their way into the real economy, as consumers and businesses pull back, causing an economic slowdown or a recession, with negative effects on earnings and cash flows, at least in the near term. In the long term, the trigger event can change the economic dynamics, causing a resetting of real growth and inflation expectations, which then feed back into markets;

To illustrate, consider the 2008 banking crisis, where the Lehman collapse over the weekend before September 15 triggered a sell off in the stock market that caused equities to drop by 28% between September 12 and December 31, 2008, and triggered a steep recession, causing unemployment to hit double digits in 2009. The earnings for S&P 500 companies took a 40% hit in 2008, and long term, neither the economy nor earnings recovered back to pre-crisis levels until 2012.

During that crisis, I started a practice of estimating equity risk premiums by day, reflecting my belief that it is day-to-day movements in the price of risk that cause equity markets to move as much as they do in a crisis:

Spreadsheet with raw data

Equity risk premiums which started the crisis at around 4% peaked at almost 8% on November 21, 2008, before ending the year at 6.43%, well above the levels at the start of 2008. Those equity risk premiums did not get back to pre-2008 levels until almost 15 years later.

Moving to 2020 and looking at the COVID crisis, the trigger event was a news story out of Italy about COVID cases in the country that could not be traced to either China or cruise ships, shattering the delusion that the pandemic would be contained to those settings. In the weeks after, the S&P 500 shed 33% of its value before bottoming out on March 23, 2020, and treasury rates plunged to historic lows, hitting 0.76% on that day. The key difference from 2008 was that the damage to the economy and earnings was mostly short term, and by the end of the year, both (economy and earnings) were on the mend, helped undoubtedly by multi-trillion dollar government support and central banking activism:

As in 2008, I computed equity risk premiums by day all through 2020, and the graph below tells the story:

Spreadsheet with raw data

As you can see, the equity risk premium which started at 4.4% on February 14, 2020, peaked a few weeks later at 7.75% on March 23, 2020, and as with the economy and earnings, it was back down to pre-crisis levels by September 2020.

The Perils of Post Mortems

Each crisis gives rise to postmortems, where investors, regulators and researchers pore over the data, often emerging with conclusions that extrapolate too much from what happened.

For investors: The lesson that many investors get out of looking at past crises is that markets come back from even the worst meltdowns, and that contrarian investing with a long time horizon always works. While that may be comforting, this lesson ignores the reality that the fact that a catastrophe did not occur in the crisis in question does not imply that the probability of it occurring was always zero. Markets assess risks in real time.
For regulators: To the extent that crises expose the weakest seams in markets and businesses, regulators often come in with fixes for those seams, mostly by dealing with the symptoms, rather than the causes. After the 2008 crisis, the conclusions were that the problems was banks behaving badly and ratings agencies that were not doing their job, both merited judgments, but the question of risk incentives that had led them on their risk taking misadventures were largely left untouched.
For researchers: With the benefit of hindsight, regulators weave stories about crises that are built around their own priors, by selectively picking up data items that support them. Thus, behavioral economists find every crisis to be an example of bubbles bursting and corrections for irrational investing, and efficient market theorists use the same crisis as an illustration of the magic of markets working.

It is worth remembering that each crisis is a sample size of one, and since each crises is different, aggregating or averaging across them can be difficult to do. Thus, the danger is that we try to learn too much from past crises rather than too little.

The Tariff Crisis?

I don't believe that it is premature to put the tariff news and reaction into the crisis category. It has the potential to change the global economic order, and a market reaction is merited. It is, however, early in the process, since we are just past the trigger event (tariff announcement) and the initial market reaction, with lots of unknowns facing us down the road:

There are clearly stages of this crisis that have played out, but based on what we know now, here is how I see them:

After shocks: The tariff story will have after shocks, with both negatives (other countries imposing their own tariffs, and the US responding) and positives (a pause in tariffs, countries dropping tariffs). Those after shocks will create more market volatility, and if history is any guide, there is more downside than upside in the near term. In addition, the market volatility can feed itself, as levered investors are forced to close out positions and fund flows to markets reflect investor concerns and uncertainty. If you add on top of that the possibility that global investors may decide to reduce their US equity holdings, that reallocation will have price effects.
Real economy (near term): In the near term, the real economy will slow down, with the plus being that while tariff-related price increases are coming, a cooling down in the economy will dampen inflation. The likelihood of a recession has spiked in the days since the tariff announcement, and while we will have to wait for the numbers on real growth and unemployment to come in, it does look likely that real growth will be impacted negatively. The steep declines in commodity prices suggests that investors see an economic slowdown on the horizon. As
Real economy (long term): Global economic growth will slow, and the US, as the world’s largest economy, will slow with it.. There are other dynamics at play including a restructuring of old economic and political alliances (Is there a point to having a G7 meeting?) and a new more challenging environment for global companies that have spent the last few decades building supply chains that stretch across the globe, and selling to consumers all over.

It is worth noting that if we measure winning by not the size of the pie (the size of the entire economy) but who gets what slice of that economy, it is possible that tariffs could reapportion the pie, with capital (equity markets) getting a smaller slice, and workers getting a larger slice,. In fact, much of this administration's defense of the tariff has been on this front, and time will tell whether that works out to be the case.

In the two days after the announcement, stock prices have dropped and the price of risk has risen, as investors reassess the economy and markets:

The implied equity risk premium has risen from 4.57% on April 2 to 5.08% by the close of trading on Friday. The road ahead of us is long, but I plan to continue to compute these implied equity risk premiums every day for as long as I believe we are in crisis-mode, and I will keep these updated numbers on my webpage. As stocks have been revalued with higher prices of risk, that same uncertainty is playing out in the corporate bond market, where corporate default spreads widened on Thursday (April 3) and Friday (April 4):

As with the equity risk premiums, the price of risk in the bond market had already risen between the start of 2025 and March 28, 2025, but they surged last week, with the lowest ratings showing the biggest surges. With treasury rates, equity risk premiums and default spreads all on the move it may be time for companies and investors to be reassessing their costs of equity and capital.

What now?

If you have stayed with me so far on this long and rambling discourse, you are probably looking for my views on how this crisis will unfold, and how investors should respond now. I am afraid that dishing out investment advice is not my cup of tea, but I will try to explain how I plan to deal with what's coming, with the caveat that what I do may not work for you

A (Personal) Postscript

In the midst of every market meltdown, you will see three groups of experts emerge. The first will be the "I told you so" group, eager to tell you that this is the big one, the threat that they have spent a decade or more warning you about. They will of course not let on that if you had followed their advice from inception, you would have been invested in cash for the last decade, and even with a market crash, you would not be made hold again. The second will include "knee jerk contrarians", arguing that stock markets always come back, and that every market dip is a buying opportunity, an extraordinarily lazy philosophy that gets the rewards (none) that its deserves. The third will be the "indecisives", who will present every side of the argument, conclude that there is too much uncertainty right now to either buy or sell, but to wait until the uncertainty passes. There are elements of truth in all three arguments, but they all have blind spots.

In the midst of a crisis, the market becomes a pricing game, where perception gets the better of reality, momentum overwhelms fundamentals and day-to-day movements cannot be rationalized. Anyone who tells you that their crystal balls, data or charts can predict what's coming is lying or delusional, and there is no one right response to this (or any other) crisis. It will depend on:

Cash needs and time horizon: If you are or will soon be in need of cash, to pay for health care, buy a home or pay tuition, and you are invested in equities, you should take the cash out now. Waiting for a better time to do so, when the clock is ticking is the equivalent of paying Russian Roulette and just as dangerous. Conversely, if you do not need the cash and are patient, you have the flexibility of waiting, though having a longer time horizon does not necessarily mean that you should wait to act.
Macro views: The effects on markets and the real economy will depend on how you see the tariffs playing out, with the outcomes ranging from a no-holds-barred trade war (with tariffs and counter tariffs) to a partial trade war (with some countries capitulating and others fighting) to a complete clearing of the air (where the tariff threat is scaled down or put on the back burner). While you may be inclined to turn this over to macro economists, this is less about economics and more about game theory, where an expert poker player will be better positioned to forecast what will happen than an economic think tank.
Investment philosophy: I have long argued (and teach a class to that effect) that every investor needs an investment philosophy, attuned to his or her personal make up. That philosophy starts with a set of beliefs about how markets make mistakes and corrects them, and manifests in strategies designed to take advantage of those mistakes.

My investment philosophy starts with the belief that markets, for the most part, do a remarkable job in aggregating and reflecting crowd consensus, but that they sometimes make big mistakes that take long periods to correct, especially in periods and portions of the market where there is uncertainty. I am terrible at gauging market mood and momentum, but feel that I have an edge (albeit a small one) in assessing individual companies, though that may be my delusion. My response to this crisis (or any other) will follow this script:

Daily ERP: As in prior crises, I will continue to monitor the equity risk premiums, treasury rates and the expected return on stocks every day until I feel comfortable enough to let go. Note that this process lasted for months after the 2008 and 2020 crises, but as earnings updates for the S&P 500 reflect tariffs, my confidence in my assessments will increase. (As mentioned earlier, you will find these daily updates at this link)
Revalue companies in my portfolio: While I was comfortable with the companies in my portfolio on March 28, viewing them as under valued or at least not over valued enough to merit a sell, the tariffs may have an significant effect on their values, and I plan to revalue them in batches, starting with my big tech holdings (the Mag Five, since I did sell Tesla and most of my Nvidia holdings) and working through the rest.
Buy value: I have drawn a contrast between great companies and great investments, with the former characterized by large moats, great management and strong earnings power, and the latter by being priced too low. There are companies that I believe are great companies, but are priced so highly by the market that they are sub-standard investments and I choose not to invest in them. During a crisis, where investors often sell without discrimination, there companies can become buys, and I have to be ready to buy at the right price. Since buying in the face of a market meltdown can require fortitude that I may not have, I have been scouring my list of great companies, revaluing them with the tariff effects built in, and putting buys at limit prices below those values. In the last week, both BYD, a company that I said that I liked, a few weeks ago in my post on globalization and disruption, and Mercado Libre, a Latin American powerhouse, that has the disruptive potential of an Amazon combined with a fintech enterprise, have moved from being significantly overvalued to within shouting distance of the limit prices I have on them.
Go back to living: I certainly don't see much gain watching the market hour-to-hour and day-to-day, since its doings are out of my control and anything that I do in response is more likely to do harm than good. Instead, I plan on living my life, enjoying life's small pleasures, like a Yankee win or taking my dog for a walk, to big ones, like celebrating my granddaughter's birthday in a couple of days.

I hope that you find your own path back to serenity in the face of this market volatility, and that whatever you end up doing with your portfolio allows you to pass the sleep test, where you don't lie awake at night thinking about your portfolio (up or down).

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Investing Politics: Globalization Backlash and Government Disruption!

2025-03-15T12:50:00.002-04:00

I will start with a couple of confessions. The first is that I see the world in shades of gray, and in a world where more and more people see only black and white, that makes me an outlier. Thus, if you are reading this post expecting me to post a diatribe or a tribute to Trump, tariffs or Tesla, you are likely to be disappointed. The second is that much of my work is in the micro world, where I look at companies and their values, and the work that I do on macro topics or variables is to help me in that pursuit. Thus, my estimates of equity risk premiums, updated every month, are not designed to make big statements about markets but more to get inputs I need to value companies. That said, to value companies today, I have no choice but to bring in the economics and politics of the world that these companies inhabit. The problem with doing so, though, is that with Trump and tariffs on the one hand, and Musk and DOGE on the other, it is easy to be reactive, and to let your political leanings drive your conclusions. That is why I want to step back and look at the two larger forces that have brought us to this moment, with the first being globalization, a movement that has shaped economics and markets for much of the last four decades, but that has now, in my view, crested and is facing pushback, and the other being disruption, initiated by technology start-ups in the 1990s, and extended to lay waste to the status quo in many businesses in the decades since, but now being brought into the political/government arena.

Globalization – The Rise, Effects and Blowback

Globalization has taken different forms through the ages, with some violent and toxic variants, but the current version of globalization kicked into high gear in the 1980s, transforming every aspect of our lives. I am no historian, but in this section, I will start with a very short and personal history of how globalization has played out in my classroom, examine its winners and losers, and end with an assessment of how the financial crisis of 2008 caused the movement to crest and create a political and economic backlash that has led us to today.

A Short (Personal) History of Globalization

The best way that I can think of illustrating the rise of globalization is to talk about how it has made its presence felt in my classroom over the last four decades. When I started my teaching journey at the University of California at Berkeley in 1984, business education was dollar-centric, with business schools around the world using textbooks and cases written with US data and starring US companies. My class had a sprinkling of European and Japanese students but students from much of the rest of the world were underrepresented. The companies that they went to work for, after graduation, were mostly domestic in operations and in revenues, and multinationals were more the exception than the rule, with almost all of them headquartered in the United States and Europe.

Today, business education, both in terms of location and material, has become global, with European and Asian business schools routinely making the top business school list, and class materials reflecting this trend. My classes at NYU often have more students from outside the United States than from within, and very few will go to work for entities with a purely domestic focus. Many of these hiring firms have supply chains that stretch across the world and sell their products and services in foreign markets. As businesses have globalized, consumers and investors have had no choice but to follow, and the things we buy (from food to furniture) and the companies that we invest in all reflecting these global influences.

The Winners from Globalization

As consumers, companies and investors have globalized, there have clearly been many who have benefited from its rise. Without claiming to be comprehensive, here is my list of the biggest winners from globalization.

China: The biggest winner from globalization has been China, which has seen its economic and political power surge over the last four decades. Note that the rise has not been all happenstance, and China deserves credit for taking advantage of the opportunities offered by globalization, making itself first the hub for global manufacturing and then using its increasing wealth to build its infrastructure and institutions. To get a measure of China’s rise, I look at its GDP, relative to GDP from the rest of the world over the last few decades:
Source: World Bank
China's share of global GDP increased ten-fold between 1980 and 2023, and its centrality to global economic growth is measured in the table below, where I look at the percentage of the change in global GDP each decade has come from different parts of the world:
Between 2010 and 2023, China accounted for almost 38% of global economic growth, with only the United States having a larger share, though the winnings for the US were on a larger base and are more attributable to the other global force (disruption) that I will highlight in the next section.
Consumers: Consumers have benefited from globalization in many ways, starting with more products to choose from and often at lower prices than in pre-globalization days. From being able to eat whatever we want to, anytime of the year, to wearing apparel that has become so cheap that it has become disposable, many of us, at least on the surface, have more buying power.
Global Institutions : While the World Bank and the IMF predate the globalization shift, their power has amped up, at least in many emerging markets, and the developed world has created its own institutions and agreements (EU and NAFTA, to name just two) making it easier for businesses and individuals to operate outside their domestic borders. In parallel, International Commercial Courts have proliferated and been empowered to enforce the laws of commerce, often across borders.
Financial Markets (and their centers): Over the last few decades, not only have more companies been able to list themselves on financial markets, but these markets has become more central to public policy. In many cases, the market reaction to spending, tax or economic proposals has become the determinant on whether they get adopted or continued. As financial markets have risen in value and importance, the cities (New York, London, Frankfurt, Shanghai, Tokyo and Mumbai) where these markets are centered have gained in importance and wealth, if not in livability, at the expense of the rest of the world.
Experts: We have always looked to experts for guidance, but globalization has given rise to a new cadre of experts, who are positioned to identify what they believe are the world’s biggest problems and offer their solutions in forums like Davos and Aspen, with the world’s policy makers as their audience.

The Losers from Globalization

When globalization was ascendant, its proponents underplayed its costs, but there were losers, and that list would include at least the following:

Japan and Europe: The graph that shows the rise of China from globalization also illustrates the fading of Japan and Europe over the period, with the former declining from 17.8% of global GDP in 1995 to 3.96% in 2023 and the latter seeing its share dropping from 25.69% of global GDP in 1990 to 14.86%. You can see this drop off in the graph below:

While not all growth from globalization is zero-sum, a significant portion during this period was, with economic power and wealth shifting from Europe and Japan to newly ascendant economies.
Consumers, on control: I listed consumers as winners from globalization, and they were, on the dimensions of choice and cost, but they also lost in terms of control of where their products were made, and by whom. To provide a simplistic example, the shift from buying your vegetables, fish and meat from local farmers, fishermen and butchers to factory farmers and supermarkets may have made the food more affordable, but it has come at a cost.
Small businesses: While there are a host of other factors that have also contributed to the decline of small businesses, globalization has been a major contributor, as smaller businesses now find themselves competing against companies who make their products thousands of miles away, often with very different cost structures and rules restricting them. Larger businesses not only had more power to adapt to the challenges of globalization, but have found ways to benefit from it, by moving their production to the cheapest and least restrictive locales. In one of my data updates for this year, I pointed to the disappearance of the small firm effect, where small firms historically have earned higher returns than large cap companies, and globalization is a contributing factor.
Blue-collar workers in developed markets: The flip side of the rise of China and other countries as manufacturing hubs, with lower costs of operation, has been the loss of manufacturing clout and jobs for the West, with factory workers in the United States, UK and Europe bearing the brunt of the cost. While the job losses varied across sectors, with job skills and unionization being determining factors, the top line numbers tell the story. In the United States, the number of manufacturing jobs peaked at close to 20 million in 1979 and dropped to about 13 million in 2024, and manufacturing wages have lagged wage growth in other sectors for much of that period.
Democracy: In my view, globalization has weakened the power of democracy across the world. The fall of the Iron Curtain was greeted by optimists claiming the triumph of democracy over authoritarianism and the dawn of a new age of democratic freedom. That promise has largely been dashed, partly because the biggest winners from the globalization sweepstakes were not paragons of free expression and choice, but also because voters in democracies were frustrated when they voted for change, and found that the policies that followed came from a global script. The Economist, the newsmagazine, measures (albeit with their own biases) democracy in the world, and its findings in its most recent update are troubling.
Not only does the world tilt more authoritarian than democratic in 2024, the trend line indicates that the world is becoming less democratic over time. While there are other forces (social media, technology) at play that may explain this shift as well, the cynicism that globalization has created about the capacity to create change at home has undoubtedly contributed to the shift away from democracy.

I believe that globalization has been a net plus for the global economy, but one reason it is in retreat is because of a refusal on the part of its advocates to acknowledge its costs and the dismissal of opposition to any aspect of globalization as nativist and ignorant.

The 2008 Crisis and its Aftermath

Coming into this century, the march of globalization seemed unstoppable, but the wave crested in 2008, with the financial market crisis. That crisis exposed the failures of the expert class, leading to a loss of trust that has never been recovered. While the initial public responses to the financial crisis were muted, the perception that the world was still being run by hidden (global) forces, unelected and largely unaccountable to anyone, has continued, and I believe that it has played a significant role in British voters choosing Brexit, the rise of nationalist parties in Europe, and in the elections of Donald Trump in the United States. Trump, a real estate developer with multiple international properties, is an imperfect spokesperson of the anti-globalization movement, but it is undeniable that he has tapped into, and benefited from, its anger. While he was restrained by norms and tradition in his first term, those constraints seem to have loosened in this second go around, and he has weilded tariffs as a weapon and is open about his contempt for global organizations. While economists are aghast at the spectacle, and the economic consequences are likely to be damaging, it is not surprising that a portion of the public, perhaps even a majority, are cheering Trump on.

To those who are nostalgic for a return to the old times, I don't believe that the globalization genie can go back into the bottle, as it has permeated not only every aspect of business, but also significant portions of our personal lives. The world that will prevail, if a trade war plays out, will be very different than the one that existed before globalization took off. China, the second largest economy in the world today, is not returning to its much smaller stature, pre-globalization, and given the size of its population, it may be able to sustain its economy and grow it, with a domestic market focus. While investors are being sold the India story, it is worth recognizing that India will face much more hostility from the rest of the world, as it tries to grow, than China did during the last few decades. For Europe and Japan, a combination of an aging populations and sclerotic governments limit the chances of recovery, and for the United States, the question is whether technology can continue to be its economic savior, especially if global markets become more difficult to access.

Disruption – Origins and Extensions

In the world of my youth, disruption was not used as a compliment and disruptors were consigned to the outside edges of society, labeled as troublemakers or worse. That has changed in this century, as technology evangelists have used disruption as a sword to slay the status quo and offer, at least, in their telling, more efficient and better alternatives.

The Disruptor Playbook

I have written about disruption in earlier posts, and at the risk of repeating myself, I will start with a generalized description of the playbook used by disruptors to break up the status quo.

Find a business to disrupt: The best businesses to disrupt are large (in terms of dollars spent on their products/services), inefficient in how they make and sell these products, and filled with dissatisfied players, where no one (or at least very few) is happy. For the most part, these businesses have made legacy choices, which made sense at the time they were made, have long outlived their usefulness, but persist, because systems and practices have been built around them, and changes are fought by the beneficiaries of these inefficient systems.
Target their weakest links: Legacy businesses have a mix of products and services, and it is inevitable that some of these products are services have high margins and pay for other products that are offered at or below cost. Disruptors go after the former, weaning away unhappy customers by offering them better deals, and in the process, leaving legacy businesses with a less profitable and viable product mix.
Move quickly and scale up: Speed is of the essence in disruption, since moving quickly puts status quo companies at a disadvantage, as these companies not only take more time to respond, but must weather fights within their organizations, often driven by politics and money. With access to significant capital from venture capital, private equity and even public investors, disruptors can scale up quickly, unencumbered by the need to have well formed business models or show profits at least in the near term.
Break rules, ask for permission later: One feature shared by disruptive models, albeit to varying degrees, has been a willingness to break rules and norms, knowing fully well that their status quo competitors will be more averse to doing so, and that the rule makers and regulators will take time to respond.
There is no alternative: By the time the regulators or legal system catches up with the disrupters, they aim to have become so ascendant, and the status quo so damaged, that there is no going back to the old ways.

In the last three decades, we have seen this process play out in industry after industry, from the retail business (with Amazon), the music business (with Apple iTunes first and Spotify later), the automobile business (with Tesla) and advertising (with Google and Facebook), to name just a few.

Disruption's Winners and Losers

The obvious winners from disruption are the disruptors, but since many of them scaled up with unformed business models, the payoff is less in the form of profits, and more in terms of their market capitalizations, driven by investors dazzled by their potential. That had made the founders of these businesses (Bezos, Musk and Zuckerberg) not only unbelievably wealthy, but also given them celebrity status, and created a host of winners for those in the ecosystem, including the disruptors' employees and investors. As these disrupted businesses prioritized scaling up over profitability, consumers benefited as they received products and services, at bargain-basement prices, sometimes below cost.

The clearest loser from disruption is the status quo. As legacy companies melt down, in terms of profitability and value, the damage is felt in concentric circles, with employees facing wage cuts and job losses, and investors seeing write downs in their holdings The peripheral damage is to the regulatory structures that govern these businesses, as the rule breakers became ascendant, leaving rule makers impotent and often on the side lines. To the extent that these regulations and rules were designed to protect the environment and the public, there are side costs for society as well.

In short, disruption may have been a net positive for society, but there are casualties on its battlefield. In the battle for the global economic pie, the fact that so much of the disruption has originated in the United States, aided both by access to a capital and a greater tolerance for rule-breaking, has helped the United States maintain and even grow its share of global GDP. In practical terms, this has manifested in the soaring market capitalizations of the biggest technology companies, and it is their presence that has allowed the United States to ward off the decline in economic power and market cap that you have seen in much of the rest of the developed world.

Disruption goes macro

For much of its history, disruption has been restricted to the business space and it has had only limited success when directed at systemic inefficiencies in less business-driven settings. Health care clearly meets all of the criteria for a good disruption target, consuming 20% of US GDP, with a host of unhappy constituencies (doctors, patients, hospitals and payers). However, attempts at disruption, whether it be from Mark Cuban’s pharmaceutical start-up or from Google and Amazon’s health care endeavors, have largely left the system intact. I have described education, at the school and college level, as deserving of disruption for more than two decades, but notwithstanding tries at online education, not much has changed at universities (yet).

Can entire governments be disrupted? After all, it is hard to find anyone who would describe government organizations and systems as efficient, and the list of unhappy players is a mile long. The pioneers of government disruption have been in Latin America, with El Salvador and Argentina being their venues. Nayib Bukele, in El Salvador, and Javier Milei, in Argentina, have not just pushed back against the norms, but have reveled in doing so, and they were undoubtedly aided by the fact that the governments in both countries were so broken that many of their citizenry viewed any change as improvement. As we watch Elon Musk and DOGE move at hyper speed (by government standards), break age-old systems and push rules and laws to breaking point, I see the disruption playbook at play, and I am torn between two opposing perspectives. On the one hand, it is clear the US government has been broken for decades and tinkering at its edges (which is what every administration has done for the last forty years) has accomplished little to reduce the dysfunctionality of the system (and the deficits and debt that it creates). On the other, though, disrupting the US government is not the same as disrupting a business, since there are millions of vulnerable people (social security, Medicare and veteran care) whose lives rest on government checks, and a break in that process that is not fixed quickly could be catastrophic. There is a middle ground here, and unless DOGE finds it quickly, this disruption story will have lots of casualties.

Market and Micro Effects

As I have wrestled with the barrage of news stories in the last few weeks, many with large consequences for economies and markets, I keep going back to what this means for my micro pursuits, i.e., analyzing how companies make decisions on investing, financing and dividends and what the values of these companies are. It is still early in that process, and there is much that I still don’t know the answer to, but here the ways I see this playing out.

In markets

There are two key inputs that are market-driven which affect the values of every company. The first is interest rates, across the maturity spectrum, since their gyrations will play out across the market. In the graph below, I look at US treasury rates and how they have moved since the Trump election in early November:

The ten-year US treasury rate has declined from 4.55% on Election Day (November 5) to 4.27% on March 13, 2025, but since that treasury rate is driven of expectations about inflation and real economic growth, Trump supporters will attribute the decline to markets anticipating a drop in inflation in a Trump administration and Trump critics suggesting that the rate drop is an indicator of a slowing economy and perhaps even a recession. The yield curve has flattened out, with the 10-year rate staying higher than the 2-year rate, pushing that very flawed signal of economic recession into neutral territory.

The other number that I track is the equity risk premium, which at least in my telling, is a forward-looking number backed out of the market and the receptacle for the greed and fear in markets. In the table below, I show my estimates of the implied equity risk premium for the S&P 500 at the start of every month, since January 2024, and on March 14, 2025.

The equity risk premium at the start of March was at 4.35%, surprisingly close to the 4.28% on Election Day, but that number has jumped to 4.68% in the first two weeks of March, indicating that uncertainty about tariffs and the economy is undercutting the resilience that the market has shown so far this year. In my view, the pathway that the equity risk premium takes for the rest of the year will be the key driver in whether equities level off, continue to decline or make a comeback. If equity risk premiums continue to march upwards, driven by increased uncertainty and the potential for trade wars, stock prices will drop, even if the economy escapes a recession, and adding a recession, with the damage it will create to expected earnings, will only make it worse. In one of the first posts I wrote this year, I looked at US equities, and valued the S&P 500 at 5262, putting it about 12% below the index level (5882) at the start of the year. Even with the drawdown in prices that we have seen through March 10, the index remains above my estimated value, and while that value reflected what I saw at the start of the year, what has happened in the last few weeks has lowered the fair value, not raised it.

In companies

Changes in interest rates and risk premiums will affect the valuations of all companies, but assuming that the tariff announcements and government spending cuts will play out over the foreseeable future, there will be disparate effects across companies. I will draw on a familiar structure, where I trace the value of a company to its key drivers:

By narrowing our focus to the drivers of value, we can look at how company exposure to trade wars and DOGE will play out:

1. Revenue growth: On the revenue growth front, companies that derive most or all of their revenues domestically will benefit and companies that are dependent on foreign sales will be hurt by tariff wars. To assess how that exposure varies across sectors, I look at the percentage of revenues s in each sector that companies in the S&P 500 get from foreign markets:

Based on revenues in 2023

Collectively, about 28% of the revenues, in 2023, of the companies in the S&P 500 came from foreign markets, but technology companies are most exposed (with 59% of revenues coming from outside the country) and utilities least exposed (just 2%) to foreign revenue exposure. It is also worth noting that the larger market cap companies of the S&P 500 have a higher foreign market revenue exposure than smaller market cap companies. On the DOGE front, the attempts to cut costs are likely tol hit healthy care and defense, the two businesses that are most dependent on the government spending, most directly, with green energy, a more recent entrant into the government spending sweepstakes, also on the cutting block.

2. Operating margins: A company that gets all of its revenues from the domestic markets can still be exposed to trade wars, if its production or supply chains is set in other countries. The data on this front is far less visible or reported than revenue data and will require more company-level research. It is also likely that if the attempts to bring production back to the United States come to fruition, wages for US workers will increase, at least in the longer term, pushing up costs for companies. In short, a tariff war will lower the operating margins for many firms, with the size of the decline depending on their revenues,

3. Reinvestment: To the extent that companies are altering their decisions on where to build their next manufacturing facilities, as a result of tariff fears or in hope of government largesse, there should be an effect on reinvest, with an increase in reinvestment (lower sales to capital ratios) at businesses where this move will create investment costs. Looking across businesses, this effect is likely to be more intense at manufacturing companies, where moving production is more expensive and difficult to do, that at technology or service firms.

4. Failure risk: Since 2008, the US government has implicitly, if not explicitly, made clear its preference for stepping in to help firms from failing, especially if they were larger and the cost of failure was perceived as high. It is not clear what the Trump administration's views are on bailing out companies in trouble, but may initial read is that government is less likely to jump in as a capital provider of last resort.

There is another way in which you can reframe how the shifts in politics and economics will play out in valuation. I have long argued that every valuation is a bridge between stories and numbers, and that to value a company, you have a start with a business story for the company, check to make sure that it is possible, plausible and probable, and connect the story to valuation inputs (revenue growth, margins, reinvestment and risk). Staying with that structure, I have also posited that the value of a company can sometimes be affected by its political connections or by the government acting as an ally or an adversary, making the government a key player in the company's story. While that feature is not uncommon in many emerging market companies, when analyzing US and European companies, we had the luxury, historically, of keeping governments out of company stories, other than in their roles of tax collectors and regulators. That time may well have passed, and it is entirely possible that when valuing US companies now, you have to bring the government into the story, and in some cases, a company's political connections can make or break the story.

The company where you are seeing the interplay between economics and politics play out most visibly right now is Tesla, a company that has had a rollercoaster history with the market. In 2024, its stock soared, especially so after the election, but it has now given up almost of its gains, almost entirely because of its (or more precisely, Elon Musk's) political connections. I revisited my Tesla valuation from January 2024, when I valued the stock at $182, triggering a buy in my portfolio when the stock price dropped to $170. In the intervening year, there were three developments that have affected the Tesla narrative:

A rethiinking the "electric cars are inevitable" story: For the last few years, it has become conventional wisdom that electric cars will eventually displace gas cars, and the question has been more about when that would happen, rather than whether. In 2024, you saw second thoughts on that narrative, as hybrids made a comeback, and the environmental consequences of having millions of electric cars on the road came into focus. To the extent that Tesla's value has come from an assumption that the electric car market will be huge, this affects end revenues and value.
The rise of BYD as a competitor for electric cars: Since its founding, Tesla has dominated the electric car business, and legacy car makers have struggled to keep up with it. in 2024, BYD, the Chinese electric car company, sold more electric cars than Tesla for the first time in history, and it is clearly beating Tesla not just in China, but in most Asian markets and even in Europe, with lower prices and more choices. Put simply, it feels like Tesla has its first real competitor in the electric car business.
The politicization of the Tesla story: There has been a backlash building from those who do not like Musk's political stances and it is spilling over into Tesla's sales, in Europe and the United States. As long as Musk remains at the center of the news cycle, this is likely to continue, and there is the added concern, even for Tesla shareholders who agree with Musk's politics, that he is too distracted now to provide direction to the company.

These developments have made me more wary than I was last year on the end game for Tesla. While I do believe that Tesla will be one of the lead players in the electric car market, the pathway to a dominant market share of the electric car market has become rockier, and it seems likely that the electric car market will bifurcate into a lower-priced and a premium market, with BYD leading in the first (lower priced) market, especially in much of Asia, and Tesla holding its own in the premium car market, with a clear advantage in the United States. I remain skeptical that any of the legacy auto companies, notwithstanding the money that they have spend on electric cars and the quality of these cars, will challenge the newcomers on this turf. My updated valuation for Tesla is below:

Download Tesla valuation (March 2025)

My estimate of value for Tesla stands at about $150 a share, about $30 less than my value last year, and about $70 below its stock price. As an investor, I have been wary of taking a position in BYD, because of its Chinese origins and the presence of Beijing as a player in its story, but given that Tesla is now a political play, it may be time to open the door to the BYD investment, but that will have to wait for another post.

The Bottom Line

While it is easy to blame market uncertainty on Trump, tariffs and trade wars for the moment, the truth is that the forces that have led us here have been building for years, both in our political and economic arenas. In short, even if the tariffs cease to be front page news, and the fears of an immediate trade war ease, the underlying forces of anti-globalization that gave rise to them will continue to play out in global commerce and markets. For investors, that will require a shift away from the large cap technology companies that have been the market leaders in the last two decades back to smaller cap companies with a more domestic focus. It will also require an acceptance of the reality that politics and macroeconomic factors will play a larger role in your company assessments, and create a bigger wild card on whether investments in these companies will pay off.

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Valuation of Tesla in March 2025

Data Update 9 for 2025: Dividends and Buybacks - Inertia and Me-tooism!

2025-03-05T15:14:00.002-05:00

In my ninth (and last) data post for 2025, I look at cash returned by businesses across the world, looking at both the magnitude and the form of that return. I start with a framework for thinking about how much cash a business can return to its owners, and then argue that, in the real world, this decision is skewed by inertia and me-tooism. I also look at a clear and discernible shift away from dividends to stock buybacks, especially in the US, and examine both good and bad reasons for this shift. After reporting on the total cash returned during the year, by public companies, in the form of dividends and buybacks, I scale the cash returned to earnings (payout ratios) and to market cap (yield) and present the cross sectional distribution of both statistics across global companies.

The Cash Return Decision

The decision of whether to return cash, and how much to return, should, at least in principle, be the simplest of the three corporate finance decisions, since it does not involve the estimation uncertainties that go with investment decisions and the angst of trading of tax benefits against default risk implicit in financing decisions. In practice, though, there is probably more dysfunctionality in the cash return decision, than the other two, partly driven by deeply held, and often misguided views, of what returning cash to shareholders does or does not do to a business, and partly by the psychology that returning cash to shareholders is an admission that a company's growth days are numbered. In this section, I will start with a utopian vision, where I examine how cash return decisions should play out in a business and follow up with the reality, where bad dividend/cash return decisions can drive a business over a cliff.

The Utopian Version

If, as I asserted in an earlier post, equity investors have a claim the cash flows left over after all needs (from taxes to debt payments to reinvestment needs) are met, dividends should represent the end effect of all of those choices. In fact, in the utopian world where dividends are residual cash flows, here is the sequence you should expect to see at businesses:

In a residual dividend version of the world, companies will start with their cash flows from operations, supplement them with the debt that they think is right for them, invest that cash in good projects and the cash that is left over after all these needs have been met is available for cash return. Some of that cash will be held back in the company as a cash balance, but the balance can be returned either as dividends or in buybacks. If companies following this sequence to determine, here are the implications:

The cash returned should not only vary from year to year, with more (less) cash available for return in good (bad) years), but also across firms, as firms that struggle on profitability or have large reinvestment needs might find that not only do they not have any cash to return, but that they might have to raise fresh capital from equity investors to keep going.
It also follows that the investment, financing, and dividend decisions, at most firms, are interconnected, since for any given set of investments, borrowing more money will free up more cash flows to return to shareholders, and for any given financing, investing more back into the business will leave less in returnable cash flows.

Seen through this structure, you can compute potential dividends simply by looking for each of the cash flow elements along the way, starting with an add back of depreciation and non-cash charges to net income, and then netting out investment needs (capital expenditures, working capital, acquisitions) as well as cash flow from debt (new debt) and to debt (principal repayments).

While this measure of potential dividend has a fanciful name (free cash flow to equity), it is not only just a measure of cash left in the till at the end of the year, after all cash needs have been met, but one that is easy to compute, since every items on the list above should be in the statement of cash flows.

As with almost every other aspect of corporate finance, a company's capacity to return cash, i.e., pay potential dividends will vary as it moves through the corporate life cycle, and the graph below traces the path:

There are no surprises here, but it does illustrate how a business transitions from being a young company with negative free cash flows to equity (and thus dependent on equity issuances) to stay alive to one that has the capacity to start returning cash as it moves through the growth cycle before becoming a cash cow in maturity.

The Dysfunctional Version

In practice, though, there is no other aspect of corporate finance that is more dysfunctional than the cash return or dividend decision, partly because the latter (dividends) has acquired characteristics that get in the way of adopting a rational policy. In the early years of equity markets, in the late 1800s, companies wooed investors who were used to investing in bonds with fixed coupons, by promising them predictable dividends as an alternative to the coupons. That practice has become embedded into companies, and dividends continue to be sticky, as can be seen by the number of companies that do not change dividends each year in the graph below:

While this graph is only of US companies, companies around the world have adopted variants of this sticky dividend policy, with the stickiness in absolute dividends (per share) in much of the world, and in payout ratios in Latin America. Put simply, at most companies, dividends this year will be equal to dividends last year, and if there is a change, it is more likely to be an increase than a decrease.

This stickiness in dividends has created several consequences for firms. First, firms are cautious in initiating dividends, doing so only when they feel secure in their capacity to keep generate earnings. Second, since the punishment for deviating from stickiness is far worse, when you cut dividends, far more firms increase dividends than decrease them. Finally, there are companies that start paying sizable dividends, find their businesses deteriorate under them and cannot bring themselves to cut dividends. For these firms, dividends become the driving force, determining financing and investment decisions, rather than being determined by them.

This is, of course, dangerous to firm health, but given a choice between the pain of announcing a dividend suspension (or cut) and being punished by the market and covering up operating problems by continuing to pay dividends, many managers choose the latter, laying th e pathway to dividend madness.

Dividends versus Buybacks

As for the choice of how to return that cash, i.e., whether to pay dividends or buy back stock, the basics are simple. Both actions (dividends and buybacks) have exactly the same effect on a company’s business picture, reducing the cash held by the business and the equity (book and market) in the business. It is true that the investors who receive these cash flows may face different tax consequences and that while neither action can create value, buybacks have the potential to transfer wealth from one group of shareholders (either the ones that sell back or the ones who hold on) to the other, if the buyback price is set too low or too high.

It is undeniable that companies, especially in the United States, have shifted away from a policy of returning cash almost entirely in dividends until the early 1980s to one where the bulk of the cash is returned in buybacks. In the chart below, I show this shift by looking at the aggregated dividends and buybacks across S&P 500 companies from the mid-1980s to 2024:

While there are a number of reasons that you can point to for this shift, including tax benefits to investors, the rise of management options and shifting tastes among institutional investors, the primary reason, in my view, is that sticky dividends have outlived their usefulness, in a business age, where fewer and fewer companies feel secure about their earning power. Buybacks, in effect, are flexible dividends, since companies, when faced with headwinds, quickly reduce or cancel buybacks, while continuing to pay dividends: In the table below, I look at the differences between dividends and buybacks:

If earnings variability and unpredictability explains the shifting away from dividends, it stands to reason that this will not just be a US phenomenon, and that you will see buybacks increase across the world. In the next section, we will see if this is happening.

There are so many misconceptions about buybacks that I did write a piece that looks in detail at those reasons. I do want to reemphasize one of the delusions that both buyback supporters and opponents use, i.e., that buybacks create or destroy value. Thus, buyback supporters argue that a company that is buying back its own shares at a price lower than its underlying value, is effectively taking an investment with a positive net present value, and is thus creating value. That is not true, since that action just transfers value from shareholders who sell back (at the too low a price) to the shareholders who hold on to their shares. Similarly, buyback opponents note that many companies buy back their shares, when their stock prices hit new highs, and thus risk paying too high a price, relative to value, thus destroying value. This too is false, since paying too much for shares also is a wealth transfer, this time from those who remain shareholders in the firm to those who sell back their shares.

Cash Return in 2024

Given the push and pull between dividends as a residual cash flow, and the dysfunctional factors that cause companies to deviate from this end game, it is worth examining how much companies did return to their shareholders in 2024, across sectors and regions, to see which forces wins out.

Cash Return in 2024

Let's start with the headline numbers. In 2024, companies across the globe returned $4.09 trillion in cash to their shareholders, with $2.56 trillion in dividends and $1.53 trillion taking the form of stock buybacks. If you are wondering how the market can withstand this much cash being withdrawn, it is worth emphasizing an obvious, but oft overlooked fact, which is that the bulk of this cash found its way back into the market, albeit into other companies. In fact, a healthy market is built on cash being returned by some businesses (older, lower growth) and being plowed back into growth businesses that need that capital.

That lead in should be considered when you look at cash returned by companies, broken down by sector, in the table below, with the numbers reported both in US dollars and scaled to the earnings at these companies:

To make the assessment, I first classified firms into money making and money losing, and aggregated the dividends and buybacks for each group, within each sector. Not surprisingly, the bulk of the cash bering returned is from money making firms, but the percentages of firms that are money making does vary widely across sectors. Utilities and financials have the highest percentage of money makers on the list, and financial service firms were the largest dividend payers, paying $620.3 billion in dividends in 2024, followed by energy ($346.2 billion) and industrial ($305.3 billion). Scaled to net income, dividend payout ratios were highest in the energy sector and technology companies had the lowest payout ratios. Technology companies, with $280.4 billion, led the sectors in buybacks, and almost 58% of the cash returned at money making companies in the sector took that form.

Breaking down global companies by region gives us a measure of variation on cash return across the world, both in magnitude and in the type of cash return:

It should come as no surprise that the United States accounted for a large segment (more than $1.5 trillion) of cash returned by all companies, driven partly by a mature economy and partly by a more activist investor base, and that a preponderance of this cash (almost 60%) takes the form of buybacks. Indian companies return the lowest percentage (31.1%) of their earnings as cash to shareholders, with the benign explanation being that they are reinvesting for growth and the not-so-benign reason being poor corporate governance. After all, in publicly traded companies, managers have the discretion to decide how much cash to return to shareholders, and in the absence of shareholder pressure, they, not surprisingly, hold on to cash, even if they do not have no need for it. It is also interesting that buybacks seems to be making inroads in other paths of the world, with even Chinese companies joining the party.

FCFE and Cash Return

While it is conventional practice to scale dividends to net income, to arrive at payout ratios, we did note, in the earlier section, that you can compute potential dividends from financial statements, Here again, I will start with the headline numbers again. In 2024, companies around the world collectively generated $1.66 trillion in free cash flows to equity:

As you can see in the figure, companies started with net income of $6,324 billion, reinvested $4,582 billion in capital expenditures and debt repayments exceeded debt issuances by $90 billion to arrive at the free cash flow to equity of $1.66 trillion. That said, companies managed to pay out $2,555 billion in dividends and bought back $1,525 billion in stock, a total cash return of almost $4.1 trillion.

As the aggregate numbers indicate, there are many companies with cash return that does not sync with potential dividends or earnings. In the picture below, we highlight four groups of companies, with the first two focused on dividends, relative to earnings, and the other two structured around cash returned relative to free cash flows to equity, where we look at mismatches.

Let's start with the net income/dividend match up. Across every region of the world, 17.5% of money losing companies continue to pay dividends, just as 31% of money-making companies choose not to pay dividends. Using the free cash flows to equity to divide companies, 38% of companies with positive FCFE choose not to return any cash to their shareholder while 48% of firms with negative FCFE continue to pay dividends. While all of these firms claim to have good reasons for their choices, and I have listed some of them, dividend dysfunction is alive and well in the data.

I argued earlier in this post that cash return policy varies as companies go through the life cycle, and to see if that holds, we broke down global companies into deciles, based upon corporate age, from youngest to oldest, and looked at the prevalence of dividends and buybacks in each group:

As you can see, a far higher percent of the youngest companies are money-losing and have negative FCFE, and it is thus not surprising that they have the lowest percentage of firms that pay dividends or buy back stock. As companies age, the likelihood of positive earnings and cash flows increases, as does the likelihood of dividend payments and stock buybacks.

Conclusion

While dividends are often described as residual cash flows, they have evolved over time to take on a more weighty meaning, and many companies have adopted dividend policies that are at odds with their capacity to return cash. There are two forces that feed this dividend dysfunction. The first is inertia, where once a company initiates a dividend policy, it is reluctant to back away from it, even though circumstances change. The second is me-tooism, where companies adopt cash return policies to match their peer groups, paying dividends because other companies are also paying dividends, or buying back stock for the same reasons. These factors explain so much of what we see in companies and markets, but they are particularly effective in explaining the current cash return policies of companies.

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Data Updates for 2025

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Dividend fundamentals, by industry (US, Global, Emerging Markets, Europe, Japan, India, China)
Cash return and FCFE, by industry (US, Global, Emerging Markets, Europe, Japan, India, China)