•  AI Chatbots Know More About You Than You Realise: A handful of casual questions is enough for a chatbot to assemble a strikingly detailed profile of the user. The language we use is full of signals we don’t know we’re sending; AI has learnt to read them. (Straits Times)

• ‘It Beats Pitchfork Rebellions and the Guillotine’: Why These Super-Rich Americans Are Asking For Higher Taxes: As far as political protest goes, this was among the most civilized I have ever witnessed. The organizers did not make any noise beyond the idling truck covered in changing digital billboards. There were no chants on the sidewalks, no signs to leave behind as litter. The workers at the estate of billionaire Jeff Bezos looked with curiosity that quickly gave way to indifference as the visitors’ vehicle flipped through a three-minute slide deck mocking the Amazon founder: “Congratulations! You won capitalism! Now pay your damn taxes. It beats pitchfork rebellions and the guillotine” — a very civilized class of protesters. (Time) see also What I Learned About Billionaires at Jeff Bezos’s Private Retreat: For the richest men on Earth, everything is free and nothing matters. (The Atlantic)

• The Man Who Invented the Future: The Widener family fortune was built on electricity — and on a founder who imagined the modern world before it arrived. Are we the conflicted heirs of the world according to Francis Bacon? (Hedgehog Review)

• The Great Ozempic Experiment: Online and in doctors’ offices, people are finding that GLP-1s may help with everything from arthritis to addiction to migraines.It’s a new era of D.I.Y. medicine. Now the health establishment needs to catch up. GLP-1s may help with everything from arthritis to addiction — and we’re all the test subjects. (New York Times)

• 10 Things That Matter in AI Right Now: MIT Tech Review’s take on the ten AI trends, tools, and debates that actually matter heading into the rest of 2026. (MIT Technology Review see also The Warehouse, in Plain Sight: How the American warehouse — now being repurposed by DHS — became invisible infrastructure hiding in plain sight. That concrete box off the freeway wasn’t designed for storage so much as capture — of markets, workers, and, now, people detained by immigration agents. It’s a disappearing machine. We need to see it clearly. (Places)

• AI, Iran and the Gulf 101: Deutsche Bank’s primer on how AI investment, Iran policy, and Gulf capital fit together. (Deutsche Bank)

• It’s Been Quite the Year for Victoria Beckham: The Spice Girl turned fashion designer clawed her way out of debt and posted record profits. Family troubles aren’t standing in the way of her success. (WSJ)

• OnlyFans Model, 20, Made $43 Million Last Year. To Her, It Doesn’t Conflict with Christian Values: ‘The Lord’s Very Forgiving’ (Exclusive) At just 20 years old, Sophie Rain is making a life-changing yearly income of over $43 million — and sees no contradiction with her faith. (People)

• The invention of the soul: Humans weren’t given souls by God or genes. We made them ourselves with language – turning sentience into something sacred (Aeon) see also You’ve lived this life before: The mystical insight came to Nietzsche like a lightning flash: time eternally recurs – and life must be lived accordingly (Aeon)

• Marilyn Monroe’s Nudes Made Her Notorious. “Surprisingly Good” Acting Made Her a Star. In an exclusive excerpt from their new book, The Marilyn Monroe Century: From Norma Jeane to Icon―A Story in Photographs, John Miller and Mark A. Fortin trace the icon’s meteoric rise from scandal to stardom. Plus, a selection of previously unpublished photographs by Bruno Bernard. (Vanuty Fair)

Be sure to check out our Masters in Business interview this weekend with David Gardner, cofounder of The Motley Fool in 1993 (with his brother Tom Gardner). Originally launched as a print investment newsletter based on the idea that ordinary investors could beat Wall St., it gained traction when promoted on America Online (AOL) in 1994; it soon became a major presence on AOL and then Fool.com. His latest book is “Rule Breaker Investing: How to Pick the Best Stocks of the Future and Build Lasting Wealth.”

There’s never been an investment like the investment in railroads

Source: @paulg

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~~~

To learn how these reads are assembled each day, please see this.

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The stock market is hitting all-time highs, even as consumer sentiment hits all-time lows.

Is this a paradox?

Hardly.

The confusion stems from people who imagine market prices move off their personal economic experiences (as well as broader consumer sentiment). This is a false belief, easily disproven with a few data points and charts.

“This market makes no sense!”

A similar anomaly occurred during the pandemic. The S&P 500 kept making new all-time highs even as your local economy was faltering. Stores were closing, unemployment was surging, and airlines, hotels, and retailers were going bust.

New all-time highs seemingly ignored all of that. The explanation for this was simple, albeit wonky: Your personal economy is local, visible, and “availability-weighted” while the S&P 500 is global, but more importantly, market-cap weighted. 1

Today’s anomaly is similar.

As it turns out, psychology matters – just not your psychology. When we look at the ownership structure of assets in the United States, we see a very lopsided distribution. The top 1% owns half of all equities in the US; the top 10% owns 87%.

How much do you think the sentiment of the bottom 90% of the population, by net worth – they own just 13% of stocks – matters to the stock market?

Not very much.

A related point is the so-called Wealth Effect – it’s mostly nonsense, a case of correlation, not causation.2

The lopsided distribution of equity ownership in the country explains a lot of things; it is especially useful when explaining why sentiment at all-time lows does not seem to have much effect on markets.

What is impacting overall sentiment? Consider:

Inflation had dropped from 9% down to 2.5ish%, mostly under control – until the tariffs began to drive prices higher. 3

Iran War took most Americans by surprise; the reasons were not explained to the country, and so it remains unpopular. (Sending gas prices up $1 a gallon is not popular either).

Home prices remain high, with starter homes out of reach for most young people.

Measurement issues are a very real problem when it comes to identifying sentiment. The same problem exists in polling and other measures of intention and psychology.

The K-Shaped Economy has led to a majority of Americans not feeling optimistic about the current or future economic situations.

That K is a real phenomenon: The wealthy are doing better than ever – their biggest assets are real estate (ATHs), stocks (ATHs) and businesses (awash in PE money) are all doing great; Oh, and thanks for renewing the 2016 TCJA giant tax cuts for another decade.

We see this manifest in spending patterns also. About half of all retail sales are driven by the top 10% of consumers.4

~~~

It’s never quite as clear-cut as some claim – extreme rallying cries are great clickbait but hardly explain the complexities and nuances of the markets.

Yes, markets have been democratized (somewhat) over the past 50 years. But ownership is still primarily held by the wealthy. If you want sentiment to match market prices, try surveying billionaires and millionaires instead of ordinary people…

See also:
If America’s So Rich, How’d It Get So Sad? (Derek Thompson Apr 23, 2026)

Why the Stock Market Makes No Sense Right Now (New York Times, April 18, 2026)

Aftermath: Wall Street Is Lying to Itself (Prospect, April 23, 2026)

Previously:
Revisiting the Wealth Effect (October 23, 2025)

The Probability Machine (August 28, 2025)

The K-Shaped Recovery (September 4, 2020)

Maybe Mr. Market Is Rational After All… (August 7, 2020)

Wealth Effect Rumors Have Been Greatly Exaggerated (November 16, 2010)

Why the Treasury Secretary is Wrong on the Wealth Effect of Stocks vs Real Estate (October 26, 2006

UPDATE: April 24, 2026  10:00 am  

Latest U Mich Comsumer Sentiment Final April data

Source: U Mich

__________

1. The “availability heuristic” is our tendency to use information that comes to mind quickly and easily as opposed to the more nuanced, complex real world.

Wikipedia: “mental shortcut that relies on immediate examples that come to a given person’s mind when evaluating a specific topic, concept, method, or decision. This heuristic, operating on the notion that, if something can be quickly recalled, it must be important, or at least more important than alternative solutions not as readily recalled.”

2. There IS a real wealth effect with housing – the bottom 90% own 87% of the houses – pretty close to what you expect. But even those numbers are skewed; the lower half of households – AKA renters – only own 10% of the housing stock. So the wealth effect of housing real, but somewhat muted to the 100 million owners of their primary residences.

3. The media has a big impact on sentiment, and except for the Artemis II mission, the headlines have been mostly negative. Other factors be weighing on sentiment include Home prices, health care costs, Ukraine War, ICE murders, Epstein files, etc.

4.Bloomberg: “A Moody’s Analytics analysis of Fed data found the top 10% of earners were responsible for about 49.2% of total U.S. consumer spending in Q2 2025, the highest share in data going back to 1989.” (September 16, 2025)

5. Markets trade off of profits and growth which often have nothing to do with your personal economic situation.

The post All Time Highs (SP500) versus All Time Lows (Consumer Sentiment) appeared first on The Big Picture.

• It Was on Your Table Every Morning Growing Up. It’s Dying Before Our Eyes: Florida’s orange industry — long a breakfast-table staple — is collapsing, and no one in the state wants to face it.  Deep in desiccated Southern groves, the powerhouse of American citrus is suffering a brutal, unrelenting decline. No one wants to face what that means. Who Killed the Florida Orange? (Slate)

• Private Assets May Be Coming to Your 401(k). You Should Know the Risks.: Tara Siegel Bernard on alternative investments — private credit, private equity, crypto — about to start appearing on 401(k) menus, with risks that aren’t fully priced in.  (New York Times) see also U.S. Officials Try to Get a Grip on Risks Bubbling Inside Private Credit: The SEC is sending sweeping information requests to private-credit managers, targeting valuations, loan selection, and other practices. (Wall Street Journal)

• The Billionaire Math Geek Who Turned AI Into a Money-Printing Machine: Alex Gerko’s XTX Markets uses Nvidia chips and deep learning to forecast price moves — and print money at scale. (Wall Street Journal)

• 6 Tax Tips You Should Start Thinking About Now: Simple questions to ask year-round that can help you keep more of what you earn. (Morningstar)

• Warfare in a Box: Executive Outcomes and the making of the modern mercenary: How drone warfare commoditized killing at scale. The moral distance is now shorter and the supply chain is global.  Placing profit over ideology, modern mercenaries are as at home in the boardroom as on the frontline. Their companies are registered in the appropriate tax haven, like the City of London, and operate through shell firms. (The Baffler)

• Where Did the Middle East Go? Satellite Imaging in the Fog of War: Planet Labs’ decision to restrict Middle East satellite imagery is raising questions within a multibillion-dollar industry about commercial independence and global accountability. (Businessweek) see also Putin’s High-Tech Russian Submarines Goad NATO Deep Below the Atlantic: The undersea cable game heats up — and NATO is still catching up to how much of the modern economy literally runs through cables at the bottom of the ocean. NATO is fighting back against Russia’s submarine threat in cat-and-mouse games reminiscent of the Cold War.  (Bloomberg)

• Richard Feynman’s Notes For Self-Education: The great physicist’s approach to learning was as elegant as his physics — curiosity-driven, self-directed, and gloriously undisciplined. A masterclass in how to stay intellectually alive.  (Noted)

• Rocket Science for Monkeys: Early disquisitions on language frequently puzzled over the question of how rational speech could have emerged from the non-speech of animal calls and cries, and Enlightenment treatises on the origins of speech sometimes proposed that iconic words might have been a halfway house.A review of recent work on animal communication and the origins of language. (London Review of Books) 

• MAGA Is Increasingly Convinced the Trump Assassination Attempt Was Staged: Conspiracy theories about the Butler, Pennsylvania, shooting have ramped up in recent weeks as once steadfast Trump supporters turn on the president. As intra-MAGA criticism of Trump grows, the “it was staged” conspiracy is gaining real traction inside the movement. (Wired)

• The Interview Violence Shaped Charlize Theron. It Doesn’t Define Her: Lulu Garcia-Navarro’s long-form interview for The Interview. (New York Times)

Be sure to check out our Masters in Business interview this weekend with David Gardner, cofounder of The Motley Fool in 1993 (with his brother Tom Gardner). Originally launched as a print investment newsletter based on the idea that ordinary investors could beat Wall St., it gained traction when promoted on America Online (AOL) in 1994; it soon became a major presence on AOL and then Fool.com. His latest book is “Rule Breaker Investing: How to Pick the Best Stocks of the Future and Build Lasting Wealth.”

Job Market Gets Tougher for College Grads as Competition and AI Rise

Source: Bloomberg

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For the past 30 years, I have been sharing my ideas on investing, markets, the economy, policy, and related topics. Online, in public, no hold barred; writing for a broad audience invites comments, criticism, and general interaction.

So it was no surprise that when Twitter came along, I jumped in (Thanks, Howard!). It was an amazing technology that gave you access to awesome people you otherwise would not have met; it enabled cool interactions with readers. It became a community of like-minded individuals across a variety of topics. For me, this included everything from markets and investing, economic analysis, and behavioral finance to cosmology and collectible cars. Before Chart Kid Matt, many of the most-liked charts seen on The Big Picture were originally found on Twitter.

My partner Josh, always ahead of the curve, saw the writing on the wall and tapped out in 2020. I hadn’t noticed the slow bleed on the site until I was hacked in July 2023. Someone took control of the account until Bloomberg helped me get it back 3 months later. Nadig warned mw I wouldn’t recognize it. The site had changed so radically over those three months that it was hard to wrap my head around it.1

Trying to scale and monetize the site only led to a change for the worse; new ownership accelerated the collapse. Today, there is very little engagement, too many bots, and endless trolls;2  constant fake versions of myself and impersonations 3 of my colleagues have revealed how little the site cares about the security and safety of its users.4  What could have been a global town square morphed into something far more insidious. 5

And so it is with a heavy heart that I belatedly say farewell to X.

I’ll still allow the blog to auto-post; I’ll lurk on my favorite lists, and occasionally retweet others. But that’s it. You can find me here and on LinkedIn.

__________

1. Recently, a number of my favorite follows have headed for the exits; EFF was merely the latest.

2. Social media fraud channels contributed to 56% of all crypto scam cases in 2025, led by Telegram, X, and Instagram; AI-generated deepfake scams rose roughly 700%; fake endorsements impersonating Musk accounted for 32% of social media scam attempts. (Source: CoinLaw) According to Medium, the most visible pattern of fraud is the stock-pick scam chain: a low-follower bot posts a reply with replies disabled, tags a “financial expert” account using a stock photo and a generic handle ending in a number, and that account pushes penny-stock picks.

3. New ownership made everything worse: Paid verification inverted the old signal: a blue check now correlates positively, not negatively, with scam risk, and the algorithmic boost for paying accounts means scam replies surface above legitimate ones. (via Claude)

4. AI has lowered the cost of producing convincing content and increased variability across a botnet, making pattern-based takedowns harder, while “reply-and-block” tactics let bots register engagement and then suppress the original poster’s ranking. (via Claude)

5. It’ll be interesting to see the Harvard Business School case study of how to light $38 billion on fire.

The post Farewell, Twitter appeared first on The Big Picture.

• Women get worse investment advice: There are plenty of differences between men and women; how they invest their money, how they deal with risk, etc. But given the same circumstances and risk preferences, men and women should get the same advice from their financial advisers. Guess what? That doesn’t happen. New research confirms that women systematically receive lower-quality investment advice than men. (Klement on Investing)

• Has the Stock Market Really Been More Volatile Than Usual This Year? For most investors, stock market volatility is something to endure, not act upon. Dan Lefkovitz runs the numbers against the vibes. The vibes, as usual, are losing. (Morningstar)

• Will China get richer before it gets much, much smaller?  China does look likely to age more rapidly than many counterparts. According to the United Nations Department of Economic and Social Affairs Population Division, the share of its population aged 15-64 years old peaked in 2012 at 73 per cent (it is hovering now at around 70 per cent) but is set to plunge below half in the next fifty years — taking it lower than equivalent shares for the US, Europe and even Japan. (Financial Times)

• The Father-Daughter Showdown That Shook an $18 Trillion Investing Empire: Before her winning streak at Fidelity, Abby Johnson had to beat back the doubts of her rivals, and her father, too. The Abby-and-Ned Johnson succession drama at Fidelity — the sort of dynastic asset-management intrigue you rarely get to see in public. (Wall Street Journal)

• Fed Chair Apprentice: three policy themes: Fed independence, Warsh’s theory of inflation, and financial deregulation. Claudia Sahm on Kevin Warsh’s Senate confirmation heaxring for Fed Chair. (Stay-At-Home Macro)

• Helium Is Hard to Replace: Helium is produced as a byproduct of natural gas extraction. It collects in the same underground pockets that natural gas collects in. Qatar is responsible for roughly 1/3rd of the world’s supply of helium, which was formerly transported through the Strait of Hormuz in specialized containers. Thanks to the closure of the strait, helium prices have spiked, suppliers are declaring force majeure, and businesses are scrambling to deal with looming shortages. (Construction Physics)

• Why thinking hard feels bad: the emotional root of deliberation: When an intuitive answer to a problem feels slightly off, the human brain generates an uncomfortable state known as doubt. This negative feeling acts as an internal alarm bell that prompts individuals to abandon simple mental shortcuts and engage in heavy, analytical thinking. The new findings detailing this emotional trigger were published in Thinking & Reasoning. New research on why System 2 is exhausting — and why that cost is not a bug but a feature of how attention allocates itself. (PsyPost)

• The Ancient Weapons Active in Your Immune System Today. Dozens of new discoveries reveal that defenses evolved by bacteria and viruses billions of years ago still define our own innate immune system. Dozens of new discoveries reveal that defenses evolved by bacteria and viruses billions of years ago still define our own innate immune system. (Quanta Magazine)

• Searching for the ‘Smoking Gun’ in US Pedestrian Deaths: Why did American streets get so deadly for those on foot or bikes? A leading transportation safety researcher sees some surprising factors behind the crisis. (CityLab)

• ‘Wagyu’ Used to Guarantee Quality Beef. What Are You Paying for Today?: The term has been stretched to near-meaninglessness on American menus — here’s how to tell A5 from marketing. (New York Times)

Be sure to check out our Masters in Business interview this weekend with David Gardner, cofounder of The Motley Fool in 1993 (with his brother Tom Gardner). Originally launched as a print investment newsletter based on the idea that ordinary investors could beat Wall St., it gained traction when promoted on America Online (AOL) in 1994; it soon became a major presence on AOL and then Fool.com. His latest book is “Rule Breaker Investing: How to Pick the Best Stocks of the Future and Build Lasting Wealth.”

What is the price of oil—the real one?

Source: Bloomberg

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At The Money: Looking Beyond Market Cap Weighted Indexes (April 22, 2026)

Cap weighted indexes have come to dominate ETFs. Is it time for investors to consider a strategy based on fundamental weightings, such as profits or revenue growth?

Full transcript below.

~~~

About this week’s guest:

Rob Arnott is known as the “godfather of smart beta” and founder of Research Affiliates, which oversees strategies for over $100 billion in assets.

For more info, see:

Professional Bio

Masters in Business

LinkedIn

~~~

Find all of the previous At the Money episodes here, and in the MiB feed on Apple Podcasts, YouTube, Spotify, and Bloomberg. And find the entire musical playlist of all the songs I have used on At the Money on Spotify

TRANSCRIPT: Rob Arnott

Intro:
Boy, you’re gonna carry that weight
Carry that weight a long time
Boy, you’re gonna carry that weight
Carry that weight a long time

Barry Ritholtz:  Big, broad market-cap-weighted indexes, like the S&P 500, have dominated investor inflows and performance really since the financial crisis. But lately, critics of cap weighting point out that increased market concentration of just a handful of stocks — AKA the Magnificent Seven — is increasing risks for investors. What should a portfolio manager do about this? Well, to help us unpack all of this and what it means for your portfolio, let’s bring in Rob Arnott, founder of Research Affiliates and a longstanding critic of market cap weighted indexes. RAFI runs a variety of fundamental indexes that are based on things outside of cap weighting. Let’s jump right into it. So, Rob, you’ve spent decades challenging cap weighted indexes as simply just owning more of what just went up. Frame the case for alternative weighting — regardless of what it is, equal weight, fundamental, whatever — versus traditional cap weighted indices.

Rob Arnott:  Let’s play a thought experiment. Suppose I came to you and said, I have a brilliant strategy. You’re gonna love it. This strategy involves watching companies and waiting until their market value gets above a certain threshold and buying them. On average, I’m buying them when they’re up 75% relative to the market in the last year and trading at twice the market multiple. Some of these go on to achieve great success, some don’t. And our sell discipline is very simple: when the market cap falls below a certain threshold, we’re gonna sell them, and we’ll sell them at, on average, half the market multiple, at a loss of about 7,000 basis points relative to the market. What do you think?

Barry Ritholtz:  Hard pass. Hard pass.

Rob Arnott:  What I’ve just described is the active side of indexing. I’ve got a monograph coming out shortly — CFA Institute Research Foundation — called The Active Side of Indexing. Indexing is described as passive, but if it has 5% turnover, the 95% is passive — it moves up and down with the market movements and it’s blissfully ignorant and indifferent to what’s going on in the economy or the companies or whatever it is. Really passive. The 5% looks like a hypergrowth manager on crystal meth. The weighting is also an issue. Why? If I came to you and said, I’ve got a brilliant idea, I’m gonna weight stocks proportional to their price — so the more expensive they are, the bigger its weight in your portfolio — don’t you just love it?

Barry Ritholtz:  So let’s dive into that a little bit. Anybody who’s an indexer watches in horror every time something gets added to the index, and then there’s this grace period where the stock runs up and it’s even more expensive when it gets added. It’s even worse when there’s a deletion — they announce a deletion and the stocks plummet. Anticipating, front-running the sell — is this just a hidden cost?

Rob Arnott:  It’s legal front-running.

Barry Ritholtz:  I mean, if you’re gonna tell me — hey, we have $2 trillion in this index, we’re gonna sell this position in a month — why would you hold onto that?

Rob Arnott:  Exactly. The S&P’s a beautiful example. The S&P is now big enough that the stocks held in S&P index funds represent roughly 25% of the total market cap of every stock that’s in the index — not each individual ETF or index fund, but aggregated. And that means that, to the extent that indexers are obsessed with having no tracking error with matching the index, they’re gonna buy that stock at the same price that it’s added to the index, which means a market-on-close price. I will pay whatever the price is at the close on the day that it’s added to the index. Now, if you’re a hedge fund, you’re gonna wanna accommodate that and help out by buying it early and then flipping it to the indexers. And that’s been going on for a quarter century or more. I documented the pattern back in 1986 in an article called “S&P Additions and Deletions: A Market Anomaly.” And I heard anecdotally that that article was used in part to lobby S&P to pre-announce so that the index funds wouldn’t get nailed by the index changes. Now they gotta buy this and sell that, and they’re buying it higher and selling this lower, and so they have an automatic drag. The magnitude of that drag is actually very simple. If you could transact at the price at which S&P announced the decision, not the price at which it becomes effective, you would add 15 basis points per annum. So the indexes lose 15 basis points just from trading costs, with 5% annual turnover or less — three to 5% annual turnover. That’s equivalent to three to 500 basis points per stock per trade. That’s a heavy trading cost, but it’s because it’s a crowded space. It’s a herd of elephants trying to go through a single revolving door.

Barry Ritholtz:  Let’s talk about the flip-flop problem. Every time there’s an addition, something like 28% within a decade get dropped. And similarly, after there’s a deletion, almost half of those deletions rejoin the S&P, right, within a decade. What does this flip-flop do to performance?

Rob Arnott:  Well, it does what you would expect. When I did my little thought experiment describing a brilliant strategy, I was actually citing statistics from our flip-flops paper. On average, stocks that are added are added after 75 percentage points of outperformance. If they falter and are kicked back out, they’re removed at a 7,000 basis point loss. Now, if you gain 75 and lose 70, you aren’t back where you started — you’re down 50. And it’s worse than that, because you didn’t participate in the 75, you did participate in the down 70. The deletion flip-flops — stocks that are deleted and re-added — are even more dramatic. They underperform by 3,500 basis points, give or take, in the year before they’re dropped, and then they outperform by 180 percentage points. They roughly triple relative to the market before they’re added back in. So flip-flops are very, very costly, and none of this is disrespect to the index providers. This stuff has not been studied much until we took a deep dive into it. If you don’t know you have a problem, how are you gonna fix it? And the problem is big, but it’s on a very small part of the portfolio. It’s on the active side of indexing — the little sliver of active trading.

Barry Ritholtz:  Really, really interesting. So let’s talk about fixing it. You have been discussing for as long as I’ve known you — which is decades — economy-weighting indices rather than cap weighting or price weighting. Define what a fundamental economic weighting of an index is. What goes into that?

Rob Arnott:  Let’s suppose you want an index that studiously mirrors the economy instead of studiously mirroring the market. Well, you wouldn’t weight companies by market cap, you wouldn’t choose them based on market cap. Let’s choose them based on how big their business is. Well, how do you define that? How big are its sales? How big are its profits? How big is its net worth? Today we would go a step further and say, net worth adjusted for intangibles. How much does it distribute to shareholders in dividends and buybacks? Four different measures. You could argue endlessly about which is right, or you could simply say, I’m gonna take the average of the four weights. So Nvidia is a decent slug of total profits in the economy, but it’s not seven or 8% — not its market weight. It’s in the 2% range in terms of sales. It’s in the 2% range in terms of dividends or net worth. It rounds to a very, very small number. So you could argue, is it half a percent or 1% or 2%? Average those, and you’re gonna say it’s about one, one-and-a-half percent of the economy. Okay, that’s big enough to make the cut. We’re gonna include it, and we’ll include it at a one, one-and-a-half percent weight. Now, if you do that, what you’re doing is taking the frothy growth stocks — beloved and expected to grow fabulously — and down-weighting them to their current economic footprint. You’re taking the value stocks — the unloved, out-of-favor, cheap stocks — and you’re saying, let’s reweight those up to their economic footprint. So you wind up with a stark value tilt. And that means the sensible way to measure RAFI, the fundamental index, is to measure it against the value indexes. And that’s where it gets really interesting. Schwab and Invesco have ETFs and mutual funds, PIMCO has some ETFs tied to RAFI — the fundamental index — and collectively those three organizations have over a hundred billion dollars in RAFI assets. So this is not new, it’s not small. We introduced the idea about 20 years ago. If you compare it with the cap weighted value indexes, you get an astonishing result. On average, RAFI beats the cap weighted value indexes by two to 2.5% per year compounded, and does so with variability.

Barry Ritholtz:  How does that tracking error compare to the cap weighted growth indexes?

Rob Arnott:  The growth indexes have outperformed hugely, but they’ve outperformed by dint of becoming more and more expensive relative to fundamentals. The underlying fundamentals of the value indexes — in terms of sales, profits, book value, dividends — have grown roughly pari passu with growth portfolios this century to date, which shocks most people, because the relative performance has been about two to 3% per annum for a quarter century. And the notion that, wow, this has beat this now by — call it something on the order of two to one, 10,000 basis points of outperformance — but the underlying fundamentals have grown in parallel.

Barry Ritholtz:  Let me re-ask that question in a different way, which is: if we know there’s a disadvantage to cap weighted indexes, well isn’t the obvious and simple alternative just equal weight? Why not just go equal weight?

Rob Arnott:  Equal weighting is a perfectly legitimate way to create a portfolio. It’s gonna have a stark small cap tilt, because a tiny company will get the same weight as Nvidia or ExxonMobil. It will have a stark value bias, because companies that are trading at low multiples will get the same weight as stocks trading at high multiples. It will have a rebalancing alpha. If a stock soars, you’re gonna trim it. If it tumbles, you’re gonna top it up. The only Achilles heel that I think matters for equal weighting is: equal weighting what stocks? Equal weighting the S&P, for instance — you’re going to be equal weighting a portfolio that includes companies that have soared into being big enough to be added. You’re gonna be leaving out companies that have performed badly enough to be really cheap, and the result is that you’re going to have a portfolio that’s biased towards higher multiple stocks. So, interestingly, equal weighting over long periods of time performs about the same as fundamental index, which we launched 20 years ago — but with much more variability.

Barry Ritholtz:  Got it. That makes a lot of sense. So if we’re looking at a fundamental-driven index in a period where mega caps are dominating or growth is dominating, how do you ride that out? Up until last year, it felt like if you weren’t overweight the Mag Seven, you were underperforming — until we learned, last year, five of the seven Mag Seven underperformed in 2025.

Rob Arnott:  Yeah, yeah. Shocking. The thing that I find interesting here is, we introduced fundamental index in 2005 — live strategies at PIMCO go back to mid-2005, at Invesco go back to late 2005. So it’s live, it’s been investable for 20 years. The thing that’s interesting is, just two years later, in 2007, value crested, and it underperformed ferociously until summer of 2020. Since then it’s been bottom-bouncing — outperforming handily, then crashing, outperforming, then crashing, bottom-bouncing. And so at the end of 2025, value had underperformed — Russell Value had underperformed the Russell 1000 peak-to-trough by 3,800 basis points. Wow. You were 38% poorer than a simple Russell or S&P index investor. That’s a horrific headwind for anything with a value tilt. RAFI Fundamental Index has a rebalancing alpha: a stock soars and its fundamentals don’t validate that, then you’re gonna say, thanks for the nice high price, I’m gonna trim it. If it tanks and the fundamentals don’t falter, you’re gonna say, thanks for the bargain, I’m gonna top it up.

Barry Ritholtz:  So let’s talk a little more about that rebalancing strategy. What sort of alpha does that create? How does that drive returns?

Rob Arnott:  The best way to measure the performance of RAFI is against the cap weighted value indexes. Relative to the value indexes — this is live — the RAFI indexes have beat the cap weighted value indexes by a little over 2% per year compounded. Now, with compounding, that’s a big number. That means that you’re over 50% richer than you were with a cap weighted value index after 20 years. So that’s important. Now the other thing that’s interesting is, relative to the value indexes, the tracking error is pretty tight. It’s about two-and-a-half percent variability in that 2% value add, which means that RAFI has beat cap weighted value in most years when value’s been winning and in most years when value’s been losing. It doesn’t matter. RAFI has been winning about three out of every four years. And this is live. This is not a back test.

Barry Ritholtz:  So, to wrap up: investors who are concerned about market concentration, concerned about valuation, but a little skittish on the underperformance that value has created in a cap weighted format, should consider a fundamental index. It trades differently than both growth and value, and has a better risk profile and a better valuation profile. I’m Barry Ritholtz. You are listening to Bloomberg’s At the Money.

The post At the Money: Looking Beyond Market Cap Weighted Indexes appeared first on The Big Picture.

• What Do You Believe About Investing? Asking questions about what an investor believes, or what their investment philosophy is, can increasingly feel like a mundane tick box exercise quickly covered by some generic, unfalsifiable answers. This really shouldn’t be the case. Investment beliefs are the foundation to any approach and will likely define its success or failure. An underwhelming process supported by some robust beliefs will likely do just fine, whereas flawed beliefs won’t be saved by a good process. The investment beliefs we hold should underpin any decision we make, without them it becomes incredibly difficult to make coherent choices – particularly dur ing spells of market stress. (Behavioural Investment)

• The Trade: The war produced a market mechanism. Someone knew how it worked before the public did. Every announcement moved markets and every extension of a deadline crashed oil. Then escalation would push it back up and the cycle would repeat. This happened seven times in fifty-two days. (The Omission)

• Has the Stock Market Really Been More Volatile Than Usual This Year? For most investors, stock market volatility is something to endure, not act upon. (Morningstar)

• Tim Cook Was Very, Very Good at Making Money: How Cook turned Jobs’s creation into a $4 trillion machine. Whatever you think of him as a product visionary, the operator’s ledger is unassailable. (New York Times)

• Breaking cover: the future of the business book: Authors are experimenting with new technology and formats to better communicate ideas. Business books are being replaced by podcasts, Substacks, and LinkedIn posts. The 300-page hardback is becoming a vanity artifact, not a vehicle for actual ideas.  (Financial Times)

• San Diego Now Has So Much Water That It’s Selling It: Once a drought poster child, the California city now generates enough water to rescue parched states like Arizona—and brew beer from recycled sewage  (Wall Street Journal)

• You Should Be More Freaked Out by Shingles: The viral infection leaves millions with chronic pain, increased stroke risk, and lifelong nerve damage—yet vaccination rates remain dangerously low. (Wired)

• Scientists identify five ages of the human brain over a lifetime: Four major turning points around ages nine, 32, 66 and 83 create five broad eras of neural wiring over the average human lifespan. (University of Cambridge)

• Ukraine Has Finally Given Up on Trump: Kyiv has started hitting Russian oil-export facilities — a sign it’s no longer waiting on Washington. (The Atlantic)

• The man who saw the future: the legacy of cultural theorist Mark Fisher: Capitalist Realism: Is There No Alternative? was published in 2009 to critical silence—now a film revisits Mark Fisher’s prescient legacy. Touching on everything from late-stage capitalism to Adele, the work of the late writer is proving increasingly influential. Now a documentary on him is looking to live up to his ideals. Touching on everything from late-stage capitalism to Adele, the work of the late writer is proving increasingly influential. Now a documentary on him is looking to live up to his ideals. (The Guardian)

Be sure to check out our Masters in Business this week with Philippe Bouchaud, co‑founder, chair & head of research/chief scientist at Capital Fund Management (CFM). The $20 billion firm specializes in managed futures. He began his career in theoretical physics, was awarded the IBM Young Scientist Prize (1990) and the C.N.R.S. Silver Medal (1996), and has published over 300 scientific papers and several books in physics and finance.

Renewables overtook coal power in 2025 for the first time in over 100 years

Source: @nicolasfulghum

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The post 10 Wednesday AM Reads appeared first on The Big Picture.

Everybody’s attention has been focused on the Iran War,1 but I want to draw your attention back to a different war — the prior Trade War.

Eighteen months ago, the Trump administration was elected to its second term. After November 2024, there was a lot of noise about what people thought would happen. When Liberation Day rolled around in 2025, many observers, including long-standing Wall Street supporters, were shocked.

The first tariffs were on Canada and Mexico, voiding the deal the president himself had renegotiated; then came 100% tariffs on China, then a long list of other tariffs.

There was a problem with this executive-branch muscle-flexing: It was (obviously) unconstitutional. Article I, Section 8 of the U.S. Constitution specifically delegates the power to lay and collect duties, taxes, and excises to Congress.2 This was an unambiguously executive-branch overreach—arguably done in bad faith.3

The U.S. Court of International Trade found the tariffs unconstitutional; the Federal Circuit Court of Appeals (en banc, with every judge in the district participating) overwhelmingly affirmed that finding. It was fast-tracked to the Supreme Court, which ruled 6–3 against the administration.4

The President, when pushing the tariffs, promised four things: 1) Creation of industrial jobs, 2) Lower inflation, 3) Reduction of the trade deficit,  and 4) Reduction in the federal budget deficit.

None of those things happened.

All of them are now worse than before the tariffs were introduced (before the Iran assault began).

Making it worse was the sheer chaos of how the policy was implemented. According to the Cato Institute, there were 50 separate reversals, new policies, changes, pauses, and exemptions (traders called it “TACO”). The chaos froze CFOs and CEOs in place. They stopped hiring. They stopped building new factories and plants. They cut capital expenditures.  “Until we know what the policy is, how can we commit hundreds of millions (or billions) of dollars?”

As the chart at top shows, only a few agreements were actually negotiated; normally, trade agreements take years to be hammered out, especially when more than two parties are involved. But even that small handful is now void. No counterparty is going to stick with promises based on a policy the highest court in the land has declared unconstitutional.

But what about those promised benefits of the Tariffs over the year it was in effect? Let’s consider the data, to see how the reasons for implementing tariffs have fared:

Employment: roughly 100,000 manufacturing jobs were lost over the past year:

Inflation: As soon as the IEEPA tariffs were announced on Canada and Mexico, prices shot up substantially. After April 2nd, prices continued higher. Federal Reserve Chair Jay Powell specifically said that part of the reason the Fed is on hold is the higher prices consumers are paying because of tariffs.

In fact, about 90% of companies that import or retail goods said they were forced to raise prices because of tariffs. At the same time, about 75% of those companies said their margins were compressed.

Trade deficit: Reached an all-time high. The promised reduction of U.S. imports never happened, and angry allies boycotted U.S. goods.

Budget Deficit: The craziest data point of the whole tariff episode: we collected about $156 billion in tariff revenue, but we already owe back roughly $166 billion—and that figure is estimated to grow to close to $200 billion by 2029. How is that possible?

The law is that if you take money that isn’t yours—money you’re not entitled to, either legally or constitutionally — when you eventually lose that litigation, you don’t just have to return that money; you must return that money plus interest.

~~~

~~~

Tariffs were the signature economic policy of Trump’s second term; he not only campaigned on them (the 10% universal tariff floor, 60%+ on Chinese goods, reciprocal tariffs), but also insisted that the revenue source would fund tax cuts and close the deficit, create manufacturing jobs, end the trade deficit, and reduce inflation.

None of the promised benefits occurred over the year when tariffs were in effect. The data shows the exact opposite happened.

This did not come as a surprise to students of economic history. We have studied the Tariff regime in the 1930s, which more or less had the exact same results. They may not have caused the Great Depression, but they certainly made it much worse.

After losing the SCOTUS case, the president lashed out with a different series of tariffs. CATO, the conservative think tank in DC,  responded by observing that the New Tariffs Are Just as Illegal as the Old Ones.5 Look for those to be overturned eventually, also.6

I suspect the war in Iran prevented us from fully digesting what it means for the Tariffs to be overturned.7 That will come about eventually…

See also:
Trump Administration to Begin Refunding $166 Billion in Tariffs (NY Times, April 20, 2026)

Businesses start applying for US tariff refunds (Semafor, April 20 2026)

Section 122 Is an Anachronism, Not a License for New Tariffs (CATO, April 14, 2026)

Previously:
Winners of SCOTUS Decision Striking Down Tariffs (February 20, 2026)

IEEPA Tariff Ruling’s Losers (February 23, 2026)

IEEPA Tariffs Update (January 27, 2026)

It’s Tariff Week! * (January 12, 2026)

Tariffs Likely To Be Overturned (November 5, 2025)

Might Tariffs Get “Overturned”? (July 31, 2025)

The Muted Impact of Tariffs on Inflation So Far (July 17, 2025)

Are Tariffs a New US VAT Tax? (March 31, 2025)

MiB: Special Edition: Neal Katyal on Challenging Trump’s Global Tariffs (September 3, 2025)

Neal Katyal on Challenging Trump’s Global Tariffs (September 8, 2025)

Which States Could Suffer the Most From Trade War Tariffs? (September 16, 2019)

__________

1. Whether it was authorized by Congress or not, ~10 weeks later, with three carrier groups, and 100,000 troops, countless missiles, drones and bombs, it is hard to call this anything other than a “War.”

2, Article I, Section 8, Enumerated Powers:

“The Congress shall have Power To lay and collect Taxes, Duties, Imposts and Excises, to pay the Debts and provide for the common Defence and general Welfare of the United States; but all Duties, Imposts and Excises shall be uniform throughout the United States.”

The same issue exists with spending money appropriated by Congress as well.

3. In his first term, with a less pliable House Speaker and a more assertive legislative branch, the administration’s desire to use tariffs as a negotiation tool had been informally rejected by Congress.

4. An embarrassing dissent opinion came from Kavanaugh, joined by Alito and Thomas. (See this for more)

5. SCOTUS has given POTUS the ability to enact unconstitutional actions for about a year at a time. I wish Justice Roberts understood that “Justice delayed is justice denied.”

6. Hopefully, the judicial system won’t take another year to locate and constitution to figure out what it actually says…7. “Why are so obsessed with Tariffs?” a few emailers have asked. I am reminded of similar emails pre-GFC: “Why do you care so much about Derivatives and CDS?

7. That too, requires Congressional approval, which may or may not be forthcoming. You may be detecting a pattern here…

The post Tariff War, Post-SCOTUS Update appeared first on The Big Picture.

• 50 years. 50 facts. Indexing since 1976. As the golden anniversary of index fund investing approaches, the strategy is widely available across and within asset classes. Its appeal is evident: Indexing is an easy-to-understand, cost-effective strategy that can offer diversification, tax efficiency, and consistent performance relative to the results of the market. (Vanguard)

• The U.S. Is Manufacturing a Ton of Grid Batteries: Demand for energy storage is surging on the U.S. grid — and the country now has more than enough battery-making factories to meet it. Domestic energy-storage capacity is scaling fast as grid demand climbs: (Reasons To Be Cheerful) see also AI Is Using So Much Energy That Computing Firepower Is Running Out: The AI bubble has a physics problem: there aren’t enough megawatts on the grid. Compute-constrained means capex-constrained, which means the models won’t scale the way investors are pricing.  (Wall Street Journal)

• A Catechism for Robots: Kevin Kelly’s first draft FAQ dedicated to embodied AIs with persistent memories, dynamic learning, and a large dose of autonomy. (KK)

• Deals Hurt Returns. Thaler explains that auctions—especially when there are large numbers of bidders—can cause some participants to become emotional, to the point that they become undisciplined and end up bidding too much. The winners in these situations are thus “cursed” because they’re the ones who were willing to overpay the most and thus tend to be most disappointed. (HumbleDollar)

• Americans Have Always Hated Taxes. The 250-Year Battle Over Your Wallet. From the Whiskey Rebellion of the 1790s to Henry David Thoreau’s refusal to pay taxes to the 1,400 elected officials who signed an antitax pledge. Barron’s A look at the long history of American tax rebellions. (Barrons)

• How Austin’s stunning drop in rents explains housing in America: We finally have some good news about housing affordability. (Vox)

• Some of the Most Popular Graduate Degrees Don’t Pay Off Financially: New data shows the ROI on many master’s programs is worse than advertised.The report found advanced degrees in social work and psychology may have a zero to negative return, while medicine, law and pharmacy degrees show the highest return. (Washington Post)

• Why the Strait of Hormuz is a geological wonder: The continental collisions that created this narrow Mideast waterway also made it the most strategically important 21 miles on Earth. Geology is destiny, and Iran controls the choke point.  (National Geographic)

• ‘An Operational Success and a Huge Strategic Failure’ Perhaps the most apt description of Trump’s policy toward Iran is an “incoherent maze” — a phrase Pete Hegseth applied in 2016 to Barack Obama’s foreign policy. Lost in his own labyrinth, Trump granted sanctions relief to Iran even as he bombed it, and careened from threatening war crimes unless Iran opened the strait to suggesting that the strait wasn’t our concern. (NYTimes) see also The Mythology of Pete Hegseth: Garrett Graff dismantles the mythology Hegseth has built around himself as the Iran War’s cheerleader-in-chief. The alternate history he’s selling is dangerous. (Doomsday Scenario)

• NBA Playoffs: Ranking the Superstar Matchups We Want to See in the First Round: The giants who rule atop the game must go to war with one another, over and over again, until only one is left standing. There’s no room for error or quarter on the way to the pinnacle of the sport. The quality of each team still matters quite a bit but it’s the superstar matchups that will determine who will be featured on the biggest stage come the Finals…(Sports Illustrated)

Be sure to check out our Masters in Business this week with Philippe Bouchaud, co‑founder, chair & head of research/chief scientist at Capital Fund Management (CFM). The $20 billion firm specializes in managed futures. He began his career in theoretical physics, was awarded the IBM Young Scientist Prize (1990) and the C.N.R.S. Silver Medal (1996), and has published over 300 scientific papers and several books in physics and finance.

The gap between Wall Street (S&P 500) and Main Street (Consumer Sentiment) has never been this wide before. Wakl St ATH, Main St ATL

Source: @charliebilello

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The post 10 Tuesday AM Reads appeared first on The Big Picture.

The transcript from this week’s, MiB: Philippe Bouchaud, Founder/Chief Scientist, Capital Fund Management, is below.

You can stream and download our full conversation, including any podcast extras, on Apple Podcasts, Spotify, YouTube (video), YouTube (audio), and Bloomberg. All of our earlier podcasts on your favorite pod hosts can be found here.

~~~

Masters in Business Jean-Philippe Bouchaud
Chief Scientist, Head of Research, Chairman & Co-founder, CFM

Barry Ritholtz  [00:00:16]  This weekend on the podcast. Yet another extra special guest, Jean-Philippe Bouchaud is Chief Scientist, Head of Research, Chairman and Co-founder at CFM. They’re a quantitative trend following hedge fund. They run over $20 billion in client money.

Barry Ritholtz  [00:00:34]  They’ve been around for almost 35 years, put together a very impressive track record. They also run a number of interesting academic research labs and things like that. Jean-Philippe has published something like 300 plus academic papers. They are deep into all the things that drive markets from a quantitative perspective.

Barry Ritholtz  [00:00:59]  I thought this conversation was fascinating, and I think you will also. With no further ado, my interview of CFM’s Jean-Philippe Bouchaud. So what do people call you, JP, Jean-Philippe? What do you like?

Jean-Philippe Bouchaud  [00:01:13]  Jean-Philippe in France. JP in Anglo-Saxon countries. JP.

Barry Ritholtz  [00:01:17]  All right. It seems a little informal, but I’ll go with JP. So, JP, let’s start with your background: PhD in theoretical physics from ENS. You spent some years at very prestigious research institutions. I mentioned Cavendish Labs.

Barry Ritholtz  [00:01:35]  What was the original career plan?

Jean-Philippe Bouchaud  [00:01:38]  Yeah, I was planning to be a physicist, but then, studying statistical physics, and we can go into that later if you wish, I realized that physics can offer much more than studying physics. And then I was always fascinated by numbers. I’ve always liked statistics, and financial markets spit statistics every day.

Jean-Philippe Bouchaud  [00:02:03]  And I thought, this is a very interesting complex system. There are crises, crashes, jumps, the system seems to be driven by its own dynamics. Physicists have to do something about this. And so,

Barry Ritholtz  [00:02:17]  Very — sounds very similar to chaos theory.

Jean-Philippe Bouchaud  [00:02:20]  Yeah, exactly. So I mean, that was the high days of chaos theory.

Barry Ritholtz  [00:02:23]  So I pulled some phrases from some of your papers. One was titled, and I’m gonna mangle this, disordered systems and complex phenomena, which can be either physics or finance. Exactly, it sounds like. But what are the dynamics of glassy systems and granular media?

Barry Ritholtz  [00:02:42]  That sounds fascinating.

Jean-Philippe Bouchaud  [00:02:45]  But it’s all — the problem is how do interacting elements give rise to something surprising? Granular matter is grains that interact with one another. And then you have these strange phenomena called avalanches, where you drop a grain on a slope, and most of the time nothing happens. But sometimes there’s a big landslide that takes all the grains down.

Jean-Philippe Bouchaud  [00:03:13]  And so this, again, is very reminiscent of financial markets, right? I mean, many things happen, nothing much follows, and then sometimes there’s a crash, right? And so this was really intriguing for physicists like me.

Barry Ritholtz  [00:03:27]  So as you’re talking, I’m just thinking of a concept in physics that really applies to markets — the three body problem. When you have those three gravitational masses interacting with each other, it’s fairly unpredictable, which kind of seems like markets themselves.

Jean-Philippe Bouchaud  [00:03:49]  Yeah, I mean, there are two ways to be unpredictable. One is that the system is by itself unpredictable. That even with deterministic laws like the three body problem, you can’t say much after a few seconds, days, or weeks. But there are other kinds of unpredictability when there’s a true source of exogenous noise that hits the system, and you can’t say anything. So that’s the traditional way economists think about markets.

Jean-Philippe Bouchaud  [00:04:16]  They’re kind of buffeted by things you can’t predict because they come from outside. And then I think the physics hunch is that there can be self-generated shocks, self-generated randomness that come from large assemblies of individuals — IE traders, agents that trade and buy and sell to each other. And this can generate intrinsic randomness that is not of the same kind as the three body problem, but really comes from the interaction of a huge number of elements.

Barry Ritholtz  [00:04:53]  So I see the parallels between theoretical physics and finance. What led you to begin shifting in the early nineties from studying theoretical physics to becoming fascinated by market microstructure and physics?

Jean-Philippe Bouchaud  [00:05:13]  Yeah, so as I said, initially, I’ve always been excited by data and trying to make sense of data. So there was something there anyway. But what really drove the transition was, in a sense, the 1987 crash and the Black-Scholes theory. I didn’t know anything about that. And then I wrote a paper on what I was working on, which was physics systems with large jumps, if you want, large crashes,

Jean-Philippe Bouchaud  [00:05:43]  that happened from time to time. And someone who was working in the banking industry called me and said, hey, it’s really interesting because it resembles what happens in finance, and in particular, what just happened, the 1987 crash. And there’s this theory, the actual theory, that is a theory that only works in a world where there are no crashes, where all the motions are small and predictable. They’re random, they’re kind of predictable, even if they’re random in some strange way.

Jean-Philippe Bouchaud  [00:06:16]  And I thought, this is really weird. And this guy said, why don’t you try to generalize Black-Scholes to a world where there are crashes? And I thought, well, that’s really interesting. So I read Black-Scholes and I thought, it can’t be right, they must be wrong, these guys.

Jean-Philippe Bouchaud  [00:06:30]  So I kind of redid everything myself, and found something that looked more interesting than BS because it could be extended to non-Gaussian heuristics, as they’re called — non-normal distributions, bell curves and so on. And so it looked to me interesting, and I thought, okay, maybe we can do software out of that and commercialize it. And so I went and knocked on several doors, and suddenly the door of Jean-Pierre Aguilar opened, and Jean-Pierre Aguilar was someone who had founded actually CFM in ’91. That was ’94.

Jean-Philippe Bouchaud  [00:07:07]  And I started explaining what I had been doing and that I was interested in transferring ideas from physics to finance. And he said, why don’t we create something together? And so at the time, we created a company called Science and Finance, and this was done in two weeks. It was like amazing the way we met.

Jean-Philippe Bouchaud  [00:07:28]  There was a fluid that was flowing between us immediately. And so CFM then merged with Science and Finance in 1990. So it’s now the same firm. But the idea he had at the time — he had this small CTA trading firm, and he thought, I need to beef up my research team.

Jean-Philippe Bouchaud  [00:07:51]  And this guy seems to be interesting. So we just partnered and that’s how it all started.

Barry Ritholtz  [00:07:57]  And that CTA firm specialized in managed futures.

Jean-Philippe Bouchaud  [00:08:00]  Yeah, exactly. Pretty much.

Barry Ritholtz  [00:08:01]  Now I know most of the futures traders, they all seem to be trend followers. How do you think about applying quantitative research and theoretical physics to dealing with futures?

Jean-Philippe Bouchaud  [00:08:15]  Yeah, well that was exactly Jean-Pierre Aguilar’s idea. He said, okay, I’m doing trend following. It’s good, but it’s not rocket science, maybe we can do much better. And so he said, why don’t we work on something more beefy than just trend following?

Jean-Philippe Bouchaud  [00:08:32]  And so, that started the whole thing. And the main idea is data. Physicists are good at looking at data and extracting structures, looking at data and imagining that from that data you can build theories. You can identify what’s important and what’s not.

Jean-Philippe Bouchaud  [00:08:51]  And that’s, I think, the way it all works in physics — that you scrutinize data and then there’s a flash and you think, okay, I can model that. And it’s really the same process in finance, at least as far as we were concerned. And we are concerned now. It hasn’t changed.

Jean-Philippe Bouchaud  [00:09:08]  It’s the same process.

Barry Ritholtz  [00:09:10]  Really quite fascinating. You keep your professorships at ENS and you’ve maintained a foot in academia, even as you’re building and running an asset management firm. Tell us about that.

Barry Ritholtz  [00:09:23]  You’re still publishing papers? What keeps you interested in the academic side of finance?

Jean-Philippe Bouchaud  [00:09:29]  Well, first of all, it is me. I feel I’m a researcher’s researcher at heart, and I need to continue. It’s like people running the marathon — they’re doing something else in life, and then there’s an urge to run the marathon. For me, there’s an urge to understand what I’m doing and understand also things that I’m not doing even now.

Jean-Philippe Bouchaud  [00:09:52]  Even physics problems — I can get excited about them, or trying new things like the ML revolution. How does ML work? Why do large language models work

Jean-Philippe Bouchaud  [00:10:05]  so well, learn so well? I think it’s fascinating. I want to understand. But there’s another reason for doing this: to attract talent, you need to identify them. You need to attract them, you need to be their professor at one point.

Jean-Philippe Bouchaud  [00:10:23]  And I think a lot of the success of CFM has been attracting talents. And I think part of that — only part of that, of course, it’s a teamwork — is due to the fact that I’m still very connected in academic circles, and young students have listened to me giving talks, lecturing, they’ve read my papers, and so they feel, let’s go and work for that firm because it seems that they’re really doing cool stuff.

Barry Ritholtz  [00:10:50]  So is that the thinking behind establishing the research division at CFM? I mean, you run that as a full academic research department, as opposed to a lot of asset management shops. They have a couple of CFPs and MBAs and CFAs working on their quantitative models. You guys seem like you’ve taken it to a whole different level.

Jean-Philippe Bouchaud  [00:11:14]  Yeah, I mean, most — maybe even all — our researchers have a PhD. It doesn’t mean that we’re an academic lab. We’re really working on concrete stuff. We’re really there to make models that work, build portfolios that are robust, model risk, model execution, control costs.

Jean-Philippe Bouchaud  [00:11:34]  All these things are bread and butter for everyday work. But at the same time, we feel that when we find something that is beyond the kind of daily work, and that can be published because it brings something to the academic debate or to the public debate — why, how do markets work? Why are there crashes?

Jean-Philippe Bouchaud  [00:11:53]  Are markets efficient? What about the economy? Do people understand inflation? Do we need new theories to understand inflation, monetary policy, and all these things?

Jean-Philippe Bouchaud  [00:12:05]  We believe it’s our role also, because we have access to so much data and we are privileged. Academics — they don’t have access to so much data. And so we have to give back in a way. And the reason we are doing this is, as I said, not only because we’re driven to do that, but also because it creates an atmosphere where people are happy to work at CFM. I hope. I don’t want to put words in their mouth.

Barry Ritholtz  [00:12:34]  Well, you guys opened up — or expanded — a big New York office. You don’t seem to be having much difficulty recruiting people there. What’s the headcount there now?

Jean-Philippe Bouchaud  [00:12:43]  We have 115 researchers, and 15% of them are in New York.

Barry Ritholtz  [00:12:50]  What motivated expanding the New York office as much as you have?

Jean-Philippe Bouchaud  [00:12:54]  Well, first of all, a lot of our investors are in the US, so we need to be there and interact with them. And we need to have a presence, if only for investor relations, but also because there’s a lot of talent in the US that we want to grab and attract. There’s a lot of data, a lot of brokers, so it makes a lot of sense. So we’ve been in New York for 20 years, and it’s obvious that it is a hub and we should expand there.

Barry Ritholtz  [00:13:26]  So you mentioned earlier your co-founder, Jean-Pierre Aguilar, passed away in 2009. What was the impact on the firm? How did you guys manage around

Barry Ritholtz  [00:13:39]  that? That’s a big loss when you lose a founder.

Jean-Philippe Bouchaud  [00:13:41]  Yeah, it was a tragedy because he died in a glider accident. We knew that he was gliding. We knew that gliding was dangerous, but in a sense, it really means bad risk management, right? We never thought that he could crash. It never occurred to us, which was strange,

Jean-Philippe Bouchaud  [00:14:00]  because these things happen. And so it was tragic because we were not prepared. And it was tragic because he was not only a friend, but he was the public figure of CFM. He was not involved in constructing models.

Jean-Philippe Bouchaud  [00:14:15]  I mean, quants — in a way, what’s great about quant investing is that you don’t need star traders. You don’t need PMs that know everything. It’s a collective effort. And so when someone disappears or resigns or dies, it’s not a tragedy.

Jean-Philippe Bouchaud  [00:14:31]  But in the case of Jean-Pierre, it was even — he was not really involved in the construction of models. He was just very inspiring, generous, and he was really great. He had a vision, when we met, and he thought, okay, with that guy, we can build something great. It’s amazing to think that he was so enthusiastic about creating what we created together.

Jean-Philippe Bouchaud  [00:14:57]  And so we owe him a lot. So when he passed away, it was really difficult. There were several issues. One is that he had 57% of the company. So we had to negotiate with the estate to get back control.

Jean-Philippe Bouchaud  [00:15:15]  That was pretty difficult. But we went through that. And also we needed to reassure our investors. Jean-Pierre seemed to be the public figure.

Jean-Philippe Bouchaud  [00:15:26]  He was a public figure. And he seemed to be the inspiration behind everything. And so we had to communicate that we were at the helm and that we would navigate that, and it worked. And so it was very stressful, but it was very rewarding as well to go through that.

Barry Ritholtz  [00:15:46]  And the firm carries on as his legacy. Coming up, we continue our conversation with Jean-Philippe Bouchaud, Head of Research and Chief Scientist at CFM, talking about the growth of Capital Fund Management. I’m Barry Ritholtz, you’re listening to Masters in Business on Bloomberg Radio.

Barry Ritholtz  [00:16:16]  I’m Barry Ritholtz. You are listening to Masters in Business on Bloomberg Radio. My extra special guest this week is Jean-Philippe Bouchaud. He is the Head of Research, Chairman, and Chief Scientist at Capital Fund Management hedge fund, managing over $20 billion, a quantitative shop specializing in managed futures and other quant type funds.

Barry Ritholtz  [00:16:40]  So let’s talk a little bit about the building of the fund. You co-founded Science and Finance in 1994 with Jean-Pierre Aguilar. What was the thought process? Did you think you were building a quant fund, a research shop?

Barry Ritholtz  [00:17:01]  What was the original plan?

Jean-Philippe Bouchaud  [00:17:03]  A quant fund.

Barry Ritholtz  [00:17:04]  From day one?

Jean-Philippe Bouchaud  [00:17:04]  Yeah. From day one, a quant fund. But from day one, we knew that we wanted to be strongly associated with academia. We knew that the only way to innovate — again, coming back to the fact that financial markets are complex systems — it’s really difficult to beat the market.

Jean-Philippe Bouchaud  [00:17:23]  We know that everybody’s trying to beat the market. If we want to have something else to say and not follow the crowd, we have to innovate. And innovating is hard. You have to spend time, you have to have new ideas that nobody else has.

Jean-Philippe Bouchaud  [00:17:37]  And so this means investing heavily in research. So the two are not contradictory. We really wanted to be a quant fund. We knew already about Renaissance.

Jean-Philippe Bouchaud  [00:17:48]  We knew that these guys at Renaissance were very close in spirit and in culture to what we were. And so we thought we were going to try to emulate them. Of course, they’re so great that there’s no way to emulate them. But anyway, this was the aim.

Barry Ritholtz  [00:18:03]  They had a 40 year head start on you guys. So it’s funny you mentioned Renaissance Technologies. When I’m doing my research for CFM, I’m kind of reminded of D.E. Shaw and AQR and a few others, a little bit of Millennium — although they do so much of everything — the thought process behind being a quant shop when there’s so many other quant shops.

Barry Ritholtz  [00:18:32]  If we don’t create our own models, if we don’t create our own findings and innovations, we’re just an also-ran. Is that the thought process behind it?

Barry Ritholtz  [00:18:42]  We have to do this, otherwise — because all of these other quant shops I’ve mentioned, none of them are quite the academic lab that you’ve

Jean-Philippe Bouchaud  [00:18:53]  created. AQR is, I think, closest to us on that front. Renaissance initially took a completely different turn. They thought, we have to be completely secretive about everything and be a kind of black hole where everything goes in but nothing goes out.

Jean-Philippe Bouchaud  [00:19:12]  And that was not our philosophy. We thought that life is too short as well. This is not only — we want to make money for ourselves, for our investors, we want to excel, but not at any cost. We think that there’s something else in life, that there’s a legacy that we want to leave.

Jean-Philippe Bouchaud  [00:19:29]  And this legacy is intellectual as well.

Barry Ritholtz  [00:19:32]  Pursuing the truth as to what drives markets. And what leads to alpha and returns.

Barry Ritholtz  [00:19:36]  Every new discovery of alpha eventually gets arbitraged away. Is that the thinking?

Jean-Philippe Bouchaud  [00:19:44]  Yeah, not exactly. I mean, trend following — it’s not arbitraged away at all. And actually, if you think about it, it’s very hard to arbitrage trend following. If people trend follow, it is going to lead to more trend, not less.

Barry Ritholtz  [00:19:59]  Right? Momentum is not only a Fama-French factor, but it takes on its own life, right?

Jean-Philippe Bouchaud  [00:20:04]  Well, Fama doesn’t like momentum, but anyway.

Barry Ritholtz  [00:20:06]  But isn’t it part — it’s not part of the original three factor model, but wasn’t it in one of the later models?

Jean-Philippe Bouchaud  [00:20:14]  Reluctantly, I think he had to add it, but it’s a disgrace for efficient market theory. So he doesn’t like momentum at all.

Barry Ritholtz  [00:20:22]  Listen, if the math is there, it doesn’t matter if you like it. If it works, if it’s a valid factor, it’s a valid factor. I agree.

Jean-Philippe Bouchaud  [00:20:30]  I agree. I agree. But that’s, again, a physicist’s point of view. Experiments are above everything else.

Jean-Philippe Bouchaud  [00:20:39]  But sometimes when you talk to economists, they have a strange view that theorems and axioms supersede any empirical observation. I was told that by an economist. And so there’s a very strong difference in perception.

Barry Ritholtz  [00:20:54]  I recently had Richard Thaler and Alex Imas in the studio, and I was shocked to learn from them they still aren’t teaching behavioral finance and economics courses at a college level, which is kind of shocking. I agree — you’d think everything we’ve learned.

Barry Ritholtz  [00:21:13]  So let’s talk about another technology. Over the past decade, but especially the past few years, there have been huge advances in artificial intelligence and machine learning, to say nothing about large language models. How are you guys thinking about real world investing driven by AI, and what sort of opportunities does this open up?

Jean-Philippe Bouchaud  [00:21:35]  Well, AI is really an advanced form of data analysis. And in a way, we’ve been doing machine learning forever. The thing is that techniques have evolved. It’s now much more efficient.

Jean-Philippe Bouchaud  [00:21:50]  There are many more things that one can do, in particular reading text. For many years we were just using numbers. And actually for many years we were just using prices and volumes and not anything else — and fundamental information about companies.

Jean-Philippe Bouchaud  [00:22:06]  But now there’s so much data that you can use. There’s a new data set every day that we are presented with by the data vendors. And so there’s a need to handle the data, to read sometimes huge data files. For example, if you think about microstructure high frequency data, there are events happening in the order book of major exchanges at the millisecond level or even faster.

Jean-Philippe Bouchaud  [00:22:31]  This generates a huge amount of information that has to be dealt with, analyzed. And machine learning helps you very much doing that. Reading texts that no human would be able to read and extracting statistical information from that text. So for us it is, I wouldn’t say a revolution, but it’s an acceleration of things that we were trying to do before.

Jean-Philippe Bouchaud  [00:22:56]  And obviously we’re much in tune with that. We’ve actually created a lab at CFM to help transferring technology from what ML people are constructing to what researchers at CFM may be using. But also to try to understand how these things work, right? Because we are very uncomfortable with the idea of black boxes.

Jean-Philippe Bouchaud  [00:23:19]  A black box is something that can improve the research process. But when you think about implementing that in production and having models trading with these models, you really want to be sure that the machine has done something that makes sense. And so understanding what machine learning is actually doing, why are these things working to start with — what is strange is that it works so well, but nobody understands why. When you’re driving a car, the car works really well, but we know exactly why it works, how it works. Machine learning — nobody really understands what’s the magic.

Jean-Philippe Bouchaud  [00:23:58]  And I think it’s a huge intellectual challenge and we want to be part of that.

Barry Ritholtz  [00:24:03]  How much of that is pattern recognition? Because when I think about it, whenever I read about LLMs, it’s really just statistically what makes the most sense for the next letter, the next word, the next sentence. It’s kind of hard to think of crafting a document based on probabilities of the most likely word, if you have these few words beginning. But apparently that’s a big part of how they work.

Barry Ritholtz  [00:24:31]  Or am I grossly over

Jean-Philippe Bouchaud  [00:24:32]  by that? Yeah, so why is that, why does it work, and can it work in finance too? Is it because the language or images have such a strong structure that there’s an internal logic to language or to pictures or to other things that the model is able to capture, and using these relatively simple ideas of statistical prediction of what’s going to happen next is enough to generate meaningful sentences? But maybe there’s part of that — the structure of the data. Is it the case in finance too? Maybe, maybe

Barry Ritholtz  [00:25:12]  not. Well, that’s literally exactly where I want to go. If you’re training an LLM on billions and billions of documents, pages, books, whatever, and it now has a giant data source, so when you get these first few words or first few sentences, here’s what’s most common, here’s what’s second most common.

Barry Ritholtz  [00:25:30]  And here’s a reference check for you to say, how does this compare grammatically, structurally, to the giant corpus we have? I can see the math behind that, because there are only so many trillions of combinations of letters, words, sentences. But when you now apply it to markets, which seem to be so random tick to tick, day to day, can you apply the same sort of logic to investing?

Jean-Philippe Bouchaud  [00:26:01]  Well, there are two problems. One is fundamental: are there structures that you can extract? And we believe that there are, because otherwise we wouldn’t be here. I mean, trend following is a structure, it’s a pretty trivial one, but it is a structure. Now, many other types of structures in the data that we’ve extracted without using ML, or using ML now, or recovering with ML, or even more complicated ones with ML.

Jean-Philippe Bouchaud  [00:26:31]  But the major difference between finance and languages or pictures is, one, the amount of data. Because in the end, stock markets have only existed since, I don’t know, 1900 or 1800, if

Barry Ritholtz  [00:26:45]  you want. Years. So, small data set.

Jean-Philippe Bouchaud  [00:26:47]  Small data set. Except if you go to high frequency — as I said, if you go tick by tick, all the book data, there are huge amounts of data. And there you can think that there’s more to it. But yeah, so there’s the problem of the availability of data, and the frequency at which you want to predict.

Jean-Philippe Bouchaud  [00:27:11]  So for high frequency, I think there’s a lot of structure. For lower frequency, it’s not clear yet that it is going to be useful, used as a kind of technical model, which only looks at prices without reading text. For reading text, we know that there’s a lot of structure which responds to the structure of language. But having said everything you said, there’s still something strange about LLMs or generative AI — that with this process of constructing sentences that are statistically valid, you can invent new things.

Jean-Philippe Bouchaud  [00:27:49]  And that’s the thing that is really strange, right? I mean, you can learn pictures, for example — this celebrity database where you make the machine learn these pictures, and then you ask the machine to generate new ones. And it does. And these are pictures that look exactly

Jean-Philippe Bouchaud  [00:28:08]  — I mean, you look and you think it’s a celebrity, but the celebrity doesn’t exist, right? So there’s something still weird about this that, as I said, nobody really understands.

Barry Ritholtz  [00:28:19]  Really kind of interesting.

Jean-Philippe Bouchaud  [00:28:20]  And so continuing on that, what we are trying to do is to do the same thing with financial markets. So, as I said, a hundred years of data is not a lot, but maybe you can use these gen AI models to generate a million years of fictitious financial markets. That’s interesting.

Barry Ritholtz  [00:28:40]  Very interesting. So let’s talk a little bit about trend following and managed futures. It’s had a few real standout years — in particular, 2022, a real challenging year, and managed futures were at the top of the asset quilt. What does that episode tell us about what strategies work, why they work?

Barry Ritholtz  [00:29:05]  And the question I always find with trend following, why do so few investors tend to stay with them? They all seem to get nervous and tap out right before things — almost when you see people giving up, it’s just about when the turn occurs.

Jean-Philippe Bouchaud  [00:29:23]  I agree.

Barry Ritholtz  [00:29:24]  So first of all, why was 2022 such a standout year?

Jean-Philippe Bouchaud  [00:29:29]  I don’t know.

Barry Ritholtz  [00:29:30]  I mean, aside from the fact that we had big Fed rate hikes and fixed income and equities both got shellacked double digits — kind of rare occurrence the same year. I think you have to go back about 40, 41 years to see both of them down significantly. How do you think about what environment leads to best results for trend following?

Jean-Philippe Bouchaud  [00:29:59]  It is very difficult to say, because otherwise we would have a meta-model that arbitrages and increases the weight of trend following when it’s going to work. Well, there probably is more research to do. And we’ve been trying; we haven’t found anything that’s very convincing. But the beginning of 2026 is also a very good period for trend following.

Jean-Philippe Bouchaud  [00:30:21]  Actually, we wrote a paper in 2014 called ‘200 years of trend following.’ And we were reporting on the fact that since 1800, if you paper trade a very simple trend following strategy, you make money every decade with ups and downs. There are years that are not so good. But as you say, what is striking about the very point you made about people getting out of trend following just before it gets back on, is I think it’s ingrained in people’s behavior to chase performance.

Jean-Philippe Bouchaud  [00:30:57]  So if performance has been bad for a few years, everybody declares it dead. And that was the case in 2014 when we wrote our paper — trend following had been flat for the last five years. And people said, okay, well, trend following is dead now. And we were absolutely convinced that it was not the case, that trend following is such a strong behavioral bias — performance chasing is so ingrained in every one of us, even rational — we can’t help it.

Jean-Philippe Bouchaud  [00:31:26]  And so we bet, and it was confirmed, that trend following would come back. And since 2014 it has been very good actually overall.

Barry Ritholtz  [00:31:35]  Well, you had a market that very much was trending mostly in one direction — I mean, you have Q4 of 2018, and I think 2016 was so-so, but for the past 15 years, the bias has been pretty much in one direction. If you’re on the right side of that, you should do pretty well.

Jean-Philippe Bouchaud  [00:31:52]  Yeah, but I’m not speaking about being long. I mean, right, I’m really speaking about

Barry Ritholtz  [00:31:56]  different asset classes, different assets, and trends up, you long and short.

Jean-Philippe Bouchaud  [00:32:00]  Yeah.

Barry Ritholtz  [00:32:00]  Sure. So it doesn’t matter as long as the trend is in place, you want to participate in it — up or down. And for people who are not familiar with managed futures, there’s a decent amount of leverage used in that product. So you have to really manage the risk of the downside.

Barry Ritholtz  [00:32:17]  But how do you think about the potential upside relative to the risk you’re taking in a futures product?

Jean-Philippe Bouchaud  [00:32:23]  What do you mean exactly? I mean, we just think about risk. Risk is a very complex object actually. There’s volatility, but there’s also correlation.

Jean-Philippe Bouchaud  [00:32:34]  If you deal with a portfolio of futures that has like 150 futures, there’s a very subtle correlation structure between all the assets that you have in your portfolio. So if you think about risk, you really have to think about how all these products interact with one another, talk to one another. And so it’s not only a question of volatility that goes up and down that you have to control, but also a question of how these assets co-move together or anti-co-move together. But the way we think about upside risk is the same as the way we think about downside risk — it is just a question of risk.

Barry Ritholtz  [00:33:10]  So you mentioned 150 different assets. I’m assuming some of these are commodities — wheat,

Jean-Philippe Bouchaud  [00:33:16]  crude oil, gold, commodities,

Barry Ritholtz  [00:33:18]  stocks, bonds, interest rates, right? What else is in the full list of 150?

Jean-Philippe Bouchaud  [00:33:24]  Well, there are different maturities, different countries’ futures, futures in China. I mean, if you count everything, it goes up to — I don’t have the exact number, but it’s in the 150s altogether.

Barry Ritholtz  [00:33:39]  Has CFM been looking at prediction markets, things like Kalshi and Polymarket?

Jean-Philippe Bouchaud  [00:33:44]  No, we haven’t. They’re not liquid enough for us, really.

Barry Ritholtz  [00:33:47]  And you need size, and they can’t provide it. Exactly. Really quite fascinating.

Barry Ritholtz  [00:33:52]  Coming up, we continue our conversation with Jean-Philippe Bouchaud, co-founder and Chief Scientist at Capital Fund Management, talking about how market structures are changing today. I’m Barry Ritholtz, you’re listening to Masters in Business on Bloomberg Radio. I am Barry Ritholtz. You are listening to Masters in Business on Bloomberg Radio.

Barry Ritholtz  [00:34:28]  My extra special guest this week, Jean-Philippe Bouchaud, Chief Scientist and Chairman at CFM, a quantitative hedge fund managing over $20 billion in assets. So let’s just talk a little bit about risk management. I know that when you’re dealing with leveraged or long/short or futures, there’s a very robust thought process around risk management.

Barry Ritholtz  [00:34:56]  Tell us a little bit about how you think about correlation and risk.

Jean-Philippe Bouchaud  [00:35:02]  Yeah, we have a disciplined and systematic approach, not only to alpha signals, to building prediction, but also to risk management. We have a pretty sophisticated tool to predict the volatility of tomorrow, the volatility of our portfolio tomorrow. And we are pretty good at that. So of course, we know financial markets are difficult beasts.

Jean-Philippe Bouchaud  [00:35:25]  And even if you have the best model in the world, you can still have unexpected events that blow up your portfolio. That’s something that we can’t say will never happen. But in a way, if you don’t want to take any risk, you shouldn’t be in financial markets, right? You shouldn’t be in that business.

Jean-Philippe Bouchaud  [00:35:43]  So we accept that there might be, I don’t know, a completely unexpected event that breaks the whole financial markets everywhere in the world, and everything is going to fail and there’s nothing to do about that. So that can happen. But barring these extreme events, we think we’re pretty good at predicting what’s going to happen.

Jean-Philippe Bouchaud  [00:36:05]  And over the last 35 years of the existence of CFM — actually our anniversary is this year, we’re celebrating our 35th anniversary in June in Paris, very proud of that — it kind of resisted these 35 years, although we’ve become much better with time. But having said that, there’s always an element that you have to be ready to intervene, even if you’re a quant shop.

Jean-Philippe Bouchaud  [00:36:38]  And so on several occasions in the past 35 years, we decided that our risk model couldn’t know about things that we humans knew, like, I don’t know, the Brexit votes. And in these cases,

Barry Ritholtz  [00:36:53]  so let’s talk about that. Just in the past 12 months, between the tariffs and Venezuela and now the ongoing war in Iran, how does global market volatility around all these geopolitical events — how does a quant shop deal with that? What I’m hearing is the humans have to do what humans do and sometimes override the machines.

Jean-Philippe Bouchaud  [00:37:18]  Sometimes. Yes.

Barry Ritholtz  [00:37:19]  I mean, when unexpected geopolitical events disrupt the world, our models — your models — are just not built to really work their way through that.

Jean-Philippe Bouchaud  [00:37:31]  You’re right. Some events are okay, and like the war in Iran for the moment is not something that our risk models are completely blind to, right? It doesn’t mean that they’ve predicted it at all. It just means that we’re comfortable with the risk that our models have predicted, and they’ve adapted sufficiently fast to the events so that we’re comfortable with the risk level, no human intervention.

Jean-Philippe Bouchaud  [00:37:58]  On the other hand, in some cases it’s completely unexpected. Like tariffs and liberation day — this created havoc. Although, strangely enough, liberation day was announced. Everybody knew what was going to be said, and still everybody

Barry Ritholtz  [00:38:14]  was surprised — didn’t think of the depth of it. I don’t believe — despite ‘I’m tariff man, it’s the most beautiful word in the dictionary’ — I think the 100, 150% tariffs on specific countries, later found to be completely unconstitutional, but at the time I think people were genuinely shocked

Barry Ritholtz  [00:38:33]  by this. And then a week later, a little bit of a TACO trade where, let’s just put a pin in this for 90 days. Again, back to the volatility, how do you deal? Oil is trending upwards and then you have a tweet, ‘the war’s over,’ and then it resumes, and then you have a tweet,

Barry Ritholtz  [00:38:54]  ‘I think we’ve got a deal.’ And then the other side says, we’re not even negotiating. I don’t recall a period in history where the president of the United States just constantly disrupted the normal flow of market activity. How disruptive is this to a quant model?

Jean-Philippe Bouchaud  [00:39:14]  Well, as I said, the two cases seem to be pretty different. Liberation day was really a surprise. And we had to manually intervene. There was something in our models that was completely blind to these things, and we had to make a judgment call.

Jean-Philippe Bouchaud  [00:39:28]  I think the idea really is that humans should use their best judgment in these cases and decide whether it’s reasonable that the risk model knows something about what’s going on or not. In some cases it does. In some cases it doesn’t. The tricky part is not to overreact, because you said you don’t remember periods of the world where things like this happened.

Jean-Philippe Bouchaud  [00:39:51]  But looking back, I’ve been in the markets for 35 years and every year there seems to be something unexpected that happens.

Barry Ritholtz  [00:40:00]  Just not every day.

Jean-Philippe Bouchaud  [00:40:02]  Not every day, but every year. Every

Barry Ritholtz  [00:40:03]  day seems like a lot, right?

Jean-Philippe Bouchaud  [00:40:05]  But in a way, every day means that it becomes a new normal,

Barry Ritholtz  [00:40:09]  I guess.

Jean-Philippe Bouchaud  [00:40:10]  And so it’s not that bad. If it’s every day — but really, this idea that this time is different is something that’s strange. If you look at the world and the history of financial markets, it’s really being normal that’s not normal.

Jean-Philippe Bouchaud  [00:40:27]  And we’ve become used to that.

Barry Ritholtz  [00:40:29]  So let’s stay with the idea of modeling. You’ve been kind of skeptical of certain applications of deep learning in finance. There’s overfitting — no one’s ever seen a bad back test because they all seem to work perfectly in the past. You have a lot of signal-to-noise issues. What are some of the problems with models that you are focusing on improving?

Jean-Philippe Bouchaud  [00:40:57]  Yeah, well, for example, this, exactly what you just said: can you have indicators that tell you whether your back test is overfitted or not? And for many years we struggled with that, and we used judgment again to say this is plausible, this is not plausible.

Jean-Philippe Bouchaud  [00:41:16]  We can believe that we kind of replace the trader that trades every day his signals or his beliefs to a higher level where we are traders of models. We kind of judge — we say this model is good enough to go in production, this model is not convincing enough. But it would be great to have something more systematic.

Jean-Philippe Bouchaud  [00:41:41]  And over the years we’ve been struggling, and I think with some success, to have meta-models that predict whether your back test is really fudged or if it’s decent enough to go in production. So we are kind of industrializing this process of selecting models that will go into production. Does that make sense?

Barry Ritholtz  [00:42:05]  That makes perfect sense. You’re a quant, and we’ve seen some issues with a lot of quant shops in the US where crowding became a structural risk. You have all these systematic strategies, and math is math.

Barry Ritholtz  [00:42:20]  So essentially you end up with a crowded trade. We had what people called the Quant Quake way back when. How do you think about that? How do you manage that problem when you’re constructing portfolios?

Jean-Philippe Bouchaud  [00:42:34]  Sure, it’s something that every day we think about. We were in the Quant Quake in 2007. And actually we were fortunate enough to be out of the markets, or to have de-leveraged already, two weeks before the worst day of the Quant

Barry Ritholtz  [00:42:54]  Quake. Was that an external signal, or one of your model signals, or what led you

Jean-Philippe Bouchaud  [00:42:58]  to it? It was something in the performance already in July. The Quant Quake, the really bad day, happened maybe 9th of August — I don’t remember exactly — but yeah, it was early

Barry Ritholtz  [00:43:08]  August, dog days of summer, for sure. Right.

Jean-Philippe Bouchaud  [00:43:10]  But starting like the 10th of July already, there was something really very strange in our portfolio and we started reflecting on what was going on, and decided someone was de-leveraging and hitting us by shorting our longs and buying our shorts. And this thought process of imagining that even if a fund that was like 10% correlated with ours — not a lot, but 10% — and having every day a kind of systematic de-leveraging policy, it would create exactly the kind of signals that we were seeing in our portfolio. So we thought, okay, this is maybe going to lead to a crash because people are going to suffer, and at one point they’re going to

Barry Ritholtz  [00:43:56]  — it cascades and

Jean-Philippe Bouchaud  [00:43:57]  cascades, and so on. And so that was the rationale for getting out. So in some cases you’re lucky enough to have strong enough signals that tell you that your standard risk model is wrong and you should do something else.

Barry Ritholtz  [00:44:12]  That’s really fascinating. So you are now almost 35 years into being a market quant.

Barry Ritholtz  [00:44:30]  What’s the most important thing about markets that the mainstream funds still get wrong?

Jean-Philippe Bouchaud  [00:44:39]  Well, I think it’s this question of what is price doing? What are moving prices? And a lot of people still believe that there’s something like a fundamental value and that the price is really moving because fundamentals are moving. Whereas we believe — and this touches very recent academic papers that Gabaix and Koijen, two economists, have put forward.

Jean-Philippe Bouchaud  [00:45:06]  They’ve called it the inelastic market hypothesis. And we’ve contributed to that debate as well. And the idea is really that markets are not driven by fundamentals — or at least they are, to some extent, driven by fundamentals. But this is a small long-term effect. On the short run — short run meaning from one day to one year, which is pretty long already —

Jean-Philippe Bouchaud  [00:45:31]  it’s really flows that matter. That is, people buying or selling stuff — whatever the reason they buy or sell is going to move prices. It’s not going to move prices on a short timescale and then disappear. It’s really going to leave a trace in markets.

Jean-Philippe Bouchaud  [00:45:47]  And this is really a fundamental change of point of view that I think is going to percolate and convince more and more people looking forward. But having this change of tag is really important, because in one case, what you need to do to make money is to predict fundamentals. In the other case, you need to predict what people are going to do. And so in a sense, crowding can be a good thing.

Jean-Philippe Bouchaud  [00:46:15]  Because if there’s crowding, it’s easier to predict what the crowd is going to do. And so if — whatever the reason people do things, they move prices — and you’re able to predict what people are going to do because you have behavioral models and structural models that tell you, everything else being equal, people are more likely to do this and that, then you can build models. And I think that’s the reason why we’ve been successful — this change of philosophy. We’re not kind of anchored to fundamentals, we’re anchored to flows.

Barry Ritholtz  [00:46:46]  So this sounds a little bit like the Ben Graham line — I think it’s Graham — in the short run, markets are voting machines; in the long run, they’re weighing machines. Is that the balance between flows and fundamentals?

Jean-Philippe Bouchaud  [00:46:59]  Yeah, I think it’s an old idea. I mean, it’s Keynes also — things like that. But in the long run, we’re all dead — Keynes said that. So it is really a question of whether you’re going to be solvent.

Jean-Philippe Bouchaud  [00:47:16]  I mean — ooh, I’m getting tired. I’ll take that again. What’s the word?

Barry Ritholtz  [00:47:27]  Was that Keynes, by the way, or Graham? I initially thought it was Keynes, and then I checked myself.

Jean-Philippe Bouchaud  [00:47:32]  I’m not sure. But what Keynes said is that — what was his quote? Markets can remain irrational longer than you can remain solvent.

Barry Ritholtz  [00:47:45]  He never said that, but it’s always attributed to him. There’s a great website called Quote Investigator.

Barry Ritholtz  [00:47:52]  And you give them a quote. Because people, as a sort of appeal to authority, they’ll put somebody sophisticated as the source. Einstein never said ‘compounding is the most powerful force in the universe,’ but they always attribute it to

Jean-Philippe Bouchaud  [00:48:09]  — I didn’t know that one.

Barry Ritholtz  [00:48:10]  I spend way too much time perusing quotes on the site. But you would be shocked who said ‘markets are a voting machine in the short run, but a weighing machine in the long run.’

Jean-Philippe Bouchaud  [00:48:23]  I don’t think it’s Keynes, by the way.

Barry Ritholtz  [00:48:25]  No, I don’t remember if it was Keynes or Graham, but I thought it was one or the other. And it’ll tell me — yeah, Benjamin Graham. But the first thing that came to mind was Keynes, or Galbraith. They have so many favorite quotes from

Jean-Philippe Bouchaud  [00:48:38]  — from both. He said many relevant things, even today.

Barry Ritholtz  [00:48:41]  Yes, absolutely.

Jean-Philippe Bouchaud  [00:48:42]  But actually we have models that predict exactly that — on the short run you can have trends and irrational behavior, and on the long run it reverts back to fundamentals. But the long run, from our estimate, is like five, ten years. I’m very long timescale.

Barry Ritholtz  [00:48:58]  I’m trying to remember — it might have been Fama, from Fama-French, who said you can’t really tell if a manager is skilled until you have 20 years of data, because it could just be good luck over five or ten years. Which is kind of fascinating. I want to stay with the efficient market, or the inelastic market hypothesis.

Barry Ritholtz  [00:49:19]  I’m curious as to your thoughts on EMH. I’ve always thought markets were kind of eventually efficient, but not very efficient in the short run. What’s your criticism of EMH?

Jean-Philippe Bouchaud  [00:49:35]  Well, it really depends what you mean by efficient. If you mean that they’re very close to unpredictable, then you’re right. But I think it’s a very dumbed-down version of EMH. The question is whether prices reflect something fundamental that is in principle not knowable, that reflects reality, or not at all.

Jean-Philippe Bouchaud  [00:49:57]  And one smoking gun of that is, do you have long-term mean reversion? That is, can prices do random things, in particular trending — which is really completely against EMH on the short run, that is, from a week to six months, markets are trending. Over six months, one year — and then on the longer timescale, they kind of hover around some long-term trend.

Jean-Philippe Bouchaud  [00:50:28]  And I think this is true, but this is really at odds with efficient markets, which tells you that every day markets are around the correct price, right? And there’s no trend, no mean reversion ever.

Barry Ritholtz  [00:50:42]  Which is not exactly what your day-to-day experience is if you’re in the markets. It seems — it’s not magic. The collective votes of all market participants don’t magically bring you to the correct, in quotes, price.

Jean-Philippe Bouchaud  [00:50:59]  But that’s the assumption of efficient markets. Right? Or actually, not even an assumption — it’s the argument that collectively, if you have — that’s the difference between having rational investors that all take decisions based on noisy observation, but independent from one another.

Jean-Philippe Bouchaud  [00:51:20]  Then because they’re independent, they realize the mean. I mean, some overpriced, some underpriced, and then it’s a voting machine and the vote comes out, right? Because there are enough investors and they’re uncorrelated to one another. But the problem with markets is that it’s not the way it works.

Jean-Philippe Bouchaud  [00:51:36]  People are influenced by what other people are doing and what other people are saying. So instead of having independent guys doing random stuff, it’s kind of one guy who’s doing only one thing, which is a fictitious body that aggregates everybody in the same way.

Barry Ritholtz  [00:51:58]  Let’s jump to our favorite questions that we ask all of our guests, starting with: tell us about your mentors who helped shape the direction of

Jean-Philippe Bouchaud  [00:52:07]  your career. Oh, that’s an easy one. I have several mentors, but two of them are really close to my heart. One is Mandelbrot, of course — the fractal guy.

Barry Ritholtz  [00:52:18]  I knew him personally.

Jean-Philippe Bouchaud  [00:52:20]  Oh, really?

Barry Ritholtz  [00:52:20]  Yes. And he did a lot of things in physics as well. So he influenced a lot.

Jean-Philippe Bouchaud  [00:52:27]  My wife — my wife was a physicist before turning to a playwright now. And she worked on fracture surfaces. The way, when you break a material, what emerges from the fracture is a kind of very rough landscape that is fractal, and Mandelbrot had worked on that, and there was a lot of interaction with Mandelbrot.

Barry Ritholtz  [00:52:50]  Fractal at a molecular level or at a larger level?

Jean-Philippe Bouchaud  [00:52:53]  Well, fractal from the very fine structure to macroscopic length scales. And so Mandelbrot also did his work on financial markets. And for me it was really a revelation. It was something very influential, and out of the dogma of Brownian statistics and Gaussian phenomena and so on.

Jean-Philippe Bouchaud  [00:53:17]  And so it was also very close to what I was doing myself in physics. So it was clear that he influenced me enormously on that.

Barry Ritholtz  [00:53:26]  Didn’t he write a book on market crashes? And how there’s a fractal nature within those? I’m trying to

Jean-Philippe Bouchaud  [00:53:33]  — The Misbehavior of

Barry Ritholtz  [00:53:34]  Markets. That’s right. There you

Jean-Philippe Bouchaud  [00:53:35]  go. I’m actually quoted in that book.

Barry Ritholtz  [00:53:37]  Oh, get out. That’s fascinating.

Jean-Philippe Bouchaud  [00:53:39]  And then Pierre-Gilles de Gennes, who was a Nobel Prize in physics, a French physicist, who was so fantastic. And both these two, and also Phil Anderson, who was a Nobel Prize in physics as well, in the US — these three people, they convinced me that you shouldn’t be stuck to your own field. You should broaden your scope.

Jean-Philippe Bouchaud  [00:54:04]  And what you learn from one field can be very useful in understanding another field. The three of them, they’ve really kind of hovered around and not got tied to their specific initial field. And I think this creates — well, at least for me, this uninhibited me in the sense that I thought, okay, maybe I’m not legitimate to speak about finance because I’m a physicist. But no, it doesn’t matter.

Jean-Philippe Bouchaud  [00:54:34]  If I have things that I strongly believe in, I should better say them and go to the end of them. So I think they were really influential in that

Barry Ritholtz  [00:54:42]  way. So since we mentioned The Misbehavior of Markets, let’s talk about some books. What are some of your favorites? What are you reading right now?

Jean-Philippe Bouchaud  [00:54:50]  Wow, so many. That’s really a very broad question. So my last book is a book on John and Paul, by Ian Leslie — John Lennon and Paul McCartney.

Jean-Philippe Bouchaud  [00:55:03]  It’s a beautiful book. I really loved it.

Barry Ritholtz  [00:55:05]  What’s the name of it?

Jean-Philippe Bouchaud  [00:55:06]  John and Paul.

Barry Ritholtz  [00:55:08]  Is it John and Paul: A Love Story? Am I remembering it correctly?

Jean-Philippe Bouchaud  [00:55:10]  Yeah. I’m a big Beatles fan.

Barry Ritholtz  [00:55:15]  Me too. That’s in my queue.

Jean-Philippe Bouchaud  [00:55:16]  You should read it. Very emotional.

Barry Ritholtz  [00:55:16]  I’m gonna make a recommendation to you for a YouTube channel called ‘You Can’t Unhear This.’ They take apart Beatles songs in ways that — just little things that were done in the recording process that in a million years you never would’ve noticed. And then once you hear it, you just can’t unhear it. And if you’re a Beatles fan, it’s a rabbit hole. You’ll love this. What else besides John and Paul?

Jean-Philippe Bouchaud  [00:55:43]  Yeah, I’m reading something that I should have read for years — Mrs. Dalloway, Virginia Woolf. I’m really a great admirer of Virginia Woolf.

Barry Ritholtz  [00:55:54]  Really interesting. Do you do much streaming TV, podcasts, anything like that? Or we can skip over that.

Jean-Philippe Bouchaud  [00:56:03]  Podcasts a bit, but they’re kind of French podcasts.

Barry Ritholtz  [00:56:07]  So let’s — people are always looking for stuff. So what are you streaming today? What sort of podcasts are you listening to?

Jean-Philippe Bouchaud  [00:56:15]  Yeah, in France we are very fortunate. We have something called — it’s a radio where there’s an enormous — I mean, you could stay tuned all day if you wanted. There’s so many interesting things going on about everything — cultural, literature, but also movies, politics.

Barry Ritholtz  [00:56:34]  We have NPR here. It’s very similar. It’s a rabbit hole. You could fall down.

Jean-Philippe Bouchaud  [00:56:38]  Okay. So lives of major celebrities in culture, in cinema, and theater — all these things. So I’m really a big fan.

Barry Ritholtz  [00:57:06]  Our final two questions. First, what sort of advice would you give to a recent college grad interested in a career in either quantitative investing or theoretical physics?

Jean-Philippe Bouchaud  [00:57:29]  Well, study theoretical physics, and study everything that’s related to data. Pay attention to data, and think about something that you strongly believe in and that you feel has not been investigated. And it doesn’t matter if it’s big or small — make the effort of building something you strongly believe in.

Barry Ritholtz  [00:57:40]  Really good answer. And our final question: what do you know about the world of investing today that you wish you knew 35 years or so ago when you were first getting started?

Jean-Philippe Bouchaud  [00:57:44]  Oh, that it is extremely competitive. Much more than we thought.

Barry Ritholtz  [00:57:47]  Really? Wow. That’s really fascinating.

Barry Ritholtz  [00:58:14]  Jean-Philippe, thank you so much for being so generous with your time. We have been speaking with Jean-Philippe Bouchaud, CFM’s co-founder, Chairman, and Chief Scientist. If you enjoy this conversation, check out any of the 600-plus we’ve done over the past 12 years. You can find those at Apple Podcasts, Spotify, YouTube, Bloomberg, wherever you find your favorite podcasts.

Barry Ritholtz  [00:58:28]  I would be remiss if I didn’t thank the craft team that helps me put these conversations together each week. Meredith Frank is my video producer. Anna Luke is my podcast producer. Sean Russo is my head of research. I’m Barry Ritholtz. You’ve been listening to Masters in Business on Bloomberg Radio.

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