frankly speaking

The Formula that Killed Wall Street

Frank Voisin — Sat, 04 Apr 2009 03:12:38 +0000

There’s an interesting article in Wired about David Li, a quant who derived the Gaussian copula function, was touted as a lock for the Nobel Prize in Economics, and recently fell from grace due to the roll his function had in the current economic meltdown.

The Gaussian copula function models risks in a different manner, supposedly with greater accuracy. It was quickly adopted by ratings agencies, regulators and the greater financial community. In particular, it was instrumental in the securitization of assets like sub-prime mortgages. More specifically, Li’s function estimates the correlation of defaults in specific tranches, by looking at the movement of credit default swaps in the past. It allowed ratings agencies to assign Triple-A ratings to the “safest” tranches, which have since been found to be overrated. These unrealistic ratings led to lower financing costs (since lower risk or higher safety means lower required compensation for risk and thus lower financing costs) which quickly caught on and helped drive significant economic growth.

In finance, you can never reduce risk outright; you can only try to set up a market in which people who don’t want risk sell it to those who do. But in the CDO market, people used the Gaussian copula model to convince themselves they didn’t have any risk at all, when in fact they just didn’t have any risk 99 percent of the time. The other 1 percent of the time they blew up. Those explosions may have been rare, but they could destroy all previous gains, and then some.

Fast forward a few years, and the limitations of the Gaussian copula function become more obvious as correlations between defaults skyrockets and bankers, ratings agencies and regulators continued to ignore the effects of the assumptions they have made in using Li’s formula and fail to adjust these assumptions to reflect the new economic reality. Li himself warned of the way his model was being used, though these warnings fell on deaf ears. Perhaps if the model’s creator had been listened to, some of the current mess could have been avoided. Anyways, it is a good article that I’d recommend. Enjoy.

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The Day Wall Street Exploded

Frank Voisin — Tue, 03 Mar 2009 23:35:13 +0000

You’ve heard of 9/11, but what about 9/16? I hadn’t either, but September 16, 1920 was the deadliest attack on US soil until the Oklahoma City bombing, and most people have never heard of it.

More surprising was that this terrorist attack, like the first World Trade Centre attack and the attack on 9/11, was also aimed at Wall Street.

A Yale Professor is now arguing (in her book, The Day Wall Street Exploded: A Story of America in its First Age of Terror) that 9/16 is relevant today given the parallels between that attack and 9/11.

The NY Times has a book review here. I’ve got this on my reading list and will post my own review once I get a chance to read it.

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Barbarians at the Gate: The Fall of RJR Nabisco

Frank Voisin — Mon, 01 Sep 2008 15:00:00 +0000

I recently finished Barbarians at the Gate: The Fall of RJR Nabisco, by Bryan Burrough and John Helyar, about the 1988 leveraged buyout of RJR Nabisco. Although it is an older book dealing with an event that occurred twenty years ago, it is still called one of the best business books ever written, and has been on my “To Read” list for a long time. Having finished it, I am satisfied that it is one of the better business books that I have read, and would recommend it to others interested in leveraged buyouts and tales of Wall Street mega deals.

One of the best features of Barbarians at the Gate is that the authors did an excellent job discussing the backgrounds of the key players and how each party got to a position where they were bidding upwards of $20 billion (in the late 80s!). What’s more, many of the players are still relevant today (Kohlberg Kravis Roberts, to name just one which is often in the media).

The story begins with Ross Johnson who, through a series of cunning moves, became the head of RJR Nabisco. Early in the story, Johnson, fed up with the low valuations the market is assigning his company (due to the market’s distaste for tobacco-derived revenues), decides to launch a management-led leveraged buyout (LBO) to purchase the public shares of RJR Nabisco and take the company private. Such deals were exceedingly popular in the 80s. Critics argued that LBOs saddle good American companies with debt in order to finance the purchase and then leave the company unable to compete effectively against overseas counterparts. Proponents responded by saying LBOs force companies to become lean in order to pay off the high levels of debt, which makes the companies more competitive.

After Johnson launches the bid (with the help of Salomon Bros and Shearson Lehman Hutton), Kohlberg Kravis Roberts (KKR) makes a bid (with the help of Drexel Burnham Lambert’s junk bonds), which prompts Ted Forstmann’s Forstmann Little & Co to enter the fray (due to Forstmann’s hatred of Henry Kravis of KKR and the junk bonds it employs).

What struck me about the story was the way in which the parties progressed with their bids. The initial round allowed for several weeks of due diligence, with each party offering competitive bids. The second round was much quicker, lasting just eight days. The third round lasted just one night and the fourth round lasted all of a few minutes. I use the term “round” extremely loosely, as only the first two rounds were formal processes, while the latter two were more back-and-forths between the parties. Given that the bids increased a substantial amount in the last two rounds, I found it shocking that the parties abandoned their rationality to drive the prices higher out of ego. My wife thinks I am naive in being shocked at this, but I had somehow convinced myself that people with billions of dollars at stake get to that position by making rational decisions more often than not, and that they would rarely let their ego drive their decisions. Perhaps that is the reason this book was written – to detail how the biggest transaction of the 80s was based on ego rather than rational calculation.

One more thing that struck me while reading this book was the level of excess and greed displayed by the main character, Ross Johnson. He spends money with Olympian determination and still seems disappointed he couldn’t spend more. One of his quotes that I will remember is often repeated throughout the book: “Another million lost… in the sands of time.” He just simply doesn’t have any concern for financial conservatism and does his shareholders a major disservice with the excessive levels of spending he insists upon.

The book is an incredibly interesting read and does a good job detailing the LBO process and the fights that can erupt during takeovers, and I would highly recommend it. If you’ve read it, please leave your thoughts in the comments!

It’s Your Ship: Management Techniques from the Best Damn Ship in the Navy

Frank Voisin — Wed, 27 Aug 2008 15:00:00 +0000

During an MBA course on leadership, I was given an article to read called It’s Your Ship, by Captain D. Michael Abrashoff. I recently found out that the article became the basis for a book entitled It’s Your Ship: Management Techniques from the Best Damn Ship in the Navy about Captain Abrashoff’s leadership education as commanding officer of the USS Benfold.

Abrashoff took over the Benfold at a time when the military was having problems attracting and retaining strong candidates and disillusionment ran high. The Benfold had a “sullen crew that resented being there and wanted to go home.” Abrashoff was able to turn the ship around to be “the best damn ship in the navy” by all accounts. As the junior ship in its area, the Benfold became the leader in all of the navy’s metrics, winning commendations and setting the standard for other ships to meet. Even more impressive is the fact that Abrashoff accomplished this without having to fire or replace a single crewmember – instead, he found the potential that had not been recognized to that point. It’s Your Ship is about how Abrashoff was able to accomplish this turnaround.

When reading the book, I was struck by the simplicity of Abrashoff’s actions. No act was complex or difficult, but the sum total was nothing short of remarkable. Abrashoff freed his subordinates (something counterintuitive in the armed forces) to solve problems creatively without fear of reprimand, and by doing so he created a team of problem solvers who didn’t need to turn to him for directions.

Abrashoff recognized that most obstacles that limit an organization’s potential are set in motion by that organization’s leadership, which often fails because of their own shortcomings. It is very rare that an organization fails because of the collective inadequacy of the workforce – too often the workforce is hampered by leadership. Abrashoff recognized that the more control he gave up, the better his team performed.

Abrashoff arrived at several leadership lessons that we can all learn from:
1. Lead by Example
Most often, the problem is you. You know what you want to see in your employees, but do you see the same in yourself? If not, you have no hope of seeing it in others! Hold yourself as accountable as you want to employees to hold themselves.

2. Listen Aggressively
Abrashoff began his command of the Benfold by interviewing each of the 310 crewmembers individually and privately. He listened to their concerns and stories. This helped him develop relationships and gave him ideas of how to improve the ship’s performance. He was able to empathize with them and created an environment where new ideas could be presented by anyone on the ship and would be considered equally, regardless of rank. Many of the best ideas were from the lowest ranked members. Keep this lesson in mind when running your organization – how well do you know your employees? How often do you listen to their ideas and thoughts? How often do you consult them when coming up with new directives?

3. Communicate Purpose and Meaning
Be open with how decisions are being made and why they are being made. Involve people in the decisionmaking process and you will get buy-in. If you make decisions in secret and then send them out in a top-down, thou shalt manner, you will fail to generate buy-in and set yourself up for failure.
Review all actions as a group, regardless of whether the acts were successful or not. Allow people to speak openly during these reviews so that everyone can improve together.

4. Create a Climate of Trust
You have to earn trust, and you can earn it only by giving it. You have to give people responsibility and freedom to fulfill that responsibility and then step back and watch them exceed your expectations. Have the courage to believe in your team. If you don’t truly trust them, and micromanage, you will get poor performance and alienate those that could have exceeded your expectations.

5. Look for Results, Not Salutes
Making orders from the top is “demoralizing and squashes initiative.” Encourage your staff at all levels to come up with ideas about how to improve results and give them the ability to take iniative.

6. Take Calculated Risks
7. Go Beyond Standard Procedure
#6 and 7 are closely linked. Standard operating procedure will not get you outstanding results, so unless your goal is only to not get fired, then you have to exceed the standard operating procedures. You need to take calculated risks in order to improve the organization’s performance. “Treat rules as guidelines, not as immutable laws, unless they are critical” – Pay more attention to the reasoning behind the rules and try to uphold that thinking, rather than the strict rule itself (since rules, in some situations, accomplish the opposite of the original goal!)

8. Build Up Your People’s Confidence
“Praise is more productive than punishment.” You should work to improve the confidence of your employees and work hard to help them accomplish their goals. High-confidence individuals outperform those with low-confidence, and so you should work to help people improve rather than berate them when they fail.

9. Generate Unity
Rather than focusing on prohibiting people from discriminating against others (as many corporations do through diversity training initiatives), you should focus your employees on their common interests and the reasons they should value each other. Prohibiting employees from devaluing others will not be successful, because top-down initiatives rarely work. Maximize uniqueness and help channel it toward the common goals of the group.

My key take-away from this book is that “Leadership is the art of doing simple things very well.” Don’t focus on major, immediate change through drastic changes, but instead on small initiatives that, combined, will provide the sought-after results.

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Tags: Book Review, Leadership

The Black Swan – Part 4

Frank Voisin — Sat, 23 Aug 2008 15:00:17 +0000

This is the fifth of a five-part series reviewing Nassim Nicholas Taleb’s The Black Swan: The Impact of the Highly Improbable. You will find the review navigation at the bottom of this post.

Part 4: The End

What can we do about the Black Swan?

The Black Swan occurs relative to expectations. Expectations are derived from past experience, which as we have seen is fallacious (since a single disconfirming Black Swan is sufficient to destroy the soundness of our conclusions). Thus, if have undue confidence in your expectations, you will be open to the effects of Black Swans. On the other hand, if you keep an open mind about expectations, then you will be more likely to not let yourself fall prey to the Black Swan.

Recognize that we live in Extremistan and that a single event is enough to have an enormous impact. By recognizing this, you will be less likely to leave yourself vulnerable even to a single negative event. Hedge and be careful.

The Great Asymmetry
You should try to put yourself in situations where favorable consequences are much larger than unfavorable ones. This is known as the Great Asymmetry. Taleb gives some tips for how to do this:

Distinguish between activities which are exposed to negative Black Swans (e.g. Banking, Lending), and which are exposed to positive Black Swans (e.g. Publishing, Scientific Research)and try to minimize the former and maximize the latter.
Pasteur: “Chance Favours the Prepared” – Do not try to predict specific Black Swans, and instead focus on becoming more prepared for any type of Black Swan. Hedge your bets!
Seize every opportunity, because this will increase the likelihood of reaching a positive Black Swan.
Be leery of “experts” that provide predictions.
If you do follow a prediction, keep in mind that its accuracy decreases in relation to its time horizon.
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Critically assess your decisions, rather than relying on a superficial understanding of the causal connections.
Recognize your own biases. Just recognizing these will help you avoid falling prey to making big mistakes.

On a Personal Level

As in his previous book, Taleb takes the last few paragraphs to discuss how his work could impact you personally. He says that the odds against our being alive were of monstrous proportions. We are all Black Swans, so we should stop sweating the small stuff and live happier lives knowing that we defied great odds to come into existence.

Review Nagivation (Links will work once all posts are published)

Introduction | Part 1 | Part 2 | Part 3 | Part 4

Inventory Valuation

Frank Voisin — Fri, 22 Aug 2008 15:00:00 +0000

While inventory is supposed to be valued at the lower of cost and market value (LCM), firms have discretion over whether the inventory they sell – either using First-In-First-Out (FIFO) or Last-In-Last-Out (LIFO). This decision makes a big difference in the firm’s financial statements.

For example, in an inflationary environment where the firm’s inventory is being purchased at increasing prices, the LIFO method results in lower inventory costs (since its inventory is made up of items purchased when prices were lower) and its cost of goods sold (COGS) is higher (because it is using items purchased most recently at the new, inflated prices). When I say “lower inventory costs” or “higher COGS”, this is in relation to FIFO methods.

This example explains an important rule for analysts:

FIFO provides the most useful estimate of inventory value
LIFO provides the most useful estimate of COGS

The reasoning for this is as such: As the price of new inventory increases, FIFO reacts quickly, since FIFO inventories are made up of newer goods. Therefore, if you are valuing a company, check the FIFO value of the inventory for the best representation of what the company’s inventory assets are worth.

However, if you are considering the future cash flows of the company, then you should look at the firm’s LIFO-based COGS because this gives the most accurate representation of where COGS is trending (since LIFO is quickest to react in terms of COGS representing replacement cost).

What if the figures are given in different terms than you want? Look to the footnotes! If the company uses LIFO, US GAAP requires that the company provide what is known as “LIFO Reserve” which is the difference between the LIFO inventory and the FIFO inventory values. So you can arrive at the FIFO inventory with LIFO values by simply adding the LIFO Reserve. It is a bit more difficult (and by no means an exact science) going from FIFO COGS to LIFO, because it requires you to have an idea of the rate of inflation of the inventory.

Do you have any other analysis tips you’d like to see on this blog? Let me know!

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Tags: Finance, Investing, Valuation

REIT Managers on the Credit Crunch

Frank Voisin — Thu, 21 Aug 2008 15:00:08 +0000

Fascinating article HERE about how REIT managers are handling the Credit Crisis. Luck favours the prepared! Those who have staggered & fixed debt maturities haven’t flinched.

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Tags: Credit Crunch, Investing, Real Estate, REIT

The Black Swan – Part 3

Frank Voisin — Tue, 19 Aug 2008 15:00:15 +0000

This is the fourth of a five-part series reviewing Nassim Nicholas Taleb’s The Black Swan: The Impact of the Highly Improbable. You will find the review navigation at the bottom of this post.

Part 3: Those Gray Swans of Extremistan

In Part 3, Taleb does two things. First, he discusses how the world is becoming increasingly dominated by Extremistan, making single events far more important than in the past. Second, he explains why the Bell Curve is “the great intellectual fraud.”

Extremistan and Its Consequences

The world is moving further and further into Extremistan each day. Single events (observations) are having a greater and greater effect on the whole, making the past less relevant as fuel for our predictions of the future.

The long tail, an idea revived by Chris Anderson, says that the current environment allows small, marginal players to exist in the “antechamber of success” until luck in the form of positive Black Swans, swing them into prime time, subverting the previous market leaders. The market may be dominated by acute concentration where the top players have a disproportionate amount of the business, but the long tail of Extremistan allows the little guy to take over very quickly. The internet is fueling much of this subversion, which aids to the further movement of our world into Extremistan.

The Bell Curve

Taleb says the Bell Curve is inadequate and does not apply to Extremistan. Take this with the previous assertion that the world is becoming more dominated by Extremistan, and it becomes clear that the Bell Curve is of little use.

The main problem with the Bell Curve is that it disregards Black Swans. The Bell Curve focuses on the ordinary and considers outliers (Black Swans) to be secondary. This ignores the impact of Black Swans. Instead, the Black Swan should be the focus, with the ordinary as secondary. This takes into account the events with the most impact and works outward from there.

Another problem with the Bell Curve is its inaccuracy in predicting unlikely events. Small errors in determining sigma lead to wildly different odds for high-sigma (low probability events). This leads to big problems when you combine highly inaccurate estimates with the events that cause the greatest impact – the end result is an unclear understanding of the most important things.

The Bell Curve should only be used with looking at single variables, where aggregates and magnitudes don’t matter (e.g. Yes/No), because in all other situations, where aggregates and magnitudes DO matter, a single observation can disrupt everything and wipe out a century of profits.

Part 3 Conclusion

Ignore the Bell Curve and reports or studies based on the Bell Curve for most things. It is an incredibly limited tool and not to be used for prediction.

Review Nagivation (Links will work once all posts are published)

Introduction | Part 1 | Part 2 | Part 3 | Part 4

Technical Analysis

Frank Voisin — Sun, 17 Aug 2008 15:00:00 +0000

I’ve posted here and here on different aspects of technical analysis, but it occurred to me that perhaps I shouldn’t assume everyone knows what that is, so I thought I would do a brief introduction.

Technical analysis is the study of past market behaviour in order to forecast the market’s future. The basis for this is that market behaviour (and security pricing behaviour) follow patterns that can be recognized by studying the past and can be expected to reoccur in the future.

Patterns can be found by looking at pricing, volume and their relaitonships to other variables. However, pure technical analysts focus solely on price, using the Efficient Market Hypothesis as the basis for assuming that price already reflects all relevant information and so the price behaviour is all that is relevant.

Technical analysis has many critics. I would think Nassim Nicholas Taleb would be critical given his book, The Black Swan: The Impact of the Highly Improbable (which I have been reviewing here).

No one claims technical analysis is perfect. Rather, proponents argue that it increases the analyst’s odds of being right, such that the gains from successful trades outweigh the losses from losing trades.

If you want to learn more about technical analysis, check out the wikipedia article.

What do you think of technical analysis?

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Tags: Finance, Technical Analysis, Trading

Fun with Securitization

Frank Voisin — Sat, 16 Aug 2008 15:00:00 +0000

Securitization is the process of taking groups of loans and putting them into a trust, which then issues securities to be sold. The securities are called “asset-backed” because they are secured by the cash-flow producing assets (loans) that they consist of. Securitization has become increasingly popular, leading to trillions of dollars of new issuance each year ($3.07 trillion in 2005 in the US markets alone), covering a diverse set of assets, such as residential mortgages, commercial real estate, auto and college tuition loans.

It is important to note that the issuer of the debt transfers all risk for the debt through the process of securitization. The issuer creates the debt, then subsequently sells it and the buyer of the resulting securities absorbs the risk. The debt issuers love this because they don’t have to hold on to the debt through its entire maturity, which has benefits over the historical portfolio approach to lending. The issuing bank can attain significantly higher ROEs by selling the debt after the securitization process (for a profit) and eliminate the credit risk. Higher profit and lower risk!

Wikipedia has an excellent summary of the motives and risks for securitization for both the issuer and the investor.

What is interesting is the process of enhancing the credit rating of the resulting securities beyond the credit rating of the underlying assets. This is known as Credit Enhancement and is based on the concept of tranching. Essentially, you take the resulting securities and split them into tranches with different levels of subordination, credit protection and exposure. So, if the entire group of securities would have a credit rating of BBB, and you cut it into several tranches, the highest tranche with no subordination, could have a credit rating of AAA, because it gets paid first, and the only way it would not get paid would be if a huge group of the underlying loans were unpaid (i.e. the highest tranche has the lowest risk). This is a complex process that is fairly new, and has led to the problems we are seeing in the United States – the debt rating agencies were ill equipped to rate the credit risk of the tranches, and then the housing market crashed leading to the perfect storm of unpaid loans that left all the tranches devastated. Some have argued that the recent events in the United States have shown the flaws of the securitization process which is resulting in the “dismantling” of this as a business model.

One of the major benefits of securitization is that it solves the problem of mismatched assets & liabilities. Using the old portfolio-model of debt issuance, lenders would raise money with one set of characteristics (specific duration and perhaps fixed interest), and then lend the money on a different set of characteristics (perhaps a different duration, or floating interest). As time goes on, the risk of floating interest rates differing from the fixed rates increases, and once profitable loans may become unprofitable. The securitization process solves this because the debt is sold very quickly to the market and its price changes similar to bond price fluctuations.

So, given all the problems recently created by securitization, it does solve a major problem (the problem of mismatched assets & liabilities led to the collapse of the Savings & Loan industry in the 80s). But the problem seems to be that the originators (lenders) are creating new problems by becoming a little too loose in how they make the loans, because they know that they will not hold the debt for very long. The question is whether this problem can be solved, and it seems we are in for a ride over the next few years as we see the full ramifications of the ABCP debacle and the resulting policy changes.

What are your thoughts? Is this something that can be solved? How can you ensure lender-discipline as well as save the securitization process?

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The Black Swan – Part 2

Frank Voisin — Fri, 15 Aug 2008 15:00:13 +0000

This is the third of a five-part series reviewing Nassim Nicholas Taleb’s The Black Swan: The Impact of the Highly Improbable. You will find the review navigation at the bottom of this post.

Part 2: We Just Can’t Predict

In this section of the book, Taleb tells us that the world is far more complicated than we think. We tend to ignore this, and platonify the future, believing that there is something predictable about it, something like the past. This is wrong. As long as we recognize this and keep an open mind rather than platonify and tunnel, we should alright.

We saw in the review of Part 1 how we tend toward the Narrative Fallacy, oversimplifying our understanding of the past, and becoming overconfident.

Poor Record

Taleb points out that humans have a terrible record of predicting things. He calls this the “Scandal of Prediction.” Consider the major events throughout history and think of how many of them had been predicted beforehand (please leave them in the comments if you can think of any!). Think about your own life – how many of the major, defining moments did you predict? Odds are none. We don’t predict when we will find the person we will marry or when people will die (generally), and we don’t predict major events like 9/11, yet we are confident in our ability to predict. The fact is, we predict things that are largely irrelevant. The Black Swans are responsible for the bulk of the major change, and they remain unpredictable.

Confidence Over Knowledge

Humans have a built-in tendency to increase in confidence further than we increase in knowledge. The lowly cab driver will admit he does not know the future of the markets, whereas the investment banker in the back seat will express his confidence in being able to predict how the markets will turn. As our knowledge increases, our confidence increases faster.

“Almost” Right
When we are wrong, we rationalize it away. We focus on how we were “almost right” or we reframe the game to show how we were right but-for an unexpected event that wasn’t part of the original prediction rules. Psychologists have shown how people are quick to attribute their successes to their own expertise and equally quick to rationalize away our failures. We have a built-in tendency to protect our egos at all cost, and this causes us to be unaware of our inability to predict.

Variability Matters

Too often we platonify and make projections without considering the fuzziness of the thing being projected. We ignore the variability and focus only on the projection. By ignoring variability, we ignore the Black Swan. In most cases, it is the range of possible outcomes that is most important – not the final result. Knowing the range allows you to prepare for all possible eventualities.

PART 2 CONCLUSION: Be a Fool in the Right Places

We all have epistemic arrogance (we are terrible at making predictions with accuracy). The key is to focus not on the reliability of the predictions (which we cannot know) but instead the potential consequences. Make predictions that you rely on about things that have little effect (e.g. a prediction about whether it will rain when you go to the park). Do not make predictions about big things that have the potential to ruin you – for these things, you must keep an open mind, consider variability, and plan for all eventualities.

Rank your beliefs based on the harm they may cause, not on their perceived plausibility. Be prepared for all outcomes associated with those beliefs that may cause great harm.

Example: Taleb’s Barbell Strategy: Suppose you want a medium-risk portfolio. Rather than investing 100% of your money in securities which you think are medium-risk (hence, fully trusting your prediction about the riskiness of the securities), invest 90% of your portfolio in certainly safe instruments like government bonds, and the other 10% in extremely speculative bets as leveraged as possible. Instead of having false medium risk, you have high risk on one side and no risk on the other, protecting you from negative Black Swans and leaving you open to positive Black Swans.

Review Nagivation (Links will work once all posts are published)

Introduction | Part 1 | Part 2 | Part 3 | Part 4

Dealing with Vacancies

Frank Voisin — Thu, 14 Aug 2008 15:00:00 +0000

The United States has 18.6 million vacant homes. This is the highest share of homes since we began measuring the number of vacancies. Why do we care? Vacant houses lead to lower home prices (Econ 101: greater supply with equal demand leads to lower prices). As the prices decrease, people lose their equity and abandon the homes or have more difficulty refinancing (since the banks won’t refinance at the original amount), which leads to increased foreclosures and more vacancies. The social implications are great – vacant houses lead to lost municipal taxes and invite looting, vandalism and other criminal activity.This is a negative-feedback cycle which is difficult to reverse.

One solution being proposed is to demolish the surplus vacant homes. Ben Bernanke supports this, saying that it will “mitigate safety hazards and reduce supply.” This solution is expensive, as it requires cities to spend significant amounts of money demolishing homes – money spent from budgets already stressed from the lack of property taxes that were counted upon.

Congress is looking to an even more expensive solution which would lead to $7,500 tax credits and government guaranteed mortgages at below-market rates in order to slow the rate of foreclosures.

The Economist argues that it is a mistake to inflate the market artificially, and that we should just accept the pain of a market correction.

Some suggest a creative solution of granting temporary immigration to foreigners willing to invest in residential properties and to hold them for a certain period of time, which would decrease the surplus housing. What do you think of this? Would this just lead to a glut of extra workers which the economy would be unable to find work for? It seems to me that this solution would just swap the real estate problem for an unemployment problem (and all the associated social problems).

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Tags: Politics, Real Estate

A Fan of Fanlines

Frank Voisin — Wed, 13 Aug 2008 15:00:00 +0000

As discussed previously, Fibonacci numbers play an important role in technical analysis due to their predictive power in security pricing movements. A related concept is the use of fanlines. Fanlines are used to compare the slop of an initial movement to the slope of the Fibonacci retracement in order to determine support or resistance points.

Essentially, the analyst takes the initial pricing movement and creates fanlines at 38.2%, 50% and 61.8% (the key Fibonacci points) on either side of the initial movement. These lines carry on into the future, diverging from the initial point. This allows the Technical Analyst to determine the likely regions in which the security’s price will trend during retracements.

This is just another way in which Fibonacci numbers play a role in technical analysis. On the Bloomberg, the tool is “Fans” in the Fibonacci submenu under Annotations when looking at a graph.

Have you had success with this? Let me know what it’s like in practice!

The Black Swan – Part 1

Frank Voisin — Tue, 12 Aug 2008 15:00:11 +0000

This is the second of a five-part series reviewing Nassim Nicholas Taleb’s The Black Swan: The Impact of the Highly Improbable. You will find the review navigation at the bottom of this post.

Part 1: Umberto Eco’s Antilibrary, or How We Seek Validation

In the first part of the book, Taleb introduces us to the major concepts that are used throughout the rest of the book. I will discuss some of these here:

The Opaqueness of History: Taleb describes history as opaque, in that we see only the output (or outcome), and not the inner workings. Consider history to be a black box from which we have experiences (or worse, read others’ account of their experiences). The problem with this is that we have natural biases that cause us to form conclusions about causality (perhaps erroneously) that lead us to believe we understand why something happened the way it did. This is further exacerbated when we try to convey this understanding to others, in which case we simplify further into narratives for ease of transmission of the information. When we form our own conclusions and reduce history to narratives, we necessarily eliminate all of the information that we feel is not relevant. Taleb says that this information is important to get a full understanding of causality and so by eliminating it, we fool ourselves into thinking we understand.

Three reasons history is opaque:

Illusion of Understanding: The world is more complicated and random than we realize, yet we convince ourselves that we understand and ignore that which we think is not relevant.
Retrospective Distortion: We assess events after the fact and through the lens of our biases, filtering out important information.
Overvaluation of Factual Information: People overvalue information and believe that the facts they have witnessed in the past lead to general conclusions for the future.

Platonicity: Taleb uses this word to describe our tendency to focus on pure and well-defined “forms” that are constructs existing only in our minds. We prefer these over less easily understood concepts, like randomness, which we ignore. It is easier to accept “the CEO did X, the company was successful, therefore the CEO must be good at his job” than to recognize the amount of randomness involved in the company’s success. This builds upon Taleb’s Fooled By Randomness, to explain that the common tendency to ignore the role of chance in our lives (as discussed in Fooled by Randomness) has the consequence of our focusing on certain clean (and simple) constructs as to how the world operates. By focusing on these constructs, we think we understand more about the world than we really do.

Tunneling: When we do allow ourselves to consider the unknown, we tend to focus on well-defined sources of uncertainty, such as future sales or economic conditions. This gives us a sense of security in that we have the uncertainty “covered” and makes us more prone to the Black Swans which do not easily come to mind.

Confirmation Fallacy: We use our experiences to draw conclusions that experience is evidence of no possible Black Swan. Instead, we have proven there is no evidence of the possibility of Black Swans. The absence of evidence is not the same as evidence of the absence. We have too much confidence in what we think is right and pay too little attention to how quickly we could be disproven.

Narrative Fallacy: We summarize and simplify, reducing the complexity of situations. We do this so that things are easier to remember and convey to others, and we do this to convince ourselves that we understand the environment in which we live. This distorts our understanding of the world. We can’t help it, but it is important to be aware of it and recognize that the world is not nearly as simple as we believe. Favour experimentation over storytelling.

The Ludic Fallacy: Very closely related to Platonicity is the Ludic Fallacy which is the act of confusing a model (or a platonified concept) for reality. By believing our simple, clean concept of reality to be the way the world works, we ignore all of the things that exist outside of our concept, which includes the Black Swan (because it is unknown), and this leaves us open to the Black Swan’s extreme impact.

As discussed in my review of Roger Lowenstein’s When Genius Failed: The Rise and Fall of Long-Term Capital Management, the Ludic Fallacy was a major factor in the fall of LTCM, as the geniuses that ran that hedge run believed that their models accurately showed the risk associated with their trades, ignoring the Black Swan. They believed their risk assessment models were reality and that nothing would happen outside of their models. They were wrong, and they collapsed as a result.

Taleb argues that statistics only work in games with well-defined rules. In reality, we cannot know the odds with accuracy because we don’t have all the information (since it hasn’t happened yet) and that a small variation in any variable could have a huge impact.

Scalable vs. Non-Scalable: Things which are non-scalable require more effort to yield greater impact. Think of lawyers, dentists, and other people paid by the hour: to earn more, you must work more. Things which are scalable require no extra effort to yield greater impact. Consider the investor, who may earn higher returns through the same amount of time spent considering investments. Scalability creates giants and dwarves. Some will be wildly successful, while others will be terribly unsuccessful. Non-scalability create groups of the mediocre.

Extremistan vs. Mediocristan: This is closely linked to the scalable vs. non-scalable discussion.

Mediocristan is a place where events don’t contribute very much individually – only collectively. Events are non-scalable. Black swan events are impossible as a result. Example: The average height of a large group of people. Any single person will not disproportionately alter the average height, because no one is tall enough to do such a thing (e.g. no one exists who is 21 miles tall)

Extremistan is a place where inequalities are great because one single event can disproportionately impact the aggregate (because events are scalable). Black swan events are likely because any single event can have a disproportionate effect. Example: The average wealth of a large group of people. Any single person may have enough m oney to materially affect the average (e.g. Bill Gates would cause a major change to the average, even in massively large groups).

Socially constructed concepts, like wealth, exist in Extremistan, whereas physical characteristics like height exist in Mediocristan. People make a big mistake in believing they live in Mediocristan, when in reality they exist in Extremistan, where Black Swans exist.

Review Nagivation (Links will work once all posts are published)

Introduction | Part 1 | Part 2 | Part 3 | Part 4

“Fake” Trading & Prediction Markets

Frank Voisin — Mon, 11 Aug 2008 15:00:00 +0000

Yesterday I wrote about the new US & UK government restrictions on short selling certain investment banks. If that has got you down, there is a solution: “fake” trading in the prediction markets!

What are Prediction Markets?
Very briefly, prediction markets are markets where parties bet on certain events. For example, people may bet on Barack Obama winning the California primaries by 5%. Some will bet that this will occur. When these bets happen, the price ot further betting increases. Others will bet that this event will not occur, and they will short sell, driving the price down. Once the event has occurred (or the timeline for it to occur has elapsed without the event occurring), the bets are settled. Interestingly, predictive markets have been found to be extremely accurate forecasting tools for political and sporting events, as well as seemingly unpredictable things like natural disasters and temperature changes.

The success of predictive markets seems to be tied to the wisdom of crowds: as more and more people bet, each taking into account different variables that they have been exposed to (e.g. news reports), the accuracy increases.

The most popular predictive market platform is InTrade. Recently, companies like Tradefair and HedgeStreet have introduced predictive markets for use in forecasting interest rates, commodity prices and other financial variables.

So, while you cannot short sell certain investment banks in the US or UK, perhaps you can try your hand at predictive markets and bet on the next bank failure. (Tip: InTrade users are betting against BankUnited Financial).