Review of Taleb's The Black Swan

I wrote this a while ago, mainly based on posts I had done over at Mahalanobis, and posted it on my website. I figure I'd update it and put it out here where more people might see it, as the book in question is still quite popular.

Nassim Taleb is a former trader who wrote a textbook on option and market making, and then became more philosophical in his best seller Fooled by Randomness, and now in The Black Swan. His big idea is that sometimes, unexpecting things happen: countries dissolve into anarchy, wars start, unknown authors become famous. His secondary ideas are variations on this theme, that people, especially experts, are generally biased, overconfident, and rationalize past event so they appear deterministic. Stated baldly, these assertion are hardly novel but true enough, and one can argue about their relevance in various cases. As a highly popular presentation of ideas near to my interests and vocation, I think it is worth critically examining if there is anything to his particular contribution to the literature on cognitive biases or social failures. My conclusion, in short, is no.

Taleb’s style is to severely criticize experts and authorities--lots of 'morons', 'idiots', and 'fools' out there--while implying that both he and his reader or listener are exempt from their many biases. Reading someone deflating puffed-up egos, criticizing the insular world of academics, and suggesting the experts have a huge blind spot on something important, can be fun reading. But it has to be making points that are true if new, or important if true, and here he fails to deliver.

For someone advocating doubt and criticizing expert and 'regular' people’s overconfidence and arrogance, Taleb’s writings are filled with certainty, anger, and immodesty, having the Godelian impossibility of someone shouting 'I am the most humble!' Indeed, his current popularity based on prescience in forecasting recent events, and his emphasis that this proves him correct is exactly the kind of naive confirmation based on small samples that he argues is sloppy thinking. Consistency is not a hobgoblin in Taleb's mind.

While people are generally overconfident about their diving ability or common sense, does that same overconfidence lead people to underestimate the probability of market crashes, and thus the price of insurance (eg, put options?) The data suggest the opposite is true, that is, that people overpay for such improbabilities based on hope. Survey data on beliefs are not necessarily economically important, because markets elicit results not from unmotivated an ignorant masses, but from a highly motivated and informed subset. People willing to offer ‘a side’ to such a bet tend not to be biased, and also pad their bets with a considerable safety margin so that their errors are not catastrophic, which in practice means you obtain much lower odds for improbable events than what simple surveys would imply.

A major theme of Taleb is that models of uncertainty are too precise, and this thread has a long history. Taleb's sometime co-author Benoit Mandelbrot has been trying to sell the world on the big idea of fractals in finance for several decades. James Gleick’s Chaos outlined the essence of Benoit Mandelbrot’s fractals, which takes a simple few lines of inputs to create graphics of insane complexity yet also beautiful recursive symmetry, in many cases eerily similar to nature (eg, ferns, snowflakes). In dynamic systems, you have chaotic systems that are purely deterministic though sufficiently complex that they appear random. These systems have large jumps, or phase shifts, reminiscent of market crashes or sudden bankruptcies; they have butterfly effects where small changes produce big differences in outcomes. Mandelbrot and others have been trying to apply these ideas to financial markets for many decades now (since 1962!), and the effort has not gained any traction, in spite of many papers applying this concept (search skew or kurtosis in any financial journal and you will see many papers). Mandelbrot’s big idea in finance is that finance relies on a profoundly flawed assumption, mainly that market prices are normally distributed. Mandelbrot argues market prices have much fatter distributions described by Cauchy distributions, as evidenced by the high number of 5+ standard deviation moves in financial markets.

The result of these mistaken assumptions is to understate risk, according to Mandelbrot, and so overprice stocks and underprice options, and also understate the capital cushion financial institutions need to withstand market risk. Mandelbrot’s alternative approach is based on new parameters that would replace the mean and standard deviation. His first parameter is Alpha, derived from Pareto's Law, is an exponent that measures how wildly prices vary. It defines how fat the tails of the price change curve are. The second one, the H Coefficient, is an exponent that measures the dependence of price changes upon past changes. Unfortunately, Mandelbrot himself acknowledges in The Misbehavior of Markets that no two individuals calculate the same Alpha and H Coefficient when using the exact same historical data: there is no unique way to calculate these two parameters. Thus, using one method, you could derive Alpha and H coefficients that suggest a stock is risky, using another method you would reach the opposite conclusion. This flaw probably has some bearing on its lack of practitioner popularity.

Frank Knight, meanwhile, in his classic Risk, Uncertainty, and Profit in 1921, outlined the basic idea that it is uncertainty, in the form of non-quantifiable dispersion, that is at the root of profits. The basic idea is that risk, once quantified, is diversifiable, and thus becomes risk-free. If you know that your champagne bottle could burst while fermenting with probability p, that number becomes very manageable the larger your operation via the law of large numbers. Economists have been intrigued by this notion ever since, but by definition it is unquantifiable so when you write down a random process, it is no longer Knight-like, making it rather elusive. However, when we come up with uncertainty proxies, such as volatility (surely more volatile assets are generally more uncertain), leptokurtosis (asymmetric tails), or analyst or investor disagreement, the results do not have any obvious empirical implication beyond the fact that they exist. That is, assets with greater downside tail, or analyst disagreement, conditional upon gaussian volatility measures, are not predictive of future returns.

Taleb’s career as a talking head started in 1996 when, as the author of a niche derivatives text, his claim in Derivatives Strategy magazine that the new Value-at-Risk phenomenon was worse than useless made for great debate in risk management circles. I was leading a Value-at-Risk project at the time, so of course I found his criticisms of interest. JPMorgan had just introduced this method of aggregating risks in a highly popular practitioner brief. Their approach, RiskMetrics, outlined in detail the methods of estimating volatility when you had a portfolio of currencies, bonds, equities, and even options. Previously, financial books that contained bonds, currencies, equities, etc., each had little silos of risk reports, but this showed how they could be combined, basically by putting everything into a factor approach, in which every asset has a sensitivity to a factor, and every factor has a certain correlation and volatility. This was not new—factor analysis had been around for a while—but its clear application to a tangible problem was insightful, and created a lot of buzz.

Value-at-Risk was not a panacea, but it was an improvement (the only people who use the word 'panacea' are critics). Taleb’s criticisms of VAR then are similar to his criticisms now: that a metric is not flawless, and those who believe parametric applications of VAR are fools. In a trivial sense he is right, but in the case of VAR, or specific parametric statistics, or expectations in general, there are many users who understand that tools need to be supplemented by judgment, adjustments for the parochial realities of various asset classes with their various deviations from pure 'normality'. It is a cliché on the risk management lecture circuit that you need not just technical knowledge, but judgment, mainly by senior executives who don’t have any technical knowledge. Even in these stressed times, Taleb was dead wrong on VAR, in that in spite of his criticisms it is ubiquitous as a method for amalgamating short-term risks from different instruments into a single metric.

VaR is not useful for allocating capital, or estimating the cost of equity, but it is useful in keeping your traders honest. It allows one to measure risk given various assumptions, and like any model it is garbage in-garbage out. The recent financial crisis has often been blamed on VaR. To the extent certain banks applied VAR to mortgages, using, say, a 10-day VAR based on data from the benign 1990s, was an error. Yet, the ubiquity of this error suggests it was not a mathematical mistake (math errors are random and go in both directions), rather a flawed assumption that implied benign VAR exercises: the fact that housing prices do not decline. One can say with hindsight, this was incredibly stupid, yet no one was arguing for financial institutions must be robust to this scenario prior to 2007, and the government regulators were actively encouraging no downpayment, no documentation loans as part of a multipronged effort to increase home ownership. That is, the mistaken assumption was part of a broader mistake, comprehensible to all, not some technical error by risk managers, because that assumption was not theirs to make; it was part of a zeitgeist that people seem to forget like all those who forgot voting for Nixon after he resigned. Most importantly, VAR is not perfect, nor a panacea, but the onus is on critics to describe a better alternative. Using 'judgment' or 'all one's information' seems better with hindsight, yet as foresight this is so undefinable it would be a signficant step backward.

If you were to list all the financial company bankruptcies, the one common thread would be that they blindsided investors with their exposures. Who knew Orange County had such a position against interest rates ex ante in 1994? Who knew Barings had such an exposure to a trader in Singapore in 1997? These were not properly calculated risks that went awry, nor were they outright fraud where an unauthorized intraday position blew up. They were the result of investors or management not fully understanding the risks that were being taken, which often a correctly calculated VAR number, correctly communicated, would have easily shown. The errors were problems in getting an accurate VAR, which clearly needs people getting accurate data on positions.

If operating risk is the primary reason why trading operations fail, emphasis on refining VAR seemingly misses the point. Operating risk is neglected for good reason, however, in that it is extremely difficult to quantify existing operating risks, which in turn makes it nearly impossible to evaluate methods of monitoring and reducing these risks. Just as Eisenhower stated it is essential to plan prior to battle even though once a battle has commenced the plan is useless, VAR is essential in planning the allocation of capital, yet in risky situations becomes useless. This is not a paradox, but merely the fact that when we train for competition, we practice tactics and strategies. Inevitably competition, especially competitions we ‘lose’, will bring forth situations we have not prepared for, but the best preparation for such an occurrence is not nihilism, but more practice. And indeed many new situations are avoided by practice, which is why we learn math by solving old problems, because we think these tools are useful to unknown new problems. The alternative, to instead focus on operational risk, is such an undefined objective, that it is much less salutary.

Taleb argues that the unpredictability of important events implies we should basically forget about all that is predictable, because that’s not where the real money or importance is. So from a risk management perspective, we should ignore Value at Risk, which measures anticipated fluctuations. Further, we should ‘go long’ on these unanticipated events by engaging in quirky activities on the off-chance that we randomly find something, or someone, really valuable.

Success in markets, like life, is a combination of ability, effort, and chance. Much of intelligent thought is distinguishing between what is predictable versus what is unpredictable; it is to any organism's advantage to find out what we can figure out and change, and what is forever mysterious and unalterable (eg, the Serenity Prayer). The brain is constantly predicting, trying to figure out cause and effect so it can better understand the world. Most of what humans process is predictable, but because we take predictable things for granted, they are uninteresting. We can't predict some things, but instead of resorting to nihilism, we merely buy insurance or manage
our portfolios--in the broad sense of the term--to have an appropriate robustness. Discovering certain things are basically unpredictable does not diminish our constant focus on trying to predict more and more things. People will disagree on which risks at the margin are predictable, but that's to be expected, and we all hope to be making the right choices that optimize our serenity at the margin of our predictable prowess.

From Taleb's Wikipedia entry circa July 2006, we see where Black Swan thinking goes when applied to an investment strategy:

When he was primarily a trader, he developed an investment method which sought to profit from unusual and unpredictable random events, which he called "black swans." His reasoning was that traders lose much more money from a market crash than they gain from even years of steady gains, and so he did not worry if his portfolio lost money steadily, as long as that portfolio positioned him to profit greatly from an extremely large deviation (either a crash or an unexpected jump upwards).

In fact, Mandelbrot also argues for this strategy. Taleb co-authored a paper arguing that most people systematically underestimate volatility. Furthermore, he argues there exists not only a lack of appreciation of fat tails, but a preference for positive skew, in that people prefer assets that jump up, not down, which would imply the superiority of buying out-of-the-money puts as opposed to calls because those negative tails that increase the price of puts are unappreciated.He is affiliate with some fund that tend to be long tail risk, presumably by being long deep out-of-the-money options, but selling at-the-money options, a locally delta and vega neutral strategy.

These assertions present some straightforward tests, which a Popperian like Taleb should embrace. Specifically, buying out-of-the-money options, especially puts (because of negative skew), should, on average, make money. But insurance companies, which basically are selling out-of-the-money options, tend to do as well as any industry (Warren Buffet has always favored insurance companies, especially re-insurers, as equity investments). Studies by Shumway and Coval (2001) and Bondarenko (2003) have documented that selling puts is where all the extranormal profit seems to be. Of all the option strategies, selling, not buying, out-of-the-money puts has been the best performer historically. Further, Sophie Ni finds that out-of-the-money options are more overexpensive the degree they are out-of-the-money.

Malcom Gladwell wrote a 2002 New Yorker article contrasting the thoughtful, pensive Taleb versus the brash cowboy Victor Niederhoffer: Taleb buys out-of-the-money puts, Niederhoffer sells them. Taleb is betting on the big blow up, Niederhoffer on the idea that people overpay for insurance. Who was right? Well, Niederhoffer ran his flagship fund until September 2007 from a chalet-style mansion in Weston Connecticut . Taleb shut down his Empirica Kurtosis fund at the end of 2004, and the only public data on it suggest a rather anemic Sharpe ratio (up 60% in 2000, but then fluttered). Later Taleb described the fund as a hedge or laboratory. While neither strategy was great, and returns are proprietary, I venture that Niederhoffer's was better if you would just look at their lifetime Sharpes. Taleb's latest funds, which he is less involved with day-to-day but implement his basic beliefs in extremes, were up significantly in 2008, though this is to be expected given the extreme decline, and is similar to how Empirica started.

Taleb's big problem is that he misinterprets the mode-mean trade. A mode-mean trade is where a trader finds a strategy with a positive mode, but zero or negative mean. He then uses someone else’s capital to make money off several years of good returns, making good money for creating or managing the strategy, then, when the strategy gives it all back, the investor bears all the loss. The zero mean means that all the modal returns come crashing down in short time, generating large losses, and the manager walks away with his il-gotten bonuses from the benign modal periods. That’s a bad strategy for the investor, and the trader who manages it is either naïve or duplicitous. High Yield debt is a good example, as the stated yields are quite high, but the total returns to B rated bonds is the same as for BBB rated bonds, at several times the risk (and very concentrated in recessions). However, just because selling puts is a bad strategy, it doesn't mean buying puts is a good strategy. A Sharpe of 0.2 is a bad long position, but a worse short.

A ‘Black Swan’ is something that is totally unexpected and important. [European] people assumed all swans were white, but then they saw a black swan, and everyone certain all swans were white was wrong! That’s a 'gotcha game' for people who really take seriously someone’s assertions on the color of birds. But when there’s a price involved, the payoff to such an insight is not obvious, if not totally absent. For example, London bookmakers offer ‘only’ 250-1 odds a perpetual motion machine will not be discovered, and 100-1 odds aliens won’t be contacted: longshots ignored in a casual context are usually overpriced in actual markets.

In option markets, there is a volatility smile, whereby out-of-the-money options have higher implied volatilities, especially on the downside. For example, in May 2006 when rumors of GM's woes were large and its stock price was around 32, GM options had a one-year at-the-money implied volatility of 60, but down at a strike price of 15 its volatility is a much higher 140. The fact that Black-Scholes assumes lognormal returns does not imply market participants think likewise, so it is simply incorrect to assert that a market collapse of 23 standard deviations has a infintesimal probability based on the normal distribution, because real markets are aware of fat tails. Perhaps options were priced this way in the 1980's, but since then, there is a volatility smile that directly captures non-nomality. You can't profit from the idea that market returns have fat tails because that's priced into the market via the volatility smile, and this volatility smile shows up in 'disaster' insurance of all types: People pay a lot to sleep easy. Many people have looked at option prices, and they all find that out-of-the-money puts are the most overpriced of all options—people are expecting ‘Black Swans’ too much on average.

Taleb responds by noting that the 1987 stock market crash changes everything, because if you bought puts then, you would have made enough money to make up for decades of otherwise weak performance. While Shumway and Coval do not include 1987 in their academic study, Bonderanko does, and gets the same results. Taleb points out several anecdotes of financial market crises as further evidence of the importance of financial debacles, such as the 1998 crisis related to Long Term Capital Management (implied vols, libor spreads, skyrocked), or the emerging markets blow up of 1997. These events made money for people with long volatility, or specifically long puts, but the plural of anecdote is not data, so he should have cited an empirical paper showing the positive abnormal returns to taking on fat-tailed or asymmetric risk. Though it is easy to recall extreme events that would generate large fortunes to those on the correct side, one has to put them in full context, against the cost of insuring against these events over long periods of time. What is the sample space of all things one is insuring against? Stasis is data, as Stephen Jay Gould used to say. The volatility smile, and large bid-ask spreads in the extremes as a function of price, imply you can’t make extranormal profits over the long run by going long ‘Black Swans’ - at least in the markets where Taleb has the most experience (though not, according to him, expertise, which is more philosophically oriented).

The bottom line is that people tend to underappreciate low probability events when they are immaterial--because they are immaterial! So they underestimate the prevalence of Black Swans because if you find one, who cares? But hurricane insurance, a 3-delta put option on GM? You will pay up for that.

In the end, he promises to teach us how to take advantage of these Black Swans. His strategy is pretty simple. He argues for a barbell strategy of much safety, and a dollop of wild risks, which is, basically, an exposure to something totally unquantifiable, like Llama farms, or any of the myriad opportunities neighbors, spammers, and late-night paid-TV tout. In the context of Tobin’s two fund separation theorem, this means the ‘efficient’ risky portfolio is the most insanely unquantifiable and risky portfolio you can imagine, tempered by its modest allocation. Yet this implies the unquantifiable and risky portfolio has very good returns, which by definition (unquantifiable) is merely an assertion. As per the super safe assets, the only consistent risk premiums are from extending from overnight to a couple years in bond maturity, and from going from AAA to BBB credit risk. Super safe, is generally 'too safe', in that economists find this risk premium outsized relative to its volatility or covariance difference.

There is good reason to suspect one loses money, on average, on the wildest risks. Consider longshot odds at the racetrack and the highest payout (and thus riskiest) lottery tickets. Researchers have found a negative return premium for highly volatile stocks. Applied to ‘uncertainty’, this same pattern holds, as stocks with the most earnings forecast estimation error also have the most volatility, so it is no surprise they too have a negative premium. Truly improbable scenarios generally involve more hope than rational investment, as people will pay you to help them dream of the chance to become incredibly rich in the same way that the biggest lotteries, with 100 million to 1 odds, have the highest jackpots and the lowest mean returns. There are an infinite number of companies that directly target people wishing to make an end run around the rat race, and most of these companies are engaged in selling nothing more than hope (estimates are that only 2% of proposed home-based businesses touted on the internet are legitimate business opportunities).

Black Swan argues that standard statistics is flawed because it is backward looking — it uses ‘historical’ data — and argues that standard measures of risk like the normal distribution are ‘frauds’. I too prefer future data, but it is hardly a practical alternative. The Gaussian distribution is common in theory because it is so analytically tractable; it often creates closed form solutions that allow one to see how one variable affects another, and has nice properties, such as the fact that two Gaussian random variables added together is also a Gaussian random variable. In practice, no one actually believes in this view, and makes ad hoc adjustments, such as the volatility smile for option prices. The key is that from an expositional point of view, the Gaussian distribution usually gives one the gist of the true ‘fatter-tailed’ distribution, and allows easy exposition. Non-economists often giggle at the term ‘fat-tailed’ or homoskedasticity, but indeed most real world distributions are not ‘Normal’ or Gaussian, they simply have fatter tails than average. Does this imply statistics is a fraud? Well, if you mistake the map for the territory, indeed, this is news. For everyone with some common sense it’s an approximation or expositional device.

Taleb belittles predictions that have large or unmentioned error rates, yet any specific error metric (standard deviation, value-at-risk, correlation, R2, etc) is, in his mind, a fraud and useless because it relies on an assumption, one that is 'wrong'. He argues we reward those who imagine the impossible, but what does that mean in practice? That we encourage people to enumerate everything possible no matter how improbable? In finance, these risk reports are all too common because they reflect a lot of work, in that generating a list of unprioritized things that could happen is easy but practically useless, because you simply can’t address all the points and so must leave them as mere ‘I told you so’ observations. One can remember Richard Clarke’s vague warning about Al Qaeda prior to 9/11, which in no way suggested that changes to hijacking protocols or airline boarding should be made, but rather that something could happen, true but unhelpful.

Getting people to highlight wild risks comes easy, which I think is a big part of his book’s appeal. Legislators and personal-injury lawyers eagerly hype risks with negligible real impact, like secondhand smoke, or getting cancer from trace amounts of chemicals. Sometimes they create considerable public concern about risks that don't exist, like that of contracting anti-immune disease from breast implants, or cell phones causing cancer. Newsrooms are full of English majors who make confident pronouncements about global-warming or some other complicated process, all in hopes of getting viewers or readers activated.

I could imagine Taleb teaching a statistics class to freshman and instead of starting with the arithmetic mean and standard deviation, asking 'what was the probability of an airplane taking down the World Trade Center on September 10, 2001?', and waxing poetic about how ‘we just don’t know!” Students might think such talk is much "cooler" than boring formulas, but such confused thinking leads nowhere in particular and can be indulged indefinitely without producing anything useful, as Taleb demonstrates. Of course one needs technical knowledge and common sense in anything, but while you can teach one, you can't teach the other. We teach statistics, calculus, etc., not because it solves every problem, but because it can help in many problems to delineate and potentially manage that which we can change from that which we can’t. Surely a college department of 'wisdom' or 'good judgment' would be a valuable thing, unfortunately no one can agree on the curriculum.

Martin Gardner wrote a popular column for Scientific American, and in the process received a lot of mail from ‘cranks’ telling him about perpetual motion machines and the like. So he wrote a book called Fads and Fallacies. In the book he describes "cranks" as having five invariable characteristics:

1. They have a profound intellectual superiority complex.


2. They regard other researchers as idiotic, and always operate outside the peer review


3. They believe there is a campaign against their ideas, a campaign compared with the persecution of Galileo or Pasteur.


4. They attack only the biggest theories and scientific figures.


5. They coin neologisms.

On his personal website, Taleb once described himself as being "an essayist, belletrist, literary-philosophical-mathematical flâneur," a conception that some people finding endearing, me not so much. Literary-philosophical-mathematical types,- especially flâneurs - tend to be 'full of themselves,' supporting Gardner ’s characteristic #1. He prides himself on not submitting articles to refereed journals, considers most people who are indifferent to him as fools, and disdains editors, even spellcheckers (#2). He proudly notes that someone told him “in another time he would have been hanged [for what, inanity?].” Wilmott Magazine, a quant publication published by his colleague Paul Wilmott, wrote a fawning article about him in which they noted that he is “Wall Street’s principal dissident. Heretic! Calvin to finance’s Catholic Church” (#3). His website states his modest desire to understand chance from the viewpoint of “philosophy/epistemology, philosophy/ethics, mathematics, social science/finance, and cognitive science”, supporting #4. Lastly, for #5, he has gone so far as to print a glossary for his neologisms (eg, “epistemic arrogance” for “overconfidence”). In Martin Gardner’s taxonomy, Taleb is a classic crank.

Clearly his experience as an options trader gives him credibility, but I think this is a big issue, that of successful brokers thinking they made their money off investing insights. Before he became a regular on the talking-head circuit and expert on Judeo-Arabic philosophy, he was primarily a trader for large market makers. These are not speculators investing their wealth based on insights, but more like brokers, making money off customer flow, only their buyers and sellers are the guys on the phones talking to retail clients. Such traders spend most of their time looking at a model such as Black-Scholes that tells them what price to buy and sell based on some underlying parameters. These models are more or less standard, and so the main thing the market maker has to do is keep his model inputs fresh, post prices to potential buyers and sellers, fill market orders, and pick off stale limit orders. Customers generally have access to older prices, and in a situation where the current price moves every second, this clearly puts a trader at an informational advantage, which is why it was such a lucrative field, especially in the days before the internet became big (ie, Taleb's time). The trader makes money irrespective of movements in the underlying model price, as in general he keeps his exposure to first-order (eg, delta) and second order (eg, vega) risks as close to zero as possible.

But such trading skill is quite distinct from what a speculator or investor does, which involves a directional bet on the first or second order risks that traders normally try to erase. Traders know as much about what makes prices move as plankton knows about what makes the tides move. Much of being a trader is encouraging trading activity from a hesitant broker, and so many traders are quite adept at presenting themselves as more than middlemen, but also men with an angle or a story. A good trader is probably truly delusional about his prognostic abilities because this allows him to appear sincere in his sales pitch for the latest trade idea; those who don't believe their own stories make weak sales pitches (see Robert Trivers, who Taleb mentions favorably in The Black Swan, and note the irony endemic in his writings). Most of these traders are certain they could make money without their customer flow, because the same self-deception that serves them well chatting up brokers or impressing their boss generates delusions of strategic grandeur. Supreme self-assurance, even if undeserved, just as much as knowing your Greeks, makes for a good trader . Thus Taleb’s ‘narrative fallacy’ argument plays right into his own biases, that is, he has fooled himself into thinking he knows 'the big picture' because that delusion was helpful in his own career.

Rich investor or rich broker: Who is the more easily fooled about his alpha? Notice the relation to the theme of Fooled by Randomness? Taleb is consistently amusing because his criticisms of others apply so neatly to himself: he claims he is an empiricist yet supports his points with anecdotes. The Black Swan makes fun of ‘experts’ with credentials, but he states he does not deign to engage with anyone not sufficiently expert; he states he is not interested in being a speaker-bureau commodity , but routinely travels the rubber chicken circuit; he derides forecasters who don't give a full accounting of their prior forecasting history, yet delinks old remarks about Value-at-Risk, and recategorized his extinct Hedge Fund as a hedge, not a fund; he claims to prize humility, yet is most immodest; he argues against applying the law of large numbers, and also of inferring too much from small samples; people apply models to reality in biased manner, people naively extrapolate data without the appropriate theory; forward thinking is adaptive, forward thinking is error-laiden. Some people think inconsistency is a sign of genius; I think it just reflects confused thinking.

Inconsistency is the major problem with Taleb's oeuvre. For example, he often praises the work of Danny Kahneman as one of the uniquely prophetic economists, famous for his 'prospect theory' that explains why people will be risk seeking in small losses in but risk loving over large gambles. That is, Prospect Theory was invented to explain why people will pay small amounts to gamble, but are risk averse over gains. Yet Taleb argues that a predominant financial vice is the mode-mean trade, where people desire to make a little bit every day, often at the expense of blowing up on 'fat tail' events. These are opposite theories, which would not be a big deal if they were minor assertions from these men, but in fact they are the signature financial hypotheses of each. Taleb may appreciate Kahneman's diverse work, but one would expect him to be a harsh critic of this seminal idea, not the huge unqualified fan he is.

To be popular it is helpful to make people think they are learning something new about something novel and important. Yet the masses do not really like novelty, they like affirmation of their inchoate prejudices. Thus, a reader can leave The Black Swan thinking that any expert is either a charlatan or a fool except Taleb and those smart enough to appreciate him, a group that prides itself on knowing what they don't know, that any specific model is imperfect and therefore evidence is naive Platonism. The current financial crisis may make radical theories that suggest junking existing theory more attractive, but remember that the Great Depression was a Black Swan, and this did not help macroeconomic theory so much as lead it into the desert for 40 years, giving many a wasted life championing not merely a welfare state, but socialism and all its unintended horrors. If something really unpredictable happens, the large number of perennial disparate forecasters of disaster, combined with bayesian statistics, still implies those calling for the end of times are probably 'lucky fools', as Taleb would say. I do agree his claims of an extreme event were spot on over the past year, but this is no less impressive that Henry Blodget's Amazon call in 1998, Elaine Garzarelli calling the 1987 stock market crash, or Angelo Mozilo's subprime investment success up to 2006 made him a business visionary. I look to broader historical data and see buying out-of-the-money options is poor investment strategy, so I don't consider recent events proof of some really useful truth.

To the degree The Black Swan has arguments about the essence of risk they are at least a generation old, even if many are pleasantly introduced to them for the first time (fat tails see Mandlebrot (1962), nonquantifiable risk see Knight (1921), for various cognitive biases see Kahneman, Slovic and Tversky (1982) which was a compilation of papers mainly done in the 1970's), and these books have spawned, or are clearly referenced, by literally thousands of books. The Black Swan may popularize the concept of low probability events, what were called 'peso problems' (see Rietz 1988), and that would be a good thing. But ultimately, the bumper sticker "shit happens" is kind of funny, kind of true, but hardly profound.

Stewart Lectures Cramer

If you haven't seen it, Jim Cramer went on The Daily Show and was totally owned by John Stewart. Cramer went on hoping to merely apologize and then get in Stewart's good graces, but Stewart didn't let him off the hook. Stewart played some clips showing Cramer bragging about manipulating markets. Specifically, Cramer bragging about how easy it was to push the S&P futures down! Cramer is clearly exagerrating there, as this market is too large for Cramer to profitably move around, like you thinking you can push IBM up or down $10. He also disavowed Rick Santelli's rant against bailing out renters (oops! homeowners), saying "I don't know where he grew up, but I've lived out of my car", in a clear attempt to pander to the audience as if he grew up in some great hardship (if you graduate from Harvard and then sleep in your car as a journalist during your first job, I don't think that's the same as growing up in Detroit, or being truly homeless). Clearly, Cramer is a braggart. He's also overconfident about his opinions. He does not appear to have a good batting average. After this performance, I would add he's a moral and intellectual coward. He didn't defend his behavior, he tried to suck up to a celebrity bigger than him in a sycophantic way, and it didn't work.

Nevertheless, Stewart is still profoundly wrong in his assumptions about this crisis. Stewart seemed to think that Cramer and CNBC knew that the market was a pure 'greater fool' play all along, where people are selling worthless crap to the next guy in a pure ponzi scheme. The implication was that CNBC had a financial incentive in hyping stocks, even though they knew it was worthless (I guess he thinks they are evil and stupid, because it would have to bust). But I don't think the people at CNBC 'knew' the market was overvalued. There are bears on the show every day, but there are always bears. If CNBC knew about 2008 in 2007, they would have been very prescient, but they did not. Its a very simplistic view of the world to think bad things happen out of bad faith, as with individuals this is often the case, with the madness of markets, it is much more complex.

Further, Stewart emphasized the 30 to 1 leverage, as if this highlighted the investment banks were being imprudent. With hindsight, that was a bad call. But as I mentioned earlier (see here and here), many investment banks were at that level of leverage for over a decade. Only a handful of investment banks really increased their leverage (not commercial banks) over the past decade, and the market decline occurred throughout the financial sector of thousands of firms.

So, as frustrated as Stewart is with this crisis, his premise that this was caused by bad faith and 30 to 1 corporate leverage, is simply wrong. Most of the banks selling these mortgages had them on their balance sheets. They incorrectly believed in the business model. Stupid, but not a conspiracy. As per 30 to 1 leverage, yes this is a bad idea, and I went over why this is so in my earlier post, but many investment banks had been doing this for over a decade so it is not specific to this crisis, just a perennially bad corporate decision.

Government Incentives

In abstract, a company run by the government is better than the private sector, because they operate maximizing total social welfare, not just that of the shareholders. Thus, the community, employees, even global warming, are simultaneously considered in their calculus. This is clearly better than merely maximizing shareholder profits.

That's the logic of socialism, a logic that was compelling to most intellectuals from, say, 1930 to 1970. Einstein, like many other smart, but economically ignorant writers, noted that "production for use" is obviously better than "production for profit", and legions of college students think that getting rid of profits would simply lower costs, and create a better focus on serving people rather than profits. The problem as Hayek pointed out, is that only a price rationing system in a free economy incents the right people, at the right time, to make the right decisions. It decentralizes decision making. The alternative is a political clusterfluck, as interests wrestle for turf over a perceived fixed economic pie. Hayek mentions Einstein specifically in his Fatal Conceit about this common misperception. With hindsight, this is clearly balderdash, as the difference between the prosperity, and freedom, of Taiwan vs. China, North vs. South Korea, East vs West Germany shows.

Consider the regulation of banks, which many Democrats think needs to be stepped up. Is regulation disinterested? Maxine Waters can be counted on for making all sorts of paranoid accusations, as she represents her inner city Los Angeles district in a way that would make Huey Long proud. But she and her husband have ties to OneUnited, the largest black owned bank in the US. Her husband is a director. The Wall Street Journal and New York Times report she owned $250 to $500k in bank stock, and also received "received interest payments from a separate holding at the bank, also worth between $250,000 and $500,000". Now, that's a return on capital!

Interestingly, OneUnited (an unlisted company) seems to have had most of its assets in Fannie Mae and Freddie Mac stock, obvious a bad investment, but also, suggests a fundamental lack of true banking, making loans, as opposed to merely investing in the stock market, which is a poor use of bank capital. it's privately held, so who knows what the solvency of this bank truly is, but if they had a lot of Fannie stock, they are in big trouble.

If they gave her a special dividend of $500k, she's worth it. At a hearing on minority lending in 2007, Ms. Waters criticized regulators for not doing enough to help minority banks stave off mergers with non-minority institutions. A provision designed to aid OneUnited was written into the federal bailout legislation by Mr. Frank, who is chairman of the financial-services panel. Meanwhile, it was disclosed that OneUnited pays for a Porsche used by one of its executives and its chairman's $6.4 million beachfront home in Pacific Palisades, Calif., a luxury enclave between Malibu and Santa Monica.

When and How to Recapitalize Banks

In this crisis, the problem of how, and when, to recapitalized banks, is forefront. Several assume that the market, or the government, should merely add equity. This is often conflated with 'nationalization', but really we are not contemplating the US government actually buying and running banks prospectively. They are really considering various ways of become silent partners.

In any case, the recapitalization issue really falls into three separate cases that should be handled differently. Let us examine the case where this is prudent. In scenario 1 below, the bank starts out with a traditional 10% equity ratio, or 10 to 1 leverage. This is a 'safe' level of equity, in that it is sufficient for both regulators, that is, by law, and for debtholders, that is, by the market.



If there is a loss of 0.5 'units', we now have an undercapitalized bank. Both regulators, and debt holders, are unhappy with this state of affairs. In this case, the optimal thing to do is merely let the market recapitalize. Equity owners are unwilling to lose the franchise value of the bank, say from brand name and existing customer relationships. So, they are willing to appease regulators and debtholders by accept dilution to maintain the little value they have. One might facilitate this via the Meltzer plan, of having the government match fund equity investors. The bottom line is that equity owners, that is, those who direct management, will agree to this remedy.

In scenario 2 below, the bank starts out again with 10 to 1 leverage, but loses enough value (say 1.1 units) via write-downs so that the bank is insolvent, its debts are worth more than the assets. In this case, the government may wish to make the debt holders whole to retain the franchise value of the bank. Equity owners are wiped out, however. That is, all they need do here, is put in 0.1 units, and then equity owners are willing to add the other unit. This cost of 0.1 units, is less than if they acquired the bank, and sold off the pieces. There is a significant going concern value that is preserved if the failed bank is sold to another bank, the main way a bank is recapitalized, compared to if the bank is merely liquidated by the government. These losses are twofold. First, there is the value loss from exiting customer relations that are now reassigned, and brand value of the bank (really, capitalized future customer relationships. Secondly, the government is probably a less efficient in liquidating assets than the private sector because they do not have an incentive to merely maximize the return on the bank assets. These costs are large and significant, and was documented in examining the S&L crisis in the late 1980's (see Christopher James, Journal of Finance, Sep 1991).



In scenario 3 below, the losses are so large (say 5 units), no amount of equity infusion would be greater than any gain from maintaining the franchise value, or avoiding the extra costs of relative government inefficiency. If the government burns, say, 1 unit of franchise value, and also 1 unit via their incompetence, this is 2 units, still better to wipe out the bank and sell it off as opposed to paying debt owners 5 units to save the 2. The optimal action if for the bank to be simply acquired by the government, small depositors are made whole but bank bond owners lose principle.



For banks that are presumed insolvent, the question is whether the loss is sufficiently small so that it is less expensive to taxpayers to maintain its franchise value. The crucial question, therefore, is whether banks are solvent given their current values, not in some stress test. The adequacy of capital refers to scenario 1, but this should be self-correcting, because equity owners will accept dilution in order to avoiding losing franchise value. Insolvent banks need government action, but there is a difficult decision about liquidation versus bailing out bank debtholders, and here one should focus on the current value of the assets, and the bank's franchise value, as opposed to a stress test.

The Treasury should tread lightly, because a currently solvent bank that is merely undercapitalized under certain hypothetical scenarios is like a bank who is technically insolvent when one writes off Goodwill. This latter scenario resulted in several billions of US liability in the S&L crisis, as several banks found that changing the rules on bank goodwill merely destroyed an accounting contrivance, in the process creating a bank that violated the regulatory requirements and destroyed the bank's franchise value because by law it had to be taken over, even though equity and debt owners were willing to operate the bank. Destroying a bank's franchise value in such a manner destroys real value, and the courts agreed with this. It is not the legal liability I am concerned with, rather, the suboptimality of destroying bank value, from the relative inefficiency of government bureaucrats or the franchise value loss.

A zombie bank is like scenario 3 where the bank is not shut down by the government. The government keeps it alive, via forcing debtholders to renegotiate their terms, but the bank is insolvent no new equity owners are forthcoming. The bank owners, afraid of losing the option value of their ownership, merely wait for inflation to make the nominal value of assets rise above the value of the debt, but if inflation remains low, this may take a long time. Interestingly, in these situations, the small amount of equity does not generate the inordinate risk taking from taking advantage of the 'heads I win, tails the government loses' approach to the bank, but rather, a timid owner waiting for nominal prices to make the bank solvent.

The key is that for solvent banks one might think match funding equity infusions is worthwhile, and indeed it seems reasonable to assume that such an investment will be ok, because investors are also investing new capital on equal terms. But for insolvent banks, the only way to get the capital ratios back to solvency involves government injections, not mere match funding. No equity owner will take scenario 3 back to solvency, because investing 5 units to bring it back to normal, would be a transfer of 4 units to the debt owners, meaning, new investors would add 5 units to get 1 unit of bank ownership, a poor investment relative to buying stock in a solvent bank, of which there are many.

Meltzer's Bank Plan

Timmy Geithner's stress test looks at the effects of macro economic changes on bank balance sheets, an exercise surely to not generate effects larger than their standard errors. This will be manifest in the absence of standard errors in what they produce, and any scientific forecast includes standard errors.

But one might say, just stress the market by 3 standard deviations. Yet, if the market falls another 50%, one must anticipate a total collapse of the financial markets, so I think yes, if we can't figure out how to keep financial markets from going into a tailspin, it's "start over" for the American financial system. I don't think that's going to happen, I'm just saying in a scenario where banks on average are writing down their risky (ie, non-Treasury) assets by 20%, implies imagining everyone is insolvent because there is an accelerator mechanism at play here. Thus, I don't think such an exercise would shed much light on the puzzle of whom to shut down. With that scenario, the answer is trivial: everyone. Then the question is, how is the government going to replace our current financial system, as they will no doubt feel obligated to do?

Longtime Federal Reserve scholar Allan Meltzer suggests the Fed merely say they will match fund equity investors in banks. Say a bank wants $20B, if it can raise $10B, the government will add $10. If banks think they are solvent, then bank equities owners will prefer dilution to being stopped out due to liquidity concerns. If a bank is insolvent, it will find no equity investors, and so then be taken over in the way the S&L's were disposed of in the early 1990's. This has the advantage of using the market to determine who is solvent, not some arbitrary stress test.

If I were Treasury Secretary, I would do nothing. Luckily, Geithner has such low gravitas that's all he can get away with, going through the motions, generate reports, testify in front of Congress 3 times a week, that in the end does not amount to much. Thus, I am pleasantly pleased with Geitner because if he had more credibility he might actually be able to do something; given the predilections of the Obama team it would be worse than doing nothing.

Economists Aren't More Stupid than Other Scientists

While economists have been taking a beating for not predicting the future correctly, it is useful to remember that when you take a physicist to the real world, he too has little to say. Consider our understanding of our solar system, a seemingly straightforward issue.

Mercury: Has an abnormally large iron core. For its size, its density seems too high. This is a puzzle. Leading theory: hit by asteroid.

Venus: Venus rotates in a clockwise fashion, in contrast to other planets. Leading theory: an asteroid hit it to spin the other way

Earth: No one knows where the water came from. Leading theory: snow comets hit us. The moon's origin, also, is not obvious, because it doesn't have enough iron, and if it just spun out of a really fast spinning earth, the earth would still be spinning fast or the energy release needed to slow the earth down would raised the temperature to 1000 degrees Celsius. Nebula forming two planets, or planetary capture, don't work. Leading theory: an asteroid hit the early Earth, creating the moon.

Mars:There appears to be evidence that Mars used to have water, as it seems to have lots of old lake beds and dry river beds. Why did the water leave? The atmosphere is currently about 0.3% of the earth's, so currently water goes immediately from ice to water vapor and then floats into space. So where did the atmosphere go? Solution: solar winds, or an asteroid hit it and pulled away the atmosphere.

Jupiter: Jupiter is 1300 times the volume of the earth, yet spins around once every 10 hours. Puzzling.

Saturn: Where did the rings come from? At some point in Saturn's early history, a moon about 300 km across got too close to Saturn and was torn into pieces. Or, it's also possible that two moons collided together, or a moon was struck hard enough by an asteroid that it just shattered.

Uranus: Uranus spins on its side, rolls around like a ball, unlike the other planets. This is a puzzle, because in theory, the solar system started as a spinning nebula of debris that generated a similar spin to it agglomerated parts, and also because its moons circle the planet on a different axis. Solution: an asteroid hit it. The moon Miranda has a very strange surface. There are huge faults, smooth plains, and curiously shaped rifts, smooth in some areas, rocky in others, that make it seem like it has different parts. A leading explanation: hit by asteroids.

You can only use the 'hit by an asteroid' explanation so many times, before it starts sounding like a filler for 'we have no good theory'.

We have a lot of work to do figuring out lots of prosaic stuff. Nobel physicist Robert Laughlin points out, much interesting physics are emergent phenomenon, they are not derived from basic forces. Very little, if any, collective organizational phenomenon, such as crystallization and magnetism, has ever been deduced from its lower lower-level parts.Those predicting the End of Physics are ignoring all the interesting problems that are not potentially generalizable to everything.

A physicist's ability to predict the terminal velocity of rocks falling from the Tower of Pisa, is like an economist predicting that when you subsidize something you get more of it. True in itself, but few are interested in such isolated phenomena.

TARP Recipients Discover One of Many Conditions

An amendment prohibits any recipient of TARP funding from hiring H-1B visa holders. These are the visa given to most highly skilled analytical types in finance. Bank of America recently rescinded job offers to 66 foreign born students graduating from US business schools.

Number of H-1B visas per year: 65,000
Number of illegal immigrants per year: at least 500,000

I am not a libertarian who believes in open borders, because as Milton Friedman noted, we have a welfare state that makes this not merely a transaction between two consenting adults. If we allow in the 3 billion worldwide who make less than a dollar a day, they will be happy for a while, but then they will complain they are being treated poorly and demand more rights, paid for by taxpayers. The second their kids are born here, they are US citizens. And basically, once in, they are de facto citizens, because it is politically impossible to deport illegals in any large number. Further, we apply exclusions only to those who follow the law. If you break the law, you get in. This is not a good filter.

Heck, Mexico doesn't allow illegals from Guatemala or Honduras, and you don't see illegals marching in Mexico City. Indeed, you don't see this in Tokyo, Frankfurt, or Paris. I like the Canadian, or Singaporean system. Why not have open borders to someone with a college degree who has a job here? As per unskilled workers, we have an excess supply here, and a simple way to raise their wages would be to stop this supply from increasing further.

I know banks are stressed, but I imagine over the next several years there will be many public interest inspired conditions on the TARP monies that will turn out to be similarly stupid. When you receive a favor from the Don, he owns you.

Good News for Cougars

It seems the highest IQ children would have 15 year old men mating with 40 year old women. According to a new study, published Monday in the online journal PLoS Medicine (see here), paternal age at conception generates a linear decrease in child IQ, whereas maternal age rises sharply to age 25, then rises very slightly throughout her life. See graph below cropped from the paper. The solid line is the IQ of the child as a function of the mother's age, the dotted line as a function of the father's age. They held constant race, gestational age, socioeconomic factors, marital status, other parent's age, and mental illness, and had about 33,000 observations. Sounds pretty solid.



Thus, by having my kids at 35, 37, and 41 I probably cost my kids about 4 IQ points. Then again, if I had kids in my early 20's, they wouldn't be here, some other DNA would, so they really can't complain. Plus, they enjoy the advantage of a ripened mother. I'm sure they will complain anyway.

Out-Sourcing Risk Management

The latest Basel Accord consultation document raises the prospect that banks must conduct their own due diligence on each of the assets underlying securitisation transactions – a requirement that could make the securitisation market out of reach for many small firms. They also note that

A bank should conduct analyses of the underlying risks when investing in the structured products and must not solely rely on the external credit ratings assigned to securitization exposures by the CRAs (Credit Rating Agencies). A bank should be aware that external ratings are a useful starting point for credit analysis, but are no substitute for full and proper understanding of the underlying risk, especially where ratings for certain asset classes have a short history or have been shown to be volatile. Moreover, a bank also should conduct credit analysis of the securitisation exposure at acquisition and on an ongoing basis. It should also have in place the necessary quantitative tools, valuation models and stress tests of sufficient sophistication to reliably assess all relevant risks.

The question raised is whether out-sourcing risk management is ever acceptable. Clearly, in the latest crisis the rating agencies errored. Yet, the rating agencies generally don't go against conventional wisdom. I see no indicators suggesting that while the rating agencies ignored the risk of falling housing prices, more than a handful out of tens of thousands standard financial institutions were applying a 25% stress test to these assets.

There are clearly problems with relying on someone else, especially when they are wrong. But there are problems with in-house analysis too. Consider the pros and cons of an external risk management, such as provided by traditional rating agencies, that centralizes risk measurement:

Pros:
1) regulators need only analyze one framework, and can simultaneously address the risk to an asset class with one thorough vetting process.
2) avoid conflicts of interests within banks that may lead to excessive pressure on risk managers to understate risks.
3) create tranparency in risk measures that allow for greater liquidity, including the hypothecation of assets as collateral, or the existence of trading markets that enable a bank to sell or buy assets to meet liquidity or capital needs.
4) there may be scale economies in evaluating risk. Do we need each bank to reunderwrite IBM's unsecured credit? What about Citibank's credit card receivables?


Cons:
1) If the centralized authority makes an error, it has systemic implications (one error is magnified)
2) There are conflicts of interest between the centralized risk monitor and the various entities it is evaluating.These are inescabable, because if one moves from an issuer pays model, to a buyer pays, then the buyer's intests are in play. He probably will have a large inventory, and would probably want to see continued stable, high ratings for an asset class, just as an issuer would. Further, there would be a free rider problem among credit analysis buyers, which would potentially cause an underinvestment in them, because the centralized authority can not seize sufficient revenue from the value they produce. So, in any scenario, there is conflict of interest for an external ratings provider.

In a crisis, the first reaction is usually to centralize various decentralized functions. Think about the reaction to 9/11 and the calls for one unit in charge of intelligence gathering. Centralization has considerable benefits, which are often alluded to if you ever have to deal with an institutional IT infrastructure and their latest plan to centralize information.

I think the fundamental question is whether one thinks systematic errors are caused by key focal points, in this case, Moody's and S&P, or one thinks the more basic error was a ubiquitous incorrect belief. I tend to the latter. The rating agencies were not leading the adoption of NINJA loans, they were led by regulators, legislators, the government sponsored housing entities, and of course investors. It is too simplistic to blame a formula, or a specific group for this crisis, as no one was arguing that assets should have a stress test for a 30% housing decline back in 2006. No one. With hindsight, that was a mistake, but one everyone made. It is nice to think that people would have independently applied this if it was in their bailiwick, but just as no congressman wanted to appear to squelch more homeowning, and thus prohibit the US Housing and Urban Development Department from encouraging no down payment mortgages.

Consider David A. Andrukonis, risk manager for Fannie, was let go for fighting Bill Syron's plans to accept more mortgages with weaker underwriting standards (Syron got $38MM, Andrukonis was let go). He was a risk manager telling his CEO that a risk that has never materialized, but is based on theory, should inhibit more volume into a higher revenue and ROE activity. He lost, as has every risk manager in such a scenario. With hindsight, NINJA loans are indefensible, but in real time objecting was futile. When there is big money to be made doing X, a theory but no data arguing against X, is not compelling.

I don't think this mental blind spot came from outside, it crept in through a lack of confounding data to the idea that these weaker (err, innovative) lending criteria were too risky, and this belief was so endemic because it reflected the righteous goal of increasing home ownership to the poor, especially minorities. People believe bad analysis when they get the answer they like, and everyone liked the answer. Even the American Economic Association granted great honors on those writing that existing lending criteria were (see the seminal prized pub in early form here), if unintentially, racist, while their critics were left in obscure universities and journals. With over $2 Trillion (that's 2 thousand Billion) in loans targeted towards 'traditionally underserved communities' (ie, people with bad credit, no down payment), the money created deep and broad vested interests. The rating agencies were not necessary for this problem.

Lastly, regulators are already overwhelmed by data, as the process of outsiders evaluating a complex financial institution is simply very difficult in the best of circumstances. They ask for risk reports, they will get them, but tables of exposures cross-tabbed by originator, region, etc., will just be butt-covering documentation. To think regulators have the ability to analyze anew thousands of institutions individual risk underwriting just means they will be more overwhelmed. They will look for key words, perfunctory analysis that consultants will teach banks that are necessary, which in the end makes the underwriting just as susceptible to group-think as before. But at least the external rating agency is monitored critically, in a straightforward fashion, and there is some competition (ie, Fitch, S&P and Moody's). The myriad decentralized reports will have the added baggage of serving two purposes. Hitting the hot-buttons everyone knows regulators are looking for, while pretending to be some great alpha-inspired individualized edge, built from the bottom up.

Rahm Emmanuel stated the Obama administration should never let a crisis go to waste. I think crisis bring out the worst in individuals and collectives because they overreact indiscriminately, and this usually merely kills an error that is now so obvious as to be irrelevant going forward anyway. The bottom line is that people need to have good incentives, good data, and good judgment. If losing 80% of one's market cap is not sufficient incentive for firms to address this optimally given their specific contexts, why does one think the incentives, data, and judgment of legislators and bureaucrats in Washington or Basel are going to be better? Because they are selfless public servants? That's a grade-school view of politics. I'm not saying markets are perfect, merely that regulators mandating decentralization of all risk measurement, is worse.

We Need More Depressions

One problem in this crisis is that economics has basically one Great Depression dominating its data. Thus, we are like doctors looking at a patient who is sick, having only one experience with a very sick man. Now, if that man had pneumonia, and the new patient has something else, your cures are going to be no good. Cross country studies are good, but even there we are left with a handful of comparables, and all come with obvious dissimilarities that prevent generalizations. As Russ Roberts says, 'It's one event!'

Judge Defines Douchebaggery. Really.

Some women found their pictures in the book Hot Chicks with Douchebags. They sued for defamation. The judge dismissed the complaint, basically after noting the book was clearly satirical, and so was protected by the first amendment. Some of the commentary in the ruling is very funny, primarily because the judge avoids obvious jokes and puns:

In light of these guidelines, the Court has carefully scrutinized the book and the context in which the photographs appear. On page 70 the title heading is “The Federbag”. It contains three paragraphs, all of which describe a type of male who the author considers a “douche celebrity.” He described a federbag as “famous in their own minds, they live the celebrity rock star life style while being neither celebrity nor rock star.”
...
The book begins by defining a “douchebag” and including a definition which is not recognized in the dictionary. In fact, it appears to be one made up in order to be humorous. At the end of the book, in his acknowledgment, the author states “I must give a special round of thanks to all the participants and contributors on the blog whose enthusiasm and hilarious commentary mocking the douchescrote and celebrating the hott have kept me going.” On the rear cover there is a quote “Douchebags need a smack.” This quote is attributable to “Gandhi”.

The Court concludes that there is no actionable defamation. The book is replete with obvious attempts at satirical humor. For example, how can a person reasonably believe that in 1981 archaeologist Renee Emile Bellaqua uncovered in a cave in Gali Israel a highly controversial Third Century religious scroll suggesting that the “douchey/hotty” coupling was a troublesome facet in early social religious structures? Or would a reasonable person believe that Jean-Paul Sartre stated “man is condemned to be douchey because once thrown into the world he is responsible for every douchey thing that he does”? Or that John Hopkins has a Department of Scrotology or that there was a Theban King Seqenenra Tag, in ancient Egypt known as “gito of the southern city”? An examination of the book reveals that old photographs of paintings are doctored to suit the satire in the book. The author also defines a completely fictitious time period “BG”, before the actor Richie Grieco and “AG” after the actor’s impact on the douchebag male style.

The Court finds that the text and photographs do not constitute defamatory falsehood of or concerning any of the plaintiffs.

I wish I had a judge with common sense like that.

A Totally Unfair, Very Funny Takedown of CNBC

Check out John Stewart mocking CNBC.

The Daily Show With Jon StewartM - Th 11p / 10c

CNBC Gives Financial Advice

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Corporate Leverage Did Not Cause the Bubble

The attached graph shows leverage ratios for Commercial and Investment Banks over time. There's a little jump at the end for investment banks, and I suspect a lot of that was due to the fact that as the Asset Backed market shut down mid 2007, all the stuff coming on their books they used to sell, they had to keep.

Note that many people say leverage caused the bubble. Only in a certain sense. Home buyers were too leveraged (no money down). Any owners of these assets were implicitly too leveraged by owning them. Basically, if the base constituents of the portfolio is leveraged, everything above it is too. But strategically, at corporate level, the Assets to Liabilities ratio was not a major player in this crisis. And, of course, no bank should ever own investment grade securities trying to make money on the spread.

Leverage and the Crisis

The recent crisis has created a cottage industry in articles with various primary culprits: hubris, too little regulation, government encouraging lending to poor neighborhoods, credit default swaps, Viking machismo, copulas, regulatory capital requirements, value-at-risk, the Basel 2 regulator accord, Greenspan's easy money, too-big-to-fail, Chinese investment, asymmetric bonuses, the repeal of Glass-Steagall, and finally excessive leverage by financial institutions. Leverage, what the Brits call 'gearing', is the ratio of assets to equity (think Archimedes) I have worked a lot examining bank default models based on financial ratios, and how ratings, equity returns or spreads relate to leverage, and for banks there just is not that much there historically. Leverage, historically, is not a very powerful indicator of financial performance for financial institutions (it works for nonfinancials, however). Not that it does not matter, only that leverage ratios observed represent equilibrium solutions to various problems, and the residual cross sectional correlation is generally uninformative. The nature of the assets and liabilities is orders of magnitude more important than what the balance sheet shows, which is why I do not actively trade financial institutions. Bank financial statements are too opaque to be useful, a problem that has really exacerbated this crisis because they now find they can't demonstrate they are not insolvent.

Consider first the return on a strategy going long BBB (Baa) bonds, short AAA (Aaa) bonds. This strategy's total return is listed below.


Note that on average, you make money in this strategy. Indeed, this is a prominent financial puzzle, because the return on this trade represents a higher return than generated via standard utility functions. That is, the annualized Sharpe ratio is about 0.4, which is around what it is for the stock market, which itself generates the well known 'equity premium puzzle'.

Note also that the drawdown to such a strategy varies over time. It was about 20% in the Depression (1930's), and recently experienced a 15% drawdown. One can imagine someone thinking the Depression is no longer a relevant benchmark, in which case, a 6% drawdown is a reasonable worst case scenario. Now, most investors apply capital of around 3 times the worst case scenario, which means, using the lenient 6% worst case scenario, 18% capital. The return, per year, is merely 1.4% annually, so a 1.4% return on 18% in capital (these are a function of par bonds at work) is about 7.8% return on economic capital, which is generally not a good investment for something with so much uncertainty. Thus, while on a Sharpe ratio this may seem a puzzle, given the leptokurtosis (fat downward tail) in bonds, one should not base the risk capital as a function of the standard deviation, but rather, a stress test, which in this case makes this a bad trade (alas, utility function theorists focus on standard deviations). The strategy makes sense for banks only in the context of a product that is 1) not market to market and 2) generates auxiliary revenue via servicing of the loan.

Thus, I am truly puzzled that many banks ended up, basically, with a highly levered position long high grade mortgages funded at the bank funding rate (usually A rating), because even if this crisis did not happen, and the BBB rated Mortgaged Backed Securities did not experience defaults, the mark to market from standard variations in the BBB-AAA spread makes it a loser for investors. I suspect many backed into this trade as UBS did, via first acquiring the securities to repackage into mezzanine securities sold at fat margins, but then were stuck with the high grade securities, and rationalized this as not a problem by looking at historical BBB default rates and ignoring the mark to market. In any case, they should have had a leverage ratio of 5.5:1 (1/0.18) if they want to 'arb' the AAA-BBB spread, so the really high leverage rates of investment banks implies they were not looking at the spread volatility, irrespective of default rate and housing price assumptions.

But is this a key to our financial debacle? Did they merely take on 30 times leverage to arb some thin investment grade spread? Looking at investment bank leverage ratios (assets/book equity), we see that Lehman, Merril, and Bear Stearns were all around 30 in December 2007. Yet, they were around these levels in 1995. Sure, some, like Citi and Morgan Stanley, moved up considerably over that period, but these moves were the exception. Thus, in general, there was not a big increase in leverage, as it was confined to a handful of investment banks. Nevertheless, we do see a relation between leverage and stock returns. Using all US investment banks with market cap over $1B in 2006, and looking at stock returns from 2006 to present, we see a modest, but significant, negative relation. Higher leverage implies a higher future market decline.


But these are investment banks, and when someone on television says 'banker', they usually mean 'investment banker', which is a very different animal. Bankers have modest incomes, and except for the highest echelons, are not not your quintessential Masters of the Universe. They understand the nitty-gritty of underwriting and servicing loans, as opposed to trading and making deals. The leverage ratios of commercial banks, if anything, went down over the past decade, from an average of 12 to around 10. Most banks are commercial banks, not investment banks. We see little relation between leverage in 2006 and subsequent stock market performance (using those US banks with market cap greater than $1B in 2006). This is a typical banking relation, where book leverage, in general, is not correlated with things like ratings, spreads, and other financial performance metrics.


Thus, I think the investment banks that warehoused significant amounts of market traded BBB rated debt were making a bad trade, and were much too highly levered to make this trade. This seems to explain some of the problems for these firms. Yet, most banks were not increasing leverage, and there is little relation between the leverage and future returns we see in commercial banks, as both have lost about 60% in market cap since 2006. So although leverage has some relevance, this is a second order consideration in the big picture.

The key is the quality of the mortgages, and for investors looking at financial statements, there was basically zero meaningful information on how the nature of mortgage assets changed for banks back in 2006.

The Unpredictability of This Crisis

As this crisis wears on, fewer and fewer people remember being surprised by it. For example, in a Blogginheads episode, Megan McCardle and Dean Baker both noted they anticipated the collapse even though Baker called for a 10% decline in housing back in 2002, which is about 20% below December 2008 level. But last April, as these problems were becoming apparent, I was at a meeting by the National Bureau of Economic Research, where all the top financial economists got together. Markus Brunnemeier gave a talk on the housing bubble and noted that about $200B in value had been destroyed via the housing price decline, and this represented only a couple percent in the stock market. The implication was the market had already overreacted. The consensus was this was correct, and I must admit I was no different.

I think what is most surprising about this is the accelerator mechanism that propelled a housing bubble into so many other sectors. It destroyed the market value of all sorts of assets, and seems caught in a positive feedback loop. A related puzzle is that the crisis seemed to start in the US, but the US equity market has declined less than most other countries, and our currency has strengthened. One of my favorite bank analysts, Tom Brown, called for a bank stock bottom last summer. I don't feel bad not understanding or anticipating this, because I know many thoughtful people missed it too, and that very few crises are understood in real time. Sure, with hindsight, I'm seeing connections, how a total lack in trust has created declines in market values which cause declines in banks that have to mark more and more of their assets to market, but realistically going back to last year I don't see how I could have anticipated this other than being a permabear, or knowing about the prevalence of NINJA loans. Indeed, maybe that is the missing variable, that one could not have predicted this problem without knowing how crazy mortgage underwriting became.

Popular not Original

In an interview on YouTube, Michael Hardt, author of Empire, noted that

Q: The two of you wrote a book Empire ... and has become kind of an intellectual best seller, it has been translated into 22 languages ... Why do you think the book had such an impact?

Hardt: Um. One never knows these things. But, one I idea I've had is that that the book the book is not terribly original. And I mean that in a good way, not out of any kind of modesty. Truly original books don't get read. Truly original books, no one can understand. What we are saying is not exactly obvious, but is, but people are already thinking. ... Books in general that have a kind of intellectual success are books people are ready for.

That's a pretty refreshing take by a successful author.

All Banks are Insolvent if You Think They Are

A major difference between banks, and nonbanks, is the inherent susceptibility towards runs. No bank can withstand a run, which is a major reason why we have Central Banks to act as Lenders of Last resort. They key is simply that a bank is a way to intermediate between savers and investors. Most investments into real good take a year, or 5 years, to generate any return. If you sell them before completed, you will usually sell them at a loss irrespective of their worth. Most savers want the ability to retrieve all of their money instantly, in case they need to pay for some emergency.

The solution to this problem, is to rely on the statistics that imply most people will not want all their money right away at the same time. Indeed, with deposit insurance, there are few consumer runs on banks as happened in the 1930's and before. But, most banks and large complex financial institutions have lots of short term debt that rolls over, and if a sufficient number of investors do not roll over their debt, the bank either has to issue long term debt, or liquidate their assets. In times like 2008, both scenarios were inconsistent with viability.

Thus, a problem today is that we have no idea what the assets of a bank are really worth, because the information provided by banks does not allow someone outside to know the proportion of mortgages they hold with Fico scores<600, LTV's>100%, vintage less than 5 years, etc. Without this necessary information people make extrapolations based on what they know, such as where various types of debt trades. But to say the traded debt is a decent proxy for all their assets is absurd, and unfortunately that's how assets are being treated.

In the past week, prominent internet economists Tyler Cowen and Paul Krugman suggested that banks are insolvent, meaning, the value of their assets is below the value of their liabilities. They offered no data to support this assertion, I presume they merely inferred it via the stock market. Unfortunately, they are definitely correct if investors refuse to roll over short term debt, many large banks are insolvent, but this is just a self-fulfilling prophesy true at any time in banking.

It's a difficult problem, especially because simultaneous to financial stabilization plans, we have vague stress tests that could indiscriminately wipe out banks, as any official proclamation a bank is insolvent implies, through the dynamics above, they are insolvent. We also have various home owner bills that could drastically reduce mortgage values, such as changing the bankruptcy laws so that not only are these loans non-recourse, but the banks effectively do not have a first claim on the collateral as previously assumed. Further, such legislation could encourage a second wave of mortgage defaults as people try to qualify for the government's booty.

The markets are in such a funk that market valued accounting, applying market valuation estimates based on proxies, probably does imply a lot of insolvency. Yet non-mortgage loss rates are not outside the norm of past recessions and thus far are containable. The proxy approach using market values would unnecessarily destroy a great amount of franchise value so needed at this point in time. I get the feeling I'm reliving the US history from 1931-33, where the government turned a major recession into a Depression by misunderstanding the importance of providing liquidity and other ham-fisted efforts to fix things (Smoot-Hawley tariffs). Merely heeding M2 will not be sufficient to save us. Bernanke understands the importance of banks in recessions, as he thought Friedman was right to blame the Federal Reserve for its role in the Great Depression, stating on Nov. 8, 2002:

Let me end my talk by abusing slightly my status as an official representative of the Federal Reserve. I would like to say to Milton and Anna: Regarding the Great Depression. You're right, we did it. We're very sorry. But thanks to you, we won't do it again.

He needs to also remember that one characteristic of recessions is that they are all different.