Retire Standard Deviation

Wednesday, January 22nd, 2014

“The statistician cannot evade the responsibility for understanding the process he applies or recommends,” Sir Ronald A. Fisher said, so it is time to retire standard deviation from common use and replace it with mean deviation, Nassim Nicholas Taleb suggests:

Standard deviation, STD, should be left to mathematicians, physicists and mathematical statisticians deriving limit theorems. There is no scientific reason to use it in statistical investigations in the age of the computer, as it does more harm than good — particularly with the growing class of people in social science mechanistically applying statistical tools to scientific problems.

Say someone just asked you to measure the “average daily variations” for the temperature of your town (or for the stock price of a company, or the blood pressure of your uncle) over the past five days. The five changes are: (–23, 7, –3, 20, –1). How do you do it?

Do you take every observation: square it, average the total, then take the square root? Or do you remove the sign and calculate the average? For there are serious differences between the two methods. The first produces an average of 10.8, the second 15.7. The first is technically called the root mean square deviation. The second is the mean absolute deviation, MAD. It corresponds to “real life” much better than the first — and to reality. In fact, whenever people make decisions after being supplied with the standard deviation number, they act as if it were the expected mean deviation.

It is all due to a historical accident: in 1893, the great Karl Pearson introduced the term “standard deviation” for what had been known as “root mean square error”. The confusion started then: people thought it meant mean deviation. The idea stuck: every time a newspaper has attempted to clarify the concept of market “volatility”, it defined it verbally as mean deviation yet produced the numerical measure of the (higher) standard deviation.

But it is not just journalists who fall for the mistake: I recall seeing official documents from the department of commerce and the Federal Reserve partaking of the conflation, even regulators in statements on market volatility. What is worse, Goldstein and I found that a high number of data scientists (many with PhDs) also get confused in real life.

Learning to Love Volatility

Wednesday, November 21st, 2012

Nassim Nicholas Taleb shares his five rules for anti-fragility:

  1. Think of the economy as being more like a cat than a washing machine.
  2. Favor businesses that benefit from their own mistakes, not those whose mistakes percolate into the system.
  3. Small is beautiful, but it is also efficient.
  4. Trial and error beats academic knowledge.
  5. Decision makers must have skin in the game.

Taleb is a classic crank

Wednesday, August 19th, 2009

In Martin Gardner’s taxonomy, Eric Falkenstein explains, Nassim Nicholas Taleb is a classic crank:

  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 attack only the biggest theories and scientific figures.

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.
[...]
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.

Read the whole thing. I skipped ahead to the punch-line. Falkenstein poked fun at Taleb earlier, too.

Nassim Nicholas Taleb to David Cameron

Tuesday, August 18th, 2009

Nassim Nicholas Taleb writes an open letter to David Cameron, leader of Britain’s Conservative party:

We live in an increasingly complex system and complexity causes Black Swans. How? The more interdependent we become, the harder it is to trace the cause of an event and the tougher to forecast accurately, meaning the traditional tools of economics will fail us. And since the spread of the internet, rumours go round the world in minutes. Consider the run on Icelandic banks. It took place at BlackBerry speed. So the economic variables, such as sales, commodity prices, unemployment or GDP growth, are subject to ever more extreme variations. The over-efficiency of the systems means things run smoothly, but are subject to rare but violent blow-ups.

David, you must counter this complexity by lowering indebtedness. We have known since Babylonian times that debt is treacherous and allows no room for mistakes: felix qui nihil debet goes the Roman proverb (“happy is he who owes nothing”). The combination of debt levels swollen from two decades of over-confidence with modern finance’s complex derivatives has been disastrous.

Be careful, too, of the so-called science of economics. Economists have been no better in their predictions than cab drivers. We have an “expert” problem, in which the expert provides you with misplaced confidence, but no information. Because we think, correctly, that the dermatologist, the baker, the chemist are true experts (they know more about their respective subjects than the rest of us), we swallow the canard that the economists at the International Monetary Fund, the World Bank, the Bank of England and the US Federal Reserve are also experts, without checking their record. This reliance on faux experts is, for the most part, what got us here. Now it is continuing with the build-up of government deficit and an increased reliance on flimsy forecasts by the Obama administration.

This problem with experts was particularly acute when it came to the “risk models” on which bankers built those positions that turned sour. So it is that you are coming under pressure to provide more regulation. Alas, the need for more regulation is a myth. I have been fighting risk models both as a Wall Street trader and as a professor and my worst nightmares were the results of regulators. It was they who promoted the reliance on ratings by credit agencies. The “value-at-risk” models regulators promoted made us take more risks.

If we are to have regulators, we need them to operate along conservative lines and conserve the rich knowledge and understanding of risk transmitted through generations of practice, of trial and error. We replaced the heuristics of the elders with arrogant (and incompetent) beliefs, breaking, in the name of science, the chain of knowledge. Old, conservative bankers and traders have been replaced by keen young mathematical analysts, yet anyone who listened to a grandmother who survived the Depression would have been warned against debt and been better prepared than Ben Bernanke and Alan Greenspan, respectively chairman and former chairman of America’s Federal Reserve.

The solution is obvious: build an economy that increases the role of well-tested traditions. Ban financial derivatives that require advanced mathematics rather than trial and error. Look at mother nature. There is a complex system built around sound principles that has insured both evolution and survival. It does not let anything get too big to fail. It breaks things early. I don’t understand why people who stand against tampering with nature accept tampering with the economy that would have organically grown too. Work on building a “robust” society, capable of withstanding errors, in which the role of finance (hence debt) would be minimal. We want a society in which people can make mistakes without risk of total collapse. Silicon Valley offers a good example, where people have the chance to fail fast (and repeatedly).

The best blueprint is the very opposite of the Obama administration’s economic policies (its foreign policy is commendable). It has been administering pain-killers without addressing the cause of disease. Obama is strengthening those who do the wrong thing.

Ten principles for a Black Swan-proof world

Thursday, April 9th, 2009

Nassim Nicholas Taleb offers up ten principles for a Black Swan-proof world:

  1. What is fragile should break early while it is still small. Nothing should ever become too big to fail. Evolution in economic life helps those with the maximum amount of hidden risks — and hence the most fragile — become the biggest.
  2. No socialisation of losses and privatisation of gains. Whatever may need to be bailed out should be nationalised; whatever does not need a bail-out should be free, small and risk-bearing. We have managed to combine the worst of capitalism and socialism. In France in the 1980s, the socialists took over the banks. In the US in the 2000s, the banks took over the government. This is surreal.
  3. People who were driving a school bus blindfolded (and crashed it) should never be given a new bus. The economics establishment (universities, regulators, central bankers, government officials, various organisations staffed with economists) lost its legitimacy with the failure of the system. It is irresponsible and foolish to put our trust in the ability of such experts to get us out of this mess. Instead, find the smart people whose hands are clean.
  4. Do not let someone making an “incentive” bonus manage a nuclear plant — or your financial risks. Odds are he would cut every corner on safety to show “profits” while claiming to be “conservative”. Bonuses do not accommodate the hidden risks of blow-ups. It is the asymmetry of the bonus system that got us here. No incentives without disincentives: capitalism is about rewards and punishments, not just rewards.
  5. Counter-balance complexity with simplicity. Complexity from globalisation and highly networked economic life needs to be countered by simplicity in financial products. The complex economy is already a form of leverage: the leverage of efficiency. Such systems survive thanks to slack and redundancy; adding debt produces wild and dangerous gyrations and leaves no room for error. Capitalism cannot avoid fads and bubbles: equity bubbles (as in 2000) have proved to be mild; debt bubbles are vicious.
  6. Do not give children sticks of dynamite, even if they come with a warning. Complex derivatives need to be banned because nobody understands them and few are rational enough to know it. Citizens must be protected from themselves, from bankers selling them “hedging” products, and from gullible regulators who listen to economic theorists.
  7. Only Ponzi schemes should depend on confidence. Governments should never need to “restore confidence”. Cascading rumours are a product of complex systems. Governments cannot stop the rumours. Simply, we need to be in a position to shrug off rumours, be robust in the face of them.
  8. Do not give an addict more drugs if he has withdrawal pains. Using leverage to cure the problems of too much leverage is not homeopathy, it is denial. The debt crisis is not a temporary problem, it is a structural one. We need rehab.
  9. Citizens should not depend on financial assets or fallible “expert” advice for their retirement. Economic life should be definancialised. We should learn not to use markets as storehouses of value: they do not harbour the certainties that normal citizens require. Citizens should experience anxiety about their own businesses (which they control), not their investments (which they do not control).
  10. Make an omelette with the broken eggs. Finally, this crisis cannot be fixed with makeshift repairs, no more than a boat with a rotten hull can be fixed with ad-hoc patches. We need to rebuild the hull with new (stronger) materials; we will have to remake the system before it does so itself. Let us move voluntarily into Capitalism 2.0 by helping what needs to be broken break on its own, converting debt into equity, marginalising the economics and business school establishments, shutting down the “Nobel” in economics, banning leveraged buyouts, putting bankers where they belong, clawing back the bonuses of those who got us here, and teaching people to navigate a world with fewer certainties.

Greed and Stupidity

Monday, April 6th, 2009

David Brooks looks at the two prominent narratives of the economic crisis, the greed and stupidity narratives.

The best single encapsulation of the greed narrative, he says, is Simon Johnson’s The Quiet Coup, in The Atlantic.

To Brooks, the more persuasive theory revolves around ignorance and uncertainty:

The primary problem is not the greed of a giant oligarchy. It’s that overconfident bankers didn’t know what they were doing. They thought they had these sophisticated tools to reduce risk. But when big events — like the rise of China — fundamentally altered the world economy, their tools were worse than useless.

Many writers have described elements of this intellectual hubris. Amar Bhidé has described the fallacy of diversification. Bankers thought that if they bundled slices of many assets into giant packages then they didn’t have to perform due diligence on each one. In Wired, Felix Salmon described the false lure of the Gaussian copula function, the formula that gave finance whizzes the illusion that they could accurately calculate risks. Benoit Mandelbrot and Nassim Taleb have explained why extreme events are much more likely to disrupt financial markets than most bankers understood.

To me, the most interesting factor is the way instant communications lead to unconscious conformity. You’d think that with thousands of ideas flowing at light speed around the world, you’d get a diversity of viewpoints and expectations that would balance one another out. Instead, global communications seem to have led people in the financial subculture to adopt homogenous viewpoints. They made the same one-way bets at the same time.

Jerry Z. Muller wrote an indispensable version of the stupidity narrative in an essay called “Our Epistemological Depression” in The American magazine. What’s new about this crisis, he writes, is the central role of “opacity and pseudo-objectivity.” Banks got too big to manage. Instruments got too complex to understand. Too many people were good at math but ignorant of history.

The greed narrative leads to the conclusion that government should aggressively restructure the financial sector. The stupidity narrative is suspicious of that sort of radicalism. We’d just be trading the hubris of Wall Street for the hubris of Washington.

Recipe for Disaster: The Formula That Killed Wall Street

Tuesday, February 24th, 2009

Felix Salmon calls David Li‘s Gaussian copula function the formula that killed Wall Street:

In 2000, while working at JPMorgan Chase, Li published a paper in The Journal of Fixed Income titled “On Default Correlation: A Copula Function Approach.” (In statistics, a copula is used to couple the behavior of two or more variables.) Using some relatively simple math — by Wall Street standards, anyway — Li came up with an ingenious way to model default correlation without even looking at historical default data. Instead, he used market data about the prices of instruments known as credit default swaps.

D’oh!

When the price of a credit default swap goes up, that indicates that default risk has risen. Li’s breakthrough was that instead of waiting to assemble enough historical data about actual defaults, which are rare in the real world, he used historical prices from the CDS market.

All the ratings agencies needed was one variable, correlation, to rate a tranche:

As a result, just about anything could be bundled and turned into a triple-A bond — corporate bonds, bank loans, mortgage-backed securities, whatever you liked. The consequent pools were often known as collateralized debt obligations, or CDOs. You could tranche that pool and create a triple-A security even if none of the components were themselves triple-A. You could even take lower-rated tranches of other CDOs, put them in a pool, and tranche them — an instrument known as a CDO-squared, which at that point was so far removed from any actual underlying bond or loan or mortgage that no one really had a clue what it included. But it didn’t matter. All you needed was Li’s copula function.

The CDS and CDO markets grew together, feeding on each other. At the end of 2001, there was $920 billion in credit default swaps outstanding. By the end of 2007, that number had skyrocketed to more than $62 trillion. The CDO market, which stood at $275 billion in 2000, grew to $4.7 trillion by 2006.

We know it all doesn’t end well:

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.

Li’s copula function was used to price hundreds of billions of dollars’ worth of CDOs filled with mortgages. And because the copula function used CDS prices to calculate correlation, it was forced to confine itself to looking at the period of time when those credit default swaps had been in existence: less than a decade, a period when house prices soared. Naturally, default correlations were very low in those years. But when the mortgage boom ended abruptly and home values started falling across the country, correlations soared.

Managers didn’t understand how the black box worked:

Bankers should have noted that very small changes in their underlying assumptions could result in very large changes in the correlation number. They also should have noticed that the results they were seeing were much less volatile than they should have been — which implied that the risk was being moved elsewhere. Where had the risk gone?

They didn’t know, or didn’t ask. One reason was that the outputs came from “black box” computer models and were hard to subject to a commonsense smell test. Another was that the quants, who should have been more aware of the copula’s weaknesses, weren’t the ones making the big asset-allocation decisions. Their managers, who made the actual calls, lacked the math skills to understand what the models were doing or how they worked. They could, however, understand something as simple as a single correlation number. That was the problem.

Taleb of course proclaims, “Anything that relies on correlation is charlatanism.”

The Oracle of Doom

Tuesday, January 27th, 2009

Robert Langreth, writing in Forbes, calls Nassim Nicholas Taleb (The Black Swan) The Oracle of Doom — but not everyone thinks the oracle draws the right conclusions from the realization that markets can be unpredictable;

The critique from Paul Seabright of the Toulouse School of Economics in France: “He is right that economists have put too much faith in their tools, but he doesn’t stop there; he implies that all the tools are useless.” NYU economist Robert Engle says Taleb draws “precisely the wrong lesson” from the meltdown. “This episode has told us we need to work harder and harder on our risk models — not to ignore them entirely.” Texas Tech University statistician Peter Westfall complains that Taleb, while correct about fat tails, is slippery when it gets to mathematical assumptions underlying his theories. “He simply refuses to be pinned down,” says Westfall.

Taleb has been slow to offer up much in the way of advice. My own conclusion was that debt, which tends to be inflexible, was more dangerous than people realized, and that we shouldn’t be subsidizing it with its current tax-advantage. (Interest payments are a deductible expense; dividend payments are not.) It looks like Taleb is now suggesting the same thing — although he makes an analogy I wouldn’t be so quick to make and some policy suggestions that seem unpalatable:

He thinks the world would be a more stable place if there were fewer debt instruments and more equity stakes, perhaps along the lines of the Islamic musharaka system of profit sharing. He thinks complex derivatives such as swaps should be banned because they are tools for hiding massive risk. He envisions a two-tier financial system: Banks that might have to be bailed out someday should be treated like utilities and limited to all but the simplest investing and lending activities. Hedge funds that speculate with private money should do so with the knowledge they will never be bailed out again. Retail investors should keep more money in inflation-indexed bonds and not count on the stock market.

(Hat tip à mon père.)

The Fourth Quadrant

Monday, September 22nd, 2008

Nassim Nicholas Taleb (The Black Swan, Fooled by Randomness) maps decisions onto four quadrants — much like any MBA, really. In his case, the two criteria are whether the decision itself is simple (binary) or complex, and whether the probability distribution is known and thin-tailed (Mediocristan) or unknown and/or fat-tailed (Extremistan).

The Fourth Quadrant, naturally, is where all hell breaks loose:

In response, Taleb has developed a list of Phronetic RulesWhat Is Wise To Do (Or Not Do) In The Fourth Quadrant:

1) Avoid Optimization, Learn to Love Redundancy. Psychologists tell us that getting rich does not bring happiness — if you spend it. But if you hide it under the mattress, you are less vulnerable to a black swan. Only fools (such as Banks) optimize, not realizing that a simple model error can blow through their capital (as it just did). In one day in August 2007, Goldman Sachs experienced 24 x the average daily transaction volume — would 29 times have blown up the system? The only weak point I know of financial markets is their ability to drive people & companies to “efficiency” (to please a stock analyst’s earnings target) against risks of extreme events.

Indeed some systems tend to optimize — therefore become more fragile. Electricity grids for example optimize to the point of not coping with unexpected surges — Albert-Lazlo Barabasi warned us of the possibility of a NYC blackout like the one we had in August 2003. Quite prophetic, the fellow. Yet energy supply kept getting more and more efficient since. Commodity prices can double on a short burst in demand (oil, copper, wheat) — we no longer have any slack. Almost everyone who talks about “flat earth” does not realize that it is overoptimized to the point of maximal vulnerability.

Biological systems — those that survived millions of years — include huge redundancies. Just consider why we like sexual encounters (so redundant to do it so often!). Historically populations tended to produced around 4-12 children to get to the historical average of ~2 survivors to adulthood.

Option-theoretic analysis: redundancy is like long an option. You certainly pay for it, but it may be necessary for survival.

2) Avoid prediction of remote payoffs — though not necessarily ordinary ones. Payoffs from remote parts of the distribution are more difficult to predict than closer parts.

A general principle is that, while in the first three quadrants you can use the best model you can find, this is dangerous in the fourth quadrant: no model should be better than just any model.

3) Beware the “atypicality” of remote events. There is a sucker’s method called “scenario analysis” and “stress testing” — usually based on the past (or some “make sense” theory). Yet I show in the appendix how past shortfalls that do not predict subsequent shortfalls. Likewise, “prediction markets” are for fools. They might work for a binary election, but not in the Fourth Quadrant. Recall the very definition of events is complicated: success might mean one million in the bank …or five billions!

4) Time. It takes much, much longer for a times series in the Fourth Quadrant to reveal its property. At the worst, we don’t know how long. Yet compensation for bank executives is done on a short term window, causing a mismatch between observation window and necessary window. They get rich in spite of negative returns. But we can have a pretty clear idea if the “Black Swan” can hit on the left (losses) or on the right (profits).

The point can be used in climatic analysis. Things that have worked for a long time are preferable — they are more likely to have reached their ergodic states.

5) Beware Moral Hazard. Is optimal to make series of bonuses betting on hidden risks in the Fourth Quadrant, then blow up and write a thank you letter. Fannie Mae and Freddie Mac’s Chairmen will in all likelihood keep their previous bonuses (as in all previous cases) and even get close to 15 million of severance pay each.

6) Metrics. Conventional metrics based on type 1 randomness don’t work. Words like “standard deviation” are not stable and does not measure anything in the Fourth Quadrant. So does “linear regression” (the errors are in the fourth quadrant), “Sharpe ratio”, Markowitz optimal portfolio, ANOVA shmnamova, Least square, etc. Literally anything mechanistically pulled out of a statistical textbook.

My problem is that people can both accept the role of rare events, agree with me, and still use these metrics, which is leading me to test if this is a psychological disorder.

The technical appendix shows why these metrics fail: they are based on “variance”/”standard deviation” and terms invented years ago when we had no computers. One way I can prove that anything linked to standard deviation is a facade of knowledge: There is a measure called Kurtosis that indicates departure from “Normality”. It is very, very unstable and marred with huge sampling error: 70-90% of the Kurtosis in Oil, SP500, Silver, UK interest rates, Nikkei, US deposit rates, sugar, and the dollar/yet currency rate come from 1 day in the past 40 years, reminiscent of figure 3. This means that no sample will ever deliver the true variance. It also tells us anyone using “variance” or “standard deviation” (or worse making models that make us take decisions based on it) in the fourth quadrant is incompetent.

7) Where is the skewness? Clearly the Fourth Quadrant can present left or right skewness. If we suspect right-skewness, the true mean is more likely to be underestimated by measurement of past realizations, and the total potential is likewise poorly gauged. A biotech company (usually) faces positive uncertainty, a bank faces almost exclusively negative shocks. I call that in my new project “concave” or “convex” to model error.

8) Do not confuse absence of volatility with absence of risks. Recall how conventional metrics of using volatility as an indicator of stability has fooled Bernanke — as well as the banking system.

9) Beware presentations of risk numbers. Not only we have mathematical problems, but risk perception is subjected to framing issues that are acute in the Fourth Quadrant. Dan Goldstein and I are running a program of experiments in the psychology of uncertainty and finding that the perception of rare events is subjected to severe framing distortions: people are aggressive with risks that hit them “once every thirty years” but not if they are told that the risk happens with a “3% a year” occurrence. Furthermore it appears that risk representations are not neutral: they cause risk taking even when they are known to be unreliable.

I didn’t realize he also has a seminar DVD out, Nassim Nicholas Taleb: The Future Has Always Been Crazier Than We Thought.

Nassim Nicholas Taleb: the prophet of boom and doom

Sunday, June 1st, 2008

Bryan Appleyard, writing in the Times, calls Nassim Nicholas Taleb the prophet of boom and doom:

Last May, Taleb published The Black Swan: The Impact of the Highly Improbable. It said, among many other things, that most economists, and almost all bankers, are subhuman and very, very dangerous. They live in a fantasy world in which the future can be controlled by sophisticated mathematical models and elaborate risk-management systems. Bankers and economists scorned and raged at Taleb. He didn’t understand, they said. A few months later, the full global implications of the sub-prime-driven credit crunch became clear. The world banking system still teeters on the edge of meltdown. Taleb had been vindicated. “It was my greatest vindication. But to me that wasn’t a black swan; it was a white swan. I knew it would happen and I said so. It was a black swan to Ben Bernanke [the chairman of the Federal Reserve]. I wouldn’t use him to drive my car. These guys are dangerous. They’re not qualified in their own field.”

In December he lectured bankers at Société Générale, France’s second biggest bank. He told them they were sitting on a mountain of risks – a menagerie of black swans. They didn’t believe him. Six weeks later the rogue trader and black swan Jérôme Kerviel landed them with $7.2 billion of losses.

As a result, Taleb is now the hottest thinker in the world. He has a $4m advance on his next book. He gives about 30 presentations a year to bankers, economists, traders, even to Nasa, the US Fire Administration and the Department of Homeland Security. But he doesn’t tell them what to do – he doesn’t know. He just tells them how the world is. “I’m not a guru. I’m just describing a problem and saying, ‘You deal with it.’”

It seems he has embraced an important criticism of his work and presented it as a strength: he describes the hubris of quantitative analysts, but he doesn’t suggest a better way to analyze the world.

Anyway, as much as I enjoyed The Black Swan, I can’t say it added much to Fooled by Randomness, which leaves me wondering how much his next book — which has earned him a $4-million advance — will add to the previous two.

The writer, Appleyard, is clearly fascinated by Taleb’s evolutionary fitness routine, which he picked up from Art De Vany:

But the biggest rule of all is his eccentric and punishing diet and exercise programme. He’s been on it for three months and he’s lost 20lb. He’s following the thinking of Arthur De Vany, an economist – of the acceptable type – turned fitness guru. The theory is that we eat and exercise according to our evolved natures. Early man did not eat carbs, so they’re out. He did not exercise regularly and he did not suffer long-term stress by having an annoying boss. Exercise must be irregular and ferocious – Taleb often does four hours in the gym or 360 press-ups and then nothing for 10 days. Jogging is useless; sprinting is good. He likes to knacker himself completely before a long flight. Stress should also be irregular and ferocious – early men did not have bad bosses, but they did occasionally run into lions.

He’s always hungry. At both lunches he orders three salads, which he makes me share. Our conversation swings from high philosophy and low economics back to dietary matters like mangoes – bad – and apples – good as long as they are of an old variety. New ones are bred for sugar content. His regime works. He looks great – springy and fit. He shows me an old identity card. He is fat and middle-aged in the photo. He looks 10 years younger than that. “Look at me! That photo was taken seven years ago. No carbs!”

I wouldn’t describe an evolutionary fitness routine as “eccentric and punishing” — because it’s not particularly punishing at all.

This is odd:

Startlingly, this great sceptic, this non-guru who believes in nothing, is still a practising Christian. He regards with some contempt the militant atheism movement led by Richard Dawkins.

“Scientists don’t know what they are talking about when they talk about religion. Religion has nothing to do with belief, and I don’t believe it has any negative impact on people’s lives outside of intolerance. Why do I go to church? It’s like asking, why did you marry that woman? You make up reasons, but it’s probably just smell. I love the smell of candles. It’s an aesthetic thing.”

Take away religion, he says, and people start believing in nationalism, which has killed far more people. Religion is also a good way of handling uncertainty. It lowers blood pressure. He’s convinced that religious people take fewer financial risks.

This parable is silly, almost trite, but instructive:

Let me introduce you to Brooklyn-born Fat Tony and academically inclined Dr John, two of Taleb’s creations. You toss a coin 40 times and it comes up heads every time. What is the chance of it coming up heads the 41st time? Dr John gives the answer drummed into the heads of every statistic student: 50/50. Fat Tony shakes his head and says the chances are no more than 1%. “You are either full of crap,” he says, “or a pure sucker to buy that 50% business. The coin gotta be loaded.”

Here’s how Taleb stumbled into his millions:

In 1985, Taleb discovered how he could play Fat Tony in the markets. France, Germany, Japan, Britain and America signed an agreement to push down the value of the dollar. Taleb was working as an options trader at a French bank. He held options that had cost him almost nothing and that bet on the dollar’s decline. Suddenly they were worth a fortune. He became obsessed with buying “out of the money” options. He had realised that when markets rise they tend to rise by small amounts, but when they fall – usually hit by a black swan – they fall a long way.

The big payoff came on October 19, 1987 – Black Monday. It was the biggest market drop in modern history. “That had vastly more influence on my thought than any other event in history.”

It was a huge black swan – nobody had expected it, not even Taleb. But the point was, he was ready. He was sitting on a pile of out-of-the-money eurodollar options. So, while others were considering suicide, Taleb was sitting on profits of $35m to $40m. He had what he calls his “f***-off money”, money that would allow him to walk away from any job and support him in his long-term desire to be a writer and philosopher.

In the middle of a crisis, this certainly seems true:

“Complex systems don’t allow for slack and everybody protects that system. The banking system doesn’t have that slack. In a normal ecology, banks go bankrupt every day. But in a complex system there is a tendency to cluster around powerful units. Every bank becomes the same bank so they can all go bust together.”

He points out, chillingly, that banks make money from two sources. They take interest on our current accounts and charge us for services. This is easy, safe money. But they also take risks, big risks, with the whole panoply of loans, mortgages, derivatives and any other weird scam they can dream up. “Banks have never made a penny out of this, not a penny. They do well for a while and then lose it all in a big crash.”
[...]
“Governments and policy makers don’t understand the world in which we live, so if somebody is going to destroy the world, it is the Bank of England saving Northern Rock. The biggest danger to human society comes from civil servants in an environment like this. In their attempt to control the ecology, they don’t understand that the link between action and consequences can be more vicious. Civil servants say they need to make forecasts, but it’s totally irresponsible to make people rely on you without telling them you’re incompetent.”

Bear Stearns – the US Northern Rock – was another vindication for Taleb. He’s always said that whatever deal you do, you always end up dealing with J P Morgan. It was JPM that picked up Bear at a bargain-basement price. Banks should be more like New York restaurants. They come and go but the restaurant business as a whole survives and thrives and the food gets better. Banks fail but bankers still get millions in bonuses for applying their useless models. Restaurants tinker, they work by trial and error and watch real results in the real world. Taleb believes in tinkering – it was to be the title of his next book. Trial and error will save us from ourselves because they capture benign black swans. Look at the three big inventions of our time: lasers, computers and the internet. They were all produced by tinkering and none of them ended up doing what their inventors intended them to do. All were black swans. The big hope for the world is that, as we tinker, we have a capacity for choosing the best outcomes.

So, what does Taleb recommend?

Well, the good investment strategy is to put 90% of your money in the safest possible government securities and the remaining 10% in a large number of high-risk ventures. This insulates you from bad black swans and exposes you to the possibility of good ones. Your smallest investment could go “convex” – explode – and make you rich. High-tech companies are the best. The downside risk is low if you get in at the start and the upside very high. Banks are the worst – all the risk is downside. Don’t be tempted to play the stock market – “If people knew the risks they’d never invest.”

The article ends with Taleb’s top life tips:

  1. Scepticism is effortful and costly. It is better to be sceptical about matters of large consequences, and be imperfect, foolish and human in the small and the aesthetic.
  2. Go to parties. You can’t even start to know what you may find on the envelope of serendipity. If you suffer from agoraphobia, send colleagues.
  3. It’s not a good idea to take a forecast from someone wearing a tie. If possible, tease people who take themselves and their knowledge too seriously.
  4. Wear your best for your execution and stand dignified. Your last recourse against randomness is how you act — if you can’t control outcomes, you can control the elegance of your behaviour. You will always have the last word.
  5. Don’t disturb complicated systems that have been around for a very long time. We don’t understand their logic. Don’t pollute the planet. Leave it the way we found it, regardless of scientific ‘evidence’.
  6. Learn to fail with pride — and do so fast and cleanly. Maximise trial and error — by mastering the error part.
  7. Avoid losers. If you hear someone use the words ‘impossible’, ‘never’, ‘too difficult’ too often, drop him or her from your social network. Never take ‘no’ for an answer (conversely, take most ‘yeses’ as ‘most probably’).
  8. Don’t read newspapers for the news (just for the gossip and, of course, profiles of authors). The best filter to know if the news matters is if you hear it in cafes, restaurants… or (again) parties.
  9. Hard work will get you a professorship or a BMW. You need both work and luck for a Booker, a Nobel or a private jet.
  10. Answer e-mails from junior people before more senior ones. Junior people have further to go and tend to remember who slighted them.

I find it mildly ironic that a man who rails against hubris asks us to “avoid losers” who say things are too difficult.

Inside Wall Street’s Black Hole

Sunday, March 16th, 2008

Michael Lewis (Liar’s Poker, The Real Price of Everything) looks Inside Wall Street’s Black Hole:

One of the revolt’s leaders is Nassim Nicholas Taleb, the bestselling author of The Black Swan and Fooled by Randomness and a former trader of currency options for a big French bank. Taleb can precisely date the origin of his own personal gripe with Black-Scholes: September 22, 1985. On that day, central bankers from Japan, France, Germany, Britain, and the United States announced their intention to torpedo the U.S. dollar — to reduce its value in relation to the other countries’ currencies. Every day, Taleb received a list of his trading positions from his firm and a matrix describing his risks. The matrix told him how much money he stood to make or lose, given various currency fluctuations. That September 22, when the central bankers announced their plan to lower the dollar’s value, he made money but didn’t know it. “I didn’t know what my position was,” he says, “because the movement was outside the matrix they’d given me.” The French bank’s risk-analysis program assumed that a currency crash of this magnitude would occur once in several million years and therefore wasn’t worth considering.

Taleb made a killing that day, but it wasn’t thanks to a grand plan and it wasn’t happy money. “People in dark suits started coming from Paris,” he says. “They said that the only way I could have made that much was to have taken far too much risk.” But he hadn’t. They had simply failed to account for the true nature of risk in financial markets. “Then I started looking at the history of markets,” he says. “And I saw that these sorts of things happened all the time.” Taleb became obsessed with the way prices in the options market, based on the famous Black-Scholes model, underestimated the risk of extreme and rare events. He set up his trading to profit from such events by buying up disaster insurance that would, according to Black-Scholes, be considered overpriced. When October 19, 1987, arrived, he was prepared. “Ninety-seven percent of all the returns I ever made as a trader, I made on that day,” he says.

He’s Still Beating The House

Tuesday, September 25th, 2007

He’s Still Beating The House:

Ed Thorp’s moment is coming — again. Thorp is an investor, mathematician, and crack blackjack player whose winning system got him expelled from Reno casinos in the 1960s. Now his 1967 work, Beat the Market: A Scientific Stock Market System, has been named one of the most sought-after out-of-print books of the past year by BookFinder.com.

Beat the Market, which sells for up to $750 on Amazon.com, describes his investing system, a precursor of the Black-Scholes formula. Why is the book so hot now? Perhaps it’s rising interest in the relation between gambling and investing. Thorp also gets mentions in recent books, including Nassim Nicholas Taleb’s best-seller on probability, The Black Swan. Another attention-getter: publicity about a cigarette-pack-size computer co-invented by Thorp in the ’60s, to be exhibited next spring at Germany’s Heinz Nixdorf computer museum. “It could predict where a roulette ball would land,” he says.

Colonial shipwreck yields $500M in coins

Friday, May 18th, 2007

Colonial shipwreck yields $500M in coins — a new record:

Deep-sea explorers said Friday they have mined what could be the richest shipwreck treasure in history, bringing home 17 tons of colonial-era silver and gold coins from an undisclosed site in the Atlantic Ocean. Estimated value: $500 million.

A jet chartered by Tampa-based Odyssey Marine Exploration landed in the United States recently with hundreds of plastic containers brimming with coins raised from the ocean floor, Odyssey co-chairman Greg Stemm said. The more than 500,000 pieces are expected to fetch an average of $1,000 each from collectors and investors.
[...]
The richest ever shipwreck haul was yielded by the Spanish galleon Nuestra Senora de Atocha, which sank in a hurricane off the Florida Keys in 1622. Treasure-hunting pioneer Mel Fisher found it in 1985, retrieving a reported $400 million in coins and other loot.

It seems that the Black Swan meme is spreading:

In keeping with the secretive nature of the project dubbed “Black Swan,” Odyssey also isn’t talking yet about the types, denominations and country of origin of the coins.

Interestingly, Odyssey is a public, for-profit company:

“We have treated this site with kid gloves and the archaeological work done by our team out there is unsurpassed,” Odyssey CEO John Morris said. “We are thoroughly documenting and recording the site, which we believe will have immense historical significance.”

The news is timely for Odyssey, the only publicly traded company of its kind.

The company salvaged more than 50,000 coins and other artifacts from the wreck of the SS Republic off Savannah, Ga., in 2003, making millions. But Odyssey posted losses in 2005 and 2006 while using its expensive, state-of-the-art ships and deep-water robotic equipment to hunt for the next mother lode.

“The outside world now understands that what we do is a real business and is repeatable and not just a lucky one shot deal,” Stemm said. “I don’t know of anybody else who has hit more than one economically significant shipwreck.”

In January, Odyssey won permission from the Spanish government to resume a suspended search for the wreck of the HMS Sussex, which was leading a British fleet into the Mediterranean Sea for a war against France in 1694 when it sank in a storm off Gibraltar.

Historians believe the 157-foot warship was carrying nine tons of gold coins to buy the loyalty of the Duke of Savoy, a potential ally in southeastern France. Odyssey believes those coins could also fetch more than $500 million.

But under the terms of a historic agreement Odyssey will have to share any finds with the British government. The company will get 80 percent of the first $45 million and about 50 percent of the proceeds thereafter.

Shattering the Bell Curve

Thursday, April 26th, 2007

In Shattering the Bell Curve, David Shaywitz reviews Nassim Taleb’s The Black Swan and notes that “the power law rules”:

Mr. Taleb is fascinated by the rare but pivotal events that characterize life in the power-law world. He calls them Black Swans, after the philosopher Karl Popper’s observation that only a single black swan is required to falsify the theory that “all swans are white” even when there are thousands of white swans in evidence. Provocatively, Mr. Taleb defines Black Swans as events (such as the rise of the Internet or the fall of LTCM) that are not only rare and consequential but also predictable only in retrospect. We never see them coming, but we have no trouble concocting post hoc explanations for why they should have been obvious. Surely, Mr. Taleb taunts, we won’t get fooled again. But of course we will.

Writing in a style that owes as much to Stephen Colbert as it does to Michel de Montaigne, Mr. Taleb divides the world into those who “get it” and everyone else, a world partitioned into heroes (Popper, Hayek, Yogi Berra), those on notice (Harold Bloom, necktie wearers, personal-finance advisers) and entities that are dead to him (the bell curve, newspapers, the Nobel Prize in Economics).

A humanist at heart, Mr. Taleb ponders not only the effect of Black Swans but also the reason we have so much trouble acknowledging their existence. And this is where he hits his stride. We eagerly romp with him through the follies of confirmation bias (our tendency to reaffirm our beliefs rather than contradict them), narrative fallacy (our weakness for compelling stories), silent evidence (our failure to account for what we don’t see), ludic fallacy (our willingness to oversimplify and take games or models too seriously), and epistemic arrogance (our habit of overestimating our knowledge and underestimating our ignorance).

Catastrophic Black Swans

Tuesday, September 19th, 2006

John Robb discusses the potential for Catastrophic Black Swans — very bad, unpredictable events — at the hands of terrorists:

If we follow this trend line, the path in development is clear. First, over the next decade, the size of the group necessary for global warfare will continue to decrease and decentralize (through a near term shift to systems disruption and open source organizational forms). Second, we will eventually reach a point when the weaponry available to these groups will enable them to initiate a catastrophic black swan (an event that [is] impossible to predict).

RAND’s Charles Meade and Roger Molander provide a great example of a catastrophic black swan in their contemplation of the effects of the explosion of a nuclear bomb, smuggled in a shipping container, at the port of Long Beach CA (PDF). Of particular interest are the cascading effects of such an attack — such as port closures across the US, which would result in immediate global economic isolation of an indeterminate duration. Of course, viewed within the context of a catastrophe like this, it is important to consider the first expression of this trend line (global terrorism using conventional weaponry) as a grace period. History has given us an opportunity to get security right before the next wave hits. So far, it doesn’t look like we have learned anything at all.