Nassim Nicholas Taleb, “The Black Swan” (The Impact of the Highly Improbable), Random House 2007
Main Point I got from book: all statistical methods used to define financial risk that are based on standard deviation and the Gaussian Bell Curve are inappropriate. This includes all Modern Portfolio Theory, Sharpe, Treynor, Scholes, Merton, etc... Many financial people know this but they are safe as long as they stay in the fold using accepted industry risk measurements so when the predictions fail they can be justified as using conventionally accepted concepts. Some do not know this and include Nobel winners: ala 1998 LTCM or 1982 lending to South American countries, or how about the 1989 Savings and Loan debacle and then, of course, NOW (subprime).
“A Black Swan is a highly improbable event with three principal characteristics: It is unpredictable; it carries a massive impact; and, after the fact, we concoct an explanation that makes it appear less random, and more predictable, than it was. The astonishing success of Google was a black swan; so was 9/11. For Taleb, black swans underlie almost everything about our world, from the rise of religions to events in our own personal lives.
“Why do we not acknowledge the phenomenon of black swans until after they occur? Part of the answer, according to Taleb, is that humans are hardwired to learn specifics when they should be focused on generalities. We concentrate on things we already know, and time and time again fail to take into consideration what we don’t know. We are, therefore, unable to truly estimate opportunities, too vulnerable to the impulse to simplify, narrate, and categorize, and not open enough to rewarding those who can imagine the ‘impossible.’”
“For years, Taleb has studied how we fool ourselves into thinking we know more than we actually do. We restrict our thinking to the irrelevant and inconsequential, while large events continue to surprise us and shape our world.”
pg xxi: Contrary to social-science wisdom, almost no discovery, no technologies of note, came from design and planning—they were just Black Swans. The strategy for the discoverers and entrepreneurs is to rely less on top-down planning and focus on maximum tinkering and recognizing opportunities when they present themselves. So I disagree with the followers of Marx and those of Adam Smith: the reason free markets work is because they allow people to be lucky, thanks to aggressive trial and error, not by giving rewards or “incentives” for skill. [also: note capitalism allows industries to fail; socialism props up failing industries].
pg. 35: Matters that seem to belong to Mediocristan (subjected to what we call type 1 randomness): height, weight, calorie consumption, income for a baker, a small restaurant owner, a prostitute, or an orthodontist; gambling profits (in the very special case, assuming the person goes to a casino and maintains a constant betting size), car accidents, mortality rates, ‘IQ’ (as measured). You can apply the bell curve to these.
Matters that seem to belong to Extremistan (subjected to what we call type 2 randomness): wealth, income, book sales per author, book citations per author, name recognition as a ‘celebrity,’ number of references on Google, populations of cities, uses of words in a vocabulary, numbers of speakers per language, damage caused by earthquakes, deaths in war, deaths from terrorist incidents, sizes of planets, sizes of companies, stock ownership, height between species (consider elephants and mice), financial markets (but your investment manager does not know it), commodity prices, inflation rates, economic data. The Extremistan list is much longer than the prior one. (and you cannot use the bell curve to describe these; the distribution is either Mandelbrotian ‘gray’ Swans (tractable scientifically) or totally intractable Black Swans.
pg 43: In the summer of 1982, large American banks lost close to all their past earnings (cumulatively), about everything they ever made in the history of American banking—everything. They had been lending to South and Central American countries that all defaulted at the same time—‘an event of an exceptional nature.’ So it took just one summer to figure out that this was a sucker’s business and that all their earnings came from a very risky game. In fact, the travesty repeated itself a decade later, with the ‘risk-conscious’ large banks once again under financial strain many of them near-bankrupt, after the real estate collapse of the early 1990s in which the now defunct savings and loan industry required a taxpayer-funded bailout of more than half a trillion dollars. Another recent event is the almost instant bankruptcy, in 1998 of a financial investment company (hedge fund) called Long-Term Capital Management (LTCM) which used the methods and risk expertise of two Nobel economists, geniuses, but who were in fact using phony, bell-curve style mathematics. [the turkey problem math: 1000 days of good feed, then Thursday, Thanksgiving!!; the history does not lead to that conclusion].
Pg 53: We react to a piece of information not on its logical merit, but on the basis of which framework surrounds it, and how it registers with our social-emotional system. Logical problems approached one way in the classroom might be treated differently in daily life. Indeed they are treated differently in daily life. Knowledge, even when it is exact, does not often lead to appropriate actions because we tend to forget what we know, or forget how to process it properly if we do not pay attention, even when we are experts. Statisticians, it has been shown, tend to leave their brains in the classroom and engage in the most trivial inferential errors once they are let out on the streets. In 1971, the psychologists Kahneman and Tversky plied professors of statistics with statistical questions not phrased as statistical questions: e.g.: Assume that you live in a town with two hospitals—one large, the other small. On a given day 60 percent of those born in one of the two hospitals are boys. Which hospital is it likely to be? Many statisticians made the equivalent of the mistake (during a casual conversation) of choosing the larger hospital, when in fact the very basis of statistics is that large samples are more stable and should fluctuate less from the long-term average—here, 50 percent.
Pg 146: Experts who tend to be experts: livestock judges, astronomers, test pilots, soil judges, chess masters, physicists, mathematicians (when they deal with mathematical problems, not empirical ones), accountants, grain inspectors, photo interpreters, insurance analysts (dealing with bell curve style statistics).
Experts who tend to be...not experts: stockbrokers, clinical psychologists, psychiatrists, college admissions officers, court judges, councilors, personnel selectors, intelligence analysts (CIA), economists, financial forecasters, finance professors, political scientists, ‘risk experts’, and personal financial advisers.
Simply, things that move, and therefore require knowledge, do not usually have experts, while things that don’t move seem to have some experts.
Pg. 211: This idea that in order to make a decision you need to focus on the consequences (which you can know) rather than the probability (which you can’t know) is the central idea of uncertainty. Much of my life is based on it.
Pg. 277: The Nobel Prize committee has gotten into the habit of handing out Nobel Prizes to those who ‘bring rigor’ to the process with pseudo-science and phony mathematics. After the stock market crash, they rewarded two theoreticians, Harry Markowitz and William Sharpe, who built beautifully Platonic models on a Gaussian base, contributing to what is called Modern Portfolio Theory. Simply, if you remove their Gaussian assumptions and treat prices as scalable, you are left with hot air. So the Bank of Sweden and the Nobel Academy are largely responsible for giving credence to the use of the Gaussian Modern Portfolio Theory as institutions have found it a great cover-your-behind approach.
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