FIGURE 14
By removing the ten biggest one-day moves from the U.S. stock market over the past fifty years, we see a huge difference in returns – and yet conventional finance sees these one-day jumps as mere anomalies. (This is only one of many such tests. While it is quite convincing on a casual read, there are many more-convincing ones from a mathematical standpoint, such as the incidence of 10 sigma events.)
Clearly, anyone who wants more than the high number of six sigma as proof that markets are from Extremistan needs to have his head examined. Dozens of papers show the inadequacy of the Gaussian family of distributions and the scalable nature of markets. Recall that, over the years, I myself have run statistics backward and forward on 20 million pieces of data that made me despise anyone talking about markets in Gaussian terms. But people have a hard time making the leap to the consequences of this knowledge.
The strangest thing is that people in business usually agree with me when they listen to me talk or hear me make my case. But when they go to the office the next day they revert to the Gaussian tools so entrenched in their habits. Their minds are domain-dependent, so they can exercise critical thinking at a conference while not doing so in the office. Furthermore, the Gaussian tools give them numbers, which seem to be “better than nothing”. The resulting measure of future uncertainty satisfies our ingrained desire to simplify even if that means squeezing into one single number matters that are too rich to be described that way.
I ended Chapter 1 with the stock market crash of 1987, which allowed me to aggressively pursue my Black Swan idea. Right after the crash, when I stated that those using sigmas (i.e., standard deviations) as a measure of the degree of risk and randomness were charlatans, everyone agreed with me. If the world of finance were Gaussian, an episode such as the crash (more than twenty standard deviations) would take place every several billion lifetimes of the universe (look at the height example in Chapter 15). According to the circumstances of 1987, people accepted that rare events take place and are the main source of uncertainty. They were just unwilling to give up on the Gaussian as a central measurement tool – “Hey, we have nothing else”. People want a number to anchor on. Yet the two methods are logically incompatible.
Unbeknownst to me, 1987 was not the first time the idea of the Gaussian was shown to be lunacy. Mandelbrot proposed the scalable to the economics establishment around 1960, and showed them how the Gaussian curve did not fit prices
And now a brief history of the “Nobel” Prize in economics, which was established by the Bank of Sweden in honor of Alfred Nobel, who may be, according to his family who wants the prize abolished, now rolling in his grave with disgust. An activist family member calls the prize a public relations coup by economists aiming to put their field on a higher footing than it deserves. True, the prize has gone to some valuable thinkers, such as the empirical psychologist Daniel Kahneman and the thinking economist Friedrich Hayek. But the 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.
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. Software vendors have sold “Nobel crowned” methods for millions of dollars. How could you go wrong using it? Oddly enough, everyone in the business world initially knew that the idea was a fraud, but people get used to such methods. Alan Greenspan, the chairman of the Federal Reserve bank, supposedly blurted out, “I’d rather have the opinion of a trader than a mathematician”. Meanwhile, the Modern Portfolio Theory started spreading. I will repeat the following until I am hoarse: it is contagion that determines the fate of a theory in social science, not its validity.
I only realized later that Gaussian-trained finance professors were taking over business schools, and therefore MBA programs, and producing close to a hundred thousand students a year in the United States alone, all brainwashed by a phony portfolio theory. No empirical observation could halt the epidemic. It seemed better to teach students a theory based on the Gaussian than to teach them no theory at all. It looked more “scientific” than giving them what Robert C. Merton (the son of the sociologist Robert K. Merton we discussed earlier) called the “anecdote”. Merton wrote that before portfolio theory, finance was “a collection of anecdotes, rules of thumb, and manipulation of accounting data”. Portfolio theory allowed “the subsequent evolution from this conceptual potpourri to a rigorous economic theory”. For a sense of the degree of intellectual seriousness involved, and to compare neoclassical economics to a more honest science, consider this statement from the nineteenth-century father of modern medicine, Claude Bernard: “Facts for now, but with scientific aspirations for later”. You should send economists to medical school.
So the Gaussian[60] pervaded our business and scientific cultures, and terms such as
Things got a lot worse in 1997. The Swedish academy gave another round of Gaussian-based Nobel Prizes to Myron Scholes and Robert C. Merton, who had improved on an old mathematical formula and made it compatible with the existing grand Gaussian general financial equilibrium theories – hence acceptable to the economics establishment. The formula was now “useable”. It had a list of long forgotten “precursors”, among whom was the mathematician and gambler Ed Thorp, who had authored the bestselling
The postcrash years were entertaining for me, intellectually. I attended conferences in finance and mathematics of uncertainty; not once did I find a speaker, Nobel or no Nobel, who understood what he was talking about when it came to probability, so I could freak them out with my questions. They did “deep work in mathematics”, but when you asked them where they got their probabilities, their explanations made it clear that they had fallen for the ludic fallacy – there was a strange cohabitation of technical skills and absence of understanding that you find in idiot savants. Not once did I get an intelligent answer or one that was not ad hominem. Since I was questioning their entire business, it was understandable that I drew all manner of insults: “obsessive”, “commercial”, “philosophical”, “essayist”, “idle man of leisure”, “repetitive”, “practitioner” (this is an
