James Owen Weatherall, assistant professor of logic and philosophy of science at the University of California, Irvine, tells the story of physicists in finance in The Physics of Wall Street: A Brief History of Predicting the Unpredictable. He begins by describing the extraordinary success of Jim Simons, a physicist and the founder of the hedge fund Renaissance Technologies. Simons’s Medallion Fund earned an unparalleled 2,478.6% return from 1988 to 1998 and a 73.7% return in 2007. Approximately one-third of Renaissance employees have PhDs—not in finance but, rather, like Simons, in such fields as physics, mathematics, and statistics. According to an MIT mathematician, Renaissance Technologies is the best physics and mathematics department in the world. That, say Simons and others, is why the firm has excelled.
Weatherall explores how the quants came to be and how to understand the “complex mathematical models” that have become central to modern finance. In 2008, he concedes, sophisticated models fell into the hands of people who did not understand their purpose and did not care. Despite the recent financial crisis, Weatherall argues against giving up on models and in favor of continuing to seek new ideas from physics and related fields to solve the ongoing economic problems faced by countries around the world.
The author discusses the contributions to finance of several great scientists, mathematicians, and physicists. Louis Bachelier, a French mathematician, was the first to attempt to apply new ideas from statistical physics to financial markets. He was a pioneer in using probability theory. Maury Osborne, an American astrophysicist, found that returns, not prices, are normally distributed. Benoit Mandelbrot, a French mathematician, developed fractal geometry and discovered that normal and lognormal distributions cannot capture the full wildness of financial markets. Drawing on still more sophisticated ideas from physics, Ed Thorp, a mathematician, and Fischer Black, a physicist, showed investors how to use the tools developed by Bachelier, Osborne, and Mandelbrot in day-to-day trading. Other scientists, including Didier Sornette (originally a geophysicist), demonstrated how new developments in physics can be used to fill in the gaps in the random walk/efficient market thinking behind the Black–Scholes model. They achieved this insight by using black box models to identify local, short-term inefficiencies and then capitalize on them as quickly as possible. In essence, they used physics to become the most sophisticated investors in the market. Sornette took Mandelbrot’s observation that in wildly random markets, such extreme events as market crashes have dominating effects and asked whether it is possible to predict such catastrophes. He coined a new term, dragon kings—as opposed to Nassim Taleb’s black swans—for extreme events.
Dragon kings are similar to black swans in that they can both have enormous consequences. The word “dragon” is meant to capture the fact that these kinds of events do not have a natural place in the normal bestiary. Dragon kings are tyrannical when they appear, but unlike black swans, their approach is detectable. Many events that seem to be black swans really do issue warnings, which often take the form of log-periodic precursors—oscillations in some form of data that happen only when the system is in the special state in which a massive catastrophe can occur. These precursors arise only when the right combination of positive feedback and amplifying processes is in place, together with the self-organization necessary to make a bang and not a whimper. Mandelbrot observed that extreme events occur more often than a random walk would predict. Sornette believed that catastrophic crashes happen even more frequently than Mandelbrot proposed. In other words, extreme events occur more often than predicted even under an assumption of fat-tailed distributions. It is a way of saying that even if markets are wildly random and extreme events occur all the time, at least some extreme events can be anticipated if the telltale warnings are known. Although these dragon kings can upend the entire world economy, they can be studied and understood.
Weatherall argues—correctly, in this reviewer’s opinion—that the mathematical modeling of financial markets by using methods from physics may be somewhat flawed in that the stock market is composed of humans who exhibit irrational tendencies, such as overconfidence, home bias, and frame dependence. Physics might be appropriate for billiard balls and inclined planes—even for space travel and nuclear reactors—but as Newton said, it cannot predict the madness of men. This kind of criticism draws heavily on ideas from behavioral economics, which attempts to understand economics by drawing on cognitive psychology and sociology. From the behavioral point of view, markets are all about the foibles of people and cannot be reduced to the formulas of physics and mathematics.
Ultimately, Weatherall argues convincingly that given the proven track record of applying to finance ideas from physics, it is time to do it again. The Physics of Wall Street is about the future of finance and the new set of tools that will facilitate the proper functioning of the world’s economies. Jim Simons’s Renaissance Technologies returned 80% in 2008 by being smarter than the competition and by doing science on Wall Street. Renaissance Technologies employees have not forgotten how to think like physicists, question their assumptions, and constantly search for the chinks in their model’s armor. The hedge fund has a large group of dedicated researchers who are given 40 hours a week of unstructured time to pursue their own ideas. Renaissance Technologies shows that mathematical sophistication is the remedy, not the disease.