The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It
The book jacket states that, “The Quants is at once a timeless masterpiece of explanatory journalism, a gripping tale of ambition and hubris…and an ominous warning about Wall Street’s future.” Unfortunately, the book doesn’t quite live up to this characterization, but 50 pages are essential to an understanding of financial risk management.

Scott Patterson, the author and a staff reporter at The Wall Street Journal, opens the book in March 8, 2006 at the Wall Street Poker Night Tournament (now the Math for America Poker Night Tournament). Gambling is a common interest with many of the quants, and has obvious parallels to the men who developed the science of probability hundreds of years earlier. The main plot centers on four quants: Peter Muller, head of Morgan Stanley’s PDT hedge fund; Ken Griffin, Manager of Chicago hedge fund Citadel Investment Group; Cliff Asness, founder of AQR Capital Management; and Boaz Weinstein, derivatives trader at Deutsche Bank and head of its Saba internal hedge fund.
The non-linear narrative shifts between events central to the financial collapse in 2007 and 2008, the back-stories of how the main characters rose from math whizzes to high-powered hedge fund managers, and the role complex mathematics (differential calculus, quantum physics, and advanced geometry), high-speed computing, and the use of immoderate leverage (trading on margin with over 97% borrowed money) played in enabling people who knew little about finance to control billions of dollars in global trading positions. The story reaches its climax as it describes events in August of 2007, when the billions the hedge funds had gained over a decade of using quantitative techniques were wiped out over the course of a few days. While this approach makes for a lively story, it is too superficial in its explanation of the role quantitative analysts played in developing the complex structured asset-backed securities (ABS), such as collateralized debt obligations (CDO), that spread risk and uncertainty about the value of the underlying assets more widely, rather than reducing risk through diversification, as promoted.
50 Pages Well Worth Reading
The fifty pages that comprise chapters two, three, and four are a concise explanation of how quantitative analysis used models from the physical sciences to create a statistical trading advantage, and how strict adherence to these models eventually lead to disaster. The author uses Ed Thorp, the “Godfather” of the quants, to make what could be a dry, academic subject interesting and engaging.
As with others who contributed to the science of probability, Ed Thorp initially applied his research to gambling, specifically blackjack. After reading a ten-page article on blackjack strategy in the Journal of the American Statistical Association by U.S. Army mathematician Roger Baldwin and three colleagues, Thorp contacted Baldwin and obtained the data from the study in 1959. Thorp was in the process of moving from UCLA, where he had completed his Ph.D. in physics, to an instructor position at MIT. Once there, he used a university computer (IBM 704) to evolve the blackjack strategy into what would become the basis for his success in casinos and his 1962 bestselling book, Beat the Dealer. What separated Thorp from those who only focus on probability was his incorporation of a wager strategy that would mitigate his risk of a prolonged losing streak (a statistically wide deviation from the norm) busting him. He derived his betting strategy from a 1956 paper, A New Interpretation of Information Rate, by John Kelly Jr., a physics researcher at Bell Laboratories. Using Kelly criterion, he could determine how much to wager, depending on the situation—larger bets when the odds were in his favor and smaller bets when they were not—that maximized return during advantageous periods of play while minimizing the risk of losing everything during periods of disadvantage.
In 1964, Thorp shifted his sights from blackjack to Wall Street—“the biggest casino of all”—and devised a precise, quantitative method for pricing stock warrants (basically long-term contracts, much like a call option, that investors can convert into common stock). He combined his pricing formula with a strategy of shorting warrants he judged to be overpriced while going long (buying) an appropriate amount of stock to hedge his bet. Theoretically, the inverse correlation of the warrant and stock would offset each other, providing the necessary hedge if the warrant price did not fall within the contract period. In the best-case scenario, the price of the (overpriced) warrant would decline and the stock would rise, closing the inefficiency gap and providing a gain on each side of the trade.
This strategy, commonly called convertible bond arbitrage (a specific application of delta hedging) became one of the most successful and lucrative trading strategies ever devised. The only risk was the unlikely event that the price of the warrant would increase while the price of the underlying stock continued to decline. Thorp understood this risk, and continued to apply Kelly criterion to determine how much he should wager on each trade in order to mitigate the risk of such an unlikely event. Unlike many hedge funds that focused only on outcome probability, Thorp understood and accounted for the other critical variable in risk management—the potentially devastating consequence of a low probability outcome.
Another person who figures prominently in financial risk management is Benoit Mandelbrot, who showed that unlikely events in financial markets are not that unlikely. He starts with the dissertation of Louis Bachelier at the University of Paris in 1900 called, “The Theory of Speculation,” that applied Brownian motion from physical sciences to market prices. Bachelier’s formula showed that the future course of the market is essentially a coin flip. This discovery came to be called the random walk, and is visually described by a bell curve. But Mandelbrot observed that the behavior of prices in financial markets (using data from cotton, wheat, railroad stocks, and interest rates that he ran on the supercomputers at IBM, where he worked at the time) frequently demonstrated huge leaps to the outer edges of the bell curve. In a paper detailing his findings. “The Variation of Certain Speculative Prices,” he asserted that the standard bell curve was not applicable to model financial markets because, “Large price changes are much more frequent than predicted.” Mandelbrot borrowed a mathematical technique devised by the French mathematician Paul Levy, whom he had studied under in Paris, to measure the erratic behavior of prices. When plotted on a chart, the curve produced bubbled out on both ends, and became known as “fat tails.”

Paul Cootner, an MIT finance professor viciously attacked Mandelbrot’s paper in his 1963 book, The Random Character of Stock Market Prices. He, and other critics, claimed that while Mandelbrot’s model might be accurate for brief time periods, over longer time periods, prices appear to move in a more orderly Brownian fashion. However, this misses Mandelbrot’s main point that “prices can gyrate wildly over short periods of time—wildly enough to cause massive, potentially crippling losses to investors who’ve made large leveraged bets.” Mandelbrot’s theories were shelved by the financial engineers who preferred the orderliness of the bell curve and a random walk to the messy, chaotic world of a fat-tail distribution. But from time to time events such as Black Monday (October 19, 1987), the Long Term Capital Management (LTCM) bailout in September 1998, and the Emergency Economic Stabilization Act of 2008, the mother of all bailouts (at least until several countries in the European Union default on sovereign debt), remind us of the consequences of ignoring unlikely events.
Nassim Nicholas Taleb, a critic of quant models, argues that investors who believe the market moves according to a random walk are “fooled by randomness,” and that models based on historical trends and expectations of a random walk, are bound to lead their users to destruction. That’s the subject of the next book.