The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution

by Gregory Zuckerman

“The lesson was: Do what you like in life, not what you feel you ‘should’ do,” Simons says. “It’s something I never forgot.”

“Jim understood at an early age that money is power,” Barbara says. “He didn’t want people to have power over him.”

“Bad ideas is good, good ideas is terrific, no ideas is terrible.”

They posited that the market had as many as eight underlying “states”—such as “high variance,” when stocks experienced larger-than-average moves, and “good,” when shares generally rose.

The whys didn’t matter, Simons and his colleagues seemed to suggest, just the strategies to take advantage of the inferred states.

Simons and the code-breakers proposed a similar approach to predicting stock prices, relying on a sophisticated mathematical tool called a hidden Markov model. Just as a gambler might guess an opponent’s mood based on his or her decisions, an investor might deduce a market’s state from its price movements.

A hidden Markov process is one in which the chain of events is governed by unknown, underlying parameters or variables. One sees the results of the chain but not the “states” that help explain the progression of the chain. Those not acquainted with baseball might throw their hands up when receiving updates of the number of runs scored each inning—one run in this inning, six in another, with no obvious pattern or explanation. Some investors liken financial markets, speech recognition patterns, and other complex chains of events to hidden Markov models.

The Baum-Welch algorithm provided a way to estimate probabilities and parameters within these complex sequences with little more information than the output of the processes. For the baseball game, the Baum-Welch algorithm might enable even someone with no understanding of the sport to guess the game situations that produced the scores. If there was a sudden jump from two runs to five runs, for example, Baum-Welch might suggest the probability that a three-run home run had just been hit rather than a bases-loaded triple. The algorithm would allow someone to infer a sense of the sport’s rules from the distribution of scores, even as the full rules remained hidden.

“What you’re really modeling is human behavior,” explains Penavic, the researcher. “Humans are most predictable in times of high stress—they act instinctively and panic. Our entire premise was that human actors will react the way humans did in the past … we learned to take advantage.”

No one ever made a decision because of a number. They need a story. Daniel Kahneman, economist

Not completing desired trades resulted in more than just poor performance. The factor-trading system generated a series of complicated and intertwined trades, each necessary to score profits while also keeping risk at reasonable levels. By contrast, futures trading was simple stuff; if a trade didn’t happen, there were few consequences. With Frey’s stock-trading system, failing to get just a few moves done threatened to make the entire portfolio more sensitive to market shifts, jeopardizing its overall health. And missed trades sometimes cascaded into bigger, systemic problems that compromised the accuracy of the entire model. Getting it even a little wrong caused big problems that Frey and his team, using mid-1990s’ technology and their own subpar software engineering skills, couldn’t address.

By 1997, Medallion’s staffers had settled on a three-step process to discover statistically significant moneymaking strategies, or what they called their trading signals. Identify anomalous patterns in historic pricing data; make sure the anomalies were statistically significant, consistent over time, and nonrandom; and see if the identified pricing behavior could be explained in a reasonable way.

Investment professionals generally judge a portfolio’s risk by its Sharpe ratio, which measures returns in relation to volatility; the higher one’s Sharpe, the better. For most of the 1990s, Medallion had a strong Sharpe ratio of about 2.0, double the level of the S&P 500. But adding foreign-market algorithms and improving Medallion’s trading techniques sent its Sharpe soaring to about 6.0 in early 2003, about twice the ratio of the largest quant firms and a figure suggesting there was nearly no risk of the fund losing money over a whole year.

The options also were a way of shifting enormous risk from Renaissance to the banks. Because the lenders technically owned the underlying securities in the basket-options transactions, the most Medallion could lose in the event of a sudden collapse was the premium it had paid for the options and the collateral held by the banks. That amounted to several hundred million dollars. By contrast, the banks faced billions of dollars of potential losses if Medallion were to experience deep troubles. In the words of a banker involved in the lending arrangement, the options allowed Medallion to “ring-fence” its stock portfolios, protecting other parts of the firm, including Laufer’s still-thriving futures trading, and ensuring Renaissance’s survival in the event something unforeseen took place. One staffer was so shocked by the terms of the financing that he shifted most of his life savings into Medallion, realizing the most he could lose was about 20 percent of his money.

“We’re right 50.75 percent of the time … but we’re 100 percent right 50.75 percent of the time,” Mercer told a friend. “You can make billions that way.”

Simons and his colleagues generally avoid predicting pure stock moves. It’s not clear any expert or system can reliably predict individual stocks, at least over the long term, or even the direction of financial markets. What Renaissance does is try to anticipate stock moves relative to other stocks, to an index, to a factor model, and to an industry.

Simons shared a few life lessons with the school’s audience: “Work with the smartest people you can, hopefully smarter than you … be persistent, don’t give up easily. “Be guided by beauty … it can be the way a company runs, or the way an experiment comes out, or the way a theorem comes out, but there’s a sense of beauty when something is working well, almost an aesthetic to it.”