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

by Gregory Zuckerman

He was hoping to discover and codify universal principles, rules, and truths, with the goal of furthering the understanding of these mathematical objects.

Albert Einstein argued that there is a natural order in the world; mathematicians like Simons can be seen as searching for evidence of that structure.

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. Here’s what was really unique: The paper didn’t try to identify or predict these states using economic theory or other conventional methods, nor did the researchers seek to address why the market entered certain states. Simons and his colleagues used mathematics to determine the set of states best fitting the observed pricing data; their model then made its bets accordingly. The whys didn’t matter, Simons and his colleagues seemed to suggest, just the strategies to take advantage of the inferred states.

Simons told another colleague that some academics were “super smart” yet weren’t original thinkers worthy of a position at the university.

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.

“Truth in life is broad and nuanced; you can make all kinds of arguments, such as whether a president or person is fantastic or awful,” he says. “That’s why I love

behavioral economics, which explored the cognitive biases of individuals and investors. Among those identified: loss aversion, or how investors generally feel the pain from losses twice as much as the pleasure from gains; anchoring, the way judgment is skewed by an initial piece of information or experience; and the endowment effect, how investors assign excessive value to what they already own in their portfolios.

“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.”

By the time Mercer turned sixty-four in 2010, he was convinced government should play a minimal role in society, partly because governments empower incompetence.

Mercer shared concerns about the level of discourse on college campuses, saying schools “churn out a wave of ovine zombies steeped in the anti-American myths of the radical left, ignorant of basic civics, economics, and history, and completely unfit for critical thinking.”11

For all the unique data, computer firepower, special talent, and trading and risk-management expertise Renaissance has gathered, the firm only profits on barely more than 50 percent of its trades, a sign of how challenging it is to try to beat the market—and how foolish it is for most investors to try. 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. During his time helping to run the Medallion fund, Elwyn Berlekamp came to view the narratives that most investors latch on to to explain price moves as quaint, even dangerous, because they breed misplaced confidence that an investment can be adequately understood and its futures divined. If it was up to Berlekamp, stocks would have numbers attached to them, not names. “I don’t deny that earnings reports and other business news surely move markets,” Berlekamp says. “The problem is that so many investors focus so much on these types of news that nearly all of their results cluster very near their average.”