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

by Gregory Zuckerman

“It was the elegance of it all, the concepts were beautiful,” he says.

Simons joined during a tumultuous period for the IDA. High-level Soviet codes

Truth . . . is much too complicated to allow for anything but approximations. John von Neumann

Ax was a decent poker player, but he couldn’t find a way to beat Kochen. Growing more infuriated with each loss, Ax became convinced Kochen was gaining a crucial advantage by reading his facial expressions. Ax had to hide his tell. One summer evening, as the poker players sat down to play in a brutal heat wave, Ax showed up wearing a heavy, woolen ski mask to conceal his face. Sweating profusely and barely able to see through the mask’s narrow openings, Ax somehow lost to Kochen again. Ax stalked away from the game, fuming, never to uncover Kochen’s secret. “It wasn’t his face,” Kochen says. “Jim tended to straighten up in his chair when he had a good hand.”

Markov chains, those sequences of events in which the next event is only dependent on the current state.

It’s easy to see why they saw similarities between stochastic processes and investing. For one thing, Simons, Ax, and Straus didn’t believe the market was truly a “random walk,” or entirely unpredictable, as some academics and others argued. Though it clearly had elements of randomness, much like the weather, mathematicians like Simons and Ax would argue that a probability distribution could capture futures prices as well as any other stochastic process. That’s why Ax thought employing such a mathematical representation could be helpful to their trading models. Perhaps by hiring Carmona, they could develop a model that would produce a range of likely outcomes for their investments, helping to improve their performance.

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Berlekamp came to realize that much of human interaction is colored by shades of gray that he sometimes found difficult to discern. Mathematics, by contrast, elicits objective, unbiased answers, results he found calming and reassuring. “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 math problems—they have clear answers.”

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Technical Analysis of the Financial Markets,

“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.” =

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No one ever made a decision because of a number. They need a story.

“There’s many a slip ’twixt the cup and the lip.”

Simons and his team had yet to solve the market, however. Medallion gained 21 percent in 1997, a bit lower from the 32 percent results a year earlier, the over 38 percent gain in 1995, and the 71 percent jump in 1994. Its trading system still ran into serious issues. One day, a data-entry error caused the fund to purchase five times as many wheat-futures contracts as it intended, pushing prices higher. Picking up the next day’s Wall Street Journal, sheepish staffers read that analysts were attributing the price surge to fears of a poor wheat harvest, rather than Renaissance’s miscue.

betting on retracements. About 60 percent of investments that experienced big, sudden price rises or drops would snap back, at least partially, it turned out. Profits from these retracements helped Medallion do especially well in volatile markets when prices lurched, before retracing some of that ground.