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

by Gregory Zuckerman

“Hi,” he began, tentatively. “This is James Ax.” They spoke for hours that evening, the first of a series of lengthy and intense conversations between Ax and his two sons. Ax shared his regrets about how he had abandoned his boys and acknowledged the damage his anger had caused. The boys forgave Ax, eager to have their father back in their lives. Over time, Ax and his sons forged close relationships. In 2003, after Ax became a grandfather, he and Barbara, his ex-wife, reunited and established their own unlikely friendship. Three years later, at the age of sixty-nine, Ax died of colon cancer. On his tombstone, his sons engraved a formula representing the Ax-Kochen theorem.

Team members didn’t know a thing about the stocks they traded and didn’t need to—their strategy was simply to wager on the re-emergence of historic relationships between shares, an extension of the age-old “buy low, sell high” investment adage, this time using computer programs and lightning-fast trades.

“He needed Ferraris,” Sussman says. “We bought him Ferraris.”10 Shaw, a supercomputing expert, hired math and science PhDs who embraced his scientific approach to trading. He also brought on whip-smart employees from different backgrounds. English and philosophy majors were among Shaw’s favorite hires, but he also hired a chess master, stand-up comedians, published writers, an Olympic-level fencer, a trombone player, and a demolitions specialist. “We didn’t want anyone with preconceived notions,” an early executive says.11

In public, Simons professed confidence, encouraging his team to keep at it. “We have to keep trying,” he said in a group meeting in the summer of 1995, still an intimidating presence despite his shorts and sandals. Privately, though, Simons wondered if he was wasting his time. Maybe the team would never figure out equities, and Renaissance was destined to remain a relatively small futures-trading firm. It was a conclusion Laufer, Patterson, and others in the futures group already had reached. “We had given it years already,” Patterson says. “If I was calling the shots, I might very well have pulled the plug.” Simons remained a stubborn optimist. But even he decided enough was enough. Simons gave Brown and Mercer an ultimatum: Get your system to work in the next six months, or I’m pulling the plug.

“There’s no data like more data,” Mercer told a colleague, an expression that became the firm’s hokey mantra.

Renaissance’s goal was to predict the price of a stock or other investment “at every point in the future,” Mercer later explained. “We want to know in three seconds, three days, three weeks, and three months.”

If there was a newspaper article about a shortage of bread in Serbia, for example, Renaissance’s computers would sift through past examples of bread shortages and rising wheat prices to...

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.

outsiders staring at them. Next, Dwyer took his visitors downstairs to see Renaissance’s data group, where over thirty PhDs and others—including Chinese nationals and a few newly hired female scientists—were usually deep in thought near whiteboards filled with intricate formulas. The job of these scientists, Dwyer explained, was to take thousands of outside data feeds pumping nonstop into the company and scrub them clean, removing errors and irregularities so the mathematicians upstairs could use the information to uncover price patterns.

Other than making the occasional slipup, Simons was an effective salesman, a world-class mathematician with a rare ability to connect with those who couldn’t do stochastic differential equations. Simons told entertaining stories, had a dry sense of humor, and held interests far afield from science and moneymaking. He also demonstrated unusual loyalty and concern for others, qualities the investors may have sensed. Once, Dennis Sullivan, returning to Stony Brook after two decades in France, drove to Renaissance’s parking lot to talk with Simons. The two spent hours speaking about math formulas, but Simons sensed Sullivan was struggling with a different kind of problem. It turned out that Sullivan, who had six children from multiple marriages over forty years, was fielding financial requests from his kids and was having difficulty deciding how to treat each fairly.

One time, when neither Simons nor Brown was around to greet representatives of a West Coast endowment, Mercer joined the meeting. Asked how the firm made so much money, Mercer offered an explanation. “So, we have a signal,” Mercer began, his colleagues nodding nervously. “Sometimes it tells us to buy Chrysler, sometimes it tells us to sell.” Instant silence and raised eyebrows. Chrysler hadn’t existed as a company since being acquired by German automaker Daimler back in 1998. Mercer didn’t seem to know; he was a quant, so he didn’t actually pay attention to the companies he traded. The endowment overlooked the flub, becoming RIEF’s latest investor.

Medallion, still only available to employees, remained the heart of the firm. It now managed about $10 billion and was scoring annual gains of approximately 65 percent, before the investor fees, resulting in near-record profits. Medallion’s long-term record was arguably the greatest in the history of the financial markets, a reason investors and others were becoming fascinated with the secretive firm.

Medallion still held thousands of long and short positions at any time, and its holding period ranged from one or two days to one or two weeks. The fund did even faster trades, described by some as high-frequency, but many of those were for hedging purposes or to gradually build its positions. Renaissance still placed an emphasis on cleaning and collecting its data, but it had refined its risk management and other trading techniques.

In some ways, the Renaissance machine was more powerful than before Magerman quit. The company now employed about 250 staffers and over sixty PhDs, including experts in artificial intelligence, quantum physicists, computational linguists, statisticians, and number theorists, as well as other scientists and mathematicians. Astronomers, who are accustomed to scrutinizing large, confusing data sets and discovering evidence of subtle phenomena, proved especially capable of identifying overlooked market patterns.

The gains on each trade were never huge, and the fund only got it right a bit more than half the time, but that was more than enough. “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.” Mercer likely wasn’t sharing his firm’s exact trading edge—his larger point was that Renaissance enjoyed a slight advantage in its collection of thousands of simultaneous trades, one that was large and consistent enough to make an enormous fortune. Driving these reliable gains was a key insight: Stocks and other investments are influenced by more factors and forces than even the most sophisticated investors appreciated. For example, to predict the direction of a stock like Alphabet, the parent of Google, investors generally try to forecast the company’s earnings, the direction of interest rates, the health of the US economy, and the like. Others will anticipate the future of search and online advertising, the outlook for the broader technology industry, the trajectory of global companies, and metrics and ratios related to earnings, book value, and other variables. Renaissance staffers deduced that there is even more that influences investments, including forces not readily apparent or sometimes even logical. By analyzing and estimating hundreds of financial metrics, social media feeds, barometers of online traffic, and pretty much anything that can be quantified and tested, they uncovered ne...

How the firm wagered was at least as important as what it wagered on. If Medallion discovered a profitable signal, for example that the dollar rose 0.1 percent between nine a.m. and ten a.m., it wouldn’t buy when the clock struck nine, potentially signaling to others that a move happened each day at that time. Instead, it spread its buying out throughout the hour in unpredictable ways, to preserve its trading signal. Medallion developed methods of trading some of its strongest signals “to capacity,” as insiders called it, moving prices such that competitors couldn’t find them. It was a bit like hearing of a huge markdown on a hot item at Target and buying up almost all the discounted merchandise the moment the store opens, so no one else even realizes the sale took place. “Once we’ve been trading a signal for a year, it looks like something different to people who don’t know our trades,” an insider says. Simons summed up the approach in a 2014 speech in South Korea: “It’s a very big exercise in machine learning, if you want to look at it that way. Studying the past, understanding what happens and how it might impinge, nonrandomly, on the future.”5

IBM has estimated that 90 percent of the world’s data sets have been created in the last two years alone, and that forty zettabytes—or forty-four trillion gigabytes—of data will be created by 2020, a three-hundred-fold increase from 2005.8

The gains Simons and his colleagues have achieved might suggest there are more inefficiencies in the market than most assume. In truth, there likely are fewer inefficiencies and opportunities for investors than generally presumed. 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.