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Anticipating dramatic societal shifts, Simons harnessed algorithms, computer models, and big data before Mark Zuckerberg and his peers had a chance to finish nursery school.
I wasn’t entirely shocked. Simons and his team are among the most secretive traders Wall Street has encountered, loath to drop even a hint of how they’d conquered financial markets, lest a competitor seize on any clue.
There were compelling reasons I was determined to tell Simons’s story. A former math professor, Simons is arguably the most successful trader in the history of modern finance. Since 1988, Renaissance’s flagship Medallion hedge fund has generated average annual returns of 66 percent, racking up trading profits of more than $100 billion (see Appendix 1 for how I arrive at these numbers).
The average Renaissance employee has nearly $50 million just in the firm’s own hedge funds.
Simons never took a single finance class, didn’t care very much for business, and, until he turned forty, only dabbled in trading.
decade later, he still hadn’t made much headway. Heck, Simons didn’t even do applied mathematics, he did theoretical math, the most impractical kind.
Eventually, I conducted more than four hundred interviews with more than thirty current and former Renaissance employees.
Even Simons spoke with me, eventually. He asked me not to write this book and never truly warmed to the project. But Simons was gracious enough to spend more than ten hours discussing certain periods of his life, while refusing to discuss Renaissance’s trading and most other activities. His thoughts were valuable and appreciated.
For all his insights and prescience, Simons was blindsided by much that took place in his life. That may be the most enduring lesson of his remarkable story.
Fifty-two years old, Simons had already lived a full life, enjoying enough adventure, accomplishment, and prosperity
to satisfy the ambitions of his peers. Yet, there he was, overseeing an investment fund, sweating the market’s daily eruptions.
Simons recruited two renowned and headstrong mathematicians to trade with him, but those partnerships crumbled amid losses and acrimony. A year earlier, Simons’s results had been so awful he had been forced to halt his investing. Some expected him to pull the plug on his entire operation.
Now on his second marriage and third business partner, Simons decided to embrace a radical investing style. Working with Elwyn Berlekamp, a game theorist, Simons built a computer model capable of digesting torrents of data and selecting ideal trades, a scientific and systematic approach partly aimed at removing emotion from the investment process.
Simons had earned a PhD at the age of twenty-three and then became an acclaimed government code-breaker, a renowned mathematician, and a groundbreaking university administrator.
Merit cigarette lodged between two fingers, Simons reached for the phone to call Berlekamp one more time. “Have you seen gold?” Simons asked, the accent of his gravelly voice hinting at his Boston upbringing. Yes, I’ve seen gold prices, Berlekamp responded. And, no, we don’t need
to adjust our trading system. Simons didn’t push, hanging up politely, as usual.
There wasn’t even a proper name for this kind of trading, which involved data cleansing, signals, and backtesting, terms most Wall Street pros were wholly unfamiliar with.
Every day for lunch, Mercer ate either a tuna or peanut-butter-and-jelly sandwich packed in a used brown paper bag.
“There are patterns in the market,” Simons told a colleague. “I know we can find them.”
“The lesson was: Do what you like in life, not what you feel you ‘should’ do,” Simons says. “It’s something I never forgot.”
The way that powerful theorems and formulas could unlock truths and unify distinct areas in math and geometry captured Simons.
“It was the elegance of it all, the concepts were beautiful,” he says.
“Jim understood at an early age that money is power,” Barbara says. “He didn’t want people to have power over him.”
Q: What’s the difference between a PhD in mathematics and a large pizza? A: A large pizza can feed a family of four.
“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.
Players don’t need to know why their opponent is glum or exuberant to profit from those moods; they just have to identify the moods themselves.
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.
They were suggesting that one could deduce a range of “signals” capable of conveying useful information about expected market moves.
Around that time, Harry Markowitz, the University of Chicago Nobel laureate and father of modern portfolio theory, was searching for anomalies in securities prices, as was mathematician Edward Thorp. Thorp would attempt an early form of computerized trading, gaining a head start on Simons. (Stay tuned for more, dear reader.)
including factor investing, the use of models based on unobservable states, and other forms of quantitative investing—that would sweep the investing world decades later.
“Jim, I don’t want to be on a committee,” one potential hire told him. “How about the library committee?”
Simons said. “It’s a committee of one.”
“It’s unusual for someone to think of the well-being of colleagues,” Farkas says.
Simons made mistakes—including letting future Fields Medal winner Shing-Tung Yau get away after the young geometer demanded tenure—
but he assembled one of the world’s top centers of geometry, hiring twenty mathematicians while learning to identify the nation’s best minds and how to recruit and manage them.
By 2019, tens of thousands of citations in academic papers—approximately three a day—referenced Chern-Simons theory, cementing Simons’s position in the upper echelon of mathematics and physics.
Simons came from a different world and enjoyed a unique perspective. He was accustomed to scrutinizing large data sets and detecting order where others saw randomness.
“Let the problem be,” Baum liked to say, usually with a smile, when his kids came to him with concerns. “It will solve itself.”
To gather additional data, Simons had a staffer travel to lower Manhattan to visit the Federal Reserve office to painstakingly record interest-rate histories and other information not yet available electronically.
Some people take years to identify a profession for which they are naturally suited; others never make the discovery.
By identifying comparable trading situations and tracking what subsequently happened to prices, they could develop a sophisticated and accurate forecasting model capable of detecting hidden patterns.
To solve the problem, Straus began to model data rather than just collect it.
They didn’t have extensive cotton pricing data from the 1940s, for example, but maybe creating
the data would suffice.
Just as one can infer what a missing jigsaw puzzle piece might look like by observing pieces already in place, the Axcom team made deductions about the missing i...
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Elsewhere, statisticians were using similar approaches—called kernel methods—to analyze patterns in data sets.
“Just follow the data, Jim,” he said. “It’s not me, it’s the data.”
