Simons left Stony Brook in 1977, a year after winning the Oswald Veblen Prize, one of the highest honors in the geometry world, awarded by the American Mathematics Society every five years. With Shiing-Shen Chern, he developed what’s known as the Chern-Simons theory, which became a key component of the field of string theory, a hypothesis that the universe is composed of tiny strings of energy humming in multidimensional spaces.
Simons got serious about making money. He started an investment firm called Monemetrics in a strip mall near the East Setauket train station. He made a call to Lenny Baum, an IDA cryptanalyst who’d done work on automated speech recognition technology. Simons thought Baum, one of the sharpest mathematicians he’d ever met, could use his quantitative brilliance to make hay in the market.
Baum’s chief achievement at IDA was the Baum-Welch algorithm, which he and fellow IDA mathematician Lloyd Welch designed to unearth patterns in an obscure mathematical phenomenon called a hidden Markov process. The algorithm proved to be an incredibly effective code-breaking tool, and also has interesting applications for financial markets.
A Markov process, named after Russian mathematician Andrey Markov, models a sequence of events in a system that have no direct relation to one another. Each roll of the dice in a game of Monopoly, for instance, is random, although the outcome (which square you land on) depends on where you are on the board. It is, in other words, a kind of random walk with contingent variables that change with each step along the way.
A hidden Markov process models a system that depends on an underlying Markov process with unknown parameters. In other words, it can convey information about some kind of underlying, random sequence of events. For instance, imagine you are talking on the phone with a friend who is playing a game of Monopoly. He yells “Darn!” each time he lands in jail, or “Eureka!” each time his opponent lands on his Park Place property, as well as a sequence of other exclamatory giveaways. With enough data and a powerful computer, the Baum-Welch algorithm can tease out probabilities about this process—and at times even predict what will come next.
Baum was skeptical. He’d never been interested in investing. But Simons was persistent. “Why should I do this?” Baum asked during one of their many phone conversations. “Will I live longer?”
“Because you’ll know you lived,” Simons replied.
Baum gave in. He started commuting to Long Island from Princeton to work at Monemetrics. Both were still relative novices in the investing game, and Baum found little use for his mathematical skills in the financial realm. Instead, he proved to be a brilliant fundamental trader, wagering on the direction of currencies or commodities based on his analysis of the economy or twists and turns in government policies.
But Simons was stuck on the notion of creating mathematically grounded trading models. He turned to a Bronx-born math professor he’d hired while running the math department at Stony Brook, James Ax.
Ax looked at Baum’s algorithms and determined that he could use them to trade all kinds of securities. In the mid-1980s, Simons and Ax spun a fund out of Monemetrics called Axcom Ltd. In 1985, Ax moved the operation to Huntington Beach, California. Axcom was to act as the trading advisor for the fund, which was nominally run as an investing firm owned by a company Simons had founded in July 1982 called Renaissance Technologies.
Soon Simons’s growing crew of quants added another math wizard, Elwyn Berlekamp, a game theory expert at Berkeley. Like Ed Thorp, Berlekamp had worked with Claude Shannon and John Kelly at MIT. He’d briefly met Simons during a stint at IDA in the 1960s.
The fund put up solid returns for several years, even managing to trade through Black Monday with relatively little damage. In 1988, Ax and Simons renamed the fund Medallion in honor of a math award they’d both won. Almost as soon as they’d renamed the fund, things started going south for Medallion. In the second half of 1988, losses were piling up, and getting steeper every month. By April 1989, it had dropped nearly 30 percent. Alarmed by the shift in fortunes, Simons ordered Ax to stop trading. Ax resisted, convinced he could turn things around. He hired a lawyer, threatening to sue. Simons spoke with his own lawyer.
In June, Berlekamp, who’d been gone for several months on a trip to Egypt, swung by Medallion’s office. He was surprised at how the situation had deteriorated. He quickly provided a solution, offering to buy out Ax’s stake, which represented two-thirds of its assets. Ax agreed. So did Simons.
With Ax gone, it was time to get to work on revamping the fund’s trading system. Berlekamp moved Medallion’s headquarters north to Berkeley so he could focus on overhauling the strategy without worrying about the commute. He rented the entire ninth floor of an office building on Shattuck Avenue near the university and shipped in the fund’s computers. For several months Berlekamp and Simons sweated over how to turn around Medallion’s fortunes.
A crucial change was a shift to higher-frequency trading. Typically, the fund would hold on to positions for several days, even weeks. Berlekamp and Simons decided to shorten average holding periods to less than a day, or even just an hour, depending on how far a position moved. From a statistical point of view, they realized, the ability to predict what will happen tomorrow, or in the next few hours, is far better than the ability to predict what will happen a week or two down the road.
To Berlekamp, it was like betting strategies in card games such as blackjack. In blackjack, the bettor’s edge is small. But that’s okay, since the law of large numbers is on his side. If the bettor plays ten thousand hands a month, his chances of being down are very small (if he plays his cards right). With just one bet, a gambler has to be very sure that his advantage is quite large. That’s why the goal was to make a lot of bets, as many as possible, just as long as there was also a slight statistical edge.
By November 1989, Medallion was up and running again. And it was an immediate success. In 1990, it gained 55 percent after fees. The team at Medallion kept tweaking the models, and the performance kept improving. Simons kept bringing on board math whizzes, including Henry Laufer, another Stony Brook don, to work for Renaissance. Laufer had earned a degree in physics from Princeton in 1965 and published a book on black holes in 1971 called Normal Two-Dimensional Singularities. He was an advisor to Renaissance’s commodity traders in the 1980s and joined the firm full-time in January 1991.
Simons closed the fund to new investors in 1993 with $280 million in assets. He didn’t think the models could handle much more cash. In 1994, returns hit an eye-popping 71 percent. The great run by Medallion was on. Month after month, quarter after quarter, year after year the money kept rolling in. The fund’s success became so reliable that its researchers and traders (all sporting Ph.D.’s) forgot what it was like to lose. When Medallion posted a rare 0.5 percent loss in a single quarter of 1999, at least one employee actually wept.
Meanwhile, Simons had tapped into Morgan Stanley’s stat arb machine created in the 1980s by purchasing Kepler Financial Management, the fund set up by Robert Frey after he’d left Nunzio Tartaglia’s APT group. The fund had a rough start, but it eventually started hitting on all cylinders. In 1997, it was absorbed into the Medallion mother ship and called the Factor Nova Funds, adding stat arb firepower to an already state-of-the-art investment machine. It was the first step in making Medallion a genuine multistrategy fund.
By then, Berlekamp was gone. He’d left Renaissance at the end of 1990 to pursue academic interests at Berkeley, where he went on to crack game theory puzzlers such as mathematical chess. But the Medallion legend continued to grow. To be sure, the fund has had a few hiccups over the years. In March 2000, when the dot-com bubble began to implode, reversing trends in technology stocks that had been in place for several years, Medallion lost $250 million in three days, nearly wiping out its year-to-date profit. But the fund quickly bounced back and put up another year of stellar returns.
Every trader on Wall Street who has heard of the fund’s mind-bending performance has openly marveled: how do they do it?
Simons has let few clues drop over the years. He once remarked that the fund sifts through data for identifiable patterns in prices. “Patterns of price movements are not random,” he said, a shot across the bow of the efficient-market random walkers such as Eugene Fama. “However, they’re close enough to random so that getting some excess, some edge out of it, is not easy and not so obvious, thank God.”
After chuckling at this cryptic statement, Simons added: “God probably doesn’t care.”
One day in 2003, Paul Samuelson came to speak at Renaissance’s headquarters in East Setauket. The MIT economist and Nobel laureate had long held that it was impossible to beat the market. He qualified that statement by saying that if anyone could do it, they would hide away and not tell anyone about their secret.
“Well, it looks like I’ve found you,” Samuelson said to the laughter of the wealthy quants of East Setauket.
How does Renaissance detect nonrandom price movements? It’s almost the same as asking whether Renaissance knows the Truth.
The fact is, no one outside the offices of Renaissance Technologies knows the answer to how it detects nonrandom price movements. Few people who have joined Renaissance have ever left. Those who have aren’t talking.
There are a few clues, however. One is the large number of cryptographers who helped to create Medallion: Ax, Berlekamp, and of course Simons himself. Cryptographers are trained to detect hidden messages in seemingly random strings of codes. Renaissance has applied that skill to enormous strings of market numbers, such as tick-by-tick data in oil prices, while looking at other relationships the data have with assets such as the dollar or gold.
Another clue can be found in the company’s decision in the early 1990s to hire several individuals with expertise in the obscure, decidedly non–Wall Street field of speech recognition.
In November 1993, Renaissance hired Peter Brown and Robert Mercer, founders of a speech recognition group at IBM’s Thomas J. Watson Research Center in Yorktown Heights, New York, in the hills of Westchester County. Brown came to be known as a freakishly hard worker at the fund, often spending the night at Renaissance’s East Setauket headquarters on a Murphy bed with a whiteboard tacked to the bottom of it. Worried about his health, he became an avid squash player because he deduced that it was the most efficient method of exercising. Often seen in the fund’s office in rumpled clothes, a stack of pens stuffed in his pockets, Brown had the ability to tackle the toughest mathematical conundrums as well as wire up the most advanced computers.
Mercer, meanwhile, was simply known as the “big gun” at Renaissance. When a thorny problem cropped up that required focused attention, the firm would “just aim Bob at it and fire,” said a former employee.
Over the following years, Renaissance hired a slew of people from IBM’s voice recognition group, including Lalit Bahl and the brothers Vincent and Stephen Della Pietra. Internet searches on any of these names will spit out a series of academic papers written in the early to mid-1990s. Then the trail goes cold.
At first blush, speech recognition and investing would appear to have little in common. But beneath the surface, there are striking connections. Computer models designed to map human speech depend on historical data that mimic acoustic signals. To operate most efficiently, speech recognition programs monitor the signals and, based on probability functions, try to guess what sound is coming next. The programs constantly make such guesses to keep up with the speaker.
Financial models are also made up of data strings. By glomming complex speech recognition models onto financial data, say a series of soybean prices, Renaissance can discern a range of probabilities for the future directions of prices. If the odds become favorable … if you have an edge …
It’s obviously not so simple—if it were, every speech recognition expert in the world would be running a hedge fund. There are complicated issues involving the quality of the data and whether the patterns discovered are genuine. But there is clearly a powerful connection between speech recognition and investing that Renaissance is exploiting to the hilt.
A clue to the importance of speech recognition to Renaissance’s broader makeup is that Brown and Mercer were named co-CEOs of Renaissance Technologies after Simons stepped down in late 2009.
“It’s a statistical game,” said Nick Patterson, a former Renaissance analyst and trader who’d previously done work as a cryptographer for the British and U.S. governments. “You discern phenomena in the market. Are they for real? That’s the key question. You must make sure it’s not model error or just noise.”
If the phenomenon is “for real,” capitalizing on it can be an even tougher challenge. How much leverage should be used? How much cash can be tossed at the strategy before it vanishes into thin air? The deep thinkers at Renaissance considered all of these issues and more. “Our edge was quite small, but it’s like being the house player at a casino,” Patterson added. “You have a small edge on every bet, and you have to know how to handle that.”
A common thread runs through voice recognition technology and cryptography: information theory. Indeed, information theory sprouted in part from the government’s efforts to crack codes during World War II. In financial markets, cryptographers try to discover hidden patterns that will recur in the future.
Medallion may tweak its models more than outsiders believe. One person familiar with the fund says it adjusts models for market conditions far more frequently than most quant operations. The switches are based on complex market signals discerned by Medallion’s powerful computers. Since its trades are processed so rapidly and Medallion trades in so many markets, this gives the fund more flexibility to shift its focus than most one-trick-pony quant funds.
Perhaps no one is more astounded at the Medallion fund’s two-decades-and-running streak than Simons himself. Throughout the 1990s, employees at Renaissance braced themselves for an end to the spectacular, lotterylike success. In 1992, the senior staff held a meeting to discuss the prospects for the fund over the next decade. Most expected to be in a different line of work in ten years. Simons was known for constantly saying, “The wolf is at the door.”
So paranoid is Simons about the threat of employees leaving the fund, taking its special sauce elsewhere, that he’s more than willing to ruin the careers of such apostates. In December 2003, Renaissance sued two employees, Alexander Belopolsky and Pavel Volfbeyn, who’d left the firm to join New York hedge fund giant Millennium Partners. The suit accused the two former MIT physicists of misappropriating trade secrets. In his defense, Volfbeyn accused Renaissance of asking him to devise methods to “defraud investors trading through the Portfolio System for Institutional Trading, or POSIT,” referring to a dark pool of liquidity—essentially an electronic market that matches buy and sell orders for stocks out of the public eye. Volfbeyn said he was instructed to create a code that would “reveal information that POSIT intended to keep confidential,” according to an article by Bloomberg, and that he refused to participate in the scheme, as well as others, because he believed they were against the law. The suit also hinted at a nefarious swaps deal that he described as a “massive scam,” but didn’t explain the deal in detail.
Nothing ever came of the allegations, and the two parties eventually settled their differences. But the message to Renaissance’s employees had been sent.
Insiders say the pressure to succeed at Renaissance can be brutal. One mathematician at the fund may have succumbed to the pressure on March 1, 2006. That’s when Alexander Astashkevich, a thirty-seven-year-old MIT graduate who worked at Renaissance, shot and killed his estranged wife in the small town of Port Jefferson, Long Island, before turning the shotgun on himself. He left behind a six-year-old son named Arthur.
Perhaps the intense pressure explains why Simons was known to burn through three packs of Merit cigarettes a day. One day Patterson came into Simons’s office to discuss a management issue. After some time, he noticed that Simons, puffing away at a Merit, wasn’t listenin
g—he was transfixed by the flitting numbers on his screen: numbers showing big losses in the Medallion fund. Even though Medallion always seemed to claw back from such dips, which were part and parcel of running a fund, only to rack up more gains, they caused Simons’s stomach to churn every time. Robert Frey, who left Renaissance in 2004, said one of the biggest reasons he quit was that he couldn’t take the gut-wrenching day-to-day volatility anymore. Despite Medallion’s success, it always seemed ephemeral, as if one day the magic would go away, vanish like a genie into its bottle. As if one day the Truth wouldn’t be the Truth anymore.
In between developing the most successful trading programs in the world, Renaissance’s wealthy band of quants found time to relax in the exclusive environs of East Setauket and Port Jefferson. Simons and Laufer, the fund’s “chief scientist,” owned mansions perched on the Long Island Sound, just a few minutes’ drive from the firm’s headquarters. Simons loved to take his staff sailing on his luxury yacht or jet off to exclusive resorts such as Atlantis in the Bahamas.
Rival quants such as Peter Muller and Cliff Asness, meanwhile, looked upon Medallion’s chart-crushing success, year after year, with awe. None had any idea how Simons had done it. No matter what the market was doing, Medallion cranked out billions in profits. Many wondered: had Simons and his band of reclusive quants out in the woods of Long Island discovered the holy grail, the philosopher’s stone—the secret mythical Truth of the financial markets? Perhaps, they thought with envy, Simons really had cracked the code.
One thing was certain: Simons wasn’t talking.
By the late 1990s, Ken Griffin was swapping convertible bonds from a high tower in Chicago. Jim Simons was building his quant empire in East Setauket. Boaz Weinstein was scouring computer screens to trade derivatives for Deutsche Bank. Peter Muller was trading stocks at Morgan Stanley. Cliff Asness was measuring value and momentum at AQR. They were all making more money than they’d ever dreamed possible.
The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It Page 13