The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It (18 page)

Brown first realized an important fact about the game: you have to be highly confident to issue a challenge. In a ten-person game, if
you’re right when you issue a challenge, you gain $100—but if you’re wrong, you lose $900. In other words, you wanted to be 90 percent certain that you were correct to challenge. If he could figure out a pattern for making bets and challenges, he would have an edge over the traders, who were basically playing by gut instinct. The quants would know when to keep betting and when to challenge.

Brown crunched the numbers and came up with a key insight: A game of Liar’s Poker follows one of two paths. In one path, a single number is passed around, and no one changes the number for the entire round until a challenge is made (five 2s, seven 2s, ten 2s, etc.). In the other path, someone does change the number—usually, Brown figured, somewhere around the tenth bid. In the first path, there is virtually no chance of seeing the same digit appear fourteen times or more in a group of ten $20 bills. But in the second path, if someone changes the number and ups the bid, that often means he has a large number of the same digit on his bill, perhaps three of four. That boosts the odds significantly that there are more than fourteen instances of that digit in the circle.

Knowing how the two paths differed, along with the odds that accompanied each challenge, helped Brown crack the game. It wasn’t rocket science, but he believed it was enough to do the trick.

He started circulating his strategy on electronic bulletin boards, and even created a simulator that let the quants practice on their home computers. They focused on speed. Rapid-fire bets would unnerve the traders. They also realized it was often optimal to raise the bets dramatically if they had more than one of the same digit on their twenty, something that hadn’t normally been done in the past. A bet of eight 6s could suddenly shoot to fourteen 7s.

Their testing complete, the quants finally decided to put their strategy into action on Kidder Peabody’s trading floor. Brown surveyed the action from a distance, chuckling to himself as the bidding started off as normal. The traders were predictable, playing it safe: four 2s.

When the quants’ turn came, the bids came in fast and furious. Bid … bid … bid. Ten 7s. Twelve 8s. Thirteen 9s. They machine-gunned around the circle back to the top trader, who had started the
bidding. Kidder’s traders were dumbfounded. The silence lasted a full minute. The quants struggled to keep straight faces. Brown nearly doubled up with laughter.

The head trader finally decided to challenge the last quant. Bad move. There were fifteen 9s in the circle. He lost, but he refused to pay, accusing the quants of cheating. The quants just laughed, high-fiving. Brown had expected this. Traders never admit to losing.

The Liar’s Poker game at Kidder Peabody quietly died off soon after the quant uprising. Brown’s strategy spread to quants at other firms. Within a year, Liar’s Poker had virtually vanished from Wall Street’s trading floors. The quants had killed it.

The quants were proving themselves to be a force to be reckoned with on Wall Street. No longer would they stand at the end of the line and be victimized by the Big Swinging Dicks.

Indeed, the quants were flooding into Wall Street in the 1980s from outposts such as BARRA in Berkeley, where Muller was earning his quant chops by creating factor models, or the University of Chicago, where Asness was studying at the feet of Fama and French. The rise of the personal computer, increased volatility due to fluctuating inflation and interest rates, and options and futures exchanges in Chicago and New York created the perfect environment for the brainiacs from academia. Physicists, electrical engineers, even code breakers trained by the military-industrial complex found that they could use the math they’d always loved to make millions in the financial markets. Eventually programs dedicated to the singular goal of training financial engineers cropped up in major universities around the country, from Columbia and Princeton to Stanford and Berkeley.

The first wave of quants went to banks such as Salomon Brothers, Morgan Stanley, and Goldman Sachs. But a few renegades struck off on their own, forming secretive hedge funds in the tradition of Ed Thorp. In a small, isolated town on Long Island one such group emerged. In time, it would become one of the most successful investing powerhouses the world had ever seen. Its name was Renaissance Technologies.

It is fitting that Renaissance Technologies, the most secretive hedge fund in the world, founded by a man who once worked as a code breaker for the U.S. government, is based in a small Long Island town that once was the center of a Revolutionary War spy ring.

The town of Setauket dates from 1655, when a half dozen men purchased a thirty-square-mile strip of land facing Long Island Sound from the Setalcott Indian tribe. When the War for Independence started more than a century later, it had become the most densely settled town in the region. Long Island largely lay in the hands of the British during the war after George Washington’s defeat in the Battle of Brooklyn in 1776. Setauket, a port town, boasted its share of guerillas, however. The redcoats cracked down, turning it into a garrison town.

The Culver Spy Ring sprang up a year later. Robert Townsend of nearby Oyster Bay posed as a Tory merchant in Manhattan to gather information on British maneuvers. He passed along information to an innkeeper in Setauket who frequently traveled to New York, who relayed the messages to a Setauket farmer, who handed the intelligence to a whaleboat captain named Caleb Brewster. Brewster carried the package across Long Island Sound to Setauket native Major Benjamin Tallmadge, who was headquartered in Connecticut. At last, Tallmadge posted the messages to General Washington.

After the war, Washington made a tour of Long Island and visited Setauket to meet the spies. He stayed at Roe’s Tavern on the night of April 22, 1790, and wrote in his diary that the town was “tolerably decent.”

In Washington’s day, Roe’s Tavern was located on a road that’s now called Route 25A—the same road where Renaissance Technologies’ headquarters can be found today.

Renaissance’s flagship Medallion fund, launched in the late 1980s, is considered by many to be the most successful hedge fund in the world. Its returns, at roughly 40 percent a year over the course of three decades, are by a wide margin unmatched in the investing world. By comparison, before the recent stock market implosion, Warren Buffett’s storied Berkshire Hathaway averaged an annual return of about 20 percent. (Of course, scale matters: Medallion has about $5 billion
in capital, while Berkshire is worth about $150 billion, give or take a few billion.)

Indeed, Medallion’s phenomenal returns have been so consistent that many in quantdom wonder whether it possesses that most elusive essence of all: the Truth.

As a
toddler growing up in a small town just outside of Boston, James Harris Simons was stunned to learn that a car could run out of gas. He reasoned that if the tank was half full, and then lost another half, and another half, it should always retain half of the previous amount. He had stumbled upon a logical riddle known as Zeno’s paradox, not exactly common fare for a preschooler.

Simons excelled at math in high school, and in 1955 he enrolled at MIT. He soon caught the poker bug, playing with friends into the late hours of the night before piling into his Volkswagen Beetle and driving to Jack & Marion’s deli in nearby Brookline for breakfast.

Simons cruised through MIT’s bachelor’s program in math in three years, aced its master’s program in one, and then enrolled in Berkeley’s Ph.D. program, studying physics. At Berkeley, he got his first taste of commodities trading, making a tidy sum on soybeans. After earning his doctorate, Simons taught classes at MIT before moving up the road to Harvard. Dissatisfied with a professor’s salary, he took a job with the Institute for Defense Analysis, a nonprofit research wing of the Defense Department.

The IDA had been established in the mid-1950s to provide civilian assistance to the military’s Weapons Systems Evaluation Group, which studies technical aspects of newfangled weapons. By the time Simons arrived, the IDA had set up a branch in Princeton that had become a haven for Cold War code breakers.

The Vietnam War was raging, aggravating many of the more liberal academic types who worked at civilian research labs such as IDA. In 1967, a former chairman of the Joint Chiefs of Staff, Maxwell Taylor, president of IDA, wrote an article in favor of the war for the
New York Times Magazine
. The article elicited an acid response from Simons. “Some of us at the institution have a different view,” the twenty-nine-year-old Simons wrote in a letter to the magazine’s editors, which was
published in October 1967. “The only available course consistent with a rational defense policy is to withdraw with the greatest possible dispatch.”

The letter apparently cost Simons his job. But it didn’t take him long to find a new one. In 1968, he took the position of chairman of the math department at the State University of New York at Stony Brook, on Long Island and just up the road from Setauket. He gained a reputation for aggressively recruiting top talent, building the department into a mecca for math prodigies around the country.

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.