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

by Scott Patterson

These computer-driven investors couldn’t care less about a company’s “fundamentals,” amorphous qualities such as the morale of its employees or the cut of its chief executive’s jib. That was for the dinosaurs of Wall Street, the Warren Buffetts and Peter Lynches of the world, investors who focused on factors such as what a company actually made and whether it made it well. Quants were agnostic on such matters, devoting themselves instead to predicting whether a company’s stock would move up or down based on a dizzying array of numerical variables such as how cheap it was relative to the rest of the market, how quickly the stock had risen or declined, or a combination of the two—and much more.

The quants created a name for the Truth, a name that smacked of cabalistic studies of magical formulas: alpha. Alpha is a code word for an elusive skill certain individuals are endowed with that gives them the ability to consistently beat the market. It is used in contrast with another Greek term, beta, which is shorthand for plain-vanilla market returns anyone with half a brain can achieve. To the quants, beta is bad, alpha is good. Alpha is the Truth. If you have it, you can be rich beyond your wildest dreams.

In other words, quants, such as Griffin, Asness, Muller, Weinstein, Simons, and the rest of the math wizards who had taken over Wall Street, had helped tame the market’s volatility. Out of chaos they had created order through their ever-increasing knowledge of the Truth. Every time the market lurched too far out of equilibrium, their supercomputers raced to the rescue, gobbling up the mispriced securities and restoring stability to the troubled kingdom.

Discovering how to price volatility was the key to unlocking the stock warrant treasure trove. Say you own a warrant for IBM. The current value of IBM’s stock is $100. The warrant, which expires in twelve months, will be valuable only if IBM is worth $110 at some point during that twelve-month period. If you can determine how volatile IBM’s stock is—how likely it is that it will hit $110 during that time period—you then know how much the warrant is worth. Thorp discovered that by plugging in the formula for Brownian motion, the random walk model, in addition to an extra variable for whether the stock itself tends to rise more or less than other stocks, he could know better than almost anyone else in the market what the IBM warrant was worth. Gamblers make such time-dependent bets all the time. The time to expiration of the warrant is similar to the four quarters of a football game, baseball’s nine innings, or a lap around the racetrack. Investors are wagering on a certain outcome within a predefined time frame. Thorp simply used his math skills and his well-honed gambling instincts to quantify the problem. But to the conservative crowd—think investors in Treasuries and blue chips—all of this seemed a sort of crystal ball divination of the future, an approach better left to hucksters and charlatans. A trained physicist such as Thorp, however, saw that it was simply a matter of assigning a certain probability to a future outcome based on fixed parameters—a practice physicist...

If the desk held a block of General Motors stock, it would sell short a chunk of Ford that would pay off if the GM stock took a hit. Bamberger’s software provided traders up-to-date information on the relative positions of the pairs. Bamberger noticed that large block trades would often cause the price of the stock to move significantly. The price of the other stock in the pair, meanwhile, barely moved. This pushed the typical gap between the two stock prices, the “spread,” temporarily out of whack. Suppose GM typically traded for $10 and Ford for $5. A large buy order for GM could cause the price to rise temporarily to $10.50. Ford, meanwhile, would stay at $5. The “spread” between the two stocks had widened.

As Nassim Nicholas Taleb, a critic of quant models, later argued in several books, investors who believe the market moves according to a random walk are “fooled by randomness” (the title of one of his books). Taleb famously dubbed the wild unexpected swings in markets, and in life itself, “black swans,” evoking the belief long held in the West that all swans are white, a notion exploded when sailors discovered black swans in Australia. Taleb argued that there are far more black swans in the world than many people believe, and that models based on historical trends and expectations of a random walk are bound to lead their users to destruction.

The idea that the market is an efficient, randomly churning price-processing machine has many odd consequences. Fama postulates a vast, swarming world of investors constantly searching for inefficiencies—those hungry piranhas circling in wait of fresh meat. Without the hungry piranhas gobbling up juicy fleeting inefficiencies, the market would never become efficient. Would the piranhas exist without the fresh meat? No fresh meat, no piranhas. No piranhas, no market efficiency. It’s a paradox that continues to baffle EMH acolytes. Another offshoot of market efficiency is that, if true, it effectively makes it impossible to argue that a market is mispriced—ever. When the Nasdaq was hovering above 5,000 in early 2000, it was impossible to argue at the time that it was in a bubble, according to EMH. The housing market in 2005, when prices for many homes in the United States had doubled or tripled in a matter of a few years? No bubble.

Implicitly, EMH also showed that there is a mechanism in the market making prices efficient: Fama’s piranhas. The goal was to become a piranha, gobbling up the fleeting inefficiencies, the hidden discrepancies, as quickly as possible. The quants with the best models and fastest computers win the game.

Crucially, EMH gave the quants a touchstone for what the market should look like if it were perfectly efficient, constantly gravitating toward equilibrium. In other words, it gave them a reflection of the Truth, the holy grail of quantitative finance, explaining how the market worked and how to measure it. Every time prices in the market deviated from the Truth, computerized quant piranhas would detect the error, swoop in, and restore order—collecting a healthy profit along the way. Their high-powered computers would comb through global markets like Truth-seeking radar, searching for opportunities. The quants’ models could discover when prices deviated from equilibrium. Of course, they weren’t always right. But if they were right often enough, a fortune could be made. This

Fama and French’s prime discovery was that value stocks performed better than growth stocks over almost any time horizon going back to 1963. If you put money in value stocks, you made slightly more than you would have if you invested in growth stocks.

Fama and French also found that small stocks tended to fare better than large stocks. The notion is similar to the value and growth disparity, because a small stock is intuitively unloved—that’s why it’s small.

In other words, according to Fama and French, the forces pushing stocks up and down over time weren’t volatility or beta—they were value and size. For students such as Asness, the message was clear: money could be made by focusing purely on these factors. Buy cheap mushroom pizzas (small ones) and short jumbo pepperonis.

“Fama and French … misinterpret their own data,” a true smackdown in quantdom. Sharpe argued that the period Fama and French observed favored the value factor, since value stocks performed extremely well in the 1980s after the market pummeling in the previous decade of oil crises and stagflation.

Asness believed he had discovered a curious anomaly in a trend driving stock prices. Stocks that were falling seemed to keep falling more than they should, based on underlying fundamentals such as earnings, and stocks that were rising often seemed to keep rising more than they should. In the parlance of physics, the phenomenon was called “momentum.” According to the efficient-market hypothesis, momentum shouldn’t exist, since it implied that there was a way to tell which stocks would keep rising and which would keep falling. Asness knew that momentum was a direct challenge to Fama, and he expected a fight. He cleared his throat. “My paper is going to be pro-momentum,” he said with a wince.

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.”

The Gaussian copula was, in hindsight, a disaster. The simplicity of the model hypnotized traders into thinking that it was a reflection of reality. In fact, the model was a jury-rigged formula based on the irrationally exuberant, self-reinforcing, and ultimately false wisdom of the crowd that assigned make-believe prices to an incredibly complex product. For a while it worked, and everyone was using it. But when the slightest bit of volatility hit in early 2007, the whole edifice fell apart. The prices didn’t make sense anymore. Since nearly every CDO manager and trader used the same formula to price the fizzing bundles—yet another instance of crowding that results from popular quant methodologies—they all imploded at once.

There’s clear evidence that Wall Street’s gluttonous demand for loans and all the fat fees they spit out was the key factor that allowed, and encouraged, brokers to concoct increasingly risky mortgages with toxic bells and whistles such as adjustable interest rates that shot higher a few years—or in some cases a few months—after the loan was made. Out of twenty-five of the top subprime mortgage lenders, twenty-one were either owned or financed by major Wall Street or European banks, according to a report by the Center for Public Integrity. Without the demand from the investment banks, the bad loans would never have been made. As the CDO

Initially, Morgan wasn’t eager to join the party. A mantra at Morgan before John Mack returned was that the bank “wouldn’t be another Goldman,” according to a person who worked at the bank. Morgan would exercise caution during boom times to be prepared for the inevitable bust when the music stopped.