The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It (14 page)

His first words came as a shock to the students in the room.

“Everything I’m about to say isn’t true,” said Fama in a gruff voice tinged with the accent of his Boston youth.

He walked to his chalkboard and wrote the following:
Efficient-market hypothesis
.

“The market is efficient,” Fama said. “What do I mean by that? It means that at any given moment, stock prices incorporate all known information about them. If lots of people are drinking Coca-Cola, its stock is going to go up as soon as that information is available.”

Students scribbled on their notepads, taking it all in.

The efficient-market hypothesis, perhaps the most famous and long-lasting concept about how the market behaved in the past half century, was Fama’s baby. It had grown so influential, and had become so widely accepted, that it was less a hypothesis than a commandment from God in heaven passed down through his economic prophet of the Windy City.

“There are a number of consequences to market efficiency,” Fama said, facing the classroom. “One of the most important is that it’s statistically impossible to know where the market is going next. This is known as the random walk theory, which means that the future course of the market is like a coin toss. It either goes up or down, fifty-fifty, no one knows which.”

A student near the front row raised a tentative hand.

“What about all the guys who get paid to pick stocks? They must get paid for a reason. It can’t be all luck.”

“The evidence shows that trying to pick stocks is a complete waste of time,” Fama said flatly. “And money. Wall Street is full of salesmen trying to convince people to give them a buck. But there’s never been a study in history showing active managers consistently beat the market. It’s just not in the data. Managers have good runs, but it usually does just come down to dumb luck.”

“Why do people pay these money managers so much money?”

“Hope? Stupidity? It’s hard to say.” “What about Warren Buffett?”

Fama sighed.
That Buffett again
. Increasingly, students were obsessed with the track record of this hick investor from Omaha, whose company, Berkshire Hathaway, had beaten the S&P 500 for two decades in a row and counting.

“There do seem to be a few outliers that are impossible to explain. In every science there are freaks that seem to defy all the rules. Buffett, as well as Peter Lynch at Fidelity’s Magellan fund, have had consistent returns over the years. I’m not aware of anyone else. These freak geniuses may be out there, but I don’t know who they are. Who knows,” he said with a shrug and a smile, “maybe they’ll lose it all next year.”

The math showed it was inevitable that a few traders would stand out, but that didn’t mean they had skill. Give ten thousand people a quarter. Tell them to flip. Each round, eliminate the ones who flipped heads. After ten rounds, maybe a hundred will be left. After twenty, maybe three or four will still be in the game. If they were on Wall Street, they’d be hailed as expert coin flippers, coin flippers drenched in alpha. Buffett, according to Fama, was in all probability a lucky coin flipper.

Another student raised a hand. “But you said everything you’re going to tell us isn’t true. So does that mean that markets really aren’t efficient?”

“That’s right,” Fama said. “None of what I’m telling you is one hundred percent true. These are mathematical models. We look at statistics, historical data, trends, and extrapolate what we can from them. This isn’t physics. In physics, you can build the space shuttle, launch it into orbit, and watch it land at Cape Canaveral a week later. The market is far more unstable and unpredictable. What we know about it are approximations about reality based on models. The efficient-market hypothesis is just that, a hypothesis based on decades of research and a large amount of data. There’s always the chance we’re wrong.”

He paused. “Although I’m virtually certain that we’re right. God knows the market is efficient.”

The classroom laughed nervously. Fama was an intimidating presence, radiating a cool disdain for those unable to keep up. Cliff Asness, a twenty-three-year-old Ph.D. student, nodded and scribbled Fama’s words in his notebook:
freak geniuses … mathematical models
… None of this was new to him; he’d taken finance classes under
some of the top finance thinkers in the world at the University of Pennsylvania’s Wharton School. But he knew that Fama was the man, the top of the heap in academic finance.

But still, he couldn’t help wondering. Indeed, Fama’s words were almost a challenge:
Could I do it? Could I beat the market?

As a child, Clifford Scott Asness gave no sign of his future as a Wall Street tycoon. He was born in October 1966 in Queens, New York. When he was four, his family moved to the leafy, suburban environs of Roslyn Heights on Long Island. In school Asness received good grades, but his interest in Wall Street didn’t extend beyond the dark towers of Gotham in the pages of
Batman
. Obsessed with little besides girls and comic books, Asness was listless as a teenager, without direction and somewhat overweight. At times he showed signs of a violent temper that would erupt years later when he sat at the helm of his own hedge fund. Once a chess team rival taunted him in the school’s parking lot about a recent match. Enraged, Asness seized his tormentor and tossed him into a nearby van, over and over again.

As an undergraduate at the University of Pennsylvania’s Wharton School, Asness assumed he’d follow in the footsteps of his father, a trial lawyer. He wasn’t sure
why
he wanted to become a lawyer, aside from that it seemed a family tradition. His father, however, was mystified by his son’s plans.

“Why would you want to be a lawyer when you’re good with numbers?” he said.

Asness took his father’s words seriously. Open to new fields, he delved into the arcane world of portfolio theory as a research assistant for Wharton professor Andrew Lo, who later moved to MIT. To his surprise, he found the subject fascinating. He switched his focus to finance, picking up a degree in computer science along the way—a crackerjack quant combo.

As Asness neared graduation, he canceled his appointment to take the Law School Admission Test, the LSAT, and instead signed up for the Graduate Management Admission Test, or GMAT. With a solid score in hand, he was accepted by several business programs. His favorites were Stanford and Chicago. Decisively, Chicago offered to fly out the cash-poor Asness for a visit, while Stanford didn’t. He arrived
on a beautiful spring day—perhaps the most fortuitous sunny day of his life. It was the ultimate bait and switch, Asness would later say, joking that he must be the only person who ever chose the University of Chicago over Stanford based on weather.

Asness entered Chicago when Eugene Fama and his colleague, Kenneth French, were working on landmark research that would shake the foundations of business schools around the country. Their research would draw on the most important ideas in modern finance and push them into entirely new realms of theory and application.

Fama was the star of the duo. Born near the end of the Great Depression and raised around the rugged shipyards of Boston’s Charles-town neighborhood, Fama was one of the first economists to work intensively with computers. As a student at the University of Chicago in the early 1960s, he also had access to one of the world’s largest databases of stock market data, Chicago’s Center for Research in Security Prices, otherwise known as CRSP (pronounced “crisp”).

Fishing for subjects to teach, Fama realized that the university didn’t offer any courses on Harry Markowitz, a former Chicago student who used quantitative methods to show how investors can maximize their returns and lower their risk profiles by diversifying their portfolios—quant-speak for the old saw “Don’t put all of your eggs in one basket.”

Fama started teaching Markowitz’s theories in 1963. He soon added the works of William Sharpe, a Markowitz protégé who’d done pioneering work on the concept of beta, a measure of a stock’s sensitivity to the broader volatility in the market. A stock that had a higher beta than the rest of the market was considered more risky, while a stock with a low beta was a safer play. The more risk, the more potential reward—and also the more pain. A stock with a beta of 1 has the same volatility as the rest of the market. Ho-hum blue chips such as AT&T typically have low betas. A beta of 2 is a highly volatile stock—often technology jumping beans such as Apple or Intel. If you know a stock’s beta, you know something about how risky it is.

The result of Fama’s efforts was the first course on modern finance at Chicago, called Portfolio Theory and Capital Markets (which Fama
teaches to this day). In his research, he made extensive use of the university’s database of stocks as well as its computers, running test after test and looking for hidden patterns in the data. By 1969, Fama distilled the collected ideas of this class, and years of computerized number crunching, into the first fully formed articulation of a cornerstone of modern portfolio theory: the efficient-market hypothesis, or EMH.

While many thinkers over the years had written about market efficiency, Fama’s was the most coherent and concise statement of the idea that the market is unbeatable. The fundamental idea behind EMH is that all relevant new information about a stock is instantly priced into the stock, making it “efficient.” Fama envisioned a large, well-developed market with many players constantly on the hunt for the latest news about companies. The process of injecting new information—a lousy earnings report, the departure of a CEO, a big new contract—is like tossing a juicy piece of fresh meat into a tank of piranhas. Before you know it, the meat has been devoured.

Since all current information is built into the stock’s price and future information is essentially unknowable, it is impossible to predict whether a stock will rise or fall. The future, therefore, is random, a Brownian motion coin flip, a drunkard’s walk through the Parisian night.

The groundwork for the efficient-market hypothesis had begun in the 1950s with the work of Markowitz and Sharpe, who eventually won the Nobel Prize for economics (together with Merton Miller) in 1990 for their work.

Another key player was Louis Bachelier, the obscure French mathematician who argued that bond prices move according to a random walk.

In 1954, MIT economist Paul Samuelson—another future Nobel laureate—received a postcard from Leonard “Jimmie” Savage, a statistician at Chicago. Savage had been searching through stacks at a library and stumbled across the work of Bachelier, which had largely been forgotten in the half century since it had been written. Savage wanted to know if Samuelson had ever heard of the obscure Frenchman. He said he had, though he’d never read his thesis. Samuelson promptly hunted it down and became enthralled with its arguments.

Since the future course of the market is essentially a 50–50 random coin flip, Bachelier had written, “The mathematical expectation of the speculator is zero.” Samuelson had already started thinking about financial markets. His interest had been piqued by a controversial speech given in 1952 by Maurice Kendall, a statistician at the London School of Economics. Kendall had analyzed a variety of market data, including stock market indexes, wheat prices, and cotton prices, looking for some kind of pattern that would show whether price movements were predictable. Kendall found no such patterns and concluded that the data series looked “like a wandering one, almost as if once a week the Demon of Chance drew a random number from a symmetrical population.” Kendall said this appeared to be “a kind of economic Brownian motion.”

Samuelson realized this was a bombshell. He made the leap embedded in Bachelier’s original paper: investors are wasting their time. Mathematically, there is no way to beat the market. The Thorps of the world should put away their computers and formulas and take up a more productive profession, such as dentistry or plumbing. “It is not easy to get rich in Las Vegas, at Churchill Downs, or at the local Merrill Lynch office,” he wrote.

At the time, Samuelson was becoming an éminence grise of the economic community. If he thought the market followed a random walk, that meant everyone had to get on board or have a damn good reason not to. Most agreed, including one of Samuelson’s star students, Robert Merton, one of the co-creators of the Black-Scholes option-pricing formula. Another acolyte was Burton Malkiel, who went on to write
A Random Walk Down Wall Street
.

It was Fama, however, who connected all of the dots and put the efficient-market hypothesis on the map as a central feature of modern portfolio theory.

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.

Despite such mind-bending conundrums, the EMH became the dominant paradigm in academia as Fama spread the gospel. It was a frontal assault on the money management industry, which was built on the idea that certain people with the right methods and tools can beat the market.

The quants viewed EMH as a key weapon in their arsenal: The probabilities of various movements of an efficient market could be understood through the math spawned by Brownian motion. The most likely moves were those found near the middle of the bell curve, which could be used to make forecasts about the probable future volatility of the market over the course of a month, a year, or a decade. In the financial planning community, so-called Monte Carlo simulations, which can forecast everyday investors’ portfolio growth over time, used the idea that the market moves according to a random walk. Thus, an annual gain or loss of 5 percent a year is far more likely, since it falls near the center of the bell curve. A gain or loss of 50 percent, such as the stock market crash seen in the credit crisis of 2008 (or the 23 percent single-day plunge seen on Black Monday, for that matter) was so unlikely as to be a virtual impossibility—in the models, at least. Today, nearly all large financial services firms, such as Fidelity Investments and T. Rowe Price, offer Monte Carlo simulations to investors. Thus, the insights of Bachelier more than a century earlier and prodded on by Fama had reached into the very nuts and bolts of how Americans prepare for retirement. It had also blinded them to the chance that the market could make extreme moves. Such ugly phenomena simply didn’t fit within the elegant models spawned by the quants.