The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It (15 page)

EMH was in many ways a double-edged sword. On one hand, it argued that the market was impossible to beat. Most quants, however—especially
those who migrated from academia to Wall Street—believed the market is only
partly
efficient. Fischer Black, co-creator of the Black-Scholes option-pricing formula, once said the market is more efficient on the banks of the Charles River than the banks of the Hudson—conveniently, after he’d joined forces with Goldman Sachs.

By this view, the market was like a coin with a small flaw that makes it slightly more likely to come up heads than tails (or tails than heads). Out of a hundred flips, it was likely to come up heads fifty-two times, rather than fifty. The key to success was discovering those hidden flaws, as many as possible. The law of large numbers that Thorp had used to beat the dealer and then earn a fortune on Wall Street dictated that such flaws, exploited in hundreds if not thousands of securities, could yield vast riches.

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 was one of the major lessons Cliff Asness learned studying at the University of Chicago. But there was more.

Fama, a
bulldog with research, hadn’t rested on his efficient-market laurels over the years. He continued to churn out libraries of papers, leveraging the power of computers and a stream of bright young
students eager to learn from the guru of efficient markets. In 1992, soon after Asness arrived on the scene, Fama and French published their most important breakthrough yet, a paper that stands as arguably the most important academic finance research of the last two decades. And the ambition behind it was immense: to overturn the bedrock theory of finance itself, the capital asset pricing model, otherwise known as CAPM.

Before Fama and French, CAPM was the closest approximation to the Truth in quantitative finance. According to the grandfather of CAPM, William Sharpe, the most important element in determining a stock’s potential future return is its beta, a measure of how volatile the stock is compared with the rest of the market. And according to CAPM, the riskier the stock, the higher the potential reward. The upshot: long-term investments in risky stocks tended to pay off more than investments in the ho-hum blue chips.

Fama and French cranked up their Chicago supercomputers and ran a series of tests on an extensive database of stock market returns to determine how much impact the all-important beta actually had on stock returns. Their conclusion: none.

Such a finding was nothing short of lobbing a blazing Molotov cocktail into the most sacred tent of modern portfolio theory. Decades of research were flat-out wrong, the two professors alleged. Perhaps even more surprising were Fama and French’s findings about the market forces that did, in fact, drive stock returns. They found two factors that determined how well a stock performed during their sample period for 1963 to 1990: value and size.

There are a number of ways to gauge a company’s size. It’s generally measured by how much the Street values a company through its share price, a metric known as market capitalization (the price of a company’s shares times the number of shares). IBM is big: it has a market cap of about $150 billion. Krispy Kreme Doughnuts is small, with a market cap of about $150 million. Other factors, such as how many employees a company has and how profitable it is, also matter.

Value is generally determined by comparing a company’s share price to its book value, a measure of a firm’s net worth (assets, such as the buildings and/or machines it owns, minus liabilities, or debts).
Price-to-book is the favored metric of old-school investors such as Warren Buffett. The quants, however, use it in ways the Buffetts of the world never dreamed of (and would never have wanted to), plugging decades of data from the CRSP database into computers, pumping it through complex algorithms, and combing through the results like gold miners sifting for gleaming nuggets—flawed coins with hidden discrepancies.

Fama and French unearthed one of the biggest, shiniest nuggets of all. The family tree of “value” has two primary offspring: growth stocks and value stocks. Growth stocks are relatively pricey, indicating that investors love the company and have driven the shares higher. Value stocks have a low price-to-book value, indicating that they are relatively unloved on Wall Street. Value stocks, in other words, appear cheap.

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.

Intuitively, the idea makes a certain amount of sense. Imagine a neighborhood that enjoys two kinds of pizza—pepperoni and mushroom. For a time both pizzas are equally popular. But suddenly mushroom pizzas fall out of favor. More and more people are ordering pepperoni. The pizza man, noticing the change, boosts the price for his pepperoni pizzas and, hoping to encourage more people to buy his unloved mushroom pies, lowers the price. The price disparity eventually grows so wide that more people gravitate toward mushroom, leaving pepperoni behind. Eventually, mushroom pizzas start to gain in price, and pepperonis decline—just as Fama and French predicted.

Of course, it’s not always so simple. Sometimes the quality of the mushrooms are on the decline and the neighborhood has a good reason for disliking them, or the flavor of the pepperoni has suddenly improved. But the analysis showed that, according to the law of large numbers, over time value stocks (unloved mushrooms) tend to perform better than growth stocks (pricey pepperoni).

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. Large stocks, meanwhile, often suffer from too much love, like a celebrity with too many hit movies on the market, and are due for a fall.

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.

For the cloistered quant community, it was like Martin Luther nailing his Ninety-five Theses to the door of the Castle Church in Wittenberg, overturning centuries of tradition and belief. The Truth as they knew it—the holy CAPM—wasn’t the Truth at all. If Fama and French were right, there was a New Truth. Value and size were all that mattered.

Defenders of the Old Truth rallied to the cause. Fischer Black, by then a partner at Goldman Sachs, leveled the most damning blast, writing, “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.

Nevertheless, Fama and French’s New Truth began to take hold.

Aside from the theoretical bells and whistles of the paper, it had a crucial impact on the financial community: by bringing down the CAPM, Fama and French opened the floodgates for a massive wave of fresh research as finance geeks started to sift through the new sands for more gleaming golden nuggets. Cliff Asness was among the first in line.

In time, the findings had a more sinister effect. More and more quants crowded into the strategies unearthed by Fama and French and others, leading to an event the two professors could never have anticipated: one of the fastest, most brutal market meltdowns ever seen.

But that was years later.

One day
in 1990, Asness stepped into Fama’s office to talk about an idea for a Ph.D. dissertation. He was nervous, wracked by guilt. Fama
had given him the greatest honor any student at the University of Chicago’s economics department could hope for: he’d picked Asness to be his teaching assistant. (Ken French, Fama’s collaborator, also sang Asness’s praises. Fama and French were known to say that Asness was the smartest student they had ever seen at Chicago.) Asness felt he was double-crossing a man he’d come to worship as a hero.

The phenomenon Asness was considering as a dissertation topic flew in the face of Fama’s beloved efficient-market hypothesis. Combing through decades of data, 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.

Fama rubbed his cheek and nodded. Several seconds passed. He looked up at Asness, his massive forehead wrinkled in concentration.

“If it’s in the data,” he said, “write the paper.”

Asness was stunned and elated. Fama’s openness to whatever the data showed was a remarkable display of intellectual honesty, he felt.

He started crunching the numbers from Chicago’s extensive library of market data and noticed a variety of patterns showing long-and short-term momentum in stocks. At first Asness didn’t realize he’d made a profound discovery about hidden market patterns that he could exploit to make money. He was simply thrilled that he could write his dissertation and graduate. The money would come soon enough.

In 1992, as Asness buckled down on his dissertation on momentum, he received an offer to work in the fixed-income group at Goldman Sachs. A small but growing division at Goldman, called Goldman Sachs Asset Management, was reaching out to bright young academics
to build what would become one of the most formidable brain trusts on Wall Street.

Asness’s first real job at Goldman was building fixed-income models and trading mortgage-backed securities. Meanwhile, he spent nights and weekends toiling away at his dissertation and thinking hard about a choice he’d have to make: whether to stay in academia or pursue riches on Wall Street.

His decision was essentially made for him. In January 1992, he received a call from Pimco, the West Coast bond manager run by Bill Gross. A billionaire former blackjack card counter (in college he’d devoured
Beat the Dealer
and
Beat the Market)
, Gross religiously applied his gambling acumen to his investment decisions on a daily basis. Pimco had gotten hold of Asness’s first published research, “OAS Models, Expected Returns, and a Steep Yield Curve,” and was interested in recruiting him. Over the course of the year, Asness had several interviews with Pimco. In 1993, the company offered him a job building quantitative models and tools. It was an ideal position, Asness thought, combining the research side of academia with the applied rigor of Wall Street.

Goldman, upon learning about the offer, offered him a similar job at GSAM. Asness took it, reasoning that Goldman was closer to home in Roslyn Heights.

“So you’re taking the worse job because you’re a mama’s boy, huh?” his Pimco recruiter quipped.

Asness just laughed. He knew Goldman was the place for him. In 1994, soon after finishing his dissertation, Clifford Asness, Ph.D., launched the Quantitative Research Group at Goldman Sachs. He was twenty-eight years old.

WEINSTEIN