The Quants Part 4

He was also becoming drawn into a new hobby: poker. He started haunting the Oaks Card Room in Emeryville, a twenty-minute ride from BARRA's office. He devoured poker strategy books and was soon cleaning up at the Oaks' high-stakes tables.

Gambling turned into an obsession. Muller would spend ten to fifteen hours a week playing cards at the Oaks. Sometimes he dove into marathon sessions that tested his endurance. Once he started playing at 6:00 P.M P.M. after work on a Friday and didn't stop until 10:00 A.M A.M. that Sunday. Driving home, he was so exhausted that he fell asleep at a stoplight.

In 1989, Muller got an a.s.signment to do some work for a new BARRA client, a hedge fund operator called Renaissance Technologies. Jim Simons was looking for expert help to solve a th.o.r.n.y problem he faced with one of his funds named Medallion.

The problem involved the most efficient use of Medallion's spare cash. Muller's solution was so clever that Renaissance offered him a job. But he was skeptical and turned down the offer. Still under the spell of academia, he believed in Fama's efficient-markets hypothesis and the mounds of research that claimed it's not possible to beat the market over the long haul.

He soon changed his mind about that.

By 1991, Muller was pulling down a hundred grand a year. He lived in a beautiful house in the Berkeley Hills with his girlfriend and had a great job with enough spare time for his jazz band, gobs of poker, and surfing on the side. But he wanted more. 1991, Muller was pulling down a hundred grand a year. He lived in a beautiful house in the Berkeley Hills with his girlfriend and had a great job with enough spare time for his jazz band, gobs of poker, and surfing on the side. But he wanted more.

That year, BARRA went public. To Muller, the company seemed different after the IPO, less hungry, less energetic, less creative. A number of employees, good ones, left for other companies or to work on their own projects. Muller had an idea he thought could breathe new life into BARRA: use the quant.i.tative models it developed for clients to manage its own money. In other words, set up an internal BARRA hedge fund. He had just the right people to run it, too: his poker buddies from the Oaks, all BARRA employees.

The firm's higher-ups scotched the plan. It wasn't a good idea to launch a risky operation so soon after the IPO, they said. Andrew Rudd, BARRA's CEO, suggested Muller create new models to forecast returns for stocks and sell them to clients. It wasn't quite what Muller had in mind, but he agreed. In short order, he helped design BARRA's best-selling Alphabuilder system, a PC-based software program that could a.n.a.lyze expected returns for stock portfolios.

Then he quit.

"Who the f.u.c.k are you, and why the f.u.c.k do you get an office?" f.u.c.k are you, and why the f.u.c.k do you get an office?"

"I'm f.u.c.king Peter Muller, and I'm f.u.c.king pleased to meet you."

Muller stared bullets at the wisea.s.s Morgan Stanley salesman who'd barged into his office as though he owned it. Muller had only recently begun setting up a quant.i.tative trading group at Morgan, and this was the reception he got?

It had been like this since the day he arrived at the bank. After accepting a job at Morgan, and with it an incredible increase in pay, he'd given notice at BARRA and taken six weeks of R&R, spending most of it in Kauai, the lush, westernmost island of Hawaii. The transition from the placid green gardens of Kauai to the rock-'em-sock-'em trading floor of Morgan Stanley in midtown Manhattan had been a rude shock. Muller had been promised his own office and a battery of data sources before his arrival, but on his first day at the bank he saw that none of his requests had been met.

Until the promised office arrived, he'd found a seat at a desk in the middle of Morgan's football field of a trading floor and called a former BARRA colleague, Tom Cooper, who was working at a hedge fund in Boston.

"How can you work in an environment like this?" he asked.

Suddenly a woman sitting next to him grabbed the receiver from his hand. "I need that phone!"

Muller stared back in shock as she barked out trades that involved markets in Chicago and Tokyo. Politeness wasn't an option when money was on the line, Muller was learning. BARRA and its quaint quant models suddenly seemed a world away.

A friend sent Muller congratulatory flowers for his new job. The bouquet was delivered to his desk on the trading floor. It was raw meat to the grizzled traders around him: Look at the California quant boy and his pretty flowers Look at the California quant boy and his pretty flowers. What had he gotten into? Muller wondered. The energy was maddening. Everyone was packed like sardines, shouting, sweating-and wearing suits!

This wasn't California. This certainly wasn't Berkeley. This was New York f.u.c.king City, this was Morgan f.u.c.king Stanley, one of the biggest, most aggressive investment banks in the world, and Muller was right in the boiling heart of it.

[image]ASNESS[image]

The muscular professor strode to the podium and faced yet another roomful of bright-eyed students eager to learn the secrets of how the stock market really worked. The professor, Eugene Fama, had been teaching at the University of Chicago since the early 1960s. Now, in September 1989, he was universally acknowledged as one of the brightest thinkers about financial markets and economics on the planet. Fama ran a hand across his balding head and squinted at the gangly sprawl of twentysomethings before him.

One trait about Fama that immediately jumped out to new students was his forehead. It was unusually large, high, and wide, traced with a stack of deep-cut lines that undulated like waves as he barked out wisdom about the markets in his Boston brogue as if agitated by the powerful thoughts percolating in his basketball-sized cranium. Clad in a loose-fitting blue cotton b.u.t.ton-down shirt and tan chinos, he seemed more a refugee from the school's philosophy department than a tough-minded guru of the money set.

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 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 G.o.d in heaven pa.s.sed down through his economic prophet of the Windy City.

"There are a number of consequences to market efficiency," Fama said, facing the cla.s.sroom. "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 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 s.p.a.ce 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. G.o.d knows the market is efficient."

The cla.s.sroom 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 freak geniuses ... mathematical models ... None of this was new to him; he'd taken finance cla.s.ses 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. ... None of this was new to him; he'd taken finance cla.s.ses 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? 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 tyc.o.o.n. 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 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 a.s.sumed he'd follow in the footsteps of his father, a trial lawyer. He wasn't sure why 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. 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 a.s.sistant 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 (p.r.o.nounced "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 quant.i.tative 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 protege 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 cla.s.s, 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 n.o.bel 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 n.o.bel 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 5050 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 a.n.a.lyzed 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 bombsh.e.l.l. 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 eminence 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 d.a.m.n 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 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 a.s.sault 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 a.r.s.enal: The probabilities of various movements of an efficient market could be understood through the math sp.a.w.ned 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 sp.a.w.ned by the quants.

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 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. 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 quant.i.tative 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 a.s.set pricing model, otherwise known as CAPM. 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 a.s.set pricing model, otherwise known as CAPM.

Before Fama and French, CAPM was the closest approximation to the Truth in quant.i.tative 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 c.o.c.ktail 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 (a.s.sets, 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 a.n.a.lysis 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 d.a.m.ning 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 ma.s.sive 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 antic.i.p.ated: 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 a.s.sistant. (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. 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 a.s.sistant. (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 pa.s.sed. He looked up at Asness, his ma.s.sive 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 a.s.set 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 Beat the Dealer and and Beat the Market) Beat the Market), Gross religiously applied his gambling ac.u.men 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 quant.i.tative models and tools. It was an ideal position, Asness thought, combining the research side of academia with the applied rigor of Wall Street.