In just a few years, BARRA developed a cultlike following. Rosenberg scored a hit with the company’s Fundamental Risk Management Service, a computerized program that could forecast a stock’s behavior based on categories such as earnings, industry, market capitalization, and trading activity.
By the time Muller arrived at BARRA, thousands of managers were running money using the newfangled quantitative strategies. Rosenberg himself left BARRA in 1985, soon after Muller was hired, with a small group of colleagues, to start his own money management firm, Rosenberg Institutional Equity Management, in Orinda, California. Within a few years it was managing several billion dollars in markets around the world. (More recently, Rosenberg has drifted away from the worldly pursuit of riches and has been teaching courses on Buddhism for the Nyingma Institute in Berkeley.)
One of the first projects Muller worked on at BARRA concerned an analysis of the various components of stock returns, the bread and butter of BARRA’s factor models. Just before he left, Rosenberg took a look at Muller’s work and demonstrated his ability to see the push and pull of economic forces at work in the market.
“This factor must be oil prices,” he said. “Look at the spike during the energy crisis. … And this one must be related to interest rates.”
One problem: Muller had screwed up the math and the data were bunk. He reworked the analysis and sheepishly showed the results to Rosenberg.
“This makes much more sense,” Rosenberg said. “This factor must be the oil factor. … And here’s where the Fed came in and tightened.”
While this showed that Rosenberg could quickly convert math and models into real-world events, it also demonstrated that the models could fool the best in the business. Even with all the high-powered math, there always seemed to be a bit of the witch doctor in Rosenberg and the quant methods he spawned. The constant search for hidden factors in market prices could turn into a voodoolike hunt for prophecies in chicken entrails, dark portents in cloud shapes.
The relaxing, sun-splashed atmosphere of BARRA was something of a revelation to Muller after the do-nothing burbs of Jersey and the cloistered corridors of Princeton. It was the mid-1980s. Nostalgia for the sixties was on the rise. And there were few better places to catch that wave than Berkeley, a short hop to the surfer hangouts at Half Moon Bay and the hippie haven of Haight-Ashbury. Of course, working for a financial research outfit didn’t exactly fit the classic hippie mold, but Muller was fine with that. He’d had enough of scrounging for money, playing music for peanuts. The $33,000-a-year salary he was making at BARRA was a boon, and there was certainly more to come. Most of all, he was determined that however much money he made, he wouldn’t turn into Ebenezer Scrooge. Rosenberg had already set an example that one could make buckets of money and still retain a sense of spirituality.
And life was good at BARRA. The casual atmosphere. The go-easy dress code. The only guy seen in a suit was the company’s marketing chief. Employees would take long lunches to talk about academic theory, politics, world events. Muller had a girlfriend and was playing part-time in a jazz band. Once a month, a group of employees would take late-night runs under a full moon, followed by a trip to a bar or, even better, an ice cream parlor.
Muller quickly learned Fortran and worked on fixing code for the company, but he was itching to learn more about the real work going on at BARRA: financial modeling. He put aside his music and buried himself in the literature of modern portfolio theory: Eugene Fama, Fischer Black, Robert Merton, the classics.
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. after work on a Friday and didn’t stop until 10:00 A.M. that Sunday. Driving home, he was so exhausted that he fell asleep at a stoplight.
In 1989, Muller got an assignment 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 thorny 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.
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 quantitative 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 analyze expected returns for stock portfolios.
Then he quit.
“Who the fuck are you, and why the fuck do you get an office?”
“I’m fucking Peter Muller, and I’m fucking pleased to meet you.”
Muller stared bullets at the wiseass Morgan Stanley salesman who’d barged into his office as though he owned it. Muller had only recently begun setting up a quantitative 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. 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 fucking City, this was Morgan fucking Stanley, one of the biggest, most aggressive investment banks in the world, and Muller was right in the boi
ling heart of it.
ASNESS
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 button-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.
“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 ship
yards 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.
The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It Page 9