The Rise and Fall of the Quants

Can Wall Street do without the technogeeks who designed its complex derivatives markets?

4 min read

When the U.S. financial system melted down, fingers were quickly pointed at the “quants”—the physicists, mathematicians, and engineers who had devised the computer programs, statistical tools, and financial instruments that were supposed to help investors manage risks. Critics said that it was the flawed assumptions of those financial models that brought banking to the brink of Armageddon.

You might guess that Wall Street is now shunning physicists, mathematicians, and engineers, but you’d be wrong. Talented people with quantitative backgrounds are more welcome than ever [see sidebar, "Why Be a Quant "], says Petter Kolm, deputy director of New York University’s master’s program for mathematics in finance and formerly a quant for Goldman Sachs. Before the financial mess started in the fall of 2007, he says, investment banks typically hired fresh MBA grads as entry-level analysts. “Many investment banks I talk to today say they want to replace that portion of people with MBAs with people who have quantitative analysis skills, such as engineering and math.”

The culture of Wall Street is already relying less on traditions and personal connections and more on complex technologies. Juhua Zhu, who has a Ph.D. in electrical engineering from Princeton and is now a vice president at Morgan Stanley, saw the change coming shortly after joining the firm four years ago. She says it was soon clear that “traditional trader jobs would vanish as many transactions would be transferred to computers.”

Those changes are carving out a need for more math whizzes and data crunchers. “Quantitative jobs demand research talent—people who can read any text in a technical field and reach a high level of expertise in a short amount of time,” says Alp Atici, a Columbia University math Ph.D. who works as a quantitative researcher at hedge fund Citadel Investment Group. ”People with Ph.D.’s in science and pure math are usually accustomed to much harder and deeper research texts.”

One of the first quants was Robert Merton, who started out as an applied mathematician at Caltech before switching to economics at MIT. In 1969 he tried to work out the pricing of stock options with the help of stochastic calculus—a branch of mathematics used to model random systems such as Brownian motion, the movement of particles in liquid. A high point for quants came when Merton and Myron Scholes won the 1997 Nobel Prize in Economics for their option-pricing model. (When it comes to low points, Merton, the quant trendsetter in so many ways, was ahead of his time again. He and Scholes were members of hedge fund Long-Term Capital Management’s board of directors when that company lost more than US $4 billion in 1998; the Federal Reserve, fearing a liquidity crisis, put together a restructuring plan that presaged its aggressive liquidity interventions in 2008.)

The trickle of physicists and mathematicians eschewing low-paying academic jobs in favor of Wall Street bonuses turned into a flood in the Reagan years. The 1980s fads for junk bonds and leveraged buyouts came and went, but the quants stayed. Their direct role in today’s recession started in the booming 1990s housing market as they helped banks package mortgages, credit cards, and other credit assets, slice up the packages, and sell them as instruments known as asset-backed securities. That took risky assets off companies’ balance sheets, freed up capital, and let the companies borrow more money. Quants are credited with creating models that helped investors understand, manage, and price the risk associated with these securities.

Asset-backed securities are a great innovation if used sparingly. But this did not happen. Emanuel Derman, industrial engineering and operations research professor at Columbia and author of the memoir My Life as a Quant: Reflections on Physics and Finance, says banks “got too big for their boots and borrowed too much money.” The quants’ mathematical machinations didn’t so much dilute risk as hide it. And then came the breaking point, one that quantitative models did not take into account: record numbers of subprime borrowers defaulting on their mortgages.

So was this a lack of foresight on the quants’ part? The simple truth is, there is no one right model, Derman says. Inputs to financial models are related to how people will behave in the future. But patterns of behavior change over time.

“If you’re designing a global positioning system, the distance from here to Waterloo doesn’t change,” he says. ”Financial engineering isn’t based on financial science. It’s scientific methods applied to human variables.”

But why didn’t quants raise the alarm about risks associated with their innovations? NYU’s Kolm says that business decision makers, not quants, were calling the shots and did not always care about the risks. At a major investment bank, he says, risk managers who tried to warn about risks were unpopular: ”Either you played along or you left because you were the bad guy at the party.” It’s also becoming clear that mortgage fraud played a big role in distorting the data that the models used to predict foreclosures—a case of garbage in, garbage out.

Despite the role that physicists and engineers played in the economic crisis, the relationship doesn’t seem to have soured. Quants aren’t being recruited less or fired more—though there are fewer jobs overall as some companies, like Lehman Brothers, disappear, and others, like Bank of America and Merrill Lynch, merge. Beverly Principal, assistant director of employment services at Stanford, says that finance companies are still coming to campus, while a large number of students are still interested in financial careers. Career service representatives at other schools echo this sentiment.

Companies have started to be selective, and people with advanced degrees have the upper hand. Credit Suisse Securities has spent the past year trying to attract people with doctorates in physics, math, and engineering from top schools, says Ilias Tagkopoulos, who got his Ph.D. in EE from Princeton last summer. A trifecta of skills—programming, mathematical proficiency, and an ability to communicate—led to his job at Credit Suisse as a relationship manager. Most members of his team are also Ph.D.’s from top schools, he says.

The lack of jobs, though, has started to make itself felt. Ming Zhong, a portfolio manager at Lazard Asset Management, an investment bank in New York City, says that when he graduated from Columbia with a master’s in EE in 2004, 70 percent of the jobs available through the campus-recruiting program were finance related. Now, he says, graduates are having a hard time finding internships, even unpaid ones. “One year ago, I’d say keep trying, always have hope. Six months ago, I suggested students look around. In the spring, I told them, ’You should think about changing careers.’ ”

But things won’t always be so bleak. When the economic pendulum eventually swings back, predicts Morgan Stanley’s Zhu, “the whole pie will have shrunk, but the portion of jobs for people with a technical background will have continued to grow.”

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