May 17, 2012

The Quant School Curriculum

The students who do manage to clear the acceptance hurdle and find their way into one of the nation's top financial engineering programs aren't learning how to do their fathers' stock trading. Instead, they're studying at the finance world's answer to Hogwarts, learning the sort of financial wizardry that would make a layman's head spin.

Columbia's program lasts a full academic year, and the first semester is intense, the university's Derman says. Right from the beginning Columbia's M.F.E. students learn everything from data analysis to Monte Carlo simulations (a problem-solving technique to help calculate the likelihood of certain outcomes) to the stochastic process, which is designed to provide a deeper understanding of probability and the role it plays in where the market is heading, along with the reasons behind market fluctuations.

Once students make it through the first portion of the program, the requirements become much more elective and students venture into areas in which they have the most interest, Derman explains. "In the beginning they do core stuff, which is corporate economics and applications programming, because more and more, what's important for getting jobs is being able to program in an efficient way," he says. "The programming is so important because so much of trading is now taking place through computer programs."

New York University's program, in which students pursue a Master of Science of Mathematics in Finance (M.S.M.F.), takes a track that's similar to Columbia's. After finishing core courses that are designed to beef up their calculus, statistics and programming abilities, NYU students can take electives, such as algorithmic trading in quantitative strategy. Courses also are available in subjects such as mortgage-backed securities, energy derivatives and statistical arbitrage strategies.

NYU's Kolm concedes that it can be hard to distinguish the top quant programs from each other, since they all have gotten down to a science what the courses should look like and what employers are looking for. But in addition to the complex subject matter, he notes, these schools all teach students how to get a job after they graduate.

"We spend a lot of time at NYU teaching these young people — many of whom have never worked before — job searching skills, from resume writing to networking," Kolm says. "We teach them how to master the interviews as well — the quantitative interviews."

Putting Quants to Work

Once they've completed their respective quant programs, students are ready to hit the Street, and they typically have choices, ranging from algorithmic trading to risk management.

Rutgers' Longo breaks down traders in today's marketplace into two camps: traditional traders whose skills are more intuitive and relationship-oriented; and program traders, who are now responsible for the lion's share of the volume that's transacted on a daily basis. And since the prices of stocks and other assets now are often fragmented across a wide array of venues -- from exchanges to electronic communication networks (ECN) and dark pools -- firms place a premium on quants who are able to help their trading desks source the best, or truest liquidity, he says.

Of the quant grads who wind up working for traditional asset management firms, Longo says they usually end up in the risk management department. They may even find themselves working on enhanced index teams where the goal often is to try to outperform the market, but also to limit benchmark risk or tracking errors to a certain percentage.

"A lot of quants also end up in the fixed-income area, even convertible bonds," Longo contends. "In the hedge fund space, if it's a quant shop like James Simons' firm Renaissance, they prefer people with strong quant skills, and they primarily hire a lot of people with Ph.D.s in math or physics."

Buy-firms also turn to quants for their abilities to analyze massive amounts of market data, identify any market inefficiencies they can find and then build models to make money on those inefficiencies, says Anthony Dostellio, a recruiter at the executive search firm Objective Paradigm. "From an analysis standpoint, they're examining statistical data and either developing or helping to develop algorithms, all with the purpose of predicting future market movement," Dostellio says.

Over the next few years, Columbia's Derman adds, the roles of the trader and computer programmer will continue to converge. But even as the technology becomes more sophisticated, the role of human intuition in the trading process isn't likely to vanish anytime soon, he suggests. "The big fraction of the trading on the New York Stock Exchange is algorithmic, and to some extent it does become a battle of the programs. Even the market making — there's very little open outcry," Derman explains.

"But good traders are still going to need some combination of technical skills and have a good gut understanding of how to trade, not just relying on a program," he emphasizes. "They're going to have to build sensible behavior into their programs."