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As Wall Street clamors to attract the best and brightest students with a knack for math and a mind for analytical modeling, many graduate students who typically would continue in academia and work toward their Ph.D.s now are opting for a different route to the Street. Master in financial engineering programs, or M.F.E.s (or any other acronym that represents a master's degree with a focus on finance-geared mathematics), are becoming hotbeds for firms looking to recruit quantitative analysts.
M.F.E. students typically have entered programs immediately after earning their undergraduate degrees or after a short stint in the professional ranks. They generally come from a heavy math or physics background but have decided to forgo the traditional academic track to seek a career on Wall Street.
Quantifying a Qualified Quant
While it's difficult to define exactly what makes a good quant, the consensus seems to be that the best candidates are people with multidisciplinary backgrounds. This means an education combining mathematics, finance, computer science and statistics, according to Emanuel Derman, director of the master's program in financial engineering at Columbia University, head of risk management at hedge fund Prisma Capital Partners and author of "My Life as a Quant: Reflections on Physics and Finance."
Derman says he looks to hire quants who have learned the basics in areas such as modeling but who also understand why the model behaves the way it does and which factors are driving it in the market. "I don't just want people who can solve equations," he says. "As in physics or in any natural science, it's important to understand what the math is telling you -- not just the equation or solution."
Ultimately, Derman says, M.F.E. and similar programs are designed to prepare students for a career in financial engineering on Wall Street. The students graduate and go on to jobs across the spectrum at buy- and sell-side firms, as well as other financial services firms. "You can't learn everything in school -- a lot of the schoolwork is theoretical," Derman acknowledges. "So students go through the program and then get out and get experience in the real financial world."
Steven Janowsky, head of financial engineering at FX Solutions, agrees that the knowledge necessary to succeed on Wall Street goes beyond course work. He says he typically looks for "people who can learn." According to Janowsky, if a candidate comes out of a quality M.F.E. program, generally he or she is smart; but, Janowsky adds, he wants employees who can adapt to what they are working on.
"When I'm interviewing, I try to find out something they don't know and present it in a way they can hopefully figure out from the discussion we're having," says Janowsky. "There generally ends up being some complicating factors in developing any real model, so I see if they can at least think through different impacts of that."
As an example, Janowsky says, his quants typically wouldn't be coming up with new solutions to problems, but rather would be starting with a solution and seeing if they can improve upon it. "Someone might already have a model, but it doesn't deal with holidays, so we'd want them to incorporate holidays," he explains. "We need them to be aware of different types of things that can go wrong."
Janowsky adds that finding out how a candidate would deal with adversity is also a key factor in his hiring process. "See how people react to things being pulled out from under them," he suggests. "Some start on a problem and want to keep doing it the way they've done it originally. But change is the nature of the business, and they need to be able to adapt."
Ian Domowitz, managing director of the analytical products and research group at ITG, is looking for students with more of a focus on the computational and statistics sides. "Our emphasis is not on pricing or valuation models," he relates. "So the research we do is heavily empirical and requires statistical and mathematical sophistication."
Domowitz says he also looks for a programming requirement when recruiting from M.F.E. programs. "I think it's a differentiating factor," he comments.
Leo Murphy, University Relations Program manager at Trading Technologies, agrees that programming skills are vital for a good quant. But, he says, most of the students at the top M.F.E. programs already meet the programming requirements; if they don't, they're smart enough to learn the necessary skills quickly.
Murphy puts an emphasis on the mathematical skills for M.F.E. students. "Wall Street really values these students," he says. "The salaries these graduates are making are pretty significant."
Beyond hiring the obvious smart people, Robert Almgren, cofounder of Quantitative Brokers and adjunct faculty member at New York University Courant Institute's Mathematics in Finance program, says he values the common vocabulary that students coming out of M.F.E. programs acquire. He says M.F.E. students typically come from a math background and can learn the finance side.
"It's more difficult to take someone who had finance experience and was short on math and teach them math," Almgren asserts. "If the students don't gravitate toward math, sciences, physics or engineering as [undergraduate freshmen], they can't really make it up."
