Quants Move Beyond Model Building

Traditional quants still are involved in model creation, but they're also moving into quantitative trading, according to Joe Long, managing director in charge of front-office technology and quantitative recruitment at INET Technologies in New York. "A lot of the quantitative hedge funds are hiring Ph.D.s to trade," he says.

In general, the role of the quant on Wall Street is growing in importance, say industry sources. "It's been gradually increasing for some time now," says Peter Cotton, CEO of Julius Finance, a private research firm and development shop in New York, who previously worked at Morgan Stanley developing tools for pricing synthetic credit derivatives. Fortunately, "There's probably more supply of quants than there used to be," he adds.

The supply of quants is greater now than in the past because more masters in financial engineering (MFE) programs have proliferated at top universities such as the University of Chicago, M.I.T., Carnegie Mellon and the University of California at Berkeley, according to Lee Maclin, director of research at Pragma Financial Systems. Maclin teaches statistical trading and statistical arbitrage and algorithmic trading at the New York University Courant Institute of Mathematics, one of the top MFE programs in the Northeast. "There are courses springing up that deal with algorithmic trading, that review the background math and market microstructure math," he relates, noting that some of the top investment banks post advertisements for jobs in the hallways of these programs. "You see that their first priority is algorithmic trading," Maclin adds.

In fact, fueled by the shrinking order sizes and profit margins in U.S. equities, algorithmic trading is one of the hottest areas in which quants are working. "You can't afford to pay a person to put in every share because the trade size has gotten so small that even a moderate order needs hundreds of fills," explains Robert Almgren, head of quantitative strategies for equities at Banc of America Securities. Almgren, who has a Ph.D. in applied and computational mathematics from Princeton University, is a researcher and professor in mathematics and computer science who joined BAS in 2005 as a tenured associate professor in mathematics and computer science at the University of Toronto. He is in charge of algorithmic trading development at BAS.

People with quantitative skills are needed to develop new models, back-test the models and program the algorithms, according to Almgren. "A lot of that is dealing with technology -- you have to be able to implement your ideas and get the data out of the models," he says.

"Everything is data driven," Almgren continues. "You have to be able to work the databases and program the trading system. And ... you have to be smart enough to get the system to do the right thing from your quantitative analysis."

And the proliferation and increased frequency of data on The Street is further fueling the need for quants in algorithmic trading, notes John Comerford, EVP and global head of trading research at Instinet. For example, "Level 2 [i.e., Nasdaq depth of book] data is about 30 gigabytes a day," he says. "We're dealing with data that's closer to what they deal with in the biosciences and the genomes and not what people deal with in standard relational database technology."