B-School Master’s: Spotlight On Carnegie Mellon Tepper

Rick Bryant, executive director of the Carnegie Mellon MSCF

Rick Bryant, executive director of the Carnegie Mellon MSCF

That was more than two decades ago, in 1994, and the financial services industry has changed. “Back then the primary focus was on derivatives pricing,” Bryant says. “Exotics were coming into the fore, and term structures, the ability to build these math models and to develop proprietary research that the investment banks could then distribute as proprietary securities. Now a lot of this capability can be had off the shelf with the new derivatives software.”


Also, Bryant adds, since the financial crisis, interest among banks and investors in complicated structures has declined, while stricter regulation has imposed a need to develop capital structures that fit the regulatory regime. For that work, stochastic calculus remains essential. “What has changed is a lot more demand (exists) now from the hedge funds and proprietary trading shops for statistical analysis, the ability to parse large data sets, so certainly strong data skills . . . to find predictive signals, so using historical price patterns to try to essentially beat the market, get a sense for where future price movements will go, based on historical data.”

The financial crisis has also hugely increased demand for employees with risk-management expertise, Bryant says. “Risk management was not an area that was much considered before the financial crisis,” he says. In response, Tepper has added several risk-management courses to the computational finance degree program. Previously, the topic was covered in a few lectures, Bryant says, but now, “quite a bit of the curriculum is focused exclusively on risk management.”

Program administrators are revamping the asset management course to supplement the teaching of professors. “Some of our faculty don’t have hands-on Wall Street experience,” Bryant says. “We brought in some street-smart practitioners to help our faculty, who can certainly teach at a level of depth that would not normally be found with a practitioner.”

The administration is also examining the curriculum to see how to respond to demand among hedge funds and trading firms needing expertise in big data and statistical analysis, Bryant says.


The 16-month computational finance master’s is run jointly by Tepper and the Carnegie Mellon schools of mathematics and statistics, plus Heinz College, a Carnegie Mellon institution that combines the schools of public policy and management, and information systems and management. Tepper and the math school each provide about 30% of the curriculum, while statistics contributes 25%, and Heinz 15%. “It allows us depth in all four disciplines,” Bryant says. Students need to develop strong math, statistics, and business skills, and know how to code, Bryant says. “Those are four very different areas and you can’t expect a business school or a math department or a statistics department to have people on staff who can do all that.”

Of each class of 100 students, about half take the program at the Pittsburgh campus and half at the New York City campus.

Graduates of the full-time computational finance program make an average starting base salary of $92,000, and many students receive signing bonuses, Bryant says. Tepper tracks grads for six months, and 80% had accepted jobs within three months of graduation, while 94% had taken positions six months after finishing the program. “There are great jobs for strong quants on Wall Street, and Wall Street pays well,” Bryant says. “The hedge funds pay well. There’s a lot of competition now from the West Coast so the banks are … paying more (and) they’re actually giving them a few days off during the week; they’re giving thought to the fact that maybe they would like to get married and start a family. In part it’s a general recognition that the millennials’ outlook is different from some earlier generations but it’s largely a result of needing to compete with Silicon Valley.”


Financial technology or “fintech” firms in Silicon Valley are drawing serious quant talent, but Bryant believes the Valley’s luster will fade. “It won’t be long till some of these startups, it turns out that the equity wasn’t worth that much after all,” he says. “The pendulum does swing.”

About half the program’s grads go into the “sell side” of the financial services sector (where signing bonuses are common, as with hedge funds) and end up “working on the trading floors as desk quants hoping to have trading responsibilities down the road,” Bryant says. “They’re in the quantitative (areas) of the big banks.” About a quarter of graduates go into roles focused on risk management and analytics, supporting rather than trading at big banks and hedge funds, he says. About 20% go into portfolio management like Zhu, often in hedge funds, Bryant says. The remaining 5% spread out into companies such as Bloomberg, Moody’s, Numerics, Axioma, software companies, credit rating agencies, and Fannie Mae and Freddie Mac, he says.





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