Quant Guide 2019: Fordham University

Gabelli School of Business, New York City, New York, US

MSc in Quantitative Finance | metrics table at end of article

Ranked 14th in the Quant Finance Master’s Guide 2019

Quant finance master’s graduates expect to be stretched during the course of their studies. At Fordham University, this process starts as early as the interview, which the programme director, Sudip Gupta, admits is gruelling.

It’s a necessary step, he says, to differentiate between candidates of similarly high quality.

“Everyone has an extremely high grade point average,” he says. “You can only make distinctions once you start talking to people, and we ask very serious quant questions.”

Gupta and his academic faculty personally interview around 300 applicants every year, in contrast to many other institutions where candidates are vetted by the university admissions department. The procedure is designed to evaluate weaknesses in highly technical topics on a level traditional admissions staff often can’t.

One advantage of this approach, Gupta says, is that new students should, in theory, already be familiar to their instructors once they appear on campus. Another is that it enables staff to devise personalised pre-master’s courses for students to take over the summer, before the official start of the programme.

“We have some on finance, some on maths, some on stats, programming languages,” says Gupta. “Some students who are really good in finance might not have much of a programming background and so on. By the time they arrive in the fall, they should all be at par with each other.”

Once the playing field is levelled, recruits dive headlong into mathematics, statistics and finance courses. The programme aims to strike a balance between forward-looking, cutting-edge elements and traditional competencies. Financial regulation may lack the supposed glamour of, say, artificial intelligence, but it is still a necessary part of a quant’s training.

“We have three elective courses that deal with regulation,” says Gupta. “Risk management, one specifically on financial regulation, and then it’s also covered in our credit risk management class, which is taught by a JP Morgan managing director.”

Other modules on offer include courses in machine learning and econometrics, blockchain application in finance, the Python and R languages, algorithmic trading and big data analytics.

For now, the more advanced material is in the elective realm, but Gupta aims to change this by the time the next cohort arrives in the autumn of 2019.

“I want AI, fintech, machine learning and blockchain content as standard,” he says.

In explaining the importance of fintech to the future of the financial sector, Gupta uses an analogy of the physical process of running. “If you want to run, you need a few things: a good heart, good muscles, good endurance and good shoes. We’re starting to see now that blockchain is going to be the muscles and machine learning is going to be the heart.”

Maintaining the relevance of the programme is a priority for Gupta. Fordham helps achieve this through the programme’s board members, who meet with and advise faculty on a regular basis. Access to industrial expertise of this kind is helpful when it comes to placing students for internships and projects, Gupta adds.

“We’ve just placed two students in fintech companies that do text mining,” he says.

Most students complete the degree in 18 to 24 months. Some students are able to do a 12-month version of the programme. During the internships, which start in May, some students elect to work full-time during the day and return to campus in the evenings for additional classes.

The majority of the programme’s graduates go into finance, but students are increasingly drawn towards the tech sector, says Gupta: “Google, Amazon, Apple and Microsoft – these are companies where students are getting a lot of jobs these days. Some of our students prefer fintechs and start-ups. They believe experience [in those companies] will give them a head start in future.”

View other universities and a guide to the metrics tables