Quant Guide 2020: Princeton University

Princeton, New Jersey, US

QUANT 31-princeton_CampLife_02-web.jpg
Photo: © Princeton University, Office of Communications

Princeton University’s Master in Finance tops Risk.net’s quant guide rankings for the second year in a row – the programme boasts a 100% employment rate in financial services for its graduates in the last four years, and an average basic compensation of $120,000. Based at the Bendheim Center for Finance, it is led by professor René Carmona.

The two-year, four-semester programme looks to keep pace with the changing demands of industry: besides the usual core and elective courses and a research project, Master of Finance students can earn a recently introduced machine learning certificate from Princeton’s Center for Statistics and Machine Learning. Requirements for the certificate include the completion of machine learning, statistics and probability courses, plus a graduate seminar that does not contribute credits towards the master’s degree itself.

Carmona says several new staff have joined the programme: Mete Soner, who lectures on stochastic control and financial mathematics; Matias Cattaneo, who teaches regression and time-series analysis; and Michael Junho Lee, a visiting lecturer in finance from the Federal Reserve Bank of New York. Financial engineering professor John Mulvey will be co-teaching an online course on Python and machine learning in asset management on e-learning platform Coursera.

Single-semester master’s projects in machine learning are also proving popular; most students, Carmona says, are choosing to complete such work as a part of the optional certificate. Besides that, Master of Finance candidates are showing interest in the computational finance, C++ and high-frequency markets modules.

View this institution’s entry in the 2019 guide

View other universities and a guide to the metrics tables

Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.

To access these options, along with all other subscription benefits, please contact info@risk.net or view our subscription options here: http://subscriptions.risk.net/subscribe

You are currently unable to copy this content. Please contact info@risk.net to find out more.

Most read articles loading...

You need to sign in to use this feature. If you don’t have a Risk.net account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here