Quantcast Master’s Series: Petter Kolm, Courant Institute

The NYU programme is taught almost exclusively by elite financial industry practitioners

For the third episode of the Quantcast Master’s Series – part of Risk.net’s Tomorrow’s Quants project – we speak to Petter Kolm, director of the Master’s in Mathematics and Finance at the Courant Institute of Mathematical Sciences of New York University.

Kolm, along with research partner Nicholas Westray of Point72, is the newly anointed buy-side quant of the year in the Risk Awards 2026. He has been director of Courant’s master’s programme since 2007 and serves as a board member or external adviser to several financial firms, giving him a broad perspective on the education students need to start a career in quantitative finance.

This perspective is characteristic of the way Courant’s programme is organised: its lecturers are almost all affiliated to the industry, covering senior positions in financial institutions. They include quants of the stature of Leif Andersen, global head of quant analytics at Bank of America, and Bruno Dupire, head of quant research at Bloomberg.

Quantcast – a Risk.net Cutting Edge podcast · Petter Kolm 27/11/25 Risk Quantcast_MS

In fact, the banking sector connection was the main driver for the launch of the master’s programme in 1999. The programme’s founders had noticed a number of besuited students were attending certain of Courant’s mathematics classes in the mid-to-late ’90s and ultimately discovered they were Wall Street analysts, strengthening their knowledge of stochastic calculus, probability theory and the use of the Black-Scholes model to price options. Courant responded to the demand by designing a master’s programme that would fulfil the industry’s needs.

For several years, derivative price modelling and risk management were central to the curriculum. But, after the global financial crisis of 2008 and subsequent shifts in market dynamics, the programme’s leaders adapted its focus: “We no longer put a heavy emphasis on structured products or exotic products,” says Kolm. “It’s about automation, building algorithms to price and trade automatically, manage trade execution [and] manage transaction costs.”

Related

Deep learning alpha signals from limit order books: practical insights and lessons learned 

An analysis on network architectures applied to limit order book data is presented

The mathematical knowledge necessary to create all this is paired with the need to be able to code, predominantly in Python, Kolm says.

To put the more theoretical part of the curriculum to work, students take a capstone project and an internship before the final semester. These are key elements of the programme, giving students first-hand experience of teamwork and introducing them to real-world finance.

The conversation moves onto Kolm’s own research, in which he is prolific. In this, Kolm primarily explores trading and portfolio management, including the integration of machine learning techniques in real-world financial applications. Notably, Kolm and Westray have co-authored a paper examining the use of deep learning to derive alpha signals from limit order books. Their findings, which led to the 2026 Risk award, highlight the surprising efficacy of long short-term memory (LSTM) neural networks in predicting stock price movements from order book data – which suggests that, despite the emergence of more complex models, the robustness of LSTMs persists.

To hear the full interview, listen in the player above, or download. Future podcasts in our Quantcast series will be uploaded to Risk.net. You can also visit the main page here to access all tracks, or go to Spotify, Amazon Music or the iTunes store to listen and subscribe.

Find all the content of the Tomorrow’s Quants project here