Overview
Please join us for the September installment of the Bloomberg Quant (BBQ) Seminar Series – in-person in New York City at Bloomberg’s 731 Lexington Avenue office.
In this seminar chaired by Bruno Dupire, Agostino Capponi will present the keynote, followed by “lightning talks” of 5 minutes each in quick succession.
Please note: Seating is limited and no walk-ins will be allowed. Please register early to reserve your seat.
Agenda
- 5:00 PM
Registration
- 5:30 PM
Keynote: Agostino Capponi | Columbia University
When More Data Hurts: Navigating the Limits of AI and Market Nonstationarity
Does more training data always improve return forecasts? In non-stationary markets, longer windows expose models to structural change, while complex models that mitigate misspecification demand precisely such windows. We formalize this nonstationarity-complexity tradeoff: the excess risk of a model class decomposes into a misspecification term, an estimation-variance term governed by the class’s Rademacher complexity, and a drift term that grows with the training window under non-stationarity. We resolve it with a tournament-based selection procedure that adaptively chooses the optimal model class and training window using non-stationary validation data. We develop an oracle-type inequality guaranteeing performance close to the best model class-window pair in hindsight, uniformly over the unknown drift path. Applied to 17 industry portfolios, the method improves out-of-sample R^2 by 14–23% over rolling-window and regime-switching benchmarks, with gains concentrated in recessions.- 6:30 PM
Lightning Talks
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- Francesco Tonin | Bloomberg
Using Bell Theorem to Unmask “Lucky” Portfolio Managers - Jessica Mitra | EDHEC Business School
Does Accounting for Higher Moments Help Characteristic-Based Factors to Span the Stochastic Discount Factor? - Milind Sharma | QuantZ Capital / QMIT
The Quantamental Revolution - Xiaotong Hu | John Hopkins University
Synthetic Data for Time Series Foundation Models - Sasha Stoikov | Cornell University
Finding Good Wines on the Cheap
- Francesco Tonin | Bloomberg
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- 7:00 PM
Networking Reception
Speakers
Bruno Dupire
Bruno Dupire is the Global Head of Quantitative Research, CTO Office at Bloomberg, which he joined in 2004. Prior to this assignment in New York, he has headed the Derivatives Research teams at Société Générale, Paribas Capital Markets and Nikko Financial Products where he was a Managing Director. He is best known for having pioneered the widely used Local Volatility model (simplest extension of the Black-Scholes-Merton model to fit all option prices) in 1993 and the Functional Itô Calculus (framework for path dependency) in 2009. He is a Fellow and Adjunct Professor at NYU and he is in the Risk magazine “Hall of Fame”. He is the recipient of the 2006 “Cutting edge research” award of Wilmott Magazine and of the Risk Magazine “Lifetime Achievement” award for 2008. He was also selected as the recipient of the 2025 Financial Engineer of the Year Award by the International Association for Quantitative Finance (IAQF) . He runs and organizes the Bloomberg Quant (BBQ) Seminar, the largest monthly event of this kind.
Agostino Capponi
Agostino Capponi is a Professor in the Department of Industrial Engineering and Operations Research at Columbia University, where he is also a member of the Data Science Institute and the founding director of the Columbia Center for Digital Finance and Technology. His current research interests are in decentralized finance, machine learning in finance, market microstructure, systemic and liquidity risk, climate finance, and financial networks. Agostino’s research has been recognized with the 2018 NSF CAREER award, a JP Morgan AI Research Faculty award, the UBRI Innovator award, and the Presidential Early Career Award for Scientists and Engineers award. His research has also been covered by various media outlets, including Bloomberg, the Financial Times, Vox, and Politico. Agostino is a fellow of the crypto and blockchain economics research forum, and an academic fellow of Alibaba’s Luohan academy. Agostino is co-editor of the book Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices, published in 2023 by the Cambridge University press.