6:00 PM Seminar Begins

7:30 PM Reception

Hybrid Event

Fordham University

McNally Amphitheater

140 West 62nd Street

New York, NY 10023

Free Registration!

For Virtual Attendees: Please select virtual instead of member type upon registration.

Abstract:

We propose a data-driven dynamic factor framework where a response variable vector y(t) depends on a high-dimensional set of covariates x(t), without imposing any parametric model on the joint dynamics. Leveraging Anisotropic Diffusion Maps, a nonlinear manifold learning technique introduced by Singer & Coifman, our framework uncovers the joint dynamics of the covariates and responses in a purely data-driven way. We approximate the embedding dynamics using linear diffusions, and exploit Kalman filtering to predict the evolution of the covariates and response variables directly from the diffusion map embedding space. We generalize Singer’s convergence rate analysis of the graph Laplacian from the case of independent uniform samples on a compact manifold to the case of time series arising from Langevin diffusions in Euclidean space. Furthermore, we provide rigorous justification for our procedure by showing the robustness of approximations of the diffusion map coordinates by linear diffusions, and the convergence of ergodic averages under standard spectral assumptions on the underlying dynamics. We apply our method to the stress testing of equity portfolios using a combination of financial and macroeconomic factors from the Federal Reserve’s supervisory scenarios. We demonstrate that our data-driven stress testing method outperforms standard scenario analysis and Principal Component Analysis benchmarks through historical backtests spanning three major financial crises, achieving reductions in mean absolute error of up to 55% and 39% for scenario-based portfolio return prediction, respectively.

Bio:

 J. Antonio Sidaoui is a PhD candidate at the Department of Industrial Engineering & Operations Research at Columbia University. J. Antonio joined Columbia in 2023 after studying his MS in Statistics & Data Science at Yale University, and his undergraduate degrees in Statistics and Mathematical Economics at the Wharton School, University of Pennsylvania. J. Antonio's research focuses on the discovery and design of novel Machine Learning methodologies for financial applications, most recently he has worked on Graph Machine Learning for Asset Pricing and Manifold Learning for data-driven risk management.

Join the IAQF for virtual presentations and conversations with the winners of the IAQF Academic Paper Competition

Join the IAQF for virtual presentations and conversations with the winners of the IAQF Academic Case Competition

Tuesday, October 28th 2025

5:00pm - 6:15pm

Zoom Event

Registration is Complimentary for All

Moderator:

TBA

Schedule of Events:

• Introductions & presentation of problem.
• Questions to each of the teams.
• Following an overview of the problem that students were asked to work on, the moderator will turn it over to each of the Teams who will have 10 minutes to discuss how they came to their conclusion.
• This will be followed by a Q&A portion.

Winning Teams Are:

Team Sharpe Minds, University of California Berkeley, Master of Financial Engineering. The team was led by student team captain John Ryan. Team members included Fanghou He, Frank Wang, Jichu Han, Yingshi Li, and Benjamin Steel. The team worked under the direction of Kevin Coldiron.

Team Geeks for Greeks, University of California Los Angeles, Anderson School of Management, Master of Financial Engineering. The team was led by student team captain Matthew Atwell. Team members included Aryan Jain, Vikalp Thukral, Siddhant Kumar, Aditya Shankar, and Panagiotis Kargados Mallouchos. The team worked under the direction of Eric Reiner.

Team Psych, Columbia University, Mathematics of Finance Program. The team was led by student team captain Ruohao Yin. Team members included Thomas Cole, Paul Heyden, Alexander Pereyma, and Hua-Hsuan Shih. The team worked under the direction of Eric Yeh.

Team Violet Quants, New York University School of Engineering, Masters in Financial Engineering. The team was led by student team captain Harsh Kharabe. Team members included Shinjinee Maiti, Krish Kalyani, Vatsal Makodia, Pushkar Jain, and Arnav Sinha. The team worked under the direction of Jianing Yao.