In the Fall semester, the seminar takes place on Wednesdays 6:10 pm — 7:25 pm.
Location: TBA
For directions, please see Directions to Campus and Morningside Campus Map.
Organizer: Jaehyuk Choi
Schedule of Presentations
Scroll down for the Schedule of Past Presentations.
Wednesday, September 3, 2025
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Speaker: Nathaniel Powell, Deep Market Making Inc.
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Wednesday, September 10, 2025
Title: Marketron Games: Self-Propelling Stocks vs Dumb Money
Speaker: Igor Halperin, Fidelity Investments
Igor Halperin is an AI researcher and a Group Data Science leader at Fidelity Investments. His research focuses on using methods of reinforcement learning, information theory, and physics for financial problems such as portfolio optimization, dynamic risk management, and inference of sequential decision-making processes of financial agents. Igor has an extensive industrial and academic experience in statistical and financial modeling, in particular in the areas of option pricing, credit portfolio risk modeling, and portfolio optimization. Prior to joining Fidelity, Igor worked as a Research Professor of Financial Machine Learning at NYU Tandon School of Engineering. Before that, Igor was an Executive Director of Quantitative Research at JPMorgan, and a quantitative researcher at Bloomberg LP. Igor has published numerous articles in finance and physics journals, and is a frequent speaker at financial conferences. He has co-authored the book “Machine Learning in Finance: From Theory to Practice” (Springer 2020), and contributed to the book “Credit Risk Frontiers” (Bloomberg LP, 2012). Igor has a Ph.D. in theoretical high energy physics from Tel Aviv University, and a M.Sc. in nuclear physics from St. Petersburg State Technical University. In February 2022, Igor was named the Buy-Side Quant of the Year by RISK magazine.
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We present a model of price formation in an inelastic market whose dynamics are partially driven by both money flows and their impact on asset prices. The money flow to the market is viewed as an investment policy of outside investors. For the price impact effect, we use an impact function that incorporates the phenomena of market inelasticity and saturation from new money (the dumb money effect). Due to the dependence of market investors’ flows on market performance, the model implies a feedback mechanism that gives rise to nonlinear dynamics. Consequently, the market price dynamics are seen as a nonlinear diffusion of a particle (the marketron) in a two-dimensional space formed by the log-price x and a memory variable y. The latter stores information about past money flows, so that the dynamics are non-Markovian in the log price x alone, but Markovian in the pair (x,y), bearing a strong resemblance to spiking neuron models in neuroscience. In addition to market flows, the model dynamics are partially driven by return predictors, modeled as unobservable Ornstein-Uhlenbeck processes. By using a new interpretation of predictive signals as self-propulsion components of the price dynamics, we treat the marketron as an active particle, amenable to methods developed in the physics of active matter. We show that, depending on the choice of parameters, our model can produce a rich variety of interesting dynamic scenarios for market regimes.
Wednesday, September 17, 2025
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Speaker: Christina Qi, Databento
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Wednesday, September 24, 2025
Title: Introduction to Structured Finance
Speaker: JG Lee, Director of Modeling, FINSIGHT Group
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Wednesday, October 1, 2025
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Wednesday, October 8, 2025
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Wednesday, October 15, 2025
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Wednesday, October, 22, 2025
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Wednesday, October 29, 2025
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Wednesday, November 5, 2025
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Wednesday, November 12, 2025
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Wednesday, November 19, 2025
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Wednesday, November 26, 2025
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Wednesday, December 3, 2025
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