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Igor Halperin is currently an Adjunct Professor of Financial Machine Learning at the NYU Tandon School of Engineering. Prior to that, he was an Executive Director of Quantitative Research at JPMorgan Chase where he focused on the research and development of predictive and statistical models and machine learning methods for modeling risk of financial portfolios.
He has authored a number of publications on quantitative finance, and is a frequent speaker at financial conferences. Dr. Halperin has a Ph.D. in theoretical physics from Tel Aviv University, and M.Sc. in nuclear physics from St. Petersburg State Technical.
Case studies
Use case 1. Trading a European option with RL
Use case 2. Using RL to find optimal portfolios for both individual investors and the whole market. Connection with the Black-Litterman model
Case study and summary
Use case 3. Modeling market dynamics. Going beyond RL: the role of symmetries. The QED model of dynamics of stocks with defaults.
Summary: The role of priors and regularization for RL and ML in finance. Future directions.