Thalesians Seminar (New York) — Gordon Ritter — The Usefulness of Reinforcement Learning in Finance
Wednesday, November 14, 2018:
NYU Kimmel Center, Room 914, 60 Washington Square South, NY 10012, NY
Trading in real markets typically involves planning trades spanning multiple horizons in the future, and properly accounting for trading costs in the trade planning. Under certain assumptions on the statistical process generating returns, one may show that von Neumann-Morgenstern rational decision-makers plan their trades to optimize a utility function of final wealth, where the wealth random variable has all costs included. In a world where markets are not perfectly efficient, could machines learn to optimize utility of wealth? We show that this is indeed possible, by giving an example where trading costs are high, but there nonetheless remains a statistical arbitrage opportunity after costs. A reinforcement learning algorithm learns to trade without knowing a priori that trading costs even exist, and without any a priori model for the stochastic return-generating process. Rather, aspects of the cost function and the return-generating process are encoded in the agent's beliefs concerning the Bellman value function. We discuss the most likely applications of this new technology to markets; in particular, both optimal execution in the style of Almgren and Chriss, and optimal hedging of derivative contracts are special cases of the expected-utility framework, and hence these problems lend themselves to possible handling by agents trained using reinforcement learning. In the option-hedging case, we explain the underlying state-space model and show that the relevant "states" are the same as those considered by Arrow and Debreu.
Gordon Ritter completed his PhD in mathematical physics at Harvard University in 2007, where his published work ranged across the fields of quantum computation, quantum field theory, differential geometry and abstract algebra. Prior to Harvard he earned his Bachelor's degree with honours in Mathematics from the University of Chicago. Prof. Ritter is currently a Professor at NYU, Rutgers, and the award-winning Baruch MFE program, where his research interests are focused on portfolio optimization and statistical machine learning. Prof. Ritter is also a leader in the quantitative trading industry. He is preparing to launch his own company which will manage money for institutional clients by means of high-Sharpe pure alpha systematic trading strategies. He has ten years' experience doing this; most recently he built a successful trading system from scratch at GSA Capital, a firm which won the Equity Market Neutral & Quantitative Strategies category at the Eurohedge awards four times. Prior to GSA, Gordon was a Vice President of Highbridge Capital and a core member of the firm's statistical arbitrage group, which although less than 20 people, was responsible for billions in profit and trillions of dollars of trades across equities, futures and options with low correlation to traditional asset classes.
The IAQF-Thalesians Seminar Series is a joint effort on the part of the IAQF (formerly IAFE) and the Thalesians. The goal of the series is to provide a forum for the exchange of new ideas and results related to the field of quantitative finance. This goal is accomplished by hosting seminars where leading practitioners and academics present new work, and following the seminars with a reception to facilitate further interaction and discussion. The seminar series is limited to IAQF and Thalesians members only.
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