Reinforcement learning is an attractive alternative to traditional approaches for hedging derivatives. It involves less trading so that transaction costs are saved, It also allows the hedger more freedom in choosing an objective function. In this presentation, we explain how the reinforcement learning approach can be implemented and present some of our results

Bio:

John Hull is a professor of finance at the Joseph L. Rotman School of Management, University of Toronto. He is also academic director of FinHub, which is a financial innovation research center at Rotman. He has written four books: "Options, Futures, and Other Derivatives" (now in its 11th edition), "Fundamentals of Futures and Options Markets" (now in its 9th edition), "Risk Management and Financial Institutions" (now in its 6th edition), and "Machine Learning in Business: An Introduction to the World of Data Science" (now in its 3rd edition).

Zissis Poulos is a postdoctoral fellow at Joseph L. Rotman School of Management and a researcher at Rotman's Financial Innovation Lab (FinHub). He received his Master's and Ph.D. degrees in Electrical and Computer Engineering (ECE) from the University of Toronto (UofT) in 2014 and 2018, respectively. His research focuses on machine learning applied to derivatives hedging, risk management, volatility modeling, and applications of natural language processing in the analysis of financial soft information. From 2017 to 2019 he served as project coordinator for NSERC COHESA, a Canada-wide strategic research network promoting the adoption of AI in the country.