Modern Perspectives on Reinforcement Learning in Finance
The Journal of Machine Learning in Finance, Vol. 1, No. 1, 2020.
28 Pages Posted: 16 Sep 2019 Last revised: 16 Mar 2021
Date Written: September 6, 2019
We give an overview and outlook of the field of reinforcement learning as it applies to solving financial applications of intertemporal choice. In finance, common problems of this kind include pricing and hedging of contingent claims, investment and portfolio allocation, buying and selling a portfolio of securities subject to transaction costs, market making, asset liability management and optimization of tax consequences, to name a few. Reinforcement learning allows us to solve these dynamic optimization problems in an almost model-free way, relaxing the assumptions often needed for classical approaches.
A main contribution of this article is the elucidation of the link between these dynamic optimization problem and reinforcement learning, concretely addressing how to formulate expected intertemporal utility maximization problems using modern machine learning techniques.
Keywords: Dynamic programming, Finance, Hedging, Intertemporal choice; Investment analysis, Machine learning, Optimal control, Options, Portfolio optimization, Reinforcement learning
JEL Classification: G11, C61
Suggested Citation: Suggested Citation