Recent Advances in Reinforcement Learning in Finance

60 Pages Posted: 28 Nov 2021 Last revised: 21 Dec 2021

See all articles by Ben M. Hambly

Ben M. Hambly

University of Oxford - St. Ann's College

Renyuan Xu

University of Southern California - Epstein Department of Industrial & Systems Engineering

Huining Yang

University of Oxford - Mathematical Institute

Date Written: November 24, 2021

Abstract

The rapid changes in the finance industry due to the increasing amount of data has revolutionized the techniques on data processing and data analysis and brought new theoretical and computational challenges. In contrast to classical stochastic control theory and other analytical approaches for solving financial decision-making problems that heavily reply on model assumptions, new developments from reinforcement learning (RL) are able to make full use of the large amount of financial data with fewer model assumptions and to improve decisions in complex financial en- environments. This survey paper aims to review the recent developments and use of RL approaches in finance. We give an introduction to Markov decision processes, which is the setting for many of the commonly used RL approaches. Various algorithms are then introduced with a focus on value and policy based methods that do not require any model assumptions. Connections are made with neural networks to extend the framework to encompass deep RL algorithms. Our survey concludes by discussing the application of these RL algorithms in a variety of decision-making problems in finance, including optimal execution, portfolio optimization, option pricing and hedging, market making, smart order routing, and robo-advising.

Keywords: Reinforcement Learning, Finance, Machine Learning, Quantitative Finance, Stochastic Control

Suggested Citation

Hambly, Ben M. and Xu, Renyuan and Yang, Huining, Recent Advances in Reinforcement Learning in Finance (November 24, 2021). Available at SSRN: https://ssrn.com/abstract=3971071 or http://dx.doi.org/10.2139/ssrn.3971071

Ben M. Hambly

University of Oxford - St. Ann's College ( email )

Woodstock Road
Oxford OX2 6HS
United Kingdom
+44 1865 274800 (Phone)
+44 1865 274899 (Fax)

Renyuan Xu (Contact Author)

University of Southern California - Epstein Department of Industrial & Systems Engineering ( email )

United States

HOME PAGE: http://renyuanxu.github.io

Huining Yang

University of Oxford - Mathematical Institute ( email )

Radcliffe Observatory, Andrew Wiles Building
Woodstock Rd
Oxford, Oxfordshire OX2 6GG
United Kingdom

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