Recent Advances in Reinforcement Learning in Finance

TL;DR

This survey reviews recent reinforcement learning advancements in finance, highlighting how RL techniques leverage large data sets to improve decision-making in complex financial environments without relying heavily on traditional model assumptions.

Contribution

It provides a comprehensive overview of RL methods, including deep RL, and discusses their applications across various financial decision-making problems.

Findings

RL approaches outperform traditional methods in complex tasks

Deep RL enables handling high-dimensional financial data

RL applications improve decision quality in finance

Abstract

The rapid changes in the finance industry due to the increasing amount of data have 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 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.