A Review of Reinforcement Learning in Financial Applications

TL;DR

This survey reviews the growing use of Reinforcement Learning in finance, analyzing its applications, challenges, and future research directions to improve decision-making and industry adoption.

Contribution

It provides a comprehensive overview of RL applications in finance, identifies key challenges, and proposes future research directions including benchmarking and advanced RL methods.

Findings

RL shows promise in financial decision-making tasks

Challenges include explainability and robustness

Future directions involve benchmarking and multi-agent RL

Abstract

In recent years, there has been a growing trend of applying Reinforcement Learning (RL) in financial applications. This approach has shown great potential to solve decision-making tasks in finance. In this survey, we present a comprehensive study of the applications of RL in finance and conduct a series of meta-analyses to investigate the common themes in the literature, such as the factors that most significantly affect RL's performance compared to traditional methods. Moreover, we identify challenges including explainability, Markov Decision Process (MDP) modeling, and robustness that hinder the broader utilization of RL in the financial industry and discuss recent advancements in overcoming these challenges. Finally, we propose future research directions, such as benchmarking, contextual RL, multi-agent RL, and model-based RL to address these challenges and to further enhance the implementation of RL in finance.