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Annual Review of Statistics and Its Application

Volume 12, 2025

A Review of Reinforcement Learning in Financial Applications

  • Yahui Bai1,****, Yuhe Gao1,****, Runzhe Wan2,****, Sheng Zhang2,**** and Rui Song2
  • 1Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA 2Amazon, Seattle, Washington, USA; email: [email protected]
    **** *These authors contributed equally and are listed in alphabetical order.
  • Vol. 12:209-232 (Volume publication date March 2025)
  • First published as a Review in Advance on November 15, 2024
  • Copyright © 2025 by the author(s).
    This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third-party material in this article for license information.

Abstract

In recent years, there has been a growing trend of applying reinforcement learning (RL) in financial applications. This approach has shown great potential for decision-making tasks in finance. In this review, 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 with traditional methods. Moreover, we identify challenges, including explainability, Markov decision process 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.

Keyword(s): deep learning, finance, machine learning, meta-analysis, reinforcement learning, statistics
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2025-03-07
2025-06-17
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A Review of Reinforcement Learning in Financial Applications
Annual Review of Statistics and Its Application 12, 209 (2025); https://doi.org/10.1146/annurev-statistics-112723-034423
/content/journals/10.1146/annurev-statistics-112723-034423
/content/journals/10.1146/annurev-statistics-112723-034423
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