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.

Yahui Bai

Yuhe Gao

Runzhe Wan

Sheng Zhang

Rui Song

Annual Review of Statistics and Its Application

统计及其应用年度回顾向统计学家和统计用户介绍主要方法学进展和允许实施的计算工具。

The Annual Review of Statistics and Its Application is an annual review journal that aims to introduce major methodological advancements in the field of statistics and the computational tools that facilitate their implementation to statisticians and statistical users.

《Annual Review of Statistics and Its Application》是一本年度回顾期刊，旨在向统计学家和统计用户介绍统计学领域的主要方法学进展及其应用的计算工具。

Category	Quartile
数学	1
数学, 数学跨学科应用	1
数学, 统计学与概率论	1

A Review of Reinforcement Learning in Financial Applications

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Journal Info

Category	Quartile
STATISTICS & PROBABILITY	1