Reinforcement Learning in Financial Decision Making: A Systematic Review of Performance, Challenges, and Implementation Strategies

TL;DR

This systematic review evaluates reinforcement learning's application in financial decision making, highlighting performance, challenges, and strategies for effective implementation in complex investment scenarios.

Contribution

It provides a comprehensive analysis of 167 articles, proposing a unified framework to improve RL deployment in finance and emphasizing practical considerations over algorithm complexity.

Findings

RL outperforms traditional methods in market making.

Implementation quality and domain knowledge are crucial.

Need for interpretable and robust RL models in finance.

Abstract

Reinforcement learning (RL) is an innovative approach to financial decision making, offering specialized solutions to complex investment problems where traditional methods fail. This review analyzes 167 articles from 2017--2025, focusing on market making, portfolio optimization, and algorithmic trading. It identifies key performance issues and challenges in RL for finance. Generally, RL offers advantages over traditional methods, particularly in market making. This study proposes a unified framework to address common concerns such as explainability, robustness, and deployment feasibility. Empirical evidence with synthetic data suggests that implementation quality and domain knowledge often outweigh algorithmic complexity. The study highlights the need for interpretable RL architectures for regulatory compliance, enhanced robustness in nonstationary environments, and standardized benchmarking protocols. Organizations should focus less on algorithm sophistication and more on market microstructure, regulatory constraints, and risk management in decision-making.