The Evolution of Reinforcement Learning in Quantitative Finance: A Survey
It provides a comprehensive review for researchers and practitioners in finance, but is incremental as it synthesizes existing work without introducing new methods.
This survey evaluates 167 publications to explore how reinforcement learning (RL) is applied in quantitative finance, addressing the complexity of financial markets and advancing traditional methods with dynamic approaches like transfer learning and multi-agent solutions.
Reinforcement Learning (RL) has experienced significant advancement over the past decade, prompting a growing interest in applications within finance. This survey critically evaluates 167 publications, exploring diverse RL applications and frameworks in finance. Financial markets, marked by their complexity, multi-agent nature, information asymmetry, and inherent randomness, serve as an intriguing test-bed for RL. Traditional finance offers certain solutions, and RL advances these with a more dynamic approach, incorporating machine learning methods, including transfer learning, meta-learning, and multi-agent solutions. This survey dissects key RL components through the lens of Quantitative Finance. We uncover emerging themes, propose areas for future research, and critique the strengths and weaknesses of existing methods.