51. Reinforcement Learning for Finance: A Review.
- Author
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León Nieto, Diego Ismael
- Subjects
- *
REINFORCEMENT learning , *MARKOV processes , *MACHINE learning , *DECISION making , *PROBLEM solving , *OPTIONS (Finance) , *HEDGING (Finance) - Abstract
This paper provides a comprehensive review of the application of Reinforcement Learning (RL) in the domain of finance, shedding light on the groundbreaking progress achieved andthe challenges that lie ahead. We explore how RL, a subfield of machine learning, has been instrumental in solving complex financial problems by enabling decision-making processes that optimize long-term rewards. Reinforcement learning (RL) is a powerful machinelearning technique that can be used to train agents to make decisions in complex environments. In finance, RL has been used to solve a variety of problems, including optimal execution, portfolio optimization, option pricing and hedging, market making, smart order routing, and robo-advising. In this paper, we review the recent developments in RL for finance. We begin by introducing RL and Markov decision processes (MDPs), which is the mathematical framework for RL. We then discuss the various RL algorithms that have beenused in finance, with a focus on value-based and policy-based methods. We also discuss the use of neural networks in RL for finance. Finally, we discuss the results of recent studies that have used RL to solve financial problems. We conclude by discussing the challenges and opportunities for future research in RL for finance. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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