Data availability and accessibility have brought in unseen changes in the finance systems and new theoretical and computational challenges. For example, in contrast to classical stochastic control theory and other analytical approaches for solving financial decision-making problems that rely heavily on model assumptions, new developments from reinforcement learning (RL) can make full use of a large amount of financial data with fewer model assumptions and improve decisions in complex economic environments. This paper reviews the developments and use of Deep Learning(DL), RL, and Deep Reinforcement Learning (DRL)methods in information-based decision-making in financial industries. Therefore, it is necessary to understand the variety of learning methods, related terminology, and their applicability in the financial field. First, we introduce Markov decision processes, followed by Various algorithms focusing on value and policy-based methods that do not require any model assumptions. Next, connections are made with neural networks to extend the framework to encompass deep RL algorithms. Finally, the paper concludes by discussing the application of these RL and DRL algorithms in various decision-making problems in finance, including optimal execution, portfolio optimization, option pricing, hedging, and market-making. The survey results indicate that RL and DRL can provide better performance and higher efficiency than traditional algorithms while facing real economic problems in risk parameters and ever-increasing uncertainties. Moreover, it offers academics and practitioners insight and direction on the state-of-the-art application of deep learning models in finance.
|期刊||International Journal of Information Management Data Insights|
|出版狀態||已出版 - 11月 2022|