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The mathematical approach to many financial decision-making problems has traditionally been through modelling with stochastic processes and using techniques from stochastic control. The choice of models is often dictated by the need to balance tractability with applicability. Simple models lead to tractable and implementable strategies in closed-form or that can be found through traditional numerical methods. However, these models sometimes oversimplify the mechanisms and the behaviour of financial markets which may result in strategies that are sub-optimal in practice and that can potentially result in financial losses. On the other hand, models that try to capture realistic features of financial markets are much more complex and are often mathematically and computationally intractable using the classical tools of stochastic optimal control. | The mathematical approach to many financial decision-making problems has traditionally been through modelling with stochastic processes and using techniques from stochastic control. The choice of models is often dictated by the need to balance tractability with applicability. Simple models lead to tractable and implementable strategies in closed-form or that can be found through traditional numerical methods. However, these models sometimes oversimplify the mechanisms and the behaviour of financial markets which may result in strategies that are sub-optimal in practice and that can potentially result in financial losses. On the other hand, models that try to capture realistic features of financial markets are much more complex and are often mathematically and computationally intractable using the classical tools of stochastic optimal control. | ||
r2018} and deep RL methods <ref name="mosavi2020"/>. | r2018} and deep RL methods <ref name="mosavi2020">A.MOSAVI, Y.FAGHAN, P.GHAMISI, P.DUAN, S.F. Ardabili, E.SALWANA, and S.S. Band, ''Comprehensive review of deep reinforcement learning methods and applications in economics'', Mathematics, 8 (2020), p.1640.</ref>. | ||
Our survey will begin by discussing Markov decision processes (MDP), the framework for many reinforcement learning ideas in finance. | Our survey will begin by discussing Markov decision processes (MDP), the framework for many reinforcement learning ideas in finance. | ||
Latest revision as of 02:28, 13 May 2024
The mathematical approach to many financial decision-making problems has traditionally been through modelling with stochastic processes and using techniques from stochastic control. The choice of models is often dictated by the need to balance tractability with applicability. Simple models lead to tractable and implementable strategies in closed-form or that can be found through traditional numerical methods. However, these models sometimes oversimplify the mechanisms and the behaviour of financial markets which may result in strategies that are sub-optimal in practice and that can potentially result in financial losses. On the other hand, models that try to capture realistic features of financial markets are much more complex and are often mathematically and computationally intractable using the classical tools of stochastic optimal control. r2018} and deep RL methods [1].
Our survey will begin by discussing Markov decision processes (MDP), the framework for many reinforcement learning ideas in finance. We will then consider different approaches to learning within this framework with the main focus being on value-based and policy-based methods. In order to implement these approaches we will introduce deep reinforcement methods which incorporate deep learning ideas in this context. For financial applications we will consider a range of topics and for each we will introduce the basic underlying models before considering the RL approach to tackling them. We will discuss a range of papers in each application area and give an indication of their contributions. We conclude with some thoughts about the direction of development of reinforcement learning in finance.
General references
Hambly, Ben; Xu, Renyuan; Yang, Huining (2023). "Recent Advances in Reinforcement Learning in Finance". arXiv:2112.04553 [q-fin.MF].