A Portfolio Model with Risk Control Policy Based on Deep Reinforcement Learning

Caiyu Jiang and Jianhua Wang ()
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Caiyu Jiang: School of Science, Wuhan University of Technology, Wuhan 430070, China
Jianhua Wang: School of Science, Wuhan University of Technology, Wuhan 430070, China

Mathematics, 2022, vol. 11, issue 1, 1-16

Abstract: It was shown that deep reinforcement learning (DRL) has the potential to solve portfolio management problems in recent years. The Twin Delayed Deep Deterministic policy gradient algorithm (TD3) is an actor-critic method, a typical DRL method in continuous action space. Despite the success of DRL in financial trading, surprisingly, most of the literature ignores the element of risk control. The research is proposed to combine long- and short-term risk (LSTR) control with the TD3 algorithm to build a portfolio model with risk management capabilities. Using Chinese stock data from the Shanghai Stock Exchange, we train and validate the proposed portfolio model. Performances were compared to the TD3 model without risk control. The results indicated that our proposal offers better risk control and investment returns.

Keywords: deep reinforcement learning; portfolio management; long- and short-term risk control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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