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Stock Portfolio Management by Using Fuzzy Ensemble Deep Reinforcement Learning Algorithm

Author

Listed:
  • Zheng Hao

    (Mathematics Department, State University of New York at Oswego, Oswego, NY 13126, USA)

  • Haowei Zhang

    (School of Energy and Electrical Engineering, Chang’an University, Xi’an 710064, China)

  • Yipu Zhang

    (School of Energy and Electrical Engineering, Chang’an University, Xi’an 710064, China)

Abstract

The research objective of this article is to train a computer (agent) with market information data so it can learn trading strategies and beat the market index in stock trading without having to make any prediction on market moves. The approach assumes no trading knowledge, so the agent will only learn from conducting trading with historical data. In this work, we address this task by considering Reinforcement Learning (RL) algorithms for stock portfolio management. We first generate a three-dimension fuzzy vector to describe the current trend for each stock. Then the fuzzy terms, along with other stock market features, such as prices, volumes, and technical indicators, were used as the input for five algorithms, including Advantage Actor-Critic, Trust Region Policy Optimization, Proximal Policy Optimization, Actor-Critic Using Kronecker Factored Trust Region, and Deep Deterministic Policy Gradient. An average ensemble method was applied to obtain trading actions. We set SP100 component stocks as the portfolio pool and used 11 years of daily data to train the model and simulate the trading. Our method demonstrated better performance than the two benchmark methods and each individual algorithm without fuzzy extension. In practice, real market traders could use the trained model to make inferences and conduct trading, then retrain the model once in a while since training such models is time0consuming but making inferences is nearly simultaneous.

Suggested Citation

  • Zheng Hao & Haowei Zhang & Yipu Zhang, 2023. "Stock Portfolio Management by Using Fuzzy Ensemble Deep Reinforcement Learning Algorithm," JRFM, MDPI, vol. 16(3), pages 1-14, March.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:3:p:201-:d:1098017
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    References listed on IDEAS

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    1. XingYu Fu & JinHong Du & YiFeng Guo & MingWen Liu & Tao Dong & XiuWen Duan, 2018. "A Machine Learning Framework for Stock Selection," Papers 1806.01743, arXiv.org, revised Aug 2018.
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    Cited by:

    1. Alejandra de la Rica Escudero & Eduardo C. Garrido-Merchan & Maria Coronado-Vaca, 2024. "Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent," Papers 2407.14486, arXiv.org.

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