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A Deep Deterministic Policy Gradient-based Strategy for Stocks Portfolio Management

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  • Huanming Zhang
  • Zhengyong Jiang
  • Jionglong Su

Abstract

With the improvement of computer performance and the development of GPU-accelerated technology, trading with machine learning algorithms has attracted the attention of many researchers and practitioners. In this research, we propose a novel portfolio management strategy based on the framework of Deep Deterministic Policy Gradient, a policy-based reinforcement learning framework, and compare its performance to that of other trading strategies. In our framework, two Long Short-Term Memory neural networks and two fully connected neural networks are constructed. We also investigate the performance of our strategy with and without transaction costs. Experimentally, we choose eight US stocks consisting of four low-volatility stocks and four high-volatility stocks. We compare the compound annual return rate of our strategy against seven other strategies, e.g., Uniform Buy and Hold, Exponential Gradient and Universal Portfolios. In our case, the compound annual return rate is 14.12%, outperforming all other strategies. Furthermore, in terms of Sharpe Ratio (0.5988), our strategy is nearly 33% higher than that of the second-best performing strategy.

Suggested Citation

  • Huanming Zhang & Zhengyong Jiang & Jionglong Su, 2021. "A Deep Deterministic Policy Gradient-based Strategy for Stocks Portfolio Management," Papers 2103.11455, arXiv.org.
    • Handle: RePEc:arx:papers:2103.11455
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    References listed on IDEAS

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    1. Ziming Gao & Yuan Gao & Yi Hu & Zhengyong Jiang & Jionglong Su, 2020. "Application of Deep Q-Network in Portfolio Management," Papers 2003.06365, arXiv.org.
    2. David P. Helmbold & Robert E. Schapire & Yoram Singer & Manfred K. Warmuth, 1998. "On‐Line Portfolio Selection Using Multiplicative Updates," Mathematical Finance, Wiley Blackwell, vol. 8(4), pages 325-347, October.
    3. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059, arXiv.org, revised Jul 2017.
    4. Zhipeng Liang & Hao Chen & Junhao Zhu & Kangkang Jiang & Yanran Li, 2018. "Adversarial Deep Reinforcement Learning in Portfolio Management," Papers 1808.09940, arXiv.org, revised Nov 2018.
    5. Angelos Filos, 2019. "Reinforcement Learning for Portfolio Management," Papers 1909.09571, arXiv.org.
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    Cited by:

    1. Charl Maree & Christian W. Omlin, 2022. "Balancing Profit, Risk, and Sustainability for Portfolio Management," Papers 2207.02134, arXiv.org.
    2. Hengxi Zhang & Zhendong Shi & Yuanquan Hu & Wenbo Ding & Ercan E. Kuruoglu & Xiao-Ping Zhang, 2023. "Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning," Papers 2303.11959, arXiv.org, revised Dec 2023.

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