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Application of Deep Q-Network in Portfolio Management

Author

Listed:
  • Ziming Gao
  • Yuan Gao
  • Yi Hu
  • Zhengyong Jiang
  • Jionglong Su

Abstract

Machine Learning algorithms and Neural Networks are widely applied to many different areas such as stock market prediction, face recognition and population analysis. This paper will introduce a strategy based on the classic Deep Reinforcement Learning algorithm, Deep Q-Network, for portfolio management in stock market. It is a type of deep neural network which is optimized by Q Learning. To make the DQN adapt to financial market, we first discretize the action space which is defined as the weight of portfolio in different assets so that portfolio management becomes a problem that Deep Q-Network can solve. Next, we combine the Convolutional Neural Network and dueling Q-net to enhance the recognition ability of the algorithm. Experimentally, we chose five lowrelevant American stocks to test the model. The result demonstrates that the DQN based strategy outperforms the ten other traditional strategies. The profit of DQN algorithm is 30% more than the profit of other strategies. Moreover, the Sharpe ratio associated with Max Drawdown demonstrates that the risk of policy made with DQN is the lowest.

Suggested Citation

  • Ziming Gao & Yuan Gao & Yi Hu & Zhengyong Jiang & Jionglong Su, 2020. "Application of Deep Q-Network in Portfolio Management," Papers 2003.06365, arXiv.org.
    • Handle: RePEc:arx:papers:2003.06365
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Seyoung Park & Hyunson Song & Sungchul Lee, 2019. "Linear programing models for portfolio optimization using a benchmark," The European Journal of Finance, Taylor & Francis Journals, vol. 25(5), pages 435-457, March.
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    Cited by:

    1. Zhengyong Jiang & Jeyan Thiayagalingam & Jionglong Su & Jinjun Liang, 2023. "CAD: Clustering And Deep Reinforcement Learning Based Multi-Period Portfolio Management Strategy," Papers 2310.01319, arXiv.org.
    2. Pieter M. van Staden & Peter A. Forsyth & Yuying Li, 2023. "A parsimonious neural network approach to solve portfolio optimization problems without using dynamic programming," Papers 2303.08968, arXiv.org.
    3. Panda, Saunak Kumar & Xiang, Yisha & Liu, Ruiqi, 2024. "Dynamic resource matching in manufacturing using deep reinforcement learning," European Journal of Operational Research, Elsevier, vol. 318(2), pages 408-423.
    4. Huanming Zhang & Zhengyong Jiang & Jionglong Su, 2021. "A Deep Deterministic Policy Gradient-based Strategy for Stocks Portfolio Management," Papers 2103.11455, arXiv.org.
    5. Karush Suri & Xiao Qi Shi & Konstantinos Plataniotis & Yuri Lawryshyn, 2021. "TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution," Papers 2104.00620, arXiv.org.
    6. van Staden, Pieter M. & Forsyth, Peter A. & Li, Yuying, 2024. "Across-time risk-aware strategies for outperforming a benchmark," European Journal of Operational Research, Elsevier, vol. 313(2), pages 776-800.

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