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Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio Allocation

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  • Xinyi Li
  • Yinchuan Li
  • Yuancheng Zhan
  • Xiao-Yang Liu

Abstract

Portfolio allocation is crucial for investment companies. However, getting the best strategy in a complex and dynamic stock market is challenging. In this paper, we propose a novel Adaptive Deep Deterministic Reinforcement Learning scheme (Adaptive DDPG) for the portfolio allocation task, which incorporates optimistic or pessimistic deep reinforcement learning that is reflected in the influence from prediction errors. Dow Jones 30 component stocks are selected as our trading stocks and their daily prices are used as the training and testing data. We train the Adaptive DDPG agent and obtain a trading strategy. The Adaptive DDPG's performance is compared with the vanilla DDPG, Dow Jones Industrial Average index and the traditional min-variance and mean-variance portfolio allocation strategies. Adaptive DDPG outperforms the baselines in terms of the investment return and the Sharpe ratio.

Suggested Citation

  • Xinyi Li & Yinchuan Li & Yuancheng Zhan & Xiao-Yang Liu, 2019. "Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio Allocation," Papers 1907.01503, arXiv.org.
    • Handle: RePEc:arx:papers:1907.01503
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Eric Benhamou & David Saltiel & Serge Tabachnik & Sui Kai Wong & François Chareyron, 2021. "Distinguish the indistinguishable: a Deep Reinforcement Learning approach for volatility targeting models," Working Papers hal-03202431, HAL.
    2. Eric Benhamou & David Saltiel & Serge Tabachnik & Sui Kai Wong & Franc{c}ois Chareyron, 2021. "Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting," Papers 2104.10483, arXiv.org, revised Apr 2021.
    3. Eric Benhamou & David Saltiel & Sandrine Ungari & Abhishek Mukhopadhyay & Jamal Atif, 2020. "AAMDRL: Augmented Asset Management with Deep Reinforcement Learning," Papers 2010.08497, arXiv.org.
    4. Zijie Zhao & Roy E. Welsch, 2024. "Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Execution," Papers 2410.14927, arXiv.org.
    5. Zechu Li & Xiao-Yang Liu & Jiahao Zheng & Zhaoran Wang & Anwar Walid & Jian Guo, 2021. "FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance," Papers 2111.05188, arXiv.org.
    6. 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.
    7. Eric Benhamou & David Saltiel & Sandrine Ungari & Abhishek Mukhopadhyay, 2020. "Bridging the gap between Markowitz planning and deep reinforcement learning," Papers 2010.09108, arXiv.org.
    8. Jingyi Gu & Sarvesh Shukla & Junyi Ye & Ajim Uddin & Guiling Wang, 2023. "Deep learning model with sentiment score and weekend effect in stock price prediction," SN Business & Economics, Springer, vol. 3(7), pages 1-20, July.
    9. Huifang Huang & Ting Gao & Yi Gui & Jin Guo & Peng Zhang, 2022. "Stock Trading Optimization through Model-based Reinforcement Learning with Resistance Support Relative Strength," Papers 2205.15056, arXiv.org.
    10. Xinyi Li & Yinchuan Li & Xiao-Yang Liu & Christina Dan Wang, 2019. "Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction," Papers 1908.01112, arXiv.org.
    11. Eric Benhamou & David Saltiel & Sandrine Ungari & Abhishek Mukhopadhyay, 2020. "Time your hedge with Deep Reinforcement Learning," Papers 2009.14136, arXiv.org, revised Nov 2020.
    12. Mei-Li Shen & Cheng-Feng Lee & Hsiou-Hsiang Liu & Po-Yin Chang & Cheng-Hong Yang, 2021. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    13. Xinyi Li & Yinchuan Li & Hongyang Yang & Liuqing Yang & Xiao-Yang Liu, 2019. "DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News," Papers 1912.10806, arXiv.org.
    14. Berend Jelmer Dirk Gort & Xiao-Yang Liu & Xinghang Sun & Jiechao Gao & Shuaiyu Chen & Christina Dan Wang, 2022. "Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting," Papers 2209.05559, arXiv.org, revised Jan 2023.
    15. Ekaterina V. Orlova, 2023. "Dynamic Regimes for Corporate Human Capital Development Used Reinforcement Learning Methods," Mathematics, MDPI, vol. 11(18), pages 1-22, September.
    16. Amirhosein Mosavi & Yaser Faghan & Pedram Ghamisi & Puhong Duan & Sina Faizollahzadeh Ardabili & Ely Salwana & Shahab S. Band, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Mathematics, MDPI, vol. 8(10), pages 1-42, September.
    17. Xiao-Yang Liu & Hongyang Yang & Qian Chen & Runjia Zhang & Liuqing Yang & Bowen Xiao & Christina Dan Wang, 2020. "FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance," Papers 2011.09607, arXiv.org, revised Mar 2022.

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