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Dynamic graph reinforcement learning algorithm for portfolio management: A novel time–frequency correlated model

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  • Ma, Cong
  • Nan, Shijing

Abstract

Revealing the dynamic correlations among various assets is crucial for portfolio management. In this study, we build a novel Multi-graphs representation based on wavelet coherence to capture and learn their dynamic time–frequency correlations. Then, a novel portfolio management strategy is proposed by integrating the Multi-graphs representation with the deep reinforcement learning algorithm, referred to as the Dynamic Wavelet Coherence Graph Convolutional Reinforcement Learning (WCG-RL) algorithm. Several numerical experiments fully illustrate the performance of our proposed WCG-RL algorithm is applicable to stocks with different market capitalization, and its performance surpasses that of the state-of-the-art algorithms in the Chinese stock market.

Suggested Citation

  • Ma, Cong & Nan, Shijing, 2024. "Dynamic graph reinforcement learning algorithm for portfolio management: A novel time–frequency correlated model," Finance Research Letters, Elsevier, vol. 63(C).
    • Handle: RePEc:eee:finlet:v:63:y:2024:i:c:s1544612324004033
    DOI: 10.1016/j.frl.2024.105373
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    References listed on IDEAS

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    1. Li, Zijian & Meng, Qiaoyu, 2022. "Time and frequency connectedness and portfolio diversification between cryptocurrencies and renewable energy stock markets during COVID-19," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    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. Farzan Soleymani & Eric Paquet, 2021. "Deep Graph Convolutional Reinforcement Learning for Financial Portfolio Management -- DeepPocket," Papers 2105.08664, arXiv.org.
    4. Mensi, Walid & Rehman, Mobeen Ur & Al-Yahyaee, Khamis Hamed & Al-Jarrah, Idries Mohammad Wanas & Kang, Sang Hoon, 2019. "Time frequency analysis of the commonalities between Bitcoin and major Cryptocurrencies: Portfolio risk management implications," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 283-294.
    5. Richard H. Thaler & Shlomo Benartzi, 2001. "Naive Diversification Strategies in Defined Contribution Saving Plans," American Economic Review, American Economic Association, vol. 91(1), pages 79-98, March.
    6. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    7. Liu, Jianhe & Lu, Luze & Zong, Xiangyu & Xie, Baao, 2023. "Nonlinear relationships in soybean commodities Pairs trading-test by deep reinforcement learning," Finance Research Letters, Elsevier, vol. 58(PC).
    8. Andrea Buraschi & Paolo Porchia & Fabio Trojani, 2010. "Correlation Risk and Optimal Portfolio Choice," Journal of Finance, American Finance Association, vol. 65(1), pages 393-420, February.
    9. William N. Goetzmann & Lingfeng Li & K. Geert Rouwenhorst, 2005. "Long-Term Global Market Correlations," The Journal of Business, University of Chicago Press, vol. 78(1), pages 1-38, January.
    10. Seyed Alireza Athari & Ngo Thai Hung, 2022. "Time–frequency return co-movement among asset classes around the COVID-19 outbreak: portfolio implications," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(4), pages 736-756, October.
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