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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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