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MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction

Author

Listed:
  • Hao Qian
  • Hongting Zhou
  • Qian Zhao
  • Hao Chen
  • Hongxiang Yao
  • Jingwei Wang
  • Ziqi Liu
  • Fei Yu
  • Zhiqiang Zhang
  • Jun Zhou

Abstract

The stock market is a crucial component of the financial system, but predicting the movement of stock prices is challenging due to the dynamic and intricate relations arising from various aspects such as economic indicators, financial reports, global news, and investor sentiment. Traditional sequential methods and graph-based models have been applied in stock movement prediction, but they have limitations in capturing the multifaceted and temporal influences in stock price movements. To address these challenges, the Multi-relational Dynamic Graph Neural Network (MDGNN) framework is proposed, which utilizes a discrete dynamic graph to comprehensively capture multifaceted relations among stocks and their evolution over time. The representation generated from the graph offers a complete perspective on the interrelationships among stocks and associated entities. Additionally, the power of the Transformer structure is leveraged to encode the temporal evolution of multiplex relations, providing a dynamic and effective approach to predicting stock investment. Further, our proposed MDGNN framework achieves the best performance in public datasets compared with state-of-the-art (SOTA) stock investment methods.

Suggested Citation

  • Hao Qian & Hongting Zhou & Qian Zhao & Hao Chen & Hongxiang Yao & Jingwei Wang & Ziqi Liu & Fei Yu & Zhiqiang Zhang & Jun Zhou, 2024. "MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction," Papers 2402.06633, arXiv.org.
    • Handle: RePEc:arx:papers:2402.06633
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    References listed on IDEAS

    as
    1. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    2. Fuli Feng & Xiangnan He & Xiang Wang & Cheng Luo & Yiqun Liu & Tat-Seng Chua, 2018. "Temporal Relational Ranking for Stock Prediction," Papers 1809.09441, arXiv.org, revised Jan 2019.
    3. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    4. Qinkai Chen & Christian-Yann Robert, 2021. "Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data," Papers 2107.10941, arXiv.org, revised Dec 2021.
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