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A Hybrid Long Short-Term Memory-Graph Convolutional Network Model for Enhanced Stock Return Prediction: Integrating Temporal and Spatial Dependencies

Author

Listed:
  • Songze Shi

    (Faculty of Business Administration, University of Macau, Macau, China)

  • Fan Li

    (Faculty of Business, The Hong Kong Polytechnic University, Hong Kong, China)

  • Wei Li

    (Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China)

Abstract

Stock return prediction is a pivotal yet intricate task in financial markets, challenged by volatility and multifaceted dependencies. This study proposes a hybrid model integrating long short-term memory (LSTM) networks and graph convolutional networks (GCNs) to enhance accuracy by capturing both temporal dynamics and spatial inter-stock relationships. Tested on the Dow Jones Industrial Average (DJIA), Shanghai Stock Exchange 50 (SSE50), and China Securities Index 100 (CSI 100), our LSTM-GCN model outperforms baselines—LSTM, GCN, RNN, GRU, BP, decision tree, and SVM—achieving the lowest mean squared error (e.g., 0.0055 on DJIA), mean absolute error, and highest R 2 values. This superior performance stems from the synergistic interaction of spatio-temporal features, offering a robust tool for investors and policymakers. Future enhancements could incorporate sentiment analysis and dynamic graph structures.

Suggested Citation

  • Songze Shi & Fan Li & Wei Li, 2025. "A Hybrid Long Short-Term Memory-Graph Convolutional Network Model for Enhanced Stock Return Prediction: Integrating Temporal and Spatial Dependencies," Mathematics, MDPI, vol. 13(7), pages 1-13, March.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:7:p:1142-:d:1624520
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    References listed on IDEAS

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    3. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    4. Jiayu Qiu & Bin Wang & Changjun Zhou, 2020. "Forecasting stock prices with long-short term memory neural network based on attention mechanism," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-15, January.
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