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

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.