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 R2 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.