Graph Representation Learning of Multilayer Spatial-Temporal Networks for Stock Predictions

Accurate stock market prediction is crucial for investors seeking significant profits. With increased economic activity, various interrelations between listed companies have become important for accurate predictions. These relations can be represented as complex financial networks, aiding the development of effective graph neural network (GNN) prediction methods. However, current GNN-based methods for stock prediction typically rely on a single static network representation, which fails to capture the dynamic and multifaceted relationships inherent in financial markets. In this article, we propose the multilayer spatial-temporal graph neural network (MST-GNN) to model the complex and evolving interactions between stocks. The MST-GNN framework incorporates a novel spatial-temporal cross-layer high-order fusion mechanism, which includes two key components: spatial-temporal neighborhood aggregation and cross-layer high-order feature fusion. These components enable the model to effectively capture both the temporal evolution and cross-network feature interactions of stocks. Our extensive experiments on four stock networks from the China A-share market demonstrate that MST-GNN significantly outperforms existing GNN-based methods on stock price trend classification and return ranking tasks.