Capturing Structural Evolution in Financial Markets with Graph Neural Time Series Models

This paper proposes a stock price prediction method based on a graph neural network architecture. The method is designed to address key characteristics of the stock market, including high nonlinearity, multivariable dependencies, and dynamically changing structural relationships. It constructs a dynamic stock graph to represent the evolving relationship network among individual stocks over time. A temporal-aware graph neural network module is designed to jointly model node features through structural propagation and temporal dependence. Specifically, the model incorporates multi-source heterogeneous information to build the dynamic graph structure. This enables explicit representation of the time-varying linkages between stocks within the graph. Graph convolution is then applied to extract structural features at each time step. A temporal module is used to model the evolution of these features over time. To validate the effectiveness of the method, the model is compared with existing graph-based and time-series models across multiple evaluation metrics. Ablation studies, robustness tests, and performance assessments under different market conditions are conducted to comprehensively analyze the model's behavior in various scenarios. Experimental results show that the proposed method achieves low prediction error while maintaining strong stability and generalization ability. It significantly improves the accuracy of modeling asset price trends in financial markets. This study provides a unified solution for structural and dynamic aspects of the stock prediction problem and extends the application scope of graph neural networks in financial time series analysis.