Structure Over Signal: A Globalized Approach to Multi-relational GNNs for Stock Prediction
This addresses the problem of improving stock prediction accuracy for financial analysts during market shocks, but it is incremental as it builds on existing GNN and attention methods.
The paper tackled the problem of inefficient message propagation in multi-relational Graph Neural Networks for stock prediction during macroeconomic shocks, and the result was that OmniGNN outperformed existing models, showing strong robustness during the COVID-19 period.
In financial markets, Graph Neural Networks have been successfully applied to modeling relational data, effectively capturing nonlinear inter-stock dependencies. Yet, existing models often fail to efficiently propagate messages during macroeconomic shocks. In this paper, we propose OmniGNN, an attention-based multi-relational dynamic GNN that integrates macroeconomic context via heterogeneous node and edge types for robust message passing. Central to OmniGNN is a sector node acting as a global intermediary, enabling rapid shock propagation across the graph without relying on long-range multi-hop diffusion. The model leverages Graph Attention Networks (GAT) to weigh neighbor contributions and employs Transformers to capture temporal dynamics across multiplex relations. Experiments show that OmniGNN outperforms existing stock prediction models on public datasets, particularly demonstrating strong robustness during the COVID-19 period.