Dynamic Cross-Market Systematic Attention for Stock Prediction

Stock price prediction is challenging due to the extremely low signal-to-noise ratio in financial markets, where stock returns reflect both market-wide systematic effects and stock-specific dynamics. Recent machine learning approaches have increasingly explored cross-stock relationships for prediction, but often overlook the underlying market-wide systematic effects and their potential interactions across different markets. To address these limitations, we propose a model-agnostic dynamic cross-market systematic attention method that improves existing stock prediction models with dynamic latent systematic factors and cross-market factor interactions. The method learns market-specific latent factors that adapt to changing market conditions and models their interactions across markets through factor-level attention, providing systematic information that can be incorporated into diverse stock prediction architectures. Experiments across the China, United States, and Hong Kong stock markets demonstrate consistent improvements when the method is integrated with different prediction models. Further analysis reveals time-varying cross-market interactions, providing insights into how the method captures changing patterns of systematic dependence across markets.