Learning unified market interdependencies via networked attention for stock price forecasting

Stock price forecasting is challenging due to market volatility and complex, dynamic inter-stock relationships. Existing graph-based approaches often rely primarily on static relational structures or simple time-aligned correlations, which capture only fixed or short-term relationships and fail to model transient cross-temporal dependencies and heterogeneous information sources. We propose a novel artificial intelligence framework for stock price forecasting that integrates heterogeneous data sources, including price co-movements, corporate linkages derived from Wikipedia, and industry affiliations, into a unified dynamic relational graph that combines both structural and behavioral dependencies. The proposed model, named Learning Unified Market Interdependencies (LUMI), adaptively models evolving inter-stock connections and uncovers latent dependencies beyond sectoral or time-aligned patterns. A dual-path temporal attention mechanism disentangles long-term trends from short-term fluctuations, capturing both periodic behaviors and abrupt market shifts. Extensive experiments on four market datasets demonstrate that the proposed deep learning framework outperforms strong baselines in predictive accuracy while providing interpretable insights into market interdependencies. These findings highlight the potential of artificial intelligence for modeling complex financial systems and improving algorithmic stock price forecasting.