Stock Prediction via a Dual Relation Fusion Network incorporating Static and Dynamic Relations
This work addresses the challenge of accurate stock prediction for financial analysts and investors by improving modeling of inter-stock relationships, though it appears incremental as it builds on existing relational methods.
The paper tackles the problem of stock price forecasting by addressing the neglect of complementarity between dynamic and static inter-stock relationships, proposing a Dual Relation Fusion Network (DRFN) that significantly outperforms baselines across different markets with high sensitivity to relational strength and stock price co-movement.
Accurate modeling of inter-stock relationships is critical for stock price forecasting. However, existing methods predominantly focus on single-state relationships, neglecting the essential complementarity between dynamic and static inter-stock relations. To solve this problem, we propose a Dual Relation Fusion Network (DRFN) to capture the long-term relative stability of stock relation structures while retaining the flexibility to respond to sudden market shifts. Our approach features a novel relative static relation component that models time-varying long-term patterns and incorporates overnight informational influences. We capture dynamic inter-stock relationships through distance-aware mechanisms, while evolving long-term structures via recurrent fusion of dynamic relations from the prior day with the pre-defined static relations. Experiments demonstrate that our method significantly outperforms the baselines across different markets, with high sensitivity to the co-movement of relational strength and stock price.