{"ID":2856836,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.10695","arxiv_id":"2510.10695","title":"Stock Prediction via a Dual Relation Fusion Network incorporating Static and Dynamic Relations","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2510.10695","url_pdf":"https://arxiv.org/pdf/2510.10695v1","authors":"[\"Long Chen\",\"Huixin Bai\",\"Mingxin Wang\",\"Xiaohua Huang\",\"Ying Liu\",\"Jie Zhao\",\"Ziyu Guan\"]","published":"2025-10-12T16:48:25Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}