Proceedings

Dynamic Higher-Order Relations and Event-Driven Temporal Modeling for Stock Price Forecasting

Kijeong Park, Sungchul Hong, Jong-June Jeon

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 6048-6056.

https://doi.org/10.24963/ijcai.2025/673

Keywords

  • Machine Learning: ML: Deep learning architectures
  • Multidisciplinary Topics and Applications: MTA: Finance
  • Machine Learning: ML: Supervised Learning
  • Machine Learning: ML: Applications

Abstract

In stock price forecasting, modeling the probabilistic dependence between stock prices within a time-series framework has remained a persistent and highly challenging area of research. We propose a novel model to explain the extreme co-movement in multivariate data with time-series dependencies. Our model incorporates a Hawkes process layer to capture abrupt co-movements, thereby enhancing the temporal representation of market dynamics. We introduce dynamic hypergraphs into our model adapting to higher-order (groupwise rather than pairwise) relationships within the stock market. Extensive experiments on real-world benchmarks demonstrate the robustness of our approach in predictive performance and portfolio stability.

Kijeong Park, Sungchul Hong, and Jong-June Jeon. 2025. Dynamic Higher-Order Relations and Event-Driven Temporal Modeling for Stock Price Forecasting. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 6048–6056. https://doi.org/10.24963/ijcai.2025/673
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