Dynamic Higher-Order Relations and Event-Driven Temporal Modeling for Stock Price Forecasting
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.
Cite
Text
Park et al. "Dynamic Higher-Order Relations and Event-Driven Temporal Modeling for Stock Price Forecasting." International Joint Conference on Artificial Intelligence, 2025. doi:10.24963/IJCAI.2025/673Markdown
[Park et al. "Dynamic Higher-Order Relations and Event-Driven Temporal Modeling for Stock Price Forecasting." International Joint Conference on Artificial Intelligence, 2025.](https://mlanthology.org/ijcai/2025/park2025ijcai-dynamic/) doi:10.24963/IJCAI.2025/673BibTeX
@inproceedings{park2025ijcai-dynamic,
title = {{Dynamic Higher-Order Relations and Event-Driven Temporal Modeling for Stock Price Forecasting}},
author = {Park, Kijeong and Hong, Sungchul and Jeon, Jong-June},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2025},
pages = {6048-6056},
doi = {10.24963/IJCAI.2025/673},
url = {https://mlanthology.org/ijcai/2025/park2025ijcai-dynamic/}
}