@inproceedings{wang-etal-2025-surf,
    title = "{SURF}: A System to Unveil Explainable Risk Relations between Firms",
    author = "Wang, Yu-Hsiang  and
      Chiu, Wei-Ning  and
      Hsiao, Yi-Tai  and
      Huang, Yu-Shiang  and
      Chiang, Yi-Shyuan  and
      Wu, Shuo-En  and
      Wang, Chuan-Ju",
    editor = "Dziri, Nouha  and
      Ren, Sean (Xiang)  and
      Diao, Shizhe",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-demo.22/",
    pages = "260--267",
    ISBN = "979-8-89176-191-9",
    abstract = "Firm risk relations are crucial in financial applications, including hedging and portfolio construction. However, the complexity of extracting relevant information from financial reports poses significant challenges in quantifying these relations. To this end, we introduce SURF, a System to Unveil Explainable Risk Relations between Firms. SURF employs a domain-specific encoder and an innovative scoring mechanism to uncover latent risk connections from financial reports. It constructs a network graph to visualize these firm-level risk interactions and incorporates a rationale explainer to elucidate the underlying links. Our evaluation using stock data shows that SURF outperforms baseline methods in effectively capturing firm risk relations. The demo video of the system is publicly available."
}