@inproceedings{chiu-etal-2025-financial,
    title = "Financial Risk Relation Identification through Dual-view Adaptation",
    author = "Chiu, Wei-Ning  and
      Wang, Yu-Hsiang  and
      Hsiao, Andy  and
      Huang, Yu-Shiang  and
      Wang, Chuan-Ju",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.emnlp-main.1336/",
    doi = "10.18653/v1/2025.emnlp-main.1336",
    pages = "26301--26311",
    ISBN = "979-8-89176-332-6",
    abstract = "A multitude of interconnected risk events{---}ranging from regulatory changes to geopolitical tensions{---}can trigger ripple effects across firms. Identifying inter-firm risk relations is thus crucial for applications like portfolio management and investment strategy. Traditionally, such assessments rely on expert judgment and manual analysis, which are, however, subjective, labor-intensive, and difficult to scale. To address this, we propose a systematic method for extracting inter-firm risk relations using Form 10-K filings{---}authoritative, standardized financial documents{---}as our data source. Leveraging recent advances in natural language processing, our approach captures implicit and abstract risk connections through unsupervised fine-tuning based on chronological and lexical patterns in the filings. This enables the development of a domain-specific financial encoder with a deeper contextual understanding and introduces a quantitative risk relation score for transparency, interpretable analysis. Extensive experiments demonstrate that our method outperforms strong baselines across multiple evaluation settings."
}