{"ID":2867018,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.18775","arxiv_id":"2509.18775","title":"Financial Risk Relation Identification through Dual-view Adaptation","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. Our codes are available at https://github.com/cnclabs/codes.fin.relation.","short_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 judgm...","url_abs":"https://arxiv.org/abs/2509.18775","url_pdf":"https://arxiv.org/pdf/2509.18775v2","authors":"[\"Wei-Ning Chiu\",\"Yu-Hsiang Wang\",\"Andy Hsiao\",\"Yu-Shiang Huang\",\"Chuan-Ju Wang\"]","published":"2025-09-23T08:09:30Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":609427,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2867018,"paper_url":"https://arxiv.org/abs/2509.18775","paper_title":"Financial Risk Relation Identification through Dual-view Adaptation","repo_url":"https://github.com/cnclabs/codes.fin.relation","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}