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    <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.</abstract>
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%0 Conference Proceedings
%T Financial Risk Relation Identification through Dual-view Adaptation
%A Chiu, Wei-Ning
%A Wang, Yu-Hsiang
%A Hsiao, Andy
%A Huang, Yu-Shiang
%A Wang, Chuan-Ju
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F chiu-etal-2025-financial
%X 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.
%R 10.18653/v1/2025.emnlp-main.1336
%U https://aclanthology.org/2025.emnlp-main.1336/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1336
%P 26301-26311

Markdown (Informal)

[Financial Risk Relation Identification through Dual-view Adaptation](https://aclanthology.org/2025.emnlp-main.1336/) (Chiu et al., EMNLP 2025)

• Financial Risk Relation Identification through Dual-view Adaptation (Chiu et al., EMNLP 2025)

ACL

• Wei-Ning Chiu, Yu-Hsiang Wang, Andy Hsiao, Yu-Shiang Huang, and Chuan-Ju Wang. 2025. Financial Risk Relation Identification through Dual-view Adaptation. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 26301–26311, Suzhou, China. Association for Computational Linguistics.