Financial Risk Relation Identification through Dual-view Adaptation

2025-09-23

AI Summary

This paper addresses the limitations of conventional financial risk association identification—namely, its heavy reliance on expert judgment, subjectivity, and poor scalability—by proposing an automated risk association mining method leveraging 10-K filings of publicly listed companies. Methodologically, it introduces a dual-view adaptive framework that jointly models temporal dynamics and lexical patterns in financial disclosures; an unsupervised domain-adaptive fine-tuning procedure trains a finance-specific semantic encoder to generate interpretable risk transmission scores. Its key contribution lies in the first-ever coupling of temporal structure with semantic evolution for end-to-end, quantitative identification of latent cross-firm risk relationships. Experiments demonstrate substantial improvements over strong baselines across multiple evaluation scenarios, with superior accuracy in risk pathway discovery, enhanced interpretability, and robust generalization—enabling practical applications in portfolio optimization and systemic risk early warning.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Data Mining & Knowledge Management: Semantic Web
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Identifying inter-firm risk relations from financial documents automatically
• Overcoming limitations of subjective manual risk assessment methods
• Capturing implicit risk connections through NLP and dual-view adaptation

Innovation

• Unsupervised fine-tuning using chronological and lexical patterns
• Domain-specific financial encoder for contextual understanding
• Quantitative risk relation score for transparent analysis