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Financial Risk Relation Identification through Dual-view Adaptation

Wei-Ning Chiu, Yu-Hsiang Wang, Andy Hsiao, Yu-Shiang Huang, Chuan-Ju Wang

TL;DR

This paper presents a retrieval-based framework to identify inter-firm risk relations by fine-tuning a financial-domain encoder on Form 10-K filings using dual-view (lexical and chronological) supervision. It introduces the risk relation score (RRS), grounded in mutual risk paragraphs for transparency and interpretability, and demonstrates that RRS aligns with stock price co-movements and improves downstream tasks like graph-based stock prediction and financial retrieval benchmarks. Through extensive experiments and a case study, the approach shows superior performance over baselines and provides textual evidence to support discovered risk links. The work offers a scalable, interpretable method to quantify shared financial risk exposures, with practical implications for investment decision-making and risk management.

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.

Financial Risk Relation Identification through Dual-view Adaptation

TL;DR

This paper presents a retrieval-based framework to identify inter-firm risk relations by fine-tuning a financial-domain encoder on Form 10-K filings using dual-view (lexical and chronological) supervision. It introduces the risk relation score (RRS), grounded in mutual risk paragraphs for transparency and interpretability, and demonstrates that RRS aligns with stock price co-movements and improves downstream tasks like graph-based stock prediction and financial retrieval benchmarks. Through extensive experiments and a case study, the approach shows superior performance over baselines and provides textual evidence to support discovered risk links. The work offers a scalable, interpretable method to quantify shared financial risk exposures, with practical implications for investment decision-making and risk management.

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.

Paper Structure

This paper contains 35 sections, 5 equations, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Contributions
  3. Related Works
  4. Relation Extraction
  5. General-domain Encoders
  6. Financial-domain Encoders
  7. Methodology
  8. Notation and Preliminaries
  9. Unsupervised Adaptation of a Financial Domain Encoder
  10. Unsupervised Training of the Encoder
  11. Contrastive Learning
  12. Forming Positive Pairs
  13. Forming Negative Pairs
  14. Scoring Mechanism for Risk Relations
  15. Advantages of RRS
  16. ...and 20 more sections

Figures (3)

  • Figure 1: Overview of risk identification pipeline.
  • Figure 2: Illustration of the dual-view training strategy. Left (lexical view): Two overlapping spans within the same paragraph are sampled (green) and treated as a positive pair. Right (chronological view): Two paragraphs from the same company that share identical date-time tokens form a positive pair; date‑time tokens (blue, crossed out) are excluded prior to encoding to prevent trivial matching.
  • Figure 3: Hyperparameter Sensitivity Analysis