用10-K文件自动识别企业间风险关联，提升投资决策效率。
Financial Risk Relation Identification through Dual-view Adaptation
- 基于10-K文件的时序与词汇模式，无监督微调构建金融领域编码器。
- 量化生成风险关联分值，准确率显著优于现有方法。
- 适合金融风控、量化投资研究者使用，代码开源可复现。
众多相互关联的风险事件——从监管变化到地缘政治紧张——可能引发企业间的连锁反应。因此，识别企业间风险关系对投资组合管理与投资策略至关重要。传统方法依赖专家判断和人工分析，主观性强、耗时且难以扩展。为此，我们提出一种系统性方法，利用权威且标准化的10-K文件作为数据源，通过自然语言处理技术捕捉隐含的抽象风险关联。该方法基于文件中的时间序列与词汇模式进行无监督微调，构建具备深层上下文理解能力的领域专用金融编码器，并引入可量化的风险关系评分，实现透明且可解释的分析。大量实验表明，该方法在多个评估场景下均优于强基线模型。代码已公开于 https://github.com/cnclabs/codes.fin.relation。
原文摘要 · Abstract (English)
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.
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