从财经新闻中自动识别企业风险，助力投资决策。
Modeling and Detecting Company Risks from News: A Case Study in Bloomberg News
- 设计七维度风险标签体系，涵盖供应链、监管等关键方面。
- 在744篇标注新闻上测试，微调模型优于大模型零样本提示。
- 分析27.7万篇彭博新闻，揭示企业运营与行业动态深层风险。
识别企业相关风险对投资者及整体金融市场的稳定至关重要。本文构建了一套计算框架，用于从新闻文章中自动提取企业风险因素。提出包含七类维度（如供应链、监管、竞争等）的新标注方案，采样并标注了744篇新闻文章，并对多种机器学习模型进行基准测试。尽管大语言模型在各类NLP任务中取得显著进展，实验表明零样本和少样本提示下的前沿LLM（如LLaMA-2）仅能实现中等至较低性能；而微调的预训练语言模型在多数风险因子上表现更优。利用该模型，分析了超过27.7万篇彭博新闻，结果表明从新闻中识别风险因素可为公司与行业运作提供广泛洞察。
原文摘要 · Abstract (English)
Identifying risks associated with a company is important to investors and the well-being of the overall financial market. In this study, we build a computational framework to automatically extract company risk factors from news articles. Our newly proposed schema comprises seven distinct aspects, such as supply chain, regulations, and competitions. We sample and annotate 744 news articles and benchmark various machine learning models. While large language models have achieved huge progress in various types of NLP tasks, our experiment shows that zero-shot and few-shot prompting state-of-the-art LLMs (e.g. LLaMA-2) can only achieve moderate to low performances in identifying risk factors. And fine-tuned pre-trained language models are performing better on most of the risk factors. Using this model, we analyze over 277K Bloomberg news articles and demonstrate that identifying risk factors from news could provide extensive insight into the operations of companies and industries.
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