Modeling and Detecting Company Risks from News: A Case Study in Bloomberg News
This work addresses the need for investors and financial markets to identify company risks from news, but it is incremental as it builds on existing NLP methods with a new domain-specific application.
The study tackled the problem of automatically extracting company risk factors from news articles by proposing a computational framework with a seven-aspect schema, and found that fine-tuned pre-trained language models outperformed zero-shot and few-shot LLMs, analyzing over 277K Bloomberg news articles to provide insights into company operations.
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.