Modeling and Detecting Company Risks from News
Jiaxin Pei, Soumya Vadlamannati, Liang-Kang Huang, Daniel Preotiuc-Pietro, Xinyu Hua
Abstract
Identifying risks associated with a company is important to investors and the wellbeing of the overall financial markets. 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 competition. We annotate 666 news articles and benchmark various machine learning models. While large language mod- els have achieved remarkable 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. In contrast, fine-tuning pre-trained language models yields better results on most risk factors. Using this model, we analyze over 277K Bloomberg News articles and demonstrate that identifying risk factors from news could provide extensive insights into the operations of companies and industries.- Anthology ID:
- 2024.naacl-industry.6
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Yi Yang, Aida Davani, Avi Sil, Anoop Kumar
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 63–72
- Language:
- URL:
- https://aclanthology.org/2024.naacl-industry.6
- DOI:
- Cite (ACL):
- Jiaxin Pei, Soumya Vadlamannati, Liang-Kang Huang, Daniel Preotiuc-Pietro, and Xinyu Hua. 2024. Modeling and Detecting Company Risks from News. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track), pages 63–72, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Modeling and Detecting Company Risks from News (Pei et al., NAACL 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.naacl-industry.6.pdf