Identifying Firm-Specific Risk Statements in News Articles

Authors:
Hsin-Min Lu;Nina Wanhsin Huang;Zhu Zhang;Tsai-Jyh Chen
Affiliations:
Management Information Systems Department, The University of Arizona, Arizona 85721;Management Information Systems Department, The University of Arizona, Arizona 85721;Management Information Systems Department, The University of Arizona, Arizona 85721;Department of Risk Management and Insurance, National Chengchi University, Taipei City, Taiwan 11605
Venue:
PAISI '09 Proceedings of the Pacific Asia Workshop on Intelligence and Security Informatics
Year:
2009

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Abstract

Textual data are an important information source for risk management for business organizations. To effectively identify, extract, and analyze risk-related statements in textual data, these processes need to be automated. We developed an annotation framework for firm-specific risk statements guided by previous economic, managerial, linguistic, and natural language processing research. A manual annotation study using news articles from the Wall Street Journal was conducted to verify the framework. We designed and constructed an automated risk identification system based on the annotation framework. The evaluation using manually annotated risk statements in news articles showed promising results for automated risk identification.