Analyst Information Discovery and Interpretation Roles: A Topic Modeling Approach

• 成果类型: Article
• 署名作者: Huang, Allen H.; Lehavy, Reuven; Zang, Amy Y.; Zheng, Rong
• 署名单位: Hong Kong University of Science & Technology; University of Michigan System; University of Michigan; Hong Kong University of Science & Technology
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2017.2751
• 发表日期: 2018
• 页码: 2833-2855
• 关键词: Analysts; DISCOVERY; INTERPRETATION; topic modeling; Latent Dirichlet allocation

摘要

This study examines analyst information intermediary roles using a textual analysis of analyst reports and corporate disclosures. We employ a topic modeling methodology from computational linguistic research to compare the thematic content of a large sample of analyst reports issued promptly after earnings conference calls with the content of the calls themselves. We show that analysts discuss exclusive topics beyond those from conference calls and interpret topics from conference calls. In addition, we find that investors place a greater value on new information in analyst reports when managers face greater incentives to withhold value-relevant information. Analyst interpretation is particularly valuable when the processing costs of conference call information increase. Finally, we document that investors react to analyst report content that simply confirms managers' conference call discussions. Overall, our study shows that analysts play the information intermediary roles by discovering information beyond corporate disclosures and by clarifying and confirming corporate disclosures.