Analyst Information Discovery and Interpretation Roles: A Topic Modeling Approach

Allen H. Huang (), Reuven Lehavy (), Amy Y. Zang () and Rong Zheng ()
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Allen H. Huang: Department of Accounting, Hong Kong University of Science and Technology, Kowloon, Hong Kong
Reuven Lehavy: Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109
Amy Y. Zang: Department of Accounting, Hong Kong University of Science and Technology, Kowloon, Hong Kong
Rong Zheng: Department of Information Systems, Business Statistics, and Operations Management, Hong Kong University of Science and Technology, Kowloon, Hong Kong

Management Science, 2018, vol. 64, issue 6, 2833-2855

Abstract: 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.

Keywords: analysts; discovery; interpretation; topic modeling; latent Dirichlet allocation (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (98)

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