Cross-industry information sharing among colleagues and analyst research

• 成果类型: Article
• 署名作者: Huang, Allen H.; Lin, An -Ping; Zang, Amy Y.
• 署名单位: Hong Kong University of Science & Technology; Singapore Management University
• 刊物名称: JOURNAL OF ACCOUNTING & ECONOMICS
• ISSN/ISSBN: 0165-4101
• DOI: 10.1016/j.jacceco.2022.101496
• 发表日期: 2022
• 关键词: EARNINGS FORECASTS; career concerns; KNOWLEDGE; performance; predictability; profitability; ORGANIZATION; COMPETITION; disclosure; accuracy

摘要

We identify a specific organizational resource in brokerage housesdinformation sharing among analyst colleagues who cover economically related industries along a supply chain. After controlling for brokerage selection effects, we show evidence consistent with the benefit of this resource to analyst research performance. Specifically, we find that analysts whose colleagues cover more economically connected industries have better research performance, especially when their colleagues produce higher-quality research. We further show that colleagues' coverage of downstream (upstream) industries is positively related to the accuracy of only analysts' revenue (expense) forecasts and that analysts and their highly connected colleagues tend to issue earnings forecast revisions contempora-neously. Last, we find that analysts with economically connected colleagues tend to have a higher level of industry specialization. Overall, our findings suggest that analysts rely on organizational resources to produce high-quality research. Hence, a portion of their per-formance and reputation is not transferable across employers.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).