Multipoint contact without forbearance? How coverage synergies shape equity analysts' forecasting performance

• 成果类型: Article
• 署名作者: Uribe, Jose N.
• 署名单位: University of Michigan System; University of Michigan
• 刊物名称: STRATEGIC MANAGEMENT JOURNAL
• ISSN/ISSBN: 0143-2095
• DOI: 10.1002/smj.3188
• 发表日期: 2020
• 页码: 1901-1932
• 关键词: Analysts; multipoint contact; mutual forbearance; networks; relatedness

摘要

Research Summary:Scholars regularly use multipoint contact (MPC) to explain how encountering rivals in different domains shapes performance. While most explanations rely on mutual forbearance theory, I propose that competitive deterrence does not adequately explain how MPC shapes performance in knowledge intensive work and argue instead that cross-domain synergies may play a central role. I examine how security analysts' MPC with publicly traded firms captures synergies in their coverage portfolio, which improves forecasting accuracy and information leadership. The advantages of greater MPC for a focal analyst are counterbalanced by rivals' observational learning, which reduces the focal analyst's forecasting differentiation. A natural experiment helps corroborate my argument: rival analysts' forecasting accuracy dropped for firms in which high MPC analysts perished in the terrorist attack on September 11, 2001. Managerial Summary:Competition in the knowledge economy often unfolds across multiple domains including product markets, geographic locations, and customer segments. In these settings, an actor's level of multipoint contact (MPC) in a domain captures the knowledge and other synergies available to the focal actor, which can improve performance in the domain. In the equity research setting, an analyst's MPC on a focal firm captures the likelihood that the analyst also covers that firm's suppliers, customers and important competitors. Using data on analysts' forecasting performance between 2001 and 2013, I find that greater levels of MPC on a focal firm predicts greater forecasting accuracy and information leadership but also lowers forecasting differentiation by attracting rivals who observe and benefit from the focal analyst's knowledge.