Does Herding Behavior Reveal Skill? An Analysis of Mutual Fund Performance

• 成果类型: Article
• 署名作者: Jiang, Hao; Verardo, Michela
• 署名单位: Michigan State University; University of London; London School Economics & Political Science
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.12699
• 发表日期: 2018
• 页码: 2229-2269
• 关键词: cross-section; institutional investors; stock returns; Intangible information; public information; career concerns; INVESTMENT; IMPACT; MARKET; RISK

摘要

We uncover a negative relation between herding behavior and skill in the mutual fund industry. Our new, dynamic measure of fund-level herding captures the tendency of fund managers to follow the trades of the institutional crowd. We find that herding funds underperform their antiherding peers by over 2% per year. Differences in skill drive this performance gap: Antiherding funds make superior investment decisions even on stocks not heavily traded by institutions, and can anticipate the trades of the crowd; furthermore, the herding-antiherding performance gap is persistent, wider when skill is more valuable, and larger among managers with stronger career concerns.