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Exploring the profitability in analyst collective opinions: The role of analyst crowd characteristics

Author

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  • Wu, Xianjiao
  • Ye, Qiang
  • Liu, Xiaochen
  • Liu, Xudong

Abstract

A substantial literature studies the profitability of analyst crowds' consensus recommendations in predicting stock price changes. Nonetheless, uncertainties remain about whether the profitability is contingent upon specific factors, particularly the characteristics of the analyst crowd. This study specifically examines the impact of crowd network structure (crowd size, connection density) and crowd network content (opinion diversity, professional experience diversity) on consensus recommendation profitability. Using comprehensive data from the Chinese stock market, including stock prices, analyst recommendations, and employment histories, we find that consensus recommendations are profitable when they come from larger analyst crowds and when the diversity within these crowds is higher, both in terms of opinion diversity and professional experience diversity. Conversely, consensus recommendations are less likely to be profitable when the analyst crowds maintain denser connections. Moreover, these effects vary across different information environments, including information tone (upgrades or downgrades), stock-level information uncertainty (high versus low), and information disruptions caused by the COVID-19 pandemic.

Suggested Citation

  • Wu, Xianjiao & Ye, Qiang & Liu, Xiaochen & Liu, Xudong, 2026. "Exploring the profitability in analyst collective opinions: The role of analyst crowd characteristics," Journal of Corporate Finance, Elsevier, vol. 98(C).
    • Handle: RePEc:eee:corfin:v:98:y:2026:i:c:s0929119926000179
    DOI: 10.1016/j.jcorpfin.2026.102959
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