Recently, the research paper entitled Exploring the profitability in analyst collective opinions: The role of analyst crowd characteristics, with Dr. WU Xianjiao from the Department of Finance of the School as the first author, was published in Journal of Corporate Finance, a world top-tier journal in the field of finance.

    Based on the data of China’s stock market, this study innovatively and systematically examines the moderating effect of analyst crowd characteristics on the profitability of consensus recommendations from two dimensions: network structure (crowd size, connection density) and network content (opinion diversity, professional experience diversity). The study finds that consensus recommendations yield stronger profitability when they are put forward by larger analyst crowds with higher diversity in both opinions and professional experience. On the contrary, the profitability of consensus recommendations tends to decline when the internal connections of analyst crowds are overly dense. In addition, the study also reveals significant variations of these effects in different information environments.

    This research not only enriches the interdisciplinary studies in the fields of behavioral finance and social networks, and provides a completely new perspective for understanding the quality of analyst forecasts by introducing the cross-organizational network theory, but also offers important insights for investors to identify high-quality investment signals, for financial platforms to improve information transparency, and for regulators to optimize the analyst crowd ecosystem and guard against market risks.

Paper information: Wu, X., Ye, Q., Liu, X.*, Liu, X. (2026). Exploring the profitability in analyst collective opinions: The role of analyst crowd characteristics. Journal of Corporate Finance, 102959.