This study explores the herding behavior of security analysts, the firm characteristics in attracting herding, and the consequences of the herding recommendations on returns in China’s stock market. By applying the LSV method, the stock recommendations of analysts are partitioned into buy- and sell-recommendations. The results show that the herding level of sell-recommendation is greater than that of buy-recommendation, which is particularly evident in the bear market. Moreover, investors selling the holdings that analysts herd to recommend “sell” might help avoid losses in the bear market. Notably, when in the bull market, if analysts herd to make sell-recommendations, investors might gain more by acting conversely. These performance variations according to market sentiments appear to be partially explained by a firm’s characteristics based on additional regression analyses and are partially attributed to analysts’ individual attitudes toward market sentiments.
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