This study investigates the impact of analysts' information source diversity on forecast bias and investment returns. We combine the GPT-4o model and text similarity, to extract the names of information sources from the text of analyst in-depth reports. Using 349,200 sources, we calculate information diversity scores based on the variety of data sources to measure analysts’ ability of selecting relevant information. The findings reveal that higher information diversity significantly reduces forecast bias and enhances portfolio returns. The effect is particularly pronounced for large companies, state-owned enterprises, those with low analyst coverage, low firm-specific experience, and reports with positive forecast revisions. Institutional investors recognize the value of this skill, while retail investors remain largely unaware, which contributes to financial inequality. This study highlights the critical role of information diversity in analyst performance.

This paper examines analysts’ catering behavior to current investor demand proxied by the unbalanced stock market reactions towards optimistic forecasts and nonoptimistic forecasts (optimism premium). Using data on earnings forecasts issued by Chinese sell-side analysts during the period 2014-2018, we find that optimism premium significantly increases analysts’ tendency to issue optimistic forecasts, in other words, analysts do cater to investor demand. Implications for theory and practices are discussed.

This study examines whether sell-side analysts have the ability to disseminate information consistent with anomaly prescriptions in China. I adopt 192 trading-based and accounting-based anomaly signals to identify undervalued and overvalued stocks. Analysts tend to give more (less) favorable recommendations and earnings forecasts to undervalued (overvalued) stocks. On analyzing the information content, I find that analyst recommendations and earnings forecasts are consistent with accounting-based information rather than trading-based information. Analysts make recommendations and earnings forecasts consistent with anomalies, especially when firms experience relatively bad firm-level information. Additionally, undervalued (overvalued) stocks are associated with high (low) analyst coverage. The results indicate that analysts may contribute to mitigating anomaly mispricing and improving market efficiency in China.

This paper examines whether sell-side analysts struggle to cope with macroeconomic uncertainty. We find that analysts issue more accurate earnings forecasts when facing higher economic policy uncertainty, which conflicts with the conclusions in the US. We provide a novel explanation for this finding and exclude the view that forecast accuracy improvement comes from analysts’ efforts to actively collect private information through site visits. Further evidence supports that heuristic cognitive bias and emotional framing effect hold back analysts’ tendency to optimism in China, resulting in higher forecast accuracy. As to why Chinese analysts do not work harder but issue more accurate forecasts, we suggest that it is mainly due to the different market regimes faced by analysts in the two countries. Our study sheds light on how macroeconomic uncertainty affects analysts’ unethical behavior and explains the cognitive processes involved.

Retail investors are rising in prominence but have historically been granted little direct access to question corporate management relative to professionals like sell-side analysts and institutional investors. Because retail investors are relatively less sophisticated and can require hand-holding, we examine whether information asymmetry among investors decreases when firms answer questions from the retail investor base. We exploit ’s investor interactive platforms (IIPs), which were designed to facilitate retail investor access to management. IIPs allow questions to be anonymously and publicly posted, but answers can only pertain to previously disclosed information and there is no explicit penalty for low-quality answers. We find that IIP answers reduce bid-ask spreads, with stronger answer effects when managers respond quickly, provide direct answers, and interact with IIP users who focus on the firm. These information asymmetry reduction benefits are substantially attenuated, and in some cases non-existent, for state-owned enterprises (SOEs), who have less incentive to publicly engage with retail investors. Finally, our findings reveal that on average the marginal effects of answers are smaller than for posted questions, suggesting that while firms benefit from answering questions to lower investor integration costs, IIP activity that lowers awareness and acquisition costs is also important.

We examine how sell-side equity analysts strategically disclose information of differing quality to the public versus the buy-side mutual fund managers to whom they are connected. We consider cases in which analysts recommend that the public buys a stock, but some fund managers sell it. We measure favor trading using mutual fund managers’ votes for analysts in a Chinese “star analyst” competition. We find that managers are more likely to vote for analysts who exhibit more “say-buy/whisper-sell” behavior with these managers. This suggests that analysts introduce noise in their public recommendations, making the more-precise information provided to their private clients more valuable. Analysts’ say-buy/whisper-sell behavior results in information asymmetry: the positive-recommendation stocks bought by the managers who vote for the analysts outperform the stocks sold by these managers after the recommendation dates. Our findings help explain several puzzles regarding analysts’ public recommendations.