We examine how removing user anonymity affects social media’s ability to generate value-relevant information for the stock market. Using a difference-in-differences design that exploits the differential timing of adopting real-name verification policies by the two most popular investment-related social media websites in China, we find that content on the treated site becomes significantly more informative about future stock returns and earnings after the policy takes effect. This effect is primarily driven by continuing users who post more actively in the pre-period. Although these users post less after the policy, the informational content of their posts increases, suggesting greater prudence in expressing opinions. Our results strengthen for firms that attract regulatory scrutiny. Overall, our study suggests that real-name verification policies can discipline internet users and potentially improve the informativeness of investment-related social media, particularly in emerging markets where retail investors predominate.

Using machine learning–based algorithms, we measure key impressions about sell‐side analysts using their LinkedIn photos. We find that impressions of analysts’ trustworthiness ( TRUST ) and dominance ( DOM ) are positively associated with forecast accuracy, especially after recent in‐person meetings between analysts and firm managers. High TRUST also enhances stock return sensitivity to forecast revisions, especially for stocks with high institutional ownership. In contrast, the impression of analysts’ attractiveness ( ATTRACT ) is only positively associated with accuracy for new analysts or when a firm has a new CEO or CFO. Furthermore, while high DOM helps male analysts’ chances of attaining All‐Star status, it reduces female analysts’ accuracy and the likelihood of winning the All‐Star award. In addition, the relation between TRUST and accuracy is modulated by the disclosure environment and is attenuated by Regulation Fair Disclosure. Our results suggest that face impressions influence analysts’ access to information and the perceived credibility of their reports.

We examine the relationship between audit quality and office-level auditor turnover. Using resumes of over 106,000 Big 4 auditors, we find that audit offices with higher turnover have a greater likelihood of client annual report restatements. This detrimental effect is more pronounced when the departing auditors are more experienced and when the office faces tighter human capital constraints and is primarily attributable to voluntary turnover. Further, such negative effect is borne mostly by complex clients and intangible-intensive clients but is weakened for clients with greater product similarity to the client portfolio of the audit office. Last, the impact of office-level turnover on audit quality persists after controlling for firm-level turnover. Our findings inform the current policy debate on whether and to what extent audit firms should disclose auditor turnover as a potential indicator of audit quality. Data Availability: Data are available from the public sources cited in the text.

This study explores whether an increase in the size of firms’ reporting networks (the number of industry peer firms reporting in comparable standards) improves the quality of analysts’ information environments. We exploit the effect of the 2005 mandatory adoption of IFRS around the world on U.S. firms, as IFRS adoption did not directly affect U.S. firms but increased the size of their reporting networks. We document that mandatory IFRS adoption is associated with a significantly larger improvement in the quality of analysts’ information environment for U.S. firms with relatively few domestic industry peers than for other U.S. firms. Further, we show that there is a larger increase in the frequency with which analysts’ research reports for U.S. firms with few domestic peers mention IFRS-adopting EU industry peer firms. Our results provide evidence that mandatory reporting regulations that increase reporting comparability can result in positive information externalities from information network effects.