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.

We develop an information choice model where information costs are sticky and co-move with firm-level intrinsic uncertainty as opposed to temporal variations in uncertainty. Incorporating analysts' forecasts, we predict a negative relationship between information costs and information acquisition, as proxied by the predictability of analysts' forecast biases. Finally, the model shows a contrasting pattern between information acquisition and intrinsic and temporal uncertainty, where intrinsic uncertainty strengthens return predictability of analysts' biases through the information cost channel, while temporal uncertainty weakens it through the information benefit channel. We empirically confirm these opposing relationships that existing theories struggle to explain.

This study examines the relationship between industry litigation risk and corporate misconduct using China's A-share listed companies’ data from 2007 to 2022. The findings indicate a significant and negative association, where companies in industries with higher median litigation amounts relative to their assets exhibit reduced incidents of misconduct. This suggests that businesses in high-risk litigation sectors may adopt more cautious practices to mitigate legal challenges and protect their reputations. The robustness of these findings is confirmed through a variety of tests, including a quasi-experimental setting of the chief judges rotation implemented in 2008. Furthermore, the study finds that external monitors including financial analysts’ site visits and local law firms moderate the negative relationship between litigation risk and misconduct. We further show that legal enforcement and moral capital are the two channels through which industry litigation risk impacts corporate misconduct. Our findings underscore the role of litigation risk in shaping peer firms' behavior.

Site visits allow visitors to physically inspect productive resources and interact with onsite employees and executives face-to-face. We posit that, by allowing visitors to acquire investmentrelated information and monitor the management team, site visits offer disciplinary benefits for corporate investments. Using mandatory disclosures of site visits in China, we find that corporate investments become more responsive to growth opportunities as the intensity of site visits increases, consistent with the notion that site visits yield disciplinary benefits. We also find that the positive association between site visits and investment efficiency is more pronounced when visitors can glean more investment-related information and when they have stronger incentives and greater power to monitor managers. This positive association is also stronger among firms with more severe agency problems and higher asset tangibility. The overall evidence supports the notion that site visits serve as a unique venue for institutional investors and financial analysts to acquire valuable information and serve a monitoring function, which generates disciplinary benefits for corporate investments.

We show that the personal traits of analysts, as revealed by their political donations, influence their forecasting behavior and stock prices. Analysts who contribute primarily to the Republican Party adopt a more conservative fore- casting style. Their earnings forecast revisions are less likely to deviate from the forecasts of other analysts and are less likely to be bold. Their stock recommen- dations also contain more modest upgrades and downgrades. Overall, these analysts produce better quality research, which is recognized and rewarded by their employers, institutional investors, and the media. Stock market participants, how- ever, do not fully recognize their superior ability as the market reaction following revisions by these analysts is weaker.

Using Chinese data, we find that analysts’ earnings forecasts are more accurate and less biased when analysts are socially connected with the company’s signatory auditor. We also find that forecast performance improves following mandatory auditor rotations that result in new analyst-auditor connections and declines following mandatory rotations that terminate existing connections. We further find that our results become stronger when the information that auditors possess is likely to be more useful to analysts, that connected analysts have better career outcomes than unconnected analysts, and that investors and other analysts are more responsive to forecast revisions issued by connected analysts. Finally, we find that connected auditors provide higher quality audits to their connected clients and are more likely to retain those clients. Overall, our findings are consistent with connected analysts benefitting from private information obtained from their social connections with auditors by providing better earnings forecasts, and in turn, with auditors benefitting from information they receive from connected analysts by delivering higher quality audits that improve client retention.

We present a novel methodology for constructing portfolios to hedge economic and financial risks arising from climate change. We utilize ChatGPT-4 to identify climate-related conversations during earnings conference calls and connect these time-stamped transcripts with high-frequency stock price data pinpointed to the conversation level. This approach allows us to assess a company’s dynamic exposure to climate change risks by analyzing real-time stock price responses to discussions about climate issues between managers and analysts. Our proposed portfolio, constructed by taking long (short) positions in stocks with positive (negative) market responses to climate conversations, appreciates in value during future periods with negative aggregate climate news shocks. Compared to portfolios constructed using alternative methods, our real-time market response-based portfolios demonstrate superior out-of-sample hedge performance. A key advantage of our approach is its ability to capture time-series and cross-sectional variations in stocks’ rapidly-evolving exposures to climate risk, relying on the timing of when climate-related issues become salient topics that warrant conference call discussions and real-time market responses to such conversations. Additionally, we showcase the versatility of our approach in hedging other types of dynamic risks: namely political risk and pandemic risk.

The study finds that analyst skill plays a key factor to explain the complicated and chaotic relation between analyst coverage and external governance. We divide analysts into multiple skill groups by GMM (Gaussian mixture model) method, and explore the effect of the coverage by skilled analysts on earnings management in Chinese listed companies. The results indicate that only the coverage of skilled analysts shows a significant negative correlation with earnings management. Heterogeneity analysis reveals that the negative relationship between the coverage of skilled analysts and earnings management is primarily observed in non-state-owned companies, those with weaker external audits, and smaller-scale firms. The conclusion remains robust after considering endogeneity issues. The findings of this study suggest that incorporating analyst skill contributes to a better understanding of the mechanisms through which analysts influence corporate governance. It also highlights that the role of analysts in corporate governance cannot be generalized.

Purpose – Using a sample of 9,898 firm-year observations from 1,821 unique Chinese listed firms over the period from 2004 to 2019, this study aims to investigate whetherthe marketrewards meeting or beating analyst earnings expectations (MBE). Design/methodology/approach –The authors use an event study methodology to capture marketreactions to MBE. Findings – The authors document a stock return premium for beating analyst forecasts by a wide margin. However,there is no stock return premium forfirms that meet orjust beat analystforecasts, suggesting that the market is skeptical of earnings management by these firms. This market underreaction is more pronounced for firms with weak external monitoring. Further analysis shows that meeting or just beating analyst forecasts is indicative of superior future financial performance. The authors do not find firms using earnings management to meet or just beat analyst forecasts. Research limitations/implications – The authors provide evidence of market underreaction to meeting or just beating analyst forecasts, with the market’s over-skepticism of earnings management being a plausible mechanism for this phenomenon. Practical implications – The findings of this study are informative to researchers, market participants and regulators concerned about the impact of analysts and earnings management and interested in detecting and constraining managers’ earnings management. Originality/value – The authors provide new insights into how the market reacts to MBE by showing that the market appears to focus on using meeting or just beating analyst forecasts as an indicator of earnings management, while it does not detect managed MBE. Meeting or just beating analyst forecasts is commonly used as a proxy for earnings management in the literature. However, the findings suggest that it is a noisy proxy for earnings management.

This paper investigates the impact of local fintech development on analysts’ earnings forecast accuracy. We use the method of web text mining to construct the local fintech development index for empirical test and find that local fintech development significantly improves analysts’ earnings forecast accuracy by promoting firm digital transformation, improving firm information transparency, and alleviating the information asymmetry between firms and outsiders. Furthermore, this effect is more significant for analysts without equity pledge associations and those with weaker buy-side pressure. This study shows that local fintech development can optimize the capital market information environment.