We study how bank liberalization affects analyst forecast accuracy using two interest rate deregulations in China—the removal of the cap on bank lending rates in 2004 and the removal of the floor in 2013—as quasi-natural experiments. Our results show that the analyst forecast accuracy for high-risk firms decreases significantly after the removal of the lending rate cap, whereas analyst forecast accuracy for low-risk firms increases significantly after the removal of the lending rate floor. Moreover, interest rate liberalization affects forecast accuracy through operational risk and information asymmetry channels. Furthermore, the impact was concentrated on firms whose actual performance fell short of performance expectations and those that received more bank loans. Our findings imply that interest rate liberalization policies may have unintended consequences for analyst forecasts.

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.

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.

We develop a machine learning (ML) approach to establish new insights into how memory affects ffnancial market participants’ belief formation processes in the field. Using analyst forecasts as proxies for market beliefs, we extract analysts’ mental contexts and recalls that shape forecasts by training an ML memory model. First, we find that long-term memories are salient in analysts’ recalls. However, compared to an ML benchmark trained to fit realized earnings, analysts pay more attention to distant episodes in regular times but less during crisis times, leading to recall distortions and therefore forecast errors. Second, we decompose analysts’ mental contexts and show that they are mainly shaped by past earnings and forecasting decisions instead of current firm fundamentals as indicated by the ML benchmark. This difference in contexts further explains the recall distortion. Third, our comprehensive memory model reveals the significance of specific memory features and channels in analysts’ belief formation, including the temporal contiguity effect and selective forgetting.

Researchers have developed a number of theories to explain stock return continuation. Using stock data from 16 emerging markets (1990 to 2002), we conduct an out-of-sample test for the sources of momentum profitability. This paper examines the role of financial analyst in the exhibited stock return continuation among emerging markets. Consistent with the predictions of the gradual information diffusion theory (Hong and Stein, 1999), the evidence indicates that momentum strategies are most profitable in small firms, firms with low analyst coverage. More interestingly, we find that besides the level of analyst following, the change in analyst following, specifically, increasing analyst coverage, and the analyst forecasts with high dispersion can help explain stock return momentum.