The value-relevance of accounting information is a cornerstone of capital market research, typically measured indirectly through coefficients and R2 values from returns-earnings models, which have limitations in explaining how accounting information influences stock prices. Based on the theory of financial analyst and the generating process of accounting information, we propose a direct measurement approach using analyst consensus earnings forecasts to capture the effect of accounting information on decision-making. We also construct firm-level measures of predictive and confirmatory value, two qualitative characteristics of accounting information defined by the Financial Accounting Standards Board. Using data from the Chinese stock market, where analysts play a crucial role, we find that our measures significantly explain the relationship between accounting information and stock prices, as well as stock price synchronicity. Our study offers a novel and verifiable method to quantify the abstract concept of value-relevance of accounting information, enhancing the understanding of its effect on decision-making and stock prices.

We propose the AI Narrative Gap as a novel firm characteristic—the systematic divergence between a firm’s AI strategic narrative intensity and its subsequent AI capital expenditure commitment—and document its capital market consequences. Using Chinese A-share listed firms from 2015 to 2022, we show that firms with a wider AI Narrative Gap attract significantly more optimistic and less accurate analyst earnings forecasts. These distorted expectations, in turn, predict lower subsequent stock returns, lower industry-adjusted abnormal returns, and weaker future accounting performance. A double-sort portfolio placing firms simultaneously in the highest tercile of the AI Narrative Gap and highest tercile of analyst optimism earns a mean return 22.8 percentage points below that of the lowest tercile on both dimensions (t = −5.10). The return reduction in the AI Narrative Gap coefficient is attenuated but not eliminated after controlling for optimism, consistent with a partial expectation-distortion channel. Collectively, these results establish the AI Narrative Gap as a cross-sectionally informative firm characteristic that captures the credibility of a firm’s AI strategic identity, with systematic implications for analyst expectations and asset prices.

This paper investigates whether and how managers of highly polluting firms adjust their earnings forecast behaviors in response to the introduction of environmental legal institutions. Using the establishment of environmental courts in China as a quasi-natural experiment, our triple difference-in-differences (DID) estimation shows that environmental courts significantly increase the likelihood of management earnings forecasts for highly polluting firms compared to non-highly polluting firms. This association becomes more pronounced for firms with stronger monitoring power, higher environmental litigation risk, and greater earnings uncertainty. Additionally, we show that highly polluting firms improve the precision and accuracy of earnings forecasts following the establishment of environmental courts. Furthermore, we provide evidence that our results do not support the opportunistic perspective that managers strategically issue more positive earnings forecasts to inflate stakeholders‘ expectations subsequent to the implementation of environmental courts. Overall, our research indicates that environmental legal institutions make firms with greater environmental concerns to provide more forward-looking information, thereby alleviating stakeholders’ apprehensions regarding future profitability prospects.

This paper explores the impact of government intervention on local analysts’ earnings forecasts, based on a scenario of financial misconduct in Chinese state-owned enterprises (SOEs). The results show that, under the influence of the government, local analysts’ earnings forecasts for SOEs with financial misconduct are less accurate and more optimistically biased. Further heterogeneity analysis reveals that forecast bias by local analysts is greater when officials have stronger promotion incentives, when regions are less market-oriented and have a larger share of the state-owned economy, and when SOEs contribute more to taxation and employment. In further analysis, we find that local analysts have a more optimistic tone in reports targeting non-compliant SOEs. Local analysts who depend heavily on political information will also issue more biased and optimistic forecasts on SOEs with violations. Finally, as a reward for achieving government goals, the local brokerages affiliated with these analysts and providing these optimistic forecasts are more likely to become underwriters in seasoned equity offerings of SOEs. This paper reveals that government intervention significantly influences analyst forecasts, providing implications for understanding the sources of analyst forecast bias.

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.

In this study, we investigate the relationship between news media sentiment and management earnings forecasts. Using Ashare listed companies in China from 2007 to 2022, we find a negative relationship between media sentiment and the propensity of firms to issue management earnings forecasts. We also find that media sentiment is associated with the precision and accuracy of these forecasts. Overall, our study offers new insights into the underlying motivations and the quality of management earnings forecasts.

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.

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.