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We evaluate the performance of financial analysts versus naïve models in making long-term earnings forecasts. Long-term earnings forecasts are generally defined as third-, fourth-, and fifth-year earnings forecasts. We find that for the fourth and fifth years, analysts' forecasts are no more accurate than naïve random walk (RW) forecasts or naïve RW with economic growth forecasts. Furthermore, naïve model forecasts contain a large amount of incremental information over analysts' long-term forecasts in explaining future actual earnings. Tests based on subsamples show that the performance of analysts' long-term forecasts declines relative to naïve model forecasts for firms with high past earnings growth and low analyst coverage. Furthermore, a model that combines a naïve benchmark (last year's earnings) with the analyst long-term earnings growth forecast does not perform better than analysts' forecasts or naïve model forecasts. Our findings suggest that analysts' long-term earnings forecasts should be used with caution by researchers and practitioners. Also, when analysts' earnings forecasts are unavailable, naïve model earnings forecasts may be sufficient for measuring long-term earnings expectations.

Financial analysts act as crucial conduits of information between firms and stakeholders. However, comparatively little is known about how these information intermediaries evaluate the believability and importance of corporate disclosures. We argue that a firm’s level of managerial discretion, or latitude of executive action, acts as a cue for financial analysts, which helps them interpret and respond to voluntary management earnings forecasts. Our study provides strong, robust evidence that financial analysts find management forecasts significantly less believable in low-discretion than in high-discretion environments, and therefore tend to be much less responsive to these forecasts. We also show that managerial discretion is especially impactful on analysts’ responses in those circumstances where analysts are typically most uncertain about how to interpret management forecasts.

Although prior research documents that analysts sometimes herd their forecasts, very few studies investigate how investors’ judgments are influenced by their perceptions of the likelihood of analyst herding. I conduct an experimental study to investigate the conditions under which investors’ assessments of uncertainty about future earnings are influenced by their perceptions of the likelihood of analyst herding. As expected, and consistent with motivated reasoning, the results show that the temporal order of analyst forecasts influences investors’ estimates of the likelihood of analyst herding and investors’ uncertainty judgments when analyst forecasts are preference-inconsistent but not when analyst forecasts are preference-consistent. This study provides a potential explanation for the mixed findings of prior research in regard to investors’ reactions to the likelihood of analyst herding. In addition, this study extends research on investors’ credulity by providing evidence that motivated reasoning and skepticism may serve as a mechanism that contributes to that credulity.

The aim of this paper is to analyze the impact of corporate governance (focused on some key mechanisms as board size, board independence, managerial ownership, institutional ownership, and chief executive officer duality) on financial analysts’ behavior in US. Results from panel data analysis for 294 US listed firms observed from 2007 to 2014 show that several attributes of the board of directors and audit committee have no effects on the number of analysts who are following the firm and the properties of analysts’ earnings forecasts. Findings also suggest that firms with independent and large boards and blockholders ownership benefit of more analyst following. In addition, it is proven that analysts’ earnings forecasts are optimistic and more accurate for companies where blockholder ownership, either by managers or external entities have larger quoted spreads but of lower quality for the ones which have greater independent board members and institutional investor’s holding.

Both the SEC (Securities and Exchange Commission) and the popular press have routinely criticized firms for the complexity of their financial disclosures. This study aims to investigate how financial analysts respond to the tone complexity of firm disclosures.

Using approximately 20,000 earnings conference call transcripts of S&P 1,500 firms between 2005 and 2015, the authors first calculate the abnormal negative tone, the measure of tone complexity; then use such tone measure in econometric models to examine analyst forecast behavior. The authors also test the robustness of the results under different model specifications, tone word lists and alternative tone measure calculations.

Consistent with the notion that analysts respond to the information demand from investors and incur more costs and effort to analyze firm disclosure when the tone is more complex, the authors find that higher tone complexity is positively and significantly associated with more analyst following, longer report duration, more forecast revisions, larger forecast error and larger forecast dispersion. In addition, the authors find that tone complexity has a long-term impact on analyst following but has a limited long-term impact on analyst report duration, analyst revision, forecast error and dispersion.

This study complements existing literature by highlighting the information role of financial analysts and by providing evidence that analysts incorporate the management tone disclosed during conference calls to adjust their forecasting behaviors. The results can be used by policymakers as evidence and support for further improving firm communication from a new dimension of disclosure tone.

The authors provide a general framework of behavior under asymmetric information and develop indices of diligence, objectivity and quality by an analyst and analyst firm about a studied firm, and relate them to the accuracy of its forecasts. The authors test the associations of these indices with time.

The test of Public Information versus Non-Public Information Models provides the index of diligence, which equals one minus the p-value of the Hausman Specification Test of Ordinary Least Squares (OLS) versus Two Stage Least Squares (2SLS). The test of Objectivity versus Non-Objectivity Models provides the index of objectivity, which equals the p-value of the Wald Test of zero coefficients versus non-zero coefficients in 2SLS regression of the earnings forecast residual. The exponent of the negative of the standard deviation of the residuals of the analyst forecast regression equation provides the index of analytical quality. Each index asymptotically equals the Bayesian ex post probability, by the analyst and analyst firm about the studied firm, of the relevant behavior.

The authors find that ex post accuracy is a statistically and economically significant increasing function of the product of the indices of diligence, objectivity and quality by the analyst and analyst firm about the studied firm, which asymptotically equals the Bayesian ex post joint probability of diligence, objectivity and quality. The authors find that diligence, objectivity, quality and accuracy did not improve with time.

There has been no previous work done on the systematic and objective characterization and joint analysis of diligence, objectivity and quality of analyst forecasts by an analyst and analyst firm for a studied firm, and their relation with accuracy. This paper puts together the frontiers of various disciplines.

The authors examine the role of analysts’ prior experience of forecasting for firms exposed to epidemics on analysts’ forecast accuracy during the COVID-19 pandemic.

The authors examine the impact of analysts’ prior epidemic experience on forecast accuracy by comparing the changes from the pre-COVID-19 period (calendar year 2019) to the post-COVID period extending up to March 2023 across HRE versus non-HRE analysts. The authors consider a full sample (194,980) and a sub-sample (136,836) approach to distinguish “Recent” forecasts from “All” forecasts (including revisions).

The study's findings reveal that forecast accuracy for HRE analysts is significantly higher than that for non-HRE analysts during COVID-19. Specifically, forecast errors significantly decrease by 0.6% and 0.15% for the “Recent” and “All” forecast samples, respectively. This finding suggests that analysts’ prior epidemic experience leads to an enhanced ability to assess the uncertainty around the epidemic, thereby translating to higher forecast accuracy.

The finding that the expertise developed through an experience of following high-risk firms in the past enhances analysts’ performance during the pandemic sheds light on a key differentiator that partially explains the systematic difference in performance across analysts. The authors also show that industry experience alone is not useful in improving forecast accuracy during a pandemic – prior experience of tracking firms during epidemics adds incremental accuracy to analysts’ forecasts during pandemics such as COVID-19.

The study findings should prompt macroeconomic policymakers at the national level, such as the central banks of countries, to include past epidemic experiences as a key determinant when forecasting the economic outlook and making policy-related decisions. Moreover, practitioners and advisory firms can improve the earning prediction models by placing more weight on pandemic-adjusted forecasts made by analysts with past epidemic experience.

The uncertainty induced by the COVID-19 pandemic increases uncertainty in global financial markets. Under such circumstances, the importance of analysts’ role as information intermediaries gains even more importance. This raises the question of what determines analysts’ forecast accuracy during the COVID-19 pandemic. Building upon prior literature on the role of analyst experience in shaping analysts’ forecasts, the authors examine whether experience in tracking firms exposed to prior epidemics allows analysts to forecast more accurately during COVID-19. The authors find that analysts who have experience in forecasting for firms with high exposure to epidemics (H1N1, Zika, Ebola, and SARS) exhibit higher accuracy than analysts who lack such experience. Further, this effect of experience on forecast accuracy is more pronounced while forecasting for firms with higher exposure to the risk of COVID-19 and for firms with a poor ex-ante informational environment.

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 whether the market rewards meeting or beating analyst earnings expectations (MBE).

The authors use an event study methodology to capture market reactions to MBE.

The authors document a stock return premium for beating analyst forecasts by a wide margin. However, there is no stock return premium for firms that meet or just beat analyst forecasts, 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.

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.

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.

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 study examines the relation between the presence of analysts’ long-term growth (LTG) forecasts and the post-earnings-announcement drift (PEAD).

Using a sample of firm-quarters from 1995 to 2013, the author conducts various regression analyses.

The author finds that the magnitude of PEAD is significantly smaller for firms with LTG forecasts. The relationship holds after controlling for a wide range of explanatory variables for PEAD returns or for the presence of LTG forecasts. The author further investigates three nonexclusive hypotheses to explain this relationship. First, LTG forecasts may convey incremental value-relevant information that facilitates investors’ processing of short-term earnings information. Second, the presence of LTG forecasts may indicate superiority in analysts’ short-term forecast ability and identify firms with more efficient short-term forecasts. Third, the presence of LTG forecasts may be associated with cross-sectional differences in the persistence of earnings surprises. The author finds that none of these fully accounts for the negative relationship between the presence of LTG forecasts and PEAD returns. Instead, the relationship may be a result of the presence of LTG forecasts capturing some unobservable firm characteristics beyond those identified in prior studies.

This study contributes to the PEAD literature by identifying a novel analyst-based predictor of the cross-sectional variation in PEAD returns.

The purpose of this study is to investigate the decision usefulness of key audit matters (KAMs) disclosures from the perspective of financial analysts.

Using data from two groups of Chinese-listed firms subject to different audit standards, the authors use a quasi-natural experiment and the difference-in-differences approach to examine the impact of KAMs on analyst forecasts. The authors also conduct a textual analysis on management disclosures as well as on the content of KAM disclosures.

The results of this study show that both forecast errors and dispersion have significantly declined for the firms disclosing KAMs compared to the firms without such disclosures. Further analysis presents evidence that KAM disclosures have resulted in simultaneous increase in management disclosures and audit quality. In addition, auditor characteristics, such as auditor’s dependence on client fees and its industry specialization, and firm’s characteristics, such as its ownership structure and its social connection with the auditor, appear to affect the informativeness of KAM disclosures. The authors also perform content analysis of KAMs to provide additional insight.

As AH firms are required to adopt the expanded audit report one year before A shares firms, by design, there is only one year in which these two types of companies differ. Therefore, the results without overgeneralizing the impact of KAM disclosures should be interpreted. In addition, this study involves the Chinese market alone and, therefore, may be affected by factors peculiar to the functioning of the Chinese economy and financial markets.

The main contribution of this study lies in highlighting the salience of KAM context in shaping the relationship between auditors, managers and analysts and its collective impact on information environment. The findings of this study are significant in that they help establish the importance of KAM disclosures in helping to assure that higher quality financial information is available to capital markets, as well as information that is otherwise unavailable given disclosure mandates in China. This study adds to the literature on the importance of providing additional means of safeguarding auditor independence and on the value of auditor expertise in providing useful content in audit disclosures. Moreover, the findings suggest that the expanded audit report can help reduce the level of asymmetric information, especially for state-owned entities. They provide insight on how the new audit rule influences managers and auditors communicating complex accounting matters as well as the moderating effect of the social connections between auditors and firm executives.