Analyst characteristics, timing of forecast revisions, and analyst forecasting ability

We first examine whether analysts with certain characteristics that prior research has identified are related to superior forecasting ability systematically time their forecast revisions later in the fiscal quarter. We then examine whether this superior ability persists after controlling for the timing advantage by using relative forecast error, a measure that largely eliminates the timing advantage of recent forecasts. Using a sample of quarterly earnings forecast revisions over the 20-year period from 1990 to 2009, we find that analysts with more firm-specific and general experience and more accurate prior-period forecasts, analysts employed by larger brokerage firms, and analysts who follow fewer industries and companies tend to revise forecasts later in the quarter. We also find that analyst characteristics that are positively correlated with revision timing are negatively related to relative forecast errors. These results are consistent with analyst characteristics being useful proxies for analyst forecasting ability and analysts with greater ability revising forecasts later in the quarter.     

Financial reporting language in financial statements: Does pessimism restrict the potential for managerial opportunism?

This study shows that the proportion of total pessimistic language is higher for companies with lower earnings manipulation and higher leverage. In contrast, high growth companies display less pessimism. Companies with higher levels of pessimism tend to display higher conservatism even if they experience bad news or low cash flows. Companies that use pessimistic language tend to display stronger corporate governance. The use of pessimistic language is positively associated with forecast accuracy and analyst coverage. Annual reports tend to be more pessimistic in order to guide analysts downward and reach target earnings. Companies that meet or just beat analysts' forecasts tend to use less pessimistic language. On the other hand, they are likely to use pessimistic language in order to reduce the magnitude of a negative market reaction to underperformance. This study also shows that the change of the reporting tone to pessimistic as well as the use of unexpected pessimistic language reduces the cost of equity.     

Analyst reputation and management earnings forecasts

Prior studies show that analysts with high reputation are influential in the market. This paper examines whether managers consider analyst reputation in shaping their voluntary disclosure strategy. Using Institutional Investor magazine’s All-American (AA) rankings as a proxy for analyst reputation, we find that the coverage of AA analysts is positively associated with the likelihood of quarterly management earnings forecasts (MEFs). We also find that AA analysts’ forecast optimism is more positively associated with the likelihood of MEFs than non-AA analysts’ forecast optimism when the firm is covered by AA analysts. Analyses based on AA analyst coverage changes and AA status changes confirm the relation between analyst reputation and MEFs. We further find that analyst reputation influences other MEF properties, such as forecast news, bias, and revisions, and that our results are robust to alternative measures of analyst reputation. Further analyses show that market reactions at quarterly earnings announcements are more positive (negative) when firms meet/beat (miss) AA analysts’ forecasts than when firms meet/beat (miss) non-AA analysts’ forecasts. Collectively, our findings suggest that managers strategically provide voluntary forecasts by taking into account the reputation of individual analysts following their firms.     

Regulation Fair Disclosure and analysts’ reliance on earnings announcements

Regulation Fair Disclosure prohibits corporations from selectively disclosing material information to groups of favored analysts and institutional investors. If information previously provided is excluded by the new regulation from analysts’ information set, it is plausible that the relative importance of the other information, such as earnings announcements, which remains could increase (Arya et al., 2005). The purpose of this study is to investigate whether analysts become more reliant on firm earnings announcements in revising their forecasts after implementation of the regulation. Our empirical results show that, after the regulation, more analysts issue forecasts immediately after earnings announcements. In addition, analysts’ earnings forecasts tend to converge more after observing earnings announcements in the post-regulation period. These results, in conjunction with the finding of higher overall level of forecast errors and dispersion, indicate that earnings announcements become more important information sources in the post regulation period. These findings suggest that analysts are more reliant on earnings announcements and there is an increase in analyst herding as a result of Regulation Fair Disclosure.     

Do analysts who understand accounting conservatism exhibit better forecasting performance?

This study investigates the performance of analysts when they match the asymmetric timeliness of their earnings forecast revisions (i.e., asymmetric forecast timeliness) with the asymmetric timeliness of firms’ reported earnings (i.e., asymmetric earnings timeliness). We find that better timeliness‐matching analysts produce more accurate earnings forecasts and elicit stronger market reactions to their forecast revisions. Further, better timeliness‐matching analysts issue less biased earnings forecasts, more profitable stock recommendations and have more favorable career outcomes. Overall, our results indicate that analysts’ ability to incorporate conditional conservatism into their earnings forecasts is an important reflection of analyst expertise and professional success.     

Audit quality and properties of analysts’ information environment

We consider how audit quality impacts sell‐side analysts’ information environment. Using the method outlined by Barron et al., we examine whether higher audit quality is associated with differences in the weight analysts place on common information relative to private information, as well as the extent to which audit quality separately impacts the precision of analysts’ private and common information. Our results show that, in instances where analysts revise their earnings forecasts for year t+1 shortly after the release of year t earnings, higher audit quality results in analysts placing more weight on public information. The precision of private (as well as public) information is improved. These results extend our understanding of how audit quality impacts on attributes of analysts’ forecasts and provides support for the argument that audit quality has important capital market implications.     

Stock Recommendations for Politically Connected Firms

Social network connections of corporations can significantly affect operating performance and firm valuation. Political connections are one form of social networking which often manifests into improved firm profitability as a result of political favors granted by politicians. However, analysts often have greater difficulty forecasting the earnings of politically connected firms than those of non‐connected firms. This is because politicians often grant political favors to firms in an unpredictable manner making it difficult for market participants to time precisely when political benefits will translate into higher firm profitability. I examine how political connections affect analysts’ stock recommendations using a unique dataset of political contributions in the US over the period 1993–2012. I show that analysts’ recommendations are less profitable for firms with high connectedness than for firms with low (or no) connectedness. I also find that analysts are less effective in translating earnings forecasts into profitable recommendations for highly connected firms. Overall, the findings suggest that analysts do not impound all of the information concerning corporate political connections efficiently into their primary research outputs.     

The Effect of Issuing Biased Earnings Forecasts on Analysts' Access to Management and Survival

This study offers evidence on the earnings forecast bias analysts use to please firm management and the associated benefits they obtain from issuing such biased forecasts in the years prior to Regulation Fair Disclosure. Analysts who issue initial optimistic earnings forecasts followed by pessimistic earnings forecasts before the earnings announcement produce more accurate earnings forecasts and are less likely to be fired by their employers. The effect of such biased earnings forecasts on forecast accuracy and firing is stronger for analysts who follow firms with heavy insider selling and hard‐to‐predict earnings. The above results hold regardless of whether a brokerage firm has investment banking business or not. These results are consistent with the hypothesis that analysts use biased earnings forecasts to curry favor with firm management in order to obtain better access to management's private information.     

Determinants of analysts’ revenue forecast accuracy

This study investigates financial analysts’ revenue forecasts and identifies determinants of the forecasts’ accuracy. We find that revenue forecast accuracy is determined by forecast and analyst characteristics similar to those of earnings forecast accuracy—namely, forecast horizon, days elapsed since the last forecast, analysts’ forecasting experience, forecast frequency, forecast portfolio, reputation, earnings forecast issuance, forecast boldness, and analysts’ prior performance in forecasting revenues and earnings. We develop a model that predicts the usefulness of revenue forecasts. Thereby, our study helps to ex ante identify more accurate revenue forecasts. Furthermore, we find that analysts concern themselves with their revenue forecasting performance. Analysts with poor revenue forecasting performance are more likely to stop forecasting revenues than analysts with better performance. Their decision is reasonable because revenue forecast accuracy affects analysts’ career prospects in terms of being promoted or terminated. Our study helps investors and academic researchers to understand determinants of revenue forecasts. This understanding is also beneficial for evaluating earnings forecasts because revenue forecasts reveal whether changes in earnings forecasts are due to anticipated changes in revenues or expenses.     

High-Technology Intangibles and Analysts' Forecasts

This study examines the association between firms' intangible assets and properties of the information contained in analysts' earnings forecasts. We hypothesize that analysts will supplement firms' financial information by placing greater relative emphasis on their own private (or idiosyncratic) information when deriving their earnings forecasts for firms with significant intangible assets. Our evidence is consistent with this hypothesis. We find that the consensus in analysts' forecasts, measured as the correlation in analysts' forecast errors, is negatively associated with a firm's level of intangible assets. This result is robust to controlling for analyst uncertainty about a firm's future earnings, which we also find to be higher for firms with high levels of internally generated (and expensed) intangibles. Given that analyst uncertainty increases and analyst consensus decreases with the level of a firm's intangible assets, we also expect and find that the degree to which the mean forecast aggregates private information and is more accurate than an individual analyst's forecast increases with a firm's intangible assets. Finally, additional analysis reveals that lower levels of analyst consensus are associated with high-technology manufacturing companies, and that this association is explained by the relatively high R&D expenditures made by these firms. Overall, our results are consistent with financial analysts augmenting the financial reporting systems of firms with higher levels of intangible assets (in terms of contributing to more accurate earnings expectations), particularly R&D-driven high-tech manufacturers.     

Value relevance of analyst earnings forecasts in emerging markets

Even though research in accounting and finance has extensively examined the role of financial analysts in developed economies, this issue has not been thoroughly examined in an emerging market setting. In this paper, I examine whether, following a market opening, analyst forecast accuracy and the market's reliance on analyst forecasts increase with time. Accuracy is expected to increase over time as analysts exert more effort and gain valuable forecasting experience. Results indicate that time is positively related to analyst forecast accuracy after controlling for a number of other firm and country characteristics. Second, I posit that time should also be related to the market's propensity to use analyst forecasts to form earnings expectations. As markets open and investors become more sophisticated, the reliance on analyst forecasts should also increase. Results are consistent with this expectation. In particular, I find that in the first sub-period earnings expectations based on random walk exhibit greater relative information content than earnings expectations based on analyst forecasts. This pattern is reversed in the third sub-period where analyst forecast errors better explain returns. Incremental information content tests produce similar results. Future research should further investigate the relation between financial analysts and other important market characteristics in emerging economies.     

Analysts' forecasts of Japanese firms' earnings: additional evidence

This article examines analysts' forecasts of Japanese firms' earnings during Japan's economic burst period in the 1990s. Using the evidence of analyst earnings forecasts in the United States as a benchmark, the article documents the following three findings. First, whereas the forecast accuracy of U.S. analysts following U.S. firms improves over time, the forecast accuracy of U.S. and Japanese analysts following Japanese firms does not. Second, whereas decreases in forecast errors of U.S. analysts following U.S. firms are best explained by decreases in forecast bias of the analysts, increases in forecast errors of U.S. and Japanese analysts following Japanese firms are best explained by increases in the frequency of losses experienced by Japanese firms. Third, Japanese analysts forecast earnings less accurately than do U.S. analysts. These findings reflect the difficulty of producing accurate earnings forecasts during economic downturns. They also suggest that Japanese analysts are more bound than their U.S. counterparts by cultural ties that impede forecast accuracy.     

Analysts’ stock recommendations,earnings growth and risk

A key output of sell‐side analysts is their recommendations to investors as to whether they should, buy, hold or sell a company's shares. However, relatively little is known regarding the determinants of those recommendations. This study considers this question, presenting results that suggest that recommendations are dependent on analysts’ short‐term and long‐term earnings growth forecasts, as well as on proxies for the analysts’ unobservable views on earnings growth in the more distant future and risk. Furthermore, analysts who appear to incorporate earnings growth beyond the long‐term growth forecast horizons and risk into their recommendation decisions make more profitable stock recommendations.     

Sell‐Side Analyst Research and Stock Comovement

We document that a stock's price around a recommendation or forecast covaries with prices of other stocks the issuing analyst covers. The effect of shared analyst coverage on stock price comovement extends beyond analyst activity days. A stock's daily returns covary with the returns of other stocks with which it shares analyst coverage. These links between stock price comovement and shared analyst coverage are consistent with the coverage‐specific information we find in earnings forecasts; analysts who cover both stocks in a pair expect future earnings of the stocks to be more highly correlated than do analysts who cover only one stock from the pair. Collectively, our evidence indicates that analyst research produces coverage‐specific spillovers that raise price comovement among stocks that share analyst coverage. The strength of these spillovers is comparable to spillovers from broad industry and market information in analyst research.     

Understanding analysts' use of stock returns and other analysts' revisions when forecasting earnings

We investigate analysts' use of stock returns and other analysts' forecast revisions in revising their own forecasts after an earnings announcement. We find that analysts respond more strongly to these signals when the signals are more informative about future earnings changes. Although analysts underreact to these signals on average, we find that analysts who are most sensitive to signal informativeness achieve superior forecast accuracy relative to their peers and have a greater influence on the market. The results suggest that the ability to extract information from the actions of others serves as one source of analyst expertise.     

公平信息披露与分析师预测精度——来自中国上市公司的经验证据

证券分析师是证券市场重要的信息加工者和传播者,他们的信息行为对中小投资者和市场效率有重要影响。本文基于2003~2009年分析师的年度盈利预测数据,运用面板计量模型实证检验了公平信息披露规则的实施对分析师预测精度的影响。研究结果表明:分析师预测精度在规则实施后显著下降了;而且,随着规则实施时间的推移,分析师预测精度进一步下降;另外,分析师对信息披露水平较差的上市公司的预测精度下降幅度更大。     

An examination of the effects of management earnings forecast form and explanations on financial analyst forecast revisions

Using a sample of 978 quarterly management earnings-per-share forecasts made during the period 1993 to 1999, we document that financial analyst revisions to management earnings forecasts are a function of management forecast form. More precise forecasts (measured three different ways) lead to greater revision of financial analyst consensus EPS forecasts for a given level of unexpected earnings as predicted by Kim and Verrecchia (1991) and Bayesian adjustment models. Also, consistent with our arguments, maximum forecasts are interpreted as bad news by analysts. Our results, while consistent with theory, are inconsistent with recent experimental studies which do not reject the null hypothesis of no effect of management earnings forecast form on the association between unexpected earnings and financial analyst forecast revisions. We also re-examine Baginski, Hassell, and Kimbrough's (2004) finding that attributions used to explain management forecasts affect the reaction to the forecast using analyst data. Consistent with their findings using stock prices, the attribution presence (especially external attributions) increases financial analyst revisions pursuant to management forecasts.     

How efficient is the market for Australian firms’ earnings information?

We construct a measure of the speed with which forecasts issued by sell-side analysts accurately forecast future annual earnings. Following Marshall, we label this measure earnings information flow timeliness (EIFT). This measure avoids the aggregation problem inherent in price-based measures of information efficiency. We document large variation in EIFT across firm-years, and show that EIFT is positively associated with the extent of analyst following, consistent with increased analyst coverage improving the speed with which earnings-related information is recognised. We also find that EIFT is higher for firm-years classified as ‘bad news’ (i.e., where analysts’ forecasts at the start of the financial period exceed the reported outcome). However, when we separately consider instances where analysts appear to forecast non-GAAP (or ‘street’) earnings rather than GAAP earnings, we find that the greater timeliness of bad news is concentrated among observations where analysts forecast non-GAAP earnings, where unusual items are typically excluded. We conclude that the market for accounting information is more efficient for negative operating outcomes than for negative outcomes reflecting unusual items.     

What Drives Target Price Forecasts and Their Investment Value?

This paper examines the informativeness of analysts’ target price forecasts by relating the investment value of target prices to their primary drivers. Decomposing target price forecasts into near‐term earnings forecasts and price‐to‐earnings ratio forecasts, we show that target price revisions reflect information from both components. In addition, we also find that the relative importance of each component in target price revisions is related to firm characteristics. A portfolio based on target price implied expected returns delivers significant abnormal returns. More importantly, we find that the abnormal returns are associated with both earnings and price‐to‐earnings forecasts, which suggests that the informativeness of target price forecasts comes not only from analysts’ ability to forecast short‐term earnings but also from their ability to assess risk and long‐term growth prospect implied in price‐to‐earnings forecasts.     

The Effect of CEO Extraversion on Analyst Forecasts: Stereotypes and Similarity Bias

In an experiment with professional analysts, we study their reliance on CEO personality information when producing financial forecasts. Drawing on social cognition research, we suggest analysts apply a stereotyping heuristic, believing that extraverted CEOs are more successful. The between‐subjects results with CEO extraversion as treatment variable confirm that analysts issue more favorable forecasts (earnings per share, long‐term earnings growth, and target price) for firms led by extraverted CEOs. Increased forecast uncertainty leads to even stronger stereotyping. Additionally, personality similarity between analysts and CEOs has a large effect on financial forecasts. Analysts issue more positive forecasts for CEOs similar to themselves.