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.     

Does short selling reduce analysts’ optimism bias in earnings forecasts?

We examine the relationship between short selling and analyst optimism bias of earnings forecasts of stocks in China. We propose two possible hypotheses: short selling enhances analysts’ information set to lower optimism bias and analysts drum up optimistic earnings forecasts to counter short selling. Our results suggest that short selling and earnings optimism bias are negatively correlated. The findings are robust to a two-stage least square method, a difference in-differences fixed effect model, an alternative measure of optimism bias, incorporated different time windows to calculate short selling, considered bull and bear market conditions, accounting for media coverage, and using abnormal short selling. By leveraging rich data, we conduct additional analyses using stocks with different forecasting horizons and earnings forecasts that were made after the accounting year but prior to the earnings announcement to further support expanding the information set argument. Lastly, we document that short selling information improves the accuracy of earnings forecasts.     

Key performance indicators as supplements to earnings: Incremental informativeness,demand factors,measurement issues,and properties of their forecasts

The documented decline in the information content of earnings numbers has paralleled the emergence of disclosures, mostly voluntary, of industry-specific key performance indicators (KPIs). We find that the incremental information content conveyed by KPI news is significant for many KPIs yet diminished when details about the computation of the KPI are absent or when the computation changes over time. Consistent with analysts responding to investor information demand, we find that analysts are more likely to produce forecasts for a KPI when that KPI has more information content and when earnings are less informative. We also analyze the properties of analysts’ KPI forecasts and find that KPI forecasts are more accurate than mechanical forecasts and their accuracy exceeds that of earnings forecasts. Our study contributes to the literature on the information content of KPIs as well as research on the properties of analysts’ forecasts. We provide evidence on whether and how to regulate voluntary disclosures.

     

When does analyst reputation matter? Evidence from analysts’ reliance on management guidance

We investigate the joint effects of analyst reputation, uncertainty and guidance news valence on analysts’ reliance on management guidance. We find that, compared to less reputable analysts, reputable analysts rely less on guidance when they issue earnings forecasts. This analyst reputation effect is stronger when earnings and information uncertainty are higher or when the guidance contains good news. Further analysis suggests that both reputable and less reputable analysts sacrifice their forecast accuracy when they rely less on guidance; however, reputable analysts are compensated to a greater extent by the increased informativeness of their forecasts. Finally, we find that analysts’ future career advancement is enhanced when their reliance is low.     

THE ASSOCIATION BETWEEN ANALYSTS’ FORECAST REVISIONS AND EARNINGS COMPONENTS: THE EVIDENCE OF FRS 3

This study examines whether the earnings components as required by FRS 3 help UK analysts to predict firms’ earnings changes by investigating the statistical association between analysts’ forecast revisions and firms’ unexpected earnings components. I find that analysts’ forecast revisions made in different time horizons are consistently associated with unexpected earnings components as required by FRS 3. UK analysts are able to incorporate current-year unexpected earnings components into their current and future earnings forecasts even before firms officially release this information. However, empirical results also show that current-year unexpected earnings components are not fully incorporated into analysts’ forecasts of future earnings. Analysts appear to wait for more information releases regarding firms’ future earnings and delay their revisions of future earnings forecasts. This is consistent with the evidence that the cumulative revisions of current earnings forecasts are generally associated with prior-year unexpected earnings components, and the association appears to be stronger as time progresses. Overall, this study provides evidence suggesting that the earnings components as required by FRS 3 help UK analysts to identify firms’ permanent and transitory earnings changes over different forecast horizons. This study also provides strong evidence supporting the informativeness of earnings components for analysts’ forecasts and the information set perspective of FRS 3 that highlights the importance of earnings components in predicting a reporting entity’s future performance.     

Inside the “Black Box” of Sell‐Side Financial Analysts

Our objective is to penetrate the “black box” of sell‐side financial analysts by providing new insights into the inputs analysts use and the incentives they face. We survey 365 analysts and conduct 18 follow‐up interviews covering a wide range of topics, including the inputs to analysts’ earnings forecasts and stock recommendations, the value of their industry knowledge, the determinants of their compensation, the career benefits of Institutional Investor All‐Star status, and the factors they consider indicative of high‐quality earnings. One important finding is that private communication with management is a more useful input to analysts’ earnings forecasts and stock recommendations than their own primary research, recent earnings performance, and recent 10‐K and 10‐Q reports. Another notable finding is that issuing earnings forecasts and stock recommendations that are well below the consensus often leads to an increase in analysts’ credibility with their investing clients. We conduct cross‐sectional analyses that highlight the impact of analyst and brokerage characteristics on analysts’ inputs and incentives. Our findings are relevant to investors, managers, analysts, and academic researchers.     

Macro information environment change and the quality of management earnings forecasts

The 1990s were characterized by substantial increases in the performance of and investor reliance on financial analysts. Because managers possess superior private information and issue forecasts to align investors’ expectations with their own, we predict that managers increased the quality of their earnings forecasts during the 1990s in order to keep pace with the improved forward-looking information provided by financial analysts, upon which investors increasingly relied. Using a sample of 2,437 management earnings forecasts, we document an increase in management earnings forecast precision, management earnings forecast accuracy, and managers’ tendency to explain earnings forecasts in 1993–1996 relative to 1983–1986. Given that these forecast characteristics are linked to greater informativeness and credibility, we also document that the information content of management earnings forecasts, as measured by the strength of share price responses to forecast news, increased in 1993–1996 relative to 1983–1986. As expected, the increased information content of management forecasts primarily occurred for firms covered by financial analysts.

Detailed management earnings forecasts: do analysts listen?

We provide archival evidence on how a particular type of supplementary information affects the credibility of management earnings forecasts. Managers often provide detailed forecasts of specific income statement line items to shed light on how they plan to achieve their bottom-line earnings targets. We assess the effect of this forecast disaggregation on the credibility of management earnings forecasts. Based on a relatively large hand-collected sample of 900 management earnings forecasts, we find that disaggregation increases analysts’ sensitivity to the news in managers’ earnings guidance, suggesting that analysts find the guidance more credible. More importantly, we identify several factors that influence this relation. First, disaggregation plays a more important role when earnings are otherwise more difficult to forecast. Second, disaggregation is more important after Regulation Fair Disclosure prohibited selective disclosure, especially for firms that were more affected because they had previously provided more private guidance. Finally, in contrast to common assertions in the prior literature, we find that, in more recent years, disaggregation matters more for guidance that conveys bad news. Managers as well as researchers should be interested in evidence suggesting that financial analysts find disaggregation especially helpful in contexts where managers’ credibility is particularly important.     

Does revenue-expense matching play a differential role in analysts’ earnings and revenue forecasts?

This paper investigates whether matching has differential implications for the accuracy of analysts' earnings and revenue forecasts. We construct a novel measure of firm-level matching and document that matching improves analysts' earnings forecasts to a greater extent than their revenue forecasts. We also document matching's differential impact on analysts' earnings and sales forecasts by proposing a new count metric capturing a wedge in the accuracy of earnings and revenue forecasts. In additional tests, we report that the differential impact of matching is less (more) pronounced in a situation where the balance sheet (income statement) orientation likely dominates. We also report that matching's differential role is weaker (stronger) when firms have high intangible intensity (analysts have appropriate resources or expertise). In short window tests, matching's role in analysts' forecast revisions is more pronounced for earnings than sales forecasts. Overall, these results show how analysts benefit from better revenue-expense matching.     

Are analysts’ earnings forecasts more accurate when accompanied by cash flow forecasts?

We examine whether analysts’ earnings forecasts are more accurate when they also issue cash flow forecasts. We find that (i) analysts’ earnings forecasts issued together with cash flow forecasts are more accurate than those not accompanied by cash flow forecasts, and (ii) analysts’ earnings forecasts reflect a better understanding of the implications of current earnings for future earnings when they are accompanied by cash flow forecasts. These results are consistent with analysts adopting a more structured and disciplined approach to forecasting earnings when they also issue cash flow forecasts. Finally, we find that more accurate cash flow forecasts decrease the likelihood of analysts being fired, suggesting that cash flow forecast accuracy is relevant to analysts’ career outcomes.     

Analyst Pessimism and Forecast Timing

In this study, we show that on average relatively pessimistic analysts tend to reveal their earnings forecasts later than other analysts. Further, we find this forecast timing effect explains a substantial proportion of the well‐known decrease in consensus analyst forecast optimism over the forecast period prior to earnings announcements, which helps explain why analysts’ longer term earnings forecasts are more optimistically biased than their shorter term forecasts. We extend the theory of analyst self‐selection regarding their coverage decisions to argue that analysts with a relatively pessimistic view–compared to other analysts–are more reluctant to issue their earnings forecasts, with the result that they tend to defer revealing their earnings forecasts until later in the forecasting period than other analysts.     

Analysts’ earnings forecasts: coexistence and dynamics of overconfidence and strategic incentives

This paper formulates a two-stage model to capture the decision process of financial analysts when issuing earnings forecasts. Our model extends the model of Chen and Jiang [(2005). Analysts’ weighting of private and public information. Review of Financial Studies, 19 (1), 319–355], by allowing for a distortion of forecasts independent of whether an analyst has private information. Using quarterly earnings forecasts, we provide empirical evidence on the coexistence of overconfidence and strategic incentives. Financial analysts overweight their private information and at the same time strategically inflate their forecast.     

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.     

Contemporaneous verification of language: evidence from management earnings forecasts

Research documents that linguistic tone is incrementally informative about stock returns. What remains a puzzle is the mechanism by which investors can assess its credibility. We examine whether contemporaneous information in management earnings forecasts serves as a timely alternative to ex post verification. We document that ex post verifiable quantitative news in unbundled forecasts, and characteristics of the linguistic tone itself, affect investors’ pricing of tone. Consistent with higher quality signals enhancing the credibility of contemporaneous lower quality signals, we find that quantitative news verifies the associated linguistic tone; when the two signals have the same sign, the price effect of tone is stronger. Furthermore, the pricing attenuation of tone is increasing in the imprecision of the quantitative forecast, suggesting that lower forecast quality compromises the quantitative signal’s credibility enhancement. Managerial incentives to inflate tone lead to the verification effect being greater for optimistic language, while management’s use of hyperbole results in attenuation of the tone’s pricing.     

Real earnings management and the properties of analysts' forecasts

Prior literature generally finds analysts are able to identify and process complex financial information. However, research suggests that in certain settings, analysts struggle to fully incorporate into their forecasts all available information. We examine analysts' forecast properties in the face of a specific type of complex financial information: real earnings management (REM). First, we investigate the relation between measures of REM and analysts' forecast properties. We find REM measures are associated with greater forecast error and dispersion in the following year. However, REM measures, by definition, capture abnormal operating results, and thus include both firms engaging in manipulative REM as well as firms experiencing firm-specific economic shocks. Thus, we conduct cross-sectional tests of analysts' forecasts for firms with and without incentives to manipulate earnings. We find that firms with low incentives to engage in earnings management (i.e., firms most likely experiencing firm-specific economic shocks) generate the strongest positive relation between REM measures and the following year's analysts' forecast properties, suggesting analysts more fully incorporate the earnings implications of firms with high incentives (i.e., firms most likely engaging in manipulative REM). Our results are consistent across numerous REM proxies and indicators of earnings management incentives.     

Can Stock Recommendations Predict Earnings Management and Analysts’ Earnings Forecast Errors?

In this article we present evidence that a firm's stock price sensitivity to earnings news, as measured by outstanding stock recommendation, affects its incentives to manage earnings and, in turn, affects analysts' ex post forecast errors. In particular, we find a tendency for firms rated a Sell (Buy) to engage more (less) frequently in extreme, income–decreasing earnings management, indicating that they have relatively stronger (weaker) incentives to create accounting reserves especially in the form of earnings baths than other firms. In contrast, firms rated a Buy (Sell) are more (less) likely to engage in earnings management that leaves reported earnings equal to or slightly higher than analysts' forecasts. Our empirical results provide direct evidence of purported, but heretofore, weakly documented equity market incentives for firms to manage earnings. They are also consistent with a growing body of literature that finds analysts either cannot anticipate or are not motivated to anticipate completely in their forecasts firms' efforts to manage earnings.     

Analysts' Rationality and Forecast Bias: Evidence from Sales Forecasts

When optimistic forecasts can improve access to management, rational analysts have incentives to issue optimistically-biased forecasts (Lim, 2001). This paper proposes that the extent of this optimistic forecast bias will depend on the forecast's importance to management. If management attaches less importance to a forecasted measure, analysts should decrease their forecast bias because the expected benefits of issuing optimistic forecasts are less. We examine analysts' earnings and sales forecasts, and predict that analysts' optimistic bias will be greater for earnings than for sales. Results are consistent with our predictions and contribute to the evidence that analysts' forecast bias is rational and intentional.     

Underwriting Relationships and Analyst Independence in Europe

This paper examines the accuracy of security analysts’ earnings forecasts and stock recommendations for firms in 13 European countries. We document at least three key findings. First, we find strong evidence that lead and co‐lead underwriter analysts’ earnings estimates and stock recommendations are significantly more optimistic than those provided by unaffiliated analysts. Second, we find that lead and co‐lead underwriter analysts’ earnings forecast and stock recommendations are significantly more optimistic for underwriter stocks than for those they provide for other stocks. Third, we also find evidence that these biases found within earnings forecasts and stock recommendations are not driven by one particular country. In short, these findings suggest that affiliated analysts are more optimistic perhaps to maintain investment banking relations.     

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.     

Do banks care about analysts' forecasts when designing loan contracts?

We investigate whether banks rely on the information content in equity analysts’ annual earnings forecasts when assessing the risk of potential borrowers. While a long literature finds that analysts provide useful information to market participants, it is not clear that banks, which have access to privileged information, would benefit from publicly available analysts’ forecasts. If, however, banks do rely on this information, then more precise private information in earnings forecasts may inform banks. We focus our analysis on the requirement of collateral because it is a direct measure of default risk, whereas other loan terms such as interest spread and debt covenants can also protect against other risks, such as asset misappropriation. The direct link between collateral and default risk allows us to examine whether information from analysts is relevant to banks when designing loan contracts. Consistent with our predictions, we find that higher precision of the private information in analysts’ earnings forecasts is associated with a lower likelihood of requiring collateral, and this effect is larger when a borrower does not have a prior relationship with the lender or their accounting or credit quality is low. We also find that this association disappears after the implementation of Regulation FD, consistent with this regulation reducing analysts’ access to private information.