Refining the Degree of Earnings Surprise: A Comparison of Statistical and Analysts' Forecasts

This paper compares the relative predictive ability of several statistical models with analysts' forecasts. It is one of the first attempts to forecast quarterly earnings using an autoregressive conditional heteroskedasticity (ARCH) model. ARCH and autoregressive integrated moving average models are found to be superior statistical forecasting alternatives. The most accurate forecasts overall are provided by analysts. Analysts have both a contemporaneous and timing advantage over statistical models. When the sample is screened on those firms that have the largest structural change in the earnings process, the forecast accuracy of the best statistical models is similar to analysts' predictions.     

Can security analyst forecasts predict gold returns?

This paper examines whether security analyst earnings forecasts for firms primarily operating in the gold market can be utilised to predict returns on the price of gold. We first demonstrate that analysts are at least in part basing their earnings forecasts for gold firms on the return expectations of the gold commodity market. We show this by providing evidence that analyst coverage impounds not only market-wide and industry information, but also gold price information for these firms — as measured via its impact on stock return synchronicity. We then examine if the difference between forecast and observed earnings for these firms has predictive value for changes in the price of gold whilst controlling for a number of macroeconomic factors. We find that this difference does hold predictive power, but also has some limitations. However, there is potential for it to be used as an additional variable within gold forecasting frameworks.     

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.     

Analyst cash flow forecasts and pricing of accruals

This paper investigates how analyst cash flow forecasts affect investors' valuation of accounting accruals. We find that the strength of the accrual anomaly documented in Sloan (1996) is weaker for firms with analyst cash flow forecasts, after controlling for idiosyncratic risk, transaction costs and firm characteristics associated with the issuance of cash flow forecasts. We further show that this reduction in mispricing of accounting accruals is at least partially attributed to the improved ability of investors to price earnings manipulations imbedded in accruals. We investigate several non-mutually exclusive alternative explanations for this improvement in investors' ability and demonstrate that the increased investor attention and the improved accuracy of analyst earnings forecasts both contribute to the mitigation of the accrual anomaly.     

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.     

Why do pro forma and Street earnings not reflect changes in GAAP? Evidence from SFAS 123R

This study examines how key market participants??managers and analysts??responded to SFAS 123R??s controversial requirement that firms recognize stock-based compensation expense. Despite mandated recognition of the expense, some firms?? managers exclude it from pro forma earnings and some firms?? analysts exclude it from Street earnings. We find evidence consistent with managers opportunistically excluding the expense to increase earnings, smooth earnings, and meet earnings benchmarks but no evidence that these exclusions result in an earnings measure that better predicts future firm performance. In contrast, we find that analysts exclude the expense from earnings forecasts when exclusion increases earnings?? predictive ability for future performance and that opportunism generally does not explain exclusion by analysts incremental to exclusion by managers. Thus our findings indicate that opportunism is the primary explanation for exclusion of the expense from pro forma earnings and predictive ability is the primary explanation for exclusion from Street earnings. Our findings suggest the controversy surrounding the recognition of stock-based compensation expense may be attributable to cross-sectional variation in the relevance of the expense for equity valuation, as well as to differing incentives of market participants.     

MANAGEMENT EARNINGS FORECASTS: THEIR USEFULNESS AS A SOURCE OF FIRM-SPECIFIC INFORMATION TO SECURITY ANALYSTS

Although managers frequently release earnings forecasts, little is known about how this information affects investor beliefs. This study compares changes in analyst earnings forecasts following the release of management forecasts: (1) to changes in analyst forecasts of a control sample of nonforecasting firms; and (2) between management forecasts with differing degrees of accuracy. The forecasting error of analyst estimates for firms releasing management forecasts decreases more rapidly than the errors associated with the control firms, which implies that management forecasts are useful. Analysts apparently are capable of determining which management forecasts are most accurate and responding appropriately.     

The Level and Persistence of Growth Rates

Expectations about long-term earnings growth are crucial to valuation models and cost of capital estimates. We analyze historical long-term growth rates across a broad cross section of stocks using several indicators of operating performance. We test for persistence and predictability in growth. While some firms have grown at high rates historically, they are relatively rare instances. There is no persistence in long-term earnings growth beyond chance, and there is low predictability even with a wide variety of predictor variables. Specifically, IBES growth forecasts are overly optimistic and add little predictive power. Valuation ratios also have limited ability to predict future growth.     

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.     

Additional Evidence on Analysts’ Decision to Issue Disaggregated Earnings Forecasts: Strategic Biasing

This study examines why analysts issue disaggregated earnings forecasts to I/B/E/S. Some recent studies suggest that analysts with superior forecasting ability issue disaggregated earnings forecasts to build reputation in the marketplace and stop forecast disaggregation once their reputation has been established. Based on an analysis of I/B/E/S forecast data for U.S. firms from 1998 to 2008, we find that, in a given year, about 20%–34% of analysts disaggregate for some, but not for all the firms that they follow. This evidence of selective disaggregation by analysts suggests that reputation building alone does not fully explain the decision to disaggregate forecasts. We hypothesize that the decision to disaggregate earnings forecasts is at the firm‐level as well and is systematically related to the analysts’ bias in the issued forecasts. Our findings are that (a) analysts’ overall optimistic bias and forecast errors decrease monotonically with the level of forecast disaggregation, and (b) analysts that selectively disaggregate their forecasts for some firms or who do not persistently disaggregate a given firm's forecasts exhibit more positive bias and larger forecast errors. Our findings are consistent with the notion that the analysts who issue biased forecasts, for example, to curry favour with the management, are less likely to provide disaggregated information as part of the forecast.     

Analysts' Forecasts Following Forced CEO Changes

This paper examines analysts' earnings forecasts during the period of uncertainty following a change of chief executive officer (CEO). It distinguishes between forced and non‐forced CEO changes, and examines whether analysts utilize their information advantage to reduce the heightened uncertainty of a forced change of CEO. Examining a sample of Australian companies followed by analysts between 1999 and 2009, we find that forecasting accuracy is lower and earnings forecasts are more optimistic for firms experiencing forced CEO turnover compared to firms not undergoing such a change. However, dispersion is not statistically different. The results suggest that forced CEO turnover events provide a challenge to the forecasting environment for analysts. During CEO changes, investors should be aware that forecasts are less accurate and have an optimistic bias.     

Analyst forecast,accounting conservatism and the related valuation implications

This study investigates whether financial analysts incorporate accounting conservatism into their earnings forecasts and whether it is more difficult for them to forecast earnings for less conservative firms, and then examines the impact of the findings on the return predictability of the value‐to‐price (V/P) ratio. After controlling for the other factors affecting forecast accuracy, such as earnings predictability and information uncertainty, I find that analysts incorporate accounting conservatism into their earnings forecasts and that forecasting earnings is more difficult for less conservative firms. Consequently, the return predictability of the V/P ratio is stronger for more conservative firms, and previously reported return predictability of the V/P ratio is an average across firms with differing levels of conservatism.     

Accrual usage to manage earnings toward financial analysts' forecasts

This study examines whether management uses discretionary accounting accruals to move earnings upward toward analysts' earnings forecasts when it appears that earnings before discretionary accruals will fall short of the forecast. An earnings shortfall relative to analysts' forecasts could lead management to fear lower compensation and an increase in the likelihood of job termination. The article finds that firms whose earnings before discretionary accruals are below analysts' forecasts use income-increasing discretionary accruals and do so to a greater extent than do firms whose earnings before discretionary accruals are above analysts' forecasts.     

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.     

The Treatment of Non-Recurring Items in the Income Statement and their Consistency with FASB Concept Statements

This study provides evidence on the consistency of Accounting Principles Board Statement No. 30 (APB, 1973) classification criteria with the objectives of the Financial Accounting Standards Board's Concept Statements Nos 1 and 2 (FASB, 1978, 1980). It is hypothesized that the current APB 30 requirement to classify items of a non-recurring nature in the operating section of the income statement decreases the predictive ability of income before extraordinary items. A random sample of 50 firms with non-recurring adjustments to income, which were included in the operating section of the income statement, was selected from Standard and Poor's Corporation Records. Naive models were used to generate earnings per share forecasts for the year in which the adjustment to income occurred, the prior year and subsequent year.
The results indicate a statistically significant decrease in the predictive ability of earnings per share before extraordinary items associated with the year that the adjustment occurred and a significant increase in the variability of earnings per share. Also, differences in predictive ability were noted between small and large firms and firms with positive and negative adjustments to income.
The results of this study also imply that the managers of most firms with negative adjustments to income are not using the adjustments to smooth income for either the purpose of decreasing the variability of earnings or increasing predictability. The results are more consistent with the 'big bath' theory. These conclusions appear to be more relevant for smaller firms than larger firms.     

Financial Analyst Characteristics and Herding Behavior in Forecasting

This study classifies analysts' earnings forecasts as herding or bold and finds that (1) boldness likelihood increases with the analyst's prior accuracy, brokerage size, and experience and declines with the number of industries the analyst follows, consistent with theory linking boldness with career concerns and ability; (2) bold forecasts are more accurate than herding forecasts; and (3) herding forecast revisions are more strongly associated with analysts' earnings forecast errors (actual earnings—forecast) than are bold forecast revisions. Thus, bold forecasts incorporate analysts' private information more completely and provide more relevant information to investors than herding forecasts.     

Valuing Loss Firms: What Can Be Learned From Analysts' Forecasts?

Prior research documents an anomalous negative price–earnings relation when a simple earnings capitalization model is estimated for loss‐making firms. Collins et al. (1999 ) suggest that the model is misspecified due to the omission of book value of equity. However, results from previous studies are confusing. We try to enrich prior literature by focusing on analysts' forecasts. In particular, we assess the role of earnings and book value in valuing loss firms using several measures based on the information provided by analysts. We hypothesize that the role of accounting figures depends on whether the loss firm is supported or not by investors. According to this argument, we construct several measures of investor support based on analysts' forecasts, and then test the value relevance of accounting information depending on the degree of support. Our results confirm the usefulness of the notion of ‘investor support’. For those loss firms that are expected to liquidate, we find that the inclusion of book value of equity in the model removes the negative sign on the earnings coefficient. However, for those loss firms that are expected to reverse current losses, we find that the coefficient on earnings remains negative despite the inclusion of book value.     

The Dynamics of Earnings Forecast Management

This paper examines whether firms manage analyst forecasts andthe associated value consequences. We find that earnings forecaststend to grow pessimistic over the forecast horizon and theseforecast changes and their timing are key determinants of whetherfirms generate positive earnings surprises: Late forecasts thatraise (lower) the consensus sharply reduce (raise) the probabilityof positive surprises. This findng is the opposite of that predictedif consensus revisions reflected new information arrival. Investorsseem to be "misled": downward consensus revisions lead to largeabnormal returns following the earnings announcement. Paradoxically,downward forecast management reduces post-announcement shareprice, as the impact of reduced forecasts dominates the gainfrom generating positive surprises.     

Beating market expectations and the pricing of firms’ probability of default

This study explores the impact of beating analysts' forecasts on investors' perceptions about firms' default probability. The information contained in analysts’ forecasts, both earnings and revenues, provides additional information to investors in pricing CDSs. While previous research has focused on the impact of beating analysts’ earnings forecasts, this study shows that firms that beat analysts' revenue forecasts also experience, on average, a decrease in the CDS premium around the earnings announcement date. This study also documents that the effect is stronger when firms beat/miss both earnings and revenue forecasts. When firms beat (miss) earnings and miss (beat) revenues, the effect of earnings is the dominant signal. These effects are stronger for firms with high levels of default risk.     

The impact of meeting or beating analysts' operating cash flow forecasts on a firm's cost of debt

Extant literature provides conflicting results with respect to the usefulness and accuracy of analysts' operating cash flow forecasts. Our study empirically examines the importance and influence of meeting or beating analysts' operating cash flow forecasts on a firm's cost of debt. Results indicate that firms meeting/beating analysts' cash flow forecasts have higher initial bond ratings as well as lower initial bond yields. Additionally, based upon an analysis of rating changes, firms meeting or beating cash flow forecasts have a higher probability of receiving a debt rating upgrade and a lower probability of a ratings downgrade compared to firms missing cash flow forecasts. A direct comparison of the importance of meeting/beating cash flow versus earnings benchmarks indicates that debt market participants appear to incrementally value both types of forecasts, and contrary to selected equity market findings, neither forecast subsumes the other for debt market participants.