Analysts’ accrual-related over-optimism: do analyst characteristics play a role?

Bradshaw et al. (J Acc Res 39:45–74, 2001) find that analyst forecast over-optimism is greater for firms with high accruals. This “accrual-related over-optimism” is generally interpreted as evidence that analyst forecasts do not fully incorporate predictable earnings reversals associated with high accruals. We investigate whether analyst experience, access to resources (brokerage size), and portfolio complexity moderate the relation between over-optimistic forecasts and high accruals. We demonstrate the robustness of accrual-related over-optimism to controls for cash flow and prior forecast errors. We find that accrual-related over-optimism is lower for analysts with greater general experience and for analysts following fewer firms but find only limited evidence of lower accrual-related over-optimism for analysts from larger brokerages and for analysts following fewer industries.     

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.     

The role of accounting conservatism in management forecast bias

We investigate whether management earnings forecasts fully incorporate information in historical accounting conservatism. We find that management earnings forecasts are more optimistic for firms with greater accounting conservatism in the previous year. We further examine whether this conservatism-related optimistic bias in management earnings forecasts varies with managers’ difficulty predicting earnings accurately, managers’ opportunistic incentives, and the firms’ litigation risk. We find that the negative association between management forecast errors and conservatism increases, to various extent, with the firms’ operating cycles, earnings volatility, and the width of forecast range but does not change with proxies for opportunistic incentives or litigation risk. These results suggest that forecast difficulty is the primary reason for managers’ failure to incorporate conservatism fully in their earnings forecasts.     

How well do investors understand loss persistence?

This paper examines investors’ expectations of loss persistence. I develop a model to forecast loss firms’ future earnings based on Joos and Plesko, The Accounting Review 80: 847–870, (2005). This model produces smaller forecast errors than two random walk models and a model that assumes losses are transitory. The results suggest that investors do not fully distinguish the differences in loss persistence captured by the model and instead appear to assume that all losses are transitory. Consequently, investors are surprised by future announcements of negative earnings for firms with predicted persistent losses, and these firms experience significantly negative abnormal returns over the following four quarters. Additional results indicate that the future negative returns of firms with predicted persistent losses are smaller in magnitude when these firms are followed by analysts. The results are robust to controls for various price anomalies and are not driven by short sale constraints.     

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.     

Sources of earnings variability and their effect on earnings forecasts

Previous research shows that analysts’ forecasts of earnings do not fully incorporate information contained in reported earnings variability. This study investigates whether the inefficient forecast is because of a failure to incorporate observable information on two components of earnings variability: variability in operating performance and income smoothing. Our results show that analysts’ forecasts fully incorporate information contained in earnings variability for firms with high income smoothing and for firms with low operating variability. A smaller serial correlation of forecast errors is observed for firms with low operating variability, which suggests that analysts recognize the permanence in earnings for such firms.     

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.     

Analysts’ pre-tax income forecasts and the tax expense anomaly

This paper examines whether analysts’ pre-tax income forecasts mitigate the tax expense anomaly documented by Thomas and Zhang (J Account Res 49:791–821, 2011). They find that seasonal changes in quarterly income tax expense are positively related to future returns after controlling for the earnings surprise and conclude that investors underreact to value-relevant information in tax expense. When analysts issue both earnings and pre-tax income forecasts, they implicitly provide a forecast of income tax expense. We posit that this implicit forecast helps investors recognize the persistence of current tax expense surprise for future earnings. Accordingly, we expect that mispricing of tax expense will be less severe for firms with earnings and pre-tax income forecasts. As expected, we find that the presence of pre-tax income forecasts significantly weakens the positive relation between tax expense surprise and future returns, consistent with analysts’ implicit forecasts of tax expense mitigating the tax expense 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.     

A discussion of “Do managers use earnings guidance to influence street earnings exclusions?”

It is well known that both managers and analysts frequently define earnings by excluding various amounts from GAAP earnings. Christensen et al. (Rev Account Stud, 2011) make a prediction of causality whereby managers actively influence how analysts define earnings. They argue that the mechanism through which managers accomplish this is guidance of analysts’ earnings forecasts within a fiscal period. Using a large sample of firms actively followed by analysts, the authors examine whether the existence of earnings guidance is associated with higher levels of total exclusions in analysts’ definition of earnings. The study provides suggestive evidence that managers actively influence analysts’ definition of earnings that they forecast. However, the indirect nature of the research design calls for additional work to specifically link directed guidance of GAAP earnings exclusions to amounts actually excluded by analysts.     

Do German security analysts herd?

We employ an innovative methodology suggested by Bernhardt et al. (J. Financ. Econ. 80:657–675, 2006) to examine the herding (or anti-herding) behavior of German analysts regarding earnings forecasts. This methodology avoids well-known shortcomings often encountered in related studies, such as correlated information signals, unexpected common shocks to earnings, systematic optimism or pessimism, or forecast target mismeasurement. Our findings suggest that German analysts anti-herd, that is, they systematically issue earnings forecasts that are further away from the consensus forecast than their private information indicates. Furthermore, we analyze the association between herding behavior and different characteristics, including the size of the brokerage, general or firm-specific experience, and the coverage of firms on the Neuer Markt. We mainly confirm findings for the United States, for example, that anti-herding is more severe in cases of higher competition among analysts. Contrary to anecdotal evidence, we also find anti-herding behavior in earnings forecasts for Neuer Markt firms during the “new economy” bubble.

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.     

The effect of repeat restructuring charges on analysts’ forecast revisions and accuracy

The primary objective of this study is to examine the effect of prior restructuring charges on analyst forecast revisions and accuracy. We find evidence that analysts respond differently to first-time restructuring firms than to repeat restructuring firms. Analysts revise their forecasts of both one-year-ahead earnings and five-year long-term growth in earnings more negatively for first-time restructuring firms than for firms with prior charges. When we examine forecast errors in the year subsequent to the restructuring, we find that current charges complicate analysts’ earnings forecast task. We further find that the decline in analyst forecast accuracy is mitigated by prior charges within past two years.     

Intangible Assets, Information Complexity, and Analysts' Earnings Forecasts

Abstract:   We examine the relation between analysts' earnings forecasts and firms' intangible assets, including technology‐based intangibles, brand names, and recognized intangibles. We predict that high information complexity of intangible assets increases the difficulty for analysts to assimilate information and increases analysts' forecast error of intangibles‐intensive firms. We find a positive association between analysts' forecast error and the firm's intangible intensity that deviates from the industry norm. We also find that analysts' forecast errors are greater for firms with diverse and innovative technologies. In contrast, analysts' forecast errors are smaller for biotech/pharmaceutical and medical equipment firms that are subject to intangibles‐related regulation.     

IPO Underpricing, Firm Quality, and Analyst Forecasts

We find that IPO underpricing is positively related to post-IPO growth in sales and EBITDA, but is not significantly related to growth in earnings. Our evidence suggests that accrual reversals or earnings management may cause this inconsistency. We interpret the growth rates of sales and EBITDA as measures of firm quality, and conclude that our evidence supports the notion that IPO firms with greater underpricing are of better quality. Our tests on analysts' earnings forecast errors show that analysts are less positively biased in their earnings forecasts for IPO firms that have greater underpricing.     

A Comparison of Between‐Country Measures of Legal Setting and Enforcement of Accounting Standards

Academics and practitioners agree that the enforcement of accounting standards has an important role in promoting high quality financial reporting and favourable capital market outcomes. We test three new enforcement proxies from Brown, Preiato and Tarca (2014) that focus specifically on auditing and accounting enforcement. We examine firms’ information environments, represented by the error in analysts’ consensus forecasts and the extent of disagreement among analysts, as indicated by forecast dispersion. For financial years ending from 2003 to 2009, we construct a sample of 357,034 firm–month observations on the errors and dispersion of analysts’ earnings forecasts for 10,769 firms domiciled in those 39 countries. We find that higher scores for all three proxies are associated with lower error and less disagreement in forecasts. In addition, we find that the indices have significant explanatory power when previously used enforcement proxies (such as Kaufmann et al.'s 2010 rule of law measure) are included in the regression models, pointing to the importance of specific measures of accounting enforcement. We conclude that accounting enforcement may be more important in securing favourable economic outcomes than has been previously realised, because researchers commonly have used noisier, more general legal proxies for enforcement that understate its marginal effects.     

Analysts' forecasts as earnings expectations

I examine three composite analyst forecast of earnings per share as proxies for expected earnings. The most current forecast weakly dominates the mean and median forecasts in accuracy. This is evidence that forecast dates are more relevant for determining accuracy than individual error. Consistent with previous research, I find analysts more accurate than time-series models. However prior knowledge of forecast errors from a quarterly autoregressive model predicts excess stock returns better than prior knowledge of analysts' errors. This is inconsistent with previous research, and is anomalous given analysts' greater accuracy.     

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.

The association between quarter length,forecast errors,and firms’ voluntary disclosures

Approximately 60 percent of adjacent fiscal quarters contain a different number of calendar days. In preliminary analyses, we find the change in quarter length is significantly associated with the changes in sales and earnings and that analysts condition on the prior quarter's results when making their forecasts. These results indicate that it is important for analysts to adjust for changes in quarter length when making forecasts. However, we find the quarterly change in days is positively associated with analysts’ sales and earnings forecasts errors, where forecast error equals the actual earnings minus the forecasted earnings. These results indicate that analysts systematically underestimate (overestimate) performance when quarter length increases (decreases). We find evidence indicating investors make similar errors as returns around earnings announcements are positively associated with the change in quarter length, but only when changes in firm performance is more sensitive to changes in quarter length. Corroborating these findings, managers are more (less) likely to discuss quarter length during conference calls when quarter length decreases (increases). These results are consistent with managers’ strategic disclosure incentives. In summary, our evidence suggests analysts and investors fail to fully take account of the quasi-mechanical effect that quarter length has on firm performance and managers strategically alter their voluntary disclosures to take advantage of these failures.     

A new measure of earnings forecast uncertainty

Relying on the well-established theoretical result that uncertainty has a common and an idiosyncratic component, we propose a new measure of earnings forecast uncertainty as the sum of dispersion among analysts and the variance of mean forecast errors estimated by a GARCH model. The new measure is based on both common and private information available to analysts at the time they make their forecasts. Hence, it alleviates some of the limitations of other commonly used proxies for forecast uncertainty in the literature. Using analysts' earnings forecasts, we find direct evidence of the new measure's superior performance.