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.     

Audit committee financial expertise and properties of analyst earnings forecasts

An important role of financial accounting information is to aid financial statement users in forming expectations about the firm's future earnings. Prior research finds that accounting financial expertise of the audit committee is associated with higher financial reporting quality. We extend this literature by examining the association between audit committee financial expertise and analysts' ability to anticipate future earnings. We find a significant association between accounting financial expertise on the audit committee and analyst earnings forecasts that are more accurate and less dispersed. In contrast, we do not find a significant association between non-accounting financial expertise (i.e., supervisory expertise) and forecast accuracy or forecast dispersion. These findings contribute to our understanding of the benefits of accounting expertise in audit committees by demonstrating an association between accounting financial expertise and improvements in analyst earnings forecasts.     

Accrual anomaly and mandatory adoption of IFRS: Evidence from Germany

This study examines the effect of mandatory adoption of IFRS on the accrual anomaly in Germany. Using the setting in Germany between 2002 and 2008, we find that the accrual anomaly occurs between 2002 and 2004 (pre-IFRS period) but disappears between 2006 and 2008 (post-IFRS period). Results are consistent after extending the test period to 2010 and controlling for analysts' cash forecasts. We also find that German firms with more significant improvements in analysts following, forecast accuracy, forecast dispersion, and earnings management following mandatory adoption of IFRS in 2005 have greater decline of the accrual anomaly between 2006 and 2008. Overall, our findings suggest that IFRS adoption together with improved reporting enforcement significantly reduces the accrual anomaly in Germany.     

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.     

Expected Mispricing: The Joint Influence of Accounting Transparency and Investor Base

We examine how accounting transparency and investor base jointly affect financial analysts' expectations of mispricing (i.e., expectations of stock price deviations from fundamental value). Within a range of transparency, these two factors interactively amplify analysts' expectations of mispricing—analysts expect a larger positive deviation when a firm's disclosures more transparently reveal income‐increasing earnings management and the firm's most important investors are described as transient institutional investors with a shorter‐term horizon (low concentration in holdings, high portfolio turnover, and frequent momentum trading) rather than dedicated institutional investors with a longer‐term horizon (high concentration in holdings, low portfolio turnover, and little momentum trading). Results are consistent with analysts anticipating that transient institutional investors are more likely than dedicated institutional investors to adjust their trading strategies for near‐term factors affecting stock mispricings. Our theory and findings extend the accounting disclosure literature by identifying a boundary condition to the common supposition that disclosure transparency necessarily mitigates expected mispricing, and by providing evidence that analysts' pricing judgments are influenced by their anticipation of different investors' reactions to firm disclosures.     

Do analysts’' forecast properties deter suboptimal labor investment decisions? Evidence from Regulation Fair Disclosure

We examine whether analysts' forecast properties deter inefficient labor investment decisions. Using accuracy and dispersion as analysts' forecast properties, we find that more accurate and less dispersed forecasts are associated with less inefficient corporate labor investments. Utilizing Regulation Fair Disclosure (Reg FD) as an exogenous variation to analysts' forecast activities, we find a causal relationship between analysts' forecast properties and labor investment inefficiency. We also find that more accurate and less dispersed forecasts decrease labor cost stickiness. Our results are consistent with the view that analysts' forecast properties enhance the information environment, which, in turn, improves corporate labor investment decisions.     

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.     

The brain gain of CFOs in China: The case of analyst forecasts

We study the impact of CFOs with foreign experience on analysts' forecast accuracy in emerging markets. Using a unique data set from China, we find that analysts' forecast accuracy increases when firms hire CFOs with foreign experience, confirming the brain gain effect of CFOs. Our results are robust after addressing potential endogeneity by introducing the propensity score matched (PSM) procedure and Heckman two-stage method. Channel analyses show that CFOs with foreign experience are related to decreased earnings management and a greater probability of hiring high-quality auditors, indicating that the improvement in financial reporting quality and information environment brought by returnee CFOs mainly drive our results. Further cross-section tests reveal that compared to firms with more external pressure, the positive effect of returnee CFOs on analysts' forecast accuracy is more pronounced among firms with fewer analyst coverage and belonging to less competitive industries. Returnee CFOs with foreign work experience exert a more significant impact on analysts' forecast accuracy than those with foreign study experience. Overall, we provide the first evidence on the brain gain of CFOs in terms of analyst forecasts.     

Earnings Surprises in Analysts' Forecasts,Mandatory Disclosure,and Share Repurchases

This paper examines the impact of forecast errors and the mandatory disclosure of repurchase transactions required by 2003 Securities and Exchange Commission (SEC) regulations on share repurchases. We define forecast errors as the difference between analysts' forecasted earnings and actual earnings. We argue that firms with positive forecast errors imply greater information asymmetry, which may induce them to signal through share repurchases. We show that both the repurchase target and analysts' forecast revision are positively related to forecast errors. Furthermore, these associations are more pronounced in the low disclosure period (1989–2003) where greater information asymmetry between managers and outside investors is found, while increased transparency in the high disclosure period (2004–2006) leads to more significant improvement in long‐term performances for firms with positive forecast errors. The results are consistent with our expectations that the information asymmetry implied in forecast errors, along with a shock change from the introduction of the 2003 SEC regulation, affect both corporate and analysts' behaviour.     

The supply of analysts and earnings forecasts

Using a unique set of hand-collected data, this study examines whether a larger supply of prospective analysts leads to existing financial analysts' higher earnings forecast accuracy. We analyze the effect of the analyst supply proxied by the number of universities and the number of finance and economics universities located in the same city as the headquarters of brokerage firms. Our findings suggest that brokerage firms located closer to a larger supply of potential analyst candidates are associated with higher analyst forecast accuracy, as observed by a lower forecast error. We further find that the degree of employed analysts' effort acts as a mediator between the supply of prospective analysts and the accuracy of their earnings forecasts.     

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.     

The value relevance of timely information: The case of comparable store sales growth

Investors and analysts have called for more timely disclosure of corporate information. Responding to these demands, some retail firms issue comparable store sales (CSS) on a monthly or a quarterly basis in addition to an annual basis. This study examines whether a timely disclosure of CSS provides value-relevant information to market participants by examining investors' and financial analysts' responses at the time of CSS disclosures (short-horizon) and over the month or the quarter (long-horizon). We find that both monthly and quarterly CSS are associated with contemporaneous market returns and analyst forecast revisions. More importantly, we find that quarterly CSS news becomes less important to investors when firms provide more timely CSS information, indicating that monthly CSS reports may preempt the information content of quarterly CSS. Additional tests show that investors and analysts rely less on CSS if CSS news and earnings (sales) news are inconsistent.     

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.     

Model-based earnings forecasts vs. financial analysts' earnings forecasts

Existing accounting-based forecasting models of earnings either do not fully consider information that is contained in stock prices or use an ad hoc specification that is not based on rigorous valuation theory. In this paper, we develop an earnings forecasting model built on the theoretical linkages between future earnings and stock prices as well as a number of accounting fundamental variables. We find that our model-based forecasts of earnings are in general less biased and more accurate than both existing model-based forecasts and analysts' consensus forecasts, at both shorter and longer horizons. We also show that the accuracy of both model-based forecasts and financial analysts' forecasts depend on firm-specific characteristics such as firm size and industry membership.     

Mandatory disclosure of comment letters and analysts' forecasts

Comment letters (CLs) have been adopted as the main supervision mechanism for information disclosure by the two main Chinese stock exchanges since 2013. Both CLs and firms' responses have been publicly disclosed since the end of 2014. Using nonfinancial listed firms from 2013 to 2019 as our sample, we investigate the impact of CLs and their mandatory disclosure on analysts' forecast quality. The results show that, in the pre-disclosure period, there is no significant relation between CLs and analysts' forecast quality. However, in the post-disclosure period, CLs are positively (negatively) correlated with analysts' forecast accuracy (optimism). The quality of analysts' forecasts is much higher when CLs contain more questions. In addition, the impact of CLs is larger for samples with a lower percentage of star analysts or samples with higher earnings volatility. CL recipients tend to disclose more information on their internal and external risks, which can offer additional information to analysts.     

Financial Analysts and the False Consensus Effect

Social psychologists have documented a tendency for people to overestimate their similarity to others. I investigate whether financial analysts' forecast errors are consistent with this bias. I model the bias by assuming analysts overestimate the correlation of the private signals they receive about a firm's future earnings. My model predicts a positive relationship between (i) the likelihood of an analyst's revised forecast being too close to his earlier forecast and (ii) the number of analysts issuing forecasts during the time interval between his two forecasts. I empirically confirm this prediction and consider several alternative explanations.     

Initial Evidence on the Role of Accounting Earnings in the Bond Market

We document that: (1) the incidence of bond trade increases during the days surrounding earnings announcements, (2) there is a bond‐price reaction to the announcement of earnings, and (3) there is a positive association between annual bond returns and both annual changes in earnings and annual analysts' forecast errors. All of these effects are larger when earnings convey bad news or when the underlying bond is more risky. Taken together, our results suggest that the nonlinear payoff structure of bond securities affects the role of accounting earnings in the bond market.     

Predictability in Financial Analyst Forecast Errors: Learning or Irrationality?

In this paper, we propose a rational learning‐based explanation for the predictability in financial analysts' earnings forecast errors documented in prior literature. In particular, we argue that the serial correlation pattern in analysts' quarterly earnings forecast errors is consistent with an environment in which analysts face parameter uncertainty and learn rationally about the parameters over time. Using simulations and real data, we show that the predictability evidence is more consistent with rational learning than with irrationality (fixation on a seasonal random walk model or some other dogmatic belief).     

Causal language intensity in performance commentary and financial analyst behaviour

We use automated techniques to measure causal reasoning on earnings‐related financial outcomes of a large sample of MD&A sections of US firms and examine the intensity of causal language in that context against extent of analyst following and against properties of analysts’ earnings forecasts. We find a positive and significant association between a firm's causal reasoning intensity and analyst following and analyst earnings forecast accuracy respectively. Correspondingly, analysts’ earnings forecast dispersion is negatively and significantly associated with causal reasoning intensity. These results suggest that causal reasoning intensity provides incremental information about the relationship between financial performance outcomes and its causes, thereby reducing financial analysts’ information processing and interpreting costs and lowering overall analyst information uncertainty. Additionally, we find that decreases in analyst following are followed by more causal reasoning on performance disclosure. We also find that firms with a considerable increase of causal disclosure especially attract new analysts who already cover many firms. Overall, our evidence of the relationship between causal reasoning intensity and properties of analyst behaviour is consistent with the proposition that causal reasoning is a generic narrative disclosure quality characteristic, able to provide incremental information to analysts and guide analysts' behaviour.     

Investor Reaction to Celebrity Analysts: The Case of Earnings Forecast Revisions

We examine the effects of analysts' celebrity on investor reaction to earnings forecast revisions. We measure celebrity as the quantity of media coverage analysts receive in sources included in the Dow Jones Interactive database, and find that media coverage is positively related to investor reaction to forecast revisions. The effect of celebrity on the reaction to forecast revisions remains significant after controlling for forecast performance variables examined in prior studies (ex post forecast accuracy, ex ante accuracy, award status, and other variables shown to be related to forecast accuracy). While these results are consistent with the familiarity of the analyst's name affecting the market reaction, we cannot rule out that our measure of celebrity is correlated with error in the performance measures we examine and/or correlated with other unexamined dimensions of forecast performance. A content analysis of a random subsample of the media coverage of our sample analysts suggests that our findings likely are not due to the increased availability of forecast revisions. Finally, an investigation of the excess returns around the quarterly earnings announcement date suggests that market participants react too strongly to forecast revisions issued by analysts with high levels of media coverage. Taken together, these findings suggest that an analyst's level of media coverage can affect the initial market reaction to his forecast revisions.