RATIONAL EXPECTATIONS AND THE DYNAMIC ADJUSTMENT OF SECURITY ANALYSTS' FORECASTS TO NEW INFORMATION

We report the results of unbiasedness tests of security analysts' earnings forecasts. By examining how analysts incorporate new information into their updated earnings forecasts we can analyze directly the effect of new information on analysts' forecast revisions and evaluate whether these revised forecasts converge to rational expectations forecasts. The forecasts made by security analysts participating in the Institutional Brokers Estimate System (IBES) database are analyzed. Using standard statistical tests, we reject the simple form of the rational expectations hypothesis. However, by extending the standard tests used in previous studies, we obtain results that suggest that analysts' earnings forecasts conform to a dynamic form of rationality. The tendency of revised forecasts to converge stochastically toward the rational expectations forecast cautions against the rejection of more complicated forms of rationality.     

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.     

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.     

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 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.     

Do UK Security Analysts Over-react?

Consensus analysts' earnings per share forecasts have become increasingly accessible in recent years and such measures have been widely adopted by academics as proxies for the market's expectations of earnings. Some US studies have suggested that analysts, in revising earnings forecasts, are prone to over-reaction. This study tests for evidence of over-reaction in revisions of the consensus earnings per share forecasts of UK companies reported by the Institutional Brokers Estimate System. For a series of periods, portfolios of companies attracting high and low revisions of earnings forecasts are constructed. We then compare the subsequent earnings forecast of the companies which attracted high revisions in the portfolio formation period with those of the companies which attracted low revisions in the portfolio formation period. We also compare actual changes in annual earnings with forecast changes in annual earnings. Both analyses suggest that UK analysts are prone to under-reaction, a finding which contrasts with the US studies which have identified over-reaction. There is little evidence that the market, in reacting to earnings forecast revisions, fails to recognise this under-reaction.     

The sensitivity of the credit default swap market to financial analysts’ forecast revisions

We examine the impact of analysts’ earnings per share (EPS) and cash flow per share (CPS) forecast revisions on the market for credit default swaps. We find that while the issuance of both EPS and CPS forecast revisions are inversely associated with changes in credit default swap (CDS) spreads, cash flow forecast revisions have a larger effect. We demonstrate that the relationship between CPS forecast revisions and CDS spreads tends to be stronger in cases of financial distress. We provide evidence that cash flow forecasts dominate earnings forecasts in some situations and that participants in the CDS market discriminate between analysts' forecast revisions and recommendation changes.     

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.     

Why do analysts issue forecast revisions inconsistent with prior stock returns? Determinants and consequences

We examine the informativeness of analyst forecast revisions that are directionally inconsistent with prior stock price movements (sign‐inconsistent revisions). Sign‐inconsistent revisions represent approximately one‐half of the forecast revisions from 1995 through 2010. Our tests indicate that sign‐inconsistent revisions are less informative than are sign‐consistent revisions. Sign‐inconsistent revisions are less likely to be closer to actual earnings realizations and they generate smaller stock price reactions. We also find evidence that sign‐inconsistent revisions are associated with analysts' economic incentives to generate trading volume and their behavioural limitations related to information uncertainty. These results suggest that sign‐inconsistent revisions do not necessarily benefit investors.     

The Reluctant Analyst

We estimate the dynamics of recommendations by financial analysts, uncovering the determinants of inertia in their recommendations. We provide overwhelming evidence that analysts revise recommendations reluctantly, introducing frictions to avoid frequent revisions. More generally, we characterize the sources underlying the infrequent revisions that analysts make. Publicly available data matter far less for explaining recommendation dynamics than do the recommendation frictions and the long‐lived information that analysts acquire but the econometrician does not observe. Estimates suggest that analysts structure recommendations strategically to generate a profitable order flow from retail traders. We provide extensive evidence that our model describes how investors believe analysts make recommendations, and that investors value private information revealed by analysts' recommendations.     

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.     

The impact of institutional analyst forecast divergence on crude oil market: Evidence from the mixed frequency models

As important information intermediaries, analysts play a non-negligible role in the crude oil market. Existing research often focuses on analysts' collection and interpretation of firm-specific information, but neglects the impact of analysts' forecasts on specific markets such as the crude oil market, which is crucial to the safe and stable development of the crude oil market. Therefore, this study uses historical data from January 2011 to December 2020 as a sample to construct analysts' forecast divergence indicators from 15 institutional analysts' forecast data on international crude oil futures prices. It then explores the impact of institutional analysts' forecast divergence on oil price return volatility, crude oil market jumps and crude oil futures trading volume, based on various mixed-frequency models. The results are as follows: First, volatility in oil price returns increases with a growing divergence in analysts' forecasts. Second, analysts' forecasts do not trigger jump in the crude oil market on the first six days after the information is released, but trigger a significant positive jump in the market on the seventh day. Third, the impact of analysts' forecast divergence on trading volume is weak; however, the reverse effect is significant, while the static and dynamic spillover results are consistent.     

Does Regulation FD work? Evidence from analysts' reliance on public disclosure

We examine how Regulation FD changed analysts' reliance on firms' public disclosure. Regulation FD is associated with a stronger analyst response to earnings announcements, management forecasts and conference calls—that is, analysts respond to these events more quickly, more frequently and with larger forecast revisions after FD. Further, following public disclosure, the decline in analyst forecast dispersion and forecast error accelerates after FD. We find no such changes either for foreign ADR firms or around several confounding events. Overall, Regulation FD levels the playing field between the analysts and individual investors, thereby promoting “fair game” property of the market.     

Analyst underreaction and the post-forecast revision drift

The post-forecast revision drift (PFRD), the phenomenon of delayed stock price reactions to analyst forecast revisions, is a well-documented market anomaly. Prior research attributes PFRD to underreaction by investors to analyst forecast revisions. This study investigates the role of the analyst forecast revision process itself in the PFRD anomaly. Using a large sample of US firms, we confirm prior findings of a positive serial correlation (momentum) in individual analysts’ revisions to their earnings forecasts and, based on both indirect and direct tests, document a positive association between this momentum and PFRD. Further analyses reveal that both the forecast revision momentum and PFRD vary in similar ways with respect to the nature of the news driving the revisions and the information environment. Collectively, our findings show that underreaction by individual analysts in the forecast revision process is an important contributor to the PFRD phenomenon.     

Disclosure Practices, Enforcement of Accounting Standards, and Analysts' Forecast Accuracy: An International Study

Using a sample from 22 countries, I investigate the relations between the accuracy of analysts' earnings forecasts and the level of annual report disclosure, and between forecast accuracy and the degree of enforcement of accounting standards. I document that firm‐level disclosures are positively related to forecast accuracy, suggesting that such disclosures provide useful information to analysts. I construct a comprehensive measure of enforcement and find that strong enforcement is associated with higher forecast accuracy. This finding is consistent with the hypothesis that enforcement encourages managers to follow prescribed accounting rules, which, in turn, reduces analysts' uncertainty about future earnings. I also find evidence consistent with disclosures being more important when analyst following is low and with enforcement being more important when more choice among accounting methods is allowed.     

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.     

Salient anchor and analyst recommendation downgrade

We find that analysts are more likely to downgrade stocks when prices approach the 52-week high. The results are stronger for stocks with higher information asymmetry but moderated by analysts' reputation, work experience, and educational background. We also find a strategy that shorts stocks with recommendation downgrades is less profitable for the downgrades near 52-week high than for other downgrades. Moreover, these downgraded firms with prices near 52-week high subsequently experience relatively less negative earnings forecast revisions. These results suggest that these downgrade decisions are less likely to be information-driven and consistent with our anchoring interpretation.     

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).     

The impact of aggregate uncertainty on herding in analysts' stock recommendations

This study examines whether aggregate uncertainty affects the herding tendency among analysts. The results show that, in addition to market risk and firm-level uncertainty, analysts' tendency to herd increases with aggregate uncertainty. These results are robust with respect to excluding common and earnings information, as well as using different measurements of consensus recommendation, risk and aggregate uncertainty. Herding among analysts is stronger when downgrading a stock. The tendency of herding clearly increases in tandem with aggregate uncertainty. The results are more prevalent for small stocks and inexperienced analysts.