The Impact of Analysts’ Forecast Errors and Forecast Revisions on Stock Prices
William Beaver,1 Bradford Cornell,2 Wayne R. Landsman,3 and Stephen R. Stubben1
First Draft: October, 2004
Current Draft: November, 2005
1. Graduate School of Business, Stanford University, Stanford, CA 94305.
2. California Institute of Technology, Pasadena, CA 91125
3. Kenan-Flagler Business School, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599.
We thank I/B/E/S International for providing data on analysts’ earnings estimates, and the Center for Finance and Accounting Research, University of North Carolina for providing financial support. We also thank workshop participants at the 2005 Stanford Summer Camp and the University of Florida and two anonymous referees for helpful comments. Corresponding author: William Beaver, .
The Impact of Analysts’ Forecast Errors and Forecast Revisions on Stock Prices
Abstract
We present a comprehensive analysis of the contemporaneous association between security returns, quarterly earnings forecast errors, and quarter-ahead and year-ahead earnings forecast revisions in the context of a fully specified model. We find that all three variables have significant pricing effects, indicating each conveys information content. The findings hold across years, across industries, and are robust to two procedures extending the event window. Findings also show that the fourth quarter is significantly different from the other three quarters. In particular, in the fourth quarter the relative importance of the forecast error is lower, while the relative importance of the quarter-ahead forecast revision increases. We find also a marked upward shift over time in the forecast error coefficients, even in the presence of the forecast revision variables, whose coefficient also exhibit a significant but less dramatic shift.
This finding is consistent with the I/B/E/S data base reflecting an improved quality of earnings forecasts, as well as an improved measure of actual earnings.
1. Introduction
One of the fundamental questions in finance and accounting is the impact of earnings surprises on stock prices. The question not only is important for evaluating theories that relate reported accounting numbers to firm value, but also has widespread implications for regulation and the law. For instance, in legal disputes related to financial reporting a central issue is how much the stock price would have been affected if the company released its “correct” earnings in place of allegedly inflated earnings. Proper analysis of that issue requires an appropriately specified model of the relation between earnings innovations and stock prices.
Empirical studies of this question employ analysts’ earnings forecast data as proxies for market expectations and, thereby, to measure earnings surprises. In an early paper, Cornell and Landsman (1989) demonstrate that the earnings surprise should not be identified solely with analysts’ forecast errors. They stress that a properly specified model of residual returns must simultaneously take account of both earnings forecast errors and earnings forecast revisions. They present evidence to show that if the forecast revisions are excluded, the response coefficient on the forecast error is higher because forecast revisions are in part based on forecast errors.
In this paper, we present a comprehensive analysis of the relation between stock returns, analysts’ forecast errors and analysts’ forecast revisions. As such, it incorporates numerous developments since the publication of Cornell and Landsman. First, there has been extensive new research on the relation between analysts’ forecasts and stock prices, which we review below. Interestingly, much of this literature has not taken account of the combined impact of forecast errors and forecast revisions.
Second, there have been improvements in the nature, quantity, and quality of the data used to measure forecast errors and forecast revisions. In particular, Cornell and Landsman was based on only three years of data (1984-86). Currently we have 20 years of data covering a much greater number of firms. Moreover, with respect to the I/B/E/S data that we use in this paper, there has been an increasing effort over time by I/B/E/S to ensure a consistency between the forecast and the realization of earnings, as well as a consistency across analysts in the earnings number being forecast. This consistency is attained by ensuring the same earnings components are included (and excluded) in the “actual” and forecasted earnings. Presumably, the effects of these efforts could alter the observed relation between security returns, forecast errors and forecast revisions. More specifically, as the I/B/E/S database becomes more successful in providing an “apples to apples” comparison, the quality of the forecast error is expected to improve because it becomes a better proxy for unexpected earnings. The resulting reduction in measurement error should affect the estimated coefficients in regression models. In addition to improving the quality of the data, I/B/E/S has extended coverage over time making the data more comprehensive. This alteration in the composition of the data may also affect the empirical estimation of the security return model. We examine whether there has been an increase or decrease over time in the information content of forecast errors and forecast revisions to assess the extent to which changes in the nature of the data have affected the observed relations.
Third, it has been suggested that companies have come under added pressure to “manage” earnings and that this may affect the relation between residual returns, forecast errors and forecast revisions (Matsumoto, 2002; Abarbanell and Lehavy, 2003; Burgstahler and Eames, 2003). For example, it may lead to reduced information content of the earnings forecast error over time. This possibility can also be examined by studying the relation between residual returns, forecast errors and forecast revisions over time. To provide further insight, we also look at the relation across industries.
Finally, it also has been suggested that managers are increasingly actively managing analysts’ expectations to avoid negative earnings surprises, which may also affect the relation between residual returns, forecast errors and forecast revisions (Brown, 2001; Matsumoto, 2002). Presumably, if this activity has increased over time and adversely affected the quality of both the forecast errors as well as the forecast revisions, the change in the relation between residual returns, forecast errors and forecast revisions over time should show up over time.
To study these questions, we provide a comprehensive examination of the relation between residual stock returns in the period surrounding quarterly earnings announcements, earnings forecast errors, and revisions in quarter-ahead and subsequent year-ahead analysts’ earnings forecasts during the period from 1984 to 2003. The length of the sample period permits us to examine whether changes in the properties of the earnings forecasts result in any perceptible trends in the coefficients on the forecast error and the forecast revisions. In addition, the growth in I/B/E/S coverage also permits us to control for potential mean differences in industry effects and to examine whether the observed relation is consistent across industries. Furthermore, the availability of an I/B/E/S “actual” earnings number, which was not provided when the database first became available, permits us to compare the properties of different specifications including forecast errors based on I/B/E/S actuals versus Compustat earnings.
We also examine two important specification issues: the distinct nature of fourth quarter earnings and the measurement of the residual return interval. With respect to the first issue, we consider whether the relation between residual returns, forecast errors and forecast revisions differs during the fourth quarter for a variety of reasons that we discuss later in the paper. If this is so, failure to take account of the fourth quarter effect will lead to a misspecified model and, quite likely, biased coefficients. To study this possibility, we develop specifications that permit the fourth quarter slope coefficients to differ from those of the interim quarters, and that take account of the intertemporal overlap in measurement of the quarter-ahead and year-ahead forecast revisions that occurs during the fourth quarter.
With respect to the residual return window, models that incorporate both forecast errors and forecast revisions face a unique data problem. The problem arises because the forecast error, by definition, is observed at the time of the earnings announcement, but the forecasts revisions are not made available until a later date. This raises two issues. The first issue is that at the time of the earnings announcement the market must use the information in the forecast error to anticipate its long-run impact, and thereby its effect on analysts’ forecast revisions, without observing the revisions. Therefore, the residual return reflects both the forecast error and the forecast revisions expected at the earnings announcement date. However, by necessity, the model includes actual forecast revisions, which likely measure the market’s expectations with error. To take account of this feature of the data, we extend the basic model in two ways. First, we extend the window over which the residual return is measured to the date at which the forecast revisions are observed. This assures us that the residual return will reflect both actual forecast errors and actual forecast revisions. A problem with this approach is that the window must be extended, on occasion, to more than two months after the earnings announcement to be sure the I/B/E/S consensus reflects forecasts made after the earnings announcement. By extending the return window, the coefficients on the forecast revisions will reflect information available subsequent to the earnings announcement. To counter this problem, the second approach turns to disaggregated data. Rather than using the I/B/E/S consensus forecasts, we employ the individual analysts’ forecasts to construct a custom consensus forecast following the earnings announcement. In this way, we can shorten the window by using the subset of the individual forecasts that are available soon after the earnings announcement.
The major findings are: First, in every model we estimate both the forecast error and the forecast revision coefficients are highly significant. In other words, neither the forecast error nor the forecast revisions dominate in that each provides information content not contained in the other. Second, based on twenty years of data, we find that, even in the presence of the forecast revision variables, the coefficient of the forecast error still increases substantially over time, with a marked shift in post 1991 period. Third, in contrast, the coefficients on the two forecast revisions exhibit a similar but less dramatic shift. We present evidence suggesting that the increase in the coefficients is attributable to joint effects of the improved quality of the I/B/E/S actual earnings and analysts’ earnings forecasts over the sample period.
This finding is important because it indicates that the significance of the forecast revisions in explaining the cross-sectional variation in earnings announcement residual returns is not an artifact of measurement error in the forecast error. Rather, the significance of the forecast revision coefficients is a robust finding that holds up through time despite changes in database quality and changes in the institutional features of the earnings reporting environment. Findings from separate industry regressions indicate that although there are cross-industry differences in the magnitude of coefficients on the forecast error and the forecast revisions, the basic relation holds across all industry groups.
Fourth, the results further support the view that the fourth quarter is different than other quarters. The evidence is consistent with the market reacting in the fourth quarter more strongly to the change in the next quarter forecast revision and less strongly to the forecast error. This finding suggests that a revision in the quarter-ahead forecast in the fourth quarter, which is the forecast revision for the first quarter of the next fiscal year, conveys more information than earlier quarters’ forecast revisions, which refer to later quarters in the same fiscal year
Fifth, findings from estimations that extend the announcement event window indicate the primary results are robust, but the impact of the forecast revisions, as compared to the forecast errors, increases. This supports the notion that when the market observes the actual forecast revisions prices are adjusted to take account of the difference between the forecast revisions that are observed and the forecast revisions that were expected at the time of the earnings announcement. These increased coefficients are also consistent with the forecast revisions reflecting information available after the earnings announcement. Consistent with these arguments, the subsequent move in stock price is correlated with the observed revisions, but not necessarily with the (earlier) forecast error.
To summarize, our results emphasize the importance of using a properly specified model when assessing the impact of the release of earnings information on stock prices. Models that fail to include forecast revisions, fail to take account of the changing nature of the I/B/E/S data, or fail to adjust for fourth quarter effects will produce earnings response coefficients that to not correctly characterize the relation between reported earnings and firm value.
The remainder of the paper is organized as follows. In the next section, we review the key findings of the research on the relation between analysts’ forecast errors and stock returns. Section three presents the research methodology and methods for measuring the variables. Section four describes the sample data. Section five presents the results and discusses their implications. The conclusions are summarized in the final section.
2. Prior Research
Using I/B/E/S consensus analyst forecast data, Cornell and Landsman (1989) study the pricing effects of earnings forecast errors and earnings forecast revisions in the period surrounding quarterly earnings announcements. The key finding of their study is that both the one-quarter-ahead and one-year-ahead forecast revisions have important explanatory power in addition to the earnings surprise. An important conclusion based on their findings is that a properly specified model of residual returns in response to the release of quarterly earnings must simultaneously take account of both earnings forecast errors and earnings forecast revisions. They present evidence to show that if the forecast revisions are excluded from the basic model, the coefficient on the forecast error is higher because the error serves as a proxy for the forecast revisions and must be interpreted accordingly.
In the years following the Cornell and Landsman study, few studies have examined the more completely specified model. A notable exception is Liu and Thomas (2000), which models stock returns as a function of annual forecast errors, annual forecast revisions, and an estimated annual revision in terminal value. Liu and Thomas finds that both the forecast error and forecast revisions provide incremental explanatory power. This study differs from Liu and Thomas in several respects: (1) Whereas Liu and Thomas relates annual stock returns with earnings variables, we examine the shorter-term announcement effects of the earnings variables in the spirit of an earnings announcement event study. Given the variability of stock returns, our shorter horizon tests have considerably more power. (2) Liu and Thomas examines only annual earnings; our research design measures earnings variables for annual and interim quarters. Hence, our research designs permits us to address additional issues, such as the differential behavior of the fourth quarter. (3) Liu and Thomas reports results based on pooled cross-sectional and time-series data and does not examine how the coefficients may have changed over time. Further, year-by-year estimation permits the calculation of test statistics that are not affected by cross-sectional correlation in the data leading to less biased test statistics than those based on pooled estimation. (4) Liu and Thomas includes earnings variables, including revisions in long-term earnings forecasts and terminal values, that are based on the authors’ extrapolations and are not reported by I/B/E/S. Hence, the results reflect the joint effect of I/B/E/S reported variables and their extrapolations using I/B/E/S and other data.