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, 2004Current Draft: November, 2005

1. Graduate School of Business, Stanford University, Stanford, CA 94305.2. California Institute of Technology, Pasadena, CA 911253. 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, william_beaver@gsb.stanford.edu.

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.

3

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

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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

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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

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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

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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.

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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

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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.

Although the number of studies that model stock returns as a function of both forecast

errors and revisions is relatively small, there is a much larger literature on the properties of

forecast errors and analysts’ forecasts. We briefly summarize key studies in both of this

literature that provide some background to our study. A number of papers study the properties of

earnings response coefficients using alternative earnings measures (Bradshaw and Sloan, 2002;

Brown and Sivakumar, 2003; Lougee and Marquardt, 2004: Abarbanell and Lehavy, 2005). Of

particular relevance to our study is Bradshaw and Sloan (2002), which documents that annual

earnings response coefficients are higher when the forecast error is defined using I/B/E/S (i.e.,

“Street” earnings) rather than Compustat net income (i.e., “GAAP” earnings), and the difference

in price response based on the two measures has increased over time. In particular, in the post-

1992 period there is a significant increase in the earnings response coefficient for I/B/E/S

earnings forecast errors. Bradshaw and Sloan (2002) attributes these findings to analysts

excluding over time an increasing number of special items from their earnings estimates, and to

the increasing prevalence of special items, which predominately occur in the fourth quarter.

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The key distinction between our study and prior studies examining the properties of

earnings response coefficients, including Bradshaw and Sloan (2002), is that we include in our

regressions analysts’ forecast revisions for quarter-ahead and year-ahead earnings. Not only

does this permit us to study the price response to forecast revisions, but this also changes the

interpretation of the coefficient on the forecast error. In particular, in a fully specified model the

forecast revisions control for the future implications of the forecast error, which results in a

coefficient on the forecast error that is not affected by the persistence of current earnings. This

model allows us to examine if there is a shift in the earnings response coefficients in the presence

of earnings forecast revisions for reasons other than a change in earnings persistence over time.

Further, we are able to examine whether there has been a similar upward trend over time in the

coefficients on the earnings revisions variables themselves. Neither is possible in the context of

a model that contains only earnings forecast errors.

One issue raised by Cornell and Landsman is whether the structural relation between the

earnings variables and stock return in the fourth quarter could differ from that of the interim

quarters. They raise the possibility that fourth quarter could differ because of the increased

frequency of special items and because the fourth quarter result will reflect the effects of the

audit process. If the fourth quarter is significantly different, and if this fact is not taken into

account in the model specification, the estimated relation between stock returns and forecasts

errors will not be properly measured. In the context of a model that includes only the earnings

forecast error, Mendelhall and Nichols (1988) finds that the market reacts relatively less strongly

to bad news in the fourth quarter because of the ability of managers to delay the reporting of bad

news in earlier quarterly earnings, but which is effectively leaked to the market in earlier

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quarters. However, it is difficult to predict whether their results would hold in the presence of

forecast revisions.

Prior research examining the properties of analysts’ forecasts is substantial. Brown

(1996) synthesizes a vast literature of the forecasting accuracy of analysts’ versus naïve

statistical models, concluding that analysts’ forecast outperform statistical models, that the

forecast error has not increased over time, and that over subperiods of time analysts’ forecasts

have been pessimistically, rather optimistically biased. Lys and Sohn (1990) find that even

though security returns can predict a portion of the forecast revision, the analysts’ forecasts are

incrementally informative. One key paper, Abarbanell and Bernard (2000), suggests that

analysts’ forecasts do not fully reflect the implications of earnings forecast errors in their forecast

revisions. Subsequently, Gleason and Lee (2003) document a post-revision price drift and

suggest the market does not fully reflect the information content of the forecast revision. In

particular, their evidence suggests that the market does not make a sufficient distinction between

revisions that provide new information and those that merely move toward to consensus.

Another strand of analyst research has examined the contention that mangers have increasingly

guided analysts’ forecasts downward so that earnings meet or beat analysts’ forecasts (Brown,

2001; Matsumoto, 2002). Presumably, to the extent that this pressure on analysts has affected

their forecasts, it could also to affect the relation between residual returns, forecast errors and

forecast revisions.

Other research related to analysts focuses on the suggestion that companies have faced

increasing pressure over time to “manage” earnings and that this may have affected the relation

between residual returns, forecast errors and forecast revisions (Matsumoto, 2002; Abarbanell

and Lehavy, 2003; Burgstahler and Eames, 2003). In particular, successful earnings

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management could affect the earnings surprise coefficient over time as earnings management

increases. In addition, if analysts do not fully incorporate the effects of earnings management in

their forecast revisions, this also could affect their coefficients over time as earnings

management increases. The only way to explore these issues fully is in the context of a model

that includes both forecast errors and forecast revisions, takes account of a possible fourth

quarter effect, and then examines how the coefficients change over time and across industries.

These streams of research motivate our interest in examining several issues: (1) Is the rise

in the coefficient on the I/B/E/S forecast still observed in the presence of the forecast error

revisions? (2) Is there a similar increase in the coefficients on the forecast revisions over time?

(3) In a fully specified model, is the structural relation of the model in the fourth quarter different

from that of the interim quarters and has that model also changed over time? (4) Are the findings

robust with respect to the alternative specifications of the announcement window?

3. Research Design

3.1 Cornell Landsman Model

Based on a valuation model that expresses equity market value as the present value of

future cash flows, Cornell and Landsman derives a model where change in equity value is equal

to a linear function of the cash flow forecast error and a series of revisions in expectation about

future periods’ cash flows. Assuming that cash flow forecast errors (changes in future expected

cash flows) are collinear with the earnings forecast errors (forecast revisions), they then derive

an empirical estimation equation that appears as equation (1) below.

Subsequent to Cornell and Landsman, Ohlson (1995) and Feltham and Ohlson (1995)

developed a characterization of equity market value as a linear function of equity book value and

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the present value of future expected abnormal earnings. Moreover, Feltham and Ohlson (1996)

demonstrates an equivalency between the cash flow and abnormal earnings representations.

Here, we present a valuation model based on the Feltham and Ohlson abnormal earnings

formulation. Empirically the stream of future expected abnormal earnings is truncated at some

point and a terminal value is estimated. From this price levels equation, it is straightforward to

derive an expression for the unexpected security return as a function of unexpected current

earnings and the change in the future expected abnormal earnings, and in the case of truncation a

change in expected terminal value.

In particular, the model developed by Liu and Thomas (2000) expresses unexpected

security returns as: [their Equation (10)]

URit = 0 + 1UEit + 2RAE2it + 3RAE3it + 4RAE4it + 5RAE5it + 6RTERMit + eit,

where UR is the expected stock return, UE is the earnings surprise with respect to current

abnormal earnings, RAE is the revised expectations about future abnormal earnings for the next

four accounting periods, and RTERM is the revision in the estimated terminal value at the end of

the horizon. The Liu and Thomas model is developed in context of annual returns and annual

revisions in future expected earnings. In our context, which is announcement period returns for

quarterly announcements, UE is represented by the forecast error on current quarterly earnings.

In the most general model, there would be separate estimates for each of the revisions in future

quarterly earnings for a finite period and the revision in expected terminal value. Our estimating

model is a parsimonious version of the Liu and Thomas model, which, as described in detail

below, reflects the structure of the I/B/E/S analysts’ forecast data, including the availability of

the data, the frequency with which the forecast variables are revised and the collinearity among

the forecasted variables. In particular, our estimating equation is:

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ARit = a0 + a1 FEit + a2 FRQit + a3 FRYit + eit (1)

where AR is the unexpected security return, FE is the forecast error for current quarterly

earnings, FRQ is the revision in the I/B/E/S consensus forecast for the next quarter, and FRY is

the revision in I/B/E/S consensus forecast for the next fiscal year.

One set of potentially omitted variables is the revisions in the quarterly earnings for the

remaining portion of the current fiscal year. For example, for the first quarter, FE is the first

quarter forecast error, FRQ is the revision in the forecast for the second quarter, and the forecast

revisions for the third and fourth quarter are omitted. There are several problems with including

these additional variables. First, the number of observations for which the forecast revision is

available more than one period ahead is limited. Second, the length of the remaining portion of

the current fiscal year shrinks as for each of the later quarters (e.g., for the second quarter there

are only two quarters remaining), and it unclear how one would incorporate the varying time

horizon into estimating equation (1). Third, the revisions in forecasts for the remaining quarters

are significantly correlated with one another. However, notwithstanding these difficulties, we

conducted a complete specification for the first quarter for those observations where a forecast

revision was reported. We found the overall explanatory power to be essentially the same as that

of Equation (1). Hence, we rely on the more parsimonious form of the estimating equation. The

main point to emphasize is that the coefficient on FRQ reflects the pricing multiplier that reflects

the revisions for the remaining quarters as well.1

1 Alternatively, we could construct our own estimates of the forecast revision for the remaining quarters based upon some extrapolation of the FRQ. This is the approach employed by Liu and Thomas (2000) to project annual forecasts beyond those reported by I/B/E/S. We have chosen not to use such an approach here because then the findings would be a joint function of I/B/E/S data and our extrapolation procedure. Moreover, in conducting preliminary calculations over our interval of revision (two months as opposed to one year in Liu and Thomas), we found the extrapolated variables to be so highly correlated with the reported variable (FRQ) that no increase in explanatory power was provided. It is more straightforward to simply include only FRQ and to interpret its coefficient accordingly.

15

Similarly, revisions in annual earnings beyond FRY are potentially omitted variables from

Equation (1). As Liu and Thomas (2000) point out, the limited availability of I/B/E/S annual

forecast revisions beyond one year results in a substantial reduction in number of observations.

To require additional FRY for two-years hence would reduce the sample size by 65 percent and

to further require FRY terms beyond two years would reduce the sample size by over 90 percent.

Moreover, regressions including these variables does not produce any increase in explanatory

power.2 Liu and Thomas (2000) constructs estimates of long-term earnings revisions based on

the reported I/B/E/S short-term earnings forecasts and the I/B/E/S long-term growth rate. We

examined the feasibility of using similar extrapolated variables. For our period of revision (two

months versus one year), we found that the revision in long-term growth rates was zero for 68

percent of our observations. Because of this, the resulting extrapolations would produce revision

variables that would either be zero or highly collinear with FRY. As a result, incorporation of

these extrapolated variables would not add significantly to explanatory power and would only

provide an illusion of additional variables that are in fact linear extrapolations of the FRY

variable and a growth variable that is predominately zero.3 As with the possibility of including

additional terms for interim quarter forecasts revisions beyond FRQ, it is more straightforward to

simply include only FRY and to interpret its coefficient accordingly—namely, the coefficient

also reflects the extent to which FRY is correlated with revisions in expected subsequent

earnings. Further, there is no revision in terminal value calculation in Equation (1). Not only it

is a purely extrapolated value in the sense that I/B/E/S does not report terminal value, but

revisions in terminal value are greatly affected by revisions in the long-term growth rate, which

2 Not surprisingly, the coefficients on these variables are positive, much smaller than for FRY and closer to zero. As a result, the coefficient on FRY is slightly lower but the overall explanatory power remains the same.3 One might dismiss 68 percent of the observations being zero as not being a sufficient reason for not using the growth rate. However, we feel the smaller propensity to update long-term forecasts is not a reflection of changing expectations and hence is a stale variable measured with considerable error.

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was zero for a 68 percent of our observations. For this reason, we do not revision in terminal

value in the estimating equation.

Consistent with the standard approach in the literature, we measure analysts’ forecast-

based variables using consensus forecasts in the I/B/E/S summary file. On the Thursday before

the third Friday of each month, I/B/E/S calculates the consensus forecast as the mean or median

of all outstanding estimates for a particular fiscal period. Forecasts are available for a variety of

fiscal periods, including the current quarter, the next quarter, the current fiscal year, and the next

fiscal year. Additional horizons are available, but analysts’ forecasts for these periods are less

frequent.

The ideal measurement of the response of security prices to earnings announcements and

earnings forecast revisions would use a consensus forecast made just prior to an earnings

announcement, and another made just after. However, consensus forecasts are compiled only

monthly. Preannouncement forecasts, then, are the most recent consensus forecasts compiled

before the earnings announcement date. Postannouncement forecasts are compiled the second

month after the earnings announcement. Consensus forecasts for the first month after the

earnings announcement are not used because they may contain individual forecasts issued both

before and after the earnings announcement.

As shown in the hypothetical example in figure 1, preannouncement forecasts are

gathered on the last forecast date before the earnings announcement, March 19. In general, the

time between the preannouncement forecast date and the earnings announcement will vary up to

one month. Since the April forecast period might contain forecasts made both before and after

the earnings announcement, postannouncement forecasts are instead gathered on May 21.

Abnormal stock returns are calculated from the close of the preannouncement date, March 19,

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until one trading day after the earnings announcement, March 24, and abnormal stock returns

over the extended window regressions described in section 3.3 are calculated until the end of the

postannouncement period, May 21.

Our initial tests are based on the Cornell and Landsman regression given by equation (1)

above, where

i, t are indices referring to a sample firm and an announcement quarter.

ARit = the abnormal stock return for firm i associated with quarterly earnings announcement t.

ARit is measured from the close of the day of the announcement of the most recent

I/B/E/S consensus forecast prior to the earnings announcement date (which we refer to as

the last day of the preannouncement forecast period) through the trading day following

the earnings announcement (see figure 1). The abnormal return is computed by

subtracting the compounded daily mean return for the corresponding size decile, rdec,

from the compounded daily firm return, r, over the period described above. That is,

FEit = the forecast error for firm i and quarterly earnings announcement t. FEit, which is

measured over the same time interval as ARit, is given by (EPSit – E(EPSit |0))/Pit, where

EPSit is the realized quarterly earnings per share taken from I/B/E/S, E(EPSit |0) is the

median preannouncement I/B/E/S consensus forecast of EPSit, and Pit is the security price

of firm i on the last day of the preannouncement forecast period (0 refers to the set of

information available on the preannouncement forecast date).4

4 All variables used to compute FE, FRQ, and FRY are adjusted for stock splits and stock dividends.

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FRQit = the forecast revision for firm i for quarter t+1, made subsequent to the earnings

announcement for quarter t. FRQit is given by (E(EPSi,t+1 |2) – E(EPSi,t+1 |0))/Pit, where

E(EPSi,t+1 |0) is the preannouncement forecast of EPS for quarter t+1, and E(EPSi,t+1 |2)

is the postannouncement forecast of EPS for quarter t+1. 2 refers to the set of

information available at the postannouncement date. As discussed above, this is the

second, not the first I/B/E/S consensus forecast available after the earnings

announcement.

FRYit = the forecast revision for firm i for the next fiscal year. FRYit is given by (E(EPSYi,t+k |2) –

E(EPSYi,t+k |0))/ Pit, where E(EPSYi,t+k |0) is the preannouncement forecast of EPS for the

fiscal year which ends in quarter t+k, and E(EPSYi,t+k |2) is the postannouncement

forecast of EPS for the fiscal year ending in quarter t+k.5 Note that the number of

quarters ahead for the subsequent fiscal year depends on the quarter of observation. For

example, if the current quarter is the first quarter of the year, the subsequent fiscal year

begins with k =4 and ends with k=7 quarters ahead, but if the current quarter is the third

quarter of the year, the subsequent fiscal year is begins with k=2 and ends with k=5

quarters ahead.

3.2 Incorporation of by Year and by Industry Fixed Effects

We estimate equation (1) several ways. These include (a) a pooled estimation with year

and industry fixed effects, where year is determined by the quarter end date and industry is based

on industry groupings used in Barth, Beaver, Hand, and Landsman (2005) (see table 1); (b) year-

by-year estimations with industry fixed-effects; and (c) for each industry, year-by-year

estimations. The fixed effects are included to capture sources of time dependence or cross

5 AR and the two forecast revisions, FRQ and FRY, are affected by the information revealed in the earnings release, 1. However, AR does not reflect information in the postannoucement period, 2.

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sectional dependence of a particular form (i.e., a constant for a given year and a constant for a

given industry across years). We assess statistical significance of coefficients in the year-by-year

estimations using Fama-MacBeth (1973) t-statistics and Z1 and Z2 statistics.6

3.3 Measurement of “Actual” Earnings per Share

Cornell and Landsman estimate equation (1) measuring earnings forecast errors using a

Compustat measure of actual earnings per share, earnings per share before extraordinary items,

which we hereafter refer to as the Compustat “actual”. Because I/B/E/S forecasts and I/B/E/S

actual earnings are measured more similarly, i.e., exclude similar items, the I/B/E/S constructed

forecast error is expected to be a better measure of earnings surprise. We assess whether this is

the case directly by estimating equation (1) using FE_COMP in place of FE, where:

FE_COMPit = the forecast error calculated as FEit, except EPSit is earnings before extraordinary

items taken from Compustat, divided by shares outstanding taken from I/B/E/S.

Even though the forecast errors measured using consistent I/B/E/S actuals likely have more

explanatory power, it is still possible that the market derives additional insight from the

information conveyed by the Compustat actuals. This may occur, for instance, if the Compustat

actuals provide information about GAAP related variables, such as special items, that the market

considers relevant, at least in some circumstances, but which are not included in the earnings

measure reported by I/B/E/S. To examine this possibility, we estimate equation (2) which adds

the term ADJ, the difference between the Compustat and I/B/E/S actuals:

ARit = a0 + a1 FEit + a2 FRQit + a3 FRYit + a4 ADJit + eit (2)

6 The Fama-MacBeth t-statistic = , where N is the number of years. Z1 equals

, where tj is the t-statistic for year j, kj is the degrees of freedom, and N is the number

of years. Z2, which equals , corrects for potential upward bias in Z1 arising from lack of independence of parameters across industries. See Barth (1994).

20

If the Compustat actuals provide additional information to the market, the ADJ coefficient, a4,

should be significantly different from zero. However, for the reasons indicated, we expect a4 to

be less than a1.

3.4 Impact of the Fourth Quarter

In their original paper, Cornell and Landsman conjecture that estimating the basic model

across all four quarters was potentially misleading because the fourth quarter could be different

than the other three quarters. They argue, for instance, that analysts might wait until year end to

revise year-ahead forecasts and that the market might place more weight on annual forecast

errors because annual financial results are audited. Although they produce some preliminary

results to support those conjectures, it is based on a sample of only three years and uses

Compustat actuals.

There are reasons other than those suggested by Cornell and Landsman for believing that

the fourth quarter may be unique. First, as one moves from the first to the fourth quarter, the

forecasting horizon implicit for FRY becomes shorter. It is reasonable to expect that as the

forecasting horizon becomes shorter the perceived precision and hence response coefficient

would increase. This horizon is shortest at the time of the announcement of fourth quarter

results, which is actually sometime within the first quarter of the next year. Second, the

information environment, as well as the nature of quarterly earnings, may differ in the fourth

quarter. For example, fourth quarter earnings contain more adjustments and special charges than

the prior quarters, in part because of auditing of the annual financial statements. It is possible

that these items are leaked to the market in earlier quarters (Mendelhall and Nichols, 1988),

which could result in a lower response coefficient for the fourth quarter forecast error relative to

the other quarters. Also, more information, in the form of a full set of financial statements, more

21

elaborate management discussion and analysis, and potentially more information gathering by

analysts may also occur. As a result, the fourth quarter is more than simply another “interim”

report. It is, in fact, the final quarter in the firm’s annual financial statements. Similarly, the

quarterly forecast revision, FRQ, is more than simply a forecast for a later quarter in the same

fiscal year. It is, in fact, a forecast of the first quarter of the next fiscal year.

Third, in addition to these substantive reasons, there are econometric reasons for

separating the fourth quarter. For the first three quarters, there is no temporal overlap between

FRQ and FRY. However, in the fourth quarter, FRQ is a component of FRY. Hence, the

interpretation of the coefficients differs for the fourth quarter.

To take account of these possibilities, we begin by estimating a version of equation (1)

permitting the intercept and FE, FRQ and FRY coefficients to differ for fourth quarter earnings

announcements:

ARit = a0 + a1 FEit + a2 FRQit + a3 FRYit + a4 Dit +

a5 DFEit + a6 DFRQit + a7 DFRYit + eit (3)

where D it is an indicator variable that equals one (zero) if the announcement is made in the

fourth (interim) quarter, and DFE, DFRQ, and DFRY are interactions between D and the

corresponding three variables. In this model, the full impact of the forecast error and the quarter-

ahead and year-ahead forecast revisions in the fourth quarter is , , and

, respectively. The reason the full impact of the quarter-ahead revision is more

complicated is that an increase in FRQ mechanically increases FRY.

To account directly for the temporal overlap between FRQ and FRY in the fourth quarter,

we also estimate the following model:

ARit = a0 + a1 FEit + a2 FRQit + a3 FRY*it + a4 Dit +

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a5 DFEit + a6 DFRQit + a7 DFRY*it + eit (4)

where FRY* equals FRY for announcement quarters 1, 2, and 3, and FRY FRQ for

announcement quarter 4. In this model, the full impact of the quarter-ahead forecast revision for

the fourth quarter is 62 αα + , while that for the year-ahead forecast revision for the fourth quarter

is 73 αα + .

3.5 Extending the Event Window

Ideally the forecast error and forecast revisions should be measured over the same time

period, so that the market reacts to all three simultaneously. Because of the reporting lag and

analyst aggregation issues, this ideal is not met. The aggregation problem arises because

analysts do not release their forecasts simultaneously. A measure of a consensus forecast

requires individual forecasts to be aggregated over time, and over time subsequent events that are

unrelated to the earnings announcement may influence forecast revisions. Reporting lag refers to

the time between an analyst forecast and its inclusion in the database. This becomes a problem

when a forecast should be included in the current consensus but is not added to the database until

after it is calculated. Cornell and Landsman address these problems by extending the

measurement window for the forecast revisions. As a result, whereas FE is measured over the

same time period as AR, the measurement periods of FRQ and FRY extend several weeks beyond

the earnings announcement event window. This feature of the data raises the issue that at the

time of the earnings announcement the market must use the information in FE to anticipate its

long-run impact, and thereby its effect on analysts’ forecasts without observing the forecasts.

Therefore, AR reflects both the forecast error and the expected forecast revisions. Because the

estimating equations include actual forecast revisions, FRQ and FRY, even if the market’s

23

expectation of the forecast revisions is unbiased, FRQ and FRY measure these expectations with

error, thereby biasing their coefficients towards zero.7

We address the non-simultaneous variable measurement issue by modifying the basic

model in two ways. First, we extend the window over which the abnormal 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. Other things equal, with forecast

revisions better aligned in time with the residual return, we would expect their coefficients to

increase. However, the problem with this approach is that the window must be extended, on

occasion, up to two months after the earnings announcement to make it unlikely that the

postannoucement consensus I/B/E/S forecast is sensitive to preannouncement forecasts made by

individual analysts. The example illustrated in figure 1 shows the event window runs from

March 23 until May 21. The longer event window has the effect of causing both the stock return

and the forecast revisions to reflect information that is unrelated to the earnings announcement.

While this may increase the slope coefficients on the forecast revisions, the longer event window

results in a regression that moves the research question away from discerning the informational

effects of the earnings announcement.

Therefore, we develop a second approach that utilizes disaggregated data. Rather than

using the I/B/E/S consensus forecasts, we employ the individual analysts’ forecasts to construct a

consensus forecast following the earnings announcement. The I/B/E/S detail file contains

individual analysts’ forecasts that can be combined to create custom consensus forecasts at any

date and for any time interval. This permits us to shorten the postannoucement event window

7 Because FRQ and FRY are measured beyond the abnormal return event window, this also raises the possibility that forecast revisions are (at least partially) responding to the abnormal return, thereby creating a potential endogeneity issue. Cornell and Landsman (1989, p. 686) recognize this, but argue there is no economic reason to believe that the information contained in analysts’ recommendations can be costlessly discerned by observing the change in price when earnings are released.

24

considerably relative to that associated with the consensus forecasts. The shorter event window

mitigates the effects of the aggregation problem by shrinking the forecast periods and aligning

them more closely to the earnings announcement.

The timeline in figure 2 illustrates how the detail data are used to construct the forecast

revisions and to compute the abnormal return over the extended event window. Each

constructed forecast revision is computed as the median of analysts’ forecasts available 19

trading days or less after the earnings announcement less the median of analysts’ earnings

forecasts made from 20 trading days through 1 trading day prior to the earnings announcement.

We use median forecasts rather than mean forecasts to lessen the effect of “stale” forecasts, i.e.,

those which may be out of date.8 The return window is computed from to the day of the earnings

announcement through 19 trading days after the earnings announcement. The forecast error is

simply I/B/E/S actual earnings less the median earnings forecast made in the twenty trading days

prior to the earnings announcement.

As shown in the hypothetical example in figure 2, preannouncement forecasts are

gathered over the twenty trading day period ending the trading day before the earnings

announcement, March 22. Postannouncement forecasts are gathered over the twenty trading day

period beginning on the earnings announcement date, or March 23 to April 20. Abnormal stock

returns over the extended window are calculated from the close of the preannouncement date,

March 22, until the end of the postannouncement period, April 20, a twenty-day window.

As we discuss below, one cost of using detail forecasts is that there is a significant

reduction in sample size because we require new forecasts to be issued both before and after the

8 Note that I/B/E/S consensus forecasts likely suffer from effects of stale forecasts, as I/B/E/S includes all available forecasts to construct their consensus measure. For this reason, we use the consensus median rather than mean.

25

announcement. Thus, the benefit of shortening the event window may be offset to some extent

by the loss of precision associated with a smaller estimation sample.

4. Sample Data

Firm-quarters included in the sample meet four criteria:

1. Median monthly earnings forecasts, actual earnings, and earnings announcement dates

are available from the I/B/E/S summary forecast data file for quarters ending between

1984 and 2003.

2. Quarterly earnings are available on the Compustat file for the same period. Consistent

with prior research, earnings is measured as income before extraordinary items and

discontinued operations.

3. Daily security price and return data are available from the CRSP file for each earnings

announcement “event” interval (defined above).

4. To mitigate the effects of outliers, for abnormal return, forecast error and forecast

revisions, we treat as missing observations that are in the extreme top and bottom one

percentile (Kothari and Zimmerman, 1995; Collins, Maydew and Weiss, 1997; Fama and

French, 1998; Barth, Beaver, Hand, and Landsman, 1999).

Table 1, panels A and B, report descriptive statistics and correlations among the variables

used in the study; panel C reports annual descriptive statistics for the forecast errors using

I/B/E/S actuals and Compustat actuals, and is discussed in detail in section 5 below. Table 1,

panel D, lists the industry composition of sample firms. Panel A reveals that mean abnormal

return is positive and, consistent with prior research (Abarbanell and Lehavy, 2003), mean

forecast errors are negative. In addition, means for both forecast revisions are negative. Panel B

reveals that the forecast errors and forecast revisions are correlated with abnormal return and

26

with each other. Panel C shows that sample observations are increasing throughout the sample

period until 1998, which is consistent with I/B/E/S coverage expanding over time. Untabulated

statistics reveal that sample observations are drawn from a wide variety of industries, with

Financials (14.6%) and Computers (14.2%) comprising the largest percentage.

5. Results

5.1 Pooled Estimation

To get an overview at the outset, we begin with a pooled model that covers the entire data

set. Table 2 presents the results for the pooled regression of equation (1), estimated over 1984 to

2003 with year and industry fixed effects. Consistent with the original findings of Cornell and

Landsman, all three coefficients are highly significant.9 In part because of the immense sample,

over 150,000 observations, all of the t-statistics exceed twenty. The finding implies that each

variable conveys information to the market not contained in the other. In particular, the

significance of the forecast error coefficient above the theoretical value of 1 implies that the

market perceives that the forecast revisions do not fully capture the implications of the forecast

error for future earnings (cash flows) performance.10 We expect the forecast revision variables to

be significant, because prices are viewed as a function of future earnings. Hence, price changes

9 The term significant indicates statistical significance at the 0.05 level or less using a two-sided test. Some of our tests clearly have directional predictions, e.g., we predict the pooled forecast error coefficient to be positive. However, we adopt a two-sided convention because our tests involving changes in coefficient magnitudes over time are associated with two-sided predictions. 10 Note that the theoretical value 1 is based on the assumption that a forecast error would be priced on a dollar-for-dollar basis after controlling for its implications for future earnings by inclusion of the forecast revisions. Finding the forecast error coefficient exceeds 1 is also consistent with the estimating equation not including all forecast revisions. To assess this possibility, we also estimated specifications including forecast revisions of two-quarter-ahead and two-year-ahead earnings forecasts. Untabulated findings from these regressions indicate that forecast error coefficients falls closer to 1 when both additional forecast revision variables are included. However, significance levels tend to be smaller, largely because data availability constraints associated with the additional regressors result in higher regression standard errors. In addition, the coefficients on the two-quarter-ahead and two-year-ahead earnings forecast revisions, while often significant, are of much smaller magnitude than the one-quarter-ahead and one-year-ahead revisions.

27

are expected to be related to the market’s revision in expected future earnings, and analysts’

forecast revisions are expected to serve as proxies for those expectational changes.

Note that the two forecast revisions, for the next quarter and for the next year, also

compete with each other for incremental explanatory power. However, neither dominates and

each is significant. This implies that analysts are able to distinguish the implications of

information arriving at the time of quarterly earnings announcements in terms of the short-run

and longer-run implications for future earnings.

5.2 Year-by-year Estimations

The issues discussed in the introduction, however, suggest that the pooled regression

hides potential changes in the relation between forecast errors, forecast revisions, and residual

returns. To examine this possibility, table 3, panel A, presents results from annual estimations of

the model. There is an increase in the number of observations per year, reflecting the increased

coverage by the I/B/E/S database with decrease in the 2000-2003 period presumably reflecting

the reduction in the number of firms forecasted due to the economic downturn.11 The results

show that the relation is robust. Except for the FRQ coefficient in 1984, a year in which there

are only 1,013 observations, all of the coefficients are positive and significant.

To compare the annual results with those from the pooled regression, we use a Fama-

MacBeth (1973) procedure. In particular, table 3, panel A reports the mean coefficient across all

the years, and Fama-MacBeth t-statistics and Z1 and Z2 statistics to assess statistical significance

of the coefficients over time. Because the Fama-MacBeth t-statistic does not use cross-sectional

data within a given year to calculate the standard error used in its calculation, and the Z2 statistic

corrects for the effect of cross-sectional dependence in the data, each is a less biased test statistic 11 McNichols and O’Brien (1999) investigate in detail the reasons why analyst coverage might be dropped. Basically, unfavorable information increases the likelihood of an analyst dropping a stock rather than continuing to report, which would have required a downward revision in forecasts. This self censoring could potentially affect the distribution of forecast errors and forecast revisions, although it is less clear how it would affect the coefficients.

28

than Z1 in the presence of cross-sectional dependence in the data. The test statistics likely are

not affected by time-series dependence in the data because the event-window abnormal returns

are non-overlapping in time and expected to be serially uncorrelated. In addition, we further

expect both the forecast errors and the forecast revisions, which are separated by a year, are also

serially uncorrelated.12

The comparison reveals that the Fama-MacBeth t-statistic and Z2 statistics are noticeably

lower than those in the pooled regression, which is consistent with positive cross sectional

dependence in the data even after extracting fixed effects. However, as in the pooled regression,

the magnitude of the forecast error coefficient is larger than that of either of the forecast

revisions.

Table 3, panel B, presents regression summary statistics from a specification that includes

only the forecast error as a regressor. Comparison of the FE coefficients in panel B to those in

panel A indicate that on average the FE increases approximately 60 percent when the forecast

revisions are excluded from the estimating equation. The increase is not surprising given the

positive correlation between forecast errors and forecast revisions. When the forecast revisions

are excluded, the forecast errors pick up some of their impact on stock prices. These findings

underscore the importance of including forecast revisions when explaining earnings

announcement period stock returns. Failure to do so results in an incorrectly specified model

when assessing the relation between forecast errors and residual returns.

Returning to panel A, which presents findings from the specification including the

forecast revisions and the forecast error, we also find evidence that the coefficients exhibit an

interesting pattern. Similar to Bradshaw and Sloan (2002) and Abarbanell and Lehavy (2005), 12 Adjacent-quarter forecast errors and forecast revisions would be expected to have some slight serial correlation (see Abarbanell and Bernard, 2000, among others). However, each year’s variables are separated from the next year’s variables by an average of four quarters.

29

there appears to be an abrupt shift in the FE coefficients beginning in 1991. For example, from

1984 through 1990, none of the coefficients is above one, while from 1991 through 2003 none of

the coefficients is below one. Needless to say, this is statistically significant using a simple

binomial test. Prior studies observe this may reflect an improvement in the quality of actual

earnings as reported by I/B/E/S in the sense of increasing the consistency between what is being

forecasted and what is included in actual. A second possibility is an improvement in the quality

of the earnings forecast in the sense of producing more consistency across analysts that comprise

the consensus. Either could induce this shift in coefficients. Although this shift in similar to that

found in prior research, there is an important difference in that the shift is still observed in the

presence of the forecast revisions variables. Hence, the observed shift is not explained solely by

a temporal change in the persistence of earnings.

To the best of our knowledge, no prior research has examined whether a similar shift

exists in forecasts revisions. To the extent that the shift in the FE coefficient is attributable to

improvements in the quality of the forecasts, we might expect to see that improvement reflected

in the coefficients on the forecast revisions as well. Both FRQ and FRY coefficients are higher

in the post 1991 period, although the shift is not as dramatic. For FRQ (FRY), 5 (5) coefficients

are below one while 2 (2) are above one from 1984-1990. For 1991-2003, 3 (4) coefficients are

below one while 10 (9) are above one. Using a binominal test, both coefficients are significantly

greater in the post-1991 period at less than the 0.05 level. Hence the improvements in the nature

of the database or the underlying quality of analysts’ forecasts appears to be a partial explanation

for the increase in the FE coefficients as well as the increase in the FRQ and FRY coefficients.13

13 Other possible reasons for the upward trend in the forecast error coefficient include enhanced earnings management and increased management’s guiding of analysts’ forecasts (Matsumoto, 2002; Abarbanell and Lehavy, 2003; Burgstahler and Eames, 2003), and increasing exclusion of transitory items over time by I/B/E/S (Bradshaw and Sloan, 2002).

30

One way to assess the importance of improvement in consistency of measures of forecast

and actual earnings is to use another measure of actual earnings. The obvious alternative is to

use Compustat data to measure actual earnings. Table 4 reports the results from estimation of

equation (1) which employs forecast errors measured using Compustat actuals, FE_COMP,

instead of FE. The most striking feature of the results is that the upward trend in the forecast

error coefficient disappears entirely. This is consistent with the hypothesis that increase in the

coefficients observed when using the I/B/E/S actuals is attributable to the success of the effort by

I/B/E/S to match the forecasts and the actuals on a more consistent basis. Second, the

coefficients on the forecast revisions are somewhat larger when FE_COMP is used as the

forecast error. This implies that the revisions are picking up some of the variance left

unexplained by use of an incorrect measure of the forecast error.

To further investigate the impact of measurement consistency of the components of the

forecast error, we examine the forecast errors directly. The findings are presented in table 1,

panel C. The results strongly support the hypothesis. Whereas the forecasts errors computed

from Compustat data, FE_COMP, show no evidence of a downward trend—if anything they

appear to increase— there is a pronounced downward trend in the magnitude of the I/B/E/S

consensus forecast errors, FE, particularly in the first ten years of the sample. This matches the

period over which the FE coefficients increase in Table 3, panel A. The evidence strongly

suggests that the trend in the FE coefficient is likely attributable to the success of the efforts by

I/B/E/S to more accurately align the actuals that are reported with the measure that analysts

forecast. This underscores the importance of defining actual and forecast earnings in precisely

the same fashion.

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Even though the forecast errors measured using consistent I/B/E/S actuals have more

explanatory power, it is still possible that the market derives additional insight from the

information conveyed by the Compustat actuals. Equation (2), which includes an additional

term, ADJ, to capture the difference between the Compustat actual and the I/B/E/S actual tests

this proposition directly. The findings reported in table 5 reveal that ADJ does increase the

explanatory power of the regression, but not a great deal. Its coefficient, a4, is positive in every

year but one, and is significantly so in over half of the years. In addition, the Z2 statistic is

highly significant. Nonetheless, the average coefficient is only 0.16, which is several orders of

magnitude less than the other coefficients. This low coefficient is consistent with prior research

by Elliott and Hanna (1996), among others, which shows that special items and other transitory

items are priced at much less than a dollar for dollar basis. In this regard, the coefficient on

FE_COMP reported previously can be thought of as being biased toward zero because it consists

of two components, one of which has a coefficient of 0.16.

5.3 Cross-industry Results

When estimating earnings response coefficients, researchers often implicitly assume that

firms in different industries can be treated identically. That is, forecast errors and forecast

revisions have the same impact on stock prices independent of the underlying business in which

the company operates. However, this may not be the case. For example, the pressure to guide or

manage earnings may be greater in one industry because of competitive factors are regulatory

concerns. To test the assumption that the coefficient are equal across industries, we return to the

fundamental model given by equation (1) and use the full time series to estimate a series of

regressions across industries.

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Untabulated results for the industry regressions reveal that the results observed for the

pooled sample holds generally in every industry. In addition, most of the coefficients for the

individual industries are quite close to the cross industries means. There are, however, some

exceptions. Insurance and real estate are found to be low coefficients while those for the retail

restaurant industry are high. We leave to future research an effort to determine whether these

differences are stationary and, if so, what accounts for them.

5.4 The Impact of the Fourth Quarter

Table 6, panel A, reports the results from estimating equation (3), which includes an

incremental intercept and slope coefficients for fourth quarter announcements, but does not

consider the overlapping measurement of FRQ and FRY in the fourth quarter. Findings from the

pooled estimation indicate that the incremental coefficients for the fourth quarter forecast error

and quarter-ahead forecast revision, DFE and DFRQ, are significant and negative while the

coefficient on the year-ahead forecast revision, DFRY, is positive and marginally significant.

Findings based on the year-by-year regressions reported in table 6, panel B, are similar to those

from the pooled regression in that they also indicate that the coefficients for DFE and DFRQ are

also negative and significant.

Table 6, panels C and D, reports the pooled and year-by-year estimation results for

equation (4), which takes into account the temporal overlap between FRQ and FRY in the fourth

quarter. Both panels reveal similar insights. In particular, with the overlap eliminated, the

coefficient on DFRQ is positive and significant, whereas that on DFRY* is insignificant. By

definition, the coefficient on DFE is unaffected. Focusing on the year-by-year results in panel D,

the mean incremental coefficient for FE is 0.50, which implies that that the total earnings

response coefficient for fourth quarter forecast errors is 0.78 (1.28.50), which is now less than

33

that for FRQ (1.84 = 1.10 + 0.74) or FRY* (1.14 = 1.00 + 0.14.). The lower coefficient implies

that the market responds less to FE in the fourth quarter. This is consistent with more

information arriving in the fourth quarter, such as more elaborate management discussion and

analysis and more comprehensive year end reviews by analysts, that is reflected in the forecast

revisions but not in the forecast errors. It is also consistent with the fourth quarter I/B/E/S actual

containing more transitory factors that the first three quarters. For example, if some year-end

adjustments are implicitly imbedded in revenue or expenses and not explicitly shown as a special

charge, it would be difficult for I/B/E/S to extract these effects when forming an I/B/E/S actual.

These findings contrast with that of Cornell and Landsman, which finds that the forecast

error has significant explanatory power only in the fourth quarter, the quarter-ahead forecast

revision has no explanatory power in the fourth quarter, but the year-ahead forecast revision

coefficient is significantly larger in the fourth quarter than in the interim quarters. Nonetheless,

the results confirm the conjecture that the fourth quarter is different than other quarters.

Consequently, properly specified models of the reaction of stock prices to forecast errors and

forecast revisions must not only include the revisions to be properly specified, they must also

take account of the unique nature of the fourth quarter.

5.5 Event Window Extensions

As discussed in section 3.3, our research design uses forecast revisions that occur after

the earnings announcement window ends. This results in FRQ and FRY likely measuring the

market’s expectations of analysts’ forecasts with error, thereby biasing their coefficients towards

zero. This section presents findings from two approaches designed to mitigate the effects of this

problem by extending the event window so that the abnormal return and forecast revisions are

measured over the same time period. Table 7 presents results from estimations based on

34

consensus I/B/E/S forecasts that extend the return window to the end of revision period. Table 8

presents results from estimations based on the consensus forecast we construct from I/B/E/S

detail data; the forecast error, quarter-ahead and year-ahead forecast revisions constructed from

the detail data are denoted FE_DET, FRQ_DET, and FRY_DET. For these regressions, each

constructed forecast revision is computed as the median of analysts’ forecasts available 19

trading days or less after the earnings announcement less the median of analysts’ earnings

forecasts made from 20 trading days through 1 trading day prior to the earnings announcement.

The return window is computed from the day of the earnings announcement through 19 trading

days after the earnings announcement. In both tables 7 and 8, panel A presents findings from the

pooled estimations, and panel B presents the year-by-year results.

The results for the pooled regression reported in table 7, panel A, are similar to those

reported in table 2, in that all three regressors, FE, FRQ, and FRY have significantly positive

coefficients. That is, each informational variable has pricing effects incremental to the others.

However, relative to the findings reported in table 2 and consistent with the conjecture that the

expanded return window better captures the price effects of FRQ and FRY, the coefficients on

these variables increase substantially. For example, the coefficient on FRY essentially doubles

from 1.02 to 2.01. Furthermore, in contrast to the basic estimation results reported in table 2, in

table 7, panel A, both the FRQ and FRY coefficients are larger than the FE coefficient.

The results of the year-by-year estimations reported in table 7, panel B, yield similar

insights from the pooled results in panel A. Relative to the year-by-year results from the basic

estimations reported in Table 3, there is general increase in the FRQ and FRY coefficients, with

overall means increasing from 1.08 and 0.99 to 1.76 and 1.89. In addition, the FRQ and FRY

coefficients are generally larger than the FE coefficient, where the overall mean FE coefficient is

35

1.31. The panel B results indicate the presence of a slight increase in the coefficient of FE over

time, although it is far less noticeable than when the shorter event window is examined. Hence,

the basic conclusions are robust to this extension with the added insight that, as expected,

sensitivity coefficients on FRQ and FRY are higher when the event window is extended.14

The findings for the pooled sample and year-by-year estimations reported in table 8,

panels A and B, show that coefficients on all three regressors are significant. Hence, the basic

findings are robust to this second extension as well. The magnitude of the coefficients can be

compared with those reported in tables 2, 3, and 8. However, a word of caution is required.

Whereas the findings presented in tables 2, 3 and 8 were based on essentially the same firm-

quarter observations, the number of observations used to estimate the table 8 regression results is

considerably smaller. The substantial reduction in sample size occurs because many of the

individual analysts do not provide a quarter-ahead and year-ahead forecast in the twenty trading

days before each quarterly earnings announcement. Hence, FRQ_DET and FRY_DET are not

available for many firm-quarters. The reduction in sample size reduces the efficiency of the

coefficient estimates.

Notwithstanding these caveats the basic findings are robust to this second extension as

well. The most noticeable difference between the coefficients in reported in table 8 and those

reported in the earlier tables is the substantial decrease in the FRY_DET coefficient, which

14 We also examined an alternative specification to alleviate the disparity in the timing of the forecast revision variables and announcement return. Specifically, we include as an additional explanatory variable the abnormal stock return beginning the day after the announcement period return through the date of the postannouncement earnings forecast. This postannouncement return will reflect information arriving after the earnings announcement but available to analysts (and the market) up to the time when the postannouncement earnings forecast is provided. Under these assumptions, this return will be correlated with the measurement error in the forecast revision variables, and therefore its inclusion will potentially reduce the revision variable coefficients (Brown, Griffin, Hagerman, and Zmijewski, 1987; Collins, Kothari, Shanken, and Sloan, 1994). When we conduct such an estimation, the coefficient on the added return variable is not significantly different from zero and the revision coefficients are unaffected by the inclusion of this variable. Hence, our basic finding is robust to this specification as well. This is consistent with the measurement error being uncorrelated with the subsequent security returns.

36

suggests that the potential limitations of this constructed consensus outweigh the potential

benefits. The coefficients on FE_DET and FRQ_DET are similar to those reported in table 8.

6. Concluding Remarks

We offer the first comprehensive analysis, both over time and across industries, of

security returns and analyst forecast errors that also takes account of forecast revisions and a

possible fourth quarter effect. We find that all analysts forecast errors, quarter ahead forecast

revisions and year ahead forecast revisions all have significant pricing effects, indicating each

conveys incremental information content. This finding is remarkably robust, holding across

years, industries, and two procedures extending the event window. Further, findings from the

expanded event window tests reveal that although forecasts revision coefficients increase, they

do not do so at the expense of the forecast error coefficients. Over the narrower event window,

the forecast error has the highest coefficient, while over the longer window the forecast revisions

have larger coefficients than the forecast error.

In addition, we document that the fourth quarter is significantly different from the other

three quarters. In particular, the relative importance of the forecast error is lower (although still

highly significant), while the relative importance of the quarter-ahead forecast revision increases.

We attribute this difference not only to the nature of fourth quarter earnings but also to the

enhanced information available near year-end that conveys information about the next fiscal

year.

Finally, we document an increase in the forecast error coefficient over time even in the

presence of the forecast revision variables and a similar but less dramatic shift in the forecast

revision coefficients. Consistent with prior research, the evidence supports the view that the

I/B/E/S measure of actual earnings is superior for calculating forecast errors because it is more

37

comparable to the earnings being forecasted by analysts. In this respect, the quality of that

number has improved over time, underscoring the importance of defining the forecast and the

actual earnings measure in precisely the same fashion. Moreover, the shift in the forecast

revision coefficients is consistent with improvements in the analysts forecast being a factor in

explaining the upward shift in both the forecast error and forecast revision coefficients.

Finding the forecast revisions continue to play a significant role in explaining earnings

announcement period returns after controlling for improvement in the measurement of the

forecast error over time suggests that 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. Thus, the findings from this study

underscore the importance of including forecast revisions in addition to forecast errors, and

allowing for a fourth quarter effect, when examining how stock returns are affected by earnings

announcements.

38

References

Abarbanell, J. and V. Bernard (2000). “Is the US Stock Market Myopic?” Journal of Accounting

Research 38, 221-242.

Abarbanell, J., and R. Lehavy (2003). “Can Stock Recommendations Predict Earnings

Management and Analysts’ Earnings Forecast Errors?” Journal of Accounting Research 41,

1-31.

Abarbanell, J., and R. Lehavy (2005). “Letting the ‘Tail Wag the Dog’: The Debate over GAAP

versus Street Earnings Revisited.” Contemporary Accounting Research (forthcoming).

Barth, M.E. (1994). “Fair Value Accounting: Evidence from Investment Securities and the

Market Valuation of Banks.” The Accounting Review 69, 1-25.

Barth, M.E., W.H. Beaver, J.M. Hand, and W.R. Landsman (1999). “Accruals, Cash Flows, and

Equity Values.” Review of Accounting Studies 3: 205-229.

Barth, M.E., W.H. Beaver, J.M. Hand, and W.R. Landsman (2005). “Accruals, Accounting–

Based Valuation Models, and the Prediction of Equity Values.” Journal of Accounting,

Auditing & Finance 20, 311-345.

Bradshaw, M.T., and R.G. Sloan (2002). “GAAP versus The Street: An Empirical Assessment of

Two Alternative Definitions of Earnings.” Journal of Accounting Research 40, 41-66.

Brown, L. (1996) “Analyst Forecasting Errors and Their Implications for Security Analysis: An

Alternative Perspective” Financial Analysts Journal 52, 40-47.

Brown, L. (2001). “A Temporal Analysis of Earnings Surprises: Profits versus Losses.” Journal

of Accounting Research 39, 221-241.

39

Brown, L., P. Griffin, R. Hagerman, and M. Zmijewski (1987). “An Evaluation of Alternative

Proxies for the Market’s Assessment of Unexpected Earnings.” Journal of Accounting and

Economics 9, 159-193.

Brown, L. and K. Sivakumar (2003). “Comparing the Value Relevance of Two Operating

Income Measures.” Review of Accounting Studies, 561-572.

Burgstahler, D., and M. Eames (2003). “Earnings Management to Avoid Losses and Earnings

Decreases: Are Analysts Fooled?” Contemporary Accounting Research 20, 253-272.

Collins, D., S.P. Kothari, J. Shanken, and R. Sloan (1994), “Lack of Timeliness and Noise as

Explanations for the Low Contemporaneous Returns-Earnings Association.” Journal of

Accounting and Economics 18, 289-324.

Collins, D.W., Maydew, E.L., and I.S. Weiss (1997). “Changes in the Value–Relevance of

Earnings & Equity Book Values Over The Past Forty Years.” Journal of Accounting and

Economics 24, 39-67.

Cornell, B., and W. Landsman (1989). “Security Price Response to Quarterly Earnings

Announcements and Analysts’ Forecast Revisions.” The Accounting Review 64, 680-692.

Elliott, J., and D. Hanna (1996). “Repeated Accounting Write-Offs and the Information Content

of Earnings.” Journal of Accounting Research 34, 135-155.

Fama, E.F., and K.R. French (1998). “Taxes, Financing Decisions, and Firm Value.” Journal of

Finance 53, 819-843.

Fama, E.F., and J.D. MacBeth (1973). “Risk, Return, and Equilibrium: Empirical tests.” Journal

of Political Economy 81, 607-636.

Feltham, J., and J. Ohlson (1995). “Valuation and Clean Surplus Accounting for Operating and

Financial Activities,” Contemporary Accounting Research 11, 689-731.

40

Feltham, J., and J. Ohlson (1996). “Uncertainty Resolution and the Theory of Depreciation

Measurement,” Journal of Accounting Research 34, 209-234.

Gleason, C. and C. Lee (2003). “Analyst Forecast Revision and Market Price Discovery.” The

Accounting Review 78, 192-225.

Kothari, S.P., and J. Zimmerman. (1995). “Price and Return Models.” Journal of Accounting and

Economics 20, 155-192.

Liu, J. and J. Thomas (2000). “Stock Returns and Accounting Earnings.” Journal of Accounting

Research 38, 71-101.

Lougee, B. and C. Marquardt (2004). “Earnings Informativeness and Strategic Disclosure: An

Empirical Examination of ‘Pro Forma’ Net Income.” The Accounting Review 79, 769-795.

Lys, T. and S. Sohn (1990). “The Association between Revisions of Financial Analysts’

Earnings Forecasts and Security-Price Changes.” Journal of Accounting and Economics 13,

341-363.

Matsumoto, D. (2002). “Management’s Incentives to Avoid Negative Earnings Surprises.” The

Accounting Review 77, 483-514.

McNichols, M., and P. O’Brien (1999). “Self Selection and Analyst Coverage.” Journal of

Accounting Research (Supplement) 37, 167-199.

Mendelhall, R. and W. Nichols (1988). “Bad News and Differential Market Reactions to

Announcements of Earlier-Quarters Versus Fourth-Quarter Earnings.” Journal of

Accounting Research 26, 63-86.

Ohlson, J. (1995). “Earnings, Book Values and Dividends in Security Valuation,” Contemporary

Accounting Research 11, 661-687.

41

FIGURE 1

FIGURE 2

42

Earnings Announcements and the Timing of I/B/E/S Summary Forecasts:An Example Using a Hypothetical Announcement on March 23

MAR 23: Earnings Announcement Date

MAY 21: End of Postannouncement

Forecast Period

APR 16:End of April

Forecast Period

MAR 19: End of Preannouncement

Forecast Period

Return Cumulation Period

Extended Return Cumulation Period

Earnings Announcements and the Timing of I/B/E/S Custom Summary Forecasts:An Example Using a Hypothetical Announcement on March 23

MAR 23: Earnings Announcement Date

MAR 23 – APR 20:Post-announcement

Forecast Period

FEB 22 – MAR 22: Pre-announcement

Forecast Period

Extended Return Cumulation Period

Return Cumulation Period

TABLE 1

Summary Statistics (n = 156, 993) Panel A: Descriptive Statistics

Percentiles

Mean Std. Dev. 25 50 75

AR 0.004 0.096 –0.045 0.001 0.049FE –0.001 0.008 –0.001 0.000 0.001FE_COMP –0.002 0.020 –0.002 0.000 0.003FRQ –0.001 0.005 –0.002 0.000 0.000FRY –0.003 0.012 –0.003 0.000 0.001

Panel B: Correlations (Pearson above diagonal, Spearman below)

AR FE FE_COMP FRQ FRY

AR 0.159 0.099 0.165 0.194FE 0.238 0.381 0.327 0.314FE_COMP 0.184 0.573 0.206 0.210FRQ 0.201 0.324 0.259 0.551FRY 0.249 0.384 0.306 0.507

43

TABLE 1 – Continued Panel C: Forecast Errors Over Time

FE FE_COMP

Year N Mean Q1 Median Q3 Mean Q1 Median Q3

1984 1,013 –0.286 –0.471 –0.042 0.286 –0.071 –0.307 0.024 0.4201985 2,944 –0.267 –0.410 –0.062 0.153 –0.124 –0.344 –0.005 0.3161986 3,659 –0.245 –0.346 –0.043 0.133 –0.186 –0.293 0.024 0.3201987 3,254 –0.243 –0.297 –0.018 0.131 –0.103 –0.260 0.039 0.3161988 3,555 –0.175 –0.270 0.000 0.201 –0.097 –0.233 0.052 0.3531989 4,721 –0.245 –0.345 –0.034 0.143 –0.226 –0.333 0.015 0.3001990 5,036 –0.222 –0.328 –0.031 0.132 –0.243 –0.363 0.012 0.2831991 5,774 –0.156 –0.238 0.000 0.126 –0.227 –0.268 0.028 0.2881992 6,664 –0.104 –0.167 0.000 0.135 –0.163 –0.181 0.051 0.3191993 8,075 –0.079 –0.145 0.000 0.136 –0.146 –0.178 0.060 0.3131994 10,021 –0.072 –0.125 0.000 0.144 –0.104 –0.145 0.080 0.3471995 10,866 –0.075 –0.106 0.014 0.144 –0.197 –0.192 0.067 0.3261996 12,265 –0.073 –0.075 0.021 0.126 –0.226 –0.176 0.063 0.2821997 13,691 –0.039 –0.049 0.027 0.126 –0.183 –0.138 0.071 0.3081998 13,632 –0.069 –0.052 0.018 0.114 –0.316 –0.246 0.049 0.2641999 12,784 –0.050 –0.036 0.029 0.144 –0.229 –0.203 0.059 0.2982000 10,650 –0.033 –0.023 0.035 0.161 –0.386 –0.342 0.038 0.2822001 9,820 –0.033 –0.051 0.024 0.142 –0.670 –0.607 –0.005 0.1972002 9,877 0.029 0.000 0.041 0.180 –0.308 –0.279 0.049 0.2882003 8,692 0.031 –0.021 0.043 0.188 –0.111 –0.202 0.061 0.316

AR = stock return measured from the close of the earnings forecast date to the close of the

weekday following the earnings announcement, less the mean return for the firm’s corresponding size decile over the same period; FE = realized I/B/E/S quarterly earnings per share less the median preannouncement I/B/E/S consensus forecast; FE_COMP = same as FE, except realized earnings is taken from Compustat—income before extraordinary items divided by the number of shares outstanding taken from I/B/E/S; FRQ = earnings forecast revision for the subsequent fiscal quarter, calculated as the I/B/E/S median forecast after the earnings announcement less the I/B/E/S median forecast before the announcement; FRY = earnings forecast revision for the subsequent fiscal year, calculated as the I/B/E/S median forecast after the earnings announcement less the I/B/E/S median forecast before the announcement.

Forecast variables are scaled by stock price on the pre-announcement forecast date and adjusted for stock splits and dividends.

All correlations in Panel B as significant at the .001 level.Forecast errors in Panel C are scaled by 100.

44

TABLE 2Pooled Estimation

ARit = a0 + a1 FEit + a2 FRQit + a3 FRYit + eit

  Estimate t-statistic

FE 1.18 36.58FRQ 1.21 21.24FRY 1.05 43.74

N 156,993Adj. R2 0.0528

Variables are defined in table 1.

45

TABLE 3By-Year Estimation

ARit = a0 + a1 FEit + a2 FRQit + a3 FRYit + eit

Panel A: All Variables

FE FRQ FRY  Est. t-stat Est. t-stat Est. t-stat   N Adj. R2

     1984 0.44 3.06 –0.05 –0.13 0.61 3.16 1,013 0.041985 0.44 4.25 0.67 2.72 1.06 9.51 2,944 0.071986 0.71 6.31 1.04 4.30 0.89 7.79 3,659 0.071987 0.46 3.56 0.63 2.02 0.87 5.83 3,254 0.031988 0.44 3.99 0.70 2.95 1.02 8.76 3,555 0.061989 0.64 6.61 0.77 3.90 0.82 8.52 4,721 0.061990 0.77 6.36 1.14 5.34 0.91 8.45 5,036 0.071991 1.33 9.69 0.85 3.67 1.00 8.77 5,774 0.071992 1.10 7.78 1.04 4.47 0.78 6.83 6,664 0.051993 1.57 10.84 1.01 4.41 0.90 8.57 8,075 0.051994 1.57 12.33 0.99 4.68 1.16 12.43 10,021 0.071995 1.21 9.44 1.86 8.13 1.27 12.89 10,866 0.081996 1.43 11.56 1.61 6.86 1.03 10.68 12,265 0.061997 2.02 15.34 1.54 7.12 1.18 13.38 13,691 0.071998 1.33 10.42 1.03 4.57 0.95 10.84 13,632 0.051999 1.41 10.26 1.18 5.27 1.08 11.18 12,784 0.052000 1.29 7.98 0.81 2.83 1.02 8.79 10,650 0.042001 1.45 9.28 1.35 5.76 0.76 8.75 9,820 0.052002 1.47 9.44 1.24 5.24 1.24 14.22 9,877 0.072003 1.80 11.00 2.11 8.64 1.16 11.42  8,692 0.09

     Mean 1.14 8.48 1.08 4.64 0.99 9.54 7,850 0.06FM t 10.39 9.57 24.13Z1   37.90  20.74  42.65Z2   11.41  9.68  15.55

46

TABLE 3 – Continued Panel B: Forecast Error only

FE  Est. t-stat   N Adj. R2

 1984 0.59 4.35 1,013 0.031985 0.71 7.01 2,944 0.031986 1.13 10.67 3,659 0.041987 0.80 6.62 3,254 0.011988 0.92 8.86 3,555 0.031989 1.11 12.35 4,721 0.041990 1.39 12.16 5,036 0.031991 2.01 15.88 5,774 0.051992 1.74 13.23 6,664 0.031993 2.26 16.87 8,075 0.041994 2.47 21.24 10,021 0.051995 2.39 20.40 10,866 0.051996 2.36 20.73 12,265 0.041997 3.06 24.75 13,691 0.051998 2.04 16.81 13,632 0.031999 2.24 17.43 12,784 0.032000 1.90 12.26 10,650 0.032001 2.21 14.90 9,820 0.032002 2.34 15.55 9,877 0.032003 3.03 19.43  8,692 0.05

 Mean 1.83 14.58 7,850 0.03FM t 10.76Z1   65.17Z2   11.75

Variables are defined in table 1.Z1 = , where tj is the t-statistic for year j, kj is the degrees of

freedom, and N is the number of years. Z2 = , and the Fama-MacBeth t-statistic (FM t) = , where N is the number of years.

47

TABLE 4Compustat Actuals

ARit = a0 + a1 FE_COMPit + a2 FRQit + a3 FRYit + eit

Panel A: Pooled Estimation

  Estimate t-statistic

FE_COMP 0.27 22.31FRQ 1.48 26.26FRY 1.14 47.85

N 156,993Adj. R2 0.0477

48

TABLE 4 – Continued Panel B: By-year Estimation

FE_COMP FRQ FRY  Est. t-stat Est. t-stat Est. t-stat   N Adj. R2

     1984 0.54 3.53 –0.08 –0.19 0.55 2.78 1,013 0.041985 0.56 6.56 0.59 2.42 1.04 9.38 2,944 0.081986 0.29 4.50 1.31 5.53 0.97 8.56 3,659 0.071987 0.36 3.45 0.62 2.00 0.91 6.20 3,254 0.031988 0.26 3.44 0.76 3.24 1.06 9.23 3,555 0.061989 0.29 5.09 0.88 4.53 0.91 9.65 4,721 0.061990 0.21 3.24 1.21 5.65 1.04 9.88 5,036 0.061991 0.37 6.43 1.10 4.78 1.21 10.87 5,774 0.061992 0.23 4.15 1.33 5.80 0.88 7.76 6,664 0.041993 0.30 5.59 1.41 6.20 1.09 10.47 8,075 0.041994 0.39 8.30 1.34 6.41 1.35 14.75 10,021 0.071995 0.22 4.93 2.22 9.89 1.42 14.61 10,866 0.081996 0.28 6.34 2.03 8.77 1.18 12.26 12,265 0.061997 0.30 7.34 2.00 9.26 1.38 15.75 13,691 0.061998 0.16 3.69 1.38 6.22 1.05 12.05 13,632 0.041999 0.31 6.32 1.49 6.78 1.18 12.23 12,784 0.042000 0.33 6.22 1.04 3.66 1.07 9.25 10,650 0.042001 0.16 3.74 1.74 7.53 0.82 9.38 9,820 0.042002 0.28 5.99 1.61 6.96 1.25 14.34 9,877 0.072003 0.18 3.16 2.59 10.71 1.30 12.76  8,692 0.08

     Mean 0.30 5.10 1.33 5.81 1.08 10.61 7,850 0.06FM t 12.39 9.29 21.66Z1   22.81  25.97  47.43Z2   14.48  9.22  14.74

Variables are defined in Table 1.Z1 = , where tj is the t-statistic for year j, kj is the degrees of

freedom, and N is the number of years. Z2 = , and the Fama-MacBeth t-statistic (FM t) = , where N is the number of years.

49

TABLE 5I/B/E/S Actuals, I/B/E/S Adjustments

ARit = a0 + a1 FEit + a2 FRQit + a3 FRYit + a4 ADJit + eit

Panel A: Pooled Estimation

  Estimate t-statistic

FE 1.19 37.07FRQ 1.18 20.75FRY 1.04 43.08ADJ 0.14 10.78

N 156,993Adj. R2 0.0535

50

TABLE 5 – Continued Panel B: By-year Estimation

FE FRQ FRY ADJ  Est. t-stat Est. t-stat Est. t-stat Est. t-stat   N Adj. R2

       1984 0.62 3.72 –0.12 –0.29 0.54 2.73 0.40 2.12 1,013 0.041985 0.64 5.83 0.55 2.26 1.03 9.27 0.51 5.11 2,944 0.081986 0.74 6.56 1.05 4.32 0.89 7.71 0.14 2.03 3,659 0.071987 0.51 3.83 0.59 1.91 0.86 5.74 0.21 1.57 3,254 0.031988 0.48 4.21 0.68 2.86 1.00 8.63 0.14 1.61 3,555 0.061989 0.68 6.91 0.74 3.77 0.80 8.35 0.15 2.49 4,721 0.061990 0.77 6.37 1.14 5.30 0.90 8.43 0.03 0.43 5,036 0.071991 1.34 9.77 0.83 3.60 1.00 8.74 0.18 2.83 5,774 0.071992 1.11 7.82 1.02 4.40 0.77 6.75 0.09 1.43 6,664 0.051993 1.57 10.83 1.00 4.34 0.90 8.54 0.10 1.73 8,075 0.051994 1.57 12.34 0.95 4.47 1.16 12.40 0.20 4.10 10,021 0.071995 1.21 9.42 1.84 8.03 1.27 12.84 0.08 1.62 10,866 0.081996 1.43 11.55 1.60 6.80 1.02 10.58 0.11 2.34 12,265 0.061997 2.04 15.45 1.51 6.94 1.17 13.21 0.14 3.32 13,691 0.081998 1.34 10.42 1.02 4.54 0.95 10.82 0.02 0.38 13,632 0.051999 1.42 10.31 1.16 5.19 1.06 10.91 0.16 2.97 12,784 0.052000 1.30 8.00 0.79 2.73 0.99 8.54 0.21 3.88 10,650 0.042001 1.46 9.36 1.33 5.64 0.75 8.60 0.08 1.76 9,820 0.052002 1.49 9.60 1.22 5.16 1.22 13.93 0.18 3.71 9,877 0.082003 1.80 10.99 2.12 8.65 1.16 11.43 –0.03 –0.45  8,692 0.09

       Mean 1.18 8.66 1.05 4.53 0.97 9.41 0.16 2.25 7,850 0.06FM t 11.42 9.11 23.06 5.78Z1   38.75  20.26  42.07  10.06Z2   12.40  9.30  15.18  7.22

ADJ = Compustat actual (income before extraordinary items) – I/B/E/S actual, scaled by stock price on the pre-announcement forecast date.

All other variables are defined in table 1.Z1 = )2/(/1 /1 −∑ = jkjktN N

j j , where tj is the t-statistic for year j, kj is the degrees of freedom, and N is the number of years. Z2 = ))1(/)(/( −Ntstddevt , and the Fama-MacBeth t-statistic (FM t) = ))1(/)(/( −Nstddev , where N is the number of years.

51

TABLE 6Estimation of Fourth-quarter Effects

ARit = a0 + a1 FEit + a2 FRQit + a3 FRYit + a4 Dit + a5 DFEit + a6 DFRQit + a7 DFRYit + + eit

Panel A: Pooled Estimation

  Estimate t-statistic

FE 1.35 35.00FRQ 1.28 19.89FRY 1.03 38.03D 0.00 –1.47DFE –0.59 –8.53DFRQ –0.53 –3.84DFRY 0.14 2.32

N 156,993Adj. R2 0.0534

52

TABLE 6 – Continued Panel B: By-year Estimation

FE FRQ FRY DFE DFRQ DFRY  Est. t-stat Est. t-stat Est. t-stat Est. t-stat Est. t-stat Est. t-stat   N Adj. R2

           1984 0.61 2.52 –0.55 –0.89 1.40 4.86 –0.26 –0.86 0.85 1.02 –1.39 –3.64 1,013 0.051985 0.42 3.27 0.64 2.35 0.83 6.73 0.04 0.21 –0.26 –0.41 1.22 4.38 2,944 0.081986 0.83 5.93 1.09 3.89 0.85 6.66 –0.41 –1.75 –0.10 –0.17 0.20 0.68 3,659 0.071987 0.74 4.61 0.39 1.09 0.98 5.74 –0.69 –2.56 0.99 1.34 –0.48 –1.38 3,254 0.031988 0.52 3.57 0.56 2.00 1.01 7.48 –0.15 –0.65 0.44 0.82 0.05 0.21 3,555 0.061989 0.72 6.31 0.93 4.11 0.84 7.79 –0.26 –1.22 –0.65 –1.42 –0.03 –0.13 4,721 0.061990 0.87 6.05 1.39 5.79 0.86 7.26 –0.40 –1.51 –1.27 –2.38 0.34 1.23 5,036 0.071991 1.56 9.73 0.70 2.75 0.93 7.56 –0.88 –2.84 0.47 0.79 0.46 1.44 5,774 0.071992 1.23 6.99 1.10 4.13 0.58 4.46 –0.39 –1.30 –0.26 –0.46 0.88 3.24 6,664 0.051993 1.97 11.31 0.95 3.65 0.82 7.01 –1.32 –4.17 0.01 0.02 0.41 1.51 8,075 0.061994 1.72 11.16 0.99 4.04 1.26 11.78 –0.51 –1.89 –0.21 –0.43 –0.41 –1.87 10,021 0.071995 1.35 8.78 2.33 8.84 1.36 11.99 –0.61 –2.19 –2.11 –4.01 –0.23 –1.02 10,866 0.091996 1.63 10.69 1.63 6.05 1.12 10.10 –0.62 –2.36 –0.45 –0.81 –0.28 –1.21 12,265 0.061997 1.99 12.52 1.73 7.08 1.20 12.11 0.08 0.29 –0.88 –1.63 0.04 0.20 13,691 0.081998 1.49 9.77 1.13 4.30 0.91 9.13 –0.57 –2.03 –0.50 –0.98 0.14 0.69 13,632 0.051999 1.37 8.65 1.13 4.59 1.14 10.77 0.19 0.61 0.63 1.08 –0.36 –1.39 12,784 0.052000 1.35 7.16 0.97 2.93 1.05 7.72 –0.28 –0.76 –0.73 –1.10 –0.06 –0.22 10,650 0.042001 1.86 10.09 1.38 5.34 0.59 6.06 –1.49 –4.30 –0.59 –0.95 0.85 3.92 9,820 0.052002 1.54 8.58 1.37 5.10 1.22 12.58 –0.37 –1.03 –0.77 –1.32 0.19 0.85 9,877 0.082003 1.87 11.00 2.22 8.82 1.10 10.53 –1.18 –1.77 –2.63 –2.41 1.27 2.83  8,692 0.09

           Mean 1.28 7.93 1.10 4.30 1.00 8.42 –0.50 –1.60 –0.40 –0.67 0.14 0.52 7,850 0.06FM t 11.11 7.50 19.23 –4.89 –2.12 1.22Z1   35.48  19.22  37.63  –7.17  –3.00  2.31Z2   11.72  7.89  14.53  –5.38  –2.16  1.11

53

TABLE 6 – Continued Panel C: Pooled Estimation, with adjusted FRY

  Estimate t-statistic

FE 1.35 35.00FRQ 1.28 19.89FRY* 1.03 38.03D 0.00 –1.47DFE –0.59 –8.53DFRQ 0.63 5.17DFRY* 0.14 2.32

N 156,993Adj. R2 0.0534

54

TABLE 6 – Continued Panel D: By-year Estimation, with adjusted FRY

FE FRQ FRY* DFE DFRQ DFRY*  Est. t-stat Est. t-stat Est. t-stat Est. t-stat Est. t-stat Est. t-stat   N Adj. R2

           1984 0.61 2.52 –0.55 –0.89 1.40 4.86 –0.26 –0.86 0.86 1.10 –1.39 –3.64 1,013 0.051985 0.42 3.27 0.64 2.35 0.83 6.73 0.04 0.21 1.78 3.00 1.22 4.38 2,944 0.081986 0.83 5.93 1.09 3.89 0.85 6.66 –0.41 –1.75 0.95 1.70 0.20 0.68 3,659 0.071987 0.74 4.61 0.39 1.09 0.98 5.74 –0.69 –2.56 1.49 2.16 –0.48 –1.38 3,254 0.031988 0.52 3.57 0.56 2.00 1.01 7.48 –0.15 –0.65 1.50 2.91 0.05 0.21 3,555 0.061989 0.72 6.31 0.93 4.11 0.84 7.79 –0.26 –1.22 0.16 0.38 –0.03 –0.13 4,721 0.061990 0.87 6.05 1.39 5.79 0.86 7.26 –0.40 –1.51 –0.07 –0.15 0.34 1.23 5,036 0.071991 1.56 9.73 0.70 2.75 0.93 7.56 –0.88 –2.84 1.86 3.47 0.46 1.44 5,774 0.071992 1.23 6.99 1.10 4.13 0.58 4.46 –0.39 –1.30 1.19 2.32 0.88 3.24 6,664 0.051993 1.97 11.31 0.95 3.65 0.82 7.01 –1.32 –4.17 1.24 2.35 0.41 1.51 8,075 0.061994 1.72 11.16 0.99 4.04 1.26 11.78 –0.51 –1.89 0.64 1.41 –0.41 –1.87 10,021 0.071995 1.35 8.78 2.33 8.84 1.36 11.99 –0.61 –2.19 –0.98 –2.10 –0.23 –1.02 10,866 0.091996 1.63 10.69 1.63 6.05 1.12 10.10 –0.62 –2.36 0.39 0.81 –0.28 –1.21 12,265 0.061997 1.99 12.52 1.73 7.08 1.20 12.11 0.08 0.29 0.37 0.80 0.04 0.20 13,691 0.081998 1.49 9.77 1.13 4.30 0.91 9.13 –0.57 –2.03 0.56 1.25 0.14 0.69 13,632 0.051999 1.37 8.65 1.13 4.59 1.14 10.77 0.19 0.61 1.41 2.75 –0.36 –1.39 12,784 0.052000 1.35 7.16 0.97 2.93 1.05 7.72 –0.28 –0.76 0.27 0.46 –0.06 –0.22 10,650 0.042001 1.86 10.09 1.38 5.34 0.59 6.06 –1.49 –4.30 0.85 1.60 0.85 3.92 9,820 0.052002 1.54 8.58 1.37 5.10 1.22 12.58 –0.37 –1.03 0.64 1.30 0.19 0.85 9,877 0.082003 1.87 11.00 2.22 8.82 1.10 10.53 –1.18 –1.77 –0.25 –0.30 1.27 2.83  8,692 0.09

           Mean 1.28 7.93 1.10 4.30 1.00 8.42 –0.50 –1.60 0.74 1.36 0.14 0.52 7,850 0.06FM t 10.60 9.06 19.08 –4.43 6.44 0.54Z1   35.48  19.22  37.63  –7.17  6.09  2.31Z2   11.72  7.89  14.53  –5.38  4.45  1.11

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FRY* = FRY for Q1 – Q3, = FRY – FRQ for Q4All other variables are defined in table 1.Z1 = , where tj is the t-statistic for year j, kj is the degrees of freedom, and N is the number of years.

Z2 = , and the Fama-MacBeth t-statistic (FM t) = , where N is the number of years.D is suppressed in Panels B and D.

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TABLE 7Extended Return Window

ARit = a0 + a1 FEit + a2 FRQit + a3 FRYit + eit

Panel A: Pooled Estimation

  Estimate t-statistic

FE 1.32 22.47FRQ 2.01 19.27FRY 2.00 45.13

N 153,974Adj. R2 0.047

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TABLE 7 – Continued Panel B: By-year Estimation

FE FRQ FRY  Est. t-stat Est. t-stat Est. t-stat   N Adj. R2

     1984 –0.01 –0.05 –0.07 –0.10 1.78 5.27 1,011 0.081985 0.81 4.46 0.87 2.01 1.97 10.01 2,943 0.101986 1.26 6.02 1.27 2.78 1.45 6.65 3,658 0.061987 0.44 2.12 1.07 2.14 1.90 7.79 3,247 0.051988 0.69 3.53 1.62 3.85 1.77 8.73 3,548 0.061989 1.35 7.27 0.93 2.49 1.71 9.30 4,710 0.061990 1.09 4.65 1.15 2.80 1.84 9.05 5,036 0.061991 1.16 4.50 1.83 4.22 2.34 10.93 5,762 0.071992 1.37 5.29 1.50 3.51 1.81 8.59 6,664 0.051993 1.71 6.52 2.50 5.98 1.98 10.25 8,072 0.061994 1.46 6.47 2.32 6.19 1.55 9.34 10,031 0.061995 1.63 7.38 2.36 5.91 2.42 14.04 10,872 0.081996 1.57 7.38 2.69 6.62 1.88 11.26 12,281 0.061997 2.01 8.03 3.48 8.53 2.05 12.36 13,549 0.061998 1.06 4.78 3.43 8.68 1.57 10.12 13,416 0.041999 1.48 5.58 1.65 3.79 2.24 12.01 12,526 0.062000 1.53 5.35 1.94 3.83 1.84 9.02 10,615 0.082001 1.74 5.47 1.85 3.74 1.22 6.61 7,979 0.042002 1.94 7.14 0.85 2.06 2.18 14.20 9,528 0.072003 1.99 6.53 1.93 4.23 2.29 12.02  8,526 0.07

     Mean 1.31 5.42 1.76 4.16 1.89 9.88 7,699 0.06FM t 10.91 8.70 26.60Z1   24.24  18.62  44.16Z2   12.15  8.12  18.20

Variables are defined in table 1.AR is measured through the end of the postannouncement period.Z1 = , where tj is the t-statistic for year j, kj is the degrees of

freedom, and N is the number of years. Z2 = , and the Fama-MacBeth t-statistic (FM t) = , where N is the number of years.

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TABLE 8Extended Return Window, I/B/E/S Detail Data

ARit = a0 + a1 FE_DETit + a2 FRQ_DETit + a3 FRY_DETit + eit

Panel A: Pooled Estimation

  Estimate t-statistic

FE_DET 1.41 11.48FRQ_DET 1.94 12.65FRY_DET 0.85 15.71

N 47,612Adj. R2 0.028

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TABLE 8 – Continued Panel B: By-year Estimation

FE_DET FRQ_DET FRY_DET  Est. t-stat Est. t-stat Est. t-stat   N Adj. R2

     1984 –0.65 –1.45 0.41 0.51 0.40 1.04 292 0.021985 0.45 1.17 0.82 1.45 0.12 0.45 673 0.031986 –0.09 –0.17 –0.35 –0.49 0.24 0.90 565 0.031987 0.70 1.37 0.67 0.92 0.14 0.50 574 0.021988 0.37 1.06 0.00 0.00 0.48 2.24 839 0.041989 0.63 1.67 1.41 2.51 0.61 2.84 1,148 0.031990 0.52 1.25 2.12 3.75 0.48 2.14 1,502 0.051991 1.48 3.78 0.86 1.71 0.83 3.83 1,887 0.041992 1.05 2.20 1.34 2.06 0.74 2.95 1,904 0.031993 0.75 1.50 1.33 1.87 0.86 3.34 1,912 0.081994 1.86 4.28 1.23 2.22 0.99 4.89 3,077 0.051995 2.25 4.62 0.89 1.67 1.15 5.99 3,328 0.041996 2.65 5.24 1.91 3.43 0.98 5.22 3,481 0.041997 2.48 4.70 2.69 4.10 0.47 2.22 3,878 0.031998 1.50 2.85 3.36 5.40 0.84 4.07 4,047 0.041999 2.11 3.46 3.11 4.29 0.70 2.99 3,898 0.042000 1.51 2.14 2.29 3.03 0.99 3.93 3,599 0.092001 2.50 4.41 1.72 2.87 0.57 2.87 3,955 0.022002 3.89 7.38 2.12 3.63 1.18 5.96 3,769 0.052003 1.25 2.78 3.97 6.88 0.90 4.22  3,284 0.06

     Mean 1.36 2.71 1.59 2.59 0.68 3.13 2,381 0.04FM t 5.46 6.20 9.41Z1   12.13  11.58  13.99Z2   5.74  6.25  8.16

FE_DET = forecast error using detail data; FRQ_DET = subsequent fiscal quarter forecast revision using detail data; FRY_DET = subsequent fiscal year forecast revision using detail data. AR is measured through the end of the postannouncement period.

Z1 = , where tj is the t-statistic for year j, kj is the degrees of freedom, and N is the number of years. Z2 = , and the Fama-MacBeth t-statistic (FM t) = , where N is the number of years.

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