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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
4
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
5
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
6
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
7
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.
8
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
9
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.
10
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
11
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
12
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
13
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:
14
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.
16
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,
17
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.
18
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.
19
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 +
22
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.
31
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.
32
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
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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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Recommended