Earnings Announcement Disclosures That Spur
Differences in Interpretations
Orie E. Barron
Pennsylvania State University
Donal Byard
Baruch College, CUNY
Yong Yu
University of Texas at Austin
April 2010
We would like to thank Masako Darrough, Leslie Davis Hodder, Jim McKeown, Nate Sharpe,
Jim Vincent, Bo Zhang, and Ph.D. seminar participants at Baruch College and Penn State
University, workshop participants at the University of Kentucky, Syracuse University, and
conference participants at the 2007 AAA annual meetings, the 2008 FARS mid-year meetings,
and the 2008 Accounting and Financial Economics conference at the University of
TexasAustin, for their helpful comments. In addition, we thank Irfan Ahmed, Monil Doshi,
Ruodan Lin, Hanna Rosen, and Ritesh Veera for their help in collecting the data. We also thank
10k Wizard LLC for providing 8K transcripts, CallStreet LLC for providing conference call
transcripts, and I/B/E/S International Inc. for providing earnings per share forecast data. Donal
Byard gratefully acknowledges financial support provided by a Lang Fellowship from Baruch
College, and a PSC-CUNY grant from the City University of New York (PSC-CUNY # 6972900 38).
Earnings Announcement Disclosures That Spur
Differences in Interpretations
Abstract
This study examines the joint information content of the disclosures made
simultaneously with firms’ earnings announcements, with a particular focus on
the disclosures that spur differences in interpretations. Using the approach
Garfinkel (2009) advocates for measuring differences in interpretations, we find
that the disclosure of balance sheets, range management forecasts, segment
reports, and more lengthy conference calls are all associated with abnormal
trading volume that is not explained by price changes. Additionally, we also find
that the same disclosures associated with this volume-based measure of
differences in interpretations are also associated with analyst-forecast-based
measures of differences in interpretations,
Keywords: Earnings announcements, price and volume reactions, differences in
interpretations, voluntary disclosures.
I. Introduction
This study examines the information content of disclosures made simultaneously with
firms’ earnings announcements with a particular focus on the disclosures that spur differences in
interpretations. We follow the approach of Beaver (1968) who, in addition to examining price
reactions, also examines trading volume reactions. Consistent with Beaver’s (1968) assumptions
and a significant amount of theoretical and empirical research over the subsequent decades (e.g.,
Karpoff 1986; Ziebart 1990; Kim and Verrecchia 1991, 1997; Atiase and Bamber 1994; Bamber
and Cheon 1995; Barron 1995; Bamber, Barron, and Stober 1997; Barron, Harris, and Stanford
2005), we assume that price changes reflect changes in investors’ average belief while trading
volume reflects differences in interpretations among investors. This assumption is supported by
Garkinkel’s (2009) research finding that abnormally high levels of trading volume that cannot be
explained by price changes reflect differences in interpretations (see also Kandel and Pearson
1995; Kim and Verrecchia 1997).
More disclosures may foster more differences in interpretations if at least a subset of
investors have limited information processing capabilities and do not all focus their attention on
the same disclosure items. That investors have limited information processing capabilities has
long been recognized. As Herbert Simon (1971) puts it:
"...in an information-rich world, the wealth of information means a dearth of something
else: a scarcity of whatever it is that information consumes. What information consumes
is rather obvious: it consumes the attention of its recipients. Hence a wealth of
information creates a poverty of attention and a need to allocate that attention efficiently
among the overabundance of information sources that might consume it" (p. 40-41).
Give the large number of disclosures typically now made with earnings announcements (see
below), such limited attention suggests that more disclosure may be associated with more
differences in interpretations. This is likely particularly to be the case for complex discloses that
do not have very clear implications for future performance.
On the other hand, more disclosures may foster a more common interpretation. This
would be the case if investors’ limited information processing capabilities are not very
1
constraining factors and that as more items are disclosed this allows each investor to fill in more
of the total information “puzzle” that they themselves were lacking. As more investors see the
total information package in a more homogeneous way, this would lead to a more common
interpretation of a firm’s future prospects.
Our priors are that during a short investment window around earnings announcements
limited information processing capabilities place a significant constraint on individual investors.
As Beaver (1968) puts it:
“Since investors may differ in the way they interpret the report, some time may
elapse before a consensus is reached, during which time increased volume would
be observed.” (p.69)
Thus we expect that, in general, more disclosure will lead to more differences in interpretations
and trading volume. This expectation is supported by Hope, Thomas, and Winterbotham (2009).
They investigate the effect of eliminating firms’ requirement to disclose geographic data and
conclude that ceasing geographic disclosures decreased differences in interpretations. Other than
this study there is no extant empirical work on whether any type of earnings announcement
disclosure, when added to the total disclosure package, increases differences in interpretations.
Thus, our study remains a somewhat exploratory analysis of the association between different
types of disclosures made with earnings announcements and differences in interpretations.
As part of their “total disclosure package,” firms typically provide various types of
disclosures with their earnings announcements which may be incrementally informative and may
be associated with differences in interpretations. At a minimum, firms provide their announced
“bottom line” earnings. However, firms typically also provide additional financial disclosures
such as an income statement, a balance sheet, segment disclosures, or a management forecast of
future earnings (e.g., see Hoskin, Hughes, and Ricks 1986; and Francis, Schipper, and Vincent
2002).
We limit our sample to a period shortly after the adoption of Reg G in 2003. We use a
post-Reg G sample period because Reg G initiated a new requirement that earnings
2
announcement disclosures be consistent with GAAP.1 The requirement that financial statement
disclosures be either in U.S. GAAP or reconciled to U.S. GAAP avoids non-comparability issues
arising from pro forma reporting.2 Additionally, we limit our sample to earnings announcements
where we can match individual analysts who forecast next quarter’s earnings both before and
after earnings announcements. We require these matched individual analysts’ forecasts before
and after our sample earnings announcements to generate analyst-forecast-based measures of
differences in interpretations. These analyst-based measures serve to bolster our volume-based
evidence regarding differences in interpretations.
We collect data on the earnings announcement disclosures made with our sample
earnings announcements. First, we obtained copies of the earnings announcement press releases
filed as 8Ks with the Securities and Exchange Commission (SEC) and copies of the conference
call transcripts. Then, using these sources, we hand-collect a sample that identifies the presence
(or absence) of the following types of disclosures in the 8K: a balance sheet, a statement of cash
flows, segment disclosures, and a managerial forecast of future earnings. Additionally, we
generate a measure of the length of conference calls using the transcript word count.
We find that the disclosure of balance sheets, range management forecasts, segment
reports, and more lengthy conference calls are all associated with abnormally high levels of
trading volume and that these four associations persist after controlling for coincident price
reactions. In contrast, point forecasts are associated with significant price reactions, but not with
volume reactions.
In other words, the presence of more complex disclosures in earnings
announcements (i.e., disclosures that are not a simple forecast of future performance) results in
more trading volume which either does not coincide with or cannot be explained by price
1
2
For details, see SEC Release No. 33-8176 Final rule: Conditions for use of non-GAAP financial measures. See:
http://www.sec.gov/rules/final/33-8176.htm
Also, during the period soon after the adoption of Reg G there was still a significant degree of variation in the
number and type of financial statements firms disclosed with their earnings announcements. With the ongoing
over-time increase in the disclosures firms include in their earnings announcements (Francis et al. 2002), this
variation was significantly reduced in later periods. For example, in our sample period that spans 2003 and 2004,
86-percent of the earnings announcements in our sample contain a balance sheet disclosure. Using the same
sample selection criteria but a 2008 sample period yields a sample where 98-percent of the earnings
announcements include a balance sheet disclosure.
3
changes. We infer that balance sheets, range forecasts, segment reports, and more lengthy
conference calls are all associated with more differences in interpretations, while point forecasts
are more commonly interpreted. Our examination of analyst-based measures of differences in
interpretations largely corroborates these conclusions.
Our study makes a number of contributions. First, our findings increase understanding of
why trading volume reactions around earnings announcements are observed more than price
reactions (see Bamber and Cheon 1995). Volume reactions are more commonly observed than
price reactions because some of the disclosures that are typically made with earnings
announcements are associated with more differences in interpretations. Second, our results also
explain why volume reactions have generally increased over time (see Landsman and Meydew
2002). Specifically, our results confirm that volume reactions have increased over time because
several types of concurrent voluntary disclosures which firms are increasingly likely to include
in earning announcements (see Francis et al. 2002) are associated with differences in
interpretations and abnormal trading that either does not coincide with, or is not explained by,
price reactions. Third, our study adds to prior studies (e.g., Kandel and Pearson 1995) that
highlight differences in interpretations around earnings announcements: our results show that
differences in interpretations are associated with particular types of disclosures. Finally, our
results should also be of interest to managers and policymakers who may be interested in
understanding the market reaction associated with various types of earnings announcement
disclosures.
The rest of this paper is organized as follows. Section II outlines the background and
literature review. Section III describes the research design and sample selection. Section IV
presents our primary results, and Section V outlines our robustness analysis, including our twostage Heckman model that controls for self-selection. The paper concludes with a discussion in
Section VI.
4
II. Background and Literature Review
The typical total disclosure package firms provide with their earnings announcements has
changed markedly over the last 20 years. Over this period, firms have significantly increased the
number of disclosures they typically provide with their earnings announcements (see Francis et
al. 2002). A number of changes in firms’ disclosure behavior have contributed to this trend.
First, and perhaps most critically, firms have increased the number of financial statement
disclosures they typically include in their earnings announcement press releases. For example,
during the 1990s many firms started releasing financial statements with their earnings’
announcements. Using a sample of all the quarterly earnings announcements made by 30 firms
over the period 1980 to 1999, Francis et al. (2002) document an increasing tendency for firms to
include an income statement, a balance sheet, or a statement of cash flows in their earnings
announcement press releases over this period of time. Similarly, Chen, DeFond, and Park (2002)
find that the proportion of firms that include a balance sheet in their earnings announcement
press releases increased from 31 percent in Q4 of 1992 to 46 percent in Q3 of 1995.
Second, firms have also increased the amount of non-financial disclosures they provide
with their earnings announcements. Earnings announcement press releases typically start with
some narrative discussion of the results for the quarter. On average, the length of this narrative
disclosure has increased over time. For example, for a constant sample of 30 firms, Francis et al.
(2002) document that the average word count of earnings announcement press releases increased
from 517 words in 1980 to 2,427 words in 1999.3 These longer narrative disclosures are also
increasingly likely to include forward-looking information: the frequency of management
earnings forecasts has also increased over time and management forecasts are frequently
included in firms’ earnings announcement press releases (see Heflin, Subramanyam, and Zhang
3
Davis, Piger, and Sedor (2009) report similar results for a larger cross-section of firms. They find that the average
word count of the narrative section of earnings announcement press releases increased from about 900 words in
1998 to to just over 1,700 words in 2003.
5
2003).4 The adoption of Regulation Fair Disclosure (Reg FD) in October 2000 also spurred
firms to make more voluntary public disclosures of forward-looking earnings information: there
was a marked increase in the frequency of public management earnings forecasts in the post-Reg
FD period compared to the pre-Reg FD period (see Heflin, et al. 2003).
The advent of conference calls has also served to increase the quantity of narrative
disclosures firms typically provide with their earnings announcements.
Conference calls
emerged as an important new disclosure mechanism in the 1990s (see Tasker 1998). Most
conference calls are “earnings calls” hosted on the day of, or the day following, firms’ earnings
announcements.
The typical conference call opens with a 15-20 minute management
presentation, followed by a question-and-answer session that lasts 30-45 minutes (see Frankel,
Johnson, and Skinner 1999). Given their timing, conference calls naturally focus on the firm’s
quarterly results that have just been released. Conference calls, thus, add to the “total disclosure
package” firms provide with their earnings announcements. In summary, the total disclosure
package firms typically provide with their earnings announcements has expanded markedly over
the last twenty years as firms have increased both the financial and non-financial disclosures they
provide with their earnings announcements.
Prior studies provide some evidence that the supplemental financial statement disclosures
made with earnings announcements are associated with stock returns (see Hoskin et al. 1986;
Francis et al. 2002; Collins, Li, and Xie 2009). However, these studies do not directly test the
association between these supplemental disclosures and differences in interpretations, i.e., the
association between disclosures and excess trading volume not associated with changes in stock
price. Using intra-daily data, Frankel et al. (1999) do examine the association between one type
of disclosureconference callsand trading volume during the period of time when conference
calls are underway; however, they do not include the financial statements managers choose to
4
While the frequency of public managerial earnings forecasts has increased over the last twenty years, the precision
of these forecasts has also tended to improve. Baginski, Hassell, and Kimbrough (2007) document a general
increase in the quality (or precision) of managerial earnings forecasts during the 1990s.
6
disclose with their earnings announcements as part of their analysis. They find high trading
volume during the period when conference calls are underway.
Evidence from recent longitudinal studies implies that some of the financial statements
that firms now choose to disclose with their earnings announcements may be incrementally
informative to investors. Using the framework of Beaver (1968), Landsman and Maydew (2002)
document that the information content of earnings announcementsas measured by abnormal
trading volume and price volatility in the three-day window centered on the earnings
announcement datehas increased over the period 1972 to 1998. They attribute this effect to
the over-time increase in the “total disclosure package” firms provide with their earnings
announcements, as documented by Francis et al. (2002).5 However, Landsman and Maydew
(2002) do not provide direct evidence linking the increased disclosures firms include in their
earnings announcements to an increase in differences in interpretations of earnings
announcements. In contrast, we directly examine the incremental information content of the
supplemental disclosures firms make in their earnings announcement press releases, and we
directly test whether these voluntary disclosures are associated with differences in
interpretations.
III. Research Design and Sample Selection
3.1 Research Design
Our first dependent variable is EXVOL. EXVOLiq measures firm-specific excess trading
volume around the quarter q earnings announcement of firm i. We define EXVOLiq as the
difference between the natural log of the cumulative trading volume over the 3-day window
around the quarter q earnings announcements of firm i and the natural log of the median volume
for firm i for contiguous 3-day periods over the 249 trading days prior to the quarter q earnings
5
The results of Landsman and Maydew (2002) also suggest that the increase in trading volume reactions to earnings
announcements is concentrated mainly in larger firms. This result lends support to the increase in the “total
disclosure package” explanation of Francis et al. (2002) as this increase in disclosure is more evident among larger
firms.
7
announcement (Barron et al. 2005).6
We also examine price reactions; we measure price
reactions using LARET, where LARETiq is the natural log of the absolute value of the 3-day
cumulative stock returns around the quarter q earnings announcement of firm i.7
Our primary independent variables are hand-collected from earnings announcement press
releases filed as 8Ks with the SEC. We manually identify and code the financial disclosures that
are typically contained in these press releases; specifically, we include the following variables
that measure the presence (or absence) of various financial statement disclosures: (1) BSiq is a
dummy variable coded one when firm i includes a balance sheet with its quarter q earnings
announcement, and zero otherwise; (2) SCFiq is a dummy variable coded one when firm i
includes a cash flow statement with its quarter q earnings announcement, and zero otherwise;
and (3) SEGiq is a dummy variable coded one when firm i discloses segment reporting
information in its quarter q earnings announcement, and zero otherwise. Note that we do not
include a dummy variable for the disclosure of an income statement because all the earnings
announcements in our sample contain an income statement disclosure (see Section 3.2 below).
While this differs from the findings of prior studies it is not surprising given our later sample
period and the general trend of firms increasing their earnings announcement disclosures over
time (see Francis et al. 2002).
We also include independent variables that measure other non-financial-statement
disclosures made with quarterly earnings announcements. First, we find that earnings conference
calls are held concurrently with all the earnings announcements in our sample (see Section 3.2
below). We use a word count to proxy for the amount of disclosure in these conference calls:
CCiq is defined as the total word count of the conference call transcript for firm i in quarter q.
Second, we also collect information about whether or not management includes a forecast of
6
7
Our results are robust to using this same measure after adjusting for market-wide trading volume.
Our inferences are unchanged using these alternative specifications of LARET; specifically: (1) using marketadjusted returns (i.e., raw returns minus value-weighted market returns); (2) using raw returns less the median
return for contiguous 3-day periods over the 249 trading days prior to the earnings announcements; or (3) using a
measure similar to Beaver’s (1968) abnormal volume measure, i.e., a measure of LARET computed by dividing
the absolute value of the 3-day raw returns around earnings announcements by the median value of the absolute
value of raw returns for contiguous 3-day periods over the 249 trading days prior to the earnings announcements.
8
future earnings in the earnings announcement press release, and the type of any such
management earnings forecasts. Specifically, MF_POINTiq, MF_RANGEiq, and MF_QUALiq are
three dummy variables set equal to one when the quarter q earnings press release of firm i
includes either a point, range, or qualitative managerial forecast of future earnings, and zero
otherwise.
To ensure that our results are not caused by our main disclosure variables being
correlated with some firm or earnings announcement characteristics, we control for firm or
earnings announcement characteristics that are likely to be associated with market reactions. In
addition, it is worth noting that many of these variables are also associated with firms’ choices to
provide supplemental disclosuresour explanatory variables.
Specifically, we include the
following control variables:
SURPiq = the absolute value of the quarter q earnings surprise (SURP), calculated as:
Actual EPS - Consensus EPS Forecast Stock Price ;8
SIGNiq = a dummy variable equal to one if SURPiq is negative, and zero otherwise;
SIZEiq = market capitalization of firm i at the end of quarter q; and
BMiq = book-to-market ratio of firm i at the end of quarter q.
We also include two sets of dummy variables to control for year (YR_DUM) and quarter
(QTR_DUM) fixed effects in our main regression analysis. Thus, for our first set of crosssectional analyses, we use the following regression model to investigate the relation between
various types of disclosures and trading volume around earnings announcements:
EXVOLiq =  0 +  1 BS iq +  2 SCFiq   3 SEGiq   4CCiq   5 MF_POINTiq   6 MF _ RANGEiq   7 MF _ QUALiq
T 4
 8 LARETiq   9 SURPiq  10SIGNiq  11SIZEiq  12 BM iq   t QTR_DUM t   y DUM 04 y + εiq .
(1)
t 2
We also examine price reactions to the same earnings announcements using the log of absolute
returns (LARET) as the dependent variable:
LARETiq =  0 +  1 BS iq +  2 SCFiq   3 SEGiq   4 CCiq   5 MF_POINTiq   6 MF _ RANGEiq  7 MF _ QUALiq
T 4
  8 SURPiq   9 SIGN iq   10 SIZEiq   11 BM iq    t QTR_DUM t   y DUM 04 y  εiq .
t 2
8
Our results are unchanged if we use the median forecast to calculate SURP.
9
(2)
We test if 1 to 7  0 and if 1 to 7 > 0. We estimate Equation (1) with and without LARET as
a control variable because we are primarily interested in understanding abnormal volume that
cannot be explained by price reactions. In Equation (1) LARET controls for value-relevant
information reflected in changes in the average belief (see Barron et al. 2005). Theory predicts
that trading volume that either does not coincide with or is unexplained by price reactions is the
result of differences in interpretations (Kandel and Pearson 1995; Kim and Verrecchia 1997;
Garfinkel 2009). As already discussed, Equations (1) and (2) do not include a variable for
income statement disclosures as all the earnings announcements in our sample include an income
statement disclosure (see Section 3.2 below).
While our control variables in Equations (1) and (2), i.e., SURP, SIGN, SIZE, and BM,
serve in part to control for firms’ choices to provide supplemental disclosures with their earnings
announcements, in our robustness analysis (see Section 5.6 below) we undertake an extensive
examination of whether our results are attributable to additional firm characteristics likely
associated with firms’ incentives to disclose rather than the disclosures themselves. Specifically,
we use two-stage Heckman estimation procedures that incorporate twenty additional control
variables for firm characteristics associated that prior studies identify as being associated with
firms’ disclosure choices (see Chen et al. 2002; Francis, Nanda, and Olsson 2008; Lang and
Lundholm 1993; Miller 2002).9 The results of this analysis suggest that our main results are not
primarily attributable to self-selection.
We focus on disclosures that may cause trading volume reactions and, specifically,
trading volume reactions that differ from price reactions. In keeping with this focus, after we
document a relation (or lack thereof) between different types of disclosures and abnormal trading
volume reactions to earnings announcements that are not associated by price changes we then
examine the association between these same disclosures and analyst-based empirical measures of
differences in interpretations. The purpose of this analysis is to provide corroborating evidence
9
See Appendix A for a comprehensive description of the calculation of the additional twenty disclosure choice
variables we include in our two-stage Heckman estimates in Section 5.6.
10
as to any association between firm-provided disclosures and differences in interpretations.
Therefore, we estimate the following regression model:
Difference in Interpretationsiq = 0 + 1 BS iq +  2 SCFiq  3 SEGiq   4 CCiq   5 MF_POINTiq
  6 MF _ RANGEiq   7 MF _ QUALiq  8 LARETiq  9 SURPiq  10 SIGN iq
(3)
T 4
 11 SIZE iq  12 BM iq  ∑ t QTR _ DUM t   y DUM04 y + ε iq ,
t 2
We test if 1 to 7  0.
We use two different analyst-based measures of Difference in
Interpretations: (1) KP, the Kandel and Pearson (1995) belief “jumbling” measure designed
specifically to detect the presence of differences in interpretations; and (2) RESIDUAL
JUMBLING, a modified version of the belief jumbling measure used by Bamber et al. (1997)
which we modify to better capture differences in interpretations. These two measures (which are
described in detail in Section 4.4.1 below) require the use of a matched set of forecasts (of future
earnings) made by the same analysts both before and after each earnings announcement.10 As a
result, our sample is restricted to observations with available data for matched analysts’ forecasts
before and after earnings announcements from the same individual analysts.
3.2 Sample Selection
We limit our sample period to the post-Reg G period: our sample period spans the 18
months from 30 March 2003 to 30 September 2004. Reg G, which applies to all earnings
announcements made after 28 March 2003, requires companies to file a copy of their earnings
announcement press releases as an 8K with the SEC and that firms using pro forma reporting
reconcile their pro forma numbers to GAAP (see SEC 2002). We end our sample period in
September 2004 because the ongoing increase in firms’ provision of financial statement
disclosures with their earnings announcements (see Francis et al. 2002) reduces the across-firm
variance in disclosures for later periods. For example, using the same sample selection criteria
(which favor large heavily-followed firms), we randomly selected a sample of quarterly earnings
10
We do not examine forecast dispersion measures because Garfinkel (2009) suggests that forecast dispersion is a
poor measure of investor disagreement. Rather, Barron, Stanford, and Yu (2009) suggest forecast dispersion is
more likely to proxy for uncertainty levels.
11
announcements made during the first quarter of 2008. For this later sample period we found that
98-percent of earnings announcements include a balance sheet disclosure (compared to 86percent for our 2003-2004 sample period).
We further require that the earnings announcement date for quarter q is available from
Compustat. In order to empirically measure differences in interpretations among investors, we
require that there be at least three individual analysts who issue a forecast of quarter q+1
earnings in the 45-day period before the quarter q earnings announcement date, and who also
revise their forecasts in the period after the quarter q earnings announcement date but prior to the
10Q/K filing date.11
We use data from the unadjusted IBES Detail file to identify these
individual analysts’ forecasts. To collect information about the voluntary disclosures made with
each earnings announcement, we obtain a copy of the 8K earnings announcement press release
for quarter q from the 10K Wizard database and a copy of the conference call transcript for
quarter q from the CallStreet database. In addition, we require that:
1) Actual earnings for quarter q and quarter q+1 are available from the IBES;
2) CRSP data be available to compute price and volume reactions to the quarter q
earnings announcement;
3) Data are available from IBES to calculate the quarter q earnings surprise: at least
one forecast of quarter q earnings is available from the 45-day period before the
quarter q earnings announcement date; and
4) Compustat data are available to calculate the other control variables.
These sample selection criteria result in a final sample of 1,445 quarterly earnings
announcements made by 676 unique firms. Because of the analyst forecast data requirement, our
sample is comprised of relatively large firms followed by relatively large numbers of analysts.12
11
This ensures that the earnings forecasts made in the post-earnings announcement window only reflect the
information disclosed in the earnings announcement but not the information disclosed in the 10Q/K filings. If the
10K/Q is filed more then 30 days after the quarter q earnings announcement date, then the post-announcement
period is capped at 30 days.
12
We test if our findings differ dramatically between the relatively larger and smaller firms in our sample. We find
that our results are similar for both subsets of firms (see Section 5.2).
12
IV. Primary Results
4.1 Descriptive Statistics
Table 1 outlines the descriptive statistics for our sample of 1,445 quarterly earnings
announcements. Consistent with prior studies (e.g., Barron et al. 2005), we find that trading
volume in the three-day event period around earnings announcements is considerably above
normal for these firms: the mean value of EXVOL is 0.533. For the sample firms, on average
6.4-percent of the outstanding shares are traded during this three-day period. This level of
trading volume is somewhat higher than that observed in prior studies (e.g., see Bamber et al.
1997), possibly because we use a later sample period and trading volume has been generally
increasing over time (Statman, Thorley, and Vorkink 2006), particularly around earnings
announcements (Landsman and Maydew 2002).
< Insert Table 1 About Here >
All the earnings announcements in our sample include an income statement. As can be
seen in Table 1, 86-percent of earnings announcements in our sample include a balance sheet.
Statement of cash flows and segment reporting information disclosures are less common: mean
SCF and SEG are 50.4 and 33.4 percent, respectively. Although not reported in Table 1, it is
noteworthy that only 3-percent of the earnings announcements in our sample include a statement
of cash flows (SCF) but not a balance sheet (BS). As Table 1 shows, the mean length of
conference calls in our sample is 9,194 words. Similar to prior studies (see Baginski, Conrad,
and Hassell 1993), we find that most management earnings forecasts made with earnings
announcements are range forecasts: 39.7-percent of earnings announcements include a range
management forecast (of future period earnings).
Compared to prior studies using earlier sample periods, these descriptive statistics
suggest that there has been a continuing increase in the prevalence of earnings announcement
voluntary disclosures, particularly financial statement disclosures, in recent years. This suggests
that the trend of increasing earnings announcement disclosures Francis et al. (2002) document
over the period 1980 to 1999 has continued over the more recent time period. For example, for
13
the last year in their sample, 1999, Francis et al. (2002) report that 77.8 percent (25.9 percent) of
the earnings announcements in their sample include a detailed income statement (balance sheet).
The corresponding figures for our sample are 100 percent (86 percent). In particular, our results
suggest that while the inclusion of financial statement disclosures in earnings announcements is
voluntary, in the case of the income statement this disclosure is now de facto mandatoryat
least for firms with a large analyst following.
The descriptive statistics for our control variables indicate that our sample is comprised
of large heavily-followed firms: mean market capitalization (SIZE) is $14.4bn, while the mean
number of analysts following each firm is 9.157. The average book-to-market ratio (BM) for
these firms is 0.436. Finally, as can be seen from the mean value of SIGN, 27.5-percent of the
earnings surprises in the sample are negative.
4.2 Correlation Analyses
Table 2 shows the Spearman correlations between our dependent variables (EXVOL,
LARET) and our explanatory variables for earnings announcement disclosures (BS, SCF, SEG,
CC, MF_POINT, MF_RANGE, and MF_QUAL) using decile ranks.
The results of the
correlation analysis are generally consistent with prior studies. For example, firm size (SIZE) is
significantly (p<0.01, two-tailed) negatively correlated with the magnitude of the earnings
surprise (SURP), and the magnitude of price (LARET) and volume reactions (EXVOL).
< Insert Table 2 About Here >
4.3 Results for the Volume-based Measure of Differences in Interpretations
Our initial analysis of volume and price reactions to earnings announcements is based on
estimates of Equations (1) and (2) using decile rank regressions (i.e., we rank the continuous
variables into 0-9 deciles and then divide the ranks by 9). This mitigates the influence of
extreme values and relaxes the Ordinary Least Squares (OLS) linearity assumption. Our results
are, however, robust to using regular rank regression specifications. We report p-values based
14
on Rogers’ (1993) standard errors that allow for heteroskedasticity and correlation among
observations for the same firm.
< Insert Table 3 About Here >
The first two columns of Table 3 reports the results from estimating Equation (1), which
examines the association between excess trading volume (EXVOL) and the different types of
disclosures firms typically include in their earnings announcements. We estimate Equation (1)
with and without LARET as a control variable: LARET is a control for trading volume associated
with price change; as a result, this regression tests the association between disclosures and excess
trading volume around earnings announcements (EXVOL) that is not explained by stock returns
(LARET). This is the part of trading volume expected to be attributable to differences in
interpretations (or differences in belief revisions).
The results from estimating Equation (1) without LARET show that four types of
disclosures are positively associated with EXVOL: balance sheet (BS), segment disclosures
(SEG), the length of the conference call (CC), and range management earnings forecasts
(MF_RANGE), p<0.01, two-tailed for all.13 The results from estimating Equation (1) including
LARET as a control variable show that the same four disclosuresBS, SEG, CC, and
MF_RANGEare still statistically significant positively associated with EXVOL when LARET is
included in the model.14 Thus, several types of disclosures made with earnings announcements
are associated with abnormal trading that is not explained by price reactions. Based on the
evidence presented by Garfinkel (2009), we conclude that these four disclosure types are likely
to be associated with more differences in interpretations.
13
Two-tailed p-values are reported for EXVOL regressions because disclosures could increase or decrease trading,
depending on whether they increase or decrease differences in interpretations.
14
In a supplemental test, similar to Bamber and Cheon (1995) we also estimate a regression model using the
difference between the ranks of EXVOL and LARET (DIFF) as the dependent variable in the analysis, where
DIFF equals the rank of EXVOL minus the rank of LARET. The untabulated results largely confirm our
expectations: SEG, CC, and MF_RANGE are all positively associated with DIFF (p<0.05 or better, one-tailed, for
all). BS, however, is not associated with DIFF, which may simply be because of the rather crude nature of this
test and the fact that BS is significantly related to price changes, unlike the other disclosure variables.
15
The results for the control variables in Equation (1) are largely consistent with prior
studies. Similar to Bamber (1987), SURP is significantly positively associated with EXVOL
(p<0.01, two-tailed), indicating that there is greater excess trading volume around larger earnings
surprises; and SIZE is negatively associated with excess trading volume (p<0.01, two-tailed).15
BM, the book-to-market ratio, is significantly negatively associated with EXVOL (p<0.01, twotailed), indicating that there is less excess trading volume around the earnings announcements of
high book-to-market firms. This result can be interpreted as indicating that there is less excess
trading volume for value firms than for growth firms. SIGN is marginally (p=0.057 and 0.07,
two-tailed) significantly positively associated with EXVOL, indicating that there is some limited
evidence of more trading volume around negative earnings surprises.
The last column in Table 3 reports the results for Equation (2) where LARET is used as
the dependent variable. These results suggest that balance sheets (BS), longer conference calls
(CC), point forecasts (MF_POINT), and range forecasts (MF_RANGE) are all incrementally
informative relative to each other: LARET is statistically significantly positively associated with
BS (p<0.01) and MF_Point (p=0.049), and marginally significantly associated with CC
(p=0.065) and MF_RANGE (p=0.084), one-tailed for all.16 The finding that point forecasts spur
price reactions with little or no volume reaction is consistent with point forecasts being
commonly interpreted.17
The results for our control variables in Equation (2) are generally as expected and
consistent with prior studies. For example, the significant positive coefficient on SURP indicates
that larger absolute earnings surprises are associated with larger stock price reactions. Similarly,
the significant negative coefficient on SIZE confirms the results of numerous prior studies that
larger firms tend to have smaller stock price reactions to their earnings announcements.
15
Our results are unchanged when a control for market-wide trading volume is added to the regression model.
One-tailed p-values are reported for LARET regressions because we have no reason for suspecting that any
disclosure could decrease the magnitude of returns, on average.
17
This finding is also consistent with Barron and Karpoff’s (2004) argument that very precise news may dampen
trading volume due to the effects of transaction costs.
16
16
The results for the statement of cash flows (SCF) are insignificant in Equations (1) and
(2). This suggests that SCF does not have incremental information content in this setting. The
lack of evidence of information content for the statement of cash flows (SCF) may arise because
97-percent of the earnings announcements in our sample that include a statement of cash flows
also include both an income statement and balance sheet which, as a result of the adoption of
Reg G, must now be in accordance with GAAP (or reconciled to GAAP). Thus, in this setting
the statement of cash flows may not contain incremental information because it can be
constructed from other disclosures.
4.4 Analysis of Analyst-Based Measures of Differences in Interpretations
We have found that some disclosures are significantly associated with volume reactions
that either do not coincide with or are not fully explained by price reactions. Thus, when
estimating Equation (3)which uses two different analyst-based measures of differences in
interpretations as the dependent variablewe have an expectation that these same four types of
disclosures (BS, SEG, CC, and MF_RANGE) will be positively associated with these analystbased measures of differences in interpretations around earnings announcements.
The two
analyst-based measures of differences in interpretations we use to estimate Equation (3) are:
KP = the Kandel and Pearson (1995) jumbling measure; designed to capture differences
in interpretations; and
RESIDUAL JUMBLING = a modified version of the belief jumbling used by Bamber et
al. (1997).
4.4.1 Overview of Analyst-Based Measures of Differences in Interpretations
Figures 1, and 2 illustrate our two analyst-based measures of differences in
interpretations.
17
Case 1
F1
Case 2
111\
1
F2
F2
1
1
Case 3
Case 5
Differential
interpretation
F1
F1
1
Case 4
Differential
interpretation
F1
1
1
F2
1
F2
F1
1
1
F2
1
Figure 1: From Bamber, Barron and Stober (1999, Figure 1)
Kandel and Pearson (1995 hereafter KP) develop a measure specifically designed to
detect the occurrence of differences in interpretations. Bamber, Barron, and Stober (1999) find
that this measure explains cases where there is trading volume coincident with earnings
announcements that spur minimal price changes.18 The KP measure Bamber et al. (1999) use is
the proportion of paired analysts’ forecast revisions that move in opposite directions and either
flip or diverge. In Figure 1, the KP measure is the proportion of paired forecast revisions that
move like Cases 4 and 5. KP argue theoretically that forecast revisions like Cases 1, 2, and 3 in
Figure 1 are consistent with analysts interpreting news in a similar fashion (i.e., confirming news
that is viewed by one analyst as good and the other as bad (Case 1), news that both analysts view
as good (Case 2), or news that both analysts view as bad, (Case 3), whereas Cases 4 and 5 are
inconsistent with analysts having common interpretations.
Thus, our first measure of differences in interpretations (denoted KP) is the ratio of the
number of pairs of analysts’ forecast revisions that are like Cases 4 or 5 in Figure 1 relative to
the total number of possible pairs of analysts’ forecast revisions around earnings announcements.
One potential weakness of this measure is that it is only designed to detect differences in
interpretations that coincide with small price reactions. However, the strength of this measure is
that it was specifically designed to detect the presence of differences in interpretations. Thus, we
consider evidence concerning KP to be the most conclusive regarding differences in
interpretations.
18
We confirm this evidence concerning the KP measure in our sample.
18
Based on our volume- and price-reaction evidence shown in Table 3, we strongly suspect
that KP is positively associated with SEG, because segment disclosures are associated with
significant volume reactions but insignificant price reactions. CC and MF_RANGE are also
expected to be positively associated with KP if the price reactions to longer conference calls and
range forecasts are not very large. Here it is important to note that if no significant association
is found then we cannot distinguish between there being no significant link with differences in
interpretations or simply that the KP measure is inappropriate because the concurrent price
reactions are too large. Finally, we do not expect a positive association between KP and BS,
because balance sheet disclosures clearly coincide with significant price reactions. Thus, to test
for corroborating evidence that balance sheet disclosures are associated with more differences in
interpretations we need to use a second, and somewhat less persuasive, measure of differences in
interpretations.
F1
1
F2
Belief
Jumbling
1
1
F3
F4
F5
1
Earnings
Announcement
Figure 2: Modified from Bamber, Barron and Stober (1997, Figure 1)
Figure 2 partially depicts our second measure of differences in interpretations, which is a
measure of the repositioning of analysts’ forecasts relative to each other, measured as one minus
the Pearson correlation between analysts’ forecasts before and after the earnings announcement.
Barron (1995) introduces this “belief jumbling” measure to the literature; Bamber et al. (1997)
show that that this jumbling measure is associated with trading volume around earnings
19
announcements.19
While similar to KP, this belief jumbling measure is broader in scope.
Specifically, while the KP measure is designed to detect the presence of differences in
interpretations in a “narrow sense,” i.e., strictly-speaking beliefs that move in opposite directions
around small price reactions, this belief jumbling measure captures the degree of “disagreement”
in a more general sense. Unlike the KP measure, this broader belief jumbling measure has the
potential to measure differences in interpretations when there is a large price reaction.
The primary weakness with using belief jumbling as a measure of differences in
interpretations is that this measure will also be influenced by differences in the precision of
analysts’ prior information, i.e., differential priors. Excess trading volume around earnings
announcements can result from greater differential priors or more differences in interpretations.
Kim and Verrecchia (1991) predict that private pre-disclosure information of different precision
causes investors with more (less) precise private information to place more (less) reliance on that
private pre-disclosure information and less (more) reliance on the public information released by
the earnings announcement. Thus, there is more differential belief revision around earnings
announcementsand hence more tradingwhen there is greater pre-announcement differential
precision.
We address this weakness of the belief jumbling measure in two ways. First, we observe
that differences in the precision of analysts’ pre-disclosure information are unlikely to be linked
in a causal way with managers’ disclosure decisions. The degree of analysts’ pre-announcement
differential precision is determined before the disclosure, so the disclosure itself is unlikely to
cause differential precision. Second, although it is plausible that the degree of analysts’ preannouncement differential precision could influence a manager’s disclosure choice, this is
unlikely because persistent differences in the precision of forecasts across analysts have been
very difficult for empirical researchers to detect. Brown and Mohd (2003) examine if analyst
characteristics can be used to predict variation in forecast accuracy out-of-sample; they find that
19
We confirm this evidence regarding this “belief jumbling” measure in our sample.
20
analyst characteristics have no relevance for predicting out-of-sample variation in forecast
accuracy. Thus, it is unlikely that managers are able to detect such differences in the short run.20
Nevertheless, it is possible that for purely mechanical reasons differential preannouncement precision causes belief jumbling to be related to a disclosure choice even though
differential pre-announcement precision is not related to the choice. This is possible because
belief jumbling due to differential pre-announcement precision increases with the magnitude of
change in the average belief as implied by Kim and Verrecchia (1991); and the magnitude of
change in the average belief may vary with a disclosure choice. For this reason, we introduce a
new measure of belief jumbling denoted RESIDUAL JUMBLING. RESIDUAL JUMBLING is
the residual from a regression of the belief jumbling measure of Bamber et al. (1997) on the
absolute change in the mean forecast. From our evidence that balance sheet disclosures coincide
with both price and volume reactions, we expect that BS will be positively associated with
RESIDUAL JUMBLING. We also expect that other disclosures that cause significant trading
volume will be associated with RESIDUAL JUMBLING.
4.4.2 Results for the Analyst-Based Measures of Differences in Interpretations
Our focus is primarily on disclosures and the KP measure designed specifically for
detecting the presence of differences in interpretations. Table 4 shows that of the three types of
disclosures (SEG, CC, and MF_RANGE) that are associated with abnormal trading volume, both
SEG (p<0.01, one-tailed) and CC (p<0.05, one-tailed) are significantly positively associated with
KP. This result is consistent with the trading volume associated with the disclosure of segment
reports and lengthy conference calls being associated with greater differences in interpretations.
In contrast, MF_RANGE, is not positively associated with KP, which may be due to price
reactions that are large enough to invalidate the KP measure. We do find, however, that
MF_RANGE is marginally positively associated with RESIDUAL JUMBLING (p-value=0.07,
20
The observed forecast dispersion and error do not reveal the degree of differential forecast precision, but rather the
degree of uncertainty and the ratio of common-to-total information (e.g., Barron, Kim, Lim, and Stevens 1998).
21
one-tailed). Thus, we do find limited evidence that range forecasts are associated with greater
differences in interpretations.
< Insert Table 4 About Here >
Balance sheet disclosure (BS) is also not significantly associated with KP.
This is
expected because BS is associated with significant price changes and the KP measure is not
predicted to explain trading volume associated with significant price changes. However, Table 4
also shows that BS is significantly positively associated with RESIDUAL JUMBLING (p=0.017,
one-tailed), which explains why there is both significant price changes and trading volume
associated with BS. SEG is also significantly positively associated with RESIDUAL JUMBING
(p=0.07, one-tailed), which just confirms our earlier result indicating a positive association
between KP and SEG, suggesting that segment disclosures are associated with more differences
in interpretations.
We also find a marginally significant negative association between the
disclosure of point forecasts (MF_POINT) and RESIDUAL JUMBLING (p=0.10, one-tailed),21
suggesting that the presence of point management forecasts is associated with less differences in
interpretations. Again, this results is also consistent with our earlier volume- and price-based
inference which indicate that point management forecasts are associated with more common
interpretations, i.e., less differences in interpretations.
We conclude that the evidence based on analyst forecast-based measures of differences in
interpretations is largely consistent with our inferences based on volume and price reactions.
Since the insignificant relation between MF_RANGE and KP may be due to price changes that
are too large to make KP a good indicator of differences in interpretations, we are left with only
one test that clearly does not support our inferences from the volume based evidence. That is,
we do not find a significantly positive relation between the length of conference calls (CC) and
RESIDUAL JUMBLING. Nevertheless, we do find a positive relation between CC and KP,
which we consider to be the more compelling evidence. In summary, we conclude that the same
21
It is inappropriate for us to make predictions concerning the relation between KP and MF_POINT because
MF_POINT is associated with significant price reactions.
22
disclosures related to our volume based measure of differences in interpretations are also
associated with analyst-forecast-based measures of differences in interpretations, although in
some cases the association is weak.
V. Robustness
We undertake a number of different analyses to examine the robustness of our results to
industry effects, the inclusion of additional disclosure measures (e.g., the use of pro form
reporting), and the possible effect of firm characteristics associated with firms’ disclosure
choices, i.e., self-selection. Our inferences are unchanged by these additional analyses.
5.1 Controlling for Industry-Specific Effects
One potential concern is that (part of) our findings may be due to industry effects. First,
it is possible that some voluntary disclosures are more prevalent in certain industries because
managers’ incentives to make certain disclosures vary across industries. Second, investors may
disagree more and trade more on some disclosures in certain industries than in other industries.
To test this, we rerun all of our regressions adding a set of industry dummy variables for each 4digit SIC code represented in our sample. Our inferences are unchanged using this alternative
specification.
5.2 Comparing Larger and Smaller Firms
Because we require necessary analyst forecast data to compute the empirical measures of
differences in interpretations used in our analysis, our sample is comprised of relatively large
firms. This raises a potential concern that our findings may not generalize to smaller firms. To
provide some evidence on this issue, we split our sample into two sub-samples based on median
firm size. Then, we examine if our findings differ across the relatively larger and smaller firms
in our sample; the results are reported in Table 5. It is also important to note that while the
average firm in our sample is large, firm size varies greatly across the firms in our sample: the
23
average firm size for the smaller- (larger-) firm sub-sample is about $ 1.5 billion ($27.2 billion).
This cross-sectional variation in firm size allows for a powerful test.
< Insert Table 5 About Here >
The left two columns ((1) and (2)) of Table 5 report the results of estimating Equation (1)
for the two sub-samples. Column (3) reports the p-values for a comparison of the magnitude of
each regression coefficient across the larger and smaller firm sub-samples. As can be seen in
column (3), none of the regression coefficients in the EXVOL regression are statistically different
across the larger and smaller firm sub-samples. The results for BS are now only marginally
statistically significant for both sub-samples, possibly because of the loss of power due to the
reduced sample size. Columns (4) to (6) show the same analysis for the LARET regression:
column (6) reports the p-values for comparisons of the magnitude of each regression coefficient
across the larger and smaller firm sub-samples. As the results in column (6) show, none of the
regression coefficients in the LARET regression are significantly different across the larger and
smaller firm sub-samples. Overall, the results reported in Table 6 indicate that our main findings
are not only attributable to either the larger or smaller firms in our sample. These results
mitigate concerns that our results may not generalize to smaller firms.
5.3 Variation in the Total Disclosure Level
We also explore whether one can generalize our results by saying that more disclosure
generally tends to result in more trading volume, but not greater price changes. To explore this
issue we create a new independent variable that sums BS, SCF, SEG, MF_POINT, MF_RANGE,
and MF_QUAL. This overall disclosure variable is highly significant in explaining trading
volume but insignificant in explaining price changes.
5.4 Controlling for the Length of the MD&A
We include the word count of firms’ conference calls (CC) as one of our disclosure
measures. Earnings announcement press releases also sometimes include a similar managerial
24
discussion and analysis (MD&A). We also separately measure the word count of this section of
firms’ earnings announcement press release and include this measure as an additional
independent variable. The results are insignificant for this additional disclosure variable.
5.5 Control for Pro Forma Financial Reporting
When hand-collecting our data we also coded a dummy variable for the presence/absence
of pro-forma disclosures (PFORMA). We re-ran all our analysis including PFORMA as an
additional dummy control variable. Our results are qualitatively unchanged by this analysis.
5.6 Additional Controls for Possible Self Selection
We view firms’ voluntary disclosure choices as likely being exogenous to our dependent
variables in Equations (1) and (2) which capture market reactions in the 3-day window around
earnings announcements. We take this view because: (1) it seems unlikely that the market
reaction in a short window event study around earnings announcements would have determined
the disclosure choice itself (i.e., this is an event study); and (2) disclosure choices can be heavily
influenced by managerial style (Bamber, Jiang, and Wang 2010). Nevertheless, similar to prior
association-type voluntary disclosure studies (see Leuz and Verrecchia 2000), we also use the
two-stage Heckman estimation procedures to address this concern.
Prior empirical studies suggest that firms’ voluntary disclosure decisions are related to:
(1) the level of uncertainty; (2) fundamental firm characteristics like firm size, i.e., market
capitalization, (3) firm complexity; (4) firm performance; (5) performance variability; (6)
proprietary costs; and (7) whether or not a firm is about to issue stock. To address this concern,
we identified a list of 20 control variables that have been used in prior empirical studies (e.g.,
Lang and Lundholm 1993; Tasker 1998; Chen, et al. 2002; Miller 2002; Botosan and Stanford
2005; and Francis et al. 2008) to control for these fundamental factors that effect firms’
voluntary disclosure decisions. See Appendix A for a complete description of each of these
additional 20 self-selection control variables.
25
In the first-stage probit model of firms’ disclosure decision, for each disclosure type we
model firms’ decision to make that type of disclosure as a function of our four existing control
variablesSURP, SIGN, SIZE, and BMand the additional 20 self-selection control variables
we identify from prior studies. That is, we estimate the first-stage probit model six times, once
for each of the different types of disclosures BS, SCF, SEG, MF_Point, MF_Range, and
MF_Qual (e.g., see Hamilton and Nickerson 2003). This yields six different inverse Mills ratios.
We substitute these six different inverse Mills ratios, in turn, into the second stage OLS model.
We estimate the second stage OLS model for both LARET and EXVOL.22
After we match our sample with data for the additional 20 self-selection control variables
used in the first stage probit models, our sample size decreases to 1,319 earnings announcements.
In the untabulated estimating of the second stage OLS estimates with LARET as the dependent
variable, the results for BS and MS_Point continue to hold: both BS and MF_Point are significant
in the predicted direction at the 5-percent level or better. Only 2 of the 6 inverse Mills ratios in
the LARET regressions are significant: those for the balance sheet and segment disclosures; both
are significant at the 1-percent level. When EXVOL is the dependent variable in the second stage
OLS the results are also broadly consistent with our main results: the same four types of
disclosures (BS, SEG, CC, and MF_Range) are still significantly positively related to EXVOL
(p<0.01, two-tailed, for most). None of the six inverse Mills ratios are significant in these
regressions. In summary, our results continue to hold when we include a comprehensive list of
control variables to control for self-selection bias.
However, we are not able to explain much of the particular disclosure choices in our
study: the pseudo R2s for the first-stage probits range from 4.3-percent to 19.8-percent. These
pseudo R2s are considerably lower than those reported in association-type voluntary disclosure
22
Note, we do not estimate a selection probit model for the presence/absence of our seventh disclosure
typeconference callsbecause a conference call accompanies every earnings announcement in our sample.
Nevertheless, as an additional analysis, we did model the length of the conference call word count (using a
dummy variable coded one (zero) is the conference call was greater (less) then the sample median word count).
Our inferences are unchanged using this alternative specification.
26
studies that use a similar two-stage Heckman model to control for self-selection (e.g., see Leuz
and Verrecchia 2000). This is likely because the disclosure choices we model are primarily
disclosure timing choices for BS, SEG and SCF, rather then strictly a decision as to whether to
disclose an item or not. Also, our study is an event study rather then an association study and, as
a result, the degree of model fit can be expected to be lower. Finally, the disclosure choices we
model may be very idiosyncratic and largely a matter of idiosyncratic managerial style (see
Bamber et al. 2010).
VI. Concluding Remarks
We find that certain disclosures that firms typically include in their earnings
announcements are associated with higher trading volume that is not explained by concurrent
price changes. Specifically, segment reporting, longer conference calls, and range management
forecasts (of future period earnings) are associated with abnormal trading around earnings
announcements that is not explained by price reactions. On the other hand, point management
forecasts are associated with significant price reactions but not trading volume reactions.
Finally, balance sheet disclosures are associated with both price and volume reactions. We also
find that the same disclosures that are associated with volume-based measure of differences in
interpretations are also associated with empirical analyst-based measures of differences in
interpretation.
Our findings make a number of contributions. First, our results support Landsman and
Maydew’s (2002) argument that the over-time increase in volume reactions to earnings
announcements is attributable to the over-time growth in earnings announcement disclosures (see
Francis et al. 2002). Specifically, we show that many of the disclosures increasingly made with
earnings announcements are associated with trading that is indicative of investor differences in
interpretations. This is consistent with our expectation that investors have limited information
processing capabilities and do not focus all their attention on the exact same disclosure items.
Second, our findings increase our understanding of why, compared to price reactions around
27
earnings announcements, volume reactions are observed more often (Bamber and Cheon 1995).
Investors appear to disagree and trade on the information contained in more disclosures, and
much of this trading does not coincide with, or is not explained by price reactions. Third, our
results extend prior studies that have documented differences in interpretations around earnings
announcements (e.g., Kandel and Pearson 1995): we show that these differences in
interpretations are associated with the disclosures made with earnings announcements.
Fourth, our results also suggest that there has been a marked change in firms’ earnings
announcement disclosures in recent years. We find much higher levels of voluntary financial
statements disclosures for our post-Regulation G (Reg G) sample period than earlier studies that
have examined earnings sample periods. For example, for the last year in their sample (1999),
Francis et al. (2002) report that 77.8 percent (25.9 percent) of the earnings announcements in
their sample include a detailed income statement (balance sheet). The corresponding figures for
our sample are 100 percent (86 percent). Our analysis thus suggests that income statement
disclosure is now de facto mandatoryat least for firms with a large analyst following. This
perhaps also explains why we find that the statement of cash flows is not incrementally
informativeand these financial statements must now be reconciled to GAAP, so that the
statement of cash flows can now be constructed from alternative sources. While our results
should also be of interest to managers and policymakers who may be interested in understanding
the market reaction associated with various types of earnings announcement disclosures, a
limitation of our study is that we do not explain a great deal of the variation in firms’ disclosure
choices associated with the “total disclosure package” released with earnings announcements.
Although, this may be because these disclosure choices are somewhat idiosyncratic (see Bamber
et al. 2010).
28
APPENDIX A
Description of Additional Self-Selection Control Variables
We use a two-stage Heckman model to control for self-selection. Prior empirical studies
suggest that firms’ voluntary disclosure decisions are related to: (1) the level of uncertainty; (2)
fundamental firm characteristics like firm size, i.e., market capitalization, (3) firm complexity;
(4) firm performance; (5) performance variability; (6) proprietary costs; and (7) whether or not a
firm is about to issue stock. We identify an additional 20 control variables that are intended to
control for these factors; these additional control variables are included in the first stage probit
model. Outlined here is a description of these additional 20 control variables, their calculation,
and our motivation for including them in the first stage selection model.
DISPiq = the dispersion of analysts’ forecasts prior to earnings announcements, defined as the
coefficient of variation in analysts’ earnings forecasts for quarter q earnings issued in a
45-day window immediately prior to quarter q earnings announcements. Bamber and
Cheon (1995) argue that the pre-announcement dispersion is associated with investor
disagreement that generates more trading volume.
Moreover, the higher the pre-
announcement dispersion, the higher investors’ uncertainty about the forthcoming
earnings announcement, which may lead to more demand for voluntary disclosures.
RANGEiq = the range of analysts’ forecasts prior to earnings announcements, defined as the
absolute difference between the most optimistic forecast and the most pessimistic
forecast among all forecasts for quarter q earnings of firm i, issued in the 45-day window
immediately prior to quarter q earnings announcement, scaled by the absolute mean
forecast. Bamber and Cheon (1995) also include this variable in their analysis. Like
DISP, higher levels of RANGE are likely associated with investors’ uncertainty about the
forthcoming earnings announcement, which may lead to more demand for voluntary
disclosures.
UEDIFFiq = the difference between (1) the absolute percentage forecast error for quarter q
earnings based on a seasonal random-walk earnings expectation model, and (2) the
29
absolute percentage forecast error for quarter q earnings based on the mean analyst
forecast of all the forecasts issued within a 45-day window prior to quarter q earnings
announcements of firm i. Bamber and Cheon (1995) find an association between this
variable and investor disagreement that generates more trading volume.
PRICEUPiq = a dummy variable that is equal to 1 if there is a price increase around quarter q
earnings announcement of firm i and 0 otherwise. Bamber and Cheon (1995) find an
association between this variable and investor disagreement that generates more trading
volume.
LOSSiq = a dummy variable equal to one if firm i reports a loss in quarter q, and zero otherwise.
Chen et al. (2002) report that firms are more likely to include a balance sheet disclosure
in their earnings announcement press release in a loss quarter.
RET_VOLiq = the return volatility of firm i in quarter q. Stock return volatility is likely to be
associated with voluntary disclosures because greater stock return volatility denotes
greater uncertainty among investors. In such a setting investors are likely to have a
greater demand for supplemental voluntary disclosures (see Chen et al. 2002). Similar to
Chen et al. (2002), we measure stock return volatility as the standard deviation of daily
stock returns over the 253 trading days (1 calander year) ending 2 trading days prior to
the quarter q earnings announcement date.
ROAiq = return on assets (ROA) for firm i in quarter q. A number of prior studies have linked
firm voluntary disclosure choices to firms’ level of performance (see Lang and Lundholm
1993; Miller 2002). Similar to Francis et al. (2008), we measure ROA in the period one
year before the event quarter q.
A_CARiq = annual market-adjusted returns for firm i for the year just prior to the quarter q
earnings announcement. Similar to Lang and Lundholm (1993), we include this variable
to control for firm performance since performance is likely to affect firms’ voluntary
disclosure decisions (see Lang and Lundholm 1993; Miller 2002). We measure A_CAR
30
as the market-adjusted returns for firm i over the 253 trading days ending 2 trading days
before the quarter q earnings announcement date.
Q_CARiq = quarterly market-adjusted returns for firm i for the quarter just prior to the quarter q
earnings announcement. Similar to Lang and Lundholm (1993), we include marketadjusted returns to control for firm performance since performance is likely to affect
firms’ voluntary disclosure decisions (see Lang and Lundholm 1993; Miller 2002). We
measure Q_CAR as the market-adjusted returns for firm i over the 64 trading days ending
2 trading days before the quarter q earnings announcement date.
#QTRS_UPiq = the number of (historic) quarters of continuous increasing seasonally-adjusted
earnings for firm i in quarter q; that is, the length of firm i’s string of quarterly earnings
increases. Earnings are defined as earnings before extraordinary items here. Miller
(2002) finds that firms provide more voluntary disclosures in periods of strong sustained
earnings performance, that these increased disclosures tend to be bundled with earnings
announcements, and that this increased level of voluntary disclosure ends when the firm’s
period of sustained strong earnings performance comes to an end.
FOLLOWiq = the number of analysts that issued forecasts for quarter q earnings within a 45-day
window immediately prior to the quarter q earnings announcement measured using data
from the IBES Detail database. The motivation for controlling for analysts’ following is
similar to firm size.
Greater analyst following is associated with higher quality
disclosures (Lang and Lundholm 1996); in addition, when the number of analysts
increases, there is likely to be more competition among analysts to provide incremental
information (see Frankel, Kothari and Webber 2006) and, thus more pressure on
management to make voluntary disclosures.
HTECHiq = a dummy variable equal to one if firm i is in a high-tech industry and zero otherwise.
Chen et al. (2002) find that firms in these industries are more likely to disclose a balance
sheet as part of their earnings announcement press releases. HTECH is equal to one for
the following four-digit codes: 2833-2836 (Drugs), 8731-8734 (R&D Services), 737131
7379 (Software), 3570-3577 (Computers), 3600-3674 (Electronics), 3810-3845
(Instruments), and zero otherwise
AGEiq = the age of the firm, measured as the number of years from the quarter when firm i first
went public to quarter q, the event quarter. This variable controls for the greater
uncertainty surrounging younger firms. As a result, the management of these firms may
face greater pressure to make supplemental voluntary disclosures (see Chen et al. 2002).
#_SEGiq = number of business segments reported by firm i. This variable proxies for operational
complexity. Investors may face more difficulty analyzing more complex firms with
multiple business segments. As a result, management of these firms may face greater
pressure to make voluntary disclosures (see Nagar, Nadar, and Wysocki 2003).
AQiq = a proxy for the accruals quality of firm i. We measure accruals quality using McNichols’
(2002) modification of Dechow and Dichev’s (2002) model as follows (all variables are
scaled by average total assets in year t and t-1):
TCAi ,t  0,t  1,i CFOi ,t 1  2,i CFOi ,t  3,i CFOi ,t 1  4,i REVi ,t  5,i PPEi ,t   i ,t , (A1)
where:
TCAi,t = firm i’s total current accruals in year t;
CFOi,t = firm i’s cash flow from operations in year t;
REVi,t = the change in firm i’s revenues between year t-1 and year t; and
PPEi,t = firm i’s gross value of property, plant, and equipment in year t.
Similar to Francis et al. (2008), we estimate Equation (A1) above for each firm using the
10-years of data prior to the year of the even quarter. The standard deviation of the
residuals from these firm-specific model estimates yield firm-specific estimates of AQ for
firm i in quarter q.
CORR_ERiq = the correlation between annual earnings and returns for firm i. If the correlation
between earnings and returns is low then this means that little information about firm
value is captured by earnings. As a result, information asymmetry may be high and the
firm may face more pressure to provide supplemental voluntary disclosures (see Lang
and Lundholm 1993). Following Francis, LaFond, Olsson, and Schipper (2004) our
32
measure of the value relevance of earnings is the explained variability from the following
firm-specific regression of returns on the level and change in earnings:
RETi ,t   0,t   1,t EARN i ,t   2,t EARN i ,t   i ,t ,
(A2)
where:
RETi,t = firm i’s 15-month return ending three months after the end of fiscal year t;
EARNi,t = firm i’s income before extraordinary items in year t, scaled by the market
value of firm i at the end of year t-1; and
EARNi,t = change in firm i’s income before extraordinary items in year t, scaled by
the market value of firm i at the end of year t-1.
Similar to Francis et al. (2004), we estimate Equation (A2) above for each firm over a 10year window prior to the year of the even quarter, and we use the adjusted-R2 from these
firm-specific estimates as our measure of the value-relevance of earnings for firm i in
quarter q.
IND_HERFiq = the industry Herfindahl index for firm i’s industry, which proxies for the
proprietary costs associated with firm i’s voluntary disclosures. We measure IND_HERF
by summing the squared market share of all firms in the same industry, where industries
are defined using 3-digit SIC codes.
IND_PROFITADJiq = a measure of the persistence in the deviation of firm i’s return on assets
(ROA) from the industry average ROA (see Botosan and Stanford 2005). This is a
measure of the persistence of a firm’s abnormal profits, which proxies for a firm’s
proprietary costs. We use the same approach as Botosan and Stanford (2005) to estimate
IND_PROFITADJA which is based upon estimates of the following model:
ROA _ ADJ t   0  1 Dn * ROA _ ADJ t 1    2 Dp * ROA _ ADJ t 1    t ,
(A3)
where:
ROA_AD,t = return on assets (ROA) of a firm in year t minus the average ROA
for firms in that industry (3-digit SIC code) in year t;
Dn = a dummy variable equal to 1 if ROA_ADJ,t-1  0, and 0 otherwise; and
Dp = a dummy variable equal to 1 if ROA_ADJt-1 > 0, and 0 otherwise.
33
We estimate Equation (A3) above separately for each industry (3-digit SIC code) using
the three years of data prior to the year of the event quarter. IND_PROFITADJA is equal
to the estimate of 2 in Equation (A3) which reflect the persistence of ROA above the
industry average for that industry.
M&Aiq = a dummy variable equal to one if firm i reports a merger or acquisition in the event
quarter, and zero otherwise.
ISSUEiq = a dummy variable proxy for the presence of a common stock issue by firm i in quarter
q. We measure ISSUE using the same approach as Francis et al. (2008). If the number
of firm i’s (split-adjusted) common shares outstanding increases by 20-percent or more in
quarter q relative to quarter q-1, then ISSUE equals one; otherwise ISSUE equals zero.
34
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38
TABLE 1
Descriptive Statistics
The sample consists of 1,445 quarterly earnings announcements made between 30 March 2003 and 30 September
2004. For each earnings announcement q we require that a minimum of three individual analysts make (or revise)
forecasts of quarter q+1 earnings in the 45-day period before the earnings announcement and also revise their
forecast in the period between the quarter q earnings announcement date and 10Q/K filing date, as long as this
post-announcement period does not exceed 30 days. All of our sample earnings announcements are after
Regulation G came into effect on 28 March 2003. We obtained copies of the earnings announcement press
releases filed as 8K statements with the SEC. While all of these 8Ks include income statement disclosures, the
amount of supplemental financial disclosure varies: some earnings announcement 8Ks include a balance sheet,
segment reporting information, a statement of cash flows, or a management earnings forecast for a subsequent
period. Our sample contains a series of dummy variables for the presence/absence of these supplemental
disclosures. In addition, each earnings announcement in our sample is accompanied by an earnings conference
call. We use the transcripts of these conference calls to measure the word count for these conference calls.
25th Percentile
Median
75th Percentile
0.486
0.084
1.208
0.188
0.022
-4.161
0.480
0.039
-3.331
0.824
0.074
-2.703
Earnings Announcement Voluntary Disclosures
BS
0.860
0.348
SCF
0.504
0.500
SEG
0.334
0.472
CC
9,194
2,359
MF_Point
0.055
0.229
MF_Range
0.397
0.489
MF_Qual
0.081
0.273
0.000
0.000
0.000
7,792
0.000
0.000
0.000
1.000
1.000
0.000
9,091
0.000
0.000
0.000
1.000
1.000
1.000
10,388
0.000
1.000
0.000
Control Variables
SURP
SIGN
SIZE
BM
0.000
0.000
1,321
0.259
0.001
0.000
3,558
0.409
0.002
1.000
11,071
0.584
Variable
EXVOL
VOL (% traded)
LARET
Mean
0.533
0.064
-3.525
0.003
0.275
14,354
0.436
Std. Deviation
0.016
0.447
33,877
0.383
EXVOL is the event-period excess trading volume, defined as the natural log of the cumulative trading volume
over the three-day event window (days -1 to +1 relative to the quarterly earnings announcement date) minus
the natural log of the firm-specific median trading volume. Median volume is defined as the median trading
volume for contiguous three-day periods over the 249 trading days prior to the earnings announcement event
window;
VOL is the percentage of outstanding shares traded over the three-day event window (days -1 to +1 relative to the
quarterly earnings announcement date);
LARET is the natural log of the absolute value of the sum of a firm’s daily returns over the three-day event
window (days -1 to +1 relative to the quarterly earnings announcement date);
BS is a dummy variable equal to one if the quarter q earnings announcement press release contains a balance sheet
and zero otherwise;
SCF is a dummy variable equal to one if the quarter q earnings announcement press release contains a statement of
cash flows and zero otherwise;
SEG is a dummy variable equal to one if the quarter q earnings announcement press release contains segment
reporting information and zero otherwise;
CC the word count for the earnings conference call made concurrent with the quarter q earnings announcement,
i.e., on the day of the earnings announcement or on the following day;
39
MF_POINT is a dummy variable set equal to one when management includes a point forecast of future period
earnings in the quarter q earnings announcement press release and zero otherwise;
MF_RANGE is a dummy variable set equal to one when management includes a range forecast of future period
earnings in the quarter q earnings announcement press release and zero otherwise;
MF_QUAL is a dummy variable set equal to one when management includes a qualitative forecast of future period
earnings in the quarter q earnings announcement press release and zero otherwise;
SURP is calculated as: Actual EPS - Mean EPS Forecast Stock Price , where actual and forecasted EPS are for
quarter q and are from the IBES database. The mean forecast is calculated as the mean of the forecasts of
quarter q earnings made in the 45-day period before the quarter q earnings announcement. Stock price is at
the end of fiscal quarter q;
SIGN is a dummy variable equal to one if the earnings surprise in quarter q is negative (i.e., if quarter q IBES
actual earnings is less than the median IBES forecast made in the 45-day period before the quarter q earnings
announcement), and zero otherwise; and
SIZE is market capitalization at the end of quarter q, calculated using Compustat data at the end of fiscal quarter q;
BM is the book-to-market ratio, calculated using Compustat data at the end of fiscal quarter q.
40
TABLE 2
Spearman Correlation Analysis
(Two-tailed p-values are shown in parentheses)
EXVOL
LARET
BS
EXVOL
LARET
BS
SCF
SEG
CC
1.00
0.420
(<0.001)
1.00
0.135
(<0.001)
0.112
(<0.001)
1.00
-0.005
(0.846)
-0.065
(0.013)
0.241
(<0.001)
1.00
-0.018
(0.504)
-0.128
(<0.001)
-0.111
(<0.001)
0.105
(<0.001
1.00
0.048
(0.066)
-0.010
(0.698)
0.091
(<0.001)
0.059
(0.026)
0.048
(0.065)
1.00
SCF
SEG
CC
MF_POINT
MF_RANGE
MF_QUAL
SURP
SIGN
SIZE
BM
All variables are defined in the endnotes of Table 1.
41
MF_
POINT
MF_
RANGE
MF_
QUAL
-0.010
(0.718)
0.027
(0.299)
-0.007
(0.801)
0.022
(0.402)
-0.004
(0.867)
0.007
(0.780)
1.00
0.107
(<0.001)
0.052
(0.049)
0.092
(<0.001)
0.037
(0.165)
-0.033
(0.204)
0.047
(0.071)
-0.196
(<0.001)
1.00
-0.013
(0.625)
-0.033
(0.208)
-0.048
(0.069)
-0.036
(0.176)
0.075
(0.004)
0.008
(0.776)
-0.072
(0.006)
-0.241
(<0.001)
1.00
SURP
SIGN
SIZE
BM
0.084
(0.001)
0.102
(<0.001)
0.021
(0.436)
0.021
(0.418)
0.028
(0.296)
-0.040
(0.130)
0.011
(0.690)
-0.113
(<0.001)
0.028
(0.291)
1.00
0.044
(0.093)
0.011
(0.669)
-0.005
(0.854)
0.019
(0.464)
-0.029
(0.274)
-0.014
(0.592)
-0.061
(0.020)
-0.072
(0.006)
0.021
(0.416)
-0.001
(0.981)
1.00
-0.206
(<0.001)
-0.253
(<0.001)
-0.176
(<0.001)
0.090
(<0.001)
0.299
(<0.001)
0.225
(<0.001)
0.041
(0.119)
-0.051
(0.054)
0.045
(0.090)
-0.178
(<0.001)
-0.075
(0.004)
1.00
-0.099
(<0.001)
-0.077
(0.003)
0.033
(0.210)
0.104
(<0.001)
0.097
(<0.001)
-0.007
(0.787)
-0.037
(0.161)
-0.059
(0.026)
0.051
(0.054)
0.241
(<0.001)
0.094
(<0.001)
-0.200
(<0.001)
1.00
TABLE 3
Regression of Association Between Excess Trading Volume and Absolute Stock Returns Around
Earnings and Earnings Announcement Voluntary Disclosures
EXVOLiq =  0 +  1 BS iq +  2 SCFiq   3 SEGiq   4CCiq   5 MF_POINTiq   6 MF _ RANGEiq   7 MF _ QUALiq
T 4
  8 LARETiq   9 SURPiq  10SIGNiq  11SIZEiq  12 BM iq   t QTR_DUM t   y DUM 04 y + εiq .
(1)
t 2
LARETiq =  0 +  1 BS iq +  2 SCFiq   3 SEGiq   4 CCiq   5 MF_POINTiq   6 MF _ RANGEiq  7 MF _ QUALiq
T 4
  8 SURPiq   9 SIGN iq   10 SIZEiq   11 BM iq    t QTR_DUM t   y DUM 04 y  εiq .
(2)
t 2
The coefficients for the 2004 year dummy (DUM04) and the quarterly (QTR_DUM) dummy variables are omitted. All other
variables are defined in the endnotes of Table 1. We estimate Equations (1) and (2) using decile rank regressions; that is,
independent variables that are not dummy variables are transformed to decile ranks. This mitigates the influence of extreme
values and to relax the linearity assumption.
Eq (1): EXVOL
Intercept
Eq (2): LARET
Coefficient
(p-value)a
Eq (1): EXVOL
Controlling for LARET
Coefficient
(p-value)a
0.492
(<0.001)
0.292
(<0.001)
0.552
(<0.001)
0.050**
(0.030)
0.007
(0.665)
0.065***
(<0.001)
0.072***
(0.003)
-0.001
(0.989)
0.054***
(0.001)
0.036
(0.205)
0.063***
(0.005)
-0.028
(0.951)
-0.017
(0.821)
0.040*
(0.065)
0.059**
(0.049)
0.024*
(0.084)
-0.001
(0.510)
0.361
(<0.001)
0.060
(0.015)
0.035
(0.040)
-0.155
(<0.001)
-0.122
(<0.001)
0.220
0.098
(<0.001)
0.011
(0.549)
-0.238
(<0.001)
-0.139
(<0.001)
0.097
Earnings Announcement Disclosures
BS
0.073***
(0.003)
SCF
-0.003
(0.845)
SEG
0.059***
(0.001)
CC
0.086***
(0.001)
MF_POINT
0.021
(0.560)
MF_RANGE
0.063***
(<0.001)
MF_QUAL
0.036
(0.241)
Control Variables
LARET
SURP
SIGN
SIZE
BM
Adjusted R2
a
0.095
(<0.001)
0.039
(0.033)
-0.241
(<0.001)
-0.173
(<0.001)
0.103
Coefficient
(p-value)a
*/**/*** indicates significance at the 10/5/1-percent level. Coefficients on disclosure variables that are significant at the
10% level are indicated simply by bold face type. P-values are two-tailed when EXVOL is the dependent variable and onetailed when LARET is the dependent variable. All p-values are based on Rogers’ (1993) method to compute standard
errors; this method allows for hetroskedasticity and correlation between observations for the same firms.
42
TABLE 4
Regression Analysis of Association Between Analyst-Based Measures of Differences in
Interpretations and Earnings Announcement Disclosures
Difference in Interpretations iq = 0 + 1 BS iq +  2 SCFiq  3 SEGiq   4 CCiq   5 MF_POINTiq
  6 MF _ RANGEiq   7 MF _ QUALiq  8 LARETiq  9 SURPiq  10 SIGN iq
(3)
T 4
 11 SIZE iq  12 BM iq iq  ∑ t QTR _ DUM t   y DUM04 y + ε iq .
t 2
The coefficients for the 2004 year dummy (DUM04) and the quarterly (QTR_DUM) dummy variables are omitted. As our
dependent variables, we use two different analyst-based measures of differences in interpretations: (1) KP, the Kandel and
Pearson (1995) belief “jumbling” measure designed to capture differences in interpretations, this is the proportion of analyst
pairs that are either a Case 4 or 5 shown in Figure 1, i.e., where two analysts’ beliefs move in opposite directions and
indicate less agreement, and (2) RESIDUAL JUMBLING, the residual from a regression of the jumbling measure used in
Bamber et al. (1997), on the absolute value of the forecast revision (of quarter q+1 earnings) around the quarter q earnings
announcement. All other variables are defined in the endnotes of Table 1. We estimate Equation (3) using decile rank
regressions; that is, independent variables that are not dummy variables are transformed to decile ranks. This mitigates the
influence of extreme values and relaxes the linearity assumption.
KP
Predicted
Sign
Intercept
Coefficient
(p-value)a
RESIDUAL
JUMBLING
Predicted
Sign
0.407
(<0.001)
Earnings Announcement Disclosures
None
BS
SEG
+
CC
+
MF_RANGE
+
-0.019
(0.445)
0.047***
(0.001)
0.053**
(0.042)
-0.035
(0.966)
-0.051
(0.299)
+
+
+
+
Other Voluntary Disclosures NOT Associated with Significant Excess Trading Volume
SCF
0.023
(0.176)
MF_POINT
None
-0.059
(0.098)
MF_QUAL
0.003
(0.911)
Control Variables
LARET
SURP
SIGN
SIZE
BM
Adjusted R2
-0.072
(0.006)
0.000
(0.998)
-0.013
(0.484)
0.068
(0.027)
0.052
(0.059)
0.05
43
Coefficient
(p-value)a
0.055**
(0.017)
0.028*
(0.070)
-0.031
(0.865)
0.022*
(0.070)
-0.026
(0.155)
-0.053*
(0.100)
-0.019
(0.555)
0.042
(0.146)
-0.033
(0.244)
0.010
(0.596)
-0.004
(0.903)
0.009
(0.745)
0.02
a
*/**/*** indicates significance at the 10/5/1-percent level. Coefficients on disclosure variables that are significant at the
10% level are indicated simply by bold face type. One-tailed p-values are reported where the coefficient sign is predicted;
otherwise two-tailed p-values are reported. All p-values are based on Rogers’ (1993) method to compute standard errors;
this method allows for hetroskedasticity and correlation between observations for the same firms.
44
TABLE 5
Analysis of Large and Small Firm Sub-Samples
We split the sample based on the median sample market capitalization (SIZE). Then, we re-ran Equation (1)
and Equation (2) separately for the large and small firm sub-samples. We estimate the version of Equation (1)
that includes LARET as a control variable. The mean firm size for the small-size sub-sample (N=723) is $1.5
billion and for the large-size sub-sample (N=722) is $27.2 billion. The coefficients for the year (YR_DUM) and
quarter (QTR_DUM) dummy variables are omitted. All variables are defined in the endnotes of Table 1.
EXVOL Regression (Eq. (1))
Variable
Intercept
(1)
(2)
Large SubSample
(4)
(5)
Small SubSample
(3)
Comparison of
Coefficients
Across Large
Vs. Sample
Large SubSample
Small SubSample
(6)
Comparison of
Coefficients
Across Large
Vs. Sample
Coeff.
(p-value)a
Coeff.
(p-value)a
(p-value)a
Coeff.
(p-value)a
Coeff.
(p-value)a
(p-value)a
0.234
(0.001)
0.292
(<0.001)
0.585
0.568
(<0.001)
0.546
(<0.001)
0.713
0.773
0.075**
(0.013)
-0.006
(0.809)
-0.029
(0.200)
0.040
(0.280)
0.050
(0.292)
0.019
(0.422)
0.007
(0.867)
0.043**
(0.040)
-0.048
(0.359)
0.009
(0.794)
0.040
(0.290)
0.063
(0.257)
0.025
(0.337)
-0.004
(0.930)
0.504
0.126
(0.001)
-0.004
(0.869)
-0.231
(0.002)
-0.172
(<0.001)
0.047
0.071
(0.063)
0.024
(0.351)
-0.253
(0.002)
-0.110
(0.006)
0.045
0.332
Earnings Announcement Disclosures
BS
0.050*
0.043
(0.061)
(0.186)
SCF
0.007
0.003
(0.754)
(0.911)
SEG
0.066***
0.067**
(0.001)
(0.018)
CC
0.024
0.103***
(0.483)
(0.003)
MF_POINT
-0.033
0.036
(0.441)
(0.489)
MF_RANGE
0.071***
0.051**
(0.001)
(0.036)
MF_QUAL
0.069*
-0.002
(0.052)
(0.964)
Control Variables
LARET
0.371
0.356
(<0.001)
(<0.001)
SURP
0.067
0.048
(0.047)
(0.175)
SIGN
0.056
0.019
(0.017)
(0.427)
SIZE
-0.115
0.043
(0.085)
(0.571)
BM
-0.086
-0.151
(0.016)
(<0.001)
Adjusted R2
0.216
0.174
a
LARET Regression (Eq. (2))
0.924
0.948
0.176
0.312
0.471
0.258
0.213
0.398
0.924
0.912
0.783
0.834
0.739
0.667
0.324
0.068
0.223
0.465
0.822
0.300
*/**/*** indicates significance at the 10/5/1-percent level. Two-tailed p-values based on Rogers’ (1993)
method to compute standard errors; this method allows for hetroskedasticity and correlation between
observations for the same firms.
45