Earnings Announcement Disclosures and Changes in
Analysts’ Information*
ORIE E. BARRON, Pennsylvania State University
DONAL BYARD, Baruch College – CUNY
YONG YU, University of Texas at Austin
ABSTRACT
This study examines how financial disclosures with earnings announcements affect sell-side
analysts’ information about future earnings, focusing on disclosures of financial statements
and management earnings forecasts. We find that disclosures of balance sheets and segment
data are associated with an increase in the degree to which analysts’ forecasts of upcoming
quarterly earnings are based on private information. Further analyses show that balance
sheet disclosures are associated with an increase in the precision of both analysts’ common
and private information, segment disclosures are associated with an increase in analysts’
private information, and management earnings forecast disclosures are associated with an
increase in analysts’ common information. These results are consistent with analysts processing balance sheet and segment disclosures into new private information regarding nearterm earnings. Additional analysis of conference calls shows that balance sheet, segment,
and management earnings forecast disclosures are all associated with more discussion
related to these items in the questions-and-answers section of conference calls, consistent
with analysts playing an information interpretation role with respect to these disclosures.
Information livree dans les communiques sur les resultats et
modification de l’information produite par les analystes
RESUM
E
Les auteurs se demandent comment l’information financiere livree dans les communiques
sur les resultats influe sur l’information produite par les analystes de maisons de courtage
en ce qui a trait aux resultats futurs, en s’interessant plus particulierement aux renseignements contenus dans les etats financiers et les previsions de resultats de la direction. Les
auteurs constatent que les renseignements que contiennent les bilans et les donnees sectorielles sont associes a une augmentation de la mesure dans laquelle les previsions des analystes quant aux resultats trimestriels a venir s’appuient sur de l’information privilegiee.
*
Accepted by Sudipta Basu. We thank the editor for many helpful suggestions and comments. We also thank
two anonymous reviewers, Linda Bamber, Anna Brown, Masako Darrough, Andy Leone, Melissa Lewis,
Edward Li, Pat O’Brien, Min Shen, Jim Vincent, Eric Wang, Ryan Wynne, Chris Yust, and conference participants at the AAA Annual Meetings, the Accounting and Financial Economics conference at the University of Texas – Austin, and seminar participants at Baruch College – CUNY, the University of Queensland,
and the University of Technology, Sydney. We are grateful to Irfan Ahmed, Monil Doshi, Xiaoge Fan,
Hao Li, Ruodan Lin, Shijian Liu, Hanna Rosen, Zhiyuan Tu, Ritesh Veera, and Eric Wang for their help
in collecting the data. In addition, we acknowledge the contribution of 10-K Wizard for providing 8-K transcripts, CallStreet for providing conference call transcripts, and I/B/E/S International Inc. for providing
earnings per share forecast data. Donal Byard gratefully acknowledges the financial support provided by a
Lang Fellowship from Baruch College, and a PSC-CUNY grant from the City University of New York
(PSC-CUNY # 69729-00 38).
Contemporary Accounting Research Vol. 34 No. 1 (Spring 2017) pp. 343–373 © CAAA
doi:10.1111/1911-3846.12275
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Des analyses plus poussees revelent que les renseignements contenus dans les bilans sont
associes a une augmentation de la precision de l’information tant commune et que privilegiee dont disposent les analystes, que les renseignements sectoriels sont associes a une
augmentation de l’information privilegiee dont disposent les analystes, et que l’information
relative aux previsions de resultats de la direction est associee a une augmentation de l’information commune dont disposent les analystes. Ces resultats confirment que les analystes
transforment l’information contenue dans les bilans et l’information sectorielle en nouvelle
information privilegiee relative aux resultats a court terme. Une analyse supplementaire des
conferences telephoniques revele que l’information contenue dans les bilans, l’information
sectorielle et les previsions de resultats de la direction sont toutes associees a de plus amples
discussions de ces elements dans la portion des conferences telephoniques consacree aux
questions et reponses, ce qui confirme que les analystes jouent un r^
ole d’interpretation de
l’information relativement a ces informations.
1. Introduction
This study examines how financial disclosures with firms’ quarterly earnings announcements affect financial analysts’ information about future earnings. It is common practice
now for firms to include various financial disclosures in addition to “bottom line” earnings
information in their earnings announcement press releases (see Chen, DeFond, and Park
2002; Francis, Schipper, and Vincent 2002). However, to date there is little evidence as to
whether and how these financial disclosures are useful to sell-side analysts. We fill this gap
by examining how disclosures of financial statements and management earnings forecasts,
the most common types of financial disclosures included in firms’ earnings announcements, affect financial analysts’ information about upcoming earnings, as reflected in their
quarterly earnings forecasts.
Analysts discover new information that preempts subsequent public disclosures, and
interpret and process public disclosures into new insights (Chen, Cheng, and Lo 2010). We
expect the information interpretation role to dominate for financial statement disclosures.
Financial statements are complex, disaggregated disclosures, and analysts apply their knowledge and skills to process the data contained in these statements into forecasts of future earnings. Because analysts vary in their backgrounds, knowledge, and skills, the insight analysts
extract from their interpretations of a financial statement disclosure are also likely to differ.
Hence, financial statement disclosures are likely to facilitate more private information
regarding future earnings, causing analysts’ earnings forecasts to be based relatively more on
private information. In contrast, we expect analysts’ information discovery role before an
earnings announcement to dominate the effect of management earnings forecasts on analysts’ information at the time of the announcement. Unlike financial statements, management earnings forecasts are a relatively simple form of disclosure requiring less processing by
analysts forecasting future earnings, and are thus more likely to be commonly interpreted by
analysts. As a result, management earnings forecasts are more likely to substitute for analysts’ private information discovered before the announcement, causing analysts’ earnings
forecasts after earnings announcements to be based relatively more on common information.
We hand-collect data on disclosures of financial statements and management earnings
forecasts for a sample of 1,445 quarterly earnings announcements from 2003 to 2004. We
use the analyst consensus measure developed by Barron, Kim, Lim, and Stevens (1998,
hereafter BKLS) to gauge the degree to which analysts’ forecasts are based on common
vs. private information. If, when forecasting earnings, analysts primarily play an information interpretation (discovery) role with respect to a disclosure, then we expect the disclosure to trigger more private (common) information, resulting in a decrease (increase) in
analyst consensus regarding future earnings.
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We find that balance sheet and segment disclosures are associated with a decrease in
analyst consensus about upcoming quarterly earnings, indicating that analysts’ quarterly
earnings forecasts incorporate relatively more private information after these disclosures
are released.1 Further, we find that balance sheet disclosures are associated with an
increase in the precision of both analysts’ common and private information, segment disclosures are associated with an increase in analysts’ private information, and management
earnings forecast disclosures are associated with an increase in analysts’ common information. We also find that both balance sheet and segment disclosures are associated with an
increase in the accuracy of analysts’ average forecasts. Overall, these results are consistent
with analysts processing balance sheet and segment disclosures into new insights regarding
near-term earnings and analysts primarily playing an information interpretation role with
respect to balance sheet and segment disclosures.
To provide more general evidence on analysts’ information interpretation role, we
examine the relation between the same financial disclosures and the content of the questions-and-answers (Q&A) section of conference calls. Since each analyst is typically only
allowed to ask a limited number of questions in a conference call, we expect analysts to
ask questions about important inputs to their various forecasting and valuation models. If
a disclosure provides useful inputs for any of analysts’ models and analysts focus on interpreting the disclosure, then we expect the disclosure to trigger more analysts questioning
management about the content of that disclosure in the conference call Q&A. Because
analysts can ask questions to extract information they need for any of their forecasting
and valuation tasks (rather than just forecasting next quarter’s earnings), this analysis
sheds light on analysts’ information interpretation role more generally. We find that balance sheet, segment, and management earnings forecast disclosures are all associated with
more discussion related to these items in the conference call Q&A, consistent with these
disclosures generally providing useful inputs for analysts’ research and analysts playing an
information interpretation role with respect to these disclosures.
Our results are robust to using alternative disclosure measures that consider disclosure
quality, controlling for other qualitative disclosures that firms may also include in their
earnings announcements, and controlling for the tone of language used in press releases.
Our results are also robust to controlling for firm characteristics associated with firms’ disclosure policies, and correcting for the endogeneity of firms’ disclosure choices using a
two-step Heckman model.
Our study contributes to our understanding of the relation between financial disclosures and analysts’ informational roles by showing that balance sheet and segment disclosures enhance the information interpretation role of analysts and help analysts produce
new private information about upcoming quarterly earnings. Our study also adds to
research examining the usefulness of the financial disclosures firms increasingly include in
their earnings announcements.
The rest of the paper is organized as follows. Section 2 reviews related research and
develops our hypotheses. Section 3 describes our data and design. Sections 4–6 report our
main tests, additional analyses, and robustness checks, respectively. Section 7 concludes.
2. Related research and hypothesis development
Related research
Prior research examining the informational roles of financial analysts has largely focused
on the relation between firms’ aggregate disclosures and analysts’ information, and does
not differentiate among different types of disclosures. Using different settings and different
1.
We do not examine income statement disclosures because all of the earnings announcements in our sample
contain an income statement.
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methodologies, these studies provide evidence consistent with analysts fulfilling both an
information discovery role and an information interpretation role. On the one hand, consistent with an information discovery role, some studies find a negative relation between
the information content of financial disclosures and that of analyst research. For example,
Shores (1990) finds that market reactions to earnings announcements are smaller for firms
with larger analyst following, consistent with analysts’ reports preempting earnings
announcement disclosures.2 Ayers and Freeman (2003) report that current stock returns
are more strongly associated with future earnings for firms with higher analyst following,
suggesting that analysts’ reports preempt the information contained in firms’ future earnings. Studies examining analysts’ activities for high-intangible firms also show that analysts
exert more effort and provide more informative reports for high-intangible firms, which
tend to have poorer-quality financial disclosures (Barth, Kasznik, and McNichols 2001;
Barron, Byard, Kyle, and Riedl 2002).
On the other hand, supporting the information interpretation role, other studies find a
positive relation between the information content of financial disclosures and that of
analysts’ research. For example, Lang and Lundholm (1996) find that firms with higherquality disclosures tend to attract more analyst following. Barron, Byard, and Kim (2002)
find a decrease in analyst consensus around earnings announcements, consistent with earnings announcements on average triggering private information production by analysts.
Both Francis, Schipper, and Vincent (2002) and Frankel, Kothari, and Weber (2006) find
a positive association between the absolute abnormal returns associated with analyst
reports and the absolute abnormal returns associated with earnings announcements, suggesting that more informative earnings announcements enhance the information content
of analysts’ reports.3
In a recent study, Chen et al. (2010) show that a different informational role dominates depending on the timing of analysts’ reports with respect to firms’ earnings
announcements. They find a negative (positive) relation between the information content
of earnings announcements and that of analysts’ reports issued in the week prior (subsequent) to earnings announcements, indicating that the information discovery (interpretation) role dominates in the period before (after) earnings announcements. They also
examine all weeks around earnings announcements and report an overall negative relation
between the information content of earnings announcements and that of analyst reports.
They show that the overall positive relation reported in Francis et al. (2002) could be
attributable to the research design employed.
While prior studies have largely focused on the aggregate relation between firms’ overall disclosures and analysts’ information, a notable exception is prior work that examines
changes in analysts’ information around a regulatory change in segment reporting (i.e.,
the adoption of Statement of Financial Accounting Standard (SFAS) 131). Berger and
Hann (2003) find that analysts have access to some of the new segment information before
it was made public by the adoption of SFAS 131. Botosan and Stanford (2005) show that,
after the adoption of SFAS 131, analysts tend to rely relatively more on common information when forecasting future earnings. These results are consistent with the disclosure of
additional segment information under SFAS 131 replacing private information acquired
by analysts prior to the rule change.
2.
3.
Bhushan (1994) finds no relation between analyst following and the magnitude of the post-earningsannouncement drift after controlling for share price and trading volume. But Zhang (2008) finds a negative
relation between analyst responsiveness and the post-earnings-announcement drift.
Altinkilicß and Hansen (2009) find that confounding news events can introduce noise into daily return-based
measures of the information contents of analysts’ stock recommendations (see also Li, Ramesh, Shen, and
Wu 2015).
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Hypothesis development
Theoretical models of information acquisition and processing can help illustrate analysts’
two informational roles. Consistent with the information discovery role, theory suggests
that a public disclosure can decrease private information that analysts acquire before the
disclosure (e.g., Kim and Verrecchia 1991; Demski and Feltham 1994). In such a model, a
public disclosure serves to provide common information that substitutes for the private
information acquired before the disclosure, thereby increasing common information and
reducing private information. On the other hand, consistent with the information interpretation role, a public disclosure can also trigger private information production (e.g., Indjejikian 1991; Kim and Verrecchia 1994; Harris and Raviv 1993). These models stress the
role of unique knowledge and skills in interpreting and processing public disclosures. For
example, an analyst with accounting expertise can better understand the impact of changes
in accounting methods on future earnings, while an analyst with a background in international trade can better predict the impact of growth in emerging markets on future earnings. When analysts apply their heterogeneous knowledge and skills to process the same
disclosure, they are likely to obtain unique interpretations and insights.
We posit that, when forecasting future earnings, analysts’ information interpretation
role is likely to dominate for disclosures of financial statements. Financial statement disclosures convey useful information regarding future earnings. For example, fundamental
analysis research provides evidence consistent with balance sheet components predicting
future earnings and returns (e.g., Ou and Penman 1989a,b; Lev and Thiagarajan 1993;
Abarbanell and Bushee 1997, 1998; Piotroski 2000). However, financial statement components are also complex, disaggregated financial disclosures. In order to transform the useful information contained in financial statements into specific forecasts of future earnings,
analysts need to process these disclosures using their own (unique) knowledge and skills.
Because analysts have heterogeneous backgrounds, knowledge, and skills, the information
(about future earnings) that each analyst is able to extract from the same financial statement is likely to differ, causing analysts’ earnings forecasts to contain relatively more private information after such disclosures.4 Thus, we expect financial statement disclosures to
be associated with a decrease in the degree to which analysts base their earnings forecasts
on common information. Our first hypothesis, stated in the alternative form, is:
H1. The disclosure of financial statements in earnings announcements is associated with
a decrease in the degree to which analysts base their earnings forecasts on common
information.
We expect analysts’ information discovery role before the announcement to dominate
the effect of management earnings forecasts on the change in analysts’ information at the
time of the announcement. Compared with financial statements, management earnings
forecasts are a relatively straightforward disclosure that directly predicts future earnings.
Management earnings forecasts are thus likely to require less processing and interpretation
by analysts when they are forecasting future earnings. As a result, analysts are more likely
to agree upon the meaning of a management earnings forecast (as it relates to future earnings), such that the common information it conveys serves mainly to substitute for analysts’ predisclosure private information regarding future earnings. Thus, we expect that the
disclosure of management earnings forecasts will be associated with an increase in the
4.
Alternatively, to the extent that financial statement disclosures serve as common inputs to analysts’ forecasting models and/or analysts focus on searching for common information in response to those disclosures,
financial statement disclosures could also cause analysts’ earnings forecasts to rely relatively more on common information.
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degree to which analysts base their earnings forecasts on common information.5 Our second hypothesis, stated in the alternative form, is:
H2. The disclosure of management earnings forecasts in earnings announcements is associated with an increase in the degree to which analysts base their earnings forecasts
on common information.
3. Sample selection and research design
Sample selection
We hand-collect press releases for a sample of quarterly earnings announcements between
March 30, 2003 and September 30, 2004. Our sample period represents a window immediately after new Securities and Exchange Commission (SEC) disclosure rules came into
effect. Directed by the Sarbanes-Oxley Act of 2002, the SEC adopted new disclosure rules
including the addition of Item 12 to Form 8-K, amendments to Item 10 of Regulation
S-K, and the adoption of Regulation G. These new rules require companies―for the first
time―to file their earnings announcement press releases with the SEC on Form 8-K and
provide more information about any non-GAAP financial information.6 These new rules
subjected earnings announcement press releases to SEC oversight for the first time and are
thus likely to have altered firms’ reporting incentives with respect to their earnings
announcement disclosures. Additionally, these new rules increased the transparency of any
non-GAAP financial information included in earnings announcements.
Selecting a sample period after these new rules came into effect benefits our study in a
number of ways. First, it provides a sample of earnings announcements subject to a consistent regulatory regime. Second, the availability of earnings announcement press releases
through 8-K filings significantly reduces costs and errors in data collection. Third, to the
extent that the new rules enhance the quality of earnings announcement disclosures (e.g.,
Kolev, Marquardt, and McVay 2008), we are able to conduct more powerful tests of the
effects of these disclosures. We focus our sample period immediately after the enactment
of the new rules because the cross-sectional variation in firms’ earnings announcement disclosures is much lower in later periods.7
Specifically, we select a sample of quarterly earnings announcements made between 30
March 2003 and 30 September 2004 that meet the following requirements:
•
5.
6.
7.
The earnings announcement date for quarter q is available from COMPUSTAT and
I/B/E/S. When the two sources disagree, we use the earlier of the two dates (Dellavigna
and Pollet 2009);
Alternatively, it is also possible that in order to differentiate themselves and stimulate trading, analysts may
react to simple disclosures like management earnings forecasts by searching for more private information.
Another possibility is that analysts may disagree on how to interpret management earnings forecasts
because they differ in how credible they perceive these management earnings forecasts to be (Hutton and
Stocken 2009).
Regulation G requires companies releasing non-GAAP financial information to provide “a presentation of
the most directly comparable GAAP financial measure and a reconciliation of the disclosed non-GAAP
financial measure to the most directly comparable GAAP financial measure” (see http://www.sec.gov/rules/
final/33-8176.htm).
The over-time increase in the incidence of disclosures made with earnings announcements significantly
reduces the cross-sectional variation in firms’ disclosures in later periods. For example, for our 2003–2004
sample period, we find that 86 percent of the earnings announcements in our sample include a balance
sheet. Using the same sample-selection criteria but a sample period confined to the first fiscal quarter of
2008, we find that 98 percent of the earnings announcements in this later sample period contain a balance
sheet disclosure (untabulated).
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Disclosures and Analysts’ Information
•
•
•
•
•
349
Data to calculate market capitalization and the book-to-market ratio at the end of
quarter q are available from COMPUSTAT;
Data are available from the I/B/E/S unadjusted detail file for 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 these forecasts in the period
between the quarter q earnings announcement and the 10-K/Q filing date.8 Matching
individual analysts before and after the quarter q earnings announcement date ensures
that we compute the change in BKLS consensus using forecasts from the same set of
individual analysts. We use I/B/E/S unadjusted data to avoid potential measurement
errors resulting from rounding (see Payne and Thomas 2003);
Actual earnings per share for quarter q+1 are available from the I/B/E/S unadjusted
database;
Data are available to calculate the surprise in quarter q earnings: at least one forecast
of quarter q earnings is available from the 45-day period immediately before the quarter
q earnings announcement date, and actual earnings per share for quarter q are available
from the I/B/E/S unadjusted detail file; and
A copy of the 8-K earnings announcement press release for quarter q is available from
the 10-K Wizard database.
We use the 8-Ks to hand-collect data for the disclosure of financial statements (i.e., an
income statement, a balance sheet, a statement of cash flows, and segment data) and management earnings forecasts in the earnings press release. Our final sample consists of 1,445
quarterly earnings announcements.
Study design
To test our hypotheses, we estimate the following regression model:
Dqiq ¼ u0 þ u1 BSiq þ u2 SCFiq þ u3 SEGiq þ u4 MEFiq þ CONTROLSiq þ eiq ;
ð1Þ
where Dqiq is the change in analyst consensus around the quarter q earnings announcement for firm i. Following prior research (e.g., Barron et al. 2002; Barron, Byard, and Yu
2008; Botosan and Stanford 2005; Byard, Li, and Yu 2011), we estimate analyst consensus
using the BKLS model. BKLS show that consensus (q) can be expressed in terms of the
variance of forecasts (D), the squared error in the mean forecast (SE), and the number of
SED=N
forecasts (N) as follows q ¼ ð11=N
ÞDþSE specifically, using matched individual forecasts of
quarter q+1 earnings made before and after the quarter q earnings announcement, we
estimate analyst consensus (q) both before and after the quarter q earnings announcement
for firm i and then calculate the change in consensus (Δqiq). Consensus (q) captures the
degree to which analysts’ forecasts are based on common information, that is, the ratio of
common-to-total information in analysts’ forecasts. In other words, consensus (q) is equal
to one minus the proportion of private information in analysts’ forecasts. The change in
consensus (Δq) thus measures the change in the degree to which analysts’ earnings forecasts are based on common information. One caveat of this consensus measure is that the
BKLS model assumes that any difference between analysts’ forecasts reflects analysts’ private information and not noise. As a result, in section 4 below, we examine the robustness
of our inferences to using a non-BKLS measure (i.e., analysts’ forecast accuracy).
8.
If the 10-K/Q is filed more than 30 days after the earnings announcement date, the post-announcement period is capped at 30 days. This ensures that earnings forecasts issued in the post-announcement window
reflect the earnings announcement disclosures. Prior research shows that analysts’ forecast revisions tend
to be concentrated in the days immediately after earnings announcements (e.g., Stickel 1989; Ivkovic and
Jegadeesh 2004).
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Contemporary Accounting Research
Our explanatory variables are as follows. BSiq is an indicator variable equal to one if
the earnings announcement press release for firm i in quarter q contains a balance sheet,
and zero otherwise. SCFiq is an indicator variable equal to one if the earnings announcement press release for firm i in quarter q contains a statement of cash flows, and zero
otherwise. SEGiq is an indicator variable equal to one if the earnings announcement press
release for firm i in quarter q contains segment data, and zero otherwise.9 MEFiq is an
indicator variable equal to one if the earnings announcement press release for firm i in
quarter q contains a management earnings forecast, and zero otherwise. H1 predicts that
the disclosure of balance sheets, cash flow statements, and segment data will all be associated with a decrease in the degree to which analysts’ forecasts are based on common information, that is, a decrease in consensus, so we expect that: u1, u2 and u3 < 0. H2 predicts
that management earnings forecasts will be associated with an increase in the degree to
which analysts’ forecasts are based on common information, that is, an increase in consensus, so we expect that: u4 > 0.
CONTROLSiq refer to a number of control variables we include in equation (1) to
capture earnings announcement and firm characteristics that are likely to affect the change
in analyst consensus. Specifically, we define the quarter q earnings surprise (SURP) as the
actual quarter q earnings minus the mean forecast of quarter q earnings issued in the 45day period immediately before the quarter q earnings announcement, scaled by stock price
at the end of quarter q. We control for the absolute size of SURP (|SURP|). We also
include an indicator variable to control for the sign of the earnings surprises: SIGN is
equal to one if SURP is negative, and zero otherwise. Barron et al. (2008) find that analyst consensus decreases more around earnings announcements that report a larger absolute earnings surprise (|SURP|) or a negative earnings surprise (SIGN). We also control
for firm size (SIZE), defined as market capitalization at the end of quarter q, since there is
likely more demand for analysis of larger firms (Bhushan 1989). Additionally, we include
the book-to-market ratio (BM) measured at the end of quarter q to control for the effect
that analysts’ forecasts may contain relatively more private information for firms with
earnings that are more difficult to forecast (Barth et al. 2001). Finally, we control for calendar quarter fixed effects.
4. Main results
Descriptive statistics and correlations
Table 1 reports descriptive statistics (panel A) and correlations (panel B) for our sample of 1,445 quarterly earnings announcements. Panel A shows that our sample is composed of relatively large firms: mean (median) market capitalization (SIZE) is
$14.4 billion ($3.6 billion). This is not surprising given our requirement that at least
three analysts issue forecasts both before and after each earnings announcement. However, this likely reduces the power of our tests since earnings announcements for larger
firms tend to be less informative (e.g., Freeman 1987; Collins, Kothari, and Rayburn
1987). Our sample firms have a mean (median) book-to-market ratio (BM) of 0.436
(0.409). The earnings surprise is negative for 27.5 percent of the earnings announcements in our sample (see SIGN, an indicator variable equal to one when an earnings
surprise is negative); the mean (median) value of |SURP|, the absolute price-scaled
earnings surprise, is 0.003 (0.001). Consistent with Barron et al. (2002), we find that on
average there is a decrease in consensus around earnings announcements―the mean
(median) change in analyst consensus (Δq) is: 0.006 (0.001). The mean of BS (0.86)
indicates that 86 percent of the earnings announcements in our sample include a
9.
Our sample includes 156 earnings announcement by single segment firms. We verify that our inferences are
unaffected if we drop these observations from our sample.
CAR Vol. 34 No. 1 (Spring 2017)
TABLE 1
Descriptive statistics and correlations
Panel A: Descriptive statistics
Variable
Mean
25th percentile
Median
75th percentile
0.442
2.603
13.210
0.008
0.182
0.262
0.564
0.002
0.001
0.626
1.021
0.001
0.196
2.609
7.539
0.000
0.348
0.500
0.472
0.499
0.000
0.000
0.000
0.000
1.000
1.000
0.000
1.000
1.000
1.000
1.000
1.000
0.016
0.447
34.877
0.383
0.000
0.000
1.321
0.259
0.001
0.000
3.558
0.409
0.002
1.000
11.071
0.584
Δq
ΔCOMMON
ΔPRIVATE
Δq
ΔCOMMON
ΔPRIVATE
ΔABSMFE
BS
SCF
SEG
MEF
|SURP|
SIGN
SIZE
BM
1.000
0.315
(<0.001)
1.000
0.767
(<0.001)
0.080
(0.003)
1.000
0.592
(<0.001)
0.179
(<0.001)
0.688
(<0.001)
0.027
(0.299)
0.057
(0.044)
0.084
(0.002)
0.010
(0.697)
0.006
(0.819)
0.010
(0.705)
0.044
(0.094)
0.021
(0.433)
0.051
(0.038)
0.008
(0.759)
0.034
(0.211)
0.006
(0.834)
0.075
(0.075)
0.037
(0.083)
0.056
(0.037)
0.033
(0.210)
0.002
(0.932)
0.041
(0.128)
0.026
(0.317)
0.046
(0.084)
0.021
(0.439)
0.021
(0.415)
0.027
(0.312)
0.001
(0.963)
0.146
(<0.001)
0.291
(<0.001)
0.005
(0.845)
(The table is continued on the next page.)
351
CAR Vol. 34 No. 1 (Spring 2017)
Panel B: Spearman correlations (above the diagonal) and Pearson correlations (below the diagonal)
Disclosures and Analysts’ Information
Dependent variables
Δq
0.006
ΔCOMMON
1.596
ΔPRIVATE
7.334
ΔABSMFE
0.002
Earnings announcement financial disclosures
BS
0.860
SCF
0.504
SEG
0.334
MEF
0.536
Control variables
|SURP|
0.003
SIGN
0.275
SIZE ($billion)
14.354
BM
0.436
SE
CAR Vol. 34 No. 1 (Spring 2017)
0.189
(<0.001)
0.039
(0.110)
0.020
(0.445)
0.027
(0.310)
0.004
(0.894)
0.034
(0.139)
0.044
(0.095)
0.014
(0.587)
0.019
(0.472)
0.004
(0.894)
0.025
(0.341)
0.025
(0.344)
0.046
(0.078)
0.013
(0.623)
0.032
(0.223)
0.024
(0.358)
0.014
(0.456)
0.025
(0.147)
ΔCOMMON
0.108
(<0.001)
0.034
(0.202)
0.015
(0.349)
0.042
(0.112)
0.011
(0.851)
0.005
(0.852)
0.017
(0.499)
0.075
(0.004)
0.033
(0.206)
ΔPRIVATE
0.043
(0.080)
0.005
(0.839)
0.018
(0.450)
0.016
(0.498)
0.550
(<0.001)
0.117
(<0.001)
0.060
(0.024)
0.028
(0.289)
1.000
ΔABSMFE
0.241
(<0.001)
0.111
(<0.001)
0.059
(0.025)
0.025
(0.438)
0.001
(0.971)
0.172
(<0.001)
0.135
(0.004)
0.125
(<0.001)
1.000
BS
0.105
(<0.001)
0.241
(<0.001)
0.021
(0.434)
0.019
(0.465)
0.024
(0.373)
0.067
(0.011)
0.017
(0.524)
0.241
(<0.001)
1.000
SCF
0.111
(<0.001)
0.001
(0.960)
0.029
(0.274)
0.151
(<0.001)
0.072
(0.006)
0.055
(0.026)
0.111
(<0.001)
0.105
(<0.001)
1.000
SEG
0.023
(0.388)
0.091
(<0.001)
0.004
(0.879)
0.041
(0.119)
0.005
(0.844)
0.059
(0.025)
0.241
(<0.001)
0.111
(<0.001)
1.000
MEF
0.006
(0.678)
0.040
(0.128)
0.021
(0.245)
0.168
(<0.001)
0.040
(0.129)
0.021
(0.419)
0.028
(0.296)
0.095
(<0.001)
1.000
|SURP|
0.047
(0.076)
0.056
(0.031)
0.078
(0.003)
0.001
(0.971)
0.019
(0.465)
0.029
(0.274)
0.091
(<0.001)
0.001
(0.982)
1.000
SIGN
0.094
(<0.001)
0.157
(<0.001)
0.160
(<0.001)
0.090
(<0.001)
0.299
(<0.001)
0.001
(0.981)
0.178
(<0.001)
0.075
(0.004)
1.000
SIZE
0.069
(0.010)
0.140
(<0.001)
0.104
(<0.001)
0.097
(<0.001)
0.044
(0.094)
0.241
(<0.001)
0.094
(<0.001)
0.200
(<0.001)
1.000
BM
This table reports descriptive statistics (panel A) and Spearman (above the diagonal) and Pearson (below the diagonal) correlations (panel B) for our
sample of 1,445 quarterly earnings announcements. For each earnings announcement q, we require a minimum of three analysts to issue forecasts of
quarter q+1 earnings in the 45-day period before the quarter q earnings announcement and also revise the forecasts in the period between the quarter
q earnings announcement date and the 10-Q/K filing date. We use these analyst earnings forecasts to calculate our dependent variables (Δq is our
main dependent variable). See the Appendix for variable definitions. Two-tailed p-values are reported in parentheses for the Spearman correlations.
Notes:
BM
SIZE
SIGN
|SURP|
MEF
SEG
SCF
BS
ΔABSMFE
Δq
Panel B: Spearman correlations (above the diagonal) and Pearson correlations (below the diagonal)
TABLE 1 (continued)
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Disclosures and Analysts’ Information
353
balance sheet. Similarly, 50.4 percent, 33.4 percent, and 53.6 percent of the earnings
announcements in our sample include a cash flow statement, segment data, and a management earnings forecast, respectively.10
Panel B of Table 1 reports both Spearman and Pearson correlations. Of the four indicator variables for the financial disclosures we examine, the Spearman correlations show
that only SEG is marginally significantly negatively (0.044, p = 0.094, two-tailed) associated with the change in consensus, Δq, indicating that SEG is associated with a decrease
in consensus; the Pearson correlations show no significant relations. These pairwise correlations do not indicate that BS and SCF are associated with a decrease in consensus, or
that MEF is associated with an increase in consensus. However, these pairwise correlations
should be interpreted with caution as they do not control for other determinants of analysts’ consensus.
Results of examining changes in analyst consensus
Columns (1) and (2) of Table 2 show our predictions from H1 and H2 and our results
from estimating equation (1) respectively. Consistent with prior research (see Lang and
Lundholm 1993, 1996; Barron et al. 2008), we use a decile rank specification in all of our
regression analyses to mitigate the influence of skewness and outliers.11 All of the continuous variables are transformed to [0, 1] decile ranks (i.e., we rank the original variables into
0–9 deciles and then divide the ranks by 9). Our inferences are, however, robust to using
regular rank regressions. We report p-values in parentheses based upon two-way (firm and
calendar quarter) clustering; we report one-tailed (two-tailed) p-values for explanatory
variables with (without) predictions.
We find that balance sheets (BS) and segment disclosures (SEG) are both negatively
associated with Δq (0.036 and 0.041, p = 0.032 and 0.020, one-tailed). Consistent with
H1, these results suggest that balance sheet and segment disclosures are associated with a
decrease in the degree to which analysts’ forecasts (of next quarter’s earnings) are based
upon common information. However, we find no evidence that the disclosure of cash flow
statements (SCF) is associated with a significant decrease in consensus (0.016, p = 0.805,
one-tailed). Similarly, we find no evidence that the disclosure of management earnings
forecasts (MEF) is associated with a significant increase in consensus (0.001, p = 0.467,
one-tailed), providing no support for H2.
To assess the economic significance of our findings, we benchmark the effects of balance sheet (BS) and segment (SEG) disclosures with those of the magnitude and sign of
earnings surprises (|SURP| and SIGN, respectively). Table 2 shows that consistent with
Barron et al. (2008), larger and negative earnings surprises are both associated with a
decrease in analyst consensus (the coefficients on |SURP| and SIGN are both negative).
10.
11.
The relatively low frequency of segment disclosures is partly due to the fact that our sample includes 156
earnings announcements from single segment firms (see also footnote 9). Our sample also has a relatively
high frequency of management earnings forecasts. As a comparison, we merge the I/B/E/S and FirstCall
databases and find that over our sample period about 27 percent of firm-quarters have at least one management earnings forecast issued on the earnings announcement date. The higher frequency of management earnings forecasts in our sample is likely because: (i) our sample contains relatively larger firms,
which may be more likely to issue more management earnings forecasts or data providers like FirstCall
may be more likely to collect forecasts by these firms (Chuk, Matsumoto, and Miller 2013); (ii) since analysts are more likely to revise their forecasts in response to management earnings forecasts, the data
requirement for computing analyst consensus (three or more matched analysts before and after an earnings
announcement) may tilt our sample more toward earnings announcements that contain management earnings forecasts; and (iii) management earnings forecasts are increasingly “bundled” with earnings announcements (Anilowski, Feng, and Skinner 2007; Rogers and Van Buskirk 2013).
The raw dependent variables (i.e., BKLS variables and forecast errors) and control variables (|SURP| and
SIZE) are highly skewed with a significant number of extreme values.
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TABLE 2
Main regression analysis
Main analysis
Δq
Additional analyses
ΔCOMMON
ΔPRIVATE
ΔABSMFE
Dependent variable:
Predict
(1)
Financial disclosures
BS
SCF
SEG
MEF
+
Controls
|SURP|
SIGN
SIZE
BM
Coeff.
(p-value)
(2)
0.036
(0.032)
0.016
(0.805)
0.041
(0.020)
0.001
(0.467)
0.055
(0.007)
0.029
(0.061)
0.025
(0.237)
0.033
(0.862)
Calendar quarter
fixed effects
Adjusted R2 (%)
Yes
0.75
Predict
(3)
?
?
?
+
+
+
?
?
Coeff.
(p-value)
(4)
0.068
(0.001)
0.008
(0.644)
0.003
(0.857)
0.020
(0.041)
0.051
(0.095)
0.001
(0.476)
0.014
(0.449)
0.011
(0.693)
Yes
1.97
Predict
(5)
+
+
+
+
+
?
?
Coeff.
(p-value)
(6)
0.080
(<0.001)
0.016
(0.828)
0.052
(0.001)
0.002
(0.553)
0.057
(0.040)
0.035
(0.008)
0.021
(0.497)
0.021
(0.338)
Yes
1.63
Predict
(7)
?
?
?
Coeff.
(p-value)
(8)
0.098
(<0.001)
0.027
(0.265)
0.043
(0.008)
0.015
(0.181)
0.147
(<0.001)
0.054
(0.001)
0.109
(<0.001)
0.014
(0.603)
Yes
6.82
Notes:
This table shows the results for our main regression analysis where we estimate equation (1) to
examine the relation between the change in analyst consensus (Δq) around earnings
announcements and the financial disclosures made with earnings announcements (BS, SCF,
SEG, and MEF). This table also shows (i) the results of estimating equation (2), which
examines the change in the precision of analysts’ common (ΔCOMMON) and private
information (ΔPRIVATE) around earnings announcements, and (ii) the results of estimating
equation (3), which examines the change in the absolute error in the mean forecast (ΔABSMFE)
around earnings announcements. The financial disclosure variables are BS, SCF, SEG and
MEF, and the control variables are |SURP|, SIGN, SIZE, and BM. See the Appendix for
variable definitions. All regressions include calendar quarter fixed effects. Additionally, all
models are estimated using decile rank regressions where all the continuous variables are
transformed into [0,1] decile ranks by ranking the original variables into 0–9 deciles and then
dividing the ranks by 9. The sample is comprised of 1,445 quarterly earnings announcements
from 2003 to 2004. We report one-tailed p-values for explanatory variables where we make a
directional prediction; otherwise we report two-tailed p-values. All p-values reported are based
upon standard errors that use two-way (firm and calendar quarter) clustering.
Tests comparing the coefficients on BS and SEG with those on |SURP| and SIGN indicate
that they are not statistically different (two-tailed p-values = 0.60, 0.79, 0.62, and 0.64 for
comparing BS with |SURP|, BS with SIGN, SEG with |SURP|, and SEG with SIGN,
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respectively). Since we estimate equation (1) using decile rank regressions, these results
suggest that the impacts of balance sheet and segment disclosures (SEG or BS) on the
change in consensus are comparable to those of a change from the bottom to the top decile (i.e., from decile 0 to decile 1) of absolute earnings surprise or a change in the sign of
the earnings surprise.
Results of examining changes in analysts’ common and private information
In this subsection, we further examine the relation between the disclosure of financial
statements and management earnings forecasts at earnings announcements and changes in
the precision of analysts’ common and private information (COMMON and PRIVATE).
BKLS show that COMMON and PRIVATE can be measured using the variance of analysts’ forecasts (D), the squared error in the mean forecast (SE), and the number of forecasts (N) as follows:
COMMON ¼
SE D=N
½ð1 1=NÞD þ SE
2
and PRIVATE ¼
D
½ð1 1=NÞD þ SE2
:
However, one caveat to keep in mind when using the precision measures of BKLS is
that these measures require the additional assumption that all analysts possess private
information of equal precision (see Barron et al. 2002). This additional assumption may
not hold when analysts have unequal abilities or issue forecasts sequentially after observing other analysts’ forecasts.
Using the same matched individual forecasts (of quarter q+1 earnings) used to calculate Δq, we estimate COMMON and PRIVATE before and after the quarter q earnings
announcement and then calculate the change in COMMON and PRIVATE for firm i in
quarter q, denoted ΔCOMMONiq and ΔPRIVATEiq. Consistent with prior research (e.g.,
Barron et al. 2008; Byard et al. 2011), for comparability across firms, we scale ΔCOM
MONiq and ΔPRIVATEiq by their respective preannouncement levels. We examine the
relation between the same financial disclosures (BS, SCF, SEG, and MEF) and changes in
both the precision of analysts’ common and private information (ΔCOMMON and ΔPRIVATE) using the following model:
DCOMMONiq or DPRIVATEiq ¼ u0 þ u1 BSiq þ u2 SCFiq þ u3 SEGiq þ u4 MEFiq
þ CONTROLSiq þ eiq ;
ð2Þ
where CONTROLSiq refers to the same control variables used in equation (1). Panel A
(B) of Table 1 shows descriptive statistics (correlations) for ΔCOMMON and ΔPRIVATE.
Our hypotheses predict that BS, SCF and SEG are positively associated with ΔPRIVATE,
and MEF is positively (negatively) associated with ΔCOMMON (ΔPRIVATE).
Columns (3) to (6) of Table 2 report results from estimating equation (2) for both
ΔCOMMON and ΔPRIVATE. We find that BS is significantly positively associated with
both ΔCOMMON (0.068, p = 0.001, two-tailed) and ΔPRIVATE (0.080, p < 0.001, onetailed), suggesting that the disclosure of balance sheets increases the precision of both
analysts’ common and private information. In contrast, SEG is significantly positively
associated with ΔPRIVATE (0.052, p = 0.001, one-tailed) but is not significantly associated
with ΔCOMMON (0.003, p = 0.857, two-tailed), suggesting that the disclosure of segment
data increases the precision of analysts’ private information only.12 These results show
that balance sheet and segment disclosures have different effects on the precision of analysts’ common vs. private information regarding upcoming quarterly earnings: balance
sheet disclosures trigger an increase in both analysts’ common and private information,
12.
These results are robust to restricting our sample to only multisegment reporting firms.
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while segment disclosures primarily spur analysts’ private information production.13 However, these prior studies do not examine the effect of segment disclosures on changes in
analysts’ common and private information. Additionally, we find that MEF is positively
associated with ΔCOMMON (0.020, p = 0.041, one-tailed), providing evidence that management earnings forecasts help increase analysts’ common information regarding upcoming quarterly earnings. We find no evidence that the disclosure of cash flow statements is
associated with changes in either analysts’ common or private information about next
quarter’s earnings.14
Results of examining changes in analysts’ forecast accuracy
Our analyses so far use the BKLS measures of analysts’ consensus and the precision of
analysts’ common and private information. As discussed earlier, a potential concern using
these measures is that the BKLS model assumes that differences in analysts’ earnings forecasts only reflect analysts’ private information; if, however, differences in analysts’ earnings forecasts reflect noise rather than private information, then a decrease in analyst
consensus may reflect more idiosyncratic noise (rather than more private information) in
analysts’ forecasts. Therefore, to test the robustness of our inferences based upon BKLS
measures, we also examine changes in the accuracy of analysts’ average forecasts.
If balance sheet, segment, and management earnings forecast disclosures increase analysts’
common and/or private information, then we would expect these disclosures to also be associated with an increase in analysts’ forecast accuracy. In contrast, if changes in the BKLS measures merely reflect noise or confusion on the part of analysts, then we would expect these
disclosures to be associated with a decrease in the accuracy of analysts’ average forecasts. We test
the association between the same earnings announcement disclosures and changes in the absolute error in analysts’ average forecasts (ΔABSMFE) using the following model:
DABSMFEiq ¼ u0 þ u1 BSiq þ u2 SCFiq þ u3 SEGiq þ u4 MEFiq þ CONTROLSiq þ eiq ;
ð3Þ
where ΔABSMFEiq is the change in the absolute error in the mean forecast (using the same
matched forecasts of quarter q+1 earnings made before and after the quarter q earnings
announcement) around the quarter q earnings announcement, scaled by stock price at the
end of quarter q. CONTROLS refers to the same control variables included in equations (1)
and (2). Panel A (B) of Table 1 shows descriptive statistics (correlations) for ΔABSMFE.
Based upon our findings for ΔCOMMON and ΔPRIVATE in Table 2, we expect BS, SEG
and MEF to be negatively associated with ΔABSMFE (u1, u3, and u4 < 0).
Our predictions and results from estimating equation (3) are shown in columns (7)
and (8) of Table 2 respectively. Consistent with our expectations, we find that BS and
SEG are both significantly negatively related with ΔABSMFE (0.098 and 0.043,
p < 0.001 and p = 0.008, one-tailed, respectively), indicating that balance sheet and segment disclosures are associated with a decrease in the absolute error in the mean forecast,
that is, an increase in the accuracy of analysts’ average forecasts. We also find that the
coefficient on MEF is negative (0.015), though it is not significant at the conventional
level (p = 0.181, one-tailed).15 We find no evidence that the disclosure of cash flow
13.
14.
15.
This result is consistent with prior findings that segment disclosures improve analyst forecast accuracy (see
Baldwin 1984; Swaminathan 1990; Duru and Reeb 2002; Berger and Hann 2003).
However, Previts, Bricker, Robinson, and Young (1994) find that, in their research reports, analysts do
sometimes discuss cash flow-related items and produce non-GAAP cash flow schedules (see also Asquith,
Mikhail, and Au 2005; Brown, Call, Clement, and Sharp 2015).
Note that tests of the effect of common information on mean forecast errors are less powerful than tests
of the effect of private information. Equation (20) of BKLS (p. 427) shows that common information has
relatively less of an effect on mean forecast errors because, unlike private information, common information contains error that is not diversified away from the mean forecast.
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statements (SCF) is negatively related to ΔABSMFE. These results using analysts’ forecast
accuracy mitigate concerns that our inferences could be confounded by potential violations
of assumptions underlying the BKLS model and that the decrease in BKLS consensus is
driven by more noise in analysts’ forecasts.
Overall, the results in Table 2 are consistent with balance sheet and segment disclosures playing an important part in analysts’ information interpretation role and analysts
developing new insights regarding near-term earnings from interpreting and processing
these disclosures. In contrast, we fail to find any significant relation between disclosures of
cash flow statements and changes in analysts’ information regarding upcoming quarterly
earnings. Our failure to find a significant relation for cash flow statement disclosures seems
somewhat surprising. Cash flow statements are an important financial statement that standard setters believe conveys useful information to investors. There is also evidence that the
cash flow statement is incrementally value-relevant (see Livnat and Zarowin 1990; Cheng,
Liu, and Schaefer 1997). One possible explanation is that our tests do not have sufficient
power to detect the effects of the disclosure of cash flow statements. Another possible
explanation is that cash flow statements may not be a key input to analysts’ earnings forecasting models: when forecasting earnings, analysts may use a “balance sheet approach”
that primarily focuses on information contained in the income statement and balance
sheet. Consistent with such a “balance sheet approach,” analysts typically focus on earnings (e.g., EBITDA) and balance sheet items in their forecasting and valuation analyses,
and financial statement analysis textbooks commonly emphasize balance sheet and income
statement items in earnings forecasting models (see, e.g., Palepu and Healy 2008; Penman
2010).
Additionally, while we find that disclosures of management earnings forecasts bundled
with earnings announcements are associated with an increase in analysts’ common information regarding upcoming quarterly earnings, we fail to find strong evidence that they are
associated with an increase in the accuracy of the mean forecast. One likely reason is that
our tests may lack power due to our relatively smaller sample size compared with prior
research and our focus on only bundled management earnings forecasts, which may be routine disclosures that are well anticipated by analysts and thus convey relatively less new
information compared to nonbundled forecasts (see Rogers and Van Buskirk 2013).16,17
One caveat of our Table 2 tests is that our regression models have low explanatory
power (the adjusted R2 is 0.75 percent, 1.97 percent, 1.63 percent, and 6.82 percent for the
Δq, ΔCOMMON, ΔPRIVATE, and ΔABSMFE regressions, respectively), though we note
that these low adjusted R2s are comparable with prior research using similar measures
(e.g., Barron et al. 2008; Byard et al. 2011). Further comparisons of the adjusted R2s in
Table 2 to “baseline” regressions (i.e., regressions including only control variables)
16.
17.
We also examine management earnings forecasts issued at the end of fiscal quarters over the period of
1995–2011. Such forecasts can be more informative because they are more likely to be unexpected and are
issued at a time when managers typically have more insider information regarding current period results.
We identify management earnings forecasts issued in the 10-day window ending on each fiscal quarter-end
(results are similar when we use a 5-day or 30-day window), and test the relation between the issuance of
management earnings forecasts in this window and changes in analysts’ information. Using a large sample
of 11,519 observations with required I/B/E/S/FirstCall/COMPUSTAT data for measuring analysts’ information, management forecast, and control variables, we find that the issuance of management earnings
forecasts is associated with a significant increase in analysts’ common and private information and a significant decrease in analysts’ forecast errors (untabulated).
To test whether our results for management earnings forecasts vary with managers’ forecast credibility, we
rerun our tests allowing the coefficient on MEF to vary with prior management forecast accuracy (measured using management earnings forecasts issued over the prior quarter or the prior four quarters), and
find that the coefficient on the interaction between MEF and the prior accuracy variables is insignificant
(untabulated).
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indicate that our test variables as a whole provide significant (marginal) incremental
explanatory power for ΔPRIVATE and ΔABSMFE (Δq and ΔCOMMON).18
Results using alternative disclosure variables
Our analyses so far are based upon indicator variables (BS, SCF, SEG and MEF) that identify the presence or absence of a disclosure in earnings announcements. We also examine
multiple alternative disclosure measures that further take into account disclosure quality:
•
•
For the balance sheet and cash flow statement disclosures, we create alternative measures (BS_ALT and SCF_ALT) which are defined as the number of line items in the
disclosure included in the earnings announcement divided by the number of line items
in the same disclosure included in the subsequent 10-K/Q filing (see D’Souza, Ramesh,
and Shen 2010).19 We also include the number of line items in the income statement disclosed in the earnings announcement divided by the number of line items in the income
statement disclosed in the subsequent 10-K/Q filing (IS_#LINES) as an additional control variable.
We construct an alternative measure of management earnings forecasts (MEF_ALT)
based upon forecast precision, which equals 4 for a point forecast, 3 for a range
forecast, 2 for single-bound forecast, 1 for a qualitative forecast, and 0 if no forecast is
provided.20
Table 3 reports the results of reestimating equation (1) using the above alternative disclosure measures. The results are similar to our main results using indicator variables.21
5. Additional analysis of the content of the Q&A section of conference calls
Our main analyses focus on analysts’ information about upcoming quarterly earnings. To
provide more general evidence on analysts’ informational role as it relates to earnings
announcement disclosures, we also examine the relation between earnings announcement
disclosures and the content of the questions-and-answers (Q&A) section of conference
calls.
Conference calls have become a standard disclosure medium for public companies
over the past two decades. Prior research finds that the Q&A section of conference calls,
in particular, conveys useful information to analysts (Tasker 1998; Hollander, Plonk, and
Roelofsen 2010; Matsumoto, Pronk, and Roelofsen 2011). Participation in the Q&A section of conference calls has been shown to be important to an analyst’s own analysis and
private information production (Mayew 2008; Mayew, Sharp, and Venkatachalam 2012).
Because it affords analysts an opportunity to question management, the Q&A section of
18.
19.
20.
21.
The adjusted R2s of the baseline regressions (two-tailed p-value from the Vuong test comparing the
Table 2 regressions with their corresponding baseline regressions) are: 0.58 percent (0.21), 1.34 percent
(0.10), 0.75 percent (0.02), and 5.68 percent (0.01) for Δq, ΔCOMMON, ΔPRIVATE, and ΔABSMFE,
respectively.
For cash flow statement disclosures, we also test a second alternative measure based upon the preparation
method. Cash flow statements prepared using the direct method are more likely to convey new information
incremental to balance sheets and income statements. We create a new measure equal to 2 if the direct
method is used, 1 if the indirect method is used, and 0 if no cash flow statement is disclosed at the earnings announcement. Of the 728 cash flow statement disclosures in our sample, 3 (725) are prepared using
the direct (indirect) method. Our inferences remain unchanged using this alternative measure for cash flow
statement disclosures.
Among the 774 management earnings forecast disclosures in our sample, 80 are point forecasts, 573 are
range forecasts, 2 are single-bound forecasts, and 119 are qualitative forecasts. Results are similar if we
group single-bound forecasts with either range forecasts or with qualitative forecasts.
We also reestimate equations (2) and (3) using these alternative disclosure measures and find similar
results.
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359
TABLE 3
Results using alternative disclosure measures
Predict
Financial disclosures
BS_ALT
SCF_ALT
SEG
MEF_ALT
+
Controls
|SURP|
SIGN
SIZE
BM
IS_#LINES
?
Calendar quarter fixed effects
Adjusted R2 (%)
Δq Coeff.
(p-value)
0.055
(0.051)
0.011
(0.691)
0.040
(0.043)
0.002
(0.333)
0.054
(0.009)
0.028
(0.063)
0.024
(0.247)
0.037
(0.897)
0.126
(0.863)
Yes
0.82
Notes:
This table shows the results of reestimating equation (1) using alternative measures of the financial
disclosures (i.e., disclosures of balance sheets, statements of cash flows, segment data, and
management earnings forecasts) made with earnings announcements. The dependent variable is
the change in analyst consensus (Δq). For the financial disclosure variables, we replace BS with
BS_ALT, SCF with SCF_ALT, and MEF with MEF_ALT. See the Appendix for variable
definitions. All regressions include calendar quarter fixed effects. Additionally, all models are
estimated using decile rank regressions where all the continuous variables are transformed into
[0,1] decile ranks by ranking the original variables into 0–9 deciles and then dividing the ranks
by 9. The sample is comprised of 1,445 quarterly earnings announcements from 2003 to 2004.
We report one-tailed p-values for explanatory variables where we make a directional
prediction; otherwise we report two-tailed p-values. All p-values reported are based upon
standard errors that use two-way (firm and calendar quarter) clustering.
conference calls provides a rare opportunity for researchers to observe the types of information analysts are seeking. Since analysts are typically only allowed to ask a limited
number of questions, it is natural to expect analysts to ask questions related to the most
important information they need for their forecasting and valuation tasks. If a disclosure
provides important inputs to analysts’ forecasting or valuation models and analysts focus
on interpreting the disclosure, then we expect the disclosure to trigger more related discussion in the Q&A section of conference calls. If, for example, balance sheet disclosures are
used as an important input in any of analysts’ models and analysts can extract new
insights from interpreting balance sheet disclosures, then we expect balance sheet
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Contemporary Accounting Research
disclosures to be associated with more analysts questioning management about the content
of the balance sheet in the Q&A section of conference calls. Because analysts can ask
questions to extract information they need for any of their forecasting and valuation tasks
(rather than just forecasting next quarter’s earnings), this conference call analysis complements our main tests by providing more general evidence on the relation between earnings
announcement disclosures and analysts’ information interpretation role.
All earnings announcements in our sample are accompanied by a conference call. To
measure the content of the Q&A section of conference calls related to financial statement
disclosures, we first analyze the text of financial statement disclosures (e.g., the balance
sheet disclosure) to generate lists of the most common keywords and phrases that occur
exclusively in each type of financial statement disclosure.22 Next, we use a PERL program
to search the transcripts of the Q&A section of conference calls to identify the number of
times these keywords and phrases occur in the Q&A section of conference calls. This
yields four numerical scores (CCQA_BS, CCQA_SCF, CCQA_SEG, and CCQA_IS) that
represent the number of times the most common keywords or phrases contained in balance sheets, cash flow statements, segment data, and income statements disclosed in earnings announcements occur in the Q&A section of conference calls.
To identify analysts questioning management regarding management earnings forecasts, we first apply a search string similar to Chuk et al. (2013, footnote 1) to the transcript of the Q&A section of conference calls to identify possible places containing
discussions about management earnings forecasts; we then manually check each place to
identify analysts questioning managements regarding a management earnings forecast. We
create an indicator variable (CCQA_MEF) equal to one if the conference call Q&A
includes analysts questioning management about a management earnings forecast (e.g.,
questions about earnings “guidance”).
To examine the relation between financial disclosures and the content of the Q&A section of conference calls, we estimate the following OLS (CCQA_BS/SCF/SEG) or logistic
(CCQA_MEF) model:
CCQA BSiq ; CCQA SCFiq ; CCQA SEGiq or CCQA MEF ¼ d0 þ d1 BSiq
þ d2 SCFiq þ d3 SEGiq þ d4 MEFiq þ CONTROLSiq þ eiq ;
ð4Þ
where CONTROLS includes the control variables used in equations (1–3), the word count
of the Q&A section of conference calls (CCQA_#WORDS), and the word count for the
number of unique income statement-related terms in the conference call Q&A (CCQA_IS).
Table 4 shows the results for estimating equation (4). Columns (1) and (3) show that
BS is significantly positively related to CCQA_BS (0.180, p < 0.001, two-tailed), and SEG
is significantly positively related to CCQA_SEG (0.152, p < 0.001, two-tailed), indicating
that balance sheet and segment disclosures are associated with more analysts questioning
and management discussion related to these disclosures in the conference call Q&A. Further, column (4) shows that MEF is significantly positively related to CCQA_MEF (0.577,
p = 0.035, two-tailed), indicating that disclosures of management earnings forecasts are
associated with a higher likelihood of analysts questioning management about a management earnings forecast in the conference call Q&A. In contrast, column (2) shows no evidence that the disclosure of cash flow statements (SCF) is related to analysts questioning
and management discussion of cash flow statement items (CCQA_SCF) in the conference
call Q&A. These results are consistent with disclosures of balance sheets, segment data
22.
Please see supporting information, “Appendix S1: Creation of Unique Word/Phrase Lists for Each Financial Disclosure” as an addition to the online article for details of the method we use to identify the keywords/phrase and the lists of the unique words/phrases.
CAR Vol. 34 No. 1 (Spring 2017)
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TABLE 4
Association between financial disclosures and the content of the conference call Q&A
Dependent variables:
Financial disclosures
BS
SCF
SEG
MEF
Controls
|SURP|
SIGN
SIZE
BM
CCQA_#WORDS
CCQA_IS
Calendar quarter fixed effects
Adjusted R2 (%)
Pseudo R2 (%)
CCQA_BS
Coefficient
(p-value)
(1)
CCQA_SCF
Coefficient
(p-value)
(2)
CCQA_SEG
Coefficient
(p-value)
(3)
CCQA_MEF
Coefficient
(p-value)
(4)
0.180
(<0.001)
0.017
(0.468)
0.001
(0.950)
0.029
(0.138)
0.017
(0.505)
0.025
(0.158)
0.014
(0.494)
0.001
(0.948)
0.051
(0.065)
0.032
(0.124)
0.152
(<0.001)
0.005
(0.651)
0.056
(0.888)
0.149
(0.580)
0.390
(0.298)
0.577
(0.035)
0.111
(<0.001)
0.008
(0.703)
0.086
(0.030)
0.153
(<0.001)
0.125
(<0.001)
0.051
(0.092)
Yes
11.31
0.010
(0.761)
0.040
(0.049)
0.002
(0.944)
0.010
(0.737)
0.055
(0.064)
0.077
(0.008)
Yes
1.55
0.026
(0.195)
0.038
(0.003)
0.116
(<0.001)
0.009
(0.397)
0.136
(<0.001)
0.365
(<0.001)
Yes
14.91
0.522
(0.218)
0.658
(0.053)
0.303
(0.944)
0.030
(0.944)
0.257
(0.550)
0.500
(0.245)
Yes
4.03
Notes:
This table shows the results from estimating equation (4), which tests the association between the
content of the questions-and-answers (Q&A) section of earnings conference calls and the
financial disclosures made with earnings announcements. See the Appendix for variable
definitions. All regressions include calendar quarter fixed effects. Additionally, we estimate
equation (4) as an OLS (for CCQA_BS/SCF/SEG) or a logistic model (for CCQA_MEF) using
decile rank regressions where all the continuous variables are transformed into [0,1] decile
ranks by ranking the original variables into 0–9 deciles and then dividing the ranks by 9. The
sample is comprised of 1,445 quarterly earnings announcements from 2003 to 2004. We report
one-tailed p-values for explanatory variables where we make a directional prediction; otherwise
we report two-tailed p-values. All p-values reported are based upon standard errors that use
two-way (firm and calendar quarter) clustering.
and management earnings forecasts generally providing useful inputs for analysts’
research, and analysts performing an information interpretation role with respect to these
disclosures. Consistent with our main analyses using analysts’ earnings forecasts, our conference call analysis yields no evidence that analysts perform a similar information interpretation role with respect to cash flow statement disclosures. Further comparison of the
adjusted R2s in Table 4 to the baseline regressions with only control variables indicates
CAR Vol. 34 No. 1 (Spring 2017)
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that our test variables provide significant incremental explanatory power for CCQA_BS
and CCQA_SEG.23,24
6. Robustness analyses
Controlling for other disclosures made with earnings announcements
Our analyses so far focus on the disclosure of financial statements and management earnings forecasts. However, firms also include many other supplemental disclosures in their
earnings announcements (see Watts 1973; Kane, Lee, and Marcus 1984; Hoskin, Hughes,
and Ricks 1986; Brown and Kim 1993; Kerstein and Kim 1995; Amir and Lev 1996; Chen
et al. 2002; Wasley and Wu 2006). To ensure that our results are not driven by omitting
some other disclosures made with earnings announcements, we also hand-collect data from
earnings announcement press releases for an additional 16 qualitative disclosures firms
may include in their earnings announcements.25 In addition, we also control for the tone
of language used in earnings announcement press releases using the measure developed by
Davis, Piger, and Sedor (2012). These additional 17 disclosure/tone variables are listed in
panel A of Table 5 and defined in the Appendix.
Panel A of Table 5 shows the descriptive statistics for these additional 17 disclosure/
tone variables. Few qualitative disclosures are common. For example, 18.3 percent of
earnings announcements include a discussion of production-related issues (the mean value
of this variable is 0.183). Panel B of Table 5 shows the results of reestimating equation (1)
after including these additional 17 disclosure/tone variables as additional controls. We find
that our main results continue to hold. We also reestimate equations (2)–(4) including
those additional disclosure/tone variables and find that our inferences remain unaffected
(untabulated).26
Controlling for the endogeneity of disclosures
The earnings announcement disclosures we examine are endogenously determined, raising
concerns that our results may be confounded by some correlated omitted variable problem. To mitigate this concern, we examine the robustness of our findings to controlling for
a variety of firm characteristics and to correcting for self-selection using a two-stage Heckman model.
Theory suggests a number of underlying factors that determine firms’ disclosure policy
choices, including: (i) the level of uncertainty; (ii) fundamental firm characteristics like firm
23.
24.
25.
26.
The adjusted R2s of the baseline regressions (two-tailed p-value from the Vuong test comparing the
Table 4 regressions with the baseline regressions) are: 7.19 percent (<0.01), 1.53 percent (0.95), and 9.70
percent (<0.01) for CCQA_BS, CCQA_SCF, and CCQA_SEG, respectively. The pseudo R2 for the baseline regression for CCQA_MEF is 3.78 percent.
As another additional test, we also consider the relation between the inclusion of forecasts of financial
statements in analysts’ research reports and the disclosure of the same financial statements in firms’ earnings announcements. For an announcement including a particular statement, issuance of a forecast of this
statement by analysts only before (after) the announcement would be more consistent with an information
discovery (interpretation) role. We randomly select 100 announcements from our sample, and hand-collect
matched analysts’ reports issued before and after these announcements from the ThomsonOne database.
We find little variation in the inclusion of financial statement forecasts between an analyst’s preannouncement reports and her post-announcement reports.
In addition to these 16 items, we also collected data for the disclosure of (i) comprehensive income, and
(ii) a new restatement. We find only one (six) earnings announcement that includes a statement of comprehensive income (a restatement announcement). Our results are robust to controlling for these two additional disclosures.
Our results are unlikely to be driven by the fair value disclosures included in balance sheets. Of the 1,445
earnings announcements in our sample, only one announcement includes a statement of comprehensive
income, and only 13 include a discussion of changes in fair values. Results are similar if we exclude these
observations.
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TABLE 5
Analysis including additional qualitative disclosure variables
Panel A: Univariate statistics for additional qualitative disclosure variables
Fair Value
Back Order
Capex
Dividends
Stock Split
Cash Flow Forecast
Revenue Forecast
Restatement
Litigation
New Products
Restructuring
Unusual Items
Accounting Changes
Shipment
Contract Issues
Production Issues
NETOPT
Mean
SE
25th percentile
Median
75th percentile
0.009
0.063
0.070
0.006
0.006
0.019
0.162
0.004
0.046
0.018
0.089
0.009
0.003
0.080
0.109
0.183
0.993
0.094
0.243
0.255
0.079
0.078
0.138
0.369
0.064
0.209
0.133
0.284
0.094
0.058
0.271
0.311
0.387
0.737
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.498
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.900
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
1.364
Panel B: Reestimating equation (1) including controls for additional qualitative disclosure variables
Dependent variable: Δq
Financial disclosures
BS
SCF
SEG
MEF
Controls
|SURP|
SIGN
SIZE
BM
Additional qualitative disclosure variables
Fair Value
Back Order
Capex
Dividends
Stock Split
Cash Flow Forecast
Revenue Forecast
Restatement
Litigation
New Products
Restructuring
Unusual Items
Predict
Coefficient
p-value
+
0.028
0.017
0.038
0.000
0.068
0.843
0.036
0.518
0.061
0.029
0.017
0.037
0.006
0.076
0.285
0.947
0.070
0.042
0.031
0.011
0.047
0.055
0.003
0.003
0.036
0.009
0.010
0.060
0.448
0.393
0.371
0.937
0.516
0.360
0.869
0.978
0.246
0.909
0.801
0.518
(The table is continued on the next page.)
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Contemporary Accounting Research
TABLE 5 (continued)
Panel B: Reestimating equation (1) including controls for additional qualitative disclosure variables
Dependent variable: Δq
Predict
Accounting Changes
Shipment
Contract Issues
Production Issues
NETOPT
Calendar quarter fixed effects
Adjusted R2 (%)
Coefficient
p-value
0.132
0.006
0.039
0.036
0.013
Yes
0.19
0.130
0.820
0.246
0.205
0.655
Notes:
This table shows the results from reestimating equation (1) after including additional control
variables for qualitative disclosures firms may include in their earnings announcements. The
dependent variable is Δq, the change in analyst consensus. See the Appendix for variable
definitions. All regressions include calendar quarter fixed effects. Additionally, all models are
estimated using decile rank regressions where all the continuous variables are transformed into
[0,1] decile ranks by ranking the original variables into 0–9 deciles and then dividing the ranks
by 9. The sample is comprised of 1,445 quarterly earnings announcements from 2003 to 2004.
We report one-tailed p-values for explanatory variables where we make a directional
prediction; otherwise we report two-tailed p-values. All p-values reported are based upon
standard errors that use two-way (firm and calendar quarter) clustering.
size (i.e., market capitalization); (iii) firm complexity; (iv) firm performance; (v) performance variability; (vi) proprietary costs; and (vii) whether or not a firm is about to issue
stock. Following prior studies (e.g., Lang and Lundholm 1993; Tasker 1998; Chen et al.
2002; Miller 2002; Botosan and Stanford 2005; Francis, Nanda, and Olsson 2008), we construct a total of 17 variables that proxy for these underlying factors that determine firms’
disclosure policy choices (these additional firm characteristics are listed in Table 6; see the
Appendix for variable definitions).
Requiring data to measure these additional controls reduces our sample to 1,258
earnings announcements. We first reestimate equation (1) after including these additional
controls. As shown in Table 6, our main results continue to hold. We also estimate
two-stage Heckman models to correct for potential endogeneity of the disclosures, and
find that our inferences remain unchanged (untabulated).27 These results mitigate concerns
that our findings are affected by the endogeneity or self-selection of disclosures.
Additional robustness tests
Our results still hold when we:
•
27.
Control for information leakage by including the number of days between the earnings
announcement date and the 10-K/Q filing date, the actual (raw or market adjusted)
returns between these two dates, or both.
In the first stage, we estimate a probit model for each of the four disclosure variables (BS, SEG, SCF and
MEF) as a function of the control variables in equation (1) plus the additional 17 firm characteristics (defined in the Appendix), and construct an inverse Mills ratio for each of the four variables (see Heckman
1979). In the second stage, we then reestimate equation (1) including these four inverse Mills ratios (e.g.,
see Song 2004).
CAR Vol. 34 No. 1 (Spring 2017)
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TABLE 6
Reestimating equation (1) controlling for firm characteristics that determine firms’ disclosure policy
Dependent variable: Δq
Predict
Financial disclosures
BS
SCF
SEG
MEF
+
Controls
|SURP|
SIGN
SIZE
BM
Firm characteristics (that determine firms’ disclosure policy)
LOSS
DISP
RET_VOL
ROA
A_CAR
Q_CAR
QTRS_UP
FOLLOW
HTECH
AGE
#_SEG
AQ
CORR_ER
IND_HHI
IND_PROFITADJ
M&A
ISSUE
Include calendar quarter fixed effects
Adjusted R2 (%)
Coefficient
p-value
0.040
0.021
0.043
0.004
0.073
0.816
0.034
0.463
0.103
0.040
0.105
0.024
0.003
0.007
0.036
0.714
0.011
0.109
0.089
0.011
0.037
0.050
0.019
0.040
0.042
0.043
0.023
0.039
0.017
0.040
0.012
0.029
0.049
Yes
1.53
0.614
0.002
0.041
0.772
0.403
0.386
0.283
0.509
0.166
0.234
0.360
0.207
0.591
0.163
0.827
0.289
0.657
Notes:
This table shows the results from reestimating equation (1) after including additional control
variables for firm characteristics that determine firms’ disclosure choices. See the Appendix for
variable definitions. All regressions include calendar quarter fixed effects. Additionally, all
models are estimated using decile rank regressions where all the continuous variables are
transformed into [0,1] decile ranks by ranking the original variables into 0–9 deciles and then
dividing the ranks by 9. The sample consists of 1,258 quarterly earnings announcements
(requiring data to calculate the additional 17 controls reduces the sample size from 1,445
quarterly earnings announcements). We report one-tailed p-values for explanatory variables
where we make a directional prediction; otherwise we report two-tailed p-values. All p-values
reported are based upon standard errors that use are two-way (firm and calendar quarter)
clustering.
•
Control for pro forma reporting by including a dummy variable for the presence of a
reconciliation between pro forma and GAAP in the earnings announcement press
release.
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•
•
•
•
•
Contemporary Accounting Research
Split our samples into larger and smaller firms (based upon median market capitalization) and rerun our analyses separately for the two subsamples. We find that our results
are similar across both subsamples, mitigating concerns that our results are driven only
by very large firms and may not generalize to smaller firms.
Use I/B/E/S split-adjusted data to construct our BKLS and forecast accuracy
variables.
Include additional management earnings forecasts disclosed in conference calls. We also
collect data for new management earnings forecasts disclosed in conference calls and
recode the MEF variable one if either the earnings announcement press release or the
conference call transcript contains a management earnings forecast.
Use unscaled COMMON and PRIVATE or scale the change in COMMON and
PRIVATE by stock price (rather than scale by their respective preannouncement
levels).
Adjust standard errors in the regressions using two-way clustering by firm and year
(rather than clustering on firm and calendar quarter).
7. Conclusion
This study examines the relation between disclosures of financial statements and management earnings forecasts at earnings announcements and changes in analysts’ information about future earnings. Using a sample of 1,445 earnings announcements, we
find that balance sheet and segment disclosures are associated with an increase in the
degree to which analysts’ forecasts of upcoming quarterly earnings are based on private
information. Further analyses show that balance sheet disclosures are associated with
an increase in the precision of both analysts’ common and private information, segment disclosures are associated with an increase in the precision of analysts’ private
information, and management earnings forecasts are associated with an increase in analysts’ common information. We also find that balance sheet and segment disclosures
are associated with an increase in the accuracy of analysts’ average forecasts. In contrast, we fail to find evidence that the disclosure of cash flow statements is associated
with changes in analysts’ information about upcoming quarterly earnings. These results
are consistent with analysts processing balance sheet and segment disclosures at earnings announcements into new private information regarding upcoming quarterly earnings. Additional analysis of the questions-and-answers (Q&A) section of conference
calls shows that disclosures of balance sheets, segment data, and management earnings
forecasts are all associated with more related discussion in the conference call Q&A,
consistent with these disclosures enhancing the information interpretation role of analyst research in general.
Our study increases our understanding of the relation between financial disclosures
and the informational role of financial analysts. To the extent that our tests have sufficient power, our failure to find a significant relation between the disclosure of cash
flow statements and analysts’ information could suggest that cash flow statement disclosures may not be an important input to analysts’ forecasting and valuation models.
One possibility is that analysts primarily use a “balance sheet approach” (e.g., see
Palepu and Healy 2008; Penman 2010) to predict earnings, which tends to focus on
the information contained in balance sheets and income statements. However, it
remains unclear whether analysts use such a balance sheet approach because cash flow
statements do not provide significant information about future earnings incremental to
balance sheets (in combination with income statements), or analysts fail to fully understand and use the information contained in cash flow statements when forecasting
earnings.
CAR Vol. 34 No. 1 (Spring 2017)
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Appendix
Definition of variables
Main dependent variables
Δq
Change in analyst consensus (q), which measures the degree to which analysts’
forecasts are based on common (public) information. BKLS express q in
terms of the variance of forecasts (D), the squared error in the mean forecast
(SE), and the number of forecasts (N) (see Barron et al., 1998, 426,
SED=N
Proposition 2) as follows: q ¼ ð11=N
ÞDþSE : Using three or more forecasts
of quarter q+1 earnings that are matched before and after the quarter
q earnings announcement, we calculate q (for q+1 earnings) both before
and after the quarter q earnings announcement. Therefore, Δq is the change
in q regarding quarter q+1 earnings around the quarter q earnings
announcement
ΔCOMMON and
Change in the precision of analysts’ common and private information calculated
ΔPRIVATE
using the BKLS model. COMMON and PRIVATE are expressed in terms of
the variance of forecasts (D), the squared error in the mean forecast (SE), and
the number of forecasts (N) (see Barron et al., 1998, 428, Corollary 1)
SED=N
as follows: COMMON ¼ ½ð11=N
and PRIVATE ¼ ½ð11=NDÞDþSE2 :
ÞDþSE2
Using three or more matched analysts’ forecasts (of quarter q+1 earnings)
made before and after the quarter q earnings announcement, we estimate
COMMON and PRIVATE (for quarter q+1 earnings) both before and
after the quarter q earnings announcement and then compute the
change in COMMON and PRIVATE (i.e., ΔCOMMON and ΔPRIVATE).
We scale ΔCOMMON and ΔPRIVATE by their preannouncement levels
for comparability across firms
ΔABSMFE
Change in the absolute error in the mean forecast of quarter q+1
earnings around the quarter q earnings announcement, scaled by stock
price. Using three or more forecasts of quarter q+1 earnings that are
matched before and after the quarter q earnings announcement, we first
calculate the change in the absolute error in the mean forecast
(of quarter q+1 earnings) around the quarter q earnings announcement.
We then scale this change in the absolute error in the mean forecast by
stock price at the end of quarter q
Financial disclosure variables
BS
Indicator variable equal to one if the quarter q earnings announcement
press release contains a balance sheet, and zero otherwise
SCF
Indicator variable equal to one if the quarter q earnings announcement
press release contains a statement of cash flows, and zero otherwise
SEG
Indicator variable equal to one if the quarter q earnings announcement
press release contains segment data, and zero otherwise
MEF
Indicator variable equal to one if the quarter q earnings announcement
press release contains a management earnings forecast, and zero otherwise
Main control variables
|SURP|
Calculated as:
j Actual EPS Mean EPS Forecast j = Stock Price;
where actual and forecasted EPS are for quarter q earnings. 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 quarter q
(The Appendix is continued on the next page.)
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Contemporary Accounting Research
Indicator variable equal to one if the earnings surprise for quarter q is
negative (i.e., if the quarter q actual earnings is less than the mean forecast
made in the 45-day period before the quarter q earnings announcement),
and zero otherwise
SIZE
Market capitalization (in $m) at the end of quarter q
BM
Book-to-market ratio at the end of quarter q
Alternative disclosure measures
BS_ALT
The ratio of the number of line items in the balance sheet disclosed in the
quarter q earnings announcement press release to the number of line items
in the balance sheet disclosed in the subsequent 10-K/Q filing for quarter
q (see D’Souza et al. 2010)
SCF_ALT
The ratio of the number of line items in the cash flow statement disclosed
in the quarter q earnings announcement press releases to the number of
line items in the cash flow statement disclosed in the subsequent 10-K/Q
filing for quarter q (see D’Souza et al. 2010)
MEF_ALT
Coded 4 for point forecasts, 3 for range forecasts, 2 for single bound forecasts,
1 for qualitative forecasts, 0 if no management earnings forecast is disclosed
in the quarter q earnings announcement press release
IS_#LINES
Control variable that is the number of line items in the income statement
included in the quarter q earnings announcement press release scaled by
the number of line items in the income statement in the subsequent
10K/Q for quarter q (see D’Souza et al. 2010)
Variables used in the conference call Q&A analysis
CCQA_BS
Number of times the most common keywords and phrases that are exclusive
to the balance sheet (e.g., “assets”) occur in the Q&A section of the quarter
q conference call
CCQA_SCF
Number of times the most common keywords and phrases that are exclusive
to the statement of cash flows (e.g., “cash flows”) occur in the Q&A
section of the quarter q conference call
CCQA_SEG
Number of times the most common keywords and phrases that are exclusive
to segment disclosures (e.g., “segments”) occur in the Q&A section of
the quarter q conference call
CCQA_MEF
Indicator variable coded one (zero) if the Q&A section of the quarter
q conference call includes at least one instance where analysts question
management about a management earnings forecast (e.g., a question
about earnings “guidance”)
CCQA_#WORDS
Total word count of the Q&A section of the quarter q conference call
CCQ&A_IS
Control variable that is a word count for the number of times the most
common keywords and phrases that are exclusive to the income
statement (e.g., “profit”) occur in the Q&A section of the quarter
q conference call
Additional qualitative disclosure variables
Fair Value
Indicator variable coded one if the quarter q earnings announcement
press release includes an explanation of the change in Accumulated
Other Comprehensive Income (AOCI) on the Balance Sheet (i.e., an
explanation of the changes in fair values going through AOCI), and
zero otherwise
Back Order
Indicator variable coded one if the quarter q earnings announcement
press release includes a discussion of customer backorders, zero otherwise
Capex
Indicator variable coded one if the quarter q earnings announcement
press release includes a discussion of capital expenditures, zero otherwise
SIGN
(The Appendix is continued on the next page.)
CAR Vol. 34 No. 1 (Spring 2017)
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369
Indicator variable coded one if the quarter q earnings announcement
press release includes an announcement of dividend changes, and zero
otherwise
Stock Split
Indicator variable coded one if the quarter q earnings announcement
press release includes an announcement of a stock split, and zero otherwise
Cash Flow Forecast Indicator variable coded one if the quarter q earnings announcement
press release includes a forecast of future cash flows, and zero otherwise
Revenue Forecast
Indicator variable coded one if the quarter q earnings announcement
press release includes a forecast of future revenue, and zero otherwise
Restatement
Indicator variable coded one if the quarter q earnings announcement
press release includes a discussion of an accounting restatement,
and zero otherwise
Litigation
Indicator variable coded one if the quarter q earnings announcement
press release includes a discussion of ongoing litigation issues,
and zero otherwise
New Products
Indicator variable coded one if the quarter q earnings announcement
press release includes a discussion of new product(s), and zero otherwise
Restructuring
Indicator variable coded one if the quarter q earnings announcement
press release includes a discussion of an ongoing restructuring,
divestiture, or discontinued operation, and zero otherwise
Unusual Items
Indicator variable coded one if the quarter q earnings announcement
press release includes a discussion of unusual items, and zero otherwise
Accounting Changes Indicator variable coded one if the quarter q earnings announcement
press release includes a discussion of a change in accounting policies,
and zero otherwise
Shipment
Indicator variable coded one if the quarter q earnings announcement
press release includes a discussion of customer shipments, and zero otherwise
Contract Issues
Indicator variable coded one if the quarter q earnings announcement
press release includes a discussion of significant customer or supplier
contract issues (e.g., a defense contractor), and zero otherwise
Production
Indicator variable coded one if the quarter q earnings announcement
press release includes a discussion of ongoing production-related issues,
and zero otherwise
NETOPT
Percentage of optimistic words (including praise, satisfaction and inspiration
words listed by DICTION) minus the percentage of pessimistic words
(including blame, hardship, and denial words listed by DICTION) in the
quarter q earnings announcement press release, measured as a percentage.
See Davis et al. (2012)
Additional control variables for firm characteristics that determine firms’ disclosure policy
LOSS
Indicator variable if the quarter q earnings announcement reports a loss
DISP
Dispersion of analysts’ forecasts prior to the quarter q earnings announcement,
defined as the coefficient of variation in analysts’ earnings forecasts
RET_VOL
Return volatility in quarter q, measured as the standard deviation of daily
stock returns over the 253 trading days (1 calendar year) ending 2 trading
days prior to the quarter q earnings announcement
ROA
Return on assets (ROA) for quarter q
A_CAR
Market-adjusted returns for the 253 trading days ending 2 trading days
before the quarter q earnings announcement
Q_CAR
Quarterly market-adjusted returns over the 64 trading days ending
2 trading days before the quarter q earnings announcement
QTRS_UP
Number of (historic) quarters of continuous increasing seasonally-adjusted
earnings prior to quarter q
Dividends
(The Appendix is continued on the next page.)
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Contemporary Accounting Research
FOLLOW
HTECH
AGE
#_SEG
AQ
CORR_ER
IND_HHI
IND_PROFITADJ
M&A
ISSUE
Number of analysts that issue forecasts of quarter q earnings within
a 45-day window prior to the quarter q earnings announcement
Indicator variable equal to one for firms in a high-tech industry
(4-digit SIC: 2833–2836 (Drugs), 8731–8734 (R&D Services), 7371–7379
(Software), 3570–3577 (Computers), 3600–3674 (Electronics),
3810–3845 (Instruments))
Number of years, by the quarter q earnings announcement,
since a firm first went public
Number of business segments reported by a firm
Accruals quality, measured by McNichols’ (2002) modification of
Dechow and Dichev’s (2002) model. We estimate the model for each
firm using 10 years of data prior to the year of the quarter q earnings
announcement. The standard deviation of the residuals from these
firm-specific model estimates yields the firm-specific estimates of AQ
Correlation between annual earnings and returns, measured as the
adjusted R2 from firm-specific regressions of annual returns on the
level of earnings and the change in earnings over the year using a 10-year
window prior to the year of the quarter q earnings announcement
(Francis, LaFond, Olsson, and Schipper 2004)
Fitted Herfindahl-Hirschman Index (HHI) index as computed by
Hoberg and Phillips (2010) for an industry (3-digit SIC codes).
See also Dhaliwal, Huang, Khurana, and Pereira (2014)
Persistence in the deviation of a firm’s return on assets (ROA) from
the industry average ROA from estimating the following model
(Botosan and Stanford 2005): ROA_ADJt = a0 + a1Dn 9 ROA_ADJt-1
+ a2Dp 9 ROA_ADJt-1 + et, where ROA_ADt is the firm’s ROA in year
t minus the average industry ROA (3-digit SIC code) in year t, Dn (Dp)
is an indicator variable equal to one if ROA_ADJt-1 ≤ 0 (ROA_ADJt-1 > 0),
and 0 otherwise. The model is estimated separately for each industry using
the three years of data prior to the year of the quarter q earnings
announcement. IND_PROFITADJA is equal to the estimate of a2
Indicator variable equal to one if a firm reports a merger or acquisition in
quarter q, and zero otherwise
Indicator variable for the presence of a common stock issuance in quarter q.
Following Francis et al. (2008), we measure ISSUE as equal to one if
the number of split-adjusted common shares outstanding increases
by 20 percent or more in quarter q relative to quarter q1, and
zero otherwise
References
Abarbanell, J. S., and B. J. Bushee. 1997. Fundamental analysis, future earnings, and stock prices.
Journal of Accounting Research 35 (1): 1–24.
Abarbanell, J. S., and B. J. Bushee. 1998. Abnormal returns to a fundamental analysis strategy. The
Accounting Review 73 (1): 19–45.
Altinkilicß, O., and R. Hansen. 2009. On the information role of stock recommendation revisions.
Journal of Accounting and Economics 48 (1): 17–36.
Amir, E., and B. Lev. 1996. Value-relevance of nonfinancial information: The wireless communications industry. Journal of Accounting and Economics 22 (1–3): 3–30.
Anilowski, C., M. Feng, and D. Skinner. 2007. Does guidance affect market returns? The nature and
information content of aggregate earnings guidance. Journal of accounting and Economics 44 (1–
2): 36–63.
Asquith, P., M. B. Mikhail, and A. S. Au. 2005. Information content of equity analyst reports. Journal of Financial Economics 75 (2): 245–82.
CAR Vol. 34 No. 1 (Spring 2017)
Disclosures and Analysts’ Information
371
Ayers, B. C., and R. N. Freeman. 2003. Evidence that analyst following and institutional ownership
accelerate the pricing of future earnings. Review of Accounting Studies 8 (1): 47–67.
Baldwin, B. A. 1984. Segment earnings disclosures and the ability of security analysts to forecast
earnings per share. The Accounting Review 59 (3): 376–89.
Barron, O. E., O. Kim, S. C. Lim, and D. E. Stevens. 1998. Using analysts’ forecasts to measure
properties of analysts’ information environment. The Accounting Review 73 (4): 421–33.
Barron, O. E., D. Byard, and O. Kim. 2002. Changes in analysts’ information around earnings
announcements. The Accounting Review 77 (4): 821–46.
Barron, O. E., D. Byard, C. Kyle, and E. J. Riedl. 2002. High technology intangibles and analysts’
forecasts. Journal of Accounting Research 40 (2): 289–312.
Barron, O. E., D. Byard, and Y. Yu. 2008. Earnings surprises that motivate analysts to reduce the
average forecast error. The Accounting Review 83 (2): 303–26.
Barth, M. E., R. Kasznik, and M. F. McNichols. 2001. Analyst coverage and intangible assets. Journal of Accounting Research 39 (1): 1–34.
Berger, P. G., and R. Hann. 2003. The impact of SFAS 131 on information and monitoring. Journal
of Accounting Research 41 (2): 163–223.
Bhushan, R. 1989. Firm characteristics and analyst following. Journal of Accounting and Economics
11 (2–3): 255–74.
Bhushan, R. 1994. An informational efficiency perspective on the post-earnings announcement drift.
Journal of Accounting and Economics 18 (1): 45–65.
Botosan, C. A., and M. Stanford. 2005. Managers’ motives to withhold segment disclosures and the
effect of SFAS no. 131 on analysts’ information environment. The Accounting Review 80 (3):
751–71.
Brown, L. D., and K.-J. Kim. 1993. The association between nonearnings disclosures by small firms
and positive abnormal returns. The Accounting Review 68 (3): 668–80.
Brown, L. D., A. C. Call, M. B. Clement, and N. Y. Sharp. 2015. Inside the “black box” of sell-side
financial analysis. Journal of Accounting Research 53 (1): 1–47.
Byard, D., Y. Li, and Y. Yu. 2011. The effect of mandatory IFRS adoption on financial analysts’
information environment. Journal of Accounting Research 49 (1): 69–96.
Chen, S., M. L. DeFond, and C. W. Park. 2002. Voluntary disclosure of balance sheet information
in quarterly earnings announcements. Journal of Accounting and Economics 33 (2): 229–51.
Chen, X., Q. Cheng, and K. Lo. 2010. On the relationship between analyst reports and corporate
disclosures: Exploring the roles of information discovery and interpretation. Journal of Accounting and Economics 49 (3): 206–26.
Cheng, A., C-S. Liu, and T. Schaefer. 1997. The value-relevance of SFAS No. 95 cash flows from
operations as assessed by security market effects. Accounting Horizons 11 (3): 1–15.
Chuk, E. C., D. A. Matsumoto, and G. S. Miller. 2013. Assessing methods of identifying management forecasts: CIG vs. researcher collected. Journal of Accounting and Economics 55 (1): 23–42.
Collins, D. W., S. P. Kothari, and J. D. Rayburn. 1987. Firm size and the information content of
prices with respect to earnings. Journal of Accounting and Economics 9 (2): 111–38.
Davis, A. K., J. M. Piger, and L. M. Sedor. 2012. Beyond the numbers: Measuring the information
content of earnings press release language. Contemporary Accounting Research 29 (3): 845–68.
Dechow, P. M., and I. D. Dichev. 2002. The quality of accruals and earnings: The role of accrual
estimation error. The Accounting Review 77 (Supplement): 35–59.
Dellavigna, S., and J. M. Pollet. 2009. Investor inattention and Friday earnings announcements.
Journal of Finance 64 (2): 709–49.
Demski, J. S., and G. A. Feltham. 1994. Market response to financial reporting. Journal of Financial
Economics 17 (1–2): 3–40.
Dhaliwal, D. S., S. X. Huang, I. K. Khurana, and R. Pereira. 2014. Product market competition
and conditional conservatism. Review of Accounting Studies 19 (4): 1309–45.
CAR Vol. 34 No. 1 (Spring 2017)
372
Contemporary Accounting Research
D’Souza, J. M., K. Ramesh, and M. Shen. 2010. Disclosure of GAAP line items in earnings
announcements. Review of Accounting Studies 15 (1): 179–219.
Duru, A., and D. M. Reeb. 2002. International diversification and analysts’ forecast accuracy and
bias. The Accounting Review 77 (2): 415–33.
Francis, J., K. Schipper, and L. Vincent. 2002. Expanding disclosures and the increased usefulness
of earnings announcements. The Accounting Review 77 (3): 515–46.
Francis, J., K. Schipper, and L. Vincent. 2002. Earnings announcements and competing information.
Journal of Accounting and Economics 33 (3): 313–42.
Francis, J., R. LaFond, P. M. Olsson, and K. Schipper. 2004. Cost of equity and earnings attributes.
The Accounting Review 79 (4): 967–1010.
Francis, J., D. Nanda, and P. M. Olsson. 2008. Voluntary disclosure, earnings quality, and cost of
capital. Journal of Accounting Research 46 (1): 53–99.
Frankel, R. M., S. P. Kothari, and J. P. Weber. 2006. Determinants of the informativeness of analysts’ research. Journal of Accounting and Economics 41 (1–2): 29–54.
Freeman, R. N. 1987. The association between accounting earnings and security returns for large
and small firms. Journal of Accounting and Economics 9 (2): 195–228.
Harris, M., and A. Raviv. 1993. Differences in opinion make a horse race. Review of Financial Studies 6 (3): 473–506.
Heckman, J. J. 1979. Sample selection bias as a specification error. Econometrica 47 (1): 153–61.
Hoberg, G. T., and G. M. Phillips. 2010. Product market synergies and competition in mergers and
acquisitions: A text-based analysis. Review of Financial Studies 23 (10): 3373–811.
Hollander, S., M. Plonk, and E. M. Roelofsen. 2010. Does silence speak? An empirical analysis of
disclosure choices during conference calls. Journal of Accounting Research 48 (3): 531–63.
Hoskin, R. E., J. S. Hughes, and W. E. Ricks. 1986. Evidence on the incremental information content of additional firm disclosures made concurrent with earnings. Journal of Accounting
Research 24 (Supplement): 1–32.
Hutton, A. P., and P. C. Stocken. 2009. Prior forecasting accuracy and investor reaction to management earnings forecasts. Working paper, Boston College.
Indjejikian, R. J. 1991. The impact of costly information interpretation on firm disclosure decisions.
Journal of Accounting Research 29 (2): 277–301.
Ivkovic, Z., and N. Jegadeesh. 2004. The timing and value of forecast and recommendation revisions. Journal of Financial Economics 73 (3): 433–63.
Kane, A., Y. K. Lee, and A. Marcus. 1984. Earnings and dividend announcements: Is there a corroborative effect? The Journal of Finance 39 (4): 1091–9.
Kerstein, J., and D. Kim. 1995. The incremental information content of capital expenditures. The
Accounting Review 70 (3): 513–26.
Kim, O., and R. E. Verrecchia. 1991. Trading volume and price reactions to public announcements.
Journal of Accounting Research 29 (2): 302–21.
Kim, O., and R. E. Verrecchia. 1994. Market liquidity and volume around earnings announcements.
Journal of Accounting and Economics 17 (1–2): 41–67.
Kolev, K., C. A. Marquardt, and S. E. McVay. 2008. SEC scrutiny and the evolution of non-GAAP
earnings numbers. The Accounting Review 83 (1): 157–84.
Lang, M. H., and R. J. Lundholm. 1993. Cross-sectional determinants of analyst ratings of corporate disclosures. Journal of Accounting Research 31 (2): 246–71.
Lang, M. H., and R. J. Lundholm. 1996. Corporate disclosure policy and analyst behavior. The
Accounting Review 71 (4): 467–92.
Lev, B., and S. R. Thiagarajan. 1993. Fundamental information analysis. Journal of Accounting
Research 31 (2): 190–215.
Li, E. X., K. Ramesh, M. Shen, and J. S. Wu. 2015. Do analyst stock recommendations piggyback
on recent corporate news? An analysis of regular-hour and after-hours revisions. Journal of
Accounting Research 53 (4): 821–61.
CAR Vol. 34 No. 1 (Spring 2017)
Disclosures and Analysts’ Information
373
Livnat, J., and P. A. Zarowin. 1990. The incremental information content of cash-flow components.
Journal of Accounting and Economic 13 (1): 25–46.
Matsumoto, D. A., M. Pronk, and E. M. Roelofsen. 2011. What makes conference calls useful? The
information content of managers’ presentations and analysts’ discussion sessions. The Accounting Review 86 (4): 1383–414.
Mayew, W. J. 2008. Evidence of management discrimination among analysts during conference calls.
Journal of Accounting Research 46 (3): 627–59.
Mayew, W. J., N. Y. Sharp, and M. Venkatachalam. 2012. Using earnings conference calls to identify analysts with superior private information. Review of Accounting Studies 18 (2): 386–413.
McNichols, M. F. 2002. Discussion of “the quality of accruals and earnings: The role of accrual estimation error”. The Accounting Review 77 (Supplement): 61–9.
Miller, G. S. 2002. Earnings performance and discretionary disclosure. Journal of Accounting
Research 40 (1): 173–204.
Ou, J. A., and S. H. Penman. 1989a. Accounting measurement, price-earnings ratio, and the information content of security prices. Journal of Accounting Research 27 (3): 111–44.
Ou, J. A., and S. H. Penman. 1989b. Financial statement analysis and the prediction of stock
returns. Journal of Accounting and Economics 11 (4): 295–329.
Palepu, K., and P. Healy. 2008. Business analysis and valuation using financial statements, 4th edn.
Cincinnati, OH: South-Western.
Payne, J. L., and W. B. Thomas. 2003. The implications of using stock-split adjusted I/B/E/S data
in empirical research. The Accounting Review 78 (4): 1049–69.
Penman, S. H. 2010. Financial statement analysis and security valuation, 4th edn. New York:
McGraw-Hill.
Piotroski, J. D. 2000. Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research 38 (Supplement): 1–41.
Previts, G. J., R. J. Bricker, T. R. Robinson, and S. J. Young. 1994. A content analysis of sell-side
financial analyst company reports. Accounting Horizons 8 (2): 55–70.
Rogers, J. L., and A. Van Buskirk. 2013. Bundled forecasts in empirical accounting research. Journal
of Accounting Economics 55 (1): 43–65.
Shores, D. 1990. The association between interim earnings and security returns surrounding earnings
announcements. Journal of Accounting Research 28 (1): 164–81.
Song, W. L. 2004. Competition and coalition among underwriters: The decision to join a syndicate.
The Journal of Finance 59 (5): 2421–44.
Stickel, S. E. 1989. The timing of and incentives for annual earnings forecasts near interim earnings
announcements. Journal of Accounting and Economics 11 (1–2): 275–92.
Swaminathan, S. 1990. The impact of SEC-mandated segment data on price variability and divergence of beliefs. The Accounting Review 66 (1): 23–41.
Tasker, S. C. 1998. Bridging the information gap: Quarterly conference calls as a medium for voluntary disclosure. Review of Accounting Studies 3 (1–2): 137–67.
Wasley, C. E., and J. S. Wu. 2006. Why do managers voluntarily issue cash flow forecasts? Journal
of Accounting Research 44 (2): 389–429.
Watts, R. L. 1973. The information content of dividends. The Journal of Business 46 (2): 191–211.
Zhang, Y. 2008. Analyst responsiveness and the post-earnings-announcement drift. Journal of
Accounting and Economics 46 (1): 201–15.
SUPPORTING INFORMATION
Additional Supporting Information may be found in the online version of this article:
Appendix S1: Creation of Unique Word/Phrase Lists for Each Financial Disclosure
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