Lost in the Details:
Market Consequences of Disaggregating Items with Homogeneous Characteristics
Eric R. Holzman
Ph.D. Candidate
Indiana University
[email protected]
Nathan T. Marshall
Assistant Professor
University of Colorado
[email protected]
Joseph H. Schroeder
Assistant Professor
Indiana University
[email protected]
Teri Lombardi Yohn
Professor and Conrad Prebys Professorship
Indiana University
[email protected]
April 20, 2017
We appreciate helpful comments from Paul Fischer, Pat Hopkins, Russell Lundholm, Mark Nelson, Jonathan
Rogers, David Veenman, Jim Wahlen and the workshop participants at the College of William and Mary, Florida
International University, Indiana University, University of Louisville, University of Kentucky, Maastricht
University, Rotterdam School of Management, Erasmus University, University of British Columbia, University of
Texas – Dallas, and University of Queensland.
Lost in the Details:
Market Consequences of Disaggregating Items with Homogeneous Characteristics
Abstract
While prior research generally concludes that there are significant benefits to increased earnings
disaggregation in earnings announcements, we document adverse consequences associated with
the disaggregation of earnings into components with homogeneous characteristics. Specifically,
we replicate the finding in prior research that the components of operating earnings are
homogenous in their persistence and that their disaggregation is not beneficial for forecasting
future profitability. Relying on emerging differences-of-opinion theory, we predict that the
disaggregation of homogeneous operating earnings line items leads investors to erroneously
place differential weights on these line items resulting in increased opinion divergence.
Consistent with our predictions, we document a significant positive relation between the extent
of operating earnings disaggregation into items with similar persistence in the earnings
announcement and excess volume, the change in idiosyncratic return volatility, and the change in
the standard deviation of analyst forecasts around the earnings announcement. In addition, we
show that the extent of operating earnings disaggregation into items with similar persistence is
positively associated with absolute analyst forecast errors after the earnings announcement and
post-earnings announcement drift. Collectively, our results highlight an important factor for
managers and regulators to consider when increasing disaggregation: while increasing the
disaggregation of heterogeneous items may be associated with positive market outcomes (as
documented in prior research), increasing the disaggregation of homogenous items can lead to
investor opinion divergence and less efficient pricing of the earnings information.
Keywords: Disaggregation, Opinion divergence, Market efficiency, Earnings announcements
1. Introduction
Earnings announcements are important market events with substantial variation in how
earnings are presented. Prior work generally suggests that increased earnings disaggregation is
associated with favorable market outcomes, through increased informativeness (e.g., Francis et
al. 2002), more accurate earnings expectations and valuations (Lipe 1986; Fairfield et al. 1996;
Hewitt 2009), increased credibility (Hirst et al. 2007; Merkley et al. 2013), and a reduction in
earnings fixation (Elliott et al. 2011). While prior research documents benefits to increased
disaggregation, a fundamental presumption in this research is that increased disaggregation
segregates items with heterogeneous characteristics (i.e., differential persistence, precision, or
subjectivity).
The relation between increased disaggregation and market outcomes is less clear,
however, when items with homogeneous characteristics are disaggregated. We conjecture that
the disaggregation of items with homogeneous characteristics leads some investors to rely on the
disaggregation and to assume that the disaggregated items differ even though they do not. That
is, some investors assume that the disaggregation provides incremental information, or an
informative signal, when it does not. Bloomfield and Fischer (2011) refer to the reliance on
uninformative signals as errors of commission. They suggest that errors of commission are a
source of investor opinion divergence that lead to less precise beliefs about future stock returns
and therefore a higher cost of capital. Based on this notion, we argue that the disaggregation of
earnings into components with homogeneous characteristics leads to greater opinion divergence
and less efficient pricing of the earnings information.
We examine the extent of operating earnings disaggregation in the earnings
announcement, as the findings in Fairfield et al. (1996) suggest that errors of commission are
1
more likely to occur when operating earnings are disaggregated. Specifically, Fairfield et al.
(1996) show that the components of operating earnings have similar persistence and that the
disaggregation of operating earnings is not useful for forecasting profitability. This suggests that
the disaggregation of operating earnings is unique because it disaggregates items of similar
persistence and is not informative for forming more accurate next period earnings expectations,
but affords investors the opportunity to differentially interpret the component line items. We
therefore hypothesize that greater operating earnings disaggregation into components with
similar persistence is positively associated with opinion divergence around the earnings
announcement.
We also make predictions regarding opinion divergence surrounding the subsequent
mandatory filing. While firms demonstrate significant variation in operating earnings
disaggregation in the earnings announcement, regulatory requirements yield comparable levels
of disaggregation for firms with similar operating environments in the mandatory filing. 1 As
such, firms with aggregated earnings announcement disclosures ultimately reveal the
disaggregated operating earnings items at the filing date. We, therefore, predict firms with
greater operating earnings disaggregation at the time of the earnings announcement to experience
less opinion divergence at the time of the 10-K filing. We also note, however, that Li and
Ramesh (2009) document a lower investor response to regulatory filings than to earnings
announcements, implying that any reduction in opinion divergence that occurs at the filing is
likely to be of lower absolute magnitude than the increased opinion divergence at the earnings
announcement. Based on this prior research, we predict greater net opinion divergence around
1
For example, the SEC materiality rules require that material line items be separated in firms’ SEC filings.
2
the combined earnings announcement and 10-K filing period for firms with greater operating
earnings disaggregation at the earnings announcement date.
Finally, we hypothesize that greater operating earnings disaggregation is associated with
larger analyst forecast errors after the earnings announcement and more pronounced postearnings announcement drift. We argue, based on the findings in Fairfield et al. (1996), that
disaggregation of operating earnings components with homogeneous persistence leads not only
to increased opinion divergence but also to less accurate forecasts. If analysts and investors rely
on the earnings components in the earnings announcement, then they are likely to erroneously
place differential weights on the operating earnings components when they should place
equivalent weights on the components. This leads to less accurate consensus forecasts and a
lower ability to extrapolate the implications of the information in the current earnings
announcement for future earnings. Based on this notion, we predict larger analyst forecast errors
after the earnings announcement and greater post-earnings announcement drift for earnings
announcements with greater operating earnings disaggregation.
To empirically test our predictions, we examine the relation between the extent of
operating earnings disaggregation and opinion divergence around the earnings announcement for
a sample of firm quarters from 1998 to 2012. Based on the findings in Fairfield et al. (1996), we
measure operating earnings disaggregation based on six sub-components with similar
persistence: (i) sales; (ii) cost of goods sold; (iii) selling, general, and administration expenses;
(iv) depreciation expense; (v) research and development expenses; and (vi) interest expense. To
ensure that the findings in Fairfield et al. (1996) are robust we replicate their study to our sample
period and find that operating earnings disaggregation does not significantly improve forecasts of
future profitability, on average, and significantly improves the average profitability forecast in
3
only three out of the 65 (two-digit SIC code) industries examined. This suggests that
disaggregating operating earnings is unique as it provides no new information, in terms of
forming more accurate next period earnings expectations, but affords investors the opportunity to
differentially interpret the component line items, leading to errors of commission. In addition, to
mitigate concerns that the one period ahead rolling forecasting model used by Fairfield et al.
(1996) imperfectly proxies for the valuation implications of the disaggregation , we employ a
long-window returns test. Specifically, we regress changes in aggregated operating income as
well as changes in the components of operating income on long window returns and find that the
explanatory power of the disaggregated model is not greater than the aggregated model.
Our primary proxy for opinion divergence is excess trading volume, which is consistent
with prior research (Bamber et al. 1997; Kandel and Pearson 1995; Bamber and Cheon 1995;
Bailey et al. 2003; Bamber et al. 2011; Garfinkel and Sokobin 2006; Lee and Swaminathan
2000) and abstracts from trading related to liquidity and price changes. We document a robust
positive relation between the level of operating earnings disaggregation and the magnitude of
excess volume around earnings announcements in a cross-sectional panel with industry, year,
and quarter fixed effects. In contrast, we document no consistent evidence of increased excess
trading around earnings announcements when firms disaggregate earnings components that
exhibit heterogeneous levels of persistence such as non-operating items, special items, or
discontinued operations. Because it is likely that the decision to provide disaggregated operating
earnings in the earnings announcement is endogenous, we also show that the result is robust to a
within-firm analysis (i.e. firm fixed effects), a first-differences specification, and a propensityscore matching design based on the likelihood of reporting high versus low disaggregated
operating earnings. We also show that these results hold for alternative proxies for opinion
4
divergence: the change in idiosyncratic return volatility and the change in analyst forecast
dispersion around the earnings announcement (Diether et al. 2002; Chatterjee et al. 2012).
In addition, as predicted, we find that firms with greater operating earnings
disaggregation in the earnings announcement experience less excess volume around the
subsequent filing date. We also find, however, that the net excess volume over the combined
earnings announcement and regulatory filing windows is higher for firms with greater operating
earnings disaggregation at the earnings announcement.
Finally, we find that greater operating earnings disaggregation is associated with less
efficient earnings pricing. Specifically, we document that consensus analyst forecasts errors
increase around earnings announcements with more operating earnings disaggregation, and these
same firms have stock prices that exhibit significantly greater post-earnings announcement drift
than firms with less operating earnings disaggregation.
Our study provides important contributions to the literature. While prior research
generally suggests that greater disaggregation is beneficial for investors, our results suggest that
there may be costs associated with specific types of disaggregation. In particular, we document
that the disaggregation of items with homogeneous characteristics increases opinion divergence
at the time of the information release. Given that prior research documents that opinion
divergence is associated with a higher risk premium (e.g., Carlin et al. 2014) and with greater
post-earnings announcement drift (e.g., Garfinkel and Sokobin 2006), our findings suggest that
disaggregation of financial statement items with homogeneous characteristics can be costly.
Further, our results speak to the body of research that seeks to understand the total
information set included with the release of bottom-line earnings (e.g., Francis et al. 2002). This
line of research generally focuses on concurrent disclosures in the earnings announcement and
5
concludes that more disclosure is beneficial to investors in the form of increased information
content. Instead, we show that some disclosures – particularly those with homogenous
characteristics – can actually lead to negative market consequences, in the form of increased
investor opinion divergence, greater earnings forecast errors, and greater post-earnings
announcement drift.
The findings of this study are also important for practice. Managers and regulators appear
to be focused on increasing the amount of disaggregation in financial reporting. Specifically, one
key objective in the FASB Disclosure Framework proposal is the provision of more
disaggregated information to investors to help inform their future cash flow projections (FASB
2008). Furthermore, the SEC is calling for increased disaggregation based on XBRL tagging
(across items and within items). The presumption is that more disaggregation is better for
stakeholders. However, our results suggest that managers and regulators should give more
consideration to the types of disaggregation, especially with respect to items with homogeneous
characteristics, as disaggregating items with similar persistence has the potential to lead to
negative capital market consequences such as greater opinion divergence and less efficient
pricing of the earnings information.
The remainder of the paper proceeds as follows. In Section 2, we review the literature
and develop our hypotheses. In Section 3, we describe our research design and sample selection.
In Section 4, we provide the results of our empirical tests. Section 5 concludes the paper with a
summary of our results and a discussion of their implications.
6
2. Hypothesis development
Earnings announcements are important events that contain substantial variation in the
manner in which earnings are presented to the market. Specifically, earnings are not released in
isolation, but rather are part of a detailed and informative press release containing both the
bottom-line earnings and other line items and disclosures (Francis et al. 2002). As such, prior
research has sought to understand the market implications of the variation in earnings
announcement presentation. The general consensus of this prior work is that increased
disaggregation is associated with favorable market outcomes. For example, Francis et al. (2002)
document that the disclosure of disaggregated earnings items increases the usefulness of the
earnings announcement press release. Fairfield et al. (1996) document forecasting improvements
from earnings disaggregation, Lipe (1986) shows that earnings components provide more
accurate valuations than bottom-line earnings alone, and Elliott et al. (2011) document that
earnings disaggregation shifts investor focus from short-term results to long-term potential (i.e.,
it reduces the amount of earnings fixation). Prior research also suggests that earnings
disaggregation increases the credibility of the information (Hirst et al. 2007; Merkley et al.
2013).
While prior research documents significant benefits to increased earnings disaggregation,
a fundamental presumption in this research is that increased disaggregation helps to segregate
items with heterogeneous characteristics, such as differential persistence, precision, or
subjectivity. The relation between increased disaggregation and market outcomes is less clear,
however, when items with homogeneous characteristics are disaggregated. We hypothesize that
there are negative market consequences from the disaggregation of earnings into components
with homogeneous characteristics in the earnings announcement. These earnings subcomponents
7
may be viewed as a series of repetitive (or uninformative) signals when compared to the
aggregated earnings signal. Consequently, we conjecture that earnings disaggregation into
components with homogeneous characteristics can induce reliance on uninformative signals if
some traders inappropriately apply different weights to each of the line items.
This conjecture is consistent with the growing “differences-of-opinion” theoretic
literature which suggests that investors with homogenous information about fundamentals may
still disagree on how to interpret common public signals (e.g., Varian 1985, 1989; Harris and
Raviv 1993; Kandel and Pearson 1995; Banerjee and Kremer 2010; Bloomfield and Fischer
2011). For example, Bloomfield and Fischer (2011) refer to reliance on uninformative signals as
errors of commission. They show analytically that errors of commission are a source of
disagreement, or opinion divergence, which can lead investors to have less precise beliefs about
future stock returns. Based on this literature, we predict that greater disaggregation of earnings
into components with homogeneous characteristics leads to errors of commission which results
in greater opinion divergence. This leads to the following hypothesis:
H1: Greater disaggregation of earnings into components with
homogeneous characteristics in an earnings announcement is positively
associated with opinion divergence.
We also make predictions regarding opinion divergence surrounding the mandatory
regulatory filing. While firms demonstrate significant variation in earnings disaggregation at the
time of the earnings announcement, regulatory requirements yield comparable levels of
disaggregation for firms with similar operating environments in the mandatory filing. As such,
firms with aggregated earnings announcement disclosures ultimately reveal the disaggregated
earnings items at the filing date. Because the filing date represents the first instance for investors
to apply their differing views of the earnings items for the firms which initially disclosed
8
aggregated earnings, the opportunity for errors of commission is higher. Thus, following similar
intuition as our first hypothesis, we present the following hypothesis:
H2a: Greater disaggregation of earnings into components with
homogeneous characteristics in an earnings announcement is negatively
associated with opinion divergence at the subsequent 10-K filing date.
Li and Ramesh (2009) document that regulatory filings engender lower investor
responses than earnings announcements. Further, investors likely make many of their valuation
decisions at the time of the earnings announcement. 2 As such, because disaggregation is delayed
for more aggregated earnings announcements, we expect the combined influence of errors of
commission to be smaller. Thus, we present the following hypothesis:
H2b: Greater disaggregation of earnings into components with
homogeneous characteristics in the earnings announcement is positively
associated with net opinion divergence during the combined earnings
announcement and 10-K filing period.
Finally, we argue that the disaggregation of earnings into components with homogeneous
characteristics leads not only to increased opinion divergence but also to less accurate
expectations of future earnings. Fairfield et al. (1996) suggest that forecasts using earnings
disaggregated into components with similar prior persistence are significantly less accurate than
forecasts that rely on more aggregated earnings. If analysts rely on the earnings components in
the earnings announcement, then there is more opportunity to erroneously place differential
weights on the components when they should place equivalent weights on the components. This
increased opportunity to commit errors of commission should lead to less accurate analyst
earnings forecasts overall. In addition, if investors make errors of commission by erroneously
placing differential weights on earnings items with similar persistence, then they are less able to
Research on analyst forecast revisions supports this presumption. For example, prior work documents a significant
concentration of earnings and recommendation revisions around the time of the earnings announcement (e.g.,
Bowen et al. 2002; Ivković and Jegadeesh 2004).
2
9
extrapolate the implication of current earnings for future earnings, which leads to more
pronounced post-earnings announcement drift. This prediction is consistent with the findings in
Garfinkel and Sokobin (2006) that opinion divergence is positively associated with post-earnings
announcement drift. This leads to our final hypothesis:
H3: Greater disaggregation of earnings into components with similar
characteristics in the earnings announcement is positively associated with
analyst forecast errors after the earnings announcement and postearnings announcement drift.
3. Sample selection and research design
3.1 Sample selection
Table 1 provides the details of our sample selection. It begins with all firm-quarter
observations in the intersection of the Compustat Preliminary Earnings and I/B/E/S databases
from 1998 to 2012 (114,988 observations) where the earnings announcement occurs before the
regulatory filing (i.e. non-concurrent filers). 3 We require I/B/E/S coverage to better identify the
actual trading day and time of the earnings announcement. For our initial analyses we drop
3,459; 1,396; 17,222; and 7,208 observations due to inadequate CRSP data available to calculate
our opinion divergence proxies, inadequate CRSP data available to calculate control variables,
inadequate Compustat data to calculate control variables, and those instances where there is less
than a calendar week between the earnings announcement and the related SEC filing (i.e.,
We start our sample in 1998 because I/B/E/S started providing large scale time stamp information which is
necessary for our identification of earnings release dates. We end our sample in 2012 because the Quarterly
Compustat Preliminary History and Unrestated Quarterly databases transitioned to Compustat Snapshot in 2013 and
we want to ensure consistency for our OCORE measure. We also note that prior to 2000 the preliminary history
database used to identify earnings announcement line items collected information from either the earnings
announcement itself or the Wall Street Journal summary announcement, which is not as detailed as the primary
earnings announcement (D’Souza et al. 2010). As a robustness test we replicate all primary analyses for the sample
period 2000-2012 and find qualitatively similar results.
3
10
concurrent filers), respectively. 4 This yields our primary sample of 85,703 firm quarters, which
we use in tables 2 through 5. Each of our subsequent analyses (in tables 6 through 9) necessitates
additional data restrictions, which we detail in Table 1.
[Please place Table 1 about here]
3.2 Variable definitions
Our research questions contain two primary constructs: (i) opinion divergence and (ii) the
extent of earnings disaggregation into components with homogeneous characteristics. We
discuss each of these in turn. Additionally, we define all variables (including controls) in
Appendix A.
3.2.1 Opinion divergence
Our primary proxy for opinion divergence is excess volume, or abnormal volume that is
unproductive in terms of changing prices or providing information content. The use of excess
trading volume as a proxy for opinion divergence is consistent with prior research (Bamber et al.
1997; Kandel and Pearson 1995; Bamber and Cheon 1995; Bailey et al. 2003; Bamber et al.
2011; Garfinkel and Sokobin 2006; Lee and Swaminathan 2000).
A reliable measure of excess volume needs to abstract away three components of total
volume that are potentially productive: (i) volume to meet liquidity trading needs; (ii) volume
attributed to price movements; (iii) volume attributed to earnings announcement information. We
follow Garfinkel (2009) to control for the first two components by measuring unexplained
volume because his study finds that measures of unexplained volume are the most reliable
proxies for abnormal investor disagreement around earnings announcements. Specifically, we
We require firm earnings announcements to occur at least a week prior to their regulatory filing, in order to isolate
the market’s reaction to the earnings announcement.
4
11
use the two unexplained volume proxies in Garfinkel (2009): change in market-adjusted turnover
(DTO) and standardized unexpected volume (SUV). Further, since Garfinkel (2009) suggests
potential benefits from a factor analysis, our primary analyses utilize the first principal
component of DTO and SUV, which we label as EXVOL. These proxies are common in the
extant literature (e.g., Gebhardt et al. 2001; Garfinkel and Sokobin 2006).
Intuitively, these proxies seek to isolate excess volume by controlling for firm-specific
and market-wide trading activity, along with the normal influence of price movements. However,
it is important to note that these measures are derived using an expectation of normal daily
volume from market days that do not include the earnings announcement. Consequently, we also
include several measures in our subsequent models to control for information that is specific to
the earnings announcement window.
Our procedure mimics that of Garfinkel and Sokobin (2006) and Garfinkel (2009).
Specifically, for DTO we begin with a firm’s daily turnover (i.e., the percentage of outstanding
shares traded on any particular day). Next, to control for market-wide trading activity, we
subtract market-wide turnover (calculated in the same fashion for all NYSE / AMEX stocks).
Finally, to control for firm-specific liquidity trading, we subtract trading activity over a control
period (EA-54 to EA-5, where EA is the earnings announcement date). Formally, our DTO
measure for any trading day t is calculated as follows:
𝐸𝐸𝐸𝐸−5
𝑉𝑉𝑉𝑉𝑉𝑉𝑖𝑖,𝑡𝑡
𝑉𝑉𝑉𝑉𝑉𝑉𝑖𝑖,𝑡𝑡
𝑉𝑉𝑉𝑉𝑉𝑉𝑖𝑖,𝑡𝑡
𝑉𝑉𝑉𝑉𝑉𝑉𝑖𝑖,𝑡𝑡
𝐷𝐷𝐷𝐷𝐷𝐷 = ���
�
−�
� �� − � ��
�
−�
� � / 50
𝑆𝑆ℎ𝑠𝑠𝑖𝑖,𝑡𝑡 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓
𝑆𝑆ℎ𝑠𝑠𝑖𝑖,𝑡𝑡 𝑚𝑚𝑚𝑚𝑚𝑚
𝑆𝑆ℎ𝑠𝑠𝑖𝑖,𝑡𝑡 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓
𝑆𝑆ℎ𝑠𝑠𝑖𝑖,𝑡𝑡 𝑚𝑚𝑚𝑚𝑚𝑚
𝐸𝐸𝐸𝐸−54
(1)
In addition to DTO, we measure standardized unexpected volume (SUV) which controls
for both the liquidity effect and the informedness effect (i.e., that the arrival of new information
about a stock can lead to more volume). This approach mirrors the market model approach for
estimating abnormal returns. Specifically, we follow the methodology in Garfinkel and Sokobin
12
(2006) and calculate SUV as a scaled prediction error from a regression of trading volume on the
absolute value of returns for day t and firm i, where the estimation window for t is between EA54 and EA-5, and where EA is the earnings announcement date. Formally:
𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖,𝑡𝑡 =
𝑈𝑈𝑈𝑈𝑖𝑖,𝑡𝑡
,
𝑆𝑆𝑖𝑖,𝑡𝑡
(2)
𝑈𝑈𝑈𝑈𝑖𝑖,𝑡𝑡 = 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑖𝑖,𝑡𝑡 − 𝐸𝐸�𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑖𝑖,𝑡𝑡 �,
+
(3)
−
𝐸𝐸�𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑖𝑖,𝑡𝑡 � = 𝛼𝛼𝑖𝑖 + 𝛽𝛽1 ∗ �𝑅𝑅𝑖𝑖,𝑡𝑡 � + 𝛽𝛽2 ∗ �𝑅𝑅𝑖𝑖,𝑡𝑡 � .
(4)
The plus and minus superscripts indicate when returns are positive or negative. This treatment is
designed to recognize that the relation between volume and the absolute value of returns differs
when returns are positive versus negative (Karpoff 1987). Finally, Si,t is the standard deviation of
residuals from the regression, calculated over the model’s estimation period. EXVOL is the first
principal component of SUV and DTO on day t+x for firm-quarter i, with day t as the earnings
announcement date and with x as -2 through +2 (i.e., two days before through two days after t).
Prior research has also used changes in idiosyncratic return volatility and analyst forecast
dispersion around the earnings announcement as proxies for opinion divergence (Diether et al.
2002; Chatterjee et al. 2012). Based on this research, we examine the robustness of our results to
alternative proxies for opinion divergence using the change in idiosyncratic stock return
volatility and the standard deviation of analyst forecasts. We define the change in idiosyncratic
stock return volatility (ΔIDVOL) as the standard deviation of idiosyncratic returns for days [2,10]
less the standard deviation of idiosyncratic returns for days [-10,-2], where the days are relative
to the earnings announcement date. Idiosyncratic returns are the residuals from a three-factor
model estimated over the [-50,50] event window. We define the change in the standard deviation
of analyst forecasts (ΔSTDEV) as the difference between the standard deviation of analyst
forecasts for the subsequent quarter’s earnings five days after the earnings announcement, and
13
the standard deviation of analyst forecasts for the subsequent quarter’s earnings five days prior to
the earnings announcement.
3.2.2 Extent of earnings disaggregation into components with homogeneous characteristics
We examine whether the disaggregation of items with similar persistence in the earnings
announcement is positively associated with opinion divergence. We focus on persistence (or the
extent to which an earnings item is indicative of its future amount) as the characteristic of
interest because investors generally use earnings announcements to update their assessment of
firm value. Given that forecasting future payoffs is fundamental for valuation, determining the
persistence of earnings is of central importance in interpreting the information provided in the
earnings announcement. In addition, standard setters have made the classification of earnings
items based on their differential persistence a fundamental feature of financial statements. For
example, the FASB has required firms to disaggregate items that are unusual and/or infrequent
from other earnings since the 1970s (FASB 1973). In addition, prior research finds that
persistence classifications are useful to investors (Bowen 1981; Lipe 1986; Strong and Walker
1993) as well as analysts (Bradshaw and Sloan 2002; Christensen et al. 2011; Ali et al. 1992).
Fairfield et al. (1996) examine the accuracy of profitability forecasting models that
systematically consider more disaggregated earnings. Fairfield et al. (1996) find that the
disaggregation of earnings into operating income, non-operating income and income taxes,
special items, and non-recurring items improves the accuracy of profitability forecasts. However,
they find that further disaggregation of operating income into gross margin, selling general and
administrative expenses, depreciation, and interest does not improve forecasts of future
profitability. In-sample estimations suggest that the lack of forecast accuracy improvement from
operating earnings disaggregation is driven by similar persistence of the component line items.
14
Based on this finding, to proxy for the extent of earnings disaggregation into components
with homogeneous characteristics within the earnings announcement, we develop an index of six
Compustat line items that are documented to have similar persistence: (i) sales; (ii) cost of goods
sold; (iii) selling, general, and administrative expenses; (iv) depreciation expense; (v) research
and development expenses; and (vi) interest expense. We compute a ratio (OCORE) of these
operating line items that are disclosed in the earnings release to what is disclosed in SEC
regulatory filing. The numerator is the number of these non-missing and non-zero operating
earnings components from the Quarterly Compustat Preliminary History database, which is
populated from the earnings announcement. The denominator is the number of non-missing and
non-zero operating earnings components from the Compustat Unrestated Quarterly database,
which is populated from the initial SEC regulatory filing. This approach builds on the work in
D’Souza et al. (2010) for a refined set of operating earnings line items. 5 The ratio compares the
degree of disaggregation in the earnings announcement to the disaggregation in the subsequent
SEC regulatory filing.
We present a histogram of the OCORE distribution in Figure 1. For our primary analyses
we utilize a scaled tercile (between 0 and 1) variable of OCORE, which we designate OCORE3.
The cutoffs for OCORE3 are also indicated in Figure 1. Essentially, our sample consists of three
operating earnings disaggregation groups: (i) an aggregated group, with less than 80 percent of
the line items disaggregated in the earnings announcement (OCORE3=0); (ii) a moderately
disaggregated group, with greater than or equal to 80 percent but less than 100 percent of the line
D’Souza et al. (2010) verify the accuracy of the quarterly Preliminary History and Unrestated Quarterly Compustat
files. Additionally, this approach has been used in Evans (2015) to examine the association between balance sheet
disclosures and cost of capital and in Schroeder (2016) to examine the association between audit completeness and
quality and earnings announcement GAAP disclosure details.
5
15
items disaggregated (OCORE3=0.5); and (iii) a fully disaggregated group, with 100 percent of
the line items disaggregated in the earnings announcement (OCORE3=1). We note that, on
average, the firms in the OCORE3=0 group have three, the firms in the OCORE3=.5 group have
four, and the firms in the OCORE3=1 group have five of the operating earnings line items
disaggregated in the earnings announcement.
[Please place Figure 1 about here]
3.3 Model specification
We examine the relation between opinion divergence (DIVOP) around an earnings
announcement and the extent of operating earnings disaggregation. To test our first hypothesis,
we synthesize models in prior work exploring excess volume around earnings announcements
(see Bamber et al. 2011 for a review) and attempt to directly control for factors related to
earnings announcements. Specifically, we begin by estimating the following regression model
with variable definitions found in Appendix A:
DIVOPi,t = α0 + β1 OCORE3i,t + Announcement Controls + Firm Controls +
Industry Fixed Effects + Year Fixed Effects + Quarter Fixed Effects + εi,t.
(5)
The dependent variable, DIVOP, is either EXVOL, ∆IDVOL, or ∆STDEV. OCORE3 is the scaled
tercile ranking (between 0 and 1) of OCORE, or the operating earnings disaggregation in the
earnings announcement.
Following prior research, we include a series of announcement controls to isolate the
opinion divergence around the earnings announcement and a series of firm controls that are
expected to influence opinion divergence around the earnings announcement. Specifically, we
include |ABNRET| and |SURP| to control for the total information content of the earnings
announcement (e.g., Beaver 1968; Karpoff 1987; Landsman and Maydew 2002). We also control
for the presence of expanded or concurrent disclosures with the earnings announcements, such as
16
a balance sheet, a cash flow statement, non-operating line items, a bundled management forecast,
and the presence of street or pro forma earnings (Hoskin et al. 1986; Francis et al. 2002; Collins
et al. 2009; Pawlewicz 2011; Miao et al. 2013). Finally, we control for earnings announcements
that disclose losses because investors treat losses differently than gains (Hayn 1995). As for firm
controls, we control for firm size (Bamber 1987) and preannouncement uncertainty (Kim and
Verrecchia 1991, 1997). We also control for firm growth, financial distress, analyst following,
and underlying complexity.
To test our second hypothesis, we compare the magnitude of excess volume at the filing
date to that around the earnings announcement. Specifically, we stack the fourth quarter earnings
announcement and filing date observations and estimate the following linear regression:
EXVOLi,t = α0 + α1FILE+ β1 EA*OCORE3i,t + β1 FILE*OCORE3i,t +
Announcement Controls + Firm Controls + Industry Fixed Effects +
Year Fixed Effects + εi,t
(6)
where FILE (EA) is an indicator variable set to one if day t represents the filing (earnings
announcement) date, and zero if it represents the earnings announcement (filing) date.
To test our third hypothesis, we use two related specifications. First, we examine absolute
analyst forecast errors with a specification that is similar to that in equation (5), however we
replace opinion divergence with absolute analyst forecasts errors. Specifically, we estimate the
following model:
|POST FEi,t+1| = α0 + β1 |PRE FEi,t+1| + β2 OCORE3i,t + Announcement
Controls + Firm Controls + Year Fixed Effects + Quarter Fixed Effects + εi,t,
(7)
where, |POST FE| (|PRE FE|) is the absolute value of the analyst forecast errors for the
subsequent quarter based on analyst forecasts 5 days after (5 days before) the earnings
announcement. All other control variables are defined in Appendix A.
17
Second, we examine whether post-earnings announcement drift is associated with the
extent of operating disaggregation. For this analysis, we perform a regression with the buy-andhold returns calculated with three-factor model residuals for days [2,20], relative to the earnings
announcement, as the dependent variable (BHAR (2,20)). The explanatory variables include the
demeaned quarterly decile rank of the earnings surprise (D(FEt)), OCORE3, and the interaction
of these two variables. Formally, we estimate the following regression:
BHARi,t = α0 + β1 D(FEi,t) + β2 OCORE3i,t + β3 D(FEi,t) * OCORE3i,t +
β4 MFi,t + β5 D(FEi,t) * MFi,t + Firm Controls + D(FEi,t) * Firm Controls +
Firm Fixed Effects + Year Fixed Effects + Quarter Fixed Effects + εi,t.
(8)
Following prior research, we include a control variable for whether the firm provided a bundled
forecast in its earnings disclosure (MF) and we also interact this MF variable with the earnings
surprise as prior research finds that the provision of a forecast can influence drift (Ng et al.
2013). Moreover, we also include controls for company characteristics and interact these controls
with the earnings surprise because prior research finds that major firm characteristics like analyst
following (Zhang 2008) and share price (Bhushan 1994) are associated with the magnitude of
post-earnings announcement drift.
4. Empirical results
4.1 Descriptive statistics
Table 2 presents details for our opinion divergence measures and descriptive statistics
across the three partitions of the extent of the operating earnings disaggregation. Panel A shows
that the average eigenvalue for the first principal component of DTO and SUV, which we label
EXVOL, is 1.56. Further, it shows that EXVOL explains 78 percent of the variance in DTO and
SUV. Because Garfinkel (2009) documents the usefulness of DTO and SUV in capturing opinion
18
divergence, this evidence of common variance across the two measures suggests that we are
adequately capturing opinion divergence.
In Panel B, we provide descriptive statistics across the three partitions of the extent of
operating earnings disaggregation. The top portion provides univariate evidence in support of our
first hypothesis, as excess volume at the earnings announcement date is significantly higher for
the disaggregated partition (i.e., OCORE3=1) compared to the aggregated partition (i.e.,
OCORE3=0). The descriptive statistics also highlight the importance of our announcement and
firm control variables, as the majority of the variables differ significantly across the partitions.
As such, we consider multivariate analyses, firm-fixed effect regressions, a first-difference
analysis, and a propensity-score matched analysis to evaluate our first hypothesis.
[Please place Table 2 about here]
4.2 Replication of Fairfield et al. (1996)
To ensure that the findings in Fairfield et al. (1996) are robust to our time period and to
our operating earnings disaggregation, we first replicate the previous findings that the
disaggregation of operating earnings is not useful for forecasting profitability. We use seven-year
rolling windows to estimate three forecasting models that increasingly use more disaggregated
earnings. The ROE model uses ROE before special items to explain year-ahead ROE before
special items. The OPINC model uses operating income and non-operating income plus income
taxes to explain year-ahead ROE before special items. The FULL model further disaggregates
operating income into sales, cost of goods sold, selling general and administrative expenses,
depreciation, research and development expense, and interest expense, and disaggregates nonoperating income from income taxes to explain year-ahead ROE before special items. The
estimations are calculated using cross-sectional pooled time-series regressions for seven-year
19
rolling windows. The in-sample estimations are presented in Panel A of Table 3. While we rely
on out-of-sample forecast accuracy to interpret the usefulness of the models, we note that the
components of operating income have very similar persistence.
We apply the estimates from each of the seven-year rolling windows to the component
line items in year t to forecast ROE before special items in year t+1 out-of-sample. We then test
for forecast improvement from the disaggregated model relative to the more aggregated model.
The forecast accuracy improvement results from the OPINC model over the ROE model are
presented in Panel B, and the forecast accuracy improvement results for the FULL model over
the OPINC model are presented in panel C. Panel B documents a significant median
improvement and a significant economic improvement in forecast accuracy from the OPINC
model over the ROE model. 6 The improvement holds for all three subsets of observations based
on the absolute value of the difference in the two forecasts. In contrast, Panel C documents the
FULL model does not improve the median accuracy of profitability forecasts and leads to
significantly worse forecasts in economic terms. In addition, the FULL model improves forecasts
at the median only for the 1,140 of the 46,128 observations with the greatest difference in
magnitude between the forecasts.
In untabulated analyses, we also perform the in-sample estimation and out-of-sample
forecast accuracy tests by two-digit SIC code. We find that the OPINC model yields significantly
more accurate forecasts than the ROE model in 26 of the industries and significantly less
accurate forecasts in four of the industries. In contrast, we find that the FULL model yields
significantly more accurate forecasts than the OPINC model in three of the industries (i.e., SIC
The economic improvement is presented by providing the percentage of observations for which the disaggregated
model improves forecast accuracy by more than .005 and the percentage of observations for which the disaggregated
model worsens forecast accuracy by more than .005.
6
20
code 35, 87, and 89) and significantly less accurate forecasts in 27 of the industries. These results
suggest that on average and for the vast majority of firms, the disaggregation of operating
earnings does not improve forecast accuracy and that the disaggregation of operating earnings is
an uninformative signal, which we predict could lead to errors of commission.
[Please place Table 3 about here]
As a complementary test, we also examine the informativeness of the disaggregation of
the change in operating earnings for long-window returns. This test is designed to help mitigate
concerns that a rolling forecast model for year-ahead earnings does not capture the implications
of the disaggregation for valuation. 7 To examine the robustness of the implications regarding the
informativeness of operating earnings disaggregation from Fairfield et al. (1996), we estimate the
following least squares regressions:
ADJRETit = α + β1 ΔROEit + εit
(9a)
ADJRETit = α + β2 ΔOPINCit + β3 ΔNONOPTAXit + εit
(9b)
ADJRETit = α + β4 ΔSALESit + β5 ΔCOGSit + β6 ΔSGAit + β7 ΔDEPRECit + β8 ΔRNDit
+ β9 ΔINTERESTit + β10 ΔNONOPit + β11 ΔTAXit + εit,
(9c)
where ADJRET is the 16-month compounded firm return starting at the beginning of the fiscal
year less the compounded market return over the same period (Hanlon, LaPlante, and Shevlin
2005). All variables are defined in Appendix A.
Columns (1) through (3) of Table 4 tabulate the results of estimating equations 9a, 9b,
and 9c on all firm-years with sixteen months of returns coverage in the CRSP monthly file and
financial accounting data coverage in Compustat during the period of 1998 to 2012. To assess
For example, the operating earnings disaggregation could be useful for forecasting long-term profitability or for
assessing risk (i.e., the cost of capital).
7
21
whether a model is more informative for long-window returns we test for differences in the R2 of
the models using the Vuong test. We find that model 9b tabulated in column (2) has higher
explanatory power for returns than model 9a (Vuong statistic: 10.65***). This is consistent with
the findings from Fairfield et al. (1996) that the disaggregation of ROE into operating (ΔOPINC)
and non-operating income (ΔNONOPTAX) yields more accurate forecasts of future earnings
than aggregated earnings. Moreover, we find that the informativeness of model 9b is greater than
model 9c (Vuong statistic: 3.44***). These findings complement those from Table 3 and suggest
that the disaggregation of operating earnings does not provide incremental information content
over aggregated operating income in explaining stock returns.
[Please place Table 4 about here]
4.3 Primary Tests of H1
Prior to exploring multivariate analyses, we begin by plotting the median daily values of
DTO and SUV around earning announcements for the disaggregated (OCORE3=1) and
aggregated (OCORE3=0) partitions in Figure 2. For both measures of excess volume, we
document higher levels of excess volume for the disaggregated group than for the aggregated
group at the time of the earnings announcement. This provides preliminary support of our first
hypothesis, which we formally test in Tables 5 and 6 that follow.
[Please place Figure 2 about here]
Table 5 presents the results of the examination of the relation between the extent of
operating earnings disaggregation and excess volume after controlling for earnings
announcement information. Specifically, Table 5 presents the results of multivariate regressions
using equation (5). We estimate the regression separately for each trading day in the window t-2
to t+2, where t is the earnings announcement date. Results are consistent with higher excess
22
volume for firm-quarters with greater operating earnings disaggregation, but only on or after the
earnings announcement date. Specifically, the coefficient on OCORE3 is positive and significant
for days t, t+1, and t+2, but not for days t-1 or t-2. These results not only support hypothesis H1,
but also lend credence to the notion that the increased excess volume stems from information in
the earnings announcement rather than unspecified firm-specific factors. Further, our coefficient
estimate of 0.03618 on day t suggests that, after controlling for announcement and firm
characteristics, a switch from low disaggregation to high disaggregation (i.e., OCORE3=0 vs.
OCORE3=1) explains approximately 18.5 percent of the difference in EXVOL between the two
groups.
Additionally, our results highlight the importance of disaggregating items with
homogeneous characteristics versus disaggregating items with heterogeneous characteristics. For
example, disaggregation of non-recurring items (i.e., NONOP, SPI, and DISCO) has no effect or
actually reduces excess volume. This is consistent with the findings in Fairfield et al. (1996) and
our replication above that disaggregating non-operating items captures differential persistence
and improves forecasts of profitability.
Our results also show the importance of our earnings announcement controls. For
example, we find a positive association between excess volume (EXVOL) and the absolute value
of the earnings surprise (|SURPt|) and the absolute value of the returns in the announcement
window (|ABNRET|). Further, we find that the inclusion of a management forecast (MF), and a
statement of cash flows (CF) is positively associated with our proxy for opinion divergence
around the earnings announcement. We also note that we do not delve into the level of
disaggregation within the MF or CF because prior research provides no guidance as to whether
the line items in these disclosures have differential characteristics.
23
That said, we also perform robustness tests to ensure that the results are not driven by
other information bundled into the earnings announcement. Specifically, we re-run our analysis
in Table 5 after deleting observations with a bundled management forecast and after deleting
observations with pro forma earnings. The coefficient on OCORE3 remains positive and
significant when these observations are excluded.
[Please place Table 5 about here]
4.4 Robustness Tests for H1
We also examine the robustness of our results in support of H1 across four additional
dimensions. First, in order to provide insight into whether greater operating earnings
disaggregation is associated with higher levels of excess volume around the earnings
announcement not only in a cross-sectional setting, but also for a specific firm over time, the first
column of Table 6 presents results of the regression analyses for equation (5) with firm fixed
effects. The inclusion of firm fixed effects allows an examination of whether the deviation of a
firm’s excess volume from its average is positively associated with the deviation of the firm’s
level of operating earnings disaggregation from its average. Consistent with the results in Table
5, we document a positive and significant coefficient on OCORE3 during the t to t+1
announcement window, documenting that our results are robust to firm fixed effects. In addition
to the within-firm inference, this result also has the added benefit of controlling for potential
time-invariant unobservable characteristics that may not have been controlled for in Table 5.
Second, in the second column of Table 6, we examine a regression of first differences in
OCORE3 and excess volume. This analysis provides greater insight into whether changes in a
firm’s excess volume is positively associated with changes in the firm’s level of operating
earnings disaggregation. Consistent with the Table 5 and firm fixed effect results, we document a
24
positive and significant coefficient on ∆OCORE3 during the t to t+1 announcement window.
Like the firm fixed effect analysis, this analysis controls for potential time-invariant
unobservable characteristics that may not have been controlled for in Table 5.
Third, in the third column of Table 6, we examine the robustness of our results to a
HIOCORE dummy variable in lieu of the scaled tercile variable. In essence, we drop the middle
tercile of OCORE3 and re-estimate our regression. Results are consistent with those in Table 5,
as the HIOCORE variable is positive and significant during the t to t+1 announcement window.
Fourth, in the fourth column of Table 6, we examine the robustness of our H1 result to a
propensity score-matched sample across high- and low-OCORE subgroups. While the extent of
operating earnings disaggregation in an earnings announcement is exogenous to investors’
information set, it is an endogenous choice for managers. To the extent that managers determine
the extent of disaggregation based on the type of information to be disclosed, our results may
reflect this self-selection. To account for this possibility, we predict the operating earnings
disaggregation choice using disclosure models from prior research (e.g., D’Souza et al. 2010;
Schroeder 2016) and re-estimate our results with a propensity score-matched sample of treatment
(high operating earnings disaggregation) and control (low operating earnings disaggregation)
observations that have the same predicted probabilities of operating income disaggregation based
on observable characteristics with covariate balance. See Appendix B for further details on our
procedure and the results of the propensity-score model. Consistent with the results thus far, we
find a positive and significant coefficient on HIOCORE during the t to t+1 announcement
window using the propensity score matched sample. Collectively, our results provide strong
support for our first hypothesis that greater operating earnings disaggregation within an earnings
announcement is associated with greater opinion divergence.
25
[Please place Table 6 about here]
Finally, we note that the in-sample coefficient estimates reported in Table 3 suggest that
certain components of operating earnings are less homogenous than other items. To ensure that
our results are not driven by one of these line items, we re-estimate our Table 5 analyses in a
variety of subsamples. Specifically, in untabulated analyses, we remove observations from the
sample in which research and development is disclosed, depreciation is disclosed, or interest
expense is disclosed in the earnings announcement. The coefficient on OCORE3 remains
positive and significant in each of these analyses.
While our focus thus far has been on differential magnitude of opinion divergence around
earnings announcements, we also expect differential duration of the opinion divergence
following the earnings announcement. To assess duration, we use DTO and SUV as proxies for
excess trading volume and graph Kaplan-Meier survival estimates (i.e., the likelihood of
remaining at an abnormal level of trading volume) separately for firms with a high level of
operating earnings disaggregation (OCORE3=1) and for firms with a low level of operating
earnings disaggregation (OCORE3=0). Figure 3 shows (for both a one week and a two week
horizon) that the survival estimates for firms with disaggregated operating earnings consistently
lie above those for firms with aggregated operating earnings, suggesting a longer duration of
opinion divergence. 8
[Please place Figure 3 about here]
Additionally, in untabulated analyses, we also assess the duration of opinion divergence in a multivariate design
with hazard model estimation and a full panel of control variables. Consistent with the results in Figure 3, we
document that excess volume persists for a longer period when operating earnings are more disaggregated in the
earnings announcement compared to when they are less disaggregated. Specifically, we document a negative and
significant coefficient on OCORE3, which suggests that as the extent of operating earnings disaggregation increases,
the likelihood of returning to normal levels of trading volume decreases. Results are robust to either measure of
excess volume (DTO or SUV).
8
26
4.5 Alternative proxies for opinion divergence
Table 7 reports the results of testing H1 with alternative proxies for opinion divergence.
Specifically, we examine the change in idiosyncratic return volatility (∆IDVOL) and the change
in the standard deviation of analyst forecasts (∆STDEV) around the earnings announcement. The
first column documents a significant positive relation between OCORE3 and the change in
idiosyncratic return volatility, suggesting that greater operating earnings disaggregation is
associated with a significant increase in idiosyncratic volatility. Column (2) documents a
significant positive relation between the extent of operating earnings disaggregation and the
change in the standard deviation of analyst forecasts. These results provide further support of our
hypothesis H1 result of a positive association between operating earnings disaggregation and
opinion divergence.
[Please place Table 7 about here]
4.6 Tests of H2
To test hypothesis H2, we exploit the fact that while firms may demonstrate variation in
the extent of operating earnings disaggregation in the earnings announcement, regulatory
requirements yield comparable levels of disaggregation for firms with similar operating
environments in the mandatory filing. Because the regulatory filing is likely the first disclosure
of disaggregated operating earnings for our set of aggregated firms (and the propensity for errors
of commission is therefore higher), we predict a negative association between the extent of
operating earnings disaggregation at the time of the earnings announcement and the amount of
excess volume at the time of the 10-K filing.
In Table 8, we present the results of our estimation of equation (6). We stack the fourth
quarter earnings announcement and the corresponding firm 10-K daily excess volume
observations and split the coefficient on OCORE3 by interacting it with a dummy for the
27
observations that relate to the earnings announcement (EA*OCORE3), and a dummy for the
observations that relate to the filing (FILE*OCORE3). We also include a dummy variable that
controls for a main effect of excess volume around the filing observations. We note that our
analysis is restricted to only 10-K filings with a minimum of two weeks between the earnings
announcement and 10-K filing. We make this restriction to minimize concerns of overlapping
periods and to ensure that sufficient time has passed following the earnings announcement. We
focus on the 10-K filing instead of the 10-Q filing because prior research documents little market
reaction to interim filings (e.g., Li and Ramesh 2009).
As predicted in hypothesis H2a, we show a significant negative coefficient on
FILE*OCORE3 for the days around the 10-K filing. Additionally, consistent with the prior
results we document a positive coefficient on EA*OCORE3 for the days around the earnings
announcement. Thus, we believe that these results provide evidence of differential excess
volume around the regulatory filing, which is consistent with H2a. In Table 8, we also evaluate
hypothesis H2b, which predicts that the positive association between the level of operating
earnings disaggregation and excess volume at the earnings announcement date is more
pronounced than the negative association between the level of operating earnings disaggregation
and excess volume at the 10-K filing date. Specifically, we perform f-tests and document that
EA*OCORE3 plus FILE*OCORE3 is significantly greater than zero on the day after the filing is
released (i.e., on day t+1). However, we do not document a difference from zero for days t and
t+2. As such, these results partially support H2b.
[Please place Table 8 about here]
4.7 Tests of H3
Table 9 presents our results on the association between operating earnings disaggregation
in the earnings announcement and the magnitude of the analyst forecast errors after the earnings
28
announcement. Consistent with our predictions in H3, we find a positive and significant relation
between analyst forecast errors and the extent of operating earnings disaggregation. This analysis
suggests that not only does greater operating earnings disaggregation lead to increased investor
opinion divergence, but it also leads to less accurate earnings forecasts by analysts.
[Please place Table 9 about here]
Lastly, we also examine whether post-earnings announcement drift is associated with the
extent of operating earnings disaggregation. Consistent with our predictions in H3, we find a
significant positive coefficient on the interaction between the earnings surprise and OCORE3.
This suggests that investors are less able to extrapolate the implication of current earnings for
future earnings for firms with greater operating earnings disaggregation, presumably due to
greater errors of commission. In other words, post-earnings announcement drift is more
pronounced for firms with greater operating earnings disaggregation.
[Please place Table 10 about here]
5. Conclusion
While prior research documents significant benefits to increased earnings disaggregation,
a fundamental presumption in this research is that increased disaggregation helps to segregate
items with heterogeneous characteristics. In this study, we explore the market consequences of
disaggregation of components with homogeneous characteristics. Specifically, we examine the
relation between operating earnings disaggregation and the magnitude of opinion divergence
around earnings announcements. Consistent with our predictions, we document a significant
positive relation between the extent of operating earnings disaggregation in the earnings
announcement and excess volume, the change in idiosyncratic return volatility, and the change in
the standard deviation of analyst forecasts around the earnings announcement.
29
In addition, we show that the relation reverses around the filing (i.e., firms with more
disaggregated operating earnings in the earnings announcement have less excess volume around
the subsequent filing). We also find, however, that the net excess volume over the combined
earnings announcement and regulatory filing windows is higher for firms with greater operating
earnings disaggregation at the earnings announcement. Finally, we show that the extent of
operating earnings disaggregation is positively associated with the absolute analyst forecast
errors after the earnings announcement and post-earnings announcement drift.
Our findings make important contributions to the accounting research streams on
earnings disaggregation, announcements, and disclosure. While prior research generally suggests
that greater disaggregation is beneficial to investors and managers, our results suggest that there
may be costs associated with specific types of disaggregation. In particular, we document that
opinion divergence around an earnings announcement is increasing in the amount of operating
earnings disaggregation. This finding is important for understanding the implications of greater
disaggregation, as prior research documents a significant risk premium (Carlin et al. 2014) and
greater post-earnings announcement drift (Garfinkel and Sokobin 2006) for firms with higher
levels of opinion divergence. Additionally, we contribute to the body of research that seeks to
understand the total information set included with the release of earnings by demonstrating that
more is not always better. In fact, our results suggest that greater disaggregation of components
with homogenous characteristics is associated with costs in the form of increased opinion
divergence.
Our results also have implications for practice, especially managers, the FASB, and the
SEC. While the presumption of managers and regulators is that more disaggregation is better for
stakeholders, our results suggest that managers and regulators should consider the types of
30
disaggregation within the financial statements. Specifically, disaggregation of financial statement
line items into components with homogeneous characteristics has the potential to lead to
negative market consequences, such as greater opinion divergence. Thus, managers should
consider this issue when determining the level of disaggregation in voluntary disclosures and
regulators should consider this potential issue when exploring changes to the mandatory level of
disaggregation in the financial statements, as is currently in discussion within the Financial
Statement Presentation Project.
31
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35
Appendix A
Variable Definitions
Variable
Table 4 Variables
ADJRET
Definition
=
16-month compounded firm returns starting at the beginning of the fiscal year less compounded
market returns over the same period.
{(SALE-COGS-XSGA-DP-XINT+NOPI-TXT) - [LAGGED (SALE-COGS-XSGA-DPXINT+NOPI-TXT)]} / [LAGGED (PRCC_F * CSHO)]}
ΔOPINC
= {(SALE-COGS-XSGA-DP-XINT) - [LAGGED (SALE-COGS-XSGA-DP-XINT)]} /
[LAGGED (PRCC_F * CSHO)]}
ΔNONOPTAX
= {(NOPI - TXT) - [LAGGED (NOPI - TXT)]} / [LAGGED (PRCC_F * CSHO)]}
ΔSALES
= {(SALE) - [LAGGED (SALE)]} / [LAGGED (PRCC_F * CSHO)]}
ΔCOGS
= {(COGS) - [LAGGED (COGS)]} / [LAGGED (PRCC_F * CSHO)]}
ΔSGA
= {(XSGA - XRD) - [LAGGED (XSGA - XRD)]} / [LAGGED (PRCC_F * CSHO)]}
ΔDEPREC
= {(DP) - [LAGGED (DP)]} / [LAGGED (PRCC_F * CSHO)]}
ΔRND
= {(XRD) - [LAGGED (XRD)]} / [LAGGED (PRCC_F * CSHO)]}
ΔINTEREST
= {(XINT) - [LAGGED (XINT)]} / [LAGGED (PRCC_F * CSHO)]}
ΔNONOP
= {(NOPI - TXT) - [LAGGED (NOPI - TXT)]} / [LAGGED (PRCC_F * CSHO)]}
ΔTAX
= {(TXT) - [LAGGED (TXT)]} / [LAGGED (PRCC_F * CSHO)]}
* - Compustat Variable Names Referenced Above
Primary Dependent Variables
DTO t+x
= Calculating change in market-adjusted turnover (DTO) is a multi-step process. First, daily turnover
ΔROE
=
ratios (i.e. VOL / SHROUT) are calculated for day t+x (where day t is the earnings announcement
date) in the relevant event window (e.g. [-2,+2]). Next, to control for the correlation between firmspecific and market-wide trading, we subtract market-wide turnover calculated the same way, but
across all NYSE/AMEX stocks (MATOi). Lastly, due to the fact that some firms stocks are more
highly traded than others we subtract the firm-specific average market-adjusted turnover (MATOi)
over a pre-event window [-54,-5].
SUV t+x
=
Standardized unexpected volume (SUV ) is the scaled (by estimation window [-54, -5] standard
deviation of prediction errors) day t+x (where day t is the earnings announcement date) prediction
error from a market model–style regression of volume on absolute valued returns, where positive and
negative returns are allowed to have different slope coefficients. We use the natural logarithm of
volume in measuring this variable to mitigate concerns about skewness.
EXVOL t+x
=
The first principal component from a principal component analysis of SUV and DTO on day t+x
(where day t is the announcement/filing date).
|POST FE t+1 |
=
BHAR (2,20)
=
The absolute value of the forecast error for quarter t+1 where the actual IBES EPS reported
for quarter t is subtracted from the consensus forecast calculated five days after the earnings
announcement for quarter t scaled by price as the end of quarter t.
The buy-and-hold three factor adjusted return (Fama and French 1993) from two business
days to twenty business days after the announcement.
Test Variables
OCORE
=
Proxy for the amount of operating earnings disaggregation. We construct this proxy by dividing the
presence or absence of an earnings component in the earnings announcement by the presence or
absence of the earnings component in the 10-Q filing. We define the possible operating earnings
components: sales; cost of goods sold; selling, general and administrative expenses; depreciation
expense; research and development expenses; and interest expense.
OCORE3
=
The tercile ranking of OCORE minus one and scaled by two. Values include zero, one-half, and one.
EA * OCORE3
=
OCORE3 interacted with a binary variable set to one when the disagreement measures examined are
from the [-2,+2] earnings announcement event window, and zero otherwise.
FILE * OCORE3
=
OCORE3 interacted with a binary variable set to one when the disagreement measures examined are
from the [-2,+2] SEC filing event window, and zero otherwise.
HIOCORE
=
A binary variable set equal to one when firm-quarter i's OCORE3 is equal to one, and set to zero
when firm i's OCORE3 in quarter x is equal to zero.
36
Appendix A
Variable Definitions
Variable
Definition
Announcement Control Variables
SURP [t,t+x]
=
FE t
=
LOSS
MF
=
=
PRO
=
TAX
=
NONOP
=
SPI
=
DISCO
=
BS
=
CF
=
The prediction error, for day t+x, from a standard market model, where the estimation period for the
market model parameters is [t-200,t-21], and the earning announcement date is day t.
The earnings surprise calculated by subtracting the actual IBES EPS value from the median consensus
forecast from the month prior to the earnings announcement date.
This is a binary variable set equal to one if actual IBES EPS is negative, and zero otherwise.
A binary variable set equal to one if a management forecast occurred, according to IBES, on the date
of the earnings announcement, and otherwise equal to zero.
A binary variable set equal to one if the absolute difference between Compustat EPS (adjusted for
dilution where identified in IBES) and IBES EPS is greater than .02, and otherwise equal to zero.
A binary variable set equal to one when a firm reports tax expense in the earnings announcement for
quarter t, and zero otherwise.
A binary variable set equal to one when a firm reports non-operating expenses in the earnings
announcement for quarter t, and zero otherwise.
A binary variable set equal to one when a firm reports a special item in the earnings announcement for
quarter t, and zero otherwise.
A binary variable set equal to one when a firm reports discontinued operations in the earnings
announcement for quarter t, and zero otherwise.
A binary variable set equal to one when a firm presents balance information in the earnings
announcement for quarter t, and zero otherwise.
A binary variable set equal to one when a firm presents cash flow statement information in the
earnings announcement for quarter t, and zero otherwise.
Firm Control Variables
SIZE
INVPRICE
BTM
GROW
NUMEST
=
=
=
=
=
PVOL
BUSSEG
GEOSEG
=
=
=
The natural logarithm of beginning of quarter total assets.
The inverse (i.e. 1/price) of the stock price at the beginning of the quarter.
The beginning of quarter book-to-market ratio.
The percentage change in seasonally differenced firm total assets.
The number of analyst forecasts included in the consensus forecast from the month prior to the
respective earnings announcement.
The standard deviation of firm stock prices during the pre-event [-54,-5] window.
The number of business segments during the fiscal year.
The number of geographic segments during the fiscal year.
Additional Variables for Supplemental Tests
ΔIDVOL
=
ΔSTDEV
=
|PRE FE t+1 |
=
The change in idiosyncratic stock return volatility (i.e., post idvol - pre idvol) around the
earnings announcements, where the pre (post) period is defined as the [-10, -2] ([2, 10]) day
event window. Additionally, idiosyncratic volatility is calculated using the residuals from a
three factor model (Fama and french 1993) estimated over the [-50, 50] day event window.
The change in the standard deviation (i.e., post stdev - pre stdev) of analyst forecasts for
quarter t+1, around the earnings announcement for quarter t scaled by price as the end of
quarter t. The pre stdev (post stdev) is calculated five days before (after) the earnings
announcements for quarter t.
The absolute value of the forecast error for quarter t+1 where the actual IBES EPS reported
for quarter t is subtracted from the consensus forecast calculated five days before the earnings
announcement for quarter t scaled by price as the end of quarter t.
37
Appendix B
Propensity Score Match
We use the following first stage probit model to predict the likelihood that the earnings
announcement contains high operating income disaggregation in order to predict the probabilities
necessary to construct a 1-to-1 propensity score matched (PSM) sample. Variable definitions for
the following model are found in Appendix A with additional discussion below.
HIOCORE =
β0 + β1BADNEWSi,t + β2INFOENVTi,t + β3INSTITi,t+ β4LITRISKi,t
+ β5COMPLEXi,t + β6PROPi,t + β7SIZEi,t+ year fixed effects +
industry fixed effect + quarter fixed effects + ε i,t
(8)
The dependent variable (HIOCORE) equals 1 (0) if the observation is in the top (bottom)
tercile of our OCORE3 measure. The covariates included in the model are based on EA
disclosure models used in prior research (i.e. D’Souza et al. 2010; Schroeder 2016). We use
principle component factor analysis to reduce the dimensionality of 17 common disclosure
measures used in prior studies. 9 The factor loadings are presented in panel A of table B1. The six
identified factors are consistent with D’Souza et al. (2010) and Schroeder (2016) and explain a
combined 64 percent of the total variation in the 17 disclosure measures.
The first factor, which we label bad news (BADNEWS), loads positively on whether the
company experienced a current period loss and missed earnings expectations. It also loads
negatively on measures of return on assets and operating cash flows. The second factor, which
we label information environment (INFOENVT), loads positively on measures of market value of
equity, number of analyst estimates, number of shareholders and market-to-book ratio. The third
Consistent with D’Souza et al. (2010) and Schroeder (2016) we use promax oblique rotation to allow the extracted
factors to be correlated, which is consistent with the idea that the relationships underlying the constructs are related
(i.e. bad news and litigation risk are likely correlated).
9
38
factor, which we label institutional investors (INSTIT), loads positively on the three institutional
investor classifications from Bushee (1998). The fourth factor loads positively on measures of
whether the company is in a high litigation industry, experiences high trade volume, experiences
high stock return volatility and the number of analyst estimates. The fifth factor, which we label
complexity (COMPLEX), loads positively on whether the company has special items and the
number of business segments and negatively on market-to-book ratio. The final factor, which we
label proprietary costs (PROP), loads positively on our market share measure and litigation risk.
In addition to the six principle component factors we also include the natural log of assets
as a control for firm size as prior literature demonstrates that larger companies provide more
detailed GAAP disclosures. We also include quarter, year and industry fixed effects to control
for unobservable factors that may also impact the decision to provide high or low disaggregated
operating income.
Table B1, panel B, presents the results of the first stage probit model. The sample used
for the first stage prediction model consists of 52,579 observations that represent the upper and
lower terciles of the OCORE3 measure with available data for the covariates. The model has a
pseudo R2 of 0.184 and an area under the ROC curve of 78 percent, which is considered
acceptable discrimination (Hosmer and Lemeshow 2000). We find that institutional ownership,
firm complexity, proprietary cost and firm size are associated with higher operating income
disaggregation. Bad news and information environment are associated with lower operating
income disaggregation.
The PSM design produces a sample of treatment and control observations that have the
closest predicted probabilities for operating income disaggregation. This allows us to control for
39
observable determinants of operating income disaggregation so that our inferences about
operating income disaggregation’s effect on unproductive volume is not biased by the
endogenous disclosure choice. We construct the PSM sample by performing a 1-to-1 match,
without replacement, of treatment (HIOCORE=1) and control (HIOCORE=0) observations
predicted probabilities within a caliper range of 3.0 percent (Guo and Fraser 2010; Lawrence,
Minutti-Meza, and Zhang 2011). This results in a total of 14,393 matched pairs (28,786
observations) for the PSM sample analysis reported in Table 6. Per panel B of Table B1, we
achieve covariate balance on the observable covariates from the first stage model.
40
Table B1
Propensity Score Match
PANEL A: FACTOR LOADINGS
Variable
LNMVE
NUMEST
SHAREHOLDER
DEDOWN
QIXOWN
TRAOWN
ROA
OCF
UENEG
LOSS
SPITEM
LIT
VOLUME
STDRET
LNBUSEG
MBR
INVMRKTSHR
BADNEWS
-0.0913
-0.0342
0.0659
0.2223
-0.0821
-0.1461
-0.8953
-0.8001
0.5192
0.7683
0.0895
0.0074
-0.0194
0.2890
-0.0993
0.1363
-0.0073
INFO
0.8849
0.7997
0.7638
0.0432
0.0465
-0.1034
-0.0102
0.0452
0.0201
-0.0680
0.2263
0.0034
0.1795
-0.2833
0.2350
0.4127
-0.0579
INSTIT
0.0889
0.0389
-0.1883
0.7836
0.7829
0.6920
-0.0175
-0.0286
0.1006
-0.0375
0.0278
-0.0587
0.0594
-0.3022
-0.0648
-0.0016
0.0417
LITIGATION
0.0619
0.4130
-0.1830
-0.1577
0.0176
0.3744
-0.0148
0.1382
0.0632
0.0664
0.2712
0.4505
0.8820
0.4592
-0.2559
0.0026
-0.1282
COMPLEX
-0.1403
-0.0942
0.0092
-0.1001
0.1230
-0.0284
-0.0133
0.1405
0.3529
-0.0329
0.5982
-0.1238
0.1059
0.0041
0.4180
-0.7614
0.0505
PROP
-0.0025
-0.0840
-0.0075
0.0615
-0.0247
0.0200
0.0016
-0.0411
-0.0881
-0.0240
0.1687
0.4440
-0.1295
-0.0001
0.1070
0.0641
0.9419
PANEL B: FIRST STAGE MODEL
DV:
HIOCORE
Mean Comparison
Treated
Control
t-stat
BADNEWS
-0.0689***
(-9.58)
-0.05115
-0.05614
0.46
INFO
-0.1189***
(-10.05)
0.0533
0.04413
0.78
INSTIT
0.0617***
(8.74)
0.01255
0.02117
-0.74
LITIGATION
0.0321***
(4.33)
-0.00762
-0.01321
0.47
COMPEX
0.0386***
(5.51)
0.06509
0.05991
0.45
PROP
0.0265**
(2.15)
0.02335
0.01144
1.03
SIZE
0.0746***
(10.25)
6.5576
6.5521
0.27
Observations
2
Psuedo R
Area Under ROC Curve
Industry FE
Year FE
Quarter FE
52,579
18.4%
0.78
Yes
Yes
Yes
41
FIGURE 1
Sample Distribution of Operating Income Disaggregation Ratio
% of Op Inc Num Observations
No. of Op Items
OCORE3 = .5
[0‐20]
5712
3664
7743
13538
30392
24654
0.99
1.48
2.91
3.65
4.35
4.86
Avg. No. of Items
Disagg = 4
35,000 (20‐40]
(40‐60]
(60‐80)
30,000 [80‐100)
= 100
Number of Observations
25,000
OCORE3 = 1
Avg. No. of Items
Disagg ≈ 5
OCORE3 = 0
Avg. No. of Items
Disagg ≈ 3
20,000
15,000
10,000
5,000
0
[0-20]
(20-40]
(40-60]
(60-80)
[80-100)
Percentage of Operating of Items Disaggregated
42
= 100
FIGURE 2
Median Excess Volume by Day Around Earnings Announcements
High/Low Tercile of OCORE3
Panel A: Excess Volume Measure = DTO
0.006
0.005
HIGH OCORE
(OCORE3=1)
0.004
LOW OCORE (OCORE3=0)
0.003
0.002
0.001
0
‐2
‐1
0
1
2
‐0.001
‐0.002
Day relative to earnings announcement
Panel B: Excess Volume Measure = SUV
1.2
HIGH OCORE
(OCORE3=1)
1
LOW OCORE
(OCORE3=0)
0.8
0.6
0.4
0.2
0
‐2
‐1
0
Day relative to earnings announcement
43
1
2
FIGURE 3
Persistence of Earnings Announcement Excess Volume
Kaplan-Meier Survival Curves
Panel A: One Week Survival Horizon
Excess Volume Measure = DTO
0.50
0.75
1.00
Kaplan-Meier survival estimates
0.25
OCORE3
ocore3 ==0 0
0.00
ocore3 ==11
OCORE3
0
1
2
3
number of days
4
5
4
5
Excess Volume Measure = SUV
0.50
0.75
1.00
Kaplan-Meier survival estimates
0.25
OCORE3
ocore3 ==0 0
0.00
ocore3 ==11
OCORE3
0
1
2
3
number of days
44
FIGURE 3
Persistence of Earnings Announcement Disagreement
Kaplan-Meier Survival Curves
Panel B: Two Week Survival Horizon
Excess Volume Measure = DTO
0.75
1.00
Kaplan-Meier survival estimates
OCORE3
ocore3 ==00
0.00
0.25
0.50
OCORE3
ocore3 ==11
0
2
4
6
number of days
8
10
Excess Volume Measure = SUV
0.75
1.00
Kaplan-Meier survival estimates
OCORE3
ocore3 ==00
0.00
0.25
0.50
OCORE3
ocore3 ==11
0
2
4
6
number of days
8
10
Figure 3 Panels A and B display Kaplan-Meier survival curves estimated over one and two week horizons, respectively, where "failure" is
defined as a return to a SUV or DTO value of zero or less. The curves are estimated separately for both proxies for excess volume (i.e.,
DTO and SUV).
45
TABLE 1
Sample Creation
Adjustments
Total firm-quarters from the intersection of IBES and the Compustat Preliminary Earnings Database
from 1998 to 2012
Less: Firm-quarters missing data to calculate excess volume measures during the [-2,+2] earnings
announcement event window
Less: Firm-quarter missing necessary CRSP data to calculate control variables
Less: Firm-quarter missing necessary COMPUSTAT data to calculate control variables
Less: Firm-quarters dropped due to less than a calendar week between the earnings announcement
and the related SEC filing
Primary Sample Examined in Tables 2, 3, 4, and 5
114,988
114,988
114,988
114,988
(3,459)
(1,396)
(17,222)
(3,459)
(1,396)
(17,222)
(3,459)
(1,396)
(17,222)
(3,459)
(1,396)
(17,222)
(7,208)
(7,208)
(7,208)
(7,208)
85,703
Less: Firm-quarters missing at least two analyst forecasts made within 90 days of two dates. The first
date is five days prior to the earnings announcement for quarter t. The second date is five days after
the earnings announcement for quarter t.
Sample Examined in Table 6 - Column 2
(26,446)
59,257
Less: Interim firm-quarters and firm-quarters missing data to calculate excess volume measures
during the [-2,+2] filing event window
(64,324)
Add: Corresponding earnings announcement data
21,379
Sample Examined in Table 7
42,758
Less: First-quarters missing at least one analyst forecasts made within 90 days of two dates. The first
date is five days prior to the earnings announcement for quarter t. The second date is five days after
the earnings announcement for quarter t.
Sample Examined in Table 8
(12,332)
73,371
46
TABLE 2
Excess Volume and Descriptive Statistics
PANEL A: EXCESS VOLUME MEASURE STATISTICS
Average Eigenvalue over [t-2,t+2] Event Window for the First Principal Component (i.e. EXVOL) of DTO and SUV:
1.56
Average Proportion of the Variance in DTO and SUV Explained by EXVOL over [t-2,t+2] Event Window:
78%
Average Pearson (Lower) and Spearman (Upper) Pairwise Correlations between DTO, SUV, and EXVOL over [t-2,t+2] Event Window:
Variable:
(1) DTO
(2) SUV
(3) EXVOL
(1)
0.56
0.88
(2)
0.72
0.88
(3)
0.89
0.94
-
PANEL B: DESCRIPTIVE STATISTICS
Variable
N
OCORE3 = 0
MEAN
STDEV
N
OCORE3 = 0.50
MEAN
STDEV
N
OCORE3 = 1
MEAN
STDEV
Hi - Lo
Excess Volume Measures
DTO t
SUV t
EXVOL t
30,657
30,657
30,657
0.0118
1.0064
-0.1402
0.0252
1.394
1.0783
30,392
30,392
30,392
0.0174
1.2037
0.0967
0.0303
1.4609
1.1918
24,654
24,654
24,654
0.0157
1.2046
0.0551
0.028
1.4571
1.1324
0.0039 ***
0.1982 ***
0.1953 ***
30,657
30,657
30,657
30,657
30,657
30,657
30,657
30,657
30,657
30,657
30,657
0.0511
0.0058
0.2385
0.1806
0.3648
0.7184
0.4045
0.2716
0.067
0.6943
0.1285
0.0525
0.0129
-
30,392
30,392
30,392
30,392
30,392
30,392
30,392
30,392
30,392
30,392
30,392
0.0594
0.006
0.2367
0.275
0.4144
0.8869
0.8091
0.3484
0.076
0.8986
0.2403
0.0569
0.013
-
24,654
24,654
24,654
24,654
24,654
24,654
24,654
24,654
24,654
24,654
24,654
0.0515
0.0056
0.173
0.3104
0.4042
0.928
0.9744
0.3359
0.0929
0.8676
0.5238
0.0523
0.0126
0.3782
0.4627
0.4907
0.2584
0.1579
0.4723
0.2903
0.339
0.4994
0.0004
-0.0002
-0.0655
0.1298
0.0394
0.2096
0.5699
0.0643
0.0259
0.1733
0.3953
**
###
###
###
###
###
###
###
###
###
30,657
30,657
30,657
30,657
30,657
30,657
30,657
30,657
3,200
0.1007
0.5233
0.2500
6.984
2.2771
2.0921
2.5334
8,400
0.1361
0.4376
0.6713
6.0838
30.1615
1.535
1.8743
30,392
30,392
30,392
30,392
30,392
30,392
30,392
30,392
2,700
0.1065
0.5149
0.2405
7.5803
1.9582
1.9464
2.7712
7,300
0.1416
0.4281
0.6841
6.438
3.418
1.4178
2.0222
24,654
24,654
24,654
24,654
24,654
24,654
24,654
24,654
3,600
0.0894
0.5619
0.2028
8.3792
1.6943
2.0584
2.2572
7,900
0.1212
0.4587
0.5469
6.5748
2.3406
1.4703
1.8251
400
-0.0113
0.0386
-0.0472
1.3952
-0.5828
-0.0337
-0.2762
***
***
***
***
***
***
***
***
30,657
19,701
-0.0014
-0.0003
0.0178
0.0026
30,392
21,470
-0.0014
-0.0003
0.0172
0.0027
24,654
18,086
-0.0005
-0.0003
0.0146
0.0025
0.0009 ***
0.0000 *
Announcement Controls
|ABNRET t |
|SURP t|
LOSS
MF
PRO
TAX
NONOP
SPI
DISCO
BS
CF
Firm Controls
TOTAL ASSETS ($MM)
INV_PRC
BTM
GROW
NUMEST
PVOL
BUSSEG
GEOSEG
Alternative Op Div Proxies
ΔIDVOL
ΔSTDEV
Table 2 Panel A displays descriptive statistics for the EXVOL measure used in the multivariate tests to proxy for excess trading volume. Table 2 Panel B displays descriptive statistics
for the primary sample. These statistics are partitioned by OCORE tercile. To minimize the effects of outliers, all continuous variables are winsorized at the 1% and 99% levels. All
variable definitions are provided in Appendix A. ***, **, * (###,##,#) indicate two-tailed (one-tailed) statistical significance at the 1%, 5%, and 10% levels, for t-tests (chi-squared
tests) run on the difference between the high and low OCORE3 partitioned variables.
47
TABLE 3
Extension of Fairfield, Sweeney and Yohn (1996)
Statistical Forecasting Results over 1998 - 2012 period
Panel A: Cross Sectional Means (Standard Deviations) of Coefficients for Fifteen 7-year rolling Regressions
of ROEBSI, on Disaggregations of ROEBSI t-1, 1998 - 2012
Model:
Adjusted R2
Intercept
ROE
OPINC
NONOPTAX
SALES
COGS
SGA
DEPREC
RND
INTEREST
NONOP
TAX
ROE
OPINC
FULL
0.5249 (0.0309)
-0.0054 (0.0042)
0.7201 (0.0127)
-
0.5526 (0.0348)
-0.0130 (0.0047)
0.6755 (0.0182)
0.2629 (0.0220)
-
0.5536 (0.0348)
-0.0139 (0.0068)
0.6672 (0.0189)
0.6669 (0.0188)
0.6680 (0.0203)
0.6451 (0.0399)
0.7108 (0.0198)
0.6709 (0.0235)
0.2953 (0.0251)
0.2371 (0.0382)
Panel B: Forecast Improvement from OPINC model over ROE model
N
Median
Econ Improve
All Observations
46,128
0.00249***
45.54 #/36.94
Absolute Magnitude of Difference in Forecasts
Greater than .05
Between .005 and .05
Less than .005
6,600
31,435
8,093
0.0535478***
.00710971***
0.00013*
54.45 #/ 45.55
55.36 #/ 44.64
51.13 # / 48.87
Panel C: Forecast Improvement from FULL model over OPINC model
N
Median
Econ Improve
All Observations
46,128
-0.00002
18.51/19.28 #
Absolute Magnitude of Difference in Forecasts
Greater than .05
Between .005 and .05
Less than .005
1,140
16,289
28,699
0.0535544***
-0.00513209
0.00002
53.86 #/ 46.14
48.64/51.36 #
50.29/49.71
Notes:
ROE Model = The base model where we use the historical relationship (estimated using 7-year rolling windows) between lagged ROE
before special items and next period ROE before special items to forecast out of sample period ahead ROE before special items.
OPINC Model = A model where we use the historical relationship (estimated using 7-year rolling windows) between lagged operating
income and non-operating items and next period ROE before special items to forecast out of sample period ahead ROE before special
items.
FULL Model = A model where we use the historical relationship (estimated using 7-year rolling windows) between lagged sales, cost of
goods sold, SG&A, depreciation, R&D, interest expense, tax, and non-operating and next period ROE before special items to forecast
out of sample period ahead ROE before special items.
Median = represents the improvement over the comparison model, where improvement is based upon absolute forecast errors. The
Wilcoxon signed rank test is used to calculate statistical significance. ***, **, * indicate two-tailed statistical significance at the 1%, 5%,
and 10% level.
Econ Improve = instances where there was an economically significant forecast improvement. The first (second) number is the
percentage of observations for which disaggregation improved (worsened) forecast accuracy based upon the category. For all
observations, the cutoff was an improvement or worsening of forecast by .005 or greater. A # indicates that the proportion of
observations with improved accuracy exceeds the proportion with reduced accuracy using the binomial test and a 5% significance level.
48
TABLE 4
Informativeness of Earnings Components for Market Returns
DV = ADJRET
ΔROE
ΔOPINC
(1)
(2)
(3)
1.600***
(9.14)
-
-
-
ΔNONOPTAX
-
ΔSALES
-
1.851***
(9.45)
-0.217*
(-1.86)
-
ΔCOGS
-
-
ΔSGA
-
-
ΔDEPREC
-
-
ΔRND
-
-
ΔINTEREST
-
-
ΔNONOP
-
-
ΔTAX
-
-
0.0922
(1.63)
0.0818
(1.46)
1.280***
(9.92)
-1.240***
(-13.85)
-0.984***
(-4.56)
-1.966***
(-2.60)
-1.806**
(-2.51)
-4.434***
(-4.01)
0.155
(0.42)
0.879***
(5.10)
0.0878
(1.48)
38,845
7.6%
Firm & Year
38,845
11.0%
Firm & Year
38,845
9.4%
Firm & Year
Col (2 ) > Col(1)
Col (2 ) > Col(3)
10.65***
3.44***
Intercept
Observations
R-Squared
SE Clustered
R-Squared Test
Vuong Statistic
-
Table 4 reports linear regressions where ADJRET is the dependent variable with t-statistics reported in
parentheses below each coefficient. Standard errors are clustered by firm and year. All variables are
winsorized at the 1% and 99% levels. All variable definitions are provided in Appendix A. ***, **, and *
indicate two-tailed statistical significance of coefficient estimates when no predicted sign is indicated and
one-tailed significance of coefficient estimates when a predicted sign is indicated, at the 1%, 5%, and 10%
levels, respectively.
49
TABLE 5
Excess Volume by Day Around Earnings Announcements
Linear Regressions
Test Variable
OCORE3
DV:
Pred.
EXVOLi,t-2
(1)
EXVOLi,t-1
(2)
EXVOLi,t
(3)
EXVOLi,t+1
(4)
EXVOLi,t+2
(5)
EXVOLi,(t,t+1)
(6)
(+)
0.00269
[0.17]
0.01351
[0.69]
0.03618***
[2.57]
0.03475***
[2.43]
0.03116**
[1.77]
0.03437***
[2.49]
10.77954***
[10.45]
8.46908***
[9.33]
5.93211***
[25.25]
8.92124***
[10.81]
9.28520***
[11.34]
6.96577***
[15.41]
Announcement Controls
| ABNRET t+x |
(+/-)
|SURP t|
(+/-)
-0.39812
[-0.55]
1.90620***
[2.75]
2.79177***
[4.66]
4.54508***
[4.91]
3.64276***
[4.07]
2.75927***
[4.13]
LOSS
(+/-)
-0.14062***
[-10.50]
-0.20829***
[-10.00]
-0.22891***
[-11.94]
-0.30523***
[-18.87]
-0.27495***
[-15.16]
-0.30627***
[-14.71]
MF
(+/-)
0.02660*
[1.85]
0.06853***
[2.72]
0.11833***
[5.60]
0.07532***
[4.39]
0.05603***
[4.06]
0.11887***
[5.56]
PRO
(+/-)
-0.01173
[-0.94]
0.06242***
[3.87]
0.03743***
[3.38]
0.01726
[1.24]
0.01607
[1.37]
0.01971
[1.54]
TAX
(+/-)
-0.00275
[-0.23]
-0.03637**
[-2.15]
0.02484
[1.45]
0.03999**
[2.24]
0.03896*
[1.82]
0.03523*
[1.71]
NONOP
(+/-)
0.01040
[0.78]
0.01445
[0.79]
0.00480
[0.28]
-0.00353
[-0.27]
0.00163
[0.10]
0.00247
[0.15]
SPI
(+/-)
-0.02690**
[-2.07]
-0.03821**
[-2.54]
-0.01551
[-1.38]
-0.01487
[-1.27]
-0.03376**
[-2.53]
-0.02487**
[-2.50]
DISCO
(+/-)
-0.01970
[-0.99]
-0.04913**
[-2.51]
-0.01556
[-0.67]
-0.01145
[-0.69]
-0.02675
[-1.20]
-0.02376
[-1.10]
BS
(+/-)
-0.01052
[-0.53]
0.02100
[1.09]
0.03205
[1.44]
0.03945**
[2.00]
0.02658
[1.46]
0.02376
[0.99]
CF
(+/-)
0.04929***
[4.53]
0.04540**
[2.40]
0.02042
[1.21]
0.06045***
[2.96]
0.06629***
[3.18]
0.04295**
[2.40]
Firm Controls
SIZE
(+/-)
0.02730***
[4.03]
-0.02256**
[-2.45]
-0.03552***
[-3.61]
-0.00975
[-1.10]
0.00791
[1.29]
-0.00696
[-0.73]
INVPRICE
(+/-)
-0.12975*
[-1.81]
-0.20479**
[-2.11]
-0.69417***
[-6.49]
-0.84610***
[-6.25]
-0.70746***
[-5.56]
-0.98917***
[-7.58]
BTM
(+/-)
-0.05546***
[-6.37]
-0.05297***
[-3.07]
-0.06897***
[-5.12]
-0.05289***
[-3.66]
-0.04295***
[-3.45]
-0.07581***
[-5.33]
GROW
(+/-)
-0.01684
[-1.37]
0.05902***
[4.21]
0.08062***
[5.02]
0.03922***
[3.70]
0.02585**
[2.07]
0.05868***
[4.11]
NUMEST
(+/-)
0.00976***
[6.88]
0.03651***
[9.20]
0.03187***
[16.82]
0.01792***
[10.94]
0.00419**
[2.21]
0.02841***
[14.83]
PVOL
(+/-)
-0.00169
[-0.57]
0.01978***
[4.35]
0.02994***
[7.31]
0.01571***
[4.94]
0.00201
[0.66]
0.02028***
[4.89]
BUSSEG
(+/-)
-0.00732**
[-2.17]
-0.02809***
[-5.53]
-0.01973***
[-4.51]
-0.01038***
[-2.60]
-0.01152***
[-3.32]
-0.01464***
[-3.40]
GEOSEG
(+/-)
0.00632**
[2.33]
0.01662***
[3.52]
0.01202***
[2.88]
0.00025
[0.07]
0.00172
[0.56]
0.00733*
[1.84]
(+/-)
-0.45239***
[-11.02]
-0.40127***
[-6.33]
-0.66822***
[-8.25]
-0.63989***
[-10.21]
-0.51182***
[-11.26]
-1.03419***
[-12.02]
85,703
4.6%
Yes
Yes
Yes
Firm & Year
85,703
11.2%
Yes
Yes
Yes
Firm & Year
85,703
24.1%
Yes
Yes
Yes
Firm & Year
85,703
11.5%
Yes
Yes
Yes
Firm & Year
85,703
6.8%
Yes
Yes
Yes
Firm & Year
85,703
28.7%
Yes
Yes
Yes
Firm & Year
Intercept
Observations
Adj. R2
Industry FE
Year FE
Quarter FE
SE Clustered
Table 5 reports linear regressions where EXVOL t+x is the dependent variable with t-statistics reported in parentheses below each coefficient. Industry, year, and
quarter fixed effects are included in each model and standard errors are clustered by firm and year. All variables are winsorized at the 1% and 99% levels. All
variable definitions are provided in Appendix A. ***, **, and * indicate two-tailed statistical significance of coefficient estimates when no predicted sign is
indicated and one-tailed significance of coefficient estimates when a predicted sign is indicated, at the 1%, 5%, and 10% levels, respectively.
50
TABLE 6
Excess Volume by Day Around Earnings Announcements
Robustness Tests
FIRM FIXED
EFFECTS
FIRST
DIFFERENCES
HI-LO OCORE
PSM
HI-LO OCORE
Pred.
(1)
(2)
(3)
(4)
Test Variables
OCORE3
(+)
0.0456***
[2.58]
-
-
-
ΔOCORE3
(+)
-
0.0804**
[1.72]
-
-
HIOCORE
(+)
-
-
0.0364***
[2.51]
0.0310***
[2.31]
85,703
33.0%
Yes
No
No
No
Firm & Year
43,563
12.9%
Yes
No
Yes
Yes
Firm & Year
55,311
28.7%
Yes
Yes
Yes
Yes
Firm & Year
28,786
28.4%
Yes
Yes
Yes
Yes
Firm & Year
DV: EXVOLi,(t,t+1)
Observations
2
Adj. R
Controls
Industry FE
Year FE
Quarter FE
SE Clustered
Table 6 reports linear regressions where EXVOLi,(t,t+1) is the dependent variable with t-statistics reported in parentheses below each coefficient.
Column 1 reports linear regressions where fixed effects are included in each model and standard errors are clustered by firm and year. Column 2
reports a linear first difference estimator where OCORE3 is replaced with its seasonal first difference, ΔOCORE3, similarly the controls and
dependent measure are replaced with the corresponding seasonal first difference, and year and quarter fixed effects are included. Column 3 reports
linear regressions where OCORE3 is replace with HIOCORE and industry, year, and quarter fixed effects are included in each model and standard
errors are clustered by firm and year. Column 4 reports linear regressions where the sample is propensity score matched on the likelihood that a firm's
operating income is fully disaggregated in its earnings announcements. All variables are winsorized at the 1% and 99% levels. All variable definitions
are provided in Appendix A. ***, **, and * indicate two-tailed statistical significance of coefficient estimates when no predicted sign is indicated and
one-tailed significance of coefficient estimates when a predicted sign is indicated, at the 1%, 5%, and 10% levels, respectively.
51
TABLE 7
Alternative Opinion Divergence Proxies
Linear Regressions
Test Variable
OCORE3
DV:
Pred.
ΔIDVOL
(1)
ΔSTDEV
(2)
(+)
0.00045***
[2.40]
0.00009**
[2.03]
0.00945***
[5.89]
0.00073***
[3.74]
Announcement Controls
|ABNRET (-1,1) |
(+/-)
|SURP t|
(+/-)
-0.01379
[-1.53]
0.01184***
[3.37]
LOSS
(+/-)
0.00012
[0.43]
-0.00032***
[-4.17]
MF
(+/-)
-0.00048***
[-4.30]
-0.00010***
[-2.87]
PRO
(+/-)
-0.00051**
[-2.31]
-0.00005*
[-1.73]
TAX
(+/-)
0.00021
[0.84]
0.00002
[0.38]
NONOP
(+/-)
-0.00055**
[-2.00]
0.00000
[0.04]
SPI
(+/-)
0.00003
[0.18]
0.00006*
[1.81]
DISCO
(+/-)
-0.00013
[-0.67]
0.00005
[0.95]
BS
(+/-)
-0.00063***
[-2.79]
-0.00008***
[-3.31]
CF
(+/-)
0.00026*
[1.82]
0.00004
[1.19]
Firm Controls
SIZE
(+/-)
0.00035***
[5.59]
-0.00003***
[-3.14]
INVPRICE
(+/-)
-0.00285***
[-2.84]
-0.00335***
[-7.15]
BTM
(+/-)
0.00060**
[2.20]
-0.00021***
[-2.92]
GROW
(+/-)
-0.00074***
[-3.86]
0.00005**
[2.49]
NUMEST
(+/-)
-0.00014***
[-10.77]
-0.00000*
[-1.85]
BUSSEG
(+/-)
0.00006
[1.57]
0.00002***
[3.06]
GEOSEG
(+/-)
-0.00010***
[-2.67]
-0.00002***
[-3.16]
(+/-)
-0.00332***
[-4.35]
0.00058***
[8.10]
85,703
59,257
1.2%
Yes
Yes
Firm & Year
2.6%
Yes
Yes
Firm & Year
Intercept
Observations
Adj. R2
Year FE
Quarter FE
SE Clustered
Table 7 reports linear regressions where ΔIDVOL or ΔSTDEV is the dependent variable with t
statistics reported in brackets below each coefficient. Year and quarter fixed effects are included
in each model and standard errors are clustered by firm and year. All variables are winsorized at
the 1% and 99% levels. All variable definitions are provided in Appendix A. ***, **, and *
indicate two-tailed statistical significance of coefficient estimates when no predicted sign is
indicated and one-tailed significance of coefficient estimates when a predicted sign is indicated,
at the 1%, 5%, and 10% levels, respectively.
52
TABLE 8
Excess Volume by Day Around Earnings Announcement & Annual 10-K Filings
Linear Regressions
Test Variables
EA * OCORE3
FILE * OCORE3
DV:
Pred.
EXVOL i,t-2
(1)
EXVOL i,t-1
(2)
EXVOL i,t
(3)
EXVOL i,t+1
(4)
EXVOL i,t+2
(5)
(+)
-0.01546
[-0.44]
0.05848**
[1.84]
0.09571***
[4.41]
0.08025***
[3.83]
0.11294***
[4.07]
(-)
-0.01902
[-0.63]
-0.05177***
[-2.43]
-0.08023***
[-4.00]
-0.04932***
[-2.93]
-0.01431
[-0.53]
42,758
4.1%
Yes
Yes
Yes
Yes
Firm & Year
42,758
8.8%
Yes
Yes
Yes
Yes
Firm & Year
42,758
31.4%
Yes
Yes
Yes
Yes
Firm & Year
42,758
14.7%
Yes
Yes
Yes
Yes
Firm & Year
42,758
8.5%
Yes
Yes
Yes
Yes
Firm & Year
0.45
0.03
0.54
1.51
4.58**
Observations
Adj. R2
Filing Dummy
Controls
Industry FE
Year FE
SE Clustered
F-Test Results
OCORE3_EA + OCORE3_F =0 :
Table 8 reports linear regressions where EXVOLt+x is the dependent variable with t-statistics reported in parentheses below each coefficient. Industry,
and year fixed effects are included in each model and standard errors are clustered by firm and year. Table 7 examines disagreement around fourth
quarter earnings announcements and annual SEC 10-K filings. Meaning that, each firm-quarter examined has two observations per day t+x, an earnings
announcement observation and a SEC 10-K filing observation. In addition, to the previous controls an additional binary controls was added to indicate
which observations are related to the filing event window. A two week minimum temporal distance between the earnings announcement and 10-K filing
was required to be included in the sample. All variables are winsorized at the 1% and 99% levels. All variable definitions are provided in Appendix A.
***, **, and * indicate two-tailed statistical significance of coefficient estimates when no predicted sign is indicated and one-tailed significance of
coefficient estimates when a predicted sign is indicated, at the 1%, 5%, and 10% levels, respectively.
53
TABLE 9
Analyst Forecast Errors
Linear Regressions
DV:
Pred.
|POST FE t+1|
Test Variable
OCORE3
(+)
0.00011***
[3.47]
Announcement Controls
|PRE FE t+1|
(+/-)
0.71789***
[49.55]
(+/-)
-0.00353***
[-9.11]
|SURP t|
(+/-)
0.01818***
[2.83]
LOSS
(+/-)
-0.00001
[-0.11]
MF
(+/-)
-0.00018***
[-2.78]
PRO
(+/-)
0.00002
[0.57]
TAX
(+/-)
-0.00017***
[-5.10]
NONOP
(+/-)
-0.00006**
[-2.02]
SPI
(+/-)
-0.00001
[-0.21]
DISCO
(+/-)
0.00016**
[2.52]
BS
(+/-)
-0.00005
[-1.29]
CF
(+/-)
-0.00001
[-0.26]
Firm Controls
SIZE
(+/-)
0.00002
[0.99]
INVPRICE
(+/-)
0.00359***
[6.40]
BTM
(+/-)
0.00007
[0.92]
GROW
(+/-)
0.00003
[1.43]
NUMEST
(+/-)
-0.00001***
[-5.01]
PVOL
(+/-)
0.00000
[0.31]
BUSSEG
(+/-)
-0.00001
[-0.54]
GEOSEG
(+/-)
-0.00006***
[-4.51]
(+/-)
0.00087***
[7.43]
|ABNRET
Intercept
(-1,1)|
(1)
Observations
73,371
Adj. R2
Year FE
Quarter FE
SE Clustered
82.4%
Yes
Yes
Firm & Year
Table 9 reports linear regressions where |POST FEt+1| is the dependent variable with t-statistics reported in
brackets below each coefficient. Year, and quarter fixed effects are included in each model and standard errors
are clustered by firm and year. All variables are winsorized at the 1% and 99% levels. All variable definitions
are provided in Appendix A. ***, **, and * indicate two-tailed statistical significance of coefficient estimates
when no predicted sign is indicated and one-tailed significance of coefficient estimates when a predicted sign
is indicated, at the 1%, 5%, and 10% levels, respectively.
54
TABLE 10
Post Earnings Announcement Drift
Linear Regressions
DV:
Pred.
BHAR (2,20)
(1)
Test Variables
D(SURP t)
(+)
0.00089***
[4.60]
OCORE3
(+/-)
-0.00024
[-0.15]
D(SURP t ) * OCORE3
(+)
0.00070**
[1.71]
(+/-)
-0.00246**
[-1.97]
(+/-)
-0.00036
[-1.08]
(+/-)
-0.00407***
[-5.14]
D(SURP t) * D(SIZE)
(+/-)
0.00003
[0.28]
D(INVPRICE)
(+/-)
0.00039
[0.90]
D(SURP t) * D(INVPRICE)
(+/-)
-0.00044***
[-4.51]
D(BTM)
(+/-)
0.00180***
[5.22]
D(SURP t) * D(BTM)
(+/-)
0.00006
[0.94]
D(GROW)
(+/-)
-0.00064***
[-3.21]
D(SURP t) * D(GROW)
(+/-)
-0.00006
[-1.00]
D(NUMEST)
(+/-)
-0.00136***
[-4.17]
D(SURP t) * D(NUMEST)
(+/-)
-0.00046***
[-6.30]
D(PVOL)
(+/-)
-0.00068***
[-2.67]
D(SURP t) * D(PVOL)
(+/-)
-0.00024***
[-2.82]
(+/-)
0.00897***
[5.27]
Forecast Control
MF
D(SURP t ) * MF
Firm Characteristics
D(SIZE)
Intercept
Observations
2
Adj. R
Firm FE
Year FE
Quarter FE
SE Clustered
85,703
3.8%
Yes
Yes
Yes
Firm
Table 10 reports linear regressions where BHAR (2,20) is the dependent variable with t-statistics reported
in brackets below each coefficient. Firm fixed effects are included in the model and standard errors are
clustered by firm. Those variables denoted with a D(_), represent demeaned decile ranks calculated every
calendar quarter. All variables are winsorized at the 1% and 99% levels. All variable definitions are provided
in Appendix A. ***, **, and * indicate two-tailed statistical significance of coefficient estimates when no
predicted sign is indicated and one-tailed significance of coefficient estimates when a predicted sign is
indicated, at the 1%, 5%, and 10% levels, respectively.
55