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Daniel A. Bens†
dbens@email.arizona.edu
University of Arizona
Eller College of Management
1130 E. Helen Street, P.O. Box 210108
Tucson, AZ 85721
Steven J. Monahan
steven.monahan@insead.edu
INSEAD – Accounting and Control Area
Boulevard de Constance
PMLS No. 1.24
F-7705 Fontainebleau Cedex, 77305
France
Logan Steele
logans@email.arizona.edu
University of Arizona
Eller College of Management
1130 E. Helen Street, P.O. Box 210108
Tucson, AZ 85721
Preliminary and Incomplete – Please do not Cite or quote without the Authors’ permission
†
Corresponding author.
The authors gratefully acknowledge financial support from the Ratoff Family Fellowship at the University of
Arizona and the INSEAD Alumni fund. The authors are solely responsible for any errors.
The Association Between Inter-Segment Profit Smoothing and the
Conservatism of Accounting Earnings
Abstract
We devise a measure of inter-segment profit smoothing for multi-segment firms. This measure
equals the ratio of the variance of profits for a portfolio of single segment firms to the variance of
profits across a firm’s multiple business units. The single segment firms are matched to the
firm’s segments by industry and size. We interpret increases in this ratio as capturing the
smoothing of inter-segment profit. Specifically, as the ratio increases the underlying profit
volatility (as captured by the control group) has increased while reported volatility stays
constant.
We find that our inter-segment profit smoothing measure is positively correlated with the level of
information asymmetry at the firm (as measured by the bid-ask spread and the probability of an
informed equity trade). As our main contribution, we document that increased inter-segment
profit smoothing reduces the asymmetric timeliness (or conservatism) in firm level accounting
earnings by as much as 24%. We use a Basu [1997] reverse regression framework for this
analysis. We also demonstrate that inter-segment profit smoothing reduces the likelihood that
the firm records a non-recurring negative accrual, namely an asset impairment. These accruals
are generally used to communicate bad economic news, and thus our evidence suggests they are
less likely to be recorded when inter-segment smoothing is high.
I. Introduction
In this paper we examine whether multi-segment firms that publicly report relatively
smooth operating performance across their segments exhibit a low association between bad
economic news and its recognition in accounting earnings. That is, we test for a negative
We measure inter-segment profit smoothing for publicly traded U.S. firms that report
more than one business segment in their annual financial reports. Our measure is a decile rank
variable within the population of multi-segment firms; the higher the rank within the sample, the
higher the classification of smoothing. We first measure the variance of profit margins across a
portfolio of single segment firms that are matched on size and industry to the multi-segment
firm’s business segments; this serves as the numerator of our ranked smoothing variable. We
then measure the variance of profit margins across the firm’s segments; this serves as the
denominator of our ranked smoothing variable. The higher this ratio, the more the firm is using
either industry aggregation schemes or cost allocation strategies to make their underlying income
appear less volatile across business units than the underlying fundamentals (as captured by the
Our proxy for conservatism is the measure of the asymmetric timeliness of accounting
earnings first used by Basu [1997]. Basu’s intuition, also articulated by Ball [1998] and Watts
[2003] among others, is that U.S. generally accepted accounting principles (GAAP) exhibit an
asymmetric bias towards reporting bad news on a more timely basis than good news. These
standards restrict the recognition of good news in accounting earnings while encouraging the
1
As we discuss later in this section, our definition of conservatism follows Basu [1997], which is often referred to as
“conditional conservatism.” Watts [2003] and Ryan [2006], among others differentiate this from “unconditional
conservatism,” which consists of an understatement of all assets irrespective of the economic condition of the
company. For simplicity, we use the term “conservatism” to mean “conditional conservatism” and the “asymmetric
timeliness of earnings”
1
recognition of bad news. Further, in the judgmental application of GAAP accountants generally
require less verification for the recognition of bad news vis-à-vis good news. Basu uses a
reverse regression specification where accounting earnings are the dependent variable and stock
returns the independent variable; the coefficient on stock returns is then allowed to vary by
whether the measure for the year is positive (i.e., good economic news) or negative (i.e., bad
economic news). Basu documents that the coefficient on the bad news is larger than that on
good news, in both statistical and economic terms. This evidence confirms, empirically, the
presence of an asymmetric bias towards accelerated bad news recognition in applied GAAP.
asymmetry in the capital markets. We estimate univariate correlations between our smoothing
measure and other publicly available measures of information asymmetry to confirm this
prediction. We document that the level of inter-segment smoothing is positively associated with
the bid-ask spread and the probability of an informed trade (PIN), both of which have been
conservatism. Given our initial results that inter-segment smoothing is associated with greater
information asymmetry, we conjecture that this smoothing obscures the underlying value of firm
assets. This obfuscation complicates the task of identifying unrecognized losses (such as asset
impairments) and creates uncertainty as to the extent of these losses. Therefore, we expect that
inter-segment smoothing reduces the ability of the accounting system to communicate bad news
in a timely fashion. Our empirical approach for testing this conjecture is based on the
regression, the dependent variable is annual earnings per share divided by beginning stock price.
2
The proxy for “economic news” is the concurrent stock return. A dummy variable that equals
one whenever returns are negative for the year is then interacted with the returns variable,
allowing for a differential coefficient for “bad news.” Our hypothesis variable is an additional
interaction term with the negative stock returns variable. This hypothesis variable is the decile
ranking of the ratio of the variance of matched single-segment firm profits to the variance of the
firm’s profits across its multiple segments. We interpret the higher ranked decile as containing
Our main regression results are consistent with our prediction that when the smoothing
variable is high then the sensitivity of accounting earnings to bad economic news is reduced.
earnings, and that this manifests itself via the delayed accounting recognition of economic losses.
This is the main contribution of our study. Our results are strengthened by the inclusion of other
news and test whether a higher level of inter-segment smoothing reduces their use. We focus on
asset write-downs (both tangible and intangible). In a probit regression, we find that the
smoothing variable is negatively associated with the likelihood of recording one of these charges,
after conditioning on several market and accounting return variables. This provides further
evidence that inter-segment smoothing reduces the timely recognition of bad news via firm-level
accounting earnings.
Our paper contributes to the growing body of literature on the conservatism of accounting
earnings. Ball [1998] and Watts [2003] posit that the contracting and governance demands on
3
accounting logically lead to the asymmetric timeliness of accounting earnings documented by
Basu [1997]. We examine how the combination of organizational structure (i.e., the
diversification into multiple business segments) and financial reporting choice (i.e., the
smoothing of accounting profits across segments) can reduce the accounting system’s ability to
The next section of this paper reviews the existing smoothing and conservatism
literatures while placing our hypotheses in this context. The third section describes our inter-
segment smoothing variable in more detail. The fourth section describes our sample selection
process and presents descriptive statistics. The fifth section empirically documents results
related to our hypothesis that the inter-segment smoothing variable is associated with
information asymmetry. The sixth section presents empirical results related to our main
section presents additional findings that certain non-recurring negative accruals used to
communicate bad news are less likely when inter-segment smoothing is high. The final section
Income smoothing has been the subject of a number of accounting research studies, both
analytical and empirical, over the past three decades. A comprehensive review of that literature
is beyond the scope of this study, but others have periodically reviewed this research (see Ronen
and Saden [1981]; Schipper [1989]; and Dechow and Skinner [2000], for examples). The
traditional income smoothing study examines the inter-temporal recognition of firm-level profit
figures. These models or empirical specifications seek to identify situations where revenues or
4
expenses are shifted by the firm from one period to another in an attempt to present a time-series
of earnings with a lower variance. Yet this definition is problematic because GAAP, when
neutrally applied, requires firms to “smooth” cash flows via the accrual process. Dechow and
Skinner [2000] discuss the problems a researcher faces when trying to determine whether there is
With any earnings management study, the challenge for the researcher is in identifying
what the “unmanaged” portion of earnings is before testing whether the “managed” piece
corresponds to the economic forces of their hypothesis. In the smoothing literature, for example,
DeFond and Park [1997] study the descriptive validity of Fudenberg and Tirole’s [1995] theory
that managers smooth income across time to prevent the “intervention” of the principal/owner in
the manager’s division (e.g., by sacking him). DeFond and Park interpret their empirical
evidence as consistent with the theory. But in a follow up paper, Elgers, Pfeiffer and Porter
[2003] point to methodological problems in measuring expected accruals via the modified-Jones
model, and how this biases the DeFond and Park conclusions in favor of the smoothing
In our study, we take a different tack from much of the prior literature and focus on inter-
segment income smoothing by the firm across segments as opposed to across time. The
smoothing of income across segments can take two forms. The first approach is to aggregate
different business activities into segments such that the variance of the underlying profits across
units is reduced. This is the form of smoothing described analytically by Hayes and Lundholm
[1996]. In their setting, firms choose to aggregate business segments with dissimilar profitability
so as to hide information from competitors. The second approach to smoothing income involves
the manipulation of the cost allocation of expenses that are components of segment profits.
5
Hann and Lu [2008] find that the incidence of segment losses appears to be unusually low, and
With the exception of Hann and Lu [2008], most past research that examines
segments into a common segment(s). The early literature in this area focused on the proprietary
cost incentive as the source of demand for this behavior. For example, Harris [1998] finds that
firms with segments in highly concentrated industries tend to aggregate those segments with
others in external reports; she also finds that segments in industries where abnormal profits tend
to persist are aggregated by firms. She concludes that these managers engage in this behavior to
hide information from competitors, as predicted by the Hayes and Lundholm [1996] model.
More recent work in this area focuses on agency cost incentives to withhold information on
disparate segments. Berger and Hann [2007] examine the revelation of previously hidden
segments that was mandated by a change in accounting rules effective in 1998.2 They conclude
that the newly revealed segments were previously withheld for agency cost reasons; managers
were attempting to prevent shareholders and other stakeholders from pressuring the firm to
In this paper, we examine the smoothing of internal profits across segments via either
aggregation or cost allocation.3 Our measure, which will be more fully described in the next
section, captures the variance of profits reported across a firm’s multiple segments and compares
this to a benchmark of the variance of profits reported across a sample of single segment firms
2
The Financial Accounting Standards Board (FASB) issued Statement of Financial Accounting Standard (SFAS)
No. 131, Disclosures about Segments of an Enterprise and Related Information effective for fiscal years beginning
after December 15, 1997.
3
It is beyond the scope of this project to determine exactly how the profits are smoothed across the segments.
However, we present evidence in Section VI that the autocorrelation in our smoothing measures is 0.394. This
suggests that the smoothing measure we use is fairly consistent through time, and thus likely due to business
aggregation or cost allocation decisions that cannot be easily changed from year to year.
6
matched by size and industry to the segments. Our interpretation is that when the benchmark
matched sample has a high variance, but the treatment firm has a low variance, then the
treatment firm is engaging in income smoothing across its segments. The advantage to capturing
smoothing this way, as opposed to the traditional inter-temporal smoothing measures, is that it
alleviates our need to specify a level of expected earnings (or accruals) in a time-series for a
sample firm. This is the methodological problem that DeFond and Park [1997] face per the
analysis of Elgers et al. [2003]. In a sense, our smoothing measure is akin to Beaver’s [1968]
research design of examining the information content of earnings not by modeling the first
moment expectations of the stock market, but rather the historical pattern in the second moment
of variance in stock returns, and how that variance changes around earnings announcements.
Our goal in this paper is to examine how inter-segment profit smoothing affects the
ability of the overall firm earnings figure to communicate timely information. Our first
hypothesis is that inter-segment smoothing suppresses the information in the components of firm
earnings because investors are now less aware of the underlying source of firm profits. We
predict that inter-segment smoothing will thus be positively associated with other measures of
information asymmetry that are publicly observable, namely, bid-ask spreads and the probability
a specific manifestation of how accounting information may be garbled with this smoothing. We
focus on the conservatism inherent in GAAP, and how inter-segment smoothing might affect the
verification for recognizing good news than bad news in financial statements” (p. 4). This
7
definition leads to the natural prediction that bad news will be reported on a more timely basis
than good news, since the accountant will require less corroborating evidence to recognize a loss
than a gain. Basu empirically documents the application of this principle in the U.S. over a
period of roughly four decades (1963-1990); he also cites sources that claim it had been applied
in Europe over the past six centuries. Ball [2001] and Watts [2003] explain the demand for
conservatism as coming from the contracting process where, largely due to governance needs
An exogenous force that has been studied as a source of conservatism is the legal nature
of a corporation’s national environment. Ball, Kothari and Robin [2000] find that firms in
common-law countries, where accounting standards are more likely to arise from the “bottom-
up” via commercial transactions, contracts and court precedent, are likely to exhibit more
conservative earnings numbers than those in code-law countries, where accounting standards
come from the “top-down” via government action. Bushman and Piotroski [2006] examine
additional macro factors in a cross-country study, including legal origin, securities laws and
Less research has examined conservatism at the firm level within the U.S. to determine
forces that might lead to cross-sectional variation in its application. Ryan [2006] identifies many
of the problems associated with applying the Basu framework generally (see especially pp. 515-
516), as does Givoly, Hayn and Natarajan [2006]. Khan and Watts [2007] suggest that three
firm fundamentals are natural determinants of a firm’s conservatism measure: the market-to-
book ratio, firm size, and firm leverage. They use these variables to form a “C_Score” that they
validate with tests that are direct (e.g., higher C_Score firms exhibit more asymmetric timeliness
as defined by Basu) and indirect (e.g., higher C_Score firms tend to experience underlying
8
fundamentals that lead to a higher demand for conservatism, such as greater information
asymmetry).
Our focus is whether a discretionary reporting decision, the smoothing of profits across
the segments of a firm, impairs the ability of the accounting system to fulfill an important
objective, the communication of bad news in a relatively timely manner. Our maintained
assumption is that bad news events, such as revenue or cost shocks that reduce profits, increase
the underlying volatility of the income stream. Yet a reporting system that smoothes these
shocks across the business units of a firm will obscure theses shocks and cause firm level
accounting numbers to be less responsive to bad economic news. Thus, our second hypothesis
predicts that firm-years classified as higher smoothers will exhibit less conservatism. That is, the
incremental “bad news” coefficient in the Basu regression will be attenuated in firm-years that
One of the ways in which bad news is recognized in accounting earnings is through the
use of negative, non-recurring accruals. Compustat uses the non-GAAP term “special items” to
describe these accruals. We believe that either asset write-downs or restructuring charges are
the accounting technology frequently used to actually record the bad news. We refer to these
Our third hypothesis is that these negative non-recurring accruals will be recorded with a
lower frequency for smoothing firms. Although this prediction may seem trivial in light of our
second hypothesis, the role of these accruals in the application of conservatism is unclear.
Callen, Hope and Segal [2009] conclude that “special items” per Compustat are a noisy
manifestation of asymmetric timeliness. Further, Frankel and Roychowdhury [2007] find that
9
[2007] find that restructuring charges are frequently more persistent (i.e., not as non-recurring as
they are purported to be) for firms that score low on the Khan and Watts [2007] C_Score. As
such, these charges may reflect big bath or cookie jar reserve behavior more than they do the
accrual recognition.
Our main hypothesis variable in this study is a within sample decile ranking of a ratio of
variances, denoted RVR (for ranked variance ratio). In the denominator of this ratio is the
variance of profits across a firm’s segments.4 In the numerator is the variance of profits across a
sample of single segment firms that are matched by size and industry to the multi-segment firm’s
business segments. The higher the ratio, the more smoothing that can be inferred. We assume
that the variance in profit across the matched single segment firms represents the expected level
of variation in profits within a multi-segment firm. As RVR increases above one, the more the
expected performance variance exceeds the reported performance variance. We interpret this as
inter-segment profit smoothing. We use ranks as opposed to a continuous measure of the ratio to
control for extreme values that can result when measuring a variance across a small number of
data points.
Both profit figures in the raw variance ratio are deflated by the respective sales of the unit
of analysis (i.e., segment sales for the multi-segment firms and firm sales for the single segment
group). We match on industry at the most precise level possible using SIC classification,
limiting ourselves to the two digit level. That is, where possible, we match first on the four-digit
4
Only multi-segment firms are included in our sample. We elaborate on the sample selection in Section IV.
10
level, second on the three-digit level if a four-digit match is not possible, and third on the two-
digit level if no match occurs in the first two attempts. After matching at the industry level, we
then choose the single segment firm with the value of sales closest to the business segment’s.
Because the definition of “profit” at the segment level may vary across the sample firms,
we use an algorithm to infer how the firms are measuring this figure at the segment. This is
necessary because we wish to remain consistent in our definition of “profit” with our benchmark
single-segment firms. We sum the profit figures across the segments, and then compare it to
several likely firm level earnings figures. We identify the minimum difference between the sum
of segment profit and the firm level measures. We then use that firm level measure for our
single segment firms when calculating the variance of profit for this matched portfolio. Our
candidates for firm level earnings in this process include (all identifying numbers refer to
Compustat fields):
• net income before extraordinary items plus depreciation expense (#18 + #14);
• net income before extraordinary items plus interest expense (#18 + #15);
• net income before extraordinary items plus tax expense (#18 + #16);
• net income before extraordinary items plus depreciation expense plus interest expense
• net income before extraordinary items plus depreciation expense plus tax expense
• net income before extraordinary items plus interest expense plus tax expense (#18 +
#15 + #16);
11
• and, net income before extraordinary items plus depreciation expense plus tax
Our measure of smoothing is similar to one used by Ettredge, Kwon, Smith and Stone
[2006], however there are significant differences. Ettredge et al. use the range of return on sales
(ROS) across a firm’s segments as the measure of variability.5 This serves as a dependent
variable in their analyses. The primary research question they ask is whether this variability
increased following SFAS 131. They construct a benchmark inherent variability measure by
looking at the range of mean profits of the single segment firms operating in the same industries
as the multi-segment firms various segments. This inherent variability is then used as a control
in an OLS regression where the aforementioned range of ROS across firm segments serves as the
dependent variable.
While our ranked variance ratio (RVR) measure differs significantly from the Ettredge et
al. approach, the variables are similar. But more importantly, the research question we ask is
considerably different from this other study. Ettredge et al. are concerned with how a new
accounting standard, SFAS 131, affected the inter-segment smoothing behavior of multi-segment
firms. Our research question is whether the inter-segment smoothing behavior of multi-segment
firms can affect the asymmetric timeliness of earnings. Thus our contribution is to assess
whether the accounting system’s role of communicating bad news on a timely basis is
5
In footnote 11 (p. 98) they report that their results are robust to using the standard deviation of ROS rather than the
range.
6
We do explore the role of SFAS 131 in our tests in Section VI.
12
IV. Sample Selection, Variable Definitions and Descriptive Statistics
Firm-level data is gathered from the Compustat Annual Industrial Files. Firms must have
data on sales (#12), diluted EPS including extraordinary items (#169), net income before
extraordinary items (#18), the current and lagged fiscal year-end stock price (#199), the current
and lagged fiscal year-end shares outstanding (#25), the book value of equity (#60), the book
value of long-term debt including the current portion (#9 + #34), and assets (#6) to be included in
our sample. Firms must also have a continuous string of stock returns on the Center for Research
in Security Pricing (CRSP) database from April of the sample year to March after the sample
year-end.
We gather segment information from the Compustat Segment database requiring segment
sales (SALE), an income variable (OPS, OIBD, or NI), and an SIC code (SSIC1 or SSIBC1).
Segments are deleted if segment sales are equal to or less than zero. Compustat includes single-
segment firms as segments in the segment database, therefore segments are also deleted if
We identify multi-segment firms as those firms that are successfully matched to at-least
two segments on SPC Permanent Number and year-end date. Multi-segment firms are deleted
from the sample if the sum of segment sales for segments successfully matched to the multi-
segment firm is not within 1% of aggregate firm sales. We identify single-segment firms as
those firms that are successfully matched to zero or one segments on SPC Permanent Number
and year-end date. We do this matching before any segments are deleted from the segment file
to reduce the likelihood that multi-segment firms are mistakenly identified as single-segment
firms.
13
Table 1 presents two panels of descriptive statistics. Panel A includes all of the
observations (17,367) that will be included in our main Basu regression tests. That panel
presents the descriptive statistics regarding the distributional characteristics of the variables that
will be used in the regressions. This sample is truncated by deleting observations in the top and
bottom 1% of deflated earnings per share (DEPS), annual returns (R), book-to-market (BTM),
and the top 1% of leverage (LEV). The variables are defined as follows:
DEPS: Diluted earnings per share including extraordinary items (#169) divided by the
RVR: A ranking of the firm’s variance ratio into deciles, higher deciles indicate higher
Neg: A dummy variable equal to one if returns for the 12 month period beginning in
April of the sample year are less than zero; else the dummy variable takes on a
value of zero.
BTM: The book value of equity (#60) divided by the market value of equity (#199 *
#25)
LEV: The book value of long-term debt including the current portion (#9 + #34) divided
Panel B of Table 1 presents the univariate correlations for all of the variables above. Pearson
14
V. Correlations between Inter-Segment Profit Smoothing and Information Asymmetry
Measures
In our first set of analyses we examine the univariate correlations between RVR and other
publicly observed measures of information asymmetry. Our objective is to gain comfort that the
smoothing RVR measure does indeed measure a phenomenon that is associated with information
asymmetry. We focus on univariate correlations between RVR and both the bid-ask spread
The bid-ask spread is a common measure of information asymmetry in the equity market.
Moreover, it is one that more transparent disclosure or accounting policies are thought to reduce.
For example, Healy, Hutton and Palepu [1999] find that sustained increases to voluntary
disclosure (as measured by a qualitative rating of disclosure quality by sell-side equity analysts)
are associated with a decline in the spread. Leuz and Verrecchia [2000] document that German
companies that voluntarily commit to a more transparent accounting system (as measured by a
switch from German GAAP to the more transparent IFRS or U.S. GAAP in the mid-1990s)
The PIN measure, as developed by Easley, Kiefer and O’Hara [1997] is another variable
equity security has been initiated by an informed trader. Hence, it is increasing in the
information asymmetry between informed and uninformed investors. Like Healy et al.’s [1999]
analysis of disclosure and bid-ask spreads, Brown and Hillegeist [2007] find that voluntary
disclosure quality is also inversely related to the PIN, the implication being that a firm’s
reporting strategy can reduce this information asymmetry measure that raises the cost of capital
for a firm.
15
We predict with our first hypothesis that the smoothing measure RVR is positively
associated with both BA_S and PIN. Our logic is that when firms smooth profits across their
segments, this increases the level of information asymmetry as captured by these proxies. This
prediction is similar to the conjectures of Healy et al. [1999] and Brown and Hillegeist [2007].
BA_S is calculated as the average closing bid-ask spread per CRSP throughout the year
that RVR is measured. We use calendar year PIN scores calculated by Assistant Professor
Soeren Hvidkjaer for the years 1983 to 2001, posted on his webpage;
PIN score measures abnormal trading activity by inferring imbalances in the volume of buy
versus sell orders in a given stock. The PIN score employs the assumption that the market maker
should incorporate new public information directly into prices for subsequent trades of a given
stock, thus creating no systematic order imbalance. In contrast, the market maker is not capable
of incorporating private information into stock prices for subsequent trades. Thus, an order
imbalance will exist whereby “bad” private information generates an excess of sell-orders and
“good” private information generates an excess of buy orders. Firms with relatively greater
abnormal trading volume (a higher PIN score) likely have more information asymmetry between
outside investors and insiders. Easley & O’Hara (1992), Easley, Hvidkjaer, & O’Hara (2002 &
2004) provide an extensive treatment of the PIN score and its relation to information asymmetry.
We limit our analysis for our first hypothesis to univariate correlations because the
ultimate goal of this analysis is to gain comfort that RVR actually captures a measure of
information asymmetry. This provides us with a cleaner interpretation of our primary analyses
in the next section where we examine whether RVR inhibits the asymmetric timeliness of
earnings.
16
Panels A and B of Table 2 present all of the regression variables defined in Section IV for
smaller sub-samples where the BA_S and PIN are available. We have only 11,785 observations
with a usable bid-ask spread, and 3,801 with a PIN. The use of the PIN score imposes several
sample size limitations on our study, which account for the dramatic drop in the number of
viable observations. The PIN score is calculated on a calendar year basis only, so firms that
don’t have a December fiscal year-end are lost. Further, the PIN score is only available for firms
on the NYSE/AMEX stock exchange for the years 1983 through 2001.
In Panel C of Table 2 we present the correlations between our smoothing measure RVR,
and BA_S and PIN. Pearson correlations are presented in the upper diagonal, Spearman in the
lower. The correlations across all variables are positive and significant, as predicted. The
magnitudes of the correlations between PIN and BA_S are quite high, which is to not surprising
as they are both designed to capture the primitive information asymmetry construct. Our
variable of interest, RVR, is positively correlated with each of the other measures, but the
magnitudes of the correlations are modest. This is to be expected, however, as the primitive
measures should encompass all of the features that lead to information asymmetry (e.g.,
disclosure policy, organizational structure, other accounting choices, etc.) whereas RVR captures
a single aspect.
The basic Basu regression uses earnings as the dependent variable, with
contemporaneous stock returns as an explanatory variable that proxies for “economic news.”
Returns are interacted with a dummy variable that takes on a value of one if the return is
17
negative, zero otherwise. In equation form, the model is presented below (all variables are
We augment the basic Basu model by including our proxy for inter-segment profit
DEPSit = β0 + β1 Negit + β2 Rit + β3 Negit * Rit + β4 RVRit + β5 Negit * RVRit + β6 RVRit * Rit
Our prediction for our second hypothesis is that the β7 coefficient is negative. This
would suggest that inter-segment smoothing reduces the ability of the accounting system to
Table 3 presents estimates of several variants of model [2]. In the first column of results,
the model above is estimated for the entire pool of 17,367 observations. The results of this
estimation support our second hypothesis. The β7 coefficient is negative and statistically
significant at the 1% level. The magnitude of the coefficient is -0.0081. Thus, if a firm moved
from the lowest decile to the highest decile of smoothing, then that would suggest the Basu
asymmetric timeliness coefficient would fall by 0.0729 (or nine multiplied by 0.0081); this
reflects a decline of 24.0% of the baseline estimated β3 coefficient of 0.3036. Even a more
conservative change of a one standard deviation increase in RVR of would suggest a decline in
the asymmetric timeliness coefficient of 0.0232 (or 2.861 multiplied by 0.0081); a decline of
As an extension of model [2], we use the lagged value of RVR. We expect that
smoothing should represent a fairly static decision by the firm, especially if it is driven by the
18
choice in how business activities are aggregated into reportable segments; we do not expect this
decision to change much from year to year. Indeed, when we estimate a univariate regression
where current RVR is the dependent variable and lagged RVR the explanatory variable, the
adjusted R2 of the model is 0.156 and the estimated coefficient for the lagged value is 0.394,
phenomenon. In the third column of Table 3 we replace the contemporaneous RVR with its
lagged values. The statistical significance and economic magnitude of the coefficient do not
In the second and fourth columns of Table 3 we estimate model [2] annually, using either
the contemporaneous (second column) or lagged (fourth column) RVR as our main variable of
interest. Then, we take the average β7 coefficient across the 24 or 23 years and test for its
statistical significance using the Fama-MacBeth method. This approach controls for cross-
sectional dependence in the financial data that likely exists. The results presented in columns
two and four of Table 3 suggest that the β7 coefficient is statistically insignificant when using the
Fama-MacBeth approach.
To examine this result further, we examined the time series of the β7 coefficient from the
annual model [2] estimations using the contemporaneous RVR value interacted with R and Neg.
We noticed that it was after the application of SFAS 131 in 1998 that the stability of the β7
coefficient began to deteriorate. Recall that SFAS 131 was designed to improve segment
footnotes by aligning the external reporting with internal management tracking of business units.
Ettredge et al. [2006] find that following the mandatory adoption of this standard the range of
reported profits across business segments increases, on average; they conclude that this implies
that SFAS 131 reduced the smoothing by management of profits across their segments. In our
19
sample, there appears to be some evidence that the higher pre-SFAS 131 smoothing levels were
average value of β7 over the 15 years from 1983-1997 was -0.0170; the Fama-MacBeth t-statistic
for this average coefficient is -2.0358. Moreover, the estimated coefficient is negative in 11 of
the 15 years. Yet in the 9 years from 1998-2006, the average β7 value is 0.0097; the Fama-
MacBeth t-statistic for this is 0.6927 and the estimated coefficient is negative in 4 years.7 We
examine the effects of this accounting regime change further in our next set of analyses that
We augment model [2] by adding the variables identified by Khan and Watts [2007] as
BTM: The book value of equity (#60) divided by the market value of equity (#199 *
#25)
LEV: The book value of long-term debt including the current portion (#9 + #34) divided
Including the asymmetric timeliness measure interactions with these firm fundamentals will
insure that our smoothing measure is not driven by these other forces. The resulting model is
presented below:
DEPSit = β0 + β1 Negit + β2 Rit + β3 SIZEit * Rit + β4 BTMit *Rit + β5 LEVit * Rit + β6 RVRit * Rit
+ β7 Negit * Rit + β8 SIZEit *Negit * Rit+ β9 BTMit *Negit * Rit + β10 LEVit *Negit * Rit
7
One year in particular drives up the average in the post-SFAS 131 era: in 2003 the estimated coefficient is 0.0987.
Excluding that year the post-SFAS 131 coefficient is essentially zero: -0.0014.
20
We present the results of estimating model [3] in Table 4. We specified model [3] with
minimal intercept terms to reduce multi-colinearity problems consistent with Khan and Watts
[2007]. Our results are quantitatively and quantitatively similar with and without the control
variables, although SAS diagnostic output suggests that strong multi-colinearity problems exist
when the full set of intercept terms is included. The unconditional asymmetric timeliness
coefficient (β7) is positive and significant, as expected. The Khan and Watts conditional
variables also take on their expected signs. Our variable of interest is β11, which conditions the
asymmetric timeliness on the degree of inter-segment smoothing. Consistent with our earlier
analyses, this estimated coefficient is negative and statistically significant in column one where
the pooled regression model is presented. Thus, the results continue to suggest that inter-
segment smoothing reduces the asymmetric timeliness of earnings. The second column of the
table presents the Fama-MacBeth coefficients and t-statistics. In this specification, the β11
timeliness of earnings in the years following the enactment of SFAS 131 (1998-2006). If we try
to control for this temporal effect via an additional dummy variable for the change in accounting
regime, we end up with a model that suffers collinearity problems per the SAS diagnostic output.
This is not surprising given the various interactions involved with such a model. That is, we
have dummy variables for both time and negative returns, as well as our smoothing measure
RVR. With all combinations of these variables interacted with the economic income proxy (i.e.,
In lieu of that approach, we present a simpler model in Table 5. In the first column of
results, we present model [3] which includes the Khan and Watts determinants of asymmetric
21
timeliness, for the pre-SFAS 131 period only. In this model, our smoothing interaction,
coefficient β11 is still negative and significant with a magnitude of -0.0195. In the post-SFAS
131 era presented in the second column, we see that the magnitude of the coefficient has fallen
by roughly 25% to -0.0147, although it is still statistically significant. While this suggests that
SFAS 131 has reduced the ability of inter-segment smoothing to compromise the asymmetric
Overall, the results from Tables 3-5 suggest that inter-segment smoothing reduces the
asymmetric timeliness of earnings, consistent with our second hypothesis. Additional analyses
suggest that this effect has attenuated post-SFAS 131, but is still present.
The results above suggest that inter-segment profit smoothing reduces accounting
conclusion. We analyze whether smoothing reduces the likelihood of accounting accruals that
are used to communicate “bad news,” namely asset impairments or restructuring charges.
fundamental operational problems, then we expect a negative association between the measure
RVR and the likelihood of an asset impairment or restructuring charge after conditioning on
We examine only the time period 2001-2006 as this is when Compustat fields for specific
impairment and restructuring charges are more complete. Compustat has presented the annual
data field “Special Items” (#17) as a record of impairment and restructuring charges since 1950.
22
However, that field contains a myriad of other charges that are not impairments or restructurings.
After reading detailed earnings announcements, Bens and Johnston [2008] screen out a number
of special item charges that are unrelated to restructuring charges or asset impairments for their
study of earnings management via these accruals (see their Table 1). Since 2001 Compustat has
been recording specific charges that are either tangible asset impairments (#380), intangible asset
impairments (#368) or restructuring charges (#376).8 Frankel and Roychowdhury [2007] find
that restructuring charges may reflect opportunistic behavior more than they do the asymmetric
timeliness feature of earnings. Therefore, our primary test focuses on asset impairments alone.
We code our dependent variable D_Spec as one if the firm has recorded an impairment charge to
taking on a value of one if a charge is recorded in any of the three fields for the year, zero
otherwise.
We base most of our control variables on the study by Francis, Hanna and Vincent
[1996], which identified several firm and industry level economic determinants of asset write-
offs and restructuring charges. We also include the change in GDP as a macro variable that is
correlated with restructuring charges (Bens and Johnston [2008]). Finally, we include the three
determinants of asymmetric timeliness identified by Khan and Watts: size, book-to-market, and
leverage. Our variable of interest remains our ranked inter-segment profit smoothing variable,
RVR, which we predict will be negatively associated with the likelihood of recording a write-off.
We present our probit model below, and the specific definitions of the variables follow:
8
While some of these fields appear with non-zero values in 2000, the Compustat dataset appears to be incomplete
for this year. Lee [2008] reaches the same conclusion and ignores this year (see footnote 15).
23
D_Specit = ß0 + ß1Rit + ß2∆Saleit + ß3∆IND_Saleit + ß4∆(∆Sale) it + ß5∆(∆IND_Sale) it + ß6ROAit
Consistent with Francis, Hanna and Vincent [1996] we include a variety of variables
intended to control for the economic characteristics of the firm that likely impact the probability
of an asset impairment. We remove special items (of which our dependent variable is a
component) when determining firm financial performance so as to remove the possibility that the
explanatory power of our variable of interest, RVR, is unduly sapped. These variables are
defined as follows:
(#18 – #17)/#6.
IND_ROA: The average return on assets for the firm’s 2-digit SIC code industry.
∆ROA: The firm’s change in return on assets from the previous year.
∆IND_ROA: The average change in return on assets from the previous year for the firm’s 2-
∆Sales: The firm’s change in sales (#12) from the previous year.
∆GDP: The year’s change in chained GDP from the Federal Reserve Bank of St. Louis
web-site: http://research.stlouisfed.org/fred2/.
∆IND_Sales: The average change in sales from the previous year for the firm’s 2-digit SIC code
industry.
∆(∆Sales): The change in the firm’s change in sales from the previous year.
∆(∆IND_Sales): The average change in the industry’s change in sales from the previous year for
24
We present the results of estimating model [4] in Table 6. The first column of results
used independent variables that are raw values, with the exception of RVR which is a ranked
variable by definition. In the second column, all explanatory variables are decile ranked, except
for ∆GDP which is ranked from one to six, corresponding to the number of years in the panel.
Firm specific data is ranked within 2-digit SIC code, whereas industry level data is ranked across
industries.
In both columns, we see a negative and significant [ß11] association between the
marginal effect from the probit model evaluated at sample means is -0.0067. Thus if a firm
moved from lowest decile to the highest decile of smoothing, then that would suggest the
probability of recording an asset impairment is 6.05% lower (or nine multiplied by .0067).
Moving from one standard deviation below the average RVR to one standard deviation above
decreases the probability of an asset impairment by 3.85%. In untabulated results we find that
when the dependent variable D_Spec is formed using both asset impairments and restructuring
charges the coefficient on our variable of interest, RVR, becomes insignificant but still negative.
This is consistent with evidence in Frankel and Roychowdhury [2007] indicating that
restructuring charges may reflect opportunistic behavior more than they do the asymmetric
reduces the conservatism applied in accounting reports. Our analysis in Section VI demonstrates
that the asymmetric timeliness coefficient declines as smoothing increases – suggesting that
recognition of bad economic news is delayed when smoothing is high. In this section we
25
analyzed how a specific set of “bad news” accruals are used with less frequency when smoothing
increases.
VIII. Conclusion
this smoothing is positively associated with the information asymmetry between informed and
uninformed shareholders. The intuition is that smoothing profits across segments obfuscates the
between our inter-segment profit smoothing measure and two common proxies for information
asymmetry: the bid-ask spread and the probability of an informed equity trade.
Past research has demonstrated that U.S. GAAP are applied with a conservative or
asymmetric bias: bad economic news is communicated faster than good economic news. This
inter-segment profit smoothing reduces the ability of the accounting system to communicate bad
news in a timely manner. Our empirical results support our prediction. We estimate a regression
of earnings on stock returns, where the returns are separated into good news (i.e., greater than
zero) and bad news (i.e., less than zero), similar to Basu [1997]. We find that the estimated
via the accounting system: through the use of negative non-recurring accruals, specifically asset
impairments and restructuring charges. We predict that the probability of a firm recording such
26
charges will be reduced as they increase the level of inter-segment smoothing. Our results
Our paper examines how firm structure and external reporting choices influence an
important role of financial accounting: the communication of bad news in a timely manner. The
structure we study is the segment reporting choice of the firm. We find that when discretion is
used to reduce the variation across segment profits, the ability of firm level earnings to
27
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29
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30
Table 1
Panel A: Descriptive statistics for variables used in the conservatism tests (n=17,367)
Variable Mean Median Std Deviation Min Max
DEPSit 0.025 0.055 0.145 -1.953 0.781
Rit 0.125 0.076 0.498 -0.968 8.383
RVRit 5.642 6.000 2.861 1 10
Negit 0.411 0.000 0.492 0 1
SIZEit 6.333 6.359 2.088 -0.650 13.112
BTMit 0.670 0.571 0.547 -1.837 11.194
LEVit 0.653 0.341 1.113 0.000 20.238
Panel B: Correlation table for variables used in the conservatism tests (n=17,367)
Pearson Correlation Coefficients Top Right / Spearman Correlation Coefficients Bottom Left
Prob > |r| under H0: Rho=0
DEPSt Rt RVRt Negt SIZEt MTBt LEVt
DEPSit 1 0.1930 0.0403 -0.2435 0.1675 -0.1185 -0.2613
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001
Rit 0.3566 1.0000 0.0087 -0.6465 0.0747 -0.2208 -0.1618
<.0001 0.2499 <.0001 <.0001 <.0001 <.0001
RVRit 0.0282 0.0015 1.0000 -0.0007 -0.1019 0.0155 -0.0149
0.0002 0.8399 0.929 <.0001 0.0406 0.0503
Negit -0.3337 -0.8522 0.0003 1.0000 -0.1696 0.1949 0.1533
<.0001 <.0001 0.973 <.0001 <.0001 <.0001
SIZEit 0.1585 0.1438 -0.1037 -0.1684 1.0000 -0.1399 0.0652
<.0001 <.0001 <.0001 <.0001 <.0001 <.0001
MTBit 0.0582 -0.2307 0.0195 0.1829 -0.1538 1.0000 0.3985
<.0001 <.0001 0.0102 <.0001 <.0001 <.0001
LEVit -0.0242 -0.1499 -0.0188 0.1165 0.1754 0.4040 1.0000
0.0014 <.0001 0.0131 <.0001 <.0001 <.0001
31
Table 2
Panel A: Descriptive statistics for observations with a caclulable B/A Spread (n=11,785)
Panel B: Descriptive statistics for observations with B/A Spread and PIN (n=3,801)
32
Table 3
Primary accounting conservatism regressions
Prediction Basic Fama & MacBeth Lagged RVR Fama & MacBeth
Intercept ß0 ? 0.0591 *** 0.0515 *** 0.0574 *** 0.0510 ***
(0.0040) (0.0059) (0.0042) (0.0058)
Negit ß1 ? -0.0255 *** -0.0132 -0.0050 -0.0041
(0.0069) (0.0107) (0.0077) (0.0109)
Rit ß2 ? -0.0254 *** 0.0066 -0.0061 0.0147
(0.0065) (0.0223) (0.0071) (0.0161)
Negit*Rit ß3 ? 0.3036 *** 0.3056 *** 0.3054 *** 0.2602 ***
(0.0179) (0.0538) (0.0216) (0.0576)
RVRit ß4 ? 0.0001 0.0004
(0.0006) (0.0008)
Negt*RVRit ß5 + 0.0021 * 0.0011
(0.0011) (0.0015)
RVRit*Rit ß6 ? 0.0020 ** 0.0008
(0.0010) (0.0024)
Negit*RVRit*Rit ß7 - -0.0081 *** -0.0072
(0.0028) (0.0078)
RVRit-1 ß4 ? 0.0001 0.0004
(0.0007) (0.0005)
Negit*RVRit-1 ß5 ? -0.0010 -0.0008
(0.0012) (0.0015)
RVRit-1*Rit ß6 ? -0.0007 -0.0019
(0.0011) (0.0022)
Negit*RVRit-1*Rit ß7 - -0.0081 ** 0.0015
(0.0034) (0.0079)
33
Table 4
Accounting conservatism regressions using the Watts & Khan C_Score specification
34
Table 5
Conservatism regressions using the Watts & Khan C_Score specification before and after FAS 131
35
Table 6
Probability of Writedown
36