Journal of Accounting and Economics 56 (2013) 40–56
Contents lists available at SciVerse ScienceDirect
Journal of Accounting and Economics
journal homepage: www.elsevier.com/locate/jae
Do managers define non-GAAP earnings to meet
or beat analyst forecasts?$
Jeffrey T. Doyle a, Jared N. Jennings b, Mark T. Soliman c,n
a
b
c
Jon M. Huntsman School of Business, Utah State University, 3540 Old Main Hill, Logan, UT 84322, USA
Olin Business School, Washington University in St. Louis, One Brookings Drive, St. Louis, MO 63130, USA
Marshall School of Business, University of Southern California, 3660 Trousdale Parkway, Los Angeles, CA 90089, USA
a r t i c l e in f o
abstract
Article history:
Received 12 June 2009
Received in revised form
8 March 2013
Accepted 12 March 2013
Available online 25 March 2013
We provide evidence consistent with firm managers opportunistically defining non-GAAP
earnings in order to meet or beat analyst expectations. This result is robust to controlling
for other tools of benchmark beating (e.g., discretionary accruals, real earnings management, and expectation management). We also find that managers tend to exclude more
expenses from non-GAAP earnings when it is costlier to use accrual earnings management
due to balance sheet constraints, indicating that these tools are substitutes. Lastly, we find
that investors discount positive earnings surprises when accompanied by exclusions from
GAAP earnings, suggesting that the market partially understands the opportunistic nature
of these exclusions. Our evidence is consistent with managers opportunistically defining
non-GAAP earnings in a way that analysts fail to fully anticipate, resulting in an increased
likelihood of exceeding analyst forecasts.
& 2013 Elsevier B.V. All rights reserved.
JEL classification:
M4
Keywords:
Non-GAAP earnings
Meet or beat analyst forecasts
Earnings definition
Earnings management tools
1. Introduction
Prior research documents a disproportionally high number of firms reporting earnings per share that meet or slightly
exceed consensus analyst forecasts (e.g., Degeorge et al., 1999). This phenomenon has motivated other studies to explore
why managers place such importance on this benchmark and what methods they use to achieve it. The prior accounting
literature focuses (with mixed results) on three tools managers use to exceed analyst forecasts: (1) accrual manipulation
(Abarbanell and Lehavy, 2003; Burgstahler and Eames, 2006; Dechow et al., 2003), (2) expectations management (Kasznik
and Lev, 1995; Matsumoto, 2002), and (3) real activities manipulation (Roychowdhury, 2006; Gunny, 2010). Our study offers
an alternative tool: managers use the discretion afforded them in defining non-GAAP earnings to achieve this benchmark.
Specifically, we study three aspects of this behavior. First, we examine whether managers use non-GAAP exclusions to meet
or beat analyst forecasts. Second, we examine whether this tool is incremental to the other tools mentioned above and
☆
We would especially like to thank Professors Ted Christensen and Erv Black for allowing us to use their hand-collected non-GAAP earnings data. We
would also like to thank Jeffrey Abarbanell, Mary Barth, Bill Beaver, John Core (our editor), Patricia Dechow, Michael Eames, Brooke Elliott, Yonca Ertimur,
Ron Kasznik, SP Kothari, Russell Lundholm, Tom Lys, Carol Marquardt, Maureen McNichols, Sarah McVay, Karen Nelson, Sugata Roychowdhury, Ewa Sletten,
Sarah Shonka, Joseph Weber, Teri Yohn as well as seminar participants at AAA Annual Meeting, Georgetown University, Massachusetts Institute of
Technology, University of North Carolina, University of Oregon, Santa Clara University, Stanford Summer Camp and the AAA Western Region Annual
Meeting for helpful comments and suggestions. This paper was formerly titled, “Do Managers use ‘Pro Forma’ Earnings to Exceed Analyst Forecasts?”.
n
Corresponding author. Tel.: þ 1 425 647 1711.
E-mail addresses: [email protected] (J.T. Doyle), [email protected] (J.N. Jennings), [email protected] (M.T. Soliman).
0165-4101/$ - see front matter & 2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.jacceco.2013.03.002
J.T. Doyle et al. / Journal of Accounting and Economics 56 (2013) 40–56
41
whether it is a substitute for them. Finally, we investigate whether the market prices the earnings surprise differently if
exclusions were involved in achieving the target.
Managers generate non-GAAP earnings to augment GAAP earnings and to provide additional information not allowed by
GAAP.1 However, prior research suggests that managers may be using their discretion in defining non-GAAP earnings in an
opportunistic manner (e.g., Doyle et al., 2003; Bowen et al., 2005) by reclassifying some actual recurring expenses as nonrecurring exclusions. If managers are acting opportunistically, they may strategically exclude just enough expenses from
their non-GAAP earnings to meet or beat analyst expectations. Of course, this assumes that analysts do not fully unwind the
opportunistic non-GAAP exclusions (i.e., they are sometimes fooled by managers). If analysts perfectly identify and
anticipate all reported non-GAAP exclusions in their forecasts, income-increasing exclusions should not be associated with a
higher likelihood of meeting or beating their forecasts. Thus, to test our hypothesis we separate total income-increasing
exclusions into two constructs: (1) expected exclusions and (2) unexpected exclusions. We proxy for this decomposition by
using Special Items as expected exclusions (items identified by Compustat) and Other Exclusions.2 We predict that firms will
be more likely to meet or beat analyst forecasts when they use income-increasing unexpected exclusions.
Consistent with this hypothesis, we find univariate and multivariate evidence that firms are more likely to meet or beat
analyst estimates when using non-GAAP earnings that are higher than GAAP earnings. When using Total Exclusions (Special
ItemsþOther Exclusions), we find that the probability of a firm meeting or beating the consensus analyst forecast increases
by 14% when the firm uses positive exclusions. When we further decompose total exclusions into special items and other
exclusions, we find that other exclusions are primarily driving our overall results. The probability of a firm meeting or
beating increases by 20% when the firm uses other exclusions, while the results are insignificant for special items. Overall,
our evidence suggests that managers opportunistically define non-GAAP earnings to exceed analyst forecasts and that
analysts do not fully incorporate this behavior in their forecasts. A potential concern about our findings is that excluding
expenses mechanically increases the probability of exceeding analyst forecasts and drives these results. However, if analysts
were able to identify all exclusions when creating their forecasts, then the use of exclusions should have no effect on the
likelihood of meeting or beating analyst expectations. This inability by analysts is ultimately the main economic prediction
in our hypothesis.3 We more fully discuss this potential mechanical relation and the tests we use to alleviate the concern in
Section 5.
Next, we attempt to distinguish the use of non-GAAP exclusions to meet analyst expectations from other tools examined
in the literature including accrual manipulation, expectations management, and real activities manipulation. Our results are
robust and incremental to these alternative tools and provide further evidence that the opportunistic use of non-GAAP
earnings is an additional tool used to meet analyst forecasts not previously documented in the literature. We then examine
how managers choose between these alternative tools. We find that non-GAAP exclusions are used as substitutes for both
discretionary accruals and discretionary cash flows to achieve analyst forecasts. We also find that managers tend to shift
towards the use of exclusions when the cost of within-GAAP earnings management is high and managers are constrained, as
proxied for by high levels of existing income-increasing accruals already on the balance sheet (Barton and Simko, 2002).
Finally, we examine the market reaction to earnings surprises when income-increasing non-GAAP exclusions are used
and firms meet or beat the consensus forecast. These excluded expenses are not completely transitory and have negative
future cash flow implications (Doyle et al., 2003; Gu and Chen, 2004). Thus, if market participants perceive that managers
are opportunistically using income-increasing exclusions to artificially meet or beat analyst expectations, we expect the
market to discount the earnings surprise of these firms. We find evidence that firms meeting or beating the consensus and
using income-increasing exclusions have significantly lower earnings response coefficients. Once again, these lower returns
are driven primarily by firms that use other exclusions rather than special items. Thus, even though analysts do not fully
unwind exclusions, the market seems to be at least partially efficient at identifying and penalizing those firms that achieve
earnings benchmarks through the use of unexpected non-GAAP exclusions.
Our study makes several important contributions. First, it is the first study using large sample evidence to show a
positive relation between the use of non-GAAP earnings and the propensity to exceed analyst earnings forecasts. As noted
previously, prior research has sought to determine what tools managers use to exceed analyst forecasts (including accrual
manipulation, expectations management, and real activities manipulation). Our study shows that managers can opportunistically define an alternative earnings measure to exceed analyst forecasts. Furthermore, we identify that it is the other
exclusions that drive the increased propensity to meet or beat, indicating that analysts are not entirely unwinding the
opportunistic expenses excluded from GAAP earnings and these unexpected exclusions.
1
Non-GAAP earnings are released by management at the same time as the quarterly earnings announcement and are not prepared in accordance with
generally accepted accounting procedures. They have many labels and are also known as ‘pro forma’, ‘Street’, or ‘core’ earnings. We discuss prior research
related to non-GAAP earnings more fully in Section 2.
2
Prior literature has also used this breakdown (e.g., Doyle et al., 2003; Kolev et al., 2008; Heflin and Hsu, 2008) and found that most of the
opportunistic behavior appears to occur in Other Exclusions.
3
We also perform two tests to alleviate the concern that we are not simply documenting a mechanical correlation between meeting or beating analyst
expectations and using exclusions. First, we find no evidence that income-decreasing exclusions decrease the likelihood of meeting or beating expectations,
which would arise if exclusions were mechanically related to meeting or beating expectations. Second, we decompose total exclusions into special items
and other exclusions and find that the use of income-increasing special items does not increase the likelihood of meeting or beating analyst expectations,
which would be the case if there was a mechanical relation between both types of exclusions and meeting or beating analyst expectations.
42
J.T. Doyle et al. / Journal of Accounting and Economics 56 (2013) 40–56
Our study specifically contributes to the literature beyond Bhattacharya et al. (2003) and Black and Christensen (2009).
They provide evidence that the firm's pro forma earnings tend to meet or beat analyst forecasts more often than the same
firm's GAAP earnings. Their work does not show that firms reporting non-GAAP earnings beat more often when compared to
other firms that just report GAAP earnings. That is the evidence we provide in this paper. Specifically, we document that
firms using income-increasing exclusions tend to meet or beat analyst expectations relative to firms that do not use nonGAAP earnings.
We also explore the tradeoffs between various managerial tools to meet or beat analyst expectations. For example, when
managers are more constrained by high levels of existing income-increasing accruals on the balance sheet, they are more
likely to use non-GAAP exclusions to beat analyst forecasts. This evidence suggests that managers likely choose between
the various methods of meeting or beating analyst expectations and choose the method least costly to implement. We
encourage future research to comprehensively assess the relative costs associated with each managerial tool used to meet or
beat analyst expectations and to control for exclusions when looking for alternative earnings management tools.
Finally, our stock return tests document that investors discount the earnings surprise of firms that meet or beat estimates
and use income-increasing exclusions, suggesting that the market is at least partially efficient by discounting these firms'
opportunistic earnings surprises. Overall, our paper makes incremental contributions to the existing literature on the use
of non-GAAP earnings, the effectiveness of analysts, benchmark beating, earnings management tradeoffs, and market
responses to accounting information.
The paper proceeds as follows: we review prior literature in Section 2. In Section 3, we motivate and present our
hypotheses. In Section 4 we describe our research design, variables, and methodology. In Section 5 we present the results.
We conclude in Section 6.
2. Prior literature
Bradshaw and Sloan (2002) were among the first to document the practice of excluding expenses (i.e., exclusions) from
GAAP earnings and issuing non-GAAP earnings (also known as Pro Forma, Street, core, or operating earnings). The rationale
made by managers and in the popular press is that these exclusions are deemed to be non-recurring, non-cash, or
uninformative of the firm's core operating performance. The stated goal is to create a higher quality measure of earnings
that is more persistent and more useful for valuation. Several papers have found evidence supporting this “informative”
view of non-GAAP earnings.4
Alternatively, since non-GAAP disclosures are lightly regulated and self-defined by managers, several studies investigate
the possible opportunistic use of these alternative performance measures (Elliott, 2006; Fredrickson and Miller, 2004).
Doyle et al. (2003) find that the expenses excluded from Street earnings are predictive of lower future cash flows and lower
future abnormal returns, casting doubt on managerial assertions that exclusions are non-recurring or irrelevant to equity
valuation. Gu and Chen (2004) examine analysts' differential treatment of items excluded by managers from non-GAAP
earnings. They find that items included (anticipated) by analysts in their definition of non-GAAP earnings are more
persistent and have higher valuation multiples than those that are excluded, demonstrating some ability by analysts to
differentiate between informative and opportunistic managerial exclusions. However, they find that the exclusions allowed
by analysts do have some predictive power, indicating that analysts do not fully unwind all recurring expenses excluded by
management. Brown et al. (2012) find that the use of income-increasing non-GAAP earnings and their relative prominence
in press releases increases with investor sentiment, which the authors attribute at least partly to opportunistic motives.
Overall, this evidence gives some indication that managers are opportunistically choosing how and when to define Street
earnings to improve the appearance of the firm by excluding expenses from earnings despite their recurring nature.
Our paper builds on the non-GAAP earnings literature by proposing an alternative tool that managers employ in meeting or
beating earnings forecasts. The prior literature documents a disproportionally high number of firms reporting earnings per share
that exceed the analysts' consensus forecast (Degeorge et al., 1999; Brown, 2001; Bartov et al., 2002; Kasznik and McNichols, 2002;
Matsumoto, 2002; Burgstahler and Eames, 2006) and has focused on three tools managers use to exceed analyst forecasts:
(1) discretionary accrual manipulation (Dechow et al., 2003; Burgstahler and Eames, 2006), (2) expectations management (Kasznik
and Lev, 1995; Matsumoto, 2002), and (3) real activities manipulation (Roychowdhury, 2006; Gunny, 2010). Our study examines an
alternative tool that managers have to opportunistically meet or beat forecasts: using their discretion to define non-GAAP earnings
that exceeds analyst forecasts.
While the prior research examines the relation between non-GAAP earnings and meeting or beating analyst forecasts
(e.g., Bhattacharya et al., 2003; Black and Christensen, 2009), it compares non-GAAP and GAAP earnings of the same firm
and is not directly comparable to our paper.5 There are two papers that test the relation between the likelihood of beating
4
See Bradshaw and Sloan (2002), Bhattacharya et al. (2003), Brown and Sivakumar (2003), Collins et al. (2009), and Frankel and Roychowdhury
(2004).
5
Neither paper documents that firms reporting non-GAAP earnings beat more often than firms only reporting GAAP earnings as is done in this paper.
This is because their samples consist of only firms that use non-GAAP earnings, which precludes any comparison between firms that use non-GAAP
earnings and firms that only use GAAP earnings. They descriptively note that a firm's pro forma earnings tend to meet or beat analyst forecasts more often
than the same firm's GAAP earnings (80% vs. 39% in Bhattacharya et al. (2003)), but their analysis compares both pro forma and GAAP earnings numbers to
the firm's same consensus analyst IBES forecast. This does not result in a true “earnings surprise” calculation for the GAAP number if the consensus analyst
J.T. Doyle et al. / Journal of Accounting and Economics 56 (2013) 40–56
43
analyst forecasts and the use of non-GAAP exclusions, but they use very small samples and the tests are not the main focus
of the studies. Both Lougee and Marquardt (2004) and Johnson and Schwartz (2005) compare a small number of firms (249
and 250, respectively) that use non-GAAP earnings to a matched sample of GAAP firms and find no significant difference in
the likelihood of a positive earnings surprise between the two groups over a small time period (1997–1999 and June–August
of 2000, respectively). In contrast to these two earlier papers, our study uses a large data set over a long sample period and
finds significant evidence that non-GAAP firms are more likely to meet or beat analyst forecasts.
Finally, our paper extends prior literature on the effectiveness of financial analysts. In particular, a growing literature has
challenged whether analysts have superior knowledge in creating forecasts and recommendations (Altinkilic and Hansen,
2009; Bradshaw et al., 2012; Hou et al., 2012). We extend these papers by examining whether analysts are able to adjust and
unwind opportunistic exclusions appropriately, given their focus on continuing operations (and lack of attention to “nonrecurring” items), and the discretion given to managers in defining non-GAAP earnings.
3. Hypothesis development
In order to motivate our hypotheses, it is important to understand the process by which non-GAAP earnings and
corresponding analyst forecasts are generated. We begin by describing five stages in this iterative process involving firm
managers, analysts, and forecast data providers.
Stage 1: for many firms, the existence of upcoming unusual items is publicly known well before the earnings
announcement. Often, a firm will issue a press release indicating the types of unusual items but not the actual amounts.
As Gu and Chen (2004) summarize, the existence of many exclusions are anticipated before the earnings announcement
but the exact amount of the exclusions is generally not known by analysts ex ante because managers generally do not
give much specific quantitative guidance on exclusions before the earnings announcement.
Stage 2: as analysts become aware of the existence of potential unusual items, they decide whether to include or exclude
the items in their earnings forecasts, based on their individual judgment and past consensus treatment. Once excluded,
unusual items are not generally specifically forecasted by analysts. Instead, analysts tend to focus on those items affecting
income from continuing operations.
Stage 3: at this point, forecast data providers, such as IBES, will survey all analysts' estimates “to establish if the majority
of them are including or excluding the event.” (Thomson Reuters, 2010). They then follow a “majority policy, where the
accounting basis of each company estimate is determined by the basis used by the majority of contributing analysts.”
Once the majority basis is determined, analysts in the minority are given the chance to adjust their forecasts to the
majority basis. If they do not conform to the majority basis, their estimates are excluded from the consensus mean
estimate. The consensus mean estimate from the forecast data provider is then made publicly available.
Stage 4: next, the firm manager observes the analysts' consensus mean estimate and which items the majority of analysts
are including/excluding in their earnings definition. The manager also privately observes his own firm's economic
performance, relative to the consensus forecast. At this point, we hypothesize that the manager will choose to either
(1) opportunistically increase the amount of excluded expenses in order to meet or beat the consensus forecast and/or
(2) artificially create a new type of exclusion that increases non-GAAP earnings. The manager then reports his version of
non-GAAP earnings in his earnings announcement press release, which must include a reconciliation between the nonGAAP number and GAAP earnings per the SEC's Regulation G.
Stage 5: finally, analysts and forecast data providers observe the earnings announcement and the non-GAAP exclusions
that are being reported. In the vast majority of cases, the forecast data provider confirms that the non-GAAP earnings
number provided by the manager is reported on the same inclusion/exclusion basis as the existing majority consensus
mean estimate and then records this number as its actual earnings number.6 If the company reports exclusions on a
different basis than the majority of analysts, the forecast data provider will adjust the number reported in the press
release to reflect the consensus analyst reporting basis and record that number as its actual earnings number. Thus, the
actual earnings number reported by IBES (which we use as our main non-GAAP earnings proxy) already includes any expost adjustments made by analysts to undo any detected managerial opportunism. The earnings surprise can now be
calculated using data from the forecast data provider, with both its actual earnings number and the consensus mean
forecast being reported on the same inclusion/exclusion basis.
(footnote continued)
forecast is also produced on a non-GAAP basis because the researcher is comparing a GAAP number to an analyst forecast based on non-GAAP earnings,
which results in a mismatched comparison. Since pro forma earnings are on average generally larger than GAAP earnings, their finding that pro forma
earnings beat the same IBES consensus more often than GAAP earnings for a given firm is mechanical.
6
We randomly hand-collect 1,000 actual earnings announcement press releases with income-increasing non-GAAP exclusions and attempt to match
the IBES-reported actual earnings to the management-produced earnings press release. Of the 969 announcement that we could locate, 915 (94.4%) contain
the reported IBES actual earnings number. This indicates that, in the vast majority of cases, analysts are in agreement with the inclusion/exclusion reporting
basis used by management in the press release.
44
J.T. Doyle et al. / Journal of Accounting and Economics 56 (2013) 40–56
If analysts anticipate and are able to unwind all actual exclusions in their forecasts, income-increasing exclusions should
not be associated with a higher likelihood of exceeding analyst estimates (i.e., actual non-GAAP earnings would increase, but
the consensus analyst forecast would increase by the same amount). However, it is plausible that analysts may not fully
adjust their forecasts for opportunistic non-GAAP exclusions for two reasons. First, since the exact amounts of excluded
items are not known ex ante by analysts, the shifting of small amounts of recurring expenses into non-GAAP exclusions by
managers would be difficult for analysts to detect. Second, Gu and Chen (2004) document that analysts do not fully reverse
all recurring expenses excluded by management. As stated in our first hypothesis, if analysts are unable to fully identify
opportunistic non-GAAP exclusions made by managers, we would expect to observe a positive relation between the use of
income-increasing non-GAAP exclusions and the likelihood of meeting or beating analyst forecasts.
H1. Managers using exclusions to increase non-GAAP earnings are more likely to meet or beat analyst earnings forecasts.
It is important to understand whether managers trade-off the opportunistic use of exclusions with other tools used to
meet or beat such as accrual manipulation, expectations management, and real activities manipulation. First, we test its
incremental explanatory power in the presence of the other tools used to meet or beat to ensure that the use of non-GAAP
earnings is an incremental tool beyond those previously documented. We then examine whether managers decrease their
use of other tools when using non-GAAP exclusions to meet or beat forecasts (i.e., the tools are substitutes). We expect that
the substitution effect between tools is driven by the relative costs of using each management tool. For example, when the
costs of accrual manipulation are relatively higher, we expect that the use of non-GAAP earnings to meet or beat to increase.
This expected substitution effect between the tools used to meet or beat analyst estimates is summarized in our second
hypothesis.
H2. Managers substitute the use of exclusions and other earnings management tools to meet or beat analyst estimates as
the costs and benefits of these tools vary.
Our third hypothesis examines the market reaction associated with meeting or beating analyst forecasts using non-GAAP
earnings. Although analysts might be misled by the use of unexpected non-GAAP exclusions resulting in a higher percentage
of firms that meet or beat, market participants may understand that at least some managers are artificially beating
the analyst benchmark through the use of non-GAAP exclusions. The requirement of Regulation G for firms to include a
reconciliation between non-GAAP and GAAP earnings in the earnings announcement may also help market participants
detect this behavior. If investors are able to identify that exclusions can be used opportunistically to meet or beat analyst
forecasts, we anticipate that investors will discount the earnings surprise when income-increasing exclusions are used. This
leads to our third hypothesis.
H3. Firms that use exclusions to increase non-GAAP earnings and meet or beat analyst estimates have lower earnings
response coefficients (ERCs) than those without.
4. Research design
4.1. Non-GAAP earnings use and meeting or beating analyst forecasts
To test H1, we compare the propensity to meet or beat analyst earnings forecasts for firms that report income-increasing
non-GAAP exclusions to all other firms. Since our hypothesis deals with firms that are attempting to increase their reported
income to beat a benchmark, we particularly focus on firms where the non-GAAP earnings figure is greater than the GAAP
earnings figure. Thus, our main independent variable of interest, Pos Excl Use, is equal to one if management has non-GAAP
earnings that are greater than GAAP earnings and equal to zero otherwise.
We define Pos Excl Use using the IBES-reported Actual EPS, consistent with Brown (2001), Bradshaw and Sloan (2002),
Abarbanell and Lehavy (2003), Brown and Sivakumar (2003), Doyle et al. (2003), Heflin and Hsu (2008) and Collins et al.
(2009). If IBES Actual EPS exceeds the per share GAAP earnings number, then Pos Excl Use is equal to one. GAAP EPS is defined
as earnings per share before extraordinary items and discontinued operations, using either basic (Compustat data item
epspxq) or diluted (data item epsfxq) EPS, depending on the IBES basic/diluted flag. We define Exclusions as IBES Actual EPS
minus GAAP EPS (i.e., a positive Exclusions number represents expenses that have been excluded from non-GAAP EPS).
Surprise is defined as the IBES Actual EPS figure minus the median consensus analyst forecast from IBES.7 MBE, our main
dependent variable, is an indicator variable that is equal to one when Surprise is greater than or equal to zero.
The advantage of using IBES Actual EPS for our determination of whether or not a firm and its analysts use a non-GAAP
earnings figure is that it allows us to create a large and comprehensive sample of GAAP/non-GAAP reporting firms to
compare our results directly with the prior studies mentioned above. Also, because our research questions do not require
the details provided in the actual press release, we can greatly expand our sample size by using the IBES figure as our
7
A potential concern with the IBES data is that the data generally used in analyst forecast studies are split adjusted and rounded to the nearest penny.
As Payne and Thomas (2003) suggest, we use the IBES unadjusted database in calculating our IBES Actual Earnings.
J.T. Doyle et al. / Journal of Accounting and Economics 56 (2013) 40–56
45
definition of pro forma earnings. Finally, as Gu and Chen (2004) indicate, the IBES actual earnings include adjustments by
analysts to unwind the expected management exclusions.8
We use multivariate logistic regressions to investigate the impact of Pos Excl Use on the propensity to meet or beat the
consensus forecast (MBE). We estimate our regression using the following equation:
MBEt ¼ γ 0 þγ 1 Pos Excl Usei,t þ γ 2 Book-to-Market i,t þ γ 3 Sales Growthi,t
þ γ 4 LnSizei,t þ γ 5 Prof itablei,t þ γ 6 ROAi,t þ υi,t
ð1Þ
the Pos Excl Use variable is designed to detect the decision by management to use income-increasing exclusions. If
management is opportunistically excluding expenses from GAAP earnings to meet or beat, we expect a positive relation
between Pos Excl Use and MBE.
We also include several control variables in the regressions that have been found to be associated with meeting or
beating analyst forecasts and the use of exclusions. Book-to-Market is measured as the book value of equity (Compustat data
item seqq) divided by the market value of equity at the end of the fiscal quarter (Compustat data item cshoq multiplied
by Compustat data item prccq). Sales Growth is the quarterly change in revenue over the same quarter in the prior year
(Compustat data item saleq). Ln Size is the natural logarithm of the market value of equity at the end of the quarter
(Compustat data item cshoq multiplied by data item prccq). Profitable is an indicator variable which is equal to one if IBES
Actual EPS is positive and zero otherwise.9 ROA controls for firm performance and is equal to IBES Actual EPS scaled by total
assets per share (Compustat data item atq divided by Compustat item cshoq). For all our tests, we also cluster the standard
errors by time and firm to correct for cross-sectional and serial-correlation (Petersen, 2009).
4.2. Distinguishing between expected and unexpected non-GAAP exclusions
As previously mentioned, although all our tests use total exclusions, we are primarily interested in the portion of total
exclusions that is unexpected or unanticipated by analysts. Exclusions that are fully understood and expected by analysts
should be incorporated into their forecasts and not increase the likelihood of meeting or beating. We attempt to proxy for
these expected exclusions by dividing exclusions into special items and other exclusions. Special Items are defined as
operating income per share (Compustat item opepsq) less GAAP EPS before extraordinary items (Compustat item epspxq or
epsfxq), consistent with prior literature. Special Items are typically thought of as “unusual or nonrecurring items.” Analysts at
Compustat help identify these special items by examining the firm's 10-K and 10-Q. Even though Special Items are unusual
and non-recurring, we expect financial analysts to more readily anticipate and identify these exclusions.
We then use the remaining total exclusions that are not captured by special items as our proxy for unexpected
exclusions. The Other Excl variable is defined as Exclusions minus Special Items. Thus, other exclusions are items that
management has excluded from non-GAAP earnings that have not been flagged by Compustat as nonrecurring items. Doyle
et al. (2003) argue that exclusions not captured by special items tend to predict negative future operating cash flows with
the most regularity and are the source of most of the future abnormal returns. Other researchers also find that these
unexpected exclusions (also labeled as ‘other’ exclusions in these papers) have different properties than special items (e.g.,
Heflin and Hsu, 2008; Kolev et al., 2008). In other words, these other exclusions tend to behave as if they are recurring
operating expenses that management has strategically excluded from GAAP earnings under the guise of being non-recurring
and transitory.
If other exclusions are used more opportunistically than special items due to increased manager discretion, we hypothesize
that those items will have a greater effect than special items in our tests specified above. Pos Special Items Use is an indicator
variable that is equal to one when managers use income-decreasing special items and equal to zero otherwise. Pos Other Excl Use
is an indicator variable that is equal to one when other exclusions are greater than zero and equal to zero otherwise. Despite
the fact that we anticipate stronger results for the unexpected exclusion variables, we still examine the effect of total exclusions
because total exclusions are readily identifiable in the quarterly earnings announcement, whereas the relative amounts of special
items and other exclusions may not be completely identifiable at the earnings announcement.
4.3. Non-GAAP earnings use vis-à-vis other methods of meeting or beating analyst forecasts
In our first hypothesis, we propose that managers opportunistically use exclusions to meet or beat analyst forecasts.
However, we need to rule out the possibility that other forms of earnings management and expectations management tools
documented in the prior literature are not correlated with exclusion use and driving our results. Therefore, we include
8
Since we are studying potential managerial opportunism, we want to ensure that our Pos Excl Use variable captures what managers are actually
disclosing in their earnings announcements. We obtain a sample of management-reported non-GAAP earnings hand-collected from actual press releases,
described by Black and Christensen (2009). When using this alternative dataset in an additional robustness test, we test whether the use of positive
exclusions increase the propensity for the firm to meet or beat analyst expectations. We redefine Pos Excl Use using the dataset of non-GAAP earnings
figures hand-collected from management press releases (Black and Christensen, 2009). Results are both qualitatively and quantitatively similar to our
results and lead to similar inferences.
9
Brown (2001) shows that the discontinuity around analyst forecast errors is significantly larger for firms with positive earnings than with negative
earnings. Accordingly, we estimate all the regressions on firm-quarter observations with positive earnings. Results are similar to those included in the
tables.
46
J.T. Doyle et al. / Journal of Accounting and Economics 56 (2013) 40–56
proxies for accruals manipulation, expectations management, and real activities manipulation in our model to provide
evidence that the use of exclusions to meet or beat analyst forecasts is incremental to other tools that could be used to meet
or beat expectations. Accordingly, we estimate the following regression equation:
MBEt ¼ γ 0 þ γ 1 Pos Excl Usei,t þ γ 2 Pos Disc Acci,t þ γ 3 Pos Disc CFOi,t
þγ 4 Pos Disc Prodi,t þ γ 5 Pos Disc Expi,t þγ 6 Neg Exp Mgmt i,t
þγ 7 Book-to-Market i,t þγ 8 Sales Growthi,t þ γ 9 LnSizei,t
þγ 10 Prof itablei,t þ γ 11 ROAi,t þ υi,t
ð2Þ
We control for accrual manipulation using annual performance-adjusted discretionary accruals, as calculated by Kothari
et al. (2005). Similar to the Pos Excl Use variable, we set the Pos Disc Acc variable equal to one when discretionary accruals is
greater than zero. We control for expectations management and follow Matsumoto (2002) to calculate the quarterly abnormal
forecast, controlling for downward management guidance. The Neg Abn Forecast variable is equal to one when the abnormal
forecast is negative (i.e., the median analyst forecast is less than the predicted forecast) and equal to zero otherwise.10 We
also include several proxies for real activities manipulation, as described by Roychowdhury (2006). We control for sales
manipulation using annual discretionary cash flows, overproduction of inventory using annual discretionary production costs,
and the abnormal reduction of discretionary expense using annual discretionary expenses. Pos Disc CFO, Pos Disc Exp, and Pos
Disc Prod are equal to one when discretionary CFO, discretionary expenses, and discretionary production costs are greater than
zero and equal to zero otherwise. Roychowdhury (2006) finds some evidence that these measures are associated with the
increased probability of meeting and beating analyst forecasts. In a robustness check, we replace the Pos Disc Acc, Pos Disc Exp,
Pos Disc Prod, Pos Disc CFO, and Neg Abn Forecast variables with the continuous variables and find similar qualitative results.11
4.4. Trade-offs between using exclusions and other forms of earnings and expectations management
We perform two primary tests to examine whether managers use exclusions and other earnings/expectations management tools as substitutes or complements. We first examine whether exclusion levels are positively or negatively associated
with other forms of earnings and expectation management using Eq. (3).
Exclusions Leveli,t ¼ γ 0 þ γ 1 Disc Acci,t þ γ 2 Disc Prodi,t þ γ 3 Disc CFOi,t
þ γ 4 Disc Expi,t þγ 5 Abn Forecast i,t þ λj ΣCONTROLSj,i,t þ υi,t
ð3Þ
The Exclusions Level variable is equal to the Exclusions divided by price (Compustat item prccq). Disc Acc, Disc Prod, Disc
CFO, and Disc Exp are equal to the level of annual discretionary accruals, discretionary production costs, discretionary CFO,
and discretionary expenses as previously described. Negative coefficients on γ1, γ2, γ3, or γ4 suggest that exclusions are
substitutes with the other earnings management variables. A positive coefficient on the Abn Forecast variable suggests that
managers are walking down analysts less as exclusions increase, indicating a substitution effect.
We then identify a sub-sample of firm/quarters that are more likely to be managing earnings. We run the same
regression specified in Eq. (3) for those firm-quarter observations that meet or beat analyst expectations and have positive
exclusions. We expect the relation between the exclusions and other earnings/expectations management variables to be
more negative (positive) during periods where managers are more likely to use exclusions as a substitute (complement) for
other earnings/expectation management tools. We also include the Book-to-Market, Sales Growth, Ln Size, Profitable, and ROA
control variables, as previously defined, to control for potential correlated omitted variables.
In our second test, we examine whether firms are more likely to use exclusions to meet or beat expectations when they
are more constrained in their ability to utilize within-GAAP earnings management (i.e., when the costs of other tools are
higher). Barton and Simko (2002) argue that the net operating assets (NOA) on the balance sheet partially reflects the extent
of previous earnings management activities and proxies for managers' constraints in using within-GAAP earnings management. They provide evidence that firms, on average, are less likely to meet or beat analyst expectations when net operating
assets are high. As mentioned earlier, exclusions do not involve an actual accounting system entry (no debits and credits)
and are not necessarily constrained by the balance sheet, as accruals would be. Therefore, we predict that firms will be more
likely to use exclusions to meet or beat analyst expectations when the firm's NOA are high, suggesting a decreased ability of
the firm to utilize within-GAAP earnings management.
10
We calculate the predicted forecast similar to Matsumoto (2002) using the following regression: ΔEPSi,q/Piq−4 ¼ αþ β1(ΔEPSiq−1/Piq−5) þCRETiq þ ε.
Similar to Matsumoto (2002), we run the regression by year and industry (four-digit SIC code), we include only observations where there are at least 10
observations in each industry/year, and we delete the top and bottom 1% of each variable. The EPSiq variable is defined as IBES earnings per share for firm i
in quarter q. Piq is defined as price per share at the end of quarter q for firm i. CRETiq is defined as the cumulative abnormal return between the three days
after the earnings announcement in quarter q−4 and 20 days prior to the earnings announcement in quarter q. We then multiply the coefficients calculated
in the prior year regression by the current quarter variable values to obtain a predicted value for the change in EPSiq. We then multiply the predicted change
in EPSi,q by the price in quarter q−4 and add the earnings per share from quarter q−4 to obtain the predicted forecast. To calculate the abnormal forecast we
subtract the median consensus forecast by the predicted forecast and divide by the price per share from quarter q−4.
11
Because of the seasonal nature of accruals, we focus on annual earnings management variables in order to reduce the noise created when they are
estimated quarterly (consistent with prior research). We recalculate the accruals and real earnings management variables on the quarterly basis and run a
similar regression to Eq. (2). We find qualitatively similar results for the coefficient on the Pos Excl Use variable.
J.T. Doyle et al. / Journal of Accounting and Economics 56 (2013) 40–56
47
First, we estimate the following equation for all firms-quarters with sufficient data to calculate the dependent and
independent variables:
Pos Excl Usei,t ¼ γ 0 þ γ 1 High NOAi,t þ λj ΣCONTROLSj,i,t þ υi,t
ð4Þ
12
We define Net Operating Assets (NOA) similarly to Fairfield et al. (2003). NOA is then ranked by firm. The High NOAi,t
variable is an indicator variable that is set to one when the firm's quarterly NOA is in its own highest quintile. We rank NOA
by firm because within-GAAP earnings management is likely to be constrained at different NOA levels for individual firms
and industries and therefore we use the firm as its own control.13 The coefficient of interest is γ1, representing how the use
of exclusions is affected when within-GAAP earnings management is constrained for a particular firm.
We then estimate Eq. (4) separately for those firms that meet or beat analyst forecasts and those that do not. If managers
desire to meet or beat analyst expectations, they utilize the earnings/expectations management tool with the lowest cost. As
a result, we expect that managers are more likely to utilize positive exclusions opportunistically when within-GAAP
earnings management is constrained and the firm meets or beats expectations. When firms do not meet or beat analyst
expectations, they have less of a need to utilize any of the earnings/expectation management tools, suggesting a weaker
relation between the High NOA and Pos Excl Use variables. Therefore, we compare the effect of the High NOA variable on the
Pos Excl Use variable when the firm does and does not meet analyst expectations. We expect the coefficient on the High NOA
variable to have a more significant effect on the Pos Excl Use variable when managers meet or beat expectations.
4.5. Market reaction to non-GAAP benchmark beaters
To test H3, we use the following equation, which captures the market's response to the earnings announcement:
3DayRet i,t ¼ γ 0 þγ 1 Surprisei,t þ γ 2 Pos Excl Usei,t þγ 3 Pos Excl Usei,t Surprisei,t
þ γ 4 Book-to-Market i,t þ γ 5 LnSizei,t þγ 6 Sales Growthi,t þ γ 7 Accrualsi,t þυi,t
ð5Þ
The dependent variable, 3DayRet, represents the three-day market-adjusted buy-and-hold returns centered on the
earnings announcement date. All independent variables are decile ranked and take a value between zero and one.14 All
independent variables are defined as follows before being ranked. Surprise is equal to the firm's earnings surprise, divided by
the firm's market price.15 We anticipate the coefficient on Surprise, or the ERC, to be significantly positive. For our return
tests, we only include observations where MBE ¼1, as we are most interested in comparing firms that meet or beat with
income-increasing exclusions versus those firms that meet or beat without using income-increasing exclusions. We are
most interested in the coefficient on the interaction between Pos Excl Use and Surprise. If the market penalizes firms that
meet or beat while using exclusions, a negative coefficient on Pos Excl Use Surprise will indicate a lower ERC for firms that
use income-increasing exclusions.
5. Results
5.1. Descriptive statistics and univariate tests
Panel A of Table 1 presents descriptive statistics for our entire sample. Since the focus of this paper is on firms that increase
non-GAAP earnings by using positive exclusions, we compare those firms with positive Exclusions to those firms with nonpositive Exclusions. The full sample consists of 237,617 firm-quarter observations from 1988 to 2009 with sufficient Compustat
and IBES data to be included in our least restrictive test. Of the 237,617 total firm-quarters, 60,682 of them are observations
where IBES Actual EPS is greater than GAAP EPS (Exclusions40), resulting in an overall mean Pos Excl Use of 25.5%. The number
of observations in any particular test varies depending on the availability of data necessary for that particular test.
The main variable of interest in Table 1 is MBE. H1 predicts that the firms with positive exclusions will meet or beat more
often, and that is what we find in the univariate t-test of means. The non-GAAP users meet or beat 65.6% of the time, while
the other firms only meet or beat 63.3% of the time (p-value o0.0001). Just Meet or Beat Expectations (JMBE) is used when
we truncate the sample to only those firms with earnings surprises between −4 cents per share and þ3 cents per share.
JMBE is equal to one if the earnings surprise is between zero and þ3 cents per share, and zero otherwise. This 2.3%
advantage is driven by earnings surprise observations between −4 cents per share and þ3 cents per share, as shown by the
12
NOA is equal to the sum of accounts receivable (Compustat item rectq), inventory (Compustat item invtq), other current assets (Compustat item
acoq), net PPE (Compustat item ppentq), intangible assets (Compustat item intanq), and other assets (Compustat item aoq) less accounts payable
(Compustat item apq), other current liabilities (Compustat item lcoq), and other liabilities (Compustat item loq). We scale NOA by lagged total assets.
13
We also rank NOA into quintiles for the entire sample across all firms and find qualitatively similar results when comparing the differences between
those firms that meet or beat analyst expectations and those that do not.
14
We rank each variable into deciles, subtract one, and divide by nine in order to facilitate interpretation of the coefficients. As an additional
robustness check, we define the independent variables as levels and obtain qualitatively similar results.
15
Cheong and Thomas (2011) and Ball (2011) provide evidence that the magnitude of the firm's earnings surprise, using the consensus analyst forecast,
does not vary as a function of the scalar. Thus, in unreported tests, we calculate Surprise without scaling by price and find that all statistical inferences are
unchanged.
48
J.T. Doyle et al. / Journal of Accounting and Economics 56 (2013) 40–56
Table 1
Descriptive statistics.
Panel A: descriptive statistics for observations with exclusions greater than and less or equal to zero
All other observations
MBE
JMBE
Pos Excl Use
Pos Other Excl Use
Pos Special Items Use
Street EPS
GAAP EPS
Surprise
Exclusions
Other Exclusions
Special Items
Book-to-Market
Sales Growth
Ln Size
Profitable
ROA
N
Mean
176,935
93,935
176,935
176,935
176,935
176,935
176,935
176,935
176,935
176,935
176,935
176,935
176,935
176,935
176,935
176,935
0.633
0.697
0.000
0.041
0.163
0.273
0.298
−0.012
−0.025
−0.026
0.005
0.572
1.217
6.015
0.817
0.005
Positive exclusions
Median
Std dev
1.000
1.000
0.000
0.000
0.000
0.230
0.250
0.000
0.000
0.000
0.000
0.475
1.110
5.888
1.000
0.010
0.482
0.459
0.000
0.197
0.370
0.435
0.485
0.146
0.084
0.097
0.086
0.456
0.523
1.738
0.387
0.040
N
Mean
60,682
30,816
60,682
60,682
60,682
60,682
60,682
60,682
60,682
60,682
60,682
60,682
60,682
60,682
60,682
60,682
0.656
0.720
1.000
0.714
0.543
0.260
0.002
−0.008
0.239
0.094
0.129
0.612
1.183
6.516
0.788
0.005
Mean test
Median
Std dev
1.000
1.000
1.000
1.000
1.000
0.210
0.080
0.010
0.080
0.030
0.010
0.495
1.076
6.450
1.000
0.009
0.475
0.449
0.000
0.452
0.498
0.448
0.655
0.156
0.379
0.193
0.284
0.511
0.547
1.825
0.409
0.034
t-Test
p-Value
10.31
7.43
o 0.0001
o 0.0001
502.27
198.47
6.21
117.60
6.31
274.14
196.83
163.18
18.07
13.96
60.50
15.48
0.12
o 0.0001
o 0.0001
o 0.0001
o 0.0001
o 0.0001
o 0.0001
o 0.0001
o 0.0001
o 0.0001
o 0.0001
o 0.0001
o 0.0001
0.91
Panel B: mean values for quartiles of exclusion levels where exclusions are positive
Mean values
Excl
Street EPS
GAAP EPS
MBE
JMBE
Surp
Book-to-Market
Sales Growth
Ln Size
Q1
Q2
Q3
Q4
0.02
0.42
0.39
0.71
0.74
0.00
0.45
1.23
7.25
0.08
0.31
0.24
0.70
0.74
0.00
0.51
1.21
6.84
0.19
0.25
0.06
0.66
0.71
0.00
0.61
1.17
6.45
0.66
0.06
−0.67
0.55
0.66
−0.04
0.88
1.12
5.52
The full sample consists of 237,617 firm-quarter observations from 1988 to 2009. The variables are defined as follows: MBE is an indicator variable equal to one
if the earnings surprise (Surprise) is greater than or equal to zero. JMBE is an indicator variable equal to one if the earnings surprise greater than or equal to
zero for all observations between −4 and 3 cents per share. Pos Excl Use is an indicator variable equal to one if Exclusions is greater than zero. Pos Other Excl Use
is an indicator variable equal to one if Other Excl is greater than zero. Pos Special Items Use is an indicator variable equal to one if Special Items are greater than
zero. Street EPS is the IBES reported actual earnings per share (IBES item VALUE). GAAP EPS is the applicable basic or diluted income per share (matched to the
IBES definition) before extraordinary items and discontinued operations (Compustat items epspxq and epsfxq). Surprise is equal to Street EPS less the consensus
median earnings forecast (IBES item MEDEST). Exclusions is equal to Street EPS less GAAP EPS (i.e., a positive Exclusions indicates an excluded expense). Other
Excl is equal to Exclusions less Special Items. Special Items is equal to operating income per share (Compustat item opepsq) less GAAP EPS. Book-to-Market is
measured as the book value of equity (Compustat item seqq) divided by the market value of equity (Compustat item cshoq multiplied by Compustat item
prccq) at the end of the fiscal quarter. Sales Growth is equal to sales (Compustat item saleq) in quarter t divided by sales in quarter t−4. Ln Size is equal to the
natural log of price (Compustat item prccq) multiplied by shares outstanding (Compustat item cshoq). Profitable is an indicator variable that is equal to one if
Street EPS is greater than or equal to zero. ROA is equal to Street EPS divided by total assets per share (Compustat item atq divided by Compustat item cshoq).
Exclusion Levels is equal to Exclusions divided by price (Compustat item prccq). All continuous variables are winsorized at the 1% and 99% levels.
just meet or beat variable (JMBE), which indicates that the tails of the earnings surprise distribution are not driving this
result (Abarbanell and Lehavy, 2007).
The mean value of Exclusions for the positive Exclusions group is 23.9 cents per share, which represents the gap between
IBES Actual EPS or Street Earnings (26.0 cents per share) and GAAP EPS (0.2 cents per share). The differences in the other
control variables are significant, with the exception of ROA, and show that firms in the positive Exclusions group tend to have
higher book-to-market ratios, lower sales growth, and higher market values.16
The other control variables (with the exception of Ln Size) indicate that the underlying economic condition of firms
reporting large exclusions is systematically poorer than firms reporting relatively small exclusions (see Abarbanell and
Lehavy, 2002). Table 1, Panel B shows mean values of our main dependent and independent variables in each of the quartiles
of Exclusion Levels (i.e., Exclusions divided by price). As Exclusion Levels increase from 0.02, 0.08, 0.19, and 0.66 across the
quartiles, the MBE variable decreases from 71% to 55%. Firms reporting relatively large exclusions are often taking large
16
We also test the medians using a Wilcoxon Rank test. Similar to the test of means, all medians are significantly different between the two groups in
the same direction with the exception of ROA, median ROAs are not significantly different from each other.
J.T. Doyle et al. / Journal of Accounting and Economics 56 (2013) 40–56
49
Table 2
Pairwise correlations.
1
2
3
4
5
6
7
8
9
Street EPS
Pos Excl Use
Pos Other Excl Use
Pos Special Items Use
MBE
Sales Growth
Book-to-Market
Ln Size
Profitable
1
2
3
4
5
6
7
8
9
1.000
−0.022
−0.032
0.035
0.271
0.102
−0.066
0.501
0.680
−0.013
1.000
0.718
0.377
0.021
−0.073
0.026
0.124
−0.032
−0.015
0.718
1.000
0.057
0.026
−0.062
0.035
0.078
−0.032
0.018
0.377
0.057
1.000
0.001
−0.053
0.000
0.152
−0.025
0.258
0.021
0.026
0.001
1.000
0.173
−0.147
0.167
0.259
−0.033
−0.029
−0.017
−0.040
0.095
1.000
−0.270
0.045
0.147
−0.139
0.037
0.044
0.015
−0.148
−0.173
1.000
−0.329
−0.048
0.461
0.123
0.076
0.151
0.167
−0.002
−0.345
1.000
0.286
0.610
−0.032
−0.032
−0.025
0.259
−0.020
−0.148
0.284
1.000
Pearson (Spearman) correlations are presented above (below) the diagonal based on a sample of 237,617 firm-quarter observations from 1988 to 2009. The
variables are defined in Table 1. All continuous variables are winsorized at the 1% and 99% levels. All correlations that are significant at the 5% level are in
bold print.
one-time write-offs, restructuring charges, etc. and appear to be much less concerned about meeting or beating analyst
forecasts in that particular quarter, which is consistent with Brown (2001) and Degeorge et al. (1999) who find that loss
firms have a much lower propensity to exceed expectations. Thus, firm performance is an important control in addressing
our research question.17 We also include univariate Pearson and Spearman correlations of key variables in Table 2. We see
that both Pos Excl Use and Pos Other Excl Use are positively correlated with MBE, as predicted by H1. However, the
correlations between Pos Special Items Use and MBE are not significant.
5.2. Results for H1: non-GAAP earnings use and meeting or beating analyst forecasts
Table 3 presents multivariate logistic regressions that help to control for other firm characteristics found to be associated
with meeting or beating analyst forecasts. As predicted by H1, the coefficient on the Pos Excl Use variable in column 1 of
Panel A is positive and statistically significant (z-stat¼4.94), indicating that firms that use income-increasing non-GAAP
earnings tend to meet or beat more often.18 In untabulated results, we find that the odds ratio for Pos Excl Use is 1.14,
suggesting that the probability of a firm meeting or beating increases by 14% when it uses positive exclusions, holding other
firm characteristics constant.
In column 2 of Panel A, we decompose exclusions into other exclusions and special items. As predicted, the main driver
of our overall result is the Pos Other Excl Use variable. The coefficient on Pos Other Excl Use is significantly positive
(z-stat¼6.90), and the coefficient on Pos Special Items Use is insignificant.19 The odds ratio is 1.20 for the Pos Other Excl Use
variable and 1.00 for the Pos Special Items Use variable. The effect of the Pos Other Excl Use variable on the propensity to meet
or beat analyst forecasts appears to have a greater impact than the Pos Special Items Use variable. Special items and other
exclusions share many of the same characteristics, in the sense that both are non-GAAP adjustments and can theoretically
be used to increase non-GAAP earnings. However, they do not appear to be used equally to meet or beat analyst forecasts.
This evidence suggests that management may be using the increased managerial discretion of other exclusions, which
analysts are less able to unwind, to opportunistically to meet or beat analyst forecasts.20
The coefficients on the control variables are consistent with prior research. Specifically, the coefficients on Book-toMarket and Sales Growth are expected to be negative and positive, respectively, because low book-to-market (glamour) firms
and high growth firms tend to meet or beat more often. The coefficient on Ln Size is expected to be positive since prior
research suggests that larger firms have less optimistic biases in analyst forecasts (Brous and Kini, 1993). Profitability is
positive as expected (Brown, 2001), as is ROA.
In Panel A of Table 3, the control group consists of firms with zero or negative exclusions. However, firms with negative
exclusions (i.e., excluded gains) may differ systematically from firms with no exclusions. Another concern is that our results
in Panel A for firms with income-increasing exclusions may be driven by some kind of mechanical relation. Although firms
that use income-increasing exclusions will, by definition, report non-GAAP earnings that are higher than their GAAP
earnings, a finding that these firms tend to meet or beat more often is not indicative of a mechanical relation, because
analysts are also forecasting on a non-GAAP basis. If analysts are able to identify opportunistic exclusions defined by
managers, the use of exclusions should have no effect on the likelihood of meeting or beating expectations. However, if
income-increasing exclusions are somehow mechanically related to a higher probability of MBE, then it should also be true
17
We also run our tests defining Pos Excl Use using only those observations below the median value of Exclusions (among those firms with positive
exclusions). Our results using these “small” exclusions use variables are qualitatively similar to those presented in the paper.
18
In untabulated results, we also examine the level of total exclusions and unexpected exclusions and find significant economic and statistical results.
We do not present those tests due to the possibility that the extreme levels of exclusions may unduly influence our tests.
19
The coefficient on the Pos Other Excl Use variable is statistically greater than the coefficient on the Pos Special Items Use variable (p-valueo 0.01).
20
The results in Table 3 are robust to using the JMBE variable, defined as earnings surprise between [−0.04, 0.03] which focuses more narrowly around
the zero earnings surprise benchmark.
50
J.T. Doyle et al. / Journal of Accounting and Economics 56 (2013) 40–56
Table 3
Logistic regressions of meet or beat indicator (MBE) on exclusion variables.
Panel A: main results testing the relation between MBE and positive exclusion use variables
[1]
Intercept
Pos Excl Use
Pos Other Excl Use
Pos Special Items Use
Book-to-Market
Sales Growth
Ln Size
Profitable
ROA
[2]
Coeff
z-Stat
Coeff
z-Stat
−0.900***
0.131***
−11.54
4.94
−0.909***
−11.63
0.180***
0.000
−0.369***
0.404***
0.080***
0.840***
5.979***
6.90
0.00
−10.36
15.20
9.79
25.66
15.71
−0.367***
0.405***
0.079***
0.838***
6.002***
Observations
Psuedo-R2
−10.17
15.21
9.47
25.39
15.79
237,617
0.074
237,617
0.074
Panel B: results including indicator variables for negative exclusion use variables
[1]
Intercept
Pos Excl Use
Neg Excl Use
Pos Other Excl Use
Neg Other Excl Use
Pos Special Items Use
Neg Special Items Use
Book-to-Market
Sales Growth
Ln Size
Profitable
ROA
Observations
Psuedo-R2
[2]
Coeff
z-Stat
Coeff
z-Stat
−0.900***
0.141***
0.032
−11.58
4.94
1.29
−0.906***
−11.70
0.156***
0.011
0.014
0.181***
−0.372***
0.407***
0.077***
0.840***
5.973***
5.90
0.39
0.52
6.15
−10.53
15.27
9.30
25.16
15.63
−0.368***
0.404***
0.078***
0.835***
5.989***
−10.19
15.19
9.09
25.25
15.64
237,617
0.074
237,617
0.074
In column 1 we investigate the relationship between the use of exclusions and the probability of meeting or beating (MBE). In column 2 we investigate the
relationship between the use of other exclusions and the probability of meeting or beating (MBE). In Panel B, we add negative exclusion use variables that
are equal to one when exclusions, other exclusions, or special items are less than zero (i.e., they decrease non-GAAP earnings relative to GAAP). All other
variables are defined in Table 1. All observations with sufficient data to calculate the dependent or independent variables are included. Following Petersen
(2009), we cluster the standard errors by time and firm to correct the standard errors for cross-sectional and serial-correlation. All continuous variables are
winsorized at the 1% and 99% levels. All p-values are two-sided. *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.
that income-decreasing exclusions should mechanically produce lower levels of MBE. When we add the negative exclusions
use variables in Panel B, we do not find lower levels of MBE for these negative exclusion variables (Neg Excl Use and Neg
Other Excl Use are insignificant, while Neg Special Items Use is significantly positive), lending some evidence that our primary
finding is not mechanically induced. We are able to simultaneously include the positive and negative exclusion use variables
in the regression because the third group, which is picked up in the intercept, consists of firm/quarter observations with
zero exclusions. In addition, the fact that we only find an increased likelihood of MBE for income-increasing other exclusions
and not for income-increasing special items makes it less likely that we are merely documenting a mechanical relation
between income-increasing exclusions and MBE (in which case both income-increasing types of exclusions should be
associated with higher levels of MBE).
In Table 4, column 1, we present evidence that the coefficient on the Pos Excl Use variable is significantly positive
(z-stat¼5.88) after controlling for other earnings/expectation management tools that could be used to meet or beat analyst
forecasts. We find that the probability of a firm meeting or beating expectations increases by 21% when the firm has positive
exclusions. In Table 4, column 2, we decompose total exclusions into unexpected exclusions (Pos Other Excl Use) and special
items (Pos Special Items Use). We find that the coefficient on the Pos Other Excl Use variable is significantly positive and the
coefficient on the Pos Special Items Use variable is insignificant, suggesting that firms are more likely to meet or beat analyst
forecasts using unexpected exclusions but not special items. The odds ratios are 1.27 and 1.04 for the Pos Other Excl Use and
Pos Special Items Use variables. These results suggest that the use of unexpected exclusions to meet or beat analyst forecasts
is an alternative tool managers can use to meet or beat analyst forecasts that has not been documented in the prior
literature.
J.T. Doyle et al. / Journal of Accounting and Economics 56 (2013) 40–56
51
Table 4
Logistic regressions of meet or beat indicator (MBE) on exclusion variables and other tools used to meet or beat analyst expectations.
[1]
Coeff
Intercept
Pos Excl Use
Pos Other Excl Use
Pos Special Items Use
Pos Disc Acc
Pos Disc CFO
Pos Disc Prod
Pos Disc Exp
Neg Abn Forecast
Book-to-Market
Sales Growth
Ln Size
Profitable
ROA
Observations
Psuedo-R2
[2]
z-Stat
nnn
−11.20
5.88
−1.471
0.194nnn
0.002
0.147nnn
−0.024
0.120nnn
0.383nnn
−0.268nnn
0.731nnn
0.082nnn
0.633nnn
10.031nnn
0.08
5.09
−0.97
4.07
15.48
−5.75
11.93
7.93
14.98
12.05
98,690
0.086
Coeff
z-Stat
nnn
−11.32
0.238nnn
0.036
−0.002
0.146nnn
−0.026
0.118nnn
0.383nnn
−0.267nnn
0.732nnn
0.083nnn
0.637nnn
9.978nnn
7.63
1.21
−0.07
5.03
−1.05
4.07
15.39
−5.79
11.88
8.24
15.06
11.95
−1.485
98,690
0.087
In column 1 we investigate the relationship between the use of exclusions and the probability of meeting or beating (MBE). In column 2 we investigate the
relationship between the use of other exclusions and the probability of meeting or beating (MBE). If discretionary accruals are greater than zero, Pos Disc
Acc is set equal to one. We calculate annual discretionary accruals following Kothari, Leone, and Wasley (2005). If discretionary cash flows are greater than
zero, Pos Disc CFO is set equal to one. If discretionary production costs are greater than zero, Pos Disc Prod is set equal to one. If discretionary expenses are
greater than zero, Pos Disc Exp is set equal to one. Discretionary cash flows, discretionary production costs, and discretionary expenses are calculated at the
annual level, following Roychowdhury (2006). We calculate the abnormal forecast variable following Matsumoto (2002), which is equal to the median
forecast less the predicted forecast. The Neg Abn Forecast is set equal to one when the abnormal forecast is negative. All other variables are defined in
Table 1. All observations with insufficient data to calculate the dependent or independent variables are deleted. Following Petersen (2009), we cluster the
standard errors by time and firm to correct the standard errors for cross-sectional and serial-correlation. All continuous variables are winsorized at the 1%
and 99% levels. All p-values are two-sided. n, nn, and nnn represent significance at the 10%, 5%, and 1% levels, respectively.
5.3. Results for H2: trade-offs between using exclusions and other forms of earnings and expectations management
Panel A of Table 5 presents the results using Eq. (3) with the Exclusion Levels variable as the dependent variable.21 The
coefficient on the Disc Acc and Disc CFO variables in column 1 are negative and significant at the 1% level, suggesting that
using exclusions is a substitute for the use of discretionary accruals and discretionary CFO. When isolating the analysis to
those firms that meet or beat analyst expectations and use exclusions (column 2), it appears that the substitutive relation
between exclusions and discretionary accruals (discretionary CFO) strengthens, as evidenced by a more negative coefficient
on the Disc Acc (Disc CFO) variable. In untabulated results, we find that the relation between the Exclusions Level and the
Disc Acc (Disc CFO) variables is statistically more negative at the 1% level for the sample of firm observations that meet or
beat analyst expectations and have positive exclusions when compared to all other firm observations in the sample. This
evidence suggests that discretionary accruals (discretionary CFO) and exclusions are more likely to be used as substitutes
when the likelihood of the opportunistic exclusion use increases.
Panel B of Table 5 presents the results with Other Exclusion Levels as the dependent variable. Similar to column 1 in Panel A,
the coefficient on the Disc Acc and Disc CFO variables are negative and significant at the 1% level, suggesting that discretionary
accruals and discretionary expenses are generally used as substitutes for other exclusions. In column 2, the coefficients are
more negative on the Disc Acc and Disc CFO variables when compared to the coefficient on the same variables in column 1,
suggesting that unexpected exclusions are more of a substitute for discretionary accruals (discretionary CFO) when the
likelihood of the opportunistic exclusion use increases. In untabulated results, we find that the coefficients on the Disc Acc and
Disc CFO variables are statistically more negative for those observations in which the firm meets or beats analyst expectations
and uses positive other exclusions. In addition, the coefficient on Abn Forecast variable is significant and positive, suggesting
unexpected exclusions are a substitute for expectation management when the likelihood of the opportunistic exclusion use
increases. In untabulated results, we find that the coefficient on the Abn Forecast variable is statistically more positive when
firms meet or beat analyst expectations and have positive exclusions.
Column 1 in Panel A of Table 6 presents the results for Eq. (4) when all observations with sufficient data to calculate the
dependent and independent variables are included in the analysis. The coefficient on the High NOA variable is equal to 0.128
(odds ratio equal to 1.14) and is significantly different from zero, suggesting that firm observations with high NOA are more
likely to use positive exclusions.
21
Before examining the multivariate regression results we investigate the simple correlations between exclusion levels and the other earnings/
expectation management variables. The Exclusions Levels variable is most negatively associated with annual discretionary accruals (correlation¼ −0.084)
and positively associated with annual discretionary production costs (correlation¼ 0.031).
52
J.T. Doyle et al. / Journal of Accounting and Economics 56 (2013) 40–56
Table 5
Earnings management trade-offs.
Panel A: OLS regression with Exclusions Level as the dependent variable
[1]
All observations
Coeff
Intercept
Disc Acc
Disc Prod
Disc Exp
Disc CFO
Abn Forecast
Book-to-Market
Sales Growth
Ln Size
Profitable
ROA
t-Stat
0.008nnn
−0.055nnn
0.000nn
0.000
−0.024nnn
−0.023
0.008nnn
−0.002nnn
0.000
−0.007nnn
−0.014n
Observations
R2
[2]
MBE and Pos Excl Use¼ 1
6.24
−9.64
1.99
0.35
−9.03
−1.48
5.39
−3.08
0.95
−10.63
−1.70
Coeff
t-Stat
0.035nnn
−0.119nnn
0.000
0.000
−0.055nnn
0.032
0.019nnn
−0.002
−0.002nnn
−0.011nnn
−0.058nn
98,690
0.079
12.02
−9.63
1.19
−0.29
−9.20
0.92
6.54
−1.36
−10.35
−8.55
−2.17
19,589
0.228
Panel B: OLS regression with Other Exclusions Level as the dependent variable
[1]
All observations
Intercept
Disc Acc
Disc Prod
Disc Exp
Disc CFO
Abn Forecast
Book-to-Market
Sales Growth
Ln Size
Profitable
ROA
Observations
R2
[2]
MBE and Pos Other Excl Use¼ 1
Coeff
t-Stat
Coeff
0.003nnn
−0.015nnn
0.000nn
0.000
−0.008nnn
−0.007
0.002nnn
−0.001nnn
0.000
−0.002nnn
0.015nnn
5.81
−8.14
2.92
−0.23
−9.33
−1.29
6.03
−2.99
0.00
−7.23
3.27
0.021nnn
−0.039nnn
0.000
0.000
−0.021nnn
0.038nnn
0.009nnn
−0.001
−0.001nnn
−0.007nnn
0.006
98,690
0.030
t-Stat
13.51
−8.98
1.59
−1.35
−10.37
2.81
10.41
−1.57
−11.07
−9.43
0.37
15,671
0.237
Panel A (Panel B) includes the regression results with the Exclusions Level (Other Exclusions Level) variable as the dependent variable. Column 1 in each
panel includes all observations with sufficient data to calculate the dependent and independent variables. Column 2 in Panel A (Panel B) includes all
observations that meet or beat analyst expectations and have positive exclusions (other exclusions). We calculate annual discretionary accruals (Disc Acc)
following Kothari, Leone, and Wasley (2005). We calculate annual discretionary cash flows (Disc CFO), discretionary production costs (Disc Prod),
discretionary expenses (Disc Exp) following Roychowdhury (2006). We calculate the Abn Forecast variable following Matsumoto (2002), which is equal to
the median forecast less the predicted forecast. All other variables are defined in Table 2. All variables are winsorized at the 1% and 99% levels. Following
Petersen (2009), we cluster the standard errors by time and firm to correct for cross-sectional and serial-correlation. n, nn, and nnn represent significance at
the 10%, 5%, and 1% levels, respectively.
As we have suggested in our previous analyses, we argue that the use of positive exclusions increases the likelihood of a
firm meeting or beating analyst expectations. Similarly, we expect the relation between using positive exclusions and higher
NOA to be stronger when the firm meets or beats analyst expectations, which would suggest that managers are more likely
to opportunistically use positive exclusions when they are constrained in utilizing within-GAAP earnings management.
We divide the firm/quarter observations into those firms that do and those that do not meet or beat analyst expectations.
Column 2 in Panel A of Table 6 presents the results when the firm does not meet or beat analyst expectations. The coefficient
on the High NOA variable is insignificant, providing no evidence that a firm with a higher NOA is more likely to use positive
exclusions when managers do not meet or beat analyst expectations. In column 3 in Panel A of Table 6, the coefficient on the
High NOA variable is positive and significant, suggesting that firm observations with higher NOA are more likely to have
positive exclusions when the firm meets or beats analyst expectations. In untabulated results, we find that difference
between the High NOA coefficients in Columns 2 and 3 is significant at the 1% level. This evidence is consistent with
managers using positive exclusions more to meet or beat analyst expectations when the manager is constrained in his/her
ability to utilize within-GAAP earnings management.
In Panel B of Table 6, we perform a similar analysis to that found in Panel A. Instead of including the Pos Excl Use variable
as the dependent variable, we include the Pos Other Excl Use variable as the dependent variable. Similar to Panel A, we find
J.T. Doyle et al. / Journal of Accounting and Economics 56 (2013) 40–56
53
Table 6
Logistics regression of positive exclusions use (other positive exclusions use) on high net operating assets.
Panel A: logit regression with Pos Excl Use as the dependent variable
[1]
All observations
Intercept
High NOA
Book-to-Market
Sales Growth
Ln Size
Profitable
ROA
[2]
MBE¼0
[3]
MBE¼ 1
Coeff
z-Stat
Coeff
z-Stat
Coeff
z-Stat
−2.259nnn
0.128nnn
0.455nnn
−0.082nn
0.217nnn
−0.394nnn
0.022
−24.59
5.93
12.96
−2.17
22.86
−8.46
0.05
−2.078nnn
0.020
0.386nnn
−0.155nnn
0.202nnn
−0.435nnn
0.565
−26.70
0.61
12.71
−4.88
19.68
−11.94
1.08
−2.291nnn
0.186nnn
0.527nnn
−0.063
0.222nnn
−0.418nnn
−0.690
−18.09
8.02
11.24
−1.40
19.72
−6.05
−1.38
Observations
Psuedo-R2
200,285
0.024
71,342
0.023
128,943
0.026
Panel B: logit regression with Pos Other Excl Use as the dependent variable
[1]
All observations
Coeff
Intercept
High NOA
Book-to-Market
Sales Growth
Ln Size
Profitable
ROA
Observations
Psuedo-R2
[2]
MBE¼ 0
z-Stat
nnn
−2.118
0.134nnn
0.393nnn
−0.026
0.147nnn
−0.412nnn
1.207nn
−23.36
6.56
12.09
−0.71
15.05
−8.74
2.46
Coeff
z-Stat
nnn
−2.026
0.030
0.303nnn
−0.111nnn
0.142nnn
−0.400nnn
1.493nnn
200,285
0.013
[3]
MBE¼1
−25.83
0.99
9.41
−3.69
13.37
−10.18
2.66
71,342
0.013
Coeff
z-Stat
nnn
−2.038
0.189nnn
0.495nnn
−0.011
0.148nnn
−0.527nnn
0.831
−16.09
8.41
11.83
−0.26
12.16
−8.31
1.62
128,943
0.015
Panel A (Panel B) includes the regression results with the Pos Excl Use (Pos Other Excl Use) variable as the dependent variable. Column 1 in each panel
includes all observations with sufficient data to calculate the dependent and independent variables. Column 2 in Panel A (Panel B) includes all observations
that do not meet or beat analyst expectations. Column 3 in Panel A (Panel B) includes all observations that meet or beat analyst expectations. All
observations with sufficient data to calculate the independent variables, dependent variables, and net operating assets (NOA) between 1988 and 2009 are
included in the sample. We calculate NOA following Fairfield, Whisenant, and Yohn (2003). NOA is equal to the sum of accounts receivable (Compustat item
rectq), inventory (Compustat item invtq), other current assets (Compustat item acoq), net PPE (Compustat item ppentq), intangible assets (Compustat item
intanq), and other assets (Compustat item aoq) less accounts payable (Compustat item apq), other current liabilities (Compustat item lcoq), and other
liabilities (Compustat item loq). The High NOA variable takes values equal to 1 when the NOA variable is in its highest quintile for each firm. All variables are
winsorized at the 1% and 99% levels. Following Petersen (2009), we cluster the standard errors by time and firm to correct the standard errors for crosssectional and serial-correlation. n, nn, and nnn represent significance at the 10%, 5%, and 1% levels, respectively.
that the positive relation between the High NOA and Pos Other Excl Use variables occurs when the firm meets or beats analyst
expectations, suggesting that managers are more likely to opportunistically use positive other exclusions to meet or beat
analyst expectations when the manager is more constrained in his/her ability to utilize within-GAAP earnings management.
5.4. Results for H3: market reaction to non-GAAP benchmark beaters
In Table 7, we examine the differential market reaction to the earnings announcement for firms that meet or beat
estimates and use income-increasing exclusions. We examine how the use of exclusions to meet or beat analyst forecasts
impacts the firm's short window ERC using the interaction between Surprise and the Pos Excl Use variables. As noted earlier,
the Pos Excl Use variable is equal to one if the firm meets or beats the consensus analyst forecast and uses income-increasing
exclusions, and zero otherwise. We only examine firms that meet or beat analyst forecasts (MBE equal to one), which
simplifies the interpretation of the ERC. However, our interpretation is limited because our total exclusions variable contains
both informative and opportunistic choices by managers and it is difficult to distinguish between the two.
In column 1, the ERC (the coefficient of 0.052 on Surprise) is positive and significant, as expected. However, the ERC is
significantly reduced when the firm uses income-increasing exclusions. The coefficient on the interaction between Surprise
and Pos Excl Use is −0.005 and statistically significant at the 5% level (t-statistic ¼−2.20). This suggests that investors
discount earnings surprises associated with the use of income-increasing exclusions by approximately 10%. We also sum the
coefficients on the Surprise variable and the interaction between the Pos Excl Use and the Surprise variables to determine
whether investors still positively value earnings surprises that are associated with income-increasing exclusions. The sum of
the coefficients equals 0.047 and is significant at the 1% level.
54
J.T. Doyle et al. / Journal of Accounting and Economics 56 (2013) 40–56
Table 7
OLS regressions of earnings announcement returns on earnings surprise, exclusion variables, and interactions.
[1]
Coeff
Intercept
Surp Level Rank
Pos Excl Use
Pos Excl UsenSurp Level Rank
Pos Other Excl Use
Pos Other Excl UsenSurp Level Rank
Pos Special Items Use
Pos Special Items UsenSurp Level Rank
Book-to-Market Rank
Sales Growth Rank
Ln Size Rank
Accruals Rank
Test
Surp Level Rankþ Excl UsenSurp Level Rank¼ 0
Pos Other Excl Use−Pos Special Items Use ¼0
Pos Other Excl UsenSurp Level Rank−Pos Special Items UsenSurp Level Rank¼ 0
Surp Level Rankþ Pos Other Excl UsenSurp Level Rank ¼ 0
Surp Level Rankþ Pos Special Items UsenSurp Level Rank¼ 0
t-Stat
nnn
−0.012
0.052nnn
−0.004nnn
−0.005nn
0.006nnn
0.016nnn
0.004nnn
−0.006nnn
Observations
R2
[2]
−9.01
29.69
−3.94
−2.20
4.75
11.41
2.98
−5.10
Coeff
t-Stat
nnn
−0.012
0.051nnn
−8.77
31.66
−0.005nnn
−0.007nnn
−0.002nn
0.006nnn
0.006nnn
0.016nnn
0.003nnn
−0.005nnn
−4.78
−2.71
−1.99
2.59
4.74
11.47
2.71
−4.82
139,391
0.041
139,391
0.042
Value
p-Value
0.047nnn
0.000
Value
p-Value
−0.003nnn
−0.012nnn
0.044nnn
0.057nnn
0.003
0.000
0.000
0.000
This table only includes observations with earnings surprises greater than or equal to zero (MBE ¼1) from 1988 to 2009 with sufficient data to calculate the
dependent and independent variables. In column 1 (column 2) we investigate the relationship between 3-day market-adjusted buy-and-hold returns
around the earnings announcement date and the use of exclusions (other exclusions) while meeting or beating analyst expectations. All variables are decile
ranked and take value between 0 and 1 [i.e. (decile−1)/9]. Surprise Level is equal to Surprise divided by price. Accruals is measured as GAAP EPS less cash
flows from operations [i.e., GAAP EPS less cash flow from operations per share (Compustat item oancfy divided by Compustat item cshprq)] divided by price.
All other variables are defined in Table 1. All variables are winsorized at the 1% and 99% levels. Following Petersen (2009), we cluster the standard errors by
time and firm to correct the standard errors for cross-sectional and serial-correlation. n, nn, and nnn represent significance at the 10%, 5%, and 1% levels,
respectively.
In column 2, we examine the market reaction to meeting or beating forecasts while using other exclusions (Pos Other Excl
Use) or special items (Pos Special Items Use). Similar to our other results, we see that the main driver of the results is
unexpected exclusions as proxied by other exclusions. The coefficient on the interaction between Pos Other Excl Use and
Surprise (the ERC penalty for beating and using other exclusions) is −0.007 (t-statistic of −2.71), which translates into a 14%
ERC discount from the overall ERC of 0.051. The ERC difference for beating and using special items is surprisingly positive at
0.006 (t-statistic of 2.59). The interactions for other exclusions and special items are significantly different from each other,
suggesting that investors more heavily discount earnings surprises that are associated with income-increasing other
exclusions. Similar to above, we sum the coefficients on the Surprise variable and the corresponding interaction variable to
examine the total information content for earnings surprises that are associated with using unexpected exclusions and
special items. We find that the earnings surprises for firms using positive unexpected exclusions and positive special items
are equal to 0.044 and 0.057, which are both significant at the 1% level.22
Overall, two conclusions emerge from Table 7. First, the market seems to be at least partially efficient in penalizing firms
that meet or beat analyst forecasts and use non-GAAP earnings numbers. Second, the overall ERC for these non-GAAP firms
is still positive, which may indicate that (1) the market is able to distinguish between those firms using exclusions
opportunistically and are pricing those surprises accordingly (the average positive ERC indicates that the majority of the
non-GAAP firms are not manipulators) and/or (2) the market is still partially fooled by at least some non-GAAP firms and
inappropriately rewards them with (albeit lower) positive ERCs. The market penalty may act as a disincentive for some
managers to use (or abuse) non-GAAP earnings to meet or beat estimates and may help to explain why all managers do not
always use this strategy to meet or beat analyst forecasts. Another interpretation of these results is that the broader equity
market is not fooled as easily as stock analysts by the use of exclusions to exceed analyst forecasts. It is also possible that
market participants may not rely on analysts to properly anticipate exclusions but rather use their own set of information
(e.g., disclosures from Reg G) in ascertaining whether or not a firm achieved this benchmark.
22
To determine if extreme, income-increasing exclusions are driving our ERC results we exclude all firms with positive exclusions above the cutoff for
the highest quartile of exclusions (i.e., Q4 in Table 1, Panel B). The results are qualitatively similar to those in Table 7.
J.T. Doyle et al. / Journal of Accounting and Economics 56 (2013) 40–56
55
6. Conclusion
Our objective in this paper is to determine whether managers define non-GAAP earnings opportunistically to meet or
beat analyst forecasts. The prior literature examines whether managers opportunistically manage accruals (Dechow et al.,
2003; Fedyk et al., 2012), manage expectations (Matsumoto, 2002), and manipulate real activities (Roychowdhury, 2006;
Gunny, 2010) to meet or beat analyst forecasts. This paper provides evidence for a previously undocumented tool that
managers use to meet or beat forecasts: managing the actual definition of earnings. We find that when firms report nonGAAP exclusions, they are more likely to meet or beat analyst forecasts, indicating that analysts do not fully anticipate and
unwind the exclusions that managers propose at the time of the earnings announcement. Upon further investigation, we
find that when total non-GAAP exclusions are broken down into unexpected exclusions and special items, it appears that the
use of other (unexpected) exclusions is the primary means by which analyst forecasts are met or exceeded.
We also find some evidence that managers assess the costs and benefits of using different methods to meet or beat
analyst expectations. We find evidence that managers are more likely to use exclusions to meet or beat analyst expectations
when within GAAP earnings management is constrained. One cost to the use of exclusions to meet or beat expectations may
be that the market discounts the firm's earnings surprise. We find that the market discounts the firm's earnings surprise by
10% to 14% when the earnings surprise is associated with the use of income-increasing exclusions. We also find that this new
“tool” of benchmark beating is incremental to other forms of earnings management documented in the literature and
appears to be a substitute to those others tools.
Despite our ERC and within GAAP earnings management results, we do not extensively examine the relative costs to
using each managerial tool used in meeting or beating analyst expectations. We believe that this could be a fruitful line
of research and encourage future research to more comprehensively examine the relative costs of using each tool and
determine when managers use various tools in differing settings. We also encourage future research to control for the
opportunistic use of exclusions when looking for other tools used to meet or beat analyst expectations.
References
Abarbanell, J., Lehavy, R., 2002. Differences in Commercial Database Reported Earnings: Implications for Empirical Research. Working Paper. University of
North Carolina and University of Michigan.
Abarbanell, J., Lehavy, R., 2003. Biased forecasts or biased earnings? The role of reported earnings in explaining apparent bias and/or underreaction in
analysts' earnings forecasts. Journal of Accounting and Economics 36, 105–146.
Abarbanell, J., Lehavy, R., 2007. Letting the ‘tail wag the dog’: the debate over GAAP versus street earnings revisited. Contemporary Accounting Research 24,
675–723.
Altinkilic, O., Hansen, R., 2009. On the information role of stock recommendation revisions. Journal of Accounting and Economics 48, 17–36.
Ball, R., 2011. Discussion of why do EPS forecast error and dispersion not vary with scale? Implications for analysts and managerial behavior. Journal of
Accounting Research 49, 403–412.
Barton, J., Simko, P., 2002. The balance sheet as an earnings management constraint. Accounting Review 77, 1–27.
Bartov, E., Givoly, D., Hayn, C., 2002. The rewards to meeting or beating earnings expectations. Journal of Accounting and Economics 33, 173–204.
Bhattacharya, N., Black, E., Christensen, T., Larson, C., 2003. Assessing the relative informativeness and permanence of street earnings and GAAP operating
earnings. Journal of Accounting and Economics 36, 285–319.
Black, D., Christensen, T., 2009. US managers' use of ‘pro forma’ adjustments to meet strategic earnings targets. Journal of Business Finance and Accounting
36, 297–326.
Bowen, R., Davis, A., Matsumoto, D., 2005. Emphasis on pro forma versus GAAP earnings in quarterly press releases: determinants, SEC intervention, and
market reactions. Accounting Review 80, 1011–1038.
Bradshaw, M., Drake, M., Myers, J., L. Myers., 2012. A re-examination of analysts' superiority over time-series forecasts of annual earnings. Review of
Accounting Studies, 17-4, 944-968.
Bradshaw, M., Sloan, R., 2002. GAAP versus the street: an empirical assessment of two alternative definitions of earnings. Journal of Accounting Research
40, 41–65.
Brous, P., Kini, O., 1993. A reexamination of analysts' earnings forecasts for takeover targets. Journal of Financial Economics 33, 201–225.
Brown, L., 2001. A temporal analysis of earnings surprises: profits versus losses. Journal of Accounting Research 39, 221–241.
Brown, L., Sivakumar, K., 2003. Comparing the quality of two operating income measures. Review of Accounting Studies 4, 561–572.
Brown, N., Christensen, T., Elliott, B., Mergenthaler, R., 2012. Investor sentiment and pro forma earnings disclosures. Journal of Accounting Research 50,
1-40.
Burgstahler, D., Eames, M., 2006. Management of earnings and analysts' forecasts to achieve zero and small positive earnings surprises. Journal of Business
Finance and Accounting 33, 633–652.
Cheong, F., Thomas, J., 2011. Why do EPS forecast error and dispersion not vary with scale? Implications for analysts and managerial behavior. Journal of
Accounting Research 49, 359–401.
Collins, D., Li, O., Xie, H., 2009. What drives the increased informativeness of earnings announcements over time? Review of Accounting Studies 14, 1–30.
Dechow, P., Richardson, S., Tuna, I., 2003. Why are earnings kinky? An examination of the earnings management explanation. Review of Accounting Studies
8, 355–384.
Degeorge, F., Patel, J., Zeckhauser, R., 1999. Earnings management to exceed thresholds. Journal of Business 72, 1–33.
Doyle, J., Lundholm, R., Soliman, M., 2003. The predictive value of expenses excluded from ‘pro forma’ earnings. Review of Accounting Studies 2–3, 145–174.
Elliott, W., 2006. Are investors influenced by pro forma emphasis and reconciliations in earnings announcements? Accounting Review 81, 113–133.
Fairfield, P., Whisenant, J., Yohn, T., 2003. Accrued earnings and growth: implications for future profitability and market mispricing. Accounting Review
78-1, 353–371.
Fedyk, T., Singer, Z., Soliman, M., 2012. The Sharpest Tool in the Tool Shed: Simultaneous Equations of Financial Statement Management Tools During IPOs.
Working Paper. USC.
Frankel, R., Roychowdhury, S., 2004. Testing Explanations for the Difference Between IBES and GAAP Earnings. Working paper.
Fredrickson, J., Miller, J., 2004. Proforma earnings disclosures: do analysts and nonprofessional investors really differ? Accounting Review 79, 667–686.
Gu, Z., Chen, T., 2004. Analysts' treatment of nonrecurring items in street earnings. Journal of Accounting and Economics 38, 129–170.
Gunny, K., 2010. The relation between earnings management using real activities manipulation and future performance: evidence from meeting earnings
benchmarks. Contemporary Accounting Research 27, 855–888.
56
J.T. Doyle et al. / Journal of Accounting and Economics 56 (2013) 40–56
Heflin, F., Hsu, C., 2008. The impact of the SEC's regulation of non-GAAP disclosures. Journal of Accounting and Economics 46, 349–365.
Hou, K., van Dijk, M., Zhang, Y., 2012. The implied cost of capital: a new approach. Journal of Accounting and Economics 53, 504–526.
Johnson, W.B., Schwartz, W., 2005. Are investors misled by “pro forma” earnings? Contemporary Accounting Research 22 (4), 915–963.
Kasznik, R., Lev, B., 1995. To warn or not to warn: management disclosures in the face of an earnings surprise. Accounting Review 70, 113–134.
Kasznik, R., McNichols, M., 2002. Does meeting earnings expectations matter? Evidence from analyst forecast revisions and share prices. Journal of
Accounting Research 40, 727–759.
Kolev, K., Marquardt, C., McVay, S., 2008. SEC scrutiny and the evolution of non-GAAP reporting. Accounting Review 83, 157–184.
Kothari, S.P., Leone, A., Wasley, C., 2005. Performance matched discretionary accrual measures. Journal of Accounting and Economics 39, 163–197.
Lougee, B., Marquardt, C., 2004. Earnings informativeness and strategic disclosure: an empirical examination of “pro forma” earnings. Accounting Review
79, 769–795.
Matsumoto, D., 2002. Management's incentives to guide analysts' forecasts. Accounting Review 77, 483–514.
Payne, J., Thomas, W., 2003. The implications of using stock-split adjusted IBES data in empirical research. Accounting Review 78, 1049–1067.
Petersen, M., 2009. Estimating standard errors in finance panel data sets: comparing approaches. Review of Financial Studies 22, 435–480.
Roychowdhury, S., 2006. Earnings management through real activities manipulation. Journal of Accounting and Economics 42, 335–370.
Thomson Reuters, 2010. Methodology for Estimates—A Guide to Understanding Thomson Reuters Methodologies, Terms and Policies for the First Call and
I/B/E/S Estimates Databases (November).