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Do firms issuing short-term earnings guidance
exhibit worse earnings quality?
Andrew C. Call
Assistant Professor
University of Georgia
Email: [email protected]
Shuping Chen*
Associate Professor
University of Texas at Austin
Email: [email protected]
Bin Miao
Assistant Professor
NUS Business School
National University of Singapore
Email: [email protected]
Yen H. Tong
Associate Professor
Nanyang Business School
Nanyang Technological University
Email: [email protected]
September 2010
Abstract: We study whether short-term earnings guidance leads to worse earnings quality, as
alleged by influential practitioners and academics in recent years. Contrary to conventional
wisdom, we find that firms issuing short-term earnings forecasts exhibit significantly lower
abnormal accruals, our proxy for earnings quality, than do firms that do not issue earnings
forecasts. We also find that regular guiders have better earnings quality than less regular
guiders. Further investigation shows it is firms facing higher capital market pressure (as proxied
by ex ante and ex post measures of the need for external financing) that exhibit worse earnings
quality, whereas guidance issuance and guidance regularity actually mitigate the negative impact
of capital market pressure on earnings quality. These findings are consistent with the
implications of theoretical research by Dutta and Gigler (2002); managers who provide guidance
are less likely to manage earnings. Our results continue to hold after we control for self-
selection bias and potential reverse causality concerns. In all, our findings suggest criticism of
short-term earnings guidance is misplaced and that myopic earnings management behavior
comes from short-term capital market pressure, not earnings guidance.
__________________________ *Corresponding author. We thank Steve Kachelmeier, Bill Kinney, and University of Texas at Austin brownbag
participants for comments. All errors are our own.
1
Do firms issuing short-term earnings guidance exhibit worse earnings quality?
“CEOs and CFOs put themselves in a bind by providing earnings guidance and then
making decisions designed to meet Wall Street’s expectations for quarterly earnings.” -- J. Fuller & M. C. Jensen, “Just Say No to Wall Street: Putting a Stop to the Earnings Game,”
Journal of Applied Corporate Finance, Winter 2010, 22 (1)
1. Introduction
In recent years, influential investors, academics, and regulators have criticized firms for
playing the “earnings game,” where executives, under pressure from Wall Street, guide market
expectations and manage earnings to meet or beat short-term earnings targets given by analysts.
For example, Warren Buffet, Michael Jensen, and Arthur Levitt criticize the practice of
providing short-term earnings guidance as fostering myopic management behavior and
subsequent earnings management, and call for the ending of the earnings guidance game (Buffet
2000; Fuller and Jensen 2010, Levitt 2000).1 Practitioner organizations such as the CFA
Institute, the Business Roundtable Institute for Corporate Ethics, and the Conference Board voice
similar concerns (CFA Institute 2006; McCafferty 2007). As a result, a small number of high
profile companies such as McDonald‟s, Coca-Cola, and Pepsi have announced the
discontinuation of quarterly earnings guidance (Chen, Matsumoto, and Rajgopal 2011).
Despite such mounting criticism, we know of no empirical study to date that has directly
addressed the impact of short-term earnings guidance behavior on earnings quality. This issue is
the focus of our study.
While critics allege that firms that offer short-term earnings guidance and firms that
guide earnings regularly are subject to managerial myopia and more likely to manage earnings,
the implications from theoretical research are opposite of such criticism. Dutta and Gigler
1 In this study we use “earnings guidance” and “earnings forecasts” interchangeably. Both refer to earnings
forecasts issued by management.
2
(2002) argue that when managers issue earnings forecasts, the set of measures available to assess
managerial performance expands to include the forecasts as well as reported earnings. They
model the impact of managers‟ forecasts on the extent of earnings management and show that it
is easier to deter managers from managing earnings if they are also asked to forecast earnings.
Thus, ex ante, it is unclear whether the issuance and regularity of earnings guidance, on average,
results in better or worse earnings quality.
Since our primary motivation for this study comes from the debate on whether short-term
forecasts lead to earnings management and consequently worse earnings quality, we focus on
short-term annual as well as short-term quarterly management earnings forecasts. Specifically,
we define a short-term forecast as an annual (quarterly) forecast of year-end (subsequent quarter)
earnings issued within a window of [-90, +45] days relative to the fiscal year-end (quarter-end)
but before earnings are announced.2 This focus on short-term earnings forecasts corresponds
with the criticism that short-term guidance leads to myopic actions such as earnings
management.
Using two abnormal accrual measures based on Jones (1991) and Dechow and Dichev
(2002), both modified as suggested by Ball and Shivakumar (2006), we document firms that
issue short-term earnings forecasts exhibit smaller magnitudes of abnormal accruals relative to
firms that give no earnings forecasts.3 We also find that, within the sample of firms that provide
earnings guidance, those that guide regularly report higher quality earnings than do those that
guide less regularly. Thus, firms that provide earnings guidance and more regular earnings
2 Of the entire CIG database on First Call, only a very limited number of forecasts (2.3%) are issued more than 30
days after the fiscal period end. Our results are not affected if we use a 30 day window after the fiscal period end. 3 We elect not to use meet-or-beat as a measure of earnings quality because Dechow, Ge, and Shrand (2010) point
out that the evidence suggesting meeting-or-beating earnings benchmarks is driven by earnings management is at
best mixed and specific to certain settings.
3
guiders exhibit higher, not lower earnings quality. This evidence is contrary to practitioners‟
criticisms but consistent with the theoretical intuition in Dutta and Gigler (2002).
If earnings guidance does not lead to worse earnings quality, then what does? To further
explore the criticism that managerial myopia impairs earnings quality, we focus on a setting
where managers are documented to have strong incentives to manage earnings: when firms need
to raise external capital (Stein 1989; Teoh, Welch, and Wong 1998a; Bhojrai and Libby 2005).
We use both an ex ante and an ex post proxy to capture this short-term capital market pressure,
and find that guidance issuance mitigates the negative impact of capital market pressure on
earnings quality. Thus, these results corroborate our main findings that earnings guidance
improves earnings quality.
To lend further credence to our primary findings, in an additional test we focus on critics‟
concern that guidance firms are more likely to inflate earnings in an effort to meet expectations.
Using the subsample of observations with positive abnormal accruals, we find that earnings
guidance issuance and regularity are associated with lower levels of positive abnormal accruals,
contrary to the allegation that these firms manage earnings upwards to meet or beat expectations.
We also conduct additional analyses to rule out the alternative explanation that firms
issue earnings forecasts to walk down analysts‟ expectations, resulting in less need to manage
earnings to meet or beat expectations. We find that firms guiding earnings upward (i.e., firms
issuing forecasts higher than analysts‟ existing forecasts) exhibit higher quality earnings. Thus,
our primary results are not driven by firms walking down analysts‟ expectations as a substitute
for earnings management. Our results are also robust to using a propensity-score matched control
sample to account for self-selection and to using Granger-causality tests to account for reverse
causality concerns.
4
Our study makes the following contributions. First, our research informs the debate on
whether short-term earnings guidance fosters earnings management and leads to worse earnings
quality, as alleged by critics of short-term earnings guidance. Our findings of a positive relation
between earnings guidance activity and earnings quality are consistent with the implications
from theoretical research (Dutta and Gigler 2002), and contrary to recent criticisms of short-term
earnings guidance. We further find that it is firms facing demand for external financing that
exhibit lower earnings quality, while earnings guidance actually mitigates the negative impact of
capital market pressure on earnings quality. Taken together, our results suggest criticism that
earnings guidance impairs earnings quality is misplaced. Rather, it is capital market pressure,
not short-term earnings guidance behavior, that leads to myopic earnings management behavior.
Second, our research also contributes to the voluntary disclosure literature in the
following two ways. First, we complement existing studies on the relationship between earnings
guidance and earnings quality. Unlike Kasznik (1999), who finds managers issuing annual
earnings forecasts are more likely to manage earnings to meet or beat their own forecasted
earnings, we focus on the issuance and regularity of short-term earnings guidance, and our
inference is opposite to that which can be drawn from Kasznik (1999).4 Our findings that firms
issuing guidance (regularly) exhibit higher earnings quality also complement Choi, Myers,
Zhang, and Ziebart (2011), who find that more frequent and more precise guidance helps
investors better incorporate future earnings into price.
4 We offer the following thoughts on why our results differ from those reported by Kasznik (1999): First, our focus
is on short-term earnings forecasts issued for the upcoming fiscal year-end or quarter, while the forecasts in
Kasznik‟s (1999) study are longer term in nature. In particular, Kasznik (1999) only examines annual earnings
forecasts issued prior to the fourth fiscal quarter. No such forecasts are examined in our study. Second, our sample
period is from 2001-2008, whereas Kasznik‟s (1999) sample period is from 1987-1991. The non-overlapping
sample periods could account for the difference in results, especially given the significant changes in the financial
reporting environment over the last few decades. Third, Kasznik (1999) examines 499 firm-year observations,
whereas we examine several thousand firm-year observations. Finally, the methodology used in the estimation of
discretionary accruals has also seen significant improvement over the model that was available when the Kasznik
(1999) study was conducted.
5
In addition, we add to the larger literature on the dynamics between voluntary disclosure
and mandatory disclosure. While other studies investigate how mandatory disclosure attributes
affect voluntary disclosure, such as how accounting conservatism and earnings informativeness
affect the probability of management issuing earnings forecasts (e.g., Lennox and Park 2006;
Gong, Li, and Xi 2009; Hui, Matsunaga, and Morse 2009), we show that voluntary disclosure
can also impact mandatory disclosure attributes in a meaningful way, as suggested by Dutta and
Gigler (2002).
We acknowledge several caveats with our paper. First, our inferences depend entirely on
our two abnormal accrual proxies. To the extent these two abnormal accruals metrics fail to
capture earnings quality, our inference will be affected. However, we note that this is an issue
facing all researchers using abnormal accruals to study earnings quality, and that our use of
abnormal accruals as a proxy for earnings quality is driven by the criticism that earnings
guidance leads specifically to earnings management. Second, while our results are robust to
propensity-score matching as a solution to self-selection concerns, our inference is only valid to
the extent the self-selection model based on prior research is valid. Third, the Granger-causality
test documents some evidence that earnings quality also impacts earnings guidance behavior.
However, we note that this is expected given prior research (e.g., Lennox and Park 2006; Hui et
al. 2009). More importantly, the existence of reverse causality does not negate our primary
conclusion that short-term earnings guidance does not lead to worse earnings quality, as alleged
by critics.
The rest of the paper is organized as follows. In section two we discuss the institutional
background, review relevant literature and develop our empirical prediction. Section three
outlines our research design and section four discusses the results of our empirical tests. Section
6
five presents the analyses to address self-selection and potential reverse causality concerns, and
section six concludes.
2. Background and hypothesis development
2.1 Criticism of short-term earnings guidance
In recent years there has been mounting criticism of short-term earnings guidance (e.g.,
Fuller and Jensen 2010; McCafferty 2007; Burns 2007; Levitt 2000). The crux of this criticism
is that earnings guidance, and in particular short-term earnings guidance, leads managers to
behave myopically. Critics are concerned that short-term earnings guidance reinforces the
obsession with short-term performance, and creates immense pressure on managers to meet or
beat analysts‟ earnings expectations through unwise operating decisions and through accounting
manipulations, as illustrated by this paper‟s opening quote.
Critics further argue that stopping the practice of issuing short-term earnings forecasts
can help eliminate the fixation on short-term earnings results, thus significantly mitigating
earnings management behavior and further helping firms to refocus on the long-term (Rappaport
2005; Fuller and Jensen 2010). For example, Fuller and Jensen (2010) urge companies to put
“an end to the „earnings game‟” and encourage CEOs to “reclaim the initiative by avoiding
earnings guidance.” Even corporate executives themselves are calling for a move away from a
“Wall-street obsession.” For example, Ann Mulcahy, Chairman and ex-CEO of Xerox, notes
that she “applaud[s] companies that have pulled back from setting earnings expectations and are
trying to reshape the rules of the road.”5 Following such criticism, in recent years a small
number of companies, including high profile firms such as McDonald‟s, Coca-Cola, and Pepsi,
5 See online article at http://knowledge.wharton.upenn.edu/index.cfm?fa=viewARticle&id=1318&specialID=41
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publicly announced their decisions to discontinue short-term earnings guidance (Chen et al.
2011).
2.2 Short-term earnings guidance and earnings quality
The theoretical model in Stein (1989) suggests that even in a fully efficient capital
market, managers under capital market pressure will resort to myopic actions that endanger long-
term value creation. One such action as alleged by critics is managing earnings in order to meet
or beat either analysts‟ earnings forecasts or managers‟ own earnings forecasts; that managers
will engage in “accounting shenanigans” to increase accounting earnings rather than the value of
the firm (Rappaport 2005). Following this argument, such intervention leads to worse earnings
quality, as reported earnings do not reflect the true underlying economics of the firm. These
concerns have led to calls from influential investors, analysts, and academics for managers to
give up quarterly earnings guidance.
Kasznik (1999) offers empirical evidence largely consistent with the above criticism.
Kasznik (1999) focuses exclusively on (longer-term) annual management forecasts and finds that
when managers overestimate earnings in their forecasts, discretionary accruals in the forecasting
years are significantly more positive than are those in the non-forecasting years. He interprets
this evidence as consistent with managers managing reported earnings toward their own
forecasts.6
In contrast, implications from theoretical research in accounting suggest the opposite.
Specifically, Dutta and Gigler (2002) argue that when managers issue earnings forecasts,
shareholders can rely on both the forecasted earnings and reported earnings as performance
measures to induce productive effort and to deter earnings management. They invoke the
6 In a related paper, Cheng, Subramanyam, and Zhang (2005) find frequent guiders exhibit lower long-term earnings
growth rates and invest less in R&D, suggesting managers who issue more earnings guidance more regularly engage
in myopic behavior to the detriment of long-term value.
8
Revelation Principal to allow for the truthful revelation of managers‟ private information, which
is enforced because reported earnings serve a confirmatory role in disciplining the managers‟
earnings forecasts, and state that:
“…the communication of the manager‟s private information generally leads to an
expansion of the region in which earnings management can be eliminated, because a
communication contract can use the earnings forecast to control the productive effort and
the accounting earnings to ensure honest forecasts and deter earnings manipulation
(pg.640).”
Therefore, managers would be less likely to manage earnings when they provide an
earnings forecast of their own. Following this intuition, firms that offer earnings guidance
should exhibit higher (instead of lower) earnings quality, contrary to the criticism that earnings
guidance results in earnings management to meet or beat short-term earnings targets.7 Evidence
that firms with more frequent short-term as well as long-term guidance provide more informative
disclosure to the capital market (Choi et al. 2011) is broadly consistent with the above theoretical
implications.
In summary, theoretical research and the limited empirical evidence present conflicting
predictions and evidence on the effect of earnings guidance on earnings quality. Therefore, we
present the following non-directional hypothesis:
H1: Earnings quality is systematically associated with the issuance of short-term
earnings guidance.
A direct implication of the criticism of short-term earnings guidance is that firms that
guide regularly impose higher pressure on themselves to meet or beat targets than do
firms that guide less regularly, and are thus more likely to manage earnings and report
7 In addition, Verrecchia (1990) argues that managers who issue voluntary disclosures have higher-quality private
information. If the quality of private information is positively correlated with the quality of public information such
as reported earnings, Verrecchia (1990) suggests firms issuing earnings forecasts will exhibit higher quality
earnings. Furthermore, Trueman (1986) predicts that managers with superior forecasting ability are more likely to
forecast to signal their ability. To the extent that more capable managers are less likely to manage earnings, firms
issuing earnings forecasts should exhibit high quality earnings.
9
lower quality earnings. However, Dutta and Gigler (2002) would suggest that regular
guidance can impose added discipline on managers, leading to exactly the opposite
prediction that regular guiders exhibit higher earnings quality. Thus, we present our
second non-directional hypothesis:
H2: Earnings quality is systematically associated with the regularity of short-term
earnings guidance.
2.3 Empirical studies on earnings attributes and voluntary disclosure
Our paper is related to an emerging empirical literature that analyzes the relationship
between earnings attributes and voluntary disclosure. Most of this literature focuses on how
earnings attributes (such as earnings informativeness) or a reporting attribute (such as
conservatism) affect the issuance of management earnings forecasts. For example, Lennox and
Park (2006) find that higher market sensitivity to earnings (earnings response coefficients from
16-quarter rolling windows) leads to a greater likelihood of management earnings forecasts.8
Hui et al. (2009) find that more conservative financial reporting leads to fewer management
forecasts. Gong et al. (2009) assume that managers‟ judgment errors cause similar biases in both
their earnings forecasts and reported earnings, and find management forecast errors for the
subsequent year to be positively related to current year‟s working capital accruals. Using
quarterly management earnings forecasts, Xu (2010) presents very similar findings to that of
Gong et al. (2009); higher accruals in the current period are associated with more optimistic
management quarterly forecasts in future periods.
These findings suggest the potential for endogeneity in our setting. Specifically, while
we are motivated by criticism of short-term earnings guidance and examine the effect of earnings
8 Francis, Nanda, and Olsson (2008) capture voluntary disclosure quality through a self-constructed index based on
2001 annual reports and 10-K filings for 677 firms, and find that higher earnings quality drives the quality of
disclosure in firms‟ mandatory filings. However, unlike Lennox and Park (2006), Francis et al. (2008) do not find
any relation between management earnings forecast frequency and their earnings quality metric.
10
guidance on earnings quality, earnings quality can also impact earnings guidance. Another
possibility is that earnings guidance and earnings quality are simultaneously determined by a
third factor, such as managers‟ overall reporting policy. Thus, we test the robustness of our
results after taking endogeneity concerns into consideration and present our results in Section 5.
3. Empirical Design
To test whether short-term earnings guidance affects the quality of reported earnings, we
estimate the following regression:
ACCRit = + 1MFit + 2LEVit-1 + 3BTMit-1 + 4OPCYCLEit-1 + 5CAPINTit-1 +
6(CFO)it-1 + 7LOSSit-1 + 8SIZEit-1 + 9INSTit-1 + IND + YEAR + it (1)
Our earnings quality proxy, ACCR, is measured using two different abnormal accrual
approaches: the absolute value of the residual (ABAC) obtained from the cross-sectional Jones
model, and the absolute value of the residual from the cross-sectional Dechow-Dichev (2002)
model (ABDD). We modify both measures as suggested by Ball and Shivakumar (2006). Both
earnings quality metrics have been widely used in the literature to investigate a range of issues,
such as auditor quality, corporate governance, cost of capital, and in particular earnings
management (e.g., Francis, LaFond, Olsson, and Schipper 2004; Teoh, Welch, and Wong 1998b;
see Dechow, Ge, and Schrand 2010 for a comprehensive review). Lower values of the absolute
residuals from both the modified Jones model (ABAC) and the Dechow-Dichev model (ABDD)
indicate higher earnings quality. As these two measures are widely used in the literature, we
relegate the detailed definition of these two variables to the appendix.
We capture short-term management earnings guidance by focusing on forecasts issued
for the earnings to be announced within the next quarter. This includes forecasts of annual
earnings to be announced at the end of the fourth quarter as well as quarterly forecasts of the
subsequent quarter‟s earnings. To more accurately capture the short-term nature of management
11
forecasts, we exclude forecasts that are issued more than 90 days before or 45 days after the
fiscal year/quarter end.9 This restriction corresponds more closely to critics‟ focus on short-term
earnings guidance and leads to a „cleaner‟ test of our hypotheses. We form a control sample of
observations consisting of firm with no annual or quarterly forecasts (i.e., firms that never appear
on CIG).
MF in equation (1) is measured in two ways. The first is a dichotomous variable,
MF_ISSUE, coded as 1 for firm–year observations with at least one short-term annual or
quarterly earnings forecast, and 0 for the control group of firm-year observations with no annual
or quarterly forecasts. We examine whether firm-year observations where MF_ISSUE equals
one are different in terms of earnings quality than the control group. Recall that larger abnormal
accruals indicate worse earnings quality. If the issuance of short-term earnings guidance leads to
higher earnings quality, 1 in equation (1) will be negative. On the other hand, if short-term
guidance fosters myopic earnings management and lowers earnings quality, 1 will be positive.10
This constitutes the test of Hypothesis 1.
To test Hypothesis 2, we capture the regularity of short-term earnings guidance by
counting the number of quarters in which the firm gives at least one short-term annual or short-
term quarterly forecast. Thus, the second measure of MF in equation (1), MF_REG, ranges from
1 to 4. We believe counting the number of quarters with short-term forecasts is a more
appropriate way to establish the regularity of guidance behavior than is counting the number of
unique short-term forecasts, as the latter approach can be interpreted as capturing forecast
9 We want to emphasize that even though we include annual forecasts in our earnings guidance sample, these annual
forecasts are short-term in nature because we require them to be issued within a [-90, +45] day window of the fiscal
year end, where day 0 is the last day of the fiscal year. Thus, annual forecasts issued earlier in the year are excluded
from our guidance sample. 10
We control for self-selection bias using a propensity score matching approach. We present detailed discussions of
our efforts to address self-selection in Section 5.1.
12
revisions. To test Hypothesis 2, we estimate equation (1) on the subset of firms with earnings
guidance and examine 1, the coefficient on MF_REG. If regular short-term earnings guidance
leads to higher earnings quality, 1 will be negative, and if regular short-term earnings guidance
lowers earnings quality, 1 will be positive. 11
We include control variables likely to be associated with incentives to manage earnings
and that can affect earnings quality. We include the ratio of debt to equity (LEV) to control for
the effects of leverage on earnings quality because DeFond and Jiambalvo (1994) document that
managers of high leverage firms manipulate earnings to avoid violating debt-covenants, while
Barton and Waymire (2004) provide evidence that managers‟ incentives to supply high-quality
reporting increases with the level of debt. We control for growth opportunities using the book-
to-market ratio (BTM) because Skinner and Sloan (2002) find growth firms have stronger
incentives to manage earnings as the market severely penalizes growth firms for negative
earnings surprises.
In addition, we control for operating cycle (OPCYCLE) because Dechow and Dichev
(2002) find the mapping of accruals into cash flows is less precise for firms with longer
operating cycles. We also control for capital intensity (CAPINT) as Cohen (2008) finds more
capital-intensive firms have better quality earnings. We include the standard deviation of
operating cash flows ((CFO)) because Hribar and Nichols (2007) argue that tests of earnings
management using unsigned earnings quality measures are misspecified without controls for
operating volatility. We also control for firm performance using a loss dummy (LOSS), firm size
using the natural log of sales (SIZE), and ownership of institutional investors (INST). Finally, we
11
Note that, in equation (1), even though MF is measured using the same annual window as the dependent variable,
ACCR, there exists a lead-lag relation between the measures of forecast guidance (i.e., MF_ISSUE and MF_REG)
and the abnormal accrual proxies (ABAC and ABDD).
13
include industry dummies (IND) based on Fama and French‟s 12 industry groupings. Detailed
definitions of the control variables are provided in the appendix.
As is common in studies of this nature, our earnings quality metrics, measures of
voluntary disclosure practices, as well as several control variables (e.g., firm size, capital
structure, etc.) are likely to be „sticky‟ over a relatively short time period. Thus, both the
residuals and the independent variables are susceptible to time-series dependence. To deal with
this concern, we cluster the standard errors by firm (Petersen 2009). We also include year
dummies in equation (1) to mitigate concerns over cross-sectional dependence (i.e., firm i‟s
earnings quality correlated with firm j‟s earnings quality in a given year). This is equivalent to
including a fixed time effect into our model. We do not use both firm- and time-clustering
because insufficient time clustering can produce biased t-statistics (Petersen 2009).12
4. Sample and Empirical Results
4.1 Sample and Descriptive Statistics
We obtain our initial sample from the First Call‟s CIG database from 2001 to 2008. We
start our sample in 2001 to mitigate errors in the measurement of management forecasts that are
not captured by the CIG database prior to Regulation Fair Disclosure (Reg FD). Reg FD, which
prohibits selective disclosures, is only effective beginning in late 2000.
We merge this initial sample with the Compustat database and extract financial statement
data to calculate our proxies for earnings quality, ABAC and ABDD. We exclude firms in the
utilities and regulated industries (SIC codes 4900 to 4949) because these firms are subject to
specific institutional and regulatory constraints. We also exclude firms in the financial services
12
Petersen (2009) uses simulation to show that while the bias in the standard errors declines with the number of
clusters, ten clusters (on time) is too small to produce meaningful adjustment. He concludes:
“When there are only a few clusters in one dimension, clustering by the more frequent cluster yields results that are
almost identical to clustering by both firm and time (p26).” Our results are not affected when we cluster by both
firm and time, consistent with Petersen (2009).
14
industry (SIC codes 6000 to 6999) because accruals in the financial services industry are not
comparable with accruals in other industries.
We begin by setting MF_ISSUE equal to one based only on the existence of short-term
annual earnings forecasts. Excluding short-term quarterly earnings forecasts ensures that our
annual earnings quality measures correspond to forecasts of annual earnings of the same year.
Although we believe that short-term quarterly earnings guidance can also impact the quality of
annual earnings (to the extent that quarterly earnings guidance behavior affects annual reported
earnings), imposing such a restriction lends further credibility to our statistical tests. This initial
sample of firms issuing short-term annual earnings forecasts consists of 18,160 firm-year
observations with non-missing ABAC data and 14,507 firm-year observations with non-missing
ABDD data. Alternatively, when we set MF_ISSUE equal to one for firms that issue either a
short-term annual or a short-term quarterly earnings forecast, our sample consists of 22,168
observations with non-missing ABAC data and 18,035 observations with non-missing ABDD
data. These latter samples are slightly larger due to the addition of firm-years that issue short-
term quarterly earnings forecasts only.
Panel A of Table 1 provides summary statistics on the earnings quality proxies and
control variables. Panel B and Panel C further present the mean and median values of the two
earnings quality proxies classified by guidance issuance (MF_ISSUE) and by guidance regularity
(MF_REG) within the sample of firms that issue short-term earnings guidance. For expositional
purposes, we multiply ABAC and ABDD by 100 in these two panels.
Panel B shows that compared to firms issuing no guidance, firm-years with short-term
annual guidance exhibit lower mean and median values of both ABAC and ABDD.13
Untabluated
13
For expositional purposes, in Panel B of Table 1 we report descriptive statistics where MF_ISSUE is coded as one
based only the existence of short-term annual management forecasts. The descriptive statistics in Panel B remain
15
results show that the differences in the mean and median values of ABAC and ABDD between the
two groups are statistically significant. Panel C further shows that among the firms that issue
short-term guidance, both ABAC and ABDD decrease monotonically as guidance regularity
increases. The differences in ABAC and ABDD between firms that issue short-term guidance in
just one quarter (MF_REG = 1) and firms that issue short-term guidance in all four quarters
(MF_REG = 4) are statistically significant (untabulated). These numbers are consistent with
firms that give guidance (firms that give regular guidance) exhibiting higher earnings quality
than firms that issue no guidance (less regular guiders). These initial figures are inconsistent
with critics‟ assertions of a negative association between earnings guidance and earnings quality.
4.2 Tests of H1 and H2
Table 2 presents the regression results of testing Hypothesis 1. Recall that larger
magnitudes of abnormal accruals indicate lower earnings quality. Thus, if short-term guidance
leads to higher earnings quality, the coefficient 1 in equation (1) will be significantly negative.
On the other hand, if short-term guidance leads to lower earnings quality, 1 will be significantly
positive.
We estimate equation (1) using two measures of earnings quality (ABAC and ABDD) and
two measures of short-term guidance issuance (based on short-term annual guidance only and
based on short-term annual and quarterly guidance), as discussed in Section 3. In Columns (1)
and (2), MF_ISSUE is equal to one if a firm issues a short-term annual forecast, and zero if the
firm issues no annual or quarterly forecasts for the year. In Columns (3) and (4), MF_ISSUE is
equal to one if the firm issues either a short-term annual or short-term quarterly forecast, while
the control group remains the same. The second measure of MF_ISSUE assumes that short-term
qualitatively the same when MF_ISSUE is coded as one based on the existence of either short-term annual or short-
term quarterly management forecasts.
16
quarterly earnings guidance also affects annual earnings quality. We believe this is a reasonable
assumption because if firms manage quarterly earnings, it is likely that such interim earnings
management behavior will affect annual earnings.
For ease of exposition, we multiply the estimated coefficients by 100. Our results across
all four measures are highly consistent; 1 is significantly negative in all four regressions at
better than the 1% level with firm-clustered t-statistics ranging from -3.11 to -5.28. Thus, short-
term earnings guidance issuance is associated with higher, rather than lower, earnings quality.14
In Table 3 we present the results of testing Hypothesis 2. Specifically, within the sample
of firms that issue short-term earnings guidance, we examine whether regular guiders report
higher quality earnings than less regular guiders. The results show that the coefficients on
MF_REG are significantly negative at the 1% level, indicating more regular guiders exhibiting
smaller magnitudes of abnormal accruals relative to less regular guiders. These multivariate
results are consistent with the univariate results presented in Panel C of Table 1. Thus, more
regular guiders exhibit higher, instead of lower, earnings quality.
Taken together, the results in Tables 2 and 3 are inconsistent with assertions that short-
term earnings guidance impairs the quality of earnings. Rather, these findings support the
theoretical intuition in Dutta and Gigler (2002) that management earnings forecasts discipline
managers in the reporting of earnings. In addition, these results are also broadly consistent with
recent empirical finding by Choi et al. (2011) that the returns of firms that issue earnings
guidance are more informative about future earnings.
4.3 Additional tests on capital market pressure
14
Because our results are qualitatively the same whether we base our definition of guidance issuance (MF_ISSUE)
on annual short-term forecasts alone or on both annual and quarterly short-term forecasts, for parsimony of
presentation, going forward we present only the results based on the broader definition of short-term guidance using
both annual and quarterly forecasts.
17
The above findings are inconsistent with the criticism that short-term earnings guidance
adversely affects earnings quality. If earnings guidance does not lead to worse earnings quality,
then what does? Theoretical research by Stein (1989) suggests that under capital market
pressure, managers resort to myopic actions that endanger long-term value creation. Using an
experiment, Bhojrai and Libby (2005) find that managers make myopic project choices in
response to increased stock market pressure in the form of a pending stock issuance.
We examine the impact of earnings guidance on the relation between capital market
pressure and earnings quality. We measure capital market pressure using both an ex post and an
ex ante proxy. Our ex post measure of capital market pressure, SHR, is an indicator variable for
stock issuance, coded as one if the number of shares outstanding at the end of fiscal year t is at
least 20% higher than the number of shares outstanding at the end of year t-1, and zero otherwise
(Xu 2010). Our ex ante measure of capital market pressure, DFCF, is the decile ranking of the
firm‟s free cash flows, FCF, defined as the difference between cash flows from operations and
the three-year average of capital expenditure. We multiply DFCF by negative one, such that
higher DFCF values are consistent with lower free cash flows and greater need for external
capital. We include these two proxies separately into equation (1) and also interact these proxies
with our short-term earnings guidance variable as follows:
ACCRit = + 1MFit +2SHRit +3MFit*SHRit + 4LEVit-1 + 5BTMit-1 +
6OPCYCLEit-1+7CAPINTit-1 + 8(CFO)it-1 + 9LOSSit-1 + 10SIZEit-1 +
11INSTit-1 + IND + YEAR + it (1‟)
ACCRit = + 1MFit +2DFCFit-1 +3MFit*DFCFi-1t + 4LEVit-1 + 5BTMit-1 +
6OPCYCLEit-1 + 7CAPINTit-1 + 8(CFO)it-1 + 9LOSSit-1 + 10SIZEit-1 +
11INSTit-1 + IND + YEAR + it (1”)
If capital market pressure leads to managerial myopia and earnings management, 2 will
be positive in equations (1‟) and (1‟‟). Though we do not have an ex ante prediction on the
18
interaction variable between MF and both DFCF and SHR, a positive 3 would indicate that
short-term earnings guidance exacerbates the negative impact of capital market pressure on
earnings quality, and a negative 3 would indicate that short-term earnings guidance mitigates the
negative impact of capital market pressure on earnings quality.
The results of estimating equations (1‟) and (1”) are presented in Panels A and B of Table
4. We report results based on both the issuance (MF_ISSUE) and regularity (MF_REG) of
earnings guidance. Consistent with our conjecture and with prior literature (e.g., Teoh et al.
1998a; Bhojraj and Libby 2005), the coefficients on SHR and DFCF are significantly positive
across both measures of abnormal accruals and both measures of earnings guidance, suggesting
firms facing capital market pressure report lower quality earnings. More importantly, the
coefficients on the interaction variable (MF*SHR and MF*DFCF) are significantly negative,
suggesting that instead of leading to worse earnings quality, earnings guidance actually mitigates
the negative impact of capital market pressure on earnings quality.
4.4 Additional test using the positive abnormal accrual subsample
Critics of the “earnings guidance game” are primarily concerned about firms managing
earnings upwards to meet or beat expectations. For example, Rappaport (2005) argues that
“failure to meet earnings targets is seen (by executives) as a sign of managerial weakness” and
alleges that “companies can boost their earnings without creating value through accounting
shenanigans”. Thus, we offer an additional test using the subsample of observations with
positive abnormal accruals. If the above allegations are true, we would expect firms providing
short-term earnings guidance to report more positive discretionary accruals. In other words, we
should see a positive 1 in the following regression:
19
POS_ACCRit = + 1MFit + 2LEVit-1 + 3BTMit-1 +4OPCYCLEit-1 +5CAPINTit-1 +
6(CFO)it-1 + 7LOSSit-1 + 8SIZEit-1 + 9INSTit-1 + IND + YEAR + it, (1‟‟‟)
where the dependent variable, POS_ACCR, is positive abnormal accruals and all other variables
are as defined previously. We report the results of this test in Table 5. Contrary to criticisms
that short-term earnings guidance leads firms to manage earnings upwards, we find these firms
have less positive abnormal accruals, as is evidenced by the significantly negative 1 across all
four specifications.15
Taken together, the combined evidence in Tables 2 through 5 indicates that firms issuing
earnings guidance exhibit higher earnings quality, and that earnings guidance actually mitigates
the negative impact of capital market pressure on earnings quality. In short, the allegations that
short-term earnings guidance leads to earnings management seem to be misplaced. Our results
suggest it is capital market pressure, not earnings guidance, that impairs earnings quality. These
findings support the prediction of Dutta and Gigler (2002).
4.5 Additional tests based on the direction of earnings guidance
Prior research argues that firms not only engage in earnings management but also use
expectations management to meet or beat analysts' earnings forecasts. For example, Matsumoto
(2002) and Richardson, Teoh, and Wysocki (2004) find evidence consistent with firms issuing
earnings guidance to walk analysts‟ forecasts down to a more beatable target. To the extent that
expectations management is a substitute for earnings management, an alternative explanation for
our results is that firms issuing earnings guidance exhibit higher earnings quality simply because
these firms have less need to manage earnings.
To ensure that our finding of a positive relation between earnings guidance and earnings
quality is not simply driven by expectations management, we conduct further empirical tests
15
Our results on the subsample of observations with negative abnormal accruals reveal no significant difference in
the abnormal accruals for firms that guide regularly.
20
based on the sign of the firm‟s earnings guidance. To capture whether the firm is engaged in
expectations management, we define three variables to capture the direction of earnings
guidance: UPMF is coded as 1 for firms that issue only positive short-term forecasts in the year,
and 0 otherwise; DOWNMF is coded as 1 for firms that issue only negative short-term forecasts
in the year, and 0 otherwise; and, MIDMF is coded as 1 for firms that issue both positive and
negative short-term forecasts in the year, and 0 otherwise. We determine the direction (positive
versus negative) of a management forecast by comparing the forecast to the median of all
analysts' forecasts issued within 90 days before the issuance of the earnings guidance.16
As such,
we include only point and range earnings forecasts in this analysis to facilitate numerical
comparisons.17
We use these three variables, UPMF, DOWNMF, and MIDMF, in place of
MF_ISSUE and re-estimate equation (1). If our main finding that earnings guidance activity
leads to less earnings management is not driven solely by a trade-off between expectations
management and earnings management, we should find a significantly negative coefficient on
UPMF, as firms issuing positive earnings guidance are unlikely to be engaged in expectations
management to walk down analysts' forecasts.
As reported in Panel A of Table 6, we find the coefficients on UPMF are significantly
negative in Columns (1) through (3). Thus, firms issuing only upwards earnings guidance
exhibit higher earnings quality compared to firms with no earnings guidance. In comparison, the
coefficient on DOWNMF is significantly negative only in Column (1). Moreover, although the
coefficient on UPMF is insignificant in Column (4), the coefficient on DOWNMF is also
16
Neutral forecasts (forecasts that are equal to analysts‟ consensus) are included with the positive forecasts (e.g.,
UPMF = 1). 17
The requirement that a management forecast be a point or range forecast reduces our sample sizes by an average
of 8.5% across the four tests (based on the two earnings management proxies and the two definitions of short-term
forecasts). For range forecasts, we use the mid-point of the range and compare this value with the median analyst
forecast.
21
insignificant. Rather, in Column (4), the significantly negative coefficient on MIDMF suggests
firms that issue both upward and downward forecasts exhibit higher earnings quality. Thus, the
evidence in Panel A suggests that our finding of a positive relation between short-term earnings
guidance issuance and earnings quality is unlikely to be driven by firms trading off expectations
management and earnings management to meet or beat analysts‟ expectations.
We also examine the impact of the direction of earnings guidance on the positive
association between forecast regularity and earnings quality. In this test, instead of counting the
number of quarters in which a firm issues at least one short-term annual or short-term quarterly
forecast, we count the number of quarters in which a firm issues at least one positive short-term
forecast (UPMF_REG). Correspondingly, we also count the number of quarters in which the
same firm issues at least one negative short-term forecast (DOWNMF_REG). The correlation
(untabulated) between UPMF_REG and DOWNMF_REG is significantly negative (-0.14),
suggesting a firm that issues at least one positive earnings forecast is less likely to also issue a
negative earnings forecast, and vice versa. Within the sample of firms that issue short-term
earnings guidance, we use these two variables (UPMF_REG and DOWNMF_REG) instead of
MF_REG to examine the effect of forecast regularity on earnings quality.
As reported in Panel B of Table 6, the coefficients on DOWNMF_REG are not significant
across the two measures of earnings quality. Thus, we find no evidence that firms that regularly
issue negative guidance exhibit higher earnings quality, which is inconsistent with the notion that
expectations management mitigates earnings management because the former is a substitute for
the latter. In contrast, we find the coefficients on UPMF_REG are significantly negative across
both measures of earnings quality, suggesting that firms that regularly issue positive forecasts
22
(firms that are least likely to be engaged in expectations management) are driving the
documented results.
In summary, the results in Table 6 indicate that our finding of higher earnings quality for
firms that issue earnings guidance (and for firms that that issue more regular earnings guidance)
cannot be fully attributed to a trade-off between expectations management and earnings
management. In contrast, the weight of the evidence in Table 6 suggests that firms that issue
positive earnings guidance and those that do so regularly exhibit higher earnings quality.
5. Investigation on endogeneity
5.1 Self-selection
Firms issuing earnings guidance likely differ from firms that do not provide earnings
guidance. As a result, rather than using all non-issuing firms as a control sample, we instead
compare firms that issue earnings guidance to a control sample matched on the propensity to
issue earnings guidance (Armstrong, Jagolinzer, and Larcker 2010; Doyle, Ge, and McVay 2007;
Francis and Lennox 2008; LaLonde 1986). This method matches control observations based on
the predicted probability of issuing earnings guidance. The advantage of using this propensity-
score matched control sample is that it allows us to compare firms that issue earnings guidance to
a set of firms that are similar along all other observable dimensions. This procedure mitigates
the concern that underlying firm characteristics associated with the choice to issue earnings
guidance drive any observed differences in the relation between earnings guidance and earnings
quality.
We generate propensity scores using a probit regression modeling the likelihood a firm
will issue short-term earnings guidance, as outlined by prior literature (e.g., Ajinkya, Bhojraj,
and Sengupta 2005):
23
MFit = + 1BTMit-1 + 2SIZEit-1 + 3INSTit-1 + 4ACit-1 + 5DISPit-1 + 6RVOLit-1 +
7ROAit-1 + IND + YEAR + it (2)
Detailed data definitions are available in the Appendix.18
For each firm that issues short-
term earnings guidance, we find a firm that does not issue earnings guidance with the closest
predicted probability of issuing guidance, based on equation (2). Using this propensity-score
matched control sample, we re-estimate our primary tests (equation (1)) and report the results in
Table 7.19
The coefficients on the earnings guidance variable (MF_ISSUE) remain significantly
negative at the 1% level in three out of four specifications (t-statistics range from -2.75 to -4.03),
and significantly negative at the 10% level in the fourth specification (t-statistic is -1.66).
In summary, our findings are robust to controls for self-selection. Firms that issue short-
term earnings guidance exhibit better earnings quality than do firms that do not issue guidance.
5.2 Reverse causality
Our analysis so far has focused on whether management earnings forecasting activity has
an impact on the quality of reported earnings. However, recent voluntary disclosure research
(e.g. Lennox and Park 2006; Hui et al. 2009) suggests that it is possible that earnings quality has
a causal effect on management earnings forecast activity. Yet another possibility is that earnings
quality and earnings guidance are jointly determined. We examine the direction of causality by
conducting a Granger lead-lag test. Our goal is to examine whether our findings that
management earnings guidance leads to higher earnings quality continue to hold after controlling
for the potential for reverse causality and simultaneity.
For the Granger causality test, we estimate the following equations:
18
To avoid considerably reducing the sample size, AC and DISP are set to sample mean for firms that are not
followed by any analysts. 19
Because we document our initial findings in Table 2 using both measures of MF_ISSUE (one using only annual
short-term forecasts and the other using both annual and quarterly short-term forecasts), we report the results using
both measures in the robustness test outlined in Table 6.
24
ACCRit = + 1MFit + 2ACCRit-1 + 3LEVit-1 + 4BTMit-1 + 5OPCYCLEit-1 +
6CAPINTit-1 +7(CFO)it-1 + 8LOSSit-1 + 9SIZEit-1 + 10INSTit-1 +
IND + YEAR + it (3a)
MFit = + 1ACCRit-1 + 2MFit-1 + 3BTMit-1 + 4SIZEit-1 + 5INSTit-1 +
6ACit-1 + 7DISPit-1 + 8RVOLit-1 + 9LITit-1 + 10ROAit-1
+ IND + YEAR+ it (3b)
Equation (3a) tests whether earnings guidance impacts earnings quality even after the
inclusion of lagged earnings quality (ACCRit-1). The purpose of this specification is to examine
the incremental explanatory power of earnings guidance on earnings quality after controlling for
any association between prior earnings quality and current earnings guidance. A significant 1
coefficient would be consistent with earnings guidance impacting earnings quality.20
Equation
(3b) examines whether earnings quality impacts earnings guidance after controlling for any
association between prior earnings guidance and current earnings quality. A significant 1
coefficient would be consistent with earnings quality impacting earnings guidance. We estimate
the above equations using both measures of earnings quality (ABAC and ABDD) and both
measures of earnings guidance (MF_ISSUE and MF_REG). We estimate equation (3b) using a
binary probit model for the dichotomous guidance issuance variable (MF_ISSUE), and an
ordered probit model for the guidance regularity variable (MF_REG).
The results are presented in Table 8. To simplify presentation and highlight our main
findings, we do not tabulate the coefficients and t-statistics for the control variables. Panel A of
Table 8 reports the results of estimating equations (3a) and (3b) using the dichotomous
MF_ISSUE variable, and Panel B of Table 8 presents the results using the guidance regularity
variable, MF_REG. The combined results across the two panels show that in the regressions of
earnings quality on earnings guidance (equation (3a)), the coefficients on earnings guidance
20
Note that even though MF is measured using the same annual window as the dependent variable ACCR, there is a
lead-lag relation between these two variables.
25
continue to be significantly negative, even after controlling for any association between prior
earnings quality and current earnings guidance. These findings corroborate our earlier finding
that issuing earnings guidance results in higher earnings quality. We also find (weak) evidence
that earnings quality impacts guidance behavior; ACCR in equation (3b) is significant at 10%
level in the regressions of MF_REG on lagged guidance and abnormal accruals variables. We
note that this finding is not surprising given prior research (e.g., Lennox and Park 2005). More
importantly, our results that short-term earnings guidance and short-term guidance regularity
lead to higher earnings quality continue to hold.
6. Conclusion
We study whether short-term management earnings guidance negatively impacts earnings
quality, as alleged by recent critics of the practice of short-term earnings guidance (e.g., Fuller
and Jensen 2010). We capture earnings quality using two abnormal accrual measures (Jones
(1991) and Dechow and Dichev (2002), as modified by Ball and Shivakumar (2006)). We
examine short-term earnings guidance based on both the issuance and regularity of short-term
earnings guidance.
Contrary to the conventional wisdom that short-term guidance fosters earnings
management, which ultimately impairs earnings quality, we find firms providing short-term
earnings guidance exhibit lower abnormal accruals, indicating these firms have higher quality
earnings. Regular earnings guiders also exhibit higher earnings quality than less regular guiders.
These findings are consistent with the theoretical implications from Dutta and Gigler (2002) that
when managers forecast earnings, earnings management activity is mitigated. These results also
reinforce the findings of Choi et al. (2011) that returns reflect more information about future
earnings for firms with more frequent short-term and long-term earnings guidance. Our findings
26
are also robust to empirical specifications that address self-selection and other potential
endogeneity concerns.
We also find evidence that capital market pressure leads to worse earnings quality,
consistent with Bhojrai and Libby (2005), and that the existence and regularity of earnings
guidance actually mitigate the negative impact of capital market pressure on earnings quality.
Furthermore, we find that firms issuing guidance and firms issuing regular guidance report less
positive abnormal accruals, contrary to the allegation that managers who guide more or guide
regularly inflate earnings through accounting manipulations. Thus, our findings suggest
concerns that earnings guidance impairs the quality of reported earnings are misplaced.
We also examine whether our results are driven by firms that issue negative earnings
guidance to walk down analysts‟ earnings expectations. Our evidence shows that these firms are
not driving our results, suggesting that the association between guidance activity and earnings
quality cannot be attributed to firms trading off the use of expectations management and earnings
management to meet or beat analysts‟ expectations. In contrast, we find evidence that even firms
that issue positive earnings guidance exhibit higher earnings quality.
Our paper informs the current debate on whether firms that issue short-term earnings
guidance are more likely to engage in myopic earnings management behavior. We also extend
the literature on the dynamic interactions between voluntary and mandatory disclosure. While
most studies in this area examine how mandatory disclosure attributes affect voluntary
disclosure, our study shows that voluntary disclosure behavior can also have a meaningful
impact on mandatory disclosure attributes, as suggested by the theoretical research of Dutta and
Gigler (2002).
27
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30
Appendix
Definition of Variables
Variables Definition ABAC
Absolute value of the residuals based on the Jones (1991) model after controlling for
economic losses as in Ball and Shivakumar (2006). The following regression model
is estimated annually for each industry (based on 2-digit SIC codes) with at least 20
observations:ACCit = 0 + 1∆REVit + 2GPPEit + 3INDADJ_CFOit + 4DINDit
+5(DINDit x INDADJ_CFOit) + it
ACC is total accruals calculated from statement of cash flow (SCF) data as earnings
before extraordinary items (IBC) minus cash flows from operations (OANCF).
ΔREV is change in revenue (SALE). GPPE is gross property, plant and equipment
(PPEGT). INDADJ_CFO is cash flows from operations (OANCF) minus the median
cash flows from operations for all firms in the same industry (based on 2-digit SIC
code) in the same year. DIND is a dummy variable set to one if INDADJ_CFO is
less than zero, and set to zero otherwise. All variables except DIND are deflated by
average total assets (AT). The absolute values of the regression residuals (εi,t) is our
measure of discretionary accruals (ABAC). Lower values of ABAC indicate higher
earnings quality. ABDD
Absolute value of the residuals from the Dechow-Dichev (2002) model after
controlling for economic losses as in Ball and Shivakumar (2006). The following
regression model is estimated annually for each industry (based on 2-digit SIC
codes) with at least 20 observations: ACCit = 0 + 1CFOit-1 + 2CFOit + 3CFOit+1 + 4DINDit + 5(DINDit x
INDADJ_CFOit) +it ACC is total accruals calculated from statement of cash flow (SCF) data as earnings
before extraordinary items (IBC) minus cash flows from operations (OANCF). CFO
is cash flows from operations (OANCF). INDADJ_CFO is cash from operations
minus the median cash from operations for all firms in the same industry (based on
2-digit SIC code) in the same year. DIND is a dummy variable set to one if
INDADJ_CFO is less than zero, and set to zero otherwise. All variables except
DIND are deflated by average total assets (AT). The absolute values of the
regression residuals (εi,t) is our measure of discretionary accruals (ABDD). Lower
values of ABDD indicate higher earnings quality. MF_ISSUE An indicator variable coded as 1 if a firm issues a short-term annual/quarterly
earnings guidance within a [-90, +45] day window of the fiscal year/quarter end and
before the earnings announcement, and 0 if the firm issues no annual or quarterly
guidance. MF_REG The number of unique quarters in year t with at least one short-term annual/quarterly
earnings guidance issued within a [-90, +45] day window of the fiscal year/quarter
end. This variable ranges from 1 to 4. UPMF An indicator variable coded as 1 if a firm issues only positive short-term
annual/quarterly earnings guidance in year t, and 0 otherwise. The sign of a
management forecast is determined by comparing the forecast to the median analyst
forecast based on all analyst forecasts issued within 90 days before the issuance of
the management forecast. Neutral forecasts (forecasts that are equal to analysts‟
consensus) are included with the positive forecasts. Only point or range management
forecasts are used to facilitate numerical comparisons. We use the mid-point of
range forecasts to determine the sign of these forecasts.
31
Appendix (continued)
Definition of Variables
Variables Definition
DOWNMF An indicator variable coded as 1 if a firm issues only negative short-term
annual/quarterly earnings guidance in year t, and 0 otherwise. MIDMF An indicator variable coded as 1 if a firm issues both positive and negative short-
term annual/quarterly earnings guidance in year t, and 0 otherwise. UPMF_REG The number of unique quarters in year t in which at least one positive short-term
annual/quarterly earnings forecast is issued. This variable ranges from 1 to 4. The
sign of a management forecast is determined by comparing the forecast to the
median analyst forecast based on all analyst forecasts issued within 90 days before
the issuance of the management forecast. Neutral forecasts (forecasts that are equal
to analysts‟ consensus) are included with the positive forecasts. Only point or range
management forecasts are used to facilitate numerical comparisons. We use the
mid-point of range forecasts to determine the sign of these forecasts. DOWNMF_REG The number of unique quarters in year t in which at least one negative short-term
annual/quarterly earnings forecast. This variable ranges from 1 to 4. LEV Proportion of long-term debt (DLTT) to total assets (AT)
BTM Ratio of book to market value of equity calculated as book value of equity (CEQ)
scaled by market value of equity (CSHO x PRCC_F). OPCYCLE Natural log of the firm‟s operating cycle measured in days, based on turnover in
accounts receivable and inventory. Specifically, the firm‟s operating cycle is
calculated as: 180 x ((ARt+ARt-1)/SALESt + (INVt+INVt-1)/COGSt). AR is accounts receivable (RECT), SALES is sales revenue (SALE), INV is
inventory (INVT), and COGS is cost of goods sold (COGS). CAPINT Capital intensity calculated as net property, plant and equipment (CAPX) divided by
total assets (AT).
(CFO) Standard deviation of cash flows (OANCF) deflated by average total assets.
Standard deviations are calculated over the current and prior 9 years. LOSS
Proportion of losses over the prior ten years. Losses are identified as negative net
income (NI). SIZE Natural log of total sales (SALE). AC Number of analysts following the firm. DISP
Forecast dispersion, measured as the standard deviation of one-year-ahead earnings
per share forecasts scaled by the absolute consensus forecast. We use the most
recent consensus forecast before the announcement of actual earnings. RVOL Return volatility measured as the standard deviation of daily stock returns over the
fiscal year. ROA Returns on assets, measured as income before extraordinary items (IB) divided by
total assets (AT).
SHR An indicator variable coded as one if the firm increases the number of shares
outstanding (CSHO) by at least 20%, and zero otherwise. DFCF The decile ranking of the firm‟s free cash flows, defined as the difference between
cash flows from operations and the three-year average of capital expenditure. We
multiply DFCF by negative one, such that higher DFCF values are consistent with
lower free cash flows and greater need for external capital.
This table presents the definitions of variables. Compustat mnemonics are in brackets.
32
Table 1
Descriptive Statistics
Panel A: Summary Statistics
Variable N Mean Std Dev Q1 Median Q3
ABAC 18,160 0.10 0.14 0.02 0.06 0.12
ABDD 14,507 0.08 0.12 0.02 0.04 0.09
LEV 18,160 0.16 0.20 0.00 0.09 0.25
BTM 18,160 0.60 1.16 0.25 0.45 0.78
OPCYCLE 18,160 4.70 0.87 4.28 4.74 5.16
CAPINT 18,160 0.25 0.22 0.08 0.18 0.35
σ(CFO) 18,160 0.10 0.19 0.04 0.06 0.10
LOSS 18,160 0.34 0.30 0.10 0.30 0.60
SIZE 18,160 5.28 2.46 3.64 5.22 6.95
INST 18,160 0.43 0.31 0.13 0.41 0.71
Panel B: Abnormal Accruals by Issuance of a Short-term Annual Forecast
MF_ISSUE=0 MF_ISSUE=1
N Mean Median N Mean Median
ABAC 13,907 11.05 6.36 4,253 5.56 3.81
ABDD 10,901 9.18 5.12 3,606 4.40 2.84
Panel C: Abnormal Accruals by Forecast Regularity based on the Number of Fiscal Quarters (1 to 4)
with a Short-term Annual or Quarterly forecast
ABAC ABDD
MF N Mean Median N Mean Median
1 3,104 7.27 4.48 2,632 6.14 3.64
2 1,418 6.87 4.29 1,276 5.79 3.37
3 1,243 6.52 4.11 1,097 5.20 3.26
4 2,496 5.77 3.72 2,129 4.60 2.75
Notes to Table 1:
See the appendix for variable definitions. The sample consists of firm-year observations from 2001 to 2008. The
number of observations for ABDD is smaller than all other variables because of data availability associated with its
estimation. Continuous variables are winsorized at the top and bottom 1% levels. All mean and median values
reported in Panel B and C are multiplied by 100 for expositional convenience.
33
Table 2
H1: The Impact of Short-term Management Forecast Issuance on Earnings Quality
Model:
ACCRit = + 1MF_ISSUEit + 2LEVit-1 + 3BTMit-1 + 4OPCYCLEit-1 + 5CAPINTit-1 + 6(CFO)it-1 +
7LOSSit-1 + 8SIZEit-1 + 9INSTit-1 + IND + YEAR + it (1)
MF_ISSUE =1 based only on
Short-term Annual Forecasts
MF_ISSUE =1 based on
Short-term Annual and
Quarterly Forecast Independent
Variables Predicted
Sign ABACit
(1) ABDDit
(2) ABACit
(3) ABDDit
(4)
INTERCEPT +/- 11.98*** 7.78*** 11.95*** 7.35***
(10.90) (7.50) (11.79) (7.82)
MF_ISSUEit +/- -1.00*** -0.68*** -0.79*** -0.50***
(-5.28) (-3.77) (-4.47) (-3.11)
LEVit-1 +/- 3.47*** 2.37** 2.82** 1.76*
(2.60) (1.98) (2.38) (1.67)
BTMit-1 +/- -0.36*** -0.38*** -0.33*** -0.33***
(-3.36) (-3.08) (-3.11) (-2.75)
OPCYCLEit-1 + 0.14 0.26 0.13 0.23
(0.81) (1.60) (0.82) (1.62)
CAPINTit-1 - -3.57*** -3.38*** -3.60*** -3.30***
(-5.72) (-5.19) (-6.49) (-5.74)
(CFO)it-1 + 17.48*** 13.47*** 17.24*** 13.19***
(8.24) (5.63) (8.51) (5.83)
LOSSit-1 + 5.95*** 6.64*** 6.23*** 6.78***
(10.92) (11.76) (12.70) (13.49)
SIZEit-1 - -0.76*** -0.66*** -0.68*** -0.57***
(-10.07) (-9.25) (-10.07) (-9.03)
INSTit-1 - -1.48*** -1.53*** -1.71*** -1.68***
(-3.59) (-3.75) (-4.56) (-4.53)
INDUSTRY & YEAR DUMMIES Included Included Included Included
N 18,160 14,507 22,168 18,035
ADJ-R2
19.8% 19.6% 18.8% 18.4%
Notes to Table 2:
See the appendix for variable definitions. For expositional convenience we multiply the coefficients by 100.
Continuous variables are winsorized at the top and bottom 1% levels. Firm clustered t-statistics are reported in
parentheses. *,**,*** denote significance at the 10%, 5% and 1% level (two-sided), respectively.
34
Table 3
H2: The Impact of Short-Term Management Forecast Regularity on Earnings Quality
Model:
ACCRit = + 1MF_REGit + 2LEVit-1 + 3BTMit-1 + 4OPCYCLEit-1 + 5CAPINTit-1 +6(CFO)it-1 + 7LOSSit-1 +
8SIZEit-1 +9INSTit-1 + IND + YEAR + it (1)
Independent Variables
Predicted Sign ABACit ABDDit
INTERCEPT +/- 10.34*** 5.18***
(7.03) (4.31)
MF_REG it +/- -0.21*** -0.22***
(-2.87) (-3.04)
LEV it-1 +/- -1.80*** -1.98***
(-2.69) (-3.08)
BTM it-1 +/- 0.29 0.55**
(1.01) (1.98)
OPCYCLE it-1 + 0.11 0.13
(0.58) (0.76)
CAPINT it-1 - -3.10*** -2.77***
(-4.93) (-4.52)
(CFO)it-1 + 11.35*** 8.90***
(3.09) (2.72)
LOSS it-1 + 5.50*** 5.54***
(8.73) (9.10)
SIZE it-1 - -0.13* -0.14**
(-1.71) (-2.04)
INST it-1 - -2.85*** -2.07***
(-5.09) (-3.98)
INDUSTRY & YEAR DUMMIES Included Included
N 8,261 7,134
ADJ-R2
10.7% 11.9%
Notes to Table 3:
See the appendix for variable definitions. For expositional convenience we multiply the coefficients by 100.
Continuous variables are winsorized at the top and bottom 1% levels. Firm clustered t-statistics are reported in
parentheses. *,**,*** denote significance at the 10%, 5% and 1% level (two-sided), respectively.
35
Table 4
The Impact of Capital Market Pressure
Panel A: Ex Post Measure of Capital Market Pressure
Model:
ACCRit = + 1MFit +2SHRit +3MFit*SHRit + 4LEVit-1 + 5BTMit-1 + 6OPCYCLEit-1 +
7CAPINTit-1 + 8(CFO)it-1 + 9LOSSit-1 + 10SIZEit-1 + 11INSTit-1 + IND + YEAR + it (1‟)
MF = MF_ISSUE (0 or 1) MF = MF_REG (1 to 4)
Independent Variables
Predicted
Sign ABACit ABDDit ABACit ABDDit
INTERCEPT +/- 9.00*** 7.24*** 6.20*** 4.95***
(8.13) (7.75) (4.67) (4.22)
MFit +/- -0.52*** -0.33** -0.18** -0.18**
(-2.91) (-2.04) (-2.30) (-2.47)
SHRit + 4.62*** 3.85*** 3.99*** 3.71***
(6.77) (5.80) (2.81) (2.70)
MFit*SHRit +/- -3.75*** -2.94*** -1.13** -1.09**
(-3.79) (-3.08) (-2.30) (-2.26)
LEVit-1 +/- 2.29 1.41 -1.97*** -2.10***
(1.62) (1.33) (-2.73) (-3.26)
BTMit-1 +/- -0.42*** -0.33*** 0.14 0.56**
(-3.74) (-2.77) (0.50) (2.03)
OPCYCLEit-1 + 0.10 0.22 0.09 0.12
(0.61) (1.51) (0.49) (0.73)
CAPINTit-1 - -3.46*** -3.37*** -3.06*** -2.78***
(-5.75) (-5.95) (-4.64) (-4.53)
(CFO)it-1 + 16.28*** 12.55*** 9.56*** 8.76***
(7.17) (5.61) (2.75) (2.70)
LOSS it-1 + 5.80*** 6.31*** 5.00*** 5.37***
(10.91) (13.02) (8.03) (8.97)
SIZEit-1 - -0.60*** -0.54*** -0.05 -0.12*
(-8.20) (-8.59) (-0.74) (-1.86)
INSTit-1 - -1.84*** -1.58*** -2.76*** -1.98***
(-4.63) (-4.32) (-4.99) (-3.88)
INDUSTRY & YEAR DUMMIES Included Included Included Included
N 18,539 18,035 7,284 7,134
ADJ-R2
20.6% 19.0% 10.5% 12.1%
36
Table 4 (continued)
The Impact of Capital Market Pressure
Panel B: Ex Ante Measure of Capital Market Pressure
Model:
ACCRit = + 1MFit +2DFCFit-1 +3MFit*DFCFit-1 + 4LEVit-1 + 5BTMit-1 + 6OPCYCLEit-1 + 7CAPINTit-1 +
8(CFO)it-1 + 9LOSSit-1 + 10SIZEit-1 + 11INSTit-1 + IND + YEAR + it (1”)
MF = MF_ISSUE (0 or 1) MF = MF_REG (1 to 4)
Independent Variables
Predicted
Sign ABACit ABDDit ABACit ABDDit
INTERCEPT +/- 17.17*** 12.52*** 16.56*** 12.16***
(15.70) (11.90) (8.61) (7.85)
MF it +/- -3.04*** -1.99*** -0.98*** -0.93***
(-5.29) (-3.79) (-2.91) (-2.89)
DFCFit-1 + 0.82*** 0.81*** 0.78*** 0.90***
(13.72) (13.19) (5.58) (6.96)
MFit*DFCFit-1 +/- -0.44*** -0.31*** -0.13*** -0.12***
(-4.97) (-3.70) (-2.59) (-2.64)
LEVit-1 +/- 2.63** 1.54 -1.91*** -2.23***
(2.17) (1.44) (-2.81) (-3.44)
BTMit-1 +/- -0.34*** -0.34*** 0.06 0.26
(-3.20) (-2.78) (0.19) (0.90)
OPCYCLEit-1 + -0.12 -0.03 -0.25 -0.31*
(-0.76) (-0.18) (-1.27) (-1.68)
CAPINTit-1 - -4.34*** -3.87*** -3.52*** -3.25***
(-7.88) (-6.90) (-5.38) (-5.21)
(CFO)t-1 + 17.39*** 13.25*** 10.32*** 7.73**
(9.94) (6.73) (2.79) (2.35)
LOSSit-1 + 3.58*** 4.01*** 3.92*** 3.47***
(7.42) (8.08) (6.26) (5.70)
SIZEit-1 - -0.52*** -0.42*** -0.07 -0.06
(-8.00) (-6.92) (-1.02) (-0.99)
INSTit-1 - -1.65*** -1.60*** -2.46*** -1.63***
(-4.43) (-4.40) (-4.39) (-3.17)
INDUSTRY & YEAR DUMMIES Included Included Included Included
N 21,396 17,377 7,984 6,880
ADJ-R2
20.4% 20.4% 11.9% 14.2%
Notes to Table 4:
See the appendix for variable definitions. For expositional convenience we multiply the coefficients by 100. Firm
clustered t-statistics are reported in parentheses. *,**,*** denote significance at the 10%, 5% and 1% level (two-
sided), respectively.
37
Table 5
Tests Using Positive Abnormal Accrual Sub-sample
Model:
POS_ACCRit = + 1MFit + 2LEVit-1 + 3BTMit-1 +4OPCYCLEit-1 +5CAPINTit-1 + (CFO)it-1 + 7LOSSit-1 +
8SIZEit-1 + 9INSTit-1 + IND + YEAR + it, (1‟‟‟)
MF = MF_ISSUE (0 or 1) MF = MF_REG (1 to 4)
Independent Variables
Predicted
Sign ABACit ABDDit ABACit ABDDit
INTERCEPT +/- 12.08*** 8.18*** 9.47*** 6.67***
(10.37) (8.40) (7.43) (5.60)
MFit +/- -0.92*** -0.45** -0.21*** -0.23***
(-5.08) (-2.56) (-2.86) (-3.13)
LEV it-1 +/- 0.72 0.09 -1.73*** -1.81***
(0.57) (0.09) (-2.59) (-2.81)
BTM it-1 +/- -0.90*** -0.67*** -1.22*** -0.59**
(-5.22) (-4.30) (-3.53) (-2.10)
OPCYCLE it-1 + 0.07 0.28* 0.35* 0.25
(0.37) (1.84) (1.96) (1.44)
CAPINT it-1 - -2.76*** -3.78*** -1.25** -3.00***
(-4.81) (-7.23) (-2.01) (-4.65)
(CFO)it-1 + 19.61*** 13.41*** 19.24*** 16.34***
(8.59) (6.85) (6.37) (5.70)
LOSS it-1 + 4.83*** 5.22*** 3.20*** 3.85***
(9.14) (11.07) (5.61) (7.13)
SIZE it-1 - -0.66*** -0.62*** -0.22*** -0.28***
(-8.96) (-9.24) (-3.11) (-4.28)
INST it-1 - -2.09*** -1.87*** -3.33*** -2.73***
(-5.28) (-4.79) (-6.42) (-5.64)
INDUSTRY & YEAR DUMMIES Included Included Included Included
N 14,001 10,517 5,349 4,151
ADJ-R2
25.7% 26.2% 15.8% 20.2%
Notes to Table 5:
POS_ACCR is positive abnormal accruals. See the appendix for other variable definitions. For expositional
convenience we multiply the coefficients by 100. Firm clustered t-statistics are reported in parentheses. *,**,***
denote significance at the 10%, 5% and 1% level (two-sided), respectively.
38
Table 6
Tests Based on the Sign of Earnings Guidance
Panel A: Issuance of Earnings Guidance
Model:
ACCRit = + 1UPMFit +1MIDMFit +1DOWNMFit + 2LEVit-1 + 3BTMit-1 + 4OPCYCLEit-1 + 5CAPINTit-1 +
6(CFO)it-1 + 7LOSSit-1 + 8SIZEit-1 + 9INSTit-1 + IND + YEAR + it (1)
MF_ISSUE =1 based only on
Short-term Annual Forecasts
MF_ISSUE =1 based on
Short-term Annual and
Quarterly Forecast Independent
Variables Predicted
Sign ABACit
(1) ABDDit
(2) ABACit
(3) ABDDit
(4)
INTERCEPT +/- 12.15*** 7.79*** 11.90*** 7.34***
(10.69) (7.27) (11.26) (7.41)
UPMFit +/- -0.86*** -0.56** -1.11*** -0.40
(-3.31) (-2.38) (-3.86) (-1.37)
MIDMFit +/- -0.43 -0.32 -0.71*** -0.52**
(-1.33) (-1.10) (-3.16) (-2.53)
DOWNMFit +/- -0.62** -0.33 -0.16 -0.07
(-2.45) (-1.40) (-0.63) (-0.27)
LEVit-1 +/- 3.60** 2.47* 3.02** 1.89*
(2.57) (1.96) (2.39) (1.67)
BTMit-1 +/- -0.36*** -0.37*** -0.32*** -0.34***
(-3.30) (-2.99) (-3.02) (-2.79)
OPCYCLEit-1 + 0.13 0.25 0.15 0.25
(0.75) (1.51) (0.92) (1.63)
CAPINTit-1 - -3.68*** -3.38*** -3.56*** -3.22***
(-5.62) (-4.93) (-6.02) (-5.25)
(CFO)it-1 + 17.47*** 13.36*** 17.53*** 13.46***
(8.16) (5.57) (8.34) (5.70)
LOSSit-1 + 6.05*** 6.77*** 6.23*** 6.89***
(10.69) (11.56) (12.08) (12.91)
SIZEit-1 - -0.80*** -0.68*** -0.71*** -0.59***
(-10.17) (-9.09) (-9.77) (-8.59)
INSTit-1 - -1.49*** -1.58*** -1.77*** -1.80***
(-3.43) (-3.69) (-4.42) (-4.53)
INDUSTRY & YEAR DUMMIES Included Included Included Included
N 16,898 13,523 20,012 16,200
ADJ-R2
19.7% 19.4% 19.1% 18.5%
39
Table 6 (continued)
Tests Based on the Sign of Guidance
Panel B: Regularity of Earnings Guidance
Model:
ACCRit = + 1UPMF_REGit + 2DOWNMF_REGit +3LEVit-1 + 4BTMit-1 + 5OPCYCLEit-1 + 6CAPINTit-1
+7(CFO)it-1 + 8LOSSit-1 + 9SIZEit-1 +10INSTit-1 + IND + YEAR + it (1)
Independent Variables
Predicted Sign ABACit ABDDit
INTERCEPT +/- 8.00*** 3.62***
(5.49) (2.79)
UPMF_REG it +/- -0.39*** -0.34***
(-3.93) (-3.49)
DOWNMF_REGIT +/- 0.09 -0.06
(1.00) (-0.67)
LEV it-1 +/- -1.95** -2.39***
(-2.48) (-3.06)
BTM it-1 +/- 0.93** 0.89**
(2.25) (2.13)
OPCYCLE it-1 + 0.27 0.24
(1.37) (1.32)
CAPINT it-1 - -2.63*** -1.97***
(-3.58) (-2.78)
(CFO)it-1 + 12.71*** 12.75***
(3.57) (3.10)
LOSS it-1 + 5.29*** 5.57***
(7.84) (7.89)
SIZE it-1 - -0.02 -0.04
(-0.26) (-0.51)
INST it-1 - -2.94*** -2.38***
(-4.67) (-3.85)
INDUSTRY & YEAR DUMMIES Included Included
N 6,318 5,331
ADJ-R2
10.0% 10.6%
Notes to Table 6:
See the appendix for variable definitions. In Panel A, the percentage of UPMF. DOWMF and MIDMF observations
in each test are as follows: Column (1): UPMF = 50%, DOWNMF = 38%, MIDMF = 12%; Column (2): UPMF =
51%, DOWNMF = 37%, MIDMF = 12%; Column (3): UPMF = 21%, DOWNMF = 32%, MIDMF = 47%; Column
(4): UPMF = 21%, DOWNMF = 32%, MIDMF = 47%. For expositional convenience we multiply the coefficients
by 100. Continuous variables are winsorized at the top and bottom 1% levels. Firm clustered t-statistics are
reported in parentheses. *,**,*** denote significance at the 10%, 5% and 1% level (two-sided), respectively.
40
Table 7
Self-Selection: Propensity-Score Matching
Model:
ACCRit = + 1MF_ISSUEit + 2LEVit-1 + 3BTMit-1 + 4OPCYCLEit-1 + 5CAPINTit-1 + 6(CFO)it-1 + 7LOSSit-1
+ 8SIZEit-1 + 9INSTit-1 + IND + YEAR + it (1)
MF_ISSUE =1 based only on
Short-term Annual Forecasts
MF_ISSUE =1 based on
Short-term Annual and
Quarterly Forecast
Independent Variables
Predicted
Sign ABACit
(1) ABDDit
(2) ABACit
(3) ABDDit
(4)
INTERCEPT +/- 9.00*** 4.80*** 11.88*** 6.97***
(7.97) (4.42) (10.08) (6.20)
MF_ISSUE it +/- -0.79*** -0.73*** -0.56*** -0.33*
(-4.03) (-3.99) (-2.75) (-1.66)
LEV it-1 +/- -0.80 0.16 -0.49 -0.14
(-1.18) (0.16) (-0.58) (-0.13)
BTM it-1 +/- 0.16 -0.06 -0.47*** -0.18
(0.67) (-0.24) (-4.34) (-0.99)
OPCYCLE it-1 + 0.17 0.44** 0.06 -0.03
(1.05) (2.50) (0.35) (-0.20)
CAPINT it-1 - -1.86*** -2.13*** -3.79*** -3.46***
(-2.92) (-3.63) (-5.84) (-5.16)
(CFO)it-1 + 12.13*** 13.84*** 14.77*** 11.00***
(3.90) (3.66) (3.57) (3.73)
LOSS it-1 + 3.32*** 4.92*** 5.67*** 5.00***
(5.69) (6.41) (7.79) (8.24)
SIZE it-1 - -0.22*** -0.28*** -0.34*** -0.27***
(-3.89) (-4.86) (-5.57) (-4.34)
INST it-1 - -1.16** -1.81*** -2.16*** -1.82***
(-2.57) (-4.00) (-3.92) (-3.95)
INDUSTRY & YEAR DUMMIES Included Included Included Included
N 8,118 6,858 15,872 13,674
ADJ-R2
8.6% 13.6% 12.0% 12.0%
Notes to Table 7:
See the appendix for variable definitions. For expositional convenience we multiply the coefficients by 100. Firm
clustered t-statistics are reported in parentheses. *,**,*** denote significance at the 10%, 5% and 1% level (two-
sided), respectively.
41
Table 8
Granger Causality Tests
Models:
ACCRit = + 1MFit + 2 ACCRit-1 + 3LEVit-1 + 4BTMit-1 + 5OPCYCLEit-1 + 6CAPINTit-1 +7(CFO)it-1 +
8LOSSit-1 + 9SIZEit-1 + 10INSTit-1 + IND + YEAR + it (3a)
MFit = + 1ACCRit-1 + 2MFit-1 + 3BTMit-1 + 4SIZEit-1 + 5INSTit-1 + 6ACit-1 + 7DISPit-1 +8RVOLit-1 +
9LITit-1 + 10ROAit-1 + IND + YEAR+ it (3b)
Panel A: MF is defined as MF_ISSUE (0 or 1)
Variables Predicted
Sign ABACit Eq. (3a)
MFit Eq. (3b)
ABDDit Eq. (3a)
MFit Eq. (3b)
INTERCEPT +/- 10.02*** -238.23*** 5.75*** -196.35***
(11.32) (-24.17) (6.64) (-19.24)
MFit +/- -0.68*** -0.43***
(-4.23) (-3.02)
ACCRit-1 + 21.04*** -19.06 22.51*** -10.73
(9.15) (-1.13) (8.69) (-0.53)
MFit-1 + 210.66*** 210.82***
(71.72) (64.75)
CONTROL VARIABLES, IND. & YEAR DUMMIES
Included Included Included Included
N 21,933 16,932 17,841 13,434
Panel B: MF is defined as MF_REG (1 to 4)
Variables Predicted
Sign ABACit Eq. (3a)
MFit Eq. (3b)
ABDDit Eq. (3a)
MFit Eq. (3b)
INTERCEPT +/- 9.33*** -86.65*** 4.23*** -88.38***
(7.14) (-5.53) (4.23) (-5.70)
MFit +/- -0.15*** -0.15**
(-2.29) (-2.40)
ACCRit-1 + 20.71*** -43.77* 21.60*** -53.34*
(9.51) (-1.76) (12.16) (-1.71)
MFit-1 + 66.73*** 64.90***
(46.25) (40.89)
CONTROL VARIABLES, IND. & YEAR DUMMIES
Included Included Included Included
N 8,199 5,501 7,071 4,594
Notes to Table 8:
See the appendix for variable definitions. For expositional convenience we multiply the coefficients by 100. Firm
clustered t-statistics are reported in parentheses. *,**,*** denote significance at the 10%, 5% and 1% level (two-
sided), respectively.