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Journal of Accounting and Economics 35 (2003) 405–422
Anticipatory income smoothing:
a re-examination$
Pieter T. Elgers, Ray J. Pfeiffer Jr*, Susan L. Porter
Department of Accounting and Information Systems, Isenberg School of Management,University of Massachusetts, Amherst, MA 01003-4915, USA
Received 1 August 2002; accepted 28 May 2003
Abstract
This paper reassesses evidence of anticipatory income smoothing reported in DeFond and
Park (DP) (J. Accounting Econom. 23 (1997) 115) in light of knowledge about measurement
error in discretionary accrual estimates. We argue that the method DP use to measure un-
managed earnings mechanically biases the evidence in a manner consistent with anticipatoryincome smoothing. Using an approximate randomization approach, we find that DP’s results
cannot be distinguished from those achieved when discretionary accruals are randomly
assigned to firm-years in our sample. Overall, these results show that the ‘backing out’
approach to measuring un-managed earnings is ineffective in testing earnings management
hypotheses.
r 2003 Elsevier B.V. All rights reserved.
JEL classification: M41
Keywords: Earnings management; Smoothing
1. Introduction
Earnings management through the use of discretionary accruals to achieve target
levels of reported earnings has been the subject of considerable public press,
$We are grateful to Bill Brown, Mark DeFond, the workshop participants at the University of
Connecticut, and an anonymous reviewer for helpful comments and suggestions and for research support
provided by the Isenberg School of Management at the University of Massachusetts. We thank I/B/E/SInternational, Inc. for providing earnings per share forecast data at an academic rate.
*Corresponding author. Tel.: +1-413-545-5653; fax: +1-413-545-3858.
E-mail address: [email protected] (R.J. Pfeiffer Jr).
0165-4101/$ - see front matterr 2003 Elsevier B.V. All rights reserved.
doi:10.1016/S0165-4101(03)00039-9
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regulatory attention, and academic study. One particular manifestation of earnings
management is ‘smoothing,’ or managing reported earnings to achieve earnings
targets across accounting periods. A recent study, DeFond and Park (1997) (DP),
draws upon Fudenberg and Tirole (1995) to predict and test hypotheses thatmanagers smooth earnings to meet earnings targets. Specifically, they propose that
managers consider both current-year earnings and expected year-ahead earnings
when making decisions about current-year discretionary accruals.
A fundamental issue in assessing earnings management is the unobservability of
the managed and un-managed components of reported earnings. Much of prior
research relies upon the Jones (1991) model of unmanaged earnings. Recent
evidence, however, calls into question the precision and power of discretionary
accruals estimates using the Jones model (e.g., Dechow et al., 1995; Guay et al.,
1996).
The paper’s primary purpose is to reassess whether the ‘backing-out’ approach to
defining un-managed earnings influences the DP results. Our analysis indicates that
classifying firm-years in this manner, where un-managed earnings is defined as the
difference between reported earnings and an error-prone discretionary accrual
estimate, creates patterns of measured discretionary accruals that appear to support
the anticipatory income smoothing hypothesis, even in the absence of earnings
management. Lim and Lustgarten (2000) examine this problem in a regression-based
setting and illustrate similar biases in other earnings-management studies. DeFond
and Park acknowledge this self-selection problem throughout their paper and
conduct several empirical tests to address the self-selection issue as an alternativeexplanation. However, DP are unable to rule out the self-selection bias as an
alternative explanation.
To address the problem of measuring managed and unmanaged components of
earnings, we use an approximate randomization procedure to generate an empirical
distribution of the discretionary accrual characteristics for each pair-wise classifica-
tion of current and expected future relative earnings performance. We then compare
the actual statistics obtained using the DP methodology to the empirical distribution
as a means of assessing whether the observed behavior is sufficient in magnitude to
exceed the thresholds using conventional confidence intervals.1 We find that the
results using the DP methodology are statistically indistinguishable from the randomresults. We conclude that the ‘backing out’ method is not capable of providing
evidence of smoothing.
We next extend our analysis in two ways. First, Abarbanell and Lehavy (2000)
hypothesize that if financial analysts are not motivated to or able to perfectly predict
the extent of earnings management, their forecasts may be proxies for the un-
managed component of earnings (and thus their forecast errors may be proxies for
the managed component of earnings). For purposes of illustration, we show that
even though analysts’ forecast errors are potentially less error-prone than modified-
Jones-model estimated discretionary accruals, replacing discretionary accruals with
forecast errors does not solve the ‘backing out’ problem in this context. We obtain
1For a more detailed description of applications of approximate randomization tests, see Noreen (1989).
P.T. Elgers et al. / Journal of Accounting and Economics 35 (2003) 405–422406
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patterns of forecast errors that, like those from the DP methodology, appear to be
supportive of the hypothesized smoothing behavior. However, the analysts’ forecast
results are also indistinguishable from the results obtained when randomly assigning
forecast errors across firm-years.In the second extension of our analysis, we investigate the use of operating cash
flow as a proxy for un-managed earnings. As one of their robustness tests, DP
employ this approach. Also, more recently, Ahmed et al. (2001) use cash flows in an
attempt to avoid the problems created by the ‘backing out’ approach. Importantly,
DP and Ahmed, Lobo, and Zhou (ALZ) implement this idea slightly differently: DP
compute operating cash flow as the difference between earnings and measured total
accruals, while ALZ use disclosed operating cash flow from firms’ Statements of
Cash Flows.
We show that DP’s means of defining operating cash flow, referred to by ALZ and
by Collins and Hribar (1999) as the ‘balance sheet approach,’ is in itself a form of
‘backing out’ that potentially leads to the same sort of mechanical pattern of
discretionary accruals as we illustrate for the DP methodology. Using cash flows
defined in this manner yields results that cannot be discriminated from those using a
random assignment of discretionary accruals across firm-years.
In addition to problems caused by measurement error, a second issue in
addressing earnings management is that economic theory is at odds about whether
managing earnings is a rational economic decision on the part of managers. While
Fudenberg and Tirole (1995) are motivated by the fact that earnings is a readily
available and often-used basis for contracting, their model is of a profit centermanager, for whom there are no available measures of equity-market-based
performance. For Chief Executive Officers, other relevant information about firms
is available for use in contracting, including equity market performance. The
presence of these other means of solving contracting problems weakens the
applicability of the earnings smoothing hypothesis because earnings manipulation
has at best a very indirect effect on the termination decision.
The remainder of the paper is organized as follows. Section 2 develops
our hypotheses, explains the research design, describes the sample selection
criteria, and presents the empirical results. Section 3 summarizes and concludes
the paper.
2. Research design, sample selection and empirical results
We seek to replicate and re-evaluate the evidence of anticipatory income
smoothing presented in DP. For this reason, we adopt the same the research
design, sample selection and variable definitions. We begin with a replication of the
results in DP, which are based on the modified-Jones model of discretionary
accruals. We next apply a randomization approach to assessing the statistical
significance of the observed sample statistics in the presence of measurement error inthe discretionary accrual proxy, and we use that approach to show that it is not
possible to test the smoothing hypothesis with this methodology.
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2.1. Research design and sample selection
Based on the predictions of their theory, DP classify firm-years into three groups:
(1) firm-years where managers have incentives to manage current earningsdownward; (2) firm-years where managers have incentives to manage earnings
upward; and (3) firm-years for which the theory makes no prediction regarding
managers’ incentives. The grouping is based on current-year and expected next-year
un-managed earnings relative to annual industry medians. For example, in firm-
years where current-year un-managed earnings are relatively low but expected year-
ahead earnings are relatively high, managers are hypothesized to have the incentive
to ‘borrow’ from year-ahead earnings to avoid reporting of sub-standard current
year earnings.
DP examine descriptive statistics of the measured discretionary accruals for the
firm-years in each group. For firm-years where managers are predicted to manage
earnings upward (downward), average discretionary accruals will be positive
(negative). Their results indicate that discretionary accruals fit this prediction very
well: upward (downward) smoothing appears in 87% (92%) of the predicted cases.
The key research design issues are the measurement of discretionary accruals, the
measurement of management’s expectation of year-ahead earnings, and the
classification of current and expected year-ahead un-managed earnings as ‘high’ or
‘low.’ We follow DP’s sample selection and measurement choices to ensure
comparability of our results, as described in the following sections.
2.1.1. Measuring discretionary accruals
Discretionary accruals are computed using the modified Jones model as was
employed in DP. Un-managed earnings in a given year are equal to reported
(asset-scaled) earnings before extraordinary items less estimated discretionary
accruals.
2.1.2. Measuring management’s expectation of year-ahead earnings
Using the I/B/E/S Summary History data updated through March of 1999, we
obtain consensus analysts’ forecasts of year t þ 1 earnings made during March of year t þ 1 for all December fiscal year firms. These forecasts serve as management’s
expectation of period t þ 1 un-managed earnings as of the time that they are making
their discretionary accrual decision for year t; as in DP.2 In each year, firms’ un-
managed earnings are classified as ‘good’ or ‘poor’ relative to the 2-digit SIC-code
industry median un-managed earnings.
2DeFond and Park (1997, pp. 120–121) acknowledge that this design choice presumes (1) that managers
choose their discretionary accruals for year t just prior to their announcement of period t earnings; and (2)
that analysts’ March t þ 1 forecasts of year t þ 1 earnings are proxies for managers’ expectations of
year t þ 1 earnings. DP’s sensitivity results indicate that their inferences are not affected by the use
of actual earnings in t þ 1 as proxies for managers’ expectations, suggesting that this is not a critical
design choice.
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2.1.3. Sample selection
We duplicate DP’s sample selection. We begin with 152,883 firm-years on the 1998
Standard and Poors’ Compustat PC-Plus database of active and research companies
having a CUSIP identifier and an SIC code. We then retain firms in industries with atleast 20 members in each year. We further require that all variables needed for our
subsequent analyses be available in the I/B/E/S and Compustat databases (including
the items needed to compute the modified-Jones-model discretionary accrual
estimates). This results in a sample of 14,684 firm-years. We eliminate the extreme
1% of firm-years based on scaled discretionary accruals, non-discretionary accruals,
and operating cash flows, and we eliminate firm-years having less than $1 million in
assets. Finally, we exclude all financial institutions and unclassified firms (SIC
between 5999 and 7000 and SIC=9999). These selection criteria yield a sample of
14,194 firm years, which compares with DP’s sample of 10,167 analysts’ forecast
errors based on 1994 Compustat and I/B/E/S data.
We add one last criterion not included by DP. We exclude all non-December
fiscal-year-end firm-years for the following reason: management is posited to act
strategically based on its projection of the firm’s position relative to the industry
median. When all industry members do not share the same fiscal year, it is difficult
for the management to identify the median firm, for it would require comparisons of
industry members across many months of the year. This last criterion results in a
sample of 8,535 firm-years, and as shown later in Table 2, does not appear to affect
our ability to replicate the results in DP. Table 1 contains descriptive statistics for
our sample and shows that the means, medians and inter-quartile distributions of thevariables in our sample correspond closely to those in DeFond and Park (1997,
Table 1, p. 123).
2.2. Results
We present the results in the following sequence. We begin with a straightforward
replication of the main results supportive of anticipatory income smoothing reported
in DP. Next, we demonstrate that we obtain essentially the same results by randomly
assigning firm-years to sample partitions where anticipatory income smoothing
incentives are hypothesized to exist. We re-assess the statistical significance of theseresults using our approximate randomization tests. We then proceed in parallel
fashion to examine results using two alternative means of partitioning firms based on
current performance: first using analysts’ forecasts and then using operating cash
flows as proxies for un-managed earnings.
2.2.1. Replication of Defond and Park (1997)
Table 2, Panel A replicates DeFond and Park’s (1997, Table 2) main results. The
table presents a two-by-two classification of the sample firm-years based on current
un-managed earnings and expected next-year earnings above/below annual industry
medians. The columns partition the cases by current-year un-managed relativeperformance, and the rows partition the cases by expected next-year un-managed
performance. Current-year un-managed performance is measured by subtracting the
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modified-Jones-model discretionary accrual estimate from reported earnings (income
before extraordinary items). Expected next-year un-managed performance is
measured as the March t þ 1 consensus forecast of year t þ 1 annual earnings.
Poor or good performance is defined relative to annual industry medians.
The anticipatory smoothing hypothesis developed in DP predicts earnings
management via discretionary accruals in the second and third quadrants of theclassification matrix in Table 2, panel A. In the second quadrant, discretionary
accruals are expected to be negative, as managers attempt to defer current year
Table 1
Descriptive statistics (n=8,535)
Variablesa Mean s Lower
quartile
Median Upper
quartile
Net earnings (NE ) 0.069 0.064 0.036 0.059 0.098
Operating cash flows (OCF ) 0.114 0.093 0.063 0.111 0.165
Assets ($millions) 3085 6294 246 865 3886
Total accruals (TA) À0.046 0.079 À0.088 À0.047 À0.006
Discretionary accruals (DA) 0.000 0.069 À0.032 0.001 0.033
Pre-managed earnings (NDE ) 0.069 0.091 0.019 0.064 0.116
Median net earnings, by industry and year (MNE ) 0.066 0.027 0.048 0.065 0.085
Current pre-managed earnings minus sample
median earnings, by industry and year (RP )
0.003 0.087 À0.043 0.001 0.047
Expected earnings (ENE ) 0.085 0.069 0.043 0.068 0.113
Median expected earnings, by industry and year
(MENE )
0.077 0.030 0.045 0.077 0.098
Earnings forecast errors (FE ) 0.001 0.026 À0.004 0.001 0.006
Expected earnings minus sample median expected
earnings, by industry and year (FD)
0.008 0.061 À0.020 0.000 0.025
Earnings forecast (AF ) 0.068 0.059 0.034 0.055 0.095
Median earnings forecast, by industry and year
(MAF )
0.063 0.025 0.043 0.061 0.079
Earnings forecast minus sample median expected
earnings, by industry and year (AFD)
0.005 0.053 À0.019 0.000 0.022
aVariable definitions: NE : Net income before extraordinary items scaled by lagged assets; OCF : Cash
flow from operations scaled by lagged assets; Assets ($millions): Total assets; TA: Net income beforeextraordinary items minus operating cash flows, scaled by lagged assets; DA: Discretionary accruals are
prediction errors from fitted values of the variant of the Jones (1991) model as was employed by DeFond
and Park (1997), where total accruals are defined as TAit=DCAit À DCLit À DCashit þ DSTDit À Depit
(DCAit=change in current assets, DCLit=change in current liabilities, DCashit=change in cash and cash
equivalents, DSTDit=change in debt included in current liabilities, and Depit=depreciation and
amortization expense); NDE : Non-discretionary earnings=NE ÀDA; MNE : Median net earnings in the
sample firm’s industry (2-digit SIC), measured in the year of interest using data from Compustat; RP :
Current pre-managed earnings minus median net earnings, by industry and year; RP =NDE – MNE ; ENE :
I/B/E/S consensus forecast of NE tþ1 as of March t þ 1; scaled by lagged assets; MENE : Median expected
future earnings (ENE ) in the sample firm’s industry (2-digit SIC); FE : NE 2AF ; FD: Expected future
earnings minus sample median earnings, by industry and year; FD=ENE ÀMENE ; AF : Analysts’ forecast
of E t as of December of year t; MAF : Expected future earnings (AF ) in the sample firm’s industry based on
the December forecasts; AFD: Expected future earnings based on the December forecast, less median net
earnings; AFD ¼ AF À MNE :
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Table 2
Discretionary accruals partitioned by current relative performance and expected relative performance
Current relative performance
Poor Good All
Panel A: Modified-Jones-model discretionary accruals for the current period
Expected future relative performance
Poor (i) (ii)
Mean 0.026 À0.062 À0.002
Median 0.017 À0.044 0.001
% positive 72% 3% 49%
N 2726 1316 4042
Good (iii) (iv)
Mean 0.060 À0.025 0.003
Median 0.040 À0.017 0.002
% positive 96% 30% 52%
N 1473 3020 4493
All
Mean 0.038 À0.037 0.000
Median 0.027 À0.026 0.001
% positive 80% 22% 51%
N 4199 4336 8535
Panel B: Randomly assigned modified-Jones-model discretionary accruals for the current period
Expected future relative performance
Poor (i) (ii)
Mean 0.028 À0.063 À0.001
Median 0.018 À0.045 0.000
% positive 72% 3% 50%
N 2756 1286 4042
Good (iii) (iv)
Mean 0.060 0.027 0.001Median 0.044 0.017 0.001
% positive 95% 30% 51%
N 1443 3050 4493
All
Mean 0.039 À0.037 0.000
Median 0.027 À0.026 0.001
% positive 80% 22% 51%
N 4199 4336 8535
Discretionary accruals for the current period are measured using the modified-Jones model; un-managed
earnings are measured as: reported earnings less estimated discretionary accruals (Panel A); reported
earnings less randomly assigned estimated discretionary accruals (Panel B); expected earnings are based onMarch consensus financial analysts’ earnings forecasts; ‘Good’ and ‘Poor’ partitions are determined
relative to annual industry medians.
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income to offset expected poor performance in the subsequent year. In the third
quadrant, discretionary accruals are expected to be positive, as managers attempt to
shift subsequent year income to the current year to offset poor current performance.
The anticipatory smoothing hypothesis does not predict smoothing behavior for themain diagonal (quadrants one and four) observations because relative un-managed
performance is expected to persist over adjacent years.
The results of the replication correspond closely to those in DP. In cell ii (the
upper right cell of the table), mean discretionary accruals are À6.2% of total assets,
and 97% of the firm-years in that cell have negative discretionary accruals. Similarly,
in cell iii (lower left), mean discretionary accruals are 6.0% of total assets, and 96%
of the firm-years have positive discretionary accruals.3 We thus replicate the DeFond
and Park results (Table 2, panel A, p. 126) although they perform significance tests
based upon pooled rather than annual statistical tests. The comparative statistics in
their sample are À4.4%of assets (and 92% negative) for cell ii and 3.8% of assets
(and 87% positive) for cell iii.
2.2.2. Re-evaluation of Defond and Park (1997) results
Potential errors in measuring discretionary accruals and the ‘backing out’
approach to determining the classifications of current performance suggest a
cautious interpretation of the DeFond and Park results and the replicated results in
Table 2. Note that current relative performance, RP ; which is used to assign the
sample firm-years to the columns in panel A of Table 2, is
RP ¼ NE À MNE À D #A; where D #A ¼ DAtrue þ e; ð1Þ
where NE is the reported earnings, MNE the annual industry median earnings, D #A
the estimated discretionary accruals, DAtrue the amount of unobservable ‘true’
discretionary accruals, and e the error in measuring discretionary accruals.4 Because
the discretionary accrual proxy, D #A; is subtracted in measuring RP ; positive
(negative) measurement errors in D #A increase the likelihood that a given firm-year
will be classified in the ‘Poor’ (‘Good’) column of the matrix in panel A of Table 2.
Under the null hypothesis of no smoothing, the true discretionary accruals are
zero (that is, DAtrue 0). We assume that e is unbiased (that is, E½e ¼ 0), but that
measurement error in estimating discretionary accruals causes e for individual firm-
years to be non-zero. Stated equivalently, estimated discretionary accruals are by
definition random noise ðD #A ¼ eÞ: We also assume under the null that NEt À MNEt
is persistent.5
3The mean and median discretionary accruals differ significantly from zero in both cells based on t-tests
constructed using the means and standard errors of the 17 annual means and medians. The percentages of
positive errors differ significantly from 50% in both cells based on binomial tests of the annual percentages
relative to a null of 50%.4Note that in general, NE ÀMNE is a measure of the departure of actual, un-managed earnings from
target earnings. MNE can be thought of as an arbitrary target, and as such it has no direct role in our
analysis. Our focus is on the effects of subtracting DA from NE in the measurement of RP .5Under the null hypothesis of no smoothing, we expect reclassification across the industry median to
occur infrequently.
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Table 3 illustrates expectations for D #A under these assumptions. As in DP, the
column classifications are based on year t relative performance above and below
annual industry medians, and the column classifications are based on relative
expected year t þ 1 performance based on analysts’ expectations above and belowindustry medians. Eq. (1), which defines relative performance, can be rewritten as
follows for firm-years whose current performance is below the industry median:
RP o03NE À MNE À D #Ao03D #A > NE À MNE : ð2Þ
Similarly, for firm-years whose current performance is above the industry median,
D #AoNE À MNE : The column headings reflect these expressions of Eq. (1). The row
headings are as defined in DP, and we include in the headings the implications of our
assumption regarding the persistence of relative performance.
Focusing for the moment on cell iii; Eq. (2) implies that E D #At Ã
> E½NE t À MNE t:
And using the properties of firm-years in the ‘Good’ row and the assump-
tion of persistence of NE t À MNE t; it follows that E NE tþ1 À MNE tþ1½ > 0
) E NE t À MNE t½ > 0: Combining these two inequalities yields E D #At
 Ã>
E NE t À MNE t½ > 0 ) E D #At
 Ãc0: A similar analysis for cell ii leads to the
prediction that for those firm-years, E D #At
 Ã{0:
6 And thus, under the null
hypothesis of no smoothing behavior, we nonetheless obtain predicted results that
appear to support the alternative hypothesis of anticipatory smoothing. This is the
essence of the problem with the ‘backing out’ approach to measuring un-managed
earnings.
Table 3
Cell membership and expected discretionary accruals
Expected future relative performance (t þ 1) Current relative performance (time t)
Poor Good
D #A > NE À MNE D #AoNE À MNE
Poor
E NE tþ1 À MNE tþ1½ o0 E D #At
 ÃoE NE t À MNE t½
) E NE t À MNE t½ o0 E NE t À MNE t½ o0
) E D #At
 Ã{0
Good
E½NE tþ1 À MNE tþ140 E D #At
 Ã> E NE t À MNE t½
) E½NE t À MNE t40 E½NE t À MNE t40
) E D#
At Ã
c0
Note: Column classifications are determined (as in DP) by comparing ‘un-managed earnings’ ðNE t À D #AtÞ
to industry median net earnings (MNE t). Row classifications are determined by comparing expected
earnings (ENE tþ1) to expected median net earnings (MENE tþ1). Current relative performance (RP t) is
defined as NE t À D #At À MNE t:
6We do not make predictions for cells i and iv, because the row and column inequalities are opposite,
and thus the sign of the expected discretionary accruals is indeterminate.
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The foregoing discussion and analysis assume that discretionary accruals are
entirely error. Note, however, if we instead assume that firms do manage earnings as
hypothesized, and that discretionary accruals are measured perfectly, the expecta-
tions for cells ii and iii are identical to those in Table 3. Thus, estimating un-managedearnings by subtracting estimated discretionary accruals when such discretionary
accruals are measured with error produces discretionary accrual distributions in the
cells of interest (ii and iii) that are observationally equivalent to those if the
discretionary accruals were entirely random errors. Therefore, valid inferences
cannot be drawn from sample statistics in the 2 Â 2 classifications. The ‘backing out’
of error-prone discretionary accrual measures biases the statistics toward rejection of
the null of no smoothing. And it is important to note that this will be true regardless
of the discretionary accrual proxy chosen.
We next evaluate whether the results of tests such as in DP can be ascribed entirely
to the problems caused by the ‘backing out’ approach. We employ a randomization
procedure whereby we generate an empirical distribution of the means, medians, and
percentage of positive cases in Table 2, panel A. We compare the characteristics of
the discretionary accruals from each candidate discretionary accruals proxy to the
empirical distribution. We test whether the number of cases from our randomized
iterations of test statistics exceeds the corresponding statistics from the actual
analysis.
2.2.3. Results based on random assignment of discretionary accruals
To gauge the effects of errors in measuring discretionary accruals on the results in
Table 2, Panel A, we induce the no smoothing condition suggested in the foregoing
discussion. We randomly assign the discretionary accrual estimates generated by the
modified-Jones model to the sample firm-years (Noreen (1989)). This re-assignment,
in effect, creates a random measurement error variable that is distributed identically
to the discretionary accrual measure.7
This random assignment is repeated 1,000 times, each time capturing the mean
and median discretionary accrual and the percentage of cases in each cell where
measured discretionary accruals are positive. We interpret the resulting statistics
from the 1,000 iterations as representative of the distribution that would exist under
the null of no income smoothing. We then repeat the tests of the anticipatory
income-smoothing hypothesis by comparing the characteristics of the distribution of
measured discretionary accruals with the empirical distribution. p-values are then
computed as the proportion of the 1000 iterations in which the mean and median
exceed the actual statistics.
Table 2, panel B presents a representative outcome from the 1,000 iterations, and
the first three columns of Table 6 summarize and test the differences between panels
A and B of Table 2. The results show that there is no reliable, statistically discernable
7As an alternative approach, we also replaced the discretionary accrual estimates with random numbers
drawn from a normal distribution with the same mean and standard deviation as the vector of Jones-
model-based estimates. The results from this procedure were virtually identical to those reported in
Table 2, Panel A.
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difference between the actual discretionary accrual measures and the random error
variable. While the median in cell ii and the percentage positive in cell iii are
statistically different, there is no general pattern of reliable differences, either
statistically or economically. These results indicate that the observed pattern of discretionary accruals does not provide a test of management’s earnings smoothing
behavior because the result is guaranteed by errors in the column classifications that
are related mechanically to the magnitudes of the discretionary accrual estimates.
2.2.4. Results using analysts’ forecast errors as discretionary accrual proxies
Abarbanell and Lehavy (2000) argue that analysts’ forecast errors might proxy for
the managed portion of annual earnings. They assume that late in the earnings year,
for reasons of motivation or ability, analysts may not forecast the managed
component of earnings. If analysts’ forecasts are to be a useful proxy for un-
managed earnings, we must also assume that forecast errors caused by reasons other
than management’s late in the year earnings management are reasonably well
diversified across the sample. To the extent that these assumptions are descriptive,
analysts’ forecasts have several characteristics that are desirable relative to modified-
Jones-model estimates, including lower variance, the ability to capture earnings
management of all types (not only accruals management), and no need to require
that parameters of a model be constant over extended periods.
To implement this approach, we repeat the analysis in Table 2, dividing firms into
‘Poor’ and ‘Good’ current relative performance based on December t forecasts of
year t earnings relative to annual industry median earnings, rather than usingreported income less discretionary accruals as in DP. Analysts’ forecast errors then
become proxies for the managed component of earnings. We present the results of
this test in Table 4, panel A. As indicated therein, there is apparent support for the
income-smoothing hypothesis; cells ii and iii have mean (median, % positive)
forecast errors of À0.009 (À0.004, 25%) and 0.015 (0.006, 82%), respectively,
suggesting downward earnings management in cell ii and upward earnings
management in cell iii:
However, this too is a ‘backing out’ approach. The column classifications in Table
4, while nominally based on analysts’ forecasts, can equivalently be described as
earnings less forecast error since forecast error is defined as actual earnings lessanalysts’ forecasts. Thus large forecast errors will likely lead to classification in the
‘Poor’ column and vice-versa, and hence the sample statistics for forecast errors may
be mechanically induced as in Table 2.
To investigate whether this potential problem renders the approach useless in
testing the smoothing hypothesis, we again employ a randomization procedure. We
randomly re-assign all forecast errors across the sample firm-years, re-compute the
implied consensus analyst forecast as reported earnings less the re-assigned forecast
error, and re-classify the firm-years relative to current-year annual industry median
earnings. Panel B of Table 4 reports a representative outcome from the 1000
iterations of this procedure. The second three columns of Table 6 show thecomparisons of mean and median forecast errors from the actual and random
versions of the test, together with a p-value that indicates the number of the 1000
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Table 4
Analysts’ forecast errors partitioned by current relative performance and expected relative performance
Current relative performance
Poor Good All
Panel A: Actual forecast errors
Expected future Relative Performance
Poor (i) (ii)
Mean 0.000 À0.009 À0.001
Median 0.001 À0.004 0.001
% positive 58% 25% 55%
N 3694 348 4042
Good (iii) (iv)Mean 0.015 À0.001 0.003
Median 0.006 0.000 0.001
% positive 82% 50% 57%
N 964 3529 4493
All
Mean 0.003 À0.002 0.001
Median 0.002 À0.000 0.001
% positive 63% 48% 56%
N 4658 3877 8535
Panel B: Randomly Assigned analysts’ forecast errors
Expected future relative performance
Poor (i) (ii)
Mean 0.005 À0.021 0.000
Median 0.002 À0.010 0.001
% positive 64% 17% 55%
N 3285 757 4042
Good (iii) (iv)
Mean 0.018 À0.003 0.002
Median 0.007 À0.000 0.001
% positive 85% 48% 57%
N 1053 3440 4493
All
Mean 0.008 À0.006 0.000
Median 0.003 À0.001 0.001
% positive 69% 43% 56%
N 4338 4197 8535
Discretionary accruals for the current period are represented by December consensus analysts’ forecast
errors; un-managed earnings are measured as: reported earnings less analysts forecast errors (Panel A);
reported earnings less randomly assigned forecast errors (Panel B) ‘expected earnings’ are based on March
consensus financial analysts’ earnings forecasts; ‘Good’ and ‘Poor’ partitions are determined relative toannual industry medians.
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cases where the sample statistics from the random iterations exceeded the actual
statistics.
The results show that none of the actual statistics can be distinguished from the
results of the randomized iterations, and thus we infer that the ‘backing out’procedure inherent in this approach renders the evidence useless as a test of the
smoothing hypothesis. Note that the very large p-values have (at least) one likely
explanation. In contrast to the modified-Jones-model discretionary accrual
estimates, December forecast errors are very tightly distributed around actual
earnings. As a result, randomly re-assigning a forecast error to another firm-year is
more likely than not to result in an implied forecast that lies further from actual
earnings than the actual forecast. Under the mechanics underlying the column
partitions, this means that a large number, or perhaps all, of the random iterations
will result in forecast errors that exceed the forecast errors using the actual data.
2.2.5. Results based on partitions of current performance using operating cash flows
Recognizing the problems inherent in the ‘backing out’ approach, both DP and
ALZ investigate the use of operating cash flows as a proxy for un-managed earnings.
Note that using cash flows as a proxy for un-managed earnings introduces its own
challenges. For example, assuming that cash flows equal un-managed earnings is
equivalent to assuming that all accruals represent earnings management, which is not
likely to be descriptive. And for cash flows to make sense as a proxy for un-managed
earnings, we would have to assume that it is relative levels of cash flows that cause
managers to manage earnings, even though the theory clearly refers to managers’sensitivity to industry earnings, not cash flows. An additional challenge is that there
is a strong negative correlation between operating cash flows and accruals (Dechow
et al., 1998). The negative correlation raises the concern that partitions based on cash
flow are highly overlapping with partitions based on accruals. Nevertheless, in this
section, we investigate whether the use of cash flows mitigates the inference problems
caused by the ‘backing out’ approach.
One important distinction between the use of cash flows in each of the two studies
in question is that DP define cash flows as earnings less measured total accruals,
while ALZ use firms’ disclosed operating cash flow from the Statement of Cash
Flows. Collins and Hribar (1999) point out that accruals computed using the‘balance sheet approach,’ whereby changes in selected current asset and liability
accounts are used to measure accruals, are subject to potentially important
measurement error, especially as the result of business combinations, but more
generally when balance sheet changes reflect phenomena other than accounting
accruals. Together with the well-documented negative correlation between accruals
and cash flows and the low explained variance of the modified Jones model, there is
reason to be concerned that results using cash flows obtained in this manner are
subject to a somewhat different, but equally important ‘backing out’ problem.
Specifically, if total accruals are measured with error, and given that the R2 from
estimating the discretionary accrual model is typically quite low, discretionaryaccruals (the residual portion of total accruals) will reflect the error in total accruals.
And given that accruals and cash flows are strongly negatively correlated, errors in
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Table 5
Discretionary accruals partitioned by current relative performance (based on cash flows) and expected
relative performance
Current relative performance
Poor Good All
Panel A: Modified-Jones-model discretionary accruals
Expected future relative performance
Poor (i) (ii)
Mean 0.043 À0.027 À0.002
Median 0.037 À0.016 0.001
% positive 79% 33% 49%
N 1433 2609 4042
Good (iii) (iv)
Mean 0.081 À0.013 0.003
Median 0.071 À0.006 0.002
% positive 92% 44% 52%
N 724 3769 4493
All
Mean 0.056 À0.019 0.000
Median 0.046 À0.010 0.001
% positive 83% 40% 51%
N 2157 6388 8535
Panel B: Randomly assigned modified-Jones-model discretionary accruals
Expected future relative performance
Poor (i) (ii)
Mean 0.043 À0.026 0.002
Median 0.033 À0.016 0.001
% positive 77% 33% 51%
N 1631 2411 4042
Good (iii) (iv)
Mean 0.076 À0.015 À0.001Median 0.067 À0.007 0.001
% positive 91% 43% 50%
N 691 3802 4493
All
Mean 0.053 À0.019 0.000
Median 0.043 À0.011 0.001
% positive 81% 39% 51%
N 2322 6213 8535
Discretionary accruals for the current period are measured using the modified-Jones model; un-managed
earnings are measured as operating cash flows: reported earnings less measured total accruals (Panel A);
reported earnings less randomly assigned total accruals (Panel B) expected earnings are based on Marchconsensus financial analysts’ earnings forecasts; ‘Good’ and ‘Poor’ partitions are determined relative to
annual industry medians.
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Table 6
Statistical comparisons of discretionary accrual magnitudes with empirical (randomized) distributions
Table 2: Partitions based on ‘backing out’
modified-Jones-model discretionary
accruals
Table 4: Partitions based on analysts’
forecasts; forecast errors as discretionary
accrual proxies
Table
modifi
accrua
Actual
(Panel A)
Random
(Panel B)
p-value Actual
(Panel A)
Random
(Panel B)
p-value Actual
(Panel
Cell ii
Mean À0.062 À0.063 0.341 À0.009 À0.021 1.000 À0.02
Median À0.044 À0.045 0.031 À0.004 À0.010 1.000 À0.01
Cell iii
Mean 0.060 0.060 0.157 0.015 0.018 1.000 0.08
Median 0.040 0.044 0.261 0.006 0.007 0.971 0.07
‘Actual’ represents the statistics from Tables 2, 4, and 5 from employing each of the alternative specifications of
description in the second row of this table. ‘Random’ indicates the statistics obtained after randomly re-assigning th
analysts’ forecast error (Table 4), or both the discretionary and non-discretionary accruals (Table 5) across firm-years a
indicate the number of cases (out of 1000 iterations) where the indicated statistic was more negative (positive) i
corresponding actual condition for cell ii (iii).
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accruals are likely to coincide with errors in cash flow in the opposite direction. As a
result, firm-years classified as having had ‘Good’ current performance based on large
operating cash flow will be on average those firms who have had relatively small
accruals. If the given firm-year had small accruals because of a large negative error inmeasuring accruals, and the negative error flows through the discretionary accrual
model to be included in discretionary accruals, once again, there will be a mechanical
explanation for observed signs and magnitudes of discretionary accruals that are
obtained using the type of analysis under scrutiny in this paper.
Table 5 presents the results of our investigation of the use of cash flows obtained
from the ‘balance sheet approach’ as a proxy for current un-managed earnings
performance. We partition firm-years into ‘Poor’ and ‘Good’ columns based on
operating cash flows, where operating cash flows are defined as reported earnings less
balance-sheet-derived total accruals, as in DP. The rest of the methodology in Table
5 is similar to that in the preceding tables.
Panel A shows that, as DP find in un-tabulated results, this approach yields
patterns in discretionary accruals that are supportive of the income smoothing
hypothesis. Panel B shows the results obtained after randomly re-assigning
discretionary accrual estimates across firm-years, computing an implied cash flow
from operations based on the sum of income, discretionary accruals, and implied
non-discretionary accruals, and using the resulting cash flow from operations to
partition firms based on current performance. As before, this procedure essentially
enforces the null of no income smoothing by removing the economic correspondence
between cash flow (the partitioning variable) and discretionary accruals (the variableof interest).
The results in Panel B are strikingly similar to those in Panel A. The last three
columns of Table 6 summarize the comparisons and provide statistical tests of
differences based on 1000 iterations of the randomization. As in Tables 2 and 4,
there is no reliable statistical difference between the results in Panel A and the results
obtained from a random classification of firms. Once again, this evidence leads to the
conclusion that using cash flows measured in this manner lead to empirical tests that
are incapable of providing evidence about the income smoothing hypothesis.
3. Conclusion
This paper re-examines evidence on the multi-year smoothing hypothesis proposed
by Fudenberg and Tirole (1995) and tested by DP. The hypothesis predicts that
managers consider both relative current-year and expected year-ahead performance
when contemplating their current-year discretionary accrual decisions. They do so to
reduce the threat of dismissal caused by under-performance in either year.
We first demonstrate analytically that the mechanical relationship between
measured discretionary accruals and un-managed earnings that is used to identify
firms’ earnings management incentives guarantees the results in DP. We thenintroduce an alternative approach to testing for earnings management in the
presence of errors in the measurement of discretionary accruals. We use a
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randomization approach to construct an empirical distribution of discretionary
accruals under the null of no earnings smoothing. Summary statistics from the
original analysis are then compared to the empirical distribution as a means of
assessing their statistical significance. The results of this approach indicate thatevidence consistent with the theory of earnings smoothing is not distinguishable
from that obtained when discretionary accrual estimates are purely random
numbers.
We next investigate analysts’ December forecast errors as an alternative managed
earnings proxy, but demonstrate that despite the possibility that analysts’ forecast
errors contain less measurement error than modified-Jones-model estimates, the
relationship between forecasts as the measure of un-managed earnings and forecast
errors as the measure of managed earnings causes this to also be a ‘backing out’
approach, and as such it is not possible to draw conclusions from this alternative
test.
We also investigate the use of cash flows as a proxy for un-managed earnings.
While on the surface, it might appear that cash flows are not subject to the ‘backing
out’ problem because they are not directly related to the magnitudes of discretionary
accruals, we show that when operating cash flows are measured using as the residual
of income and total accruals computed using a balance sheet approach, a different
sort of ‘backing out’ problem arises that renders the cash-flow-based analysis useless
in assessing the smoothing hypothesis.
Overall, our results do not provide evidence supportive of anticipatory income
smoothing that is incremental to the results obtained by a simple random assignmentof discretionary accrual estimates to the analysis firms. Our results do not reject the
underlying theory proposed in Fudenberg and Tirole (1995). Rather, we show only
that tests using the framework applied in DP are not capable of testing the theory.
More generally, the randomization approach illustrated in this paper provides a
threshold that is appropriate in assessing the results of studies that use error-prone
measures of managed earnings in order to partition reported earnings into its
managed and un-managed components. Given the prevalence of the ‘backing out’
approach in both classification tests as used in this context and in regression-based
tests, this paper highlights the difficulties inherent in measuring un-managed
earnings.
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