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http://ssrn.com/abstract=885368
Strategic Revenue Recognition to Avoid Negative Earnings
Surprises
Marcus L. CaylorUniversity of South Carolina
Moore School of Business
1705 College StreetColumbia, SC 29208
Email: [email protected]: 803-777-6081
Fax: 803-777-0712
This study is based in part on my doctoral dissertation at
Georgia State University. I am grateful forhelpful comments and
suggestions from my dissertation committee: Larry Brown (chair),
Lynn Hannan,Jayant Kale, and Siva Nathan. This paper has also
benefited from the helpful comments and suggestions ofAshiq Ali,
Tony Chen, Bill Cready, Robert Freeman, Artur Hugon, Scott Jackson,
Ross Jennings, SteveKachelmeier, Bill Kinney, Krishna Kumar, Yen
Lee, Andrew Leone, Tom Lopez, Arianna Pinello, SureshRadhakrishnan,
Galen Sevcik, Scott Vandevelde, Rich White and workshop
participants at the 2006American Accounting Association Annual
Meetings, Georgia State University, the University of
SouthCarolina, the University of Texas at Austin and the University
of Texas at Dallas. I am grateful toThomson Financial/I/B/E/S for
providing data on analysts' earnings forecasts.
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http://ssrn.com/abstract=885368
1
Strategic Revenue Recognition to Avoid Negative Earnings
Surprises
Abstract
I examine whether managers use discretion in two accounts
related to revenuerecognition, accounts receivable and deferred
revenue, to avoid negative earningssurprises. I find that managers
use discretion in both accounts to avoid negative
earningssurprises. For a common sample of firms with both deferred
revenue and accountsreceivable, I show that managers prefer to
exercise discretion in deferred revenue vis--vis accounts
receivable. I distinguish between two theories for why managers
prefer tomanage a deferral rather than an accrual: lower
disclosures versus lower costs to manage(no future cash
consequences). I find that firms using gross accounts receivable to
beat
the analyst benchmark are not assessed a lower premium,
indicating that disclosure is notan explanation. My results suggest
that if given the choice, managers prefer to useaccounts that do
the least harm to the firm (i.e., no future cash consequences).
Keywords:Revenue recognition, earnings surprises, earnings
management, accounts
receivable, deferred revenue.
Data availability:All data are available from public databases
identified in the paper.
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2
Strategic Revenue Recognition to Avoid Negative Earnings
Surprises
1. Introduction
Revenue recognition is one of the most important issues facing
firms today,
usually representing the largest line item on income statements.
Dechow, Sloan, and
Sweeney (1996) find that SEC restatements generally resulted
from improperly
recognized revenues, suggesting this as a powerful setting to
examine earnings
management. Depending on the nature of a firms business, there
are two accounts that
relate to the amount of revenue recognized in an accounting
period: deferred revenue and
accounts receivable.1 I examine both accounts to see if
discretion in revenue recognition
is used to avoid negative earnings surprises. Accounts
receivable and deferred revenue
are alternative ways for recognizing revenue so it represents a
unique opportunity to
examine how managers choose between two rather different types
of revenue
management. I examine whether firms with both accounts available
to them express a
preference for discretion in one account versus the other.
I develop two theories that suggest that discretion in deferred
revenue would be
preferred. First, gross accounts receivable is managed primarily
through real activities,
such as easing credit policies. Management of deferred revenue,
on the other hand,
represents a situation where cash has already been received.
Thus, management of
deferred revenue relates more to manipulation of estimates.
Managing gross accounts
1Deferred revenue goes by several other names including advances
from customers, unearned revenue andrevenue received in advance.
Surprisingly, little research has examined the deferred revenue
account. Theonly study that directly examines this account is
Bauman (2000). Using a sample of 22 firms from thepublishing
industry, he finds that sales increases in the current year do not
persist into the future unlessaccompanied by increases in deferred
revenue, suggesting that deferred revenues are a leading indicator
offuture earnings.
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3
receivable should be more costly to firms as it relates to
accelerating sales where cash has
not been collected yet. Thus, it has future cash consequences
whereas deferred revenue
does not. Second, deferred revenue is subject to less disclosure
relative to accounts
receivable. To determine which explanation is more descriptive,
I examine whether
investors discount the premium awarded to positive earnings
surprises that result from
discretion in gross accounts receivable.
I construct a model for the normal change in short-term deferred
revenue to
determine abnormal changes in short-term deferred revenue.2 I
derive a similar model for
the normal change in gross accounts receivable to determine
abnormal changes in gross
accounts receivable.3 I create a pre-managed distribution of
earnings by removing the
discretionary component related to the account in question
(Dhaliwal, Gleason and Mills
2004; Frank and Rego 2006), and then test whether abnormal
changes in each of these
revenue accounts are higher than would be expected for firms
with pre-managed earnings
that just miss the analyst benchmark. Next, I examine firms with
both accounts to see if
managers prefer one account over the other as a means for
revenue management. I test
whether investors can see through attempts to manage accounts
receivable to beat the
analyst benchmark as evidenced by the magnitude of the premium
awarded to these
firms.
2I use the short-term deferred revenue component and ignore the
long-term component because the long-term component of deferred
revenue does not reflect revenue that should have been recognized
during thecurrent period.3I use gross accounts receivable in lieu
of net accounts receivable because abnormal changes in netaccounts
receivable could reflect changes in the allowance for bad debt.
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My results indicate that both deferred revenue and accounts
receivable are
managed in an attempt to avoid negative earnings surprises.4 I
provide evidence that
firms prefer to exercise discretion in deferred revenue relative
to accounts receivable
when given the choice. I find that the premium awarded to
beating analyst expectations
is similar for both accounts so I rule out the lower disclosure
cost explanation.
My study makes several contributions to the literature. First, I
provide the first
descriptive evidence on deferred revenue, showing that many high
technology industries
have it on their balance sheets. Second, I provide evidence that
discretion is used in
revenue recognition to avoid negative earnings surprises. Third,
my study is the first to
examine a common sample of firms with two accounts available
that differ in terms of
transparency and costliness to see whether managers prefer to
manage one type of
account over another. I provide evidence that deferred revenue
is the revenue account of
choice, indicating that when given the choice, managers prefer
to exercise discretion in a
manner that minimizes costs to the firms. Fourth, I show that
investors do not distinguish
between varying levels of account disclosure when a firm beats
the analyst benchmark.
Fifth, I derive a discretionary model of deferred revenue for
potential use in future
research on discretionary deferrals.
The remainder of my paper is organized as follows. The second
section reviews
the relevant literature and develops hypotheses. Section three
introduces the research
design. Section four provides results of my study, and section
five contains implications
and avenues for future research.
4In a contemporaneous study, Stubben (2007) also finds that
accounts receivable are used to exceed theanalyst benchmark.
However, his study does not examine deferred revenue nor does it
examine whether apreference exists.
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2. Hypotheses Development
Firms with pre-managed earnings that just miss an earnings
benchmark in the
current period have incentives to accelerate revenue because
they need current revenue to
meet or just beat the current benchmark (Cheng and Warfield
2005; Healy 1985). Firms
can recognize more revenue to meet or just beat earnings
benchmarks via discretion in
accounts receivable, deferred revenue or a combination of both.
In two recent studies,
researchers find no evidence that discretion is used in gross
accounts receivable to avoid
losses and earnings decreases (Marquardt and Weidman 2004;
Roychowdhury 2006).5
Recent evidence suggests that the analyst benchmark is the most
important benchmark
sought by managers (Dechow, Richardson and Tuna 2003; Brown and
Caylor 2005) so I
focus on this benchmark to increase the power of my tests. I
hypothesize that managers
will use discretion in revenue to avoid negative earnings
surprises.
However, I may not find results for the analyst benchmark.
First, I examine a
post-SAB 101 environment where accounting regulations on revenue
recognition are
specifically written to prevent aggressive recognition (see SAB
101). However, Rountree
(2006) finds that firms targeted by SAB 101 were less likely to
be earnings managers and
that deferred revenue was more likely to be targeted by SAB 101
suggesting that deferred
revenue may be more likely to be affected in comparison to
accounts receivable. Second,
managers use other (non-revenue-based) accruals for avoiding
negative surprises
(Moehrle 2002; Frank and Rego 2006; among others) potentially
mitigating the effect I
5Marquardt and Weidman (2004) find that managers do not exercise
discretion in gross accountsreceivable to avoid an earnings
decrease. Roychowdhury (2006) finds no significant evidence that
grossaccounts receivable are managed to avoid a loss. In
untabulated analyses, I find similar results to bothstudies using
gross accounts receivable and deferred revenue. For this analysis,
I use the samemethodology as discussed below and interval widths of
0.5% and 0.25% for the avoidance of loss andavoidance of earnings
decreases benchmarks, respectively (Burgstahler and Dichev
1997).
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6
expect to find for revenue-based accruals. Third, while using
avoidance of negative
surprises is more powerful than using other benchmarks, the fact
that researchers find no
evidence that discretion is used in gross accounts receivable to
avoid losses and earnings
decreases makes it possible that I will find nothing for the
stronger earnings benchmark.
I hypothesize that managers do use discretion in revenue
recognition to avoid
negative earnings surprises. More formally, my first two
hypotheses are:
HYPOTHESIS1:Firms with pre-managed earnings that just miss the
analyst
benchmark have an abnormal increase in gross accounts
receivable.
HYPOTHESIS2:Firms with pre-managed earnings that just miss the
analyst
benchmark have an abnormal decrease in short-term deferred
revenue.
According to the Financial Accounting Standards Board (FASB),
revenue
recognition involves consideration of two main factors: when is
the revenue realizable
and when is it considered to be earned (Statement of Financial
Accounting Concepts No.
5 (paragraph 83))? In 1999, the Securities Exchange Commission
(SEC) provided further
guidance in Staff Accounting Bulletin (SAB) No. 101, which
states that revenue can be
recognized only when the following four criteria are met: 1)
persuasive evidence of an
arrangement exists, 2) delivery has occurred or services have
been rendered, 3) the
seller's price to the buyer is fixed or determinable, and 4)
collectibility is reasonably
assured.
Major differences exist between manipulation of accounts
receivable and short-
term deferred revenue. First, with deferred revenue, cash has
already been received and a
journal entry has been made. When the aforementioned four
criteria for revenue
recognition are considered, three of them have usually been met
with the recording of
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deferred revenue (i.e., persuasive evidence of an arrangement
exists, the seller's price to
the buyer is fixed or determinable, and collectibility is
reasonably assured). Thus,
discretion arises in deferred revenue as to when delivery has
occurred or services have
been rendered. Second, with deferred revenue, managers are less
able to manipulate
through real activities. For gross accounts receivable, managers
accelerate the
recognition of revenue through real activities manipulation,
such as providing favorable
credit terms, easing creditworthiness restrictions, and speeding
up the shipment of
goods.6 In contrast to gross accounts receivable, managers
accelerate recognition of
deferred revenue by increasing estimates of services
provided.
7
This causes manipulation
of deferred revenue to be relatively less costly vis--vis
accounts receivable in terms of
its future cash consequences. For example, providing favorable
credit terms to speed up
the recognition of a receivable has future cash consequences. In
addition, managing
gross accounts receivable has long-term reputation effects with
customers, even if it has
no direct future cash consequences. For instance, a large
supplier may push unwanted
merchandise on small retailers to speed up recognition of a
receivable.
Third, information related to deferred revenue is disclosed at
lower levels relative
to accounts receivable. Whereas accounts receivable is always a
prominent separate line
item on the balance sheet, short-term deferred revenue is often
an indistinguishable
portion of the other current liabilities category on the balance
sheet. In addition, since
it is a deferral, it is absent from the cash flow statement in
contrast to changes in accounts
6This can be managed through subjective estimates of how much
revenue has been earned, but only applyto a very few industries
where long-term construction projects exist.7While managers can use
real activities manipulation to determine when a credit sale is
recorded, withdeferred revenue the use of real activities
manipulation is less likely. For instance, it is unlikely
thatmanagers would withhold services to customers to reduce the
amount of revenue recognized. Deferredrevenue could be manipulated
through altering contract terms, however, because a customer has to
agree tothe new terms, such an action is less likely to occur.
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8
receivable so investors will find it difficult to determine if a
change is unusually high (or
low) relative to a change in sales.
I expect managers to prefer discretion in deferred revenue
relative to accounts
receivable as a result of the real costs imposed by and/or the
lower disclosure of accounts
receivable. My third hypothesis is:
HYPOTHESIS3:Firms with deferred revenue will use less discretion
in gross
accounts receivable to avoid negative earnings surprises.
My final hypothesis attempts to distinguish between the reasons
why managers
prefer deferred revenue over accounts receivable, real cash flow
consequences or level of
disclosure. If investors award firms that use gross accounts
receivable to achieve positive
earnings surprises a lower premium, then the level of disclosure
matters in managements
preference for discretion in deferred revenue. If investors do
not give firms which use
gross accounts receivable to achieve positive earnings surprises
a lower premium,
managers should not have any preference for their use related to
disclosures. My final
hypothesis examines these two views, allowing me to discriminate
amongst the two
theories:
HYPOTHESIS4a:The premium to beating is lower for firms that use
discretion in
gross accounts receivable.
HYPOTHESIS4b:The premium to beating is not lower for firms that
use discretion
in gross accounts receivable.
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3. Sample Selection and Research Design
Sample Selection
I obtain annual earnings, short-term deferred revenue, accounts
receivable, sales,
total assets, cash flow from operations and other financial
statement accounts from the
2005 Annual Compustat File. I obtain annual analyst earnings
forecast data and reported
annual earnings for computing earnings surprises from the
split-unadjusted I/B/E/S Detail
File. To be consistent with prior literature on earnings
management (e.g., Burgstahler
and Dichev 1997), I exclude utilities and financial firms (i.e.,
SIC codes between 4400
and 5000 and SIC codes between 6000 and 6500). I also exclude
any firms related to
public administration (i.e., SIC codes of 9000 or higher).
Modeling Normal Changes in Gross Accounts Receivable
To derive a model for the expected amount of gross accounts
receivable in time t,
I need to make certain assumptions. I assume that gross accounts
receivable is some
proportion of current periods sales, as accounts receivable are
included in this periods
sales.8 I also assume that gross accounts receivable are some
proportion of next periods
cash flow from operations, since accounts receivable will turn
over in the next period.
This implies that changes in gross accounts receivable should be
positively related to
contemporaneous changes in sales and future changes in cash flow
from operations.9 I
include both of these variables in my model to capture any
non-discretionary component
that is not captured by the other. Thus, if gross accounts
receivable are a greater
proportion of this periods sales or next periods cash flow from
operations than
8This assumption is adopted from Dechow, Kothari, and Watts
(1998).9I find that changes in gross accounts receivable are
significantly and positively correlated with bothchanges in current
periods sales and changes in future periods cash flow from
operations using bothPearson and Spearman correlations.
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expected, more accounts receivablemay have been recorded than
expected.10 Based on
these assumptions, I estimate abnormal changes in gross accounts
receivable by running
linear regressions by industry (2-digit SIC code) and fiscal
year using all available firms
with the requisite data:11
Gross A/Rt/ At-1= 0+ 1*(1/ At-1) + 1*(St/ At-1) + 2*(CFOt+1/
At-1) + t (1)
where:
Gross A/Rt = change in gross accounts receivable during year t
(change in Compustat
Annual Data Item 2 plus Compustat Annual Data Item 67),
St = change in sales during year t (change in Compustat Annual
Data Item 12),
CFOt+1 = change in cash flow from operations during year t+1
(change in Compustat
Annual Data Item 308), and
At-1= beginning of the year total assets (Compustat Annual Data
Item 6).
In addition to the scaled intercept term found in prior
discretionary accrual
studies, I also include a constant term based on Kothari, Leone
and Wasley (2005) who
find that it results in better-specified, more symmetric
discretionary models.12 I compute
the abnormal change in gross accounts receivable for the current
period as the difference
between the actual change in gross accounts receivable and the
predicted (or expected)
change obtained from these industry-year regressions. An
abnormal increase in gross
accounts receivable occurs when the actual change exceeds the
predicted normal
10The opposite implies that less accounts receivable may have
been recorded than expected.11I estimate at the 2-digit level to be
consistent with prior literature. I also winsorize all variables
enteringboth of my discretionary models at the extreme (1st and
99th) percentiles of their respective distributions tobe consistent
with prior literature. In addition, I require at least eight
industry-year observations to estimatethe model. Roychowdhury
(2006) uses a similar model.12Kothari, Leone, and Wasley (2005)
include both a scaled and unscaled intercept term in
theirdiscretionary accrual models.
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change. Abnormally low growth in gross accounts receivable
occurs when the actual
change is less than the predicted normal change.
Modeling Normal Changes in Deferred Revenue
To derive a model for the expected amount of short-term deferred
revenue, I make
a similar set of assumptions to those made for accounts
receivable modified for the
opposite behavior of deferrals relative to accruals. More
specifically, I assume that short-
term deferred revenue is a proportion of next periods sales,
since deferred revenue is to
be recognized in the next period. I also assume that short-term
deferred revenue is a
proportion of the current periods cash flow from operations,
because deferred revenue in
the current period is reflected in the current periods cash flow
from operations. This
implies that changes in short-term deferred revenue should be
positively related to
contemporaneous changes in cash flow from operations and future
changes in sales.13
Thus, if short-term deferred revenue is a greater proportion of
either the current periods
cash flow from operations or next periods sales than expected,
more short-term deferred
revenue remains than expected. Based on these assumptions, I
estimate abnormal
changes in deferred revenue by running linear regressions by
industry (2-digit SIC code)
and fiscal year using all available firms with the requisite
data:14,15
Def Revt/ At-1= 0+ 1*(1/ At-1) +1*(St+1/ At-1) + 2*(CFOt/ At-1)
+ t (2)
where:
13I find that changes in short-term deferred revenue are
significantly and positively correlated with changesin future sales
and changes in current periods cash flow from operations using both
Pearson and Spearmancorrelations.14I require at least eight
industry-year observations to estimate this model similar to the
constraint for thegross accounts receivable model.15I chose a
cross-sectional industry discretionary model in lieu of a
firm-specific model due to datarestrictions (i.e., a small
time-series of data).
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12
Def Revt = change in short-term deferred revenue during year t
(change in Compustat
Annual Data Item 356),
St+1 = change in sales during year t+1 (change in Compustat
Annual Data Item 12),
CFOt = change in cash flow from operations during year t (change
in Compustat Annual
Data Item 308), and
At-1= beginning of the year total assets (Compustat Annual Data
Item 6).
I compute the abnormal change in deferred revenue for the
current period as the
difference between the actual change in deferred revenue and the
predicted change from
these industry-year regressions. An abnormal increase in
deferred revenue occurs when
the actual change exceeds the predicted change. Similarly, an
abnormal decrease in
deferred revenue occurs when the actual change falls short of
the predicted change. My
measure is a proxy for the change in deferred revenue relative
to what the expected or
normal value should be given changes in cash flow from
operations and future sales. 16
I use the short-term component and ignore the long-term
component because the latter
does not reflect revenue that should be recognized during the
current period.
Empirical Models
I use pre-managed earnings in lieu of post-managed earnings
because this best
reflects ex-ante behavior. Pre-managed earnings are obtained by
removing the
discretionary component of earnings (Dhaliwal, Gleason, and
Mills 2004; Frank and
16In an attempt to provide some evidence on my model, I formed
deciles based on the level of discretionaryaccruals for all firms
in the Compustat universe for the same time period using a
modified-Jones model andexamined the mean abnormal change in
deferred revenue within these deciles. An interesting feature
ofthis comparison is that deferred revenue is likely to be only a
small proportion of aggregate accruals thatmake up an average firm
and discretionary deferred revenue will move in an opposite
direction toaggregate discretionary accruals, thus any negative
relation between the two will provide comfort that mymodel is
effectively picking up discretionary behavior. Untabulated analyses
reveal that for every decilethe sign of mean abnormal changes in
deferred revenue is opposite to the sign of mean
discretionaryaccruals.
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Rego 2006; among others). To compute pre-managed earnings for
gross accounts
receivable, I subtract the abnormal change in gross accounts
receivable from reported
earnings. To compute pre-managed earnings for deferred revenue,
I add the abnormal
change in deferred revenue to reported earnings. I convert the
abnormal change of the
revenue account to an undeflated amount by multiplying by lagged
total assets and then
scaling by common shares outstanding used to calculate EPS
(Compustat Annual Data
Item #54) in order to adjust I/B/E/S reported earnings per
share. I define a pre-managed
earnings surprise in year t as pre-managed earnings in year t
minus the consensus analyst
forecast of earnings in year t.
17
To test my first two hypotheses, I examine how abnormal changes
in gross
accounts receivable (deferred revenue) are related to instances
where a firms pre-
managed earnings just misses the analysts forecast. I estimate
the regression:
AbnormalGross A/Rt(or AbnormalDef Revt) = 0 + 1* PRE-
MANAGED_JUSTMISSt+ 2* PRE-MANAGED_MEETJUSTBEATt+
1*SIZEt-1 + 2*BMt-1 + t (3)
where AbnormalGross A/R (AbnormalDef Rev ) is the abnormal
change in gross
accounts receivable (deferred revenue). PRE-MANAGED_JUSTMISS is
defined as an
indicator variable equal to 1 if a firm reports a pre-managed
negative earnings surprise in
year t of no more than 0.2% of the end of the prior fiscal years
stock price. PRE-
MANAGED_MEETJUSTBEAT is defined as an indicator variable equal
to 1 if a firm
reports a pre-managed non-negative earnings surprise in year t
of less than 0.2% of the
17I calculate the consensus annual earnings forecast based on
the median of the last individual earningsforecasts made by all
analysts in the 90-day period preceding the end of the fiscal year.
This has theadvantage over using I/B/E/S summary forecasts because
it avoids the stale forecast problem.
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end of the prior fiscal years stock price.18
PRE-MANAGED_MEETJUSTBEAT is used
as a natural reference group since these firms should have no
motives to manage revenue.
These firms were able to achieve the benchmark before discretion
in revenue is
considered. I expect to find a significant and positive
(negative) coefficient on PRE-
MANAGED_JUSTMISS for accounts receivable (deferred revenue). An
F-test is
conducted between PRE-MANAGED_JUSTMISS and PRE-
MANAGED_MEETJUSTBEAT when PRE-MANAGED_JUSTMISS is
significantly
different from zero and PRE-MANAGED_MEETJUSTBEAT is of the same
sign. To
control for systematic differences in abnormal changes in gross
accounts receivable, I
include SIZE, the natural logarithm of a firms beginning of the
year market value of
equity. To control for growth opportunities, I include BM, the
book-to-book ratio.19
The sample related to testing my first hypothesis pertaining to
accounts receivable
is 4,562firm-year observations for fiscal years 2001-2003. My
sample to test my second
hypothesis pertaining to deferred revenue is 1,378 firm-year
observations for fiscal years
2001-2003. Fiscal years before 2001 are not used because
deferred revenue data
coverage in Compustat begins in fiscal year 2000. Fiscal year
2004 is not included in my
final samples because I require cash flow from operations one
year ahead in order to
compute the abnormal change in accounts receivable and I require
sales one year ahead in
order to compute the abnormal change in deferred revenue.
18Any definition of small miss or small beat is arbitrary. My
choice is based on prior research that hasexamined the avoidance of
negative earnings surprises. I use a 0.2% interval width for
earnings surprisesconsistent with Burgstahler and Eames (2006). An
additional advantage of this choice is that it representsthe best
trade-off between the smallest interval width and the most
observations to make reliable statisticalinferences. However, my
results are qualitatively similar using other interval widths, such
as 0.3%.19I winsorize this ratio at the extreme percentiles of its
distribution.
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15
To test my third hypothesis, I require firms to have both
accounts receivable and
deferred revenue. This reduces my sample to 962 firm-year
observations. I then re-
estimate model 3 for this common sample. If my third hypothesis
is correct, I should not
find significance on PRE-MANAGED_JUSTMISS for gross accounts
receivable.
To test my last hypothesis, I estimate the following model:
CAR = 0+ 1*MISS + 2*BEAT + 3*BEAT*A/R + 4*BEAT*D/R + 5*BEAT*BOTH
+
*UE+t (4)
where CAR is the buy-and-hold abnormal return using the CRSP
value-weighted market
index measured in a window extending from one day before the
earnings announcement
date to one day after the release of a firms 10-K to ensure
adequate investor access to
balance sheet information. The intercept in my model represents
firms that meet analyst
expectations. MISS is an indicator variable equal to 1 if the
firm misses the analyst
forecast, BEAT is an indicator variable equal to 1 if the firm
beats the analyst forecast,
BEAT*A/R is an indicator variable equal to 1 if the firm used
accounts receivable to beat
the analyst forecast, BEAT*D/R is an indicator variable equal to
1 if the firm used
deferred revenue to beat the analyst forecast, BEAT*BOTH is an
indicator variable equal
to 1 if a firm used both accounts to beat the analyst forecast,
and UE is the magnitude of
unexpected earnings (I/B/E/S reported earnings less the
consensus forecast deflated by
stock price at the beginning of the returns period).
Koh, Matsumoto and Rajgopal (2006) provide evidence suggesting
that during
and after the scandals period there is no penalty to missing
expectations (see Table 2,
panel A of their paper). Based on Koh et al. (2006), I expect to
find no significance for
MISS as my sample falls primarily into this time period.
Consistent with prior studies
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(e.g., Lopez and Rees 2002), I expect a positive and significant
coefficient on BEAT and
UE. If the a(b)part of my fourth hypothesis is correct, I do (do
not) expect a negative
and significant coefficient on the interaction term BEAT*A/R. I
do not offer
expectations for BEAT*BOTH and BEAT*D/R. For this analysis, I do
not require a
common sample but instead include all firms in both my deferred
revenue and accounts
receivable samples. The sample for this analysis is 3,703
firm-year observations with at
least one of these two accounts and the requisite CRSP data.
4. Results
Descriptive Evidence
Little is known about the deferred revenue account. I begin by
providing some
industry-specific evidence of this account. For fiscal year
2004, I find that 30.4% of
firms reported non-zero short-term deferred revenue.20
Table 1 provides descriptive
information for the 48 Fama-French (FF) industry groups for
fiscal years 2001-2004,
ranked in ascending order by percentage of firms in the industry
with non-zero short-term
deferred revenue (Fama and French 1997). All 48 FF industry
groups have some firms
with short-term deferred revenue on their balance sheets. The
top ten industry groups in
terms of percent of firms with short-term deferred revenue were
Printing and Publishing
(66.67%), Computers (55.56%), Business Services (51.14%),
Measuring and Control
Equipment (45.38%), Telecommunications (45.11%), Pharmaceutical
Products (39.81%),
Personal Services (37.22%), Electronic Equipment (35.65%),
Defense (33.33%), and
Medical Equipment (33.17%). With the exceptions of Printing and
Publishing and
Personal Services, the rest are high technology sectors that
relate to medical or
20Firms with missing assets were excluded. I do not exclude
utilities and financial firms for purposes ofproviding descriptive
evidence in Table 1. A similar proportion is found for earlier
years in my sample.
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17
computer/electronic technology. The bottom ten industry groups
in terms of percent of
firms with short-term deferred revenue were Banking (1.37%),
Textiles (4.17%), Utilities
(5.01%), Shipbuilding, Railroad Equip. (5.26%), Precious Metals
(5.39%), Agriculture
(6.45%), Aircraft (6.84%), Food Products (7.06%), Steel Works,
Etc. (7.25%), and
Rubber and Plastic Products (7.30%).
---------------------------
Insert Table 1 here
----------------------------
Table 2 reports the mean coefficient estimates from estimating
the models for the
normal change in gross accounts receivable and the normal change
in deferred revenue.
T-statistics are computed by dividing the mean of the
distribution across all industry-year
observations for each of the variables in the model by the
standard error of this
distribution.
---------------------------
Insert Table 2 here
----------------------------
The model for gross accounts receivable has an adjusted R-square
of almost 37%.
As expected, there is a significant and positive relationship
between changes in gross
accounts receivable and changes in current sales (coefficient =
0.0979; t-statistic = 14.27)
and future cash flow from operations (coefficient = 0.0573;
t-statistic = 2.92). The model
for deferred revenue has an adjusted R-square of almost 30%.
Also, as expected, there is
a significant and positive relationship between changes in
short-term deferred revenue
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18
and changes in future sales (coefficient = 0.0234; t-statistic =
4.45) and current cash flow
from operations (coefficient = 0.0365; t-statistic = 1.94).
Table 3 provides descriptive statistics for all firms with
either accounts receivable
or deferred revenue for fiscal years 2001-2003. Gross accounts
receivable has a mean of
nearly 291 million dollars and a median of nearly 17.69 million
dollars. The mean
change in gross accounts receivable is approximately 0.1% of
beginning assets. Deferred
revenue has a mean of nearly 40 million dollars and a median of
nearly 2.14 million
dollars. The mean change in deferred revenue is approximately
0.9% of beginning assets.
By definition, the mean abnormal change in short-term deferred
revenue and the mean
abnormal change in gross accounts receivable are zero since I
include a constant term in
my discretionary models.
---------------------------
Insert Table 3 here
----------------------------
Avoidance of Negative Earnings Surprises Results
Table 4 reports the results of OLS regressions examining my
first two hypotheses
related to abnormal changes in gross accounts receivable and
short-term deferred
revenue. Consistent with my first hypothesis, I find that
abnormal changes in gross
accounts receivable are more positive than normal for
PRE-MANAGED_JUSTMISS
(coefficient = 0.0017; t-statistic = 1.65).21
I find a negative and significant coefficient on
PRE-MANAGED_MEETJUSTBEAT. I also find a negative and significant
coefficient
on the book-to-market ratio and an insignificant coefficient on
size. In the second
21All coefficient estimates are presented in decimal form. For
instance, this coefficient translates into anabnormal increase in
gross accounts receivable that was 0.17% of beginning total
assets.
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19
column of Table 4, I also find support for my second hypothesis.
More specifically, there
is a negative and significant coefficient on
PRE-MANAGED_JUSTMISS (-0.0061; t-
statistic = -4.14) for abnormal changes in deferred revenue. I
find an insignificant
coefficient on PRE-MANAGED_MEETJUSTBEAT. In addition, neither
control
variable is significant.22
To provide some evidence on the prevalence of revenue
recognition to avoid negative earnings surprises, I also conduct
an analysis similar to that
of Frank and Rego (2006). I examine the proportion of firms that
use discretion in
revenue accounts to cross over the analyst threshold. I find
that 75.8% (72.7%) of firms
that had pre-managed earnings just missing analyst forecasts
were able to use discretion
in gross accounts receivable (short-term deferred revenue) to
meet or beat the benchmark.
---------------------------
Insert Table 4 here
----------------------------
Do Managers Express a Preference for Revenue Management?
Table 5 provides results related to my third hypothesis.
Consistent with my
expectations, I fail to find significant evidence that firms
with deferred revenue use
discretion in gross accounts receivable (coefficient = 0.0028;
t-statistic = 1.33). I
continue to find a significant and negative coefficient on
abnormal changes in deferred
revenue (coefficient = -0.0052; t-statistic = -3.00). An
alternative explanation could be
that these firms have a smaller stock of receivables than
deferred revenue so these firms
22To the extent that my proxy for discretionary revenue
recognition contains measurement error, acorrelation may be induced
between pre-managed earnings and the abnormal change in revenue
account(Leone and Rock 2002). However, it is unclear why such a
relation would exist for only the PRE-MANAGED_JUSTMISS interval in
relation to the other pre-managed intervals. Nonetheless, I
performtwo additional analyses. In the first, I regress the
abnormal change in revenue account on pre-managedearnings and find
insignificant coefficients for both revenue measures. I also
include PRE-MANAGED_EARNINGS, the magnitude of pre-managed
earnings, in the regressions reported in tables 4-5 to control for
this correlation if it exists. I obtain qualitatively similar
results.
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20
would not find discretion in accounts receivable to be as
economically feasible.
However, I find that these firms actually have a much higher
mean stock of receivables
than deferred revenue by a factor of 10 indicating that such an
alternative explanation is
not plausible.
---------------------------
Insert Table 5 here
----------------------------
Is There a Differential Market Response To Using Discretion in
Revenue to Beat
Analysts Forecasts?
Table 6 provides results pertaining to my final hypothesis. The
a (b)portion of
fourth hypothesis posits that the premium awarded to firms that
beat analysts forecasts
will (will not) be lower when discretion in gross accounts
receivable is used. As
expected, I find a significant and positive coefficient on BEAT
(coefficient = 0.033; t-
statistic = 2.14) and UE (coefficient = 0.134; t-statistic =
1.92). I find support for the b
portion of my fourth hypothesis. The coefficient on BEAT*A/R is
statistically
insignificant (coefficient = -0.011; t-statistic = -0.84). The
coefficients on BEAT*D/R
and BEAT*BOTH are also statistically insignificant. Table 4
suggests that
managements preference for discretion in deferred revenue is not
because accounts
receivable is more transparent, but rather because it is more
costly.23
23A caveat of this analysis is that both abnormal change models
require one year-ahead variables so it ispossible that I may not
find results for this reason. This is particularly problematic for
abnormal changes indeferred revenue where the variable with the
heaviest weight in the predictive model is one-year-ahead.However,
investors should still be able to use contemporaneous realizations
of these variables in lieu ofthose to form assessments. I
re-estimate the returns analysis using a definition of abnormal
changes ingross accounts receivable that does not require
one-year-ahead cash flow changes. I obtain qualitativelysimilar
results using this definition suggesting thatinvestors cannot see
through manipulations of grossaccounts receivable to beat analysts
forecasts.
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21
---------------------------
Insert Table 6 here
----------------------------
5. Conclusions and Implications
I examine whether managers use accounting discretion in two
accounts related to
revenue recognition, short-term deferred revenue and gross
accounts receivable, to avoid
negative earnings surprises. I find that managers accelerate the
recognition of revenue
using both accounts when pre-managed earnings miss the analyst
benchmark by a small
amount. Using a common sample, I find that managers prefer to
exercise discretion in
deferred revenue as opposed to accounts receivable to avoid
negative earnings surprises.
I distinguish between two competing theories that suggest why
deferred revenue would
be the preferred account. I rule out the explanation related to
lower disclosure of deferred
revenue by providing evidence that the premium to beating the
analyst benchmark is not
reduced when discretion in accounts receivable is used. I
conclude that the lower cost of
discretion in deferred revenue is the reason for the preference.
While some allege that
managers are only short-term focused at the expense of long-term
value creation, my
results suggest that managers prefer the revenue recognition
mechanism that has the least
future cash consequences. However, if managers do not have a
choice they will choose a
mechanism that does have future consequences in order to avoid
negative surprises.
Finally, I introduce a discretionary model for deferred revenue
that future researchers can
use when studying deferrals.
I close with some suggestions for future research. One avenue
for future research
is to examine how discretion in deferred revenue is used to
maintain sales momentum
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22
(i.e., a string of sales increases). Livnat (2004) shows that
revenue surprises are related
to post-earnings-announcement drift, so one promising avenue is
to examine how
deferred revenue relates to post-earnings-announcement drift.
Another avenue is to
examine how analysts use changes in deferred revenue to
formulate their revenue
forecasts. Future studies could examine whether and to what
extent managers substitute
management of revenue recognition accounts in lieu of expense
recognition accounts.
Zhang (2005) shows that early revenue recognition for software
firms in the early 1990s
are associated with a lower time-series predictability of
reported revenue. A related issue
that could be examined is whether discretion used to delay
revenue recognition has
greater predictive ability than discretion used to accelerate
revenue recognition.
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23
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Lopez, T., and L. Rees. The Effect of Beating and Missing
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TABLE 1Short-Term Deferred Revenue by Fama-French Industry
Group
FF Industry N % of Firms Mean Median
Banking 52 1.37% 0.8908% 0.0041%Textiles 4 4.17% 0.0004%
0.0005%Utilities 71 5.01% 1.2811% 0.7466%
Shipbuilding, Railroad Equip. 3 5.26% 0.0001% 0.0001%Precious
Metals 18 5.39% 1.5744% 0.4912%Agriculture 6 6.45% 8.4644%
10.9046%Aircraft 8 6.84% 13.6581% 2.1550%Food Products 29 7.06%
0.9494% 0.0003%Steel Works, Etc. 28 7.25% 1.8094% 0.4445%Rubber and
Plastic Products 20 7.30% 3.9312% 0.8864%Fabricated Products 7
8.05% 6.5478% 2.4093%Construction 26 8.36% 2.0928% 0.4549%Apparel
35 9.72% 1.4117% 0.6474%Alcoholic Beverages 11 9.73% 0.6457%
0.0000%Shipping Containers 9 10.11% 7.0398% 0.0820%
Candy and Soda 8 10.13% 1.2758% 0.0975%Construction Materials 52
11.21% 0.6635% 0.2653%Miscellaneous 72 12.37% 3.8251%
0.8868%Automobiles and Trucks 59 13.02% 5.4741% 0.6906%Petroleum
and Natural Gas 158 13.45% 2.0836% 0.2878%Consumer Goods 60 13.51%
5.8827% 1.9024%Business Supplies 41 13.71% 0.8698% 0.7057%Wholesale
148 14.04% 4.0790% 1.4291%Nonmetallic Mining 32 14.55% 0.6888%
0.0000%Trading 250 14.59% 3.1930% 0.1235%Chemicals 86 14.73%
2.5281% 0.3522%Healthcare 72 15.48% 3.8437% 1.4809%
Real Estate 50 15.77% 0.6608% 0.2240%Recreational Products 38
15.97% 5.5731% 2.4691%Electrical Equipment 77 18.69% 3.3509%
1.0171%Transportation 153 19.52% 2.9695% 0.7972%Tobacco Products 9
20.00% 0.000004% 0.000003%Insurance 194 20.25% 2.5555%
0.00003%Machinery 200 22.10% 4.7438% 1.5317%Retail 310 23.27%
2.3083% 1.3028%Coal 11 23.40% 0.4165% 0.4244%Restaurants, Hotel,
Motel 141 24.96% 2.6732% 1.5745%Entertainment 159 29.01% 4.5661%
1.2433%Medical Equipment 342 33.17% 5.7052% 2.1536%
Defense 16 33.33% 2.6914% 1.2218%Electronic Equipment 668 35.65%
3.7270% 1.6831%Personal Services 118 37.22% 18.4702%
9.1641%Pharmaceutical Products 783 39.81% 4.8710%
1.3794%Telecommunications 627 45.11% 3.3730% 1.2645%Measuring and
Control Equip 285 45.38% 43.5412% 1.4289%Business Services 2320
51.14% 13.1616% 6.3803%Computers 715 55.56% 7.9333% 3.9117%Printing
and Publishing 156 66.67% 6.5961% 2.8049%
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27
This table reports the Fama-French industry name, total number
of non-missing and non-zero observations for the ratio of
short-term deferred revenue-to-total assets (CompustatAnnual Data
Item 356 divided by Compustat Annual Data Item 6), percentage of
firms inthat industry with non-missing and non-zero short-term
deferred revenue, as well as themean and median of the ratio of
short-term deferred revenue-to-total assets. I define
industries consistent with Fama and French (1997). I multiply
ratios by 100 to convert topercentages for expositional
purposes.
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TABLE 2Model Parameters for Normal Change Models
Gross A/Rt/ At-1= 0+ 1*(1 / At-1) + 1*(St/ At-1) + 2*(CFOt+1/
At-1) + tDef Revt/ At-1= 0+ 1*(1 / At-1) + 1*(St+1/ At-1) +
2*(CFOt/ At-1) + t
Independent Variables Expected Sign Dependent Variable:Gross
A/Rt/ At-1
Dependent Variable:Def Revt/ At-1
Intercept ? -0.0027**(-2.03)
0.0025(1.20)
1 / At-1 ? -0.0004(-0.01)
0.0162(0.39)
St/ At-1 + 0.0979***(14.27)
N/A
St+1/ At-1 +N/A
0.0234***(4.45)
CFOt/ At-1 + N/A 0.0365*(1.94)
CFOt+1/ At-1 + 0.0573***(2.92)
N/A
Adjusted R-square 36.6% 29.2%
This table provides parameter estimates for the normal change
models of gross accounts
receivable and short-term deferred revenue. I require at least
eight non-missing observationswithin an industry-year for
estimation. To be consistent with prior literature on
earningsmanagement (e.g., Burgstahler and Dichev 1997), I exclude
utilities and financial firms (i.e., SICcodes between 4400 and 5000
and SIC codes between 6000 and 6500). I also exclude any
firmsrelated to public administration (i.e., SIC codes of 9000 or
higher). I winsorize all variables thatenter the models at the top
and bottom percentiles of their respective distributions.
Thecoefficient estimates are based on means of industry-years and
t-statistics are based on thestandard error of those means. The
coefficient estimates for the abnormal change in grossaccounts
receivable model is based on 46 industries and 130 industry-years
over 2001-2003, andthe coefficient estimates for the abnormal
change in deferred revenue is based on 22 industriesand 39
industry-years over 2001-2003. I also report the associated mean of
the adjusted R2sacross these industry-years. The dependent
variables are Gross A/Rt , defined as the change in
gross accounts receivable (change in Compustat Annual Data Item
2 plus Compustat Annual DataItem 67), and Def Revt, defined as the
change in short-term deferred revenue (change inCompustat Annual
Data Item 356). The independent variables include a constant term,
anintercept scaled by lagged total assets, 1/At-1(Compustat Annual
Data Item 6), change in sales foryear t, St(change in Compustat
Annual Data Item 12), change in sales in year t+1, St+1, changein
cash flow from operations during year t, CFOt (change in Compustat
Annual Data Item 308),and change in cash flow from operations
during year t+1, CFOt+1.***, **, and * denote statistical
significance at the 1%, 5% and 10% two-tailed
levels,respectively.
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TABLE 3Descriptive Statistics
Panel A:Firms with Accounts Receivable
Panel B: Firms withDeferred Revenue
This table provides descriptive statistics. All variables are
scaled by lagged total assets,except for book-to-market ratio, and
log of size. Gross A/R is defined as the change ingross accounts
receivable (change in Compustat Annual Data Item 2 plus
CompustatAnnual Data Item 67). AbnormalGross A/R is the abnormal
change in gross accountsreceivable defined using the model in the
text. Deferred revenue is defined as thechange in short-term
deferred revenue (change in Compustat Annual Data Item
356).AbnormalDef Rev is the abnormal change in short-term deferred
revenue defined usingthe model developed in the text. Log (MVE) is
the natural logarithm of a firms size
using beginning of the year market value of equity (Compustat
Annual Data Item 25 Compustat Annual Data Item 199). Book-to-market
is the beginning of the year book-to-market ratio ((Compustat
Annual Data Item 60 + Compustat Annual Data Item 74) /
(Compustat Annual Data Item 25 Compustat Annual Data Item
199)).
Mean Std. Dev. 25% Median 75%
Gross A/R (in $ mil) 290.787 3121.742 3.351 17.693 84.603Gross
A/Rt 0.001 0.085 -0.027 0.000 0.023AbnormalGross A/Rt 0.000 0.063
-0.023 -0.001 0.021Log (MVE)t 4.650 2.522 2.874 4.665
6.394Book-to-Markett 0.609 1.945 0.244 0.548 1.055
Mean Std. Dev. 25% Median 75%
Deferred Revenue (in $ mil) 39.324 284.213 0.269 2.135 11.837Def
Revt 0.009 0.053 -0.003 0.001 0.014AbnormalDef Revt 0.000 0.048
-0.015 -0.002 0.008log (MVE)t 4.856 2.362 3.283 4.958
6.389Book-to-Markett 0.498 0.956 0.192 0.431 0.814
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TABLE 4Abnormal Changes in Gross Accounts Receivable (Deferred
Revenue) to Avoid
Negative Earnings Surprises
AbnormalGross A/Rt(AbnormalDef Revt) = 0 +
1*PRE-MANAGED_JUSTMISSt
+ 2*PRE-MANAGED_MEETJUSTBEATt+ 1*SIZEt-1 + 2*BMt-1 + t
This table provides regression results for my first and second
hypotheses using theabnormal change in gross accounts receivable
and short-term deferred revenue as themeasures of revenue
management. The primary independent variable is
PRE-MANAGED_JUSTMISS corresponding to the range in which a firm
just misses analystsforecasts using a distribution based on
pre-managed earnings. PRE-MANAGED_MEETJUSTBEATis included as a
natural reference group, in which Iconduct an F-test between this
coefficient and that of the primary variable. The controlvariables
are SIZE, defined as the natural logarithm of a firms size using
beginning ofthe year market value of equity and BM, defined as the
beginning of the year book-to-market ratio. T-statistics are
reported in parentheses under the coefficient estimates
based on the Newey-West standard error correction for
autocorrelation andheteroskedasticity (Newey and West 1987).
Coefficient estimates are reported in decimalform.***, ** and *
denote statistical significance at the 1%, 5% and 10% two-tailed
levels,respectively (except for F-tests which are based on
one-tailed significance levels).
AbnormalGross A/R AbnormalDef Rev
0 0.0062*(1.82)
-0.0003(-0.06)
1 0.0017*(1.65)
-0.0061***(-4.14)
2 -0.0055***
(-5.62)
-0.0019
(-1.13)
1 -0.0003(-0.71)
0.0004(0.56)
2 -0.0028**(-2.56)
-0.0020(-1.00)
F-test:
1 = 2 N/A 10.68***
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TABLE 5Abnormal Changes in Gross Accounts Receivable (Deferred
Revenue) to Avoid
Negative Earnings Surprises Using a Common Sample
AbnormalGross A/Rt(AbnormalDef Revt) = 0 +
1*PRE-MANAGED_JUSTMISSt
+ 2*PRE-MANAGED_MEETJUSTBEATt+ 1*SIZEt-1 + 2*BMt-1 + t
This table provides regression results for my third hypothesis
using a common samplewith accounts receivable and short-term
deferred revenue. The primary independentvariable is
PRE-MANAGED_JUSTMISS corresponding to the range in which a firm
justmisses analysts forecasts using a distribution based on
pre-managed earnings. PRE-MANAGED_MEETJUSTBEAT is included as a
natural reference group, in which Iconduct an F-test between this
coefficient and that of the primary variable. The controlvariables
are SIZE, defined as the natural logarithm of a firms size using
beginning ofthe year market value of equity and BM, defined as the
beginning of the year book-to-market ratio. T-statistics are
reported in parentheses under the coefficient estimatesbased on the
Newey-West standard error correction for autocorrelation and
heteroskedasticity (Newey and West 1987). Coefficient estimates
are reported in decimalform.***, ** and * denote statistical
significance at the 1%, 5% and 10% two-tailed levels,respectively
(except for F-tests which are based on one-tailed significance
levels).
AbnormalGross A/R AbnormalDef Rev
0 -0.0034(-0.47)
-0.0028(-0.48)
1 0.0028(1.33)
-0.0052***(-3.00)
2 -0.0025
(-1.16)
-0.0016
(-0.74)
1 0.0006(0.65)
0.0006(0.87)
2 -0.0002(-0.07)
-0.0019(-0.78)
F-test:
1 = 2 N/A 5.28**
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TABLE 6Differential Premium to Using Deferred Revenue or
Accounts Receivable to Beat
Analysts Forecasts?
CAR = 0+ 1*MISS + 2*BEAT + 3*BEAT*A/R + 4*BEAT*D/R +
5*BEAT*BOTH + *UE+t
0 1 2 3 4 5
coefficientestimate(t-statistic)
0.013(1.01)
-0.001(-0.06)
0.033**(2.14)
-0.011(-0.84)
-0.012(-0.46)
-0.023(-0.57)
0.134*(1.92)
Adjusted R2 0.25%
This table provides regression results for my fourth hypothesis.
CAR is the buy-and-hold
abnormal return using the CRSP value-weighted market index
measured in a windowextending from one day before the earnings
announcement date to one day after therelease of a firms 10-K to
ensure adequate investor access to balance sheet information.MISS
is an indicator variable equal to 1 if the firm misses analysts
forecasts, BEAT is anindicator variable equal to 1 if the firm
beats analysts forecasts, BEAT*A/R is anindicator variable equal to
1 if the firm used accounts receivable to beat analystsforecasts,
BEAT*D/R is an indicator variable equal to 1 if the firm used
deferred revenueto beat analysts forecasts, BEAT*BOTH is an
indicator variable equal to 1 if a firm usedboth accounts to beat
analysts forecasts, and UE is the magnitude of unexpected
earnings(I/B/E/S reported earnings less the consensus forecast
deflated by stock price at thebeginning of the returns period).
***, **, and * denote statistical significance at the 1%, 5% and
10% two-tailed levels,respectively.
