Strategic Revenue Recognition to Avoid Negative Earnings
Surprises∗
Marcus L. Caylor
University of South Carolina
Moore School of Business
1705 College Street
Columbia, SC 29208
Email: marcus.caylor@moore.sc.edu
Office: 803-777-6081
Fax: 803-777-0712
∗
This study is based in part on my doctoral dissertation at Georgia State University. I am grateful for
helpful 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 of
Ashiq Ali, Tony Chen, Bill Cready, Robert Freeman, Artur Hugon, Scott Jackson, Ross Jennings, Steve
Kachelmeier, Bill Kinney, Krishna Kumar, Yen Lee, Andrew Leone, Tom Lopez, Arianna Pinello, Suresh
Radhakrishnan, Galen Sevcik, Scott Vandevelde, Rich White and workshop participants at the 2006
American Accounting Association Annual Meetings, Georgia State University, the University of South
Carolina, the University of Texas at Austin and the University of Texas at Dallas. I am grateful to
Thomson Financial/I/B/E/S for providing data on analysts' earnings forecasts.
Electronic copy of this paper is available at: http://ssrn.com/abstract=885368
Strategic Revenue Recognition to Avoid Negative Earnings
Surprises
Abstract
I examine whether managers use discretion in two accounts related to revenue
recognition, accounts receivable and deferred revenue, to avoid negative earnings
surprises. I find that managers use discretion in both accounts to avoid negative earnings
surprises. For a common sample of firms with both deferred revenue and accounts
receivable, I show that managers prefer to exercise discretion in deferred revenue vis-àvis accounts receivable. I distinguish between two theories for why managers prefer to
manage 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 not
an explanation. My results suggest that if given the choice, managers prefer to use
accounts 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.
1
Electronic copy of this paper is available at: http://ssrn.com/abstract=885368
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 firm’s 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
1
Deferred revenue goes by several other names including advances from customers, unearned revenue and
revenue received in advance. Surprisingly, little research has examined the deferred revenue account. The
only study that directly examines this account is Bauman (2000). Using a sample of 22 firms from the
publishing industry, he finds that sales increases in the current year do not persist into the future unless
accompanied by increases in deferred revenue, suggesting that deferred revenues are a leading indicator of
future earnings.
2
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.
2
I use the short-term deferred revenue component and ignore the long-term component because the longterm component of deferred revenue does not reflect revenue that should have been recognized during the
current period.
3
I use gross accounts receivable in lieu of net accounts receivable because abnormal changes in net
accounts receivable could reflect changes in the allowance for bad debt.
3
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.
4
In a contemporaneous study, Stubben (2007) also finds that accounts receivable are used to exceed the
analyst benchmark. However, his study does not examine deferred revenue nor does it examine whether a
preference exists.
4
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
5
Marquardt and Weidman (2004) find that managers do not exercise discretion in gross accounts
receivable to avoid an earnings decrease. Roychowdhury (2006) finds no significant evidence that gross
accounts receivable are managed to avoid a loss. In untabulated analyses, I find similar results to both
studies using gross accounts receivable and deferred revenue. For this analysis, I use the same
methodology as discussed below and interval widths of 0.5% and 0.25% for the avoidance of loss and
avoidance of earnings decreases benchmarks, respectively (Burgstahler and Dichev 1997).
5
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:
HYPOTHESIS 1: Firms with pre-managed earnings that just miss the analyst
benchmark have an abnormal increase in gross accounts receivable.
HYPOTHESIS 2: 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 shortterm 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
6
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-à-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
6
This can be managed through subjective estimates of how much revenue has been earned, but only apply
to a very few industries where long-term construction projects exist.
7
While managers can use real activities manipulation to determine when a credit sale is recorded, with
deferred revenue the use of real activities manipulation is less likely. For instance, it is unlikely that
managers would withhold services to customers to reduce the amount of revenue recognized. Deferred
revenue could be manipulated through altering contract terms, however, because a customer has to agree to
the new terms, such an action is less likely to occur.
7
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:
HYPOTHESIS 3: 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 management’s
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:
HYPOTHESIS 4a: The premium to beating is lower for firms that use discretion in
gross accounts receivable.
HYPOTHESIS 4b: The premium to beating is not lower for firms that use discretion
in gross accounts receivable.
8
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 period’s sales, as accounts receivable are included in this period’s
sales.8 I also assume that gross accounts receivable are some proportion of next period’s
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 period’s sales or next period’s cash flow from operations than
8
This assumption is adopted from Dechow, Kothari, and Watts (1998).
I find that changes in gross accounts receivable are significantly and positively correlated with both
changes in current period’s sales and changes in future period’s cash flow from operations using both
Pearson and Spearman correlations.
9
9
expected, more accounts receivable may 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/R t / A t-1= α0 + α1*(1/ At-1) + β1*(∆St / A t-1) + β2*(∆CFOt+1 / A t-1) + ε t
(1)
where:
∆ Gross A/R t = 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
A t-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”
10
The opposite implies that less accounts receivable may have been recorded than expected.
I estimate at the 2-digit level to be consistent with prior literature. I also winsorize all variables entering
both of my discretionary models at the extreme (1st and 99th) percentiles of their respective distributions to
be consistent with prior literature. In addition, I require at least eight industry-year observations to estimate
the model. Roychowdhury (2006) uses a similar model.
12
Kothari, Leone, and Wasley (2005) include both a scaled and unscaled intercept term in their
discretionary accrual models.
11
10
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 shortterm deferred revenue is a proportion of next period’s 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 period’s cash flow from operations, because deferred revenue in
the current period is reflected in the current period’s 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 period’s
cash flow from operations or next period’s 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= α0 + α1*(1/ At-1) + β1*(∆St+1/ At-1) + β2*(∆CFOt / At-1) + ε t
(2)
where:
13
I find that changes in short-term deferred revenue are significantly and positively correlated with changes
in future sales and changes in current period’s cash flow from operations using both Pearson and Spearman
correlations.
14
I require at least eight industry-year observations to estimate this model similar to the constraint for the
gross accounts receivable model.
15
I chose a cross-sectional industry discretionary model in lieu of a firm-specific model due to data
restrictions (i.e., a small time-series of data).
11
∆Def Rev t = 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
A t-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
16
In an attempt to provide some evidence on my model, I formed deciles based on the level of discretionary
accruals for all firms in the Compustat universe for the same time period using a modified-Jones model and
examined the mean abnormal change in deferred revenue within these deciles. An interesting feature of
this comparison is that deferred revenue is likely to be only a small proportion of aggregate accruals that
make up an average firm and discretionary deferred revenue will move in an opposite direction to
aggregate discretionary accruals, thus any negative relation between the two will provide comfort that my
model is effectively picking up discretionary behavior. Untabulated analyses reveal that for every decile
the sign of mean abnormal changes in deferred revenue is opposite to the sign of mean discretionary
accruals.
12
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 firm’s premanaged earnings just misses the analyst’s forecast. I estimate the regression:
Abnormal∆Gross A/Rt (or Abnormal∆Def Revt) = α0 + α1* PREMANAGED_JUSTMISS t + α2* PRE-MANAGED_MEETJUSTBEAT t +
β1*SIZEt-1 + β2*BMt-1 + εt
(3)
where Abnormal∆ Gross A/R (Abnormal∆Def 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 year’s stock price. PREMANAGED_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
17
I calculate the consensus annual earnings forecast based on the median of the last individual earnings
forecasts made by all analysts in the 90-day period preceding the end of the fiscal year. This has the
advantage over using I/B/E/S summary forecasts because it avoids the stale forecast problem.
13
end of the prior fiscal year’s 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 PREMANAGED_JUSTMISS for accounts receivable (deferred revenue). An F-test is
conducted between PRE-MANAGED_JUSTMISS and PREMANAGED_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 firm’s 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 4,562 firm-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.
18
Any definition of small miss or small beat is arbitrary. My choice is based on prior research that has
examined the avoidance of negative earnings surprises. I use a 0.2% interval width for earnings surprises
consistent with Burgstahler and Eames (2006). An additional advantage of this choice is that it represents
the best trade-off between the smallest interval width and the most observations to make reliable statistical
inferences. However, my results are qualitatively similar using other interval widths, such as 0.3%.
19
I winsorize this ratio at the extreme percentiles of its distribution.
14
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 reestimate 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 = α0 + α1*MISS + α2*BEAT + α3*BEAT*A/R + α4*BEAT*D/R + α5*BEAT*BOTH +
β*UE + εt
(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 firm’s 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
15
(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
20
Firms with missing assets were excluded. I do not exclude utilities and financial firms for purposes of
providing descriptive evidence in Table 1. A similar proportion is found for earlier years in my sample.
16
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
17
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
21
All coefficient estimates are presented in decimal form. For instance, this coefficient translates into an
abnormal increase in gross accounts receivable that was 0.17% of beginning total assets.
18
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; tstatistic = -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
22
To the extent that my proxy for discretionary revenue recognition contains measurement error, a
correlation 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 PREMANAGED_JUSTMISS interval in relation to the other pre-managed intervals. Nonetheless, I perform
two additional analyses. In the first, I regress the abnormal change in revenue account on pre-managed
earnings and find insignificant coefficients for both revenue measures. I also include PREMANAGED_EARNINGS, the magnitude of pre-managed earnings, in the regressions reported in tables 45 to control for this correlation if it exists. I obtain qualitatively similar results.
19
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; tstatistic = 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
management’s preference for discretion in deferred revenue is not because accounts
receivable is more transparent, but rather because it is more costly.23
23
A caveat of this analysis is that both abnormal change models require one year-ahead variables so it is
possible that I may not find results for this reason. This is particularly problematic for abnormal changes in
deferred 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 of
those to form assessments. I re-estimate the returns analysis using a definition of abnormal changes in
gross accounts receivable that does not require one-year-ahead cash flow changes. I obtain qualitatively
similar results using this definition suggesting that investors cannot see through manipulations of gross
accounts receivable to beat analysts’ forecasts.
20
--------------------------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
21
(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 1990’s
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.
22
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25
TABLE 1 Short-Term Deferred Revenue by Fama-French Industry Group
FF Industry
Banking
Textiles
Utilities
Shipbuilding, Railroad Equip.
Precious Metals
Agriculture
Aircraft
Food Products
Steel Works, Etc.
Rubber and Plastic Products
Fabricated Products
Construction
Apparel
Alcoholic Beverages
Shipping Containers
Candy and Soda
Construction Materials
Miscellaneous
Automobiles and Trucks
Petroleum and Natural Gas
Consumer Goods
Business Supplies
Wholesale
Nonmetallic Mining
Trading
Chemicals
Healthcare
Real Estate
Recreational Products
Electrical Equipment
Transportation
Tobacco Products
Insurance
Machinery
Retail
Coal
Restaurants, Hotel, Motel
Entertainment
Medical Equipment
Defense
Electronic Equipment
Personal Services
Pharmaceutical Products
Telecommunications
Measuring and Control Equip
Business Services
Computers
Printing and Publishing
N
% of Firms
Mean
Median
52
4
71
3
18
6
8
29
28
20
7
26
35
11
9
8
52
72
59
158
60
41
148
32
250
86
72
50
38
77
153
9
194
200
310
11
141
159
342
16
668
118
783
627
285
2320
715
156
1.37%
4.17%
5.01%
5.26%
5.39%
6.45%
6.84%
7.06%
7.25%
7.30%
8.05%
8.36%
9.72%
9.73%
10.11%
10.13%
11.21%
12.37%
13.02%
13.45%
13.51%
13.71%
14.04%
14.55%
14.59%
14.73%
15.48%
15.77%
15.97%
18.69%
19.52%
20.00%
20.25%
22.10%
23.27%
23.40%
24.96%
29.01%
33.17%
33.33%
35.65%
37.22%
39.81%
45.11%
45.38%
51.14%
55.56%
66.67%
0.8908%
0.0004%
1.2811%
0.0001%
1.5744%
8.4644%
13.6581%
0.9494%
1.8094%
3.9312%
6.5478%
2.0928%
1.4117%
0.6457%
7.0398%
1.2758%
0.6635%
3.8251%
5.4741%
2.0836%
5.8827%
0.8698%
4.0790%
0.6888%
3.1930%
2.5281%
3.8437%
0.6608%
5.5731%
3.3509%
2.9695%
0.000004%
2.5555%
4.7438%
2.3083%
0.4165%
2.6732%
4.5661%
5.7052%
2.6914%
3.7270%
18.4702%
4.8710%
3.3730%
43.5412%
13.1616%
7.9333%
6.5961%
0.0041%
0.0005%
0.7466%
0.0001%
0.4912%
10.9046%
2.1550%
0.0003%
0.4445%
0.8864%
2.4093%
0.4549%
0.6474%
0.0000%
0.0820%
0.0975%
0.2653%
0.8868%
0.6906%
0.2878%
1.9024%
0.7057%
1.4291%
0.0000%
0.1235%
0.3522%
1.4809%
0.2240%
2.4691%
1.0171%
0.7972%
0.000003%
0.00003%
1.5317%
1.3028%
0.4244%
1.5745%
1.2433%
2.1536%
1.2218%
1.6831%
9.1641%
1.3794%
1.2645%
1.4289%
6.3803%
3.9117%
2.8049%
26
This table reports the Fama-French industry name, total number of non-missing and nonzero observations for the ratio of short-term deferred revenue-to-total assets (Compustat
Annual Data Item 356 divided by Compustat Annual Data Item 6), percentage of firms in
that industry with non-missing and non-zero short-term deferred revenue, as well as the
mean 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 to
percentages for expositional purposes.
27
TABLE 2 Model Parameters for Normal Change Models
∆Gross A/R t / A t-1= α0 + α1*(1 / A t-1) + β1*(∆St / A t-1) + β2*(∆CFOt+1 / A t-1) + ε t
∆Def Rev t / A t-1= α0 + α1*(1 / A t-1) + β1*(∆St+1 / A t-1) + β2*(∆CFOt / A t-1) + ε t
Independent Variables
Expected Sign
Dependent Variable:
∆Gross A/R t / A t-1
-0.0027**
(-2.03)
Dependent Variable:
∆Def Rev t / A t-1
0.0025
(1.20)
Intercept
?
1 / A t-1
?
-0.0004
(-0.01)
0.0162
(0.39)
∆St / A t-1
+
0.0979***
(14.27)
N/A
∆St+1 / A t-1
+
N/A
0.0234***
(4.45)
∆CFOt / A t-1
+
N/A
0.0365*
(1.94)
∆CFOt+1 / A t-1
+
0.0573***
(2.92)
N/A
36.6%
29.2%
Adjusted R-square
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 observations
within an industry-year for estimation. 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). I winsorize all variables that
enter the models at the top and bottom percentiles of their respective distributions. The
coefficient estimates are based on means of industry-years and t-statistics are based on the
standard error of those means. The coefficient estimates for the abnormal change in gross
accounts receivable model is based on 46 industries and 130 industry-years over 2001-2003, and
the coefficient estimates for the abnormal change in deferred revenue is based on 22 industries
and 39 industry-years over 2001-2003. I also report the associated mean of the adjusted R2s
across these industry-years. The dependent variables are ∆Gross A/R t , defined as the change in
gross accounts receivable (change in Compustat Annual Data Item 2 plus Compustat Annual Data
Item 67), and ∆Def Rev t, defined as the change in short-term deferred revenue (change in
Compustat Annual Data Item 356). The independent variables include a constant term, an
intercept scaled by lagged total assets, 1/A t-1 (Compustat Annual Data Item 6), change in sales for
year t, ∆St (change in Compustat Annual Data Item 12), change in sales in year t+1, ∆St+1, change
in 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.
28
TABLE 3
Descriptive Statistics
Panel A: Firms with Accounts Receivable
Gross A/R (in $ mil)
∆Gross A/Rt
Abnormal∆Gross A/Rt
Log (MVE)t
Book-to-Markett
Mean
290.787
0.001
0.000
4.650
0.609
Std. Dev.
3121.742
0.085
0.063
2.522
1.945
25%
3.351
-0.027
-0.023
2.874
0.244
Median
17.693
0.000
-0.001
4.665
0.548
75%
84.603
0.023
0.021
6.394
1.055
Std. Dev.
284.213
0.053
0.048
2.362
0.956
25%
0.269
-0.003
-0.015
3.283
0.192
Median
2.135
0.001
-0.002
4.958
0.431
75%
11.837
0.014
0.008
6.389
0.814
Panel B: Firms with Deferred Revenue
Deferred Revenue (in $ mil)
∆Def Revt
Abnormal∆Def Revt
log (MVE)t
Book-to-Markett
Mean
39.324
0.009
0.000
4.856
0.498
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 in
gross accounts receivable (change in Compustat Annual Data Item 2 plus Compustat
Annual Data Item 67). Abnormal∆Gross A/R is the abnormal change in gross accounts
receivable defined using the model in the text. ∆Deferred revenue is defined as the
change in short-term deferred revenue (change in Compustat Annual Data Item 356).
Abnormal∆Def Rev is the abnormal change in short-term deferred revenue defined using
the model developed in the text. Log (MVE) is the natural logarithm of a firm’s 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-tomarket ratio ((Compustat Annual Data Item 60 + Compustat Annual Data Item 74) /
(Compustat Annual Data Item 25 × Compustat Annual Data Item 199)).
29
TABLE 4
Abnormal Changes in Gross Accounts Receivable (Deferred Revenue) to Avoid
Negative Earnings Surprises
Abnormal∆Gross A/Rt (Abnormal∆Def Revt ) = α0 + α1*PRE-MANAGED_JUSTMISS t
+ α2*PRE-MANAGED_MEETJUSTBEAT t + β1*SIZEt-1 + β2*BMt-1 + εt
α0
Abnormal∆Gross A/R
0.0062*
(1.82)
Abnormal∆Def Rev
-0.0003
(-0.06)
α1
0.0017*
(1.65)
-0.0061***
(-4.14)
α2
-0.0055***
(-5.62)
-0.0019
(-1.13)
β1
-0.0003
(-0.71)
0.0004
(0.56)
β2
-0.0028**
(-2.56)
-0.0020
(-1.00)
N/A
10.68***
F-test:
α1 = α2
This table provides regression results for my first and second hypotheses using the
abnormal change in gross accounts receivable and short-term deferred revenue as the
measures of revenue management. The primary independent variable is PREMANAGED_JUSTMISS corresponding to the range in which a firm just misses analysts’
forecasts using a distribution based on pre-managed earnings. PREMANAGED_MEETJUSTBEAT is included as a natural reference group, in which I
conduct an F-test between this coefficient and that of the primary variable. The control
variables are SIZE, defined as the natural logarithm of a firm’s size using beginning of
the year market value of equity and BM, defined as the beginning of the year book-tomarket ratio. T-statistics are reported in parentheses under the coefficient estimates
based on the Newey-West standard error correction for autocorrelation and
heteroskedasticity (Newey and West 1987). Coefficient estimates are reported in decimal
form.
***, ** 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).
30
TABLE 5
Abnormal Changes in Gross Accounts Receivable (Deferred Revenue) to Avoid
Negative Earnings Surprises Using a Common Sample
Abnormal∆Gross A/Rt (Abnormal∆Def Revt ) = α0 + α1*PRE-MANAGED_JUSTMISS t
+ α2*PRE-MANAGED_MEETJUSTBEAT t + β1*SIZEt-1 + β2*BMt-1 + εt
α0
Abnormal∆Gross A/R
-0.0034
(-0.47)
Abnormal∆Def Rev
-0.0028
(-0.48)
α1
0.0028
(1.33)
-0.0052***
(-3.00)
α2
-0.0025
(-1.16)
-0.0016
(-0.74)
β1
0.0006
(0.65)
0.0006
(0.87)
β2
-0.0002
(-0.07)
-0.0019
(-0.78)
N/A
5.28**
F-test:
α1 = α2
This table provides regression results for my third hypothesis using a common sample
with accounts receivable and short-term deferred revenue. The primary independent
variable is PRE-MANAGED_JUSTMISS corresponding to the range in which a firm just
misses analysts’ forecasts using a distribution based on pre-managed earnings. PREMANAGED_MEETJUSTBEAT is included as a natural reference group, in which I
conduct an F-test between this coefficient and that of the primary variable. The control
variables are SIZE, defined as the natural logarithm of a firm’s size using beginning of
the year market value of equity and BM, defined as the beginning of the year book-tomarket ratio. T-statistics are reported in parentheses under the coefficient estimates
based on the Newey-West standard error correction for autocorrelation and
heteroskedasticity (Newey and West 1987). Coefficient estimates are reported in decimal
form.
***, ** 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).
31
TABLE 6
Differential Premium to Using Deferred Revenue or Accounts Receivable to Beat
Analysts’ Forecasts?
CAR = α0 + α1*MISS + α2*BEAT + α3*BEAT*A/R + α4*BEAT*D/R +
α5*BEAT*BOTH + β*UE + εt
coefficient
estimate
(t-statistic)
Adjusted R2
α0
0.013
(1.01)
α1
-0.001
(-0.06)
α2
0.033**
(2.14)
α3
-0.011
(-0.84)
α4
-0.012
(-0.46)
β
0.134*
(1.92)
α5
-0.023
(-0.57)
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 window
extending from one day before the earnings announcement date to one day after the
release of a firm’s 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 an
indicator variable equal to 1 if the firm beats analysts’ forecasts, BEAT*A/R is an
indicator variable equal to 1 if the firm used accounts receivable to beat analysts’
forecasts, BEAT*D/R is an indicator variable equal to 1 if the firm used deferred revenue
to beat analysts’ forecasts, BEAT*BOTH is an indicator variable equal to 1 if a firm used
both 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 the
beginning of the returns period).
***, **, and * denote statistical significance at the 1%, 5% and 10% two-tailed levels,
respectively.
32