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Master thesis Department Accountancy,
Faculty of Economics and Business Studies,
Tilburg University
Revenue recognition: determinants of
the accounts receivable and the deferred
revenue account
Arthur Schothuis BSc
Administration number: 672053
Master Accountancy and Management Control
Supervisor Tilburg University: Dr. A. Yim
Second Reader: Dr. Y. Zeng
Internship: KPMG accountants, Den Bosch
Internship supervisor: Drs. C.G.W. Laureijssen RA
Date of completion: August 2010
REVENUE RECOGNITION: DETERMINANTS OF THE
ACCOUNTS RECEIVABLE AND THE DEFERRED REVENUE
ACCOUNT
BY ARTHUR SCHOTHUIS*
(August 2010)
Abstract: This study investigates how opportunistic behaviour, as well as some other
determinants, might have an impact on the normal changes in the deferred revenue account
and the accounts receivable. I focus on opportunistic behavior arising from cash deficiency or
more generally from financial constraints. From previous literature it is stated that the
importance of revenue recognition is big for managers, standard setters, investors and
auditors. The amount as well as the timing of revenue recognition is important, while it is a
fact that some managers behave opportunistic regarding this recognition. Also in this study is
tried to explain why it is important to know what the influences of opportunistic behavior are.
Previous literature has shown that if a firm is cash or financial deficient there is a
tendency to influence the numbers in the financial statements. A sample of firms that have
US-GAAP as accounting standard are being used in this thesis. I create ten regression models
in which I also control for risk factors that were omitted in prior research. The results imply
that behaving opportunistic, measured in characteristics of cash deficient firms, leads to a
negative extent of changes of the accounts receivable. With respect to the deferred revenue
account, cash deficiency leads to a positive extent of changes of the deferred revenue account.
Keywords: Revenue recognition, accounts receivable, deferred revenue, cash
deficiency.
Data Availability: All data are available from public sources.
* Arthur Schothuis is a Master’s student at Department of Accountancy, Faculty of
Economics and Business Administration, Tilburg University. Email: [email protected].
Acknowledgements
While writing this thesis I got help and support of several people. First, I want to thank
Dr. Andrew Yim, who supervised me at Tilburg University, for his supervision and advices. I
appreciate his very quick responses on my questions and the inspiration he gave me. Second, I
want to thank Drs. Corneel Laureijssen, who supervised me during the period of the
internship at KPMG accountants Den Bosch, for his help and taking care of me. And also for
the nice time I had when doing the internship. Third, I want to thank my parents, brothers and
friends who had always been supporting me and keeping their faith in me.
Tilburg/Den Bosch, August 2010
Arthur Schothuis
Table of contents
Abstract
Acknowledgements
Table of contents
Section 1 Introduction
Page 1
1.1
Motive
Page 1
1.2
Problem definition
Page 1
1.3
Research method
Page 2
1.4
Goal of the thesis
Page 3
1.5
Reminder of the thesis
Page 4
Section 2 Literature Review and Hypothesis Formulation
Page 5
2.1
Definition revenue recognition
2.2
Economic event occurring and the timing of recognition Page 6
2.3
The importance of a good revenue recognition system
Page 7
2.4
Revenue recognition and earnings management
Page 9
2.5
Formulating hypotheses
Page 11
Section 3 Regression Models and Sample Construction
Page 5
Page 13
3.1
Research method
Page 13
3.2
Data collection
Page 19
Section 4 Results
Page 20
4.1
Descriptive statistics
Page 20
4.2
Results
Page 20
4.3
Comparison with Caylor’s (2009) model
Page 23
Section 5 Discussion
Page 24
5.1
Conclusions and implications
Page 24
5.2
Limitations
Page 28
5.3
Possibilities for follow-up research
Page 28
References
Page 30
Appendix
Page 34
REVENUE RECOGNITION: DETERMINANTS OF THE
ACCOUNTS RECEIVABLE AND THE DEFERRED REVENUE
ACCOUNT
Section 1 Introduction
1.1 Motive
Revenue is usually the largest single item in financial statements, and the issues
involving revenue recognition are among the most important and difficult ones that standard
setters and accountants face. In recent years, concerns related to the recognition of revenue in
accordance with accounting standards have heightened significantly. Quite often, companies
end up tweaking the revenue numbers.
An informed market recognizes the effects of economic events when they occur, but
revenue recognition must await compliance with formal accounting recognition criteria
(Warfield and Wild, 1992). The cause of this lag is a function of cross-sectional differences in
the application of accounting recognition criteria.
If the revenue recognition rules are not defined clearly some forms of earnings
management can appear and also the lag causes that the true economic substance is not
presented well. An example of revenue recognition rules can be found in (the differences
between) the IFRS and US-GAAP accounting standard. Because of the different revenue
recognition rules, the level of earnings management as well as the size of the lag is different.
There is some criticism on the IFRS as well as on the US-GAAP rules that these are not
sufficient and that these need to be revised. (Schipper et al., 2009, Sunder, 2009 and
Wustemann and Kierzek, 2005)
Healy and Wahlen (1999) investigated that accounting standards determine the value
of the financial statements. Leuz (2003) claim that increasing the level or precision of
disclosure should reduce the likelihood of information asymmetries between investors and
increase market liquidity. Investors add value to the disclosures that a company provides. So
differences in revenue recognition rules can lead to different value of reported performances
to the users of the financial statements.
1.2 Problem definition
Despite the accounting standards, some managers still see some opportunities to act
opportunistic. Burgstahler and Dichev (1997) show that managers try to influence profit
numbers to avoid that a lower profit or a loss is presented. The reason why managers want to
1
avoid this, is because firms with a consistent pattern of earnings increases command higher
price-to-earnings multiples. Also firms breaking a pattern of consistent earnings growth
experience an average of 14% negative abnormal stock return in the year the pattern is
broken. Burgstahler and Dichev (1997) have found evidence that two components of earnings:
cash flow from operations and changes in working capital, are used to achieve increases in
earnings. Because the changes in accounts receivable and deferred revenue are part of the
changes in working capital, it is interesting to investigate what the influence of opportunistic
behavior is on how those accounts are built.
Prakash and Sinha (2009) have found that small changes in the deferred revenue
liability can have a disproportionately large impact on future profitability. While Marquardt
and Wiedman (2004) have found that firms issuing equity appear to prefer to manage earnings
upward by lifting up accounts receivable to accelerate revenue recognition.
The main question that is being asked through the thesis is: What are the main
determinants of the accounts receivable and the deferred revenue account?
Previous literature (Chevalier and Scharfstein, 1996 and Fazzari and Petersen, 1993)
has shown that cash or financial constraint firms have greater incentive to cut prices to get
short-run profits. Also constrained firms will draw working capital down during low cashflow periods and accumulate it during high cash-flow periods. Therefore when a firm is cash
constraint, there is a tendency to act opportunistic.
The first hypothesis is that behaving opportunistic, measured in terms of
characteristics of cash deficient firms, leads to a negative extent of changes of the accounts
receivable between years.
With respect to the deferred revenue account, the second hypothesis is that behaving
opportunistic, measured in terms of characteristics of cash deficient firms, leads to a positive
extent of changes of the deferred revenue account between years.
1.3 Research method
The method of recognizing accounts receivable and deferred revenue is taken from
Caylor (2009), this method is a combination between the methods distracted from Dechow et
al. (1998) and Kothari et al. (2005). However Caylor did not include some risk factors and the
opportunistic behavior variable in his model. Therefore 47 Fama-French industry dummies
are included as controls for differences between industries, for both models. Also I include
some year variable dummies to control for changes in accounting rules between the years of
my investigation. Furthermore, I included some control variables, based on Richardson et al.
2
(2005) to control for changes of other accruals and its components. Finally I include a dummy
variable for opportunistic behavior in my model to see how opportunistic behavior is
influencing the change in accounts receivable and deferred revenue and a control variable or
the opportunistic behavior variable times the sales in a certain period.
The model claims that changes in gross accounts receivable depend on total assets,
changes in sales and changes in cash flows from operations. Changes in deferred revenue
depends on the same financial numbers, but then from different time periods. Both models are
controlled for the mentioned risk factors and the opportunistic behavior dummy variable.
To be consistent with Caylor (2009), I investigated US-GAAP firms in the years 20012005.
1.4 Goal of the thesis
This study can contribute to existing literature by giving the building blocks of how
accounts receivable and deferred revenue are recognized, given that some managers act
opportunistic regarding those accounts. Because I have tried to control for industry, year and
accruals, those risk factors are captured as well. While theory has shown that there are some
differences between those determinants, for example differences between industries, the
practical, real numbers of how these accounts are built has not been investigated so far. It is
important to know what the differences are because decisions of investors can be influenced
by differences in those determinants. It can be useful to see what the influence of behaving
opportunistic is on the measurement of those accounts. A financial number regarding the
accounts receivable and the deferred revenue account can therefore be better interpreted. This
thesis shows if the assumptions that Caylor (2009) make are correct under my model: is the
accounts receivable indeed influenced, or influenced in the same way by the factors he
mentioned, or do the factors that I include play a bigger role. It also answers the question:
does the introduction of more variables in recognizing the accounts receivable and deferred
revenue account lead to a more significant model.
I expect that this investigation will confirm my hypothesis regarding the recognition of
gross accounts receivable and deferred revenue. This means that opportunistic behavior, leads
to a negative change the extent of changes in gross accounts receivable between years and to a
positive change in the extent of changes in deferred revenue between years. This could be an
indication of earnings management with respect to these accounts. But this thesis will show if
my reasoning is correct.
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1.5 Reminder of the thesis
After the introduction section, in the second section is shown what the theoretical base
is of this thesis. In this part, based on the existing literature and investigations, a frame is
created on which this thesis is based. Also certain points that are important for this paper are
mentioned. The hypotheses that follow from the introduction and the existing literature are
formulated. In section 3 the research method is further explained, as well as the data
collection. In section 4 the descriptive statistics and the results are mentioned. Finally in
section 5 the conclusions and implications of this paper are explained. Also there are some
limitations of this thesis and some possibilities for follow-up research mentioned.
In the appendix the tables are displayed.
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Section 2 Literature Review and Hypothesis Formulation
2.1 Definition revenue recognition
The Financial Accounting Standards Board (FASB) and the International Accounting
Standards Board (IASB) both use a different definition concerning revenue: “Revenues are
inflows or other enhancements of assets of an entity or settlements of its liabilities (or a
combination of both) from delivering or producing goods, rendering services, or other
activities that constitute the entity’s ongoing major or central operations.” (FASB Concepts
Statement No. 6 Elements of Financial Statements, paragraph 78), and “Revenue is the gross
inflow of economic benefits during the period arising in the course of the ordinary activities
of an entity when those inflows result in increases in equity, other than increases relating to
contributions from equity participants.” (IAS 18, paragraph 7)
In the FASB Concepts Statement number 5, paragraph 58 you can read that
“Recognition is the process of formally recording or incorporating an item in the financial
statements of an entity as an asset, liability, revenue, expense, or the like. Recognition
includes depiction of an item in both words and numbers, with the amount included in the
totals of the financial statements.” Mentioned in the IAS 18 standard about recognition is:
“Recognition means incorporating an item that meets the definition of revenue in the income
statement when it meets certain criteria”
So, according to the concepts and standards mentioned above, revenue recognition is
recording revenue, which suffices certain conditions, in the financial statements.
Although these standards and definitions are notable dated (1984 and 1993), they are
still applicable. Only concerning the implementation of the revenue recognition process some
discussion is possible. As an addition to the FASB Concepts Statement, the Securities and
Exchange Committee (SEC) published in the end of 1999 some detailed rules on the area of
revenue recognition, in the Staff Accounting Bulletin 101 (SAB 101). The SAB 101 was
revised in 2003 and the SAB 104 was published as its substitute. The reason of publishing
more detailed rules was that the SEC expressed in public their concern about the big amount
of points of controversy and the problems that companies experience concerning the current
revenue recognition.
Schipper et al. (2009) describes some different conceptual models for revenue
recognition: the customer consideration model and the measurement model. These models
were proposed at an AAA/FASB Financial Reporting Issues Conference, in order to replace
the current revenue recognition models. Some participants believe that the notion of an
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earnings process is insufficiently precise, to provide a sound conceptual basis for revenue
recognition standards. Therefore two new models were presented at that conference. Both
models are based on contract asset and contract liability, so they both recognize revenue when
contract asset increases or contract liability decreases. The difference between the two
proposed models were that, at the customer consideration model revenue is recognized based
on the prices that are mentioned in the contract, while at the measurement model recognized
revenue is based on what really is paid.
2.2 Economic event occurring and the timing of recognition
Accrual accounting distinguishes cash inflows from revenues and cash out-flows from
expenses, recognizing the differences between cash flows and income as liabilities or assets.
The principles which govern the recognition of revenues (and expenses) are the key
determinants of the properties of accrual accounting information. (Dhutta and Zhang, 2002)
The recognition of economic events in accounting revenues tends to lag that of the
market. An informed market recognizes the effects of economic events when they occur, but
revenue recognition must await compliance with formal accounting recognition criteria. The
application of these criteria involves basic concepts as reliability, objectivity, conservatism,
and verifiability. It affects earnings in two ways: (1) current earnings will include recognition
of certain prior periods' economic events, and (2) current earnings do not recognize all of the
current period's economic events until future periods. (Warfield and Wild, 1992)
The reason that this is a big issue is because events occur now, they are recognized
over some time and therefore future periods' earnings possess explanatory power for current
returns. The incremental explanatory power of future periods' earnings varies inversely with
the length of the reporting period. Warfield and Wild (1992) indicated that in certain
instances, the recognition lag is of such magnitude that the explanatory power of future
periods' earnings for current returns more than triples that of current earnings.
For investors and other stakeholders it is sometimes hard to say what the explanatory
power of current returns and earnings is. It is unclear in which period current returns will lead
to future earnings. And what the influence of previous returns on current earnings is.
Therefore it is important that there are good rules to determine when some item should
be recognized. In the following paragraph it is shown more extensively why it is important to
have good revenue recognition rules.
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2.3 The importance of a good revenue recognition system
The importance of revenue recognition (rules) is big for managers, standard setters,
investors and auditors. The amount as well as the timing of revenue recognition is important.
Many decisions of investors depend on the effects that revenue recognition has on the
financial statements. Because of the big importance of revenue recognition it is important that
good quality accounting standards exist. So that it should be clear what the interpretation of
an amount of a certain account is to all the users of the financial statements.
In the current conceptual framework of the IASB two objectives of financial
statements are mentioned. Financial statements should “provide information about the
financial position, performance and changes in financial position of an enterprise that is useful
… in making economic decisions” (Framework 12). They also should “show the results of the
stewardship of management, or the accountability of management for the resources entrusted
to it” (Framework 14).
It is important that financial statements are reliable and they provide a true and fair
view of the economic reality. Revenue recognition is therefore a crucial part of the financial
statements. “Revenue is a crucial part of an entity’s financial statements. Capital providers use
an entity’s revenue when analyzing the entity’s financial position and financial performance
as a basis for making economic decisions. Revenue is also important to financial statement
preparers, auditors and regulators.” (Discussion paper FASB, 2008)
The importance of good revenue recognition cannot be shown better by the discussion
paper “Preliminary Views on Revenue Recognition in Contracts with Customers”. The two
biggest standard setters in the world (actually two competitors) decided to work together on
the revenue recognition area by developing a new revenue recognition model. They do that
because they both see that it is important to have good revenue recognition system. I will
discuss the current issues, which are going on in getting one main accounting standard, more
extensively in the discussion section.
Srivastava (2008) explain that “Revenue is typically the largest and most value
relevant item in firms’ financial statements” and Wustemann and Kierzek (2005) confirm this
view with “It is widely recognized that revenue is one of the most important items in financial
statements and that revenue recognition is one of the most difficult issues that standard-setters
and accountants have to deal with”
Users of financial statements attach much value to the revenue that is reported. Users
have the intention to make investment decisions based on the financial statements. On the
base of trends and growth-development numbers, they evaluate the companies’ past
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performance and make predictions about the possibilities for companies to create future
company value or future cash flows.
The timing of revenue recognition has a direct or indirect effect on almost all the parts
of the balance sheet and the income statement. This timing has significant influence on the
stock price. Analysts compare actual results with the predicted results. The classification and
recognizing of certain items related to revenue activities can have an effect on the
interpretation of the financial statements. (Chlala and Landry, 2001)
Revenue recognition and classification decisions can be subjective if accounting
standards are missing, unclear, or any form of authorization is not present. Management can
pressure auditors so that they must accept the choice of their accounting policies. This has an
effect on the quality of the information provided by the company in the financial statements.
In an investigation from March 1999 the National Commission on Fraudulent
Financial Reporting has investigated that more than fifty percent of all the cases of fraudulent
reporting has to do with overestimating revenue. The most common abuses included
recording sales that never took place, shipping products before customers agreed to delivery,
and booking revenue up front from long-term contracts.
Dobler (2008) mentioned in his investigation, to rethinking of the revenue recognition
process that “revenue recognition is one of the most crucial issues in financial reporting
internationally and was the prevalent source for recent accounting scandals”.
Revenue recognition does not only have an effect on external stakeholders, also
decisions inside the company depend on revenue recognition. Revenue recognition has an
effect on the amount of the accounts receivable, on the deferred revenue, and also on the
amount of cash. In proportion of recognizing revenue earlier or later, the amount of the
accounts receivable and the deferred revenue account will change.
This in turn has an effect on the net working capital of a company. With the net
working capital you can calculate a couple of ratios, with the net working capital you can
calculate the profitability of the company, calculate the company’s risks, or even calculate the
whole firm value. (Smith, 1980) This is one of the main issues why there should be good rules
to determine the accounts receivable, the deferred income and the amount of cash that has to
be reported in the financial statements.
However it is a fact that managers behave opportunistic, so the calculations or
expectations of some working capital ratios may be wrong. Therefore in this paper the
accounts receivable and the deferred revenue account are unraveled so that the standard
setters, investors and auditors can see how these accounts are built, and what exactly the
8
influence of all the chosen determinates, especially the opportunistic behavior variable is, on
those accounts.
2.4 Revenue recognition and earnings management
Revenue is usually the largest single item in financial statements, and the issues
involving revenue recognition are among the most important and difficult ones that standard
setters and accountants face. In recent years, concerns related to the recognition of revenue in
accordance with accounting standards have heightened significantly. Quite often, companies
end up tweaking the revenue numbers, besides some other reasons. Recording revenue
improperly is also a commonly used earnings management technique. The ever evolving
business models and the growing online economy have only compounded the issue. Earnings
management/issues with revenue recognition have been the subject of headlines in the United
States and in the other parts of the world in the last few years.
From prior studies it is shown that companies and managers try to influence the profit
numbers is such a way that no earnings decreases or losses must be presented in the financial
statements. (Burgstahler and Dichev, 1997) In this study it is showed that the number of
companies that show a small profit is much higher than the number of companies show a
small loss. The main reason for managers to prevent that a small loss is presented is a lower
cost of capital that they can achieve, higher stock rates and obtaining a bonus in case of
certain profit targets.
One way of earnings management is influencing the revenue recognition process.
(Marquardt and Wiedman, 2004) According to this paper earnings management consist from
specific changes in accruals: the unexpected component in the changes of: accounts
receivable, inventory, accounts payable, accrued liabilities, depreciation expense, and special
items.
As a reaction on Marquardt and Wiedman (2002) and Burgstahler and Dichev (1997),
Caylor (2009) has investigated that managers try to influence the accounts receivable and the
deferred revenue in order to prevent that they must show negative earnings surprises. When
managers think they could not fulfill the profit-expectations of investors they try to upward
the revenue recognition by making the amount of accounts receivable higher, and deferred
revenue lower. The research method that Caylor mentioned to model normal changes in
accounts receivable and normal changes in deferred revenue, which determine the causes and
consequences of earnings management on the accounts receivable and the deferred revenue, is
the same method that I am going to use in my investigation. However I am going to extend
9
this model by putting some more variables, control variables and an opportunistic behavior
variable in the model.
Healy and Wahlen (1999) said before that “accounting standards can provide
corporate managers with a relatively low-cost and credible means of conveying private
information on their firms’ performance to external capital providers and other stakeholders.”
and “standards add value if they enable financial statements to effectively portray differences
in firms’ economic positions and performance in a timely and credible manner”
Hunton et al. (2006) said that greater transparency in reporting formats facilitates the
detection of earnings management. The size of the transparency depends on the demand of the
accounting standard. According to this paper, the importance of standards of good quality is
very high.
Martínez-Solano and García-Teruel (2007) have investigated that “managers can
create value by reducing their firm’s number of days accounts receivable and inventories.
Equally, shortening the cash conversion cycle also improves the firm’s profitability.”
Feroz et al. (1991) claim that 50% of the SEC enforcement actions between 1982 and
1989 are a function of overestimating the accounts receivable. The main cause of this is that
revenue of sold goods is recognized too early. The SEC pays much attention to good revenue
recognition.
Shortly, the accounting standards influence the intensity of earnings management, one
way of earnings management is influencing the revenue recognition, and accounts receivable
and deferred income is a part of the revenue recognition process. With other words: the
amount of the accounts receivable and deferred income and how you can influence this
amount depend on the accounting standards.
Burgstahler and Dichev (1997) confirm this reasoning by claiming they found
evidence that two components of earnings, cash flow from operations and changes in working
capital, are used to achieve increases in earnings. Cash flow from operations and changes in
working capital, are directly linked with changes in accounts receivable and deferred income.
At the calculation of earnings management is normally the abnormal change in gross
receivables calculated, however I am going to calculate what the normal change is. That is
because the goal of this paper is to find what the determinants of the accounts receivable are
under normal circumstances, and not if the expectations are met or not.
10
2.5 Formulating hypotheses
Despite the accounting standards, some managers still see some opportunities to act
opportunistic. Burgstahler and Dichev (1997) show that managers try to influence profit
numbers to avoid that a lower profit or a loss is presented. The reason why managers want to
avoid this, is because firms with a consistent pattern of earnings increases command higher
price-to-earnings multiples. Also firms breaking a pattern of consistent earnings growth
experience an average of 14% negative abnormal stock return in the year the pattern is
broken. Burgstahler and Dichev (1997) have found evidence that two components of earnings:
cash flow from operations and changes in working capital, are used to achieve increases in
earnings. Prakash and Sinha (2009) have found that small changes in the deferred revenue
liability can have a disproportionately large impact on future profitability. While Marquardt
and Wiedman (2004) have found that firms issuing equity appear to prefer to manage earnings
upward by lifting up accounts receivable to accelerate revenue recognition.
Because the changes in accounts receivable and deferred revenue are part of the
changes in working capital, it is interesting to investigate what the influence of opportunistic
behavior is on how those accounts are built.
A way of defining opportunistic behavior is looking at the cash deficient firms. The
accounts receivable and the deferred revenue account both have to deal with differences
between receiving cash and the recognition of revenue. If a firm is short on cash, or also said
cash deficient, especially when the competence between firms is high, firms can try to lift up
the amount of cash by creating huge deferred revenue accounts. Chevalier and Scharfstein
(1996) investigated that when the demand is high, firms have greater incentive to cut prices
because the short-run profits from stealing market share are high relative to the long-run
profits from collusion. For example firms can try to sell their product by giving big price
reductions if customers pay in advance. In that case the company has a lot of demand for their
products in the future and receives now a lot of cash. These firms want to avoid that
customers pay in a later period. Constrained firms will draw working capital down during low
cash-flow periods and accumulate it during high cash-flow periods (Fazzari and Petersen,
1993).
The explanation for the negative coefficient on working capital is that working capital
competes with fixed investment for the limited pool of finance. Thus, other things equal,
when firms choose to decrease (increase) working-capital investment, fixed investment should
rise (fall). Similarly, fluctuations in cash flow will affect the inventory investment of
constrained firms (Carpenter et al, 1994). Therefore this kind of behavior wants to decrease
11
the size of the accounts receivable and increase the size of the deferred revenue account. To
investigate how this behavior influences exactly the accounts receivable and the deferred
revenue, this variable is included to measure opportunistic behavior. 1994).
Therefore the main question that is being asked through the thesis is:
What are the main determinants of the accounts receivable and the deferred
revenue account?
While the following hypotheses are formulated:
H1: Opportunistic behavior, measured in terms of characteristics of cash
deficient firms, leads to a negative extent of changes of the accounts receivable between
years.
H2: Opportunistic behavior, measured in terms of characteristics of cash
deficient firms, leads to a positive extent of changes of the deferred revenue account
between years.
12
Section 3 Regression Models and Sample Construction
3.1 Research Method
For the first, as well as the second hypothesis the research method that was mentioned
in Caylor (2009) is used. This method is a combination between the methods distracted from
Dechow et al (1998) and Kothari et al (2005).
In the paper of Caylor two models are mentioned: a standard model and a model that is
adjusted for that paper. I am going to use the standard model. That model claims that changes
in gross accounts receivable depend on total assets, changes in sales and changes in cash
flows from operations. Changes in deferred revenue also depend on total assets, changes in
sales and changes in cash flows from operations, but then from different time periods. For
example at deferred revenue there is a connection between sales in next year and cash flows
from operations in this year. While at accounts receivable there is a connection between sales
this year and cash flows from operations next year.
However Caylor did not include some risk factors in his model. Therefore 47 FamaFrench industry dummy variables are included as controls for differences between industries
in both models. Also I include some year variable dummies to control for changes in
accounting rules between the years of my investigation. I included some control variables,
based on Richardson et al (2005) to control for changes in other accruals and its components.
Despite the accounting standards, some managers still see some opportunities to act
opportunistic. Burgstahler and Dichev (1997) show that managers try to influence profit
numbers to avoid that a lower profit or a loss is presented. Therefore a variable to account for
this kind of opportunistic behavior is included as well.
Hypothesis 1
Dechow et al (1998) has indicated that the relation between sales and cash flow from
operations is not one-to-one because some sales are made on credit. Specifically, they assume
that a proportion of the firm’s sales remain uncollected at the end of the period.
The accounts receivable accrual incorporates future cash flow forecasts (collection of
accounts receivable) into earnings. Because in the next period some of the current amount of
accounts receivable is being paid. Therefore current sales will lead to future cash flows.
Changes in accounts receivable should be positively correlated to changes in current sales and
changes in future cash flow from operations.
13
Because changes in accounts receivable also have an effect on the total assets, this is
part of the model as well. This parameter measures for which part the accounts receivable is
dependent on the total assets of period t-1.
Kothari et al (2005) use assets as the deflator to mitigate heteroskedasticity in the
model. If every part of the model is divided by the total assets this would lead to more relative
results.
Furthermore, a constant term that is based on Kothari et al (2005) is included. The
advantages of including a constant term are that it provides an additional control for
heteroskedasticity not alleviated by assets as the deflator. Second, it mitigates problems
stemming from an omitted size (scale) or risk variable. And discretionary accrual measures
based on models without a constant term are less symmetric, making power of the test
comparisons less clear-cut. Thus, model estimations including a constant term allows to better
address the power of the test issues that are central when doing the analysis.
The reason that is chosen for gross accounts receivable instead of net accounts
receivable is that the provision for bad debt and the amount that is mentioned on the balance
sheet in the accounts receivable should be both included to calculate the total gross accounts
receivable. According to Caylor (2009), if I use the net accounts receivable this could
influence the results.
Also some control variables for risk are introduced. I control for differences between
industries. Because Prakash and Sinha (2009) indicate that industry characteristics also affect
predictability, 47 Fama-French industry dummies are included as controls in both models.
Year dummy variables are included to control for changes in accounting rules or some other
influences between years. It could be the case that accounting rules are subject to change over
the time period of my investigation, so therefore there has to be controlled for them.
Prakash and Sinha (2009) also explain that the inclusion of accruals and its
components as controls is a good thing to do to control for the changes in accruals.
Richardson et al (2005) decompose total accruals into three components: changes in working
capital (∆WC), changes in non-current assets (∆NCO) and changes in financing (∆FIN). They
showed that ∆NCO has greater explanatory power for future earnings than changes in
operating accruals (measured as ∆WC). To be consistent with Caylor (2009), I divide the
accrual control variables by total assets.
By definition, a firm is said to be cash deficient if the amount of cash that a firm owns
is lower than the short term or current liabilities minus the short term or current assets. The
motivation to use this measure was mentioned in Fazzari and Petersen (1993). They
14
mentioned that previous literature explains why working capital is one of the key elements of
the firm: “Inventory components of working capital enter directly into the production
function. For example, firms stockpile materials to reduce the probability of stockouts that
could slow production. They also use work-in-process inventories to achieve economies of
scale by running large batch sizes. Other components of working capital such as trade credit
and finished-goods inventories facilitate sales. Accounts receivable, in particular, can affect
sales to customers who are themselves liquidity constrained. Finally, cash and equivalents and
current liabilities affect costs through the liquidity of the firm. For example, compensating
cash balances can reduce financing costs, and adequate cash stocks allow firms to take
advantage of discounts for prompt payment.” So the liquidity or the ability to repay short-term
debt of the firm is one of the main issues to determine if the firm is cash deficient or not.
In this paper it is mentioned that is has been investigated before that firms use liquid
assets as collateral for short-term borrowing, which reduces working capital through an
increase in current liabilities. Therefore the measure mentioned above is used to see if firms
are cash deficient or not.
Cash deficient firms will draw working capital down during low cash-flow periods and
accumulate it during high cash-flow periods. Therefore when a firm is cash deficient, there is
a tendency to act opportunistic.
A second way of determining if a firm is cash deficient or cash constraint or not is
looking at its characteristics. Kaplan and Zingales (1997) have investigated that firms that are
financial constraint have certain characteristics. For example, they have a low sales growth,
compared to firms that are not financial constraint. They have a high debt-to-capital ratio, the
ratio between investments and capital is relatively low and the relationship between cash flow
and capital is even negative. Therefore these four characteristics are taken as measure for
opportunistic behavior as well. So that not only by definition, but also in real numbers there is
a measurement for opportunistic behavior.
So the opportunistic behavior variable is measured in terms of cash deficiency. The
dummy variable is equal to 1 if firms are cash deficient by definition or have certain
characteristics that a financial constraint firm has and equal to 0 if firms are not cash deficient.
The five measures of opportunistic behavior are run in parallel to see what the influence of
every measure is on the determination of the accounts receivable. Therefore, I create five
regression equations.
15
As another control variable I multiply the five opportunistic behavior dummy
variables by the change in sales in the current period divided by the assets at the beginning of
the year.
The formula to measure this hypothesis is:
Hypothesis 1: ∆gart / At-1 = α0 + α1*(1/At-1) + α2*(∆St/At-1) +
α3*(∆CFOt+1/At-1) +
α4*(∆WCt /At-1) + α5*(∆NCOt /At-1) + α6*(∆FINt /At-1) + ∑ δ*IND + ∑ φ*YEAR + α7*opp +
α8*opp*(∆St/At-1)+ εt
Whereby:
∆gart = change in gross accounts receivable during year t
∆St = change in sales during year t
∆CFOt+1 = change in cash flow from operations during year t+1
At-1 = total assets at the beginning of year t
∆WCt = changes in working capital during year t
∆NCOt = changes in non-current assets during year t
∆FINt = changes in financing during year t
∑ δ*IND = 47 Fama-French industry dummies depending on the SIC-code of the company
∑ φ*YEAR = year dummy variables for the years 2005, 2006 and 2007
opp = a dummy variable equal to 1 if a manager behaves opportunistic
α0 = constant term
εt = error term
Hypothesis 2
For hypothesis 2 the same assumptions can be made as for hypothesis 1. Except, the
sales and cash flow from operations behave just the other way round: there has been paid for a
product while there are no sales facing it. The changes in deferred revenue are linked to future
sales. Products which has been paid for now, and that may recognized as sales in the future
are expressed now in a deferred revenue.
Deferred revenue is therefore linked to current cash flow from operations, because in
this period the deferred revenue generates cash flow. Changes in deferred revenue should be
positively correlated to changes in future sales. This is because the amounts that are
recognized as deferred revenue now, are recognized as sales in the future. Because changes in
16
deferred revenue also have an effect on the total assets, this is part of the model as well. In
this model a constant term and the mentioned control variables are included as well.
As for the first hypothesis the way of measuring opportunistic behavior is looking at
the cash deficient firms. If a firm is short on cash, or cash deficient, firms can try to lift up the
amount of cash by creating huge deferred revenue accounts. For example firms can try to sell
their product by giving big price reductions if customers pay in advance. In that case the
company receives now a lot of cash. The amount of deferred revenue will in that case be
higher. The dummy variable is equal to 1 if firms are cash deficient and equal to 0 if firms are
not.
Again, the five measures of opportunistic behavior are run in parallel and five
regression equations are created, one for every measure/characteristic of opportunistic
behavior.
As for hypothesis one I use one other control variables: I multiply the opportunistic
behavior dummy variables by the change sales in the next period divided by the assets at the
beginning of the year.
The formula to measure this hypothesis is:
Hypothesis 2: ∆def revenuet / At-1 = α0 + α1*(1/At-1) + α2*(∆St+1/At-1) + α3*(∆CFOt /At-1) +
α4*(∆WCt /At-1) + α5*(∆NCOt /At-1) + α6*(∆FINt /At-1) + ∑ δ*IND + ∑ φ*YEAR + α7*opp
α8*opp*(∆St+1/At-1)+ εt
Whereby:
∆def revenuet = change in short-term deferred revenue during year t
∆St+1 = change in sales during year t+1
∆CFOt = change in cash flow from operations during year t
The remaining the variables are defined as mentioned at hypothesis 1.
Regression analyses
I make in total ten regression analyses (five measures times the two hypotheses) to see
what exactly the influence is of all the parameters of the model on the changes in gross
accounts receivable and deferred revenue. During the whole process there is controlled for
some risk factors. Some differences between industries and years are therefore captured. It is
important to see what exactly the influence is of all those building blocks on the determination
of the two accounts. Investors can better interpret the value of these two accounts if they
17
know perfectly how these accounts are built. They can see for example that there are
differences between industries. Especially it is interesting to see how behaving opportunistic
influences the results. Will the result of the Marquardt and Wiedman (2004) paper, that firms
issuing equity appear to prefer to manage earnings upward by lifting up accounts receivable,
be confirmed under this setting or not?
There is one main difference between doing my regressions and the way that Caylor
(2009) does his regressions. Caylor (2009) runs his regressions by industry and fiscal year
using all available firms with the requisite data. The coefficient estimates are based on means
of industry-years and t-statistics are based on the standard error of those means. In other
words he runs for each industry and each year the regression separately and takes a (weighted)
average of those regressions to come to his coefficients. I think the reason that Caylor (2009)
is doing that is because he does not have control variables to capture differences between
industry and firm years. By taking the mean, he takes the averages between industries and
years into account in the regression equation as well. Using this method requires some good
programming skills, which I am not capable of. If I want to perform it in the same way, I have
to do all the regressions, and take all the averages manually instead of letting the computer do
this automatically. This would take a lot of time. Therefore instead, I take a pooled sample in
which all the industries and firm years are taken into account at the same time in the same
regression, but with control variables. The reason that this is justifiable to do that instead of
Caylor’s (2009) method, is because creating dummy variables is also a way to capture the
differences between years and industries.
This thesis will also show if the assumptions that Caylor (2009) make are correct
under my model: is the accounts receivable indeed influenced, or influenced in the same way
by the factors he mentioned, or do, because of the variables that I include, the new variables
play a bigger role. If for example the parameter of 1 divided by total asset in the beginning of
the period is not significant in my model, this could imply that the assumption that accounts
receivable depend on total asset in the beginning of the period, that was correct in Caylor’s
model, is not correct in my model. This could be caused because in this model I use some
control variables that capture the differences between industries and years. In general, if the
factors that are omitted by Caylor are indeed important, the estimation of my normal changes
model should be different. A higher R-square will indicate if the introduction of more
variables in the determination of the accounts receivable and deferred revenue account will
lead to a statistically different and more significant model.
18
3.2 Data Collection
To be consistent with Caylor (2009), the investigated firms are US firms that use USGAAP as accounting standard. The data is collected from the Compustat database. In this
database I can collect all the data I need for my investigation. I have a number of all the items
that I need and that number I am using when looking up a certain item.
As Caylor (2009) did, the industry years 2001-2005 are investigated, therefore data
over the years 2000-2006 are collected. I have chosen not to take any furthermore years into
my investigation, because I do no want to take into account the effects of the financial crisis
that started late 2006/early 2007. The SIC-code for each company is looked up as well, so I
can see in which industry the companies are operating. I can use them as control variables. I
can control for and see if there are some differences between the industries. I start with a total
number of firm-year observations of 73,657.
In the paper of Caylor (2009) and in the paper of Burgstahler and Dichev (1997),
companies with a SIC company code between 4400 and 5000, between 6000 and 6500 and
companies with a code higher than 9000 are excluded from the investigation. This means that
utilities, financial institutions and firms related to public administration are deleted from the
investigation, because this could influence the results significantly. For my investigation I
also exclude them because the results can as well be influenced significantly if I include them.
It is also important that firms act in accordance with the US-GAAP standard. If I
include firms that act in accordance to a different standard this could influence my results. In
Compustat there is an option to display according to which accounting standard the numbers
are prepared. All the firms that have a different accounting standard than US-GAAP are
deleted from my sample.
For the first hypothesis it is important that the firms have an accounts receivable that is
not equal to zero, otherwise they are deleted for my investigation. The same counts for the
second hypothesis: firms must have a deferred revenue account that is not equal to zero,
otherwise they are deleted for my investigation. In the descriptive statistics it is shown how
many companies for each hypothesis per industry are taken in my investigation. Also the top
and bottom 1% of all the variables are deleted in order to take care that the outliers do not
influence the results.
This results in the total sample of 28,848 firm-year observations. From the total
sample 22,184 of them can be included is this investigation for the first hypothesis. While
7,296 firm-year observations of the total sample can be included is this investigation for the
second hypothesis.
19
Section 4 Results
4.1 Descriptive statistics
As under Caylor (2009), table 1 provides descriptive information for the Fama–French
(FF) industry groups for fiscal years 2001–2005, ranked in ascending order by percentage of
firms in the industry with non-zero short-term deferred revenue (Fama and French, 1997). I
have included all the firms in the descriptive statistics for this table, so the firms that are
excluded for my sample are included in this table to give a good oversight of all the
industries. For fiscal year 2005, 25.81% of all firms reported non-zero short-term deferred
revenue. All 48 FF industry groups have some firms with short-term deferred revenue on their
balance sheets. Table 1 displays the top industry groups in terms of percent of firms with
short-term deferred revenue. This group includes industries as Shipbuilding, Railroad
Equipment (27.42%), Healthcare (26.22%), Computer Software (24.81%), Entertainment
(24.79%), and Measuring and Control Equipment (24.16%). From the top 10 industry groups,
five are services that are provided, while the other five are industrial fabricated products.
Looking at the firms that actually have non-zero deferred revenue on their balance sheet, the
companies in the industries Pharmaceutical Products and Retail have the highest mean
deferred revenue-to assets.
Table 2 provides descriptive statistics for all firms that have either accounts receivable
or deferred revenue for fiscal years 2001–2005. Gross accounts receivable has a mean of
about 294 million dollars and a mean change of approximately 1.7% of beginning total assets.
Deferred revenue has a mean of more than 38 million dollars and a mean change in deferred
revenue of approximately 2.1% of beginning total assets. Table 2 also provides information
pertaining to the control variables, SIZE and BM, which Caylor (2009) used to test his
hypotheses. These control variables were used for the abnormal change model of the accounts
receivable and the deferred revenue account. Because I measure the normal change of those
accounts, I have not included them in my regression analysis. But to be consistent with the
descriptive statistics of Caylor (2009), I calculated and displayed them in my descriptive
statistics.
4.2 Results
Table 4 reports the results of performing the five regression analysis for the first
hypothesis. In this hypothesis the influence of certain variables on the determination of the
changes in the gross accounts receivable is explained. To take care of the differences between
20
years and industries, there are some dummy variables created. Because they are not of direct
interest for this study, they are not displayed. The results are consistent with the hypothesis
that behaving opportunistic, measured in terms of cash deficiency, would lead to a negative
change in the extent of changes in gross accounts receivable between years. Most of the
parameters are significant at the 1% level, the models have R-squares between 33.0% and
33.5%.
The results show that in three out of five ways to measure cash deficiency there is a
negative influence in determining the accounts receivable. Only one, the ratio between cash
flow and capital, shows a positive influence. This positive relationship was not expected,
because if firms are short on cash they would like to reduce the accounts receivable in order to
get as much cash flow as they can right now. But in general, looking at the parameters for
behaving opportunistic you can conclude that cash deficient firms try to lower the accounts
receivable. The explanation for this direction is that firms that are cash deficient want to
receive as much cash as they can now and not in the future, in the form of an accounts
receivable. So one part of the change of the accounts receivable can be explained by the fact
that some managers behave opportunistic, and therefore influence this account. Four
parameters are statistically significant at the 10% level.
What is notable about the other numbers in the table is, that the change in accounts
receivable are almost not dependent on the cash flow from operations in period t+1. While
was expected that, because the accounts receivable are paid in the next period, there was a
positive relationship on the cash flow from operations in period t+1, it turned out that there
was almost no relationship. This can be explained by the fact that I have included industry
dummy variables for all industries. Receiving cash for accounts receivable can occur in
different time periods per industry and is therefore captured by the industry dummy variables.
A different explanation can be found in the nature of the accounts receivable. If a big part of
them are short-term accounts receivable, i.e. they are paid within one year, they are only
influencing the current CFO and not the CFO in next year.
If I compare the expected with the actual signs, I notice that all the signs are the same
direction as what was expected. This implies that the signs of my model are consistent with
previous literature.
What also is notable, is that the parameters under the five models hardly change,
except for the measure of opportunistic behavior and the control variables linked to these
variables of for the measure of opportunistic behavior. The statistical significance also hardly
21
differs for each variable per model. This implies that the influence of opportunistic behavior
on the models is statistically significant.
In table 5 the results for hypothesis two are presented. It was tested how changes in the
deferred revenue account depend on several variables that are found in previous literature.
In this hypothesis the same conclusion regarding the opportunistic behavior variables
can be drawn as for the first hypothesis. The results are consistent with the hypothesis that
behaving opportunistic, measured in terms of cash deficiency, would lead to a positive change
in the extent of changes in deferred revenue. The models have R-squares between 10.9% and
12.3%
Four of the variables that where part of the model to measure potential opportunistic
behavior have a positive parameter. This implies that companies that are cash deficient indeed
try to create a positive extent of changes in the deferred revenue account. Because they are
short on cash, they have incentives to lift up that account. For example, they can try to lift up
sales by giving price reductions if customers pay in advance. It turns out that this expectation
is true. So one part of the change in the deferred revenue can be explained by the fact that
some managers behave opportunistic, and therefore influence this account.
What can be seen from the other parameters is that the parameters for ∆St+1/At-1 and
∆CFOt /At-1 are in all the five models almost equal to zero. And none of the parameters that
are equal to zero, except one, are statistical significant at the 1% level. So the influence of
those variables on the change in the extent of changes in deferred revenue is not as much as
was mentioned in the literature and what I expected that the influence would be. For the
changes in sales it can be explained by the nature of that account. If there is a lot of short-term
deferred revenue included in the deferred revenue, a big part of the deferred revenue is
influencing the sales now, and not in the future. However if that is the case, the parameters for
change in CFO should be positive, but this is not the case. This can all be explained by the
industry and year dummy variables. There can be a lot of differences between industry about
when to recognize a certain transaction or not. Therefore the differences between industries
can be captured by those variables, and not in the change of CFO.
All the parameters have the same sign as expected. This implies that those signs are
consistent with previous literature. Most of the other variables are significant at the 1% level.
As for hypothesis 1, the parameters under the five models hardly change, the statistical
significance also hardly differs for each variable per model. This implies that the influence of
opportunistic behavior on the models is stable for each of the measures.
22
Table 3 displays the Pearson correlation matrix for the variables of hypothesis 1 and 2.
4.3 Comparison with Caylor’s (2009) model
Because the basis of the model in this thesis is the model of Caylor (2009), a
comparison of his model with mine (extended) model is done, in order to see if there any
similarities and differences.
I cannot directly compare the results of Caylor (2009) with the results of this paper
because there are some differences in the sample size of the two papers. Although I have tried
to reproduce Caylor’s (2009) sample as much as possible, there are some differences. To
control for differences between industries and years, Caylor (2009) used industry averages to
determine the results for the regression analysis. Because I do not have the program skills to
let the computer determine those averages, I used dummy variables for year and industries.
Therefore, to do the comparison correctly, I compare the original regression model of Caylor
(2009), performed on my data, with the results of the extended model, based on the same data.
The formula that Caylor (2009) used to measure normal changes in accounts
receivable was: ∆gart / At-1 = α0 + α1*(1/At-1) + α2*(∆St/At-1) + α3*(∆CFOt+1/At-1) + εt. And
for the measure of the normal changes in deferred revenue the model was: ∆def revenuet / At-1
= α0 + α1*(1/At-1) + α2*(∆St+1/At-1) + α3*(∆CFOt /At-1) + εt.
If I compare the result of the extended model with the results by using Caylor’s
model, the first thing that is noticeable is the adjusted R-square of the models. For the first
hypothesis, the extended model have adjusted R-squares of about 33.3%, while Caylor’s
model has an adjusted R-square of 28.5%. The signs of the parameters, are the same, while
the parameters in the extended model are as statistical significant as those from the original.
This comparison implies that, based on the adjusted R-square, including more and control
variables do lead to a statistical better model for this hypothesis.
For the second hypothesis I have adjusted R-squares between 10.9% and 12.3%, while
the original model has an adjusted R-square of 10.5%. The signs of the parameters are all the
same, while the parameters of the original model are statistical more significant. For this
model it is hard to claim which of the models is better. Although the adjusted R-squares are
slightly higher for the extended model, the parameters are more significant at the original
model.
23
Section 5 Discussion
5.1 Conclusions and implications
In this paper is being tried to investigate what the effect of behaving opportunistic is
on the revenue recognition process. From previous literature it is stated that the importance of
revenue recognition is big for even managers, standard setters, investors and auditors. The
amount as well as the timing of revenue recognition is important. For example many decisions
of investors depend on the effects that revenue recognition has on the financial statements.
Previous literature also has found that some managers try to act opportunistic when it
comes to revenue recognition. They try to lift up the revenue in order to present good results.
It has been investigated that more than 50% of all the cases of fraudulent reporting has to do
with overestimating revenue. To measure if managers act opportunistic I look at the firms that
are cash deficient. It has been investigated before that cash deficient firms will draw working
capital down during low cash-flow periods and accumulate it during high cash-flow periods.
Also, some other papers have found similar results regarding influencing the financial
numbers at cash deficient firms. Therefore when a firm is cash deficient, there is a tendency to
act opportunistic.
This paper investigates what exactly the effect is of behaving opportunistic on the
determination of the accounts receivable and the deferred revenue account. To investigate that
influence, I extended a model to determine how the accounts receivable and the deferred
revenue account are established. I based this on some other papers. The model was extended
by some more determinants of these accounts, some risk factors to capture differences
between industries and years and the measurement of opportunistic behavior.
My first hypothesis was that behaving opportunistic, measured in terms of cash
deficiency, would lead to a negative change in the extent of changes in gross accounts
receivable between years. The results are consistent with this hypothesis.
The results are consistent with the second hypothesis that behaving opportunistic,
measured in terms of cash deficiency, would lead to a positive change in the extent of changes
in deferred revenue.
These results imply that behaving opportunistic, measured in two ways, indeed
influence, in either a positive or negative way, the determination of the revenue recognition
process. Users of the financial statements must be aware of this fact. The expectation is that
users of financial statements can better interpret the value of the two accounts if they know
that it is not only the other financial numbers that influence the revenue recognition process,
24
but also the opportunistic behavior of managers can influence the process. When making
decisions that are dependent on these accounts or make predictions of these accounts,
investors must know that opportunistic behavior of managers influences the extent of changes
between years of these accounts.
Auditors must also know that these accounts are being used to lift up cash-flows,
revenue or earnings.
My results are consistent with Burgstahler and Dichev (1997) and Marquardt and
Wiedman (2004): managers try to influence the amounts mentioned on these accounts. Also
the investigation that has been done in this paper gives some academical evidence for the
claim that 50% of the SEC enforcement actions are a function of overestimating the accounts
receivable, especially in cases where firms are cash deficient.
Because of the current discussion about getting one main accounting standard in the
world instead of two, it is interesting to mention theoretically what the implications are of the
differences between the two mostly used accounting standards in the world. I think the reader
of this paper will get some more theoretical background in the area of revenue recognition, by
mentioning the current issues regarding this area that are going on in the FASB and the IASB.
The results of this paper are being used in this discussion as well.
Previous literature has shown that there are some differences in IFRS and US-GAAP
regarding revenue recognition. The question that I ask myself when reading that literature is:
knowing the differences between IFRS and US-GAAP, how can this change the way
stakeholders make their decisions?
Historically, the US has required non-US firms listing on US exchanges to provide
reconciliations to US-GAAP of earnings and book value of equity. This requirement stems
from the belief that US investors can make better investment decisions regarding non-US
firms if the investors have access to information about these firms that is “similar” and of
“similar quality” to that available for US firms (Jenkins, 1999)
But now these firms aren’t required any more to make reconciliations. So the investors
must deal with the IFRS and US-GAAP existing next to each other. And literature has shown
that there are some differences in IFRS and US-GAAP. So the decisions of investors can
change when changing the accounting standard. (Pownall, Schipper, 1999)
Economic theory suggests that information asymmetries between potential buyers and
sellers of firm shares introduce adverse selection into share markets, and hence reduce market
liquidity. Information asymmetries are costly to firms, as investors adjust prices to
25
compensate for holding shares in illiquid markets. Increasing the level or precision of
disclosure should reduce the likelihood of information asymmetries between investors and
increase market liquidity. (Leuz, 2003) The level of disclosure depends on the accounting
standards, and therefore decisions of investors are influenced, as mentioned above, can
change.
Harris and Muller (1999) examine Form 20-F reconciliations from IAS to US- GAAP.
They find that, based on reconciliation magnitudes, IAS are closer to US- GAAP than other
foreign GAAP, but that reconciliation items are incrementally value relevant. They interpret
their findings as evidence that IAS and US-GAAP accounting measures are not substitutes. So
for investors it is relevant to know that the two standards exist next to each other and the
analyzing of differences between them give incrementally value.
Dobler (2008) mentioned that the accounting scandals and the proven incapable
accounting standards have motivated the international accounting standard setters to the revise
the recognition process.
Standard setters also find it useful to know what the differences between IFRS and
US-GAAP are. Gordon et al. (2008) state that US-GAAP differs from IFRS because of
differences in the enforcement and regulatory reporting environments. The need for users of
financial statements, to receive useful information, can be provided by preparers at a
reasonable cost, as a basis for making economic decisions. If there are differences in the rules
provided by the preparers, this will lead to different information that is provided to the users.
To take care that these differences on the area of revenue recognition will be as low as
possible the FASB and the IASB initiated a joint project on revenue recognition, primarily to
clarify the principles for recognizing revenue. (The discussion paper “Preliminary Views on
Revenue Recognition in Contracts with Customers”) They compared the two standards on the
area of revenue recognition and come to the conclusion that there are many differences
between the standards. Therefore the board, that deals with the joint project, make the claim
that a new model have to be developed to improve financial reporting by providing clearer
guidance on when an entity should recognize revenue.
That model should use one recognition principle, that is constant applicable to all kind
of transactions. The focus of the new revenue recognition model should be on changes in
assets and liabilities, because changes in these two parts of the financial statements can show
the revenue in the most distinct way. In the paper it is mentioned that “revenue is an increase
in assets, a decrease in liabilities or some combination of the two.” In the continuation of that
paper a whole new recognition model is explained. Another issue in this paper, and also of
26
some other literature, is the question if reporting in accordance with fair value solves the
problems that appear with the current revenue recognition processes. (The so-called Asset and
Liability Fair Value Approach).
Wustemann and Kierzek (2005) also take part into this collaboration. They
investigated three different conceptual frameworks that all are based on the asset and liability
view. (The Asset and Liability Fair Value Approach, the Asset and Liability Performance
Approach and the Asset and Liability Transaction Approach). Their main criticism on IFRS is
that the two current IFRS standards do not sufficient clarify how to deal with all kinds of
different transactions. More rules are needed to clarify revenue recognition for all those
different transactions. “Even though the general recognition criteria in the IASB Framework,
par. 83 apply to all sorts of income, the required level of probability and reliability of
measurement of the inflow of future economic benefits varies between different revenuegenerating transactions”. Their new proposed conceptual framework should remove these
objections.
This
new
model
or
framework
should
improve
the
comparability
and
understandability of revenue for users of financial statements. If the standard setter knows
what the differences between two accounting standards are, he can find the inconsistencies
between the two standards and can take action to reduce the inconsistencies by adjusting
current rules or writing new rules. For example knowing the differences will show if the
criticism regarding that rule-based standards reflecting the true economic substance more
badly than principle-based standards on this area is correct. (Van der Meulen et al., 2007)
For auditors is it also useful to know what the differences between IFRS and USGAAP are. As said before, in an investigation from March 1999 the National Commission on
Fraudulent Financial Reporting has investigated that more than fifty percent of all the cases of
fraudulent reporting has to do with overestimating revenue. Therefore, differences between
IFRS and US-GAAP, especially on the area of revenue recognition, will lead to different
kinds or possibilities of frauds and therefore there are different important pitfalls that the
auditor needs to be aware of.
Taken the results of this paper in the perspective of the critique on the US-GAAP
accounting standard, and also on joint project mentioned before between the FASB and the
IASB on revenue recognition, it turned out that acting opportunistic is still possible under USGAAP. Because acting opportunistic is still possible under the US-GAAP, this confirms the
view that maybe the makers of these standards must clarify the principles for recognizing
revenue or a new model have to be developed to improve financial reporting. For example by
27
providing clearer guidance on when an entity should recognize revenue, in order to take care
that such opportunistic behavior is not possible in the future anymore.
5.2 Limitations
There are some limitations if you look at this investigation. This investigation has been
done by comparing firms that are using US-GAAP. But if you want to get a full view it can be
better to also include firms that are using a different accounting standard. For example
European firms that use IFRS or domestic American firms that use a different standard than
US-GAAP. You can maybe run the regressions in parallel for IFRS and US-GAAP and see
what the results are. Or you can create dummy variables for the accounting standard. If you
allow different accounting standards into your sample you get more companies/data and your
investigation will be more representative and generalizable.
To get a complete view of revenue recognition you can find more items in the balance
sheet that you can compare. Now the accounts receivable and the deferred revenue account
are compared but you can also compare some other items like for example inventory or the
value of fixed assets to see what the influence of behaving opportunistic in on those accounts.
Maybe you can draw conclusions regarding the influence of behaving opportunistic on
revenue recognition in general, and not only draw conclusions on the accounts receivable and
deferred revenue account.
To be consistent with Caylor (2009) I used firm data of the years 2001-2005. I do not
know if these years are representative for the years 2006-2009, or for any years in the past.
5.3 Possibilities for follow-up research
Following from the limitations there are some possibilities for follow-up research. A
follow-up investigation can be done by including more companies or by investigating more
items in the balance sheet or income statement. You can also focus more on differences
between two industries. You can include more years to see if the results are significant over
time, or that you can see a trend in the results, or there is maybe somewhere a temporary or
structural break.
Roychowdhury (2006) suggests that activities manipulation (in this case act
opportunistic to manage earnings) seems to vary positively with the stock of inventories and
account receivables. An example that he gives is that a firm with substantial credit sales to
dealers can more easily engage in accelerating the recognition of sales by shipping goods
early to its dealers and booking receivables. In his paper opportunistic behavior is defined as
28
“positive abnormal cash flow from operations (CFO)”. The author of this paper claims that
the abnormal CFO can differ from the normal CFO because of three reasons: sales
manipulation, reduction of discretionary expenditures and overproduction. Therefore it can be
interesting as well to use abnormal CFO as indicator for opportunistic behavior.
Following up on the discussion mentioned in paragraph 5.2 it can be useful if you can
investigate if there are differences between IFRS and US-GAAP regarding the determination
of the accounts receivable and deferred revenue account. As it has turned out, the importance
of a good revenue recognition system is high. Pownall and Schipper (1999) think that this
comparison might be assessed in any of four ways:
-
two or more sets of standards might be directly compared to see if their requirements
differ
-
accounting policies for a set of firms could be compared cross-sectionally, and
differences in accounting policies for demonstrably similar events/transactions would
be taken as evidence of noncomparability.
-
accounting practices within a given accounting policy could be assessed
-
the reported numbers themselves might be assessed
For example at the first one, you can give an exact description of how IFRS and USGAAP differ. You can write down how, in theory, what the differences are. With the second
research method you can use the 20-F reconciliation forms to see what the differences are.
The third method focus on differences in the notes that explain the financial statements. You
can determine what the real accounting practices of the rules are. You can see for example,
that the IFRS and US-GAAP rules are the same, but that in practice a different interpretation
is given to these standards. Or in the fourth case you can compare the reported numbers
themselves. You can compare the heights of the accounts receivable and deferred revenue
account and see how they differ.
29
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33
Appendix: Tables
On the next pages the four tables are displayed. The notes belonging to the tables can
be found after each table.
34
Table 1
Short-term deferred revenue by Fama–French industry group.
FF Industry
N
Proportion of firms with
Mean deferred
Median deferred
36
deferred revenue (%)
11.11
revenue-to-assets (%)
1.49
revenue-to-assets (%)
1.27
319
15.36
11.91
5.31
Construction Materials
446
15.70
10.17
2.86
Beer & Liquor
102
17.65
5.12
1.91
Consumer Goods
458
18.12
8.96
2.97
Textiles
108
18.52
6.85
2.85
Automobiles and Trucks
438
18.95
7.78
3.50
Precious Metals
281
19.22
7.38
4.00
Tobacco Products
Rubber and Plastic Products
Shipping Containers
96
19.79
5.31
3.59
Aircraft
150
20.00
7.03
4.87
Electrical Equipment
504
20.24
7.91
3.00
Computer Hardware
815
20.37
9.33
2.81
Food Products
516
20.54
7.73
2.28
Agriculture
130
20.77
7.18
4.09
Mining
274
20.80
13.77
1.70
Candy & Soda
105
20.95
5.76
2.04
Non-Metallic and Industrial Metal
Utilities
1164
21.13
8.75
2.67
Business Supplies
293
21.16
8.56
3.86
Chemicals
694
21.18
7.80
2.87
Recreation
313
21.41
10.66
2.29
1437
21.43
10.67
3.19
Petroleum and Natural Gas
Printing and Publishing
242
21.49
5.99
2.70
Almost Nothing
1251
21.66
9.55
3.24
Pharmaceutical Products
2655
21.69
52.67
3.14
Medical Equipment
1292
21.75
8.68
3.07
Insurance
1290
21.78
7.33
2.76
78
21.79
5.14
1.76
10967
21.89
14.02
3.03
502
21.91
10.49
3.66
Coal
Banking
Steel Works Etc
Construction
332
21.99
9.70
3.83
1730
22.02
39.51
2.92
967
22.13
9.31
2.49
1180
22.37
8.42
3.11
62
22.58
12.26
1.72
Personal Services
446
22.65
11.25
2.69
Real Estate
388
22.94
8.09
3.33
Retail
Transportation
Wholesale
Defense
Restaraunts, Hotels, Motels
Electronic Equipment
Machinery
692
22.98
10.35
3.72
1914
23.20
7.85
2.92
950
23.37
8.23
2.39
1232
23.38
7.96
2.89
483
23.40
8.47
2.30
Trading
2404
23.50
7.98
2.89
Business Services
2065
23.54
8.93
3.96
89
23.60
17.64
8.35
Communication
Apparel
Fabricated Products
35
Measuring and Control Equipment
592
24.16
8.27
2.94
Entertainment
710
24.79
7.23
2.25
3221
24.81
47.50
2.73
675
26.22
8.30
3.20
62
27.42
10.93
2.97
47150
22.22
16.41
3.04
Computer Software
Healthcare
Shipbuilding, Railroad Equipment
Total and average
Note to table 1: this table reports the Fama and French (1997) industry name, total number
of non-missing and non-zero observations for the ratio of short-term deferred revenue-to-total
assets, 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 revenueto-total assets. Ratios are multiplied by 100% to convert to percentages for expositional
purposes.
36
Table 2
Descriptive statistics
Firms with accounts receivable
Gross A/R (in $ mil)
∆Gross A/R
SIZE
BM
Firms with deferred revenue
Deferred revenue (in $ mil)
∆Def Rev
SIZE
BM
Mean
Std. dev.
25%
75%
293.538
0.017
2.105
0.468
2988.251
0.115
1.118
1.182
2.720
-0.018
1.281
0.199
90.658
0.032
2.787
0.791
38.291
0.021
2.125
0.328
260.469
0.627
1.049
0.749
0.351
-0.005
1.405
0.139
12.752
0.018
2.723
0.597
Note to table 2: this table provides descriptive statistics for firms with accounts receivable
and firms with deferred revenue. All variables are scaled by lagged total assets, except for the
raw values of gross accounts receivable and deferred revenue, book-to-market ratio, and log
of size. ∆Gross A/R is defined as the change in gross accounts receivable. ∆Deferred revenue
is defined as the change in short-term deferred revenue. SIZE is the natural logarithm of a
firm’s size using beginning of the year market value of equity. BM is the beginning of the year
book-to-market ratio.
37
Table 3
Panel A: Pearson correlation matrix for the variables of hypothesis 1
∆gart /
At-1
1/At-1
∆St/At-1
∆CFO t+1/
At-1
∆gart / At-1
Pearson
1,000
1/At-1
Sig. (2-tailed)
Pearson
,026***
Sig. (2-tailed)
,000
∆St/At-1
Pearson
∆CFOt+1/At-1
-,012
,085
-,107***
1,000
Sig. (2-tailed)
Pearson
,522***
,000
,014**
,044
,000
,099
∆WCt /At-1
Sig. (2-tailed)
Pearson
,209***
-,267***
,125***
,098***
Sig. (2-tailed)
,000
,000
,000
,000
∆NCOt /At-1
Pearson
,233
,000
∆FINt /At-1
Sig. (2-tailed)
Pearson
Sig. (2-tailed)
Cash deficiency Pearson
Sig. (2-tailed)
∆NCOt
/At-1
∆FINt /
At-1
,000
-,051*** -,244***
,000
,000
-,085*** ,261***
-,035***
,000
-,085***
,017**
,015
-,037***
,138***
,000
-,186***
,038***
,000
-,093***
1,000
,000
,000
,000
,000
,000
,000
,107
,000
Sales growth
Sig. (2-tailed)
Pearson
Sig. (2-tailed)
Debt to
capital
CFt/Kt+1
,000
,265
-,001
,094
,839
***
1,000
,104
,000
***
Sales
growth
1,000
,000
-,062
***
-,011
,071***
Pearson
Cash
I /K
deficiency t t+1
1,000
,011
It/ Kt-1
***
∆WCt /
At-1
***
,000
,075
,000
-,061***
-,161***
-,039***
,059***
,381***
,000
,000
,000
,000
,000
,000
,454
,000
-,174*** ,069***
-,348***
-,011
,000
,000
,107
,000
Debt to capital
Pearson
-,009
-,051
,192
,000
CFt/ Kt-1
Sig. (2-tailed)
Pearson
,027***
Sig. (2-tailed)
,000
***
-,027
***
1,000
-,034***
,037
***
,031
***
-,238***
-,013
-,005
***
1,000
-,019
***
,044***
,000
,004
,000
-,062***
,064***
,228***
,215***
-,016**
,000
,000
,000
,000
,017
,024
-,260
,000
,070
,000
,006
,000
,173***
-,073***
-,001
-,008
-,072***
,000
,000
,886
,251
,000
***,**,* denotes statistical significant at the 1%, 5% and 10% two-tailed levels respectively
38
,178
***
1,000
-,020***
,012
***
1,000
1,000
1,000
Panel B: Pearson correlation matrix for the variables of hypothesis 2
∆def revt /
1/At-1
At-1
∆St+1/At-1
∆CFOt/
At-1
∆WCt /
At-1
∆NCOt /
At-1
∆FINt /
At-1
∆def revt / At-1
Pearson
1,000
1/At-1
Sig. (2-tailed)
Pearson
,226***
Sig. (2-tailed)
,000
∆St+1/At-1
Pearson
∆CFOt/At-1
-,077***
,000
-,008
1,000
Sig. (2-tailed)
Pearson
-,030
,052
-,008
,617
,599
,967
∆WCt /At-1
Sig. (2-tailed)
Pearson
-,030
-,008
,044***
,025
Sig. (2-tailed)
,052
,611
,005
,117
∆NCOt /At-1
Pearson
,123***
,193***
-,027
-,005
,470***
Sig. (2-tailed)
Pearson
,000
,000
,091
,746
,000
∆FINt /At-1
Sig. (2-tailed)
Pearson
-,419***
,000
-,124***
,049***
,002
-,024
-,022
,175
-,026
-,122***
,000
-,003
-,356***
,000
,004
1,000
Cash deficiency
-,144***
,000
-,118***
Sig. (2-tailed)
,000
,000
,117
,099
,851
,803
,001
Cash
deficiency
It/Kt+1
Sales
growth
Debt to
capital
CFt/Kt+1
1,000
-,001
1,000
It/ Kt-1
Pearson
,030
,026
-,023
-,039
,057
,102
,139
Sales growth
Sig. (2-tailed)
Pearson
,021
,127***
Sig. (2-tailed)
,186
Debt to capital
Pearson
CFt/ Kt-1
1,000
**
1,000
1,000
-,050***
,007
,022
,002
,657
-,021
-,002
-,098***
-,151***
,367***
,183
,173
,886
,000
,000
,000
-,015
,035**
,041***
,009
-,157***
-,018
,025
,058***
,878
,346
,027
,009
,587
,000
,250
,112
,000
,050***
,077***
-,011
-,010
-,016
,001
-,092***
-,384***
,213***
,174***
,016
,001
,000
,492
,539
,318
,969
,000
,000
,000
,000
,294
,008
,037
,014
,623
-,027
-,021
,000
,088
,003
,002
Sig. (2-tailed)
Pearson
,863
Sig. (2-tailed)
**
,054***
***,**,* denotes statistical significant at the 1%, 5% and 10% two-tailed levels respectively
39
1,000
1,000
1,000
1,000
Table 4
Model parameters for changes in gross accounts receivable
Cash deficiency
definition
0.010
(1.304)
Investmentst/
Kapitalt-1
Sales growth
Debt to total
Capital ratio
Cash Flowt/
Kapitalt-1
0.014*
(1.739)
0.010
(1.282)
0.010
(1.292)
0.008
(1.046)
?
0.024***
(12.159)
0.020***
(10.117)
0.021***
(10.290)
0.022***
(11.367)
0.018***
(12.159)
∆St/At-1
+
0.111***
(73.233)
0.112***
(45.653)
0.110***
(61.508)
0.110***
(72.198)
0.105***
(65.846)
∆CFOt+1 /At-1
+
0.000
(1.559)
0.000
(1.065)
0.000
(0.868)
0.001*
(1.829)
0.000
(1.015)
Opp
-
-0.007***
(-4.918)
-0.004*
(-1.779)
-0.002**
(-2.105)
0.000
(-0.524)
0.008***
(6.552)
Opp*(∆St/At-1)
?
-0.021***
(-6.575)
-0.008***
(-2.868)
-0.014***
(-4.589)
-0.013***
(-4.257)
0.009***
(3.107)
∆WCt /At-1
+
0.059***
(25.123)
0.063***
(26.853)
0.063***
(26.732)
0.061***
(26.137)
0.063***
(25.123)
Independent Variables
Expected Sign
Intercept
?
1/At-1
40
∆NCOt /At-1
+
0.031***
(15.628)
0.034***
(16.725)
0.034***
(16.604)
0.032***
(16.009)
0.035***
(17.146)
∆FINt /At-1
?
-0.003***
(-7.769)
-0.003***
(-6.836)
-0.003***
(-7.076)
-0.003***
(-7.081)
-0.002***
(-6.302)
Industry dummy variables
Included
Included
Included
Included
Included
Year dummy variables
Included
Included
Included
Included
Included
Adjusted R-square
33.3%
33.3%
33.0%
33.2%
33.5%
***,**,* denotes statistical significant at the 1%, 5% and 10% two-tailed levels respectively
Note to table 4: this table provides parameter estimates for the model to measure the normal changes in gross accounts receivable. Opp is the
variable that indicates if the managers act opportunistic measured in terms of characteristics of cash deficient firms. This variable is defined in
five different ways. Firms with a SIC company code between 4400 and 5000 (utilities), between 6000 and 6500 (financial institutions) and
companies with a code higher than 9000 (public administration) are deleted from the sample. The sample is based on 22,184 US-GAAP firmyears. Between brackets the T-statistic for that variable is mentioned. The industry dummy and the year dummy variables are not displayed in
this table. The top and bottom 1% of all the variables are deleted in order to take care that the outliers do not influence the results.
41
Table 5
Model parameters for changes in deferred revenue
Cash deficiency
definition
0.005
(0.416)
Investmentst/
Kapitalt-1
Sales growth
Debt to total
Capital ratio
Cash Flowt/
Kapitalt-1
0.002
(0.175)
0.006
(0.536)
0.006
(0.545)
0.006
(0.505)
?
0.013***
(9.146)
0.014***
(10.165)
0.014***
(9.996)
0.014***
(10.346)
0.014***
(10.115)
∆St+1/At-1
+
0.001***
(4.837)
0.000
(0.260)
0.000
(0.864)
0.000
(-0.868)
0.000
(-0.855)
∆CFOt /At-1
-
0.000
(-1.508)
0.000
(-0.777)
0.000
(-0.777)
0.000
(-0.846)
0.000
(-0.790)
Opp
+
0.009***
(4.555)
0.004**
(2.002)
0.000
(-0.206)
0.002
(1.355)
0.002
(1.099)
Opp*(∆St+1/At-1)
?
0.000
(-0.768)
0.000
(-0.303)
0.000
(-1.474)
0.000
(1.089)
0.000
(1.455)
∆WCt /At-1
+
-0.006***
(-4.132)
-0.006***
(-4.356)
-0.006***
(-4.361)
-0.006***
(-4.512)
-0.006*
(-4.361)
Independent Variables
Expected Sign
Intercept
?
1/At-1
42
∆NCOt /At-1
+
0.005***
(6.181)
0.005***
(6.003)
0.005*
(6.009)
0.005***
(6.277)
0.005*
(5.949)
∆FINt /At-1
?
0.000
(-1.583)
0.000**
(-2.216)
0.000**
(-2.255)
0.000**
(-1.964)
0.000**
(-2.346)
Industry dummy variables
Included
Included
Included
Included
Included
Year dummy variables
Included
Included
Included
Included
Included
Adjusted R-square
12.3%
11.0%
10.9%
11.1%
11.0%
***,**,* denotes statistical significant at the 1%, 5% and 10% two-tailed levels respectively
Note to table 5: this table provides parameter estimates for the model to measure the normal changes in deferred revenue. Opp is the variable
that indicates if the managers act opportunistic measured in terms of characteristics of cash deficient firms. This variable is defined in five
different ways. Firms with a SIC company code between 4400 and 5000 (utilities), between 6000 and 6500 (financial institutions) and companies
with a code higher than 9000 (public administration) are deleted from the sample. The sample is based on 7,296 US-GAAP firm-years. Between
brackets the T-statistic for that variable is mentioned. The industry dummy and the year dummy variables are not displayed in this table. The top
and bottom 1% of all the variables are deleted in order to take care that the outliers do not influence the results.
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