accounting complexity, misreporting
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Accounting complexity, misreporting,and the consequences of misreporting
Kyle Peterson
Published online: 29 July 2011
� Springer Science+Business Media, LLC 2011
Abstract I examine whether accounting complexity in the area of revenue rec-
ognition increases the probability of restating reported revenue. I measure revenue
recognition complexity using the number of words and recognition methods from
the revenue recognition disclosure in the 10-K and a factor score based on the
number of words and methods. Tests reveal that revenue recognition complexity
increases the probability of revenue restatements, and these restatements are the
result of both intentional and unintentional misreporting. Furthermore, complexity
moderates the consequences of restatement—lower incidence of AAERs, less
negative restatement announcement returns, and lower subsequent CEO turnover—
suggesting that stakeholders of the firm consider accounting complexity when
responding to misreporting.
Keywords Misreporting � Restatement � Revenue recognition � Accounting
complexity � Restatement consequences
JEL Classification G38 � M41
1 Introduction
I examine how accounting complexity affects both the incidence and consequences of
misreporting, an important issue as standard setters are confronted with how to
account for increasingly complex transactions. Since the Financial Accounting
Standards Board (FASB) is not interested in reducing representational faithfulness in
favor of simplicity (FASB 2008, QC24), understanding the effects of complexity is
important in evaluating the costs and benefits of the FASB’s approach. I focus on one
K. Peterson (&)
University of Oregon, Lundquist College of Business, Eugene, OR 97403, USA
e-mail: [email protected]
123
Rev Account Stud (2012) 17:72–95
DOI 10.1007/s11142-011-9164-5
effect of accounting complexity, misreporting, because the FASB and Securities and
Exchange Commission (SEC) have both suggested complexity is a major contributor
to the increased incidence of financial statement misreporting (Cox 2005; Herz 2005).
I focus on revenue recognition specifically because (1) revenue recognition applies to
all firms; (2) revenue misreporting is a common type of restatement (Palmrose et al.
2004; GAO 2002, 2006); and (3) anecdotal evidence suggests that revenue
recognition can be complex (Sondhi and Taub 2006; Herz 2007; Turner 2001).
Complexity is ‘‘the state of being difficult to understand and apply’’ (SEC 2008).
Complex accounting specifically pertains to the difficulty in understanding the
mapping of transactions (or potential transactions) and standards into financial
statements.1 My three empirical proxies capture complexity by using the firms’
revenue recognition disclosures: (1) the number of words in the revenue recognition
policy description in the notes to the financial statements; (2) the number of methods
listed in that same description; and (3) a factor score using both the words and
methods. Due to the inherent difficulty in disentangling complexity resulting from
transactions and standards, my proxies capture overall complexity, but still allow me
to provide evidence on the effects of accounting complexity on misreporting and
various stakeholders’ reactions to misreporting when accounting is complex.
I hypothesize that revenue recognition complexity increases the likelihood of
revenue restatements due to two competing (but not exclusive) effects. First, when
accounting is complex, managers are more likely to err when applying standards to
transactions, increasing the likelihood of unintentional misreporting due to
mistakes. Second, complex accounting may allow managers to manipulate financial
statements as suggested by Picconi (2006) and Bergstresser et al. (2006). To
determine whether complexity is associated more with mistakes or manipulation, I
test whether my complexity proxies are more associated with revenue restatements
being an irregularity or an error as defined in Hennes et al. (2008). I also examine
the effect of complexity on the consequences of restatement, including the
likelihood of an SEC Accounting and Auditing Enforcement Release (AAER),
restatement announcement returns, and CEO turnover following the restatement.
I conduct my tests on a sample of 333 revenue restatements from 1997 through
2005. To test whether revenue recognition complexity increases the probability of
restating revenue, I compare firms restating revenue with two sets of control firms:
(1) firms that had a restatement during the sample period but did not restate revenue
(hereafter referred to as nonrevenue restatements) and (2) a matched sample of firms
that do not have any kind of restatement during the sample period.
Results show that firms with complex revenue recognition are more likely to
restate revenue. Depending on the model and the proxy of complexity, a one
standard deviation increase in revenue recognition complexity increases the
probability of revenue misreporting between 7.6 and 13.0 percent relative to a
matched sample. Compared with other determinants in the models, this suggests
complexity is an important determinant of revenue misreporting. However, I find
1 Prior literature has not developed a definition of accounting complexity. The SEC’s Advisory
Committee on Improvements to Financial Reporting (ACIFR) provides a definition in its final
recommendation report (SEC 2008), and it is similar to the one presented in this paper. Accounting
complexity is described more thoroughly in Sect. 2.
Accounting complexity, misreporting 73
123
revenue recognition complexity is not a significant predictor of the likelihood of
restating due to an irregularity versus error (as defined by Hennes et al. 2008), which
suggests complexity leads to both intentional and unintentional misreporting.
Examining the consequences of restatement, I show that increased complexity
reduces the likelihood of receiving an AAER, results in less negative restatement
announcement returns, and reduces the incidence of subsequent CEO turnover for
revenue restaters. Interacting my complexity proxies with the irregularity indicator
from Hennes et al. (2008) suggests that complexity generally reduces the
consequences of restatements for both errors and irregularities. These results do
not appear sensitive to alternative models or specifications, including a two-stage
partial observability model and controlling for changes in disclosure requirements.
This study contributes to accounting research in several ways. First, as one of the first
studies to measure and examine the implications of accounting complexity, it improves
our understanding about misreporting costs associated with increased complexity
(although it does not speak to other costs or potential benefits of this increased
complexity). Second, although prior research (for example, Bergstresser et al. 2006)
suggests that complexity is associated with earnings management or manipulation, I find
revenue recognition complexity leads to both errors and manipulation. Third, my results
should be informative to standard setters because they provide a better understanding of
the effects of complexity on misreporting and stakeholders’ perception of complexity.2
It appears that stakeholders of the firm temper their reactions to misreporting when
accounting is complex. This is informative given the FASB’s stated goal of not reducing
complexity at the expense of faithful representation because it suggests that users have
some understanding of the implications of accounting complexity on misreporting.
Finally, these results add to the restatement literature, which has traditionally examined
incentives and governance as determinants of misreporting (Palmrose et al. 2004; Burns
and Kedia 2006) but has ignored the effect of complexity.
In a concurrent working paper, Plumlee and Yohn (2009) also examine the causes of
financial statement misreporting by examining restatement announcement disclosures.
They conclude that the two major causes of restatements cited by managers from 2003
through 2006 were internal company errors (57 percent) or some characteristic of the
accounting standard (37 percent), while few restatements were caused by the
complexity of the transaction or manipulation (3 percent each). My study makes an
incremental contribution by developing an independent and objective measure of
complexity to test its effect on misreporting. I also examine the effect of complexity on
the consequences of misreporting, which is not addressed in Plumlee and Yohn (2009).3
2 The results in this paper do not address whether accounting complexity is pareto optimal or should be
reduced. Without an examination of all costs and benefits of complexity, it is not feasible to make any case
for social welfare. For example, potential benefits of accounting complexity relative to simpler accounting
could be reduced earnings management or better comparability, which are not examined in this study.3 My findings suggest that firms that restate revenue have more complex revenue recognition; however,
from Plumlee and Yohn (2009), it seems that firms do not necessarily highlight complexity as a reason for
the restatement but are more likely to use vague descriptions like ‘‘internal error.’’ What managers say
about the causes of misreporting does not likely include a description of all relevant factors that led to the
restatement (e.g., undue pressure to meet targets, executive compensation, governance failures,
complexity), but examining all these factors in a multivariate setting provides a better understanding
of all these effects.
74 K. Peterson
123
In the next section, I define accounting complexity and develop my hypothesis.
Section 3 discusses the sample and measurement of complexity. Results on the
probability of misreporting are presented in Sect. 4. Section 5 discusses the tests on
errors versus irregularities and consequences of misreporting. In Sect. 6, I conduct
some additional analysis, and Sect. 7 concludes.
2 Hypothesis development
2.1 Accounting complexity
I define accounting complexity as the amount of uncertainty related to the mapping
of transactions or potential transactions and standards into the financial statements.4
This definition is intended to apply to both preparers and users of financial
statements and is similar to the one proposed by the SEC’s Committee on
Improvements to Financial Reporting (ACIFR) in its recommendation report issued
in August 2008.5
Accounting complexity stems from a combination of transactions and financial
reporting standards. With regard to transactions, the ‘‘increasingly sophisticated
nature of business transactions can be difficult to understand’’ (SEC 2008). For
example, uncertainty about transactions increases for firms with numerous
customer-specific contracts or agreements documented by multiple contracts.
Reporting standards can also increase complexity, including the following
characteristics highlighted by the ACIFR (SEC 2008):
• Describing accounting principles in simple terms for highly sophisticated
transactions
• Detailed guidance for numerous specific fact patterns
• Multiple standard-setting bodies issuing guidance
• The volume of standards and interpretations make it difficult to determine
appropriate guidance
While it may be useful to separate the source of complexity into transactions and
standards, isolating these two sources empirically is difficult (or impossible)
because standards are written with transactions in mind. For example, basic
transactions generally only need simple guidance. But for complex transactions,
standard setters could provide simple guidance (for example, for revenue, waiting
for the collection of cash or full performance on a contract before recognition), or
more complex guidance (for example, recognition depending on more complex
4 No formal definition of accounting complexity exists in the academic literature. Prior research has
examined firm or organization complexity (Bushman et al. 2004), information complexity (Plumlee
2003), and information overload (Schick et al. 1990 for a review), concepts not wholly unrelated to
accounting complexity.5 The ACIFR define financial reporting complexity for preparers as the difficulty ‘‘to properly apply [US
GAAP] and communicate the economic substance of a transaction’’ and for investors as the difficulty in
understanding ‘‘the economic substance of a transaction or event and the overall financial position and
results of a company’’ (SEC 2008).
Accounting complexity, misreporting 75
123
estimation and timing). Therefore, if we observe more complex accounting, it is
difficult to attribute it to standards or transactions because it is a combination of
both. My empirical proxies (discussed in Sect. 3) are not specifically linked to each
source of complexity, but capture the combined role of both transactions and
standards. As a result, my results do not provide direct evidence on whether revenue
standards are egregiously complex and need revision. However, because my proxies
measure overall complexity, they likely reflect how some firm stakeholders view
complexity (that is, they recognize complexity generally but do not have an
understanding of, or care about, the separate roles of transactions and standards).
Anecdotal evidence suggests that accounting for revenue can be particularly
complex for preparers and users of financial statements (see preface to Sondhi and
Taub 2006). The FASB states there are over 200 revenue recognition pronounce-
ments by various standard setting bodies (Herz 2007), and much of the authoritative
guidance is industry- or transaction-specific. These issues can lead to inconsisten-
cies across pronouncements or difficulties in applying multiple standards to a
contract. In addition, complicated revenue transactions can increase uncertainty,
which may include lengthy contracts, customer-specific contracts, multiple clauses
for customer acceptance and payment terms, and side agreements (Turner 2001).
2.2 Accounting complexity and misreporting
Accounting complexity can be costly to financial markets because uncertainty limits
cognitive processing, leading to simplification, biases, and errors in estimation or
judgment (see Schick et al. 1990; Tversky and Kahneman 1974). One of these costs
is misreporting. Complexity could lead to misreporting in two distinct ways. First,
complexity from the preparer’s perspective could cause mistakes in financial
reporting (the mistake theory), with more complexity leading to more errors and
misreporting. Second, complexity could allow managers to opportunistically manage
earnings (the manipulation theory).6 In contrast to the mistake theory, which suggests
that complexity affects the preparer’s accuracy in financial reporting, the manip-
ulation theory relies on complexity creating uncertainty for investors (or information
intermediaries). For example, research shows that investors and analysts do not
understand the effect of changes in pension plan parameters on future earnings
(Picconi 2006) and that managers increase rates of return assumptions on pension
assets in settings when it benefits the firm or manager (Picconi 2006; Bergstresser
et al. 2006). The findings on pensions suggest managers believe it is more difficult for
investors to detect manipulation when accounting or reporting is complex.
2.3 Predictions
Both the mistake and manipulation theory of complexity suggest that complexity
increases the likelihood of misreporting. Assuming that the probability of detecting
6 Although this theory suggests managers take advantage of complex accounting by managing the
financial statements, complexity is not a necessary condition for manipulation. Many fraudulent practices
are implemented using simple accounting settings (e.g., fictitious sales, bill-and-hold transactions, and
capitalizing expenses).
76 K. Peterson
123
the misreporting is similar across both theories, I propose the following hypothesis,
stated in alternate form:
H1 Managers of firms with more complex revenue recognition are more likely to
misreport revenue than managers of firms with less complex revenue recognition.
Even though both the mistake and manipulation theories lead to the prediction in
H1, the null hypothesis of no result could occur if the effect of complexity on
revenue misreporting were small or if misreporting is solely driven by managerial
incentives and governance, as hypothesized in prior literature (Palmrose et al. 2004;
Burns and Kedia 2006; Zhang 2006; Callen et al. 2009). More importantly, these
tests allow me to quantify the economic importance of the effect of complexity on
the likelihood of misstating revenue. Prior literature (Hennes et al. 2008, Palmrose
et al. 2004) has shown that intentional misreporting has more severe consequences
than unintentional misreporting. Since complexity could lead to intentional or
unintentional misreporting, I examine whether my complexity proxies are
associated with the probability that the restatement is an irregularity versus an
error as defined in Hennes et al. (2008). I also examine the effect of complexity on
the consequences of misreporting. I do not make formal hypotheses for these tests
due to the competing effects of the mistake and manipulation theories on the
predictions. I discuss these tests in Sect. 5.
3 Sample and measurement
3.1 Sample selection
The analysis is conducted on a sample of revenue restatement firms from 1997 to
2005 collected by the GAO for its reports to Congress in 2002 and 2006. From the
initial GAO sample of 738 revenue restatements, I exclude restatements of financial
firms (SIC 6000-6999) due to their substantially different revenue recognition, any
restatement for the same firm within a 1 year period, and all revenue restatements in
response to SAB 101 or any revenue EITF issued during the sample period.7 Also, I
recategorize 39 revenue restatements identified by the GAO because they are not
related to revenue recognition but are related to non-operating gains and non-
operating income (such as interest income). Missing variables from 10-K
disclosures and Compustat and CRSP databases reduces the revenue restatement
sample to 333 observations. Financial data, stock returns, and analyst forecasts are
obtained from Compustat, CRSP, and I/B/E/S respectively. Option compensation
data is obtained from Execucomp. CEO turnover and characteristics are also
obtained from Execucomp where available and hand collected from the proxy filings
otherwise.
7 I consider SAB 101 and EITF restatements as mandatory restatements caused by a change in
accounting standard. During the sample period, the Emerging Issues Task Force issued EITFs 99-19,
00-10, 00-14, 00-22, 00-25 to clarify revenue recognition issues such as recognizing gross v. net, shipping
and handling costs, sales incentives, and other consideration from a vendor. Including the SAB and EITF
firms in testing H1 provides similar results.
Accounting complexity, misreporting 77
123
3.2 Control firms for H1
I test whether revenue recognition complexity increases the probability of
misreporting revenue (H1) using two different small-sample comparison groups.
This joint testing approach improves confidence in the combined results because of
the strengths and weaknesses of each comparison sample. For the first comparison
sample, I use firms that also had a restatement during the sample period but restated
something other than revenue. This design is advantageous because it inherently
controls for incentives and governance effects that lead to restatements, which are
difficult to measure, and also likely controls for other determinants of restatements
that may be missing from the model. A limitation of this comparison sample is that,
if restatement firms generally have more complex revenue recognition than
nonrestatement firms, the coefficient on complexity may not accurately reflect the
full effect of complexity on restatements. This comparison sample is also obtained
from the GAO reports, where I exclude financial firms and firms with more than one
restatement in a 1-year period as before. I also exclude any restatements for firms
that have a revenue restatement over the sample period to ensure that a single firm
cannot be in both samples. The final comparison sample is 859 restatements.
The second comparison group is a matched control sample of firms that did not
have a restatement over the sample period. The matched sample approach is
advantageous relative to the previous comparison sample because it allows me to
estimate the full effect or magnitude of complexity on misreporting. I match on
fiscal year, assets, and the book-to-market ratio because revenue restatement firms
are generally smaller firms, and matching on assets and book-to-market ensures the
firms are similar size and have similar growth prospects.8 I first identify all firms
without any restatement during the sample period that have data coverage on
Compustat and Execucomp. Firms with assets between 70 and 130% of the assets of
the sample firm in the same fiscal year are chosen as potential matches, and the
matched firm chosen with the closest book-to-market ratio to that of the sample
firm. This process yields 324 matched sample firms with necessary data. Finally, as
described below, the research design using the matched sample ideally includes a
measure of equity incentives. Since many of the revenue restatement firms are small
and are not covered on Execucomp, including this control in the model reduces the
sample size significantly to 93 revenue restatements and 93 matched firms.
3.3 Measuring revenue recognition complexity
My empirical measures of complexity are based on the description of the firm’s
revenue recognition practices. Relative to shorter disclosures, longer disclosures and
more methods capture the preparer’s need to incorporate more sophisticated or a
broader set of transactions and standards. Longer disclosures also reflect the
manager’s need to explain more involved practices or methods. This is evident in
8 I do not match on industry because it likely introduces a noisy sort on revenue recognition complexity,
potentially controlling for the effect being tested. However, I do control for industry in the regression
analysis.
78 K. Peterson
123
the ‘‘Appendix’’, which contains four sample revenue recognition disclosures and
their complexity proxies to illustrate how these disclosures capture complexity. I
collect revenue recognition disclosures contained in the summary of significant
accounting policies from firms’ most recent 10-K prior to the restatement
announcement using the SEC EDGAR Database. I measure revenue recognition
complexity using the number of words (WORDS), a proxy for the number of
methods (METHODS), and a factor score (RRC SCORE) based on WORDS and
METHODS from the disclosures.9,10 METHODS is measured as the number of
occurrences of the word-stems ‘‘recogn’’ and ‘‘record’’ found in the disclosure.
Bushman et al. (2004) and others have used measures of general organizational
complexity relying on proxies such as firm size and operating or geographic
segments. My measures of revenue recognition complexity could be associated with
these traditional measures of organizational complexity, so for the tests described in
the next section, I include controls for operating complexity and firm size.
Table 1 Panel A provides revenue recognition complexity statistics for the
revenue restatement sample and both comparison samples and tests for differences
in means and medians. RRC SCORE is calculated for each combined sample and
produces a score that is mean zero. The tests reveal that revenue restaters have
significantly higher mean and median WORDS, METHODS, and RRC SCORE than
both sets of comparison firms (p values \ 0.001).
Because managers have discretion with their revenue recognition disclosures,
they could alter their disclosures to appear more or less complex. To alleviate
concerns that managers may be manipulating revenue recognition disclosures prior
to the restatement, I also collect the revenue recognition disclosures just followingthe restatement announcement. If discretion of the disclosure exists, it should be
reduced following the restatement due to auditor scrutiny accompanying the
restatement. Table 1 Panel B shows that revenue restatement firms have more
WORDS and METHODS and higher RRC SCORE than non-revenue restatement
firms in both the pre- and post-periods, suggesting that the higher complexity for
revenue restatement firms is not driven by managerial discretion and still exists
post-restatement.11
9 Certain practices or factors could also lead to increased complexity and risk of misreporting beyond
what may be captured by disclosure length (see AICPA Practice Alert 98-3, 1998). In unreported analysis,
I measure RRC SCORE where I also include the total number of counts in the disclosure (by using key-
word searches) for the following revenue recognition practices: the percentage of completion method,
multiple deliverables, vendor-specific objective evidence, barter or nonmonetary exchange revenue, or
fair valuing aspects of the contract. Results using this measure are consistent with the results reported in
the tables for RRC SCORE.10 As a test of validity of my complexity measures, I examine whether my measures are associated with
the variation and error in analysts’ forecasts of revenue, an indication that complexity increases
uncertainty. Results (untabulated) indicate all three proxies are positively related to both the error and
variation in analysts’ revenue forecasts with p values less than 10 percent after controlling for analyst
following, size, and book-to-market.11 It is also interesting to note that for both the revenue restaters and non-revenue restaters, the number of
WORDS and METHODS increased in the post period, but the increase was greater for the revenue
restaters (91.3 and 1.57 for revenue restaters; 38.0 and 0.53 for non-revenue restaters). The greater
increase in post-restatement disclosures for revenue restaters could be an attempt to resolve confusion
over already complex revenue recognition.
Accounting complexity, misreporting 79
123
Tab
le1
Rev
enue
reco
gnit
ion
dis
closu
rest
atis
tics
Rev
enue
rest
atem
ents
Com
par
ison
gro
up
Mea
nte
sts
Med
ian
test
s
NM
ean
Med
ian
NM
ean
Med
ian
Dif
f.t
test
Dif
f.v2
Pan
elA
:re
ven
ue
reco
gnit
ion
dis
closu
rest
atis
tics
Non-r
even
ue
rest
atem
ent
com
par
ison
gro
up
WO
RD
S3
33
26
8.6
19
2.0
85
91
86
.51
03
.08
2.1
**
*5
.50
89
.0*
**
56
.7
ME
TH
OD
S3
33
5.8
85
.00
85
94
.00
3.0
01
.88*
**
7.2
32
.00*
**
51
.1
RR
CS
CO
RE
33
30
.46
0.3
68
59
-0
.18
-0
.28
0.6
3**
*7
.70
0.6
4**
*4
7.7
Mat
ched
sam
ple
com
par
iso
ng
rou
p
WO
RD
S3
24
26
4.8
18
4.5
32
41
69
.31
13
.09
5.5
**
*5
.89
71
.5*
**
26
.1
ME
TH
OD
S3
24
5.8
15
.00
32
44
.02
3.0
01
.79*
**
5.7
32
.00*
**
32
.9
RR
CS
CO
RE
32
40
.30
0.2
03
24
-0
.30
-0
.39
0.6
1**
*6
.11
0.5
9**
*2
8.5
Var
iable
Rev
enue
rest
atem
ents
Non-r
even
ue
rest
atem
ents
Mea
nte
stM
edia
nte
st
NM
ean
Med
ian
NM
ean
Med
ian
Dif
f.t
test
Dif
f.v2
Pan
elB
:pre
-an
dpost
-res
tate
men
tre
ven
ue
reco
gnit
ion
dis
closu
rest
atis
tics
WO
RD
S3
22
27
0.5
19
3.5
81
71
83
.21
02
.08
7.4
**
*5
.81
91
.5*
**
56
.5
PO
ST
WO
RD
S3
22
36
1.9
27
3.5
81
72
21
.11
36
.01
40
.7*
**
7.9
81
37
.5*
**
62
.6
Dif
fere
nce
91
.3*
**
80
.0*
**
38
.0*
**
34
.0*
**
53
.4*
**
4.4
54
6.0
**
*2
5.0
Tes
t7
.06
9.3
96
.86
9.7
2
ME
TH
OD
S3
22
5.8
65
.00
81
73
.95
3.0
01
.92*
**
7.3
12
.00
**
*4
9.4
PO
ST
ME
TH
OD
S3
22
7.4
36
.00
81
74
.48
3.0
02
.96*
**
9.5
63
.00
**
*6
3.9
Dif
fere
nce
1.5
7**
*1
.00*
**
0.5
3**
*0
.00*
**
1.0
4**
*4
.87
1.0
0*
**
12
.3
Tes
t6
.86
7.5
15
.35
7.5
0
RR
CS
CO
RE
32
20
.46
0.3
58
17
-0
.19
-0
.30
0.6
5**
*7
.81
0.6
5*
**
45
.3
PO
ST
RR
CS
CO
RE
32
20
.56
0.5
08
17
-0
.22
-0
.31
0.7
8**
*9
.41
0.8
2*
**
64
.7
80 K. Peterson
123
Tab
le1
con
tin
ued
Var
iable
Rev
enue
rest
atem
ents
Non-r
even
ue
rest
atem
ents
Mea
nte
stM
edia
nte
st
NM
ean
Med
ian
NM
ean
Med
ian
Dif
f.t
test
Dif
f.v2
Dif
fere
nce
0.1
0*0
.15*
-0
.03
-0
.02*
**
0.1
3**
2.0
40
.17
**
*7
.0
Tes
t1
.77
1.7
80
.91
3.9
4
This
table
conta
ins
reven
ue
reco
gnit
ion
dis
closu
rest
atis
tics
for
asa
mple
of
reven
ue
rest
atem
ents
and
two
com
par
ison
sam
ple
s,a
sam
ple
of
non-r
even
ue
rest
atem
ents
and
a
mat
ched
sam
ple
.P
anel
Ap
rese
nts
des
crip
tiv
est
atis
tics
for
the
reven
ue
reco
gn
itio
nco
mp
lex
ity
pro
xie
sb
ysa
mp
le.
Pan
elB
con
tain
sco
mp
aris
ons
of
rev
enu
ere
cog
nit
ion
dis
closu
rest
atis
tics
pre
-an
dpost
-res
tate
men
tfo
rboth
reven
ue
and
non-r
even
ue
rest
atem
ent
firm
s.T
he
num
ber
of
firm
sin
Pan
elB
dif
fers
from
those
pre
sente
din
Pan
elA
due
toth
ere
quir
emen
tto
hav
epost
-res
tate
men
tre
ven
ue
reco
gnit
ion
dis
closu
res.
Bold
edst
atis
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Accounting complexity, misreporting 81
123
4 Probability of misreporting tests
4.1 Empirical design
I use a restatement design and matched-sample design to test H1 based on (1)
below, where Complexity is either WORDS, METHODS, or RRC SCORE.
PðRevenueRestateÞ ¼ f ðaþ b ComplexityþX
c ControlsÞ ð1Þ
4.1.1 Restatement design
For the restatement design I use a logistic regression, where the dependent variable
is one if the firm restated revenue and zero if the firm restated something other than
revenue. Control variables from prior research to measure incremental determinants
for why managers might misreport revenue fall into three categories: (1) value
relevance, (2) governance, and (3) other.
Value relevance of revenue has been shown to be an important determinant of
firms restating revenue (Callen et al. 2009; Zhang 2006). Growth firms (Ertimur
et al. 2003) and firms with analyst revenue forecasts (Ertimur and Stubben 2005)
have revenue that is more value relevant. I include the book-to-market ratio of the
firm at the fiscal year-end just prior to the restatement (BTM) as a proxy for growth
and an indicator equal to one if the firm has a revenue forecast any time prior to the
restatement announcement and zero otherwise (SALEFCST). Revenue may also be
more value relevant when net income is less value relevant, especially for loss firms
(Hayn 1995) and firms with high earnings volatility (Zhang 2006). Therefore, I
include the proportion of loss years to total years the firm has earnings data on
Compustat (LOSSPER) and the 5-year earnings volatility (EARNVOL) of the firm
prior to the restatement announcement.
Prior research provides evidence on the effect of auditing and governance on
misreporting in general (Defond and Jiambalvo 1991; Palmrose et al. 2004) but
provides little insight to whether managers will specifically misreport revenue. It is
more likely that the previously mentioned variables on the value-relevance of
revenue already capture an increasing monitoring effect on revenue reporting by
auditors. However, Kinney and McDaniel (1989) show restatements are more likely
for firms with poor recent performance, which causes auditors to scrutinize financial
statements. I include the stock returns for the 12 months prior to the restatement
announcement (PRERET) and the average change in sales for the prior 2 years
(CHSALES) to control for this performance monitoring effect. Finally, I control for
other potential monitoring effects by including the size of the firm (LOGMVE), an
indicator if the firm is audited by a large accounting firm (BIGN), and an indicator
whether the restatement is attributed to the auditor (AUDITOR).
As discussed previously, I include a control for general operating complexity
(OPCOMPLEX), which is the log of the sum of the number of the operating and
geographic segments found in Compustat. I include the firm’s 5-year average
accounts receivable (A/R) accrual prior to the restatement to control for high A/R
accruals (AR ACCRUAL) because Zhang (2006) argues that large accounts
82 K. Peterson
123
receivable accruals increase managers’ flexibility in managing revenue. Finally, I
include industry (designations from Palmrose et al. 2004) and year indicators to
control for industry and year effects that may affect complexity and the probability
of restatements.
4.1.2 Matched sample design
Following Johnson et al. (2009), I estimate the matched sample design using a
conditional logistic regression that accounts for the non-random sampling issues a
matched sample creates. The dependent variable is one if the firm restated revenue
and zero otherwise. Control variables from prior research should capture any
determinants for why managers might misreport the financial statements and are
summarized in Burns and Kedia (2006). In addition to the control variables used in
the restatement research design as described above,12 I include controls to capture
incentives related to growth, external financing, violating debt covenants, and
managerial equity incentives. These are the earnings-to-price ratio (EP) as another
proxy for growth, cash raised from issuing equity or debt (DEBT ISSUE and
EQUITY ISSUE), leverage as a proxy for closeness to violating debt covenants
(LEVERAGE), total operating accruals (OP ACC) because misreporting firms have
higher accruals (Dechow et al. 1996), and a measure of CEO equity incentives using
the pay-for-performance sensitivity of CEO stock options (LOG PPS). This last
variable measures the change in the value of stock options held for a percentage
change in the value of the firm, but including this variable in the model reduces the
sample size significantly as discussed in Sect. 3.2. A more detailed description of
each variable can be found in Tables 2 and 3. For revenue restatement firms, these
variables are all measured as of the fiscal year just prior to the restatement
announcement. For matched firms, the variables are measured as of the match year.
4.2 Results of tests of H1
Table 2 presents results from the logistic estimation of the restatement research
design. WORDS, METHODS, and RRC SCORE all have positive, statistically
significant coefficients (Z-stats of 3.71–4.60) indicating that revenue recognition
complexity increases the likelihood that a firm will restate revenue relative to other
restatement firms. This provides support for H1. Firms with greater operational
complexity (OPCOMPLEX), firms with an analyst sales forecast (SALESFCST), and
firms with larger A/R accruals are also more likely to restate revenue relative to
other restatement firms. In terms of economic significance, a one standard deviation
change in WORDS, METHODS, or RRC SCORE increases the probability of
revenue restatement by 8.7, 6.2, and 8.1 percent respectively. In the case of
WORDS, an increase from 50 to 223 words in the disclosure increases the
probability by 8.7 percent. Compared with the marginal effects of other variables in
the model, it appears to be one of the most significant determinants for misreporting
12 I exclude the variable AUDITOR from the matched-sample design because matched sample firms do
not have a restatement.
Accounting complexity, misreporting 83
123
revenue.13 Thus, while other determinants of misreporting are also important,
revenue recognition complexity provides a significant effect on determining which
firms will misreport revenue.
Table 3 presents results from the conditional logistic estimation of the matched
sample research design. Two main specifications are presented, where the second
specification includes an additional control for compensation incentives (LOG PPS).
The table presents results using only RRC SCORE to conserve space, but results are
consistent when using the other two proxies. The coefficients on revenue complexity
Table 2 Logistic regression estimates on the relation between revenue recognition complexity and
restatements using a restatement design
Pred. WORDS METHODS RRC SCORE
Coeff Z-stat Coeff Z-stat Coeff Z-stat
Complexity ? 0.330*** 4.36 0.082*** 3.71 0.344*** 4.60
OPCOMPLEX ? 0.317** 2.17 0.316** 2.16 0.306** 2.08
BTM - -0.198 -1.29 -0.261* -1.74 -0.231 -1.52
LOSSPER ? 0.438 1.56 0.387 1.39 0.392 1.40
SALESFCST ? 0.495*** 2.61 0.533*** 2.88 0.506*** 2.70
EARNVOL ? 0.005 0.99 0.006 1.04 0.005 1.01
CHSALES - -0.279* -1.65 -0.295* -1.71 -0.288* -1.67
PRERET - -0.262** -2.31 -0.259** -2.32 -0.257** -2.32
BIGN ± -0.407 -1.54 -0.346 -1.32 -0.398 -1.51
LOGMVE ± 0.034 0.64 0.009 0.19 0.018 0.35
AUDITOR ± 0.295 1.32 0.326 1.45 0.307 1.37
AR ACCRUAL ? 4.217*** 2.60 5.003*** 3.12 4.493*** 2.81
N 1,192 1,192 1,192
Pseudo R2 0.161 0.155 0.161
This table presents estimates of a logistic regression model where the dependent variable is one if the firm
restated revenue and zero if the firm had a restatement but restated something other than revenue.
Complexity is a placeholder for each of the three complexity proxies (WORDS, METHODS, RRC
SCORE) as described in Table 1. OPCOMPLEX is the log(GEOSEG ? OPSEG), where GEOSEG
(OPSEG) are the number of geographic (operating) segments reported for the firm in COMPUSTAT.
BTM is the firm’s book-to-market ratio at the end of the fiscal year just prior to the restatement
announcement. LOSSPER is the percentage of firm years with negative income prior to the restatement
announcement. SALEFCST is an indicator equal to one if the firm had an analyst sales forecast any time
prior to the restatement and zero otherwise. EARNVOL is the standard deviation of earnings scaled by the
absolute mean value of earnings for the five fiscal years prior to the restatement. CHSALES is the average
change in annual net sales for the 2 years prior to the restatement. PRERET is the 12-month stock returns
(including delisting returns) for the firm prior to the restatement. BIGN is an indicator equal to one if the
firm was audited by a large accounting firm and zero otherwise. LOGMVE is the log market value of
equity at the fiscal year end prior to the restatement. AR ACCRUAL is the 5 year average A/R accrual
scaled by sales prior to the restatement. Z-statistics are calculated using Huber/White robust standard
errors with firm-level clustering to adjust standard errors for multiple restatements from the same firm.
Results for the intercept, industry, and year indicators are not shown but are included in the model. *, **,
and *** Indicate significance at 10, 5, and 1%
13 In untabulated results, the marginal effects of the complexity variables are not statistically different
from the marginal effects of SALEFCST, PRERET, BIGN, or AR ACCRUAL.
84 K. Peterson
123
are positive and significant in both specifications, consistent with the findings in
Table 2 and H1. Although many of the coefficients are similar to those in Table 2,
the coefficient on CHSALES is now positive and significant, indicating that restating
firms in general have higher sales growth than non-restating firms. Table 3 results
also suggest that revenue restatement firms are more likely to access the equity and
debt markets and have higher operating accruals prior to a restatement announce-
ment compared to matched-sample control firms. Many of the significant results for
Table 3 Conditional logistic regression estimates on the relation between revenue recognition com-
plexity and restatements using a matched sample design
Predict Without PPS Including PPS
Coeff Z-stat Coeff Z-stat
RRC SCORE ? 0.491*** 4.48 1.252*** 2.92
OPCOMPLEX ? -0.115 -0.60 0.220 0.42
BTM - 1.609 1.55 -8.551 -1.05
LOSSPER ? 0.716 1.47 4.832* 1.65
SALESFCST ? -0.483* -1.68 1.763 1.08
EARNVOL ? 0.012 1.54 -0.001 -0.25
CHSALES - 1.092*** 2.96 6.364** 2.14
PRERET - -0.137 -0.96 0.735 1.26
BIGN ± -1.348*** -3.08 3.975* 1.85
LOGMVE ± -0.618*** -3.02 -2.462* -1.75
AR ACCRUAL ? -1.010 -0.58 -9.440 -0.45
DEBT ISSUE ? 0.874* 1.66 2.684 0.86
EQUITY ISSUE ? 0.845* 1.90 6.972 0.81
LEVERAGE ? -1.103 -1.59 -2.922 -0.74
EP - 0.165 0.44 0.317 0.12
OP ACC ? 3.741*** 3.34 -0.209 -0.03
LOG PPS ? 0.357 1.01
N 648 186
Pseudo R2 0.339 0.636
This table presents estimates of a conditional logistic regression model using a matched sample where the
dependent variable is one if the firm restated revenue and zero if the firm is a matched firm. DEBT ISSUE
is the sum of long- and short-term debt issued (dltis ? dltr) divided by average total assets for the fiscal
year prior to the restatement. EQUITY ISSUE is equal to common and preferred stock issued (sstk)
divided by average total assets for the fiscal year prior to the restatement. LEVERAGE is the ratio of
short- and long-term debt (dltt ? bast) divided by total assets for the fiscal year prior to the restatement.
EP is the ratio of earnings per share (epspx) to price (prcc_f) at the end of the fiscal year prior to
restatement. OP ACC is operating accruals (oiadp-oancf) divided by average total assets for the fiscal year
prior to the restatement. LOG PPS is the change in the value of stock options held for a percentage change
in the value of the firm as outlined in Core and Guay (2002) and Burns and Kedia (2006). The first three
specifications exclude LOG PPS from the model because it reduces the sample size considerably. All
other variables are defined in Tables 1 and 2. Z-statistics are presented using Huber/White robust standard
errors with firm-level clustering to adjust standard errors for multiple restatements from the same firm.
Results for industry and year indicators are not shown but are included in the model. *, **, and ***
Indicate significance at 10, 5, and 1%
Accounting complexity, misreporting 85
123
control variables disappear when LOG PPS is added to the model, most likely an
indication of losing power due to the sample being restricted.
I estimate marginal effects for the regressions in Table 3 as the average change in
the predicted probability as the variable for the treatment observation moves one
standard deviation centered on the observed value, holding all the other variables
constant at their observed values (see Greene 1997). A one standard deviation
increase in revenue complexity increases the probability of misreporting between
7.6 and 13.0 percent relative to the matched sample firms, which is similar to the
marginal effects of complexity in Table 2.
5 Irregularity and consequences tests
5.1 Irregularity tests
In this section I examine whether complexity is associated more with intentional or
unintentional misreporting and the effect of complexity on restatement conse-
quences. Hennes et al. (2008) classify a restatement as intentional if the restatement
disclosure discusses an irregularity or fraud, a board-initiated independent
investigation, or an external regulatory inquiry. To test whether complexity leads
to more mistakes or manipulation, I use their classification to estimate the following
model for the revenue restatement sample:
PðIRREGÞ ¼ f ðb0þb1Complexityþb2MULTIPLEþb3AUDITORþb4MISSFCST
þb5RESTLENþb6CHREV þb7CHNIþb8LOGMVEþb9BIGN
þb10�18INDUSTRYÞ ð2ÞI include three variables that provide some indication of intent: (1) whether the
firm restated more than just revenue (MULTIPLE); (2) whether the restatement is
credited to the firm’s auditor (AUDITOR); and (3) a dummy equal to one if the
restatement caused the firm to miss the sales forecast for the first period of the
restatement and zero otherwise (MISS FCST). I also include three measures of
the magnitude of the restatement: (1) the number of periods the company is restating
in quarters (RESTLEN); (2) the percentage change in revenue over all periods of the
misreporting due to the restatement (CHREV); and (3) the percentage change in net
income over all periods of the misreporting due to the restatement (CHNI). Finally, I
include controls for size (LOGMVE), whether the firm was audited by a large
accounting firm (BIGN), and industry indicators.
5.2 Irregularity results
The results for testing whether complexity is associated with errors or irregularities
are found in Table 4. The table presents results using only RRC SCORE, but results
are consistent when using the other two proxies. The coefficient on RRC SCORE is
positive, but not significant at the 10 percent level. Since revenue complexity is not
a significant predictor of the restatement being an irregularity, these results
86 K. Peterson
123
combined with the results from H1 suggest complex firms are likely to engage in
both intentional and unintentional misreporting. However, these results are
inconsistent with managers of complex firms pervasively exploiting complexity to
manipulate financial reporting.
5.3 Consequences of misreporting tests
I also examine the consequences of misreporting to determine whether stakeholders’
response to misreporting is affected by complexity. If stakeholders are aware of
complexity when they observe misreporting, it is possible they temper their
reactions to restatements for complex firms. I examine three reactions to
misreporting that provide evidence of intent: SEC Accounting and Auditing
Enforcement Releases (AAERs), restatement announcement returns, and CEO
turnover.
First, the issuance of an AAER represents a greater likelihood of intentional
actions.14 I test the following logistic regression model, where the dependent
Table 4 Irregularity logistic regression estimates
IRREG
Coeff Z-stat
RRC SCORE 0.135 1.03
BIGN -0.63 -1.55
MISSFCST 0.198 0.46
RESTLEN 0.042** 2.05
AUDITOR 0.509 1.33
MULTIPLE 0.250 0.90
LOGMVE 0.290*** 3.77
CHREV -0.305 -0.29
CHNI -0.036 -0.47
N 333
Pseudo R2 0.126
This table contains coefficient estimates of a logistic regression of IRREG (whether the firm’s restatement
was an irregularity as defined in Hennes et al. 2008) on revenue recognition complexity and control
variables. MISS FCST is an indicator equal to one if the restatement caused the firm to miss the sales
forecast for the first period of the restatement and zero otherwise. RESTLEN is the number of firm
quarters the firm restated. AUDITOR is an indicator equal to one if the auditor identified the restatement
and zero otherwise. MULTIPLE is an indicator equal to one if the firm’s restatement included additional
areas of restatement besides revenue and zero otherwise. CHREV (CHNI) is the percentage change in
revenue (net income) over all periods of the restatement due to the restatement. All other variables are
defined in prior tables. Z-statistics are listed below each coefficient, using Huber/White Robust standard
errors with firm-level clustering. *, **, and *** Indicate significance at 10, 5, and 1%
14 Erickson et al. (2006) correctly argue that SEC actions do not necessarily imply fraud or gross
negligence. In these cases, the action ends with a settlement and an AAER, the firm admits to no
wrongdoing but agrees to avoid future securities violations. However, Karpoff et al. (2008) find that 79
percent of enforcement actions in their sample from 1978 through 2006 include charges of fraud.
Accounting complexity, misreporting 87
123
variable is one if the firm has an AAER associated with revenue or receivables
within 3 years of the restatement announcement and zero otherwise:
PðAAERÞ ¼ f ðb0þb1Complexityþb2MULTIPLEþb3AUDITORþb4IRREG
þb5MISSFCST þb6RESTLENþb7CHREV þb8CHNIþb9LOGMVE
þb10BIGNþb11�19INDUSTRYÞ ð3ÞGenerally, studies on AAERs (Dechow et al. 1996, 2007; Beneish 1999) have
compared AAER firms with either a large sample of public firms or to small
matched-samples but have not modeled the probability of SEC involvement for a
specific misreporting event. I conjecture that restatement characteristics are
important in determining if the SEC issues an AAER in this setting. These
characteristics include managers’ intent to manipulate revenue, the magnitude of the
misstatement, and SEC exposure from issuing the AAER. I include the three
variables to identify intent as used in the irregularity regression (MULTIPLE,
AUDITOR, and MISS FCST) plus IRREG as previously defined. I also include three
measures of the magnitude of the restatement (RESTLEN, CHREV, and CHNI) as
defined previously. Finally, the SEC may target large firms (LOGMVE) and firms
audited by large accounting firms (BIGN) because it benefits from enforcement of
those firms relative to smaller firms.
I also test whether the market reaction to revenue restatement announcements
differs based on revenue recognition complexity using an OLS regression where the
dependent variable is cumulative abnormal market adjusted daily returns over a
5-day window (CAR).
CAR¼ b0þb1Complexityþb2MULTIPLEþb3AUDITORþb4IRREGþb5CHREV
þb6CHNIþb7LOGMVEþb8PREPETþb9�17INDUSTRYþ e ð4ÞPalmrose et al. (2004) document that restatement announcement returns are
negatively associated with restatements that are intentional, affect multiple
accounts, decrease net income, and are attributed to auditors or management. I
control for these findings using MULTIPLE, AUDITOR, and IRREG. I control for
the magnitude of the restatement by including both CHREV and CHNI as previously
defined. The model includes LOGMVE since adverse news is likely to be magnified
for small firms, which typically have weaker information environments than large
firms (Collins et al. 1987; Freeman 1987). To control for investors’ revisions of
future growth expectations, I include the recent stock returns (PRERET) as
previously defined.
Finally, I examine the effect of complexity on subsequent CEO turnover. Desai
et al. (2006) show that CEO turnover is greater for restatement firms than matched
sample firms, and Hennes et al. (2008) show CEO turnover is greater for
irregularities than unintentional errors. I test the following logistic regression
model where the dependent variable is one if the CEO resigned or was dismissed
from the firm within 2 years following the restatement announcement and zero
otherwise:
88 K. Peterson
123
PðCEO TURNÞ ¼ f ðb0 þ b1Complexityþ b2IRREGþ b3MULTIPLE þ b4LOGMVE
þ b5CHREV þ b6CHNI þ b7PRERET þ b8POSTRET þ b9ROA
þ b10CARþ b11CEOAGE þ b12TENURE þ b13CHAIR
þ b14�22INDUSTRYÞ ð5ÞConsistent with these prior studies, I include control variables that are associated
with CEO turnover following restatements. I include IRREG and MULTIPLE as
previously defined as partial controls for managerial culpability. I control for firm
size by including LOGMVE as previously defined. I also include both CHREV and
CHNI to capture the magnitude of the restatement. Prior firm performance is also
associated with CEO turnover decisions (Engel et al. 2003). Therefore, I include the
cumulative stock returns for the year prior to (PRERET) and the year following
(POSTRET) the restatement announcement to control for market-based perfor-
mance, and return on assets (ROA) prior to the restatement to control for operating-
based performance. I include the restatement announcement return (CAR) to capture
the market’s assessment of the restatement. I also include CEO controls that should
influence the turnover decision including the CEO’s age (CEO AGE), the CEO’s
tenure (CEO TENURE), and whether the CEO is also the chair of the board
(CHAIR).
5.4 Consequences of misreporting results
Descriptive statistics on the consequences of misreporting (untabulated) show
AAERs were enforced on 20 percent of revenue restatements in the sample and 31
percent of revenue restatement firms have CEO turnover in the 2 years following
the restatement. The mean announcement CAR is -10 percent consistent with the
findings in Palmrose et al. (2004).
Table 5 contains regression estimates for the consequences of misreporting tests.
The results are presented with the RRC SCORE complexity proxy only but are
similar when using WORDS and METHODS as proxy. The results for AAERs show
RRC SCORE is negatively associated with AAERs, indicating restatements
involving complex revenue recognition are less likely to receive an AAER,
consistent with the SEC recognizing the role of complexity in misreporting. The
results also show the SEC targets firms with irregularities (IRREG) and pervasive
restatement issues (MULTIPLE). Finally, CHREV has a significant negative
coefficient, which is expected if the SEC is more concerned with revenue
overstatements.
The results for announcement returns in Table 5 also show that firms with
complex revenue recognition have less negative announcement returns (coeff 0.029,
t-stat 3.05). The economic effect of complexity on returns is also significant. A one
standard deviation increase in RRC SCORE (1.22) increases announcement returns
by 3.5 percent. With an average market capitalization of $1.9 billion prior to the
restatement, the mean change in announcement return dollars is $68 million. The
results also show that understatements of revenue (CHREV) have higher
announcement returns, and restatements that are irregularities have much lower
Accounting complexity, misreporting 89
123
announcement returns (-9.7 percent). The coefficients on PRERET are also
negative, suggesting the market revised expectations to a greater degree for firms
with higher recent returns.
The CEO turnover regression in Table 5 suggests complexity also reduces the
probability of CEO turnover. Consistent with Hennes et al. (2008), irregularities are
associated with increased CEO turnover. Performance is also negatively related to
CEO turnover, and the CEO characteristics CEO TENURE and CHAIR also have
negative coefficients as expected. Overall, the results in Table 5 provide evidence that
firms with complex revenue recognition have less severe restatement consequences.
To better understand how complexity and intent interact in affecting the
consequences of misreporting, Table 6 reports selected coefficients for the same
Table 5 Consequences of misreporting regression estimates
AAER CAR CEO TURN
Coeff Z-stat Coeff t-stat Coeff Z-stat
RRC SCORE -0.534*** -3.22 0.029*** 3.05 -0.305** -2.15
BIGN 0.869 1.33
MISSFCST 0.092 0.14
RESTLEN -0.004 -0.11
AUDITOR -0.313 -0.67 0.044 1.38
IRREG 4.892*** 4.67 -0.097*** -4.40 1.012*** 3.31
MULTIPLE 1.240*** 2.76 -0.032 -1.43 0.469 1.43
LOGMVE 0.062 0.62 -0.003 -0.51 -0.140 -1.52
CHREV -4.001*** -3.02 0.428*** 3.24 -0.860 -0.82
CHNI 0.256** 2.32 0.001 0.22 0.090 1.02
PRERET -0.038*** -3.55 -0.302 -1.06
POSTRET -0.520* -1.82
ROA -1.257** -2.26
CAR -0.543 -0.79
CEO AGE 0.681 0.74
CEO TENURE -0.057** -2.18
CHAIR -0.631** -2.20
N 333 333 326
Pseudo R2/R2 0.375 0.187 0.193
This table contains logistic and OLS regression estimates to test if revenue recognition complexity affects
the consequences of restatement. AAER is an indicator equal to one if the firm has an SEC AAER related
to revenue or receivables within 2 years of the restatement announcement. CAR is the 5-day cumulative
abnormal return (market adjusted return) centered on the restatement announcement date. CEO TURN is
an indicator set to one if the CEO resigns or is terminated within 2 years of the restatement but excludes
CEO turnover where the former CEO retains a Chair or Director position. All other variables are
previously defined in prior tables. The CEO TURN regressions have seven observations with missing
CEO AGE, CEO TENURE, and CHAIR because the data was unavailable in proxy filings. Coefficients
on the intercept and industry indicators are included but not presented. Z-statistics (Logistic) or t-statistics
(OLS) are listed next to the coefficient, using Huber/White Robust standard errors with firm-level
clustering. *, **, and *** Indicate significance at 10, 5, and 1%
90 K. Peterson
123
regressions as in Table 5 but includes interactions with the complexity proxies and
IRREG. As in Table 5, the results are only presented for RRC SCORE but are
similar when using WORDS and METHODS. To make it easier to understand the
interaction effects, the regression includes two interactions with complexity: one for
cases where IRREG is equal to zero and one for IRREG equal to one. For AAERs,
complexity reduces the incidence of AAERs for both irregularities and errors,
although the reduction is larger for errors than irregularities. Announcement returns
are also less negative for both irregularities and errors. In contrast, complexity
reduces the probability of CEO turnover only in the case of irregularities, suggesting
that managers can hide behind complexity when there is some indication of intent.
While the coefficient on the interaction with mistakes (IRREG = 0) and complexity
is insignificant, this may be due to the already low probability of CEO turnover for
mistakes in general. Collectively, these results suggest accounting complexity
tempers restatement consequences for both errors and irregularities.
6 Additional analysis
The existence of a restatement includes the sequential events of misreporting and
detection of the misreporting; therefore, modeling these events separately may yield
better parameter estimates relative to traditional logit estimation (Callen et al.
2009). The two-stage partial observability probit model allows such estimation
when only the combined event is observed. Results for tests of H1 when using this
model are consistent with the results presented in the paper (complexity coefficient
Z-statistics of 5.17–8.34).
Prior to SAB 101, firms had a choice to disclose their revenue recognition policy
if they thought it was a significant policy. Since my proxy for revenue recognition
complexity relies upon these disclosures, a positive association between complexity
Table 6 Consequences of misreporting regression estimates with irregularity interactions
AAER CAR CEO TURN
Coeff Z-stat Coeff t-stat Coeff Z-stat
IRREG 5.117*** 4.89 -0.102*** -4.12 1.130*** 3.53
RRC SCORE (IRREG = 0) -1.242*** -4.30 0.022* 1.88 -0.093 -0.37
RRC_SCORE (IRREG = 1) -0.513*** -3.07 0.033*** 2.68 -0.407** -2.45
Controls included Yes Yes Yes
N 333 333 326
Pseudo R2/R2 0.378 0.188 0.197
This table contains logistic and OLS regression estimates to test if revenue recognition complexity affects
the consequences of restatement differently if the misstatement is intentional or unintentional. The
models are the same as those presented in Table 5, except I include interactions between the revenue
recognition complexity proxy (RRC SCORE) and IRREG. Coefficients on the intercept and other control
variables are included in the model but are not presented. Z-statistics (Logistic) or t-statistics (OLS) are
listed next to the coefficient, using Huber/White Robust standard errors with firm-level clustering. *, **,
and *** Indicate significance at 10, 5, and 1%
Accounting complexity, misreporting 91
123
and misreporting may be due to a regulation change. I conduct all the prior tests
after splitting the sample into pre- and post-SAB 101 restatements (fiscal years
2001). All results are consistent with the results presented in the paper except
coefficients on complexity are insignificant for the consequence regressions in the
pre-SAB 101 period; results remain consistent in the post-SAB 101 period. The
difference in results pre- and post-SAB 101 may imply that lack of disclosure
guidance in the pre-SAB 101 period caused firm disclosures to be less reliable
measures of the firm’s real revenue recognition polices, increasing noise in my
measures of complexity in the pre-period.
7 Conclusions
I investigate the effect of accounting complexity on misreporting using a setting of
revenue recognition complexity and revenue restatements. The results suggest that
in the case of revenue recognition, accounting complexity is a key factor in the
occurrence of misreporting. However, firm stakeholders temper the negative
consequences for misreporting when revenue recognition is complex. Given the
FASB’s interest in faithfully representing complex transactions, these results help
inform the FASB on stakeholders’ reactions to misreporting resulting from
complexity. Future research could examine other effects of accounting complexity
besides misreporting.
Acknowledgments This paper is based on my dissertation at the University of Michigan. I appreciate
the guidance and advice of my dissertation committee members, Russell Lundholm and Ilia Dichev, and
especially my chair, Michelle Hanlon. Author also thankful to the following for helpful comments: David
Guenther, Angela Davis, Judson Caskey, Lian Fen Lee, K. Ramesh, Jeff Wilks, Cathy Shakespeare, Chad
Larson, Peter Demerjian, anonymous reviewers, and workshop participants at the University of Michigan,
Washington University (St. Louis), University of Oregon, and Northwestern University.
Appendix
Example revenue recognition disclosures
A.C. Moore Arts & Crafts, 2005 10-K [WORDS: 8; METHODS: 1; RRC SCORE: -1.19]
Revenue is recognized at point of retail sale.
UStel, Inc., 1997 10-K [WORDS: 9; METHODS: 1; RRC SCORE: -1.45]
Revenue is recognized upon completion of the telephone call.
Regal Entertainment Group 2004 10-K [WORDS: 161; METHODS: 4; RRCSCORE: 0.14]
Revenues are generated principally through admissions and concessions sales with
proceeds received in cash at the point of sale. Other operating revenues consist
primarily of product advertising (including vendor marketing programs) and other
92 K. Peterson
123
ancillary revenues which are recognized as income in the period earned. We
recognize payments received attributable to the marketing and advertising services
provided by us under certain vendor programs as revenue in the period in which the
related impressions are delivered. Such impressions are measured by the concession
product sales volume, which is a mutually agreed upon proxy of attendance and
reflects our marketing and advertising services delivered to our vendors. Proceeds
received from advance ticket sales and gift certificates are recorded as deferred
revenue. The Company recognizes revenue associated with gift certificates and
advanced ticket sales at such time as the items are redeemed, they expire or
redemption becomes unlikely. The determination of the likelihood of redemption is
based on an analysis of our historical redemption trends.
Brooks Automation, 2002 10-K [WORDS: 284; METHODS: 7; RRC SCORE: 1.14]
Revenue from product sales are recorded upon transfer of title and risk of loss to the
customer provided there is evidence of an arrangement, fees are fixed or determinable,
no significant obligations remain, collection of the related receivable is reasonably
assured and customer acceptance criteria have been successfully demonstrated.
Revenue from software licenses is recorded provided there is evidence of an
arrangement, fees are fixed or determinable, no significant obligations remain,
collection of the related receivable is reasonably assured and customer acceptance
criteria have been successfully demonstrated. Costs incurred for shipping and handling
are included in cost of sales. A provision for product warranty costs is recorded to
estimate costs associated with such warranty liabilities. In the event significant post-
shipment obligations or uncertainties remain, revenue is deferred and recognized when
such obligations are fulfilled by the Company or the uncertainties are resolved.
Revenue from services is recognized as the services are rendered. Revenue from
fixed fee application consulting contracts and long-term contracts are recognized
using the percentage-of-completion method of contract accounting based on the
ratio that costs incurred to date bear to estimated total costs at completion. Revisions
in revenue and cost estimates are recorded in the periods in which the facts that
require such revisions become known. Losses, if any, are provided for in the period
in which such losses are first identified by management. Generally, the terms of
long-term contracts provide for progress billing based on completion of certain
phases of work. For maintenance contracts, service revenue is recognized ratably
over the term of the maintenance contract.
In transactions that include multiple products and/or services, the Company
allocates the sales value among each of the deliverables based on their relative fair
values.
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