accounting complexity, misreporting

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

intern

Highlight

intern

Highlight

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).


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.


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.


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

tics

are

signifi

cant

atth

e10

per

cent

level

.W

OR

DS

(PO

ST

WO

RD

S)

isth

e

num

ber

of

word

sin

the

reven

ue

reco

gnit

ion

footn

ote

dis

closu

rein

the

most

rece

nt

10-K

fili

ng

pri

or

to(f

oll

ow

ing)

the

rest

atem

ent

announce

men

t.M

ET

HO

DS

(PO

ST

ME

TH

OD

S)

isth

en

um

ber

of

tim

es‘‘

reco

gn

’’o

r‘‘

reco

rd’’

isu

sed

inth

ere

ven

ue

reco

gn

itio

nfo

otn

ote

dis

clo

sure

inth

em

ost

rece

nt

10

-Kfi

lin

gp

rio

rto

(fo

llo

win

g)

the

rest

atem

ent

announce

men

t.R

RC

SC

OR

E(P

OS

TR

RC

SC

OR

E)

isth

efa

ctor

score

of

(PO

ST

)W

OR

DS

and

(PO

ST

)M

ET

HO

DS

usi

ng

the

pri

nci

pal

com

ponen

tsm

ethods.

WO

RD

San

dM

ET

HO

DS

are

win

sori

zed

atth

e1

and

99

per

cen

tile

s.T

test

san

dW

ilco

xon

sig

n-r

ank

edte

sts

(bo

th2

-tai

led

)ar

eca

lcu

late

dfo

rth

ed

iffe

ren

cein

mea

ns

and

med

ian

s,re

spec

tiv

ely

.*

,*

*,

and

**

*In

dic

ate

sign

ifica

nce

at1

0%

,5

%,

and

1%


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

intern

Highlight

intern

Highlight

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.


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%


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.


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


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%


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.

References

American Institute of Certified Public Accountants (AICPA). (1998). Responding to the risk of improperrevenue recognition, AICPA practice alert no. 98–3. New York, NY: AICPA.

Beneish, M. (1999). Incentives and penalties related to earnings overstatements that violate GAAP. TheAccounting Review, 74(4), 425–457.


123

Bergstresser, D., Desai, M., & Rauh, J. (2006). Earnings manipulation, pension assumptions, and

managerial investment decisions. Quarterly Journal of Economics, 121(1), 157–195.

Burns, N., & Kedia, S. (2006). The impact of performance-based compensation on misreporting. Journalof Financial Economics, 79, 35–67.

Bushman, R., Chen, Q., Engel, E., & Smith, A. (2004). Financial accounting information, organizational

complexity and corporate governance systems. Journal of Accounting and Economics, 37(2),

139–201.

Callen, J., Robb, S., & Segal, D. (2009). Revenue manipulation and restatements by loss firms. Auditing:A Journal of Practice and Theory, 27(2), 1–29.

Collins, D., Kothari, S. P., & Rayburn, J. (1987). Firm size and the information content of prices with

respect to earnings. Journal of Accounting and Economics, 9, 111–138.

Core, J., & Guay, W. (2002). Estimating the value of employee stock option portfolios and their

sensitivities to price and volatility. Journal of Accounting Research, 40(3), 613–630.

Cox, C. (2005). Remarks before the 2005 AICPA national conference on current SEC and PCAOBdevelopments. Washington, DC, http://www.sec.gov/news/speech/spch120505cc.htm.

Dechow, P., Ge, W., Larson, C., & Sloan. R. (2007). Predicting material accounting misstatements.

Working Paper. University of Michigan and University of Washington.

Dechow, P., Sloan, R., & Hutton, A. (1996). Causes and consequences of earnings manipulation: An

analysis of firms subject to enforcement actions by the SEC. Contemporary Accounting Research,13, 1–36.

DeFond, M. L., & Jiambalvo, J. (1991). Incidence and circumstances of accounting errors. TheAccounting Review, 66, 643–655.

Desai, H., Hogan, C., & Wilkins, M. (2006). The reputational penalty for aggressive accounting: Earnings

restatements and management turnover. The Accounting Review, 81(1), 83–112.

Engel, E., Hayes, R., & Wang, X. (2003). CEO-turnover and properties of accounting information.

Journal of Accounting and Economics, 36, 197–226.

Erickson, M., Hanlon, M., & Maydew, E. (2006). Is there a link between executive equity incentives and

accounting fraud? Journal of Accounting Research, 44(1), 113–143.

Ertimur, Y., Livnat, J., & Martikainen, M. (2003). Differential market reactions to revenue and expense

surprises. Review of Accounting Studies, 8(2–3), 185–211.

Ertimur, Y., & Stubben, S. (2005). Analysts’ incentives to issue revenue and cash flow forecasts, Working

Paper. Stanford University.

Financial Accounting Standards Board (FASB). (2008). Conceptual framework for financial reporting:The objective of financial reporting and qualitative characteristics and constraints of decision-useful financial reporting information. FASB exposure draft.

Freeman, R. (1987). The association between accounting earnings and security returns for large and small

firms. Journal of Accounting and Economics, 9, 195–228.

Government Accountability Office (GAO). (2002). Financial statement restatements, trends, marketimpacts, regulatory responses, and remaining challenges. GAO-03-138.

Government Accountability Office (GAO). (2006). Financial restatements: Update of public companytrends, market impacts, and regulatory enforcement activities. GAO-06-678.

Greene, W. (1997). Econometric analysis (3rd ed., pp. 339–350). Upper Saddle River, NJ: Prentice Hall.

Hayn, C. (1995). The information content of losses. Journal of Accounting and Economics, 7, 85–107.

Hennes, K., Leone, A., & Miller, B. (2008). The importance of distinguishing errors from irregularities in

restatement research: The case of restatements and CEO/CFO turnover. The Accounting Review,83(6), 1487–1519.

Herz, R. (2005). Remarks before the 2005 AICPA national conference on current SEC and PCAOBdevelopments. Speech given by the FASB Chairman, Washington DC.

Herz, R. (2007). Specialized industry accounting and reporting. Too much of a good thing? Speech given

by the FASB Chairman, 26th Annual SEC and Financial Reporting Institute Conference.

Johnson, S., Ryan, H., Jr, & Tian, Y. (2009). Managerial incentives and corporate fraud: The sources of

incentives matter. The Review of Finance, 13(1), 115–145.

Karpoff, J., Lee, D., & Martin, G. (2008). The Consequences to managers for financial misrepresentation.

Journal of Financial Economics, 88(2), 193–215.

Kinney, W., & McDaniel, L. (1989). Characteristics of firms correcting previously reported quarterly

earnings. Journal of Accounting & Economics, 11(February), 71–93.

Palmrose, Z., Richardson, V., & Scholz, S. (2004). Determinants of market reactions to restatement

announcements. Journal of Accounting & Economics, 37, 59–89.

94 K. Peterson

123

http://www.sec.gov/news/speech/spch120505cc.htm

Picconi, M. (2006). The perils of pensions: Does pension accounting lead investors and analysts astray?

The Accounting Review, 81(4), 925–955.

Plumlee, M. (2003). The effect of information complexity on analysts’ use of that information. TheAccounting Review, 78(1), 275–296.

Plumlee, M., & Yohn, T. (2009). An analysis of the underlying causes attributed to restatements.

University of Utah and Indiana University Working Paper.

Schick, A., Gorden, L., & Haka, S. (1990). Information overload: A temporal approach. Accounting,Organizations and Society, 15, 199–220.

Securities and Exchange Commission (SEC). (2008). Final Report of the advisory committee onimprovements to financial reporting to the United States securities and exchange commissions.

August, 2008, link: http://www.sec.gov/about/offices/oca/acifr/acifr-finalreport.pdf.

Sondhi, A., & Taub, S. (2006). Miller revenue recognition guide. CCH Incorporated.

Turner, L. (2001). The times, they are a-changing. Speech by SEC chief accountant, 33rd rocky mountain

securities conference, Denver, Colorado. www.sec.gov/news/speech/spch493.htm.

Tversky, A., & Kahneman, D. (1974). Judgement under uncertainty: Heuristics and biases. Science, 185,

1124–1131.

Zhang, Y. (2006). An empirical analysis of revenue manipulation. Working Paper, Columbia University.


123

http://www.sec.gov/about/offices/oca/acifr/acifr-finalreport.pdf

http://www.sec.gov/news/speech/spch493.htm

Copyright of Review of Accounting Studies is the property of Springer Science & Business Media B.V. and its

content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's

express written permission. However, users may print, download, or email articles for individual use.

accounting complexity, misreporting

Documents