GUTMANN-SEBASTIAN-6000737-IB-THESIS

1
Master Thesis
Accounting and Auditing Enforcement Releases (AAERs)
and Internal Control over Financial Reporting
Maastricht University
School of Business and Economics
Maastricht, 10 August 2014
Gutmann, Sebastian (i6000737)
M.Sc. International Business: Accountancy
Course: Writing a Master Thesis
Thesis Supervisor: Caren Schelleman

AAERs and internal control over financial reporting S. Gutmann
2
Accounting and Auditing Enforcement Releases (AAERs) and
Internal Control over Financial Reporting
Sebastian Gutmann
SUMMARY: This paper investigates the effect of internal control weaknesses on the likelihood
to receive Accounting and Auditing Enforcement Releases (AAERs). My sample is divided into
two equally large groups of companies: one group that received at least one AAER in 2009 and
one group that did not. I find that there is a significant positive relation between internal control
weaknesses and AAERs being issued in 2009, imputing that companies that exercise weak
systems of internal control are more likely to be convicted of earnings management. This finding
is robust against alternative definitions of the control variables in my model. However, despite a
good model-fit statistics, the model itself is reported to be insignificant, imposing a very severe
limitation of my finding. Nevertheless, this study might provide valuable insights for companies,
stakeholders, auditors and regulators alike.
Keywords: AAER; internal control; financial reporting quality; misstatement; material
weaknesses; earnings management.

3
I. INTRODUCTION
n this paper I examine the relation between the quality of a system of internal control over
financial reporting and Accounting and Auditing Enforcement Releases (AAERs).
According to the Public Company Accounting Oversight Board (PCAOB), when there is a
material weakness in internal control over financial reporting, there is “more than a remote
likelihood that a material misstatement of the annual or interim financial statements will not be
prevented” (Doyle et al., 2007). Prior literature also suggests a relation between material
weaknesses in internal control and financial statement reliability. The studies of Doyle et al.
(2007) and Ashbaugh-Skaife et al. (2008) both examine the relation between internal control
weaknesses and accruals quality, a common proxy of earnings management. They find that poor
accruals quality is indeed driven by company-level material weaknesses in internal control.
Similarly, Chan et al. (2008) examine the relation between internal control weaknesses and
material restatements, another proxy of earnings management. Their results are consistent with
the studies mentioned before, as they find a significant positive relation between internal control
weaknesses and material restatements of financial statements.
While it is obvious that there is a solid foundation of prior literature in the field of internal
control quality in relation to financial statement reliability, I aim to contribute to this field of
study by examining the effect of internal control quality on the likelihood to receive an AAER.
Since AAERs are issued by the Securities and Exchange Commission (SEC) against a listed
company or its officer(s) upon misconduct with U.S. GAAP, it qualifies as a proxy for earnings
management. Based on prior research, I expect to find a positive relation between internal control
weaknesses and AAERs being issued by the SEC.
I read and categorized all AAERs that were issued in 2009 (n=180). After eliminating
redundant observations, such as AAERs issued against auditors, or AAERs concerning the period
I

4
prior to 2004 (for which no internal control reports are available), I arrived at a sample of 31
companies that received an AAER in 2009. I matched these AAER companies with control firms
that were (close to) identical to the AAER companies in terms of size and industry. Having a
sample of 62 companies, composed in part of firms that were identified as having practiced
earnings management in the AAER, and in part of non-misstating firms, I tested whether internal
control quality differed between those two firm characteristics. After controlling for inherent firm
characteristics such as size, profitability, age, growth potential, leverage, and industry, I find that
there is indeed a significant positive relation between internal control weaknesses and AAERs
being issued in 2009. This finding is robust against alternative definitions of control variables in
my model and in line with prior research (Ashbaugh-Skaife et al., 2008; Chan et al., 2008; Doyle
et al., 2007). My findings imply that a strong system of internal control over financial reporting
can have significant long-term benefits such as increased financial statement reliability and less
risk of SEC prosecution.
My study makes several contributions. First, I add to the existing earnings management
literature by introducing a proxy of earnings management that, to my knowledge, has not yet
been used in this context, to wit AAERs. Next, I expect the study to contribute to an increased
understanding of the benefits of internal control systems by corporations. U.S. regulators noted
that it is difficult to communicate the benefits of section 404 of SOX (Bailey, 2004). My study
provides insights into how compliance with section 404 can benefit corporations by mitigating
the likelihood of receiving an AAER, which according to Dechow et al. (1996) results in a higher
cost of capital for organizations. Third, it is also relevant for regulators in the United States that
are being criticized for the increased cost of compliance with section 404 of SOX (Raghunandan
and Rama, 2006). Finally, it can provide implications for auditors that face severe risk of

5
litigation resulting from non-detection of material misstatements (in the annual report), often
exposed by the SEC in the AAERs.
The remainder of this paper is organized as follows. Section two discusses relevant prior
research that is connected to my study and motivates the hypothesis. Next, data gathering and
methodology is described. Following, the empirical findings are presented in the fourth section.
Finally, the last section contains the conclusion and limitations of my study.
II. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT
Internal Control over Financial Reporting and SOX
My research aims at further developing our understanding of how financial statement
reliability is affected by the quality of a system of internal control. The Committee of Sponsoring
Organizations of the Treadway Commission (COSO) defines internal control as being an internal
process […] designed to provide reasonable assurance regarding the achievement of objectives
relating to operations, reporting, and compliance (COSO, 2013). Clients’ weak internal control
was identified by U.S. regulators as one aspect contributing to the reporting of unreliable
financial information, exposed by the collapse of Enron in 2001 (Gordon, 2002). As a direct
response, U.S. regulators ratified SOX in 2002 to regain investor confidence in, and improve
reliability of financial accounting and reporting practices of publicly traded firms (Altamuro and
Beatty, 2007; Ettredge et al., 2006). The act aims at preventing deceptive accounting behavior by
imposing more oversight and higher penalties for managerial misconduct (Zhang, 2007).
Arguably the most prominent section of the act is section 404 that requires management to
establish an effective internal control system. Furthermore, section 404 requires management and
auditors to separately assess the effectiveness of the system of internal control and auditors to
report on management’s internal control assessment (Alexander et al., 2013: Cheng et al., 2013;

6
Ge and McVay, 2005; Goh et al., 2013; Krishnan et al., 2008). Subsequently, the internal control
assessments need to be filed along with the annual report at the SEC.
Compliance with Section 404 of SOX
Krishnan (2005) noted “the emphasis on good internal control of course arises because it
is considered to be an important factor in achieving good quality financial reporting”. However,
this increased quality of financial reporting comes at a cost for U.S. listed companies (Zhang,
2007). A main cost driver identified by management is section 404 of SOX which considerably
increased compliance costs for listed companies (Powell, 2005).
Raghunandan and Rama (2006) were among the first to examine the effect of compliance
with section 404 on audit fees. Their sample includes 660 manufacturing firms that have a fiscal
year-end on December 31, 2004. They compare fees paid to auditors for the years 2003 and 2004
and find a mean increase of 84 percent. While it is unlikely that this increase is solely attributable
to compliance with section 404, the magnitude of the increase from 2003 to 2004 suggests that a
considerable amount of money is spent on auditors’ internal control assessment. A survey
conducted by Financial Executives International (FEI) in 2005 reveals that section 404
compliance costs amount to an average of $4.36 million for 217 companies that report average
revenues of $5 billion (Cheng et al., 2012). Krishnan et al. (2008) examine the components of
compliance costs to companies and find that it can be classified into three distinct categories:
internal labor costs, external consulting and technology expenses, and auditor attestation charges.
The additional audit fees are mainly driven by the integrated audit that incorporates the internal
control assessment. Moreover they find that section 404 imposes disproportionate costs for
smaller firms and claim that it provides few benefits for investors (Krishnan et al., 2008). These
findings are supported by Alexander et al. (2013) who find that corporations do not perceive the

7
compliance benefits to outweigh the costs. They note that this is particularly apparent for smaller
companies that have a higher cost of compliance per dollar of assets.
Effect of Internal Control on Earnings Informativeness
Naturally, given the large amount of additional resources needed to comply with section
404 of SOX, scholars, management, and stakeholders alike are curious about the effect of internal
control systems on the reliability of financial statements.
Singer and You (2011) provide insights into how section 404 of SOX provides benefits
for market participants. Using abnormal accruals as a proxy of earnings accuracy, they compare
accelerated filers1
with non-accelerated filers2
and find that reliability of reported earnings is
higher for accelerated filers. Their results also suggest that section 404 contribute to earnings
quality by reducing the proportion of intentional misstatements. Furthermore, the authors claim
that the predictive power of earnings of accelerated filers increase, resulting in greater
informativeness of reported earnings to third parties (Singer and You, 2011). A similar study by
Chen et al. (2013) examines the effect of auditor attestation under section 404 of SOX on
earnings informativeness. More precisely, they examine whether annual earnings are more
closely associated with information in stock returns after adoption of section 404. They find that
earnings informativeness was greater in the section 404 adoption year than in the previous year
for companies with internal control reports that contain no material weakness. The authors
employed a control sample consisting of non-accelerated filers that were not subject to
compliance with section 404 in the adoption year. Within this control sample, there was no
1
The SEC defines accelerated filers as companies that have at least $75 million of common equity, have previously
filed at least one annual report, were subject to the Exchange Act for at least one fiscal year, and do not qualify as a
small business under SEC rules.
2
Non-accelerated filers do not meet the SEC’s definition of accelerated filers. Non-accelerated filers are
permanently exempt from compliance with SOX section 404(b).

8
change in earnings informativeness, highlighting the positive effect of audited internal control
systems on the informativeness of reported earnings (Chen et al., 2013).
Other studies examined earnings informativeness with respect to material weaknesses in
internal control. The PCAOB defines a material weakness as “a deficiency, or a combination of
deficiencies, in internal control over financial reporting, such that there is a reasonable
possibility that a material misstatement of the company's annual or interim financial statements
will not be prevented or detected on a timely basis” (PCAOB, 2004). In a very recent study,
Myllymäki (2014) investigates the persistence in association between section 404 material
weaknesses and financial reporting quality. Fundamentally, the study builds on the view that
misstatements in financial statements indicate a failure of the system of internal control. The
author examines whether section 404 material weakness disclosures allow inferences about future
financial reporting quality. She finds that there is an increased probability of undiscovered
material misstatements in financial information reported by companies that disclosed material
weaknesses in internal control as opposed to companies that disclosed no such weakness.
Furthermore, the higher probability of misstated financial information persists on average two
years after the remediation of the material internal control weakness, indicating that the system of
internal control is still not as effective in detecting and preventing misstatements in comparison
with companies that have a clean record of material weaknesses in internal control (Myllymäki,
2014). Similar results are found by Bedard et al. (2012) who investigate earnings quality in the
presence of a variety of material internal control weaknesses. The authors find evidence that
remediation of some internal control weaknesses result in significant changes in abnormal
accruals, where abnormal accruals are employed to proxy earnings quality. In particular,
remediation of entity-wide internal control weaknesses, such as improper segregation of duties
and weaknesses related to information technology, have a significant effect on a reduction of

9
abnormal accruals. However, if the weaknesses remain unremedied for two years, abnormal
accruals significantly increase. The latter is true for all internal control weaknesses, whether it
affects entity-wide weaknesses or account specific weaknesses (Bedard et al., 2012).
Other researchers suspected a relation between weaker internal control quality and higher
earnings management. This relationship is intuitively appealing since it can be assumed that
management is able to manage earnings more aggressively in environments that exhibit less than
ideal systems of internal control. Doyle et al. (2007) investigate the relationship between a weak
system of internal control and earnings management. The authors work with a large sample of
705 firms that disclose at least one material weakness and use accruals quality as a proxy of
earnings management. They employ the accruals quality measure developed by Dechow and
Dichev (2002) and find that internal control weaknesses are associated with poor accruals quality.
Furthermore they find that poor accruals quality is mainly driven by company-level material
weaknesses, rather than by account-specific material weaknesses. The results are generally robust
against the difficulty in accrual estimation, known determinants of material weaknesses, and
corrections for self-selection bias (Doyle et al., 2007). However, the authors acknowledge that
SOX was in effect for a relatively short time, which might limit inference of causality between
internal control weaknesses and accruals quality. This implies that there is a need to extend the
research on the effect of internal control on financial statement reliability and highlights the
relevance of my research.
More recent studies by Chan et al. (2008) and Ashbaugh-Skaife et al. (2008) also
investigate the relationship between internal control weaknesses and earnings management. In
their research, the authors focus on discretionary accruals as a proxy of earnings management and
find mild evidence that there are more positive discretionary accruals for firms that report internal
control weaknesses as compared to other firms. Additionally, Chan et al. (2008) introduce

10
another proxy of earnings management: material restatements. They find a significant
relationship between weak internal control quality and material earnings restatements,
consolidating a general conception about a positive relationship between weak systems of
internal control and earnings management.
The above mentioned studies reveal that there is a solid foundation of prior literature in
the field of earnings management related to internal control system quality. However, as
indicated by Francis (2011), it is by far exhaustive. I plan to contribute to the existing literature
by investigating the relationship between weak internal control systems and financial statement
reliability. I add to prior literature by introducing another proxy of financial statement reliability
that, to my knowledge, has not yet been used in this context, namely Accounting and Auditing
Enforcement Releases (AAERs). “AAERs are issued by the SEC during or at the conclusion of
an investigation against a company, an auditor, or an officer for alleged accounting or auditing
misconduct” (Dechow et al., 2011). I argue that this research is relevant and value adding for
several reasons. First, Dechow et al. (2010) point out in their study that AAERs belong to what
they call the group of “external indicators of earnings misstatements”, which delivers academic
justification that AAERs are a relevant proxy for financial statement reliability. Second, AAERs
are also likely to capture a group of economically significant manipulations as the SEC has
limited resources and likely pursues the most important cases (Dechow et al., 2011). Third,
linking AAERs as a proxy of earnings management to quality of internal control systems is to my
knowledge unprecedented in academic literature. The resulting hypothesis therefore reads as
follows.
H1: Material weaknesses in internal control are positively associated with AAERs issued
by the SEC.

11
III. METHOD
Variables and Model
I want to contribute to the existing earnings management literature by examining the
effect of internal control weaknesses on the likelihood to receive an AAER. I believe AAERs are
a suitable proxy of earnings management because as Dechow et al. (2010) indicate, AAERs
belong to the group of external indicators of earnings management. Consequently, I want to test
the effect of several variables on the presence of AAERs issued by the SEC. As a result,
AAER2009 will be the binary response variable. It takes one of two forms: 1 if the SEC issued an
AAER against the company in the sample in 2009; 0 if no AAER was issued in 2009. I opted to
include AAERs from 2009 arbitrarily. Please note that my sample includes 31 companies that
received an AAER in 2009 and another 31 control companies that match the AAER firms in size
and industry, which did not receive an AAER in 2009. The process of data collection and the
underlying rationality will be presented in the following section.
Since I want to test the effect of internal control weaknesses on the presence of AAERs,
ICWtx
3
is the binary explanatory variable. Identical to the response variable, ICWtx also takes one
of two mutually exclusive forms: 1 if the company disclosed one or more internal control
weaknesses during the period affected by the AAER4,5
; 0 if no internal control weaknesses were
disclosed in the affected period. I supplement the model with multiple control variables to
increase the conclusiveness of the model and to control for correlation bias.
SIZEtx depicts the total assets of a given company at the fiscal yearend. Similar to the
study of Ashbaugh-Skaife et al. (2008), I aim to control for the difference in size between the
3
tx reflects the period of financial statement manipulation identified by the SEC in the AAER. Control firms’ “period
affected” is equivalent to their AAER firm counterparts.
4
The “period affected” by the AAER describes the time span over which the company was accused of financial
statement manipulation by the SEC.
5
The period of financial statement manipulation, identified by the SEC in the AAER, is not limited to one fiscal
year.

12
companies in the sample. In their study, the authors examine the effect of internal control
weaknesses on accrual quality. Given that both, accrual quality and AAERs are proxies for
earnings management, I argue that it is useful to include a measure of size in the model. Dechow
and Dichew (2002) find that smaller firms have lower quality accruals. Consequently, I expect
SIZEtx to be negatively associated with AAERs being issued in 2009. ROAtx is defined as pretax
income divided by total assets and thus describes normalized pretax income at fiscal yearend. I
argue that it is useful to include normalized pretax income because it controls for differences in
profitability among companies in the sample. Myllymäki (2014) argues that adding an indicator
variable of profitability controls for the association between poor financial performance and low
financial reporting quality. Since low financial reporting quality is likely to result in the issuance
of an AAER by the SEC, I believe that including ROAtx is also applicable in this context. Based
on the results of Myllymäki (2014), I expect ROAtx to be negatively related to AAERs issued in
2009. I match SIZEtx and ROAtx to the year of manipulation6,7
, for companies that received an
AAER. The same year is used to collect the financial data on the respective control firms. I
believe matching financial data to the period of manipulation improves reliability of the model
due to increased timeliness.
I include two more company-wide control variables, namely LEVERAGEtx and
GROWTHtx. Prior literature on AAERs by Dechow et al. (2011) suggests introducing leverage as
a control variable. This is also applicable in this study since high leverage might be an incentive
to manage earnings in order to appear more attractive to investors and lenders. I used the same
definition of leverage as Dechow et al. (2011) to wit long-term debt divided by total assets. The
authors find in their study that leverage does not motivate financial misrepresentations, expressed
6
If the period of financial statement manipulation exceeds one fiscal year, the most recent year of manipulation is
used.
7
The year(s) of financial statement manipulation are indicated by the SEC in the AAER.

13
by AAERs. However, their study reveals a positive insignificant relationship between leverage
and AAERs, which leads me to expect to find the same relationship in my study. GROWTHtx is
defined as the market-to-book value of equity by Collins and Kothari (1989). In their study on the
change in earnings informativeness after the introduction of SOX, Chen et al. (2013) use this
definition to control for different levels of growth. I argue that it is useful to include an indicator
of growth in my model since different levels of growth, particularly low levels, might incentivize
managers to manipulate financial statement information.
Another aspect that needs to be controlled for is public age, depicted by AGEtx and defined
as the time span lapsed between the company’s initial public offering (IPO) and the most recent
year of financial statement manipulation identified by the SEC. Doyle et al. (2007) indicate that
firms showing material weaknesses in internal control tend to be, among other attributes,
younger. Myllymäki (2014) also includes a predictor variable for age in her study. Based on the
results of her statistical analysis, I expect to find a negative relationship between AGEtx and
AAERs issued in 2009.
Moreover, I want to control for the different industries in which my sample firms operate.
For that purpose, I construct a set of dummy variables representing the nine industries that I want
to control for in my model (IND_AGRICULTUREtx, IND_MININGtx, IND_CONSTRUCTIONtx,
IND_MANUFACTURINGtx, IND_TRANSPORTATIONtx, IND_WHOLESALEtx, IND_RETAILtx,
IND_SERVICEStx, and IND_PUBLICtx). Note that I used the Standard Industrial Classification
(SIC) codes to classify the companies into the respective groups at the most recent year of
financial statement misrepresentation. I follow the model of Chan et al. (2008) by excluding the
Finance, Insurance, and Real Estate industry from my sample, as companies in that industry
show very different characteristics, particularly with respect to the structure of their balance

14
sheet, in comparison with the other industries. A more elaborate explanation of the industry
categorization will be presented in the following section.
Furthermore, as mentioned above, all AAERs that were issued against the companies in
my sample affect one or multiple years in the period between the year 2004 and 2007. Since
macroeconomic factors are likely to change over the course of a four-year time span, I add
another set of dummy variables to control for changing macroeconomic conditions. These
variables are labeled YEAR2004, YEAR2005, YEAR2006, and YEAR2007 respectively. As with the other
binary variables in my model, the year dummies received a 1 if the AAER identified financial
statement manipulation in the respective year or a 0 if it did not. Please note that due to the nature
of AAERs, it is possible to get a 1 assigned for multiple year variables if the manipulation
prolonged over several years.
Lastly, during the process of AAER categorization, outlined in the following section, I
encountered that the SEC, on several occasions, issued multiple AAERs against the same
company for the same syndicate. This happens when for example one AAER is issued against a
company and another is issued against an officer of that exact same company. This might be in
itself informative and is worthy to control for. Therefore I include MULTIPLE_AAER2009 in my
preliminary model, which reads as follows:
AAER2009 = β0 + β1ICWtx + β2SIZEtx + β3ROAtx + β4GROWTHtx + (1)
β5LEVERAGEtx + β6AGEtx + β7IND_AGRICULTUREtx + β8IND_MININGtx +
β9IND_CONSTRUCTIONtx + β10IND_MANUFACTURINGtx +
β11IND_TRANSPORTATIONtx + β12IND_WHOLESALEtx + β13IND_RETAILtx +
β14IND_SERVICEStx + β15IND_PUBLICtx + β16YEAR2004 + β17YEAR2005 +
β18YEAR2006 + β19YEAR2007 + β20MULTIPLE_AAER2009 + ε*

15
However, after categorizing all sample firms according to SIC codes, it is apparent that
the industry variables IND_AGRICULTUREtx, IND_MININGtx, IND_TRANSPORTATIONtx, and
IND_PUBLICtx are redundant. This is because of a zero count of sample firms in these categories.
The revised final model therefore reads as follows:
AAER2009 = β0 + β1ICWtx + β2SIZEtx + β3ROAtx + β4GROWTHtx + (2)
β5LEVERAGEtx + β6AGEtx + β7IND_CONSTRUCTIONtx +
β8IND_MANUFACTURINGtx + β9IND_WHOLESALEtx + β10IND_RETAILtx +
β11IND_SERVICEStx + β12YEAR2004 + β13YEAR2005 + β14YEAR2006 + β15YEAR2007 +
β16MULTIPLE_AAER2009 + ε*
Data Gathering and Sampling
In an effort to inform stakeholders, the SEC makes AAERs publicly available. On the
institution’s online webpage, one can easily access every AAER issued from 1999 onwards. At
the time of writing this report, 3558 AAERs were issued against officers, companies and auditors.
I carefully read and categorized all AAERs issued by the SEC in 2009. An exemplary extract of
the AAER categorization is provided in exhibit 1. First, I documented the official AAER number
given by the SEC. The year 2009 contains AAERs 2914 until 3093 and thus 180 AAERs in total.
Next, I extracted data against whom the release was issued: (1) officer(s), (2) company, (3)
auditor, or (4) other. If the AAER accused either an officer or a company, the next step was to
document the company’s name. Following that, I extracted information on the reason why the
AAER was issued. I encountered that a variety of reasons can lead to SEC prosecution, ranging
from compliance failures to stock options backdating. Since I want to test the relationship

16
between internal control weaknesses and financial statement reliability, I am particularly
interested in earnings management. I therefore categorized reason for receiving an AAER into:
(1) financial statement manipulation in case of earnings management, (2) other in all other cases
where the release was issued against an officer or a company, and (3) issued against auditor, if
the affected party was an auditor. While (3) issued against auditor might appear repetitive or
redundant on first sight, it enhanced clarity for myself. In case of financial statement
manipulation, the next note describes the period affected by the manipulation(s). The SEC
provides the period that is affected by financial statement manipulation in the AAER. This period
is not necessarily limited to one fiscal year. Most of the time, the manipulations affect multiple
years. Lastly, I added a short description of the reason for receiving an AAER, if the release was
issued against either officer(s) or company.
Having categorized all 180 AAERs that were issued in the year 2009, the next step
necessary was to eliminate redundant data to arrive at a working sample (exhibit 2). First I
eliminated all AAERs that were issued against auditors and others, which decreased the sample
size to 143. Next, all entries stating reasons other than financial statement manipulation were
excluded, decreasing the total number to 99. Third, all entries that affected the period prior to
2004 were eliminated, limiting the sample to 59 observations. This was necessary because prior
to 2004, companies were not obliged to file an internal control report along with the annual report
at the SEC, which makes testing a relationship impossible. Next, I eliminated multiple AAERs
against the same company for the same period arbitrarily, which left me with an effective sample
of 42 observations. Moreover, I had to exclude 3 more entries for which no financial information
could be obtained from either Compustat or EDGAR online, reducing the AAER sample to 39
observations. Finally, I excluded 8 more companies that belonged to the SIC category of financial
institutions. This was a necessity because companies in that particular industry differ too much

17
from the rest of the sample in terms of balance sheet and income statement structure. Ideally, I
would have wished a larger sample size. Unfortunately due to time constraints, including a
second year into the AAER database was not feasible.
Having categorized and eliminated AAERs, the next step was to include an equal number
of control firms in the sample. In order to obtain maximum informativeness and to ensure
comparability, the control firms should be similarly large in size and operate in the same industry.
To facilitate good matching of firms, I first ranked the AAER firms according to size in terms of
total assets from largest to smallest. Next, I searched the Compustat North America database for
companies that were identical (or as close as possible) to the ranked AAER firms in terms of total
assets for the fiscal year at hand. The fiscal year at hand depended on the year of financial
statement manipulation identified by the SEC in the AAER. Next, I picked the control firm that
best matched the AAER firm in terms of total assets and SIC category. As mentioned previously,
I excluded the Finance, Insurance, and Real Estate industry, leaving me with 9 out of 10 initial
SIC categories. Exhibit 3 presents the 31 AAER firms and their respectively matched control
firms, ranked in size.
Having arrived at a sample size of 62 firms, containing 31 AAER firms and 31 control
firms, spread across five different industries, the last step left was to gather the data on the
variables described in equation 2 above. Data on internal control weaknesses and public age was
gathered from the Audit Analytics database. Note that rather than simply looking at one fiscal
year, I assigned control firms a 1 for the variable ICWtx if they reported an internal control
weakness in any year of the affected period of the respective AAER firm. This of course
increases the likelihood of receiving a 1 for this binary variable and might result in slightly
conservatively biased results when testing the effect of ICWtx on AAER2009. Data on the financial

18
explanatory variables and industry classification was obtained through Compustat North America
and EDGAR online for the respective years.
IV. RESULTS
My final empirical model (see equation 2) consists of 16 variables. In order to enhance
clarity, Table 1 presents an overview of the variables that will be tested in the following analyses.
Tables 2 and 3 provide an overview on the descriptive statistics. Table 2 depicts frequencies on
four sorts of binary variables: AAER2009, ICWtx, MULTIPLE_AAER2009, and the five industry
variables. From Panel A, it is observable that the sample indeed contains 31 companies that were
subject to SEC prosecution in 2009 as well as 31 control firms, which did not receive an AAER
during that year. It is also obvious that there are a significantly larger number of firms included in
the sample that did not encounter any internal control weaknesses. Furthermore, we can see that
58.1 percent of the AAER firms received multiple AAERs in 2009 for the same syndicate. This is
possible because the SEC can prosecute officer(s) and company. Panel B depicts the main
industries that were identified during the sampling process with the help of SIC codes. The
construction and wholesale trade industries are represented with 2 firms each, the retail trade
industry counts 8 firms. Services and manufacturing complete the sample with 22 and 28
observations respectively. Please note that due to the matching of size and industry, each
category consists of an equally great number of AAER firms and control firms.
Table 3 describes all variables in use. Due to the binary nature AAER2009, ICWtx,
MULTIPLE_AAER2009, the industry dummies, and the year dummies take one of two mutually
exclusive forms: 0 or 1, which naturally represent the minimum and maximum value. SIZEtx is
presented in millions of dollar. The smallest company in the sample accounts for total assets of 6
million, whereas the largest company accounts for assets of more than 21 billion. Needless to say,

19
AAER 2009
ICW tx
SIZE tx
ROA tx
AGE tx
LEVERAGE tx
GROWTH tx
IND_CONSTRUCTION tx
equal to 1 if two-digit SIC code is between 15-17; equal to 0 if not
IND_MANUFACTURING tx
IND_WHOLESALE tx
IND_RETAIL tx
IND_SERVICES tx
Equal to 1 if two-digit SIC code is between 70-89; equal to 0 if not
YEAR 2004
YEAR 2005
YEAR 2006
YEAR 2007
MULTIPLE_AAER 2009
LN_SIZE tx Natural log of total assets at most recent year of misstatement
ROA_NEW tx Standardized net income [net income / total assets]
AVE_GROWTH tx Average market-to-book value of equity over 3 years prior to misstatement
LN_LEVERAGE tx Natural log of leverage ratio [long-term debt / total assets]
LN_AGE tx Natural log of time span between IPO and misstatement year-
-
-
-
+
?
?
Total assets at most recent year of financial statement manipulation
Standardized pretax income [pretax income / total assets]
Time span between IPO and most recent year of earnings management
Leverage ratio [long-term debt / total assets]
-
?
?
?
?
?
-
-
-
+
Equal to 1 if earnings management was detected in 2007; equal to 0 if not
AAERs issued by the SEC in 2009
Material internal control weaknesses reported during period of earnings
management identified by the SEC in the AAER
Equal to 1 if multiple AAERs were issued in 2009 against the same
company; equal to 0 if not
Growth potential [market value of equity / book value of equity]
SIC category construction [two-digit SIC codes 15-17]
SIC category manufacturing [two-digit SIC codes 20-39]
SIC category wholesale trade [two-digit SIC codes 50-51]
SIC category retail trade [two-digit SIC codes 52-59]
SIC category services [two-digit SIC codes 70-89]
TABLE 1
Variable Definitions
?
?
?
Variable Name Predicted Sign Definition
+

20
(SIZE tx in millions of USD)
Minimum Maximum Mean Median Std. Dev.
AAER 2009 0.00 1.00 0.50 0.50 0.50
ICW tx 0.00 1.00 0.26 0.00 0.44
SIZE tx 6.00 21369.00 1654.94 481.50 3669.76
ROA tx -1.22 0.25 -0.06 0.03 0.26
AGE tx 1.00 73.00 10.45 8.00 11.02
LEVERAGE tx 0.00 0.66 0.16 0.10 0.17
GROWTH tx 0.95 18.00 3.65 2.52 3.33
IND_CONSTRUCTION tx 0.00 1.00 0.03 0.00 0.18
IND_MANUFACTURING tx 0.00 1.00 0.45 0.00 0.50
IND_WHOLESALE tx 0.00 1.00 0.03 0.00 0.18
IND_RETAIL tx 0.00 1.00 0.13 0.00 0.34
IND_SERVICES tx 0.00 1.00 0.35 0.00 0.48
YEAR 2004 0.00 1.00 0.81 1.00 0.40
YEAR 2005 0.00 1.00 0.48 0.00 0.50
YEAR 2006 0.00 1.00 0.23 0.00 0.42
YEAR 2007 0.00 1.00 0.16 0.00 0.37
MULTIPLE_AAER 2009 0.00 1.00 0.42 0.00 0.50
TABLE 3
Descriptive Statistics
Refer to table 1 for variable definitions
Panel A: Distributional Properties of Binary Variables
Sample (n = 62 observations)
Frequency Percentage Frequency Percentage Frequency Percentage
0 = No 31 50.0 46 74.2 36 58.1
1 = Yes 31 50.0 16 25.8 26 41.9
62 100.0 62 100.0 62 100
Panel B: Distributional Properties of Binary Variable Industry (based on SIC categorization)
Sample (n = 62 observations)
Frequency Percentage
IND_CONSTRUCTION tx 2 3.2
IND_MANUFACTURING tx 28 45.2
IND_WHOLESALE tx 2 3.2
IND_RETAIL tx 8 12.9
IND_SERVICES tx 22 35.5
62 100.0
Industry at most recent year of manipulation
Cummulative Percentage
3.2
48.4
51.6
64.5
100.0
Total
AAER 2009 ICW tx MULTIPLE_AAER 2009
TABLE 2
Descriptive Statistics

21
this is a considerable disparity. However, as mentioned in the previous section, comparability
between AAER firms and control firms is ensured by means of matching. ROAtx is defined as
pretax income divided by total assets and therefore presented as a ratio. ROAtx also varies
considerably from -1.22 to 0.25, resulting in a sample-wide average of -0.06. However, due to the
rather extreme value of -1.22, the mean of -0.06 is somewhat misleading. The median of 0.03
indicates that a majority of the sample firms are profitable. LEVERAGEtx is also given as a ratio,
ranging from 0.00 to 0.66. A value of 0.00 indicates that the firm(s) has no long-term debt,
whereas a value of 0.66 implies that a firm is largely financed by long-term debt. The mean value
of 0.16 is very close to the 0.18 that Dechow et al. (2011) find in their considerably larger
sample, leading me to conclude that my sample leverage is representative. GROWTHtx is denoted
as the market-to-book value of equity and ranges between 0.95 and 18 with a sample average of
3.65. The ratio indicates that all companies that report values greater than zero are expected to
grow in the future. Chen et al. (2013) report a similar sample wide average of 3.47. Given that
their sample consists of more than 1,500 companies, it is reasonable to assume that an average
value of 3.65 is fairly representative for the general population of firms. AGEtx describes the time
span between the companies’ IPO and the most recent year of financial statement manipulation,
mentioned by the SEC in the AAERs. From Table 3 it is obvious that the youngest firm reports a
public age of just 1 year, while the oldest went public 73 years before the most recent
manipulation. On average, the companies in my sample are publicly traded for 10.45 years at the
most recent date of financial statement manipulation.
Table 4 below reports the pair-wise correlations. Please note that the upper-right hand side
displays the Pearson product-moment correlations and the lower-left hand side the Spearman
rank-order correlations. I discuss the Spearman correlations but note that the Pearson correlations
are generally consistent with the Spearman correlations. The bold numbers indicate significance

22
1 2 3 4 5 6 7 8 9
AAER 2009 1 - 0.295 0.021 -0.247 -0.142 0.079 0.082 0.000 0.000
ICW tx 2 0.295 - -0.028 0.108 -0.109 -0.190 0.033 -0.108 -0.017
SIZE tx 3 -0.005 0.174 - 0.205 0.608 -0.068 0.085 0.081 -0.068
ROA tx 4 -0.166 -0.061 0.350 - 0.237 -0.202 0.207 0.159 -0.237
AGE tx 5 -0.137 -0.018 0.320 0.380 - 0.027 0.089 0.201 -0.070
LEVERAGE tx 6 0.170 -0.248 -0.033 -0.178 -0.173 - 0.038 -0.082 0.157
GROWTH tx 7 0.085 0.040 0.542 0.116 0.128 0.032 - 0.152 0.098
IND_CONSTRUCTION tx 8 0.000 -0.108 0.245 0.276 0.202 -0.097 0.179 - -0.166
IND_MANUFACTURING tx 9 0.000 -0.017 -0.054 -0.380 -0.062 0.168 0.085 -0.166 -
IND_WHOLESALE tx 10 0.000 -0.108 0.306 0.153 0.291 -0.026 -0.008 -0.033 -0.166
IND_RETAIL tx 11 0.000 0.103 0.000 0.091 -0.129 -0.218 -0.007 -0.070 -0.349
IND_SERVICES tx 12 0.000 0.025 -0.147 0.173 -0.027 0.023 -0.146 -0.135 -0.673
YEAR 2004 13 0.000 -0.084 0.119 0.237 0.295 0.092 0.162 0.089 -0.376
YEAR 2005 14 0.000 0.093 -0.198 0.215 0.143 -0.161 -0.148 0.189 -0.230
YEAR 2006 15 0.000 -0.054 -0.319 -0.051 0.014 -0.151 -0.084 -0.099 0.130
YEAR 2007 16 0.000 0.042 -0.039 0.038 -0.032 -0.137 0.039 -0.080 0.131
MULTIPLE_AAER 2009 17 0.000 0.096 0.259 0.226 0.198 -0.184 0.281 -0.155 -0.114
(continued below )
Pearson correlations are reported above the diagonal, and Spearman Correlations are reported below
* bold numbers indicate significance at a minimum of 0.05
TABLE 4
Spearman / Pearson Correlation Matrix
10 11 12 13 14 15 16 17
AAER 2009 1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
ICW tx 2 -0.108 0.103 0.025 -0.084 0.093 -0.054 0.042 0.096
SIZE tx 3 0.860 -0.118 -0.194 0.141 -0.112 -0.215 -0.113 0.269
ROA tx 4 0.097 0.095 0.085 0.088 0.204 -0.041 0.054 0.199
AGE tx 5 0.610 -0.130 -0.135 0.214 0.004 -0.082 -0.078 0.180
LEVERAGE tx 6 -0.050 -0.159 -0.001 0.092 -0.118 -0.168 -0.130 -0.271
GROWTH tx 7 -0.057 -0.038 -0.110 0.136 -0.161 -0.146 0.021 0.191
IND_CONSTRUCTION tx 8 -0.033 -0.070 -0.135 0.089 0.189 -0.099 -0.080 -0.155
IND_MANUFACTURING tx 9 -0.166 -0.349 -0.673 -0.376 -0.230 0.130 0.131 -0.114
IND_WHOLESALE tx 10 - -0.070 -0.135 0.089 -0.177 -0.099 -0.080 0.215
IND_RETAIL tx 11 -0.070 - -0.285 -0.055 0.012 0.022 -0.169 0.063
IND_SERVICES tx 12 -0.135 -0.285 - 0.363 0.226 -0.078 0.041 0.053
YEAR 2004 13 0.089 -0.055 0.363 - -0.016 -0.321 -0.451 0.251
YEAR 2005 14 -0.177 0.012 0.226 -0.016 - 0.403 -0.074 -0.038
YEAR 2006 15 -0.099 0.022 -0.078 -0.321 0.403 - 0.392 0.010
YEAR 2007 16 -0.080 -0.169 0.041 -0.451 -0.074 0.392 - 0.161
MULTIPLE_AAER 2009 17 0.215 0.063 0.053 0.251 -0.038 0.010 0.161 -
Pearson correlations are reported above the diagonal, and Spearman Correlations are reported below
* bold numbers indicate significance at a minimum of 0.05
TABLE 4 (continued)
Spearman / Pearson Correlation Matrix

23
at least at the 5 percent level. As predicted, receiving AAERs (AAER2009) is positively correlated
with the presence of internal control weaknesses (ICWtx) and significant at the 5 percent level.
The correlation matrix shows no further significant correlation between AAER2009 and any of the
control variables. However, it does portrait correlations among the control variables. I am not
going to discuss every significant correlation in detail but note that there is a significant positive
correlation between SIZEtx and both, ROAtx and AGEtx indicating that larger firms are likely to be
more profitable and older.
In Table 5 you can find the collinearity statistics of my model. The left column displays
the tolerance statistics for the independent variables; the right column presents the variance
inflation factor (VIF). The VIF states how much the variance of the estimated coefficient is
inflated by the existence of correlation among the independent variables (O’brien, 2007). Please
note that a VIF of 1 implies that there is no correlation present, whereas a score of 4 justifies
further investigation, and a score above 10 signals severe multicollinearity. It is apparent that
SIZEtx and IND_WHOLESALEtx show VIF values larger than 4. However, this does not by default
mean that it is necessary to exclude these variables from my model. I believe that SIZEtx might be
inflated due to the matching process and thus does not impose remediation. With regard to
IND_WHOLESALEtx, I believe that the inflation is largely attributable to the very limited amount
of observations for this industry category (n=2).
I test hypothesis 1 by running a binary logistic regression analysis on the final model (see
equation 2). I opted for this statistical test because the binary, non-linear nature of the models’
response variable prohibits using an OLS linear regression. The results of this test may be found
in Table 6 below. The results indicate that ICWtx are significantly and positively (at the 0.05
level) associated with the likelihood of receiving AAERs in 2009, suggesting that companies
with material internal control weaknesses are more likely to have misstated financial statements

24
TABLE 5
Collinearity Statistics
Tolerance VIF
Intercept
ICWtx 0.869 1.151
SIZEtx 0.178 5.618
ROAtx 0.740 1.351
GROWTHtx 0.768 1.302
LEVERAGEtx 0.784 1.276
AGEtx 0.495 2.020
IND_CONSTRUCTIONtx 0.725 1.379
IND_MANUFACTURINGtx 0.452 2.212
IND_WHOLESALEtx 0.156 6.410
IND_RETAILtx 0.655 1.527
IND_SERVICEStx 0.489 2.045
YEAR2004 0.457 2.188
YEAR2005 0.495 2.020
YEAR2006 0.484 2.066
YEAR2007 0.488 2.049
MULTIPLE_AAER2009 0.629 1.590
Bold numbers indicate possibility of
multicollinearity
Refer to Table 1 for variable definitions
in comparison with companies that show no material internal control weaknesses. This finding is
in line with prior research in the field of internal control quality in association with earnings
management (Ashbaugh-Skaife et al., 2008; Chan et al., 2008; Doyle et al., 2007). It is also
apparent from Table 6 that ROAtx is negatively related to AAERs being issued in 2009. This
relationship is, however, only of weak significance (at the 0.10 level). The untabulated odds ratio
of 0.031 suggests that increasing ROAtx by 1 percent results in a reduction of the probability of
receiving an AAER of 3.1 percent. The finding is in line with my prediction that I base on the
study of Myllymäki (2014) who also found a negative association between profitability and
earnings management. Furthermore, it is also obvious that the remaining control variables are

25
insignificant at the 0.10 level. Particularly with respect to SIZEtx, GROWTHtx, LEVERAGEtx, and
AGEtx, my results fail to support findings presented in prior literature. I argue that this might be
attributable to the very limited amount of observations (n=62) in my sample. It is also apparent
that the industry and year variables, as well as MULTIPLE_AAER2009 are highly insignificant.
Independent Variables Exp. Sign
Coefficient
Estimate
Wald Chi-
square P-values
ICW tx + 1.876 6.233 ** 0.013
SIZE tx - 0.000 0.249 0.618
ROA tx - -3.464 3.360 * 0.067
GROWTH tx - 0.089 0.752 0.386
LEVERAGE tx + 2.078 1.191 0.275
AGE tx - -0.048 1.391 0.238
IND_CONSTRUCTION tx ? 1.398 0.555 0.456
IND_MANUFACTURING tx ? -0.340 0.135 0.713
IND_WHOLESALE tx ? 1.083 0.051 0.822
IND_RETAIL tx ? 0.042 0.002 0.968
IND_SERVICES tx ? -1.083 0.051 0.822
YEAR 2004 ? 0.423 0.149 0.699
YEAR 2005 ? 0.169 0.041 0.839
YEAR 2006 ? 0.463 0.200 0.655
YEAR 2007 ? 0.300 0.067 0.796
MULTIPLE_AAER 2009 ? 0.011 0.000 0.988
Intercept -1.483 1.082 0.298
Likelihood ratio
Chi-square 16450.00 p = 0.353
Pseudo R-squared
Cox & Snell R-squared 0.233
Nagelkerkes R-squared 0.311
n 62
* significant at the two-tailed p-value ≤ 0.10
** significant at the two-tailed p-value ≤ 0.05
Dependent Variable = AAER 2009
AAER 2009 = β0 + β1ICW tx + β2SIZE tx + β3ROA tx + β4GROWTH tx + β5LEVERAGE tx +
β6AGE tx + β7IND_CONSTRUCTION tx + β8IND_MANUFACTURING tx +
β9IND_WHOLESALE tx + β10IND_RETAIL tx + β11IND_SERVICES tx + β12YEAR 2004 +
β13YEAR 2005 + β14YEAR 2006 + β15YEAR 2007 + β16MULTIPLE_AAER 2009 + ε t*
TABLE 6
Binary Logistic Regression Analysis

26
This implies that neither industrial nor macroeconomic factors are likely to increase the
probability of receiving an AAER in 2009.
Table 6 also reports the pseudo r-squared statistics for the model at hand. Similar to the
adjusted r-squared in the OLS linear regression, the pseudo r-squared tries to capture the
explained variation of the logistic regression model. The Cox & Snell r-squared (0.233) and the
Nagelkerkes r-squared (0.311) both exceed 0.200, a threshold that is generally considered a good
model fit (Henkel et al, 2012). Nevertheless, note that the likelihood ratio of 16,450 is not
significant at the 0.10 level, which implies that the model as a whole is not significant.
In order to increase confidence in my findings, I conducted a robustness test, running a
binary logistic regression on a partially altered version of my model. I used different definitions
of the variables in equation 2, that were already introduced in prior literature. Following Doyle et
al. (2007), SIZEtx was replaced by LN_SIZEtx, the natural logarithm of total assets. ROAtx which is
defined as normalized pretax income was replaced by ROA_NEWtx which reflects net income
divided by total assets (Bedard et al., 2012). Ashbaugh-Skaife et al. (2008) use average growth
over three years rather than for just one year. I replaced GROWTHtx by this alternative definition
of growth. AVE_GROWTHtx thus reflects the average market-to-book value of equity over three
years. Moreover, I replaced LEVERAGEtx and AGEtx by the natural logarithm of the variables
(Doyle et al., 2007). The industry and year variables, as well as MULTIPLE_AAER2009 remained
unchanged, resulting in the following model:
AAER2009 = β0 + β1ICWtx + β2LN_SIZEtx + β3ROA_NEWtx + β4AVE_GROWTHtx + (3)
β5LN_LEVERAGEtx + β6LN_AGEtx + β7IND_CONSTRUCTIONtx +
β8IND_MANUFACTURINGtx + β9IND_WHOLESALEtx + β10IND_RETAILtx +
β11IND_SERVICEStx + β12YEAR2004 + β13YEAR2005 + β14YEAR2006 + β15YEAR2007 +
β16MULTIPLE_AAER2009 + ε*

27
The results of the robustness test can be found in Table 7 above. It is obvious that ICWtx is
still significantly positively related to AAER2009 at the 0.05 level, which implies that this finding
is robust against alternative definitions of the variables in my model. However, recall from Table
Independent Variables Exp. Sign
Coefficient
Estimate
Wald Chi-
square P-values
ICW tx + 2.005 6.337 ** 0.012
LN_SIZE tx - 0.146 0.305 0.581
ROA_NEW tx - -2.683 1.605 0.205
LN_GROWTH tx - 0.177 3.185 * 0.074
LN_LEVERAGE tx + 0.002 0.001 0.974
LN_AGE tx - -0.481 1.157 0.282
IND_CONSTRUCTION tx ? 1.556 0.771 0.380
IND_MANUFACTURING tx ? -0.624 0.434 0.510
IND_WHOLESALE tx ? 0.738 0.128 0.720
IND_RETAIL tx ? -0.409 0.142 0.706
IND_SERVICES tx ? 0.738 0.128 0.720
YEAR 2004 ? 0.295 0.065 0.799
YEAR 2005 ? -0.172 0.040 0.842
YEAR 2006 ? 0.978 0.751 0.386
YEAR 2007 ? -0.324 0.071 0.790
MULTIPLE_AAER 2009 ? 0.376 0.220 0.639
Intercept -1.487 0.358 0.551
Likelihood ratio
Chi-square 17161.00 p = 0.309
Pseudo R-squared
Cox & Snell R-squared 0.242
Nagelkerkes R-squared 0.322
n 62
* significant at the two-tailed p-value ≤ 0.10
** significant at the two-tailed p-value ≤ 0.05
Dependent Variable = AAER 2009
TABLE 7
Robustness Test (Binary Logistic Regression)
AAER 2009 = β0 + β1ICW tx + β2LN_SIZE tx + β3ROA_NEW tx + β4AVE_GROWTH tx +
β5LN_LEVERAGE tx + β6LN_AGE tx + β7IND_CONSTRUCTION tx +
β8IND_MANUFACTURING tx + β9IND_WHOLESALE tx + β10IND_RETAIL tx +
β11IND_SERVICES tx + β12YEAR 2004 + β13YEAR 2005 + β14YEAR 2006 + β15YEAR 2007 +
β16MULTIPLE_AAER 2009 + ε t*

28
6 that I found a weakly significant relation between profitability (ROAtx) and AAERs being
issued in 2009. This finding is not robust against alternative definitions of the same variables, as I
find no (weakly) significant relation between ROA_NEWtx and AAER2009.
In sum, I find that material internal control weaknesses are positively related (at the 0.05
level) to AAERs being issued by the SEC in 2009. This finding is robust against alternative
variable definitions. Overall, the pseudo r-squared statistics report a good model fit, but the
likelihood ratio suggests that the model as a whole is not significant. Testing the model with
alternative variable definitions reveals a slightly improved likelihood ratio but given a p-value of
0.309, the model as a whole is still insignificant. This leads me to conclude that I fail to provide
sufficient evidence to support hypothesis 1.
V. DISCUSSION
This paper investigates the effect of internal control weaknesses on the likelihood to
receive AAERs. I read and categorized all AAERs that were issued in 2009 (n=180). After
eliminating redundant observations, such as AAERs issued against auditors, or AAERs
concerning the period prior to 2004 (for which no internal control reports are available), I arrived
at a sample of 31 companies that received an AAER in 2009. I matched these AAER companies
with control firms that were (close to) identical to the AAER companies in terms of size and
industry. Having arrived at a sample of 62 companies, composed in part of firms that were
identified as having practiced earnings management in the AAER, and in part of non-misstating
firms, I tested whether internal control quality differed between those two firm characteristics.
After controlling for inherent firm characteristics such as size, profitability, age, growth potential,
leverage, and industry, I find that there is indeed a significant positive relation between internal
control weaknesses and AAERs being issued in 2009. This finding is robust against alternative

29
definitions of control variables in my model and in line with prior research (Ashbaugh-Skaife et
al., 2008; Chan et al., 2008; Doyle et al., 2007). My findings imply that a strong system of
internal control over financial reporting can have significant long-term benefits such as increased
financial statement reliability and less risk of SEC prosecution.
There are several limitations to my study. First of all, I use material internal control
weaknesses as a proxy of internal control problems. Since detection and reporting of these
weaknesses is subject to human error, it is uncertain whether my sample reflects the true
underlying population of firms with internal control problems. Secondly, the total number of
observations in my sample (n=62) limits the study’s generalization, as it is arguably not large
enough to fairly represent of the population of firms. Finally and most importantly the likelihood
ratio of my model of 0.353 implies that the model is in itself insignificant, which essentially
justifies challenging all findings.
Nevertheless I believe that my study makes several contributions. First, I add to the
existing earnings management literature by introducing a proxy of earnings management that, to
my knowledge, has not yet been used in this context, to wit AAERs. Next, I expect the study to
contribute to an increased understanding of the benefits of internal control systems by
corporations. U.S. regulators noted that it is difficult to communicate the benefits of section 404
of SOX (Bailey, 2004). My study provides insights into how compliance with section 404 can
benefit corporations by mitigating the likelihood of receiving an AAER, which according to
Dechow et al. (1996) results in a higher cost of capital for organizations. Third, it is also relevant
for regulators in the United States that are being criticized for the increased cost of compliance
with section 404 of SOX (Raghunandan and Rama, 2006). Finally, it can provide implications for
auditors that face severe risk of litigation resulting from non-detection of material misstatements
(in the annual report), often exposed by the SEC in the AAERs.

30
REFERENCES
Alexander, C. R., Bauguess, S. W., Bernile, G., Lee, Y. A., and Marietta-Westberg, J. 2013.
Economic effects of SOX section 404 compliance: a corporate insider perspective. A
Journal of Accounting and Economics 56: 267-290.
Altamuro, J., and Beatty, A. 2007. Do internal control reforms improve earnings quality?
Working Paper, Ohio State University.
Ashbaugh-Skaife, H., Collins, D. W., Kinney, W. R., and LaFond, R. 2008. The effect of SOX
internal control deficiencies and their remediation on accrual quality. The Accounting
Review 83: 217-250.
Bailey, Jr., A. D. 2004. Speech by SEC staff: Remarks before the 2004 AICPA National
Conference on Current SEC and PCAOB developments. Available at:
http://www.sec.gov/news/speech/spch120604adb.htm
Bedard, J. C., Hoitash, R., Hoitash, U., and Westermann, K. 2012. Material weakness
remediation and earnings quality: a detailed examination by type of control deficiency.
Auditing: A Journal of Practice & Theory 31: 57-78.
Chan, K. C., Farrell, B., and Lee, P. 2008. Earnings management of firms reporting material
internal control weaknesses under section 404 of the Sarbanes-Oxley Act. Auditing:
A Journal of Practice & Theory 27 (2): 161-179.
Chen, L. H., Krishnan, J., Sami, H., and Zhou, H. 2012. Auditor attestation under SOX section
404 and earnings informativeness. Auditing: A Journal of Practice & Theory 32: 61-81.
Cheng, M., Dhaliwal, D., and Zhang, Y. 2013. Does investment efficiency improve after
disclosure of material weaknesses in internal control over financial reporting? Journal
of Accounting and Economics 56: 1-18.
Collins, D. W., and Kothari, S. P. 1989. An analysis of intertemporal and cross-sectional
determinants of earnings response coefficients. Journal of Accounting and Economics
11: 143-181.

31
Committee of Sponsoring Organizations of the Treadway Commission (COSO). 2013.
Internal Control – Integrated Framework. Durham, NC: COSO.
Dechow, P. M., and Dichev, I. 2002. The quality of accruals and earnings. The Accounting
Review 77: 35–59.
Dechow, P.M., Ge, W., Larson, C.R., and Sloan, R.G. 2011. Predicting material accounting
misstatements. Contemporary Accounting Research 28: 17-82.
Dechow, P. M., Ge, W., and Schrand, C. 2010. Understanding earnings quality: a review of
the proxies, their determinants and their consequences. Journal of Accounting and
Economics 50: 344-401.
Dechow, P. M., Sloan, R. G., and Sweeney, A. P. 1996. Causes and consequences of earnings
manipulation: an analysis of firms subject to enforcement actions by the SEC.
Contemporary Accounting Research 13: 1-36.
Doyle, J. T., Ge, W., and McVay, S. 2007. Accruals quality and internal control over financial
reporting. The Accounting Review 82 (5): 1141-1170.
Ettredge, M. L., Li, C., and Sun, L. 2006. The impact of SOX section 404 internal control
quality assessment on audit delay in the SOX era. Auditing: A Journal of Practice &
Theory 25 (2): 1-23.
Francis, J. R. 2011. A framework for understanding and researching audit quality. Auditing:
A Journal of Practice & Theory 30 (2): 125-147.
Ge, W., and McVay, S. 2005. The disclosure of material weaknesses in internal control after
the Sarbanes-Oxley Act. Accounting Horizons 19 (3): 137-158.
Goh, B. W., Krishnan, J., and Li, D. 2013. Auditor reporting under section 404: the
association between the internal control and going concern audit opinions. Contemporary
Accounting Research 30 (3): 970–995.

32
Gordon, J. N. 2002. What Enron means for the management and control of the modern
business corporation: some initial reflections. The University of Chicago Law Review
69 (3): 1233-1250.
Henkel, J., Luettke, R., and Kagarer, K. 2012. Empirische Wirtschaftsforschung: Logistische
Regression. Modul 15. Lecture conducted from Technische Universitaet Muenchen,
Germany.
Krishnan, J. 2005. Audit committee quality and internal control: an empirical analysis. The
Accounting Review 80 (2): 649-675.
Krishnan, J., Rama, D., and Zhang, Y. 2008. Costs to comply with SOX section 404.
Auditing: A Journal of Practice & Theory 27 (1): 169-186.
Myllymäki, E. 2014. The persistence in the association between section 404 material
weaknesses and financial reporting quality. Auditing: A Journal of Practice & Theory
33: 93-116.
O’brien, R. M., 2007. A caution regarding rules of thumb for variance inflation factors. Quality
& Quantity 41: 673-690.
Powell, S. S. 2005. Costs of Sarbanes-Oxley are out of control. Letters to the Editor. Wall
Street Journal (March 21): A-17.
Public Company Accounting Oversight Board (PCAOB). 2004. An Audit of Internal Control
over Financial Reporting Performed in Conjunction with an Audit of Financial
Statements. Auditing Standard No. 2. Washington, DC: PCAOB.
Raghunandan, K. and Rama, D. V. 2006. SOX section 404 material weakness disclosures and
audit fees. Auditing: A Journal of Practice & Theory 25: 99-113.
Singer, Z., and You, H. 2011. The effect of section 404 of the Sarbanes-Oxley Act on
earnings quality. Journal of Accounting, Auditing & Finance 26 (3): 556-589.
Solomon, D. 2005. Accounting rule exposes problems but draws complaints about cost. Wall
Street Journal (May 2): A-1.

33
Zhang, I. X., 2007. Economic consequences of the Sarbanes-Oxley Act of 2002. Journal of
Accounting and Economics 44: 74-115.

34
APPENDIX
Exhibit 1 AAER categorization extract
*Accused_Party: (1) officer(s); (2) company; (3) auditor; (4) other
**Reason_for_AAER: (1) financial statement manipulation; (2) other; (3) against auditor
Exhibit 2 Working sample AAERs in 2009
AAER Sampling Overview Total n
Complete set of AAERs in 2009 180
less:AAERs against auditors 143
less: Reasons other than earnings management 99
less: AAERs issued affecting prior 2004 59
less: Multiple AAERs against same company 42
less: AAERs without Compustat/ EDGAR data 38
less:AAERs against companies in financial sector 31

35
Exhibit 3 Firm Matching (Total Assets in Millions of USD)
AAER Firms Control Firms Industry*
Cardinal Health Inc. 21369 16240 McKesson Corp 6
Dana Holding Corp. 9019 9163 Danaher Corp 4
Terex Corporation 4179 4196 Consol Energy Inc. 4
Beazer Homes USA Inc. 3149 3387 Ryland Group Inc. 3
Comverse Technology Inc. 2925 2929 Station Casinos Inc. 8
Mercury Interactive Inc. 2013 2023 Universal Compression Holdings 8
Hayes Lemmerz International 1806 1801 Rock-Tenn Co 4
VeriFone Systems Inc. 1547 1548 Palm Inc. 4
Monster Worldwide Inc. 1544 1526 Vail Resorts Inc. 8
CSK Auto Corporation 1042 1037 Carmax Inc. 7
Brocade Communications Systems 987 988 RF Micro Devices Inc. 4
American Italian Pasta Company 748 745 Nektar Therapeutics 4
SafeNet Inc. 589 590 Vecco Instruments Inc. 4
MedQuist Inc. 541 542 Priceline Group Inc. 8
West Marine Inc. 532 532 Cost Plus Inc. 7
Krispy Kreme Doughnuts Inc. 480 483 Systemax Inc. 7
Ulticom Inc. 272 273 Kforce Inc. 8
LSB Industries Inc. 167 166 Inspire Pharmaceuticals Inc. 4
Isilon Systems Inc. 132 132 Zalicus Inc. 4
Escala Group Inc. 131 131 Captaris Inc. 8
World Health Alternatives Inc. 101 101 Navisite Inc. 8
Home Solutions of America Inc. 89 88 Fortune Industries Inc. 8
Allion Healthcare, Inc. 86 87 Design Within Reach Inc. 7
Merge Healthcare Inc. 79 79 Corillian Corp 8
Dyadic International Inc. 45 45 Qualstar Corp 4
VoIP, Inc. 36 36 Urologix Inc. 4
Tvia Inc. 23 23 Eltek Ltd. 4
UCI Medical Affiliates Inc. 18 18 Datawatch Corp 8
PowerCold Corporation 9 9 Daegis Inc. 4
Video Without Boundaries Inc. 9 9 Ikonics Inc. 8
Apogee Technology Inc. 6 6 American Commerce Solution 4
*Industry (based on SIC categories)
Agriculture, Forestry, Fishing 1
Mining 2
Construction 3
Manufacturing 4
Transportation & Public Utilities 5
Wholesale Trade 6
Retail Trade 7
Finance, Insurance, Real Estate 8
Public Administration 9
Total Assets

GUTMANN-SEBASTIAN-6000737-IB-THESIS

Recommended

Recommended

More Related Content

What's hot

What's hot (14)

Similar to GUTMANN-SEBASTIAN-6000737-IB-THESIS

Similar to GUTMANN-SEBASTIAN-6000737-IB-THESIS (20)

GUTMANN-SEBASTIAN-6000737-IB-THESIS