The Effect of Internal Control Deficiencies on
Firm Risk and Cost of Equity Capital
Hollis Ashbaugh-Skaife
University of Wisconsin-Madison
hashbaugh@bus.wisc.edu
Daniel W. Collins*
University of Iowa
daniel-collins@uiowa.edu
William R. Kinney, Jr.
University of Texas-Austin
william.kinney@mccombs.utexas.edu
Ryan LaFond
MIT-Sloan School of Management
rzlafond@mit.edu
February 2006
The Effect of Internal Control Deficiencies on
Firm Risk and Cost of Equity Capital
Abstract
Theoretically, internal control quality is related to the cost of capital through its effect on
idiosyncratic and systematic risk. The Sarbanes-Oxley Act (SOX) mandates audits of
internal control and disclosure of material internal control weaknesses, but do individual
firms potentially benefit from the resulting disclosures? The answer is important because
SOX requirements are costly to implement and may be a deadweight loss to firms. In
this paper, we use unaudited section 302 and audited section 404 disclosures to estimate
internal control deficiency (ICD) effects on idiosyncratic risk, systematic risk, and
ultimately, the cost of capital. We find that after including other risk factors the ICDs are
significantly positively related to idiosyncratic risk, systematic risk, and cost of capital,
and that the median market adjusted cost of capital increase following initial ICD
disclosure is economically important at 104 basis points. Furthermore, we observe that
the remediation ICDs is followed by significant cost of capital reductions. Overall, the
results of our cross-sectional and change analysis tests are consistent with strong internal
controls being valued by the capital market.
The Effect of Internal Control Deficiencies on
Firm Risk and Cost of Equity Capital
I. Introduction
In this study we investigate whether firms with internal control deficiencies (ICDs)
exhibit higher systematic risk, higher idiosyncratic risk and higher cost of capital relative
to firms with strong internal controls. Further, we investigate whether the existence of
ICDs and the changes in internal control systems as evidenced by auditor opinions on
internal controls that are required by the Sarbanes-Oxley Act of 2002 (SOX) are causally
related to changes in firms’ cost of equity capital. Much of the prior research that
investigates the impact of SOX has focused on the costs of SOX internal control auditing
and reporting. This study is the first to document potential benefits to firms of having
strong internal controls in terms of cost of capital consequences.
Ashbaugh-Skaife, Collins and Kinney (2005) and Doyle, Ge and McVay (2005) posit
that weak internal controls introduce both intentional and unintentional errors into the
financial reporting process that lead to lower quality earnings signals. Consistent with
this conjecture, these studies find that firms that report weak internal controls exhibit
greater noise in accruals and larger abnormal accruals relative to firms not reporting
internal control deficiencies. We posit that poor internal controls that allow increased
noise in the financial reporting process increase the information risk faced by investors
that manifests in higher cost of capital.
Recent theoretical work by Lambert, Leuz and Verrecchia (2005) investigates the
direct and indirect effects of information quality on cost of capital in a multi-security
CAPM setting. With respect to the direct effects, they show that higher quality
information reduces market participants’ assessed variances of a firm’s cash flows and
the assessed covariances with other firms’ cash flows leading to a lower cost of capital.
Moreover, they show that the quality of accounting systems, which includes not only the
financial disclosures that firms make to outsiders, but also internal control systems, has
an effect on firms’ real decisions including the assets appropriated by management that
reduces the expected value of cash flows to investors, thus contributing to an indirect
effect on firms’ cost of capital.
Building on the theoretical work in Lambert et al. (2005), we find that firms that
disclose ICDs exhibit significantly higher betas, idiosyncratic risk and cost of capital
during the period from 2002 to 2004 relative to firms not reporting internal control
deficiencies. These differences persist after controlling for other factors that prior
research has shown to be related to these variables. Our finding that risk and cost of
capital differences pre-date the first disclosures of ICDs suggests that market
participants’ assessment of non-diversifiable market risk (beta), idiosyncratic risk and
implied cost of capital incorporated expectations about internal control risks based on
observable firm characteristics. This conjecture is consistent with Ashbaugh-Skaife et al.
(2006) and Doyle et al. (2006) who demonstrate that firms with more complex
operations, recent changes in organization structure, greater accounting risk exposure and
less investment in internal control systems are more likely to disclose internal control
deficiencies.
We use the initial disclosure of an ICD by management under SOX 302 or SOX 404
auditor opinions on internal controls along with prior evidence on the determinants of
ICD disclosure to structure change analysis tests designed to investigate whether initial
2
ICD disclosures caused predictable changes in firms’ cost of capital. Our change analysis
reveals that firms that disclose an ICD experience a significant increase in marketadjusted cost of capital of roughly 93 basis points around the first disclosure of an ICD.
Moreover, we find that ICD firms with the lowest probability of reporting an ICD exhibit
a greater cost of capital change (104 basis points on average) relative to those ICD firms
with the highest likelihood of reporting an ICD based on observable firm characteristics
(62 basis points). This result is consistent with the market incorporating incomplete
adjustments for internal control risks into firms’ cost of capital assessments prior to
learning about which firms have ICDs, and then updating these risk assessments after
ICDs are revealed.
To further substantiate causality between internal control weaknesses and cost of
capital, we conduct two additional change analyses. First, we examine cost of capital
changes around SOX 404 audit opinions on internal controls for those firms that
previously disclosed ICDs under SOX 302 but rectified their control problems. If
revelation of internal control problems contributes to an increase in firms’ cost of capital
as our first set of change results suggest, then successful remediation of those problems
should lead to a decrease in cost of capital. Consistent with this prediction, we find that
ICD firms that subsequently receive an unqualified SOX 404 opinion exhibit an average
decrease in market-adjusted cost of capital of 152 basis points around the disclosure of
the opinion. In contrast, we find that ICD firms that previously disclosed internal control
problems that received adverse SOX 404 opinions exhibit no change in cost of capital
around the opinion.
3
Finally, we conduct a cost of capital change analysis on those firms that received
unqualified SOX 404 opinions that had no prior disclosure of internal control
weaknesses. Consistent with the market forming prior probability assessments of likely
internal control problems based on observable firm characteristics, we find a significant
decrease in the average market-adjusted cost of capital of 77 basis points around the
disclosure of an unqualified SOX 404 opinion for those firms that were most likely to
report ICDs. In contrast, we find no cost of capital change for those firms that received an
unqualified SOX 404 opinion that were least likely to report an ICD.
Collectively our cross-sectional and inter-temporal tests paint a very consistent
picture that internal control risk is an important determinant of both idiosyncratic risk and
systematic market risk that impacts the market’s assessment of firms’ cost of capital.
Firms that have strong internal controls or firms that remediate prior internal control
weaknesses are rewarded with significantly and economically significant lower cost of
capital. Our findings are consistent with assertions made by Congress and regulators that
the internal control reporting requirements of Sarbanes-Oxley will result in higher quality
financial reporting, which in turn will lead to lower cost of capital (U. S. House of
Representatives, 2003; U.S. Senate, 2002 and 2004; Donaldson, 2005). Thus, our study
represents one of the first to document potential benefits of a systematic reporting
structure to communicate information about internal controls in terms of cost of capital
consequences.
Our study makes several contributions to the extant literature. First, we contribute to
the literature that seeks to examine the effects of information quality on investors’ risk
assessments and equity cost of capital using a setting, internal control weaknesses and
4
their remediation, that arguably provides a cleaner partitioning of firms in terms of
information quality than in prior studies (Botosan, 1996; Botosan and Plumlee, 2002;
Bhattacharya, Daouk and Welker, 2003). Moreover, the combination of cross-sectional
and inter-temporal change tests afforded by the unique regulatory setting surrounding
SOX 404 internal control reporting provides an opportunity to triangulate our assessment
of information quality in ways that minimize competing explanations for our results.
Our study also contributes to the growing literature designed to assess the economic
consequences of the SOX legislation. Much of the prior literature focuses on the costs of
implementing SOX requirements and internal control auditing and reporting in particular
(Li, Pincus and Rego, 2005; Zhang, 2005; Berger, Li and Wong, 2005; Solomon and
Bryan-Low, 2004). This paper along with a concurrent study by Ogneva, Raghunandan
and Subramanyam (2005) are among the first to provide evidence on potential benefits of
SOX in terms of cost of capital effects. In contrast to the Ogneva, et al. (2005) study,
however, that finds no association between internal control weaknesses and implied cost
of capital, we find clear evidence that internal control risk matters to investors and that
firms reporting strong internal controls or firms that correct prior internal control
problems benefit from lower cost of capital. 1
The remainder of our paper is organized as follows. Section II summarizes the
theoretical underpinnings of our analysis and sets forth our predictions about internal
control weaknesses and remediation, market and idiosyncratic risk and cost of capital.
Section III provides institutional background and summarizes related work. Section IV
1
See later discussion for sample, design choices and cost of capital proxy differences between our study
and the Ogneva, et al. (2005) study that contribute to the differences in results.
5
describes our sample, data sources, variable measurements and provides descriptive
statistics. Section V presents our empirical findings and Section VI concludes.
II. Linkages between Internal Control Weaknesses, Firm Risk and Cost of Equity
Capital
Lambert et al. (2005) develop a model in a multi-security CAPM setting that links the
quality of accounting disclosures and information systems to firm risk and cost of equity
capital. In the Lambert, et al. framework, accounting information system quality is
broadly defined to include not only the disclosures the firm makes to outsiders, but also
the internal control systems that a firm has in place. A key insight from their analysis is
that the quality of accounting information and the systems that produce that information
influence a firm’s cost of capital in two ways: (1) direct effects—where higher quality
accounting information does not affect firm cash flows, per se, but does affect market
participants’ assessments of the variance of a firm’s cash flows and the covariance of the
firm’s cash flows with aggregate market cash flows; and (2) indirect effects—where
higher quality information and better internal controls affect real decisions within the
firm, including the amount of firm resources that managers appropriate for themselves.
The expression they derive for the expected return for a firm, i.e., cost of equity
capital, reduces to—
~
E(Vj Φ )
R f H (Φ ) + 1
~
.
E(R j Φ) =
, where H(Φ) =
H (Φ ) − 1
1
⎛~ J ~ ⎞
Cov⎜V j , ∑ Vk Φ ⎟
Nτ
k =1
⎝
⎠
(1)
Where R f is the risk free rate, Φ is the information available to investors to make their
~
assessments regarding the distribution of future cash flows for firm j, Vj is a random
6
vector of future cash flows for firm j, and Nτ is the aggregate risk tolerance of the
market. Notice that the covariance term can be decomposed into two components: the
firms’ own variance (idiosyncratic risk) plus the covariance with all other firms’ cash
flows in the market (beta risk) as follows:
⎛~ J ~ ⎞
⎛~ J ~ ⎞
~ ~
Cov⎜V j , ∑ Vk Φ ⎟ = Cov (V j , V j Φ ) + Cov⎜⎜V j , ∑ Vk Φ ⎟⎟.
k =1
⎝
⎠
⎝ k≠ j
⎠
(2)
Lambert et al. (2005) analyze the direct effects of information system quality by
~
introducing an accounting information signal, Z j (e.g., earnings), that provides a noisy
~
~
signal about the (future) end-of-period cash flows of the firm, V j . That is, Z j
=
~
V j + ε~j
where ε~j is the noise or measurement error in the information signal. Because the
(future) end-of-period firm cash flows are unobservable, the market’s assessment of the
covariance structure of firm j’s future cash flows with all other firms in the market is
conditioned by the quality or precision of firm j’s accounting signal. Specifically,
J
~ ~
∑ Cov(V , V
j
k =1
k
Zj) =
Var (ε~j )
Var (ε~j ) ⎛
⎛~
~
~ ~
~ ⎞⎞
~ Cov(V j ,Vk ) =
~ ⎜⎜ Cov (V j ,V j ) + Cov⎜⎜V j , ∑Vk ⎟⎟ ⎟⎟.
∑
Var ( Z j ) ⎝
k =1 Var ( Z j )
⎝ k ≠1 ⎠ ⎠
J
(3)
As equation (3) shows, investors’ assessment of the variance of firm j’s cash flows and
covariance of firm j’s cash flows with the cash flows of all other firms in the market is
proportional to the measurement error or noise in the information signal about firm j’s
future cash flows. Importantly, the effect of measurement error in the information signal
7
does not diversify away as the number of securities grows large--the effect is present for
each and every covariance term with firm j.
Substituting equation (3) for the covariance term in the expression for H(Φ) in
~
equation (1) and recognizing that Z j is a noisy signal from information set Φ that is
available to investors yields the following expression for expected return (cost of capital):
R f H (Φ ) + 1
~
E(R j Φ) =
,
H (Φ ) − 1
where H(Φ ) =
~
E(Vj Φ )
⎡ Var (~ε j ) ⎛
⎛~
1
~ ~
~ ⎞ ⎞⎤
=⎢
~ ⎜⎜ Cov(Vj , Vj ) + Cov⎜⎜ Vj , ∑ Vk ⎟⎟ ⎟⎟⎥
Nτ ⎢⎣ Var ( Z j ) ⎝
k ≠1
⎝
⎠ ⎠⎥⎦
.
(4)
Thus, as the noise in a firm’s accounting signals [ Var (ε~j ) ] increases, the firm’s cost
of equity capital increases. Moreover, they show that the quality of a firm’s information
system, which includes its internal controls, has an effect on a firm’s real decisions,
including the amount of firm cash flows that managers appropriate for themselves.
Weaker internal controls increase managers’ appropriation of firm resources, which
decreases the ratio of expected cash flows available to investors relative to the covariance
of firm cash flows as shown in equation (4). This translates into a higher cost of equity
capital.
In sum, we posit that the quality of a firm’s internal controls affects investors’
assessments of firm risk and cost of capital through (1) the precision of the signals
produced by the accounting system; and (2) by the amount of resources that managers
can appropriate for themselves. If a firm’s internal controls are weak, then the quality or
precision of its accounting signals is impaired. Consistent with this conjecture,
8
Ashbaugh-Skaife et al. (2005) and Doyle et al. (2005) find that firms reporting internal
control deficiencies (ICDs) exhibit noisier accruals after controlling for other firm
characteristics that affect accrual quality. Combining these empirical findings with the
theory in Lambert et al. (2005), we develop both cross-sectional and inter-temporal
predictions. In the cross-section, we predict that firms with ICDs will exhibit higher
idiosyncratic risk, higher systematic (beta) risk and higher cost of capital relative to firms
with strong internal controls. Moreover, we expect firms’ costs of capital will increase
when the first revelation of internal control problems is made public under SOX 302 or
404 reporting provisions, and will decrease when external auditor SOX 404 opinions
affirm that the firm’s internal control weaknesses have been fully remedied.
In formulating our inter-temporal tests, we recognize that investors hold priors on the
likelihood that firms will face internal control problems based on observable firm
characteristics (Ashbaugh-Skaife et al. 2006; Doyle et al. 2006). Thus, we predict that the
effect of negative or positive signals about firms’ internal control quality will have a
greater (smaller) effect on cost of capital the smaller (the greater) the probability of a firm
reporting an internal control weakness. The unique regulatory setting surrounding
disclosures on internal control weaknesses and remediation allows us to triangulate our
research question in ways that mitigate competing explanations for our results.
III. Institutional Background and Reporting Environment
Prior to passage of the Sarbanes-Oxley Act (SOX) in July 2002, public companies in
the U.S. were required to maintain books and records that would protect corporate assets
and provide GAAP-based financial reporting (e.g., FCPA 1977). However, pre-SOX
9
statutes did not require management evaluations of internal control or public assertions
about control adequacy (although some publicly disclosed internal control assertions
voluntarily (see McMullen et al. 1996)) and the statutes did not require independent
audits of internal control. 2 SOX changed the public assertion, audit, and audit reporting
landscape in two steps.
Section 302 of SOX mandates that a firm’s CEO and CFO certify in periodic (interim
and annual) SEC filings that the signing officers have “evaluated . . . and have presented
in the report their conclusions about the effectiveness of their internal controls based on
their evaluation” (302 (a) (4) (C) and (D)). However, neither section 302 of SOX nor
SEC interpretations describe procedures for evaluating controls and do not require
independent audits of internal control or management’s certifications. 3
Section 302 requirements became effective in 2002, and management certifications,
including exceptions indicating internal control deficiencies (ICDs) began appearing in
2003 (e.g., see Klein 2003). The lack of required management review and evaluation
procedures and the lack of independent audit requirements for internal control probably
lowers the likelihood of ICD detection and increases the variance of outcomes, as does
the fact that changes in control deficiencies that are judged “material weaknesses” do not
necessarily require disclosure prior to the effective date of section 404 (see SEC 2004
question 9).
2
The FCPA did require external auditors to report to the board of directors material weaknesses in the
firm’s internal control over financial reporting noted during conduct of the financial statement audit.
3
In particular, the SEC staff states “. . . we are not requiring any particular procedures for conducting the
required review and evaluation. Instead, we expect each issuer to develop a process that is consistent with
its business and internal management and supervisory practices” (SEC 2002).
10
Section 404, which became effective for fiscal years ending on or after November 15,
2004, required a formal basis for management assertions about internal control over
financial reporting and independent audits of the assertion by the registrant’s financial
statement audit firm as well as the auditor’s own opinion about internal control
effectiveness. In the empirical work to follow, we combine material weakness ICDs
made under SOX sections 302 and 404 and also disclosure of deficiencies and significant
deficiencies that are not required to be disclosed (see AS No. 2 2002). We do this for
three reasons.
First, while the necessary investigation to discover and requirements to disclose
deficiencies differ across disclosure and audit regimes, all disclosures are likely to reflect
important weaknesses in internal controls. 4 Second, sequencing 302 and 404 disclosures
for specific firms allows within sample testing of the effects of new information about
internal control through audit or management review discovery under section 404.
Disclosure of an ICD under 404 that was not preceded by a 302 disclosure could indicate
management’s lack of knowledge, inadequate review, or even intent to mislead. Further,
audited 404 disclosures may reflect remediation of prior internal control deficiencies
disclosed under 302. Our third reason for combining the disclosures is statistical in
nature. Because of the short history of mandated internal control reporting, the number of
ICD disclosures is small and the available empirical measures of risk and cost of capital
are relatively noisy. Combing regulatory regimes and voluntary disclosures increases the
power of our tests.
4
For example, voluntary disclosure of a deficiency or significant deficiency may reflect management or
auditor uncertainty about whether a deficiency might be judged a material weakness, a belief that voluntary
disclosure may facilitate a reputation for diligence and transparency or a legal defense, or perhaps a signal
that a rumored deficiency is not a material weakness.
11
IV. Sample, Variables, and Descriptive Statistics
The data for this study are compiled from the following sources:
•
Internal control deficiency disclosures and SOX 404 audit opinions–Compliance
Week, Glass-Lewis and SEC filings on Edgar
•
Accounting variables – Standard and Poor’s Compustat
•
Stock return data – CRSP and Datastream
•
Expected returns (implied cost of capital estimates) – Value Line
Our initial sample of firms providing disclosure of ICDs is obtained from
compilations of SEC filings reported in Compliance Week, a weekly electronic newsletter
published by Boston’s Financial Media Holdings Group, and Glass-Lewis & Co., LLC, a
leading investment research and proxy advisory firm. The sample period spans filings
made from November 2003 to September 2005. In addition we supplement these two
databases with additional hand collected data from SEC filings. 5 This results in an initial
sample of 1,053 firms disclosing at least one internal control deficiency in either the SOX
302 or 404 reporting regimes.
Panel A of Table 1 partitions the sample of 1,053 ICD firms based on their SOX 404
audit reports. For the sample of firms disclosing control deficiencies and having 404
reports, 380 firms received adverse 404 opinions, 13 firms received disclaimers while
133 firms received an unqualified 404 opinion from their auditor. Of the 380 firms that
received an adverse 404 opinion, 186 firms had previously disclosed control deficiencies
under the 302 regime, while the adverse 404 opinion represents the first disclosure of a
control deficiency for 194 firms. Of the 13 firms receiving disclaimers under 404, four
5
The additional hand collection was used to supplement the initial database in instances where firms had
delayed their filings yet indicated that they were expecting an adverse opinion or filed their 10-K after
sample periods covered by the Glass Lewis and Compliance Week database.
12
did not disclose a control deficiency under the 302 regime. The largest sub-sample
consists of 527 firms not having a SOX 404 opinion, indicating that these firms reported
an internal control problem under 302, but have not provided an auditor’s opinion on
internal controls because they are either non-accelerated filers or had not yet filed a 10-K
in the SOX 404 reporting period by the sample cut-off date of September 2005. In
conducting our empirical tests, we allow the samples to vary depending on the data
required to conduct the particular analysis drawing each sample from the initial
population of 1,053 internal control deficiencies reported in Panel A.
Panel B of Table 1 investigates the market reaction to the first ICD disclosure for
sample firms having the necessary data to conduct the analysis. Specifically, we collect
daily returns over the three day window centered on the first ICD disclosure date from
DataStream. In addition, we further restrict the sample by requiring firms to have
sufficient data to estimate the ICD determinant model of Ashbaugh-Skaife et al. (2006),
that is used to form an expectation on whether a firm is incurring internal control
problems (see Appendix 2 for details).
The results reported in Panel B of Table 1 indicate that both the mean (-1.65%) and
median (-0.71%) buy and hold three day market-adjusted return (BHAR) around the first
disclosure of an ICD for the sample of firms reporting control deficiencies and having the
necessary return data (n=641) are negative and significant. These results indicate that at
least a portion of the information contained in the initial ICD disclosure was not expected
by investors. The second and third rows of Panel B report the market reaction for the
least (most) likely groups, where the groups are formed based on the predicted
probability of an ICD using a logit prediction model based on the work by Ashbaugh-
13
Skaife, et al. (2006). For both the least and most likely groups, the mean and median
market reactions are negative and significant at the 0.09 level or better. The magnitude
of the market reaction is largest for the most likely group (top 30% of the ICD sample
based on the predicted probability) where stock price fell, on average, -2.97% at the
announcement of an ICD. Overall, the descriptive statistics on the market reaction to the
ICD indicate that ICD reports resulted in a revision in investors risk assessments,
expectations of future cash flows or both and that these revisions were greatest for the
sample of firms most likely to receive an ICD report.
Panel C of Table 1 reports the descriptive statistics for the sample of firms used in the
first analysis examining the association between internal control deficiencies and two
types of risk, idiosyncratic and systematic risk. To be included in this analysis we require
firms to have daily and monthly stock returns on CRSP to estimate the idiosyncratic and
systematic risk measures. Idiosyncratic risk (I_RISK) is the standard deviation of the
residuals from the following model estimated over the fiscal year using daily returns:
EXRETi ,t = β 0 + β1 RMRFt + ε i ,t
(5)
where EXRET is firm i’s return on day t minus the risk free rate, RMRF is the day t
excess
return
on
the
market
obtained
from
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html).
Systematic risk (BETA) is measured as the coefficient on the RMRF term from the
following model:
EXRETi ,τ = β 0 + β 1 RMRFτ + ε i ,τ
(6)
14
where EXRET is firm i’s return for month τ minus the risk free rate, RMRF is the month
m excess return on the market obtained from
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html).
Equation (6) is estimated using monthly returns requiring a minimum of 24 and
maximum of 60 observations over the current and prior fiscal years. In addition, we
require sufficient accounting data on Compustat to calculate control variables included in
our analysis of I_RISK and BETA •
STD_CFO (Standard deviation of Cash flow from operations (Compustat #308)
divided by total assets (Compustat #6) calculated using the prior five fiscal years,
requiring a minimum of three years of data)
•
LEV (Total debt (Compustat #9 plus Compustat #34) divided by total assets
(Compustat #6)
•
CFO (Cash flow from operations (Compustat #308) divided by total assets
(Compustat #6)
•
BM (Book value of equity (Compustat #60) divided by market value of equity
(Compustat #199* Compustat #25)
•
SIZE (Natural log of fiscal year end market value of equity (Compustat #25 *
Compustat #199)
•
DIVPAYER (One if the firm pays dividends (Compustat # 21), zero otherwise).
Finally, for comparative purposes we require firms to have data for fiscal years 2002,
2003 and 2004. 6
The upper half of panel C of Table 1 reports the descriptive statistics for the sample
of firms disclosing control deficiencies (ICD=1) and having sufficient data to be included
in the analysis (610 firms representing 1,830 firm-year observations). The lower half of
panel C of Table 1 displays the descriptive statistics for the control sample (ICD=0),
6
Our inferences our unchanged if we do not impose this requirement.
15
representing all firms with sufficient data on both CRSP and Compustat that did not
disclose an internal control deficiency in either the 302 or 404 reporting regimes (3,208
firms representing 9,624 firm-year observations). On average, firms disclosing internal
control deficiencies tend to have more volatile cash flows (mean value of
STD_CFO=0.092) than the control firms (mean value of STD_CFO=0.085). In addition,
ICD firms compared to non-ICD firms tend to be smaller (mean SIZE $5.640 billion
versus $5.955 billion), have less leverage (mean LEV 0.214 versus 0.220), have lower
cash flow from operations (mean CFO 0.017 versus 0.032), have higher book-to-market
ratios (mean BM 0.585 versus 0.570) and are less likely to pay dividends (mean
DIVPAYER 0.226 versus 0.342).
Panel D of Table 1 provides correlations among the control variables and the ICD
indicator variable. The upper right hand portion of the panel presents Pearson productmoment correlations, while the lower left hand portion presents the Spearman rank-order
correlations. To facilitate discussion, we focus on the Pearson correlations, but note that
the Spearman rank order correlations are generally consistent with the Pearson results.
Consistent with expectations, the ICD variable is positively correlated with both I_RISK
(0.072) and BETA (0.072). In addition, the ICD variable is positively correlated with
STD_CFO (0.026) and negatively correlated with CFO (-0.027), SIZE (-0.055), and
DIVPAYER (-0.091). As expected, I_RISK and BETA exhibit a relatively large positive
correlation, 0.446. However, in terms of magnitude, the correlation between I_RISK and
SIZE, -0.618, is the largest.
IV. Results
16
IV.1 Cross-Sectional Results
We begin our empirical tests by investigating the relation between the presence of
internal control weaknesses and idiosyncratic risk (I_RISK).
Panel A of Table 2
compares the mean and median I_RISK between ICD and control firms. For all three
years of our sample period, the mean and median I_RISK of ICD firms is significantly
higher than that of the non-ICD firms.
We next investigate the relation between internal control deficiencies and
idiosyncratic risk by estimating a multivariate OLS regression that controls for other
factors that prior research has shown to be related to idiosyncratic risk (Rajgopal and
Venkatachalam 2005). Specifically, we estimate the following model:
I _ RISK = β 0 + β1 ICD + β 2 STD _ CFO + β 3 LEV + β 4 CFO + β 5 BM + β 6 SIZE +
β 7 DIVPAYER + ε
(7)
where all variables are as previously defined.
Panel B of Table 2 displays the results of estimating equation (7) for each year of our
sample period. 7 As expected, we find that smaller firms that less often pay dividends and
report lower levels and greater volatility of cash flows from operations exhibit greater
idiosyncratic risk. We find a significantly negative coefficient on BM for two of the
three years. The analysis using the complete sample of ICD and control firms finds a
significant negative coefficient on LEV for two of the three years, which is contrary to
expectations. However, when we eliminate firms from the sample that do not have debt
(or trivial amounts of debt), the results indicate, as expected, that firms with more debt
7
We conduct yearly regressions because of the changes in market conditions over 2002-2004, and require
sample firms to have data in all three years. Allowing the sample to vary by year conditional on firms have
data does not change our results.
17
exhibit more idiosyncratic risk. 8 Turning to the primary variable of interest, we find a
positive and significant coefficient on ICD for each of the three years of our analysis
period. This result indicates that firms that have internal control problems have greater
idiosyncratic risk than firms that do not report internal control deficiencies.
The purpose of internal controls is to generate reliable, high quality financial
information. There is some evidence to suggest that firms with weak internal controls
have low quality financial information relative to firms operating with adequate internal
controls (e.g. Hogan and Wilkins 2005 and Ashbaugh-Skaife, et. al. 2005).
Prior
research suggests that firms reporting low quality information present greater information
risk that manifests in higher costs of capital. Thus, it could be that our model of I_RISK
suffers from a correlated omitted variable problem in that ICD could be proxying for low
information quality. To assess whether the presence of an ICD results in greater risk
beyond that of poor quality financial information, we re-estimate equation (3) including
two variables that capture firms’ information quality: abnormal accruals and noise in
working capital accruals. Abnormal accruals (AA) are defined as the absolute value of
performance adjusted abnormal accruals as estimated by the modified Jones model. The
noise in working capital accruals (DD_NOISE) is defined as in Dechow and Dichev
(2002) (see Appendix 1 for details).
Panel C of Table 2 displays the results of estimating equation (3) including AA and
DD_NOISE.
As expected, we find positive coefficients on AA and DD_NOISE
8
The tabled result that LEV is negatively associated with I_RISK is consistent with the findings of Duffee
(1995). Duffee (1995) reports that the positive relation between leverage and risk measures documented in
prior work is due to the samples used in the empirical analysis. When Duffee (1995) requires firms to have
debt, he finds a positive relation between leverage and risk. Relaxing this requirement results in either
insignificant results or in some instances the opposite result. Following Duffee’s (1995) work, we delete
firms that have leverage less than 0.10 and find that a positive association between leverage and I_RISK.
18
indicating that poor quality financial information is positively related to idiosyncratic
risk. More importantly, after controlling for the quality of firms’ financial information,
we still find a significantly positive coefficient on ICD for 2002, 2003, and 2004. Thus,
it appears that weaknesses in internal controls manifest in more idiosyncratic risk beyond
that of poor quality financial information.
Our second empirical test examines the relation between systematic risk, i.e., BETA,
and internal control deficiencies. Panel A of Table 3 displays the mean and median
values of BETA for ICD and non-ICD firms for each sample year. Univariate tests
indicate that the mean and median values of BETA for ICD firms are significantly larger
than that for non-ICD firms across all years in our sample period.
Panel B of Table 3 reports the results of the following OLS regression:
BETA = β 0 + β1 ICD + β 2 STD _ CFO + β 3 LEV + β 4 CFO + β 5 BM + β 6 SIZE +
β 7 DIVPAYER + ε
(8)
where all variables are as previously defined. After controlling for known risk factors,
we find a significantly positive coefficient on ICD for each of the sample years. As in
our idiosyncratic risk analysis, we assess the robustness of our results by adding the two
information quality measures to equation (8). The results displayed in Panel C of Table 3
indicate that firms that have problems with their internal controls exhibit greater market
risk even after controlling for the quality of their financial information.
Our third set of tests focuses on firms’ cost of equity capital. As suggested by the
analytical results in Lambert et al. (2005), our general hypothesis is that firms with
internal control deficiencies will exhibit higher cost of equity capital relative to firms
with strong internal controls.
Based on prior research (Francis, et. al. 2004 and
19
Ashbaugh-Skaife, Collins and DeFond 2006), we elect to use firms’ expected rate of
return, as reported in Value Line, as our measure of cost of equity capital. 9
To benchmark our cost of capital measure to prior research, we report in Panel A of
Table 4 the descriptive statistics for the cost of capital estimates (CC) and the risk
measures that serve as control variables in our cost of capital tests; BETA, SIZE, BM,
and I_RISK. The mean (median) CC value for ICD firms is 17.492 (15.563) whereas the
mean (median) CC value for the control firms is substantially less (14.823 and 13.625,
respectively). Panel B of Table 4 displays the CC estimates by year for ICD and control
firms. The univariate tests indicate that both the mean and median CC values for ICD
firms are larger than that of the control firms.
9
Theoretical and empirical research indicates that a “good” measure of expected return will be positively
related to beta and the book-to-market ratio and negatively related to size (Sharpe 1964; Linter 1965; Black
1972; Berk 1995; Fama and French 2004; Botosan and Plumlee 2004). Guay, Kothari, and Shu (2003)
suggest evaluating cost of equity capital measures based on the association between measures of expected
returns and realized returns. They posit that expected returns, on average, should equal realized returns if
investors expected returns reflect rational expectations. We consider two cost of capital measures. The
first is from Value Line and the other is derived using IBES forecast as inputs to Easton’s [2004] PEG
ratio. While Botosan and Plumlee conclude that these two measures are the most defensible, the PEG ratio
estimates potentially have additional drawbacks in our setting. Specifically, to derive a cost of capital
estimate the PEG ratio requires positive and increasing earnings forecasts. This introduces a potentially
serious sample selection bias against ICD firms as Ashbaugh-Skaife, et al. (2005) and Doyle et al. (2005)
find that ICD firms have a much higher incidence of losses that non-ICD firms. In assessing the validity of
the two cost of capital estimates, we find that in each year ( 2002, 2003 and 2004) both the Value Line and
PEG ratio cost of capital measures exhibits a positive (negative) and significant association with beta and
the book-to-market ratio (size), as expected. However, when validating the two estimates of the cost of
capital we find that the Value Line implied cost of capital measure meets the Guay et al. (2003) rational
expectations test, while the PEG ratio cost of capital measure fails this test. As a final means of
comparison between the two measures, we examine the implications of the PEG ratio’s positive earnings
and positive earnings growth requirements for the ICD firms. Ashbaugh-Skaife et al. (2005) and Doyle et
al (2005) find that firms reporting losses and firms in financial distress are more likely to report control
weaknesses, indicating that the PEG ratios requirements potentially restrict the ICD sample. For fiscal
2004 we are able to calculate Value Line cost of capital estimates for 220 ICD firms, while only 175 ICD
firms have PEG ratio estimates. Thus, requiring firms to have positive forecasted earnings and positive
growth in forecasted earnings results in a loss of over 20 percent of the ICD observations. In addition, the
Value Line cost of capital estimates indicate that the sample of ICD firms not meeting the PEG ratio
requirements are potentially more risky than the ICD firms meeting the PEG ratio requirements (median
Value Line cost of capital of 13.83 vs. 12.25).
Based on the results of these analyses to evaluate the
properties of the two alternative cost of capital measures, we conclude that the Value Line measure is
superior to the PEG ratio measure and is a reasonably good proxy for firms’ cost of equity capital. A
limitation of Value Line is that it covers a limited number of publicly traded firms. Thus, the sample of ICD
and control firms used for our cost tests is smaller than the samples used in our idiosyncratic and market
risk assessments.
20
We test our predictions regarding the effects of internal control deficiencies on the
cost of equity capital using the following model:
CC = β 0 + β1 ICD + β 2 BETA + β 3 SIZE + β 4 BM + β 5 IDO _ RISK + ε
(9)
where all variables are as previously defined. Panel C of Table 4 reports the results of
estimating equation (9) for 2002, 2003, and 2004. The signs of the coefficients on the
risk factors are as expected in that we find that BETA, BM, and I_RISK are positively
related to firms’ cost of equity capital. The results also indicate that SIZE is positively
related to the cost of capital in two of the three years, inconsistent with expectations. In
untabulated analyses, we drop I_RISK from equation (9) and findthat the coefficient on
SIZE is negative and significant as expected. Turning to the primary variable of interest,
we find a significantly positive coefficient on ICD in each of the three years.
Furthermore, as shown in Panel D of Table 4, this result holds after controlling for the
quality of firms’ financial information. These results indicate that firms with internal
control problems incur higher costs of equity capital than firms that do not suffer from
internal control weaknesses. This finding provides empirical evidence supporting the
theoretical work of Lambert et al. (2005) who predict that firms with stronger internal
and higher quality financial information will exhibit a lower cost of capital.
IV.2 Change Analysis
The results reported above suggest that weak internal controls are associated with
higher cost of equity capital, but these cross-sectional results do not provide a sufficient
basis for inferring causality. To further substantiate a causal relation between internal
control deficiencies and cost of equity capital, we conduct five additional change
analyses tests. The first analysis investigates whether there is a change in the cost of
21
equity capital when a firm first reports a control problem and whether the magnitude of
change in cost of capital is inversely related to the market’s assessed likelihood that a
firm will report an ICD. We start by identifying the first ICD report date for 162 sample
firms for which we have Value Line cost of capital estimates both before and after the
ICD report date and data necessary to estimated the ICD determinant model in Appendix
2. We then compare the CC estimate pre-disclosure to the CC estimate post-disclosure. 10
Because it is argued that the market can set expectations for whether a firm has an
internal control problems based on observable events (see Ashbaugh-Skaife et al. 2005
and Doyle, et. al. 2005), we divide the 162 firms into deciles based on the likelihood of
reporting an internal control problem.
Panel A of Table 5 reports the mean and median change in the cost of capital for all
162 firms as well as the bottom three and top three deciles. For the full sample we find
that the cost of capital increased significantly after firms’ first disclosure of an internal
control problem. In addition, we find that the cost of capital increase following the ICD
report appears more severe for the firms that were the least likely to report and ICD.
Specifically for the least likely group the mean change in the cost of capital is 1.036,
significant at the 0.07 level one-sided. For the sample of firms that were most likely to
report ICD problems, both the mean and median values are positive, consistent with
investors assessing a higher cost of capital after the ICD report. However, neither of these
values is significantly different from zero. Based on the results reported in Panel B of
Table 5, we conclude that the initial revelation of the ICD results in investors assessing a
10
In Tables 5, 6, and 7, we report the results using market adjusted cost of capital measures, where market
adjustments are made using all firm not reporting and ICD over the same time period. We also examine
the change in the cost of capital using unadjusted and industry adjusted cost of capital measures (untabled)
and find similar results.
22
higher cost of equity capital and this revision in cost of capital is greater for firms that
were least likely to report an ICD based on observable firm characteristics. These
findings are consistent with at least a portion of the initial market reaction to the ICD
being due to investor assessing greater information risk to firms initially reporting ICDs.
As a further test of causality, we examine the change in the cost of capital
surrounding the remediation of prior disclosed control deficiencies. Specifically, we
examine the change in the cost of capital surrounding the release of the unqualified SOX
404 opinion for the sample of firms that previously disclosed a control deficiency under
section 302 of SOX. In conducting our analysis we require firms have at least two
months between the release of the SOX 302 control deficiency and the unqualified SOX
404 auditor opinion to ensure that investors had sufficient time to revise their cost of
capital estimates. Panel A of Table 6 reports the descriptive statistics on cost of capital
pre and post SOX 404 opinion for the firms that remedied their internal control problems
and received a clean SOX 404 opinion form their external auditors. For the 38 firms that
disclosed an ICD under 302 and received an unqualified 404 opinion, we find a
significant reduction in the cost of capital following the release of the 404 report.
Specifically, in Panel B of Table 6 the mean (median) change in the market-adjusted cost
of equity capital following the release of the clean SOX 404 opinion is -1.513 (-1.313),
both of which are significantly different from zero at the 0.04 level or better.
In contrast to firms that remedied their internal control problems, there were firms
that voluntarily disclosed ICDs during the SOX 302 regime and failed to remedy these
problems as evidenced by a qualified SOX 404 audit report. For these firms, we expect
to observe no significant change in cost of capital at the time of receiving an adverse
23
opinion since the market had already adjusted its cost of capital assessments upward on
the initial disclosure (see Table 5). In Panel A of Table 7, we report the descriptive
statistics on the CC for the 119 firms that failed to correct their control problems prior to
the SOX 404 audit. 11 Panel B of Table 7 reports the mean and median change in CC
around the SOX 404 audit opinion for these firms. As expected, we find no significant
change in cost of capital around the reporting of the qualified 404 opinion (mean=-0.161,
median=-0.375).
We conduct one additional cost of capital change analysis surrounding the release of
the 2004 10-K for the sample of firms that received unqualified 404 opinions and did not
disclose a control deficiency under the 302 regime. These firms are deemed to have
strong internal controls. 12 Similar to the analysis in Table 5, we use the model from
Ashbaugh-Skaife et al (2006) to determine the likelihood of a firm reporting an ICD. We
first examine the changes in the cost of capital for the full sample of 685 firms meeting
the data requirements. Both mean and median values of the market-adjusted change in
cost of capital measures are not significantly different from zero. After partitioning firms
based on the probability of receiving a control deficiency, we find that the sample of
firms receiving a unqualified 404 that were most likely to report a control weakness have
a cost of capital decline following the release of the 404 report. Specifically, the mean
change in the market- adjusted cost of capital is -0.77% which is significant at the 0.01
level. The median change in cost of capitalof -0.25 is directionally consistent with the
11
The total number of firms used in Tables 6 and 7 is 157, whereas the sample used in Table 5 is 162. The
difference in sample size is due to the requirement that firms have 404 reports combined with requiring
data to estimate the determinant model.
12
We identify the 404 opinions for this sample of firms using a combination of Compliance Week and hand
collection.
24
mean, but is not significantly different from zero. The results for the least likely group
are similar to those for the overall sample in that there is no evidence of a cost of capital
change. Thus, as expected, there is no significant change in cost of capital for those firms
that were least likely to report an ICD and did, in fact, receive an unqualified SOX 404
opinion on their internal controls.
In summary, the results presented in Tables 5-8 provide evidence consistent with the
revelation of internal control deficiencies causing significant revisions to investors’
assessment of information risk. In addition, these results provide evidence consistent
with poor internal controls causing investor to assess higher information risk and a higher
cost of capital.
IV.3 Effects on Risk after Controlling for the Likelihood of Reporting an ICD
Our last analysis examines the effect of internal control deficiencies on risk after
controlling for the firm-specific characteristics that are associated with poor internal
controls. Ogneva, Raghunandan and Subramanyam (2005) examine the implications of
section 404 certifications for cost of capital, concluding that internal control weaknesses
do not have a direct effect on the cost of capital after controlling for firm characteristics
that are associated with ICDs. Ogneva et al. (2005) view internal control deficiencies as
having two potential effects on the cost of capital.
The first effect arises from
information risk associated with poor internal controls and the second effect arises from
internal control problems being inherently more likely for firms facing greater operating
risk.
Ogneva et al. (2005) find that the cost of capital effect of internal control
disclosures disappear after controlling for factors associated with an increased likelihood
of reporting internal control deficiencies. Based on this finding Ogneva et al. (2005)
25
conclude that internal control deficiencies do not have a direct effect on the cost of
capital.
The results of our change-analysis provide evidence inconsistent with the findings of
Ogneva et al. (2005). By examining the firm-specific change in the cost of capital, we
are able to control for other factors that are correlated with the reporting of internal
control deficiencies as well as correlated with the cost of capital. However, to provide a
more direct comparison to Ogneva et al. (2005), we re-estimate our cross-sectional
I_RISK, BETA, and CC models including the variables from the Ashbaugh-Skaife et al.
(2006) model using all firms with available data for fiscal 2004.
The first column of Panel A in Table 9 displays the association between I_RISK and
ICD including the factors affecting the likelihood of firms reporting an ICD. After
including the additional variables in the model, we continue to find a positive and
significant coefficient on ICD. The second column of Panel A of Table 9 reports the
association between ICD and BETA after controlling for the firm-specific characteristics
associated with internal control deficiency disclosures. Similar to the results reported in
Table 3 we continue to find a positive and significant association between ICD and
BETA. Finally, Panel B of Table 9 presents the results of the cross-sectional cost of
capital model including the additional control variables. While the magnitude of the
coefficient and significance is reduced relative to the results presented in Table 5, we
continue to find a positive and significant association between the cost of capital and the
ICD dummy.
Our research design examines the implications of internal control weaknesses on the
cost of capital over multiple years because the required reporting of internal control
26
problems is a new phenomena yet the existence of internal control problems in all
likelihood existed prior to the disclosure. Indeed, our results indicate that investors
viewed firms reporting internal control weaknesses to be more risky prior to the first
disclosure of an internal control weakness. Based on this result, we examine the change
in investors’ assessment of firms’ cost of capital surrounding changes in internal control
reporting. Our results indicate that investors updated their cost of capital assessments
when new information related to internal controls was released. Differences in our
research design related to the measurement of the cost of capital and samples sizes
contribute to differences in the inferences drawn from our analysis relative to the
conclusions of Ogneva et al. (2005).
V. Conclusion
The recent theoretical work of Lambert, Leuz and Verrecchia (2005) suggests that
information and information system quality have both an indirect and direct effect on the
cost of equity capital.
Strong internal controls are fundamental to a high quality
information system and high quality financial information. We use the unique setting
provided by the enactment of the Sarbanes-Oxley Act of 2002 (SOX) that requires firms
to disclose internal control problems to empirically test whether the quality of firms’
internal control systems affect idiosyncratic risk, beta risk, and costs of equity capital.
In cross-sectional tests, we find that firms that disclose problems with their internal
controls exhibit significantly higher betas, idiosyncratic risk and cost of capital during the
period from 2002 to 2004 relative to firms not reporting internal control deficiencies.
These differences persist after controlling for other factors that prior research has shown
27
to be related to these variables. We also structure change analysis tests designed to
investigate whether initial disclosure of internal control problems, repeated disclosure of
internal control problems, or remediation of internal control problems caused predictable
changes in firms’ cost of capital. In general, our findings indicate that firms that disclose
an internal control problem experience a significant increase in market-adjusted cost of
capital, firms that have persistent internal control problems have no change in their cost
of capital, and firms that improve their internal control systems as evidenced by receiving
an unqualified SOX 404 opinion exhibit an average decrease their market-adjusted cost
of capital of 152 basis points. Our study provides clear evidence that internal control risk
matters to investors and that firms reporting strong internal controls or firms that correct
prior internal control problems benefit from lower costs of equity capital.
28
Appendix 1 Variable Definitions
AA
BETA
BM
CC
CFO
DD_NOISE
DIVPAYER
ICD
I_RISK
LEV
SIZE
STDCFO
STDNIBE
Abnormal accruals measured by the absolute value of performance adjusted
abnormal accruals (see Ashbaugh et al. 2003)
Beta measured as the coefficient on RMRF from the following model:
EXRET = β 0 + β 1 RMRF + ε estimated over the 60 months prior to a firm-year
observation fiscal year end, requiring minimum of 18 months. EXRET is the
firm’s monthly return minus the risk free rate, RMRF is the excess return on
the market (source
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html).
Book to market measured as book value of equity (Compustat #60) divided
by market value of equity (Compustat #199* Compustat #25).
Cost of capital defined as Value Line’s annual expected return.
Cash flow from operations measured as cash flow from operations
(Compustat #308) scaled by total assets (Compustat #6).
The Dechow and Dichev’s (2002) quality of accruals metric measured as
negative one times the standard deviation of the firm-specific residual from
the prior three to five years. Residuals are from the following cross-sectional
estimations of the following OLS model:
WCAt = β 0 + β1CFOt −1 + β 2 CFOt + β 3 CFOt +1 + ε
where regressions are estimated by three, two, or one-digit SIC codes
conditional on having at least 10 firms in each SIC group. WCA=working
capital accruals (-(Compustat # 302+ Compustat # 303+ Compustat # 304+
Compustat # 305+ Compustat # 307)); CFO= cash flow from operations
(Compustat # 308); all variables are scaled by average total assets (Compustat
# 6).
One if the firm pays dividends (Compustat # 21), zero otherwise.
One if firm disclosed an internal control problem, zero otherwise.
Idiosyncratic risk measured as the standard deviation of the residuals from the
following model estimated over the fiscal year using daily
returns: EXRET = β 0 + β 1 RMRF + ε EXRET is the firm’s daily return minus the
risk free rate, RMRF is the excess return on the market (source
http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html).
Leverage measured as total debt (Compustat #9 plus Compustat #34) divided
by total assets (Compustat #6).
Natural log of fiscal year-end market value of equity (Compustat #25 *
Compustate #199).
Standard deviation of cash flow from operations defined as cash flows from
operations (Compustat #308) divided by total assets (Compustat #6), where
the standard deviation is calculated using the prior five fiscal years, requiring
a minimum of three years of data.
Standard deviation of net income before extraordinary items defined as net
income before extraordinary items (Compustat #18) divided by total assets
(Compustat #6), where the standard deviation is calculated using the prior five
fiscal years, requiring a minimum of three years of data.
29
Appendix 2: ACK Internal Control Model
Variables
INTERCEPT
SEGMENTS
FOREIGN_SALES
M&A
RESTRUCTURE
RGROWTH
INVENTORY
SIZE
%LOSS
RZSCORE
AUDITOR_RESIGN
RESTATEMENT
Likelihood ratio χ2
Percent Concordant
Percent Discordant
n
Predicted
Sign
+
+
+
+
+
+
+
+
+
-3.097***
0.072***
0.536***
0.173**
0.478***
0.065***
0.700**
-0.045***
0.202*
0.022
1.320***
0.543***
159.25***
62.3%
35.9%
4810
The dependent variable is set equal to one if the firm discloses an internal control deficiency, and
zero otherwise. The independent variables are defined as follows: SEGMENTS is the number of
reported business segments in 2003 (Compustat Segment file),FOREIGN_SALES is equal to one
if a firm reports foreign sales in 2003, and zero otherwise(Compustat Segment file), M&A equals
one if a firm is involved in a merger or acquisition from 2001 to 2003, and zero otherwise
(Compustat AFNT #1), RESTRUCTURE equals one if a firm was involved in a restructuring
from 2001 to 2003, and zero otherwise (this variable is coded one if any of the following
Compustat data items are non-zero: 376, 377, 378 or 379), RGROWTH is the decile rank of
average growth rate in sales from 2001 to 2003 (the percent change in Compustat #12),
INVENTORY is the average inventory to total assets from 2001 to 2003 (Compustat #3/
Compustat #6), SIZE is the natural log of average market value of equity from 2001 to 2003 in $
millions (Compustat #199 * #25), %LOSS is the proportion of years from 2001 to 2003 that a
firm reports negative earnings, RZSCORE is the decile rank of Altman (1980) z-score measure of
distress risk, AUDITOR_RESIGN equals one if the auditor resigned from the client in 2003, zero
otherwise (8-K filings), and RESTATEMENT equals one if a firm had a restatement or an SEC
AAER from 2001 to 2003 and zero otherwise.
30
Table 1
Internal Control Deficiencies Samples and Descriptive Statistics
Panel A: Sample of Firms Reporting Internal Control Problems under 302 or 404 or Both
Adverse 404 but no ICD under 302
Adverse 404 and ICD under 302
Disclaimer under 404 but no ICD under 302
Disclaimer under 404 and ICD under 302
Unqualified 404 but disclosed an ICD under 302
No 404 report but disclosed an ICD under 302
194
186
4
9
133
527
Total
1,053
Panel B: Market Reaction to First Internal Control Deficiency Disclosure for Firms that have the
Necessary Data to estimate the ICD Reporting Model of Ashbaugh-Skaife, Collins, and Kinney
(2006)
Buy and Hold Market Adjusted
Return (BHAR)
Mean
Median
3-day BHAR
3-day BHAR
All firms having necessary data (n=641)
p-value
-1.648
0.01
-0.708
0.01
Least likely group to report ICD (lower 30%) (n=192)
p-value
-0.992
0.09
-0.598
0.02
Very likely group to report ICD (upper 30%) (n=192)
p-value
-2.965
0.01
-1.259
0.01
31
Table 1 continued
Panel C: Descriptive Statistics for ICD and Control Firms that Comprise the Samples used in the
Idiosyncratic Risk and Systematic Risk Analyses
ICD=1 (610 firms, 1,830 firm year observations)
Mean
3.682
1.380
0.092
0.214
0.017
0.585
5.640
0.226
I_RISK
BETA
STD_CFO
LEV
CFO
BM
SIZE
DIVPAYER
Median
3.223
1.113
0.060
0.153
0.053
0.514
5.648
0.000
Q1
2.153
0.546
0.033
0.011
-0.008
0.288
4.424
0.000
Q3
4.616
1.953
0.106
0.342
0.110
0.822
6.723
0.000
Std Dev
2.080
1.118
0.109
0.227
0.199
0.885
1.828
0.418
Q1
1.732
0.401
0.028
0.014
0.013
0.274
4.353
0.000
Q3
4.166
1.697
0.100
0.349
0.127
0.756
7.429
1.000
Std Dev
2.119
1.044
0.104
0.220
0.203
0.655
2.163
0.474
ICD=0 (3,208 firms, 9,624 firm year observations)
Mean
3.263
1.173
0.085
0.220
0.032
0.570
5.955
0.342
I_RISK
BETA
STD_CFO
LEV
CFO
BM
SIZE
DIVPAYER
Median
2.697
0.884
0.054
0.175
0.072
0.482
5.965
0.000
Panel D: Correlations (Pearson (top) Spearman (bottom))
ICD
ICD
I_RISK
BETA
STD_CFO
LEV
CFO
BM
SIZE
DIVPAYER
0.100
0.076
0.042
-0.014
-0.065
0.028
-0.057
-0.091
I_
RISK
0.072
0.519
0.578
-0.208
-0.377
0.004
-0.665
-0.603
BETA
0.072
0.446
0.452
-0.261
-0.283
-0.154
-0.122
-0.448
STD_
CFO
0.026
0.442
0.325
-0.343
-0.222
-0.175
-0.409
-0.447
LEV
-0.009
-0.080
-0.191
-0.174
-0.009
0.008
0.161
0.185
CFO
-0.027
-0.455
-0.304
-0.448
0.019
-0.103
0.340
0.232
BM
0.007
0.024
-0.093
-0.094
-0.123
0.052
-0.257
0.042
SIZE
-0.055
-0.618
-0.120
-0.310
0.083
0.322
-0.202
0.433
Bold text indicates significance at the 0.05 level or better two-tailed. See Appendix 1 for
variable definitions.
Differences in means (medians) are assessed using a t-test (one-sample median test).
32
DIVPAYER
-0.091
-0.480
-0.411
-0.299
0.132
0.230
0.001
0.434
Table 2
Internal Control Deficiencies and Idiosyncratic Risk
Panel A: Descriptive Statistics on Idiosyncratic Risk
Fiscal Year
2004
2003
2002
Mean
Median
Mean
Median
Mean
Median
ICD=1 (n=610)
ICD=0 (n=3,208)
3.103
2.724
2.753
2.282
3.638
3.258
3.177
2.641
4.306
3.808
3.856
3.262
p-value
0.01
0.01
0.01
0.01
0.01
0.01
Panel B: Idiosyncratic Risk OLS Regressions
I _ RISK = β 0 + β 1 ICD + β 2 STD _ CFO + β 3 LEV + β 4 CFO + β 5 BM + β 6 SIZE
+ β 7 DIVPAYER + ε
Predicted
Sign
INTERCEPT
ICD
STD_CFO
LEV
CFO
BM
SIZE
DIVPAYER
Adjusted R2
n
+
+
+
?
-
2004
5.843***
0.113***
2.114***
-0.274***
-1.564***
-0.767***
-0.412***
-0.644***
0.59
3,818
2003
6.469***
0.108**
3.397***
-0.121
-2.878***
-0.604***
-0.458***
-0.879***
2002
6.291***
0.193***
2.353***
0.224**
-2.118***
-0.035
-0.411***
-1.172***
0.52
3,818
0.51
3,818
33
Table 2 Continued
Panel C: Idiosyncratic Risk OLS Regressions Controlling for Information Quality
I _ RISK = β 0 + β1 ICD + β 2 STD _ CFO + β 3 LEV + β 4 CFO + β 5 BM + β 6 SIZE
+ β 7 DIVPAYER + β 8 AA + β 9 DD _ NOISE + ε
Predicted
Sign
INTERCEPT
ICD
STD_CFO
LEV
CFO
BM
SIZE
DIVPAYER
AA
DD_NOISE
Adjusted R2
n
+
+
+
?
+
+
2004
5.454***
0.104**
1.259***
-0.141*
-1.506***
-0.674***
-0.397***
-0.568***
1.649***
2.468***
0.59
3,620
2003
5.837***
0.108**
1.997***
0.143
-2.813***
-0.494***
-0.431***
-0.716***
1.734***
5.191***
2002
5.630***
0.199***
1.742***
0.512***
-2.097***
-0.003
-0.384***
-0.946***
2.064***
4.554***
0.52
3,602
0.51
3,428
***, **, *, indicates significance at the 0.01, 0.05, 0.10 levels, respectively. See Appendix 1 for
variable definitions. Differences in means (medians) in Panel A are assessed using a t-test
(Wilcoxon rank sum test).
34
Table 3
Internal Control Weakness and Systematic Risk (Beta)
Panel A: Descriptive Statistics on Systematic Risk (Beta)
Fiscal Year
2004
2003
2002
Mean
Median
Mean
Median
Mean
Median
ICD=1 (n=610)
ICD=0 (n=3,208)
1.434
1.217
1.181
0.940
1.370
1.167
1.108
0.870
1.338
1.134
1.038
0.845
p-value
0.01
0.01
0.01
0.01
0.01
0.01
Panel B: Systematic Risk (Beta) OLS Regressions
BETA = β 0 + β1 ICD + β 2 STD _ CFO + β 3 LEV + β 4 CFO + β 5 BM + β 6 SIZE
+ β 7 DIVPAYER + ε
Predicted
Sign
INTERCEPT
ICD
STD_CFO
LEV
CFO
BM
SIZE
DIVPAYER
Adjusted R2
n
+
+
+
?
-
2004
1.317***
0.099***
1.438***
-0.634***
-0.967***
-0.264***
0.063***
-0.845***
2003
1.295***
0.113***
1.202***
-0.679***
-1.315***
-0.351***
0.068***
-0.808***
0.27
3,818
0.30
3,818
2002
1.067***
0.108***
1.427***
-0.628***
-0.860***
-0.013
0.065***
-0.782***
0.27
3,818
35
Table 3 Continued
Panel C: Systematic Risk (Beta) OLS Regression Controlling for Information Quality
BETA = β 0 + β1 ICD + β 2 STD _ CFO + β 3 LEV + β 4 CFO + β 5 BM + β 6 SIZE +
β 7 DIVPAYER + β 8 AA + β 9 DD _ NOISE + ε
Predicted
Sign
INTERCEPT
ICD
STD_CFO
LEV
CFO
BM
SIZE
DIVPAYER
AA
DD_NOISE
Adjusted R2
n
+
+
+
?
+
+
2004
0.928***
0.099***
0.995***
-0.494***
-0.959***
-0.187***
0.083***
-0.788***
0.115
3.659***
0.28
3,620
2003
0.954***
0.115***
0.919***
-0.565***
-1.348***
-0.291***
0.084***
-0.744***
-0.171
3.588***
0.30
3,602
2002
0.646***
0.100***
1.346***
-0.549***
-0.878***
-0.002
0.088***
-0.683***
0.644***
3.375***
0.29
3,428
***, **, *, indicates significance at the 0.01, 0.05, 0.10 levels. See Appendix 1 for variable
definitions. Differences in means (medians) in Panel A are assessed using a t-test (Wilcoxon rank
sum test).
36
Table 4
Internal Control Weakness and Cost of Capital
Panel A: Descriptive Statistics on the Cost of Capital (CC)
Fiscal Year
2004
2003
2002
Mean
Median
Mean
Median
Mean
Median
15.101
12.515
13.833
11.500
18.239
15.520
16.938
14.500
19.137
16.433
16.938
14.875
0.01
0.01
0.01
0.01
0.01
0.01
ICD=1 (204 firms, 612 firm year observations)
Mean
Median
Q1
Q3
Std Dev
CC
BETA
SIZE
BM
I_RISK
15.563
1.021
6.971
0.488
2.362
11.563
0.537
6.186
0.316
1.714
21.625
1.808
8.001
0.729
3.274
8.816
1.029
1.419
0.470
1.368
Q1
Q3
Std Dev
10.000
0.369
6.783
0.273
1.341
17.875
1.396
8.831
0.626
2.499
7.260
0.897
1.512
0.324
1.114
ICD=1 (n=204)
ICD=0 (n=1,104)
p-value
Panel B: Descriptive Statistics on CC Samples
17.492
1.311
7.167
0.577
2.651
ICD=0 (1,104 firms, 3,312 firm year observations)
Mean
Median
CC
BETA
SIZE
BM
I_RISK
14.823
1.004
7.854
0.480
2.088
13.625
0.755
7.742
0.436
1.796
37
Table 4 Continued
Panel C: Cost of Capital OLS Regression
CC = β 0 + β1 ICD + β 2 BETA + β 3 SIZE + β 4 BM + β 5 I _ RISK + ε
Predicted
Sign
INTERCEPT
ICD
BETA
SIZE
BM
I_RISK
+
+
+
+
Adjusted R2
n
2004
2.445**
0.817**
1.278***
0.331***
2.577***
2.968***
0.31
1,308
2003
5.131***
1.234**
0.246
0.397***
4.566***
2.489***
0.16
1,308
2002
5.084***
0.752**
1.814***
0.134
1.979***
2.611***
0.40
1,308
Panel D: Cost of Capital OLS Regression Controlling for Information Quality
CC = β 0 + β1 ICD + β 2 BETA + β 3 SIZE + β 4 BM + β 5 I _ RISK + β 6 AA
+ β 7 DD _ NOISE + β 8 STDNIBE + ε
Predicted
Sign
INTERCEPT
ICD
BETA
SIZE
BM
I_RISK
AA
DD_NOISE
STDNIBE
Adjusted R2
n
+
+
+
+
+
+
+
2004
3.330***
0.762**
1.404***
0.252**
2.321***
3.076***
-2.813
-7.612
-0.484
2003
4.700***
1.166**
0.223
0.390**
4.652***
2.589***
1.977
4.528
-2.085
2002
4.936***
0.747**
1.565***
0.153
2.036***
2.557***
-1.171
4.420
4.214**
0.32
1222
0.16
1225
0.40
1203
***, **, *, indicates significance at the 0.01, 0.05, 0.10 levels. See Appendix 1 for variable
definitions. Differences in means (medians) in Panel B are assessed using a t-test (Wilcoxon rank
sum test).
38
Table 5 Cost of Capital Change Pre-Post First ICD Disclosure
Panel A: Cost of Capital Pre and Post ICD Disclosure (n=162)
Pre ICD CC
Post ICD CC
Mean
15.878
16.990
Median
14.750
15.000
Q1
11.000
11.750
Q3
18.250
19.750
Std Dev
7.391
8.289
Pre Period Market-Adjusted CC
Post Period Market-Adjusted CC
4.042
4.969
2.917
2.979
-0.500
0.000
6.500
7.750
7.420
8.272
Panel B: Firm Specific Change in the Market Adjusted Cost of Capital
(Post-First ICD Disclosure minus Pre-First ICD Disclosure)
Mean
Change in the
Cost of Capital
0.927
0.02
Median
Change in the
Cost of Capital
1.042
0.02
Least Likely Group (lower 30%) n=48
p-value
1.036
0.07
0.750
0.24
Very Likely Group (upper 30%) n=48
p-value
0.615
0.24
0.688
0.15
Full Sample n=162
p-value
Pre and Post Cost of Capital are calculated using a maximum of 180 days before and after the
first ICD report. Least (Very Likely) Group represents the sub-samples of firms that are less
(more) likely of receiving a ICD based on the Ashbaugh-Skaife, Collins, and Kinney (2006) ICD
model. Differences in means (medians) are assessed using a t-test (one-sample median test).
39
Table 6 Remediation of Internal Control Weakness and the Cost of Capital
Panel A: Descriptive Statistics on Cost of Capital Pre and Post-404 Disclosure for Firms that
Disclosed a ICD under 302 and Received a Clean 404 Opinion (n=38)
Pre-404 CC
Post-404 CC
Mean
15.836
14.503
Median
13.750
12.750
Q1
10.500
9.000
Q3
19.000
16.000
Std Dev
8.500
8.593
Pre-404 Market Adjusted CC
Post-404 Market Adjusted CC
4.096
2.583
2.125
0.792
-1.625
-2.750
7.292
4.333
8.501
8.592
Panel B: Firm-Specific Change in Market-Adjusted Cost of Capital
(Post-404 CC minus Pre-404 CC)
Firms Previously Disclosing a weakness receiving
and clean audit opinion (n=38)
p-value
Mean Change in the
Cost of Capital
Median Change in the
Cost of Capital
-1.513
0.03
-1.313
0.04
Pre and Post Cost of Capital are calculated using a maximum of 180 days before and after the 404
report. To be included in the sample, firms are required to have disclosed a 302 weakness at least
two months prior to the 404 report, and their 404 reports indicate that they have corrected their
internal control problem (n=38). Differences in means (medians) are assessed using a t-test (onesample median test).
40
Table 7 No-Remediation of Internal Control Weakness and the Cost of Capital
Panel A: Descriptive Statistics on Cost of Capital Pre and Post-404 Disclosure for Firms that
Disclosed a ICD under 302 and Received a ICD 404 Opinion (n=119)
Pre-404 CC
Post-404 CC
Mean
16.011
15.963
Median
15.250
14.500
Q1
11.500
11.000
Q3
18.000
18.750
Std Dev
7.530
7.863
Pre-404 Market Adjusted CC
Post-404 Market Adjusted CC
4.204
4.042
3.375
2.700
-0.292
-0.875
5.833
6.750
7.520
7.878
Panel B: Firm Specific Change in the Market Adjusted Cost of Capital
(Post-404 CC minus Pre-404 CC)
Firms Previously Disclosing a ICD and Receiving
a Clean 404 Opinion (n=119)
p-value
Mean Change in the
Cost of Capital
Median Change in the
Cost of Capital
-0.161
0.37
-0.375
0.36
Pre and Post Cost of Capital are calculated using a maximum of 180 days before and after the 404
report. Sample consist of all firms disclosing internal control problems under section 302 and
receiving adverse 404 opinions having Value Line cost of capital estimates in both the pre and
post periods (n=119). Differences in means (medians) are assessed using a t-test (one-sample
median test).
41
Table 8 Cost of Capital Change Pre-Post Fiscal 2004 10-K
For Firms Receiving Unqualified 404 Opinions
with No Prior Internal Control Weakness Disclosure
Panel A: Firm Specific Change in the Cost of Capital Post minus Pre 404 Opinions Disclosure
Mean
Median
Q1
Q3
Std Dev
Post 404 CC
Pre 404 CC
13.816
13.612
12.500
12.000
9.000
9.000
17.500
16.250
7.512
6.628
Post Period Market Adjusted CC
Pre Period Market Adjusted CC
1.622
1.612
0.333
0.167
-3.333
-2.833
5.250
4.417
7.514
6.633
Panel B: Firm Specific Change in the Market Adjusted Cost of Capital
(Post 404 Disclosure minus Pre 404 Disclosure)
Mean Change in the
Cost of Capital
0.010
0.48
Median Change in the
Cost of Capital
0.250
0.11
Least Likely Group (lower 30%) n=205
p-value
0.040
0.45
0.250
0.18
Very Likely Group (lower 30%) n=205
p-value
-0.768
0.01
-0.250
0.26
Full Sample (n=685)
p-value
Differences in means (medians) are assessed using a t-test (one-sample median test).
42
Table 9 Analysis Controlling for ICD Determinants
Panel A: Idiosyncratic and Market Risk OLS Regressions Controlling for ICD Determinants
RISK = β 0 + β1 ICD + β 2 STD _ CFO + β 3 LEV + β 4 CFO + β 5 BM + β 6 SIZE +
β 7 DIVPAYER + β 8 SEGMENTS + β 9 FOREIGN _ SALES +
β10 M & A + β11 RESTRUCTURE + β12 RGROWTH + β13 INVENTORY +
β14 % LOSS + β15 RZSCORE + β16 AUDITOR _ RESIGN +
β17 RESTATEMENT + ε
Risk Measure
Predicted
Sign
INTERCEPT
ICD
STD_CFO
LEV
CFO
BM
SIZE
DIVPAYER
SEGMENTS
FOREIGN_SALES
M&A
RESTRUCTURE
RGROWTH
INVENTORY
%LOSS
RZSCORE
AUDITOR_RESIGN
RESTATEMENT
Adjusted R2
n
+
+
+
?
-
I_RISK
5.962***
0.104**
1.052***
-0.425***
-0.825***
-0.593***
-0.445***
-0.280***
-0.014
0.020
0.083*
-0.007
0.000
0.410**
0.790***
-0.069***
0.730***
-0.072
0.60
2,965
BETA
0.898***
0.078**
0.584***
-0.783***
-0.218**
-0.085*
0.076***
-0.461***
-0.076***
0.124***
0.166***
0.240***
-0.012*
-0.280**
1.018***
-0.029***
0.163
0.040
0.39
2,965
43
Table 9 Continued
Panel B: Cost of Capital OLS Regression Controlling for ICD Determinants
CC = β 0 + β1 ICD + β 2 BETA + β 3 SIZE + β 4 BM + β 5 I _ RISK + β 6 SEGMENTS +
β 7 FOREIGN _ SALES + β 8 M & A + β 9 RESTRUCTURE + β10 RGROWTH +
β11 INVENTORY + β12 % LOSS + β13 RZSCORE + β14 AUDITOR _ RESIGN +
β15 RESTATEMENT + ε
Predicted
Sign
INTERCEPT
ICD
BETA
SIZE
BM
I_RISK
SEGMENTS
FOREIGN_SALES
M&A
RESTRUCTURE
RGROWTH
INVENTORY
%LOSS
RZSCORE
AUDITOR_RESIGN
RESTATEMENT
Adjusted R2
n
+
+
+
+
2004
5.592***
0.542*
0.657***
0.172
3.720***
2.276***
-0.021
-0.946**
-0.641**
0.046
0.218***
1.173
2.596***
-0.175**
0.337
-0.131
0.32
1,027
***, **, *, indicates significance at the 0.01, 0.05, 0.10 levels. See Appendix 1 and 2 for variable
definitions.
44
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