Material Weakness in Internal Control and Stock Price Crash Risk:
Evidence from SOX Section 404 Disclosure
Abstract: This study investigates the hitherto unexplored questions of whether and how the
presence of undisclosed internal control weaknesses (ICWs) and their initial disclosure
differentially influence the likelihood that extreme negative outliers occur in firm-specific return
distributions, which we refer to as stock price crash risk. We predict and find that firms with
ICW problems are more crash-prone than firms with effective internal controls. We also find that
stock price crash risk is greater for fraud-related ICWs. We provide strong evidence that the
positive association between ICWs and crash risk is observed at least two years prior to the
initial disclosure of the ICW. More importantly, we find that the positive association gradually
decreases over the two-year period following the disclosure and essentially disappears after
publicly disclosed ICW problems are remediated. The above results hold after controlling for
various firm-specific determinants of crash risk and ICWs. Overall, our results suggest that the
presence of undisclosed ICWs tends to exacerbate managers’ bad news hoarding until the ICW
problems are disclosed to the public, which increases crash risk. On the other hand, public
disclosure of ICWs constrains managerial incentive and ability to withhold bad news from
outside investors, thereby mitigating crash risk.
Keywords: Internal control weakness, crash risk, Sarbanes-Oxley Act (SOA)
JEL Classification Codes: G12, M41, K22
1.
Introduction
The past two decades have witnessed a series of large-scale corporate debacles and
accounting and auditing failures around the world, including the cases of Enron, Tyco and
Worldcom. These scandals, which cost investors billions of dollars when the share prices of the
affected companies collapsed, dramatically shook public confidence in the stability of capital
markets and the reliability of accounting disclosures. In an effort to restore investor confidence,
the U.S. Congress passed the Sarbanes-Oxley Act (SOX) in 2002. Section 404 of SOX (hereafter
SOX 404) requires a firm’s auditor to attest to the management’s internal control evaluation and
report the auditor’s own conclusion regarding internal control effectiveness. 1 This study
investigates a hitherto unexplored question of whether and how internal control weaknesses and
their disclosure are associated with the likelihood that extreme negative outliers occur in firmspecific return distributions, which we refer to as stock price crash risk.
Prior research shows that material weaknesses in internal control over financial
reporting—or simply internal control weaknesses (ICWs)—are associated with negative stock
returns and higher cost of (both equity and debt) capitals.2 This line of research has typically
analyzed the impact of ICWs on ex post realized returns or ex ante implied costs of capital,
which is conveniently referred to as the first moment study because its focus is on the effect of
ICWs on the first moment of a firm’s return distribution.3 On the other hand, the ICW disclosure
1
In this study, we focus on SOX 404 disclosures because compared to unaudited SOX 302 disclosures, auditorattested SOX 404 disclosures are more reliable indicators of a firm’s financial reporting system quality.
2
See, for example, Hammersley, Meyers and Shakespeare, 2008; Ogneva, Subramanyan, and Raghunandan, 2007;
Kim, Song and Zhang, 2011; Costello and Wittenberg-Moerman, 2012; Ashbaugh-Skaife, Collins, Kinney and
LaFond, 2009; Beneish, Billings and Hodder, 2008; and Dahliwal, Hogan, Trezevant and Wilkins, 2011.
3
In this study, we classify prior research into the first, second, and third moment studies if it focuses on the effect of
accounting regulation such as SOX 404 or IFRS adoption on the mean, variance, and skewness or tail risk,
respectively, of firm-specific return distributions. For example, if researchers examine the impact of IFRS adoption
(an accounting regulation) on the cost of capital (i.e., the required rate of return and thus the first moment of return
1
requirements under SOX 404 were in response to the abrupt, large-scale decline in stock prices
and the associated loss of investor confidence in the quality and reliability of financial reporting.
Nevertheless, previous literature has paid little attention to the effect of ICWs on negative tail
risk or the likelihood of observing extreme negative outliers in firm-specific return distribution
(which is conveniently referred to the third moment effect of ICWs).4 As a result, little is known
about whether and how ICWs are associated with the occurrences of extreme negative returns or
stock price crashes.
To better understand the role of internal control quality in stock price formation process,
our study first investigates whether the presence of (not-yet-disclosed) ICWs prior to the initial
ICW disclosure is positively associated with stock price crash risk. In so doing, we attempt to
isolate the presence effect (the effect associated with the presence of undisclosed ICW problems
prior to the initial public disclosure of an ICW) from the disclosure effect (the effect associated
with the initial public disclosure of an ICW under SOX 404). Second, we predict that the public
disclosure of an ICW is likely to improve firm-level transparency, and thus, mitigate a firm’s
crash risk subsequently. To test this prediction, we further examine whether the public disclosure
of an ICW under SOX 404 leads to a decreases in stock price crash risk from the pre- to the postICW-disclosure period. Finally, we examine whether and how the remediation of publicly
disclosed ICW problems impacts crash risk in the post-ICW-disclosure period.
We are motivated to examine the above research questions for the following reasons.
First, as noted in SEC (2003), internal control is a much broader concept that encompasses not
only the financial reporting process but also the overall information environment of a firm. Kim
distribution) and idiosyncratic return volatility or synchronicity (i.e., the second moment of firm-specific return
distribution), such research is conveniently referred to as the first and second moment studies, respectively.
4
This third moment effect enables researchers to better capture the accumulated effect of an information-related
event such as the initial disclosure of ICWs (Kim and Zhang, 2012).
2
et al. (2011) provide evidence that internal control quality captures the overall quality of a firm’s
information production system. 5 Furthermore, Hutton, Marcus and Tehranian (2009, hereafter
HMT) document a positive association between information opaqueness (captured by the threeyear moving sum of absolute abnormal accruals) and future crash risk. Given the above evidence,
our study examines whether the impact of internal control deficiencies on stock price crash risk
goes beyond and above the effect of HMT’s information opaqueness on crash risk. In other
words, we are interested in examining whether the lack of internal control quality, as reflected in
ICWs, is incrementally important over and beyond the lack of earnings quality in determining
crash risk.
Second, SOX 404 requires managers of all public firms to assess the effectiveness of
internal controls over financial reporting and to provide periodic auditor-attested evaluations of
internal control effectiveness. In comparison with SOX 302 disclosures of ICW, Section 404
disclosures is viewed as a more comprehensive, objective, and unambiguous indicator for the
quality of a firm’s information production system. 6 Therefore, establishing the link between
internal control quality under SOX 404 and stock price crash risk can provide useful insights into
whether and how the reliability and quality of a firm’s overall information production system,
not a specific attribute per se, are incorporated into stock price formation process, particularly
the third moment of firm-specific return distribution.
Lastly and more importantly, our research setting allows us to: (i) differentiate the ICW
presence effect on crash risk from the ICW disclosure effect; and (ii) evaluate whether and how
the initial disclosure of ICWs and its subsequent remediation affect stock price crash risk. Given
5
Kim et al. (2011) provide strong evidence that ICW is significantly associated with a higher cost of private debt, as
reflected in unfavorable loan contracting terms (e.g., higher loan spread and more restrictive covenants) even after
controlling for financial reporting quality.
6
See Feng, Li and McVay (2009), Kim, Song and Zhang (2011), and Cheng, Dhaliwal and Zhang (2012).
3
that prior research on the economic consequences of ICW disclosures does not explicitly
differentiate the presence effect from the disclosure effect, our study allows us to make cleaner
inferences on whether the ICW disclosure requirement under SOX 404 accomplishes its intended
policy objectives. In short, the results of our investigation provide new insights into the ongoing
debate about the costs and benefits of SOX 404 disclosure and compliance.
Briefly, our results, using a large sample of firms with auditor-attested ICW disclosures
during the post-SOX period of 2004-2011, reveal the following. First, we find that, in the years
prior to the initial disclosure of ICW, firms with ICW problems are more prone to experience
stock price crashes relative to firms without such problems. Our results are robust to different
measures of crash risk and alternative research designs and econometric methods. The above
findings support the view that effective internal controls mitigate stock price crash risk, and thus,
help to maintain stability in stock markets. Second, we find that firms with more severe fraudrelated ICWs face higher crash risk than those with less severe ICWs. This finding suggests that
fraud-related material weaknesses point to more fundamental problems, such as maintaining an
ethical culture in the workplace (Kizirian, Mayhew and Sneathen, 2005). Finally, the results of
our over-time analyses show that the crash risk of ICW firms declines in the years subsequent to
the initial disclosure of ICWs, and virtually disappears after the firms remediate the publicly
disclosed ICW. This finding suggests that the ICW disclosure under SOX 404 constrains
managers’ ability to hoard bad news, which mitigates firm-specific crash risk and increases
stability in equity markets.
Our study adds to the existing literature in the following ways. First, this is the first study
that examines the third moment effect of ICWs, that is, the effect of ICWs on negative tail risk.
Second, to the best of our knowledge, our study is the first that explicitly separates the
4
consequences associated with the presence of undisclosed ICW problems from those associated
with the initial disclosure of ICW. Third, our study provides new evidence on the benefits of
SOX 404 compliance: the disclosure of ICWs limits management’s bad news hoarding, and thus,
improves firm-level transparency, which in turn mitigates future crash risk. Fourth, our research
provides strong and reliable evidence that internal control quality is an incrementally significant
determinant of stock price crash risk above and beyond earnings quality and other known
determinants of crash risk. This finding is particularly relevant given the evidence that investors
are increasingly concerned about negative tail risk (Pan, 2002; Yan, 2011). Finally, the results of
our study provide an important policy implication to accounting and security market regulators:
internal control deficiencies are a significant factor driving stock price crashes, and thus, internal
control quality plays an important role in influencing future crash risk and maintaining stability
in equity markets.
The paper proceeds as follows. Section 2 provides a brief review of prior literature and
develops research hypotheses. Section 3 describes the sample, data, and variable measurement.
Section 4 discusses our empirical results. Section 5 presents the results of further analyses and
robustness checks. The final section concludes.
2.
Literature Review and Hypotheses Development
Our study is related to two strands of research. One strand examines the relation between
financial reporting quality and stock price crash risk; the other strand investigates the
determinants and consequences of SOX 404 disclosure. We offer a brief review of prior research
in each strand, and then develop our research hypotheses.
2.1
Prior research on firm-specific determinants of stock price crash risk
5
Stock price crash risk at the firm level refers to the likelihood that extreme negative
outliers occur in the distribution of firm-specific returns, that is, stock returns after netting out a
portion of returns that co-move with common factors (Jin and Myers, 2006; HMT; Kim et al.,
2011a; 2011b). The investment community and security regulators have given considerable
attention to research on stock price crash risk, since a series of corporate debacles and highprofile accounting scandals occurred in the early 2000s. The recent financial crisis in 2008 has
further brought about renewed interest in firm-specific causes for stock price crash risk.
Jin and Myers (2006) examine whether the agency conflicts and information asymmetries
between corporate insiders and outsiders is related to stock price crash risk.7 Specifically, their
model predicts that opaque stocks are more likely to deliver large negative returns. Since then,
much effort has been dedicated to empirically test this prediction. Notably, HMT use the threeyear moving sum of absolute abnormal accruals as a proxy for information opaqueness and
document a positive association between information opaqueness and stock price crash risk.
Their study concludes that financial reporting transparency is crucially important for maintaining
stability in stock markets.
Similar in spirit to HMT, Kim et al. (2011b) hypothesize that complex tax shelters and
tax planning allow managers to manage earnings via restructuring real transactions, which
provides a useful means for hiding negative information. Consistent with their hypothesis, they
find that corporate tax avoidance is positively associated with stock price crash risk. In another
study, Kim et al. (2011a) find that when a firm’s managers—particularly, the chief financial
officers (CFOs)—are given option-based compensation contracts, they tend to hide bad news
within the firm to maximize their incentive compensation, which in turn engenders relatively
7
Other analytical studies include Bleck and Liu (2006), and Benmelech, Kandel and Veronesi (2010).
6
high crash risk. DeFond et al. (2012) examine whether and how mandatory IFRS adoption by
European Union countries affects stock price crash risk. They provide evidence suggesting that
mandatory IFRS adoption decreases crash risk for industrial firms by increasing transparency or
decreasing information opaqueness, while it increases crash risk for financial firms by
magnifying stock return volatility for these firms. In another related study, Kim and Zhang
(2012) posit that conservatism curbs managerial incentives to delay the release of bad news, and
thus constrains managerial ability to withhold bad news. Consistent with this view, they find that
the degree of conditional conservatism is negatively associated with future crash risk. Hamm, Li
and Ng (2012) examine how management earnings guidance, an important voluntary disclosure
channel, is related to future crash risk. They find that the positive association between opacity in
reported earnings and crash risk, as documented in HMT, is stronger when opacity interacts with
more frequent earnings guidance.
Collectively, this line of research shows that financial reporting quality is negatively
associated with stock price crash risk. However, these earlier studies rely, in large part, on
researchers’ self-constructed earnings quality proxies and/or focus only on a specific earnings
attribute such as accrual quality and accounting conservatism (e.g., HMT; Kim and Zhang, 2012).
To our knowledge, no prior research has investigated the impact of internal control quality, an
unambiguous and comprehensive measure of a firm’s information production system, on stock
price crash risk.
2.2. Prior research on economic determinants and consequences of SOX 404 disclosure
Earlier studies on SOX 404 disclosures are of descriptive nature. For example, Doyle, Ge
and McVay (2007b), among others, find that firms with weak internal controls tend to be
7
smaller, younger, less profitable, more complex, or undergoing restructuring changes. 8 More
recent studies examine the economic consequences of SOX 404 disclosure, particularly, the
impact of ICWs on the cost of equity (e.g., Ogneva et al., 2007; Ashbaugh-Skaife et al., 2009),
the cost of public debt (Dhaliwal et al., 2011), and the cost of private debt (Kim et al., 2011).
Overall, this line of research focuses its attention on the first moment effect of initial ICW
disclosures, namely, the effect of initial public disclosures of ICWs under SOX 404 on ex post
realized stock return and ex ante expected stock returns or implied costs of capital. The main
finding of this research is that initial ICW disclosures have a negative impact on the market, as
manifested in higher costs of capital.
However, no prior research has investigated the impact of internal control deficiencies on
the likelihood of observing extreme negative outliers in firm-specific return distribution.
Moreover, prior research on the economic consequences of ICW disclosures under SOX 404
does not explicitly isolate the ICW presence effect (the consequence associated with the
presence of undisclosed ICWs) from the ICW disclosure effect (the consequences associated
with initial ICW disclosures under SOX 404). As will be further explained below, it is important
to separate the ICW presence effect from the ICW disclosure effect, when examining the impact
of ICWs on stock price crash risk.
2.3
Hypotheses development
2.3.1. The effect of the presence of undisclosed ICW on crash risk
The effectiveness of internal controls is an important factor that determines the quality
and reliability of a firm’s information production system. The quality of internal controls can
8
See also Ashbaugh-Skaife, Collins and Kinney, 2007; Ge and McVay, 2005.
8
affect not only the quality of public information disclosed via external financial reports, but also
the quality of (undisclosed) private information. For example, Doyle et al. (2007a) find that
ICWs are generally associated with poorly estimated accruals that are not realized as cash flows.
Feng et al. (2009) find that management forecasts are less accurate among firms with ICW
problems. Their results suggest that internal control quality not only influences earnings reports,
but also has an economically significant effect on voluntary disclosure that relies on internal
management reports (e.g., management earnings guidance).
The presence of (undisclosed) ICW problems entails procedural and estimation errors as
well as opportunistic earnings management,9 thereby deteriorating corporate transparency. Prior
research provides evidence that lack of transparency in financial reports enables managers to
opportunistically withhold bad news or unfavorable information (Jin and Myers, 2006; HMT;
Kim et al., 2011a; Kim and Zhang, 2012), thereby increasing future crash risk.10 However, there
is a limit to the amount of unfavorable information that managers can absorb or successfully hide
from outside investors. This is because, once the total amount of hidden negative information
reaches a certain threshold, it becomes too costly or impossible to continue to withhold it. When
the total amount of the hidden negative information that has accumulated over time reaches a
tipping point, it will come out abruptly, leading to a large negative, extreme return on the
individual stocks concerned, i.e., a stock price crash (Jin and Myers, 2006; HMT; Kim and
Zhang, 2012). One can therefore expect that ceteris paribus, firms with (undisclosed) ICW
9
A material ICW is defined as “[a] deficiency, or a combination of deficiencies, in internal controls over financial
reporting such that there is a reasonable possibility that a material misstatement of the registrant’s annual or interim
financial statements will not be prevented or detected on a timely basis by the company’s internal controls”
(www.sec.gov).
10
Prior research shows that firms with ICWs tend to disseminate less transparent or more opaque financial reports
than those with no ICWs. (Doyle, Ge and McVay, 2007a; Ashbaugh-Skaife, Collins and Kinney, 2007; Feng, Li and
McVay, 2009).
9
problems are more prone to experience stock price crashes than firms with effective internal
controls.
Given the scarcity of evidence on the issue, it is interesting and important to test whether
the quality and reliability of a firm’s information production system, as reflected in ICWs, go
above and beyond HMT’s information opaqueness measure in predicting future crash risk. To
provide systematic evidence on this unexplored issue, we test the following hypothesis in
alternative form:
H1: All else being equal, the presence of material weaknesses in internal control over
financial reporting, or simply material internal control weaknesses (ICWs), prior to their
initial disclosures is positively associated with the likelihood of stock price crashes.
2.3.2. Does the severity of undisclosed ICW problems matter?
Admittedly, however, there are also other reasons why our prediction may not hold
empirically. First, prior research suggests that ICWs are attributed primarily to a firm’s
complexity and insufficient resources (Doyle, Ge and McVay, 2007b). The disclosure of ICWs
simply implies that the firm’s internal controls are not sufficient to prevent or detect potential
accounting misstatement. Therefore, ICWs do not necessarily suggest the existence of
accounting misstatement. One way to further substantiate our prediction in H1 is to see if the
association between ICWs and crash risk is stronger for firms with more severe ICW problems.
Specifically, we interpret ICWs related to unethical issues or potential restatements, i.e.,
fraud-related ICWs, as a signal for an environment in which the probability of managerial rent
extraction is at its highest. Prior research suggests that restatements are often linked to aggressive
accounting and management culpability (Efendi, Srivastava and Swanson, 2007; DeFond and
10
Jiambalvo, 1994). 11 Skaife, Veenman and Wangerin (2012) also find that managers whom
external auditors identified as lacking integrity tend to engage in more profitable insider trading.
We expect that fraud-related ICW problems are more fundamental and severe in nature, and thus,
are more closely associated with managerial opportunism in financial reporting, such as bad
news hoarding. We therefore predict that the association between ICW and crash risk is stronger
for fraud-related ICWs than for other types of ICWs. To provide empirical evidence on the above
prediction, we test the following hypothesis in alternative form:
H2: All else being equal, stock price crash risk prior to the initial disclosure of ICW is
positively associated with fraud-related ICWs, to a greater extent, than it is with other
non-fraud-related ICWs.
2.3.3. The effect of initial public disclosure of ICW on crash risk
In comparison with previous ICW-related research, our study uses the relatively long
(post-SOX) sample period of 2004-2011. This, along with our unique research setting, provides
us with an opportunity to evaluate the changes in crash risk around the first-time disclosure of
ICWs as required by SOX 404. Ex ante, it is not clear how the disclosure of ICWs will impact
crash risk. On the one hand, one can expect the disclosure of ICWs to have a negative impact on
the market. To the extent that the presence of ICW problems allows corporate insiders to
withhold bad news within the company and accumulate the hidden unfavorable information over
time, initial public disclosures of ICWs may enable outside investors to evaluate the adverse
consequences of hidden unfavorable information. In such a case, the initial ICW disclosure by a
firm may cause an increase in crash risk of that firm.
11
For example, Efendi et al. (2007), among others, find that managers’ compensation incentives are associated with
restatements. In a similar vein, DeFond et al. (1994) suggest that capital market pressure is one motivating factor
leading to restatements.
11
On the other hand, the disclosures of ICWs are expected to cause a dramatic change in a
firm’s information environment for the following reasons. First, while the presence of
undisclosed ICWs increases future crash risk, public disclosure of ICWs per se can improve
corporate transparency almost immediately, and thus mitigate stock price crash risk subsequently.
This may occur because upon the initial public disclosures, investors become aware of ICW
problems inherent in these firms, and are more likely to exercise a heightened degree of scrutiny
over these firms. Second, upon the ICW disclosures, boards of directors may impose additional
monitoring mechanisms to discipline managers. Third, facing the adverse consequences from the
public disclosures of ICWs,12 managers are likely to have strong incentives to exert greater effort
to remediate publicly disclosed ICW problems. For example, managers are likely to become
more forthcoming with respect to bad news disclosure. In such cases, the disclosures of ICWs
may mitigate stock price crash risk.
Given the two opposing predictions above, the directional effect of initial ICW disclosure
on stock rice crash risk is an empirical question. To provide systematic evidence on this
unexplored question, we test the following hypothesis in alternative form:
H3: The initial public disclosure of ICWs and the subsequent remediation of publicly
disclosed ICWs lead to a decrease in stock price crash risk, all else being equal.
3.
Sample selection and variable measurement
3.1
Data and sample selection
12
These adverse consequences may include lower compensation and higher forced turnover (Johnstone, Li and
Rupley, 2010; Wang, 2010).
12
As reported in Panel A of Table 1, the initial sample for this study includes all firm-year
observations that are jointly included in the three databases, Compustat, CRSP (Center for
Research in Security Prices), and Audit Analytics. This initial sample consists of 34,565 firmyears for our post-SOX sample period of 2004-2011. The sample period begins in 2004 as
accelerated filers were required to comply with SOX 404 starting from the fiscal year ending on
November 15, 2004. We merge CRSP weekly stock return data with Compustat financial
statement data and Audit Analytics SOX 404 audit report data. In so doing, we eliminate 338
firm-years with fewer than 26 weeks of stock-return data. We also drop 2,940 low-priced stocks
with their average price for the year less than $2.50. Finally, we eliminate 11,890 firm-years with
insufficient financial data to calculate control variables. The final sample consists of 19,397
firm-year observations for the sample period of 2004-2011.
Out of 19,397 firm-years in our final sample, 1,397 (7.2%) report ICW problems. In our
regression analyses, we create an indicator variable, denoted by MW, that equals one if the firm
reports ICW problems in a sample year and zero otherwise. Panel B of Table 1 reports the
number of sample firms in each sample year and the percentage of firms with ICW problems in
each sample year. As shown in Panel B, we clearly observe a declining pattern in the percentage
of firms with ICW disclosures over our sample period. The percentage of ICW disclosures
gradually declines from a high of 17.2% in 2004 to 3.0% in 2011. This declining pattern is
consistent with the findings of some recent related studies (e.g., Cheffers, Whalen and Thrun,
2010; Kinney and Shepardson, 2011).
3.2
Measuring firm-level crash risk
13
Following prior literature, we employ three measures of crash risk.13 We first estimate the
following augmented market model to calculate firm-specific weekly returns for each firm in
each year:
where
is the return on stock in week , and
is the return on the CRSP value-weighted
market index in week . We include the lead and lag terms for the market index to allow for
nonsynchronous trading (Scholes and Williams, 1972). The residual from Eq. (1), i.e., εjt,
captures firm-specific weekly return. Since these residuals are highly skewed, we transform them
by obtaining a log-transformed form of firm-specific weekly return, Wjt, that is the natural log of
one plus the residual return from Eq. (1); Wjt = ln (1+εjt).
The first measure of crash risk for each firm in each year, denoted by CRASH, is an
indicator variable that equals one for a firm-year that experiences one or more firm-specific
weekly returns (i.e., Wjt) falling 3.2 standard deviations below the mean firm-specific weekly
returns for that fiscal year, with 3.2 chosen to generate a frequency of 0.1% in the normal
distribution. This measure captures the likelihood of observing extreme negative outliers in firmspecific weekly return distribution.
The second measure of crash risk is the negative conditional return skewness, denoted by
NCSKEW. We calculate NCSKEW by taking the negative of the third moment of daily returns,
and dividing it by the standard deviation of daily returns raised to the third power. Therefore, for
any stock in year , we obtain:
13
For space limitation, we report results using two measures of crash risk, CRASH and NCSKEW. We conduct
robustness analysis using the third measure, DUVOL, but do not tabulate the results.
14
∑
where
∑
is the number of weakly return observations in the period.
Our third measure of crash risk is the down-to-up volatility ratio measure that was first
used by Chen, Hong and Stein (2001). For any stock over year , we separate all the weeks
with returns below the period mean (“down” weeks) from those with returns above the period
mean (“up” weeks), and compute the standard deviation for each of these sub-samples
separately. Then, for any stock over year , we calculate DUVOL as follows:
]
where
and
are the number of up and down weeks in the period, respectively.
Panel C of Table 1 reports the incidence of stock price crashes, measured by CRASH, for
each sample year. As shown in Panel C, on average, 19.8% of firms in our sample experience at
least one crash event during a given year. Not surprisingly, crash incidence is the highest in 2008
(the year of U.S. stock market crash) at 22.8%. It is also interesting to observe that the likelihood
of observing firm-level stock price crashes is greater during the pre-crisis period of 2004-2007
than during the post-crisis period of 2009-2011.
4.
Empirical results
4.1
Descriptive statistics
Table 2 presents descriptive statistics on the main variables used in this study, as well as
additional variables that are used as controls in our multivariate analysis. Detailed definitions of
all variables are provided in Appendix A. The mean value of
15
is 0.198 for the full sample,
suggesting that, on average, 19.8% of firm-years experience one or more extreme, negative
returns. Here, the mean
is higher than that reported by Kim et al. (2011b) and Kim and
Zhang (2012).14 It should be noted, however, that our sample period is more recent and covers
the financial crisis of 2008. We find that mean crash likelihood is significantly higher for the
ICW sample (26.0%) than for the non-ICW sample (19.3%), which is consistent with the
prediction in H1. The mean value of
is also much larger than that reported by Kim et
al. (2011b) and Kim and Zhang (2012), suggesting that firms in our study are, on average, more
crash-prone than those in these two studies. We also find that both mean and median of
are significantly greater for the ICW sample (0.178) than for the non-ICW sample
(0.059), which is again consistent with the prediction in H1. As is the case for CRASH and
NCSKEW, we also find that, on average, the down-to-up volatility ratio (DUVOL) is significantly
higher for the ICW sample (0.115) than for the non-ICW sample (0.031), which is, anew, in line
with the prediction in H1.
We find that the mean value of
is 7.2%, which is lower than those reported by Feng
et al. (2009) and Kim, Song and Zhang (2011). This finding is not surprising, because our sample
period covers more recent years up to 2011, and the percentage of firms with ICWs under SOX
404 disclosure has been steadily declining over the recent years.15
With respect to our control variables, we find that firms with ICW problems are smaller,
less levered, less profitable, more opaque in financial reporting, less dependent on foreign sales,
more likely to incur a loss, have restructuring activities, appoint non-Big 4 auditors, and
experience auditor changes, compared with firms without ICW problems. These differences in
14
For example, Kim et al. (2011b) reports an average crash probability of 0.161 based on the sample period from
1995-2008.
15
See Table 1 Panel C for the incidence of ICW by each year.
16
firm characteristics between ICW and non-ICW firms are, in general, consistent with those
reported in prior research on cross-sectional determinants of ICWs (e.g., Doyle et al., 2007a).
Table 3 presents the correlation matrix for the main variables used in our regression
analysis. Our three measures of crash risk,
,
, and
, are all significantly
positively correlated with each other, suggesting that they capture the same underlying construct.
We find that the correlations between the ICW indicator, i.e.,
, and the three measures for
crash risk are all positive and significant at less than the 1% level. Though only suggestive of the
underlying relation, this finding is consistent with the prediction in H1 that the presence of ICW
is positively associated with stock price crash risk. It should be noted, however, that it is
premature to draw any conclusion from the univariate analysis, because other confounding
factors can potentially drive the positive ICW-crash risk association. In the next section we
therefore perform multivariate regression analyses to test our hypotheses.
4.2
Are ICWs positively associated with stock price crash risk?
4.2.1 Test of H1
Hypothesis H1 is concerned with whether stock price crash risk is higher for firms with
undisclosed ICW problems (i.e., ICW firms) than for firms with no such problem (i.e., non-ICW
firms). To test H1, we estimate the following regression of crash risk on the presence of ICW
and control variables (firm subscripts are subsumed for brevity):
17
In the above equation, CrashRisk refers to one of our two proxies for stock price crash risk,
CRASH and NCSKEW.16
To isolate the presence effect (the effect of the presence of ICW on crash risk) from the
disclosure effect (the effect of the initial public disclosure of ICW on crash risk), we take the
following approach. As illustrated in Figure 1, suppose that a firm initially discloses its ICW
problem in year t, i.e. interval (t, t+1) in Figure 1. For each year t-1, i.e., interval (t-1, t), we
construct a treatment sample of ICW firms (MW = 1) and a control sample of non-ICW firms
(MW = 0).17 For the purpose of testing H1, crash risk is measured as of year t in which ICW
problems have existed but have not been disclosed yet. Note also that, as illustrated in Figure 1,
MW and our control variables are measured as of year t-1. Implicit here is the assumption that a
firm that discloses its ICW problem in year t should have had the same problem in year t-2, i.e.,
interval (t-2, t-1), though the problem is not yet disclosed to the public (Doyle et al. 2007a;
Schrand and Zachman 2012). The above approach allows us to effectively exclude the disclosure
year (year t) from our sample period so that the observed difference in crash risk between the
two samples of ICW and non-ICW firms captures the presence effect that is not confounded by
the initial disclosure effect. Hypothesis H1 translates into a significantly positive coefficient on
MW, i.e.,
which suggests that crash risk is significantly higher for firms with
undisclosed ICW problems than for those without such problems.
We control for seven firm-specific crash risk characteristics that are known to determine
firm-level crash risk. Chen et al. (2001) predict that stock price crashes are more likely to occur
when there are large differences of opinion among investors. Following their study, we control
16
As mentioned earlier, we also use DUVOL as an additional proxy for crash risk. Untabulated results are explained
in section 5.6.
17
The construction is based on the initial disclosure between time t and t+1. In our sample, most firms disclose their
internal control quality for year t-1 after fiscal year end, i.e., between time t and t+1.
18
for the detrended average monthly trading turnover, denoted by DTURN, which proxies for
differences of opinion among investors or investor heterogeneity. In addition, Chen et al. (2001)
also document several other variables that predict crash risk. Specifically, they find that firms
with high return skewness in the prior year, measured as lagged
, are likely to have
high return skewness in current year as well. Meanwhile, they also document a positive
association between prior stock return volatility, denoted by lagged
, and crash risk, and
that stocks with high past returns are more crash-prone in current year. Therefore, we control for
return (
) in prior period. Finally, both Chen et al. (2001) and HMT find that crash risk is
associated with firm size (
leverage (
), market to book ratio (
), return on asset (
), and
). We therefore include these variables as controls in our regression model.
HMT use the three-year moving sum of absolute abnormal accruals, denoted by
, to proxy for information opaqueness. They find that
and crash risk are
positively related. We argue that our measure of internal control quality, namely MW, is a more
comprehensive and unambiguous measure of the quality of a firm’s information production
system. We therefore include
in our regression model for two purposes. First, we
would like to validate the effects of information opaqueness on crash risk as documented in
HMT and Jin and Myers (2006) using our sample with more recent observations.18 Second, we
want to ensure that our test variable,
, captures some aspects of financial reporting quality
that are incremental over and beyond HMT’s information opaqueness.
Previous research has identified firm-specific characteristics that determine the presence
of ICW. For example, both Ge et al. (2005) and Doyle et al. (2007a) show that ICW firms are
18
In particular, HMT suggest that the effect of information opaqueness, measured as a three-year moving sum of
absolute discretionary accruals, on crash risk has diminished after the passage of SOX.
19
smaller, younger, financially weaker and more complex. To alleviate possible problems of
omitted correlated variables and potential endogeneity concerns associated therewith, we include
in regression (2) a set of control variables that are associated with ICWs. We control for a firm’s
financial performance by including a variable capturing recent losses,
, which is defined as
the percentage of the most recent three years in which the firm reports a loss. We include a
foreign sales indicator (
(
) and the natural log of one plus the number of business segment
) to control for business complexity. We also include three additional indicator
variables representing restructuring activities (
auditor changes during each sample year (
, Big 4 auditors (
and
to isolate the effect of these variables
from the effect of MW on crash risk. To address potential cross-sectional and serial dependence
in the data, we report z/t-statistics (two tailed) that are based on robust standard errors corrected
for double (firm and year) clustering (Peterson, 2009; Gow Ormazabal and Taylor, 2010).
Throughout the paper, all regressions include year and industry indicators to control for year and
industry fixed effects, respectively.
Panel A Table 4 reports the results of logistic regressions using CRASH as the dependent
variable. The baseline model presents the estimated results for Eq. (2) by excluding a set of ICW
determinants. The regression results for the baseline model show that the coefficient on our key
variable of interest,
4.84
, is highly significant with an expected positive sign and z-statistic of
. To assess the economic significance of our test results, we compute the
marginal effect of
that captures the change in
associated with a change of
from 0 to 1, holding all other independent variables at their mean values. The marginal effect of
is about 0.05, suggesting that crash risk is higher for ICW firms by about five percentage
20
points, compared with firms with no ICW problems. This is economically significant, given that
the average unconditional probability of crash occurrence is 19.8% in our sample.
Throughout our study, seven crash risk determinants, which are used as our control
variables, are all measured with a one year lag (i.e., measured in one year prior to the year when
CRASH is measured) so that current-year return distribution fully reflects the impact of these
control variables, if any. With respect to the estimated coefficients on our seven control variables,
the following are noteworthy. We find that the coefficients on known determinants of crash risk
are broadly in line with the findings of prior research. Crash risk is positively and significantly
associated with lagged detrended trading turnover (
firm size
(
and lagged market-to-book ratio
lagged stock return
lagged
The coefficient on lagged opaqueness
is positive but insignificant. This result, along with a significantly positive
coefficient on MW, indicates that the effect of ICW on increasing crash risk is incremental above
and beyond prior-period accounting opaqueness. 19 The coefficient on lagged return on assets
is both significant with a predicted negative sign.
One may argue that our test variable, MW, may suffer from potential endogeneity bias,
because MW is, to a large extent, subject to managers’ self selection. In an effort to alleviate
potential endogeneity concerns associated with this self-selection bias, we also estimate Eq. (2)
by including well-known determinants of ICW as additional controls. As shown in the second
section of Panel A, we find that the coefficient on MW remains highly significant with an
expected positive sign. This suggests that ICW is incrementally significant in explaining crash
risk even after controlling simultaneously for all known determinants of both crash risk and
19
We find that the coefficient of
is insignificant when we exclude our main test variable,
. One
possible reason is that after SOX, the relation between
and crash risk has significantly diminished, as
documented by HMT.
21
ICWs. We also find that the sign and significance of estimated coefficients on seven crash risk
determinants are, overall, similar to those obtained for the base model.20 Interestingly, we find
that crash risk is higher for firms with foreign sales (FSALE) and restructuring charges
(RESTRUCTURE), while it is lower for firms with more frequent losses (LOSS) 21 and more
business segments (SEGMENTS).
Panel B of Table 4 reports the results of ordinary least squares (OLS) regressions for Eq.
(2), using
of
as the dependent variable. As shown in Panel B of Table 4, the coefficient
is significantly positive in both the baseline model and the augmented model, which
strongly supports the prediction in H1. This result is economically significant as well: Taking the
baseline model as an example, the coefficient of
is 0.126, suggesting that ineffective
internal control is associated with an approximate 85% increase (0.126/0.068-1) in
.
Overall, the results reported in both Panels A and B of Table 4 are similar to each other
and generally consistent with the prediction in H1 that the presence of (undisclosed) ICW prior
to its initial disclosure increases stock price crash risk. This finding is robust to different
measures of crash risk, and holds even after controlling for Chen et al.’s (2001) investor
heterogeneity (DTURN), HMT’s information opaqueness (OPAQUE), and other firm-specific
determinants of crash risk. Our results hold, irrespective of whether or not we control for firmspecific characteristics that are known to determine ICW. In short, our findings are consistent
with the view that effective internal control plays a significant role in limiting managerial
incentive, ability, and opportunity to withhold or delay the disclosure of bad news, which in turn
20
One notable difference is that the coefficient on
becomes significant in the augmented model.
One possible explanation for this finding is that firms that had losses are more likely to have actually disclosed
bad news, and hence less prone to experience stock price crashes.
21
22
significantly lowers the likelihood of bad news being stockpiled within a firm, and thus, stock
price crash risk.
4.2.2 Test of H2
Hypothesis H2 is concerned with the impact of the severity or seriousness of ICW on
crash risk. To test whether (more serious) fraud-related ICWs have a stronger association with
crash risk than (less serious) other ICWs, we estimate the following regression in which ICWs
are decomposed into fraud-related and other (non-fraud related) ones:
In Eq. (3) above, as discussed earlier, CrashRisk refers to either CRASH or NCSKEW.
is an indicator variable that differentiates fraud-related ICWs from other ICWs.
Fraud-related internal control problems are based on the reason key fields in Audit Analytics that
describe the nature of the material weaknesses contributing to ineffective internal control.
Specifically,
is coded one if Audit Analytics classifies a material weakness as
related to “restatement or non-reliance of company filings” (reason key #5) or “ethical or
compliance issues with personnel” (reason key #21), and zero otherwise. Similarly,
is coded one if a firm has non-fraud related ICWs and zero otherwise. Based on this
classification, we identify 573 firm-year observations as having fraud-related weaknesses
(2.95%).22 The difference between the coefficients of
and
captures the
incremental crash risk for firms that have been identified by their auditors as not in compliance
22
551 firm-year observations are identified as having problems with “restatement or nonreliance of company
filings,” 74 firm-year observations are identified as having problems with “ethical or compliance issues with
personnel,” and 52 firm-year observations are identified as having both types of problems.
23
with regulation and standards and having a higher probability of misstatement, relative to firms
with other types of internal control problems.
Panels A and B of Table 5 present the regression results for Eq. (3), using CRASH and
NCSKEW, respectively, as the dependent variable. We find that the coefficients on both
MW_fraud and MW_other are positive and highly significant at less than the 1% level,
irrespective of whether the base model or the full model is used. More importantly, we also find
that the coefficient on MW_fraud is larger in magnitude and more significant than the coefficient
on MW_other. As indicated in the bottom part of the table in Panel A, the results of Chi-square
tests for the difference in magnitude between the two estimated coefficients indicate that the
difference is statistically significant (at about the 5% level in two-tailed tests) for the base model
as well as for the full model. This suggests that firms with fraud-related ICWs are more likely to
experience extreme negative outliers in their weekly firm-specific return distribution than firms
with other types of ICWs.
As shown in Panel B of Table 5, when
also find that the coefficients on
and
is used as the dependent variable, we
are both significantly positive, and
the former is larger in magnitude and more significant than the latter. As shown in the bottom
part of the table, the results of an F test for the difference in magnitude between the two
coefficients, MW_fraud and MW_other, indicate that the difference is statistically significant at
less than the 5% level (at two-tailed tests). Overall the results in Panel B are qualitatively
identical with those in Panel A.
In short, our results reported in both panels of Table 5 are consistent with H2, suggesting
that (a) firms with fraud-related ICWs and those with other types of ICWs are likely to have
24
higher crash risk than firms with no such problems and (b) fraud-related ICW problems are more
serious than other ICW problems in terms of their impacts on increasing crash risk.
4.3
Does the disclosure of ICW reduce stock price crash risk? --- Difference-in-differences
tests
Recall that hypothesis H1 is concerned with cross-sectional differences in crash risk
between ICW firms and non-ICW firms prior to the ICW disclosure under SOX 404. This is
based on Doyle et al.’s (2007a) conjecture that ICW problems may have actually existed in years
prior to the ICW disclosures under SOX 404. 23 In contrast, hypothesis H3 is interested in
whether and how the ICW disclosures bring about an over-time change in crash risk from the
pre-disclosure period to the post-disclosure period.
To test H3, we pool pre-SOX observations in years prior to the initial ICW disclosure and
post-SOX observations in years subsequent to the initial ICW disclosure. 24 If ICWs facilitate bad
news hoarding by corporate insiders, then the increased crash risk associated with the presence
of undisclosed ICW (that existed in years prior to the initial ICW disclosure) should diminish
once firms reveal their ICW problems to the public. This is because the ICW disclosure itself
improves corporate reporting transparency subsequently and crash risk is inversely associated
with transparency (Jin and Myers, 2006). Specifically, one can expect that in the years after ICW
firms publicly disclose their ICW problems, there should be no significant difference in crash
risk between firms with effective internal controls and firms that report ICWs. Stated another
way, ICW firms have now become transparent as they publicly disclosed their ICW problems,
and thus, in the post-disclosure period, the difference in crash risk should not be significant
23
In a similar spirit, Schrand and Zachman (2012) report a “slippery slope” to financial misreporting for firms that
are subject to AAERs.
24
By doing so, we effectively exclude observations in the initial disclosure years.
25
between firms with public disclosures of their ICW problems and firms with no ICWs (and thus
no disclosure of ICWs).
Since it is unclear how long it will take ICW firms to remediate their publicly disclosed
ICW problems, we construct an expanded sample of 22,421 firm-years that covers two years
prior to and two years subsequent to the year of the ICW disclosure under SOX 404. To test H3,
we stack the four-year observations together, and then, estimate the following regression model:
In the above equation, CrashRisk refers to either CRASH or NCSKEW.
is an
indicator variable that equals one if the observation is within the 1-year (2-year) period before
the year of the adverse internal control opinion under SOX 404 disclosure and zero otherwise. To
the extent that publicly disclosed ICW problems existed in years prior to the public disclosure,
we expect that the coefficient on
to be significantly positive.
is
an indicator variable that equals to one if the observation is within the 1-year (2-year) period
after the ICW disclosure under SOX 404 and zero otherwise.25 Our hypothesis H3 translates into
.
Panel A of Table 6 reports the results of the logistic regression in Eq. (5) using CRASH as
the dependent variable. This regression allows us to assess the temporal variation in stock price
crash surrounding the initial public disclosure of ICW. As shown in Panel A, for both baseline
and full models, we find that the coefficients on
and
are both significantly positive.
This is consistent with the prediction in H1, suggesting that crash risk is higher for ICW firms
25
For example,
is equal to one for fiscal year 2003 if the firm discloses a material weakness for fiscal year
2004.
is equal to one for fiscal year 2005 if the initial disclosure of a material weakness occurs in fiscal year
2004.
and
are defined similarly.
26
than non-ICW firms in up to two years prior to the initial ICW disclosure of an adverse SOX 404
audit opinion.
On the other hand, the coefficient on
is significantly positive for both models. As
shown in the bottom part of Panel A of Table 6, the results of Chi-square test for the difference
in magnitude between the two regression coefficients suggests that the difference,
, is
significantly negative. This is consistent with our hypothesis H3 that stock price crash risk
declines significantly from the pre-ICW-disclosure period to the post-ICW-disclosure period,
once ICWs are publicly disclosed. Interestingly, the coefficient on
is not statistically
different from zero, suggesting that crash risk differentials between ICW firms and non-ICW
firms disappear, in large part, in the second year following the initial disclosure. In other words,
it takes about two years for the crash risk differentials to dissipate in the post-disclosure period.
Panel B of Table 6 reports the results of OLS regressions for Eq. (5) using
as
the dependent variable. The results in Panel B are qualitatively identical to those in Panel A,
except that the coefficient on
, which is insignificant in Panel A, becomes significant at
the 5% level in the full-model specification.26 The F-statistics in the bottom part of Panel B
indicates that the decline of crash risk from the PRE1 period to the POST1 period is highly
significant.
In short, the results in Panels A and B are, overall, consistent with our hypothesis H3 that
the disclosure of ICWs leads to a significant decline in stock price crash risk during the postdisclosure period. Stated another way, our results in Table 6 can be interpreted broadly in such a
26
An F-test indicates that the difference between pre and post coefficients, our main variable of interest, is
negatively significant.
27
way that the public disclosure of ICW improves corporate reporting transparency, particularly,
bad news hoarding, thereby leading to a decline in crash risk in the post-disclosure period.
5. Further Analysis and Robustness Check
5.1
Post-remediation analysis
In our main analyses, we provide evidence that the presence of ICW is positively
associated with stock price crash risk. We also provide evidence suggesting that upon the initial
ICW disclosure, managers of ICW firms tend to exert extra effort to improve internal control
quality as manifested in a reduced crash risk in the post-ICW-disclosure period. For
completeness of our story, we further analyze whether the difference in crash risk, if any,
between ICW and non-ICW firms disappears after firms with adverse internal control opinions
under SOX 404 remediate publicly disclosed ICW problems. To address this issue, we estimate
the following model:
where
is an indicator variable that equals one if the observation is
within the 1-year (2-year) period after previously disclosed ICW problems are remediated and
zero otherwise.27 Once firms with adverse internal control opinions successfully remediate their
ICW problems and subsequently receive clean internal control opinions, stock price crash risk
for such firms should not differ significantly from crash risk for firms with no ICW problems. In
other words, we predict that the coefficient on
27
is insignificant.
For example,
is equal to one for fiscal year 2006 if the firm discloses a material weakness for fiscal
year 2004 and a clean opinion for fiscal year 2005.
28
Under this prediction, no differential crash risk exists between ICW firms and firms that
remediate previously disclosed ICW problems.
Panels A and B of Table 7 reports the regression results, using CRASH and NCSKEW,
respectively, as the dependent variable. In Panel A, we find that the coefficients on
and
are both insignificant at any conventional level. This is consistent with the
view that the remediation of ICW problems constrains managerial opportunism in financial
reporting, including bad news hoarding by corporate insiders. As shown in Panel B, the results
using
as the dependent variable are, overall, qualitatively similar to those in Panel A,
except that we find the coefficient on
is significant, but becomes insignificant once
we extend the post-remediation period up to two years. In short, the results of our postremediation analyses reinforce our main inference that the crash risk differential between ICW
and non-ICW firms decreases or largely disappears, once previously disclosed ICW problems are
ex post remediated.
5.2
Positive jump risk
Our regression results in Table 4 suggest that internal control quality is a significant
predictor of negative tail risk or crash risk. An alternative explanation of this finding is that firms
with ICW problems operate in volatile environments, and thus, these firms are more prone to
experience not only large, abrupt price declines but also large, unexpected price jumps. In such a
case, one can expect internal control quality or the lack thereof to be a significant predictor of not
only negative crash risks but also positive jump risks. To better understand the role of internal
control quality in predicting extreme tail risks, whether negative or positive, we now examine the
impact of ICWs on positive jump risk. For this purpose, we define a positive jump risk, denoted
29
by JUMP, symmetrically to a negative crash risk, that is the likelihood that a firm’s weakly
return falls 3.2 standard deviation above the mean of firm-specific weekly return distribution,
that is, Wit = ln (1 + εit), and then re-estimate Eq. (2), using JUMP as the dependent variable.
Table 8 reportsh the results of this logistic regression.
As shown in table, we find that the coefficient on MW is negative and marginally
significant at the 10% level. This finding does not support the argument that ICWs are associated
with volatile environments, suggesting that the observed impact of ICWs on increasing crash risk
or negative tail risk (Table 4) is unlikely to be a mere manifestation of the increased volatility
associated with ICWs. This is so because, if the increased volatility is the main cause for the
increased crash risk, we should also observe a positive association between MW and JUMP or a
significantly positive coefficient on MW in Table 8. .
5.3
ICW and Restatement
Hammersley et al. (2008) find that ICW disclosures are often accompanied by
restatements. To evaluate the possibility that findings are driven by the effect of financial
restatements on crash risk, we construct a reduced sample by excluding firms from our sample if
ICW problems are preceded by restatements in our sample. We then repeat our regression
analyses. Untabulated results show that ICWs remains still positively associated with crash risk
suggesting that our reported results are unlikely to be driven by restatements.
5.4
Endogeneity of ICW
ICW disclosure under SOX 404 is an exogenous event, and thus, potential endogeneity in
the ICW-crash risk relation is of less concern in our study. Nevertheless, we conduct additional
analyses to alleviate this endogeneity concern. Specifically, we re-estimate our main regressions
30
using a two-stage least squares (2SLS) approach. We first predict the likelihood of firms having
ICWs, using well-known ICW determinants from existing studies, and obtain the predicted
values of ICWs. We then re-estimate our main regression results reported in Table 4, using the
predicted values of ICWs as our test variable in replacement of ICWs. Untabulated results shows
that the results of 2SLS regressions are qualitatively the same as those reported in Table 4,
suggesting that our main results reported in Table 4 are unlikely to be driven by potential
endogeneity.
We also employ a propensity score matching (PSM) procedure to address the
endogeneity concern. We use a probit model to estimate propensity scores for the probability of
realizing an internal control weakness. The propensity score model includes ICW determinants
and year and industry fixed effects. The ICW determinants are the same as the ones used as
instruments in the 2SLS model. The MW observations are matched one to one with the non-MW
observations with replacement using the estimated propensity scores. Untabulated results show
that the regression results using the PSM sample are qualitatively identical with those reported in
the paper, suggesting, anew, that our results are robust to potential endogeneity concerns. .
5.5
The Cox hazard model approach
Jin and Myers (2006) point out that time can enter investors’ assessment of crash
probabilities in the sense that the probability of crash occurrence in current period depends on
the occurrence of a crash in the previous period. In a related vein, Kim and Zhang (2012) argue
that a proportional hazard approach is more appropriate for the purpose of examining firmspecific determinants of crash risk, because this approach controls for the past history of crashes
when predicting future crash likelihood. However, one drawback of this approach is that it
31
necessarily leads to a substantial reduction in sample size, because it requires that a firm be
included into the sample only when such a firm experienced at least one crash event during the
sample period.
Similar to Kim et al. (2012), in an attempt to check the robustness of our main results, we
estimate the Cox proportional hazard model as specified below:
(
)
is the “hazard” or instantaneous likelihood of crash occurrence, for firm at time ,
where
conditional on
crashes having occurred in firm by time ;28
event; and
is an unspecified function that captures the baseline hazard. Hypothesis H1
predicts that
is the time of the
th
, which can be interpreted as the extent to which the hazard of crash
occurrences increases with the lack of internal control quality given the past crash history.
To estimate the hazard model in Eq. (7), we identify a sample of firms with at least one
crash event during the sample period. For each crash event of a firm, we calculate the crash
interval, which is the length of time (in weeks) from the current crash event to the next. If no
further crash event is observed, the interval is the length of time from the current event until the
firm’s delisting date or the ending date of the sample period, whichever occurs first. The control
variables are the same as in Eq. (2) and year dummies are included. The model is estimated using
partial likelihoods developed by Cox (1975). The partial likelihood estimation makes it possible
to estimate all coefficients without specifying a particular functional form of
28
The hazard function is defined as follows:
where
is the number of events that have occurred to firm by time .
32
. Industry-level
stratification allows different industries to have different baseline hazard functions, while
constraining the coefficients to be the same across industries (Allison, 2005).
Table 9 reports the estimated results for the hazard model in Eq. (7). As shown in Table 8,
we find that the coefficients on
are significantly positive in both models. This is in line with
our earlier finding in Table 4, suggesting that the instantaneous crash likelihood of firms with
ineffective internal control at time
is higher than that of firms with no ICW, even after
controlling for past crash history. This lends further support to our main finding that the presence
of ICW is positively associated with stock price crash risk.
5.6
Alternative measures of crash risk
as the dependent variable 29 and re-
As our third proxy for crash risk, we use
estimate all the regressions reported in Table 4 through Table 7. Though not tabulated for brevity,
the results using this alternative measure of crash risk are qualitatively similar to those reported
in the paper.
6.
Conclusion
We examine whether and how the presence of ICW and its initial disclosure and
subsequent remediation are associated with stock price crash risk. Consistent with our prediction,
we find that the presence of (undisclosed) ICW is positively associated with crash risk, and this
positive association exists up to two years prior to the initial ICW disclosure. Moreover, we find
that the impact of ICWs on crash risk gradually declines upon the initial disclosures, and largely
disappears after remediation of previously disclosed ICW problems. In addition, we find that
firms with fraud-related ICWs are more crash-prone than other ICWs. The above results are
29
See section 3 and Appendix A for an empirical definition of
33
.
incrementally significant even after controlling for HTM’s information opaqueness, Chen et al.’s
(2001) investor heterogeneity, other firm-specific factors that prior research identified to be
associated with stock price crash risk, and firm-specific determinants of ICWs identified by prior
research on internal control quality. Our results are robust to the use of alternative proxies for
crash risk and different econometric designs.
Collectively, our findings support the view that the quality of a firm’s internal controls
plays an important role in constraining stock price crash risk and maintaining the stability of
stock markets. More importantly, our results highlight the importance of the disclosure of
material weaknesses in internal controls over financial reporting: ICW disclosure induces a
heightened degree of scrutiny and external monitoring by outside investors, and thus, encourages
corporate insiders to be more forthcoming with respect to bad news disclosure. This contributes
to lowering stock price crash risk. Our study provides new evidence on the market consequences
of ineffective internal controls and the potential benefits associated with SOX 404 disclosure.
34
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37
Appendix A Variable Definitions
Dependent Variables: Crash Risk Measures
An indicator variable that equals to one if a firm experiences one or
more crash events within a year. See Eq. (1) in the text for more
details.
The negative coefficient of skewness of firm-specific weekly return
for fiscal year t.
Main Test Variables: Internal Control Weaknesses
An indicator variable that equals to one if the firm reports ineffective
internal controls and zero if the firm reports effective internal
controls.
An indicator variable that equals to one if the internal control
weakness is fraud-related and zero otherwise.
An indicator variable that equals to one if the firm-year observation is
within the 2-year period before the year of the adverse internal
control opinion and zero otherwise.
An indicator variable that equals to one if the firm-year observation is
within the 1-year period before the year of the adverse internal
control opinion and zero otherwise.
An indicator variable that equals to one if the firm-year observation is
within the 1-year period after the initial disclosure of material
weakness and zero otherwise.
An indicator variable that equals to one if the firm-year observation is
within the 2-year period after the initial disclosure of material
weakness and zero otherwise.
Crash Risk Control Variables
Average monthly turnover in fiscal year t minus average monthly
turnover in fiscal year t-1.
Firm-specific average weekly returns.
Standard deviation of firm-specific weekly returns.
The natural log of market capitalization.
Market to book ratio.
Total long-term debts divided by total assets.
Income before extraordinary items divided by lagged total assets.
The prior three years’ moving sum of the absolute value of
discretionary accruals (Hutton et al. 2009). Specifically,
where
)+
)+
)
is measured using the Modified Jones Model.
38
Internal Control Weakness Control Variables
The proportion of loss years in the prior three years.
An indicator variable that equals 1 if the firm has foreign sales and 0
otherwise.
The natural log of one plus the number of reported business
segments.
An indicator variable that equals 1 if the restructuring charge is
nonzero and 0 otherwise.
An indicator variable that equals 1 if the firm is audited by a Big 4
firm and 0 otherwise.
An indicator variable that equals 1 if the firm experiences auditor
change in the year and 0 otherwise.
39
Figure 1: Timeline for variable measurement for testing H1 and H2
t-2
t-1
t
MW and Control
variables measured
as of time t-1
40
t+1
Crash risk
measured as of Auditor-attested
report disclosed
time t
Table 1
Sample selection and summary statistics on stock price crashes
Table 1 Panel A presents our sample selection process. Panel B and Panel C report over time
pattern of stock price crashes and internal control effectiveness respectively. The sample period
is from fiscal years 2004 to 2011.
Panel A: Sample selection
Initial sample of firm-year observations in the Compustat, CRSP, and Audit Analytics
databases from fiscal years 2004-2011
Less: Firm-year observations with less than 26 weeks of stock data
Less: Firm-year observations with an average stock price less than $2.50
Less Firm-year observations with insufficient data to calculate control variables
Total
Panel B: Internal control effectiveness over time
2004
2005
2006
2007
No. of firms
1,825
2,197
2,388
2,682
%firms with ICW
17.2% 12.2% 9.7%
7.8%
problems
34,565
(338)
(2,940)
(11,890)
19,397
2008
2,586
2009
2,506
2010
2,653
2011
2,560
Total
19,397
5.0%
3.0%
3.2%
3.0%
7.2%
Panel C: Summary statistics on the likelihood of stock price crashes measured by CRASH
Fiscal year
Number of firms
Number of firms with Percentage of firms with
stock price crashes
stock price crashes
2004
2005
2006
2007
2008
2009
2010
2011
Total
1,825
2,197
2,388
2,682
2,586
2,506
2,653
2,560
19,397
384
513
522
521
589
412
442
457
3,840
41
21.0%
23.4%
21.9%
19.4%
22.8%
16.4%
16.7%
17.9%
19.8%
Table 2
Descriptive statistics
Table 2 presents the descriptive statistics for the total sample of 19,397 firm-year observations,
as well as the descriptive statistics for the sub-samples partitioned on whether the firm reports
and ineffective internal control. Bold text indicates the difference between the mean (median) for
firms with ineffective internal control
and firms with effective internal control
is significant at the 0.05 level or better. Differences in means (medians) are assessed
using a t-test (Wilcoxon rank sum test). All variables are defined in Appendix A.
Full Sample
N=19,397
Mean Median
0.198
0.068
0.037
0.072
0.009
0.050
-0.170
0.052
6.670
2.786
0.176
0.025
0.241
0.245
0.051
1.223
0.312
0.830
0.052
0.000
0.019
0.015
0.000
0.004
0.003
-0.101
0.045
6.555
2.017
0.126
0.045
0.152
0.000
0.000
1.099
0.000
1.000
0.000
Std.
dev.
0.398
0.844
0.519
0.258
0.159
0.817
0.260
0.028
1.768
38.456
0.207
0.224
0.454
0.350
0.221
0.715
0.463
0.376
0.222
Non-ICW Sample
(MW =0)
N=18,009
Mean Median
Std.
dev.
0.395
0.193
0.000
0.837
0.059
0.013
0.514
0.031
0.009
0.000
0.000
0.000
0.004
0.156
0.008
0.048
0.000
0.813
0.255
-0.166 -0.099
0.028
0.051
0.045
1.776
6.730
6.622
2.781
2.017 39.861
0.207
0.177
0.129
0.221
0.029
0.048
0.462
0.238
0.150
0.345
0.236
0.000
0.218
0.050
0.000
1.223
1.099
0.714
0.462
0.310
0.000
0.369
0.838
1.000
0.211
0.046
0.000
42
ICW Sample
(MW = 1)
N=1,388
Mean Median
Std.
dev.
0.439
0.260
0.000
0.926
0.178
0.100
0.568
0.115
0.098
0.000
1.000
1.000
0.003
0.189
0.020
0.076
0.019
0.868
0.312
-0.222 -0.142
0.030
0.060
0.054
1.454
5.891
5.781
2.850
2.017
7.135
0.206
0.155
0.080
0.245
-0.025
0.013
0.330
0.277
0.182
0.380
0.365
0.333
0.250
0.067
0.000
1.233
1.099
0.729
0.474
0.339
0.000
0.445
0.729
1.000
0.325
0.120
0.000
Table 3
Correlation Matrix
Table 3 presents the Pearson correlation matrix of selected variables. Bold text indicates statistical significance at the level of 0.05 or
better. All variables are defined in Appendix A.
1
0.635
1
0.521
0.891
1
0.043
0.037
0.042
1
0.026
0.041
0.039
0.019
1
0.014
0.025
0.021
0.009
0.001
1
0.005
0.029
0.033
-0.056
-0.232
0.013
1
0.001
-0.026
-0.029
0.085
0.199
0.044
-0.886
1
0.010
0.061
0.053
-0.122
0.024
0.009
0.327
-0.480
1
0.015
0.015
0.012
0.001
0.008
-0.008
-0.003
-0.002
0.015
1
-0.026
-0.021
-0.023
-0.027
0.027
0.002
-0.026
-0.007
0.103
-0.020
1
-0.036
-0.026
-0.019
-0.061
-0.011
-0.012
0.182
-0.217
0.185
0.018
-0.024
1
0.014
0.014
0.009
0.022
-0.001
-0.017
-0.157
0.209
-0.143
0.017
-0.070
-0.093
43
1
Table 4
Internal control quality and stock price crash risk
Table 4 Panel A reports the logit regression results with CRASH as the dependent variable. Year
dummies are based on Compustat fiscal year notation. Industry dummies are based on 2-digit
SIC industry classifications from CRSP. The standard errors are clustered by firm and by year
and the z-statistic of each coefficient is provided. Significance levels are based on two-tailed
tests. ***, **, and * denoted significance at the 1%, 5%, and 10% levels, respectively. All
variables are defined in Appendix A.
Panel A: Logistic Regression Using CRASHt as the Dependent Variable
Controlling for ICW
Pred.
Baseline model
determinants
Variable
Sign
Coefficient
z-statistics
Coefficient
z-statistics
Test variable
+
0.321***
(4.84)
0.312***
(4.49)
Crash risk determinants
+
0.411**
(2.21)
0.381*
(1.95)
+
0.023
(0.94)
0.004
(0.17)
+
0.364**
(2.23)
0.510***
(2.63)
+
2.872
(1.52)
5.585**
(2.31)
?
0.054**
(1.98)
0.041
(1.47)
+
0.001***
(4.25)
0.001***
(4.65)
+
-0.008
(-0.09)
-0.007
(-0.07)
-0.267**
(-2.38)
-0.325***
(-2.76)
+
0.041
(1.39)
0.081**
(1.97)
ICW determinants
-0.332***
(-3.57)
0.110**
(2.16)
-0.075***
(-3.05)
0.275***
(10.88)
-0.002
(-0.04)
0.001
(0.03)
Year dummies
Included
Included
Industry dummies
Included
Included
n
19,397
19,352
2
Pseudo R
0.0225
0.0257
44
Table 4 (Continued)
Internal control quality and stock price crash risk
Table 4 Panel B reports the ordinary least squares (OLS) regression results with NCSKEW as the
dependent variable. Year dummies are based on Compustat fiscal year notation. Industry
dummies are based on 2-digit SIC industry classifications from CRSP. The standard errors are
clustered by firm and by year and the t-statistic of each coefficient is provided. Significance
levels are based on two-tailed tests. ***, **, and * denoted significance at the 1%, 5%, and 10%
levels, respectively. All variables are defined in Appendix A.
Panel B: OLS Regression Using NCSKEWt as the Dependent Variable
Variable
Test variable
(β1)
Pred.
Sign
Baseline model
Coefficient
t-statistics
Controlling for ICW
determinants
Coefficient
t-statistics
0.126***
(5.99)
0.123***
(5.55)
0.199***
0.019
0.206**
1.774
0.039***
0.000
-0.055
-0.096***
0.030*
(3.36)
(1.20)
(2.44)
(1.63)
(3.92)
(1.38)
(-1.34)
(-2.89)
(1.85)
0.191***
0.013
0.239**
2.439*
0.036***
0.000
-0.053
-0.112***
0.055***
(3.11)
(0.83)
(2.53)
(1.96)
(3.65)
(1.44)
(-1.29)
(-3.03)
(2.76)
-0.095***
0.040*
-0.027***
0.080***
0.001
0.018
(-5.33)
(1.84)
(-3.29)
(9.62)
(0.05)
(0.59)
Included
Included
19,352
0.0236
+
Crash risk determinants
+
+
+
+
?
+
+
+
ICW determinants
Year dummies
Industry dummies
n
Adjusted R2
Included
Included
19,397
0.0213
45
Table 5
The impact of the relative seriousness of an ICW on stock price crash risk
This table examines the effect of the relative seriousness of the ICW on crash risk. We consider
firms with fraud-related weakness as having more severe internal control problems. Panel A
reports the logit regression results with CRASH as the dependent variable. Year dummies are
based on Compustat fiscal year notation. Industry dummies are based on 2-digit SIC industry
classifications from CRSP. The standard errors are clustered by firm and by year and the zstatistic of each coefficient is provided. Significance levels are based on two-tailed tests. ***, **,
and * denoted significance at the 1%, 5%, and 10% levels, respectively. All variables are defined
in Appendix A.
Panel A: Logistic Regression Using CRASHt as the Dependent Variable
Controlling for ICW
Pred.
Baseline model
determinants
Variable
Sign
Coefficient
z-statistics
Coefficient
z-statistics
Test variable
+
0.444***
(4.87)
0.433***
(4.72)
(β1)
(β2)
+
0.229**
(2.36)
0.221**
(2.28)
Crash risk determinants
+
+
+
+
?
+
+
+
0.409**
0.024
0.362**
2.897
0.054*
0.001***
-0.010
-0.267**
0.041
(2.19)
(0.95)
(2.28)
(1.54)
(1.95)
(4.26)
(-0.11)
(-2.40)
(1.41)
0.379*
0.005
0.508***
5.596**
0.041
0.001***
-0.009
-0.326***
0.081**
(1.94)
(0.19)
(2.69)
(2.33)
(1.46)
(4.65)
(-0.09)
(-2.79)
(1.97)
-0.331***
0.111**
-0.075***
0.276***
-0.006
0.003
(-3.58)
(2.19)
(-3.07)
(10.76)
(-0.09)
(0.05)
Included
Included
19,352
0.0259
2.829
ICW determinants
Year dummies
Industry dummies
n
Pseudo R2
Chi-squared (β1= β2)
p-value
Included
Included
19,397
0.0226
2.684
0.101
46
0.0926
Table 5 (Continued)
The impact of the relative seriousness of an ICW on stock price crash risk
Table 5 Panel B reports the ordinary least squares (OLS) regression results with NCSKEW as the
dependent variable. Year dummies are based on Compustat fiscal year notation. Industry
dummies are based on 2-digit SIC industry classifications from CRSP. The standard errors are
clustered by firm and by year and the t-statistic of each coefficient is provided. Significance
levels are based on two-tailed tests. ***, **, and * denoted significance at the 1%, 5%, and 10%
levels, respectively. All variables are defined in Appendix A.
Panel B: OLS Regression Using NCSKEWt as the Dependent Variable
Pred.
Sign
Variable
Baseline model
Coefficient
t-statistics
Controlling for ICW
determinants
Coefficient
t-statistics
0.186***
(4.02)
0.182***
(3.90)
0.083***
(3.86)
0.081***
(3.85)
0.198***
0.019
0.207**
1.788
0.038***
0.000
-0.055
-0.096***
0.030*
(3.33)
(1.21)
(2.47)
(1.64)
(3.88)
(1.38)
(-1.37)
(-2.90)
(1.85)
0.190***
0.013
0.240**
2.447**
0.035***
0.000
-0.053
-0.112***
0.056***
(3.09)
(0.84)
(2.56)
(1.97)
(3.64)
(1.44)
(-1.32)
(-3.04)
(2.76)
-0.095***
0.040*
-0.027***
0.080***
-0.000
0.019
(-5.41)
(1.86)
(-3.30)
(9.74)
(-0.02)
(0.61)
Included
Included
19,352
0.0238
3.325
Test variable
(β1)
(β2)
+
+
Crash risk determinants
+
+
+
+
?
+
+
+
ICW determinants
Year dummies
Industry dummies
n
Adjusted R2
F (β1= β2)
p-value
Included
Included
19,397
0.0215
3.242
0.0718
47
0.0682
Table 6
Disclosure of weak internal control and stock price crash risk: over-time analysis
This table examines the association between internal control effectiveness and stock price crash
risk before and after the disclosure of an ICW, based on an extended sample period including
years 2002-2011. Panel A reports the logit regression results with CRASH as the dependent
variable. Year dummies are based on Compustat fiscal year notation. Industry dummies are
based on 2-digit SIC industry classifications from CRSP. The standard errors are clustered by
firm and by year and the z-statistic of each coefficient is provided. Significance levels are based
on two-tailed tests. ***, **, and * denoted significance at the 1%, 5%, and 10% levels,
respectively. All variables are defined in Appendix A.
Panel A: Logistic Regression Using CRASHt as the Dependent Variable
Variable
Baseline model
Coefficient
z-statistics
Controlling for ICW
determinants
Coefficient
z-statistics
?
0.373***
(4.99)
0.365***
(4.83)
?
0.470***
(5.94)
0.458***
(5.55)
?
0.211**
(2.35)
0.226**
(2.30)
?
0.056
(0.70)
0.074
(0.94)
+
+
+
+
?
+
+
+
0.258*
0.024
0.554**
4.411*
0.077***
0.002***
-0.006
-0.277**
0.038
(1.85)
(1.06)
(2.45)
(1.94)
(3.14)
(3.06)
(-0.07)
(-2.51)
(1.29)
0.232
0.003
0.681***
7.058**
0.065***
0.002***
0.014
-0.338***
0.078**
(1.63)
(0.11)
(2.60)
(2.51)
(2.74)
(3.29)
(0.14)
(-2.85)
(1.96)
-0.366***
0.085
-0.071***
0.272***
-0.013
-0.012
(-4.57)
(1.57)
(-3.81)
(10.92)
(-0.23)
(-0.22)
Included
Included
21,995
0.0264
Pred. Sign
Test variable
(β1)
(β2)
(β3)
Crash risk determinants
ICW determinants
Year dummies
Industry dummies
n
Pseudo R2
Included
Included
22,421
0.0231
Chi-squared
(β2= β3)
11.73
8.75
p-value
0.001
0.003
48
Table 6 (Continued)
Disclosure of weak internal control and stock price crash risk: over time analysis
Table 6 Panel B reports the ordinary least squares (OLS) regression results with NCSKEW as the
dependent variable. Year dummies are based on Compustat fiscal year notation. Industry
dummies are based on 2-digit SIC industry classifications from CRSP. The standard errors are
clustered by firm and by year and the t-statistic of each coefficient is provided. Significance
levels are based on two-tailed tests. ***, **, and * denoted significance at the 1%, 5%, and 10%
levels, respectively. All variables are defined in Appendix A.
Panel B: OLS Regression Using NCSKEWt as the Dependent Variable
Baseline model
Coefficient
t-statistics
Controlling for ICW
determinants
Coefficient
t-statistics
?
0.099**
(2.20)
0.101**
(2.19)
?
0.210***
(10.14)
0.212***
(10.18)
(β3)
?
0.130***
(3.77)
0.140***
(3.79)
(β4)
?
0.032
(1.52)
0.041**
(1.96)
+
+
+
+
?
+
+
+
0.093*
0.019
0.235***
2.220**
0.056***
0.000***
-0.074*
-0.109***
0.026
(1.89)
(1.47)
(3.15)
(2.33)
(5.43)
(2.83)
(-1.94)
(-3.37)
(1.64)
0.093*
0.013
0.270***
3.018***
0.051***
0.000***
-0.068*
-0.116***
0.052***
(1.79)
(0.96)
(3.26)
(2.70)
(5.69)
(3.03)
(-1.90)
(-3.36)
(2.72)
-0.118***
0.018
-0.028***
0.082***
0.016
0.007
(-6.21)
(0.69)
(-4.16)
(9.21)
(0.66)
(0.33)
Included
Included
21,995
0.0319
3.567
Variable
Pred. Sign
Test variable
(β1)
(β2)
Crash risk determinants
ICW determinants
Year dummies
Industry dummies
n
Adjusted R2
F (β2= β3)
p-value
Included
Included
22,421
0.0288
4.66
0.03
49
0.06
Table 7
Weak internal control and stock price crash risk: post-remediation analysis
This table examines the association between internal control effectiveness and stock price crash
risk after the remediation of an ICW, based on an extended sample period including years 20022011. Panel A reports the logit regression results with CRASH as the dependent variable. Year
dummies are based on Compustat fiscal year notation. Industry dummies are based on 2-digit
SIC industry classifications from CRSP. The standard errors are clustered by firm and by year
and the z-statistic of each coefficient is provided. Significance levels are based on two-tailed
tests. ***, **, and * denoted significance at the 1%, 5%, and 10% levels, respectively. All
variables are defined in Appendix A.
Panel A: Logistic Regression Using CRASHt as the Dependent Variable
Controlling for ICW
Pred.
Baseline model
determinants
Variable
Sign
Coefficient
z-statistics
Coefficient
z-statistics
Test variable
?
0.030
(0.42)
0.054
(0.80)
(β1)
(β2)
?
-0.023
+
+
+
+
?
+
+
+
0.335**
0.043**
0.698***
5.578**
0.081***
0.002***
0.043
-0.309***
-0.012
(-0.19)
-0.014
(-0.11)
Crash risk determinants
(2.20)
(2.03)
(3.04)
(2.53)
(3.89)
(3.13)
(0.52)
(-3.17)
(-0.45)
0.311**
0.024
0.803***
7.618***
0.068***
0.002***
0.061
-0.367***
-0.002
(2.02)
(1.08)
(3.06)
(2.78)
(3.52)
(3.27)
(0.66)
(-3.50)
(-0.08)
-0.323***
0.047
-0.056**
0.259***
-0.029
-0.021
(-4.33)
(0.71)
(-2.21)
(9.64)
(-0.54)
(-0.50)
Included
Included
27,256
0.0237
ICW determinants
Year dummies
Industry dummies
n
Pseudo R2
Included
Included
27,927
0.0209
50
Table 7 (Continued)
Weak internal control and stock price crash risk: post-remediation analysis
Table 7 Panel B reports the ordinary least squares (OLS) regression results with NCSKEW as the
dependent variable. Year dummies are based on Compustat fiscal year notation. Industry
dummies are based on 2-digit SIC industry classifications from CRSP. The standard errors are
clustered by firm and by year and the t-statistic of each coefficient is provided. Significance
levels are based on two-tailed tests. ***, **, and * denoted significance at the 1%, 5%, and 10%
levels, respectively. All variables are defined in Appendix A.
Panel B: OLS Regression Using NCSKEWt as the Dependent Variable
Pred.
Sign
Variable
Baseline model
Coefficient
t-statistics
Controlling for ICW
determinants
Coefficient
t-statistics
Test variable
(β1)
?
0.036*
(1.68)
0.047**
(2.22)
(β2)
?
-0.010
(-0.24)
-0.005
(-0.12)
+
+
+
+
?
+
+
+
0.113**
0.028**
0.292***
2.986***
0.064***
0.000***
-0.062
-0.132***
0.010
Crash risk determinants
(2.34)
(2.41)
(3.81)
(3.31)
(6.40)
(3.02)
(-1.61)
(-4.14)
(1.36)
0.109**
0.021*
0.324***
3.717***
0.059***
0.000***
-0.058
-0.146***
0.016
(2.29)
(1.79)
(3.89)
(3.66)
(6.63)
(3.17)
(-1.58)
(-3.79)
(1.50)
-0.119***
0.017
-0.022***
0.085***
0.004
0.003
(-5.53)
(0.63)
(-2.96)
(10.56)
(0.19)
(0.12)
Included
Included
27,256
0.0371
ICW determinants
Year dummies
Industry dummies
n
Adjusted R2
Included
Included
27,927
0.0340
51
Table 8
Internal control quality and jump risk
Table 8 reports the logit regression results with JUMP as the dependent variable. Year dummies
are based on Compustat fiscal year notation. Industry dummies are based on 2-digit SIC industry
classifications from CRSP. The standard errors are clustered by firm and by year and the zstatistic of each coefficient is provided. Significance levels are based on two-tailed tests. ***, **,
and * denoted significance at the 1%, 5%, and 10% levels, respectively. All variables are defined
in Appendix A.
Variable
Baseline model
Coefficient
z-statistics
Controlling for ICW
determinants
Coefficient
z-statistics
-0.150*
(-1.86)
-0.155*
(-1.83)
-0.456***
-0.012
-0.292***
-1.48
-0.125***
-0.001
0.239
-0.058
-0.116***
(-4.46)
(-0.46)
(-2.72)
(-1.25)
(-8.93)
(-0.74)
(1.25)
(-0.76)
(-2.93)
-0.455***
-0.013
-0.285***
-1.35
-0.134***
-0.001
0.233
-0.059
-0.111**
(-4.44)
(-0.52)
(-2.60)
(-0.96)
(-7.05)
(-0.72)
(1.23)
(-0.76)
(-2.52)
Test variable
Crash risk determinants
ICW determinants
-0.019
0.205***
0.041
0.017
0.021
0.062
Year dummies
Industry dummies
n
Pseudo R2
Included
Included
19,397
0.0186
52
(-0.29)
(-3.36)
(0.81)
(0.56)
(0.38)
(0.76)
Included
Included
19,352
0.019
Table 9
Weak internal control and stock price crash risk: Cox proportional hazards model
This table examines the association between internal control effectiveness and stock price crash
risk over time using a Cox proportional hazards model. Year dummies are based on Compustat
fiscal year notation. The standard errors are clustered by firm and the z-statistic of each
coefficient is provided. Significance levels are based on two-tailed tests. ***, **, and * denoted
significance at the 1%, 5%, and 10% levels, respectively. All variables are defined in Appendix
A.
Cox Proportional Hazards Regression Using CRASHt as the Failure Risk
Controlling for ICW
Pred.
Baseline model
determinants
Variable
Sign
Coefficient
t-statistics
Coefficient
t-statistics
Test variable
+
(β1)
0.204**
(2.18)
0.186**
(1.97)
Crash risk determinants
+
+
+
+
?
+
+
+
0.049
0.086***
-0.020
0.053
0.077***
0.000
0.042
-0.597***
0.087***
(0.27)
(3.39)
(-0.11)
(0.02)
(4.56)
(0.33)
(0.35)
(-4.42)
(5.60)
0.036
0.077***
0.044
1.275
0.064***
0.000
0.035
-0.614***
0.090***
(0.20)
(3.03)
(0.23)
(0.57)
(3.34)
(0.45)
(0.29)
(-4.26)
(5.95)
-0.160*
0.056
-0.031
0.150***
0.052
0.115
(-1.81)
(0.55)
(-0.72)
(2.88)
(0.70)
(0.98)
Included
Industry
10,932
-5687
ICW determinants
Year dummies
Stratification level
n
Log
pseudolikelihood
Included
Industry
10,949
-5700
53