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

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 firm-

specific 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, auditor-

attested 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

2

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 post-

ICW-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).

3

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 three-

year 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).

4

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 fraud-

related 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

5

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

6

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 high-

profile 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 three-

year 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).

7

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

8

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.

9

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).

10

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

11

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.

12

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).

13

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 firm-

years 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

14

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 firm-

specific 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.

15

∑ ∑

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 is 0.198 for the full sample,

16

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.

17

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):

18

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.

19

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 ( ), market to book ratio ( ), return on asset ( ), and

leverage ( ). 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.

20

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 ( , Big 4 auditors ( and

auditor changes during each sample year ( 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, , is highly significant with an expected positive sign and z-statistic of

4.84 . 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

21

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 ( lagged stock return lagged

firm size and lagged market-to-book ratio 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.

22

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 as the dependent variable. As shown in Panel B of Table 4, the coefficient

of 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 firm-

specific 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. 21

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.

23

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.

24

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 is used as the dependent variable, we

also find that the coefficients on and 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

25

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.

26

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.

27

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 post-

disclosure 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.

28

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 is insignificant.

27

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.

29

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 post-

remediation 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

30

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

31

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 firm-

specific 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

32

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:

( )

where is the “hazard” or instantaneous likelihood of crash occurrence, for firm at time ,

conditional on crashes having occurred in firm by time ;28

is the time of the th

event; and is an unspecified function that captures the baseline hazard. Hypothesis H1

predicts that , 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 . Industry-level

28

The hazard function is defined as follows:

where is the number of events that have occurred to firm by time .

33

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 our third proxy for crash risk, we use as the dependent variable29

and re-

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 .

34

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.

35

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38

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.

39

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.

40

Figure 1: Timeline for variable measurement for testing H1 and H2

t-2 t-1 t t+1

Crash risk measured as of time t

Auditor-attested report disclosed

MW and Control variables measured as of time t-1

41

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

34,565

Less: Firm-year observations with less than 26 weeks of stock data (338)

Less: Firm-year observations with an average stock price less than $2.50 (2,940)

Less Firm-year observations with insufficient data to calculate control variables (11,890)

Total 19,397

Panel B: Internal control effectiveness over time

2004 2005 2006 2007 2008 2009 2010 2011 Total

No. of firms 1,825 2,197 2,388 2,682 2,586 2,506 2,653 2,560 19,397

%firms with ICW

problems 17.2% 12.2% 9.7% 7.8% 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

stock price crashes

Percentage of firms with

stock price crashes

2004 1,825 384 21.0%

2005 2,197 513 23.4%

2006 2,388 522 21.9%

2007 2,682 521 19.4%

2008 2,586 589 22.8%

2009 2,506 412 16.4%

2010 2,653 442 16.7%

2011 2,560 457 17.9%

Total 19,397 3,840 19.8%

42

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 Non-ICW Sample

(MW =0)

ICW Sample

(MW = 1)

N=19,397 N=18,009 N=1,388

Mean Median Std.

dev.

Mean Median Std.

dev.

Mean Median Std.

dev.

0.198 0.000 0.398 0.193 0.000 0.395 0.260 0.000 0.439

0.068 0.019 0.844 0.059 0.013 0.837 0.178 0.100 0.926

0.037 0.015 0.519 0.031 0.009 0.514 0.115 0.098 0.568

0.072 0.000 0.258 0.000 0.000 0.000 1.000 1.000 0.000

0.009 0.004 0.159 0.008 0.004 0.156 0.020 0.003 0.189

0.050 0.003 0.817 0.048 0.000 0.813 0.076 0.019 0.868

-0.170 -0.101 0.260 -0.166 -0.099 0.255 -0.222 -0.142 0.312

0.052 0.045 0.028 0.051 0.045 0.028 0.060 0.054 0.030

6.670 6.555 1.768 6.730 6.622 1.776 5.891 5.781 1.454

2.786 2.017 38.456 2.781 2.017 39.861 2.850 2.017 7.135

0.176 0.126 0.207 0.177 0.129 0.207 0.155 0.080 0.206

0.025 0.045 0.224 0.029 0.048 0.221 -0.025 0.013 0.245

0.241 0.152 0.454 0.238 0.150 0.462 0.277 0.182 0.330

0.245 0.000 0.350 0.236 0.000 0.345 0.365 0.333 0.380

0.051 0.000 0.221 0.050 0.000 0.218 0.067 0.000 0.250

1.223 1.099 0.715 1.223 1.099 0.714 1.233 1.099 0.729

0.312 0.000 0.463 0.310 0.000 0.462 0.339 0.000 0.474

0.830 1.000 0.376 0.838 1.000 0.369 0.729 1.000 0.445

0.052 0.000 0.222 0.046 0.000 0.211 0.120 0.000 0.325

43

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 1

44

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

Variable Pred.

Sign

Baseline model

Controlling for ICW

determinants

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

Pseudo R2

0.0225 0.0257

45

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 Pred.

Sign

Baseline model

Controlling for ICW

determinants

Coefficient t-statistics Coefficient t-statistics

Test variable

(β1) + 0.126*** (5.99) 0.123*** (5.55)

Crash risk determinants

+ 0.199*** (3.36) 0.191*** (3.11)

+ 0.019 (1.20) 0.013 (0.83)

+ 0.206** (2.44) 0.239** (2.53)

+ 1.774 (1.63) 2.439* (1.96)

? 0.039*** (3.92) 0.036*** (3.65)

+ 0.000 (1.38) 0.000 (1.44)

+ -0.055 (-1.34) -0.053 (-1.29)

- -0.096*** (-2.89) -0.112*** (-3.03)

+ 0.030* (1.85) 0.055*** (2.76)

ICW determinants

-0.095*** (-5.33)

0.040* (1.84)

-0.027*** (-3.29)

0.080*** (9.62)

0.001 (0.05)

0.018 (0.59)

Year dummies Included Included

Industry dummies Included Included

n 19,397 19,352

Adjusted R2

0.0213 0.0236

46

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 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 Pred.

Sign

Baseline model

Controlling for ICW

determinants

Coefficient z-statistics Coefficient z-statistics

Test variable

(β1) + 0.444*** (4.87) 0.433*** (4.72)

(β2) + 0.229** (2.36) 0.221** (2.28)

Crash risk determinants

+ 0.409** (2.19) 0.379* (1.94)

+ 0.024 (0.95) 0.005 (0.19)

+ 0.362** (2.28) 0.508*** (2.69)

+ 2.897 (1.54) 5.596** (2.33)

? 0.054* (1.95) 0.041 (1.46)

+ 0.001*** (4.26) 0.001*** (4.65)

+ -0.010 (-0.11) -0.009 (-0.09)

- -0.267** (-2.40) -0.326*** (-2.79)

+ 0.041 (1.41) 0.081** (1.97)

ICW determinants

-0.331*** (-3.58)

0.111** (2.19)

-0.075*** (-3.07)

0.276*** (10.76)

-0.006 (-0.09)

0.003 (0.05)

Year dummies Included Included

Industry dummies Included Included

n 19,397 19,352

Pseudo R2 0.0226 0.0259

Chi-squared (β1= β2) 2.684 2.829

p-value 0.101 0.0926

47

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

Variable Pred.

Sign

Baseline model

Controlling for ICW

determinants

Coefficient t-statistics Coefficient t-statistics

Test variable

(β1) + 0.186*** (4.02) 0.182*** (3.90)

(β2) +

0.083*** (3.86) 0.081*** (3.85)

Crash risk determinants

+ 0.198*** (3.33) 0.190*** (3.09)

+ 0.019 (1.21) 0.013 (0.84)

+ 0.207** (2.47) 0.240** (2.56)

+ 1.788 (1.64) 2.447** (1.97)

? 0.038*** (3.88) 0.035*** (3.64)

+ 0.000 (1.38) 0.000 (1.44)

+ -0.055 (-1.37) -0.053 (-1.32)

- -0.096*** (-2.90) -0.112*** (-3.04)

+ 0.030* (1.85) 0.056*** (2.76)

ICW determinants

-0.095*** (-5.41)

0.040* (1.86)

-0.027*** (-3.30)

0.080*** (9.74)

-0.000 (-0.02)

0.019 (0.61)

Year dummies Included Included

Industry dummies Included Included

n 19,397 19,352

Adjusted R2 0.0215 0.0238

F (β1= β2) 3.242 3.325

p-value 0.0718 0.0682

48

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 Pred. Sign

Baseline model

Controlling for ICW

determinants

Coefficient z-statistics Coefficient z-statistics

Test variable

(β1) ? 0.373*** (4.99) 0.365*** (4.83)

(β2) ? 0.470*** (5.94) 0.458*** (5.55)

(β3) ? 0.211** (2.35) 0.226** (2.30)

? 0.056 (0.70) 0.074 (0.94)

Crash risk determinants

+ 0.258* (1.85) 0.232 (1.63)

+ 0.024 (1.06) 0.003 (0.11)

+ 0.554** (2.45) 0.681*** (2.60)

+ 4.411* (1.94) 7.058** (2.51)

? 0.077*** (3.14) 0.065*** (2.74)

+ 0.002*** (3.06) 0.002*** (3.29)

+ -0.006 (-0.07) 0.014 (0.14)

- -0.277** (-2.51) -0.338*** (-2.85)

+ 0.038 (1.29) 0.078** (1.96)

ICW determinants

-0.366*** (-4.57)

0.085 (1.57)

-0.071*** (-3.81)

0.272*** (10.92)

-0.013 (-0.23)

-0.012 (-0.22)

Year dummies Included Included

Industry dummies Included Included

n 22,421 21,995

Pseudo R2

0.0231 0.0264

Chi-squared

(β2= β3)

11.73 8.75

p-value 0.001 0.003

49

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

Variable Pred. Sign

Baseline model

Controlling for ICW

determinants

Coefficient t-statistics Coefficient t-statistics

Test variable

(β1) ? 0.099** (2.20) 0.101** (2.19)

(β2) ? 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)

Crash risk determinants

+ 0.093* (1.89) 0.093* (1.79)

+ 0.019 (1.47) 0.013 (0.96)

+ 0.235*** (3.15) 0.270*** (3.26)

+ 2.220** (2.33) 3.018*** (2.70)

? 0.056*** (5.43) 0.051*** (5.69)

+ 0.000*** (2.83) 0.000*** (3.03)

+ -0.074* (-1.94) -0.068* (-1.90)

- -0.109*** (-3.37) -0.116*** (-3.36)

+ 0.026 (1.64) 0.052*** (2.72)

ICW determinants

-0.118*** (-6.21)

0.018 (0.69)

-0.028*** (-4.16)

0.082*** (9.21)

0.016 (0.66)

0.007 (0.33)

Year dummies Included Included

Industry dummies Included Included

n 22,421 21,995

Adjusted R2

0.0288 0.0319

F (β2= β3) 4.66 3.567

p-value 0.03 0.06

50

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 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 Pred.

Sign

Baseline model

Controlling for ICW

determinants

Coefficient z-statistics Coefficient z-statistics

Test variable

(β1) ? 0.030 (0.42) 0.054 (0.80)

(β2) ? -0.023 (-0.19) -0.014 (-0.11)

Crash risk determinants

+ 0.335** (2.20) 0.311** (2.02)

+ 0.043** (2.03) 0.024 (1.08)

+ 0.698*** (3.04) 0.803*** (3.06)

+ 5.578** (2.53) 7.618*** (2.78)

? 0.081*** (3.89) 0.068*** (3.52)

+ 0.002*** (3.13) 0.002*** (3.27)

+ 0.043 (0.52) 0.061 (0.66)

- -0.309*** (-3.17) -0.367*** (-3.50)

+ -0.012 (-0.45) -0.002 (-0.08)

ICW determinants

-0.323*** (-4.33)

0.047 (0.71)

-0.056** (-2.21)

0.259*** (9.64)

-0.029 (-0.54)

-0.021 (-0.50)

Year dummies Included Included

Industry dummies Included Included

n 27,927 27,256

Pseudo R2

0.0209 0.0237

51

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

Variable Pred.

Sign

Baseline model

Controlling for ICW

determinants

Coefficient t-statistics Coefficient t-statistics

Test variable

(β1) ? 0.036* (1.68) 0.047** (2.22)

(β2) ? -0.010 (-0.24) -0.005 (-0.12)

Crash risk determinants

+ 0.113** (2.34) 0.109** (2.29)

+ 0.028** (2.41) 0.021* (1.79)

+ 0.292*** (3.81) 0.324*** (3.89)

+ 2.986*** (3.31) 3.717*** (3.66)

? 0.064*** (6.40) 0.059*** (6.63)

+ 0.000*** (3.02) 0.000*** (3.17)

+ -0.062 (-1.61) -0.058 (-1.58)

- -0.132*** (-4.14) -0.146*** (-3.79)

+ 0.010 (1.36) 0.016 (1.50)

ICW determinants

-0.119*** (-5.53)

0.017 (0.63)

-0.022*** (-2.96)

0.085*** (10.56)

0.004 (0.19)

0.003 (0.12)

Year dummies Included Included

Industry dummies Included Included

n 27,927 27,256

Adjusted R2

0.0340 0.0371

52

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 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.

Variable

Baseline model

Controlling for ICW

determinants

Coefficient z-statistics Coefficient z-statistics

Test variable

-0.150* (-1.86) -0.155* (-1.83)

Crash risk determinants

-0.456*** (-4.46) -0.455*** (-4.44)

-0.012 (-0.46) -0.013 (-0.52)

-0.292*** (-2.72) -0.285*** (-2.60)

-1.48 (-1.25) -1.35 (-0.96)

-0.125*** (-8.93) -0.134*** (-7.05)

-0.001 (-0.74) -0.001 (-0.72)

0.239 (1.25) 0.233 (1.23)

-0.058 (-0.76) -0.059 (-0.76)

-0.116*** (-2.93) -0.111** (-2.52)

ICW determinants

-0.019 (-0.29)

0.205*** (-3.36)

0.041 (0.81)

0.017 (0.56)

0.021 (0.38)

0.062 (0.76)

Year dummies Included Included

Industry dummies Included Included

n 19,397 19,352

Pseudo R2

0.0186 0.019

53

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

Variable Pred.

Sign

Baseline model

Controlling for ICW

determinants

Coefficient t-statistics Coefficient t-statistics

Test variable

(β1) + 0.204** (2.18) 0.186** (1.97)

Crash risk determinants

+ 0.049 (0.27) 0.036 (0.20)

+ 0.086*** (3.39) 0.077*** (3.03)

+ -0.020 (-0.11) 0.044 (0.23)

+ 0.053 (0.02) 1.275 (0.57)

? 0.077*** (4.56) 0.064*** (3.34)

+ 0.000 (0.33) 0.000 (0.45)

+ 0.042 (0.35) 0.035 (0.29)

- -0.597*** (-4.42) -0.614*** (-4.26)

+ 0.087*** (5.60) 0.090*** (5.95)

ICW determinants

-0.160* (-1.81)

0.056 (0.55)

-0.031 (-0.72)

0.150*** (2.88)

0.052 (0.70)

0.115 (0.98)

Year dummies Included Included

Stratification level Industry Industry

n 10,949 10,932

Log

pseudolikelihood

-5700 -5687