CORPORATE FRAUD AND LIQUIDITY - fmaconferences.org · The data set contains 81 variables. Some of those variables include: daily share volume, number of trades, opening quote, average

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CORPORATE FRAUD AND LIQUIDITY

Emre Kuvvet

Nova Southeastern University

H. Wayne Huizenga School of Business and Entrepreneurship

3301 College Avenue

Fort Lauderdale-Davie, Florida 33314-7796

[email protected]

Phone: 954-262-5000

Abstract

We examine the implications of corporate fraud on stock liquidity. We find that the revelation of corporate

fraud has an adverse effect on liquidity not only in the short term but also in the long term. We also find that the

complexity and severity of fraudulent activities do not further reduce liquidity; rather, the existence of fraud is

what matters to investors. Our results also suggest that liquidity is more adversely affected for fraudulent firms

with weak governance than for those with better governance.

JEL Codes: G10; G12; G340

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I. Introduction

Corporate fraud reduces shareholders' wealth for the fraudulent firm (Karpoff, Lee, and Martin 2008a)

and its rival firms (Goldman, Peyer, and Stefanescu 2012) while increasing the cost of capital (Lin,

Song, and Sun 2012). In this study, we identify a new channel through which fraud harms

shareholders' wealth: reduced liquidity. Former Securities and Exchange Commission (SEC)

Chairman Arthur Levitt commented that "Quality information is the lifeblood of strong, vibrant

markets. Without it, investor confidence erodes. Liquidity dries up. Fair and efficient markets simply

cease to exist." (Remarks to The Economic Club of New York, New York City, October 18, 1999.) It

is possible that fraud can increase the transaction costs associated with trading a stock in the stock

market. The revelation of fraud can decrease the credibility of firms' financial reporting and increase

informational asymmetry. But evidence of the effect of fraud on liquidity is scarce. We fill this gap by

analyzing the implications of corporate fraud for both short-term and long-term liquidity.

One of the ways to align the financial incentives of executives with those of shareholders is

through executive compensation programs. However, the prior literature extensively shows that

executive compensation provides managers the incentive to commit fraud. Johnson, Ryan and Tian

(2008) found that firms are more likely to commit fraud if the size of managers’ equity ownership is

high. Beneish (1999) showed that managers manipulate earnings in order to sell their stocks and

exercise option holdings. Burns and Kedia (2006) suggested that the sensitivity of the CEO's option

portfolio to stock price is positively related to the propensity to misreport. Similarly, Bergstresser and

Philippon (2006) found that firms are more likely to manipulate their earnings when the CEO

compensation is more closely related to the value of stock and option holdings. During the

commission of fraud, the fraudulent activities are perpetuated and hidden so well that there is little

information asymmetry among traders. At the trigger event, it is first revealed to the public that fraud

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may have been perpetuated. During this period, the magnitude of fraudulent activity is still unknown.

It is also unclear when the truth behind the fraud will be determined. Since investors are trying to

value the implications of fraud, the impending penalties, and its impact on reputation, there is an

increase in uncertainty about the firm’s value. The revelation of fraud suggests that information

previously disclosed by companies to investors is wrong; therefore, prior beliefs about firms' riskiness

have to be reassessed. Kim and Verrecchia (1994) suggested that less-accurate disclosures provide

better opportunity for certain traders to process public information into private information, thus

increasing information asymmetry among traders and thereby decreasing liquidity. This increase in

uncertainty would lead to an increase in the cost of liquidity. In addition, there will be a decrease in

management's credibility and a loss of trust by investors. Jarrell and Peltzman (1985) and Klein and

Leffler (1981) showed that corporate fraud harms firms’ reputations by changing the conditions of

trade for investors and thus causes higher uncertainty about companies' future prospects. Milgrom and

Roberts (1982) and Kreps and Wilson (1982) suggested that corporate fraud can increase the

information asymmetry identified by investors due to loss of trust. As a result, investors might not

participate in equity markets for fear that they would lose out to better informed traders and insiders,

thus reducing liquidity (Velikonja 2013). Together, the studies cited above suggest that firms

convicted of corporate fraud may have higher spreads.

To the best of our knowledge, this is the first study to examine the effect of corporate fraud on

liquidity. We compare fraudulent firms' liquidity before and after fraud incidents. We use the Karpoff

et al. (2008a, b) database to classify corporate fraud from 1993 through 2005. This database identifies

the first public revelation dates for frauds. The database allows us to focus on liquidity during the

relevant time periods. Our liquidity measures come from Vanderbilt's Market Microstructure database.

We use daily observations of liquidity measures from 1993 until 2005.

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We find that the revelation of corporate fraud has an adverse effect on liquidity not only in the

short term but also in the long term. In the short run, the quoted spread (effective spread) increases by

0.138% (0.108%) after the revelation of fraud. In the long term, the quoted spread (effective spread)

increases by 0.152% (0.098%) after the revelation of fraud. The fraudulent activities differ in terms of

complexity and the amount of harm caused to shareholders. Thus, we also examine the impact of the

severity and complexity of fraud on liquidity. The results suggest that increased complexity and

severity of fraudulent activities does not further reduce liquidity; rather, the existence of fraud is what

matters to investors.

Apart from analyzing the effect of fraud on liquidity, we also study the interaction effect of

corporate fraud and governance on liquidity. We examine whether firms with weak corporate

governance have more severe liquidity problems after the revelation of fraud than those with better

corporate governance. Our results suggest that the liquidity of fraudulent firms with weak governance

is more adversely affected than the liquidity of those with better governance. These results highlight

the importance of corporate governance as a channel through which the adverse effect of fraud on

liquidity can be moderated.

II. Literature Review

Several studies in the corporate fraud literature demonstrate that corporate fraud has a significant

negative effect on firms' financing costs, investment decisions, and shareholder wealth. For example,

Lin, Song, and Sun (2012) studied the impact of corporate fraud on fraudulent firms’ external

financing costs. They compared fraudulent firms’ cost of debt financing before and after fraud

exposure. They found that firms’ cost of debt significantly increased after the revelation of fraud. The

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log value of firms’ loan spread increased by 29.6% after the revelation of corporate fraud. Lin and

Paravisini (2011) found that banks have a weakened ability to fund syndicated loans externally after a

firm they monitor commits fraud. Tian, Udell, and Yu (2012) found that a venture capital firm’s

reputation is negatively affected when it fails to prevent fraud in its portfolio companies. They found

that venture capital firms that failed to prevent fraud had greater trouble in taking future portfolio

companies public. Wang, Winton, and Yu (2008) suggested that a firm is more likely to commit fraud

when investors are more optimistic about the industry’s prospects. Wang and Winton (2012) found

that the propensity for fraud is higher in competitive industries. They also found that poor performance

in competitive industries following booms is largely concentrated in firms that are likely to have

committed fraud during the booms. They suggested that fraud can intensify cyclical fluctuations in the

real economy.

Goldman, Peyer, and Stefanescu (2012) looked at how the announcement of an accusation of

fraud affected industry rivals of the accused firm. They found that, in competitive industries, the

announcements resulted in negative returns to rival shareholders. Velikonja (2013) also suggested that

false information associated with hiding fraud and avoiding detection impairs risk evaluation by

investors, the firms’ suppliers, and customers. It also alters the business decisions of fraudulent firms

and their rivals. Dyck, Morse, and Zingales (2013) estimated that, on average, corporate fraud costs

investors 22% of enterprise value in fraud-committing firms and 3% of enterprise value across all

firms. Karpoff et al. (2008a) found that the reputational penalty associated with financial

misrepresentation is more than 7.5 times the sum of all penalties imposed through the legal and

regulatory system. They found that, on average, for every dollar a firm deceitfully inflates its market

value, that dollar, plus an additional $3.08, is lost when the firm’s misconduct is revealed. Thus, the

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evidence in the fraud literature shows that fraud adversely affects a firm's financing costs, its

investment decisions, and its shareholders' wealth.

This study is also related to the literature examining the impact of firm-level governance

(Chung, Elder and Kim 2010) and country-level governance (Eleswarapu and Venkataraman 2006) on

stock liquidity. This literature found that poor governance lowers liquidity in the cross-section of

stocks and that improvements in governance are associated with improvements in liquidity. However,

companies with good corporate governance still commit fraud.

The literature review shows that there is a lack of direct evidence of the impact of fraud on

liquidity in the literature. In this study, we examine the effect of fraud on liquidity. We look at whether

and how fraud affects the short-term and long-term liquidity of a stock. We are the first to document

the increased transaction costs associated with trading a firm’s stock after the revelation of fraud. We

also suggest that good corporate governance is still beneficial to the firm even though good

governance might not prevent the firm from committing fraud. Our results suggest the importance of

good governance for mitigating the effect of fraud on liquidity.

III. Data, Descriptive Statistics, and Methodology

Data and Sample

We obtained a sample of frauds detected between 1976 and 2005 from Karpoff et al. (2008a, b).

Following the example of Karpoff, Koester, Lee, and Martin (2013), we identified an enforcement

action as fraud if the SEC or Department of Justice (DOJ) filed charges alleging the violation of: (i)

Section 17(a) of the Securities Act of 1933 for fraudulent interstate transactions related to the issuance

of a security or (ii) Section 10(b) of the Securities Exchange Act of 1934 for manipulative and

deceptive devices related to the trading of an already issued security. For a detailed description of the

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data, please see Karpoff et al. (2008a, b). We collected our liquidity variables from Vanderbilt's

Market Microstructure Database (Financial Markets Research Center). The Market Microstructure

Database uses the Trade and Quote (TAQ) data distributed by the NYSE to calculate the variables for

each stock-day. The data set starts on 4 January 1993 and is stored as a permanent SAS data set. It

contains daily observations of variables measuring trade and quote properties for more than 8,000

common domestic and foreign stocks. Filters are applied to the trade and quote data before the

variables are calculated. The stock-day begins at 9:30:00 hours and ends at 16:00:00 hours. Stocks

whose monthly average price is less than three dollars are excluded from the file in that particular

month. Infrequently traded stocks and foreign stocks are included. The data set contains 81 variables.

Some of those variables include: daily share volume, number of trades, opening quote, average ask

and bid size, mean half spread, mean trade-weighted effective half spread, average trade-weighted ask

and bid price, share volume at the bid and ask side, and trading halt. We use Quoted Spread and

Effective Spread in percent from the database as inverse measures of liquidity. Quoted Spread (%) is

the average trade-weighted relative half-spread. Quoted Spread in percent is computed as follows:

Quoted Spread (%) = 100*(A - B) / (A + B) / 2,

where A is the ask and B is the bid price of the quote. Effective Spread (%) is the average relative

effective half-spread of the stock-day. Effective Spread in percent is computed as follows:

The numerator is the mean of the trade-weighted effective half-spread. The effective half-

spread is the absolute difference between the trade price and the quote midpoint of the associated

quote. The relative effective half-spread is the effective half-spread divided by the trade price. We

excluded frauds detected before 1993 due to the lack of liquidity data. The Financial Markets Research

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Center also limited our access to liquidity variables to data pertaining to dates through 2005. Thus, our

final fraud sample contains 202 enforcement actions dating from 1993 through 2005.

Table 1 shows the description of our fraud sample. The sample consists of 202 SEC

enforcement actions from 1993 through 2005. The violation period extends from the beginning of the

fraud until the end of the fraudulent activity. The median duration of the violation period is 729 days.

The first public revelation date is the earliest date that information about the fraud became public. First

public revelation dates are from the Karpoff et al. (2008a, b) database. First public revelation events

include firm disclosures, internal investigations, restatements, auditor changes, management changes,

third-party actions, SEC filing delays, whistle-blower charges, related investigations, related litigation,

class action lawsuit filings, informal inquiries, formal investigations, and Wells Notices. The median

duration from the beginning of the violation until the first public announcement is 760 days. The

median durations of the violation period and the period from the beginning of the violation to the first

public revelation increased between 1993 and 2005. The number of days from the end of the violation

to the first public announcement can be interpreted as the difference between "Violation Beginning to

First Public Announcement" and "Violation Period". The median duration from the ending of the

violation until the first public announcement is 47 days and seems to decline between 1993 and 2005.

Panel B of Table 1 also reports the number of events each year by first public revelation event types.

[INSERT TABLE 1 HERE]

Table 2 shows the distribution of the fraud sample by industry using two-digit Standard

Industrial Classification (SIC) codes. While the fraudulent firms are broadly spread across 39 different

industries, there are some concentrations of firms in the business services, electronic, and industrial

machinery industries.

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Figure I, replicated from Karpoff et al. (2008a), illustrates the sequence of an SEC enforcement

action. An enforcement action starts with a trigger event that exposes the possible fraud and draws the

SEC’s attention. There are different types of trigger events (e.g., self-disclosure of malfeasance, a

restatement, a change in management or in the audit firm, a shareholder lawsuit, a complaint by an

investor or a whistle-blower, delayed SEC filings, an article in the press, investigations by other

federal agencies). Following the trigger event, the SEC collects information through an informal

inquiry that can turn into a formal investigation. The SEC then sends a Wells Notice to potential

defendants, informing them that the SEC intends to file charges and offering them an opportunity to

reply as to why civil charges should not be filed. The SEC then imposes administrative sanctions. The

SEC can also refer the case to the DOJ, which may file criminal charges (Karpoff et al. 2008a, p. 588).

[INSERT FIGURE I HERE]

Table 3 shows summary statistics for the 1-day quoted spread (%), effective spread (%), and

trading halt for important dates in our fraud sample from 1993 to 2005. Panel A presents quoted

spread (%), effective spread (%), and trading halt for the first public announcement date, which is the

earliest date that information about the fraud was available to the public. Quoted and effective spread

data are available for 181 firms at the time of first public announcement. For all the first public

announcements (181 events), the mean 1-day quoted spread is 1.205%. The mean 1-day effective

spread is 0.913%. Of those firms, 29% experienced a trading halt during those days.

The SEC identified the first public announcements for 170 firms. SEC-identified events consist

of firm disclosures (50 events), auditor changes (8 events), internal investigations (47 events),

management changes (5 events), registrations (9 events), related investigations (2 events), related

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litigation (6 events), restatements (33 events), and third-party actions (9 events). The mean 1-day

quoted spread on the SEC-identified trigger date is 1.223%. The mean 1-day effective spread on the

SEC-identified trigger date for those firms is 0.924%, and 29.3% experienced a trading halt during

those days. In 11 cases, the first public revelation date is identified in the Karpoff et al. (2008a, 2008b)

database. Other first revelation events consist of announcements of SEC informal inquiries (5 events),

formal investigations (3 events), Wells Notices (1 event), and class action lawsuits (2 events). For

those 11 events, the mean quoted spread (effective spread) is 0.935% (0.744%).

Panel B of Table 3 shows important follow-up announcements about each fraud that were

made after the first public announcement date. The follow-up announcements consist of notices of

informal SEC inquiries, formal SEC investigations, Wells Notices, initiation of regulatory

proceedings, initiation of class action lawsuits, and bankruptcy. We only use those firms that have at

least three announcements in Panel B. The mean quoted spread (effective spread) for the firms with

second announcements is 0.755% (0.604%). The mean quoted spread (effective spread) for the firms

with third announcements is 0.738% (0.576%). In Panel C, we divide our sample into low, medium,

and high levels based on market cap, volatility, and volume and show quoted spread (%), effective

spread (%), and trading halt for the first public announcement date.


Methodology

We employed the following regression to study the effect of fraud on firms’ short-term and long-term

liquidity while controlling for firm-specific variables noted in the literature (Huang and Stoll 1996).

We use Quoted spread (%) and Effective spread (%) as dependent variables (inverse liquidity

measures).

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(1)

where Post fraud is a dummy variable equal to 1 after the revelation of fraud and 0 otherwise, Price

volatility is the standard deviation of returns, Price is the average trade price, Volume is the trading

volume, and Firm size is the dollar market capitalization of the stock. There are two observations for

every firm. Day 0 is the trading day on which the fraud was publicly revealed. For the pre-fraud period

(Before [-5,-1]), the short-term liquidity is calculated as the mean of the liquidity on days -5 through -

1. For the post-fraud period (After [0,+5]), the short-term liquidity is calculated as the mean of the

liquidity on days 0 through +5. We removed firms that did not have liquidity data for both periods. For

the pre-fraud period (Before [-200,-6]), the long-term liquidity is calculated as the mean of the

liquidity on days -200 through -6. For the post-fraud period (After [+6,+200]), the long-term liquidity

is calculated as the mean of the liquidity on days +6 through +200. Pre and post averages for the

control variables are calculated the same way as the liquidity measures. Standard errors are estimated,

allowing for clustering at the two-digit SIC industry level because the fraudulent activities are

correlated with economic downturns for the industries (e.g., Wang, Winton, and Yu 2010; Wang and

Winton 2012).

IV. Empirical Results

Liquidity around the Revelation of Fraud

In Panel A of Table 4, we show the mean quoted spread (%) and effective spread (%) for our fraud

sample during the 401-trading-day window around the revelation of fraud. Number of events indicates

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the number of firms used in calculating the mean for the quoted spread and effective spread for each

trading day. Number of events changes due to the limited availability of data on liquidity for some

trading days. Quoted spread (%) and effective spread (%) increase gradually from day -200 through

day 0, hitting the highest point on day 0. Although quoted spread (%) and effective spread (%) steadily

decrease through day +200, they remain high. Panel B shows the difference in spread across time. The

notations ***, **, and * in Panel B represent 1%, 5%, and 10% significance levels, respectively.


Figure II plots the mean quoted spread (%) and effective spread (%) for our fraud sample

during the 401-trading-day window around the revelation of fraud. Day 0 is the trading day on which

the fraud was publicly revealed. Quoted spread (%) and effective spread (%) increase over the 200

days before the revelation of fraud. Quoted spread (%) and effective spread (%) remain high in the

days after the revelation of fraud.

[INSERT FIGURE II HERE]

Table 5 demonstrates changes in liquidity around the revelation of fraud. In Panel A, we show

short-term changes in liquidity. Difference (After - Before) indicates the changes in liquidity derived

by subtracting the pre-fraud levels from the post-fraud levels. The difference for quoted spread (%) is

0.306% and is statistically significant at 1%. The difference for effective spread (%) is 0.236% and is

statistically significant at 1%. In other words, quoted spread (%) and effective spread (%) increase by

0.306% and 0.236%, respectively, in the post-fraud window compared with the pre-fraud window. The

difference for trading halt (%) is 9.2% and is statistically significant at 1%.

Panel B of Table 5 presents long-term changes in liquidity. The difference for the quoted

spread (%) is 0.314% and is statistically significant at 1%. The difference for effective spread (%) is

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0.232% and is statistically significant at 1%. The decreased liquidity continues over the long run. The

difference for trading halt (%) is 0.5% and is statistically significant at 1%. Thus, the univariate results

show that liquidity deteriorates after the revelation of fraud for both the short-term and long-term

windows.


Table 6 explains the impact of severity of fraud on liquidity for both short-term and long-term

windows. We employ several indicator variables to measure the severity of fraud (Karpoff et al. 2007).

Insider trading dummy is an indicator variable taking the value of 1 for enforcement actions that

include allegations of insider trading. Department of Justice dummy is an indicator variable taking the

value of 1 for enforcement actions that include one or more civil or criminal proceedings by the DOJ.

Cooperated in investigation dummy is an indicator variable taking the value of 1 for firms identified

by regulators in the enforcement action as having self-reported, cooperated, or taken remedial actions.

Initial public offering dummy is an indicator variable taking the value of 1 for enforcement actions

associated with a firm’s initial public offering. Foreign company dummy is an indicator variable taking

the value of 1 for foreign firms. Bankruptcy dummy is an indicator variable taking the value of 1 for

firms that filed for bankruptcy.

Panel A of Table 6 presents the effect of severity of fraud on short-term changes in liquidity.

The results show that fraudulent firms with allegations of insider trading have higher quoted and

effective spreads during the short-term window than fraudulent firms without insider trading

allegations. We can also see that a firm’s liquidity suffers more severely in the short-term window if

the enforcement action is associated with the firm's initial public offering. Fraudulent firms that

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eventually filed for bankruptcy also have higher quoted and effective spreads during the short-term

window than those that did not file for bankruptcy.

Panel B of Table 6 shows the effect of fraud severity on long-term changes in liquidity. We

still see a negative effect on long-term liquidity for firms that filed for bankruptcy and firms whose

enforcement actions were associated with their initial public offerings. Insider trading accusations do

not seem to affect firms' long-term liquidity. We do not see any difference in short-term and long-term

liquidity for foreign firms, non-cooperating firms, or firms that faced additional enforcement actions

by the DOJ.


The Effect of Corporate Fraud on Short-Term Liquidity

In this section, we examine the effect of fraud on a firm’s short-term liquidity. In Panel A of Table 7,

the dependent variable is Quoted spread (%), which is the average trade-weighted half-spread of a

stock. We removed firms that did not have liquidity data for both periods, leaving 185 firms in our

sample. In Model 1 of Panel A, Post fraud dummy is 0.138 and is statistically significant at 1%. In

other words, after the revelation of fraud, the quoted spread increased by 0.138% in the short run. The

results show that the revelation of fraud adversely affects the short-term liquidity of fraudulent firms

by a considerable amount. The coefficients for post-fraud variables are statistically significant in all

regressions.

The fraudulent activities differ in terms of complexity and the degree of harm caused to

shareholders. Thus, we also examine the impact of the severity of fraud on short-term liquidity by

using several variables to measure the complexity and severity of the fraud (Karpoff et al. 2007).

Regulatory penalties are the total fines assessed against the firm and its agents by the SEC, DOJ, and

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state attorneys general. The variable # Respondents represents the number of individuals named in the

enforcement proceedings. The variable # Proceedings represents the total number of administrative,

civil, and criminal proceedings in the enforcement action. We examine the interaction of the Post

fraud variable with the variables measuring the complexity and severity of the fraud. None of the

interactions between those variables and Post fraud dummy is statistically significant. This suggests

that the complexity and severity of fraudulent activities does not further reduce a firm’s short-term

liquidity; rather, the existence of fraud is what matters to investors.1

In Panel B of Table 7, the dependent variable is Effective spread (%), which is the average

trade-weighted effective half-spread of the stock. In Model 1 of Panel B, Post fraud dummy is 0.108

and is statistically significant at 1%. The overall results suggest that short-term market liquidity

deteriorates after the revelation of corporate fraud.


The Effect of Corporate Fraud on Long-Term Liquidity

Table 8 looks at the effect of fraud on long-term liquidity. We removed firms that did not have

liquidity data for both periods, leaving a sample of 188 firms. In Panel A of Table 8, the dependent

variable is Quoted spread (%). In Model 1 of Panel A, Post fraud dummy is 0.152 and is statistically

significant at 5%. In the long term, the quoted spread increased by 0.152% after the revelation of

fraud. These findings validate the hypothesis that the revelation of fraud negatively affects long-term

liquidity. The coefficients for the Post fraud variable are statistically significant in all regressions. We

look at the effect of the severity and complexity of fraud on long-term liquidity by using several

1 Our results do not change when we use the abnormal stock returns around the announcement dates as a proxy for the

severity of the fraud.

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variables (Karpoff et al. 2007), and we examine the interaction of our Post fraud variable with those

variables. None of the interactions of those variables with Post fraud dummy is statistically significant.

This suggests that the complexity and severity of fraudulent activities does not further reduce long-

term liquidity.

In Panel B of Table 8, the dependent variable is Effective spread (%). In Model 1 of Panel B,

Post fraud dummy is 0.098 and is statistically significant at 5%. The results in Table 7 and Table 8

suggest that the revelation of corporate fraud has an adverse effect on liquidity not only in the short

term but also in the long term.


The Interaction Effect of Corporate Fraud and Governance on Liquidity

The results above suggest that the revelation of fraud decreases liquidity both in the short term and in

the long term. In this section, we examine the interaction effect of corporate fraud and governance on

liquidity. It is possible that firms with weak corporate governance have more severe liquidity problems

after the revelation of fraud than those with better corporate governance. Strong corporate governance

can assure investors that there will be adequate internal and external mechanisms through which

fraudulent activities can be prevented in the future. We examine the governance aspects of outside

director percentage, the percentage of institutional ownership, and the quality of the external audit firm

(Farber 2005). We show the interactions of Post fraud and our corporate governance proxies in Table

9. We use Quoted spread (%) and Effective spread (%) as dependent variables (inverse liquidity

measures).

In Panel A of Table 9, we look at the interaction effect of corporate fraud and governance on

short-term liquidity. The sample in Panel A contains 185 firms. Our first proxy for corporate

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governance is Outside directors %. Rosenstein and Wyatt (1990), Weisbach (1988), and Brickley and

James (1987) found that boards consisting of outside directors are more effective than boards

composed of insiders. Outside director % is the percentage of independent directors on a firm's board.

In Models 1 and 2 of Panel A, we include the interaction term between Post fraud and Outside

director % in the regression. In Model 1, the dependent variable is Quoted spread (%); the coefficient

for the interaction term is -0.554 and is statistically significant at 5%. In other words, the revelation of

fraud affects the short-term liquidity of firms with good governance less adversely than it does firms

with weak governance. In Model 2 of Panel A, the dependent variable is Effective spread (%). The

coefficient for the interaction term is -0.442 and remains significant at 5%.

Our second proxy for corporate governance is Institutional holdings %. Institutional investors

can provide external monitoring of firms (Jensen 1993). Institutional holdings % is the percentage of

ownership by institutions. In Models 3 and 4 of Panel A, we include the interaction term between Post

fraud and Institutional holdings % in the regression. In Model 3, the dependent variable is Quoted

spread (%). The coefficient for the interaction term is -0.393, and it is statistically significant at 5%. In

Model 4, the dependent variable is Effective spread (%). The coefficient for the interaction term is -

0.319 and remains significant at 1%.

Our third proxy for corporate governance is Major accounting firm dummy. DeFond (1992)

and Palmrose (1988) suggested that a major audit firm is likely to provide better monitoring than a

smaller audit firm. Major accounting firm dummy is an indicator variable taking the value of 1 to

indicate that the firm’s auditor was one of the 8/6/5/4 major accounting firms. In Models 5 and 6 of

Panel A, we include the interaction term between Post fraud and Major accounting firm dummy in the

regression. In Model 5, the dependent variable is Quoted spread (%); the coefficient for the interaction

term is -0.308 and is statistically insignificant. In Model 6, the dependent variable is Effective spread

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(%). Here, the coefficient for the interaction term is -0.27 and remains significant at 1%. These results

suggest that the short-term liquidity of fraudulent firms with weak governance, as evidenced by

inadequate monitoring, is more adversely affected than the short-term liquidity of fraudulent firms

with better governance.

In Panel B of Table 9, we look at the interaction effect of corporate fraud and governance on

long-term liquidity. The sample in Panel B contains 188 firms. In Models 1 and 2 of Panel B, we

include the interaction term between Post fraud and Outside director % in the regression. In Model 1,

the dependent variable is Quoted spread (%); the coefficient for the interaction term is -0.907 and is

statistically significant at 1%. This indicates that the revelation of fraud seems to affect the long-term

liquidity of firms with good governance less adversely than it does firms with weak governance. In

Model 2, the dependent variable is Effective spread (%). The coefficient for the interaction term is

-0.68 and remains significant at 1%.

In Models 3 and 4 of Panel B, we include the interaction term between Post fraud and

Institutional holdings % in the regression. In Model 3, the dependent variable is Quoted spread (%);

the coefficient for the interaction term is -0.528 and is statistically significant at 5%. In Model 4, the

dependent variable is Effective spread (%). The coefficient for the interaction term is -0.324 and

remains significant at 10%.

In Models 5 and 6 of Panel B, we include the interaction term between Post fraud and Major

accounting firm dummy in the regression. In Model 5, the dependent variable is Quoted spread (%);

the coefficient for the interaction term is -0.568 and is statistically significant at 10%. In Model 6, the

dependent variable is Effective spread (%). The coefficient for the interaction term is -0.383 and

remains significant at 10%. Thus, we find that the liquidity effect of corporate fraud is more severe for

firms with weak governance than for those with strong governance. These results highlight the

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importance of corporate governance as a channel through which the adverse effect of fraud on

liquidity can be moderated.


V. Robustness

We also look at the impact of fraud revelation on information asymmetry. Spread measures can be

decomposed into order processing, inventory, and information asymmetry components. There are

several methods (e.g., Glosten and Harris 1988; Huang and Stoll 1997; Easley, Keifer, O'Hara, and

Paperman 1996) that extract the adverse selection component from the spread. Unfortunately,

Vanderbilt's Market Microstructure Database (Financial Markets Research Center) only contains daily

observations of effective and quoted spreads and does not decompose the spread measures into their

adverse selection components. To calculate the information asymmetry measures, we used an

alternative database that is publicly available. We used monthly data disclosed under Securities and

Exchange Commission Rule 605 of Regulation NMS from 2001 to 2005.2 This rule requires that

exchanges and market centers release order-based performance statistics by stock, order type, and

order size.

Price impacts of trades are commonly used in the market microstructure literature to examine

the extent of information-based trading. Price impact of a trade will be zero if the trade has no new

information on the share's value. However, if it is an information-motivated trade, stock price would

increase if the trade is initiated by a buyer and decrease if the trade is initiated by a seller. Thus, price

impact shows the value of private information held by informed traders. We used two different high-

2 The reporting of execution quality was initially required under SEC Rule 11Ac1-5 starting in mid-2001. Regulation NMS

(approved on 8/29/2005) changed the numbering of Rule 11AC1-5 to Rule 605.

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frequency price impact measures as proxies for information asymmetry. Our first price impact

measure was developed by Goyenko, Holden, and Trzcinka (2009). Goyenko et al. (2009) stated that

"A static version of price impact is the slope of the price function at a moment in time. Essentially, this

is the cost of demanding additional instantaneous liquidity and can be thought of as the first derivative

of the effective spread with respect to order size. The price impact benchmark uses two (aggregated)

points on this curve to measure the slope (p. 156)." Static price impact based on Rule 605 data for a

given stock is calculated as follows:

Static price impact (650) = [($Effective spread (605)Big Orders/P) - ($Effective spread (605)Small Orders/P)]

/ [(Ave trade size (605)Big Orders) - (Ave trade size (605)Small Orders)]*1000000,

Effective spreads are reported only for market orders and marketable limit orders in the 605

data. Effective spread (605) is the dollar share-weighted average monthly spread reported by market

centers. P is the average price in a month. Big orders is the set of all orders in the range of 2000-9999

while small orders is the set of all orders in the range of 100-499. Our second price impact measure

was used by Boehmer (2005) and Bessembinder (2003). Proportional price impact (%) based on Rule

605 data for a given stock is calculated as follows:

,

Effective spread can be interpreted as the total price impact of a trade and can be decomposed

into a permanent and a temporary component (Boehmer 2005 p. 560). Realized spread can be

considered a temporary or non-informational price impact. Thus, Proportional price impact reflects

the private information conveyed by informed traders. Rule 605 data is reported monthly. Thus, for the

pre-fraud period (Before [-12,-1]), the price impact is calculated as the mean of the price impact on

months -12 through -1. For the post-fraud period (After [0,+12]), the price impact is calculated as the

21

mean of the price impact on months 0 through +12. We removed firms that did not have price impact

data for both periods. Our sample size has been reduced significantly since the Rule 605 data is

available only from 2001 until 2005.

In Model 1 of Table 10, the dependent variable is Static price impact. Post fraud dummy is

0.223 and is statistically significant at 10%. In Model 2 of Table 10, the dependent variable is

Proportional price impact (%). Post fraud dummy is 0.033 and is statistically significant at 1%. The

results show that the revelation of fraud increases information asymmetry in fraudulent firms. Thus,

our results are robust to alternative measures of liquidity.


VI. Conclusion

In this paper, we looked at whether and how corporate fraud affects the short-term and long-term

liquidity of a firm’s stock. We documented a new channel through which corporate fraud affects

shareholders' wealth (via a reduction in liquidity). We found that the revelation of corporate fraud has

an adverse effect on liquidity not only in the short term but also in the long term. In the short run, the

quoted spread (effective spread) increases by 0.138% (0.108%) after the revelation of fraud. In the

long run, the quoted spread (effective spread) increases by 0.152% (0.098%) after the revelation of

fraud.

Fraudulent activities differ in terms of complexity and the degree of harm caused to the

shareholders. Thus, we also examined the impact of the severity and complexity of fraud on firms’

liquidity. The results suggest that the complexity and severity of fraudulent activities does not further

reduce liquidity; rather, the existence of fraud is what matters to investors.

22

Apart from analyzing the effect of fraud on liquidity, we also studied the interaction effect of

corporate fraud and governance on liquidity. We examined whether firms with weak corporate

governance have more severe liquidity problems after the revelation of fraud than those with better

corporate governance. Our results suggest that the liquidity of fraudulent firms with weak governance

is more adversely affected than the liquidity of fraudulent firms with better governance. These results

highlight the importance of corporate governance as a channel through which the adverse effect of

fraud on liquidity can be moderated.

23

TABLE 1. Description of Our Fraud Sample.

Our fraud sample consists of 202 SEC enforcement actions from 1993 through 2005. The violation period

extends from the beginning of the fraud until the end of the fraudulent activity. The first public revelation date

is the earliest date that information about the fraud became public. First public revelation dates are taken from

the Karpoff et al. (2008a, b) database. First public revelation events include firm disclosures, internal

investigations, restatements, auditor changes, management changes, third-party actions, SEC filing delays,

whistle-blower charges, related investigations, related litigation, class action lawsuit filings, informal inquiries,

formal investigations, and Wells Notices. Panel B reports the number of events each year by first public

revelation event types.

Panel A:

Violation Period

(Days)

Violation Beginning

to First Public

Announcement

(Days)

Violation Ending

to First Public

Announcement

(Days)

Year Number of Events Mean Median Mean Median Mean Median

1993 13 768.38 546 928.46 748 160.08 121

1994 22 561.77 499.5 690.45 650.5 128.68 61.50

1995 12 841.42 729 807.58 820.5 -33.83 34

1996 12 978 820.5 982.58 992.5 4.58 3.50

1997 16 929.5 821 978.69 764 49.19 75

1998 20 901.45 577 970.6 667.5 69.15 79

1999 14 819.14 729 688.79 648 -130.36 20

2000 28 756.57 683.5 804.54 729 47.96 47

2001 17 778 546 751.35 657 -26.65 57

2002 30 884.27 730 872.87 819.5 -11.40 29

2003 9 1026.6 1094 1101.11 1133 74.56 107

2004 5 900 456 966.4 505 66.40 61

2005 4 2022.3 1748.5 1930.25 1830.5 -92 -4.50

Total 202 848.1 729 878.24 760 30.14 47

24

Panel B:

Year Announce

ment

Auditor

Change/With

drawn Audit

Internal

Investigation

Management

Change

Registration/

Reports

Related

Investigat

ion

Related

Litigation

Restat

ement

Third

Party

Unusual

Trading

Class

Action

Lawsuit

Filing

Informal

Inquiry

Formal

Investigat

ion

Wells

Notice

1993 4 0 2 1 0 0 1 1 0 1 0 3 0 0

1994 2 4 7 1 0 0 2 5 0 0 0 1 0 0

1995 4 0 3 0 0 0 1 2 0 0 1 0 1 0

1996 2 1 3 0 2 0 0 2 1 0 0 0 0 1

1997 0 2 7 0 1 0 1 5 0 0 0 0 0 0

1998 5 1 8 0 2 0 0 3 1 0 0 0 0 0

1999 5 0 0 0 1 0 0 7 0 0 1 0 0 0

2000 11 0 6 2 3 0 0 5 0 0 0 0 1 0

2001 6 0 4 0 1 0 1 2 3 0 0 0 0 0

2002 11 1 4 1 1 2 1 4 3 0 0 1 1 0

2003 2 0 4 0 0 0 1 0 1 0 0 1 0 0

2004 0 0 4 0 0 0 0 1 0 0 0 0 0 0

2005 0 0 2 1 0 0 0 0 0 0 0 1 0 0

Total 52 9 54 6 11 2 8 37 9 1 2 7 3 1

25

TABLE 2. Distribution of the Fraud Sample by Industry.

This table shows the distribution of the fraud sample by industry based on two-digit SIC code.

Two-Digit SIC Code Industry Number of Firms

13 Oil and Gas Extraction 3

14 Nonmetallic Minerals, Except Fuels 1

20 Food and Kindred Products 5 22 Textile Mill Products 1

23 Apparel and Other Textile Products 5

26 Paper and Allied Products 3 27 Printing and Publishing 3

28 Chemical and Allied Products 8

32 Stone, Clay, and Glass Products 1 33 Primary Metal 2

34 Fabricated Metal Products 5

35 Industrial Machinery and Equipment 18 36 Electronic and Other Electric Equipment 20

37 Transportation Equipment 2 38 Instruments and Related Products 14

39 Miscellaneous Manufacturing 1

45 Transportation by Air 1 47 Transportation Services 1

48 Communications 6

49 Electric, Gas, and Sanitary Services 9 50 Wholesale Trade - Durable Goods 7

51 Wholesale Trade - Nondurable Goods 8

53 General Merchandise Stores 1 56 Apparel and Accessory Stores 1

57 Furniture and Home Furnishings Stores 1

58 Eating and Drinking Places 1 59 Miscellaneous Retail 6

60 Depository Institutions 7

61 Nondepository Institutions 3 62 Security and Commodity Brokers 1

63 Insurance Carriers 2

64 Insurance Agents, Brokers, and Services 1 67 Holding and Other Investment Offices 2

72 Personal Services 2

73 Business Services 41 76 Miscellaneous Repair Services 1

79 Amusement and Recreation Services 2

80 Health Services 3 87 Engineering and Management Services 3

Total 202

26

TABLE 3. Liquidity Response to the Revelation of Fraud.

This table shows summary statistics for the 1-day quoted spread (%), effective spread (%), and trading halt for

important dates in our fraud sample from 1993 to 2005. Quoted Spread (%) is the average trade-weighted half-spread

of a stock. Effective Spread (%) is the average trade-weighted effective half-spread of the stock. Halt is a dummy

variable that is equal to 1 if the firm experienced a trading halt and 0 otherwise. Panel A presents quoted spread (%),

effective spread (%), and trading halt for the first public announcement date, which is the earliest date that

information about the fraud was available to the public. Most of the first public announcements are SEC-identified

events such as firm disclosures, auditor changes, internal investigations, management changes, SEC filing delays,

related investigations, related litigation, restatements, and third-party actions. In 11 cases, the first public revelation

date is identified in the Karpoff et al. (2008a, b) database. Other first revelation events consist of announcements of

SEC informal inquiries, formal investigations, Wells Notices, and class action lawsuits. Panel B reports on important

follow-up announcements about the frauds. We only use those firms that have at least three announcements in Panel

B. These follow-up announcements include notices of informal SEC inquiries, formal SEC investigations, Wells

Notices, initiation of regulatory proceedings, initiation of class action lawsuits, and bankruptcy. In Panel C, we

divide our sample into low, medium, and high levels based on market cap, volatility, and volume and show quoted

spread (%), effective spread (%), and trading halt for the first public announcement date.

N Quoted

Spread (%)

Effective

Spread (%)

N Halt

Panel A: First Public Announcement

All first public announcements 181 1.205 0.913 186 0.29

SEC-identified trigger events 170 1.223 0.924 174 0.293

-Announcement 50 1.437 0.977 50 0.36

-Auditor change/withdrawn audit 8 3.183 2.098 9 0.111

-Internal investigation 47 0.982 0.791 48 0.438

-Management change 5 0.737 0.695 5 0.2

-Registration/reports 9 1.142 1.02 9 0.222

-Related investigation 2 0.161 0.144 2 0

-Related litigation 6 0.442 0.299 7 0

-Restatement 33 1.278 1.054 34 0.206

-Third party 9 0.507 0.432 9 0.111

-Unusual trading 1 0.737 0.858 1 0

Other first revelation events 11 0.935 0.744 12 0.25

-SEC informal inquiry 5 0.625 0.464 6 0.167

-SEC formal investigation 3 1.096 0.952 3 0.333

-SEC Wells Notice 1 1.745 1.428 1 0

-Class action lawsuits begin 2 1.062 0.788 2 0.5

Panel B: Important Follow-Up Announcements

2nd announcement 108 0.755 0.604 112 0.191

3rd announcement 108 0.738 0.576 112 0.134

Panel C: First Public Announcement by Market Cap, Volatility, and Volume

N Quoted

Spread (%)

Effective

Spread (%)

N Halt

Low Market Cap 57 2.682 1.917 62 0.210

Medium Market Cap 61 0.725 0.623 61 0.295

27

High Market Cap 63 0.334 0.286 63 0.365

Low Volatility 62 0.467 0.420 66 0.273

Medium Volatility 58 0.886 0.740 59 0.373

High Volatility 61 2.259 1.579 61 0.230

Low Volume 57 1.848 1.437 62 0.226

Medium Volume 61 1.312 0.877 61 0.311

High Volume 63 0.521 0.475 63 0.333

28

TABLE 4. Liquidity around the Revelation of Fraud.

Panel A of this table shows the mean quoted spread (%) and effective spread (%) for our fraud sample during the 401-

trading-day window around the revelation of fraud. Day 0 is the trading day on which the fraud was publicly revealed.

Quoted spread (%) is the average trade-weighted half-spread of a stock. Effective spread (%) is the average trade-

weighted effective half-spread of the stock. Number of events indicates the number of firms used in calculating the

mean for quoted spread (%) and effective spread (%) for each trading day. Number of events changes due to the limited

availability of data on liquidity. Panel B shows the difference in spread across time. The notations ***, **, and * in

Panel B represent 1%, 5%, and 10% significance levels, respectively.

Panel A:

Trading Day Relative to

the Revelation of Fraud

Number of

Events

Quoted

Spread (%)

Effective

Spread (%)

-200 155 0.596 0.463

-180 158 0.593 0.478

-160 158 0.614 0.472

-140 159 0.56 0.444

-120 165 0.604 0.48

-100 172 0.637 0.498

-80 177 0.651 0.505

-60 179 0.754 0.579

-40 178 0.625 0.495

-20 184 0.67 0.549

-10 185 0.733 0.569

-9 185 0.711 0.57

-8 186 0.693 0.546

-7 185 0.684 0.54

-6 184 0.716 0.571

-5 183 0.673 0.516

-4 188 0.733 0.567

-3 186 0.756 0.616

-2 186 0.804 0.61

-1 177 0.782 0.614

0 181 1.205 0.913

1 182 1.09 0.864

2 178 0.977 0.728

3 176 0.997 0.748

4 172 0.956 0.728

5 173 0.985 0.744

6 169 0.93 0.689

7 168 0.914 0.716

8 164 0.845 0.661

9 163 0.97 0.677

10 159 0.864 0.62

29

20 153 0.797 0.633

40 133 0.815 0.604

60 130 0.727 0.601

80 129 0.832 0.654

100 129 0.726 0.576

120 116 0.643 0.513

140 117 0.663 0.539

160 116 0.71 0.597

180 117 0.672 0.556

200 113 0.625 0.53

Panel B:

Quoted Spread (%) Effective Spread (%)

Day + 1 minus Day - 1 0.308** 0.25**

Day + 2 minus Day - 2 0.173 0.118

Day + 3 minus Day - 3 0.241* 0.132

Day + 4 minus Day - 4 0.223* 0.161**

Day + 5 minus Day - 5 0.312*** 0.228***

30

TABLE 5. Changes in Liquidity around the Revelation of Fraud.

This table shows changes in liquidity around the revelation of fraud. Panel A presents short-term

changes in liquidity. The means of those variables from day -5 to day -1, Before [-5,-1], are used to

calculate short-term liquidity levels for the pre-revelation period. The means of the variables from day

0 to day +5, After [0,+5], are used to calculate short-term liquidity levels for the period following the

revelation of fraud. Panel B shows long-term changes in liquidity. The means of the variables from

day -200 to day -6, Before [-200,-6], are used to calculate long-term liquidity levels for the pre-

revelation period. The means of the variables from day +6 to day +200, After [+6,+200], are used to

calculate long-term liquidity for the period following the revelation of fraud. Difference (After -

Before) indicates short-term and long-term changes in liquidity. The notations ***, **, and * represent

1%, 5%, and 10% significance levels, respectively.

Panel A: Short-Term Changes

Before [-5,-1] After [0,+5] Difference (After - Before)

Quoted Spread (%) 0.797 1.103 0.306***

Effective Spread (%) 0.607 0.843 0.236***

Halt 0.008 0.1 0.092***

Panel B: Long-Term Changes

Before [-200,-6] After [+6,+200] Difference (After - Before)

Quoted Spread (%) 0.835 1.149 0.314***


Halt 0.005 0.01 0.005***

31

TABLE 6. The Impact of the Severity of Fraud on Liquidity.

This table shows the impact of the severity of fraud on liquidity for both short-term and long-term windows. We use several indicator variables to

measure the severity of fraud (Karpoff et al. 2007). Insider trading dummy is an indicator variable taking the value of 1 for enforcement actions that include allegations of insider trading. Department of Justice dummy is an indicator variable taking the value of 1 for enforcement actions that include one

or more civil or criminal proceedings by the DOJ. Cooperated in investigation dummy is an indicator variable taking the value of 1 for firms identified by

regulators in the enforcement action as having self-reported, cooperated, or taken remedial actions.. Initial public offering dummy is an indicator variable taking the value of 1 for enforcement actions associated with a firm’s initial public offering. Foreign company dummy is an indicator variable taking the

value of 1 for foreign firms. Bankruptcy dummy is an indicator variable taking the value of 1 for firms that filed for bankruptcy. Panel A presents the

impact of severity of fraud on short-term changes in liquidity. Panel B shows the impact of severity of fraud on long-term changes in liquidity. Difference (After - Before) shows short-term and long-term changes in liquidity calculated by subtracting pre-fraud levels from post-fraud levels. The notations ***,

**, and * represent 1%, 5%, and 10% significance levels, respectively.

Panel A: Short-Term Changes

Severity of Fraud Difference

(After - Before)

Difference

(After - Before)

Difference-

in-Difference

Insider trading dummy 1 0

Quoted Spread (%) 0.463 0.242 0.221*

Effective Spread (%) 0.376 0.179 0.197**

Halt 0.082 0.097 -0.016

Department of Justice dummy 1 0

Quoted Spread (%) 0.334 0.286 0.048

Effective Spread (%) 0.244 0.23 0.014

Halt 0.118 0.074 0.044

Cooperated in investigation dummy 1 0

Quoted Spread (%) 0.327 0.296 0.031

Effective Spread (%) 0.215 0.246 -0.032

Halt 0.091 0.094 -0.002

Initial public offering dummy 1 0

Quoted Spread (%) 1.138 0.264 0.874***


Halt 0.213 0.086 0.127**

Foreign company dummy 1 0

Quoted Spread (%) 0.575 0.293 0.283


Halt 0.122 0.091 0.031

Bankruptcy dummy 1 0

Quoted Spread (%) 0.479 0.232 0.247**


Halt 0.121 0.081 0.04

32

Panel B: Long-Term Changes

Severity of Fraud Difference

(After - Before)

Difference

(After - Before)

Difference-

in-Difference

Insider trading dummy 1 0

Quoted Spread (%) 0.413 0.276 0.137


Halt 0.013 0.002 0.011**

Department of Justice dummy 1 0

Quoted Spread (%) 0.247 0.362 -0.115


Halt 0.012 0.001 0.011***

Cooperated in investigation dummy 1 0

Quoted Spread (%) 0.232 0.359 -0.128


Halt 0.009 0.003 0.006

Initial public offering dummy 1 0

Quoted Spread (%) 1.309 0.27 1.039***


Halt -0.004 0.006 -0.01

Foreign company dummy 1 0

Quoted Spread (%) 0.171 0.321 -0.15


Halt 0.007 0.005 0.002

Bankruptcy dummy 1 0

Quoted Spread (%) 0.501 0.241 0.26*


Halt 0.009 0.004 0.005

33

TABLE 7. The Effect of Corporate Fraud on the Short-Term Liquidity.

This table shows the effect of fraud on short-term liquidity. We use Quoted spread (%) and Effective spread (%) as dependent variables (inverse liquidity measures).

(1)

where Post fraud is a dummy variable equal to 1 after the revelation of fraud and 0 otherwise, Price volatility is the standard deviation of returns, Price is the mean trade price, Volume is the trading volume, and

Firm size is the dollar market capitalization of the stock. Insider trading dummy is an indicator variable taking the value of 1 for enforcement actions that include allegations of insider trading. Department of Justice

dummy is an indicator variable taking the value of 1 for enforcement actions that include one or more civil or criminal proceedings by the DOJ. Cooperated in investigation dummy is an indicator variable taking the value of 1 for firms identified by regulators in the enforcement action as having self-reported, cooperated, or taken remedial actions. Initial public offering dummy is an indicator variable taking the value of 1 for

enforcement actions associated with a firm’s initial public offering. Foreign company dummy is an indicator variable taking the value of 1 for foreign firms. Regulatory penalties are the total fines assessed against

the firm and its agents by the SEC, DOJ, and state attorneys general. Bankruptcy dummy is an indicator variable taking the value of 1 for firms that filed for bankruptcy. The # Respondents variable represents the number of individuals named in the enforcement proceedings. The # Proceedings variable represents the total number of administrative, civil, and criminal proceedings in the enforcement action. In Panel A, the

dependent variable is Quoted spread (%), which is the average trade-weighted half-spread of a stock. In Panel B, the dependent variable is Effective spread (%), which is the average trade-weighted effective half-

spread of the stock. There are two observations for every firm. For the pre-fraud period (Before [-5,-1]), short-term liquidity is calculated as the mean of the liquidity on days -5 through -1. For the post-fraud period (After [0,+5]), short-term liquidity is calculated as the mean of the liquidity on days 0 through +5. Pre and post averages for the control variables are calculated the same way as the liquidity measures. We removed

firms that did not have liquidity data for both periods, leaving a sample of 185 firms. Standard errors are clustered at the two-digit SIC industry code. The notations ***, **, and * represent 1%, 5%, and 10%

significance levels, respectively.

Panel A: Quoted Spread (%)

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Constant 3.675*** 3.68*** 3.658*** 3.72*** 3.685*** 3.691*** 3.735*** 3.667*** 3.677*** 3.581***

Post fraud 0.138*** 0.122** 0.171** 0.094* 0.088* 0.116*** 0.116*** 0.127** 0.136*** 0.193***

Price volatility 4.475** 4.448** 4.473** 4.499** 4.029** 4.463** 4.475** 4.474** 4.474** 4.492**

Log of price -0.993*** -0.991*** -0.999*** -0.992*** -0.979*** -0.991*** -0.991*** -0.991*** -0.993*** -1.004***

Log of volume -0.115*** -0.119*** -0.115*** -0.116*** -0.121*** -0.116*** -0.114*** -0.116*** -0.115*** -0.112***

Log of market capitalization 0.057 0.061 0.06 0.055 0.063 0.057 0.052 0.059 0.057 0.064

Post fraud * Insider trading dummy 0.073

Post fraud * Department of Justice dummy -0.082

Post fraud * Cooperated in investigation dummy 0.133

Post fraud * Initial public offering dummy 1.222

Post fraud * Foreign company dummy 0.494

Post fraud * Log of regulatory penalties 0.005

Post fraud * Bankruptcy dummy 0.043

Post fraud * # Respondents 0

Post fraud * # Proceedings -0.01

Observations 370 370 370 370 370 370 370 370 370 370

R-squared 0.496 0.497 0.497 0.498 0.516 0.500 0.497 0.496 0.496 0.497

34

Panel B: Effective Spread (%)

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Constant 2.821*** 2.829*** 2.815*** 2.848*** 2.825*** 2.83*** 2.857*** 2.808*** 2.83*** 2.761***

Post fraud 0.108*** 0.083** 0.12* 0.081* 0.086** 0.095*** 0.095*** 0.091** 0.1*** 0.143***

Price volatility 3.026** 2.984** 3.025** 3.041** 2.83** 3.019** 3.027** 3.024** 3.023** 3.037**

Log of price -0.668*** -0.665*** -0.67*** -0.667*** -0.662*** -0.666*** -0.666*** -0.665*** -0.667*** -0.674***

Log of volume -0.072*** -0.079*** -0.072*** -0.073*** -0.075*** -0.073*** -0.072*** -0.074*** -0.073*** -0.07**

Log of market capitalization 0.013 0.018 0.013 0.011 0.015 0.013 0.009 0.015 0.012 0.017

Post fraud * Insider trading dummy 0.116


Post fraud * Cooperated in investigation dummy 0.083





Post fraud * # Respondents 0.001


Observations 370 370 370 370 370 370 370 370 370 370

R-squared 0.582 0.583 0.582 0.583 0.590 0.585 0.582 0.582 0.582 0.583

35

TABLE 8. The Effect of Corporate Fraud on Long-Term Liquidity.

Panel A: Quoted Spread (%)

This table shows the effect of fraud on long-term liquidity. We use Quoted spread (%) and Effective spread (%) as dependent variables (inverse liquidity measures).

(1)

where Post fraud is a dummy variable equal to 1 after the revelation of fraud and 0 otherwise, Price volatility is the standard deviation of returns, Price is the mean trade price, Volume is the trading volume, and

Firm size is the dollar market capitalization of the stock. Insider trading dummy is an indicator variable taking the value of 1 for enforcement actions that include allegations of insider trading. Department of Justice

dummy is an indicator variable taking the value of 1 for enforcement actions including one or more civil or criminal proceedings by the DOJ. Cooperated in investigation dummy is an indicator variable taking the value of 1 for firms identified by regulators in the enforcement action as having self-reported, cooperated, or taken remedial actions. Initial public offering dummy is an indicator variable taking the value of 1 for

enforcement actions associated with a firm’s initial public offering. Foreign company dummy is an indicator variable taking the value of 1 for foreign firms. Regulatory penalties are the total fines assessed against a

firm and its agents by the SEC, DOJ, and state attorneys general. Bankruptcy dummy is an indicator variable taking the value of 1 for firms that filed for bankruptcy. The variable # Respondents represents the number of individuals named in the enforcement proceedings. The variable # Proceedings represents the total number of administrative, civil, and criminal proceedings in the enforcement action. In Panel A, the

dependent variable is Quoted spread (%), which is the average trade-weighted half-spread of a stock. In Panel B, the dependent variable is Effective spread (%), which is the average trade-weighted effective half-

spread of the stock. There are two observations for every firm. For the pre-fraud period (Before [-200,-6]), long-term liquidity is calculated as the mean of the liquidity on days -200 through -6. For the post-fraud period (After [+6,+200]), long-term liquidity is calculated as the mean of the liquidity on days +6 through +200. Pre and post averages for the control variables are calculated the same way as the liquidity measures.

We removed firms that did not have liquidity data for both periods, leaving a sample of 188 firms. Standard errors are clustered at the two-digit SIC industry code. The notations ***, **, and * represent 1%, 5%, and

10% significance levels, respectively.

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Constant 2.122*** 2.116*** 2.098*** 2.117*** 2.105*** 2.122*** 2.0916*** 2.107*** 2.08*** 1.973***

Post fraud 0.152** 0.169** 0.193** 0.161* 0.106** 0.149** 0.162*** 0.139* 0.182** 0.232***

Price volatility 9.495*** 9.517*** 9.514*** 9.486*** 9.277*** 9.499*** 9.5082*** 9.497*** 9.535*** 9.573***

Log of price -1.031*** -1.032*** -1.035*** -1.032*** -1.033*** -1.032*** -1.033*** -1.029*** -1.037*** -1.053***

Log of volume -0.171*** -0.167*** -0.17*** -0.171*** -0.179*** -0.171*** -0.1701*** -0.174*** -0.168*** -0.161***

Log of market capitalization 0.198*** 0.195*** 0.2*** 0.199*** 0.209*** 0.198*** 0.2001*** 0.201*** 0.2*** 0.204***

Post fraud * Insider trading dummy -0.068


Post fraud * Cooperated in investigation dummy -0.024



Post fraud * Log of regulatory penalties -0.0023


Post fraud * # Respondents -0.005


Observations 376 376 376 376 376 376 376 376 376 376

R-squared 0.704 0.705 0.705 0.704 0.719 0.704 0.704 0.704 0.705 0.707

36

Panel B: Effective Spread (%)

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Constant 1.851*** 1.845*** 1.844*** 1.848*** 1.842*** 1.851*** 1.871*** 1.843*** 1.821** 1.763***

Post fraud 0.098** 0.113** 0.109** 0.102* 0.075* 0.098** 0.0917** 0.091* 0.118** 0.144**

Price volatility 6.618*** 6.638*** 6.624*** 6.614*** 6.507*** 6.619*** 6.6092*** 6.619*** 6.646*** 6.664***

Log of price -0.737*** -0.738*** -0.738*** -0.738*** -0.738*** -0.737*** -0.7361*** -0.736*** -0.741*** -0.75***

Log of volume -0.123*** -0.12*** -0.123*** -0.123*** -0.128*** -0.123*** -0.1239*** -0.125*** -0.121*** -0.117***

Log of market capitalization 0.123*** 0.12*** 0.123*** 0.123*** 0.129*** 0.123*** 0.1215*** 0.124*** 0.124*** 0.126***

Post fraud * Insider trading dummy -0.062


Post fraud * Cooperated in investigation dummy -0.011





Post fraud * # Respondents -0.004


Observations 376 376 376 376 376 376 376 376 376 376

R-squared 0.737 0.737 0.737 0.737 0.744 0.737 0.737 0.737 0.737 0.738

37

TABLE 9. The Interaction Effect of Corporate Fraud and Governance on Liquidity.

This table shows the regression results from the interaction effect of corporate fraud and governance on short-term and long-term liquidity. We use Quoted spread (%) and Effective spread (%) as dependent variables (inverse liquidity measures). Quoted spread (%) is the average trade-weighted half-spread of a stock. Effective

spread (%) is the average trade-weighted effective half-spread of the stock.

(1)

where Post fraud is a dummy variable equal to 1 after the revelation of fraud and 0 otherwise, Price volatility is the standard deviation of returns, Price is the average trade price, Volume is the trading volume, and Firm size is the dollar market capitalization of the stock. Outside director % is the percentage of independent directors

on the firm's board. Institutional holdings % is the percentage of ownership of institutions. Major accounting firm dummy is an indicator variable taking the value of

1 for firms with an auditor from among the 8/6/5/4 major accounting firms. In Panel A, we look at the interaction effect of corporate fraud and governance on short-term liquidity. For the pre-fraud period (Before [-5,-1]), short-term liquidity is calculated as the mean of the liquidity on days -5 through -1. For the post-fraud period

(After [0,+5]), short-term liquidity is calculated as the mean of the liquidity on days 0 through +5. The sample in Panel A contains 185 firms. There are two

observations for every firm. In Panel B, we look at the interaction effect of corporate fraud and governance on long-term liquidity. For the pre-fraud period (Before [-200,-6]), long-term liquidity is calculated as the mean of the liquidity on days -200 through -6. For the post-fraud period (After [+6,+200]), long-term liquidity is

calculated as the mean of the liquidity on days +6 to +200. The sample in Panel B contains 188 firms. Pre and post averages for the control variables are calculated

the same way as the liquidity measures. We removed firms which did not have liquidity data for both periods. Standard errors are clustered at the two-digit SIC

industry code. The notations ***, **, and * represent 1%, 5%, and 10% significance levels, respectively.

Panel A: Short-Term Liquidity

Quoted

Spread (%)

Effective

Spread (%)

Quoted

Spread (%)

Effective

Spread (%)

Quoted

Spread (%)

Effective

Spread (%)

[1] [2] [3] [4] [5] [6]

Constant 3.535*** 2.748*** 3.634*** 2.772*** 3.594*** 2.733***

Post fraud 0.458** 0.357*** 0.271*** 0.219*** 0.415** 0.346***

Price volatility 4.459** 2.99** 4.505** 3.035** 4.437** 3.005**

Log of price -0.988*** -0.66*** -1.007*** -0.671*** -0.987*** -0.663***

Log of volume -0.117** -0.077** -0.115*** -0.071*** -0.121*** -0.075***

Log of market capitalization 0.06 0.019 0.053 0.01 0.08 0.025

Post fraud * Outside director % -0.554** -0.422**

Outside director % 0.226 0.059

Post fraud * Institutional holdings % -0.393** -0.319***

Institutional holdings % 0.36** 0.21*

Post fraud * Major accounting firm dummy -0.308 -0.27***

Major accounting firm dummy -0.175 -0.051

Observations 370 370 370 370 370 370

R-squared 0.498 0.585 0.499 0.584 0.506 0.591

38

Panel B: Long-Term Liquidity

Quoted

Spread (%)

Effective

Spread (%)

Quoted

Spread (%)

Effective

Spread (%)

Quoted

Spread (%)

Effective

Spread (%)

[1] [2] [3] [4] [5] [6]

Constant 1.911*** 1.709*** 2.022*** 1.786*** 1.991*** 1.775***

Post fraud 0.678*** 0.493*** 0.34*** 0.215** 0.657** 0.44**

Price volatility 9.447*** 6.561*** 9.502*** 6.613*** 9.324*** 6.483***

Log of price -1.013*** -0.722*** -1.032*** -0.735*** -1.022*** -0.731***

Log of volume -0.175*** -0.127*** -0.169*** -0.122*** -0.181*** -0.132***

Log of market capitalization 0.201*** 0.127*** 0.197*** 0.123*** 0.224*** 0.144***

Post fraud * Outside director % -0.907*** -0.68***

Outside director % 0.306 0.185

Post fraud * Institutional holdings % -0.528** -0.324*

Institutional holdings % 0.248 0.12

Post fraud * Major accounting firm dummy -0.568* -0.383*

Major accounting firm dummy -0.105 -0.116

Observations 376 376 376 376 376 376

R-squared 0.710 0.744 0.707 0.739 0.720 0.754

39

TABLE 10. Robustness Check.

This table examines the impact of fraud revelation on information asymmetry by using direct measures of

information asymmetry. To calculate the information asymmetry measures, we used monthly data disclosed under

Securities and Exchange Commission Rule 605 of Regulation NMS from 2001 to 2005. We used two different high-

frequency price impact measures as proxies for information asymmetry. Our first price impact measure, Static Price

Impact (605), was developed by Goyenko, Holden, and Trzcinka (2009). It is defined as the difference in the

effective spread between large and small orders divided by the difference in the average share size between large

and small orders. Static Price Impact (605) is in dollars/share. Our second price impact measure was used by

Boehmer (2005) and Bessembinder (2003). Proportional Price Impact (605) is half the difference between effective

and realized spreads. Rule 605 data is reported monthly. For the pre-fraud period (Before [-12,-1]), price impact is

calculated as the mean of the price impact on months -12 through -1. For the post-fraud period (After [0,+12]), price

impact is calculated as the mean of the price impact on months 0 through +12. We removed firms that did not have

price impact data for both periods. Our sample size has been reduced significantly since the Rule 605 data is

available only from 2001 until 2005. Standard errors are clustered at the two-digit SIC industry code. The notations

***, **, and * represent 1%, 5%, and 10% significance levels, respectively.

Static Price

Impact (605)

Proportional Price

Impact (605)

[1] [2]

Constant 5.045*** 0.66***

Post fraud 0.223* 0.033***

Price volatility 23.616*** 5.454***

Log of price -0.74*** -0.151***

Log of volume -0.598** -0.065**

Log of market capitalization 0.348* 0.042*

Observations 60 62

R-squared 0.667 0.789

40

Figure I. Timeline of an SEC Enforcement Action.

This figure, replicated from Karpoff et al. (2008a), illustrates the sequence of an enforcement action.

Violation Begins

Violation Ends

Trigger Event

Initial Filing of a

Private Lawsuit

Inquiry Event

Investigation Event

Wells Notice

Initial Regulatory Proceeding

Concluding Regulatory Proceeding

41

Figure II. Liquidity Around the Revelation of Fraud.

This figure presents the mean quoted spread (%) and effective spread (%) for our fraud sample during the

401-trading-day window around the revelation of fraud. Day 0 is the trading day in which the fraud was

publicly revealed. Quoted spread (%) is the average trade-weighted half-spread of a stock. Effective

spread (%) is the average trade-weighted effective half-spread of a stock.

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

-200 -150 -100 -50 0 50 100 150 200

Trading day relative to the revelation of fraud

Quoted Spread (%) Effective Spread (%)

42

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Documents

CORPORATE FRAUD AND LIQUIDITY - fmaconferences.org · The data set contains 81 variables. Some of those variables include: daily share volume, number of trades, opening quote, average