Electronic copy available at: http://ssrn.com/abstract=2427530

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Stock Liquidity and Earnings Management*

Jing Fang+

School of Accounting and Finance

The Hong Kong Polytechnic University

* I am grateful to Alex Edmans and Vivian W. Fang for their insightful comments on the previous version

of this paper, to members of my dissertation committee (C.S. Agnes Cheng (chair), Dana Hollie, Joseph

Legoria, and Ji-Chai Lin), to Joshua Ronen for stimulating ideas that underlie several additional tests of

this paper, and to seminar participants at the Louisiana State University and the AAA 2012 annual

meeting for their helpful comments. + Email: robert.j.fang@polyu.edu.hk, School of Accounting and Finance, the Hong Kong Polytechnic

University, Hong Kong.

Electronic copy available at: http://ssrn.com/abstract=2427530

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Stock Liquidity and Earnings Management

Abstract

We find a negative relation between stock liquidity and earnings management. Our

finding is insensitive to specific stock liquidity and earnings management measures used and

remains strong after controlling for a comprehensive list of possible covariates including firm

fixed effects. We use the 2001 decimalization as a quasi-experiment to run a difference-in-

differences analysis (DiD) and find that firms experiencing greater stock liquidity improvement

encounter significantly greater drop in earnings management after the decimalization than their

matched pairs that are otherwise similar. Our DiD finding suggests a causal negative effect of

stock liquidity on earnings management. Using the matched sample for the DiD analysis we also

find differential changes in analyst followings and efforts, institutional ownership, and stock

price efficiency surrounding the decimalization, which is consistent with our hypothesized

mechanisms through which stock liquidity deters earnings management.

JEL classification: G14, M12, M41, M52.

Keywords: Stock Liquidity, Earnings Management, Difference in Differences, Decimalization.

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1. Introduction

In this study, we examine the effect of stock liquidity on accounting-based earnings

management.1 We hypothesize a negative between stock liquidity and earnings management.

One of the most cited motives for managers to manage reported earnings is that managers

attempt to maneuver investors expectations about their firms economic prospects through

earnings management and move their firms stock prices in their preferred directions at their

desired magnitudes. Stock liquidity encourages private information production by investors by

increasing the marginal value of information, shift shareholder base toward large, sophisticated

investors by inducing the formation of blockholdings, and spur arbitrage by reducing costs and

risks related to arbitrage. Therefore, we reason that stock liquidity makes it difficult for managers

to move their firms stock prices through earnings management. Moreoever, earnings

management is value-destroying because it consumes valuable organizational resources,

especially top executives limited time. Stock liquidity ensures that stock prices could timely and

faithfully reflect the value-destroying consequence of earnings management. Therefore, we

reason that stock liquidity also makes it less beneficial for managers to engage in earnings

management by timely and faithfully revealing the value-destroying consequence of earnings

management in stock prices.

We find a negative relation between stock liquidity and earnings management. Our

finding is robust to specific stock liquidity and earnings management measures used. In the main

test, we find a negative relation between the absolute value of discretionary accruals and the

stock liquidity measure proposed in Corwin and Schultz (2012). Using the stock liquidity

measure proposed in Hasbrouck (2009), our finding remains unchanged. Moreover, we find that

stock liquidity is also negatively related to the likelihood of a SEC Accounting and Auditing

Enforcement Release (AAER) and the likelihood of an accounting restatement. More importantly,

we show that the negative relation between stock liquidity and the absolute value of discretionary

accruals remains strong after controlling for a long list of possible covariates and firm - / year - /

1 In all following paragraphs, we mean accounting-based earnings management by earnings management unless

stated otherwise.

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industry- fixed effects, suggesting that our finding may not be confounded by omitted correlated

variables.

To establish causality, we adopt the difference-in-differences (DiD) approach. We use the

decimalization surrounding 2001 as a quasi-experiment to examine how the exogenous variation

in stock liquidity generated by the decimalization affects changes in earnings management.

Specifically, we match firms with changes in stock liquidity in the top one-third (namely the

treatment firms) with firms with changes in stock liquidity in the bottom one-third (namely the

control firms) by using a one-to-one nearest neighbor propensity score matching without

replacement. Our matching ensures that the treatment firms and the control firms are similar

along a host of characteristics right before the decimalization.

Using the DiD approach, we find that the treatment firms experience a significant drop in

the absolute value of discretionary accruals while the control firms experience no significant

change in the absolute value of discretionary accruals. In addition, we find no significant changes

in the absolute value of discretionary accruals for both the treatment firms and the control firms

in the year immediately prior to the decimalization. We also find no significant difference

between the treatment firms and the control firms regarding the change in the absolute value of

discretionary accruals in the year immediately prior to the decimalization. Overall, our finding

from the DiD analysis suggests that stock liquidity has a causal negative effect on earnings

management.

We use the matched sample constructed for the DiD analysis to validate underlying

mechanisms through which stock liquidity dampens managers incentives to manage reported

earnings. We use financial analysts information production efforts as a rough indicator of

overall information production efforts by market participants. We find that the control firms

experience significant decrease in analyst followings after the decimalization while the treatment

firms experience no significant change in analyst followings. We also find that the treatment

firms experience significant increase in analyst efforts while the control firms experience no

significant change in analyst efforts.

With respect to the effect of stock liquidity on shareholder base composition, we find that

the treatment firms experience significantly greater increase in institutional holdings owned by

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transient investors that are found to be better informed than other types of institutional investors

and by dedicated investors that arguably have greater incentives to deter earnings management.

Nevertheless, we find that both the treatment firms and the control firms experience similar

significant decreases in institutional holdings owned by quasi-indexing investors that arguably

have no incentives to engage in active information production or monitoring.

Regarding the effect of stock liquidity on arbitrage, we find that both the treatment firms

and the control firms experience significant increases in short interests while increases in short

interests are not significantly different between the treatment firms and the control firms. We

argue that it may not be surprising to observe no significant difference between the treatment

firms and the control firms regarding increases in short interests because the threat of ex-post

arbitrage deters ex-ante earnings management and less earnings management leaves fewer

opportunities for short arbitrage. Nevertheless, we use a broad sample and document strong

cross-section evidence about the positive relation between stock liquidity and short interests.

To examine the effect of stock liquidity on stock price efficiency, we measure stock price

efficiency as the extent to which stock prices deviate from the fundamental value of traded

stocks. We find that the treatment firms experience significant improvement in stock price

efficiency after the decimalization while the control firms experience either no significant change

or significant deterioration in stock price efficiency depending on the fundamental value measure

used. Overall, using the matched sample constructed for the DiD analysis we find that changes in

analyst followings and efforts, in institutional holdings owned by different types of investors, in

short interests, and in stock price efficiency are generally in line with our hypothesized

mechanisms.

In addition, using two different approaches to estimating the fundamental value of traded

stocks we document a significant positive relation between discretionary accruals and equity

value errors (i.e., the difference between market value and estimated fundamental value) for

firms with stock liquidity below the sample median while we find no significant relation for

firms with stock liquidity above the sample median. Our finding is consistent with our reasoning

that stock liquidity makes it difficult for managers to move stock prices in their preferred

directions at preferred magnitudes through earnings management.

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Furthermore, we find that the negative relation between stock liquidity and the absolute value

of discretionary accruals is stronger for firms whose CEOs equity portfolio delta is above the

sample median than for firms whose CEOs equity portfolio delta is below the sample median.

We measure a CEOs equity portfolio delta as the sensitivity of the CEOs equity holdings to a 1%

change in stock prices. To ensure comparability across periods and firms we scale a CEOs

equity portfolio delta by the CEO total cash compensation (i.e., the ratio of the CEOs equity

portfolio delta to the sum of the CEOs equity portfolio delta and total cash compensation).

Earnings management is costlier to managers whose equity portfolio delta is greater when stock

prices could timely reflect the value-destroying consequence of earnings management. Therefore,

our finding is consistent with our reasoning that stock liquidity makes it less beneficial for

managers to engage in earnings management by timely and faithfully showing the value-

destroying consequence of earnings management in stock prices.

Our study adds to the growing literature that examines the real effects of stock markets in

general and the role of efficient stock prices in disciplining managers in particular.2 It has long

been recognized that if stock prices could timely and faithfully reflect the consequence of

managers actions, managers would have greater incentives to take desirable actions and avoid

undesirable ones (Fama 1980; Fishman and Hagerty 1989). Our study provides evidence

that stock liquidity, the most important aspect of market microstructure with respect to its effect

on stock price efficiency (Chordia et al. 2008; OHara 2003), deters earnings management. In

our best knowledge, our study is the first empirical work that documents a causal negative effect

of stock liquidity on earnings management.

Our paper also adds to the growing literature that links stock liquidity to firm performance.

For instance, Fang et al. (2009) find a positive relation between stock liquidity and firm

performance. Studies following Fang et al. (2009) attempt to identify potential governance

channels through which stock liquidity contributes to firm value. For instance, Edmans et al.

(2013) find that liquidity facilitates governance through intervention and, to a greater degree,

through trading (also see Bharath et al. 2013). Our study suggests a new channel through which

2 Interested readers can refer to Bond et al. (2012) for their excellent review of research work that examines the real

effects of stock markets.

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stock liquidity contributes to firm value. That is, stock liquidity deters earnings management that

consumes valuable organizational resources, especially top managers limited time and attention.

Our study complements existing accounting research that examines the relation between

accounting standards and choices and market efficiency. Existing accounting research focuses on

how accounting standards and choices affect stock market efficiency. Our finding suggests that

stock liquidity and ensuing changes also affect managers decisions about accounting choices in

general and earnings management in particular. Our finding about the causal effect of stock

liquidity on earnings management suggests a more complete view about the relation between

stock market efficiency and accounting choices including earnings management. Our study

demonstrates the importance of taking into account factors affecting the price discovery process

in understanding accounting phenomena and in accounting research design (also see Lee 2001).

Our study suggests a stock liquidity-based explanation to the over-time variation in aggregate

earnings management. We find that in line with our cross-section evidence the over-time

variation in aggregate stock liquidity well accounts for the over-time variation in aggregate

earnings management. Interestingly, we find that during 2000 2005 (six years in total) decline

in aggregate absolute value of discretionary accruals occurred simultaneously with improvement

in aggregate stock liquidity ( = -0.94). Prior studies (e.g., Cohen et al. 2008) attribute the

decline in aggregate absolute value of discretionary accruals during 2002-2005 to the passage of

SOX. Our findings suggest that in addition to SOX and other concurrent events improvement in

stock liquidity and ensuing changes in information production activities, shareholder base

composition, and arbitrage may also serve as a fundamental factor that drives the decline in the

aggregate absolute value of discretionary accruals during 2000-2005.3

The rest of the paper is organized as follows. In section 2, we develop the hypothesis. Section

3 describes data sources, samples, empirical measures for stock liquidity and earnings

management, and descriptive statistics for the sample used in the main test of our hypothesis.

Section 4 presents results from the main test. Section 5 presents results from three robustness

3 Besides reductions in the minimal tick size, other concurrent changes also affect trading costs and, therefore, stock

liquidity (see Chordia et al. 2011). For instance, institutional commissions decline over time; advancement in

technology makes it easier for institutions to execute automated algorithmic trading and online brokerage accounts

make trading easier for retail investors.

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tests, the test that controls for more possible covariates and firm fixed effects, the DiD test, and

other further analyses. Section 6 concludes.

2. Hypothesis Development

Anecdotal cases, survey of executives, and findings of archival studies suggest that

managers engage in earnings management (see Dechow et al. 2011; Graham et al. 2005; Healy

and Palepu 2003). Various motives underlie managers earnings management decisions such as

avoidance of debt covenant violations, and maximization of their compensations (Fields et al.

2001; Healy and Wahlen 1999). Among the most often cited motives is that managers manage

reported earnings in an attempt to maneuver market participants expectation of their firms

economic fundamentals and move their firms stock prices in desired directions.

Findings of prior studies suggest that, at least to some extent, managers succeed in

maneuvering investors expectation of their firms economic fundamentals through earnings

management. For instance, Bartov et al. (2002) find that firms that manage reported earnings to

meet or beat analysts earnings expectations (MBE) command a valuation premium compared

with firms that do not manage reported earnings and fail to MBE. Findings with implications

similar to Bartov et al. (2002) are documented in Barth et al. (1999), Kasznik and McNichols

(2002), and Skinner and Sloan (2002).

We argue that stock liquidity affects the extent to which managers succeed in moving

stock prices toward their preferred directions at their desired magnitudes through earnings

management as a result of its effect on the value of information, the composition of shareholder

base, and information-based arbitrage. Stock liquidity reduces trading costs and assists informed

market participants in disguising their private information. Thus, stock liquidity enables market

participants to profit from private information (Holmstrm and Tirole 1993; Kyle and Vila 1991;

Maug 1998). As a result, stock liquidity motivates private information acquisition (Grossman

and Stiglitz 1980; Holmstrm and Tirole 1993). We reason that when stock liquidity is high and,

consequently, market participants spend great resources in information production including

studying the value implication of reported earnings, it becomes difficult for managers to mislead

market participants through earnings management.

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Both analytical predictions and empirical findings suggest that stock liquidity encourages

the formation of blockholdings (Brav et al. 2010; Edmans 2009; Edmans et al. 2013; Maug 1998;

Kyle and Vila 1991). Blockholders arguably possess superior information about firms

fundamental values. First, blockholders have incentives to become informed as a result of the

large amount that blockholders can sell upon negative information (Edmans 2009). Second,

because quality information acquisition incurs fixed costs such as hiring well-trained analysts

shareholders will only acquire information on large ownership stakes (Boehmer and Kelley

2009). Moreover, blockholders possess better capabilities of conducting high-quality

fundamental analysis as a result of their scale and resources (Bushee and Goodman 2007).

Furthermore, blockholders have better access to management because of their large equity

holdings (Bushee and Goodman 2007). Existing research has accumulated considerate evidence

that confirms blockholders information superiority over the general public. 4 For instance,

Collins et al. (2003) find that the accrual component of reported earnings is less mispriced in

firms with greater institutional ownership. We reason that when stock liquidity shifts the

shareholder base towards sophisticated, large investors it becomes difficult for managers to

maneuver market participants expectation of their firms economic fundamentals through

earnings management.

Stock liquidity stimulates information-based arbitrage. Arbitrage is costly and risky (Lee

2001; OHara 2003; Shleifer and Vishny 1997). Stock liquidity reduces trading costs and allows

arbitrageurs to quickly alter their holding positions at prices that do not fully reveal their private

information. Therefore, stock liquidity makes information-based arbitrage lucrative. Stock

liquidity also mitigates risks related to arbitrage by easing trading between investors and

facilitating establishment and reestablishment of arbitrage positions. Arbitrageurs are generally

well-informed, possibly motivated by the great risk and cost to be overcome by them. For

instance, Karpoff and Lou (2010) find that abnormal short interest increases steadily in the

nineteen months before financial misrepresentation is publicly revealed, suggesting that short

arbitrageurs can detect firms engaging in earnings management. We reason that when stock

liquidity is high and, consequently, information-based arbitrage is active it becomes difficult for

4 Interested readers can refer to Bushee and Goodman (2007) and Edmans (2009) for the list of references.

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managers to move stock prices in their preferred directions at their desired magnitudes through

earnings management.

Earnings management consumes valuable organizational resources such as managerial

attention and cognition (Goldman and Slezak 2006; Peng and Rell 2008). Peng and Rell (2008)

argue that the principal cost of earnings management may be not money spent by the company,

but the waste of managers' limited time that could be spent in pursuit of long-run value (p.285).

Peng and Rells (2008) argument may be of great relevancy in todays increasingly brutal

competitive environments wherein managerial attention and cognition are becoming increasingly

scarce strategic resources (Gavetti 2005; Yadav et al. 2007). When managers waste valuable

organization resources including their limited time on earnings management the fundamental

value of their firms would be lower.

Stock liquidity ensures that stock prices faithfully and timely reflect the fundamental

value of underlying traded stocks as a result of its effect on the value of information, the

composition of shareholder base, and information-based arbitrage. For instance, Holmstrm and

Tirole (1993) analytically show that in response to liquidity improvement market participants

spend more efforts in private information acquisition and meanwhile trade more aggressively on

their private information to maximize profits, which leads to more efficient prices about traded

assets (also see Edmans 2009; Grossman and Stiglitz 1980). Empirical evidence generally

confirms the positive effect of stock liquidity on stock price efficiency (e.g., Chordia et al. 2008,

2011).5 Therefore, we argue that when stock liquidity is high, stock prices will timely reflect the

value-destroying consequence of earnings management. We reason that managers will find it less

beneficial to engage in earnings management when stock liquidity is high and, consequently,

stock prices timely reflect the value-destroying consequence of earnings management.

To sum up, stock liquidity makes it difficult for managers to move stock prices in their

preferred directions at their desired magnitudes through earnings management by motivating

private information production, shifting shareholder base towards sophisticated, large investors,

and stimulating informed arbitrage; stock liquidity makes it less desirable for managers to

5 In this study, we find that firms experiencing dramatic improvement in stock liquidity resulting from the

decimalization encountered significant enhancement of price efficiency as gauged by the extent to which their stock

prices deviate from underlying fundamental values.

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engage in earnings management by ensuring that stock prices timely reflect the value-destroying

consequence of earnings management. Therefore, we reason that when stock liquidity is high

managers will engage less in earnings management, which leads to our below hypothesis:

Hypothesis 1 (H1): ceteris paribus, the greater stock liquidity the less earnings management.

3. Data, Sample, Variable Measurement, and Descriptive Statistics

3.1 Data and Sample

In the main test of H1, we obtain accounting- and auditor- related data from

COMPUSTAT, stock-related data from CRSP, and institutional ownership data from Thomson

CDA/Spectrum Institutional 13f Holdings. To carry out other tests, we obtain restatement data

from AUDITANALYTICS, AAER data from the Center of Financial Reporting and

Management, analysts-related data from I/B/E/S, executives- and compensation- related data

from EXECUCOMP, short interest data from COMPUSTAT, GDP data from Bureau of

Economic Analysis, and institutional investor classification data from Dr. Bushees website.

In the main test of H1 we use the absolute value of discretionary accruals as the proxy for

earnings management. We exclude financial (SIC 6000-6999) and utilities (SIC 4900-4999)

firms from the sample since discretionary accruals estimation is problematic for these firms

(DeFond and Subramanyam 1998). To maximize statistical power and generalizability, we only

require that a firm-year observation has no missing values for variables used in a test to be

included in the test. As a result, different tests involve different sample compositions.

3.2 Variable Measurement

3.2.1 Measuring Financial Misreporting

Following existing studies (e.g., Armstrong et al. 2013; Jiang et al. 2012; Zang 2012) we use

the absolute value of discretionary accruals as the proxy for earnings management in the main

test of H1. Discretionary accruals are the difference between total accruals and normal accruals.

We adopt the model proposed in Dechow et al. (1995) to estimate normal accruals. The model is

as follows:

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where, for firm i and fiscal year t, TAC is the earnings before extraordinary items and

discontinued operations (COMPUSTAT: ibc) minus the operating cash flows from continuing

operations taken from the statement of cash flows (COMPUSTAT: oancf xidoc) (see Hribar

and Collins 2002); A is total assets (COMPUSTAT: at); S is net sale (COMPUSTAT: sale); REC

is the accounts receivable (COMPUSTAT: rect); PPE is the gross value of property, plant, and

equipment (COMPUSTAT: ppegt); standards for change from fiscal year t-1 to fiscal year t.

For each year, we estimate the regression equation (1) for every industry classified by two-

digit SIC codes. Our estimation approach controls for industry-wide variations in economic

conditions that affect total accruals while allowing the coefficients to vary across time. We

require that the minimal number of observations for each industry-year combination is fifteen.

Discretionary accruals are the estimated residuals of regression equation (1).

3.2.2 Measuring Stock Liquidity

We adopt the high-low stock liquidity measure proposed in Corwin and Schultz (2012). The

high-low stock liquidity measure possesses desirable attributes. First, the high-low stock

liquidity measure has intuitive theoretical foundation. Corwin and Schultz (2012) base

development of the high-low stock liquidity measure on two uncontroversial empirical

regularities. Namely, daily high prices are always buyer-initiated while daily low prices are

always seller-initiated. The ratio of high-to-low prices reflects both the fundamental volatility

and the bid-ask spread of the stock. The component of the high-to-low price ratio attributed to

the fundamental volatility increases proportionately with the trading interval while the

component attributed to the bid-ask spread stays relatively constant over a short period.

Second, Corwin and Schultz (2012) show that the high-low stock liquidity measure

outperforms other low-frequency measures in capturing cross-sections of both spread levels and

month-to-month changes in spreads. When a low-frequency stock liquidity measure is used in

cross-section regressions, it is desirable for the low-frequency stock liquidity measure to have

high cross-section correlations with stock liquidity measures computed from high-frequency

intraday transaction data. In addition, the high-low stock liquidity measure is much less

computationally demanding than stock liquidity measures estimated from intraday transaction

data. Because of the large size of samples used in this study, computational feasibility requires

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us to use low-frequency stock liquidity measures. By construction, Corwin and Schultzs (2012)

high-low stock liquidity measure actually captures stock illiquidity. We use the natural log of the

inverse of the high-low estimate as the stock liquidity measure. In Appendix 2 we provide brief

background for Corwin and Schultzs (2012) high-low stock liquidity as well as Hasbroucks

(2009) stock liquidity measure.

3.3 Descriptive Statistics

Table 1 reports descriptive statistics for the sample used in the main test of H1. We refer

to prior studies (e.g., Armstrong et al. 2013; Zang 2012) to set up the regression model for the

main test of H1. Specifically, we control for auditor characteristics, various firm characteristics,

and real activities-based earnings management that are either related to or associated with

earnings management. In addition, we also control for year and industry fixed effects. Appendix

1 provides definitions for all these control variables. Table 1 Panel A shows that regarding

statistical distributions variables used in the main test of H1 are comparable to those used in prior

studies (e.g., Armstrong et al. 2013; Zang 2012).6

Table 1 Panel B reports Pearson and Spearman correlations between variables used in the

main test of H1. We are cautious about drawing inferences from correlations between variables.

However, we want to point out that in line with the prediction of H1 stock liquidity and absolute

value of discretionary accruals are significantly negatively correlated. Moreover, the stock

liquidity measure (i.e., LIQ_HL) varies with firm characteristics as expected. For instance,

LIQ_HL is higher for larger, more mature firms while its lower for firms with more volatile

business operations as measured by the standard deviations of cash flows from operations and

sales.

4. Empirical Results

To test H1, we run OLS regression to estimate below model:

6 For the sake of saving space, we only report summary statistics and correlations for variables used in the main test

of H1. Summary statistics and correlations for variables used in all other tests are provided at request.

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where ADAt is the absolute value of discretionary accruals as a percentage of total assets,

estimated by using the model proposed in Dechow et al. (1995); LIQ_HLt is the natural log of the

inverse of the high-low estimate of bid-ask spread proposed in Corwin and Schultz (2012),

computed over a period of 252 trading days that in the last month of fiscal year t. All other

variables are as defined in Appendix 1. H1 predicts that 1 < 0.

Table 2 presents OLS estimates of Equation (2). Standard errors are adjusted for

heteroscedasticity and clustered at the firm level. Consistent with the prediction of H1, the

coefficient on LIQ_HLt is negative (1 = -0.293, t = -4.04). The economic magnitude of the effect

is also significant. We would observe a 6.2% drop in the absolute value of discretionary accruals

among firms with stock liquidity one standard deviation below the sample mean if stock liquidity

of these firms is increased to one standard deviation above the sample mean.7

5. Robustness Tests and Additional Analyses

5.1 Robustness Tests

We run a battery of robust tests of H1. Table 3 reports results from three of these

robustness tests. Table 3 Panel A reports results from the robustness test that adopts the stock

liquidity measure proposed in Hasbrouck (2009). Hasbrouck (2009) shows that when measured

on an annual basis his estimate is highly correlated with the effective cost measure estimated

from intraday transactions. In Appendix 2, we provide brief background for Hasbroucks (2009)

stock liquidity measure. Table 3 Panel A shows that our inference about H1 remains unchanged

when we use the stock liquidity measure proposed in Hasbrouck (2009).

To ensure that our inference about H1 applies to earnings management in general and is

robust to specific earnings management measures used, we follow existing studies to use the

likelihood of a restatement or a SEC AAER as the proxy for earnings management in robustness

tests. Table 3 Panel B presents results from the robustness test that examines the relation between

stock liquidity and the likelihood of a SEC Accounting and Auditing Enforcement Release

(AAER). We refer to Armstrong et al. (2013) and Zang (2012) for the choice of covariates. All

variables are as defined in Appendix 1. In Table 3 Panel B the dependent variable is

7 We document a much stronger effect after controlling for more covariates and firm fixed effects (see Table 4).

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Prob({AAERi,t=1}). AAERi,t is an indicator variable that equals one if the SEC published a AAER

that identified accounting fraud or misrepresentation at firm i in fiscal year t and zero if

otherwise. We restrict our analysis to observations with fiscal years before 2008 to avoid

potential selection bias. Results presented in Table 3 Panel B are obtained by using Probit

regression. Consistent with the prediction of H1, we find that the coefficient on LIQ_HLt is

negative (t = -2.35).

Table 3 Panel C presents results from the robustness test that examines the relation

between stock liquidity and the likelihood of an accounting restatement. We refer to Armstrong

et al. (2013) and Zang (2012) for the choice of covariates. All variables are as defined in

Appendix 1. In Table 3 Panel C, the dependent variable is Prob({RESi,t=1}). RESi,t equals one if

any of firm is financial results (quarterly, annual, or otherwise) in fiscal year t are subsequently

restated and equals zero if otherwise. There are generally time lags between restatement dates

and restated financial results. As turned out in the data, more than 90% of restatements are made

within four years after the financial results. To avoid potential selection bias, we limit our

analysis to observations with fiscal years before 2009.

Results presented in Table 3 Panel C are obtained by using Probit regression. As shown in

Model 1, the relation between stock liquidity and the likelihood of an accounting restatement is

negative but statistically insignificant. In Model 2, we explore whether there is a nonlinear

relation between stock liquidity and the likelihood of an accounting restatement. In Model 2,

instead of using a continuous measure of stock liquidity, we create a dummy variable (i.e.,

D_LIQ_HL) that equals one if LIQ_HL is in the top quintile of the sample and zero if otherwise.

As shown in Model 2, the coefficient on D_LIQ_HL is negative (-0.089) and significant (t = -

2.41), suggesting that we would observe significant drop in the incidence of accounting

restatements when firms move to the top stock liquidity quintile.

In unreported results, we adopt alternative regression models of normal accruals proposed in

Dechow et al. (2003) and Jones (1991) to estimate discretionary accruals and find that our

inference about H1 is robust to the way in which discretionary accruals are estimated. Moreover,

instead of using two-digit SIC codes to classify the industry membership of firm-year

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observations we apply the latest Fama-French industry classification scheme (49 industries in

total) to estimate discretionary accruals. We find that using a different industry classification

scheme does not alter our inference regarding H1. To sum up, results from these robustness tests

further corroborate the finding documented in the main test of H1, suggesting that the negative

relation between stock liquidity and earnings management is not specific to stock liquidity and

earnings management measures used.

5.2 Endogeneity: Omitted Correlated Variables and Reverse Causality

Endogeneity problems are ubiquitous in archival studies (Roberts and Whited 2011).

Endogeneity problems mainly stem from either omitted correlated variables or reverse causality.

In our research setting, there could be reasons for stock liquidity and earnings management to be

jointly determined. Also, there might be reverse causality between stock liquidity and earnings

management. For instance, Lang et al. (2012) find that greater stock liquidity is associated with

smaller discretionary accruals. Therefore, it is possible that our finding about the effect of stock

liquidity on earnings management might be driven by potential reverse causality between stock

liquidity and earnings management.

5.2.1 Controlling for Firm Fixed Effects and More Covariates

To address the potential endogeneity problem stemming from omitted correlated

variables, we control for firm fixed effects and additional possible covariates identified from

related studies (e.g., Badertscher 2011; Bergstresser and Philippon 2006). These additional

covariates are as defined in Appendix 1. Firm fixed effects methods solve joint determination

problems in which an unobserved time-invariant variable simultaneously determines both stock

liquidity and earnings management. Controlling for firm fixed effects is also equivalent to

looking only at the relation between within-firm changes in stock liquidity and within-firm

changes in earnings management.

Table 4 presents estimates from the tests that controls for firm fixed effects and additional

possible covariates. There is still evidence of a negative relation between stock liquidity and the

absolute value of discretionary accruals. The estimate of the coefficient on LIQ_HL is -0.644

with a significant t-statistic of 2.47.

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The results from the test controlling for firm fixed effects go a long way toward

dismissing omitted variables explanations as sources of endogeneity. Because only the effects of

within-rm changes in the absolute value of discretionary accruals and in stock liquidity are

taken into account, rm-specic omitted variables cannot readily explain the observed relation

between stock liquidity and the absolute value of discretionary accruals. On top of the long list of

possible covariates, rm xed effects take care of most time-invariant unobserved variables. We

conclude that omitted variables are highly unlikely to explain the negative relation between stock

liquidity and the absolute value of discretionary accruals even though we could not completely

rule out the possibility.

5.2.2 Difference-in-differences analysis using the 2001 decimalization

To address the potential endogeneity problem stemming from reverse causality, we use

the difference-in-differences (hereafter, DiD) approach to determine the effect of a change in

stock liquidity caused by an exogenous event on earnings. Specifically, this methodology

compares changes in ADA of a sample of treatment firms with changes in stock liquidity in the

top one-third to changes in ADA of a sample of control firms with changes in stock liquidity in

the bottom one-third but that are otherwise comparable before the exogenous event.

The DiD methodology possess several desirable features. First, the DiD methodology

rules out omitted trends that are correlated with stock liquidity and ADA in both the treatment

firms and the control firms. Second, the DiD approach helps establish the direction of causality

as the experiment is conducted surrounding an exogenous change in stock liquidity. Third, as

with the firm fixed effects the DiD approach controls for time-invariant unobserved differences

between the treatment firms and the control firms.

Following prior studies (e.g., Fang et al. Forthcoming) we use the 2001 decimalization of

the minimum tick size as the opportunity for quasi experiment. Prior studies document

significant improvement in stock liquidity as a result of the decimalization (Bessembinder 2003;

Chordia et al. 2005). For the empirical relation tested in this study, the decimalization is a good

candidate for quasi experiment because decimalization directly affects stock liquidity but

unlikely affect earnings management directly. At the same time, changes in stock liquidity

surrounding the decimalization exhibit wide variation in the cross-section of stocks. More

18

importantly, we would not expect the change in future earnings management to affect the change

in stock liquidity brought about by the decimalization. Hence, an examination of the change in

earnings management following the change in stock liquidity resulting from the decimalization

provides a quasi- experiment for our test.

We construct a treatment group and a control group of firms using propensity score

matching. Specifically, we begin with all firms with non-missing matching variables including

ADA in the pre-decimalization year (t-1) and the post-decimalization year (t+1), with t indicating

the year during which the decimalization occurred. On the basis of LIQ_HLt-1 to t+1, we sort

2,576 sample firms into three equal groups and retain only the top group representing firms that

experience the greatest increase in stock liquidity surrounding the decimalization (namely the

treatment group) and the bottom group representing firms that experience the least improvement

in stock liquidity surrounding the decimalization (namely the control group).

To apply the propensity score matching, we first estimate a Probit model based on the

1,717 sample firms in the top and the bottom groups. The dependent variable is one if the firm-

year belongs to the treatment group and zero otherwise. The Probit model includes all control

variables from the main test of H1 and ADA measured in the year immediately preceding the

decimalization. We also control for industry fixed effects in the Probit model. These variables

are included to help satisfy the parallel trends assumption as the DiD estimator should not be

driven by the differences in any industry or firm characteristics. Table 5 Panel A presents

parameter estimates from the Probit model used to estimate the propensity scores. The results

show that the specification captures a significant amount of variation in the choice variable, as

indicated by a pseudo R2 of 19.5% and a p-value from the 2 test of the overall model fitness well

below 0.001.

We then use the predicted probabilities (i.e., propensity scores) to perform a nearest-

neighbor propensity score matching. Specifically, each firm in the top group (i.e., the treatment

firms) is matched to a firm from the bottom group with the closest propensity score (i.e., the

control firms) without replacement. We end up with 473 unique pairs of matched firms.

19

The validity of the DiD estimate critically relies on the parallel trends assumption.

Following prior studies (e.g., Fang et al. Forthcoming) we conduct a number of diagnostic tests

to demonstrate that we do not violate the parallel trends assumption. In the first test, we rerun the

Probit model by using the matched sample. The Probit estimates are presented in the Post-

match column of Table 5 Panel A. None of the coefficients on independent variables is

statistically significant including the coefficient on pre-decimalization ADA. Also, the Post-

match coefficient estimates are much smaller than the Pre-match ones, suggesting that the

Post-match results are not simply an artifact of a decline in the degree of freedom due to the

drop in sample size. In addition, pseudo R2 drops drastically from 19.5% prior to the matching to

1.1% post the matching. And a 2 test for the overall model fitness shows that we cannot reject

the null hypothesis that all of the coefficient estimates are zero (with a p-value of 0.9998).

In the second diagnostic test, we examine the difference between the propensity scores of

the treatment firms and those of their matched control firms. Table 5 Panel B demonstrates that

the difference is rather trivial. Finally, we report the univariate comparisons between the

treatment firms and the control firms with respect to their pre-decimalization characteristics and

corresponding t-statistics in Panel C. As shown, we observe no statistically significant

differences between the treatment firms and the control firms with respect to their characteristics

in the pre-decimalization regime. In particular, the two groups of firms have similar levels of

pre-decimalization stock liquidity (i.e., LIQ_HL) and ADA, even though their stock liquidity is

affected by the decimalization differently. Overall, our diagnostic tests suggest that the

propensity score matching process has removed material observable differences (other than the

difference in the change in stock liquidity surrounding the decimalization), which increases the

likelihood that changes in ADA are caused only by the exogenous change in stock liquidity

resulting from the decimalization.

Table 5 Panel D presents the DiD estimator. Column (1) reports the average change in

ADA for the control firms and Column (2) reports the average change in ADA for the treatment

firms. Change in ADA (i.e., CHG_ADAt-1 to t+1) is the difference between ADA immediately

before the decimalization and ADA immediately after the decimalization. In columns (3) and (4),

we report the DiD estimator and the corresponding two-tailed t-statistics.

20

Several interesting findings emerge. First, ADA of both the treatment firms and the

control firms drops after the decimalization, which is consistent with H1 that stock liquidity is

negatively related to earnings management on average. Second, the drop in ADA is much greater

for the treatment firms than for the control firms as the DiD estimator is statistically significant at

the 1% level. Actually the drop in ADA is statistically significant only for the treatment group.

Following Roberts and Whiteds (2011) suggestion, we repeat the DiD analysis in pre-

decimalization years to further verify the internal validity of our DiD finding. Specifically, we

examine whether significant difference exists between the treatment firms and the control firms

regarding change in ADA from year t-2 to year t-1. Table 5 Panel E presents results from this

falsification test. As shown, we observe no significant change in ADA for both the treatment

firms and the control firms and no significant difference between the treatment firms and the

control firms regarding change in ADA. Findings from this falsification test further suggest that

our DiD finding is more likely due to the differential impact of the decimalization on the stock

liquidity of the treatment firms and the control firms, as opposed to some alternative forces.

5.3 Possible Mechanisms

We run several tests to examine whether the hypothesized mechanisms through which

stock liquidity deters managers from engaging in earnings management change surrounding the

decimalization as predicted. It is definitely challenging to provide definitive proof of underlying

mechanisms through which stock liquidity deters earnings management. Therefore, our tests here

are only suggestive.

5.3.1 Stock Liquidity and Private Information Production

We reason that one of the mechanisms through which stock liquidity deters earnings

management is that stock liquidity motivates market participants to engage in information

production by enhancing the value of information. Essentially, we cannot observe the overall

production of information about a firm by all market participants. However, we can infer efforts

expended by financial analysts in producing information about a firm (Barth et al. 2001).

Financial analysts serve as critical information intermediaries in the capital market through their

private acquisition and processing of information about firms economic prospects. We argue

21

that the amounts of information production efforts expended by financial analysts might serve as

a rough indicator of overall information production efforts made by market participants.

Following Barth et al. (2001) we gauge financial analysts information production efforts

by the number of analysts following a firm (i.e., ANA_COV) and analysts efforts (i.e.,

ANA_EFF) where ANA_EFF is defined as

, Ni,t : number of analysts following firm i

in year t and nj: number of firms followed by analyst j in year t. We then examine the effect of

stock liquidity on financial analysts information production efforts as measured by ANA_COV

and ANA_EFF in the DiD framework using the matched sample constructed in Section 4.2.2.

Table 6 Panel A presents the DiD estimator. The treatment firms experience no

significant change in analyst followings while the control firms experience significant drop in

analyst followings. And the changes in analyst followings are statistically significantly different

between the control firms and the treatment firms. Regarding ANA_EFF, the treatment firms

experienced statistically significant increase after the decimalization while the control firms

experienced no material change in ANA_EFF. And changes in ANA_EFF are marginally

significantly different between the control firms and the treatment firms. Overall, our evidence

suggests that information production by financial analysts in particular (and by market

participants in general) in response to change in stock liquidity may be an underlying mechanism

through which stock liquidity deters earnings management.

5.3.2 Stock Liquidity and Institutional Ownership

We reason that another mechanism through which stock liquidity deters earnings

management is that stock liquidity encourages the formation of blockholders and thus shifts the

shareholder base towards sophisticated, large institutional investors. To examine the effect of

stock liquidity on the shareholder base composition, we compute changes in institutional

ownership (i.e., INST_OWN) in the DiD framework using the matched sample constructed in

Section 4.2.2. Table 6 Panel B presents the DiD estimator. As shown, the treatment firms

experience, both statistically and economically, significant increase in institutional ownership

after the decimalization while the control firms experience no material change in institutional

22

ownership. Regarding the change in institutional ownership the difference between the treatment

firms and the control firms is statistically significant.

Institutional investors are not homogenous and differ in various dimensions such as

investment horizon (Bushee 1998; Yan and Zhang 2009). We follow the institutional investor

classification scheme created by Bushee (1998, 2001) and explore changes in institutional

holdings owned by transient investors (i.e., INST_OWN_TRA), quasi-indexers (i.e.,

INST_OWN_QIX), and dedicated investors (i.e., INST_OWN_DED), respectively. We argue that

these three types of investors have differential impacts on managers incentives to engage in

earnings management.

Findings of prior studies (e.g., Yan and Zhang 2009) suggest that transient institutional

investors are adept at private information acquisition and, as a result, are well informed.

Therefore, we argue that increase in institutional holdings owned by transient investors (i.e.,

INST_OWN_TRA) makes it more difficult for managers to move stock prices through earnings

management.

Findings of prior studies (e.g., Chen et al. 2007) suggest that dedicated institutional

shareholders tend to actively monitor managers for wrongdoings due to their dedicated

ownership position. Earnings management is value-destroying because it consumes valuable

organizational resources, especially managers limited time. Also, dedicated institutional

shareholders would not benefit much from temporary changes in stock prices driven by earnings

management as a result of their dedicated ownership position. Moreover, Karpoff et al. (2008)

find that once detected accounting frauds can impose substantial penalties on firms. We argue

that dedicated institutional shareholders would bear a big portion of penalties imposed on firms

in the form of dramatic decline in the value of their shareholdings once accounting frauds are

detected. Therefore, it is expected that due to their dedicated ownership position dedicated

institutional shareholders have greater incentives to monitor managers and prevent earnings

management in the first place than the other two types of institutional shareholders. Therefore, it

is reasonable to posit that increase in institutional holdings owned by dedicated investors (i.e.,

INST_OWN_DED) leads to less earnings management.

23

Quasi-indexing institutional investors use passive indexing strategies and thus have few

or no incentives to monitor specific invested firms or spend resources in acquiring information

about specific invested firms. Therefore, changes in institutional holdings owned by quasi-

indexers (i.e., INST_OWN_QIX) may not have direct impact on earnings management. However,

we argue that decrease in institutional holdings owned by quasi-indexers may increase

institutional holdings owned by transient investors and dedicated investors as a result of the

crowding-out effect and, therefore, indirectly leads to less earnings management.

Table 6 Panel B also presents the DiD estimators for institutional holdings owned by

transient investors, quasi-indexers, and dedicated investors. Both the treatment firms and the

control firms experience significant increase in institutional holdings owned by transient

investors after the decimalization while the increase is significantly greater for the treatment

firms than for the control firms. As shown, only the treatment firms experience significant

increase in institutional holdings owned by dedicated investors. Regarding the change in

institutional holdings owned by dedicated investors, the difference between the treatment firms

and the control firms is statistically significant. Both the treatment firms and the control firms

experience significant decrease in institutional holdings owned by quasi-indexing investors after

the decimalization, and the decreases experienced by the treatment firms and the control firms

are not statistically significant. Interestingly, Table 6 Panel B reveals that for the control firms

decrease in institutional holdings owned by quasi-indexing investors cancels out the increase in

institutional holdings owned by transient investors, which accounts for why we observe no

significant change in overall institutional ownership after the decimalization for the control firms.

In summary, we show that after the decimalization firms with greater improvement in

stock liquidity experience greater increase in institutional holdings owned by transient investors

that are well informed and by dedicated investors that have great incentives to deter managers

from engaging in earnings management. Our evidence suggests that changes in institutional

holdings owned by different types of institutional investors in response to changes in stock

liquidity may explain why firms with greater improvement in stock liquidity after the

decimalization experience greater decrease in the absolute value of discretionary accruals.

5.3.3 Stock Liquidity and Arbitrage

24

Stock liquidity stimulates arbitrage by reducing costs and risks faced by arbitrageurs. We

argue that as a result of its effect on arbitrage stock liquidity discourages earnings management.

If managers succeeded in maneuvering market participants expectations about their firms

economic prospects through earnings management, incoming-increasing discretionary accruals

arguably would cause overvaluation while income-decreasing discretionary accruals arguably

would cause undervaluation. To arbitrage on undervalued stocks requires taking a long position

while arbitrage on overvalued stocks requires taking a short position. In practice, taking a short

position is costlier and riskier than taking a long position (see Hirshleifer et al. 2011). Therefore,

short arbitrageurs arguably may have greater incentives to get informed as a result of great risk

and cost to be overcome by them (Boehmer et al. 2008). Here, we examine the effect of stock

liquidity on short interests in the DiD framework using the matched sample constructed in

Section 4.2.2.

Table 6 Panel C reports the DiD estimator. As shown, on average, both the treatment

firms and the control firms experience significant increases in short interests after the

decimalization. However, regarding the increases in short interests the difference between the

treatment firms and the control firms is not statistically significant. We argue that it is not

surprising to find no significant difference between the treatment firms and the control firms

because stock liquidity prevents overvaluation in the first place as a result of its effect on

earnings management and, as a result, leaves fewer opportunities for short arbitrage. For example,

the threat of ex-post active short arbitrage deters ex-ante income-increasing earnings

management in the first place. It may be revealing to examine the cross-section relation between

stock liquidity and short interests.

Table 7 reports results from the Tobit regression that takes short interests as a function of

stock liquidity and other covariates. We refer to Dechow et al. (2001) to set up the regression

model. As shown, in the cross-section stock liquidity is positively related to short interests. In

summary, our evidence is generally consistent with a story that stock liquidity discourages

earnings management in the first place as a result of its positive effect on arbitrage.

5.3.4 Stock Liquidity and Stock Price Efficiency

25

We reason that stock liquidity and ensuing stock price efficiency deter earnings

management by ensuring that stock prices could timely reflect the value-destroying consequence

of earnings management. To examine the effect of stock liquidity on stock price efficiency, we

investigate changes in stock price efficiency in the DiD framework using the matched sample

constructed in Section 4.2.2. We measure stock price inefficiency essentially as the extent to

which stock prices deviate from the fundamental value of traded stocks. Specifically, we use two

measures: the absolute value of (MV / FV minus one) (i.e., INEFF_FL) where MV is the market

value of equity measured at the fiscal year end and FV is the fundamental value of equity

estimated by using the analyst-based valuation method proposed in Frankel and Lee (1998), and

the absolute value of the difference between LMV and LFV (i.e., INEFF_RKRV) where LMV is

the natural log of market value of equity measured at the fiscal year end and LFV is natural log

of the fundamental value of equity estimated by using the accounting-based valuation method

proposed in Rhodes-Kropf et al. (2005).

Table 6 Panel D reports the DiD estimators. Using INEFF_RKRV as the measure for

stock price inefficiency, we find that the treatment firms experience significant improvement in

stock price efficiency after the decimalization while the control firms experience significant

deterioration in sock price efficiency, and that the difference between the treatment firms and the

control firms is statistically significant regarding the change in INEFF_RKRV. Using INEFF_FL

as the measure for stock price inefficiency, we find that only the treatment firms experience

marginal improvement in stock price efficiency after the decimalization, and that the difference

between the treatment firms and the control firms is statistically insignificant regarding the

change in INEFF_FL. We conjecture that small sample size and thus low statistical power may

explain why we cannot find strong evidence using INEFF_FL. Only 60 pairs out of 473 pairs

have no missing values for INEFF_FL.8 Overall, our finding suggests that the effect of stock

liquidity on stock price efficiency may serve as a mechanism through which stock liquidity

affects earnings management.

8 Presuming no change in stock price efficiency for firms that miss values for INEFF_FL, we rerun the DiD test. We

find that change in INEFF_FL is -0.167 (t = -2.87, p = -0.004) for the treatment firms and is -0.025 (t = -0.82, p =

0.415) for the control firms, and that regarding the change in INEFF_FL the difference between the treatment firms

and the control firms is 0.143 (t = 2.17, p = 0.030).

26

5.4 Stock Liquidity and the Relation between Discretionary Accruals and Equity Valuation

Errors

We reason that stock liquidity makes it difficult for managers to move stock prices in

their preferred directions at their preferred magnitudes through earnings management as a result

of its effect on information production, shareholder base composition, and arbitrage. The direct

empirical implication of our reasoning is that we should observe weaker relation between

discretionary accruals and equity valuation errors when stock liquidity is higher. We argue that

finding evidence consistent with the implication will lend further support to the empirical

validity of our reasoning.

It is challenging to estimate the fundamental value of traded stocks. To ensure that our

finding is not sensitive to specific measures, we use two different measures. The first measure is

MV / FV minus one (i.e., EQ_VAL_ERR_FL) where MV is the market value of equity measured

at the end of the fourth month after fiscal year end and FV is the fundamental value of equity

estimated by using the analyst-based valuation method proposed in Frankel and Lee (1998). The

second measure is the difference between LMV and LFV (i.e., EQ_VAL_ERR_RKRV ) where

LMV is the natural log of the market value of equity measured at the end of the fourth month

after fiscal year end and LFV is the natural log of the fundamental value of equity estimated by

using the accounting-based valuation method proposed in Rhodes-Kropf et al. (2005).

Table 8 reports results for the analysis that examines the effect of stock liquidity on the

relation between discretionary accruals and equity valuation errors. Referring to previous studies

(i.e., Jiang et al. 2005; Zhang 2006) we control for various firm characteristics that are related to

or associated with valuation uncertainty faced by investors. As shown, regardless of the

measures used, we observe significant positive relation between discretionary accruals (i.e., DA)

and equity valuation errors for firms with their stock liquidity below the sample median while we

find no significant relation between discretionary accruals (i.e., DA) and equity valuation errors

for firms with their stock liquidity above the sample median. Our finding lends further support to

the empirical validity of our reasoning that stock liquidity makes it difficult for managers to

27

move stock prices in their preferred directions at their preferred magnitudes through earnings

management.

5.5 CEOs Equity Incentive and the Relation between Stock Liquidity and Earnings

Management

We reason that one mechanism through which stock liquidity deters earnings

management is that stock liquidity causes stock prices to timely and faithfully reflect the value-

destroying consequence of earnings management. The direct implication of our reasoning is that

the negative relation between stock liquidity and earnings management will be stronger when

managers have a greater stake in their firms stock prices.

Following existing studies, we examine the effect of CEOs stake in their firms stock

prices on the relation between stock liquidity and earnings management. Specifically, we

measure a CEOs stake in his firms stock price as the sensitivity of the CEOs equity portfolio

(i.e., stocks, restricted stocks, and unexercised exercisable / un-exercisable stock options) to a 1%

change in the stock price (see Core and Guay, 2002). Following Bergstresser and Philippon

(2006), we use the ratio of the CEOs equity portfolio delta to the sum of the CEOs equity

portfolio delta and the CEOs total cash compensation measured at the end of fiscal year t-1 (i.e.,

EQ_INC_CEO) to ensure comparability across periods and firms.9 We then refer to the sample

median to sort the observations into two groups and run the OLS regression separately for each

group.

Table 9 reports the results. As shown, consistent with the implication of our reasoning the

negative relation between stock liquidity and the absolute value of discretionary accruals is

stronger when CEOs have greater stake in their firms stock prices as measured by

EQ_INC_CEO.10

We conclude that our finding here lends further support to the empirical

validity of our reasoning.

5.6 Trends of Stock Liquidity and Earnings Management

9 We obtain qualitatively the same results by scaling the CEOs equity portfolio delta by the CEOs total annual

compensation. 10

We adopt the sample used in the test for results reported in Table 4 to run a similar test that controls for more

covariates and firm fixed effects, and obtain essentially the same results.

28

Stock liquidity varies over time (see Chordia et al. 2008). Given the strong cross-section

evidence about the negative relation between stock liquidity and earnings management, we

wonder whether earnings management and stock liquidity negatively co-vary over time in the

aggregate. We draw Figure 1 to examine the time-series patterns of aggregate stock liquidity and

the absolute value of discretionary accruals. To draw Figure 1 we just require that a firm-year

observation has no missing values for stock liquidity (i.e., LIQ_HL) and the absolute value of

discretionary accruals (i.e., ADA). We first assign percentage ranks to each firm-year observation

according to its stock liquidity and absolute value of discretionary accruals. Then we compute

equally-weighted percentage ranks for stock liquidity and the absolute value of discretionary

accruals each year.

Figure 1A reveals that consistent with the findings documented in Cohen et al. (2008)

there is an overall trend of increase in the absolute value of discretionary accruals during 1990-

2000 while there is an overall trend of decrease during 2001-2005. Figure 1A also reveals that

consistent with our cross-section evidence the absolute value of discretionary accruals and stock

liquidity negatively co-vary over time in the aggregate. More importantly, variation in aggregate

stock liquidity well accounts for variation in aggregate absolute value of discretionary accruals

( = -0.74). In a period of financial turmoil when investors face great uncertainty, investors may

not trade for information reasons such as flight-to-quality on the one hand, and on the other

hand stock liquidity may dry out (Caballero and Krishnamurthy 2008; Ns et al. 2011). We

conjecture that if excluding 1999-2001 and 2008 2009 when huge uncertainty permeates the

stock market we should observe a stronger relation between stock liquidity and the absolute

value of discretionary accruals in the aggregate. Figure 1B shows that consistent with our

conjecture variation in aggregate stock liquidity accounts for more variation in aggregate

absolute value of discretionary accruals ( = -0.81) when 1999-2001 and 2008 2009 are

excluded.

Cohen et al. (2008) attribute the decline in aggregate absolute value of discretionary

accruals during 2002-2005 to the passage of SOX. However, Figure 1A show that during 2000

2005 (six years in total) decline in aggregate absolute value of discretionary accruals occurred

29

simultaneously with improvement in aggregate stock liquidity ( = -0.94).11 In summary, our

findings suggest that besides the passage of SOX improvement in stock liquidity and ensuing

changes in information production activities, shareholder base compositions, and arbitrage

activities may also drive the decline in aggregate absolute value of discretionary accruals during

2001-2005.

6. Conclusion

This study examines the effect of stock liquidity on earnings management. We find a

negative relation between stock liquidity and earnings management. Our finding is insensitive to

specific stock liquidity and earnings management measures used, and remains strong after

controlling for a long list of possible covariates including firm fixed effects. We use the

decimalization of the minimum tick size as a quasi-experiment to carry out a difference-in-

differences (DiD) analysis, and find that firms with greater stock liquidity improvement (the

treatment firms) experience a significant drop in the absolute value of discretionary accruals

while firms with less stock liquidity improvement (i.e., the control firms) experience no

significant drop. However, before the decimalization the treatment firms and the control firms

are similar along a host of characteristics. Our results from the DiD analysis suggest that the

negative effect of stock liquidity on earnings management is causal.

Our finding has important policy implications since stock liquidity can be altered by

policies and regulations. Our study suggests an unexplored channel through which stock liquidity

contributes to firm value and social welfare. That is, at the firm level stock liquidity contributes

to firm value by deterring earnings management that consumes valuable organizational resources

such as top executives limited attention and cognition that could be used in pursuit of long-run

value; at the society level, as a result of its effect on earnings management, stock liquidity

enhances trust between managers and investors and, therefore, contributes to the functioning of

the capital market.

11

Figure 1A also shows that aggregate absolute value of discretionary accruals actually started to decline in 2001

(one year before the passage of SOX) when market-wide stock liquidity starts to improve dramatically (see Chordia

et al. 2011).

30

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34

TABLE 1

Descriptive statistics

Table 1 reports descriptive statistics for the sample used in the main test of H1. The sample is

constructed from the intersection of COMPUSTAT (accounting and auditor data), CRSP (stock

price), and Thomson CDA/Spectrum Institutional 13f Holdings (institutional ownership) for the

period of 1988 2012. The sample covers a total of 52,516 firm-year observations. Panel A presents summary statistics. Panel B presents Pearson and Spearman correlations. All variables

are as defined in Appendix 1.

Panel A: Summary statistics

Variable N Mean Std 25th Median 75th

ADA 52,516 7.16 7.66 2.14 4.80 9.29

LIQ_HL 52,516 4.34 0.76 3.88 4.41 4.88

Big8 52,516 0.86 0.35 1.00 1.00 1.00

AUD_TEN 52,516 9.73 7.67 4.00 7.00 14.00

AUD_TEN x AUD_TEN 52,516 153.57 235.13 16.00 49.00 196.00

NOA 52,516 0.80 0.89 0.34 0.55 0.89

OP_Cycle 52,516 141.39 91.90 79.96 123.04 179.58

Market_Share 52,516 0.06 0.15 0.00 0.01 0.04

Z-Score 52,516 5.35 5.70 2.30 3.59 5.95

INST_OWN 52,516 0.46 0.30 0.19 0.45 0.71

REM 52,516 -0.09 0.25 -0.21 -0.09 0.02

LMV 52,516 5.56 2.11 4.04 5.50 6.95

BTM 52,516 0.70 0.63 0.31 0.53 0.87

LEV 52,516 0.19 0.18 0.02 0.16 0.31

Firm_Age 52,516 19.78 16.29 8.00 14.00 27.00

ROA 52,516 0.01 0.16 -0.01 0.04 0.08

RET 52,516 0.16 0.67 -0.24 0.05 0.38

Capital 52,516 0.26 0.21 0.10 0.20 0.37

Intangible 52,516 0.08 0.26 0.00 0.02 0.08

Std_CashFlow 52,516 0.08 0.08 0.03 0.05 0.09

Std_Sale 52,516 0.22 0.23 0.08 0.15 0.28

35

Panel B: Pairwise Pearson (Spearman) correlations in upper (lower) triangle

Correlations significantly different from zero at p-values less than 0.05 are in boldface type

Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21)

ADA (1) -0.23 -0.11 -0.15 -0.13 0.03 0.09 -0.11 0.10 -0.21 0.14 -0.21 -0.04 -0.10 -0.19 -0.30 0.00 -0.16 0.12 0.26 0.19

LIQ_HL (2) -0.24 0.19 0.30 0.27 -0.02 -0.11 0.23 -0.02 0.54 -0.09 0.71 -0.23 0.02 0.41 0.35 0.05 0.09 -0.10 -0.30 -0.26

Big8 (3) -0.10 0.19 0.16 0.13 -0.02 -0.07 0.10 -0.03 0.28 -0.04 0.32 -0.06 0.06 0.06 0.10 0.01 0.07 -0.01 -0.12 -0.07

AUD_TEN (4) -0.14 0.29 0.17 0.95 -0.10 -0.02 0.16 -0.11 0.24 -0.04 0.30 -0.01 0.04 0.56 0.14 -0.01 0.07 -0.07 -0.21 -0.22

AUD_TEN x AUD_TEN (5) -0.14 0.29 0.17 1.00 -0.09 -0.02 0.16 -0.10 0.21 -0.03 0.28 -0.01 0.04 0.57 0.12 -0.01 0.05 -0.06 -0.18 -0.18

NOA (6) -0.06 0.06 0.00 -0.03 -0.03 0.14 -0.07 0.00 -0.01 -0.07 0.05 0.07 0.22 -0.12 -0.18 -0.06 0.26 0.11 0.04 -0.13

OP_Cycle (7) 0.08 -0.10 -0.05 0.01 0.01 0.24 -0.10 0.10 -0.12 -0.03 -0.13 0.01 -0.07 -0.03 -0.12 -0.01 -0.27 0.19 0.12 -0.12

Market_Share (8) -0.22 0.49 0.25 0.26 0.26 -0.07 -0.16 -0.09 0.18 0.03 0.27 -0.01 0.10 0.25 0.10 -0.02 0.07 -0.09 -0.15 -0.07

Z-Score (9) 0.07 0.03 -0.02 -0.05 -0.05 -0.21 0.07 -0.14 -0.02 -0.08 0.06 -0.22 -0.47 -0.17 0.03 -0.10 -0.21 0.20 0.21 0.15

INST_OWN (10) -0.20 0.55 0.29 0.26 0.26 0.05 -0.10 0.41 0.02 -0.11 0.66 -0.11 0.01 0.27 0.21 -0.06 0.02 -0.06 -0.27 -0.24

REM (11) 0.07 -0.10 -0.04 -0.03 -0.03 -0.18 -0.06 0.03 -0.09 -0.10 -0.14 0.03 0.00 -0.04 -0.30 -0.01 0.01 0.19 0.19 0.11

LMV (12) -0.22 0.71 0.32 0.27 0.27 0.11 -0.13 0.50 0.08 0.69 -0.15 -0.38 0.02 0.39 0.33 0.15 0.08 -0.03 -0.26 -0.25

BTM (13) -0.05 -0.18 -0.05 0.03 0.03 0.10 0.02 0.07 -0.28 -0.08 0.10 -0.39 0.11 0.00 -0.12 -0.31 0.10 -0.09 -0.08 -0.03

LEV (14) -0.12 0.09 0.07 0.07 0.07 0.29 -0.05 0.30 -0.67 0.02 0.02 0.05 0.12 0.10 0.02 0.02 0.31 -0.14 -0.17 -0.10

Firm_Age (15) -0.19 0.42 0.01 0.54 0.54 -0.06 0.01 0.35 -0.13 0.29 -0.02 0.30 0.09 0.14 0.17 -0.01 0.11 -0.11 -0.26 -0.25

ROA (16) -0.10 0.36 0.08 0.12 0.12 -0.12 -0.04 0.22 0.33 0.20 -0.28 0.37 -0.32 -0.13 0.14 0.21 0.07 -0.39 -0.36 -0.12

RET (17) -0.06 0.18 0.04 0.06 0.06 -0.07 -0.03 0.06 -0.09 0.03 -0.05 0.24 -0.37 0.02 0.08 0.31 -0.03 -0.02 0.00 0.00

Capital (18) -0.17 0.14 0.09 0.10 0.10 0.23 -0.23 0.24 -0.24 0.03 0.04 0.09 0.12 0.36 0.16 0.03 0.00 -0.17 -0.22 -0.19

Intangible (19) 0.07 -0.09 0.02 -0.01 -0.01 -0.02 0.34 -0.33 0.22 0.02 0.02 0.04 -0.24 -