1-s2.0-S0148296303002674-Main Earnings Management, Surplus Free Cash Flow, And

Journal of Business Research 58 (2005) 766–776

Earnings management, surplus free cash flow, and

external monitoring

Richard Chung, Michael Firth*, Jeong-Bon Kim

School of Accounting and Finance, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

Abstract

Managers engage in earnings management for various reasons. We argue that low-growth companies with high free cash flow (SFCF) will

use income-increasing discretionary accruals (DAC) to offset the low or negative earnings that inevitably accompany investments with

negative net present values (NPVs). Our results, using 22,576 company year observations over the period 1984–1996, confirm our

hypothesis. We also examine the role of high-quality auditors and institutional shareholders in mitigating the SFCF–DAC relation. Our

results show that Big 6 auditors and institutional investors with substantial shareholdings moderate the SFCF–DAC relation, which suggests

that external monitoring by these two outside stakeholders is effective in deterring managers’ opportunistic earnings management.

D 2003 Elsevier Inc. All rights reserved.

Keywords: External monitoring; Surplus free cash flow; Earnings management; Audit quality; Institutional shareholdings

Free cash flow allied to low-growth opportunities has

been identified as a major agency problem where managers

make expenditures that reduce shareholder wealth. To

camouflage the effects of the non-wealth-maximizing

investments, managers can use accounting discretion to

increase reported earnings. This opportunistic behavior is

restricted if external monitoring by outside stakeholders is

effective. In this paper, we argue that high-quality auditors

are more effective in limiting managers’ ability to make

opportunistic accounting choices than low-quality auditors.

We also argue that financial institutions with substantial

equity stakes in a company have the incentive, time, and

expertise to monitor the opportunistic actions and earnings

management of corporate executives.

This paper has three objectives. First, we investigate

whether managers of low-growth companies with high free

cash flows have incentives to boost reported earnings by

choosing income-increasing discretionary accruals (DAC).

In so doing, our analysis focuses on whether the level of

DAC is positively related to free cash flow in low-growth

firms. To ease exposition, we use the term surplus free cash

flow (SFCF) for free cash flow in low-growth firms.

0148-2963/$ – see front matter D 2003 Elsevier Inc. All rights reserved.

doi:10.1016/j.jbusres.2003.12.002

* Corresponding author. Tel.: +852-2766-7062. fax: +852-2330-9845.

E-mail address: [email protected] (M. Firth).

Second, we examine whether external monitoring by

high-quality auditors and institutional investors with sub-

stantial shareholdings are effective in deterring opportunis-

tic earnings management. Our measures of audit quality

include the traditional classification of Big 6 and the more

recent emphasis on the length of auditor tenure. Thus, we

use a more comprehensive approach to identify audit

quality. If external monitoring is effective, managers’ abil-

ities to make opportunistic accounting choices will be more

constrained than otherwise. As a result, the level of DAC

should be lower for companies with more effective moni-

toring than for those with less effective monitoring. Finally,

we investigate whether and how the incentive effect of

SFCF on DAC (i.e., the positive relation between DAC and

SFCF) is constrained or moderated by external monitoring

by high-quality auditors and substantial institutional share-

holders. If external monitoring effectively mitigates mana-

gerial opportunism, the adverse effects of SFCF will be

reduced, and the positive relation between DAC and SFCF

will be weakened.

To our knowledge, this paper is the first attempt to

examine the SFCF agency problem in the context of

managers’ opportunistic accounting choices. This study

also sheds light on the interaction between incentive

effects and monitoring effects on managers’ DAC

choices. While previous research documented that the le-

vel of DAC is lower for Big-6-audited companies (Becker

R. Chung et al. / Journal of Business Research 58 (2005) 766–776 767

et al., 1998; Francis et al., 1999) and for companies with

high institutional shareholdings (Rajgopal et al., 2002), it has

paid little attention to examining how the incentive effect on

DAC interacts with the monitoring effect.

Using a large sample of 22,576 company year observa-

tions over the period 1984–1996, we find that companies

with high SFCF use income-increasing DAC to boost

reported earnings. The results are consistent with our

hypothesis that management use positive accruals to cam-

ouflage the earnings impact of investments in negative

NPV projects and other self-serving activities. Our results

also show that Big 6 auditors are associated with less

positive accruals, and this is especially so when SFCF is

high. This result suggests that Big 6 auditors inhibit

companies with high SFCF from using income-increasing

accruals. The results for auditor tenure are somewhat

ambiguous. Long tenure is associated with higher accruals

except when SFCF is high, in which case the accruals are

lower. Financial institutions with high investment stakes in

a company also appear to restrain management from using

positive accruals if SFCF is high. In contrast, when surplus

free cash is low, institutional investors do not constrain

DAC. Our results indicate that when SFCF is high, quality

auditors and institutional shareholders monitor management

actions and deter aggressive income-increasing earnings

management.

The next section sets up our hypotheses on the DAC

and SFCF relationship. Section 2 describes the data sources

and research method, while Section 3 presents and dis-

cusses the results. Finally, Section 4 summarizes our

findings.

1. Background and hypotheses

Jensen (1986) defined the agency cost of free cash flow

as cash flows that are invested in negative net present value

(NPV) projects. Firms with low-growth opportunities are

more likely to invest free cash flow in unprofitable proj-

ects. In the absence of effective monitoring or disciplinary

actions by outside stakeholders and their agents, some

managers may choose to invest in marginal or negative

NPV projects and activities. These projects and activities

may be self-gratifying to the managers and may bring them

pecuniary benefits or other personal rewards. In many

cases, these managers may believe the investments will at

least ‘break even’ for investors, although the fact that they

‘hide’ or give little disclosure to the activities suggests that

they do not believe that the activities will withstand

scrutiny by investors.

Identifying the agency cost of free cash flow (invest-

ments in negative NPV projects) is very difficult. Managers

do not disclose to investors an investment’s cash flow

projections and the assumptions behind them. Appealing

to commercial secrecy provides a cloak for bad investment

decisions. Managers may not even internally project cash

flows for some investments; the biases managers have for

some ‘pet’ activities or personal perquisites may make them

ignore cash and profit planning. Poor investments, however,

will reveal themselves in the future profits of the company.

Non-value-maximizing investments eventually reduce earn-

ings. This will result in lower stock prices and may trigger

shareholder actions to remove directors and senior execu-

tives. To camouflage the impact of negative or marginal

NPV investments on earnings, managers may employ ac-

counting procedures that increase reported income. These

‘inflated’ profit numbers may help assuage investors and

lead to higher market valuation than would otherwise have

been the case (this assumes investors cannot completely

unravel the earnings management). Our first and primary

hypothesis is

H1: Companies with high SFCF are more likely to choose

income-increasing DAC than otherwise.

We hypothesize that auditors and institutional sharehold-

ers will reduce the SFCF–DAC relation. An important role

of the external independent auditor is to attest to the financial

statements of client companies. This verification gives as-

surance to shareholders, potential investors, and creditors

that the income statement and balance sheet accurately or

conservatively reflect the state of the client’s activities and

net assets. The audit function reduces agency costs created

by information asymmetry and reduces the control problems

caused by the separation of ownership and management

(Watts and Zimmerman, 1983). The auditor examines the

accounting procedures used by clients to see if they are

appropriate. If the procedures are considered inappropriate,

then the auditor will try to persuade the client to revise the

financial statements, and if they do not do so, the audit report

can be qualified. Krishnan and Krishnan (1997) and Francis

and Krishnan (1999), among others, provide evidence sug-

gesting that auditors are more likely to issue a qualified audit

opinion when they believe that failure to do so increases

litigation risk beyond an acceptable level. Past evidence

suggests that auditors tend to be conservative (Basu et al.,

1998; Chung et al., 2003), and so they may not agree with

aggressive income-increasing DAC. We therefore argue that

auditors will restrain managers’ abilities to choose income-

increasing DAC for companies with high SFCF.

It is now widely accepted that there are quality differences

among audit firms (DeAngelo, 1981; Simunic and Stein,

1987; Francis et al., 1999). High-quality auditors are more

likely to restrict income-increasing DAC (Becker et al.,

1998; Kim et al., 2003; Francis et al., 1999). This argument

is predicated on high-quality auditors having a lot of repu-

tation at stake. High-quality auditors want to avoid share-

holder litigation and the bad publicity associated with a client

company that aggressively uses inappropriate positive DAC.

Identifying high-quality audits is problematic. Traditionally,

Big 6 firms have been used as a proxy for high-quality

auditors (DeAngelo, 1981; Becker et al., 1998). Big 6

R. Chung et al. / Journal of Business Research 58 (2005) 766–776768

auditors have a substantial market share of listed company

clients in the United States as well as in many other

countries. They also have very large consultancy, computer,

and tax departments that use the same brand name as the

audit firm. To protect their hard-won reputations, the Big 6

auditors deploy significant resources to auditing (recruit-

ment, training, and systems), and they have the indepen-

dence to insist that clients make necessary changes to their

financial statements or else they will issue qualified audit

reports. We argue that our proxy for audit quality, the Big 6

auditors, will restrict the income-increasing DAC of clients

when compared to non-Big 6 auditors. St. Pierre and

Anderson (1984) and Palmrose (1988) show that auditors

are more likely to be sued if reported profits are alleged to

exceed the ‘true’ earnings. In contrast, there is little or no

evidence of auditors being sued if reported profits are less

than the ‘true’ earnings. Because Big 6 auditors stand to lose

more from litigation than non-Big-6 auditors, they will be

more conservative and will restrain clients from using

positive DAC (Francis et al., 1999). St. Pierre and Anderson

(1984) report a lower level of litigation among Big 6 auditors

compared with non-Big 6-auditors (after controlling for the

relative sizes of the auditors). We hypothesize that a Big 6

auditor will be even more cautious when a client company’s

agency costs are high. Thus, when SFCF is high, Big 6

auditors will restrict the use of DAC more than when SFCF

is low. This leads to our second hypothesis:

H2: Big 6 auditors moderate the SFCF–DAC relationship.

Recently, research has looked beyond the traditional

dichotomy of Big 6 and non-Big 6 as a proxy for audit

quality. One particular dimension of an auditor–client

relationship is the length of audit tenure. There are argu-

ments that a long tenure will dull an auditor’s independence

and make them more willing to accept management’s

interpretations of accounting for business transactions

(Sainty et al., 2002; Davis et al., 2003). This view has led

to calls for mandatory auditor rotation in the United States.

(Mandatory rotation is required in some European

countries—Arrunada and Paz-Ares, 1997.) In contrast, the

accounting profession argues that auditor rotation will

reduce audit effectiveness as new auditors face steep learn-

ing curves in understanding clients’ businesses (Geiger and

Raghunandan, 2002). In this circumstance, managers may

find it easier to indulge in earnings management. A number

of empirical studies, using a variety of approaches, have

examined the relationship between audit quality and audit

tenure. The findings from these studies have generally

concluded that long tenure does not harm independence

and may in fact improve audit quality (Sainty et al., 2002;

Geiger and Raghunandan, 2002; Myers et al., 2003; Johnson

et al., 2002). However, Davis et al. (2003) reach an opposite

conclusion. They find that long tenure is associated with

increased earnings management.

In light of the conflicting arguments and the somewhat

mixed empirical evidence discussed above, we include audit

tenure as a main effect and as an interaction term in our

regression models. In particular, short auditor–client tenure

(less than 5 years) is differentiated from long auditor–client

tenure (5 years or more). Because of the conflicting argu-

ments on the impact of audit firm tenure on independence

and audit quality, we do not specify directional signs on the

tenure variables.

Institutional shareholders have the expertise to analyze

company performance. If the institutions own a large

percentage of a company’s shares, then they have the

incentive and motivation to monitor management’s actions,

and they have the power to affect or change corporate

actions and decisions. When institutional investors have

substantial shareholdings, it becomes difficult for them to

sell shares immediately at the prevailing price. This lack of

liquidity means investment institutions have incentives to

closely monitor companies with high SFCFs. Other things

being equal, those companies that have substantial institu-

tional shareholders become less able to engage in opportu-

nistic earnings management. We hypothesize that the

monitoring activities of institutional shareholders will in-

hibit management from opportunistically using income-

increasing DAC (Chung et al., 2002). One way of inhibiting

the actions of management is the threat of legal action

against managers taken by institutional investors. Institu-

tional investors also have the wherewithal to remove man-

agers if they believe the managers are using DAC to

camouflage the earnings impact of their opportunistic

actions. We argue that institutional shareholders will more

closely monitor management and management’s accounting

choices if there are high agency costs. Institutional share-

holders will therefore impose more monitoring when free

cash flow is high. This leads to our third hypothesis:

H3: Large institutional shareholders moderate the SFCF–

DAC relationship.

Management ownership is also a variable that may

reduce agency costs as the motivation of managers with

relatively large share stakes are more closely aligned to the

motivations of [other] shareholders. Francis et al., 1999,

conclude, however, that there is no systematic relationship

between management ownership and accounting accruals.

Based on this evidence, we do not incorporate a manage-

ment ownership variable in the regression model.

2. Research method

2.1. Models

To test our hypothesis that companies with high levels

of SFCF will adopt income-increasing DAC, we estimate

cross-sectional regression models. These models include


two proxy measure for high-quality auditors and a

measure for institutional shareholders. We hypothesize

that these factors will have an effect on DAC and that

they will modify the SFCF–DAC relationship. The basic

model, with company (i) and time (t) subscripts, is:

DACit ¼ b0 þ b1SFCFit þ b2B6it þ b3LTit

þ b4SFCF � B6it þ b5SFCF � LTit þ b6ISit

þ b7SFCF � ISit þ b8DEBTit þ b9RELCFit

þ b10SIZEit þ b11ACit ð1Þ

where DAC is the discretionary accounting accruals

derived from the modified Jones (1991) model (see Eq.

2); SFCF is a dummy variable set equal to 1 if retained

cash flow (RCF, see Eq. 4) is above the sample median

for the year and the price-to-book ratio (PB) is below the

sample median for the year, otherwise SFCF is coded 0;

B6 is a dummy variable coded 1 if the auditor is a

member of the Big 6, otherwise B6 is coded 0; LT is a

dummy variable coded 1 if a firm has had the same

auditor for 5 years or more, otherwise LT is coded 0;

SFCF�B6 is the interaction of SFCF and B6; SFCF�LT is

the interaction of SFCF and LT; IS is a dummy variable

taking the value 1 if the sum of institutional sharehold-

ings and blockholdings (ownership above 5%) is above

the sample median for a year, otherwise IS is coded 0;

SFCF�IS is the interaction of SFCF and IS; DEBT is the

total debt divided by total assets; RELCF is the relative

cash flow measured by the difference between cash flow

for the year divided by lagged total assets (year t� 1)

and the industry median for the year; SIZE is the log of

market value of equity at fiscal year end; and AC is the

absolute value of total accruals divided by lagged total

assets (year t� 1).

The cutoff point of 5 years or more for long tenure

(LT= 1) is similar to the criterion used by Knapp (1991)

and Sainty et al. (2002). Geiger and Raghunandan (2002)

found that the auditor tenure effect in their study tapered off

after 5 years. The use of 4, 5, or 7 years as the point at which

rotation should take place has also been advocated in various

SEC or congressional reports (Davis et al., 2003; Myers et

al., 2003).

DAC are estimated cross-sectionally for each year and

for each industry using the modified Jones (1991) model

(Dechow et al., 1995). The model, with company (i) and

time (t) subscripts, is

TACit=TAi;t�1 ¼ a0ð1=TAi;t�1Þ

þ a1½ðDREVit � DARitÞ=TAi;t�1�

þ a2ðPPEit=TAi;t�1Þ þ eit ð2Þ

where TAC/TA is the total accruals divided by lagged total

assets. Total accruals (TAC) is calculated as TAC=(Dcurrent

assets�Dcash)� (Dcurrent liabilities�Dshort-term debt

�Dtaxes payable)� depreciation. D denotes change from

year t� 1 to year t; TA is the lagged total assets; #REV is

the change in sales revenues; DAR is the change in accounts

receivables; PPE denotes property, plant, and equipment;

and e denotes unspecified random factors.

The model is estimated cross-sectionally each year for

each industry (based on two-digit SIC codes). TAC/TA is

made up of non-DAC (NDAC) that arise from the normal

operations of the business, while DAC are choices made by

a firm’s managers.Thus,

TACit=TAi;t�1 ¼ NDACit þ DACit ð3Þ

NDAC are defined as the fitted values from Eq. (2),

while DAC are defined as the residual, eit, from Eq. (2). The

residual term (difference between TAC and the fitted value,

NDAC) is used as the dependent variable in Eq. 1.

Consistent with other studies, DAC is assumed to be the

outcome of managers’ opportunistic choices of accounting

methods.

We proxy the existence of an SFCF agency problem by

examining the RCF and growth prospects of a company.

Companies that retain substantial cash flows and that have

low-growth prospects are more likely to invest the cash

flows in marginal or negative NPV projects. We contend

that high RCF in and of itself does not imply that it will be

invested in wealth-decreasing projects. Companies with

high RCF and low-growth prospects are much more likely

to make ‘unwise’ investments. RCF for each company is

calculated as

RCFit ¼ ðINCit � TAXit � INTEXPit � PSDIVit

� CSDIVitÞ=TAi;t�1 ð4Þ

where RCF is the retained cash flow; INC is the operating

income before depreciation; TAX is the total taxes; INTEXP

is the interest expense; PSDIV is the preferred stock

dividends; CSDIV is the common stock dividends; and

TA is the total assets at the beginning of the fiscal year.

Growth is proxied by the price to book ratio (PB). High

PBs indicate that the stock market is expecting high growth

(Holthausen and Larcker, 1992; Skinner, 1993). Companies

with above-median RCF and below-median PB are our proxy

for firms with potential free cash flow agency problems.

DEBT, RELCF, SIZE, and AC are added as control

variables in Eq. 1. Previous studies have documented a

negative and significant coefficient for DEBT in regres-

sions explaining DAC (Becker et al., 1998). One reason

for this relationship is that companies with high debt levels

face increased monitoring by bankers and creditors, and

this inhibits the use of positive discretionary accounting

accruals. For very high levels of debt, companies may

wish to increase write-offs to the income statement (the so-

called ‘Big Bath’—DeAngelo et al., 1994), and this will


reduce positive accruals. Becker et al. (1998) report that

cash flow had a negative relationship to DAC. Companies

with high cash flows (and hence high profits) may adopt

income-decreasing DAC so as to smooth earnings. We

measure the cash flow of a company relative to its industry

median. Previous studies have also documented a positive

coefficient for SIZE and a negative coefficient for AC

(Becker et al., 1998).

2.2. Data

The sample is drawn from all companies included in

the 1998 COMPUSTAT PC-Plus Active and Research

files during the 17-year period, 1980 to 1996. As we

need 5 years of data to construct our tenure variable (LT),

our test results are from 1984 to 1996. The institutional

shareholder data are obtained from the COMPACT D/

SEC Disclosure database. This database has observations

beginning in 1988. Company year observations with

negative book values and missing values are excluded.

We winsorize observations that fall in the top 1% and

bottom 1% for each variable. Winsorization reduces the

impact of outlier observations on the results. Variables

that fall in the top 1% and bottom 1% are recoded to the

nearest permitted value (the value just below the top 1%

and the value just above the bottom 1%; see Barnett and

Lewis,1978, for a discussion of procedures to identify

and adjust for outliers). To operationalize the Jones

(1991) model, we require there to be at least 20 compa-

nies per two-digit industry code, per year. The final

sample size is 22,576 company year observations for

1984–1996 and 11,686 company year observations for

1988–1996 (that have the relevant institutional share-

holding data).

Summary statistics for the sample are reported in Table

1. The mean and median DACs are close to zero. Nineteen

percent of observations are classified as having potential

SFCF agency problems. The Big 6 audit 82% of the

sample companies. About 48% of companies have been

audited by the same auditor for 5 years or more. Approx-

imately 57% of the sample companies have substantial

( > 5%) institutional shareholders. The Big 6 and IS vari-

Table 1

Descriptive statistics for variables

Variable Mean S.D. Median Minimum Maximum

DAC 22576 � 0.003 0.113 � 0.005 � 0.313 0.387

SFCF 22576 0.189 0.391 0 0 1

B6 22576 0.819 0.385 1 0 1

IS 11686 0.570 0.310 0.596 0 1

DEBT 22576 0.457 0.213 0.455 0.055 0.944

RELCF 22576 � 0.032 0.188 0 � 0.888 0.328

SIZE 22576 3.816 1.528 3.892 0.515 6.653

AC 22576 0.099 0.100 0.070 0.001 0.547

LT 22576 0.483 0.500 0 0 1

ables indicate that most companies are audited by high-

quality firms and are monitored by institutional investors.

Debt to total assets averages 45.7%, and the cash flow to

total assets for the sample companies is slightly below their

industry averages. Absolute total accruals to total assets

average 9.9% (mean) and 7% (median). The magnitudes of

the correlations between the independent variables is small

enough that multicollinearity is not a major problem in

interpreting the regression coefficients (Judge et al., 1988,

p. 868).

3. Results

3.1. Univariate results

Table 2 shows the test results for differences in DAC

across subsamples formed on the basis of SFCF, auditor,

audit tenure, and institutional share ownership. In panel

A, average (mean and median) DAC are reported for

observations with high and low SFCF. Observations with

high SFCF have higher DAC. The differences are signif-

icant at the .01 level (t test for means) and the .10 level

(one-tail Wilcoxon Z test for distributions). This finding is

consistent with our hypothesis. Companies with high

SFCF tend to use income-increasing DAC to boost

reported earnings.

Panel B reports DAC across Big 6 and non-Big 6

partitions. Big-6-audited firms have lower DAC (signifi-

cant at the .05 level using the one-tail t test and at the .01

level for the Wilcoxon Z test). Consistent with our expect-

ations, Big 6 auditors appear to constrain managers’

discretion in choosing income-increasing DAC. This evi-

dence is consistent with studies that examined Big 6 and

accounting accruals in other contexts (Becker et al., 1998;

Kim et al., 2003; Francis et al., 1999), although we find

that the significance of the mean difference is marginal.

Panel C shows that firms with longer audit tenures have

lower DAC, and the mean difference is highly significant.

The results are directionally consistent with Myers et al.

(2003).

The division of the sample into high and low institutional

shareholdings appears to have no impact on DAC (Panel D).

The differences in DAC across the two groups of ownership

level are not statistically significant. High institutional share

ownership does not appear to constrain managers’ account-

ing choices. This finding is inconsistent with the results of

Rajgopal et al. (2002).

Panel E shows a four-way partitioning of DAC on the

basis of SFCF and B6. The evidence suggests that high

SFCF leads to high DAC, and a Big 6 auditor leads to low

DAC. The Big 6 finding is consistent across observations

with low and high free cash flow. Likewise, the SFCF

finding is consistent across observations with Big 6 and

non-Big 6 auditors. Note, however, that the lowest DAC

occurs in the low SFCF/Big 6 quadrant. Panel F reports

Table 2 (continued)

(G) Low (SFCF= 0) and high (SFCF= 1) SFCF, and low (IS = 0) and high

(IS = 1) institutional ownership subsamples


the four-way partitioning of DAC based on SFCF and

audit tenure. The evidence is consistent with Panels A and

C. High SFCF is associated with high DAC, and firms

Table 2

Univariate test differences in DAC between subsamples

(A) Low (SFCF= 0) and high (SFCF= 1) SFCF subsamples

SFCF= 0 SFCF= 1 t (Z)

Mean DAC

(median)

� 0.004

(� 0.006)

0.003

(� 0.004)

� 3.83

(� 1.36)

N 18320 4256

(B) Big 6 and non-Big 6 subsamples

Non-Big 6 Big 6 t (Z)

Mean DAC

(median)

0.001

(0.000)

� 0.003

(� 0.006)

1.89

(3.68)

N 4089 18487

(C) Short-term and long-term tenure subsamples

Short term Long term t (Z)

Mean DAC

(median)

0.001

(� 0.004)

� 0.007

(� 0.006)

5.16

(1.67)

N 11663 10913

(D) Low (IS = 0) and high (IS = 1) institutional ownership subsamples

IS = 0 IS = 1 t (Z)

Mean DAC

(median)

� 0.003

(� 0.005)

� 0.002

(� 0.005)

0.44

(0.41)

N 5906 5780

(E) Low (SFCF= 0) and high (SFCF= 1) SFCF and Big 6 and non-Big 6

subsamples

Non-Big 6 Big 6 t (Z)

SFCF= 0 Mean DAC

(median)

� 0.002

(� 0.001)

� 0.004

(� 0.007)

1.10

(2.60)

N 3407 14913

SFCF= 1 Mean DAC

(median)

0.013

(0.005)

0.001

(� 0.006)

2.57

(3.34)

N 682 3574

t (Z) � 2.94

(� 2.02)

� 2.84

(� 0.64)

(F) Low (SFCF= 0) and high (SFCF= 1) SFCF, and short-term and long-

term audit tenure subsamples

Short term Long term t (Z)

SFCF= 0 Mean DAC

(median)

0.000

(� 0.005)

� 0.008

(� 0.006)

4.36

(1.27)

N 9728 8592

SFCF= 1 Mean DAC

(median)

0.009

(� 0.001)

� 0.002

(� 0.005)

3.57

(1.88)

N 1935 2321

t (Z) � 3.29

(� 1.62)

� 2.62

(� 0.68)

(G) Low (SFCF= 0) and high (SFCF= 1) SFCF, and low (IS = 0) and high

(IS = 1) institutional ownership subsamples

IS = 0 IS = 1 t (Z)

SFCF= 0 Mean DAC

(median)

� 0.005

(� 0.006)

� 0.004

(� 0.005)

� 0.47

(� 0.32)

N 4714 4573

IS = 0 IS = 1 t (Z)

SFCF= 1 Mean DAC

(median)

0.003

(� 0.003)

0.003

(� 0.002)

0.03

(� 0.31)

N 1192 1207

t (Z) � 2.40

(� 1.23)

� 2.25

(� 1.13)

t (Z) Statistics refer to t test (nonparametric Wilcoxon median test) for

differences in means (distribution).

with a long auditor association have lower DAC. The

smallest DAC occurs in the low SFCF/long tenure quad-

rant. Panel G examines the four-way partitioning of the

sample data on the basis of SFCF and IS. SFCF is a

significant factor in explaining DAC across low and high

institutional shareholding groups. IS remains nonsignifi-

cant. The findings from the four-way partitioning used in

Panels D and E corroborate the findings in Panels A, B,

and C.

3.2. Multivariate results

The regression results for various specifications of Eq.

(1) are shown in Table 3. We report t statistics using the

Newey and West (1987) procedure to avoid problems of

residual autocorrelation. The major variables of interest are

SFCF, B6, SFCF�B6, IS, and SFCF�IS. The length of audit

tenure (LT) is also a variable of interest, although we make

no prediction on the sign. Columns A through E use the full

sample data from 1984 to 1996, while Columns F, G, and H

use data from 1988 to 1996, a period for which we have data

on institutional shareholders.

A consistent finding across all the columns is that SFCF

is positively and significantly related to DAC. This result is

consistent with our hypothesis (H1). Companies with high

SFCF use income-increasing DAC. Here, the increase in

reported profits may reduce the pressure on management

such that they can more easily engage in non-value-maxi-

mizing expenditures.

The auditor variable, B6, has a negative sign in all model

specifications and is statistically significant in most of them.

This suggests that a Big 6 auditor forces or coerces client

companies to reduce income-increasing DAC. The evidence

is consistent with the univariate results reported in Table 2

as well as in prior research (Becker et al., 1998; Kim et al.,

2003). The interaction term, SFCF�B6, has negative and

significant coefficients. Thus, Big 6 auditors act to reduce

DAC in general, but they are especially influential when

clients have SFCF. The evidence is consistent with the

prediction from H2.

Companies with a longer term auditor relationship have

higher DAC, although not all the LT coefficients are signif-

icant. The regression result is opposite to the univariate

Table 3

Regression estimates (t statistics) on DAC model

Variable Predicted

sign

A B C D E F G H

Intercept (?) � 0.029

(� 10.74)

� 0.032

(� 11.31)

� 0.033

(� 13.15)

� 0.035

(� 13.52)

� 0.033

(� 11.56)

� 0.022

(� 6.44)

� 0.023

(� 6.77)

� 0.024

(� 6.00)

SFCF (+) 0.019

(13.88)

0.037

(9.30)

0.019

(13.83)

0.028

(12.56)

0.044

(10.36)

0.018

(10.34)

0.025

(9.74)

0.047

(8.27)

B6 (� ) � 0.009

(� 4.32)

� 0.005

(� 2.26)

� 0.005

(� 2.37)

� 0.004

(� 1.37)

LT (?) 0.001

(0.64)

0.004

(2.63)

0.004

(2.50)

0.004

(1.79)

SFCF�B6 (� ) � 0.021

(� 4.91)

� 0.019

(� 4.58)

� 0.017

(� 2.92)

SFCF�LT (?) � 0.016

(� 5.64)

� 0.015

(� 5.30)

� 0.015

(� 4.16)

IS (� ) 0.000

(� 0.27)

0.003

(1.22)

0.002

(1.19)

SFCF�IS (� ) � 0.014

(� 4.03)

� 0.012

(� 3.42)

DEBT (� ) � 0.085

(� 24.34)

� 0.085

(� 24.35)

� 0.085

(� 24.23)

� 0.085

(� 24.20)

� 0.085

(� 24.31)

� 0.076

(� 16.20)

� 0.076

(� 16.18)

� 0.077

(� 16.22)

RELCF (� ) � 0.307

(� 44.95)

� 0.307

(� 45.02)

� 0.307

(� 44.86)

� 0.308

(� 44.98)

� 0.308

(� 45.06)

� 0.316

(� 30.83)

� 0.317

(� 30.86)

� 0.318

(� 30.89)

SIZE (+) 0.015

(29.83)

0.015

(29.77)

0.014

(29.26)

0.014

(29.14)

0.015

(28.90)

0.012

(15.84)

0.011

(15.79)

0.012

(15.83)

AC (� ) 0.008

(0.67)

0.008

(0.64)

0.008

(0.68)

0.008

(0.69)

0.008

(0.68)

� 0.013

(� 0.71)

� 0.013

(� 0.69)

� 0.011

(� 0.63)

N 22576 22576 22576 22576 22576 11686 11686 11686

Adjusted

r-square

.241 .242 .240 .241 .242 .237 .238 .240

t Statistics are estimated based on the Newey–West adjustment for heteroskedasticity.


results reported in Table 2. One interpretation of these results

is that long-term auditors are less vigilant in constraining

managers’ use of income-increasing DAC because they are

more complacent. The evidence is consistent with Davis et al.

(2003). However, when clients have high SFCF, long-tenure

auditors become much more vigilant in constraining manag-

ers’ use of income-increasing DAC. The signs and magni-

tudes of the coefficients on LT and SFCF�LT suggest that

long-tenure auditors have a net negative impact on DAC

when clients have high SFCFs.

Columns F, G, and H use sample data for 1988 to 1996

and include a variable reflecting institutional shareholdings.

The results show that the institutional shareholder variable is

not significant, and so our results differ from those of

Rajgopal et al. (2002). In Columns G and H, we incorpo-

rate an additional variable reflecting the interaction of

SFCF and institutional shareholdings (SFCF�IS). In both

columns, institutional shareholding (IS) is nonsignificant

while the interaction term, SFCF�IS, is negative and

significant at the .01 level. Our interpretation of this

result is that institutional shareholders act to deter

positive DAC when SFCF is high; in contrast, when

there is no free cash flow agency problem, institutional

shareholders do not constrain the use of positive DAC.

The evidence in Columns G and H is consistent with

H3.

The control variables, DEBT and RELCF, have the

anticipated negative signs, and the coefficients are signif-

icant at the .01 level. These results are consistent with

prior research (Becker et al., 1998; DeAngelo et al.,

1994; Dechow et al., 1995). Note, however, that Becker

et al. (1998) use cash flow of the company, whereas we

use ‘excess’ cash flow divided by lagged total assets

(where the industry median cash flow divided by lagged

total assets are deducted from the company’s cash flow).

The coefficient on SIZE is positive and significant.

Finally, AC is not significantly associated with DAC;

this result contrasts with Becker et al. (1998), who report

a negative and statistically significant coefficient on their

measure of accruals.

3.3. Robustness checks

To examine the robustness of our results, we carry out two

additional analyses. The first disaggregates the results on the

basis of the company’s performance, and the second exten-

sion examines yearly regression results. A common measure

of company performance is Tobin’s Q (Chung and Pruitt,

1994). This is defined as the ratio of the market value of

assets to the replacement cost of assets. Companies with

Q < 1 are said to be performing poorly as the market value is

less than the replacement cost of assets. These companies

Table 4

Regression estimates (t statistics) on DAC for Q < 1 and Q>1 subsamples

Variable Predicted Q < 1 Q>1 Q < 1 Q>1

signA B C D

Intercept (?) � 0.024 (� 7.04) � 0.038 (� 7.98) � 0.012 (� 2.49) � 0.035 (� 5.64)

SFCF (+) 0.056 (13.60) 0.027 (2.15) 0.055 (9.98) 0.040 (2.53)

B6 (� ) � 0.005 (� 1.86) � 0.004 (� 0.97) � 0.004 (� 1.03) � 0.003 (� 0.69)

LT (?) 0.012 (6.89) � 0.001 (� 0.45) 0.009 (4.17) 0.003 (0.86)

SFCF�B6 (� ) � 0.016 (� 3.79) � 0.007 (� 0.57) � 0.012 (� 2.12) � 0.016 (� 0.93)

SFCF�LT (?) � 0.020 (� 6.90) � 0.007 (� 1.01) � 0.017 (� 4.84) � 0.009 (� 1.11)

IS (� ) 0.001 (0.43) � 0.002 (� 0.54)

SFCF�IS (� ) � 0.010 (� 2.87) � 0.005 (� 0.53)

DEBT (� ) � 0.057 (� 14.52) � 0.115 (� 18.76) � 0.056 (� 11.06) � 0.100 (� 11.98)

RELCF (� ) � 0.530 (� 51.87) � 0.193 (� 23.08) � 0.557 (� 41.44) � 0.202 (� 16.49)

SIZE (+) 0.009 (16.03) 0.017 (19.52) 0.007 (9.15) 0.015 (11.54)

AC (� ) � 0.156 (� 12.09) 0.163 (8.89) � 0.176 (� 9.34) 0.145 (5.28)

N 13781 8795 7054 4632

Adjusted

r-square

.424 .189 .447 .177

t Statistics are estimated based on the Newey–West adjustment for heteroskedasticity.


may also have higher agency costs as the poor performance

may reflect willful acts by the managers. Companies with

poor stock market performance, as evidenced by low Q

Table 5

Annual regression estimates (t statistics) on DAC model

(A) Full sample

Year Intercept SFCF B6 LT SFCF�B6 SFCF

(Predicted sign) (?) (+) (� ) (?) (� ) (?)

84 0.003 0.002 � 0.011 � 0.007 � 0.003 0.0

85 � 0.034 0.028 � 0.005 0.005 � 0.003 � 0.0

86 0.004 0.015 � 0.023 0.004 � 0.002 � 0.0

87 � 0.066 0.013 � 0.016 0.005 0.012 � 0.0

88 � 0.043 0.050 � 0.010 0.012 � 0.026 � 0.0

89 � 0.035 0.041 � 0.008 � 0.006 � 0.026 � 0.0

90 � 0.004 0.043 � 0.012 0.003 � 0.009 � 0.0

91 0.019 0.030 � 0.006 0.001 � 0.030 0.0

92 � 0.006 0.033 � 0.008 0.007 � 0.006 � 0.0

93 � 0.033 0.041 � 0.005 0.002 � 0.010 � 0.0

94 � 0.046 0.083 0.001 0.005 � 0.047 � 0.0

95 � 0.055 0.058 � 0.001 0.006 � 0.036 � 0.0

96 � 0.053 0.051 � 0.003 0.011 � 0.019 � 0.0

Average � 0.027 0.037 � 0.008 0.004 � 0.016 � 0.0

t � 3.58 6.36 � 4.61 2.40 � 3.44 � 3.6

(B) Subsample with nonmissing IS variable

Year Intercept SFCF B6 LT SFCF�B6 SFCF

(Predicted sign) (?) (+) (� ) (?) (� ) (?)

88 � 0.057 0.068 0.009 � 0.010 � 0.011 � 0.0

89 � 0.019 0.030 � 0.007 � 0.002 � 0.015 0.0

90 0.013 0.026 � 0.015 � 0.001 � 0.006 � 0.0

91 0.031 0.030 � 0.014 � 0.002 � 0.015 0.0

92 0.010 0.048 � 0.014 0.005 � 0.009 � 0.0

93 � 0.019 0.058 0.003 0.002 � 0.024 � 0.0

94 � 0.031 0.070 � 0.010 0.005 � 0.031 � 0.0

95 � 0.053 0.047 0.006 0.007 � 0.020 � 0.0

96 � 0.041 0.033 � 0.009 0.009 � 0.001 � 0.0

Average � 0.019 0.046 � 0.006 0.002 � 0.015 � 0.0

t � 1.82 8.05 � 1.87 0.77 � 4.70 � 4.0

scores, may be more prone to using positive DAC in an

attempt to increase their ratings. We therefore rerun Eq. (1)

but segregate observations into those where Q < 1 and those

�LT DEBT RELCF SIZE AC

(� ) (� ) (+) (� )

08 � 0.108 � 0.408 0.012 0.017

24 � 0.101 � 0.385 0.016 0.111

04 � 0.072 � 0.398 0.012 � 0.181

14 � 0.047 � 0.393 0.019 0.086

24 � 0.094 � 0.409 0.019 0.056

02 � 0.096 � 0.381 0.019 0.059

22 � 0.108 � 0.398 0.015 � 0.113

10 � 0.071 � 0.312 0.008 � 0.263

22 � 0.085 � 0.304 0.011 � 0.095

14 � 0.080 � 0.279 0.015 0.005

30 � 0.075 � 0.238 0.016 � 0.059

12 � 0.101 � 0.263 0.018 0.088

23 � 0.099 � 0.230 0.016 0.120

13 � 0.088 � 0.338 0.015 � 0.013

8 � 17.76 � 17.73 16.05 � 0.39

�LT IS SFCF�IS DEBT RELCF SIZE AC

(� ) (� ) (� ) (� ) (+) (� )

29 � 0.006 � 0.020 � 0.122 � 0.400 0.024 0.252

00 0.007 � 0.019 � 0.104 � 0.481 0.015 0.068

13 � 0.002 0.004 � 0.085 � 0.483 0.011 � 0.077

03 0.004 � 0.025 � 0.076 � 0.396 0.007 � 0.209

14 0.011 � 0.029 � 0.075 � 0.314 0.007 � 0.144

15 � 0.003 � 0.005 � 0.065 � 0.333 0.011 � 0.097

28 0.003 � 0.013 � 0.060 � 0.263 0.014 � 0.094

16 � 0.002 � 0.010 � 0.086 � 0.299 0.016 0.078

21 � 0.001 � 0.001 � 0.096 � 0.245 0.016 0.099

15 0.001 � 0.013 � 0.085 � 0.357 0.013 � 0.014

3 0.68 � 3.55 � 13.04 � 12.17 7.46 � 1.82


where Q>1. We estimate Q, with company (i) and time (t)

subscripts, as follows:

Qit ¼ ðMVEit þ PSit þ DEBTitÞ=TAit ð5Þ

where MVE is the market value of common stock; PS is the

liquidating value of preferred stock; DEBT is the value of a

company’s short-term liabilities net of its short-term assets,

plus book value of long-term debt; and TA denotes the total

assets.

All variables aremeasured at the year’s end. This approach

to computing Q follows Chung and Pruitt (1994). Chung and

Pruitt (1994) find that this relatively simple calculation

compares very well with (gives very similar results to) the

more complex procedures used by Lindenberg and Ross

(1981).

The results of partitioning on the basis of high Q and low

Q are shown in Table 4. SFCF has significantly positive

coefficients in all columns. The magnitudes of the coeffi-

cients are much higher when Q < 1. Thus, when companies

have poor stock market ratings, SFCF has a much stronger

influence on income-increasing DAC. The Big 6 variable

has the expected negative sign in all the partitions, but only

one of the coefficients is significant at the .05 level, one-tail

test (this occurs when Q < 1). The interaction term,

SFCF�B6, is especially strong when Q < 1. When companies

Table 6

Regression estimates (t statistics) on change in DAC (DDAC) model

Variable Predicted

sign

A B C

Intercept (?) � 0.004

(� 1.82)

� 0.005

(� 1.96)

� 0.003

(� 2.30)

SFCF (+) 0.003

(2.00)

0.008

(1.72)

0.004

(2.06)

B6 (� ) � 0.000

(� 0.02)

0.001

(0.36)

LT (?) � 0.002

(� 0.92)

SFCF�B6 (� ) � 0.006

(� 1.14)

SFCF�LT (?)

IS (� )

SFCF�IS (� )

DDEBT (� ) � 0.224

(� 19.21)

� 0.224

(� 19.20)

� 0.225

(� 19.22)

DRELCF (� ) � 0.566

(� 65.64)

� 0.566

(� 65.66)

� 0.566

(� 65.67)

DSIZE (+) 0.051

(10.91)

0.051

(10.89)

0.051

(10.88)

DAC (� ) � 0.062

(� 5.34)

� 0.062

(� 5.34)

� 0.062

(� 5.32)

N 19743 19743 19743

Adjusted

r-square

.473 .473 .473

t Statistics are estimated based on the Newey–West adjustment for heteroskedast

have poor stock market performance (Q < 1), the presence

of a Big 6 auditor reduces the magnitude of the association

between SFCF and DAC. When Q < 1, companies with

long-term associations with their auditors have higher

DAC, although this is cancelled out if SFCF is high.

SFCF�IS is significant in Column C and has the expected

negative sign. DEBT, RELCF, and SIZE are significant in

all columns, and so the evidence is similar to that in Table 3.

When we partition by Q, AC becomes highly significant but

with negative signs when Q < 1 and positive signs when

Q>1. The evidence from Table 4 confirms that high SFCFs

are associated with positive DAC. The interactions of Big 6

auditors and institutional shareholders with SFCF reduce

DAC when companies have poor stock market ratings

(Q < 1). Thus, Big 6 firms and institutional investors are

more vigilant when a company’s SFCF is high and stock

market valuation is low. Long audit tenure is associated with

higher DAC when Q < 1, but these auditors become more

vigilant when SFCF is high, and so DAC is reduced. In

summary, monitoring by Big 6 auditors and institutional

investors is much more acute when companies have low

stock values as measured by the Q ratio.

We also report the regression results estimated separately

for each year. This reduces the potential effect of any serial

correlation in the regression error terms. In the annual

regressions, an individual company appears just once. We

D E F G H

� 0.003

(� 1.68)

� 0.004

(� 1.34)

� 0.005

(� 3.10)

� 0.006

(� 3.10)

� 0.008

(� 1.97)

� 0.001

(� 0.20)

0.005

(0.89)

0.003

(1.43)

0.005

(1.60)

0.007

(0.93)

0.001

(0.51)

0.002

(0.60)

� 0.003

(� 1.51)

� 0.003

(� 1.57)

0.000

(0.14)

� 0.007

(� 1.30)

� 0.006

(� 0.91)

0.007

(1.99)

0.008

(2.10)

0.006

(1.36)

� 0.003

(� 1.50)

� 0.002

(� 0.91)

� 0.002

(� 1.02)

� 0.004

(� 0.98)

� 0.004

(� 0.85)

� 0.225

(� 19.23)

� 0.225

(� 19.22)

� 0.218

(� 13.17)

� 0.217

(� 13.16)

� 0.217

(� 13.13)

� 0.566

(� 65.66)

� 0.566

(� 65.67)

� 0.580

(� 45.45)

� 0.580

(� 45.46)

� 0.580

(� 45.43)

0.051

(10.88)

0.051

(10.85)

0.053

(7.63)

0.053

(7.62)

0.053

(7.61)

� 0.062

(� 5.33)

� 0.062

(� 5.33)

� 0.059

(� 3.70)

� 0.059

(� 3.70)

� 0.059

(� 3.73)

19743 19743 10540 10540 10540

.473 .473 .481 .481 .481

icity.


use the Fama and MacBeth (1973) approach to form an

average coefficient from the 13 regressions and compute

the t statistics thereon. The results for the full 13 years of

regressions are shown in Table 5, Panel A. Themain variables

of interest, SFCF, B6, and SFCF�B6, are statistically signif-

icant and have the expected signs. The results corroborate the

evidence from Table 3, and they are consistent with our

hypotheses. The variables LT and SFCF�LT are also signifi-

cant and have the same signs as in Table 3.

Panel B of Table 5 reports the annual regression results

using data from 1988 to 1996; this sample incorporates a

variable reflecting institutional shareholdings. The results

show SFCF to be highly significant as is the case with all

the previous analyses. The Big 6 (B6) and Big 6 interaction

term (SFCF�B6) and the tenure interaction term (SFCF�LT)have negative coefficients and are significant at the .01 level.

The IS variable itself is not significant, while the interaction

term (SFCF�IS) is significant at the .01 level. This result againsuggests that institutional shareholders effectively constrain

the management’s use of income-increasing DAC when

SFCF is high.

A final test is to run a regression of changes in DAC on

the independent variables. (We thank a reviewer for sug-

gesting this analysis.) The results are shown in Table 6.

SFCF has positive signs in seven specifications of the

model, although significance levels vary. Companies that

increase DAC do so when they have high SFCF. The

monitoring variables, B6, LT, IS, and the interaction terms

are generally not significant in the analyses. However,

SFCF�LT is positive and significant in Columns D and E.

4. Summary

Discretionary accounting accruals provide mechanisms

for managers to adjust earnings towards some preferred

level. A growing body of research has examined manag-

ers’ motives for using DAC and has used these motives to

predict earnings. We extend this line of research by

investigating the relationship between SFCF and DAC

and the moderating effect of monitoring variables on the

SFCF–DAC relationship. This paper argues that compa-

nies with high SFCF use income-increasing DAC to

camouflage the earnings impact of non-value-maximizing

investments and other expenditures. Our empirical results

using data from 1984 to 1996 confirm our hypothesis of a

positive relationship between SFCF and DAC.

Big 6 auditors moderate the SFCF–DAC relationship.

Due to their conservatism and their desire to avoid litigation,

Big 6 auditors constrain management from making income-

increasing DAC. This behavior is especially strong when

SFCF is high. Institutional shareholders also have a moder-

ating effect on DAC but only when SFCF is high.

Longer term auditor–client relationships have been ar-

gued to have an impact on audit quality, but there is

considerable disagreement as to the sign of the association.

Our results indicate that auditor tenure has an impact on the

use of DAC, although there are two factors at work. Compa-

nies with a long-term auditor relationship have higher DAC,

thus suggesting that more leeway is given by the auditors.

However, when SFCF is high, companies with long-term

auditor relationships have lower DAC. These results can be

used to support auditor rotation (the LT results) or to support

long tenure (the SFCF�LT results).

Our main findings still apply when we conduct sensitivity

tests. Companies with poor stock market ratings, as measured

by Tobin’s Q, show a very strong relationship between SFCF

and DAC. Debt, relative cash flows, and size are also

significantly related to discretionary accounting accruals.

SFCF represents non-wealth-maximizing expenditures and

thus signals a significant agency cost to shareholders. We

show that companies with high SFCF use income-increasing

DAC.Management uses DAC to camouflage the poor returns

from the negative NPVexpenditures funded from RCF. Big 6

auditors, acting as agents for stockholders, and institutional

investors with large shareholdings inhibit managers from

engaging in opportunistic earnings management when com-

panies have SFCF. Our findings add to the expanding

literature that addresses discretionary accounting choice.

Acknowledgements

We are very grateful to the editors and reviewers whose

helpful and insightful comments and suggestions greatly

improved the paper. This paper has also benefited from

comments made by workshop participants at the Hong Kong

Polytechnic University. We gratefully acknowledge partial

financial support for this research from Hong Kong

Polytechnic University research grants.

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