monitoring mechanism, overvaluation, and earnings …conference/conference2010/...overvaluation and...

Monitoring Mechanism, Overvaluation, and Earnings Management

Shing-yang Hu* and Yueh-hsiang Lin

†

Abstract

Prior research suggests that overvalued equity can exert market pressures on managers to inflate

earnings. However, we posit that in a well-functioning capital market, external and internal

monitoring mechanisms can deter such behaviors. Using U.S. data from 1987 to 2005, we test

the market pressure hypothesis against the monitoring hypothesis. Consistent with the

monitoring hypothesis, we find no statistical relation between the degree of overvaluation and

future accruals or real earnings management for highly monitored firms, such as large companies,

S&P 1500 companies, or companies followed by analysts. The market pressure hypothesis holds

only when monitoring is weak.

JEL Classification: G10, G34, M40, M52

Keywords: Accrual, Earnings management, Market pressure, Overvaluation, Monitoring

* Department of Finance, National Taiwan University, Room 715, No. 85, Sec. 4, Roosevelt Road, Taipei, Taiwan

106. Telephone: +886-2-33661085; Fax: +886-2-23661299; E-mail address: [email protected] † Department of Finance, National Taipei College of Business, Room 833, No.321, Sec. 1, Jinan Rd., Zhongzheng

District, Taipei, Taiwan, 100. Telephone: +886-2-23226469; Fax: +886-2-23226378; E-mail address:

[email protected]

mailto:[email protected]

mailto:[email protected]

Optimistic visions of a new economic era set the stage for an explosion in financial

values. . . . It was an environment in which incentives for business management to keep

reported revenues and earnings growing to meet expectations were amplified.

– Paul A. Volcker, ―Finally, A Time for Auditing Reform,‖ June 25, 2002,

Remarks at the Conference on Credible Financial Disclosures,

Kellogg School of Management, Northwestern University

1. Introduction

The literature has clearly established that corporate decisions can be driven by market

value. When equity is overvalued, companies are more likely to make more investments (Baker,

Stein, and Wurgler, 2003; Polk and Sapienza, 2009; Stein, 1996), conduct more mergers and

acquisitions (e.g., Dong et al., 2006; Rhodes-Kropf, Robinson, and Viswanathan, 2005; Shleifer

and Vishny, 2003), offer more securities and pay more dividends (Baker and Wurgler, 2000,

2002, 2004), and use more accruals (Chi and Gupta, 2009). In addition, many of these

researchers also show that these decisions are followed by lower stock returns, suggesting that

they are not beneficial to investors. In fact, Jensen (2005) argued that the agency costs of

overvalued equity set into motion a series of value-destroying forces that can undermine core

firm value. Although the literature widely agrees on the problem, the solution remains an open

question. That is, researchers have yet to fully address whether any existing monitoring

mechanisms can deter managers' value-destruction behaviors. To investigate this question, we

examine the relation between overvaluation and earnings management decisions.

Earnings management is an important component of corporate decision making.

Supporting Volcker’s (2002) claim, Graham, Harvey, and Rajgopal (2005) find that executives

manage earnings to meet outsiders’ expectations of future earnings. Anecdotal evidence (e.g.,

Enron) also suggests that companies inflate their earnings when the market expectation becomes

2

excessive. However, certain monitoring mechanisms can prevent managers from inflating the

earnings. Therefore, we investigate the relation among degree of monitoring, market

overvaluation, and earnings management. We consider two hypotheses regarding the impact of

overvaluation on earnings management: the market pressure hypothesis and the monitoring

hypothesis.

The market pressure hypothesis, drawn from Jensen’s (2005) agency theory of

overvalued equity, suggests that when investors irrationally overvalue a firm, they anticipate a

growth rate of cash flows higher than what the firm can realistically achieve. As a consequence,

managers may engage in earnings management to generate earnings reports that satisfy these

investors as well as justify the firm’s overvalued market price. Therefore, under the market

pressure hypothesis, the extent of earnings management is expected to be higher for firms with

higher overvaluations.

The monitoring hypothesis acknowledges the impact of market pressure but emphasizes

the effectiveness of monitoring from either external information intermediaries or internal

corporate governance mechanisms. External intermediaries, such as analysts and rating agencies,

regularly track financial statements and interact with managers. Under constant monitoring,

inflated earnings through management are likely to be detected and, therefore, unlikely to affect

stock prices. In organizations in which internal governance mechanisms are in place, managers

may pay a high price if their management of earnings is revealed. That is, the board may

terminate their employment (Karpoff, Lee, and Martin, 2008a), and the reputation of the

company may be harmed, resulting in a drop in the market price (Karpoff, Lee, and Martin,

2008b) and, consequently, a drop in the value of any shares or options held by the manager.

Managers will, therefore, resist managing earnings even when faced with overvaluation if

3

sufficient monitoring is in place. Thus, according to the monitoring hypothesis, the extent of

earnings management is not affected by market overvaluation.

In this study, we measure the degree of market overvaluation by using the ratio of the

market value of equity to its fundamental value. The fundamental value can be estimated by

following either Rhodes-Kropf et al.’s (2005) regression approach or the discounted residual

income approach (Frankel and Lee, 1998; Ohlson, 1990). The advantage of the residual income

approach is that it based on a valuation model and uses analysts’ forecasts. The drawback is that

the requirement of analysts’ forecasts data biases our results against the market pressure

hypothesis. When analysts follow a company, they play a monitoring role by scrutinizing the

firm’s financial statements. As a result, managers are less likely to manage earnings, and we are

less likely to find evidence supporting the market pressure hypothesis. In contrast, the

Rhodes-Kropf et al.’s regression approach only uses information from financial statements and

can be applied to the entire sample. Therefore, we use the regression approach for the whole

sample and compare results from both approaches for the subsample that includes analysts’

forecasted data.

We measure the extent of earnings management by using performance-matched

discretionary accruals (Kothari, Leone, and Wasley, 2005). Using firms listed on the NYSE,

Amex, and Nasdaq from 1987 to 2005 as our sample, we find no significant difference in

discretionary accruals between high-overvalued and low-overvalued companies. Therefore, on

average, we find stronger support for the monitoring hypothesis than for the market pressure

hypothesis.

We then examine cross-sectional differences, which provide further evidence for the

monitoring hypothesis. High-profile firms that receive more attention from outsiders such as

4

analysts, the media, and investors tend to have a more symmetric information environment,

which increases the likelihood that earnings management will be discovered. Therefore, even

with overvaluation, managers will not chance earnings management. Consistent with the outside

monitoring hypothesis, we find no relation between market overvaluation and future accruals for

large firms or firms included in the S&P 1500 index in the Investor Responsibility Research

Center (IRRC) database or in the I/B/E/S database. By contrast, firms receiving less attention

from the financial community (i.e., small firms and firms not included in the S&P 1500 index or

the IRRC or I/B/E/S databases) exhibit a significantly positive relation between overvaluation

and accruals.

For firms receiving less attention from the financial community, market pressure is

related to their operating performance and their dependence on equity financing. When an

overvalued company has poor operating performance, its managers face much greater pressure

to improve performance and are more likely to engage in earnings management than managers

of better performing companies. Furthermore, when overvalued companies are highly dependent

on the equity market, managers are more likely to engage in earnings management to support the

market price to preserve their financing channel. Consistent with both predictions, we find a

strongly positive relation between market valuation and future accruals for firms with poor

operating performances and with a high degree of equity dependence but only for the sample of

non-S&P or non-I/B/E/S companies.

In addition to accruals, we also examine three measures of earnings management through

real activities (Cohen, Dey, and Lys, 2008; Cohen and Zarowin, 2010; Roychowdhury, 2006).

Our results are similar to accruals. Consistent with the market pressure hypothesis, we find no

relation between market overvaluation and future real earnings management for firms included

5

in the S&P 1500 index. We also find some weak evidence to support the market pressure

hypothesis.

Overall, this study adds to the line of research that examines how corporate decisions are

affected by overvaluation. In particular, in the investigation of the relation between

overvaluation and earnings management, our paper is closest to Efendi, Srivastava, and Swanson

(2007) and Chi and Gupta (2009). Efendi et al. use the Internet bubble as a period of

overvaluation and find that a restatement is more likely when the CEO has a sizeable holdings of

stock options. Chi and Gupta find a positive relation between overvaluation and accruals.

Neither paper discusses deterrents to the agency cost of overvaluation. Jensen (2005) discusses

many possible solutions to the agency cost associated with overvaluation (e.g., governance

mechanisms, short selling) but argues that they are ineffective. The first and the foremost

contribution of our paper is to propose the monitoring hypothesis and provide supporting

evidence that monitoring is an effective tool to limit earnings management. Specifically, we find

no significant relation between overvaluation and accruals for firms that receive more attention

from investment communities. Our second contribution is to examine the cross-sectional

implication of the market pressure hypothesis. For firms that receive less attention and

monitoring, we provide evidence that overvalued companies that are performing poorly or have

financing needs have a stronger tendency to conduct earnings management through either

accruals or real activities.

The remainder of the paper is organized as follows. Section 2 analyzes the relation

between the degree of market overvaluation and the subsequent accruals. In Section 3, we

further test the monitoring and the market pressure hypotheses by examining specific firm

characteristics. Section 4 provides robustness tests, and Section 5 concludes our findings.

6

2. A first look at the relation between market overvaluation and subsequent earnings

management

Our sample includes firms that are listed on the NYSE, Amex, and Nasdaq and included

in the COMPUSTAT and CRSP databases from 1987 through 2005. Financial companies (SIC

codes 6000–6999) are excluded. As we note in the following discussion, our results are mainly

based on accruals estimated from cash flow statements; therefore, we begin our sample period in

1987, the year that COMPUSTAT began to report cash flow data.

We use the ratio of market value of equity to its fundamental value (hereinafter M/V) as

the proxy for the degree of market overvaluation. Theoretically, a firm may be priced high due to

either the rational expectation of higher growth opportunities or investors’ irrational

overvaluation. The M/V measures the discrepancy between market valuation and fundamental

value and reflects the degree of overvaluation. To estimate the fundamental value, we follow

Rhodes-Kropf et al. (2005) by first estimating the following regression for each of the 12 Fama

and French industries for each year t:3

0 1 2 3 ( 0) 4ln( ) ln( ) ln( ) ln( )it jt jt it jt it jt NI it jt it itM B NI I NI Lev

, (1)

where ln(‧) is the natural logarithmic function, the subscript i indicates firm i, j indicates

industry j, M is the CRSP market value of equity at the end of the fiscal year, B is the

COMPUSTAT book value of equity for the fiscal year (COMPUSTAT annual data item 60), NI+

is the absolute value of the reported net income (item 172), I(NI<0) is an indicator function that

equals 1 when net income is negative and zero otherwise, and Lev is the book leverage (1 – book

3 The authors thank Kenneth French for providing data on the 12 Fama and French industry classifications on his

website (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french).

7

value of equity/total assets).4 We then average over time for each parameter α,

j

T

t jtT

1ˆ

1. (2)

The estimated fundamental value (V) is the natural exponent of the fitted value:

0 1 2 3 ( 0) 4ln( ) ln( ) ln( ) ln( )it j j it j it j NI it j itV B NI I NI Lev

. (3)

To estimate the extent of earnings management, we first calculate total accruals (Ac)

from items on the statement of cash flow:

1,

/)(

ttttCF

AssetsOCFNIAc , (4)

where the subscript CF indicates that the accruals are computed from items on the cash flow

statement and the subscript t indicates the year; OCF is the total cash flow from operations (item

308); and Assets is the total assets (item 6). To reduce the impact of extreme values, we delete

any total accruals with an absolute value greater than 1 and winsorize the total accruals at the 1st

and 99th percentiles.

An alternative method to calculate total accruals in previous studies is to use items on the

balance sheet.5 However, the literature (Collins and Hribar, 2002; Kraft, Leone, and Wasley,

2006) suggests that accruals estimated from the balance sheet can be biased. In our sample, we

4 Although the market-to-book value of equity is commonly obtained by matching the book value of equity with

the market value four months after the fiscal year, we use the fiscal year-end data to ensure that the market value is

predetermined for the first quarter of the next year’s accounting results.

5 Accruals estimated from balance sheet are calculated as the one-year change of the current assets unrelated to

cash (item 4 – item 1) minus the one-year change of the current liability excluding short-term debt and taxes

payable (item 34 – item 5 – item 71) and the depreciation and amortization expense (item 14), and then scaled by

lagged total assets.

8

find that the estimates of total accruals from cash flow statements and balance sheets are quite

different. Specifically, as Table 1 shows, the median (mean) of the total accruals is –0.0520

(–0.0555) based on cash flows statements (AcCF) and –0.0395 (–0.0306) based on balance sheets

(AcBS).

<< INSERT TABLE 1 ABOUT HERE>>

More relevant to our study is that the bias of accruals estimated from balance sheets is

correlated with market overvaluations. In the first column of Panel A in Table 2, for each year t

from 1987 to 2004, we sort M/V into quartiles. For the lowest M/V quartile in the first column,

the averages are –0.0728 for total accruals (in year t+1) estimated from the cash flow statements

and –0.0533 for total accruals estimated from balance sheets—a difference of 0.0195. In the

second column, the difference is even larger (0.0288) for the highest M/V. The correlation

between bias and overvaluation could be due to correlations between bias and merger and

acquisition (M&A) activities and between overvaluation and M&A. Recent studies have found

that M&A are more intense in higher market valuation periods (Moeller, Schlingemann, and

Stulz, 2005). To account for this bias, we use COMPUSTAT Annual footnote 1 to identify

acquisition activity (codes AA). At the year of M/V groups formation, the percentage of

acquisitions is 12.56% (20.35%) for the lowest (highest) M/V quartile. When one firm acquires

another, balance sheets are combined, and the combined net current assets (current assets minus

current liabilities) become greater than the net current assets before the combination (Collins and

Hribar, 2002). Therefore, the accruals estimated from the balance sheet are upwardly biased

from acquisitions. To eliminate this bias and avoid wrong inferences, most of our results are

9

based on accruals estimated from cash flow statements. However, in the regression analysis of

Section 4, we also report results based on accruals estimated from the balance sheet and get

similar results.


To concentrate on the effect of market overvaluation to earnings management, we filter

out the effect of the firms’ expected growth on accruals. When managers have higher

expectations of future earnings, they increase working capital to serve growth needs. Therefore,

accruals are an indicator of the firms’ expected growth (Chan, Chan, Jegadeesh, and Lakonishok,

2006).

We first estimate four widely used discretionary accruals models: (i) the Jones (1991)

model, (ii) the modified Jones model (Dechow, Sloan, and Sweeney, 1995), (iii) the CFO model

(Dechow, Kothari, and Watts, 1998), and (iv) the DD model (Dechow and Dichev, 2002). We

run cross-sectional regressions for all firms in our sample for each year and each two-digit SIC

code industry as follows:

, ,

, 0 1 2 3 ,

, 1 , 1 , 1

1[ ] [ ] [ ]

i t i t

i t i t

i t i t i t

Sales PPEAc

Assets Assets Assets

; (5)

, , ,

, 0 1 2 3 ,

, 1 , 1 , 1

1[ ] [ ]

i t i t i t

i t i t

i t i t i t

Sales AR PPEAc


; (6)

, , ,

, 0 1 2 3 4 ,

, 1 , 1 , 1 , 1

1[ ] [ ] [ ] [ ]

i t i t i t

i t i t

i t i t i t i t

Sales PPE CFOAc

Assets Assets Assets Assets

; (7)

, , , , 1

, 0 1 2 3 4 5

, 1 , 1 , 1 , 1 , 1

1[ ] [ ] [ ] [ ] [ ]

i t i t i t i t

i t

i t i t i t i t i t

Sales PPE CFO CFOAc

Assets Assets Assets Assets Assets

10

, 1

6 ,

, 1

[ ]i t

i t

i t

CFO

Assets

, (8)

where the subscript i indicates firm i, ΔSales is the change in sales (item 12), PPE is net

property, plant and equipment (item 8), ΔAR is the change in accounts receivable (item 2), and

CFO is cash from operations (item 18 – AcBS). Following Kothari et al. (2005), we require a

minimum of ten observations and include a constant for each cross-sectional regression. The

fitted values from these regressions are the nondiscretionary accruals. The residuals in equations

(5) to (8) are the discretionary accruals, JACF (estimated from the Jones model), MJACF

(estimated from the modified Jones model), CFOACF (estimated from the CFO model), and

DDACF (estimated from the DD model).

Second, we estimate performance-matched discretionary accrual. Because discretionary

accruals are not well specified for firms with extreme operating performance (Dechow et al.,

1995; Kothari et al., 2005), we use a matching-firm approach to further remove the portion of

accruals that are operations related. Specifically, for each sample firm in the highest and lowest

M/V quartiles, we choose as a matching firm that has the closest return on assets (ROA;

measured as net income divided by lagged total assets) in year t+1 from all observations in the

second and third quartiles that have the same two-digit SIC code as the sample firm. Using

middle quartiles to choose the matching firms ensures that the matching firms are similar to the

sample firms in operating performance but different in market overvaluations. We then subtract

the matching firm’s discretionary accruals from the sample firm’s discretionary accruals to

obtain the performance-matched accruals.

To test the market pressure against the monitoring hypotheses, we examine the relation

between future accruals and M/V. Recall that the market pressure hypothesis predicts a positive

relation between M/V and future accruals, and the monitoring hypothesis predicts that

11

subsequent accruals will be independent of M/V. In the sorting of all observations on M/V in

year t into quartiles, we test the difference in the mean of accruals in year t+1 between the

extreme M/V quartiles, as shown in Table 2. At first glance, as the third column of Panel B

shows, firms in the highest M/V quartile have higher discretionary accruals than those in the

lowest quartile. The mean of the DD (Dechow and Dichev, 2002) discretionary accruals is

0.0011 for the highest quartile and 0.0072 and for the lowest quartile. The difference of 0.0062 is

significant at a 0.01 level. The results are consistent with findings in Chi and Gupta (2009).

However, the significance diminishes after controlling for ROA. For the ROA-matched

discretionary accruals from the DD model, the difference between the highest and the lowest

M/V quartiles is only 0.0027 and not significantly different from zero at the 0.10 level.

Therefore, as Kothari et al. (2005) suggest, controlling for operating performance is important

for studies on accruals. The relation between M/V and future accruals is fairly weak, which is

more consistent with the monitoring hypothesis than the market pressure hypothesis.

3. A closer look: The market pressure hypothesis and the monitoring hypothesis

In our previous discussion, we observe a weak relation between future discretionary

accruals and M/V. This result generally supports the monitoring hypothesis but not the market

pressure hypothesis. However, given overvaluation, not every firm is subject to the same

incentive to manipulate earnings. Managers with strong incentives are more likely to manage

earnings than managers with little or no incentives. On the other hand, the weak relation

between M/V and future accruals may also be a result of a low monitoring threshold within a

firm. To put these arguments into perspective, we examine several firm traits that may increase

market pressures or monitoring activities.

12

3.1. Firm traits that affect the level of external monitoring

We examine two firm traits that may lead to differences in monitoring intensities on

overvalued companies: (1) The extent of external monitoring, and (2) the soundness of the

governance system. Earnings management is directly related to external monitoring. When the

investment community pays little attention to a firm, a poor information environment develops,

and investors are unable to differentiate between actual earnings and inflated earnings.

Conversely, when firms are well-researched by the investment community, earnings inflated by

accruals will be filtered by well-informed analysts and, therefore, are unlikely to affect stock

prices. Consequently, overvalued companies that receive more attention from the investment

community will be less inclined to manipulate their earnings.

Because security analysis involves a large component of fixed cost, potential revenues

are low, and liquidity is poor for smaller firms, we use firm size as our first measure of the level

of monitoring, and find a positive correlation between M/V and firm size. Panel A in Table 3

reports the frequency distribution of firm size within extreme M/V portfolios. For the highest

(lowest) M/V quartile, 48% (58%) of observations are from the largest (smallest) size quartile.

Therefore, if we fail to control for size, the average accruals of the high M/V quartile will mainly

reflect numbers from large firms and make it difficult for us to uncover relations between M/V

and accruals under the monitoring hypothesis.


We adopt a bivariate grouping method to examine whether overvalued companies are

13

more likely to engage in earnings management after controlling for size. In particular, we use a

two-way independent sorting method based on M/V and size at the end of year t. Because results

for ROA-matched and unmatched accruals are similar, for brevity, we report only the findings of

the ROA-matched DD discretionary accruals.

Panel A in Table 4 reports a two-way analysis of discretionary accruals across M/V and

size quartiles. We find that average accruals are larger for smaller and more overvalued

companies. For the largest size quartile, the difference in mean accruals between the extreme

M/V quartiles is -0.0074 which is not significantly different from zero. As firm size decreases,

the difference in mean accruals monotonically increases. Specifically, the differences in mean

accruals are 0.0078, 0.0206, and 0.0381 for the third, second, and first quartile, respectively, and

the latter two differences are significantly different from zero at the 0.01 level. Smaller size

alone is not, however, a sufficient condition for high accruals: The average accrual is only

0.0021 and is not significantly different from zero for the smallest size and lowest M/V portfolio.

Smaller firms must also be overvalued by the market to be motivated to manipulate earnings.

Therefore, market pressure hypothesis only applies to smaller firms, while our results on large

firms are consistent with the monitoring hypothesis.


As alternative measures of the degree of external monitoring, we use the inclusion in the

S&P 1500 index and tracking by the I/B/E/S database or the Investor Responsibility Research

Center (IRRC, currently RiskMetrics Group) database. Analysts pay more attention to firms that

are included in the S&P index, that are tracked by IRRC, or that the I/B/E/S database contains

14

analyst forecasts for. For each criterion, we split the whole sample into high and low monitoring

groups: The high-monitoring group contains firms included in the index or databases, and the

low-monitoring group contains other firms. Under the monitoring hypothesis, there should be no

difference in accruals between the extreme M/V quartiles for the high-monitoring group. Results

in Panel B in Table 4 support the monitoring hypothesis. For the low-monitoring group, the

discretionary accrual is significantly higher for overvalued companies. The largest difference is

0.0083 and occurs for companies without earnings forecast; presumably they face the least

external monitoring. By contrast, overvalued companies do not have significantly higher

accruals for the high-monitoring group. Take S&P companies as an example: When companies

are included in the S&P index, their average discretionary accruals are significantly negative

whether their stocks are overpriced or not. There is no significant difference in average accruals

between high and low overvalued companies.6 On the other hand, for companies not included in

the S&P index, the average accrual for overvalued companies is significantly positive and higher

than non-overvalued companies.

One concern for our measure of overvaluation is its measurement error. The fundamental

estimated from Rhodes-Kropf et al.’s (2005) regression approach may be different from the

fundamental perceived by managers. Consequently, firms measured as overvalued by this

method may in fact not be perceived as overvalued in the eyes of managers, and we therefore

6 To examine whether the S&P results are independent of the size results, we use a three-way sorting in untabulated

tests. Firms are grouped based on size, S&P status, and overvaluation. We find that, for small companies included in

the S&P index, the difference in accruals between high and low overvalued firms is not significant. By contrast, for

large non-S&P companies, overvalued firms have a significantly higher accrual. Therefore, the S&P status is a

stronger indication of market pressure and in the regression analysis followed in this study we divide our sample

based on S&P status.

15

would not observe a significantly higher accrual.

However, the measurement error is unlikely to affect our conclusions for two reasons.

First, the measurement error issue should be larger for small firms because the external

monitoring of small firms is weaker and the information asymmetry between outside investors

and managers is stronger. This would cause the measurement error to create a stronger bias

against the market pressure hypothesis for small firms. However, it is in small firms that we find

evidence to support the market pressure hypothesis.

Second, our measure of overvaluation is correlated with the extent of seasoned equity

offerings (SEO). The extent of SEO is a measure of the overvaluation perceived by managers,

because managers will issue new shares to time the market (Baker, Ruback, and Wurgler, 2005).

Specifically, we measure the extent of SEO by the amount of the sale of common and preferred

stock (item 108) deflated by the market capitalization. The median number of SEO at year t+1 is

0.13 percent for the lowest M/V quartile and 0.57 percent for the highest quartile. Therefore, the

managerial decision of SEO is consistent with our measure of overvaluation.

3.2. Firm traits that affect the level of the soundness of the governance system

In addition to external monitoring, the effectiveness of earnings management is also

related to the soundness of the governance system. That is, a manager’s ability to manage

earnings is directly related to the firm’s ability to monitor his or her behavior. If internal

monitoring leads to the discovery of earnings management, managers may pay a high price, i.e.,

the board may choose to terminate the offending manager’s employment. Furthermore, as the

reputation of the company may be hurt, it may result in a drop in market price (Jensen, 2005),

where then the value of any shares or options held by the manager decreases. Finally, the odds of

16

a takeover increase. The magnitude of the cost is related to the extent that the governance

mechanism is aligned with shareholders’ interests. Accordingly, overvalued companies with

sounder governance mechanism should be less inclined to manipulate earnings.

To measure the degree of alignment of the governance mechanism with shareholders’

interests, we first use a governance index adapted from Gompers, Ishii, and Metrick (2003).

Beginning in 1990, the IRRC has compiled a database once every two years to document

corporate governance provisions that restricts shareholder rights. Gompers et al. classified these

24 provisions into five groups: Tactics for delaying hostile bidders, voting rights, director/officer

protection, other takeover defenses, and state laws. For every firm, they add 1 point for every

provision that restricts shareholder rights and increases managerial power; the governance index

is the sum of all these points. For ease in comparing with the incentive ratio introduced later, we

define our governance index as increasing with shareholder rights. Therefore, we subtract

Gompers et al.’s (2003) governance index from 24 to obtain our governance index (hereinafter

GI).

For each year from 1990, we form two-way portfolios based on an independent sorting

of the M/V in year t and the most recent GI. Panel C in Table 4 reports the average accruals for

the valuation and governance index portfolios. The difference in mean accruals between the

extreme M/V quartiles is 0.0076 (-0.0034) for the lowest (highest) GI group. The positive

(negative) sign of the differences for the lowest (highest) GI group is consistent with the

argument that managers from overvalued companies are less aggressive in managing earnings

when the governance mechanism is more aligned with shareholders interest. These differences,

however, are not statistically significant at the 0.10 level.

We also use Bergstresser and Philippon’s (2006) incentive ratio to measure the degree of

17

alignment between the governance system and shareholders’ interests. This incentive ratio

measures the relation between the CEO’s compensation and stock price; refer to the Appendix

for a detailed description. To construct the incentive ratio, we obtain information of CEOs’

ownership of stock and stock option holdings from the ExecuComp database covering the years

1992 onward. Whether incentive compensation mitigates or aggravates earnings management is

a matter of debate. On the one hand, incentive compensation may reduce agency problems

between shareholders and managers. If it is structured to serve the shareholders’ long-term

interest, incentive compensation could reduce earnings management. On the other hand, Jensen

(2005) argues that compensation cannot solve the problem of overvalued equity. In fact, closely

linking compensation to the stock price may induce managers to manipulate earnings to make

short-term profits and increase personal wealth. Both Bergstresser and Philippon (2006) and

Efendi et al. (2007) also provide evidence that equity-based compensation is correlated with

earnings management.

Our test for the monitoring hypothesis is, in essence, an examination of whether

corporate governance among higher overvalued firms can alleviate managerial incentives to

manipulate earnings. Specifically, we use a two-way classification on the M/V and incentive

ratio portfolios. In Panel D in Table 4 we find that in the highest incentive ratio (IR) quartile, the

difference in average accruals between the extreme M/V quartiles is -0.0026, which is not

significant at the 0.10 level. This finding is consistent with the monitoring hypothesis.

Furthermore, for the most overvalued quartile, the difference in average ROA-matched

accruals between high and low incentive companies is 0.0134, which is not significant at the

0.10 level. Our results seem contradictory to earlier studies that show equity incentives lead to

earnings management. For example, Cheng and Warfield (2005) show that CEOs with high

18

equity incentive increase abnormal accruals. The difference between Cheng and Warfield and

our study is related to our control of ROA. That is, if we use DD discretionary accruals (Dechow

and Dichev, 2002) instead, we find that for the most overvalued quartile, the average accrual is

significantly higher for high incentive companies.7 This again echoes Kothari et al.’s (2005)

findings that controlling for operating performance is important for studies on accruals.

The last measure of governance mechanisms is the independence of the audit committee.

An audit committee is defined as independent if all its members are independent directors. We

obtain the data from IRRC. The results are reported in Table 4 – Panel E, which shows that

whether the audit committee is independent or not, there is no significant difference in accruals

between high or low overvalued companies.

To summarize, we find that overvalued companies do not have significantly higher

discretionary accruals as long as they are large, tracked by analysts, or included in the S&P 500

index. These results are consistent with the view that outside monitoring can reduce the

incentive to manage earnings. Although we do not find any differences in accruals between high

and low overvalued companies along the governance dimension, it does not indicate that

governance is not effective. The governance variables we use comes from the ExecuComp and

IRRC databases and these databases only cover large firms or S&P 1500 companies. Given the

effect of extensive outside monitoring on earnings management, the governance mechanism

becomes less important.

3.3. Firm traits that affect the level of market pressure

We consider two firm traits that lead to market pressures on managers of overvalued

7 We also run regressions and obtain similar results, which are available on request.

19

companies: Realized operating performance and equity dependence. All managers face greater

pressure to improve operating performance when it is poor than when it is good. However, when

overvalued firms underperform, managers face additional pressure to justify the inflated price as

these firms may face large revaluations of their stock prices. Following this rationale, operating

performance can be plausibly linked to earnings management.

We adopt a bivariate grouping method to examine whether overvalued but poor

performing firms are more likely to engage in earnings management. In particular, we use ROA

as a measure of the realized operating performance and a two-way independent sorting based on

M/V at the end of year t and ROA in year t+1.

The extent of overvaluation and future performance tends to be positively correlated but

is not monotonically so. Panel B in Table 3 shows that within the lowest M/V quartile companies

tend to have low ROA. On the other hand, within the highest M/V quartile, companies have

either very high or very low ROA: More than one third is in the highest ROA quartile and one

quarter is in the lowest ROA quartile.

The results on accruals are reported in Panel A in Table 5. In the lowest ROA quartile, the

average accruals for year t+1 are 0.0116 (-0.0103) for the highest (lowest) M/V quartile. The

difference of 0.0218 is significant at the 1% level. The differences are no longer significantly

positive for firms in the other three ROA quartiles. Therefore, not all overvalued companies will

increase their accruals; only those with bad operating performances are under market pressure to

do so.

Moreover, for the highest ROA quartile, it is the lowest M/V companies that have

significantly positive accruals (0.008). It seems that companies are aggressive in earnings

reporting only when their market valuations and operating performances are mismatched. That is,

20

not only overvalued companies with a weak operating performance want to justify their market

prices, but undervalued companies with a strong operating performance also want to boost their

market prices.


The second characteristic we examine is the degree of equity dependence. Higher levels

of equity dependence can lead to increased market pressures for overvalued companies. Prior

studies (e.g., Teoh, Welch, and Wong, 1998a, 1998b) have suggested that equity offerings are

directly related to higher accruals. To make the terms more attractive, managers manage the

earnings to keep the market price before the issuance. When firms are more equity dependent,

pressures to conserve the equity financing channel are larger. Therefore, we predict that among

overvalued firms, the extent of earnings management will be positively related to the extent of

equity dependence.

To test our assumption, we follow Kaplan and Zingales (1997), Lamont, Polk, and

Saá-Requejo (2001), and Baker et al. (2003) to construct the KZ index as a measure of equity

dependence or financial constraint, as follows:

, , , , , ,1.002 39.368 1.315 3.139 0.283i t i t i t i t i t i tKZ CF Div C Lev Q , (9)

where CF is the cash flow (item 14 + item 18), Div is cash dividends (item 19 + item 21), and C

is cash balances (item 1). All of these variables are scaled by lagged total assets.8

8 An alternative definition of the equity dependence measure excludes Tobin’s Q for conceptual cleanness of the

equity transaction and the investment opportunities (Baker et al., 2003). In untabulated tests, we construct the equity

dependence measure that excludes Tobin’s Q; the results are similar.

21

We use a two-way independent sorting on the (M/V)t and the KZ index in year t. Panel C

in Table 3 reports average discretionary accruals in year t+1. Within the highest KZ index

quartile, the average accruals are 0.0099 for the highest M/V quartile and -0.0049 for the lowest

M/V quartile. The difference of 0.0148 is significant at the 0.01 level. By contrast, for firms in

the other three KZ index quartiles, the differences are not significantly positive. Therefore,

evidence on equity dependence also supports the market pressure hypothesis.

3.4. Interaction between monitoring and market pressure

We have seen that individually both monitoring and market pressure help to decide

whether overvalued companies have higher accruals; now we want to see whether the market

pressure effect still exists within the subsample that has strong monitoring.

Panels C and D in Table 5 report the bivariate grouping based on the extent of

overvaluation and market pressure for the sample of S&P 1500 companies. We find that

overvalued S&P companies do not increase their accruals even when their operating

performances are bad or when they have high degree of equity dependence. As a result, there is

no significant difference in discretionary accruals between high and low overvalued companies

for the lowest ROA quartile and the highest KZ index quartile.

4. Robustness check

We now extend the bivariate analysis in three directions to check the robustness of our

results. We first divide our sample into S&P 1500 companies and non-S&P 1500 companies,

employ a multivariate analysis, and compare the economic significance of different variables in

Section 4.1. Section 4.2 performs a robustness check using alternative measures of earnings

management. A final robustness check using an alternative measure of fundamental values is

22

conducted in Section 4.3.

4.1. Multivariate analysis

Because our bivariate analysis suggests that high-attention firms behave very differently

from low-attention firms, we divide our sample into S&P 1500 companies and non-S&P 1500

companies. We then run multiple regressions to take all the variables into consideration. Given

that our samples include both time-series and cross-sectional data, we use the Fama–MacBeth

(1973) methodology. For each year from 1987 to 2004, we run a cross-sectional regression to

estimate coefficients. Then, we calculate the time-series average and standard deviation of

coefficients to determine whether they are significantly different from zero.

We use the DD discretionary accrual (DDACF; Dechow and Dichev, 2002) as the

dependent variable in the regression, which is calculated from the cash flow statements in year

t+1. The independent variables are (a) overvaluation, (b) size (the logarithmic of market

capitalization), ROA, and the extent of equity dependence (KZ), (c) interaction terms between (a)

and (b), (d) the lagged accrual, and (e) the industry (defined by two-digit SIC code) dummies.

We do not use the ROA-matched discretionary accruals as the dependent variable

because doing so would leave out all the matching observations and cut the total sample size in

half. Instead, we include the ROA in year t+1 as an independent variable in the regression to

control for any operating requirement effect not entirely captured by the DD discretionary model.

We also include the lagged accrual as an independent variable. We control for lagged accrual

because firms may boost earnings and thereby the valuations at year t and the accruals reverse in

the following year. We do not include the governance index and the incentive ratio variables in

the multivariate regression because they are only available for S&P firms from 1992 onwards,

23

and because they are not significant in the bivariate analyses.


Table 6 reports the time-series averages of regression coefficients. In Panel A, results are

generally consistent with those in the grouping analysis. To start, consistent with the results of

Table 2, when it comes to the effect of overvaluation on earnings management it is important to

control for ROA. For the S&P companies, when ROA is uncontrolled in Model 1, the M/V

coefficient is 2.55 and significantly different from zero at the 0.10 level.9 In Model 2, after

controlling for ROA, the M/V coefficient decreases to –1.44 and is not significantly different

from zero at the 0.10 level, while the ROA coefficient is 0.23 with t-statistic 14.21. The results

echo the positive correlation between M/V and ROA in Table 3. In S&P companies, the

effectiveness of monitoring prevents managers with overvaluation from conducting earnings

management. On the other hand, for less-monitored non-S&P companies, managers with

overvaluation engage in earnings management to meet market pressure. The effect of

overvaluation is significant for non-S&P companies even when controlling for ROA. The

t-statistic of ROA is higher than that of M/V (13.44 vs. 7.47, respectively).

To further examine the relative economic significance of M/V and ROA on the accruals, we

calculate the sensitivity of the accrual with respect to M/V and ROA. Specifically, we calculate

the unit change of a quartile deviation of the accruals with respect to per-unit change of a

quartile deviation of M/V and of ROA. A quartile deviation is one-half of the difference between

the 75th percentile and the 25th percentile. We use the quartile deviations rather than standard

9 For readability, we time the coefficients on overvaluation, size, KZ, and the interaction terms by 1000.

24

deviations because the quartile deviation is more resistant to extreme observations. Using the

results of the S&P companies in Model 2, the fitted accrual of a firm with an M/V of 0.64 (the

25th percentile; see Table 1) is 0.0016, or equivalently 0.0344 quartile deviations, less than that

of a firm with an M/V of 1.77 (the 75th percentile).10

Therefore, the sensitivity of the accrual

with respect to M/V is –0.0172. On the other hand, the sensitivity of the accrual with respect to

M/V for non-S&P companies is 0.07. In terms of the sensitivities of the accrual with respect to

ROA, they are 0.31 and 0.25, respectively, for S&P companies and non-S&P companies. Similar

to the statistical significances, the sensitivities of the estimated ROA coefficients are much larger

than those of the estimated M/V coefficients.

Model 3 in Panel A in Table 6 examines the firm characteristics related to monitoring and

market pressure. The focus is on the coefficients of the interactions of overvaluation and firm

characteristics. We find that for S&P companies, none of the interaction coefficients are

significant, while for the non-S&P companies they are. These results are generally consistent

with the predictions of the monitoring and market pressure hypotheses.

We further focus on the statistics and the economic significance of M/V on the accruals

for the non-S&P companies. For firm size, a monitoring variable, the interaction coefficient is

-2.04, which equals 4.4 standard errors from 0. For the economic significance, we compute the

sensitivity of the accrual with respect to overvaluation at different firm size levels in Table 7.

Again, the sensitivity is calculated in the unit of quartile deviation. Given the firm size at its 25th

percentile (28.04 million; see Table 1), the fitted accrual increases only 0.10 quartile deviations

10

The change in accrual is evaluated at the median of other variables. The fitted accrual, given M/V is at the 75th

(25th) percentile and the other independent variables are at the median, is around 0.0034 (0.0050). One quartile

deviation of the accrual is 0.0476.

25

as M/V increases 1 quartile deviation, i.e. the sensitivity is 0.10. The sensitivity drops to one

third of this when firm size increases to its 75th percentile. Thus, the smaller the market

capitalization is, the larger the impact of overvaluation on accruals becomes. This supports the

monitoring hypothesis, where monitoring activities reduce the incentive of overvalued

companies to manage earnings.


For the characteristics related to the market pressure hypothesis, such as ROA and the

KZ index, results are also consistent with the bivariate analysis in Table 5. The coefficient on the

interaction between overvaluation and ROA is -20.61 with t-statistic –3.71, and the sensitivity is

0.09 (0.05), given ROA at its 25th (75th) percentile. This means that the poorer the realized

operating performance is, the larger the impact of overvaluation on accruals becomes. On the

other hand, the coefficient on the interaction between overvaluation and the KZ index is 0.81

with t-statistic 2.01. The sensitivity is 0.06 (0.07), given KZ at its 25th (75th) percentile. These

results support the market pressure hypothesis that market pressure makes overvalued

companies susceptible to earnings management.

To summarize, judged by either statistical or economical significance, the effect of

market pressure is weaker than that of monitoring. The findings show that overvalued companies

use accruals more aggressively only when firms receive less attention.

One remaining concern is whether our results only exist in the Internet bubble at the end

of the 1990s. Efendi et al. (2007) document a rash of earnings manipulations during the period

between January 1997 and June 2002, which influenced the passage of the Sarbanes-Oxley Act

26

of 2002. Panels B and C in Table 6 report the time-series averages of regression coefficients of

the entire investigated period excluding 1996 to 2000, and of the period 1996 to 2000,

respectively. For S&P companies, the interaction coefficients are found to be not significant for

either period. For non-S&P companies, all the interaction coefficients are highly significant

when excluding the bubble period, while during the bubble period, firm size and ROA

interaction coefficients are also both significant. We therefore conclude that our evidence

regarding the monitoring and market pressure hypotheses is not driven by the Internet bubble

period.

4.2. Alternative estimates of earnings management

In the preceding analysis, the results are mainly based on DD discretionary accruals

(DDACF; Dechow and Dichev, 2002). As a robustness check, we present multiple regressions

using alternative estimates of earnings management documented in previous literature in Table 8.

As space is limited, we only report coefficients related to the overvaluation variable. To begin,

we focus on alternative discretionary accruals models constructed from items on the cash flow

statement. Consistent with the monitoring hypothesis, the coefficients of M/V and its interaction

terms in Panel A of Table 8 are not significantly different from zero for S&P companies, while

evidence of the market pressure hypothesis does exist for non-S&P companies. In Panel B, the

coefficients are significantly negative for the interaction between overvaluation and ROA

(t-statistics are from –5.14 to –4.73) and positive for the interaction between overvaluation and

the KZ index (t-statistics are from 2.08 to 2.38). Thus, overall, the results are unchanged when

the discretionary accruals are estimated through alternative models.

27


Results of the discretionary accruals so far are constructed from items on the cash flow

statement. Although accruals constructed from items on the balance sheet suffer an upward bias,

as we documented in Section 2, it is undisputedly an approach that has often been used in

previous studies. Consequently, we report multiple regressions using the modified Jones

discretionary accrual calculated from the balance sheet. For S&P companies, the coefficients of

M/V and its interaction terms are still not significantly different from zero. Thus, results are

similar when the accruals are calculated from the balance sheet as when they are calculated from

the cash flow statement. Furthermore, evidence that the market pressure hypothesis exists only

for non-S&P companies remains. In Panel B, the coefficients are significantly negative for the

interaction between overvaluation and ROA and positive for the interaction between

overvaluation and the KZ index.

Lastly, we examine the earnings management through real activities, which has become a

focus of recent literature (Cohen, Dey, and Lys, 2008; Cohen and Zarowin, 2010; Roychowdhury,

2006). Hence, we ask if the market pressure hypothesis and the monitoring hypothesis hold

regarding the impact of overvaluation on real earnings managements.

Following Roychowdhury (2006), we use three measures of real earnings management.

Firstly, companies can produce inventory more than necessary to lower cost of goods sold and

increase reported earnings. By producing more units, managers spread the fixed costs, causing

the fixed costs (and thereby cost of goods sold) per unit to be lower. However, the firm will still

incur much higher incremental holding costs, which cause annual producing costs to increase

relative to sales, and cause a decline in cash flow from operations given sales levels. The higher

28

the production cost is, the more extent of real earnings management is. Empirically, abnormal

production costs are estimated through the following cross-sectional regression within each

two-digit SIC code industry and each year:

, , , 1

, 0 1 2 3 4 ,

, 1 , 1 , 1 , 1

1[ ] [ ] [ ] [ ]

i t i t i t

i t i t

i t i t i t i t

Sales Sales SalesPRODA

Assets Assets Assets Assets

, (10)

where PRODA is the production cost (cost of goods sold (item 44) + change in inventory (item

3)) scaled by the lagged total assets. The abnormal productions costs, ABPRODA, are the

residuals in equation (10).

Secondly, firms can accelerate the timing of sales by offering price discounts or lenient

credit terms. Either way will cause a temporary increase in sales volume (which will disappear

when the firm reverts to old prices). Thus, the earnings in the current year will increase as the

additional sales are recorded with an assumption of positive margins. However, the current-year

cash flow from operations will decrease; this lower levels of cash flow from operations proxy

for higher levels of real earnings management. Thus, to establish the second proxy for real

earnings management, we run the following cross-sectional regression estimated for each

two-digit SIC code industry and each year:

, ,

, 0 1 2 3 ,

, 1 , 1 , 1

1[ ] [ ] [ ]

i t i t

i t i t

i t i t i t

Sales SalesOCFA


, (11)

where OCFA is the total cash flow from operations (item 308) scaled by the lagged total assets.

For ease in comparing with the results of the accruals, we define the second proxy for real

earnings management, the abnormal cash flow from operations, ABOCFA, as negative one times

the residuals in equation (11), so that a higher ABOCFA means higher levels of real earnings

management.

29

Thirdly, managers can increase reported earnings by reducing discretionary expenses

such as R&D, advertising, selling, and general and administrative expenses. Therefore, a lower

discretionary expense can reveal a higher level of real earnings management. To establish the

third proxy for real earnings management, we run the following cross-sectional regression

estimated for each two-digit SIC code industry and each year:

,

, 0 1 2 ,

, 1 , 1

1[ ] [ ]

i t

i t i t

i t i t

SalesDISEXPA

Assets Assets

, (12)

where DISEXPA is the discretionary expenses (R&D (item 46)+Advertising (item 45)+Selling,

General and Administrative expenses (item 189)) scaled by the lagged total assets. We define the

third proxy for real earnings management, the abnormal discretionary expenses, ABDISEXPA, as

negative one times the residuals in equation (12), so that higher levels of ABDISEXPA proxies

for higher levels of real earnings management.

There is no evidence that, for highly monitored S&P companies, those that are

overvalued will manage their earnings through real activities. From Panel A of Table 8, neither

the coefficients of M/V, nor the coefficients of interaction terms are significantly different from

zero for S&P companies. Combining results on accruals and real earnings management, there is

strong evidence that supports the monitoring hypothesis for S&P companies.

On the other hand, for non-S&P companies, there is weak evidence to support the market

pressure hypothesis. In Panel B, for both the abnormal production costs and abnormal cash flows

from operations, the coefficients are significantly negative for the interaction between

overvaluation and ROA (–8.19 and –59.47). For the abnormal cash flow from operations, the

coefficients are significantly positive for the interaction between overvaluation and the KZ index

(65.77). Results are most similar when the earnings management is measured by the abnormal

30

cash flow from operations.

4.3. Alternative measure of fundamental values

Our estimates of fundamental values used thus far are based on regressions, which has

the least requirement on data availability and can be easily applied to the whole sample. The

weakness is that it is not based on any valuation models. An alternative measure is based on a

discounted residual income approach (Frankel and Lee, 1998; Ohlson, 1990). This model-based

estimator requires analysts’ forecast of future earnings per share from the I/B/E/S database. As a

robustness check, we also estimate the model-based fundamental values for companies that have

the data. To estimate the fundamental value from I/B/E/S analysts’ consensus forecasts, we use

the following equation:

1 2 3, 1 22 2(1 ) (1 ) (1 )

t e t e t eA t t t t t

e e e e

FROE r FROE r FROE rV B B B B

r r r r

, (13)

where Bt is the book value; re is a three-factor industry-specific cost-of-equity (Fama and French,

1997); FROEt+i is the forecasted return on equity (ROE) for period t+i, calculated as

2 1/[( ) / 2]t i t i t i t iFROE FEPS B B ; (14)

and FEPSt+i is the I/B/E/S consensus forecasts of EPS for period t+i. Future book values are

computed as

1[1 (1 )]t i t i t iB B FROE k , (15)

where k is the dividend payout ratio, calculated as the common stock dividends paid (item 21)

divided by the net income before extraordinary items (item 237).

Table 9 reports multiple regression results using both regression-based estimates and

forecast-based estimates of the fundamental value. The sample includes all companies available

in the I/B/E/S database. The qualitative results from two estimates of the fundamental values are

31

very similar. On average, these firms receive regular scrutiny from analysts, and we find little

evidence that overvalued companies are aggressive in their management of earnings accruals.

However, we do find some evidence that overvalued companies with poor operating

performances manage earnings more aggressively. Therefore, overall, our results are robust to

different estimates of the fundamental value.


5. Conclusion

Using U.S. data from 1987 to 2005, we test the market pressure hypothesis against the

monitoring hypothesis. The monitoring hypothesis predicts that subsequent earnings

management will be independent of market overvaluation when market monitoring is strong.

The market pressure hypothesis predicts a positive relation between market overvaluation and

future earnings management when monitoring is weak and market pressure is strong.

Consistent with the monitoring hypothesis, we find no relation between market

overvaluation and different measures of accruals and real earnings management in firms

included in the S&P 1500 index. Only for companies with weak monitoring do we find evidence

consistent with the market pressure hypothesis. For companies that are not in the S&P index and

do not have any analysts following, there is a positive relation between market overvaluation

and future accruals in poor-performing and equity-dependent firms. We also find similar results

when the real earnings management is measured by cash flow from operations. These findings

are consistent with the market pressure hypothesis.

32

Thus, monitoring is found to be an important factor in the decision of whether or not to

manage earnings by overvalued companies. Whether monitoring can play a role in other

corporate decisions will be left for future research.

33

Appendix.

The incentive ratio

To measure executive incentives from the CEOs’ company stock and option holdings, we

first calculate the dollar change in the value of CEOs’ stock and option holdings for a 1%

increase in company stock price:

)(01.0 OPTIONSSHARESPRICEOnepct , (16)

where PRICE is the company’s fiscal year-end stock price, SHARES is the number of shares held

by the CEO (including restricted stocks), OPTIONS is the number of options held by the CEO,

and is the sensitivity of the CEO’s option portfolio value to the value of the underlying stock.

We follow Core and Guay’s (2002) approach to estimate . ExecuComp reports detailed

information on current year option grants and aggregate information on year-end option holdings.

Core and Guay partition the CEO’s options into three grants: Those awarded in the current year,

those awarded in previous years and currently exercisable, and those awarded in previous years

but currently unexercisable. Exercisable and unexercisable previously granted options are

treated as two single grants. For each of the three grants, the is estimated through

Black–Scholes–Merton option pricing formula (Black and Scholes, 1973; Merton, 1973). For

every 1% increase in stock price, the dollar change in the value of CEOs’ option holdings is

equal to the sums of the dollar changes in the value of the three grants.

Second, we normalize Onepct by the total compensation generated by 1% increase in

stock price:

)/( BONUSSALARYOnepctOnepctIR , (17)

where IR denotes the incentive ratio and SALARY and BONUS are the dollar value of the CEO’s

base salary and bonus, respectively. As the incentive ratio increases, the weight of equity-based

34

compensation (i.e., stocks and options) relative to the CEO’s total compensation also increases

as do the links between company stock price and the CEO’s wealth and, consequently, the

CEO’s incentives.

35

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39

Table 1

Summary statistics

Variable Mean SD Q1 Median Q3 N

AcCF –0.0555 0.1385 –0.1084 –0.0520 –0.0004 64,353

AcBS –0.0306 0.1240 –0.0874 –0.0395 0.0115 64.353

JACF 0.0028 0.1261 –0.0447 0.0074 0.0569 64,353

MJACF 0.0031 0.1274 –0.0455 0.0067 0.0570 64,353

CFOACF 0.0081 0.1276 –0.0398 0.0113 0.0619 58,048

DDACF 0.0060 0.1188 –0.0390 0.0081 0.0562 50,927

Market-to-fundamental value of

equity (M/V) 1.5400 2.4820 0.6368 1.0740 1.7731 64,353

Market capitalization (M, in $MM) 1,388.06 8,810.96 28.04 112.16 506.69 64,353

Fundamental value estimated from

regressions (V, in $MM) 769.78 3091.75 35.08 109.27 396.33 64,353

Market-to-book ratio of equity

(M/B) 3.6929 47.7782 1.0758 1.8388 3.2932 64,353

Return on assets (ROA) –0.0204 0.3233 –0.0359 0.0376 0.0899 64,353

KZ Index 0.6389 1.4494 0.0402 0.7310 1.4857 60,528

Incentive ratio 0.1862 0.2364 0.0319 0.0885 0.2378 14,411

Governance index 14.7377 2.7209 13 15 17 13,268

The sample is comprised of firm-year observations that are covered by COMPUSTAT and CRSP databases from 1987

through 2005. The total accrual (Ac) is constructed from items on the cash flow statement (CF) or items on the

balance sheet (BS) and is scaled by total asset at the end of the previous year. An observation of an accrual is dropped

if its absolute value exceeds 1, and the total accrual is winsorized at the 1st and 99th percentiles. The discretionary

accruals are residuals of cross-sectional regressions estimated for each year and each two-digit SIC code industry

using the entire COMPUSTAT data with the Jones (1991) model (JA), the modified Jones model (MJA; Dechow et al.

1995), the CFO model (CFOA; Dechow et al., 1998), or the DD model (DDA; Dechow and Dichev, 2002). Both the

M/B and firm size are at the end of each fiscal year. M/V is the market-to-fundamental value of equity and measures

the extent of overvaluation. The measure of value (V) proxies for fundamental value and is estimated by regressing

the market equity on the book equity, the net income (COMPUSTAT annual data item 172), and the book leverage

ratio (see Rhodes-Kropf et al., 2005). ROA is the net income scaled by the lagged total assets (item 6). The KZ Index

measures the degree of equity dependence (see Baker et al., 2003; Kaplan and Zingales, 1997; Lamont et al., 2001).

The governance index is adapted from Gompers et al. (2003); their index number is 24 minus the index used here. The

incentive ratio is defined as the dollar change in the value of a CEO’s stock and option holdings for a 1% increase in

the stock price (see Appendix).

40

Table 2

One-way sorting analysis on the overvaluation (M/V) in year t

Panel A: Relation between (M/V)t and total accruals in year t+1

Mean Median

Accruals t+1 Lowest (M/V)t Highest (M/V)t Highest –

Lowest Lowest (M/V)t Highest (M/V)t Highest –

Lowest AcCF, t+1 –0.0728*** –0.0600*** 0.0128*** –0.0598*** –0.0557*** 0.0041***

AcBS, t+1 –0.0533*** –0.0312*** 0.0221*** –0.0505*** –0.0393*** 0.0112***

Panel B: Relation between (M/V)t and discretionary accruals from the cash flow statement in year t+1

Without control Controlling for performance (ROAt+1)

Mean accruals t+1 Lowest (M/V)t Highest (M/V)t Highest –

Lowest Lowest (M/V)t Highest (M/V)t Highest –

Lowest JACF, t+1 –0.0079*** 0.0022** 0.0101*** –0.0015 –0.0013 0.0002

MJACF, t+1 –0.0096*** 0.0031*** 0.0127*** –0.0023** –0.0005 0.0019

CFOACF, t+1 0.0019 0.0099*** 0.0080*** –0.0013 –0.0016 –0.0003

DDACF, t+1 0.0011 0.0072*** 0.0062*** –0.0004 –0.0031** 0.0027

M/V is the market-to-fundamental value of equity and measures the extent of overvaluation. The fundamental value

(V) is estimated from regressions. The sample is comprised of firm-year observations that are covered by

COMPUSTAT and CRSP databases from 1987 through 2005. For each year t from year 1987 to 2004, we sort

observations by M/V at the end of year t into four portfolios. Panel A reports the total accrual (Ac) as constructed from

items on the cash flow statement (CF) or items on the balance sheet (BS) and scaled by total assets at the end of the

previous year. An observation of accrual is dropped if its absolute value exceeds 1, and the total accrual is winsorized

at the 1st and 99th percentiles. In Panel B, the discretionary accruals are residuals of cross-sectional regressions

estimated for each year and each two-digit SIC code industry using the entire COMPUSTAT data with the Jones (1991)

model (JA), the modified Jones model (MJA; Dechow et al., 1995) , the CFO model (CFOA; Dechow et al., 1998), or

the DD model (DDA; Dechow and Dichev, 2002). To control for performance, we first find a control observation from

the second and third M/V quartiles for each observation in the highest and the lowest M/V quartiles at the end of year

t; both observations are taken from the same industry and have the closest ROA in year t+1. The ROA-matched

discretionary accrual is the difference of discretionary accruals between the extreme and the control observations.

***, **, * denote significance at the 0.01, 0.05, and 0.1 levels, respectively.

41

Table 3

Distribution of firms over the quartiles of firm size, measures of the realized operating performance, and the

degree of equity dependence in (M/V)t extreme quartiles (%)

Panel A: Distribution over firm size in year t (Mt) quartiles

Smallest Mt 2 3 Largest Mt

Highest (M/V)t 7.09 16.94 28.18 47.79

Lowest (M/V)t 58.15 26.21 11.21 4.44

Panel B: Distribution over realized operating performance (ROAt+1) quartiles

Lowest ROAt+1 2 3 Highest ROAt+1

Highest (M/V)t 25.90 17.71 20.00 36.39

Lowest (M/V)t 31.62 31.14 23.02 14.22

Panel C: Distribution over degree of equity dependence in year t (KZt) quartiles

Lowest (KZ)t 2 3 Highest (KZ)t

Highest (M/V)t 22.27 21.98 23.85 31.91

Lowest (M/V)t 26.19 24.22 24.52 25.07


(V) is estimated from regressions. The sample comprises firm-year observations that are covered by COMPUSTAT

and CRSP databases. For each year t, we sort, two-way independent, on (M/V)t and the firm size at the end of year t

(Panel A), ROA in year t+1 (Panel B) and the KZ Index (Panel C), which generates 16 portfolios.

42

Table 4

Tests for the monitoring hypothesis: Mean ROA-matched DDACF, t+1 of two-way portfolios sorting on M/V

and the extent of external monitoring and the soundness of the governance mechanism

Panel A: Sorting on M/V and firm size in year t (Mt)

Smallest Mt 2 3 Largest Mt Largest – Smallest

Highest (M/V)t 0.0402*** 0.0170*** 0.0045 –0.0207*** –0.0609***

Lowest (M/V)t 0.0021 –0.0036 –0.0033 –0.0133** –0.0154***

Highest – Lowest 0.0381*** 0.0206*** 0.0078 –0.0074

Panel B: Sorting on M/V and the inclusion of the index or database in year t

Constituents of the S&P

1500 index

Coverage in the IRRC

database

Coverage in the I/B/E/S

database

No Yes No Yes No Yes

Highest (M/V)t 0.0051** –0.0197*** 0.0035 –0.0175*** 0.0132*** –0.0204***

Lowest (M/V)t 0.0002 –0.0219** –0.0005 –0.0095** 0.0050*** –0.0132***

Highest – Lowest 0.0048* 0.0021 0.0040* –0.0080 0.0083*** –0.0072**

Panel C: Sorting on (M/V)t and the governance index (GI) in year t

Lowest GI t 2 3 Highest GIt Lowest – Highest

Highest (M/V)t 0.0047 –0.0046 –0.0035 –0.0020 –0.0067

Lowest (M/V)t –0.0029 –0.0067 –0.0001 0.0014 0.0043

Highest – Lowest 0.0076 0.0021 –0.0034 –0.0034

Panel D: Sorting on (M/V)t and the incentive ratio (IR) in year t+1

Lowest IRt 2 3 Highest IRt Lowest – Highest

Highest (M/V)t –0.0171** 0.0021 0.0006 –0.0038 0.0134

Lowest (M/V)t 0.0004 –0.0067* –0.0058 –0.0012 –0.0016

Highest – Lowest –0.0175** 0.0088 0.0064 –0.0026

Panel E: Sorting on M/V and the independence of the audit committee

Percentage of independent directors in

the audit committee is 100%

No Yes

Highest (M/V)t –0.0049* –0.0011

Lowest (M/V)t 0.0032 –0.0041

Highest – Lowest –0.0081 0.0030


(V) is estimated from regressions. The sample is comprised of firm-year observations from the following databases:

Panels A, and B: COMPUSTAT and CRSP; Panels C and E: COMPUSTAT, CRSP, and IRRC; and Panel D:

COMPUSTAT, CRSP, and ExecuComp. Sample period coverage is as follows: Panel A and the rightmost two columns

and the leftmost two columns of Panel B: 1987–2005; middle two columns of Panel B and Panel C: 1990–2005; Panel

D: 1992–2005; and Panel E: 1997–2005. The discretionary accruals are residuals of cross-sectional regressions

estimated for each year and each two-digit SIC code industry using the entire COMPUSTAT data with the DD model

(DDACF; Dechow and Dichev, 2002), where the accruals are constructed from items on the cash flow statement. To

control for return on assets (ROA), we first find a control observation from the second and third M/V quartiles for

each observation in the highest and the lowest M/V quartiles at the end of year t; both observations are taken from the

same industry and have the closest ROA in year t+1. The ROA-matched discretionary accrual is the difference of

43

discretionary accruals between the extreme and the control observations. Except Panels B and E, for each year t, we

sort, two-way independent, on (M/V)t and the firm size at the end of year t (Panel A), the governance index in year t

(Panel C), and the incentive ratio in year t+1 (Panel D), which generates 16 portfolios. In Panel B, an independent

sorting on (M/V)t and the inclusion of the index or database at the end of year t generates eight portfolios. In Panel E,

an independent sorting on (M/V)t and the independence of the audit committee at the end of year t generates eight

portfolios. The governance index is 24 minus Gompers et al.’ (2003) index. The incentive ratio is defined as the dollar

change in the value of a CEO’s stock and option holdings for a 1% increase in the stock price (see Appendix).


44

Table 5

Tests for the market pressure hypothesis: Mean ROA-matched DDACF, t+1 of two-way portfolios sorting on

M/V and measures of the realized operating performance and the degree of equity dependence

Panel A: Sorting on (M/V)t and realized operating performance (ROAt+1)

Lowest ROAt+1 2 3 Highest ROAt+1 Highest – Lowest

Highest (M/V)t 0.0116*** –0.0014 –0.0028 –0.0140*** –0.0248***

Lowest (M/V)t –0.0103*** 0.0009 0.0037 0.0080* 0.0183***

Highest – Lowest 0.0218*** –0.0023 –0.0065* –0.0220***

Panel B: Sorting on (M/V)t and the degree of equity dependence in year t (KZt)

Lowest( KZ)t 2 3 Highest (KZ)t Highest–Lowest

Highest (M/V)t –0.0057** –0.0115*** –0.0082** 0.0099*** 0.0156***

Lowest (M/V)t 0.0065** –0.0002 –0.0023 –0.0049** –0.0115***

Highest – Lowest –0.0122*** –0.0113*** –0.0059 0.0148***

Panel C: Sorting on (M/V)t and realized operating performance (ROAt+1), S&P 1500 companies

Lowest ROAt+1 2 3 Highest ROAt+1 Highest – Lowest

Highest (M/V)t –0.0101** –0.0029 0.0004 0.0007 0.0107

Lowest (M/V)t –0.0096** 0.0021 –0.0014 –0.0071 0.0025

Highest – Lowest –0.0005 –0.0050 0.0018 0.0077

Panel D: Sorting on (M/V)t and the degree of equity dependence in year t (KZt), S&P 1500 companies

Lowest( KZ)t 2 3 Highest (KZ)t Highest–Lowest

Highest (M/V)t –0.0061* –0.0009 –0.0044* –0.0024 0.0037

Lowest (M/V)t –0.0042 –0.0087** 0.0001 –0.0062 –0.0019

Highest – Lowest –0.0019 0.0078* –0.0044* 0.0038


(V) is estimated from regressions. The sample is comprised of firm-year observations that in Panels A and B are

covered by COMPUSTAT and CRSP databases from 1987 through 2005, and in Panels C and D are covered by

COMPUSTAT, CRSP, and ExecuComp databases from 1992 through 2005. The discretionary accruals are residuals of

cross-sectional regressions estimated for each year and each two-digit SIC code industry using the entire

COMPUSTAT data with the DD model (DDACF; Dechow and Dichev, 2002), where the accruals are constructed from

items on the cash flow statement. To control for return on assets (ROA), we first find a control observation from the

second and third M/V quartiles for each observation in the highest and the lowest M/V quartiles at the end of year t;

both observations are taken from the same industry and have the closest ROA in year t+1. The ROA-matched

discretionary accrual is the difference of discretionary accruals between the extreme and the control observations. For

each year t, we sort, two-way independent, on (M/V)t and ROA in year t+1 (Panels A and C) and the KZ Index (Panels

B and D), which generates 16 portfolios.


Table 6

Time-series average of Fama-MacBeth (1973) cross-sectional regression coefficients

(M/V)t

(× 103) ROA t+1

(M/V)t

*Size t (×10

3)

(M/V)t

*ROAt+1

(× 103)

(M/V)t

*KZt

(× 103) MJACF, t

Size t (× 10

3)

KZt

(×103)

Industry

Dummies

Avg.

R2

Avg.

N

Panel A: 1987–2004

S&P 1500 companies

Model 1 2.55* 0.19*** 1.61** –3.89*** Yes 0.15 662

(1.77) (15.29) (2.00) (–4.07)

Model 2 –1.44 0.23*** 0.21*** –3.08*** 2.60*** Yes 0.24 662

(–1.55) (14.21) (14.40) (–4.65) (3.28)

Model 3 8.35 0.27*** –0.58 –18.58 –0.11 0.20*** –2.45** 3.10** Yes 0.25 662

(0.96) (7.89) (–0.92) (–1.04) (–0.15) (14.52) (–2.18) (2.11)

Non-S&P 1500 companies

Model 1 2.77** 0.17*** 1.69*** –5.41*** Yes 0.04 2,243

(2.49) (21.81) (3.65) (–10.33)

Model 2 6.26*** 0.18*** 0.15*** –4.88*** –0.73 Yes 0.14 2,243

(7.47) (13.44) (14.85) (–9.75) (–1.02)

Model 3 29.44*** 0.22*** –2.04*** –20.61*** 0.81** 0.14*** –2.55*** –1.27 Yes 0.15 2,243

(4.91) (10.72) (–4.40) (–3.71) (2.01) (14.40) (–6.93) (–1.49)

Panel B: 1987–1995 and 2001–2004

S&P 1500 11.86 0.24*** –0.85 –22.70 0.39 0.21*** –1.11 2.03 Yes 0.26 603

(1.06) (5.05) (–1.01) (–0.92) (0.37) (12.45) (–0.76) (1.00)

Non-S&P

1500

20.27*** 0.23*** –1.36*** –25.53*** 0.48 0.15*** –2.77*** –0.31 Yes 0.15 2,197

(3.10) (8.07) (–2.64) (–3.52) (1.12) (11.49) (–5.80) (–0.38)

Panel C: 1996–2000

S&P 1500 0.77 0.27*** 0.07 –13.43 –0.63 0.22*** –2.17** 4.95* Yes 0.22 805

(0.07) (9.24) (0.10) (–1.57) (–1.35) (16.77) (–2.32) (1.91)

Non-S&P

1500

52.72*** 0.23*** –3.88*** –11.43** 1.73* 0.12*** –2.55*** –1.51 Yes 0.15 2,355

(8.01) (18.84) (–7.53) (–2.46) (1.73) (11.00) (–4.91) (–0.92)

M/V is the market-to-fundamental value of equity and measures the extent of overvaluation. The fundamental value (V) is estimated from regressions. The

sample is comprised of firm-year observations that are covered by COMPUSTAT and CRSP. The discretionary accruals are residuals of cross-sectional

46

regressions estimated for each year and each two-digit SIC code industry using the entire COMPUSTAT data with the DD model (DDACF; Dechow and Dichev,

2002), where the accruals are constructed from items on the cash flow statement. Firm size is the natural log of the market capitalization at the end of each fiscal

year. The KZ index measures the degree of equity dependence. We run ordinary least squares regressions for each year t from 1987 to 2004. The coefficients are

the averages of those regressions across years.

The coefficients on the intercept and the industry (defined by two-digit SIC code) dummies are suppressed to save space. t-statistics are reported in parentheses.


47

Table 7

Sensitivities of DDACF, t+1 with respect to M/V

Non-S&P 1500 companies S&P 1500 companies

Variables X X = Q1 X = Q3 X = Q1 X = Q3

Size t 0.1007 0.0302 0.0202 0.0002

ROA t+1 0.0865 0.0546 0.0283 -0.0005

KZt 0.0602 0.0742 0.0115 0.0096

M/V is the market-to-fundamental value of equity and measures the extent of overvaluation. The fundamental value (V) is estimated from regressions. The sample

is comprised of firm-year observations that are covered by COMPUSTAT and CRSP. We run ordinary least squares regressions for each year t from 1987 to 2004.

The discretionary accruals are residuals of cross-sectional regressions estimated for each year and each two-digit SIC code industry using the entire COMPUSTAT

data with the DD model (DDACF; Dechow and Dichev, 2002), where the accruals are constructed from items on the cash flow statement. The dependent variable is

DDACF, t+1, and the independent variables are M/V in year t, ROA in year t+1, DDACF, t, firm size (the natural log of the market capitalization) at the end of year t,

the KZ index (measures the degree of equity dependence) in year t, and the interaction terms between M/V and ROA in year t+1, firm size, the KZ index, and the

industries (defined by two-digit SIC code) dummies i.e. Models 3 in Panel A in Table 6. The sensitivities are the unit change of the quartile deviation of the accruals

with respect to per-unit change of a quartile deviation of M/V, where a quartile deviation is one-half of the difference between the 75th percentile and the 25th

percentile. Specifically, we calculate the differences between the fitted accruals—those given the M/V is at the 75th percentile, the variable X (firm size, return on

assets, or KZ index) is at the 25th (or 75th) percentile, and the other variables are at the median, and those given M/V is at the 25th percentile, the variable X is at

the 25th (or 75th) percentile, and the other variables are at the median. The sensitivity is the fitted accrual difference divided by two times one quartile deviation of

the accrual.

Table 8

Regression analysis based on alternative estimates of earnings management

Dependent Variable (M/V)t

(× 103)

(M/V)t *Size t

(×103)

(M/V)t *ROAt+1

(× 103)

(M/V)t *KZt

(× 103)

Avg.

R2

Avg.

N

Panel A: S&P 1500 companies

JACF, t+1 17.63 –1.11 –25.72 –0.28 0.23 703

(1.06) (–1.00) (–1.26) (–0.33)

MJACF, t+1 18.71 –1.21 –25.48 –0.14 0.24 703

(1.05) (–0.98) (–1.13) (–0.17)

CFOACF, t+1 9.41 –0.63 –26.26 –0.25 0.25 707

(1.14) (–1.09) (–1.58) (–0.42)

MJABS, t+1 48.47 –3.65 6.15 0.34 0.07 703

(0.89) (–0.93) (0.51) (0.50)

ABPRODACF, t+1 –4.94 –0.17 –20.14 0.70 0.75 843

(–0.35) (–0.18) (–1.55) (1.02)

ABOCFACF, t+1 2.89 –0.31 –16.60 33.75 0.65 770

(0.22) (–0.36) (–1.55) (1.38)

ABDISEXPACF, t+1 19.10 –1.45 –21.81 0.43 0.73 692

(0.86) (–0.96) (–1.39) (0.50)

Panel B: Non-S&P 1500 companies

JACF, t+1 37.72*** –2.72*** –24.19*** 1.00** 0.21 2,658

(6.89) (–5.91) (–5.14) (2.38)

MJACF, t+1 41.04*** –2.92*** –23.33*** 1.06** 0.22 2,658

(7.72) (–6.43) (–4.99) (2.15)

CFOACF, t+1 31.32*** –2.27*** –25.50*** 0.74** 0.16 2,534

(5.58) (–5.03) (–4.73) (2.08)

MJABS, t+1 35.97*** –2.69*** –8.32*** 0.57*** 0.08 2,658

(5.02) (–4.29) (–2.36) (1.67)

ABPRODACF, t+1 11.40 –1.35** –8.19* 0.83 0.45 2,773

(1.44) (–2.15) (-1.85) (0.93)

ABOCFACF, t+1 32.07*** –2.71*** –59.47*** 65.77*** 0.44 2,673

(4.44) (–4.87) (–5.55) (4.41)

ABDISEXPACF, t+1 –53.97*** 2.45 13.63 –3.74*** 0.43 2,613

(–3.19) (1.55) (1.45) (–4.07)


(V) is estimated from regressions. The sample is comprised of firm-year observations that are covered by

COMPUSTAT and CRSP. The dependent variables are the discretionary accruals, respectively the residuals of


COMPUSTAT data with the Jones (1991) model (JACF), the modified Jones model (MJACF and MJABS; Dechow et al.,

1995), the CFO model (CFOACF; Dechow et al., 1998), where the accruals are constructed from items of either the

cash flow statement or the balance sheet. The abnormal production costs (ABPRODACF, t+1) are residuals of


COMPUSTAT data with the Roychowdhury (2006) model. The abnormal cash flow from operations (ABOCFACF, t+1)

49

and abnormal discretionary expenses (ABDISEXPACF, t+1) are negative one times residuals of cross-sectional

regressions estimated for each year and each two-digit SIC code industry using the entire COMPUSTAT data with the

Roychowdhury (2006) model.

The independent variables are M/V in year t, ROA in year t+1, DDACF, t, firm size (the natural log of the market

capitalization) at the end of year t, the KZ index (measures the degree of equity dependence) in year t, and the

interaction terms between M/V and ROA in year t+1, firm size, the KZ index, and the industries dummies, i.e. Models

3 in Panel A in Table 6. We run ordinary least squares regressions for each year t from 1987 to 2004. The coefficients

are the averages of those regressions across years.

Only the coefficients of M/V and the interaction terms are reported to save space. t-statistics are reported in

parentheses. ***, **, * denote significance at the 0.01, 0.05, and 0.1 levels, respectively.

Table 9

Regression analysis based on two different measures of fundamental value

(M/V)t

(× 103) ROAt+1

(M/V)t

*Size t (×10

3)

(M/V)t

*ROA t+1

(× 103)

(M/V)t

*KZt

(× 103) MJACF, t

Size t (× 10

3)

KZt

(×103)

Industry

Dummies Avg. R2 Avg. N

Panel A: Regression-based fundamental value

Model 1 –4.21*** 0.31*** 0.23*** –2.61*** 3.85*** Yes 0.20 1,328

(–4.23) (19.15) (24.96) (–4.57) (6.01)

Model 2 –1.51 0.37*** 0.08 –38.64*** –0.45 0.23*** –2.88*** 5.00*** Yes 0.20 1,328

(–0.30) (14.37) (0.20) (–3.24) (–0.92) (25.89) (–3.91) (5.37)

Panel B: Forecast-based fundamental value

Model 1 –0.79*** 0.32*** 0.23*** –3.10*** 3.73*** Yes 0.20 1,328

(–7.66) (18.35) (25.13) (–7.09) (5.21)

Model 2 1.62** 0.36*** –0.11 –7.26*** –0.10 0.23*** –2.73*** 4.70*** Yes 0.21 1,328

(2.07) (17.82) (–1.63) (–4.63) (–0.95) (25.04) (–6.24) (6.27)

M/V is the market-to-fundamental value of equity and measures the extent of overvaluation. The fundamental value (V) is estimated from respectively regressions

and the analysts’ consensus forecasts. The discretionary accruals are residuals of cross-sectional regressions estimated for each year and each two-digit SIC code

industry using the entire COMPUSTAT data with the DD model (DDACF; Dechow and Dichev, 2002), where the accruals are constructed from items on the cash

flow statement. The sample is comprised of firm-year observations that are covered by COMPUSTAT, CRSP and I/B/E/S databases. Firm size is the natural log of

the market capitalization at the end of each fiscal year. The KZ index measures the degree of equity dependence. We run ordinary least squares regressions for each

year t from 1987 to 2004. The coefficients are the averages of those regressions across years.

The coefficients on the intercept and the industry (defined by two-digit SIC code) dummies are suppressed to save space. t-statistics are reported in parentheses.


monitoring mechanism, overvaluation, and earnings …conference/conference2010/...overvaluation and...

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