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Earnings Management, Corporate Governance, and True Financial

Performance

Marcia Millon Cornett*Alan J. Marcus**

Anthony Saunders***Hassan Tehranian****

May 2005Revised: January 2006

The authors are grateful to Alex Fayman for his research assistance. We thank Mike Barry, JimBooth, Susan Chu, Richard Evans, Wayne Ferson, Edith Hotchkiss, Darren Kisgen, Gil Manzon, JeffPontiff, Jun Qian, Pete Wilson, and seminar participants at Boston College, Southern IllinoisUniversity, and University of New Orleans for helpful comments.

*College of Business and Administration, Southern Illinois University, Carbondale, IL 62901.(618) 453-1417; mcornett@cba.siu.edu

** Wallace E. Carroll School of Business, Boston College, Chestnut Hill, MA 02467. (617) 552-2767; alan.marcus@bc.edu

***Salomon Center, Stern School of Business, New York University, New York, 10020. (212) 998-0711; asaunder@stern.nyu.edu

**** Wallace E. Carroll School of Business, Boston College, Chestnut Hill, MA 02467. (617) 552-3944; hassan.tehranian@bc.edu

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Earnings Management, Corporate Governance, and True Financial Performance

ABSTRACT

This paper addresses two questions. First, do corporate governance mechanisms that have beenshown to affect firm behavior in other contexts also affect the degree to which firms advantageously

manage their reported financial performance? Second, does past research investigating the impact of

governance structure and option-based compensation on firm performance stand up when measured

performance is adjusted for the impact of earnings management? We demonstrate that corporate

governance mechanisms effectively constrain discretion in earnings management and that the

estimated impact of governance variables on corporate performance is far stronger when discretionary

accruals are removed from reported earnings. Institutional ownership of shares, institutional investor

representation on the board of directors, and the presence of independent outside directors on theboard all reduce the use of discretionary accruals in earnings management. These factors largely

offset the impact of options compensation, which we find strongly encourages earnings management.

Earnings management strongly affects patterns of reported corporate performance. While

conventional profitability measures suggest a strong relationship between option compensation and

firm performance, profitability measures that are adjusted for the impact of discretionary accruals

show no relationship with option compensation. In contrast, the estimated impact of corporate

governance variables on firm performance more than doubles when discretionary accruals are

eliminated from measured profitability.

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Earnings Management, Corporate Governance, and True Financial Performance

1. Introduction

Accountants and financial economists have recognized for years that firms use latitude in

accounting rules to manage their reported earnings in a wide variety of contexts. Healey and Wahlen

(1999) conclude in their review article on this topic that the evidence is consistent with earnings

management to window dress financial statements prior to public securities offerings, to increase

corporate managers compensation and job security, to avoid violating lending contracts, or to reduce

regulatory costs or to increase regulatory benefits. Since then, evidence of earnings management has

only mounted. For example, Cohen, Dey, and Lys (2004) find that earnings management increased

steadily from 1997 until 2002. Options and stock-based compensation emerged as a particularly

strong predictor of aggressive accounting behavior in these years (see Gao and Shrieves, 2002;

Cohen, Dey, and Lys, 2004; Bergstresser and Philippon, 2004; Cheng and Warfield, 2005).

At the extreme, earnings management has resulted in some widely-reported accounting

scandals involving Enron, Merck, WorldCom, and other major U.S. corporations. Congress

responded to the spate of corporate scandals that emerged after 2001 with the Sarbanes-Oxley Act,

passed in June 2002. Sarbanes-Oxley requires public companies to make sure their boards audit

committees have experience with applying generally accepted accounting principles (GAAP) for

estimates, accruals, and reserves.

The passage of Sarbanes-Oxley raises the question of whether corporate governance

mechanisms other than government oversight might serve to increase the quality of financial

reporting. While there is an extensive literature on opportunistic earnings management in response to

specific incentives to achieve one result or another, research looking at the impact of corporate

governance on earnings management is fairly limited. The few papers that address these issues (e.g.,

Klein, 2002 or Warfield, Wild, and Wild, 1995) focus more on the magnitude than the direction of

earnings management. Thus, they shed little light on the ability of these variables to offset the one-

sided incentive of management to increase reported earnings that results from stock and option-based

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compensation. Moreover, the range of corporate governance variables studied in the context of

earnings management has to date been limited. In particular, these papers do not address the role of

outside investors in disciplining management. Previous research (e.g., Hartzell and Starks, 2003) has

demonstrated that such investors constrain managerial behavior.

This paper addresses two questions. First, do corporate governance mechanisms that have

been shown to affect firm behavior in other contexts also affect the degree to which firms

advantageously manage their reported financial performance? Second, does past research that finds

governance structure and option-based compensation impacts firm performance stand up when

measured performance is adjusted for the impact of earnings management? Thus, this paper re-

examines the impact of incentive compensation and corporate governance on firm performance in

light of potential earnings management. We find that corporate governance variables that have been

shown to affect corporate behavior and performance in other contexts also affect firms accounting

choices. Specifically, much past research suggests that incentive-based compensation has a significant

impact on financial performance as measured by reported earnings. However, we find that once those

earnings are adjusted for discretionary accruals, the link between compensation and performance

disappears. In contrast, the estimated impact of corporate governance variables on performance more

than doubles when discretionary accruals are removed from measured profitability.

The rest of the paper is organized as follows. Section 2 briefly reviews the literature on

earnings management as it relates to our hypotheses. Section 3 discusses internal corporate

governance mechanisms shown to be important in other contexts and that might have an impact on

accounting behavior. Section 4 presents information regarding the data and methodology. Section 5

presents empirical results and Section 6 concludes the paper.

2. Earnings Management

2.1 Opportunistic Earnings Management

The opportunistic accruals-management literature largely started with Healy (1985), who

concludes that managers use accruals to strategically manipulate bonus income. For example,

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managers can defer income through accruals when an earnings target for a bonus plan cannot be

reached or when bonuses have already reached maximum levels, and can accelerate income in other

periods. Guidry, Leone, and Rock (1999) use data from businesses unit level rather than firm level

and find evidence consistent with Healeys bonus manipulation effects. Gaver, Gaver, and Austin

(1995), who study discretionary accruals rather than total accruals, also conclude that earnings are

managed, but to smooth income rather than manipulate bonuses. Finally, Holthausen, Larcker, and

Sloan (1995) also conclude that managers may use accruals to shift earnings over time with the goal

of maximizing long-term bonus income.

More recent work focuses on the use of earnings management to affect stock price, and with

it, managers wealth. For example, Sloan (1996) finds that if a firms earnings are inflated using

aggressive accruals assumptions, the market views the inflated earnings as more than cosmetic, and

the firms stock price will be affected. Teoh, Welch, and Wong (1998a, 1998b) find that firms with

more aggressive accrual policies prior to IPOs and SEOs tend to have poorer post-issuance stock price

performance than firms with less aggressive accounting policies. Their results suggest that earnings

management inflates stock prices prior to the offering. Similarly, Beneish and Vargus (2002) find that

periods of abnormally high accruals (which inflate earnings) are associated with increases in insider

sales of shares, and that after the event period, stock returns tend to be poor.

Option and restricted stock compensation is a particularly direct route by which management

can potentially increase its wealth by inflating stock prices. Indeed, considerable evidence links such

compensation to higher degrees of earnings management. Gao and Shrieves (2002), Bergstresser and

Philippon (2004), Cohen, Dey, and Lys (2004), and Cheng and Warfield (2005) all find that the use of

discretionary accruals and earnings management is more prevalent at firms where top management

compensation is more closely tied to the value of stock in general, and options more particularly.

Burns and Kedia (2003) show that firms whose CEOs have large options positions are more likely to

file earnings restatements.

2.2Earnings Management and Corporate Governance

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The literature on the impact of corporate governance on earnings management is more sparse.

Some authors (e.g., Dechow, Sloan, Sweeney, 1996; Beasley, 1996) have investigated the relationship

between outright fraud and board characteristics. These papers, however, do not focus on the strategic

use of allowable discretion in accounting policy.

Klein (2002) shows that board characteristics such as audit committee independence predict

lower discretionary accruals. She focuses on absolute rather than signed accruals, however.

Therefore, while her measure captures the noise introduced in earnings numbers due to managerial

discretion, it does not measure systematic aggressiveness of accounting choice. Warfield, Wild, and

Wild (1995) also examine the impact of corporate governance variables on earnings management.

They find that a high level of managerial ownership is positively related to the explanatory power of

reported earnings for stock returns. They also examine the absolute value of discretionary accruals

and find that it is inversely related to managerial ownership. Like Klein, they conclude that corporate

governance variables may affect the degree to which latitude in accounting rules affect the

informativeness of reported earnings, but do not address the degree to which governance or

compensation variables affect the average aggressiveness of accounting choice.

3. Corporate Governance and Earnings Management

Corporate governance variables have been shown in other contexts to affect firm behavior.

Such variables include institutional ownership in the firm, director and executive officer stock

ownership, board of director characteristics, CEO age and tenure, and CEO pay-for-performance

sensitivity. We discuss these next.

3.1 Institutional Ownership

McConnell and Servaes (1990), Nesbitt (1994), Smith (1996), Del Guercio and Hawkins

(1999), and Hartzell and Starks (2003) have found evidence that corporate monitoring by institutional

investors can constrain managers behavior. Large institutional investors have the opportunity,

resources, and ability to monitor, discipline, and influence managers. These papers conclude that

corporate monitoring by institutional investors can force managers to focus more on corporate

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performance and less on opportunistic or self-serving behavior. If institutional ownership enhances

monitoring, it could also be associated with lower use of discretionary accruals. However, at least in

principle, it is possible that managers might feel more compelled to meet earnings goals of these

investors, and thus engage in more earnings manipulation.

3.2 Director and Executive Officer Stock and/or Option Ownership

Higher stock and/or option ownership by directors and executive officers may encourage

them to use discretionary accruals to improve the apparent performance of the firm, thereby

increasing their personal wealth. Consequently, higher stock ownership by directors and executive

officers may cause managers to use discretionary accruals to inflate reported earnings and,

consequently, board members personal wealth. Thus, stock ownership by this group would be

associated with a greater use of discretionary accruals. Stock options are particularly potent in making

managers wealth sensitive to stock price, and so may have an even greater impact on earnings

management.

3.3 Board of Director Characteristics

3.3.1 Percent of Independent Outside Directors on the Board

There is considerable literature on the impact of the composition of the board of directors

(i.e., inside versus outside directors). Boards dominated by outsiders are arguably in a better position

to monitor and control managers (Dunn, 1987). Outside directors are independent of the firms

managers, and in addition bring a greater breadth of experience to the firm (Firstenberg and Malkiel,

1980; Vance, 1983). A number of studies have linked the proportion of outside directors to financial

performance and shareholder wealth (Brickley, Coles, and Terry, 1994; Byrd and Hickman, 1992;

Subrahmanyan, Rangan, and Rosenstein, 1997; Rosenstein and Wyatt, 1990). These studies

consistently find better stock returns and operating performance when outside directors hold a

significant percentage of board seats. Consequently, if outside membership on the board enhances

monitoring, it would also be associated with lower use of discretionary accruals.

3.3.2 CEO/Chair Duality

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In about 80 percent of U.S. companies, the CEO is also the chairman of the board (Brickley,

Coles and Jarrell, 1997). CEO/Chair duality concentrates power in the CEOs position, potentially

allowing for more management discretion. The dual office structure also permits the CEO to

effectively control information available to other board members and thus impedes effective

monitoring (Jensen, 1993). Consequently, if CEO/Chair duality impedes effective monitoring, it

would also be associated with greater use of discretionary accruals.

3.3.3 Board Size

Jensen (1993) argues that small boards are more effective in monitoring a CEOs actions, as

large boards have a greater emphasis on politeness and courtesy and are therefore easier for the

CEO to control. Yermack (1996) also concludes that small boards are more effective monitors than

large boards. These studies suggest that the size of a firms board should be inversely related to

earnings management. If small boards enhance monitoring, they would also be associated with less

use of discretionary accruals.

3.4 Age and Tenure of CEO

The age and tenure of the CEO may determine his or her effectiveness in managing the firm.

Some studies suggest that top officials with little experience have limited effectiveness because it

takes time to gain an adequate understanding of the company (Bacon and Brown, 1973; Alderfer,

1986). These studies suggest that the older or the longer the tenure of the firms CEO, the greater the

understanding of the firm and its industry, and the better the performance of the firm. Consequently,

if older, more experienced CEOs enhance firm performance, they would also be associated with

lower use of discretionary accruals.

4. Data and Methodology

4.1 Discretionary Accruals

Dechow, Sloan, and Sweeney (1995) compare several models of accrual management and

conclude that the so-called modified Jones model provides the most power for detecting such

management. Bartov, Gul, and Tsui (2001) also support the use of the modified Jones model, estimated

in a cross-section using other firms in the same industry. Despite concerns about its power (Kothari,

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Leone, and Wasley, 2005), it remains the most popular model for estimating accrual behavior (e.g,

Kothari, Loutskina and Nikolaev, 2005). Discretionary or abnormal accruals equal the difference

between actual and normal accruals, using a regression formula to estimate normal accruals.

The modified Jones model estimates normal accruals from the equation:1

TAjt

Assetsjt- 1= 0

1

Assetsjt- 1+ 1

SalesjtAssetsjt- 1

+ 2PPEjt

Assetsjt- 1(1)

where:

TAjt = Total accruals for firm j in year t,Assetsjt = Total assets for firmj in year t(Compustat data item 6),

Salesjt= Change in sales for firmj in year t(Compustat data item 12), andPPEjt = Property, plant, equipment for firm j in year t(Compustat data item 7)

Total accruals equal:

in current non-cash assets (Compustat data item 4 - item 1)- in current liabilities (Compustat data item 5)+ in long-term debt in current liabilities (Compustat data item 34)- Depreciation (Compustat data item 14)

Discretionary accruals,DAjt, are then measured as:

TAjt

Assetsjt- 1 -

01

Assetsjt- 1+ 1

Salesjt - ReceivablesjtAssetsjt- 1

+ 2PPEjt

Assetsjt- 1(2)

where hats denote estimated values from regression equation (1). The inclus ion ofReceivablesjt

[Compustat item 151] in regression (2) is the modification of the Jones model. This variable

attempts to capture the extent to which a change in sales is in fact due to aggressive recognition of

questionable sales.2

1 The regression is estimated as a pooled time series-cross section for the 1993-2000 sample period includingevery firm with the same 3-digit SIC code as the firm in question.

2 A criticism of the Jones model is that it may be important to control for the impact of financial performance

on accruals. Kothari, Leone, and Wasley (2005), show that (i) matching firms based on operating performancegives the best measure of discretionary accruals, and (ii) including ROA on the right-hand side of equation (1)improves the performance of the Jones models . In addition, McNichols (2002) points out that fi rms

experiencing higher growth tend to have higher accruals. We use the book-to-market ratio to measure growthsince firms with greater growth prospects are apt to have lower book-to-market ratios. Therefore, as a robustnesscheck on the modified Jones model, we also estimated an augmented Jones model for normal accruals as in

Cohen, Dey and Lys (2004) as follows:

TAjt

Assetsjt- 1= 0

1

Assetsjt-1+ 1

SalesjtAssetsjt-1

+ 2PPEjt

Assetsjt- 1+ 3CFROAjt + 4BMjt (3)

whereCFROA = cash flow return on assets (annual earnings before interest and taxes plus depreciation

divided by total assets),

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Large values of discretionary accruals are conventionally interpreted as indicative of earnings

management. Because discretionary accruals can be used to both increase or decrease earnings, in

some contexts (e.g., Klein, 2002 or Cohen, Dey, and Lys, 2004) the absolute value of discretionary

accruals is the appropriate measure to use to determine whether earnings management occurs.3

Bergstresser and Philippon (2004) study both signed and absolute accruals. They show that the late

1990s was characterized by a strong secular increase in accruals, suggesting a one-sided incentive to

raise reported earnings, consistent with increasing use of option compensation in that period.

As described below, our sample also is characterized by a strong bias toward positive

accruals. Accordingly, we examine the properties of the signed as well as the absolute value of

discretionary accruals. Moreover, when we examine measures of firm performance, we adjust cash-

flow ROA to its unmanaged value by netting out discretionary accruals. This requires that actual

discretionary accruals, not their absolute values, be deducted from reported earnings.

4.2 Other Data

The sample examined here consists of firms included in the S&P 100 Index (obtained from

Standard & Poors) as of November 1993. We use S&P 100 firms because they are among the largest

firms, representing a large share of aggregate market capitalization, and consequently command great

interest among institutional investors. Thus, these large firms enable us to test the impact of such

investors on earnings management. While institutional ownership is most prevalent in large firms

such as these, even in this group, there is considerable variation in such ownership. The sample

standard deviation of share ownership by institutional owners is 13.8 percent. Moreover, this sample

BM= ratio of book value of common to market value of shares

These additions had no material impact on our results.

3 Over long periods of time, discretionary accruals will reverse. Strategic time-shifting of income will resultin abnormally high accruals in some periods and low accruals in others. In other contexts, however, there is aclear presumption concerning the desired direction of earnings management: for example, when there are

incentives to increase the stock price in anticipation of options exercises. Moreover, using a time periodcommon to our study, Bergstresser and Philippon (2004) document a strong secular increase in accruals,consistent with a systematic and increasing bias toward inflation of earnings rather than simple transfers of

earnings across time. We provide results on both signed and absolute discretionary accruals.

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is interesting precisely because these firms are relatively stable. Prior studies have shown that

earnings management is more prevalent in poorly-performing firms (Cohen, Dey and Lys, 2004;

Kothari, Leone, Wasley, 2005) and that models of discretionary accruals are least reliable when

applied to firms with extreme financial performance (Dechow, Sloan and Sweeney, 1995). We look at

factors that influence earnings management in normal times and on the degree to which measured

performance of even blue-chip firms is affected by that management. The fact that these firms are

all free of financial distress makes the augmentation of the Jones model discussed in footnote 2 less of

an issue for our sample . This is a conservative sample-selection choice in that S&P 100 firms should

be a relatively difficult sample in which to find heavy use of discretionary accruals.

Firms that were dropped from the S&P 100 after 1993, but that remained publicly traded and

continued to operate, remain in the sample. Removing these firms would have introduced sample

selection bias as firm performance is associated with ongoing inclusion in the S&P index. However,

some firms were lost due to non-performance related events. Eleven of the 1993 S&P 100 firms

were eventually acquired by other firms over the sample period and are dropped from the sample in

the year of the merger. Another nine firms were lost by the year 2000 due to the unavailability of

proxy or institutional investor ownership data. After these adjustments to the data, we are left with a

sample of 676 firm-years. 4

Following Healy et al. (1992), who examine post-merger performance of firms, and Kothari,

Leone, and Wasley (2005), operating performance is measured as operating cash flow return on

assets, CFROA. This measure of performance is effectively independent of financial leverage. The

financial statement data needed to calculate CFROA are obtained from the Compustat database for

each year, 1993-2000.

CFROA offers several advantages over Tobins q, an alternative measure of firm

performance. While Tobins q reflects growth opportunities (and, more generally, expectations of the

4 In the regression analysis below, we trim extreme data points, eliminating the top and bottom one percent ofobservations for each right-hand side variable. Therefore, the number of data points in our regressions isreduced from 676 to 662.

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firms prospects in future years) through the impact of these factors on market value, cash flow return

on assets is a more focused measure of current performance. For example, the Tobins q of a poorly

performing firm might be inflated by expectations of a premium bid in a corporate takeover.

Regressions of Tobins q on institutional ownership are more susceptible to endogeneity problems if

institutions are attracted to growth stocks or chase recent stock-market winners. These sorts of

considerations do not affect CFROA as a measure of financial performance since operating

performance is not tied to stock prices.

Both the levels and changes in CFROA may be affected by extraneous industry trends.

Therefore, we measure firm performance in each year as an industry-adjustedROA, denoted IAROA,

i.e., as the firms cash-flow return on assets minus industry-average cash-flow return on assets in that

year. Industry-adjusted comparisons allow us to examine firm-specific performance irrespective of

any industry-wide factors that may affect CFROA. We define the industry comparison group for each

firm as all firms listed on Compustat with the same 3-digit SIC code.5 The number of firms in each

industry comparison group ranges from a min imum of 1 to a maximum of 356. Industry CFROA is

calculated as the total-asset weighted average CFROA of all firms in the industry.

Institutional investor ownership data for each year are obtained from the CDA Spectrum data

base, which compiles holdings of institutional investors from quarterly 13-f filings of institutional

investors holding more than $100 million in the equity of any firm. Institutional investors file their

holdings as the aggregate investment in each firm regardless of the number of individual fund

portfolios they manage. Our measures of institutional investor ownership follow those used in

Hartzell and Starks (2003). That is, we calculate the proportion of total institutional investor

ownership in each firm.6

5 We remove all sample firms from any industry comparison groups. For example, General Motors and Ford(both S&P 100 firms) are not included in any industry comparison groups.

6 In alternative specifications, we also investigated the impact of the leading institutional investors in each firmby using the proportion of ownership accounted for by the top-five institutional investors. The results were

unaffected by this choice. To avoid clutter, we do not report them.

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Finally, as discussed above, several studies have found that CEO compensation, board

composition, and director and executive officer stock ownership affect a firms performance.

Accordingly, we use proxy statements for each year to obtain director and officer stock ownership,

board size, independent outsiders on the board,7 CEO/chair duality, CEO age, CEO tenure, and CEO

compensation (salary, bonus, options, stock grants, long-term incentive plan payouts, and other).

Table 1 presents descriptive statistics of firm financial performance during the period of

analysis. Because discretionary accruals must be reversed at some point, their average value over long

periods should be near zero. As reported in Table 1, however, the average value of discretionary

accruals for this sample is 5.21 percent of assets using the modified Jones model as the basis for

normal accruals. The average absolute value of discretionary accruals, 8.44 percent, is not

dramatically higher than the average signed values. This result is consistent with Bergstresser and

Phillipon (2004) who also find that accruals in this period spiked dramatically.

We define unmanaged earnings as reported earnings minus discretionary accruals. While

mean CFROA based on reported earnings is 18.95 percent,8 the average value of unmanaged CFROA

is only 13.75 percent using the modified Jones model to remove the impact of discretionary accruals

on reported earnings. Not surprisingly, industry-adjusted ROA is nearly zero using either of our

performance measures, 0.71 percent for reported earnings or 0.43 percent adjusting for discretionary

accruals using the Jones model. Thus, the financial performance of our sample firms is, on average,

nearly identical to that of their industries.

INSERT TABLE 1 HERE

Table 2 presents summary statistics on corporate governance variables. Institutional

ownership is significant, averaging 58.9 percent of the outstanding shares in each firm.9 The percent

7 Specifically, independent outside directors are directors listed in proxy statements as managers in anunaffiliated non-financial firm, managers of an unaffiliated bank or insurance company, retired managers ofanother company, lawyers unaffiliated with the firm, and academics unaffiliated with the firm.

8 Recall that this is a cash flow ROA, which includes depreciation as well as net income in the numerator.

9 This is the mean value of the percentage ownership averaging across all firms in all years.

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of shares held by this group ranges from a low of 24.9 percent to a high of 74.9 percent. In contrast,

directors and executive officers hold, on average, only 3.4 percent of the outstanding shares in their

firms. On average, 413 institutional investor firms hold stock in the sample firms.

INSERT TABLE 2 HERE

While institutional investors hold a large fraction of outstanding shares, they do not often sit

on the board of directors. On average the boards of directors seat 12.29 members, and on average,

these seats are filled by 2.29 inside directors, 1.62 affiliated outside directors, and 8.04 independent

outside directors. The average number of institutional investors on the board is 0.73 and the maximum

for this group is 4. Thus, the majority of the directors are independent outsiders (albeit not

institutional investors).

The average age of the firms CEOs is 57 years (ranging from 40 to 69) and, on average, the

CEOs have been in place for just over seven years (ranging from 2 to 37 years). These CEOs are paid

an average of $2.341 million in salary and bonus annually and hold an average of $4.065 million in

options.10 CEO compensation from all sources including options positions averages $8.342 million.

Averaging across CEOs, 37.5 percent of total compensation is composed of options.

4.3. Methodology

We estimate two broad sets of regressions. The first set treats discretionary accruals as the dependent

variable. In different specifications, we examine both signed and absolute discretionary accruals. The

explanatory variables are corporate governance variables (described above) related to institutional

ownership, management characteristics, and executive compensation. The second set of regressions

examine how financial performance relates to the same set of variables, both with and without

adjustment for discretionary accruals. The explanatory variables used in the regressions are listed in

Table 3.

10 Following Hartzell and Starks (2003), we measure option value using the dividend-adjusted Black-Scholesformula. This is a better measure of ex ante value than option compensation given in the proxy statement,

which reflects exercises in any year. In any case, the two measures are highly correlated in our sample.

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INSERT TABLE 3 HERE

We estimate multivariate regressions in which discretionary accruals or the firms industry-

adjusted ROA in each year is a function of the explanatory variables in Table 3. The observations

across firms are pooled in one regression. There are 662 observations, one for each firm-year.

The regressions that treat firm performance as the dependent variable are subject to a

potential simultaneity bias. If institutions are attracted to firms with superior performance, then a

positive association between institutional ownership and performance may be observed even if that

ownership is not directly beneficial to performance. Moreover, if operating performance and

institutional ownership are both persistent over time, lagging ownership relative to performance will

not eliminate the bias.

We employ several tools to deal with this potential problem. First and foremost, we estimate

all regressions allowing for firm fixed effects. This controls for any differences in firm-specific

average performance that might remain after industry adjustment, and allows us to identify the

marginal impact of the right-hand side variables on the dependent variable for that firm.11

We also lag all measures of institutional ownership and institutional board membership by

one year. (Notice the lag structure embodied in Table 3.) Without the lag on institutional ownership, it

would be hard to distinguish between the hypothesis that institutional investors affect accounting

choice or cash flows versus the hypothesis that they are simply attracted to firms with more

conservative accounting or better recent performance. If institutions do affect management decisions,

they presumably would do so prior to the year of better performance, which also is consistent with the

use of a lag.12

11 As an additional check, we also estimated the financial performance regressions (Tables 5 and 6 below)

using first differences rather than firm fixed effects. The resulting estimates were essentially identical.12 We might also have lagged explanatory variables other than those involving institutional investors.However, endogeneity concerns are not as significant here. Board composition and membership is far less apt

to respond to perceived changes in the prospects of the firm, so, in contrast to institutional investment, there isfar less danger that it will be affected by the near-term prospects of the firm. Lagged and contemporaneousboard composition variables are essentially identical. Nevertheless, we experimented with lags on the board

membership variables, and found that such lags made virtually no difference in our regression results.

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Industry-adjusting performance eliminates another potential source of simultaneity bias that

might exist at an industry level. Suppose for example that institutional investors prefer more stable

but lower average return industries. This preference would result in a link between average ROA and

institutional holdings. However, normalizing firm ROA by industry-average ROA eliminates any

relation between relative performance and industry characteristics.

Finally, our concerns over simultaneity are mitigated by the fact that the financial

performance of our sample does not seem to be unusual. As noted above, the mean industry-adjusted

ROA of the sample firms is virtually zero, indicating financial performance about equal to that of their

industry peer groups.

The lag on executive compensation eliminates a simpler form of reverse causality. Because

bonuses are tied to firm performance, and the value of options is linked to the stock price,

management compensation is a direct function of contemporaneous operating performance. In

contrast, using lagged compensation measures enables us to measure pay-for-performance incentive

structures uncontaminated by the impact of current performance on compensation.

5. Regression Results

Table 4 presents results on the use of discretionary accruals to manage earnings.

Discretionary accruals in this table are estimated from the modified Jones model, equation (2) above.

In regression 1, the dependent variable is discretionary accruals as a percent of assets. Regression 2

presents the same specifications except that the dependent variable is the absolute value of

discretionary accruals. The results using either measure of earnings management are highly similar.

Earnings management is significantly reduced by institutional involvement in the firm, whether that is

measured by the fraction of shares owned by all institutional investors (coefficients are - 0.0269 and

- 0.0297 in the two regressions), or by the number of institutional investors (coefficients equal

0.0247 and 0.0219). These coefficients are all significant at better than a 1 percent level.

INSERT TABLE 4 HERE

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Board composition also significantly affects earnings management. The impact of the number

of institutional investors on the board is generally not significant, but the fraction of the board

composed of either institutional investors or of independent outside directors has a negative and

significant impact on earnings management, consistent with Klein (2002). Again, the point estimates

for the two specifications are highly consistent. The coefficients on the fraction of the board

composed of institutional investors are both about - 0.12 and those on the fraction of the board

composed of independent directors are about - 0.10. These coefficients are large enough to have a

significant economic impact. For example, using a coefficient estimate of - 0.10 for independent

directors, an increase of one (sample) standard deviation in this variable (i.e., using Table 2, an

increase in the fraction of independent outside directors of 0.147 or 14.7 percentage points) would

decrease discretionary accruals as a percentage of total assets by approximately .147 .10 = .0147, or

1.47 percentage points. Similarly, an increase of two institutional investors on a 12-member board (an

increase of 16.7 percent) would decrease discretionary accruals by approximately .167 .12 = .0200,

or 2.00 percentage points.

Table 4 also indicates that option compensation has a tremendous impact on earnings

management. The coefficient on option compensation as a fraction of total compensation is

approximately 0.14 in both specifications, with both t-statistics above 9. Using a coefficient estimate

of .14, an increase of .246 in option compensation as a fraction of total compensation (i.e., one sample

standard deviation) increases the contribution of discretionary accruals to measured ROA by .14

.246 = .0344 or 3.44 percentage points. Notice again that the impact on signed and absolute

discretionary accruals is effectively the same, suggesting that in this period, earnings management

was largely one-sided. Options compensation, which greatly accelerated in this period, seems to have

accelerated discretionary accruals. The other governance variables have little impact on discretionary

accruals. Neither board size nor any CEO characteristics such as age, tenure, or duality have a

significant impact on accruals policy. Not surprisingly, the firm fixed-effects (CUSIP) are significant

the 1% level in both regressions, with F-statistics above 9.

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Table 5 presents regression results of firm financial performance as a function of the same

governance and compensation variables used in the previous regressions. We measure firm

performance as industry-adjusted ROA, i.e., cash-flow ROA minus the industry mean. The cash-flow

ROA values in these regressions are based on reported earnings, i.e., without adjustment for

discretionary accruals.13

INSERT TABLE 5 HERE

The coefficient on the fraction of shares owned by all institutional investors is positive

(0.0342) and significant at the 1 percent level (t= 2.90). However, the economic impact of the

percentage of institutional ownership is relatively modest. The regression coefficient implies that an

increase of one (sample) standard deviation in institutional ownership (i.e., using Table 2, an increase

in fractional ownership of 0.138 or 13.8 percentage points) would increase industry-adjusted ROA by

only 0.0047, or 0.47 percent.

The log of the number of institutional investors holding stock in the firm is far more

influential in explaining IAROA. The coefficient on this variable is positive (0.0251), which is

significant at better than the 1 percent level (t= 2.75). Further, a one-standard deviation movement in

this variable starting from its mean value increases IAROA by 1.15 percent. These results suggest

that higher institutional investment is in fact associated with improved operating performance,

consistent with the notion that institutional ownership results in better monitoring of corporate

managers.

The coefficients on the number of institutional investors on the board and the percent of

institutional investors on the board are insignificant. However, given that so few representatives of

13 We estimated variations on this specification, for example using total option compensation in addit ion tooption compensation as a percent of total CEO compensation. The latter compensation variable provides

greater explanatory power, but highly similar point estimates. We also experimented with measures ofinstitutional ownership, for example, using total versus top-five institutional owners. Again, these twovariables seem nearly interchangeable. In light of the similarity of results across these specifications, we do

not present these variations.

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institutional investors sit on boards of directors, it is not surprising that we find no significance for

these variables.

Notice also the coefficients on the control variables. The coefficie nt on the fractional stock

ownership of directors and executive officer are insignificant (t= 1.26). This result may reflect the

fact that our sample includes only S&P 100 firms. For these firms, it would be hard for directors and

officers to have anything but minimal fractional stock holdings in the firm (the mean for the sample is

3.4 percent). Accordingly, the insignificant regression coefficient is not entirely surprising. 14

The coefficient on the fraction of the board composed of independent outside directors is

0.1083 and is significant at the 1 percent le vel. Thus, increasing the percent of independent directors

on the board appears to result in higher IAROA. Other characteristics of the board of directors have

no significant impact on industry-adjusted performance. The coefficients on the CEO/Chair duality

dummy, board size, and CEO age and tenure are all insignificant.

The coefficients on CEO option compensation is positive, 0.1109, and highly significant (t=

8.64). Higher CEO compensation paid in the form of options seems to predict higher industry-

adjusted ROA. The economic impact of option-based compensation is dramatic. An increase of one

sample standard deviation in option-based compensation as afraction of total compensation (i.e., an

increase of .246 or 24.6 percentage points) increases IAROA by .246 .1109 = .0273 or 2.73 percent.

Broadly speaking, this regression seems consistent with conventional wisdom on firm

performance. That is, performance improves with monitoring by disinterested institutional investors

and independent board members, as well as with pay-for-performance compensation, measured in this

paper by option compensation. However, recall that Table 4 shows that while earnings management

decreases with institutional monitoring, it increases with option compensation. This implies that

unmanaged performance, i.e., CFROA calculated from earnings adjusted for the impact of

discretionary accruals, will be more responsive to the monitoring variables and less responsive to the

option compensation variables.

14 This raises a general caveat concerning this study; specifically that our results may apply only to large f irms.

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Table 6 repeats the analysis of Table 5, but uses IAROA computed from unmanaged earnings

as the dependent variable. IAROA is calculated using cash-flow ROA based on reported earnings

minus discretionary accruals from the modified Jones model. The coefficient on institutional

ownership of shares, which was 0.0342 in the managed-earnings regressions (Table 5), increases to

0.0496. Similarly, the coefficient on the number of institutional investors increases from 0.0251 (t-

statistic = 2.75) in Table 5 to 0.0516 (t-statistic = 3.93) in Table 6. The coefficient on the fraction of

the board composed of institutional investors, which is positive (0.0473) but insignificant in Table 5 is

now 0.1165, which is significant at better than a 1 percent level. Finally, the coefficient on

independent directors as a fraction of the board 0.2136 (t-statistic = 8.85) is double its value in the

Table 5 regression (0.1083, t-statistic = 3.17). The economic impact of these variables increases

commensurately.

INSERT TABLE 6 HERE

In stark contrast, the impact of option compensation on performance has disappeared in Table

6. The point estimate on option compensation as a fraction of total compensation, which is positive

and highly significant (0.1109, t-statistic = 8.64) in Table 5, is now negative, - 0.0137, but

insignificant (t-statistic = - 0.38). Thus, while option compensation strongly predicts profitability

using reported earnings (Table 5), its effect seems to derive wholly from the impact of such

compensation on accounting choice. Unmanaged earnings adjusted for the impact of discretionary

accruals shows no relationship to option compensation.

6. Conclusions

The analysis in this paper suggests that earnings management through the use of discretionary

accruals responds dramatically to management incentives. Earnings management is lower when there

is more monitoring of management discretion from sources such as institutional ownership of shares,

institutional representation on the board, and independent outside directors on the board. Earnings

management increases in response to the option compensation of CEOs. Our sample period is

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characterized by a dramatic increase in accruals; both absolute values of discretionary accruals and

signed discretionary accruals show similar responses to these monitoring and incentive variables.

The results also suggest that the positive impact of option compensation on reported

profitability in this sample may have been purely cosmetic, an artifact of the more aggressive earnings

management elicited by such compensation. Once the likely impact of earnings management is

removed from profitability estimates, the relationship between performance and option compensation

disappears. Conversely, the estimates of financial performance are far more responsive to monitoring

variables when discretionary accruals are netted out from reported earnings. Therefore, the results

reinforce previous research pointing to the beneficial impact of outside monitoring, but cast doubt on

the role of pay-for-performance compensation as a means of eliciting superior performance. The

quality of reported earnings improves dramatically with monitoring, but degrades dramatically with

option compensation.

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Table 1

Descriptive Statistics on Accruals and Average Performance

CFROA is annual operating cash flow return on assets (earnings before interest and taxes plusdepreciation over the year divided by total assets at the end of the year). Financial statement dataneeded to calculate CFROA are obtained from the Compustat database for each year, 1993-2000. Foreach S&P 100 firm, we classify industry comparison firms as all firms listed on Compustat with the

same 3-digit SIC code. Industry-adjusted CFROA is a sample firms CFROA in any year minus the(total asset) weighted average industry CFROA in that year. Normal accruals are defined by equation(1) for the modified Jones model. Discretionary accruals are residuals between actual accruals andnormal accruals predicted from the model. These statistics are based on the data included in theregression analysis, i.e., excluding observations purged as extreme outliers. The sample size is 662.

StandardVariable Mean Median Deviation 25

thpercentile 75

thpercentile

Discretionary accruals

Assets.0521 .0438 .0648 -.0206 .0907

Abs(Discretionary accruals)

Assets.0844 .0727 .0637 .0089 .1204

CFROA .1895 .1824 .0906 -.0611 .2186

Unmanaged CFROA .1375 .1203 .0668 -.0309 .1656(from Jones model)

Industry-adjusted CFROA .0071 .0041 .0078 -.0063 .0138

Industry-adjusted .0043 .0060 .0031 -.0059 .0107unmanaged CFROA

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Table 2

Summary Statistics on Governance and CEO Compensation Variables

Data on institutional investor ownership for the period 1993-2000 are obtained from the CDA Spectrumdata base. These data include total shares outstanding, number of shares owned by all institutionalinvestors, and the number of institutional investors. We use proxy statements for the sample firms for

each year 1993-2000 to collect data on the fraction of director and officer stock ownership, board size,the fraction of independent outsiders on the board, CEO/chair duality, CEO age, CEO tenure, and CEOcompensation (salary, bonus, options, stock grants, long-term incentive plan payouts, and others).These statistics are based on the data included in the regression analysis, i.e., excluding observationspurged as extreme outliers. The sample size is 662.

StandardVariable Mean Median Deviation Minimum Maximum

Fraction of shares owned byall institutional investors .589 .609 .138 .249 .749

Fraction director + executiveofficer stock ownership .034 .017 .042 .004 .282

Number of institutional 412.8 359 239 91 798investors

Number of directors on board

Total 12.29 12 2.2 8 20

Inside directors 2.29 2 1.53 1 9

Affiliated outside 1.62 1 1.49 0 7

Independent outside 8.04 8 2.18 2 14

Institutional investors 0.73 1 .90 0 4

Fraction of independentoutside directors .656 .682 .147 .174 .826

CEO age (years) 57.0 57 5.1 40 69

CEO tenure (years) 7.1 5 6.6 2 37

CEO salary and bonus(in $ thousands) 2,341 1,824 2,019 642 15,656

CEO options(in $ thousands) 4,065 1,642 11,528 29 187,931

CEO totalcompensation(in $ thousands) 8,342 4,763 15,196 698 219,892

Options as fraction oftotal CEO compensation .375 .359 .246 .08 .668

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Table 3

Definitions of Regression Variables

This table lists the variable names and definitions used in the empirical analysis.

Explanatory variable Symbol

Fraction of shares of the firm owned by all institutional investors(lagged one year)

FIISOWN

natural log of the number of institutional investors holding stock infirm (lagged one year)

ln(NII)

natural log of 1 + the number of institutional investors on the boardof directors (lagged one year)*

ln(NIIOB)

fraction of board of directors composed of institutional investors(lagged one year)

FIIOB

fraction of board of directors composed of independent outsidedirectors

FINDDIR

fraction of shares in firm owned by directors and officers DOSOWN

CEO/Chair duality dummy: equals 1 if the CEO is also the chair ofthe board of directors, and 0 otherwise

CEOCHD

natural log of the size of the board of directors ln(BRDSIZE)

natural log of the CEOs age ln(CEOAGE)

natural log of the CEOs tenure ln(CEOTEN)

natural log of value of equity ln(SIZE)

CEO option compensation/total compensation (lagged one year) % OPTIONS

* There are many firms with no institutional investors on the board of directors. Therefore, wemust take log of 1 plus the number of such investors. In contrast, there are many institutionalinvestors for each firm (median = 356), so adding 1 to that number would be irrelevant.

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Table 4

Discretionary Accruals from Modified Jones Model for S&P 100 Firms

The dependent variable in Regression 1 is signed discretionary accruals. In regression 2 it is theabsolute value of discretionary accruals. Discretionary accruals are defined as the differencebetween actual accruals and the accruals predicted from the modified Jones model (equation 1).Regressions are estimated as a pooled time-series cross section for S&P 100 firms, with fixed firmeffects. The sample period is 1993 2000. t-statistics are in parentheses. The number ofobservations is 662.

Dependent Variable

Discretionary Accruals Absolute Value of Discretionaryas Percent of Assets Accruals as Percent of Assets

Explanatory Variable Regression 1 Regression 2

-0.0269 -0.0297Fraction of shares

owned by all institutional investors (-3.11) *** (-3.45)***

-0.0247 -0.0219ln(Number ofinstitutional investors) (-3.42)*** (-3.07)***

-0.0286 -0.0243ln(Number of institutionalinvestors on board) (-1.62) * (-1.47)

-0.1242 -0.1283Fraction of board composedof institutional investors (-2.52) ** (-2.64)***

0.0931 0.0809Fraction of firm owned bydirectors plus executive officer (2.14) ** (1.98)**

-0.1106 -0.0997(-3.46)*** (-2.96)***

Fraction of board composed ofindependent outside directors

CEO duality dummy 0.0073 0.0048(1.12) (0.95)

ln(Board size) -0.0042 -0.0026(-0.31) (-0.19)

ln(CEO age) 0.0054 0.0068(0.32) (0.41)

ln(CEO tenure) 0.0247 0.0189(1.27) (1.15)

ln(Firm size) -0.00108 -0.0011(lagged one year) (-1.09) (-1.15)

0.1422 0.1396Option compensation as a fractionof total compensation (9.56) *** (9.23)***

R-squared (adjusted) 42.5% 41.8%

CUSIP F Value 9.42*** 9.09***

* Significant at better than the 10% level.** Significant at better than the 5% level.*** Significant at better than the 1% level

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Table 5

Industry-adjusted ROA as a Function of Governance and Compensation Variables

The dependent variable is industry-adjusted ROA for firmjin year t, computed from reportedearnings (i.e., without adjustment for discretionary accruals). Regressions are estimated as a pooled

time-series cross-section for S&P 100 firms, with fixed firm effects. The sample period is 1993 2000. t-statistics are in parentheses. The number of observations is 662.

Explanatory Variable

Fraction of shares owned by all 0.0342institutional investors (lagged one year) (2.90)***

ln(Number of institutional investors) 0.0251(lagged one year) (2.75)***

ln(1 + number of institutional investors 0.0021on board) (lagged one y ear) (0.53)

Fraction of board composed of 0.0473institutional investors (lagged one year) (1.01)

Fraction of firm owned by directors plus 0.0265executive officer (lagged one year) (1.26)

Fraction of board composed of independent 0.1083outside directors (lagged one year) (3.17)***

CEO duality dummy -0.0021(-0.56)

ln(Board size) -0.0088(-0.85)

ln(CEO age) 0.0105(0.84)

ln(CEO tenure) -0.0131(-0.99)

ln(Firm size) 0.0010(lagged one year) (0.94)

Option compensation as a fraction of 0.1109total compensation (lagged one year) (8.64)***

R-squared (adjusted) 39.4%

CUSIP F Value 6.72***

* Significant at better than the 10% level.** Significant at better than the 5% level.*** Significant at better than the 1% level.

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Table 6

Industry-adjusted Unmanaged Cash -flow Return on Assets for S&P 100 Firms

The dependent variable is industry-adjusted ROA for firmjin year t, computed from reportedearnings (i.e., without adjustment for discretionary accruals). Regressions are estimated as a pooledtime-series cross-section for S&P 100 firms, with fixed firm effects. The sample period is 1993

2000. t-statistics are in parentheses. The number of observations is 662.

Explanatory variables

0.0496(4.02)***

Fraction of shares owned by allinstitutional investors(lagged one year)

0.0516(3.93)***

In (Number ofinstitutional investors) (lagged one year)

0.0158(1.32)

In (Number of institutional investorson board) (lagged one year)

0.1165(2.94)***

Fraction of board composed ofinstitutional investors(lagged one year)

0.0089(0.51)

Fraction of firm owned by directors plusexecutive officer(lagged one year)

0.2136(8.85)***

Fraction of board composed of independentoutside directors(lagged one year)

-0.0127(-0.58)

CEO duality dummy

-0.0096(-0.49)

In (Board size)

0.0128(0.42)

In (CEO age)

-0.0295(-0.87)

In (CEO tenure)

In (Firm size) 0.0008(lagged one year) (0.91)

-0.0137(-0.38)

Option compensation as afraction of total compensation

44.1%R-squared (adjusted)

CUSIP F-value 10.12***

*Significant at better than the 10% level.** Significant at better than the 5% level.***Significant at better than the 1% Level.