Stock Market Information Production and CEO

Incentives∗

Qiang Kang †

University of MiamiQiao Liu ‡

University of Hong Kong

First Draft: January 2003This Draft: October 2004

AbstractWe examine the relation between chief executive officers’ (CEOs’) incentives and stockmarket informativeness. Following Holmstrom and Tirole (1993), we hypothesize anddemonstrate both theoretically and empirically that CEO incentives tend to be strongerwhen stock prices contain more information. We use probability of informed trading,residual analyst coverage, analysts’ earnings forecast error, and dispersion of analysts’earnings forecast as proxies for stock market informativeness, and find that thesevariables account for the cross-sectional variations in pay-performance sensitivitieswell. Our results are robust to the choice of estimators, sample periods, measuresof incentives, model specifications, and estimation methods.

JEL Classification: D80, G14, G34, J33Keywords: Market microstructure, pay-performance sensitivity, probability of informedtrading, analysts’ earnings forecast

∗This paper is a revision of the paper entitled “Stock Trading, Information Production and IncentivePay”. We appreciate comments from Ross Watts (the editor), an anonymous referee, Tim Burch, StephenChiu, John Core, Craig MacKinlay, Wing Suen, James Wang, Xianming Zhou and seminar participants atUniversity of Hong Kong, University of Pennsylvania, and Hong Kong University of Science and Technology.An earlier draft was completed while Kang was affiliated with University of Hong Kong, whose hospitalityis gratefully acknowledged. We thank Clara Vega and Rong Qi for sharing with us programming codes andoffering computation suggestions. The financial support from University of Hong Kong and University ofMiami (Kang) and the RGC Earmarked Research Grant HKU 7128/01H (Liu) are gratefully acknowledged.All errors remain our own responsibility.

†Corresponding author. Mailing address: Finance Department, University of Miami, P.O. Box 248094,Coral Gables, FL 33124-6552. Phone: (305)284-8286. Fax: (305)284-4800. E-mail: q.kang@miami.edu.

‡School of Economics and Finance, University of Hong Kong, Pokfulam, Hong Kong. Phone: (852)2859-1059. Fax: (852)2548-1152. E-mail: qliu@hku.hk.

Stock Market Information Production and CEO Incentives

Abstract

We examine the relation between chief executive officers’ (CEOs’) incentives and stock

market informativeness. Following Holmstrom and Tirole (1993), we hypothesize and

demonstrate both theoretically and empirically that CEO incentives tend to be stronger

when stock prices contain more information. We use probability of informed trading,

residual analyst coverage, analysts’ earnings forecast error, and dispersion of analysts’

earnings forecast as proxies for stock market informativeness, and find that these

variables account for the cross-sectional variations in pay-performance sensitivities

well. Our results are robust to the choice of estimators, sample periods, measures

of incentives, model specifications, and estimation methods.

JEL Classification: D80, G14, G34, J33

Keywords: Market microstructure, pay-performance sensitivity, probability of informed

trading, analysts’ earnings forecast

1 Introduction

Market-based compensation schemes have gained huge popularity in the past two decades.

The use of stock grants and stock options, as well as other forms of equity-related incentive

pay, has become a common practice. Many researchers believe that performance-related

compensation schemes help discipline managers to act in the interest of stock investors and

that such pay arrangements are a (partial) remedy to an agency problem. However, executive

compensation has recently received a great deal of negative exposure. The consensus now

is that a large portion of what top managers get is not incentive pay, but rents extracted

by greedy CEOs from dysfunctional boards (e.g., Bertrand and Mullainathan, 2001; and

Bebchuk and Fried, 2003). Both views seem plausible. The debate thus boils down to

answering the following questions: Does incentive pay really play an effective role in aligning

the interest of executives with that of shareholders? If yes, under what conditions will

performance pay be more effective? If no, why is it used?

Our study is motivated by this debate. In this paper we focus on identifying factors that

drive the cross-sectional variation in CEO pay-performance sensitivity, a common measure

of CEO incentives in the compensation literature. In addition to the factors that have

been identified in previous research,1 we hypothesize and empirically demonstrate that stock

market informativeness enhances CEO pay-performance sensitivity. To better motivate our

empirical work, we develop a model that shows a negative relation between pay-performance

sensitivity and the (inverse) measure of stock price informativeness, V ar(δ|P ) (Kyle, 1985).

In this measure, δ captures the uncertainty of a firm’s value and P is the firm’s stock price.

Consistent with Holmstrom and Tirole (1993),2 we posit that incentive pay works more

1It has been well demonstrated that there are enormous cross-sectional variations in individual incentivemeasures such as pay-performance sensitivities (Hall and Liebman, 1998). A non-exhaustive list of variablesthat help explain this heterogeneity includes firm size (Smith and Watts, 1992; Schaefer, 1998; and Baker andHall, 2002); return volatility (Garen, 1994; Aggarwal and Samwick, 1999; and Jin, 2002); growth opportunity(Smith and Watts, 1992; and Gaver and Gaver, 1993); CEO tenure (Bertrand and Mullainathan 2001; andMilbourn, 2002); institutional investors (Hartzell and Starks, 2003); and industry and year effects (Ely, 1991;and Murphy, 1999).

2Holmstrom and Tirole (1993) analytically show that stock prices incorporate performance information

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effectively in a more efficient (or more informative) stock market.

We test this prediction using CEO compensation data drawn from Compustat’s

ExecuComp database during the period 1992–2001. Following the compensation literature

(e.g., Jensen and Murphy, 1990; Hall and Liebman, 1998; and Aggarwal and Samwick 1999),

we estimate pay-performance sensitivity by examining the empirical relation between changes

in executive firm-related wealth and changes in shareholder wealth. We use two metrics of

pay-performance sensitivity. One is stock-based pay-performance sensitivity (PPS), which

measures the change in dollar value of a CEO’s holdings of stocks and stock options per $1,000

increase in the firm’s shareholder wealth. The other is total pay-performance sensitivity

(PPS TOT ), which measures the change in dollar value of a CEO’s total firm-related

wealth per $1,000 increase in the firm’s shareholder wealth. Hall and Liebman (1998) and

Murphy (1999) find that the former accounts for the majority of CEO incentives and can

also be measured more precisely compared to other compensation components. We thus

mainly rely on the stock-based PPS to interpret the empirical results.

We construct four proxies for stock market informativeness. The first is probability of

informed trading (PIN) for each firm-year observation (Easley, Kiefer, and O’Hara (1996,

1997)). PIN directly estimates the amount of information held by traders on a specific stock.

We also exploit the information contained in the analysts’ earnings forecast data to construct

three other proxies for stock market informativeness: residual analyst coverage (RESCOV ),

analysts’ earnings forecast error (FERROR), and dispersion of analysts’ earnings forecast

(DISPER). The four variables capture different aspects of stock market informativeness.

We conduct various empirical tests of the hypothesis that pay-performance sensitivity

increases with stock market informativeness. We find strong empirical support for this

hypothesis. Since PIN and RESCOV are positively related to stock market informativeness

that cannot be readily extracted from the firm’s accounting data and that the principal can use the inferredinformation to design a better compensation contract. Thus, the higher the market liquidity is, the moreinformative the stock price is and consequently, the more effective is the performance-related pay. Thesepredictions have not been rigorously tested partly because it is not straightforward to construct an empiricalanalysis based on Holmstrom and Tirole’s (1993) model.

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but FERROR and DISPER are inversely related to stock market informativeness, the

predictions go one way for the first two variables and the other way for the last two variables.

Specifically, pay-performance sensitivity is strongly and positively related to PIN and

RESCOV , respectively. Pay-performance sensitivity is strongly and negatively related to

FERROR and DISPER, respectively. All of these results are also economically significant.

For example, using the raw measure (or the cumulative distribution function) of PIN as the

proxy for market informativeness, the pay-performance sensitivity at the maximum PIN

is $15.36 (or $2.035) larger than the pay-performance sensitivity at the median PIN as a

result of $1,000 increase in the shareholder value, and the sample median of pay-performance

sensitivities is $12.85. The overall results strongly suggest that an informationally efficient

stock market helps induce CEO incentives.

Our empirical results are robust to various robustness checks. In addition to showing that

the CEO compensation schemes work more effectively in a more informative environment,

our results identify another important underlying economic determinant, the stock market’s

microstructure, that helps account for the cross-sectional heterogeneity in CEO incentives

even after we control for the impact of various previously identified factors. Several theoretic

models have been proposed to link the market microstructure framework with corporate

finance (Holmstrom and Tirole, 1993; and Subrahmanyam and Titman, 1999). We develop

empirical evidences showing that the market microstructure matters a lot in inducing

managerial incentives.

The paper is organized as follows. Section 2 develops the main hypothesis of the paper.

Section 3 discusses the data and the empirical method. Section 4 presents the empirical

results. Section 5 offers the robustness analysis. Section 6 concludes.

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2 Development of Hypothesis

The Holmstrom and Tirole (1993) study (hereafter HT) is one of the first papers to combine

market microstructure with compensation contract theory. HT show that the stock price

incorporates performance information that cannot be extracted from the firm’s current or

future profit data. The amount of information contained in the stock price is useful for

structuring managerial incentives. An illiquid market makes the stock price less informative

and thus reduces the benefits of stock market monitoring.

HT form stock prices in expectation of a liquidation payout π = e + θ + ε, where e is the

manager’s effort and θ and ε are normally distributed mean-zero noise terms with volatilities

σθ and σε respectively. We follow HT’s notation here except that we do not report their time

subscripts.

The informed trader observes a signal of terminal value equal to e + θ + η, where η is a

mean-zero normally distributed error term with volatility ση. HT allow the informed trader

to expend resources to reduce the noise in his signal, ση. The liquidity trader submits an

order, y, which is normally distributed with mean zero and volatility σy.

Proposition 3 in HT summarizes the key comparative static results on the use of stock-

based incentives. HT show that the pay-performance sensitivity is higher when σε is lower,

or when ση is lower for exogenous reasons (e.g., increase in liquidity trade σy as a result

of reduction in ownership concentration). The HT results imply that the stock market

informativeness enhances the effectiveness of incentive contracts.

The above prediction has not been rigorously tested, which might be partially due to the

difficulty in building an empirical analysis on the HT model.3 In the Appendix, we propose a

3First, in the HT model, ownership concentration is the fundamental determinant of market liquiditywhich further determines the level of market informativeness and hence the pay-performance sensitivity.However, Hartzell and Starks (2003) point out that the ownership concentration could also exert its impacton the pay-performance relation through corporate governance channels, e.g., institutional investors monitormanagers by providing incentive compensations. Second, in the HT model, stock price volatility conveysthe precision of the informed trader’s information. A more volatile stock price implies a more liquid andinformative market resulting in a better market monitoring. This aspect has been extensively studied in the

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one-period model which provides direct empirical implications on the linkage between market

microstructure and pay-performance sensitivity.

The key insight of our model, immediately drawn from Proposition 2 in the Appendix

and the fact that Vδ(1− ρ2) = V ar(δ|P ), is

b∗ =1

1 + γkV ar(δ|P ), (1)

where b is the pay-performance sensitivity, γ is the manager’s risk aversion coefficient, k is

his disutility parameter of spending efforts, δ stands for the uncertainty of the firm value, P

is the price of the firm’s shares, and ρ is the correlation coefficient between P and δ.

As shown in Kyle (1985), V ar(δ|P ) is the inverse measure of stock price informativeness.

Lemma 2 in the Appendix states that V ar(δ|P ) = Vδ(Vδ+2Vε)(N+1)Vδ+2Vε

, where the number of informed

traders N , decreases with the cost of collecting information µ, and increases with the

underlying uncertainty Vδ, the variance of the signals Vε, and the variance of liquidity trading

Vz (see Lemma 1 in the Appendix). We summarize the results as follows:

Main Results: (1) The pay-performance sensitivity decreases with the manager’s effort

parameter k, the risk aversion parameter γ, and the inverse measure of stock price

informativeness, V ar(δ|P ). (2) The stock market informativeness increases with the number

of informed traders which is determined by various market microstructure parameters (µ,

Vδ, Vz, and Vε).

The key prediction from our model is consistent with the key prediction from HT: the

more informative the stock price, the more effective is the compensation scheme in inducing

managers’ incentives. The core hypothesis for our empirical study is:

H1: Pay-performance sensitivity increases with stock market informativeness.

empirical research on executive compensation in a different context (i.e., studying the risk-incentive relation)but with mixed evidences (Garen, 1994; Aggarwal and Samwick, 1999; Core and Guay, 2002; and Jin, 2002).Third, several key variables in HT are not directly observable (e.g., the importance of liquidity trade, σy,and the precision of noisy signals observed by the informed trader, ση).

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To implement the empirical test, we need to develop some sensible measures to serve as

proxies for stock market informativeness.

3 Data and Empirical Method

Our primary data set for executive compensation is the ExecuComp database for the period

1992–2001. We obtain stock return data from the CRSP Monthly Stock File and accounting

information from the Compustat Annual File. We use four proxies for stock market

informativeness and construct them from two databases. We compute a firm’s probability

of informed trading (PIN) for a given year by using intraday trading data extracted from

the TAQ database. We calculate three other variables by using analysts’ earnings forecast

information from the I/B/E/S History Summary File.

For our empirical study, we construct a sample containing 2,507 publicly traded firms

and 17,584 CEO-firm-year observations. All monetary terms are in 1992 constant dollars.

To remove the inflation effect from the empirical study, returns are in real terms by adjusting

nominal returns with CPI.

3.1 Measuring CEO Incentives

Because the CEO makes most major corporate decisions and exerts the greatest influence

on the firm among senior executives, we focus on incentive provisions for CEOs. We

identify CEOs by the fields “BECAMECE” and “CEOANN”. Using only “CEOANN” is

problematic, since there are many missing observations for the earlier periods of the sample

(Milbourn, 2002). We measure incentives by pay-performance sensitivity, which we define as

the dollar value change in the CEO’s firm-specific wealth per $1,000 change in the shareholder

value (Jensen and Murphy, 1990).

CEO compensation comprises several different layers. Total current compensation (TCC)

is the sum of salary and bonus. Total direct compensation (TDC) is the sum of total current

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compensation, other annual short-term compensation, payouts from long-term incentive

plans, the Black-Scholes (1973) value of stock options granted, the value of restricted stocks

granted, and all other long-term compensation. Both TCC and TDC measure the direct

payment a CEO receives from his firm within one fiscal year. However, neither TCC nor

TDC considers the indirect compensation that a CEO derives from a revaluation of stocks

and stock options already granted to him in previous years.

We compute the change in value of a CEO’s stock holdings over a given year as the

beginning-of-year value of his stock holdings multiplied by the year’s stock return. It

is important to revalue the stock option portfolios. ExecuComp contains the values of

unexercised exercisable and unexercisable in-the-money options, but the database measures

only the intrinsic value of the option portfolios. We apply the Black-Scholes (1973) model to

revalue the stock option portfolios. We adopt Core and Guay’s (1999) method to compute

the hedge ratio delta (δ) of each option.4 Delta refers to how the value of a stock option

varies given a one-dollar change in value of the underlying stock. We then multiply the option

deltas by the change in the firm’s market value (or change in shareholders’ wealth), adjusting

for the percentage of stock shares represented by the options, and then add the value from

stock option exercise to obtain the change in value of the CEO’s option holdings. The change

in CEO’s total firm-specific wealth (DTOTW ) is the sum of the direct compensation and

the indirect compensation due to the revaluation of stocks and stock option holdings.

We define the change in shareholder value (V CHANGE) over a given year as the

firm’s beginning-of-year market value times the year’s stock return. Using V CHANGE

and DTOTW , we calculate PPS TOT as the dollar value change in the CEO’s total firm-

related wealth per $1,000 change in the shareholder value.

4Core and Guay’s (1999) method quite accurately accounts for the economically meaningful value of thein-the-money options, but their method ignores the value of out-of-the-money options. This may likely createsystematic bias for firms with more underwater options and for firms with options that are at-the-money ornear-the-money. This concern is partially alleviated since our results are robust to the subperiod analysis inSection 5.2 where the sample is divided into the bubble subperiod and the post-bubble subperiod. Also, forsimplicity, some researchers use 0.7 as the δ value.

7

We define the stock-based pay-performance sensitivity (PPS) as the change in dollar

value of the CEO’s existing stock-based compensation (stocks and stock options) for a $1,000

change in the shareholders’ wealth. We express the number of shares and options held by

CEOs in units of thousands and the number of shares outstanding in millions.

PPSit =(4CEO′s Holdings of Stocks and Stock Options)it

(4Shareholder′s Wealth)it

. (2)

We can interpret the stock-based pay-performance sensitivity as the CEO’s stock

ownership percentage times 1,000 plus incentives from stock option holdings. We can do

so by using the option delta (δ):

PPSit = SHROWNPCit−1 ∗ 1000 +(Number of Options Held)it−1

(Number of Shares Outstanding)it−1

δ, (3)

where SHROWNPC is the CEO’s ownership percentage.

PPS is more appealing than PPS TOT for our empirical analysis. We can estimate

the revaluations of existing stock and stock options with relative precision compared to

measuring the impact of firm performance on the changes in total direct compensation.

Including grants of stock options and restricted stocks can be problematic since they might

serve as an ex-post mechanism like a bonus rather than an ex-ante incentive mechanism.

We therefore, focus primarily on the stock-based incentives. The analysis using PPS TOT ,

which we include as a robustness check, yields essentially the same results.

Panel A of Table 1 presents the summary statistics of the CEO’s compensation

and incentive measures. For the average (median) CEO in our sample, total current

compensation (TCC), in 1992 constant dollars, is $960,776 ($680,813). Total direct

compensation (TDC) ranges from zero to $564.17 million, with an average value of $3.3

million and a median value of $1.42 million. A CEO holds a mean of 3.33% of his firm’s stocks

with a standard deviation of 7.33%. Median CEO ownership is 0.43%, and the maximum

ownership is 99.44%. A CEO gains an average of $1.92 million from stock option revaluation

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with a standard deviation of $34.86 million. The median option revaluation is worth $0.21

million. Overall, the CEO’s total firm-related wealth (DTOTW ) has a mean value of $18.83

million and a median value of $2.3 million. We note that every compensation measure,

particularly the change in the CEO’s firm-related wealth, has a higher mean than its median

and displays a right-skewed pattern. The annual change in shareholders’ value (VCHANGE)

has a mean of $457.17 million with a median of $61.79 million.

The mean and median values of the stock-based incentive measure (PPS) as defined in

equation (3) are, respectively, $40.79 and $12.85 per $1,000 change in the shareholders’ value.

The PPS measure has a standard deviation of $74.59 with a maximum value of $994.45. The

total pay-performance sensitivity (PPS TOT ) has a mean of $46.51, which is higher than

PPS, but it also has a much larger standard deviation ($81.92). The median of PPS TOT

is $13.09 and is almost the same as the median of PPS. The stock-based incentive does not

deviate much from the total incentive, corroborating findings in prior studies that changes

in the value of stock and stock option holdings account for the majority of pay-performance

sensitivities (Hall and Liebman, 1998; and Murphy, 1999).

3.2 Measuring Stock Market Informativeness

3.2.1 Probability of Informed Trading (PIN)

Easley, Kiefer and O’Hara (1996, 1997) develop and use the PIN variable to measure the

probability of informed trading in the stock market. The measure is based on the market

microstructure model introduced in Easley and O’Hara (1992), where trades can come from

liquidity traders or from informed traders. The literature has firmly established the PIN

variable as a good measure of stock price informativeness in various settings.5 PIN directly

estimates the amount of information held by traders on a specific stock, and it is a good

5See, e.g., Easley, Kiefer, and O’Hara (1996, 1997); Easley, Kiefer, O’Hara, and Paperman (1996); Easley,O’Hara, and Srinivas (1998); Easley, Hvidkjaer, and O’Hara (2002); Chen, Goldstein, and Jiang (2003); andVega (2004).

9

empirical variable to test our hypothesis that stock prices have an active role in enhancing

pay-performance sensitivity.

Our description of the model and how we construct the PIN measure are as follows.

There are three types of players in the game, liquidity traders, informed traders, and market

makers. The arrival rate of liquidity traders who submit buy orders is εb and the arrival rate of

liquidity traders who submit sell orders is εs. Every day, the probability that an information

event occurs is α, in which case the probability of bad news is δ and the probability of good

news is (1 − δ). If an information event occurs, the arrival rate of informed traders is µ.

Informed traders submit a sell order if they get bad news and a buy order if they get good

news. Thus, on a day without information events, which occurs with probability (1−α), the

arrival rate of a buy order will be εb and the arrival rate of a sell order will be εs. On a day

with a bad information event (with probability αδ), the arrival rate of a buy order will be

εb and the arrival rate of a sell order will be εs + µ. On a day with a good information event

(with probability α(1− δ)), the arrival rate of a buy order will be εb + µ and the arrival rate

of a sell order will be εs. The likelihood function for a single trading day is:

L(θ|B, S) = (1− α)e−εb(εb)

B

B!e−εs

(εs)S

S!+ αδe−εb

(εb)B

B!e−εs+µ (εs + µ)S

S!

+α(1− δ)e−εb+µ (εb + µ)B

B!e−εs

(εs)S

S!, (4)

where θ = (εb, εs, α, δ, µ), B is the number of buy orders, and S is the number of sell orders

in a single trading day.6

Using the number of buy and sell orders in every trading day within a given year,

M = (Bt, St)Tt=1, and assuming cross-trading day independence, we can estimate the

6The trade direction is inferred from intraday data based on the algorithm proposed in Lee andReady (1991).

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parameters of the model (εb, εs, α, δ, µ) by maximizing the following likelihood function:

L(θ|M) =t=T∏t=1

L(θ|Bt, St). (5)

Thus, we estimate PIN by dividing the estimated arrival rate of informed trades by the

estimated arrival rate of all trades:

PIN =αµ

αµ + εb + εs

. (6)

We maximize the likelihood function in equation (5) for the parameter space θ and then

calculate PIN for the period 1993-2001. There are 8,456 firm-year observations after we

merge the TAQ database with the ExecuComp database.

Panel A of Table 2 presents summary statistics of PIN . In our sample, the average

PIN is 0.163 with a standard deviation of 0.051. The median value of PIN is 0.157. The

maximum PIN is 0.797 with a minimum of zero. The average sample property of our

PIN estimates is consistent with the sample proprety identified in previous studies (see, e.g,

Easley, Hvidkjaer, and O’Hara, 2002).

3.2.2 Measures Based on the Analysts’ Forecast Data

Since information-based trading can only be inferred, we supplement the PIN measure with

three other variables using the analysts’ earnings forecast data from the I/B/E/S History

Summary File: number of forecasts, forecast errors, and dispersion of forecasts. For each

firm in the ExecuComp database, as the raw measure we use the summary information on

analysts’ fiscal year 1 earnings estimates in the ninth month of a fiscal year.7

We control for the influence of size and other variables on the number of analyst forecasts

by using residual analyst coverage (RESCOV ), which we obtain by regressing the log value

7We also conduct the analysis by using summary information on each of the other month’s fiscal year1 earnings estimates within that fiscal year. The summary information is stable across months and is veryclose to the annual average value. Those analyses all yield qualitatively similar results.

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of one plus the number of analyst forecasts against firm size, market-to-book ratio, turnover

ratio, Nasdaq dummy, leverage ratio, industry dummies and year dummies.8 As in Hong,

Lim, and Stein (2000), if no I/B/E/S value is available within that fiscal year (i.e., firms

in the ExecuComp database are not matched with firms in the I/B/E/S database), we set

the number of coverage to zero. Since I/B/E/S does not include some investment banks

and hence their analysts, in unreported analysis we also drop observations with no I/B/E/S

values and the results are qualitatively similar.

Panel A of Table 2 presents summary statistics of the residual coverage that we obtain

from such a regression. RESCOV has mean of 0.023 and median of 0.264. The standard

deviation of RESCOV is 1.134, with a minimum of -3.28 and a maximum of 2.57.

In addition to RESCOV , we construct two other variables, analysts’ forecast error

(FERROR) and dispersion in analysts’ earnings forecasts (DISPER), as proxies for stock

market informativeness. We compute FERROR as the absolute value of the difference

between mean earnings forecasts and actual earnings, divided by the absolute value of

actual earnings. When a firm’s information production process is intense and effective,

analysts following the firm’s stock are more likely to agree with one another on the firm’s

earnings prospect. FERROR measures the intensity of a firm’s information production.

We merge I/B/E/S/ with ExecuComp and delete the observations with zero actual earnings

since FERROR would be undefined. This results in a sample of 9,420 observations over the

period 1992-2001. Panel A of Table 2 presents summary statistics for FERROR. It has a

mean (median) of 0.444 (0.066) and a standard deviation of 5.014.

Akinkya and Gift (1985), Diether et al. (2002), and Johnson (2004) show that the

dispersions of analysts’ earnings forecasts is a proxy for the information environment in

which various traders trade. A larger analyst forecast dispersion implies less intensive

information production or a less transparent information environment and vice versa. We

8For brevity, we do not report the results of the analyst coverage regression. The most significant variableto explain analyst coverage is firm size, followed by turnover, Nasdaq dummy and leverage. Details areavailable upon request.

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compute DISPER as the standard deviation of earnings forecasts scaled by the absolute

value of the mean earnings forecast and times 100. If the mean earnings forecast is zero, we

exclude the stock from the sample.9 Combining I/B/E/S/ with ExecuComp and deleting

the observations with zero mean earnings forecast, we obtain a sample of 9,161 observations

for the period 1992-2001. Panel A of Table 2 presents summary statistics of DISPER. Its

mean (median) is 12.116 (3.175) and its standard deviation is 66.754.

Panel B of Table 2 presents the bivariate Pearson correlation coefficients and their

significance levels of the four market informativeness variables. After merging the four

variables into a balanced panel, the number of observations drops to 4,460. In this balanced

panel, the correlation between PIN and RESCOV is -0.016 and is not significant. The

correlation between PIN and FERROR is 0.018 and is not significant either. The

correlation between PIN and DISPER is 0.040 and is significant at the 1% level. RESCOV

and FERROR are almost uncorrelated (the correlation coefficient is 2e-5). The correlation

between RESCOV and DISPER is 0.032 and is significant at the 5% level. The correlation

between FERROR and DISPER is 0.016 and is not significant. The piecewise correlations

among the four market informativeness proxies in an unbalanced panel have a similar pattern

and are not reported for brevity. Overall, the four variables have no or small correlations

among each other, indicating that they capture different aspects of a firm’s stock market

informativeness.

3.3 Control Variables

Panel B of Table 1 summarizes the control variables we use in our empirical analysis. We

obtain stock return data from CRSP. We calculate the market value (MKTV AL) at each

year-end. Our sample comprises a range of firms with an average market capitalization of

$4.29 billion and a median of $0.86 billion. The smallest firm has only $6,100 in market

9We have also assigned the observations with zero mean earnings forecast to the highest dispersion decile(90% percentile). Doing so does not qualitatively change our main results.

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capitalization and the largest one has $427.22 billion. We use the log value of market

capitalization (SIZE) as one measure of firm size. The mean (median) value of SIZE is

$6.91 million ($6.76 million) with a standard deviation of $1.6 million. The firm’s annualized

percentage stock return (ANNRET ) averages at 22.45% (median value at 11.54%). The

annualized percentage volatility of stock returns (ANNV OL), which we compute by using

the past five years of monthly stock returns, has a mean value of 39.44% and a median value

of 35.23%.

We extract firm-specific accounting data from Compustat. To measure a firm’s growth

opportunities, we calculate Tobin’s q as the ratio of the market value of assets to the book

value of assets. We obtain the market value of assets as the book value of assets (data 6)

plus the market value of common equity (data 25 times data 199) less the book value of

common equity (data 60) and balance sheet deferred taxes (data 74). Table 1 shows that

the average Tobin’s q of the firms in our sample is 2.16 with a standard deviation of 2.75.

The median is 1.48. Tobin’s q is as low as 0.28 and as high as 105.09.

Other control variables are CEO tenure, industry dummies, and year dummies. We

calculate CEO tenure as the number of years the executive has been the CEO of this firm

as of the compensation year in ExecuComp. The mean and median of the CEO tenure are

7.68 and 5.5 years, respectively. The maximum tenure in our sample is 54.92 years. Finally,

we construct 20 industry dummies based on the 2-digit SIC code from CRSP.

3.4 Econometric Specifications

There are two basic econometric methods in empirical studies of incentives. One is to directly

regress the incentive measure against a set of explanatory variables. This approach makes

it straightforward to interpret results and also provides the flexibility to control for factors

that may affect the pay-performance sensitivity. Following this approach, we specify the

14

basic model as:

PPSi,t = γ0 + γ1 ∗ INFOVi,t−1 + γ2 ∗ sizei,t−1 + γ3 ∗ annvoli,t−1 + γ4 ∗ Tobinqi,t−1

+γ5 ∗ tenurei,t +∑k≥6

γk ∗ dummiesi,t + εi,t, (7)

where PPS refers to the computed pay-performance sensitivity as specified in equation (3);

INFOV stands for the set of market informativeness variables, i.e., PIN , RESCOV ,

FERROR and DISPER; and dummies are industry dummies and year dummies.

In equation (7), we control for several empirically relevant variables: firm size, return

volatility, growth opportunities, CEO tenure, industry effects, and year effects. It is an

empirical regularity that the pay-performance sensitivity is related to the firm size (Jensen

and Murphy, 1990; Schaefer, 1998; and Baker and Hall, 2002). Aggarwal and Samwick (1999),

Core and Guay (2002), Jin (2002), among others, find that the return variability plays a

key role in explaining some of the heterogeneity in pay-performance sensitivities. Smith

and Watts (1992) and Gaver and Gaver (1993) report that a firm’s growth opportunities

have a significant impact on managerial incentives. Bertrand and Mullainathan (2001),

Milbourn (2002), and Bebchuk and Fried (2003) argue in different aspects that CEO tenure

is related to CEO incentives. Many studies document the presence of industry effects and

year effects in variations in managerial incentives (Ely, 1991; and Murphy, 1999).

In equation (7), the coefficient γ1 captures the influence of market informativeness on

incentives and is our main parameter. We test our hypothesis by examining the sign and

significance of the estimated γ1. The economic significance of market informativeness on

PPS is easy to interpret in this setting.

Another specification, adopted in Jensen and Murphy (1990), and Aggarwal and

Samwick (1999), among others, uses the CEO’s compensation level as the dependent variable.

15

Based on the approach, we specify our model as:

DTOTWi,t = α + vchangei,t ∗ (β0 + β1INFOVi,t−1 + β2sizei,t−1 + β3annvoli,t + β4Tobinqi,t−1

+β5tenurei,t + β′ddummiesi,t) + β6 ∗ INFOVi,t−1 + β7 ∗ sizei,t−1 + β8 ∗ annvoli,t

+β9 ∗ Tobinqi,t−1 + β10 ∗ tenurei,t + β′k ∗ dummiesi,t + εi,t, (8)

where DOTOWi,t denotes the changes in the CEO’s firm-related wealth while employed by

firm i in year t.10

To address cross-sectional variations in the pay-performance sensitivity, in equation (8)

we interact the shareholder dollar return (V CHANGE) with firm size, return volatility,

growth opportunities, CEO tenure, and industry and year dummies. To test the relation

between the pay-performance sensitivity and the market informativeness, we interact the

variable INFOV with V CHANGE to control for any heterogeneity in the pay-performance

sensitivity beyond that attributable to firm size, return variability, growth opportunity, CEO

tenure, industry effect, and year effect. The coefficient β1 associated with this interaction

term is the parameter of interest. If the heterogeneity of PPS does not depend on the market

informativeness, the estimated β1 will not be significantly different from zero. By including

the control variables in equation (8), we ensure that our estimates of the interaction term

coefficients are not affected by any relation between the control variables and the level of

compensation that may happen to exist in the cross section.

If we use DTOTW as the dependent variable, we do not need to measure incentives

10We can also replace DTOTW in equation (8) with the total direct compensation or its differentcomponents or their annual changes. The breakdown of TDC helps us study how its differentcomponents, e.g., salary, bonus, and stock options grants, respectively, respond to the firm performance(see Albuquerque (2004)). This approach is less appealing in our context for two reasons. First, our goal isto examine the relation between stock market informativeness and CEO incentives. Having the empiricallyappropriate incentive measures is the key, but it is still unclear whether salary, bonus, stock or stock optiongrants should be counted as ex-post bonus or ex-ante incentive mechanisms. Second, it has been shown thatCEO incentives are mainly provided through revaluations of existing stocks and stock options holdings (Halland Liebman, 1998; and Murphy 1999). Even if salary, bonus, stock or stock option grants can be treatedas the ex-ante incentive mechanisms, their individual effects on the overall CEO incentives are small.

16

directly. This approach is appealing when the proposed pay-performance sensitivity measures

are very noisy or when it is difficult to measure incentives directly. However, the approach is

less straightforward. We can only calculate the imputed pay-performance sensitivity based

on the estimated coefficients of those interaction terms, which makes it relatively difficult to

interpret the economic significance of variables of interest. For example, from equation (8),

we infer PPS as β0 +β1INFOVi,t−1 +β2sizei,t−1 +β3annvoli,t +β4Tobinqi,t−1 +β5tenurei,t +

β′ddummiesi,t for a CEO of firm i in period t. In this formula, the level of PPS and the

contribution of INFOV to PPS are not immediately clear. The imputed PPS profile is a

linear function of the variables INFOV , size, annvol, Tobinq, tenure, and dummies. β0

measures the intercept of the PPS profile. β1, β2, β3, β4, and β5 capture the respective

responses of the profile to changes in the five variables.

Another pitfall of this econometric specification is that it is still questionable whether

some components of the total CEO compensation should be included. For example, do

salary, bonus, and the grants of stocks and stock options in the current fiscal year serve as

incentive mechanisms for future CEO performance?

3.5 Testable Hypotheses and Estimation Methods

As noted earlier, the four INFOV variables, PIN , RESCOV , FERROR and DISPER,

capture the market informativeness from different perspectives. A larger value of PIN (or

RESCOV ) corresponds to a more informative market and a larger value of FERROR (or

DISPER) corresponds to a less informative market. The testing of our core hypothesis

comprises several hypothesis tests, depending on which INFOV variable and which

econometric specification is used.

Specifically, with the first econometric specification in equation (7), we have

H0’: γ1 = 0, versus

H1’: γ1 > 0 if the INFOV variable is PIN or RESCOV ; or versus

17

H1”: γ1 < 0 if the INFOV variable is FERROR or DISPER.

We replace γ1 in the above hypotheses with β1 if we use the second econometric specification

in equation (8).

Given the apparent right skewness in the CEO compensation and incentive data (as

shown in Table 1), we follow the literature and estimate median regressions to control for

the possible influence of outliers. We obtain standard errors of the estimates by using

bootstrapping methods.

As a robustness check, we replace each raw measure of the four market informativeness

proxies with its cumulative distribution function (CDF) in both model specifications. As

Aggarwal and Samwick (1999) argue, the use of CDF helps to make the highly non-

homogeneous data homogeneous and the regression results economically more sensible. It

is easier to transform the estimated coefficient values into pay-performance sensitivities at

any percentile of the distribution of the proxies. Thus, the CDFs give the regression results

more immediate economic intuition than the raw measures.

4 Empirical Results

4.1 Using PPS as the Dependent Variable

Our main empirical results are based on the econometric specification in equation (7),

where we use CEO incentive measure as the dependent variable. Table 3 presents the

median regression results of the stock-based pay-performance sensitivity PPS, defined in

equation (3), by using PIN , CDF (PIN), RESCOV , and CDF (RESCOV ) as the measures

for stock market informativeness. In all regressions we control for firm size, return volatility,

growth opportunity, CEO tenure, industry dummy, and year dummy. For brevity we do not

report coefficient estimates for the industry and year dummies.

Columns 1 and 2 show how the CEO’s incentives respond to the probability of informed

18

trading (PIN and its CDF). The estimated coefficients of the PIN measure (γ1) in both

columns are positive and statistically significant at the 1% significance level. γ1 is 23.999

in Column 1 where we use the raw measure of PIN . The difference in PPS between any

two PIN levels is calculated as the difference in the two PIN values multiplied by γ1.

For example, since the maximum, median, and minimum PIN are 0.797, 0.157, and 0,

respectively, the pay-performance sensitivity at the maximum PIN is $15.36 higher than

that at the median PIN as a result of $1,000 increase in shareholder value, all else equal. The

incentive with the median PIN is $3.77 larger than the incentive with the minimum PIN .

The economic magnitude of PIN on pay-performance sensitivity is significant, considering

that the mean (median) stock-based pay-performance sensitivity in our sample is $40.787

($12.845).

Column 2 further illustrates the economic significance of PIN . Using CDF (PIN) as

the explanatory variable, the coefficient γ1 is 4.07. The PPS at the maximum PIN , i.e.,

CDF (PIN)=1, is $2.035 larger than that at the median PIN, i.e., CDF (PIN)=0.5, per

$1,000 increase in the shareholder value.

Columns 3 and 4 show how the CEO’s incentives react to the residual analyst coverage

(RESCOV and CDF (RESCOV )). The estimated coefficients γ1 are 0.414 for RESCOV

and 1.599 for CDF (RESCOV ), respectively. Both are statistically significant at the 1%

significance level, lending support to our hypothesis. We note that the economic significance

of RESCOV is much smaller than that of PIN , which might be due to the fact that we

obtain RESCOV as the residual from the regression of the raw analyst coverage against a set

of firm-specific variables. RESCOV is likely a noisy proxy for market price informativeness.

Using CDF (RESCOV ) partially alleviates the concern. Column 4 shows that after we use

the CDF of residual coverage as the explanatory variable, the economic magnitude of the

analyst coverage on stock-based PPS almost quadruples.

Table 3 shows that the estimated coefficients of control variables are all statistically

significant and consistent in both sign and magnitude across the four regressions. The

19

coefficient of the lagged SIZE, γ2, is around -3.12 to -3.98, reaffirming that the pay-

performance sensitivity is negatively and strongly correlated with the firm size even after

we take into account the last three years of observations (1999-2001). The coefficient of

ANNV OL, γ3, ranges from 18.06 to 20.14, implying a positive risk-incentive relation. Our

results are consistent with the model prediction of Prendergast (2002) as well as the empirical

evidence in Core and Guay (2002). However, we note that the empirical evidence on the

risk-incentive relation is mixed, and the economic rationale is still subject to a heated debate

(see the survey in Prendergast (2002)).

The coefficient of the lagged Tobinq, γ4, is about 1.44 to 1.63. There is a clearly positive

relation between growth opportunities and CEO incentives, corroborating the evidences

reported in Smith and Watts (1992) and consistent with the hypothesis that a higher

incentive level is needed when CEO effort becomes more valuable. There is also a positive

relation between CEO tenure and PPS as the estimated coefficient of tenure is approximately

0.94 to 1.22.

4.2 Using DTOTW as the Dependent Variable

Table 4 reports median regression results from the four models in which we use the lagged

values of PIN , CDF (PIN), RESCOV , and CDF (RESCOV ) separately as proxies for

stock market informativeness. The coefficients of the interaction terms are related to

incentives. All the coefficients of the interaction terms are statistically significant at the

1% level. The coefficients are consistent in both sign and magnitude with the corresponding

estimates from Table 3, where we use PPS as the dependent variable. The coefficient of

V CHANGE, β0, ranges from 10.65 to 15.79. The incentive is strongly and negatively

related to SIZE and β2 is around -2.10. The incentive is strongly and positively related to

ANNV OL (β3 around 18 to 24), Tobinq (β4 around 0.10 to 0.43), and Tenure (β5 around

0.58 to 0.78).

The parameter of interest, β1, is significant and positive across all regressions. Its estimate

20

is 16.96 for PIN , 1.91 for CDF (PIN), 0.39 for RESCOV , and 1.36 for CDF (RESCOV ),

respectively. The t-statistics of the estimates are large enough to readily reject the null

hypothesis in favor of the alternative hypothesis that β1 > 0. The p-values of the estimated

coefficient β1 are zero for all four regressions. Both the t-statistics and the p-values lend

strong support to the claim that β1 > 0, clearly pointing to a strong and positive relation

between CEO incentives and the two market informativeness proxies, the probability of

informed trading and the residual analyst coverage.

Table 4 also reports the estimates of the coefficients that link the control variables to the

level of CEO pay. Firm size is strongly and positively related to the total compensation. This

finding, together with the finding of the negative relation between firm size and incentive,

might have different implications.11 The results suggest that CEOs of larger companies are

able to extract rents from shareholders in the form of higher compensation levels independent

of firm performance.12 The positive size effect on the compensation level is also consistent

with the evidence that larger firms require more talented managers who are more highly

compensated (Smith and Watts, 1992) and who are consequently expected to be wealthier

(Baker and Hall, 2002). The negative size effect on the PPS can be partially explained by

the fact that a CEO in a larger firm has difficulty in acquiring a large percentage of shares

either directly or indirectly through stock and option grants (Demsetz and Lehn, 1985; and

Baker and Hall, 2002).

Like Aggarwal and Samwick (1999), we also find a strong positive relation between return

volatility and the level of CEO compensation, although the principal-agent theory does not

give a clear prediction on whether the expected level of compensation is increasing with

the firm stock return volatility. We conjecture that as the volatility of the underlying asset

increases, the value of option portfolios held by a CEO increases, leading to a higher level of

total compensation, all else equal.

11We do not explore these implications in detail as they are beyond the scope of this study.12We thank the referee for suggesting this explanation to us.

21

We find that the estimated coefficients of Tobinq are positive and significant in all

regressions, which indicates a positive relation between the growth opportunity and the

compensation level. Together with a positive relation between the growth opportunity and

incentives, this finding is again consistent with Smith and Watts (1992). That is, a higher

growth opportunity increases the marginal value of CEO effort. Therefore higher incentive

and compensation levels are needed, holding all else fixed.

CEO tenure is positively related to both the incentive and compensation levels, which can

be subject to different interpretations. On the one hand, Bertrand and Mullainathan (2001)

and Bebchuk and Fried (2003) argue that the CEO has a lot of influence in setting his

compensation contract. CEO tenure might measure the extent to which a CEO is entrenched

and is therefore better able to appropriate money, including stock-related pay, for himself.

On the other hand, CEO tenure is argued to be a proxy for CEO reputation and experience,

which helps to improve incentives as uncertainty about a CEO’s ability is resolved over time

(Milbourn, 2002). A longer tenure also captures the accumulation of stock holdings and

hence a higher level of compensation, other things equal. A third interpretation is that

CEO tenure is a proxy for the agency cost. As CEOs approach retirement, increased equity

incentives can be used to counteract potential horizon problems (Jensen and Murphy, 1990).

The relation between the two market informativeness proxies and the compensation level

is mixed. Although it is positive, PIN is not significantly related to the CEO compensation.

RESCOV is strongly and negatively related to the CEO compensation, a finding for which

we do not have a good explanation.

4.3 Further Analysis with Alternative Price Informativeness

Measures

As noted earlier, by using the analyst earnings forecast database we can construct two other

variables, analysts’ forecast error (FERROR) and dispersion in analysts’ earnings forecasts

22

(DISPER), as our proxies for stock price informativeness. The larger the forecast error

measure or the dispersion measure is, the less efficient is the information production of

a firm’s stock and hence the lower is the stock price informativeness. When we test the

hypothesis that CEO incentives respond positively to the stock price informativeness, we

expect to find a significant negative correlation between the stock-based PPS and FERROR

or DISPER. The results are reported in Table 5.

In Table 5, Columns 1 and 2 present the median regressions results of the stock-

based PPS when we use FERROR and its cumulative distribution function as the market

informativeness measure, respectively. The parameter of interest, γ1, is -0.096 for FERROR

and significant at the 10% level. γ1 is -2.964 for CDF (FERROR) and significant at

the 1% level. Columns 3 and 4 report the results when we use DISPER and its

cumulative distribution function as the market informativeness measure, respectively. γ1

is estimated respectively, at -0.004 for DISPER, significant at the 5% level, and -4.418 for

CDF (DISPER), significant at the 1% level. Consistent with the results when we use PIN

and residual analyst coverage as the market informativeness measures, the significant and

negative estimates of γ1 in Table 5 support the hypothesis that CEO incentives do respond

positively to the stock price informativeness.

The estimated γ1 is very small, although statistically significant, if we use the raw

informativeness measures, FERROR and DISPER. This finding might cast doubt on

their economic significance. Since both variables are highly right-skewed and fat-tailed,

the estimates from the cumulative density functions of FERROR and DISPER are more

economically meaningful. Using CDF (FERROR) (CDF (DISPER)) as proxies for the

price informativeness, we find that the pay-performance sensitivity at the maximum forecast

error level (or the maximum analyst forecast dispersion level) is $1.482 ($2.136) lower

than the pay-performance sensitivity at the median forecast error level (or the median

analyst forecast dispersion level) per $1,000 change in the shareholder value. The economic

magnitude is by no means small.

23

Consistent with the results from Sections 4.1 and 4.2, the coefficients of control variables

are all statistically significant and have the expected signs. Incentive is negatively related to

firm size and is positively related to return volatility, growth opportunity, and CEO tenure.

5 Robustness Analysis

5.1 Using Alternative Pay-Performance Sensitivity Measures

PPS considers only changes in the value of CEO stock and stock option holdings. The

CEO’s direct compensation from salary and bonus, new stock and stock option grants, long-

term incentive pay and other annual compensation might also matter. PPS TOT , defined

as the dollar value change in the CEO’s total firm-related wealth per $1,000 change in the

shareholder value, measures total CEO incentives because it is derived from both the direct

and indirect stock-based CEO compensation. We repeat our analysis using PPS TOT as the

dependent variable in equation (7). We use CDFs of the four market price informativeness

proxies and report the median regression results in Table 6.

The results in Table 6 are consistent with those results using PPS. The estimates

for the parameter of interest, γ1, are 5.283 for CDF (PIN), 1.463 for CDF (RESCOV ),

-3.072 for CDF (FERROR), and -4.169 for CDF (DISPER), respectively. All estimates

are statistically significant at the 1% level. Similar to when PPS is used as the incentive

measure, we find that the coefficient estimates using PPS TOT are economically significant.

In Table 6 when we use PPS TOT as the incentive measure, the signs and magnitude

of the other parameter estimates are comparable to the corresponding signs and magnitude

of the parameter estimates reported in Tables 3 and 5, where we use PPS as the incentive

measure. These results, from another perspective, substantiate the finding of Hall and

Liebman (1998), among others, that changes in the value of CEO stock-based compensations

contribute to nearly all of the pay-performance sensitivity, and that the CEO direct

24

compensation has little impact on the pay-performance sensitivity. This result alleviates our

concerns on how to compute TDC, e.g., should we also include future direct compensations

into TDC, since firm performance might have an impact on the future pay levels? Should

new stock and option grants be treated as ex-ante incentives or ex-post bonuses? Table 6

suggests that the results are not sensitive to such choices.

Table 6 further shows that incentive is related negatively to firm size and positively to

return volatility, growth opportunities, and CEO tenure.

5.2 Subperiod Analysis

We perform a subperiod analysis for three reasons: (1) The U.S. stock market bubble burst

in 2000, rendering many executives’ options virtually worthless. The way in which we

compute the changes in value of stock option portfolios might create bias for firms with

more out-of-money options, and for firms with options that are at-the-money or near-the-

money. Comparing the results before and after the bubble burst is thus useful and interesting.

(2) We might rationally suspect that a firm’s incentive provision policies changed after the

bubble burst, which might directly affect our results. (3) Most of the prior research uses

data up to 1999. By conducting a subperiod analysis, we are able to compare our analysis

with the prior research. We can also check the stability of the relation between the incentive

and the variables of interest.

To conduct the subperiod analysis, we split our sample into two subsamples, the bubble

period 1992-1999 and the post-bubble period 2000-2001. Using the stock-based PPS as the

dependent variable and the CDF transformations of the four price informativeness measures

as the main explanatory variables, we apply median regressions to the two subperiods. Table

7 presents the results of this subperiod analysis.

Columns (1)-(4) and Columns (1’)-(4’) in Table 7 report the results for the 1992-1999

period and the 2000-2001 period, respectively. There is very little difference in the results

25

across the two periods. For each coefficient of the control variables, the corresponding

estimates from the two subperiods have the same signs. Most of the estimates have similar

magnitudes and significance levels across the two subperiods. For either subperiod, the

incentive is negatively related to firm size and positively related to return volatility, growth

opportunities, and CEO tenure.

The corresponding estimates of the parameter γ1 are consistent with each other across the

two subperiods, and for the full sample period. When we use CDF (RESCOV ) to measure

the market price informativeness, γ1 is 1.561 for 1992-1999, 1.944 for 2000-2001 and 1.599

for the full sample period 1992-2001. All three results are significant at the 5% level. When

we use CDF (FERROR), γ1 is -2.322 for 1992-1999, -2.965 for 2000-2001, -2.964 for 1992-

2001, and all are significant at the 1% level. When we use CDF (DISPER), γ1 is -3.427 for

1992-1999, -3.949 for 2000-2001, and -4.418 for 1992-2001. Again, all results are significant

at the 1% level. When we use CDF (PIN) as the market price informativeness measure, the

estimates of γ1 increase sharply from 1.464 for the first subperiod to 9.208 for the second

subperiod. Both estimates are significant at the 5% level. The estimated γ1 for CDF (PIN)

is 4.070 for the whole sample 1992-2001. Consistent with the full-sample analysis, the

subperiod analysis brings about the same results, that is, CEO incentives increase with

market informativeness. This relation is generally stable across the two subperiods despite

the stock market bubble burst in 2000. The incentive enhancement effect due to the market

informativeness seems to strengthen slightly after the bubble burst, which is obvious if we

use the CDF (PIN) as our proxy for market informativeness.

5.3 Other Robustness Checks

We examine two other types of robustness: robustness of control variables, and robustness

of estimation methods.

In addition to market capitalization, net sales and total assets are other commonly used

variables to measure firm size. We replace market capitalization with either net sales or total

26

assets in our econometric models. We also include the squared firm size proxies to control for

possible nonlinearities in the data. All these alternative specifications yield similar results

on the relation between CEO incentives and market informativeness.

In unreported median regressions we also control for other factors that may be related to

CEO incentives, such as the ratio of capital over sales, R&D expense over capital, and the

investment-capital ratio (Jin, 2002). These variables are likely to capture the value of CEO

effort. Including those control variables does not change our main results qualitatively.

Following Core and Guay (1999), we try the log value of CEO tenure in the regression

model to capture the possible concave relation between CEO incentives and CEO tenure.

Again, our main results do not change qualitatively.

Apart from the median regressions on which we build our empirical analysis, we adopt

other types of model estimation methods. We perform OLS with robust standard errors,

CEO fixed-effects OLS (Aggarwal and Samwick, 1999), and robust regression (Hall and

Liebman, 1998).13 The CEO fixed-effects OLS controls for all differences in the average level

of incentives across executives in the sample. Having CEO fixed effects helps alleviate the

potential “unobserved endogeneity” problem if those unobservables tend to stay constant

over time. All these regression methods generate qualitatively similar results. For brevity

we do not report these results.

6 Summary and Conclusion

In this paper we examine the relation between chief executive officers’ incentives and stock

market informativeness. Following Holmstrom and Tirole (1993), we develop a model that

links market microstructure to CEO incentives. We empirically investigate whether stock

market informativeness has a significant impact on CEO pay-performance sensitivity.

13A robust regression begins by screening out and eliminating gross outliers based on OLS results, andthen iteratively performs weighted regressions on the remaining observations until the maximum change inweights falls below a pre-set tolerance level, say, 1%.

27

Using probability of informed trading, residual analyst coverage, analysts’ earnings

forecast error, and dispersion of analysts’ earnings forecasts as four proxies for the market

informativeness, we conduct various empirical tests and justify the key prediction that CEO

pay-performance sensitivity increases with market informativeness. As predicted, we find a

positive relation between probability of informed trading (or residual analyst coverage) and

CEO pay-performance sensitivity, and a negative relation between analysts’ earnings forecast

error (or dispersion of analysts’ earnings forecasts) and CEO pay-performance sensitivity.

Our results are robust to alternative estimators, measures of incentives, sample periods,

model specifications, and estimation methods. Our results suggest that incentive pay is more

effective in a more informative stock market. Thus, this paper not only provides strong

empirical support for the propositions in Holmstrom and Tirole (1993), but also contributes

empirically to the research area that combines the market microstructure literature with the

corporate finance literature.

28

Appendix: A Model Linking Market Microstructure to ExecutiveCompensations

We introduce a single-period model that illustrates the link between stock trading andthe effectiveness of market-based executive compensation.

At the initial point of time 0, a publicly held firm is established with a random terminalpayoff at time 1: r = e + δ, where e is the effort level that the potential manager wouldprivately choose and e cannot be contracted on. δ is a zero-mean, normally distributedrandom variable with variances, Vδ. The shares are issued on the firm’s future cash flow.

At stage 1, the firm owner (the principal) hires one manager (the agent). The ownerwrites a compensation contract on two performance measures, – the firm’s terminal payoffr, and stock price P :

W = a + b1r + b2P, (A. 1)

where a represents the fixed salary. b1 and b2 capture the sensitivities of the manager’scompensation relative to the firm’s terminal payoff, r and its stock price, P , respectively.Given the compensation contract, the manager chooses an effort level e ∈ [0,∞), which isnot observable.

At stage 2, the stock market opens. Stock market participants can observe an informativeyet non-contractible signal on the firm’s future value δ, at a cost. We assume that there is anendogenous number of N investors who choose to do so. A potential investor will search forthe private signal on δ only if the expected value of doing so exceeds her reservation value µ.The costly signal acquired by investor i (if she so chooses) is δ + εi, where εi is i.i.d. with amean of zero and a variance of Vε. Both the informed and uninformed traders submit theirorder flows to the market maker. Informed trader i submits a market order that is linear inher signal, β(δ + εi). We assume that the total liquidity demand in the market is z and zis a zero-mean, normally distributed variable with variance Vz. Since there are N informedtraders and z liquidity demand, the total order flow observed by the market maker is givenby ω = Nβδ+

∑Ni=1(βεi)+z. The competitive market maker, given the aggregate order flow

ω, sets a price such that P = E [r|ω]. In order to obtain analytically tractable solutions, weignore the manager’s compensation W in the price function. Given that W in our model islinear in both r and P , including it in the price function does not change the informationcontent of the stock price. The stock price derived from this price function is informationallyequivalent to that from the more general price function specification, P = E [(r −W )|ω].

At time 1, the payoff is realized, the incentive contract is honored, and the firm isliquidated. The resulting liquidation proceeds are distributed between the manager andthe principal.

All agents are risk-neutral except the manager. The manager’s preference is representedby a negative exponential utility function over her compensation W with the (absolute) riskaversion coefficient γ. Her cost of choosing the effort e is denoted as C(e) = 1

2ke2. The cost

is measured in money and is independent of the manager’s wealth. The manager’s evaluationof the normally distributed income W , given her choice of effort e, can then be represented

29

in the certainty equivalent measure as follows:

U(W, e) = E(W )− γ

2V ar(W )− C(e). (A. 2)

Based on the model set-up, we can solve a rational-expectation equilibrium in which theplayers in the real sector (the principal and the manager) use the information containedin the stock price to make decisions, and both the real sector and the stock market reachequilibrium at the same time.

We begin with the stock market equilibrium. With z liquidity demand, the total orderflow observed by the market maker is ω = Nβδ +

∑Ni=1(βεi) + z. The market maker sets a

linear price schedule of the form P = e + λω (Kyle, 1985). Using standard techniques, we

obtain the equilibrium value of λ as λ = V− 1

2z Γ

12 , where Γ =

NV 2δ (Vδ+Vε)

[(N+1)Vδ+2Vε]2.

The expected profit of an informed trader is then given by ER =V 2

δ (Vδ+Vε)V12

z

Γ12 [(N+1)Vδ+2Vε]2

. A

potential trader will make an effort to search for the private signal if and only if the expectedprofit from doing so exceeds her reservation value µ. Thus, the equilibrium number ofinformed traders N , is determined by

V 2δ (Vδ + Vε)V

12

z

Γ12 [(N + 1)Vδ + 2Vε]2

= µ. (A. 3)

Using the implicit function theorem, we obtain

Lemma 1 The number of informed traders N , (1) decreases as the investors’ reservationvalue µ on becoming informed increases, (2) increases as the volatility of the firm’s cash flowVδ increases, and (3) increases as the volatility of the liquidity trading Vz increases.

Proof. Omitted and available from the authors. Q.E.D.

We also have

Lemma 2 The (inverse) informativeness of the stock price P is given by

V ar(δ|P ) =Vδ(Vδ + 2Vε)

(N + 1)Vδ + 2Vε

. (A. 4)

Proof. Given the linear price schedule P = e + λω, we have V ar(δ|P ) = V ar(δ|λω).Thus,

V ar(δ|P ) = Vδ −Cov(δ, λω)2

V ar(λω)=

Vδ(Vδ + 2Vε)

(N + 1)Vδ + 2Vε

. (A. 5)

Q.E.D.

Equation (A. 4) shows that as N increases, the conditional variance V ar(δ|P ) decreasesmonotonically. From equation (A. 4), we have:

30

Proposition 1 As the number of informed traders increases, the stock market becomes moreinformative.

We can now solve the optimal compensation contract. We set up the principal’s problemas follows:

maxa,b1,b2

E(r −W )

s.t. maxe

E(W )− γ

2V ar(W )− c(e) ≥ W (A. 6)

where W is the reservation utility to the manager.

The manager’s compensation, as specified in (A. 1), is a linear function of both therealized payoff, r, and the stock price P . Rather than dealing with W = a + b1r + b2Pdirectly, the analysis of contracting becomes analytically much easier if we transform thewage function into the following equivalent normalized function:

W = a + b1r + b2P , (A. 7)

where a = a+b2E(P ) = a+b2e∗ and P = P −E(P ) = P −e∗. Note that equation (A. 7) is a

linear transformation based on hypothesized equilibrium values. One benefit of dealing withP rather than P is that P is a zero-mean and normally distributed variable. Since the meanof P is zero, it is much easier to analytically illuminate its information role in the manager’scontracting process. At equilibrium, P is informationally equivalent to P . That is, we haveV ar(P ) = V ar(P ) and Corr(δ, P ) = Corr(δ, P ).

With W = a + b1r + b2P , we can rewrite the principal’s problem as

maxa,b1,b2

(1− b1)e− a

s.t. b1 = ke

a = W − b1e +b21γ

2V ar(δ) +

b22γ

2V ar(P ) + γb1b2Cov(δ, P ) +

ke2

2(A. 8)

The first-order conditions yield

1

k− b1γV ar(δ)− b2γCov(δ, P )− b1

k= 0, (A. 9)

and−γb2V ar(P )− γb1Cov(δ, P ) = 0. (A. 10)

Plugging Cov(δ, P ) = ρV12

δ V ar(P )12 into equation (A. 10), we have

b2 = −b1ρV

12

δ

V ar(P )12

. (A. 11)

31

Plugging b2 = −b1ρV

12

δ

V ar(P )12

back into equation (A. 9), we obtain

b1 = (1 + γkVδ(1− ρ2))−1, (A. 12)

where ρ = [ NVδ

(N+1)Vδ+2Vε]12 .

Given equation (A. 11), we can rewrite the transformed compensation function in (A. 7)as:

W = a + b1(r −ρV

12

δ

V ar(P )12

P ), (A. 13)

where r − ρV12

δ

V ar(P )12P is an aggregate measure built on two performance instruments. b1 then

captures the sensitivity of incentive pay to the aggregate performance measure. Immediately,we obtain:

Proposition 2 In the rational expectations equilibrium, the optimal pay-performancesensitivity, b∗1, is

b∗1 =1

1 + γkVδ(1− ρ2). (A. 14)

From equation (A. 14), we have ∂b1∂k

< 0, ∂b1∂γ

< 0, and ∂b1∂Vδ(1−ρ2)

< 0.

Since Vδ(1 − ρ2) = V ar(δ|P ), we have ∂b1∂V ar(δ|P )

< 0. V ar(δ|P ) captures the

informativeness of stock price (Kyle 1985).

Further, we show

Proposition 3 (1) The more costly it is to collect information about firm performance, theless sensitive is pay to performance (∂b1

∂µ< 0).

(2) Given the number of informed traders N , the more volatile the cash flow, the less sensitiveis pay to performance ( ∂b1

∂Vδ< 0).

(3) Given the number of informed traders N , the more dispersed are the informed investors’opinions (or the less informative of the performance signal), the less sensitive is pay toperformance ( ∂b1

∂Vε< 0).

Proof. Omitted and available from the authors. Q.E.D.

32

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35

36

Table 1 – Summary Statistics: CEO Compensation/Incentive Measures and Firm /CEO Characteristics Total current compensation (TCC) is the sum of a CEO's annual salary and bonus. Total direct compensation (TDC) is the sum of TCC, other annual short-term compensation, payouts from log-term incentive plans, the Black-Scholes (1973) value of stock options granted, the value of restricted stocks granted, and all other long-term compensation. SHROWNPC is the CEO’s ownership percentage at year-end. We compute the annual change in a CEO's total firm-related wealth (DTOTW) as the sum of TDC and the changes in value of the CEO's existing stocks and stock options. The change in value of stock holdings is computed as the beginning-of-year value of CEO’s stock holdings multiplied by the current year’s stock return. The change in the value of stock options (DOPTV) equals the product of option deltas and the change in the firm’s market value, adjusting for the percentage of stock shares represented by the options, and plus the value from stock option exercises. TCC, TDC, and DTOTW are expressed in thousands of dollars. We calculate the change in the firm's market value (VCHANGE) as the percentage stock returns times the market capitalization of equity at last year-end (in millions of dollars). The stock-based pay-performance sensitivity (PPS) is the change in value of the CEO’s stock-based holdings (stocks and stock options) divided by the change in the firm’s stock market value. We define the total pay-performance sensitivity (PPS_TOT) as the dollar value change in the CEO's total firm-related wealth per $1,000 change in the shareholder value. Size is the log of MKTVAL, which is the fiscal-year-end equity market value (in millions of dollars). Stock return (ANNRET) is the annualized percentage return in real terms. We compute the annualized percentage volatility of stock returns (ANNVOL) using the past five years of monthly stock return data. We calculate Tobin's Q as the ratio of the market value of assets to the book value of assets, where the market value of assets is defined as the book value of assets (data 6) plus the market value of common equity (data 25 times data 199) less the book value of common equity (data 60) and balance sheet deferred taxes (data 74). We compute TENURE as the CEO’s tenure as of year t. Nobs is the number of non-missing observations. All monetary variables are in 1992 constant dollars.

Variables Mean Std Dev Median Min Max Nobs

Panel A: Compensation and incentive measures

TCC ($K) TDC ($K)

SHROWNPC (%) DOPTV($K)

DTOTW ($K) VCHANGE ($M)

PPS PPS_TOT

960.776

3,299.286 3.331

1,920.610 18,834.77 457.171 40.787 46.506

1,348.354 9,899.765

7.326 34,856.17

497,626.39 5,541.287

74.590 81.916

680.813

1,424.882 0.428

211.803 2,296.758

61.788 12.845 13.093

0 0 0

-1,509,144 -22,056,001 -257,239.5

0 0

91,634.85

564,171.19 99.445

1,641,605 35,166,453 182,126.2 994.446 997.479

17,584 17,051 16,708 14,123 17,348 16,944 16,769 16,904

Panel B: Control Variables

MKTVAL ($M) SIZE ($M)

ANNRET (%) ANNVOL (%)

TOBIN’s Q TENURE

4,292.293

6.909 22.451 39.438 2.159 7.682

15,270.39

1.601 95.466 18.845 2.747 7.409

864.983

6.763 11.542 35.227 1.480 5.500

6.10e-3 -5.100 -96.654 10.279 0.279 0.083

427,220.76

12.965 7,010.12 347.80

105.090 54.917

17,369 17,369 17,349 16,890 17,307 16,028

37

Table 2 - Summary Statistics of Stock Price Informativeness Measures Panel A reports summary statistics of the four stock price informativeness variables used in our empirical analysis: probability of informed trading (PIN) per Easley, Kiefer and O’Hara (1992, 1996, and 1997), residual analyst coverage (RESCOV), analyst forecast error (FERROR), and forecast dispersion among analysts (DISPER). RESCOV is the residual from regressing the log value of one plus the number of analysts’ earnings forecast against firm size, market-to-book ratio, turnover ratio, Nasdaq dummy, leverage ratio, industry dummies, and year dummies. We compute FERROR as the absolute value of the difference between the mean earnings estimate and the actual earnings divided by the actual earnings. We calculate DISPER as the standard deviation of earnings forecasts scaled by the absolute value of the mean earnings forecast and times 100. Nobs is the number of non-missing observations. We compute all the variables after we match the ExecuComp database with the TAQ or I/B/E/S database. The sample period is 1993-2001 for PIN and is 1992-2001 for RESCOV, FERROR, and DISPER. Panel B presents the bivariate Pearson correlations among the four price informativeness variables in a balanced sample containing 4,460 firm-year observations. The significance level of each correlation coefficient is reported in parenthesis. Panel A: Summary statistics of the price informativeness measures

Variables Mean Std Dev Median Min Max

Nobs

PIN1

RESCOV FERROR DISPER

0.163 0.023 0.444

12.116

0.051 1.134 5.014

66.754

0.157 0.264 0.066 3.175

0

-3.280 0 0

0.797 2.574 422.5 2,550

8,456

17,204 9,420 9,161

Panel B: Bivariate correlations among the price informativeness measures

PIN

RESCOV FERROR DISPER

PIN1

RESCOV

FERROR

DISPER

1.0000 -0.0155 (0.301) 0.0184 (0.219) 0.0403 (0.007)

1.0000

2e-5 (0.999) 0.0322 (0.031)

1.0000

0.0158 (0.293)

1.000

Note 1: The sample period for PIN is 1993-2001, since we calculate PIN using data from the TAQ database which starts its data coverage from 1993.

38

Table 3 – Median Regression of Stock Based Pay-Performance Sensitivity (PPS): Using PIN and RESCOV Measures as Proxies for Price Informativeness, 1992-2001

We define stock-based pay-performance sensitivity (PPS) as the change in value of the CEO’s stock and stock options holdings per $1,000 change in shareholder value. We specify the model as: PPSi,t = γ0 + γ1*INFOVi, t-1 + γ2* Sizei, t-1 + γ3* annvoli, t + γ4*Tobinqi, t-1 + γ5*Tenurei, t + industry dummies + year dummies+ εi, t . We use raw measures of the two variables, PIN and RESCOV, and their cumulative distribution functions (CDF) as proxies for stock price informativeness. Coefficient estimates on industry dummies and year dummies are not reported for brevity. All monetary variables are in 1992 constant dollars. We report standard errors based on 20 bootstrap replications in parentheses. The sample period for the regressions involving PIN or CDF(PIN) is 1994-2001, since the PIN measure which we calculate from the TAQ database covers the period 1993-2001 and we need one-year lag for such regressions. *, **, *** - significant at the 10%, 5% and 1% levels, respectively.

Independent Variables

(1) (2) (3) (4)

Intercept

PINi, t-1

CDF(PINi, t-1)

RESCOVi, t-1

CDF(RESCOVi, t-1)

SIZEi, t-1

ANNVOLi, t

Tobin’s Qi, t-1

Tenurei, t

Sample Size Pseudo R-square

15.858***

(1.213) 23.999***

(2.721)

-3.140*** (0.099)

20.143*** (1.004)

1.633*** (0.104)

0.943*** (0.014)

7007 0.104

17.626***

(1.324)

4.070*** (0.569)

-3.115*** (0.114)

19.927*** (1.096)

1.558*** (0.113)

0.947*** (0.016)

7007 0.104

25.591***

(0 .848)

0. 414*** (0. 093)

-3.981*** (0.080)

18.055*** (0.757)

1.453*** (0.041)

1.224*** (0.014)

16207 0.111

26.348***

(0 .929)

1.599*** (0.389)

-3.977*** (0.086)

18.197*** (0.808)

1.444*** (0.043)

1.224*** (0.015)

16207 0.111

39

Table 4 – Median Regression Using Total Compensation as Dependent Variable, 1992-2001

We use the annual change in a CEO's total firm-related wealth (DTOTW) as the dependent variable. We specify the model as: DTOTWi,t = α + vchangei,t *(β0 +β1 *INFOVi, t-1 + β2*Sizei, t-1 +β3*annvoli, t +β4 *Tobinqi, t-1 +β5 *Tenurei, t+ βd*Dummies) + β6* INFORVi, t-1 + β7 *Sizei, t-1 + β8 *annvoli, t + β9*Tobinqi, t-1 + β10 *Tenurei, t + Dummies + εi, t . Dummies refer to industry dummies and year dummies. We use raw measures of the two variables, PIN and RESCOV, and their cumulative distribution functions (CDF) as proxies for stock price informativeness (INFORV). All variables are defined in Tables 1 and 2. Coefficient estimates on intercepts and dummies are not reported for brevity. All monetary variables are in 1992 constant dollars. We report standard errors based on 20 bootstrap replications in parentheses. The sample period for the regressions involving PIN or CDF(PIN) is 1994-2001, since the PIN measure which we calculate from the TAQ database covers the period 1993-2001 and we need one-year lag for such regressions. *, **, *** - significant at the 10%, 5% and 1% levels, respectively.

Independent Variables

(1) (2) (3) (3)

VCHANGEi,t

VCHANGEi,t*PINi,t-1

VCHANGEi,t*CDF(PINi,t-1)

VCHANGEi,t*RESCOVi,t-1

VCHANGEi,t*CDF(RESCOVi,t-1)

VCHANGEi,t*SIZEi,t-1

VCHANGEi,t*ANNVOLi,t

VCHANGEi,t*Tobin’s Qi,t-1

VCHANGEi,t*Tenurei,t

PINi, t-1 or CDF(PINi,t-1)

RESCOVi, t-1 or CDF(RESCOVi,t-1)

SIZEi, t-1

ANNVOLi, t

Tobin’s Qi, t-1

Tenurei, t

Sample Size Pseudo R-square

13.625***

(0.164)

16.960*** (0.209)

-2.057*** (0.012)

17.868*** (0.113)

0.099*** (0.004)

0.583*** (0.001)

1698.434 (957.534)

1046.197*** (36.057)

3640.123*** (344.143)

418.921*** (35.427)

51.873*** (4.958)

7074 0.171

15.794***

(0.188)

1.906*** (0.048)

-2.145*** (0.014)

18.016*** (0.131)

0.124*** (0.005)

0.577*** (0.001) 281.752

(212.709)

1039.389*** (43.874)

3571.816*** (401.265)

425.116*** (41.530)

52.280*** (5.783)

7074 0.171

11.231***

(0.095)

0.392*** (0.004)

-2.142*** (0.006)

23.907*** (0.052)

0.386*** (0.001)

0.777*** (0.001)

-117.662*** (21.896)

865.604*** (19.571)

2355.403*** (172.461)

427.673*** (9.936)

20.211*** (3.338)

16579 0.155

10.645***

(0.088)

1.355*** (0.019)

-2.157*** (0.006)

23.602*** (0.049)

0.427*** (0.001)

0.778*** (0.001)

-449.031*** (81.373)

868.334*** (18.502)

2363.387*** (163.125)

422.751*** (9.403)

19.988*** (3.159)

16579 0.155

40

Table 5 – Median Regression of Stock Based Pay-Performance Sensitivity (PPS): Using FERROR and DISPER as Proxies for Price Informativeness, 1992-2001

We define stock-based pay-performance sensitivity (PPS) as the change in value of the CEO’s stock and stock options holdings per $1,000 change in shareholder value. We specify the model as: PPSi,t = γ0 + γ1*INFOVi, t-1 + γ2* Sizei, t-1 + γ3* annvoli, t + γ4*Tobinqi, t-1 + γ5*Tenurei, t + industry dummies + year dummies+ εi, t . We use raw measures of the two variables, FERROR and DISPER, and their cumulative distribution functions (CDF) as proxies for stock price informativeness. All variables are defined in Tables 1 and 2. Coefficient estimates on industry dummies and year dummies are not reported for brevity. All monetary variables are in 1992 constant dollars. We report standard errors based on 20 bootstrap replications in parentheses. *, **, *** - significant at the 10%, 5% and 1% levels.

Independent Variables

(1) (2) (3) (4)

Intercept

FERRORi, t-1

CDF(FERRORi, t-1)

DISPERi, t-1

CDF(DISPERi, t-1)

SIZEi, t-1

ANNVOLi, t

Tobin’s Qi, t-1

Tenurei, t

Sample Size Pseudo R-square

25.111***

(1.134) -0.096* (0.055)

-3.980*** (0.105)

15.622*** (1.091)

1.610*** (0.056)

1.151*** (0.017)

8921 0.117

26.836***

(1.069)

- 2.964*** (0.545)

-4.073*** (0.110)

17.199*** (1.158)

1.531*** (0.058)

1.160*** (0.018)

8921 0.117

23.587***

(1.251)

-0.004** (0.002)

-3.786*** (0.116)

16.373*** (1.202)

1.600*** (0.059)

1.091*** (0.019)

8660 0.117

26.215***

(1.279)

- 4.418*** (0.582)

-3.845*** (0.115)

19.631*** (1.221)

1.352*** (0.060)

1.101*** (0.019)

8660 0.118

41

Table 6 – Median Regression of Total Pay-Performance Sensitivity (PPS_TOT): Using All Stock Price Informativeness Measures, 1992-2001

We use the total pay-performance sensitivity (PPS_TOT), which measures the dollar value change in CEO’s total direct compensation and stock-based compensation per $1,000 increase in shareholder value, as the dependent variable. We specify the model as: PPS_TOTi,t = γ0 + γ1*INFOVi, t-1 + γ2* Sizei, t-1 + γ3* annvoli, t + γ4*Tobinqi, t-1 + γ5*Tenurei, t + industry dummies + year dummies+ εi, t . INFORV includes four variables that proxy for stock price informativeness, PIN, RESCOV, FERROR, and DISPER.. All variables are defined in Tables 1 and 2. We use the cumulative distribution functions (CDF) instead of raw measures of PIN, RESCOV, FERROR, and DISPER as proxies for stock price informativeness. Coefficient estimates on industry dummies and year dummies are not reported for brevity. All monetary variables are in 1992 constant dollars. We report standard errors based on 20 bootstrap replications in parentheses. The sample period for the regressions involving CDF(PIN) is 1994-2001, since the PIN measure which we calculate from the TAQ database covers the period 1993-2001 and we need one-year lag for such regressions. *, **, *** - significant at the 10%, 5% and 1% levels, respectively.

Independent Variables

(1) (2) (3) (4)

Intercept

CDF(PINi, t-1 )1

CDF(RESCOVi, t-1)

CDF(FERRORi, t-1)

CDF(DISPERi, t-1) SIZEi, t-1

ANNVOLi, t

Tobin’s Qi, t-1

Tenurei, t

Sample Size Pseudo R-square

17.737***

(1.648) 5.283*** (0 .753)

-3.195*** (0 .151)

20.438*** (1.449)

1.554*** (0 .150)

0 .951*** (0.021)

7050 0.072

28.216***

(1.055)

1.463*** (0 .441)

-4.248*** (0 .098)

18.334*** (0 .909)

1.510*** (0.049)

1.218*** (0 .017)

16333 0.075

30.641***

(1.317)

-3.072*** (0.590)

-4.497*** (0 .119)

14.880*** (1.252)

1.700*** (0 .062)

1.195*** (0 .019)

8972 0.076

29.137***

(1.344)

-4.169*** (0 .612)

-4.250*** (0 .121)

16.797*** (1.283)

1.601*** (0.063)

1.131*** (0.020)

8706 0.076

42

Table 7 – Median Regression of Pay-Performance Sensitivity: Subperiod Analysis We divide the sample into two subperiods: 1992-1999 and 2000-2001. For each subperiod, we specify the model as: PPSi,t = γ0 + γ1*INFORVi, t-1 + γ2* Sizei, t-1 + γ3* annvoli, t + γ4*Tobinqi, t-1 + γ5*Tenurei, t + industry dummies + year dummies+ εi, t . We define stock-based pay-performance sensitivity (PPS) as the change in value of the CEO’s stock and stock options holdings per $1,000 change in shareholder value. We use cumulative distribution functions (CDF) of the four variables, PIN, RESCOV, FERROR, and DISPER, as proxies for stock price informativeness. Coefficient estimates on industry dummies and year dummies are not reported for brevity. All monetary variables are in 1992 constant dollars. We report standard errors based on 20 bootstrap replications in parentheses. The sample period for the regressions involving CDF(PIN) is 1994-2001, since the PIN measure which we calculate from the TAQ database covers the period 1993-2001 and we need one-year lag for such regressions. *, **, *** - significant at the 10%, 5% and 1% levels, respectively.

1992 – 1999 2000 – 2001 Independent Variables

(1) (2) (3) (4) (1)’ (2)’ (3)’ (4)’

Intercept

CDF(PINi, t-1 )

CDF(RESCOVi, t-1)

CDF(FERRORi, t-1)

CDF(DISPERi, t-1) SIZEi, t-1

ANNVOLi, t

Tobin’s Qi, t-1

Tenurei, t

Sample Size

Pseudo R-square

19.027*** (1.515) 1.464** (0.654)

-3.121*** (0 .146)

26.668*** (1.456)

1.633*** (0.153)

0.848*** (0.019) 5180 0.101

25.802*** (1.016)

1.561*** (0.461)

-4.065*** (0 .106)

21.015*** (1.013)

2.943*** (0.075)

1.158*** (0.018) 12998 0.115

26.207*** (1.132)

-2.322*** (0.547)

-4.274*** (0 .115)

19.291*** (1.253)

3.251*** (0.091)

1.126*** (0.018) 7047 0.122

24.978*** (1.130)

-3.427*** (0.550)

-4.060*** (0 .113)

20.615*** (1.235)

3.252*** (0.091)

1.074*** (0.018) 6815 0.123

15.757*** (2.723)

9.208*** (1.503)

-2.526*** (0 .275)

12.940*** (2.304)

1.307*** (0.239)

1.230*** (0.042) 1827 0.116

27.136*** (1.795)

1.944** (0.908)

-3.298*** (0 .173)

12.659*** (1.490)

0.368*** (0.058)

1.462*** (0.036) 3209 0.109

25.624*** (1.608)

-2.965*** (0.876)

-3.219*** (0 .156)

14.231*** (1.440)

0.401*** (0.053)

1.304*** (0.030) 1874 0.119

24.261*** (1.769)

-3.949*** (0.968)

-3.098*** (0 .172)

15.225*** (1.588)

0.362*** (0.058)

1.326*** (0.033) 1845 0.121