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Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
Stock Market Information Production and CEO Incentives
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Stock Market Information Production and CEO Incentives

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  • 1. Stock Market Information Production and CEO Incentives∗ Qiang Kang † Qiao Liu ‡ University of Miami University of Hong Kong First Draft: January 2003 This Draft: October 2004 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 ∗ This paper is a revision of the paper entitled “Stock Trading, Information Production and Incentive Pay”. We appreciate comments from Ross Watts (the editor), an anonymous referee, Tim Burch, Stephen Chiu, John Core, Craig MacKinlay, Wing Suen, James Wang, Xianming Zhou and seminar participants at University 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 hospitality is gratefully acknowledged. We thank Clara Vega and Rong Qi for sharing with us programming codes and offering computation suggestions. The financial support from University of Hong Kong and University of Miami (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.
  • 2. 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
  • 3. 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 1 It has been well demonstrated that there are enormous cross-sectional variations in individual incentive measures such as pay-performance sensitivities (Hall and Liebman, 1998). A non-exhaustive list of variables that help explain this heterogeneity includes firm size (Smith and Watts, 1992; Schaefer, 1998; and Baker and Hall, 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; and Milbourn, 2002); institutional investors (Hartzell and Starks, 2003); and industry and year effects (Ely, 1991; and Murphy, 1999). 2 Holmstrom and Tirole (1993) analytically show that stock prices incorporate performance information 1
  • 4. 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 (P P S), 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 (P P S T OT ), 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 (P IN ) for each firm-year observation (Easley, Kiefer, and O’Hara (1996, 1997)). P IN 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 (F ERROR), and dispersion of analysts’ earnings forecast (DISP ER). 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 P IN 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 inferred information to design a better compensation contract. Thus, the higher the market liquidity is, the more informative the stock price is and consequently, the more effective is the performance-related pay. These predictions have not been rigorously tested partly because it is not straightforward to construct an empirical analysis based on Holmstrom and Tirole’s (1993) model. 2
  • 5. but F ERROR and DISP ER 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 P IN and RESCOV , respectively. Pay-performance sensitivity is strongly and negatively related to F ERROR and DISP ER, respectively. All of these results are also economically significant. For example, using the raw measure (or the cumulative distribution function) of P IN as the proxy for market informativeness, the pay-performance sensitivity at the maximum P IN is $15.36 (or $2.035) larger than the pay-performance sensitivity at the median P IN 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. 3
  • 6. 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 + , where e is the manager’s effort and θ and are normally distributed mean-zero noise terms with volatilities σθ and σ 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 σ 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 3 First, in the HT model, ownership concentration is the fundamental determinant of market liquidity which 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 impact on the pay-performance relation through corporate governance channels, e.g., institutional investors monitor managers by providing incentive compensations. Second, in the HT model, stock price volatility conveys the precision of the informed trader’s information. A more volatile stock price implies a more liquid and informative market resulting in a better market monitoring. This aspect has been extensively studied in the 4
  • 7. 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 1 b∗ = , (1) 1 + γkV ar(δ|P ) 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. Vδ (Vδ +2V ) Lemma 2 in the Appendix states that V ar(δ|P ) = (N +1)Vδ +2V , 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 V , 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 V ). 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 ). 5
  • 8. 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 (P IN ) 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 6
  • 9. 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 (DT OT W ) 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 CHAN GE) over a given year as the firm’s beginning-of-year market value times the year’s stock return. Using V CHAN GE and DT OT W , we calculate P P S T OT as the dollar value change in the CEO’s total firm- related wealth per $1,000 change in the shareholder value. 4 Core and Guay’s (1999) method quite accurately accounts for the economically meaningful value of the in-the-money options, but their method ignores the value of out-of-the-money options. This may likely create systematic bias for firms with more underwater options and for firms with options that are at-the-money or near-the-money. This concern is partially alleviated since our results are robust to the subperiod analysis in Section 5.2 where the sample is divided into the bubble subperiod and the post-bubble subperiod. Also, for simplicity, some researchers use 0.7 as the δ value. 7
  • 10. We define the stock-based pay-performance sensitivity (P P S) 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. ( CEO s Holdings of Stocks and Stock Options)it P P Sit = . (2) ( Shareholder s W ealth)it 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): (N umber of Options Held)it−1 P P Sit = SHROW N P Cit−1 ∗ 1000 + δ, (3) (N umber of Shares Outstanding)it−1 where SHROW N P C is the CEO’s ownership percentage. P P S is more appealing than P P S T OT 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 P P S T OT , 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 8
  • 11. 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 (DT OT W ) 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 (P P S) as defined in equation (3) are, respectively, $40.79 and $12.85 per $1,000 change in the shareholders’ value. The P P S measure has a standard deviation of $74.59 with a maximum value of $994.45. The total pay-performance sensitivity (P P S T OT ) has a mean of $46.51, which is higher than P P S, but it also has a much larger standard deviation ($81.92). The median of P P S T OT is $13.09 and is almost the same as the median of P P S. 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 P IN 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 P IN variable as a good measure of stock price informativeness in various settings.5 P IN directly estimates the amount of information held by traders on a specific stock, and it is a good 5 See, 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); and Vega (2004). 9
  • 12. 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 P IN 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 b and the arrival rate of liquidity traders who submit sell orders is s. 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 b and the arrival rate of a sell order will be s. On a day with a bad information event (with probability αδ), the arrival rate of a buy order will be b and the arrival rate of a sell order will be s + µ. On a day with a good information event (with probability α(1 − δ)), the arrival rate of a buy order will be b + µ and the arrival rate of a sell order will be s. The likelihood function for a single trading day is: ( b )B − s ( s )S − − b ( b) B − s +µ ( s + µ) S L(θ|B, S) = (1 − α)e eb + αδe e B! S! B! S! ( b + µ)B − s ( s )S +α(1 − δ)e− b +µ e , (4) B! S! where θ = ( b , s , 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 )T , and assuming cross-trading day independence, we can estimate the t=1 6 The trade direction is inferred from intraday data based on the algorithm proposed in Lee and Ready (1991). 10
  • 13. parameters of the model ( b , s , α, δ, µ) by maximizing the following likelihood function: t=T L(θ|M ) = L(θ|Bt , St ). (5) t=1 Thus, we estimate P IN by dividing the estimated arrival rate of informed trades by the estimated arrival rate of all trades: αµ P IN = . (6) αµ + b + s We maximize the likelihood function in equation (5) for the parameter space θ and then calculate P IN 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 P IN . In our sample, the average P IN is 0.163 with a standard deviation of 0.051. The median value of P IN is 0.157. The maximum P IN is 0.797 with a minimum of zero. The average sample property of our P IN 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 P IN 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 7 We also conduct the analysis by using summary information on each of the other month’s fiscal year 1 earnings estimates within that fiscal year. The summary information is stable across months and is very close to the annual average value. Those analyses all yield qualitatively similar results. 11
  • 14. 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 (F ERROR) and dispersion in analysts’ earnings forecasts (DISP ER), as proxies for stock market informativeness. We compute F ERROR 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. F ERROR 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 F ERROR 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 F ERROR. 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 8 For brevity, we do not report the results of the analyst coverage regression. The most significant variable to explain analyst coverage is firm size, followed by turnover, Nasdaq dummy and leverage. Details are available upon request. 12
  • 15. compute DISP ER 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 DISP ER. 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 P IN and RESCOV is -0.016 and is not significant. The correlation between P IN and F ERROR is 0.018 and is not significant either. The correlation between P IN and DISP ER is 0.040 and is significant at the 1% level. RESCOV and F ERROR are almost uncorrelated (the correlation coefficient is 2e-5). The correlation between RESCOV and DISP ER is 0.032 and is significant at the 5% level. The correlation between F ERROR and DISP ER 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 (M KT V 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 9 We 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. 13
  • 16. 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 (AN N RET ) averages at 22.45% (median value at 11.54%). The annualized percentage volatility of stock returns (AN N V 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
  • 17. basic model as: P P Si,t = γ0 + γ1 ∗ IN F OVi,t−1 + γ2 ∗ sizei,t−1 + γ3 ∗ annvoli,t−1 + γ4 ∗ T obinqi,t−1 +γ5 ∗ tenurei,t + γk ∗ dummiesi,t + εi,t , (7) k≥6 where P P S refers to the computed pay-performance sensitivity as specified in equation (3); IN F OV stands for the set of market informativeness variables, i.e., P IN , RESCOV , F ERROR and DISP ER; 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 P P S 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
  • 18. Based on the approach, we specify our model as: DT OT Wi,t = α + vchangei,t ∗ (β0 + β1 IN F OVi,t−1 + β2 sizei,t−1 + β3 annvoli,t + β4 T obinqi,t−1 +β5 tenurei,t + βd dummiesi,t ) + β6 ∗ IN F OVi,t−1 + β7 ∗ sizei,t−1 + β8 ∗ annvoli,t +β9 ∗ T obinqi,t−1 + β10 ∗ tenurei,t + βk ∗ dummiesi,t + εi,t , (8) where DOT OWi,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 CHAN GE) 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 IN F OV with V CHAN GE 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 DT OT W as the dependent variable, we do not need to measure incentives 10 We can also replace DT OT W in equation (8) with the total direct compensation or its different components or their annual changes. The breakdown of T DC helps us study how its different components, 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 is to examine the relation between stock market informativeness and CEO incentives. Having the empirically appropriate incentive measures is the key, but it is still unclear whether salary, bonus, stock or stock option grants should be counted as ex-post bonus or ex-ante incentive mechanisms. Second, it has been shown that CEO incentives are mainly provided through revaluations of existing stocks and stock options holdings (Hall and Liebman, 1998; and Murphy 1999). Even if salary, bonus, stock or stock option grants can be treated as the ex-ante incentive mechanisms, their individual effects on the overall CEO incentives are small. 16
  • 19. 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 + β1 IN F OVi,t−1 + β2 sizei,t−1 + β3 annvoli,t + β4 T obinqi,t−1 + β5 tenurei,t + βd dummiesi,t for a CEO of firm i in period t. In this formula, the level of PPS and the contribution of IN F OV to PPS are not immediately clear. The imputed PPS profile is a linear function of the variables IN F OV , size, annvol, T obinq, 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 IN F OV variables, P IN , RESCOV , F ERROR and DISP ER, capture the market informativeness from different perspectives. A larger value of P IN (or RESCOV ) corresponds to a more informative market and a larger value of F ERROR (or DISP ER) corresponds to a less informative market. The testing of our core hypothesis comprises several hypothesis tests, depending on which IN F OV 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 IN F OV variable is P IN or RESCOV ; or versus 17
  • 20. H1”: γ1 < 0 if the IN F OV variable is F ERROR or DISP ER. 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 P P S, defined in equation (3), by using P IN , CDF (P IN ), 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
  • 21. trading (P IN 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 P IN . The difference in P P S between any two P IN levels is calculated as the difference in the two P IN values multiplied by γ1 . For example, since the maximum, median, and minimum P IN are 0.797, 0.157, and 0, respectively, the pay-performance sensitivity at the maximum P IN is $15.36 higher than that at the median P IN as a result of $1,000 increase in shareholder value, all else equal. The incentive with the median P IN is $3.77 larger than the incentive with the minimum P IN . The economic magnitude of P IN 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 P IN . Using CDF (P IN ) as the explanatory variable, the coefficient γ1 is 4.07. The PPS at the maximum P IN , i.e., CDF (P IN )=1, is $2.035 larger than that at the median PIN, i.e., CDF (P IN )=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 P IN , 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
  • 22. 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 AN N V 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 T obinq, 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 P IN , CDF (P IN ), 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 P P S as the dependent variable. The coefficient of V CHAN GE, 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 AN N V OL (β3 around 18 to 24), T obinq (β4 around 0.10 to 0.43), and T enure (β5 around 0.58 to 0.78). The parameter of interest, β1 , is significant and positive across all regressions. Its estimate 20
  • 23. is 16.96 for P IN , 1.91 for CDF (P IN ), 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. 11 We do not explore these implications in detail as they are beyond the scope of this study. 12 We thank the referee for suggesting this explanation to us. 21
  • 24. We find that the estimated coefficients of T obinq 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, P IN 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 (F ERROR) and dispersion in analysts’ earnings forecasts 22
  • 25. (DISP ER), 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 F ERROR or DISP ER. 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 F ERROR and its cumulative distribution function as the market informativeness measure, respectively. The parameter of interest, γ1 , is -0.096 for F ERROR and significant at the 10% level. γ1 is -2.964 for CDF (F ERROR) and significant at the 1% level. Columns 3 and 4 report the results when we use DISP ER and its cumulative distribution function as the market informativeness measure, respectively. γ1 is estimated respectively, at -0.004 for DISP ER, significant at the 5% level, and -4.418 for CDF (DISP ER), 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, F ERROR and DISP ER. 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 F ERROR and DISP ER are more economically meaningful. Using CDF (F ERROR) (CDF (DISP ER)) 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
  • 26. 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 P P S 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. P P S T OT , 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 P P S T OT 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 P P S. The estimates for the parameter of interest, γ1 , are 5.283 for CDF (P IN ), 1.463 for CDF (RESCOV ), -3.072 for CDF (F ERROR), and -4.169 for CDF (DISP ER), respectively. All estimates are statistically significant at the 1% level. Similar to when P P S is used as the incentive measure, we find that the coefficient estimates using P P S T OT are economically significant. In Table 6 when we use P P S T OT 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 P P S 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
  • 27. compensation has little impact on the pay-performance sensitivity. This result alleviates our concerns on how to compute T DC, e.g., should we also include future direct compensations into T DC, 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
  • 28. 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 (F ERROR), 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 (DISP ER), 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 (P IN ) 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 (P IN ) 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 (P IN ) 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
  • 29. 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. 13 A robust regression begins by screening out and eliminating gross outliers based on OLS results, and then iteratively performs weighted regressions on the remaining observations until the maximum change in weights falls below a pre-set tolerance level, say, 1%. 27
  • 30. 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
  • 31. Appendix: A Model Linking Market Microstructure to Executive Compensations We introduce a single-period model that illustrates the link between stock trading and the effectiveness of market-based executive compensation. At the initial point of time 0, a publicly held firm is established with a random terminal payoff at time 1: r = e + δ, where e is the effort level that the potential manager would privately choose and e cannot be contracted on. δ is a zero-mean, normally distributed random 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 owner writes a compensation contract on two performance measures, – the firm’s terminal payoff r, and stock price P : W = a + b1 r + b2 P, (A. 1) where a represents the fixed salary. b1 and b2 capture the sensitivities of the manager’s compensation 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 is not observable. At stage 2, the stock market opens. Stock market participants can observe an informative yet non-contractible signal on the firm’s future value δ, at a cost. We assume that there is an endogenous number of N investors who choose to do so. A potential investor will search for the 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 + i , where i is i.i.d. with a mean of zero and a variance of V . Both the informed and uninformed traders submit their order flows to the market maker. Informed trader i submits a market order that is linear in her signal, β(δ + i ). We assume that the total liquidity demand in the market is z and z is a zero-mean, normally distributed variable with variance Vz . Since there are N informed traders and z liquidity demand, the total order flow observed by the market maker is given by ω = N βδ + N (β i ) + z. The competitive market maker, given the aggregate order flow i=1 ω, sets a price such that P = E [r|ω]. In order to obtain analytically tractable solutions, we ignore the manager’s compensation W in the price function. Given that W in our model is linear in both r and P , including it in the price function does not change the information ˜ content of the stock price. The stock price derived from this price function is informationally equivalent 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 is liquidated. The resulting liquidation proceeds are distributed between the manager and the principal. All agents are risk-neutral except the manager. The manager’s preference is represented by a negative exponential utility function over her compensation W with the (absolute) risk aversion coefficient γ. Her cost of choosing the effort e is denoted as C(e) = 1 ke2 . The cost 2 is measured in money and is independent of the manager’s wealth. The manager’s evaluation of the normally distributed income W , given her choice of effort e, can then be represented 29
  • 32. in the certainty equivalent measure as follows: γ U (W, e) = E(W ) − V ar(W ) − C(e). (A. 2) 2 Based on the model set-up, we can solve a rational-expectation equilibrium in which the players in the real sector (the principal and the manager) use the information contained in the stock price to make decisions, and both the real sector and the stock market reach equilibrium at the same time. We begin with the stock market equilibrium. With z liquidity demand, the total order flow observed by the market maker is ω = N βδ + N (β i ) + z. The market maker sets a i=1 linear price schedule of the form P = e + λω (Kyle, 1985). Using standard techniques, we 1 −2 1 N Vδ2 (Vδ +V ) obtain the equilibrium value of λ as λ = Vz Γ 2 , where Γ = [(N +1)Vδ +2V ]2 . 1 Vδ2 (Vδ +V )Vz2 The expected profit of an informed trader is then given by ER = 1 . A Γ 2 [(N +1)Vδ +2V ]2 potential trader will make an effort to search for the private signal if and only if the expected profit from doing so exceeds her reservation value µ. Thus, the equilibrium number of informed traders N , is determined by 1 Vδ2 (Vδ + V )Vz2 1 = µ. (A. 3) Γ 2 [(N + 1)Vδ + 2V ]2 Using the implicit function theorem, we obtain Lemma 1 The number of informed traders N , (1) decreases as the investors’ reservation value µ on becoming informed increases, (2) increases as the volatility of the firm’s cash flow Vδ 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 Vδ (Vδ + 2V ) V ar(δ|P ) = . (A. 4) (N + 1)Vδ + 2V Proof. Given the linear price schedule P = e + λω, we have V ar(δ|P ) = V ar(δ|λω). Thus, Cov(δ, λω)2 Vδ (Vδ + 2V ) V ar(δ|P ) = Vδ − = . (A. 5) V ar(λω) (N + 1)Vδ + 2V Q.E.D. Equation (A. 4) shows that as N increases, the conditional variance V ar(δ|P ) decreases monotonically. From equation (A. 4), we have: 30
  • 33. Proposition 1 As the number of informed traders increases, the stock market becomes more informative. We can now solve the optimal compensation contract. We set up the principal’s problem as follows: max E(r − W ) a,b1 ,b2 γ s.t. max E(W ) − V ar(W ) − c(e) ≥ W (A. 6) e 2 where W is the reservation utility to the manager. The manager’s compensation, as specified in (A. 1), is a linear function of both the realized payoff, r, and the stock price P . Rather than dealing with W = a + b1 r + b2 P ˜ ˜ directly, the analysis of contracting becomes analytically much easier if we transform the wage function into the following equivalent normalized function: ˆ ˜ ˆ W = a + b1 r + b2 P , (A. 7) ˆ where a = a + b2 E(P ) = a + b2 e∗ and P = P − E(P ) = P − e∗ . Note that equation (A. 7) is a ˆ linear transformation based on hypothesized equilibrium values. One benefit of dealing with ˆ ˆ P rather than P is that P is a zero-mean and normally distributed variable. Since the mean ˆ of P is zero, it is much easier to analytically illuminate its information role in the manager’s ˆ contracting process. At equilibrium, P is informationally equivalent to P . That is, we have ˆ ˆ V ar(P ) = V ar(P ) and Corr(δ, P ) = Corr(δ, P ). ˆ With W = a + b1 r + b2 P , we can rewrite the principal’s problem as ˆ max (1 − b1 )e − a ˆ a,b1 ,b2 ˆ s.t. b1 = ke b2 γ 1 b2 γ ˆ ˆ ke2 a = W − b1 e + ˆ V ar(δ) + 2 V ar(P ) + γb1 b2 Cov(δ, P ) + (A. 8) 2 2 2 The first-order conditions yield 1 ˆ b1 − b1 γV ar(δ) − b2 γCov(δ, P ) − = 0, (A. 9) k k and ˆ ˆ −γb2 V ar(P ) − γb1 Cov(δ, P ) = 0. (A. 10) 1 1 ˆ ˆ Plugging Cov(δ, P ) = ρVδ2 V ar(P ) 2 into equation (A. 10), we have 1 ρVδ2 b2 = −b1 . (A. 11) ˆ 1 V ar(P ) 2 31
  • 34. 1 ρVδ2 Plugging b2 = −b1 ˆ 1 back into equation (A. 9), we obtain V ar(P ) 2 b1 = (1 + γkVδ (1 − ρ2 ))−1 , (A. 12) N Vδ 1 where ρ = [ (N +1)Vδ +2V ] 2 . Given equation (A. 11), we can rewrite the transformed compensation function in (A. 7) as: 1 ρVδ2 ˆ W = a + b1 (˜ − ˆ r P ), (A. 13) ˆ 1 V ar(P ) 2 1 ρVδ2 ˆ where r − ˜ ˆ 1 P is an aggregate measure built on two performance instruments. b1 then V ar(P ) 2 captures the sensitivity of incentive pay to the aggregate performance measure. Immediately, we obtain: Proposition 2 In the rational expectations equilibrium, the optimal pay-performance sensitivity, b∗ , is 1 1 b∗ = 1 . (A. 14) 1 + γkVδ (1 − ρ2 ) ∂b1 ∂b1 ∂b1 From equation (A. 14), we have ∂k < 0, ∂γ < 0, and ∂Vδ (1−ρ2 ) < 0. ∂b1 Since Vδ (1 − ρ2 ) = V ar(δ|P ), we have ∂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, the less sensitive is pay to performance ( ∂b1 < 0). ∂µ (2) Given the number of informed traders N , the more volatile the cash flow, the less sensitive ∂b is pay to performance ( ∂V1 < 0). δ (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 to ∂b performance ( ∂V1 < 0). Proof. Omitted and available from the authors. Q.E.D. 32
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  • 38. 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) 960.776 1,348.354 680.813 0 91,634.85 17,584 TDC ($K) 3,299.286 9,899.765 1,424.882 0 564,171.19 17,051 SHROWNPC (%) 3.331 7.326 0.428 0 99.445 16,708 DOPTV($K) 1,920.610 34,856.17 211.803 -1,509,144 1,641,605 14,123 DTOTW ($K) 18,834.77 497,626.39 2,296.758 -22,056,001 35,166,453 17,348 VCHANGE ($M) 457.171 5,541.287 61.788 -257,239.5 182,126.2 16,944 PPS 40.787 74.590 12.845 0 994.446 16,769 PPS_TOT 46.506 81.916 13.093 0 997.479 16,904 Panel B: Control Variables MKTVAL ($M) 4,292.293 15,270.39 864.983 6.10e-3 427,220.76 17,369 SIZE ($M) 6.909 1.601 6.763 -5.100 12.965 17,369 ANNRET (%) 22.451 95.466 11.542 -96.654 7,010.12 17,349 ANNVOL (%) 39.438 18.845 35.227 10.279 347.80 16,890 TOBIN’s Q 2.159 2.747 1.480 0.279 105.090 17,307 TENURE 7.682 7.409 5.500 0.083 54.917 16,028 36
  • 39. 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 0.163 0.051 0.157 0 0.797 8,456 RESCOV 0.023 1.134 0.264 -3.280 2.574 17,204 FERROR 0.444 5.014 0.066 0 422.5 9,420 DISPER 12.116 66.754 3.175 0 2,550 9,161 Panel B: Bivariate correlations among the price informativeness measures PIN RESCOV FERROR DISPER PIN1 1.0000 RESCOV -0.0155 1.0000 (0.301) FERROR 0.0184 2e-5 1.0000 (0.219) (0.999) DISPER 0.0403 0.0322 0.0158 1.000 (0.007) (0.031) (0.293) 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. 37
  • 40. 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 15.858*** 17.626*** 25.591*** 26.348*** (1.213) (1.324) (0 .848) (0 .929) PINi, t-1 23.999*** (2.721) CDF(PINi, t-1) 4.070*** (0.569) RESCOVi, t-1 0. 414*** (0. 093) CDF(RESCOVi, t-1) 1.599*** (0.389) SIZEi, t-1 -3.140*** -3.115*** -3.981*** -3.977*** (0.099) (0.114) (0.080) (0.086) ANNVOLi, t 20.143*** 19.927*** 18.055*** 18.197*** (1.004) (1.096) (0.757) (0.808) Tobin’s Qi, t-1 1.633*** 1.558*** 1.453*** 1.444*** (0.104) (0.113) (0.041) (0.043) Tenurei, t 0.943*** 0.947*** 1.224*** 1.224*** (0.014) (0.016) (0.014) (0.015) Sample Size 7007 7007 16207 16207 Pseudo R-square 0.104 0.104 0.111 0.111 38
  • 41. 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 13.625*** 15.794*** 11.231*** 10.645*** (0.164) (0.188) (0.095) (0.088) VCHANGEi,t*PINi,t-1 16.960*** (0.209) VCHANGEi,t*CDF(PINi,t-1) 1.906*** (0.048) VCHANGEi,t*RESCOVi,t-1 0.392*** (0.004) VCHANGEi,t*CDF(RESCOVi,t-1) 1.355*** (0.019) VCHANGEi,t*SIZEi,t-1 -2.057*** -2.145*** -2.142*** -2.157*** (0.012) (0.014) (0.006) (0.006) VCHANGEi,t*ANNVOLi,t 17.868*** 18.016*** 23.907*** 23.602*** (0.113) (0.131) (0.052) (0.049) VCHANGEi,t*Tobin’s Qi,t-1 0.099*** 0.124*** 0.386*** 0.427*** (0.004) (0.005) (0.001) (0.001) VCHANGEi,t*Tenurei,t 0.583*** 0.577*** 0.777*** 0.778*** (0.001) (0.001) (0.001) (0.001) PINi, t-1 or CDF(PINi,t-1) 1698.434 281.752 (957.534) (212.709) RESCOVi, t-1 or CDF(RESCOVi,t-1) -117.662*** -449.031*** (21.896) (81.373) SIZEi, t-1 1046.197*** 1039.389*** 865.604*** 868.334*** (36.057) (43.874) (19.571) (18.502) ANNVOLi, t 3640.123*** 3571.816*** 2355.403*** 2363.387*** (344.143) (401.265) (172.461) (163.125) Tobin’s Qi, t-1 418.921*** 425.116*** 427.673*** 422.751*** (35.427) (41.530) (9.936) (9.403) Tenurei, t 51.873*** 52.280*** 20.211*** 19.988*** (4.958) (5.783) (3.338) (3.159) Sample Size 7074 7074 16579 16579 Pseudo R-square 0.171 0.171 0.155 0.155 39
  • 42. 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 25.111*** 26.836*** 23.587*** 26.215*** (1.134) (1.069) (1.251) (1.279) FERRORi, t-1 -0.096* (0.055) CDF(FERRORi, t-1) - 2.964*** (0.545) DISPERi, t-1 -0.004** (0.002) CDF(DISPERi, t-1) - 4.418*** (0.582) SIZEi, t-1 -3.980*** -4.073*** -3.786*** -3.845*** (0.105) (0.110) (0.116) (0.115) ANNVOLi, t 15.622*** 17.199*** 16.373*** 19.631*** (1.091) (1.158) (1.202) (1.221) Tobin’s Qi, t-1 1.610*** 1.531*** 1.600*** 1.352*** (0.056) (0.058) (0.059) (0.060) Tenurei, t 1.151*** 1.160*** 1.091*** 1.101*** (0.017) (0.018) (0.019) (0.019) Sample Size 8921 8921 8660 8660 Pseudo R-square 0.117 0.117 0.117 0.118 40
  • 43. 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 17.737*** 28.216*** 30.641*** 29.137*** (1.648) (1.055) (1.317) (1.344) CDF(PINi, t-1 )1 5.283*** (0 .753) CDF(RESCOVi, t-1) 1.463*** (0 .441) CDF(FERRORi, t-1) -3.072*** (0.590) CDF(DISPERi, t-1) -4.169*** (0 .612) SIZEi, t-1 -3.195*** -4.248*** -4.497*** -4.250*** (0 .151) (0 .098) (0 .119) (0 .121) ANNVOLi, t 20.438*** 18.334*** 14.880*** 16.797*** (1.449) (0 .909) (1.252) (1.283) Tobin’s Qi, t-1 1.554*** 1.510*** 1.700*** 1.601*** (0 .150) (0.049) (0 .062) (0.063) Tenurei, t 0 .951*** 1.218*** 1.195*** 1.131*** (0.021) (0 .017) (0 .019) (0.020) Sample Size 7050 16333 8972 8706 Pseudo R-square 0.072 0.075 0.076 0.076 41
  • 44. 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 19.027*** 25.802*** 26.207*** 24.978*** 15.757*** 27.136*** 25.624*** 24.261*** (1.515) (1.016) (1.132) (1.130) (2.723) (1.795) (1.608) (1.769) CDF(PINi, t-1 ) 1.464** 9.208*** (0.654) (1.503) CDF(RESCOVi, t-1) 1.561*** 1.944** (0.461) (0.908) CDF(FERRORi, t-1) -2.322*** -2.965*** (0.547) (0.876) CDF(DISPERi, t-1) -3.427*** -3.949*** (0.550) (0.968) SIZEi, t-1 -3.121*** -4.065*** -4.274*** -4.060*** -2.526*** -3.298*** -3.219*** -3.098*** (0 .146) (0 .106) (0 .115) (0 .113) (0 .275) (0 .173) (0 .156) (0 .172) ANNVOLi, t 26.668*** 21.015*** 19.291*** 20.615*** 12.940*** 12.659*** 14.231*** 15.225*** (1.456) (1.013) (1.253) (1.235) (2.304) (1.490) (1.440) (1.588) Tobin’s Qi, t-1 1.633*** 2.943*** 3.251*** 3.252*** 1.307*** 0.368*** 0.401*** 0.362*** (0.153) (0.075) (0.091) (0.091) (0.239) (0.058) (0.053) (0.058) Tenurei, t 0.848*** 1.158*** 1.126*** 1.074*** 1.230*** 1.462*** 1.304*** 1.326*** (0.019) (0.018) (0.018) (0.018) (0.042) (0.036) (0.030) (0.033) Sample Size 5180 12998 7047 6815 1827 3209 1874 1845 Pseudo R-square 0.101 0.115 0.122 0.123 0.116 0.109 0.119 0.121 42

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