Heterogeneity in the Effect of Managerial Equity Incentives on Firm Value

Published date01 August 2019
AuthorJung Chul Park,Hui L. James,Bradley W. Benson
Date01 August 2019
The Financial Review 54 (2019) 583–638
Heterogeneity in the Effect of Managerial
Equity Incentives on Firm Value
Bradley W. Benson
Ball State University
Hui L. James
University of Texasat Tyler
Jung Chul Park
University of South Florida
We document significant heterogeneity in the relation between chief executive officer
(CEO) equity incentives and firm value using quantile regression. We show that CEO delta
is more effective in the presence of ample investment opportunities, while CEO vega is more
beneficial for firms lacking investment opportunities. Further, Tobin’s Qincreases in CEO
delta for more risk-tolerant firms but increases in CEO vega for more risk-averse firms. We
also observe that higher monitoring intensity after the Sarbanes-Oxley Act reduces CEO delta’s
role in compensation. Risk aversion alters the optimal incentive-value relation, and the nature
of this relation also depends on the level of Tobin’s Q.
Keywords: executive compensation, managerial equity incentives, firm value, SOX
JEL Classifications: G30, G34, J33, M52
Corresponding author: Department of Accounting, Finance, & Business Law, University of Texas at
Tyler,Tyler, TX 75799; Phone: (903) 566-7360; Fax: (903) 566-7372; E-mail: hjames@uttyler.edu.
The authors thank an anonymous referee and the editors for many helpful and insightful comments. They
also thank Garrett Smith (discussant) and participants at the Financial Management Association 2017
Annual Meeting. All errors remain the responsibility of the authors.
C2019 The Eastern Finance Association 583
584 B. W. Benson et al./The Financial Review 54 (2019) 583–638
1. Introduction
An expansive body of research examines the relation between managerial equity
incentives and firm value with mixed findings.1Previous studies, however, use tradi-
tional linear regression models (e.g., ordinary least squares [OLS] regression, fixed
effect [FE] regression) to investigate whether the conditional mean of the dependent
variable (i.e., firm value or performance) is associated with a change in covariates
(e.g., chief executive officer[CEO] delta and vega). Linear regression models assume
the estimates of the relation between firm value and CEO equity incentives are the
same regardless of where the researcher examines the conditional distribution. But
the estimated effects on the conditional mean of firm value are not necessarily in-
dicative of the nature of the relation between CEO equity incentives and firm value
when firms are at noncentral locations. Moreover, the coefficient estimates generated
from this estimation methodology are unbiased and efficient only if the residuals
are normally distributed, the variance of errors is constant, and the sample has no
influential outliers (Greene, 2008; Li, 2015).
We employ several statistical methods to test whether the aforementioned con-
ditions are met in our sample. First, a Shapiro-Wilk test rejects the normality of the
residuals at the 1% significance level, indicating nonnormality in the errors. Second,
both a Breusch-Pagan/Cook-Weisberg test and White’s test for heterogeneity rejects
the null hypothesis of constant variance at the 1% level. Finally, the absolute value
of the studentized residuals is larger than or equal to 2 in 5% of our sample firms,
suggesting the existence of outliers.2This suggests that traditional conditional mean
estimation techniques may be problematic when examining the effect of manage-
rial equity incentives on firm value. Figure 1 describes these issues graphically by
presenting the highly skewed distribution of Tobin’s Q.
We employ a quantile regression (QR) analysis to examine the effect of CEO
delta and vega on firm valueto mitigate these issues. This analysis produces estimates
by finding the values that minimize the sum of absolute residuals while OLS models
minimize the sum of squared errors. This estimation methodology overcomes many
problems associated with methods using a conditional mean framework (Koenker
1Studies find a positive correlation between managerial ownership and financial performance (Mehran,
1995; Core and Larker, 2002), a positive but decreasing relation (Morck, Schleifer and Vishney, 1988;
McConnell and Servaes 1990, 1995; Hermalin and Weisbach,1991; Hubbard and Palia, 1995; Holderness,
Krosner and Sheehan, 1999; Anderson and Reeb, 2003; Tian, 2004; Davies, Hillier and McColgan, 2005;
Adams and Santos, 2006; Pukthuanthong, Roll and Walker, 2007; McConnell, Servaes and Lins, 2008;
Tong, 2008; Benson and Davidson, 2009) or no relation (Demsetz, 1983; Demsetz and Lehn, 1985;
Agrawal and Knoeber, 1996; Loderer and Martin, 1997; Cho, 1998; Himmelberg, Hubbard and Palia,
1999; Demsetz and Villalonga, 2001; Palia, 2001; Coles, Lemmon and Meschke, 2012; Brick, Palia and
Wang, 2005; Cheung and Wei, 2006). Brick, Palmon and Wald (2012) find a negative relation between
vega and firm future stock returns. Chen and Ma (2011) find that CEO stock options increase both long-
term and short-term stock returns. Shen and Zhang (2012) show that higher vega encourages greater and
suboptimal research and development investments,leading to a lower stock and operating performance.
2The test results are not tabulated but are available from the authors on request.
B. W. Benson et al./The Financial Review 54 (2019) 583–638 585
Figure 1
Distribution of Tobin’sQ
Graph plots the distribution of Tobin’sQ. The sample is from COMPUSTATand Execucomp and includes
21,308 firm-year observations from fiscal year 1997–2013.
and Hallock, 2001; Li, 2015). First, QR makes no assumption on the distribution of
the error term. Second, QR allows different estimates to be calculated at different
points on the conditional distribution of Tobin’s Q. This is preferable to segmenting
the sample based on the distribution of Tobin’s Q(e.g., large Tobin’s Qvs small
Tobin’s Q), which truncates the sample and may lead to sample selection bias.
Finally, QR can be used to demonstrate that the conditional distribution may not
be homogeneous. Specifically, the conditional distribution is not homogenous when
estimates of different quantiles are found to be significantly differentfrom each other.
The application of the QR method is common in the economics literature and
gaining popularity in the finance literature. The QR method has been applied recently
in studies on the effect of diversification on firm value (Lee and Li, 2012), pay-
performance sensitivity (Hallock, Madalozzo and Reck, 2010), bankruptcy prediction
(Li and Miu, 2010), corporate governance and tax avoidance (Armstrong, Blouin,
Jagolinzer and Larcker, 2015), and pecking order (Chay, Park, Kim and Suh, 2015).
This study represents the first attempt to apply the QR method to the relation between
CEO equity incentives and firm value.
Weexamine the relation between CEO delta (the sensitivity of managerial wealth
to stock price) and CEO vega (the sensitivity of managerial wealth to stock return
volatility) and Tobin’s Qfor a sample of 21,308 firm-year observations from 1997 to
2013 using QR. Tobin’s Qis used in the literature as a proxy for both performance

To continue reading

Request your trial

VLEX uses login cookies to provide you with a better browsing experience. If you click on 'Accept' or continue browsing this site we consider that you accept our cookie policy. ACCEPT