ceo social status and risk taking - contesttheory.org

CEO Social Status and Risk Taking ∗

Joshua Shemesh

Department of Finance

University of Melbourne

VIC 3010 Australia

[email protected]

September 30, 2012

Abstract

I find that relative concerns, or preference for social status, affect executive officers’

risk-related business decisions and outcomes. I use prestigious CEO awards assigned by

editorials of major national publications (such as Business Week) to measure positive

shocks to CEO status. I find that firms with award-winning CEOs decrease their

idiosyncratic volatility ratios and their industry betas converge to 1. R&D spending

decreases by 20% while investment in physical assets increases relative to a matched

sample of non-winning CEOs. I argue that CEOs who reach higher status, in terms

of their reputation relative to their peers, have an incentive to increase the correlation

with respect to their reference group. By conforming, CEOs with the highest reputation

can lock-in their relative position and maximize their legacy.

∗I would like to thank my dissertation committee: Fernando Zapatero (chair), Richard John, ChristopherJones and Oguzhan Ozbas. I am especially grateful to Kevin Murphy for the data on incentives but, moreimportantly, for reading several drafts of the manuscript and providing many insightful comments. I alsothank Ashwini Agrawal, Brad Barber, Neal Galpin, David Hirshliefer, Derek Horstmeyer, Salvatore Miglietta,Jordan Neyland, Udi Peleg, Francisco Perez-Gonzalez, Luis Goncalves-Pinto, Breno Schmidt, ChristopherSchwarz and seminar participants in the 2010 FMA Annual Meeting, 2011 FIRN Annual Conference, BINorwegian School of Management, Cal-State Fullerton, City University of Hong Kong, Claremont McKennaCollege, UC Irvine, University of New South Wales, University of Missouri, University of Southern Califor-nia, University of Western Ontario and the University of Melbourne for helpful comments. I thank UlrikeMalmendier and Geoffrey Tate for providing the list of award-winning CEOs. Existing errors are my soleresponsibility.

1

1 Introduction

Social status permeates the corporate environment, which in turn can affect corporate choices.

I argue that chief executive officers (CEOs) obtain utility from social status or reputation

relative to their peers. Theory suggests that when individuals have such relative concerns,

they have incentives to conform when their status is high, and break away from the herd when

their status is low relative to their peers. I examine whether receiving a prestigious business

award—an event that highlights a CEO’s status relative to peers—affects subsequent risk-

related business decisions and outcomes. I show that changes in status affect managerial risk-

taking in several ways. Firms’ idiosyncratic volatility falls, correlations with their industry

increases, and investment policy shifts away from R&D and toward physical assets. Each of

these results supports the idea that CEOs care about reputation relative to their peers.

What should we expect to observe with managerial risk-taking as a consequence of changes

in social status? If high-ranking corporate officers value social status, CEOs with higher rep-

utation have an incentive to ”lock-in” their relative advantage with respect to their peers.

Such CEOs can decrease the probability that the market devalue their reputation by increas-

ing the correlation with their industry. CEOs in the lead will thus try to conform with, or

take the same type of risk as, other firms in their industry. Lagging CEOs, on the other hand,

will tend to leave the beaten path in an attempt to improve their position. Together, we

expect events that move CEOs from lagging to leading positions will decrease firm-specific,

or idiosyncratic, risk.

This intuition builds on the literature on relative concerns and portfolio choice. Bakshi

and Chen (1996) describe the “Spirit of Capitalism” as the accumulation of wealth far beyond

can be motivated by consumption. Bakshi and Chen show that if investors have relative-

wealth concerns, they are expected to hold a higher proportion in the index as they become

richer than others. The same prediction has also been recently studied in the mutual fund

literature, in a somewhat different setting. Basak, Pavlova and Shapiro (2007) and Chen

and Pennacchi (2009) study the optimal asset allocation of a fund manager in the context of

2

relative performance evaluation. If managers can choose the degree of exposure to systematic

versus idiosyncratic risks, their optimal strategy manifests itself not in increasing or decreas-

ing total volatility but in deviating more or less from the benchmark index. Relative concerns,

whether inherent to preferences or driven by relative performance evaluation, create a wedge

between aggregate risk and idiosyncratic risk. In the context of capital budgeting, firms have

to allocate resources, and their investment opportunities are comprised of company-specific,

industry-wide and economy-wide investments. For example, firms often have to choose be-

tween implementing a new and innovative technology and staying with the standard one. If

managers have discretion inside their firm, they may alter corporate decisions to advance

their own objectives. This paper tests whether CEO’s social status concerns, as opposed to

firm, industry, or market factors, affect managerial risk-taking.

I use prestigious business awards assigned by editorials of major national publications

(such as Business Week) to measure positive shocks to CEO status. The key criterion for

inclusion in the sample is that the award is national, so that it is prominent enough to

plausibly affect CEO status, and that the award is given to the CEO and not to the firm as a

whole. Francis et al. (2008) show that CEO awards provide a significant boost to the winner

in terms of positive media coverage and public appreciation. Since awards are not granted

randomly, I rely on a propensity-score matching procedure to test whether award-winning

CEOs change their risk-taking behavior differently than do the most similar non-winners.

The matching is based on stock returns prior to the award, along with additional firm and

CEO characteristics that are correlated with the probability of receiving an award. Using

a difference-in-differences approach, I then study how different measures of managerial risk-

taking change for the actual winners with respect to the most similar non-winners.

In line with the predictions of relative concerns, I find that firms with award-winning

CEOs monotonically decrease their idiosyncratic volatility to total volatility ratios, as expo-

sure to systematic risks becomes a means to preserve their position. Correspondingly, their

industry betas converge to 1, as the firm’s stock returns move more in line with the industry’s

3

average. This paper also finds that firms with award-winning CEOs increase their invest-

ment in physical assets while decreasing their R&D expenditure by 20% from the pre-award

expenditure level. The decrease in R&D expenditure is significant both relative to a matched

sample of non-winning CEOs, and after controlling for investment opportunities and cash-

flow. Investment decisions point to one mechanism by which CEOs affect the composition of

firm-risk, as R&D investments usually come from the firm-specific investment opportunity

set and are less dependent of the overall industry performance.

I next explore alternative channels though which awards may affect risk taking rationally,

such as compensation or termination risk. Such alternative channels either predict the oppo-

site outcome to that of social status concerns, or have no significant correlation with awards,

consistently with the use of awards as a proxy for social status. For example, Malmendier

and Tate (2009) show that award-winning CEOs may receive higher compensation, which

may then be linked to their risk taking. A manager, with decreasing absolute risk aversion

in wealth, is expected however to take on higher risks as her wealth increases. I also find

that winners face lower termination risk, as the board of directors may be reluctant to fire a

superstar. As long as termination risk suppresses managerial risk-taking (Chakraborty et al.

[2007] and Bushman et al. [2010]), winners are expected to engage in higher managerial risk

taking. In a model with asymmetric information on ability, Zwiebel (1995) argues that as

managers become less likely to be mistaken for bad managers, they will be less concerned with

relative performance, and will thus be more willing to undertake new actions. Furthermore,

Aggarwal and Samwick (2003) show that as managers become more entrenched, they tend to

expand into new industries and increase idiosyncratic exposure. The results presented in this

paper therefore suggest that the effect that awards have on managerial risk-taking through

the status channel, dominates the indirect effects that awards may have through alternative

channels. CEO awards affect firm-level decisions and outcomes in line with the predictions

of relative concerns, providing further evidence for the effect CEOs have over firm policy.

The paper is structured as follows. In section 2 I describe the related literature on the

4

relation between social status and risk taking. Section 3 describes the empirical strategy and

the measures I use for risk taking, and section 4 presents the results. Section 5 discusses

alternative channels through which awards may affect managerial risk-taking. In section 6

I explore additional direct and indirect effects that CEO awards may have on executive

compensation, and section 7 concludes.

2 Hypothesis and related literature

Several distinct yet related strands of literature support the notion that relative concerns

create a wedge between systematic risk and idiosyncratic risk. Bakshi and Chen (1996)

analytically examine the implications of the hypothesis that investors accumulate wealth not

only for the sake of consumption but also for wealth-induced social status. Bakshi and Chen

formalize the spirit-of-capitalism hypothesis by assuming that the investor’s relative social

standing enters her preferences. The authors show that if investors have relative-wealth

concerns, they are expected to hold a higher proportion into the index as they become richer

than others. Similar predictions have also been recently studied in the mutual fund literature,

in a somewhat different setting. Theoretical models in the mutual fund literature study the

optimal asset allocation of a manager in the context of relative performance evaluation.

Basak, Pavlova and Shapiro (2007) and Chen and Pennacchi (2009) consider a setup in

which managers can adjust their portfolio riskiness through taking on idiosyncratic rather

than systematic risk, as they face an increasing and convex relationship of fund flows to

relative performance. If managers can choose the degree of exposure to systematic versus

idiosyncratic risks, their optimal strategy manifests itself not in increasing or decreasing

volatility but in deviating more or less from the benchmark index (tracking error). While

any strategy entailing a deviation from the benchmark is inherently risky for managers in the

lead, underperforming managers boost the deviation of their portfolio from the benchmark,

but do not necessarily increase the volatility of their portfolios.

5

The theoretical models in the mutual fund literature may be applied in the context of this

paper, as they consider multiple players who can choose the degree of exposure to systematic

versus idiosyncratic risks. Similarly, in the context of capital budgeting, firms face complex

investment opportunities that may be company-specific, industry-specific and economy-wide.

In addition, implicit incentives in the mutual fund industry are comparable to the highly

skewed distribution of CEO public attention. Convexity can also originate from managerial

preferences, regardless of the incentive structure. CEOs value being in “first place” as an

end in itself, and so act as if they were in a winner-take-all tournament.

I thus borrow from the tournament literature, with the added assumption that CEOs

obtain utility not only from their wealth but also from their social status relative to others.

If agents care about their social status, one would expect them to act as if they were in a

tournament. Nieken and Sliwka (2010) study risk-taking behavior in a simple two-person

tournament. The Nieken-Sliwka model suggests that the optimal strategy of the front runner

depends on the correlation with the strategy of the trailing contestant 1. When the risky

strategies are correlated across players, choosing the risky strategy become a means to protect

the lead. Cabral (2002) studies the strategic choice of covariance in races. In equilibrium,

the leader is interested in increasing the correlation with respect to the laggard. By doing

so, the leader protects her leadership by managing the risk that the laggard will earn a high

return and overtake her 2. The crucial feature of the equilibrium is that the leader has less

1Nieken and Sliwka (2010) study risk-taking behavior in a simple two-person tournament, in a theoreticalmodel as well as a laboratory experiment. Suppose that two agents play a winner-take-all type game. Theagents simultaneously decide between a risky and a safe strategy. The theoretical model predicts that whenthe risky strategies are correlated across players, choosing the risky strategy become a means to protect thelead. The leading player is now more likely to choose the risky strategy than his opponent, as he imitatesthe risky strategy and can afford to gamble with a higher probability due to his lead. In this case, the higherexpected payoff of the risky strategy makes it more attractive to gamble and the leading player can affordto gamble with a higher probability due to his lead. Taylor (2003) considers the extreme case of perfectcorrelation in a mutual fund tournament. His model also predicts that the winning manager is more likelyto gamble.

2In his model, two players face two alternative R&D paths. If players choose different paths then theprobability of success is independent across players. If both players choose the same path, however, theoutcome is perfectly correlated across players. In equilibrium, the leader is interested in increasing thecorrelation with respect to the laggard. By doing so, the leader protects her leadership by managing the riskthat the laggard will earn a high return and overtake her. The laggard, on the other hand, has an incentiveto choose a different path from the leader. The laggard is willing to trade off lower expected value for lower

6

to gain from moving farther ahead than he or she has to lose from being caught up by the

laggard, whereas the laggard has more to gain from moving closer to the leader than he or

she has to lose from falling farther behind.

If high-ranking corporate officers value social status to a substantial extent, then any

effects that status concerns might have on risk taking should be easier to identify in CEO

behavior. There is little work done thus far on how managerial risk-taking is affected by

social status concerns. Adams, Almeida, and Ferreira (2005) show that stock returns are

more variable for firms run by powerful CEOs (identified by formal position and titles, status

as a founder, and status as the board’s sole insider). Hirshleifer, Low and Teoh (2012) find

that firms with overconfident CEOs (identified by press coverage or options exercise behavior)

have greater return volatility and invest more in innovation. Malmendier and Tate (2008) find

that firms whose CEOs are classified as overconfident are more likely to make an acquisition.

This effect is largest if the merger is diversifying, i.e. if the target firm is not within the

same industry group. In my paper, I focus only on the time series, not on the cross section,

and test whether shocks which affect the CEO on a personal level result in corporate policy

adjustments. The main contribution of this paper is thus to test how shocks to CEO social

status affect managerial risk-taking.

Managerial risk-taking affects almost every corporate policy, ranging from investment

choices to capital structure. Excessive risk-taking might lead to bankruptcy, while excessive

risk avoidance prevents growth and hurts shareholder value. The standard principal-agent

model highlights the tension between incentive alignment and managerial risk aversion. Since

managerial effort is unobservable, shareholders want to tie compensation to performance.

This compensation scheme, however, is also more expensive, as managers require a risk

premium for a random and uncertain wage. Understanding the factors that affect managerial

risk-taking is thus an interesting and important question in corporate finance.

correlation with respect to the leader. Cabral derives this result without assuming a convex payoff function,that is, a function with the properties that the leader has more to gain from extending his or her lead thanthe laggard has to lose from falling farther behind. Instead, payoffs are determined by the difference betweenthe two players.

7

If managers have discretion inside their firm, they may alter corporate decisions to advance

their own objectives. Bertrand and Schoar (2003), for example, identify differences in “style”

across managers by tracking top managers across different firms over time. They find that a

significant extent of the heterogeneity in investment, financial, and organizational practices

of firms can be explained by the presence of manager fixed effects. CEOs matter to firm

policy. One concern with this method is that turnover events and changes in policy may be

driven by the same forces. In my paper, I follow the same CEO at the same firm over time,

and not across different employers.

My conjecture is that CEOs wish to maximize their legacy, or accumulate as much social

status as they can during their tenure. In the quest to managerial social status, awards

provide a significant positive shock, or boost, in terms of positive media coverage and public

appreciation (Francis et al. [2008]). Once a CEO is regarded as “The Best CEO”, there is

little value in increasing her reputation any further, and so award-winning CEOs can only

lose. As managers may not be held responsible for some factors outside their control (Warner

et al. [1988]), they are mostly concerned that the market devalues their reputation based

on poor relative performance (Milbourn [2003]). In this case, one would expect winners to

decrease idiosyncratic risk but increase systematic risk. This increases the likelihood that the

firm’s stock returns move in line with the industry’s average, which in turn will perpetuate

the relative advantage of the CEO.

While most models in finance assume that relative standing is defined by relative wealth

or consumption, I conjecture that CEOs also obtain utility from their social status, in terms

of their reputation relative to their peers. CEOs want to accumulate as much social status

as they can during their tenure in order to maximize their legacy. In this paper I use awards

to measure shocks to CEO status because they are widely visible and draw a clear ranking

order. To the best of my knowledge, CEO award data have not been used in the finance

literature, except by Malmendier and Tate (2009). Malmendier and Tate (2009) study the

first-moment effects of an award on firm performance and managerial effort. The authors

8

find that award-winning CEOs subsequently underperform, spending more time on public

and private activities outside their companies, such as assuming board seats, writing books

and pursuing leisure activities such as golf. Malmendier and Tate conjecture that awards

increase CEO power within their firms. Such managers become more entrenched, and so

they put less effort and extract more rents. I explore a different aspect of awards, as they

affect CEO social status outside their firm, measured in relative terms with respect to other

CEOs.

While there is evidence that managers seem to also avoid effort when insulated from

takeovers, as in Bertrand and Mullainathan (2003), managerial effort has no clear relation

with risk taking. Whether termination risk can be linked to the composition of corporate

risk taking also remains an open question. Shleifer and Vishny (1989) argue that managers

can make it costly for shareholders to replace them by making irreversible manager-specific

investments. It is possible that all managers generally engage in such investments, but less so

as they become more entrenched. Such an investment, however, is defined as an asset whose

value is higher under the current manager than under the best alternative, and is thus not

necessarily idiosyncratic in nature. Furthermore, Aggarwal and Samwick (2003) argue that

managers diversify their firms, or expand into new industries, because they derive a private

benefit, such as entrenchment. Managers will diversify more when they feel their positions

have become less secure, or when they become more concerned with perceived ability. In

contrast to the social status argument presented in this paper, the entrenchment story thus

implies a negative relationship between diversification and managerial social status.

3 Data and empirical strategy

I use CEO awards collected by Malmendier and Tate (2009), MT hereinafter, and I test

whether the event of receiving an award affects individual and firm-wide decisions and out-

comes. The data set covers 465 awards granted during the years 1992-2003, based on 13

9

different types of awards. Business Week and Financial World are the two predominant

publications which conferred awards on CEOs during the sample period. Additional sources

include Chief Executive, Forbes, Industry Week, Morningstar.com, Time, Time/CNN, Elec-

tronic Business Magazine, and Ernst & Young. The key criterion for inclusion in the sample

is that the award is national, so that it is prominent enough to plausibly affect CEO status.

I obtain CEO characteristics from the CompuStat ExecuComp database, and so I restrict

my analysis to CEOs in the ExecuComp universe. In addition, I use CRSP for stock return

variability and CompuStat for firm fundamentals. I next consider different measures for

managerial risk taking.

3.1 Measures for risk taking in firm-level decision variables

For CEO awards to affect firm-level decisions, CEOs should have at least some control over

firm policy. An effect on firm-level decisions thus also addresses important questions in cor-

porate finance, such as the level to which CEOs matter to firm policy, and the effectiveness of

governance mechanisms. Unfortunately, firm-level decision variables are the most problem-

atic, as the mechanism through which managers affect firm risk might be unobservable. For

example, managers may engage in asset substitution, and so they do not necessarily change

investment levels, but instead choose safer or riskier projects. Thus, a change in risk can be

unrelated to investment levels. In addition, firm-level variables are based on the managerial

team as a whole and not just on the CEO. Since an award lowers the termination risk that

the CEO faces, it also lowers the probability of promotion of the VPs, and so they may

exert lower effort and have lower incentive to take risks. It is thus difficult to predict how to

aggregate the management team’s effort and risk preferences.

3.2 Measures for risk taking in firm-level outcomes

I try to learn more about the type of decisions winners make following the award by looking at

the outcome of these decisions as it is manifested in stock returns. The risk-taking tournament

10

theory predicts that winners will decrease idiosyncratic risk but increase systematic risk, and

so the decomposition of risk allows us to identify whether risk shifting is rational/strategic

in a tournament. It is also possible that the award increases investor recognition in the

CEO’s firm and its securities (Merton [1987]). As investor attention increases, one would

expect stock-price informativeness to increase accordingly and thus idiosyncratic volatility to

go up as the stock price is tracking its fundamental value more closely. One may consider an

alternative model with incomplete information, in which an award makes investors believe

that they know more about the CEO’s skills and will therefore update their beliefs to a lesser

degree following firm-specific information; in this case, the ratio of idiosyncratic volatility

to total volatility after winning an award will decrease. A consistent effect in both the

decomposition of return variability and the decomposition of investment may distinguish

between the risk-taking tournament and alternative explanations.

3.3 Empirical strategy

I rely mostly on an event-study method, by which for every award I select the years -1 to +3

relative to the year the award was made public. I then compare the last known information

prior to the award (year -1) to each of the four years following the award (years 0 to +3).

Casamatta and Guembel (2010) suggest that strategic decisions have an irreversible impact

on future cash flows, potentially beyond the managers own tenure. Specifically, even if awards

have an immediate effect on CEOs, overlapping projects from the previous years may still

dominate the risk exposure of the firm years after the award. The longer the product life

cycle, and the higher the ratio of fixed to variable cost - the longer it takes for managerial

actions to manifest in firm-level performance.

Since awards are given by corporate outsiders who rely on public information to assess

CEO quality, such variables may be used to control for cross-sectional differences between

award winners and other firms. Due to the small sample size, as prestigious business awards

are granted to very few CEOs, I use a matching method to create a control group comparable

11

to award winners. According to this method, each firm with an award-winning CEO (the

treated firm) is matched with a control firm (the predicted winner). I follow MT in using

propensity-score matching which reduces the dimensionality of the matching process. MT

implement the matching using specific independent variables that are correlated with the

probability of receiving an award: Market value, book-to-market ratio, stock return during

the last three years prior to the award, CEO gender, age, and tenure. Johnson, Young and

Welker (1993) show that CEOs are more likely to win awards from the Financial World mag-

azine following outstanding firm performance, consistent with the notion that accounting and

capital market measures of firm performance convey information about CEO productivity.

Milbourn (2003) uses CEO tenure as a proxy for reputation. CEO tenure is the result of past

retention decisions, which depends on the board of directors assessments of CEO ability.

The matching convariates are correlated not only with the independent variable (reputa-

tion), but also with the dependent variable (risk). In the context of managerial risk taking,

some of these control variables are very important. For example, executives may decrease

their risk exposure as they grow older and approach retirement age. In addition, awards

may be granted based on outstanding past performance, which is then likely to revert to the

population mean. It is also possible that risk-related events before the award was granted are

driving both the award and the adjustments following it. For example, the award could be

granted on luck (which is unsustainable by definition), successful innovation (such as FDA

approval) or turnaround (such as reorganization and recovery from bankruptcy). In order to

attend to such concerns, stock returns prior to the award are included as control variables.

Awards are given by corporate outsiders who rely on public information to assess CEO qual-

ity, and so any information they may consider should be manifested in stock returns leading

to the award. By using propensity-score matching to study the effects of CEO awards, I

make sure that I only use relevant information and include the most comparable firms in the

control group. The control firms may thus be used as a proxy of what one can expect to

occur if the CEO had not received the award.

12

I thus use a nearest-neighbor matching estimator to test whether award-winning CEOs

change over time differently than do their matched peers (predicted winners). More specif-

ically, for each variable of interest, I compare its change over time among award winners

to that of similar non-winners, a non-parametric difference-in-differences approach. This

method allows me to test how awards affect risk taking and risk preferences. Nevertheless,

matching is still not exact, i.e. there are difference in covariates between matched units and

their matches. I follow Abadie and Imbens (2007) (AI hereinafter) in adjusting the results

using auxiliary regressions of each outcome variable of interest on the control variables used

in the matching process. This regression is preformed only on the control group (predicted

winners), and the coefficients estimated are used to estimate the expected difference between

a treated firm and its match. As AI show, the simple matching estimator in finite samples

will have a bias corresponding to the matching discrepancies.

As an alternative method, I sometimes use panel regressions, in which I include all firms

common to CRSP, CompuStat and ExecuComp to control for time trends and cross-sectional

differences. Each firm-year counts for one observation, and in each observation I include

award dummies indicating past winnings. The panel regression has some potential problems:

it might capture a general cross-sectional difference in the characteristics of firms that tend

to have award-winning CEOs, which is not the question of interest in this paper. The panel

regression also makes strong assumptions on the distribution of the variables of interest. In

such a pooled model it is crucial to control for year and industry effects. Campbell et al.

(2001), for example, report a positive deterministic trend in idiosyncratic firm-level volatility.

Malmendier and Tate also address the possibility that CEOs can affect the probability

of receiving media awards by self-promotion. They find no significant differences between

winners and their matches in the number of TV interviews or in the number of mentions

or interviews in the printed press over the three years prior to awards. Awards, however,

are positively associated with press coverage during the year in which the award is granted.

Francis et al. (2008) observe a positive and highly significant association between CEO

13

awards and the number of articles containing the CEO’s name that appear in the major U.S.

and global business newspapers and business wire services.3

4 Results

Table 1 shows the logit results used to obtain the propensity scores. I include all firms in

each calendar month for which there was at least one award granted. The dependent variable

is a dummy variable equal to 1 if the CEO of the company won an award during that month.

Industry, year and award-type dummies are included to account for difference in the base

probability of winning an award in the pooled regression. Consistent with MT, CEOs of

larger firms with lower book-to-market ratios and higher past returns are significantly more

likely to win awards. CEOs with more tenure and younger CEOs are more likely to win

awards. Adding R&D intensity in the logit regression reveals that the coefficient is very

insignificant, and is thus dropped as a covariate.

[Table 1 about here.]

Based on the propensity scores obtained using the logit model in table 1, I match each

award winner with the firm with the nearest score within the same month (minimum absolute

difference), which has never won an award. The use of industry dummies allows for matching

to be made across industries, which is imporatnt for identification reasons. Comparing the

difference between treated firms and the control group within the same industry does not

identify whether the tournament effect is driven by the leaders (the treated firms), the laggers

(the control group) or both. However, comparing winners to similar non-winners across

different industries suggests that the effect is indeed driven by the treated firms. Table 2 shows

descriptive statistics of award-winning CEOs and their control group which was formed using

3In panel B of Table 2, Francis et al. report logistic regressions of a Recognition dummy (equal to 1 if theCEO is recognized in one or more lists of “top” CEOs in calendar year t, and 0 otherwise) on the number ofarticles appearing in major U.S. newspapers that mention the CEO’s name in calendar year t, the number ofarticles appearing in major international newspapers that mention the CEO’s name in calendar year t, andthe number of press releases that mention the CEO’s name in calendar year t.

14

the nearest-neighbor method as explain above. Predicted winners are similar to winners, even

in variables that are not used to match between the two. R&D intensity, as are other variabes

that are known to be correlated with risk taking, are similar for firms with award-winning

CEOs and predicted winners. It seems that winners tend to have lower idiosyncratic volatility

ratios. Note that imposing a caliper on the matching improves the similarity between winners

and predicted winners, but since the sample is small I prefer to keep all matches while

adjusting for matching discrepencies.


In table 3 I look in more detail at differences in CEO turnover between winners and

predicted winners. Termination risk may diminish managerial risk taking (Chakraborty et

al. [2007] and Bushman et al. [2010]). If the board of directors is reluctant to fire superstars,

such CEOs may thus engage in higher managerial risk taking. I compute rates of post-

award CEO turnover, due to either stepping down while not leaving the firm, or leaving

the firm following resignation/retirement. I find that winners tend to step down from the

CEO position but remain in the firm more often, while more non-winners tend to resign.

Winners may face lower termination risk because the board of directors will be reluctant to

fire a superstar. The wide recognition of award-winning CEOs may thus protect them from

termination following bad performance. This effect may also induce higher managerial risk

taking. The high frequency of CEOs who step down from the CEO position and remain on

the board is not surprising, given the high stock returns prior to the award. Brickley, Linck

and Coles (1999) report that CEO post-retirement board service is positively and strongly

related with stock returns while CEO. Note that winners tend to be older than predicted

winners, as displayed in table 2, which may explain the difference in retirement rate.


15

4.1 Firm fundamentals and decision variables

Risk taking in firm-level decisions is proxied by R&D expenditure. Firms often have to

choose between implementing a new technology or staying with the standard one. Table 4

presents differences and bias-adjusted differences between winners and predicted winners

in several investment measures. More specifically, I look at capital expenditure, research &

development expenditure and total investment (i.e. capital and R&D expenditure combined),

with all measures normalized by total assets. Table 4 suggests that firms with award-winning

CEOs display a decrease in R&D expenditure and an increase in capital expenditure relative

to predicted winners. The decrease in R&D expenditure is economically sigificant, accounting

for more than 20% from the pre-award expenditure level. One interpretation is that award

winners become myopic, and sacrifice long-term growth for short-term personal objectives

(such as the preservation of their status). This is because R&D expenditure usually represents

long-term investments. In addition, R&D investments come from the firm-specific investment

opportunity set, i.e they expose the firm to idiosyncratic risk.


Hirshleifer, Low and Teoh (2012) find that firms with overconfident CEOs invest more

heavily in innovation. If award winners become more overconfident following an award,

one would expect them to invest more heavily in R&D. Furthermore, awards may provide a

positive reinforcement to the CEO, in the sense that he must have been doing the right thing.

Winners may thus become more extreme as they amplify current investments. On the other

hand, it is also possible that the award was granted following successful innovation (such as

FDA approval), which in turn may lead to a decrease in R&D expenditure afterwards. I do

not find evidence for neither of these effects, as R&D expenditure of winners seems to remain

stable following the award (not reported). However, to further alleviate this concern, I filter

out 2 types of awards (which account for 4 awards) that may be correlated with innovation,

namely the Business Week Best Entrepreneur and the Ernst & Young Entrepreneur of the

16

Year awards. Results are similar whether I apply this filter or not.

Note however that the decrease in R&D expenditure could be driven by a decrease in

cashflow following an award. In addition, since firms with award-winning CEOs underperform

the market (as reported by MT), it is unclear whether the decrease in R&D expenditure

can be explained by a decrease in invetment opportunities following an award. I thus test

whether firms with award-winning CEOs display a decrease in R&D expenditure following

an award, controlling for investment opportunities and cashflow. I use Tobin’s Q to proxy

for investment opportunities, with the notion that managers of a firm with poor investment

opportunities (“low” Q) should invest less. Table 5 suggests that R&D expenditure is lower

for award winners-i.e., they invest less than other firms with similar investment opportunities

and cashflows following the award. The panel includes all firms with award-winning CEOs,

as well as all other firms common to CRSP, CompuStat and ExecuComp. Note that the

coefficients should be interpreted as in a difference-in-differences model, since I control for

firm fixed effects and the post-award dummy is set to 1 only during the 3 years following an

award.


R&D expenditure however is merely a rough measure for risk-taking, as it pools together

both innovative research projects and more reliable development investments. I thus try to

learn more about the type of decisions winners make following the award by looking at the

outcome of these decisions as it is manifested in stock returns.

4.2 Firm-level outcomes and stock returns

In line with the predictions of a risk-taking tournament, I study the decomposition of return

variability. Recall that I expect winners to decrease idiosyncratic risk but increase systematic

risk. The most appropriate comparison group for the decomposition of risk in this context

is the firm’s industry. I therefore regress daily stock returns on their respective industry re-

turns (Fama-French 48-industry classification) and compute the idiosyncratic volatility ratio,

17

defined as the resulting root-mean-square error divided by total return standard deviation. I

repeat the decomposition using the risk factors of Fama-French (market excess return, small

market capitalization minus big, high book-to-price ratio minus low, and momentum).

The decrease in idiosyncratic volatility normalized by total volatility is shown in figure 1.

I plot both mean and median idiosyncratic volatility ratios, estimated using either industry

returns or the Fama-French four factors (mktrf, smb, hml, and umd).

[Figure 1 about here.]

Table 6 directly tests for significance and for a cross-sectional difference in the idiosyn-

cratic volatility ratio between firms with award-winning CEOs and predicted winners. In

Panel A, I decompose stock return volatility using industry returns, so that the decomposi-

tion of risk is aligned with a potential risk-taking tournament within the industry. If CEOs

are measured relative to the industry, their safest approach is to increase the firm’s alignment

with the industry. Results show that industry betas monotonically increase for firms with

winner CEOs (converging to 1 from an average beta of 0.86 in year -1 to 0.97 in year +3). The

convergence of industry betas to a beta of 1 is shown in figure 2, as the range between the 5%

and 95% percentiles decreases by around 25%. One interpretation is that managers become

more passive, increasing exposure to industry shocks and avoiding idiosyncratic shocks. Note

that beta is increasing despite the leverage effect driven by high stock returns prior to the

award.

One concern in estimating changes in betas over time is that estimated betas may exhibit

a tendency to regress towards the grand mean of all betas, namely one. Measurement error in

estimated betas can lead to such tendency as a statistical artifact, as low betas are expected

to be measured with negative error and vice versa. I therefore follow Blume (1975) and find

that betas of firms with award winning CEOs converge to 1 even after adjusting for such

“order bias”.


18


Since winners and predicted winners are not exactly matched by their industry, comparing

their idiosyncratic volatility ratios with respect to industry is uninformative. In panel B of

table 6, I therefore use the risk factors of Fama-French (market excess return, small market

capitalization minus big, high book-to-price ratio minus low, and momentum) and compute

the idiosyncratic volatility ratio, defined as the resulting root-mean-square error divided by

total return standard deviation. Results show that idiosyncratic volatility ratios go down to

a larger extent for firms with award-winning CEOs relative to predicted winners. Comparing

winners to similar non-winners across different industries suggests that the effect is indeed

driven by the treated firms. These results are strengthened by the fact that firms with award-

winning CEOs tend to have lower idiosyncratic volatility ratio levels on average, i.e. negative

cross-sectional differences are upward biased. Note that even if managers reduce risk taking

immediately following the award, this effect may take time to manifest itself in firm-level

performance. This is due to the fact that overlapping projects take several years to manifest,

so the projects from the previous several years still dominate the risk exposure of the firm

even after the award. I therefore look for a change in firm-level risk measures over a longer

period of five years following an award.

The decrease in idiosyncratic volatility is consistent with the predictions of a risk-taking

tournament, as winners cling on to their industry. It is possible that winners engage in

earning smoothing which in turn may affect stock return variability. MT find that award

winners are significantly more likely to report negative earnings once five years have passed

from their last award than other CEOs. While this kind of earnings management may lower

total volatility, it is unclear how it would affect the idiosyncratic volatility ratio. I do not

find that total volatility (as measured by return standard deviation) significantly decreases

following the award. Even if this kind of earnings management could affect the idiosyncratic

volatility ratio, its effect is most probably dominated by actual managerial decisions. The

effects reported here are consistent with the decomposition of investment reported in table 4.

19

A consistent effect in both the decomposition of return variability and the decomposition of

investment provides further support for the risk-taking tournament explanation.

5 Discussion

5.1 CEO awards and managerial risk taking

There are additional channels though which awards may indirectly affect managerial risk

taking, even when there is no social tournament involved. It is plausible that awards provide

a positive reinforcement in the sense that the CEO must have been doing the right thing.

Winners may thus become more extreme as they amplify current investments. It is also

possible that award winners become more overconfident following an award and thus display

higher risk tolerance, as awards may cultivate CEO “hubris”. MT suggest that CEOs who

become superstars increase their outside activities and thus exert less effort in their core

responsibilities. It is not clear whether and how such distractions affect managerial risk

taking. One may suggest that superstar CEOs become more passive in their investment

decisions,

In an agency model with incomplete information, winning an award may upgrade the

beliefs the board and shareholders hold regarding CEO skill. If the CEO then does badly,

it will not change their beliefs much, and so compensation will not decrease as much-i.e.,

pay is less sensitive to performance. This effect may induce higher managerial risk taking.

Note that as documented by MT, award-winning CEOs subsequently underperform, but

their compensation is not negatively affected. This implies that pay-performance sensitivity

of award-winning CEOs indeed decreases significantly following the award. Winners not only

face lower pay-performance sensitivity but they may also face lower termination risk because

the board of directors will be reluctant to fire a superstar. The wide recognition of award-

winning CEOs may thus protect them from termination following bad performance. This

effect may also induce higher managerial risk taking.

20

In addition, higher status may mean higher CEO power in the decision-making process,

and thus more concentrated decision making. The other executives and the board members

may trust the superstar CEO and decide that they cannot stand up to him or her. If decision

making is more concentrated and made by one person and not by a group, one might expect

higher variability. Also, if award-winning CEOs experience an increase in compensation, not

shared by the next-highest paid executives in their firms, post-award compensation increases

intra-firm inequality, which may induce more group risk taking at the management level

(Kale, Reis, and Venkateswaran [2009]). There may be other spillage effects within the

management team; for example, if winning an award lowers the termination risk that the

CEO faces, it also lowers the probability of promotion of the VPs, and so they may exert

lower effort and have lower incentive to take risks. Alternatively, if a firm with an awarded

CEO faces a lower takeover risk, this affects the entire management team, not just the CEO.

It is ex-ante unclear whether awards have a direct effect on risk taking. Since most

indirect effects presented above, as distinct from status concerns, suggest a positive relation

between winning an award and managerial risk taking, finding a negative relation suggests

that direct effects, driven by status concerns, dominate managerial risk taking. A negative

relation between winning an award and managerial risk taking would thus provide evidence

for the significance of social status and competition in governing CEO behavior over and

above other factors that are commonly considered in the literature, such as compensation.

5.2 Individual-level decisions and risk aversion

In the tournament models decribed above, agents adjusted their risk exposure as an optimal

rational decision. Indeed, most of the literature that is focused on relative concerns and risk

taking assumes that agents value status as an end in itself and adjust their risk exposure to

maximize their status. Robson (1992) investigates the implications for risk-taking behavior

if individuals have identical utility functions, which are concave in wealth but convex in

relative wealth. The utility from relative wealth is defined by the wealth distribution-i.e.,

21

the number of individuals with wealth less than one’s own wealth. The model provides a

“concave-convex-concave” curve, as the middle-class gambles in an attempt to jump up the

ladder. Roussanov (2010) uses a portfolio choice framework in which households choose their

level of exposure to idiosyncratic risk in order to “get ahead of the Joneses”. In these models

agents value status as an end in itself, and adjust their risk exposure to maximize their status.

Nevertheless, status may affect the underlying risk aversion, and not merely the observed

risk taking. Classical finance mostly assumes that risk aversion is constant, either in absolute

or in relative terms of wealth or consumption. Prospect theory, however, suggests that

preferences may be risk averse or risk tolerant, depending on the agent’s reference point.

Several experimental and empirical papers document the house-money effect, by which agents

display increased risk taking following gains (Thaler and Johnson [1990]). It is thus possible

that status affects the underlying risk preferences and not merely the observed risk taking.

While most of the overconfidence literature is focused on cross-sectional differences in

managerial characteristics, one may suggest that award winners become more overconfident

following an award and thus display higher risk tolerance. Williams and Wong Wee Voon

(1999) study how mood influences subsequent risk decisions among actual managers, based

on hypothetical business decisions with realistic outcomes. They find that managers are more

likely to select riskier courses of action after they recall and describe a work-related event

they had experienced that made them feel really good. One possible explanation is that the

positive affect induced by these memories may be associated with optimism and improved

managerial expectations. If award winners indeed become more overconfident following an

award, one would expect them to invest more heavily in R&D (Hirshleifer, Low and Teoh

[2010]). An alternative behavioral theory could build on learning one’s ability: executives

may not know their ability and skill levels, and they can only learn it over time. Winning an

award increases both their own subjective estimated ability and its precision. Consequently,

winning CEOs feel more confident and are more willing to make risky choices.

There are alternative behavioral factors, however, which support the opposite view. Psy-

22

chologists such as Alice Isen show in a series of experiments that emotions affect risk taking.4

For example, Isen and Geva (1987) study the association between happiness and risk aversion

using a laboratory experiment. They find that positive affect, or happiness, made subjects

more risk averse, in comparison with those in a control group. They suggest that persons

who are feeling good tend to protect the pleasant positive state, making potential losses seem

more aversive.

Taking a more fundamental approach, one might turn to evolution in order to explore the

origins of a relation between social status and risk aversion. Such an evolutionary approach

may suggest that agents are hardwired to accept higher risks in case of feelings of inferiority, as

a human mechanism. The emotion of inferiority may have evolved to moderate the emotion

of fear, as risk tolerance serves as a signal for non-observable quality in the reproductive

cycle.5 In this case, one would expect winners to become more risk averse.

An effect of awards on individual-level decisions may suggest a change in risk aversion, and

4See Loewenstein et al. (2001) for an interdisciplinary survey on the determinants of fear. The determi-nants of fear are more complex than an assessment of the severity and likelihood of the possible outcomes ofchoice alternatives, as expected utility theory assumes.

5Evolutionary biology highlights the importance of social status in the reproductive cycle, as well as itsrelation to risky choices. While avoiding unnecessary risks is crucial for survival, risk tolerance may also serveas a signal for gene quality. Given this tradeoff, it is possible that the human brain is hardwired to accepthigher risks in case of the feeling of inferiority. I follow Grafen (1990), who develops an evolutionarily stablesignaling equilibrium deduced through the process of choice over members of the opposite sex. Evolutiondictates that the surviving organisms and strategies are those that succeed in maximizing reproductivesuccess. As Grafen shows, evolution shapes a signaling rule-that is, a scaled-response gene that in effectinstructs its bearer: “If you find yourself in a state X, emit a big signal. If the opposite, emit a small signal”.The innovation in the behavioral story I suggest is that risk tolerance may serve as a signal for quality insuch a system. One of the mechanisms through which agents signal high gene quality is by exerting theminimum level of risk aversion and disease avoidance (Fessler, Pillsworth, and Flamson [2004]). For example,consider the behavior of peacocks: the male peacock’s tail is taken to be a signaling device to prospectivemates (Niman [2006]). One view is that the exuberance of the tail is an attractive quality exactly because itmakes the peacock more vulnerable to predators, and therefore signals the male’s confidence and quality. Thishypothesis was originally proposed by biologist Amotz Zahavi (1975). Zahavi’s “handicap principle” suggeststhat reliable signals must be costly to the signaler, costing the signaler in the trait being signaled in a mannerthat an individual with less of that trait could not afford. Risk tolerance was the sole signal for gene qualityfor most of our own species’ history, and the human brain may have evolved to be hardwired to accept higherrisks in case of the feeling of inferiority. Evolutionary effects persist to the present day even though humanreproduction is now largely divorced from the factors that governed it for most of our species’ history, andhumans are unaware of any connection between these dimensions and reproductive success. Modern societyhas developed alternative signals for status, such as wealth or visible consumption, which neutralize the needto take higher risks. The relation between modern signals for status should thus be negatively related to riskaversion.

23

not merely a rational adjustment to risk taking. One direct measure of CEO risk preference

could be extracted from the CEO’s personal portfolio. The overconfidence literature uses

share ownership and option exercise activity as measures for overconfidence, inspired by

Hall and Murphy (2003) who study the subjective value of compensation for undiversified

risk-averse managers.

I focus on ownership as a proxy for risk preferences. CEO ownership is used in the

overconfidence literature as a measure for managerial risk aversion, as it is related to the

subjective level of under-diversification of the CEO’s personal portfolio. Under-diversification

means that a very high share of the CEO’s personal wealth is invested in the company

he works for. The level of under-diversification depends on the managerial subjective risk

tolerance, as this concentrated investment exposes them to company-specific risk. A visual

inspection of figure 3 suggests that award-winning CEOs decrease their share ownership

positions. The decrease is economically significant, as it accounts for more than 20% from

the pre-award ownership level.


Table 7 shows that the decrease is also statistically significant one year after the award

and onwards. Panel A displays changes in share ownership, based on the ‘SHROWN’ variable

from ExecuComp.6


It seems that predicted winners may also decrease their ownership, but not as much

as winners do. One concern is that winners may optimally adjust their ownership level in

response to higher option compensation. Managers who receive more stock and option grants

and own more shares have a greater incentive to sell equity for diversification reasons (Ofek

and Yermack [2000]). If winners recieve higher compensation, they may exercise more options

6The ‘SHROWN’ variable is said to include restricted stock but does not account for option holdings,though it seems that ExecuComp is inconsistent with regard to the inclusion of restricted stock.

24

(which I find that they do; not reported), which in turn increases their share ownership. In

many cases however, the executive will sell immediately once he or she exercises, either in the

open market or directly back to the firm. To address this concern, I study changes in total

equity holding, defined as the total dollar value of direct share ownership and option holdings,

normalized by market capitalization. This accounts for the substitution between stocks and

options, and thus adjusts for any differences in compensation between winners and predicted

winners. Surprisingly, the results presented in Panel B show that total holding of winners

decrease in the year in which the award was granted. This suggests that winners sell more

stocks than what they get through stock grants and option exercise combined. Moreover,

winners decrease their total holding by more than predicted winners do.

An alternative motive for changes in share ownership may be driven by portfolio rebal-

ancing. For example, managers have an increased incentive to sell shares after their inside

holdings have appreciated in value (Jenter [2005]). However, MT find that firms with award-

wining CEOs underperform the market, and so the rebalancing argument does not hold. On

the other hand, winners may trade on private information regarding their future underperfor-

mance. The insider trading literature finds mixed evidence. Jenter (2005) finds that insider

trades do not predict subsequent returns. On the other hand, Aboody and Lev (2000) find

that insider gains in R&D-intensive firms are substantially larger than insider gains in firms

without R&D, suggesting that R&D is an important source of private information leading to

information asymmetry and insider gains. I therefore split firms with award-winning CEOs

into low- and high-R&D intensity firms, as measured by R&D expenditure normalized by

total assets. The decrease in ownership does appear more significant in high-R&D intensity

winners, yet it is still significant at the 5% level for low-R&D intensity firms (not reported).

A major caviat for the use of holdings as a proxy for risk preferences is that there are

explicite and implicit restrictions on insider trading. An alternative proxy for risk prefer-

ences is the relation between option-based compensation and stock-return variability. Option

compensation is the principal component of the CEO’s Vega, which measures the extent to

25

which changes in risk affect the CEO’s wealth. The relation between option compensation

and firm-level risk taking is moderated by the executive’s risk aversion. Firm risk may change

for various reasons, most of which cannot be controlled by the executive. Therefore, risk shift-

ing in response to option grants may be attributed mostly to the executive’s preferences. I

therefore use the degree of risk shifting in response to option grants as a proxy for changes

in risk aversion. If award winners become more risk averse, their option compensation will

not induce risk taking as much as expected. I test this in a regression framework in table 8.

I first estimate the value of options held at the end of each year from ExecuComp using the

procedure outlined in Murphy (1999). I then estimate Vega elasticity, which measures the

extent to which changes in firm risk affect the CEO’s wealth. More specifically, Vega elastic-

ity equals the percentage change in value of outstanding options for a one percentage-point

increase in volatility. The dependent variable is annual return standard deviation.


Table 8 shows that in general option compensation induces risk taking, which is consistent

with previous literature (e.g., Cohen, Hall, and Viceira [2000]). The panel includes all firms

with award-winning CEOs, as well as all other firms common to CRSP, CompuStat and

ExecuComp. The post-award dummy is not significant, consistent with the observation that

awards don’t seem to affect total stock return variability (table 6, panel A). However, award-

winning CEOs display a negative and significant senstivity during the 3 years following the

award. I interpret the negative interaction term (Vega elasticity*Award dummy) as evidence

for a lower degree of risk shifting as a result of option grants by award winners as compared

with non-winners. Note that the coefficients should be interpreted as in a difference-in-

differences model, since I control for firm fixed effects and the post-award dummy is set to 1

only during the 3 years following an award.

To summarize, I find some evidence suggesting that award-winning CEOs become more

risk averse. The individual-level effects reported support a more general behavioral effect

governing a relation between status and underlying risk preferences, not just observed risk

26

taking. The social-science literature, including business and economics, is still debating

the relation between status and risk preferences. This paper contributes to this literature by

providing support for a positive relation between status and risk aversion.7 Putting all results

together, it seems that both a behavioral effect by which award-winning CEOs become more

risk averse and a rational effect driven by the tournament are at play. That is, a behavioral

effect by which award-winning CEOs become more risk averse may coexist with an effect

driven by the tournament.

6 Direct and indirect effects on compensation

I study effects on compensation separately from other firm-level decisions because the effects

that awards have on compensation are complex and difficult to interpret.

First, compensation adjustments following the award may be driven by the board of

directors, in an attempt to affect CEO effort and/or risk taking. The board of directors

can adjust compensation and its composition in response to changes in CEO motives and/or

CEO preferences. For example, award-winning CEOs may face better outside options, and so

higher compensation is required to keep them from leaving the firm. The board of directors

of firms with such CEOs would thus like to support retention and preserve managerial effort.

Alternatively, winner CEOs may face lower termination risk, as the board fo directors will be

reluctant to fire a superstar, and so higher incentives are required as an alternative governance

mechanism. Alternatively, the board of directors of firms with winner CEOs may want to

lower the effects of reduced risk taking by the CEO. The board may be concerned that the

CEO may no longer have an incentive to take additional risk and therefore may decrease

the risk exposure of their firms. This may result in suboptimal decisions such as abstaining

7It would be interesting to more directly test for a behavioral effect by which agents are hardwired toaccept higher risks in case of feelings of inferiority, as a human mechanism. It may be possible to developan fMRI experiment, focusing on the interplay between brain regions associated with risk perception andemotions. The idea would be to control for a feeling of status/inferiority and the riskiness of investmentopportunities, and then test whether brain regions associated with risk preferences are affected solely by theelicited emotion.

27

from growth opportunities if a desirable project (i.e. with a positive Net Present Value)

is turned down due to the risk involved. According to classical principal-agent theory, the

cash component in an optimal contract is increasing with the agent’s risk aversion. The

intuition is that when the agent is more risk averse, the cost of alignment using incentives

goes up. In these models, however, it is assumed that the agent cannot affect firm risk. Cash

compensation, however, may be more costly because of Internal Revenue Service Regulation

162(m) (which limits a company’s ability to deduct more than $1 million in cash salary for

top executives from their taxes). The board may thus instead choose to grant more stock

options to the CEO, imposing a convex preference on firm performance. Executives cannot

simply hedge these new option grants, commonly unvested for several years, because such

trades are restricted by regulation. In this case, one may expect boards in firms with higher

growth opportunities to grant more options intentionally, since in these firms the manager’s

risk aversion can affect investment decisions more significantly. Good-governance firms may

be less concerned with a decrease in managerial risk taking, since in these firms the manager

cannot affect investment decisions easily.

On the other hand, award-winning CEOs may use their increased power to affect their

compensation level and structure (Belliveau, O’Reilly, and Wade [1996]). MT find mixed

evidence, as increases in winners’ equity-based compensation are only apparent in firms with

weak pre-existing corporate governance. The authors suggest that award-winning CEOs use

their increased power to extract greater rents in the form of equity-based compensation.

If CEOs can control their compensation structure to a large extent, it may be attributed

to their personal preferences, or more specifically, their risk preferences. If managers become

more risk averse, they require more options to keep their subjective utility from compensation

at the same level. This is a certainty-equivalent (CE) argument since the CE is decreasing

with risk aversion. Lord and Saito (2006) report a negative relation between cash and total

pay. According to standard utility theory, a risk-averse manager who receives a cash com-

ponent in his or her compensation contract could be given a package with a lower expected

28

value than one who receives only risky stock compensation. While it is possible that award

winners use their increased power to increase the level of their pay, it may be more difficult

to explain why their increased power would yield higher cash weights relative to non-winners

with the same level of total pay. It is interesting therefore to test whether award winners

display a higher cash-weight-in-total-compensation when compared with non-winners with

the same level of total pay.

Second, the effects of compensation on risk taking are unclear. For example, most of the

literature assumes complete markets, in which the options’ Vega is positive, and so options

elicit risk taking. Coles, Daniel, and Naveen (2006) find that higher Vega (the sensitivity of

CEO wealth to stock volatility) leads to riskier policy choices, including relatively more in-

vestment in R&D and less investment in property, plant, and equipment (PPE). On the other

hand, Ross (2004) suggests and Cadenillas, Cvitanic, and Zapatero (2005) show that options

may have the opposite effect, as they excessively expose risk-averse executives to firm-specific

risk. Whichever of these effects prevails, it should be stronger for award winners, as they

face a lower probability of termination. A lower probability of termination means that newly

granted options will become exercisable and will not be lost in case of termination during

their vesting period. This makes options more valuable on expectation, as the probability of

staying in the firm long enough to exercise the unexercisable options goes up.

6.1 Empirical results for executive compensation

In figure 4 shows an increase in mean compensation, while medians show that compensation

in unaffected by awards in general. This suggests that there might be a strong effect but

only for a small subsection of winners. The figure also suggests that most of the increase in

mean compensation is driven by an increase in option grants. These increases however are not

significant (not reported) and are likely driven by firms with weak corporate governance. MT

split the sample by governance and show that only bad-governance firms show a significant

increase in option compensation while good-governance firms do not.

29


I next study the compensation structure, following Lord and Saito (2006). According to

standard utility theory, the certainty equivalent of equity-based compensation is lower than

that of cash. A manager who receives a higher cash component in his or her compensation

contract could be given a package with a lower expected value than the one who receives

all risky stock compensation. Lord and Saito (2006) report a negative relation between cash

weight and total pay. In table 9, I show that cash-weight-in-total-compensation increases for

winners more than it does for predicted winners.


Since total compensation levels between winner and predicted winners differ, I use a more

general panel regression in table 10. The panel includes all firms with award-winning CEOs,

as well as all other firms common to CRSP, CompuStat and ExecuComp. Award dummy is

1 if the current CEO received an award in the last three years, and zero otherwise. Missing

lagged award dummies are assumed to be zero, in order to include the first three years of the

sample. This indeed creates a measurement error; however, since the value is always assumed

to be zero the model is consistent and unbiased. This imputation only makes it harder to

get significant results.


The results in table 10 show that winners display a negative relation as standard utility

theory predicts, yet more interestingly it is significantly less negative as compared with non-

winners with the same level of total pay. While it seems plausible that award winners use their

increased power to affect the level of their pay, it is difficult to explain why their increased

power would yield a higher cash weight compared with non-winners with the same level

of total pay. In addition, the decrease in ownership reported earlier is not consistent with

increased CEO power, as more powerful CEOs will prefer to maximize ownership and use it

30

to expropriate as much as possible. I also find that the higher cash weight in compensation

is not concentrated in bad-governance firms, as reported in table 11. I use the governance

index provided by Gompers, Ishii, and Metrick (2003) to split the firms into three groups.

If compensation structure is driven by CEOs’ power to affect their pay, I expect this effect

to be stronger in, if not unique to, bad-governance firms. However, in table 11 I find the

effect to be uncorrelated with governance. This supports an alternative argument, by which

boards are adjusting compensation of winning CEOs following the award to elicit risk taking

in an attempt to mitigate managers’ flight to safety.


7 Conclusion

This paper tests the hypothesis that social status concerns affect risk taking. I test whether

changes in status affect risk taking as they are manifested in business decisions of award-

winning CEOs. The empirical exercise presented in this paper sheds some light on our

understanding of managerial risk taking. I interpret the results as evidence for the significance

of social status and competition, which govern CEO behavior over and above other factors,

such as compensation, that are commonly considered in the literature. In a more general

sense, I provide further evidence that CEOs matter, i.e. they can affect firm policy, and also

that the media matters, i.e. it affects managers.

I find that CEO awards affect firm-level decisions and outcomes in line with the predic-

tions of relative concerns. Firms with award-winning CEOs monotonically decrease their

idiosyncratic volatility ratios and their industry betas converge to 1. R&D expenditure de-

creases by 20% while investment in physical assets increases relative to a matched sample of

non-winning CEOs. I argue that CEOs who reach higher status, in terms of their reputation

relative to their peers, have an incentive to conform. By increasing the correlation with

respect to their reference group, CEOs with higher reputation lock-in their relative position

31

and maximize their legacy.

I implicitly assume that there is no skill in this setup, similar to the assumption made

with regard to money managers. A weaker assumption would be that the dispersion of

managerial ability is far lower than the dispersion of outcomes. In a way, matching each

award-winning CEO with the closest “predicted winner” makes sure of that. Several of

the covariates I match by proxy for reputation and ability (for example, outstanding firm

performance, which conveys information about CEO productivity, and CEO tenure, which

depends on the board of directors’ assessments of CEO ability). Nonetheless, it would be

interesting to explore how introducing skill can affect my main argument. Aron and Lazear

(1990) model the timing of product introduction in a sequential framework. They argue

that new markets are likely to be opened up by firms that are less dominant in existing

markets. The industry’s dominant firm then follows into the new product line, in order

to maintain its relative advantage. The reason why the leading manager follows after the

less dominant firm innovates is that managerial ability is expected to carry over to the new

market. In a model in which managers use investment decisions to manipulate the labor

market’s inferences regarding their ability, Scharfstein and Stein (1990) show that managers

may choose to mimic the investment decisions of other managers, ignoring substantive private

information. That is, an unprofitable decision is not as bad for reputation when others make

the same mistake. This ”sharing-the-blame” effect arises because mimicing suggests to the

labor market that the manager has received a signal that is correlated with theirs, and is

more likely to be informed. Zwiebel (1995) argues that average managers may stick with

the industry standard in order to differentiate themselves from bad managers. As managers

become less likely to be mistaken for bad managers, they will be less concerned with relative

performance, and will thus be more willing to undertake new actions.

32

References

Abadie, Alberto and Guido W. Imbens, “Bias Corrected Matching Estimators for AverageTreatment Effects,” Mimeo, 2007.

Aboody, David and Baruch Lev, “Information Asymmetry, R&D, and Insider Gains,” Jour-nal of Finance, 55 (2000), 2747–2766.

Aggarwal, Rajesh K. and Andrew A. Samwick, “Why Do Managers Diversify Their Firms?Agency Reconsidered,” The Journal of Finance, 58 (2003), 71–118.

Aron, Debra J. and Edward P. Lazear, “The Introduction of New Products,” The AmericanEconomic Review, 80 (1990), 421–426.

Bakshi, Gurdip S. and Zhiwu Chen, “The Spirit of Capitalism and Stock-Market Prices,”The American Economic Review, 86 (1996), 133–157.

Basak, Suleyman, Anna Pavlova and Alexander Shapiro, “Optimal Asset Allocation andRisk Shifting in Money Management,” The Review of Financial Studies, 20 (2007), 1583–1621.

Belliveau, Maura A., Charles A. O’Reilly and James B. Wade, “Social Capital at the Top:Effects of Social Similarity and Status on CEO Compensation,” The Academy of Manage-ment Journal, 39 (1996), 1568–1593.

Bertrand, Marianne and Sendhil Mullainathan, “Enjoying the Quiet Life? Corporate Gov-ernance and Managerial Preferences,” Journal of Political Economy, 111 (2003), 1043–1075.

Bertrand, Marianne and Antoinette Schoar, “Managing with Style: The Effect of Managerson Firm Policies,” The Quarterly Journal of Economics, 118 (2003), 1169–1208.

Blume, Marshall E., “Betas and Their Regression Tendencies,” The Journal of Finance, 30(1975), 785–795.

Brickley, James A., James Linck and Jeffrey L. Coles, “What Happens to CEOs AfterThey Retire? Evidence on Career Concerns and CEO Incentives,” Journal of FinancialEconomics, 52 (1999), 341–377.

Bushman, Robert, Zhonglan Dai, and Xue Wang, “Risk and CEO turnover,” Journal ofFinancial Economics, 96 (2010), 381–398.

Cabral, Luis, “Increasing Dominance with No Efficiency Effect,” Journal of Economic The-ory, 102 (2002), 471–479.

Cadenillas, Abel, Jaksa Cvitanic and Fernando Zapatero, “Executive Stock Options asa Screening Mechanism,” Working paper, University of Alberta, California Institute ofTechnology and University of Southern California, 2005.

33

Campbell, John Y., Martin Lettau, Burton G. Malkiel and Yexiao Xu, “Have IndividualStocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk,” Journal ofFinance, 56 (2001), 1–43.

Casamatta, Catherine, and Alexander Guembel, “Managerial legacies, entrenchment, andstrategic inertia,” The Journal of Finance, 65 (2010), 2403–2436.

Chakraborty, Atreya, Shahbaz Sheikh and Narayanan Subramanian, “Termination risk andmanagerial risk taking,” Journal of Corporate Finance, 13 (2007), 170–188.

Chen, Hsiu-lang and George G. Pennacchi, “Does prior performance affect a mutual fundschoice of risk? Theory and further empirical evidence,” Journal of Financial and Quanti-tative Analysis, 44 (2009), 745-775.

Cohen, Randolph B., Brian J. Hall and Luis M. Viceira, “Do Executive Stock OptionsEncourage Risk-Taking?,” Working paper, Harvard University, 2000.

Coles, Jeffrey L., Naveen D. Daniel and Lalitha Naveen, “Managerial Incentives and Risk-Taking,” Journal of Financial Economics, 79 (2006), 431–468.

Fessler, Daniel M.T., Elizabeth G. Pillsworth and Thomas J. Flamson, “Angry Men and Dis-gusted Women: An Evolutionary Approach to the Influence of Emotions on Risk-Taking,”Organizational Behavior and Human Decision Processes, 95 (2004), 107–123.

Francis, Jennifer, Allen H. Huang, Shivaram Rajgopal and Amy Y. Zang, “CEO Reputationand Earnings Quality,” Contemporary Accounting Research, 25 (2008), 109–147.

Gompers, Paul, Joy Ishii, and Andrew Metrick, “Corporate Governance and Equity Prices,”Quarterly Journal of Economics, 118 (2003), 107–155.

Grafen, Alan, “Biological Signals as Handicaps,” Journal of Theoretical Biology, 144 (1990),517–546.

Hall, Brian J. and Kevin J. Murphy, “The Trouble with Stock Options,” Journal of Eco-nomic Perspectives, 17 (2003), 49–70(22).

Hirshleifer, David A., Angie Low and Siew H. Teoh, “Are Overconfident CEOs BetterInnovators?,” Journal of Finance, 67 (2012), 1457–1498.

Isen, Alice M. and Nehemia Geva, “The influence of positive affect on acceptable level ofrisk: The person with a large canoe has a large worry,” Organizational Behavior and HumanDecision Processes, 39 (1987), 145–154.

Jenter, Dirk, “Market Timing and Managerial Portfolio Decisions,” Journal of Finance, 60(2005), 1903–1949.

Johnson, W Bruce, Mark S. Young and Michael Welker, “Managerial reputation and theinformativeness of accounting,” Contemporary Accounting Research, 10 (1993), 305–332.

34

Kale, Jayant R., Ebru Reis and Anand Venkateswaran, “Rank Order Tournaments andIncentive Alignment: The Effect on Firm Performance,” Journal of Finance, 64 (2009),1479–1512.

Loewenstein, George F., Elke U. Weber, Christopher K. Hsee and Ned Welch, “Risk asFeelings,” Psychological Bulletin, 127 (2001), 267–286.

Lord, Richard A. and Yoshie Saito, “The Effects of Idiosyncratic Risks Borne by Managerson the Design of CEO Compensation Contracts,” Working paper, Montclair State Universityand Eastern Illinois University, 2006.

Malmendier, Ulrike and Geoffrey Tate, “Who makes acquisitions? CEO overconfidence andthe market’s reaction,” Journal of Financial Economics, 89 (2008), 20–43.

Malmendier, Ulrike and Geoffrey Tate, “Superstar CEOs,” Quarterly Journal of Economics,124 (2009), 1593–1638.

Merton, Robert C., “A Simple Model of Capital Market Equilibrium with Incomplete In-formation,” Journal of Finance, 42 (1987), 483-510.

Milbourn, Todd T., “CEO reputation and stock-based compensation,” Journal of FinancialEconomics, 68 (2003), 233–262.

Murphy, Kevin J., “Executive compensation,” Handbook of Labor Economics, 3 (1999),2485-2563.

Nieken, Petra and Dirk Sliwka, “Risk-Taking Tournaments: Theory and Experimental Ev-idence,” Journal of Economic Psychology, 31 (2010), 254–268.

Niman, Neil B., “Sexual Selection and Economic Positioning,” Journal of Bioeconomics, 8(2006), 21–33.

Ofek, Eli and David Yermack, “Taking Stock: Equity-Based Compensation and the Evolu-tion of Managerial Ownership,” Journal of Finance, 55 (2000), 1367–1384.

Rege, Mari, “Why Do People Care About Social Status?” Journal of Economic Behavior& Organization, 66 (2008), 233–242.

Robson, Arthur J., “Status, the Distribution of Wealth, Private and Social Attitudes toRisk,” Econometrica, 60 (1992), 837–857.

Ross, Stephen A., “Compensation, Incentives, and the Duality of Risk Aversion and Riski-ness,” Journal of Finance, 59 (2004), 207–225.

Roussanov, Nikolai L., “Diversification and its Discontents: Idiosyncratic and En-trepreneurial Risk in the Quest for Social Status,” Journal of Finance, Forthcoming, 2010.

Scharfstein, David S. and Jeremy C. Stein, “Herd Behavior and Investment,” The AmericanEconomic Review, 80 (1990), 465–479.

35

Shleifer, Andrei and Robert W. Vishny, “Management entrenchment: The case of manager-specific investments,” Journal of Financial Economics, 25 (1989), 123–139.

Taylor, Jonathan, “Risk-taking Behavior in Mutual Fund Tournaments,” Journal of Eco-nomic Behavior & Organization, 50 (2003), 373–383.

Thaler, Richard H. and Eric J. Johnson, “Gambling with the House Money and Trying toBreak Even: The Effects of Prior Outcomes on Risky Choice,” Management Science, 36(1990), 643–660.

Veblen, Thorstein, The Theory of the Leisure Class: An Economic Study of Institutions.(London: Unwin Books, 1899)

Warner, Jeroid B., Ross L. Watts and Karen H. Wruck , “Stock Prices and Top ManagementChanges,” Journal Of Financial Economics, 20 (1988), 461–492.

Williams, Steve and Yvonne Wong Wee Voon, “The Effects of Mood on Managerial RiskPerceptions: Exploring Affect and the Dimensions of Risk,” The Journal of Social Psychol-ogy, 139 (1999), 268–287.

Zahavi, Amotz, “Mate Selection - A Selection for a Handicap,” Journal of TheoreticalBiology, 53 (1975), 205–214.

Zwiebel, Jeffrey, “Corporate Conservatism and Relative Compensation,” Journal of PoliticalEconomy, 103 (1995), 1–25.

36

Table 1: Logit regression results

The dependent variable is a dummy variable equal to 1 if the CEO of the company won anaward during each month. Market value (CRSP log(abs(PRC) ∗ SHROUT/1000)) is measuredtwo months prior to the award month and is in log form. Book-to-Market ratio (COMPUSTATSEQ/(PRCC F ∗CSHO)) is measured at the end of the last fiscal year to end at least six monthsprior to the award month. Returns x y are the total compound returns from the yth to the xth

month prior to the award month. Pseudo R-Square is the coefficient of determination as in Coxand Snell (1989, pp. 208209)

Parameter Estimate Standard Error Wald Chi-Square Pr > ChiSq

Market value 0.8820∗∗∗ 0.0454 377.4904 0.0001Book-to-market -0.2575∗∗∗ 0.0534 23.2588 0.0001Returns 2 3 0.4447∗∗ 0.1858 5.7304 0.0167Returns 4 6 1.0504∗∗∗ 0.2797 14.1008 0.0002Returns 7 12 0.5945∗∗∗ 0.1038 32.8067 0.0001Returns 13 36 0.0376∗ 0.0198 3.6203 0.0571Female (dummy) 0.9895∗ 0.5643 3.0745 0.0795CEO age -0.1181∗∗∗ 0.0091 166.8328 0.0001CEO tenure 0.0298∗∗∗ 0.0100 8.7963 0.0030

Industry dummies YesYear dummies YesAward-type dummies Yes

Pseudo R-Square 0.7392Observations 55,941

37

Tab

le2:

Desc

ripti

ve

Sta

tist

ics

of

aw

ard

win

ners

and

pre

dic

ted

win

ners

Th

esa

mp

lein

clu

des

win

ner

san

dp

red

icte

d-w

inn

ers

inal

lm

onth

sin

wh

ich

aC

EO

award

isco

nfe

rred

.P

red

icte

dw

in-

ner

sar

em

atch

edby

Mar

ket

valu

e,B

ook

-to-

mar

ket

,re

turn

s2

3,re

turn

s4

6,R

etu

rns

712

,R

etu

rns

13

36,

Fem

ale,

Age

and

Ten

ure

.R

etu

rnst

and

ard

dev

iati

onan

dId

iosy

ncr

atic

vola

tili

tyra

tio

are

bas

edon

dai

lyst

ock

retu

rns

for

each

fisc

al

year.

Idio

syn

crat

icvo

lati

lity

rati

ois

defi

ned

asth

ero

ot-m

ean

-squ

are

erro

rof

firm

retu

rnw

ith

resp

ect

toin

du

stry

retu

rns,

div

ided

by

tota

lre

turn

stan

dar

dd

evia

tion

.C

apit

alex

pen

dit

ure

(CA

PX

),R

&D

exp

end

itu

re(X

RD

),an

dto

tal

inve

stm

ent

(CA

PX

+X

RD

)ar

en

orm

aliz

edby

tota

las

sets

(AT

).M

issi

ng

valu

esof

R&

Dex

pen

dit

ure

are

set

toze

roif

cap

ital

exp

end

itu

reis

pos

itiv

e.S

har

eow

ner

ship

isnor

mal

ized

by

shar

esou

tsta

nd

ing.

Win

ner

sP

redic

ted

win

ner

sD

iffer

ence

inm

eans

NM

ean

Med

ian

NM

ean

Med

ian

W-P

Pr>|t|

Mar

ket

valu

e27

09.

796

9.82

1727

08.

804

8.94

940.

9921

∗∗∗

0.00

01B

ook

-to-

mar

ket

270

0.30

10.

2579

270

0.27

530.

3252

0.02

570.

8292

Ret

urn

s2

327

00.

0684

0.05

0927

00.

2518

0.04

5-0

.183

4∗∗

0.03

68R

eturn

s4

627

00.

0706

0.06

5827

00.

0678

0.05

440.

0027

90.

8843

Ret

urn

s7

1227

00.

2695

0.16

4127

00.

3182

0.15

38-0

.048

70.

3683

Ret

urn

s13

3627

01.

2188

0.52

4427

00.

6545

0.38

620.

5643

∗∗∗

0.00

62F

emal

e(d

um

my)

270

0.01

480

270

00

0.01

48∗∗

0.04

48C

EO

age

270

55.1

556

5627

049

.211

149

5.94

44∗∗

∗0.

0001

CE

Ote

nure

270

7.21

486

270

5.74

444

1.47

04∗∗

0.01

64

Tob

ins

Q27

03.

7764

2.01

4127

02.

946

1.75

330.

8304

∗0.

0721

Ret

urn

stan

dar

ddev

iati

on(%

)27

038

.424

731

.647

327

040

.835

35.4

063

-2.4

103

0.18

09Id

iosy

ncr

atic

vola

tility

rati

o27

00.

8637

0.88

3327

00.

8863

0.89

07-0

.022

6∗∗∗

0.00

02R

&D

exp

endit

ure

260

0.04

530.

0217

255

0.03

70.

0068

0.00

835

0.16

59C

apit

alex

pen

dit

ure

260

0.06

60.

0575

255

0.07

40.

0589

-0.0

0793

0.11

98In

vest

men

t26

00.

1114

0.09

3725

50.

1109

0.08

960.

0004

190.

9589

Shar

eow

ner

ship

(%)

248

3.32

640.

1971

265

3.73

070.

1301

-0.4

043

0.58

09

38

Table 3: CEO turnover of award winners and predicted winners

Turnover is conditional on that the firm remains in the sample (i.e. was not delisted oracquired). Turnover is imputed from ExecuComp data. If a CEO at year t is reportedas an executive but not as a CEO (CEOANN 6= CEO) in year t+1, he is assumed tostep down. If a CEO in year t is no longer reported in year t+1 he is assumed to eitherretire or resign, according to the ‘REASON’ provided by ExecuComp. Note that bothresignations and retirements can be voluntary (i.e. to take a better position elsewhere)so one should be careful when interpreting “resigned” as “fired”

Winners Predicted winners

Year Stepped down Resigned Retired Stepped down Resigned Retired

+1 7.1% 0.4% 2.6% 7.8% 1.5% 1.1%+2 8.3% 0.4% 4.5% 4.6% 2.3% 2.7%+3 6.2% 1.2% 3.1% 1.6% 2.0% 1.2%+4 4.8% 0.4% 3.6% 5.0% 0.8% 2.9%

39

Tab

le4:

Capit

al

exp

endit

ure

,R

&D

exp

endit

ure

,and

tota

lin

vest

ment

Th

eta

ble

test

sw

het

her

firm

sw

ith

awar

d-w

inn

ing

CE

Os

chan

geth

eir

risk

-tak

ing

beh

avio

rd

iffer

entl

yth

and

osi

mil

ar

non

-win

ner

s.M

ore

spec

ifica

lly,

for

each

vari

able

ofin

tere

st,

Ico

mp

are

its

chan

geov

erti

me

am

ong

award

win

ner

sto

that

ofp

red

icte

d-w

inn

ers.

Ico

mp

are

the

last

kn

own

info

rmat

ion

pri

orto

the

awar

d(y

ear

-1)

toea

chof

the

fou

rye

ars

follow

ing

the

award

(yea

rs0

to+

3).

Pre

dic

ted

win

ner

sar

em

atch

edat

year

-1by

Mark

etva

lue,

Book

-to-

mar

ket,

Ret

urn

s2

3,R

etu

rns

46,

Ret

urn

s7

12,

Ret

urn

s13

36,

Fem

ale,

Age

and

Ten

ure

.R

etu

rns

xy

are

the

tota

lco

mp

oun

dre

turn

sfr

omth

eyth

toth

exth

mon

thp

rior

toth

eaw

ard

mon

th.

Cap

ital

exp

end

itu

re(C

AP

X),

R&

Dex

pen

dit

ure

(XR

D),

an

dto

talin

vest

men

t(C

AP

X+

XR

D)

are

nor

mal

ized

by

tota

las

sets

(AT

).M

issi

ng

valu

esof

R&

Dex

pen

dit

ure

are

set

toze

roif

cap

ital

exp

end

itu

reis

pos

itiv

e.B

ias-

adju

sted

diff

eren

ceu

ses

an

auxil

iary

regr

essi

on

of

the

outc

ome

vari

able

on

the

matc

hin

gco

vari

ates

follow

ing

Ab

adie

-Im

ben

s.

Cap

ital

exp

endit

ure

R&

Dex

pen

dit

ure

Tot

alin

vest

men

t

Bia

s-ad

just

edB

ias-

adju

sted

Bia

s-ad

just

edN

Diff

eren

cediff

eren

ceD

iffer

ence

diff

eren

ceD

iffer

ence

diff

eren

ce

diff

[-1,

+0]

205

-0.0

0083

-0.0

0315

-0.0

0438

∗-0

.004

71∗∗

-0.0

0521

-0.0

0787

∗

(-0.

24)

(-0.

83)

(-1.

94)

(-2.

07)

(-1.

21)

(-1.

71)

diff

[-1,

+1]

160

0.01

02∗∗

0.01

6∗∗∗

-0.0

0456

∗-0

.003

360.

0056

90.

0127

∗∗

(2.2

8)(2

.86)

(-1.

83)

(-1.

33)

(1.0

6)(1

.97)

diff

[-1,

+2]

123

0.00

694

0.00

858

-0.0

0891

∗∗∗

-0.0

0714

∗∗-0

.001

960.

0014

4(1

.46)

(1.4

3)(-

2.76

)(-

2.17

)(-

0.33

)(0

.21)

diff

[-1,

+3]

940.

0102

∗0.

0151

∗-0

.009

78∗∗

-0.0

0598

0.00

0373

0.00

909

(1.7

1)(1

.87)

(-2.

47)

(-1.

46)

(0.0

5)(0

.92)

40

Table 5: CEO awards and R&D expenditure

Panel regression of R&D expenditure controlling for in-vestment opportunities and cashflow. Cashflow is mea-sured by Operating Income Before Depreciation (COMPU-STAT OIBDP), and investment opportunities are measuredby Tobins-Q (COMPUSTAT (AT − SEQ + (PRCC F ∗CSHO))/AT ). The post-award dummy is set to 1 onlyduring the 3 years following an award. Regression includesfirm and year fixed effects.

Parameter Estimate t Value Pr > |t|

Intercept 55.849 0.37 0.7078Award (last 3 years) -79.928∗∗∗ -9.66 0.0001Q -0.007 -0.32 0.7492Cashflow 0.068∗∗∗ 72.47 0.0001

Firm fixed effects YesYear fixed effects Yes

R-Square 0.8892Observations 46,730

41

Table 6: Decomposition of stock return volatility

Panel A: Decomposition of stock return volatility for winnersFor each fiscal year relative to the award, I regress daily stock returns either on theirrespective industry returns (Fama-French 48-industry classification) or on the risk factorsof Fama-French (market excess return, small market capitalization minus big, high book-to-price ratio minus low, and momentum). The idiosyncratic volatility ratio is defined asthe resulting root-mean-square error divided by total return standard deviation.

Stock return Idiosyncratic volatilityN standard deviation Industry beta ratios WRT industry

diff[-1,+0] 248 0.4489 0.0673∗∗ -0.0167∗∗∗

(0.6) (2.16) (-5.2)diff[-1,+1] 220 0.4905 0.0975∗∗∗ -0.0326∗∗∗

(0.47) (2.61) (-6.99)diff[-1,+2] 183 -0.5414 0.1059∗∗ -0.0427∗∗∗

(-0.34) (2.47) (-8.85)diff[-1,+3] 158 -0.9074 0.1095∗∗∗ -0.0511∗∗∗

(-0.47) (2.63) (-9.88)

Panel B: Idiosyncratic volatility ratios WRT Fama-French factorsBias-adjusted difference uses an auxiliary regression of the outcome variable on the match-ing covariates following Abadie-Imbens.

Predicted Bias-adjustedN Winners winners Difference difference

diff[-1,+0] 224 -0.024∗∗∗ -0.00905∗ -0.015∗∗ -0.0157∗∗

(-5.02) (-1.85) (-2.19) (-2.28)diff[-1,+1] 178 -0.0467∗∗∗ -0.0227∗∗∗ -0.024∗∗ -0.0363∗∗∗

(-6.59) (-3.67) (-2.55) (-3.9)diff[-1,+2] 137 -0.0621∗∗∗ -0.0395∗∗∗ -0.0226∗∗ -0.0242∗∗

(-7.82) (-5.26) (-2.07) (-2.26)diff[-1,+3] 107 -0.0543∗∗∗ -0.045∗∗∗ -0.00934 -0.029∗∗

(-6.72) (-4.94) (-0.77) (-2.23)

42

Table 7: Share ownership of award-winning CEOs

Panel A: Share ownershipShare ownership equals the number of shares held (excluding restricted stock),normalized by shares outstanding. Bias-adjusted difference uses an auxiliaryregression of the outcome variable on the matching covariates following Abadie-Imbens.


diff[-1,+0] 147 -0.2032∗∗∗ 0.2525 -0.4558 -0.4837(-2.66) (0.69) (-1.22) (-1.23)

diff[-1,+1] 110 -0.2758∗ 0.2499 -0.5257 -0.9515∗

(-1.72) (0.62) (-1.22) (-1.94)diff[-1,+2] 83 -0.565∗∗∗ 0.3139 -0.8789 -2.0756∗∗∗

(-2.69) (0.49) (-1.31) (-2.84)diff[-1,+3] 65 -0.9023∗∗∗ 0.6552 -1.5575 -4.3443∗∗∗

(-2.88) (0.56) (-1.29) (-2.99)

Panel B: Total equity holdingTotal equity holding equals the total dollar value of direct share ownership(including restricted stock) and option holdings, normalized by market capi-talization. I estimate the value of options held at the end of each year fromExecuComp using the procedure outlined in Murphy (1999). Bias-adjusted dif-ference uses an auxiliary regression of the outcome variable on the matchingcovariates following Abadie-Imbens.


diff[-1,+0] 147 -0.2065∗∗∗ 0.9512 -1.1577 -1.1196(-2.8) (1.36) (-1.65) (-1.52)

diff[-1,+1] 110 -0.2724 0.8938 -1.1662 -1.349(-1.66) (1.15) (-1.47) (-1.56)

diff[-1,+2] 83 -0.5493∗∗ 0.3071 -0.8564 -1.7799∗

(-2.53) (0.36) (-0.98) (-1.88)diff[-1,+3] 65 -0.9801∗∗∗ 0.3112 -1.2913 -2.9972∗∗

(-3.06) (0.28) (-1.13) (-2.34)

43

Table 8: Vega elasticity and return variability

Panel regression of return standard deviation on Vega elasticity. Vegaelasticity equals the percentage change in value of outstanding options fora one percentage-point increase in volatility, and is estimated at the end ofeach year from ExecuComp using the procedure outlined in Murphy (1999).The post-award dummy is set to 1 only during the 3 years following anaward. Regression includes firm and year fixed effects, and standard errorsare clustred by firm.


Intercept 40.56∗∗∗ 146,897 0.0001Award dummy (last 3 years) 1.14 1.09 0.2768Vega elasticity 3.57∗∗∗ 13.58 0.0001Vega elasticity * Award dummy -166.61∗∗ -2.17 0.0298

Firm fixed-effects YesYear fixed-effects Yes

R-square 0.7359Observations 19,622

Vega elasticity*(1+ Award) -163.04∗∗ -2.13 0.0335

44

Table 9: Cash-weight-in-compensation of winners vs. predicted winners

cash-weight-in-compensation is defined as Salary + Bonus + All Other Compensa-tion that is paid or payable in cash, divided by total compensation (EXECUCOMP(SALARY + BONUS + ALLOTHPD)/TDC1). Bias-adjusted difference uses anauxiliary regression of the outcome variable on the matching covariates followingAbadie-Imbens.


diff[-1,+0] 226 -0.011 -0.036∗ 0.0256 0.0291(-0.59) (-1.92) -0.97 -1.11

diff[-1,+1] 169 -0.015 -0.088∗∗∗ 0.0739∗∗ 0.0889∗∗∗

(-0.61) (-3.97) -2.26 -2.7diff[-1,+2] 128 -0.032 -0.053∗ 0.0208 0.0356

(-1.14) (-1.83) -0.52 -0.91diff[-1,+3] 90 -0.074∗∗ -0.147∗∗∗ 0.0727 0.0927∗

(-2.24) (-3.74) -1.42 -1.88

45

Table 10: Cash-weight-in-compensation

Panel regression of cash-weight-in-compensation (EXECUCOMP (SALARY +BONUS + ALLOTHPD)/TDC1) on total compensation (EXECUCOMPTDC1). Regression includes year and firm fixed effects.


Award dummy (last 3 years) -0.0327∗∗∗ -2.85 0.0044Total compensation -7.10E-06∗∗∗ -34.8 0.0001Total compensation * Award dummy 4.37E-06∗∗∗ 13.88 0.0001

R-Square 0.4867Observations 21,837

Total compensation*(1+Award dummy) -2.70E-06∗∗∗ -10.77 0.0001

46

Tab

le11

:C

ash

-weig

ht-

in-c

om

pensa

tion

by

corp

ora

tegovern

an

ce

Pan

elre

gres

sion

ofca

sh-w

eigh

t-in

-com

pen

sati

on(E

XE

CU

CO

MP

(SALARY

+BONUS

+ALLOTHPD

)/TDC

1)

onto

tal

com

pen

sati

on

(EX

EC

UC

OM

PTDC

1)an

dfirm

an

dye

arfi

xed

effec

ts,

by

corp

orat

ego

vern

ance

Bad

-gov

ernan

cefirm

s(G

IM>

9)7<

GIM

<=

9G

ood-g

over

nan

cefirm

s(G

IM<

=7)

Par

amet

erE

stim

ate

tV

alue

Pr>|t|

Est

imat

et

Val

ue

Pr>|t|

Est

imat

et

Val

ue

Pr>|t|

Aw

ard

dum

my

-0.0

529∗

∗∗-2

.98

0.00

29-0

.085

97∗∗

∗-3

.69

0.00

02-0

.065

∗∗-2

.36

0.01

82T

otal

com

pen

sati

on-1

.30E

-05∗

∗∗-2

7.87

0.00

01-8

.50E

-06∗

∗∗-1

7.54

0.00

01-1

.00E

-05∗

∗∗-1

7.67

0.00

01T

otal

com

p*

Aw

ard

8.08

E-0

6∗∗∗

9.73

0.00

016.

81E

-06∗

∗∗11

.28

0.00

015.

44E

-06∗

∗∗5.

210.

0001

R-S

quar

e0.

5244

0.59

420.

6O

bse

rvat

ions

7,75

74,

463

4,44

5

Tot

alco

mp*(

1+A

war

d)

-5.2

0E-0

6∗∗∗

-7.3

90.

0001

-1.7

0E-0

6∗∗∗

-40.

0001

-4.7

0E-0

6∗∗∗

-5.1

30.

0001

47

0.75

0.8

0.85

0.9

0.95

-3 -2 -1 0 1 2 3

Idio

syn

crat

ic v

ola

tilit

y ra

tio

Year

Idiosyncratic volatility WRT Industry (mean) Idiosyncratic volatility WRT Industry (median)

Idiosyncratic volatility WRT Fama-French (mean) Idiosyncratic volatility WRT Fama-French (median)

Figure 1: Decomposition of return variability for firms with award-winning CEOsGraph plots the mean and median idiosyncratic volatility ratios of firms with award-winning CEOs.

Idiosyncratic volatility ratio is defined as the root-mean-square error of firm return with respect

to either industry returns or Fama-French factors, divided by total return standard deviation.

Idiosyncratic volatility ratios are based on daily stock returns for each fiscal year, relative to the

fiscal year during which the award was granted. For consistency, the graph includes only firms for

which data are available for the full window [-3:+3] (N=70).

48

0

0.5

1

1.5

2

2.5

-3 -2 -1 0 1 2 3

Bet

a W

RT

Ind

ust

ry

Year

Mean 5% percentile 95% percentile

Figure 2: Dispersion of industry betas of firms with award-winning CEOsGraph plots the mean and 5th and 95th percentiles of factor loadings on idustry returns of stock

returns of firms with award-winning CEOs. I regress daily stock returns on their respective industry

returns (Fama-French 48-industry classification). Industry betas are based on daily stock and

industry returns for each fiscal year, relative to the fiscal year during which the award was granted.

For consistency, the graph includes only firms for which data are available for the full window [-3:+3]

(N=70).

49

0.2

0.4

0.6

0.8

1

1.2

-3 -2 -1 0 1 2 3

Ow

ne

rsh

ip (

%)

Year

Share ownership Total equity holding

Figure 3: Median ownership of award-winning CEOsGraph plots median share ownership and total equity holding of winners. Share ownership equals

the number of shares held (excluding restricted stock), normalized by shares outstanding. Total

equity holding equals the total dollar value of direct share ownership (including restricted stock) and

option holdings, normalized by market capitalization. I estimate the value of options held at the

end of each year from ExecuComp using the procedure outlined in Murphy (1999). For consistency,

the graph includes only executives for which data are available for the full window [-3:+3] (N=83).

50

0

5,000

10,000

15,000

20,000

25,000

30,000

-3 -2 -1 0 1 2 3

Co

mp

en

sati

on

($

1,0

00

s)

Year

Total Compensation (mean) Total Compensation (median)

Options Granted (mean) Options Granted (median)

Figure 4: Total compensation and option compensation of award-winning CEOsGraph plots mean and median total compensation and option compensation of award-winning

CEOs. Total Compensation includes Salary + Bonus + Other Annual + Restriced Stock Grants

+ LTIP Payouts + All Other + Value of Option Grants. Option compensation is the aggregate

value of stock options granted to the executive during the year as valued using Standard & Poor’s

Black-Scholes method. For consistency, the graph includes only firms for which data are available

for the full window [-3:+3] (N=64).

51

ceo social status and risk taking - contesttheory.org

Documents