CEO’s total wealth characteristics and implications on firm risk*
Timo Korkeamäki
Hanken School of Economics
P.O. Box 479
00101 Helsinki, Finland
[email protected]
Eva Liljeblom
Hanken School of Economics
P.O. Box 479
00101 Helsinki, Finland
[email protected]
Daniel Pasternack
Hanken School of Economics
P.O. Box 479
00101 Helsinki, Finland
[email protected]
March 22, 2017
JEL classification: G31; G32; J33
Keywords: CEO wealth, firm risk
*
We thank Lee Biggerstaff, Mara Faccio, Martin Holmén, Karin Thorburn, an anonymous referee, and seminar
participants at Aalto University Business School, Hanken School of Economics, University of Edinburgh, and
the 2014 FMA Annual meetings in Nashville, TN for helpful comments.
CEO’s total wealth characteristics and implications on firm risk
ABSTRACT
We study the connections between firm risk and the CEO’s personal wealth characteristics,
using a unique dataset on CEO wealth and its components. Consistent with decreasing
absolute risk aversion, we find that wealthier CEOs are associated with higher risk firms.
Riskier firms tend to have CEOs whose wealth is more independent of the firm. We also find
that CEOs with high personal portfolio betas run firms with higher betas. CEO’s tenure is
negatively associated with firm risk measured either as beta, idiosynchratic risk, or volatility
of accounting profitability. A possible interpretation is that risk-averse managers are better
able to imprint their risk preferences on the firm over time. Stronger corporate governance
weakens the connection between CEO wealth characteristics and firm risk.
2
1. Introduction
Risks that a manager takes as part of her job are likely to be influenced by personal
characteristics such as age, but also by the size and the decomposition of the manager’s
wealth. Personal overall attitudes towards risk tend to also affect managers’ decision making
(Cain and McKeon, 2016). Empirical studies on the connection between managerial wealth
and firm risk-taking are typically based on managerial ownership of the firm, either through
shares or executive options (see e.g. Guay 1999, Rajgopal and Shevlin 2002, Coles, Daniel,
and Naveen 2006, and Low 2009).1 However, endogeneity issues cloud the interpretation of
the relation between equity-based managerial wealth and firm risk, since it can be assumed
that firms often design executive compensation schemes to motivate managers to optimal
risk taking. Recently, Wei and Yermack (2011) study CEOs’ inside debt positions. Their
results indicate that publication of large CEO inside debt holdings lead to reductions in firm
risk, to wealth transfers from equity to debt, and to reductions in enterprise value. Moreover,
Cronqvist, Makhija and Yonker (2012) study CEOs’ personal leverage using data on their
most recent home purchases, and find support for endogenous matching of between CEO
and firm leverage. While all these recent studies shed light on the connection between
managerial wealth and corporate risk-taking, they focus only on parts of CEO wealth, as data
restrictions have not allowed studying the effects of CEOs’ total wealth. The rare exceptions
to consider CEOs’ total wealth include Becker (2006), who studies the effects of total wealth
on performance based compensation among Swedish CEOs, and Elsilä, et al. (2013), who
observe the connection between total wealth of Swedish CEOs, and their firms’ subsequent
accounting performance.
1
Share ownership is typically perceived as providing increased incentives for risk taking. The relationship
between options and risk seeking is more complex, see e.g. Guay (1999), Carpenter 2000, and Ross (2004).
We contribute to the literature by studying the connections between CEOs’ total
wealth and corporate behavior. By using a unique dataset on total CEO wealth, we at least
partly circumvent the endogeneity problems that are inherent with equity compensation
packages designed by the firm. Our detailed data set also allows us to study the effects of
decomposition of CEOs’ assets in great detail. Our data set includes the CEOs of all Finnish
publicly listed firms, and in addition to holdings in the CEOs’ own firms, it also contains
parts of the CEOs’ wealth (and indebtedness) that are not directly attributable to the firm and
its compensation structure. The data allows us to see all individual holdings such as the
CEOs’ executive stock options and private investments in different individual shares of
listed and unlisted firms (item by item), mutual funds, real estate and housing assets, land
and forest.
We find that wealthier CEOs are associated with riskier firms, a result in line with
decreasing absolute risk aversion. We also find support for the hypothesis that managers
whose wealth is more independent of the firm are associated with riskier firms, a result in
line with decreasing relative risk aversion. We further find that the CEO’s tenure is
negatively associated with firm risk measured either as beta, idiosynchratic risk, or volatility
of accounting profitability. A possible interpretation is that risk-averse managers are better
able to imprint their risk preferences on the firm over time. Our findings are also consistent
with Berger, et al. (1997) suggestion that managers with longer tenure become more
entrenched, which increases their risk aversion. Prior studies suggest that risk aversion
increases with age,2 but our results are robust to controlling for age, and they suggest tenure
with the firm, instead of age, as the primary driver of managers’ risk attitude. We also
2
See e.g. Halek and Eisenhauer (2001), and Serfling (2014).
2
consider the matching relationship between firm risk and manager’s portfolio risk, and find
firms with higher betas have CEOs with high beta personal stock portfolios.
The rest of the paper is organized as follows. In section two, we provide a literature
review and discuss our hypotheses regarding the relation between measures describing the
decomposition of the CEOs’ total wealth, and firm characteristics. In section three, we
describe our data. In section four, we study the relationship between risk characteristics of
the CEOs’ total wealth, and risk characteristics for the firm. Section five provides a summary
and conclusions.
2. Literature review and hypothesis development
Managerial attitudes toward risk can affect corporate decisions in a variety of ways.
In a recent study Cain and McKeon (2016) report that CEOs with private pilot licenses make
more risk-seeking decisions on corporate acquisitions, capital structure, and other corporate
policies. Prior studies on the relationship between components of managerial wealth and
corporate risk concentrate on the effects of managerial equity incentives. They typically
consider executive compensation design, and whether the compensation plans reach their
goals regarding incentive alignment between managers and shareholders. Guay (1999)
studies the sensitivity of CEO’s stock and option holdings to firm risk. His results support
the idea that managers are more willing to invest in risk-increasing projects when the
convexity of their payoffs, in the relation between their wealth and stock price, increases.
Guay (1999) also finds that firm risk is positively related to the sensitivity of managers'
wealth to equity risk. Cohen, Hall, and Viveira (2000) report that significant increases in
3
executive option holdings are connected to subsequent increases in firm risk (stock
volatility). 3 Rajgopal and Shevlin (2002) find that among oil and gas producing firms,
executive option incentives tend to encourage future exploration risk taking. In a study of
corporate hedging, Knopf, Nam, and Thornton (2002) show that firms’ use of derivatives is
negatively (positively) related to vega (delta) of managers’ position in the firm. Kim and Lu
(2011) find a hump-shaped relation between CEO ownership and R&D investments when
external governance is weak, and interpret it as empirical support for the theory that a high
wealth-performance sensitivity leads to insufficient risk-taking.
However, in many studies on the relationship between firm risk and managerial
ownership, the direction of causality can be questioned. Coles, Daniel, and Naveen (2006)
point out that the design of managerial incentives is likely to be influenced by risk and
policies of the firm. They address the apparent endogeneity issues that managerial
compensation has both with incentives, and with the resulting value-critical managerial
investment and debt decisions, i.e. decisions which influence firm risk. Controlling for the
endogenous feedback effects of firm risk on compensation, they find that higher prior vega
(sensitivity to stock volatility in managerial compensation schemes) is associated with riskier
policy choices, and higher leverage. Similar results are also reported by Chava and
Purnanandam (2010) concerning both delta (vega), and changes in cash holdings and
leverage. They find that higher CEO delta is associated with less risky strategies, while
higher CEO vega is connected with more risky strategies. Using an external event as a
remedy against endogeneity problems, Low (2009) finds that a greater protection for takeovers for Delaware firms resulted in reduced firm risk, mainly among firms with low CEO
3
Contradictory evidence concerning the relationship between option compensation and firm risk is offered by
Hayes et al (2012). Studying changes in option compensation before and after FAS 123R, they find little
evidence of a connection between changes in convexity and changes in firm risk.
4
wealth to stock return volatility (low vega). Most of the decrease in risk was due to a
decrease in idiosyncratic risk. Also Armstrong and Vashistha (2012) report evidence in
support of managers’ preference for systematic rather than idiosyncratic risk taking (when
being incentivized by the high vega of their option portfolio), since systematic risk can be
hedged by trading the market portfolio. The managerial dislike for idiosyncratic risk is also
documented by Panousi and Papanikolaou (2012), who find that when idiosyncratic risk
rises, firm investment falls, and more so when managers own a larger fraction of the firm.
The negative effect of managerial risk aversion is mitigated if executives are compensated
with options rather than shares.
Other studies on the effects of CEO wealth components on firm risk consider the
relationship between CEO investments in firm debt, and firm characteristics (risk and
leverage). Wei and Yermack (2011) study stockholders’ and bondholders’ reactions to
publications of CEOs’ inside debt positions (pensions and deferred compensation). They
find evidence of wealth redistribution from stockholders to bondholders, which is consistent
with risk shifting. They report a significant fall in volatility of both securities for those firms
whose CEOs have significant internal debt positions. As a test variable in their study, they
use the “relative incentive ratio” (similar to the k ratio derived in Edmans and Liu 2011),
measuring how a unit increase in the value of the firm raises the value of the CEO’s inside
debt and equity claims, scaled by a similar measure for the firm’s external debt and equity.
Cronqvist, Makhija, and Yonker (2012) study how CEO’s debt from their most recent
primary home purchase matches with firm leverage. They find support for the notion that
individual borrowing decisions are related to corporate borrowing decisions, which lends
support for the behavioral hypothesis of endogeneous matching as CEOs with certain
personal characteristics match with firms that demand similar characteristics. Their results
5
are also consistent with the idea that CEOs can imprint their personal preferences on the
capital structures of the firms they manage. Other studies of inside debt such as Chava,
Kumar and Warga (2010), Bolton, Mehran and Shapiro (2010), and Cassell, Huang, Sanchez
and Stuaart (2012) lend further support for the view that larger amounts of CEO inside debt
(pension benefits and deferred compensation) are associated with reduced firm risk. Inside
debt holdings are connected with lower future stock volatility, lower financial leverage, more
stringent bond covenants, narrower credit default swap spreads, a higher extent of firm
diversification, and higher asset liquidity.4
Although size and composition of CEO’s total wealth is crucial in theoretical utilitybased models of managerial incentives (see e.g. Lambert, Larcker and Verracchia, 1991, and
Kahl, Lui and Longstaff, 2003), data constraints tend to restrict empirical applications to
poor proxies for managerial wealth. Exceptions include Becker (2006), who study executive
compensation using Swedish data on CEOs’ total wealth (but not its decomposition),
Dittman and Maug (2007), who proxy total wealth by a time series for all compensation
received by the U.S. executives in their study, and Elsilä, et al. (2013) who study the effect
of the proportion of CEOs’ investment in their own firm on future accounting performance.
While the Elsilä, et al. (2013) paper uses the decomposition of managers’ wealth to
investment in own firm, other stock holdings, and non-stock wealth, we are not aware of any
prior studies utilizing complete data on CEOs’ portfolio compositions, including complete
data within the stock portfolio.
Our hypotheses build on both utility theory and behavioral finance. Our first
hypothesis is that, consistent with decreasing absolute risk aversion, wealthier managers
4
See also Anantharaman Fang and Gong (2010), Chen, Dou, and Wang (2010), and Wang, Xie, and Xin (2010)
for evidence of a relationship between a lower cost of debt and higher CEO inside debt.
6
should be able to bear higher project risk, when project size is controlled for. This argument
is similar to Becker (2006), who argues that wealth data provides a good proxy for absolute
risk aversion. Assuming constant or decreasing relative risk aversion, we also expect that
managers who have a smaller proportion of their wealth tied to the firm they manage should
be able to bear higher project risk, when project size is controlled for. If increasing firm risk
is optimal from the equity owners’ perspective, we expect that lower managerial risk
aversion would be manifested in higher firm risk. Naturally, executive compensation may be
used to motivate even more risk-averse managers to bear more firm risk.
Hypothesis 1: Controlling for firm size and managerial incentives, wealthier
managers are associated with more risky firms.
Hypothesis 2: Controlling for firm size and managerial incentives, managers with
portfolios more independent of the firm are associated with more risky firms.
As our unique data set allows us not only to observe total managerial wealth level and its
composition, but also to calculate risk characteristics for the CEO’s total portfolio, more direct tests
of the relationship between the risk level chosen by the manager, and firm risk, are possible. 5 We
want to compare two alternative hypotheses, those of behavioral consistency versus hedging. The
“behavioral consistency theory”6 suggests that an individual behaves in a consistent manner across
different comparable situations. Especially for risky choices, support for behavioral consistency has
been reported in the finance literature. Cronqvist, Makhija, and Yonker (2012) find support for the
behavioral consistency in the form of a match between the leverage choices of CEOs, and their firms.
Other evidence from the financial literature include Barsky, Juster, Kimball and Shapiro (1997), who
5
Our data set does not have a long time dimension, which makes it difficult to test for causality. Our
hypotheses do therefore not address causality issues (although some of our additional tests may shed some light
on the direction of causality).
6
See e.g. Allport (1937, 1966), Epstein (1979, 1980) , and Funder and Colvin (1991).
7
report on a positive correlation between measured risk tolerance and several examples of risky
behaviors such as risky investment activity, risky entrepreneurial activity, as well as tobacco and
alcohol consumption. Also e.g. the behavioral characteristic of overconfidence seems to influence an
individual’s decisions consistently on a broad basis (Malmendier and Tate, 2005, Ben-David,
Graham, and Harvey, 2007). In our case, behavioral consistency would suggest that the risk profiles
of the CEOs total personal wealth and that of their firm match.
Hypothesis 3 (behavioral consistency): There is a positive relationship between risk of the
firm, and that of the personal holdings of the CEO.
A hypothesis that contradicts behavioral consistency follows straightforward from the
standard portfolio theory. If a CEO’s income stream comes from a risky firm, and if he moreover has
executive options in such firm, portfolio concerns would imply that CEOs in riskier firms would
invest the personal part of their wealth in less risky assets, or a better diversified portfolio, i.e. hedge
firm risk with the remaining part of their portfolio.7
Managerial holdings in our data set can either be stock holdings or, more often, executive
options. While stocks have a delta one sensitivity towards the stock price of the firm, the executive
options in Finland are often issued out-of-the money and thus have a small delta towards the stock
price of the firm at the issuance. Several studies suggest that managers tend to avoid risk when the
delta of their payoffs is higher (Knopf et al, 2002; Parrino, et al., 2005; Chava and Purnanandam,
2010). To capture the effect of portfolio delta on risk taking, we create a crude measure where we
sum up the Black-Scholes values of all in-the-money executive options, i.e. all options that have a
delta higher than 0.5. We expect a negative relationship between the amount of managerial wealth in
high-delta options and firm risk.
7
Faccio, Marchica, and Mura (2011) report this type of behavior for large shareholders. They find that firms
controlled by diversified large shareholders undertake riskier investments.
8
Hypothesis 4: There is a negative relationship between risk of the firm, and that of the CEO
wealth invested in higher-delta options.
Firm risk-taking that is driven by CEO characteristics can reflect agency problems, whenever
CEO risk preferences deviate from those of the firm’s shareholders. In presence of block holders, it is
more difficult for the CEO to follow policies that conflict with shareholder preferences, as the block
holders tend to monitor management, and also retain decision power through board seats. We
therefore hypothesize that the relationships noted in Hypotheses 1 and 2 are weaker when the firm’s
ownership is more concentrated.
Hypothesis 5: The effects that managerial wealth and proportion of CEO wealth invested in
own firm have on firm risk are weaker in firms with more concentrated ownership.
3. Data and descriptive statistics
We use a unique data set of CEO wealth, wealth composition and compensation. Our
data cover the CEOs of all companies listed on the Helsinki Stock Exchange between 2002
and 2005. The wealth data indicate holdings in equities and other securities at the
security/single security level, executive options, real-estate holdings (including land and
forest). We also have similar information on each CEOs personal indebtedness. Furthermore,
the data include information on the total taxable income and capital income (capital gains,
rental income, interest income, etc.).
We obtain the wealth and income data from the Finnish tax authority, who kept
complete wealth records until the wealth tax was abolished in Finland in 2006.8 In the wealth
8
Finnish tax data can be considered very reliable. Firstly, tax records are public in Finland, and they generate a
lot of public scrutiny and media attention. A CEO who is concerned about her reputation is thus unlikely to
9
taxation, the tax authorities used so-called taxation values for wealth items. These values
were, however, converging towards actual market values at the time of our study period. We
take the taxation values as given for real estate and other wealth such as forest ownership,
but recalculate actual market values for equities (based on the exact information of stock
ownership for each CEO at each year-end) as well as executive options, using market data
from the stock market as well as the Black-Scholes model for the options. 9 The lack of
market value data on specific real estate items introduces some bias towards equities in our
data set. Also, poorly diversified managers, the use of the BS model for the executive
options may introduce an upward bias since the private value (for the CEO) of such options
may be less than their market value (Meulbroek 2001). In our robustness tests, we therefore
use the Meulbroek (2001) model with actual data on the CEO’s degree of diversification (the
part of her total wealth invested in the firm employing the CEO).
Our executive option data come from Alexander Corporate Finance Oy, a dominant
consultant of companies in Finland regarding ESOPs. Their data set covers the entire listed
market, as they have complemented their data set with information on the few programs of
listed firms that were not consulted by Alexander Corporate Finance Oy. 10 Our data set thus
includes all stock option plans for all Finnish listed firms during our sample period. The
option data contain information regarding the introduction date of stock option plans, the
target group, vesting periods and contract maturities, the exercise prices, the number of
provide wrongful information in her tax returns. Secondly, Finns are very devoted to conform to the rules. In
the 2007 Trusted Brand Survey by Reader’s Digest, 72% of the Finns viewed themselves as conformists,
compared to the global average of 19%.
9
The inputs for the standard B&S model are, besides option characteristics including time-to-maturity at the
end of each year, the past 12 month stock volatility, and the 12-month EURIBOR rate. In our robustness tests,
we consider the Meulbroek (2001) model, where also data on the manager’s relative size of other holdings as
well as the expected return on the diversified (“market portfolio”) part is needed. We have used an expected
return of 15% and an annual risk-free rate of 5% as the estimates. Our results are not sensitive to the way the
CEO’s option portfolio is calculated.
10
The same executive option data set is used by Liljeblom, et al. (2011).
10
shares obtainable upon exercise of stock options, and whether the stock options are subject
to dividend protection and/or performance-vesting/indexing. For financial statement data,
industry codes, and stock return and price data, we rely on Worldscope and Datastream. The
variables used in our analysis are defined in Appendix A.
The number of firms listed on the Helsinki Stock Exchange during our sample years
is 144 (2002), 136 (2003), 137 (2004) and 131 (2005), which sums up to 548 potential data
points. A small number of missing data points and listings/de-listings in the middle of the
year cause minor deterioration in our sample so that the final full sample consists of 532
firm-year observations for 162 different firms.11
Descriptive statistics for various measures of CEO wealth and wealth composition,
along with other firm characteristics, are reported in Table 1. The table provides information
regarding CEO characteristics, wealth, and income in Panel A, and firm characteristics in
Panel B.
Panel A of Table 1 shows that more than half of the CEOs in our sample do not own
stocks in the firm they work at, since the median for the variable HoldingsOwnFirm is zero.
On the other hand, when they do so, their ownership in the firm where they work exceeds
their average ownership of other equity (HoldingsOtherEquity) by a multiple of more than
ten. We take advantage of this cross-sectional variation in CEO holdings, and in our
regressions use Wealth%OwnFirm that controls for the CEOs stock exposure to their own
firm. CEO holdings of stocks outside their own firm is indeed markedly low, with an
11
To avoid further deterioration, we use a substitution method to replace missing data points for firm
characteristics that we use as control variables. In the substitution method we replace a missing data point by
the cross sectional median for the data item in that year. Despite the resulting smaller sample size, our results
are virtually identical when firms with any missing variables are dropped from the regressions.
11
average portfolio value of only roughly 62 thousand euros, and a maximum of 2.7 million
euros. Also the degree of diversification is in general low in terms of the number of equity
instruments in their portfolio, EquityPortf_#Instrum having an average of 2.3, and a
maximum of 34.12 The option incentives provide a very skewed pay-off profile, with a mean
of 275 692 euros, and a maximum of 50.7 million euros in terms of their Black-Scholes
values. The personal characteristics for the CEOs indicate that the average age is 50 years,
varying from 33 to 76 years, while their tenure with the firm varies from 0 to 32 years, with
an average of 6.6 years. We will later use logarithmic or winsorized values for some
variables exhibiting large outliers or a very skewed distribution.
Panel B of Table 1 reports the descriptive statistics for firms in our sample. The
average market-to-book values are rather high since the Finnish stock market during our
study period included many IT-firms, with high market-to-book values. The average ROA in
turn is slightly negative during this after-crash period of 2002 to 2005. A typical feature for
the Finnish stock market is also the large variation in ownership concentration, ranging from
widely-held firms such as Nokia, to very closely held firms. As Panel B of Table 1 indicates,
the five largest shareholders own an average of close to 50% of the aggregated ownership.
The average debt-to-assets is low, at less than 17%, although the individual values reach
very high levels for some small growth firms.13 The average for our primary proxy of firm
risk, i.e. firm beta, is 0.6, with a range from -1.16 to 2.45. The large range of beta values
reflects the turbulent time period in the Finnish stock market, after bursting of the IT-
12
CEOs do not seem to deviate much from general Finnish population. In 2005, 13% of the Finnish population
held stocks, and even among the richest one percent of the population, only 49.3% were stockholders
(Keloharju, et al., 2012).
13
The extreme values for Sales_Growth and Debt_to_Assets are both produced by a single young
pharmaceutical company engaged in product development. The drop in sales was extreme due to small
numbers (the company had hardly any sales yet), and the equity was negative one (the company was largely
financed by convertible loans). The winsorizing used in our estimations will reduce the impact of such outliers.
12
bubble.14
We begin our analysis of the determinants of firm risk-taking in Table 2, where we
compare the CEO wealth variables in two groups of firms, those with a stock beta below or
above the sample median. As expected, Table 2 indicates that higher incentives are offered
for managers in riskier firms. The average for CashPay is significantly higher for high risk
firms. The average for the variable OptionBS_value also takes a much higher value in the
group of high risk firms, but due to the high standard deviation of this variable, the
difference has a p-value of slightly over five percent. Table 2 also supports the idea that
managers in riskier firms are more diversified, since the average for OtherWealth, is
significantly (at the 1% level) higher for managers of riskier firms. Finally, CEO_Tenure is
significantly lower in high beta firms. It is consistent with new CEOs taking more risk, as in
Alderson, et al. (2014), but it may also suggest that CEO turnover is higher among high beta
firms.
4. CEO characteristics and firm risk
4.1. Beta and idiosynchractic risk
4.1.1. Baseline model for beta
Recall that our first hypothesis is based on decreasing absolute risk aversion, as we
expect that wealthier managers are able to bear higher firm risk. In tests of our Hypothesis 1,
we use Beta_Firm as our primary proxy of firm risk. This is motivated by recent evidence of
Armstrong and Vashishtha (2012) and Panousi and Papanikolau (2012), suggesting that
CEOs are most interested in adjusting the systematic risk of their firms. In our Hypothesis 2,
14
In order to reduce the effects from potential outliers, we winsorize our risk measures for our regression
models.
13
we posit that manager’s portfolio composition affects her risk-taking. We expect that
managers with portfolios that are more independent of the firm should be able to bear more
firm risk. To test the effect of managerial portfolio independence from the firm, we include
Wealth%OwnFirm in our model.15
Since a possible connection between the CEO’s personal risk aversion and the firm
risk he is willing to carry may be influenced by executive compensation packages designed
to incentivize the manager to carry more risk, we include a control variable measuring the
Black-Scholes value of the CEO’s current portfolio of executive options in the firm where he
is employed.16 We expect a positive sign for this control variable as more risky firms are
likely to grant more such options – and such options are likely to influence the managers
willingness to carry a higher firm risk (i.e. an endogeneous relationship between these
variables is likely to exist17).
As additional controls, we include CEO characteristics such as age and tenure, as
well as variables capturing the personal risk-taking of the CEO. For the latter, we use the
beta of the CEO’s equity portfolio. Besides a firm size control, our firm variables include
controls that are related to firm risk, namely leverage and an indicator for firm level
diversification. We also include a control for sales growth, as high growth may have a
15
We obtain very similar inferences if we replace Wealth%OwnFirm with an indicator for CEOs who have
exposure to their own firm.
16
In our regressions, we have used the standard B&S values for the executive options. Robustness tests (not
reported here) show that our results are qualitatively quite similar when using the Meulbroek (2001) model to
evaluate the executive options.
17
Since the incentive effect is not our focus variable in the study, we do not go further into exploring
endogeneity issues between firm risk and option compensation. Instead, we are interested in whether
managerial characteristics and wealth are, even after controlling for option incentives, related to firm risk.
14
leverage-like effect on firm risk.18 Finally, a variable for ownership concentration captures
the effect of concentrated ownership structure. Concentrated ownership may indicate poorly
diversified owners, who would have a lower preference for firm risk. Concentrated
ownership also affects the CEO’s ability to imprint his preferences to firm behavior, as block
owners tend to attain an active role in corporate governance.
Our basic model is:
Beta_Firm = +TotalWealth +Wealth%OwnFirm +EquityPf_beta+
(Other CEO characteristics) + (Firm characteristics) + 

where the vector of other CEO characteristics includes ln(OptionBS_value),
ln(CEO_Age), ln(CEO_Tenure), Wealth%OwnFirm and EquityPortf_Beta, and the firm
characteristics
include
Ln(Sales),
SalesGrowth,
Debt_to_Assets,
Many_SIC
and
5_largest_shareholders.
The results are reported in Table 3. We begin with a simple model in Column (1),
where we include neither CEO controls nor firm characteristics. However, fixed industry
effects are included in this, and all other regression specifications. We find that firm beta
increases with CEO total wealth. This supports our hypothesis 1, i.e. that in line with
decreasing absolute risk aversion, wealthier managers can carry more risk. We also obtain
the expected negative sign for our second variable of interest, Wealth%OwnFirm. This
provides support for our hypothesis 2, which was derived on the basis of decreasing relative
risk aversion. Managers with a smaller proportion of their wealth tied to the firm that they
18
See Bernardo, Chowdhry, Goyal (2007) for how to decompose a firm’s beta into the beta of assets-in-place
and the beta of growth opportunities. Their empirical results indicate that beta of growth opportunities is
greater than the beta of assets-in-place for virtually all industries over a long period of time.
15
manage seem to be more inclined to take risk. Even our hypothesis 3 on behavioral
consistency receives support, as the beta on personal equity portfolios is positively related to
firm beta. On the latter finding, it is important to note that Wealth%OwnFirm controls for the
presence of the firm’s own stock in some of the CEO portfolios.
In column (2), we add the option portfolio, CEO age, and CEO tenure as additional
controls. All three of our test variables are robust to these additions. In columns (3) and (4)
of Table 3, we include firm-level accounting variables into our specification. The findings
reported in columns (1) & (2) are robust to this addition as well, with the exception that the
coefficient on TotalWealth is no longer statistically significant. Since the accounting
variables, particularly Debt to Assets behave very differently between industrial firms and
financial firms, we follow the common practice and drop financial firms from our sample for
these regressions, and subsequent analysis done with our full set of covariates.19
Regarding CEO characteristics, the results in Table 3 indicate that CEO tenure is
inversely related to risk, i.e. firm risk is lower in firms which have had the same CEO for a
long time. This is consistent with Berger, et al. (1997) suggestion that CEOs with longer
tenure are more entrenched and subsequently also more risk averse. It is also consistent with
Alderson, et al. (2014) finding that newly-hired CEOs tend to implement riskier strategies.
Interestingly, Serfling (2014) relate risk-aversion to age, but not to tenure, whereas in our
dataset, the finding on tenure is not explained by CEO age, which we control for as well.
Among our control variables, firm size (ln(sales)) increases firm beta, and higher holdings
by top-5 shareholders decrease it. The value of CEO’s option portfolio is positively related
to firm risk, but that finding is not robust to inclusion of accounting controls in columns (3)
and (4).
19
See e.g. Armstrong and Vashishtha (2012) and Chava and Purnanandam (2010).
16
4.1.2. Option portfolios, governance, and the beta model
Next, we perform additional tests of the relationship between firm risk and
managerial wealth characteristics. First, we test our Hypothesis 4, and explore the effects of
option incentives in more detail. The model by Parrino, et al. (2005) suggests that managers
holding in-the-money options (which are more stock-like than at-the-money of out-of-themoney options) are more risk-averse to downside movements of the stock. This result is
naturally also related to prior studies that link executive option delta to both vega and firm
risk management (see e.g. Knopf, Nam, and Thornton 2002). Both effects speak for a
reduced willingness to carry risk. Since the CEOs in our data set typically hold options from
a number of different executive option program (ESOP) tranches, with different contract
terms, we create a simple measure which in essence combines the effects of the deltas and
vegas of the options in the portfolio. We divide the BS value of each manager’s total
executive options at each year-end into the value of options that are in-the-money (ITM),
and the value of all other options. The ITM options represent 40.4% of the total option
portfolio value, so about 60% of the options in our sample are out-of-the-money.
Column 1 of Table 4 replicates the last column of Table 3, with the exception that we
split
ln(OptionBS_value)
into
separate
variables
of
ln(OptionBS_not_ITM)
and
ln(OptionBS_ITM). Our results support the Parrino, et al. (2005) idea that out-of-the-money
option holdings are more likely to encourage managers to corporate risk taking, as
ln(OptionBS_not_ITM) enters with a coefficient that is highly significant with a positive sign,
while the coefficient for the ITM options is indistinguishable from zero.
Next, we test our Hypothesis 5, that corporate governance can weaken the
relationships between managerial wealth, managerial holdings in own firm, and firm risk.
Our Table 3 results already suggest that ownership structure affects corporate risk taking.
17
We define a corporate governance measure that follows Becker (2006), and proxy a stronger
governance relationship with a dummy, that takes the value of one if the combined holdings
of the five largest shareholders exceed 30% of the equity in the firm, and zero otherwise.20
We interact this dummy, Owner>30%, first with TotalWealth (Table 4, column 2), and then
with Wealth%OwnFirm (Table 4, column 3), leaving 5_largest_shareholders out to avoid
multicollinearity. We find that the interaction term is negative and significant for
TotalWealth, indicating that governance by large (potentially poorly diversified) owners is
associated with a weaker relationship between managerial wealth and firm risk than
otherwise. The latter interaction term with Wealth%OwnFirm also obtains a negative, albeit
not statistically significant coefficient. These results may be interpreted as a result of two
effects jointly pointing to the same direction and together producing a significant effect:
when the manager is poorly diversified, and the ownership is highly concentrated (owned by
most likely poorly diversified owners), i.e. when it is in the interest of both the CEO and
large owners to take less risk, the firm beta is significantly lower in comparison to other
firms.
4.1.3. CEO salary and its effect on risk taking
As noted in Table 1, the average CEO in our dataset is not particularly wealthy.
Given that the median total wealth in our sample is only slightly over 250,000 euros and the
median cash pay is 183,000, the current income seems to play a significant role on
wealthiness of our sample CEOs. In this section, we repeat our Table 3 analysis, while
replacing total wealth with a measure that proxies the CEO’s level of reliance on current
income. Variable RelSalary is defined as the ratio between current cash pay and total
20
In Finland, equity ownership is strongly concentrated also in many listed firms. On the other hand, there are
also many firms with a quite dispersed ownership. In our data set, 82% of the observations have obtain the
dummy value of one, indicating that five largest shareholders own more than 30% of the firm.
18
wealth.21 The results are reported in Table 5.
In the first two columns of Table 5, we use Beta_Firm as the dependent variable. Not
surprisingly, CEOs that rely more on the current income from the firm are connected to
lower firm risk. This can be viewed as further evidence in support of our Hypothesis 2, as
managers that are more dependent on the firm take less risk. Our earlier findings on
Wealth%OwnFirm are now somewhat weaker, but the connection between firm beta and
CEO portfolio beta persists. In the last two columns of Table 5, our dependent variable is the
firm’s idiosynchratic risk. CEOs that rely more on their current income seem to shy away
from this type of risk as well, while neither general wealth level nor equity portfolio beta
affect idiosynchratic risk. Both age and tenure enter with negative and weakly significant
signs.
4.1.4. Determinants of idiosychratic risk
Prior empirical evidence (e.g. Armstrong and Vashistha 2012, Panousi and
Papanikolau 2012) indicate that managers are more willing to undertake systematic firm risk
(which can be hedged) than idiosyncratic firm risk. In untabulated tests, we test for the
relationship between manager wealth and its components, and the firm’s idiosynchratic risk
by regressing the variable Idiosynchr_Risk on the same variables as in model (1).
None of our test variables have a significant effect on the firm’s idiosynchratic risk.
The only two variables with consistent findings are age and tenure, which are both inversely
related to idiosynchratic risk. Hence, these results fail to support the notion that managers
would be more concerned about unsystematic than systematic risk, at least not as it is
21
Recall that CashPay captures the entire taxable income from employment, including the value of any option
exercises.
19
reflected in their personal wealth decisions.
4.1.5. ROA volatility of the firm as a measure of risk
In addition to the market-based measures of firm risk, such as beta and idiosynchratic
stock return volatility, we perform tests using the primary measure for corporate risk taking
used by Faccio, Marchica, and Mura (2011). In their cross-country study, they use the
volatility of a country and industry-adjusted profitability,(ROA), calculated on the basis of
a minimum of five lagged observations. Table 6 reports the results of the tests of our CEO
wealth characteristics as determinants of (ROA)in specifications similar to those in Table 3,
columns (3) & (4). Due to data limitations, our sample shrinks to 286 firm-year observations.
We find that our results are somewhat different when risk is measured as volatility of
the ROA. Total wealth is weakly positively related to risk. Wealth in own firm enters with a
positive but insignificant coefficient, whereas in the previous tables, the variable has
obtained a negative and significant coefficient. Also, equity portfolio beta fails to affect firm
volatility. Among control variables, firm size has a negative effect on risk (in contrast to
Table 3). All in all, the results in Table 6 are consistent with our earlier findings that
wealthier CEOs are connected to riskier firms. Our earlier findings regarding the effect of
ln(CEO_Age) and ln(CEO_Tenure) on corporate risk taking also receive additional support
in Table 6.
To summarize our regression results, we find support for our hypothesis 1, i.e. that
more wealthy managers are associated with riskier firms at least when the firm’s systematic
(beta) risk is concerned. The relationship is weaker in firms with a large shareholder, our
proxy for corporate governance. We also find support for other relationships between
20
managerial characteristics and the firm’s beta risk. We find that the manager’s tenure is
negatively and typically highly significantly related to firm risk, either indicating a higher
CEO turnover in risky firms, new CEOs engaging in more risky corporate policies, or that
managers that stay as managers for a longer time can imprint a lower risk on the firm.
We also find support for a positive relationship between ln(OptionBS_value) and
firm risk. Specification tests reveal that this relationship is due to the part of the CEO’s
option portfolio where the executive options are out-of-the-money. This result is in line with
the predictions of Parrino et al (2005) and also with the idea that the willingness to undertake
risk is negatively related to option delta (Knopf et al 2002). Since endogeneity issues can be
the cause of such relationship,22 we further elaborated on it in our robustness tests. These
tests also aim to separate the connection between firm risk and executive compensation
design from the effects of managerial incentives on firm risk-taking. Our results support a
positive relationship between the CEO’s total wealth and firm risk (beta) even when a
potential effect from firm risk on executive option compensation is accounted for. Thus we
obtain support for our hypothesis 1.
We find similar evidence regarding the Wealth%OwnFirm variable. We thus obtain
support also for our hypothesis 2, i.e. more independent managers are connected to higher
firm risk. Our results regarding a matching relationship between CEO’s portfolio beta and
firm beta support our hypothesis 3, as we find a positive connection between the two
variables.
22
On one hand, high executive option compensation may be used to motivate managers to undertake riskier
investments /to carry more risk, and thus lead to a positive association between executive option values and
firm beta. On the other hand, since the option value is positively related to the volatility of the underlying asset,
the causality may come from an external shock simultaneously causing increased firm risk and increased option
values, if the firm’s total risk and beta risk are related.
21
5. Summary and conclusions
Using unique data on CEO’s total wealth and its decomposition item by item, we
study the relationship between firm risk and CEO characteristics. We contribute to the
literature on the effects of managerial risk aversion, and the incentivizing effects of
managerial compensation, on firm risk taking. Following Cronqvist, Makhija and Yonker
(2012), we test the “behavioral consistency theory” and consider the matching relationship
between managerial and corporate risk preferences. The mechanism that can cause a match
between managerial risk aversion and firm risk may either be endogenous matching (i.e.
firms seeking managers with certain personal characteristics) or the influence of the CEO on
firm risk.
We find that wealthier CEOs are associated with riskier firms in terms of both firm
beta, and ROA volatility. Our results suggest that CEOs in our sample have decreasing
absolute risk aversion. We also provide support for the hypothesis that managers whose
wealth is more independent of the firm are associated with riskier firms, which suggests
decreasing relative risk aversion. Furthermore, we get strong support for a matching
relationship between firm beta and that of the CEO’s personal stock portfolio. Stronger
corporate governance seems to mitigate the relationship between firm risk and CEO wealth
characteristics.
We also find interesting results for both CEO age and tenure. The CEO’s tenure
tends to be inversely related to firm risk. This finding is robust to various proxies of firm risk.
A possible interpretation is that risk-averse managers are better able to imprint their risk
preferences on the firm over time.
22
23
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28
Appendix A. Variable definitions for key variables
The table describes our key variables for CEO (Panel A) and the firm (Panel B). Wealth and income data are
from the Finnish tax authority; financial statement data, industry codes and stock return and price data are from
Datastream. Firm ownership data are from Pörssiyhtiöt books and the Finnish Central Securities Depository;
dividend and share repurchase data are from the Helsinki Stock Exchange, and ESOP data are from Alexander
Corporate Finance Oy. All nominal variables (“values”) are denoted in euros. By “own firm” we refer to the
firm employing the CEO. The time period is 2002 to 2005, and the sample covers 532 firm-years for 162 firms.
Panel A. CEO characteristics
CEO_Age
The age of the CEO at year-end.
CEO_Tenure
Tenure with company in years, rounded to the closest full year.
CashPay
The total taxable income from employment for a specific calendar-year.
CapitalIncome
The total taxable income from capital gains, rents and other capital income for a
specific calendar-year.
TotalWealth
The total taxable value of the CEO wealth (gross of debt). Equities are calculated
at the market value at the year-end, while for other assets, their taxational values
determined by the tax authorities have been used
PersonalDebt
CEO’s personal total debt, plus 1 euro to avoid zeros.
Wealth_ volatility
The weekly one-year return variance for the CEOs total gross wealth, using stock
market data for the listed part of the wealth, and zero for the unlisted part.
Personal_leverage
PersonalDebt to TotalWealth.
HoldingsOwnFirm
Value of gross holdings in the firm employing the CEO, in euros.
Wealth%OwnFirm
HoldingsOwnFirm to TotalWealth.
HoldingsOtherEquity
Value of other equity holdings (excluding holdings in own firm). Identical to
EquityPortfolio(ex own).
EquityPortfolio
The sum of HoldingsOwnFirm and HoldingsOtherEquity.
RealEstateValue
The total value of real-estate holdings.
FlatsValue
The total value of apartments owned
RealEstateTotal
The sum of RealEstateValue and FlatsValue.
TotalWealth minus EquityPortfolio. Includes thus RealEstateTotal as well as
OtherWealth
other taxable wealth such as forests etc.
EquityPortf_Beta
The market beta for the CEOs EquityPortfolio (i.e. sum of HoldingsOwnFirm
and HoldingsOtherEquity), measured against the OMX Helsinki Cap-index.
EquityPortf_#Instrum
The number of securities in the CEOs EquityPortfolio.
OptionBS_value
The Black-Sholes-value of the CEOs ESOP portfolio at year-end, plus 1 euro.
OptionBS_ITM
The Black-Sholes-value of that part of OptionBS_value which consists of in-themoney options.
OptionBS_not_ITM
The difference between OptionBS_value and OptionBS_ITM.
Panel B. Firm characteristivs
Sales
Sales denoted in millions of euros.
SalesGrowth
Growth of sales as compared to previous year in percent
Market_to_Book
Market value of equity/Book value of equity.
ROA
EBIT/Total Assets.
Recent ROA volatility, calculated on the basis of at least 5 observations.
(ROA)
Free_Cash_Flow
(EBIT + depreciation & amortization)/Sales.
Capex/Sales
Capital expenditures / Total sales
Debt_to_Assets
Total long term debt / Total assets.
Mkt_leverage
Book debt to the sum of book debt and market equity.
Tang
Property, plant and equipment over total assets.
For_Own
% of shares held by foreign investors.
5_largest_shareholders % of shares held by 5 largest shareholders.
Owner>30%
A dummy taking the value of 1 if 5_largest_shareholders > 30%.
Variance_Firm
The variance of weekly stock price changes during the previous year.
Beta_Firm
Market beta for the firm as measured against the OMX Helsinki Cap-index.
Idiosynchr_Risk
The firm’s unique risk , based on a variance decomposition of the firm’s total risk
(Variance_Firm) into systematic risk (Beta_Firm squared times market variance)
and unique variance. Idiosynchr_Risk is defined as the square root of unique
variance. Market risk has been proxied by the weekly standard deviation of the
OMX Helsinki Cap-index for the year in question.
29
12month_return_firm
Many_SIC
Change in the market value of the company during the last 12 months.
A dummy for firms with more than 2 SIC-codes at the 2-digit level.
30
Table 1. Descriptive statistics
This table reports descriptive statistics for key variables describing CEOs’ total wealth, income, and the
risk variables related to CEO’s wealth in Panel A (for the full sample) and in Panel C (for two subgroups
based on firm beta below or above sample median), and firms characteristics in Panel B. See Appendix A
for variable definitions. The study period is from 2002 to 2005, and the sample covers 532 firm-years for
162 different firms. The t-test in the last column of Panel C tests for differences in variable averages for
high vs low beta firms, and the asterisks denote significance at the 1% (***), 5% (**) and 10% (*) levels in
two-sided tests, respectively.
Panel A. CEO characteristics , wealth,
and income
CEO_Age
CEO_Tenure
CashPay
CapitalIncome
TotalWealth
Mean
50.0
6.6
384,136
303,604
2,557,215
50
4
183,342
7,116
254,665
33
0
0
0
0
76
32
16,200,000
24,400,000
196,822,149
529
523
531
531
532
HoldingsOwnFirm
HoldingsOtherEquity
FlatsValue
RealEstateTotal
671,478
62,591
23,094
49,051
1,823,146
0.04
0.55
0.21
2.32
275,692
Mean
0
0
0
0
221,475
0.00
0.09
0
0
0
Median
0
0
0
0
0
0
0
-1.16
0
0
141,983,541
2,676,379
2,553,400
2,780,158
126,616,747
2.85
10.72
2.45
34
50,700,000
Max
532
536
531
531
532
437
530
536
536
536
307.05
0.19
2.07
-0.01
0.22
0.17
0.18
0.48
0.01
0.60
0.05
0.24
2.21
29.17
0.02
1.70
0.04
0.15
0.14
0.08
0.48
0.00
0.52
0.04
0.19
2
0
-1
-29.77
-4.43
-5.67
0
0
0.01
0.00
-1.16
0.01
-0.88
0
10 333
20.80
83.66
0.29
15.53
2.38
0.93
1.00
0.15
2.45
0.39
11.60
8
536
522
536
511
373
494
523
523
533
521
521
515
536
OtherWealth
Wealth_volatility
Personal_leverage
EquityPortf_Beta
EquityPortf_#Instrum
OptionBS_value
Panel B. Firm characteristics
Sales (millions of euros)
Sales_Growth
Market_to_Book
ROA
Free_Cash_Flow
Debt_to_Assets
For_Own
5_largest_shareholders
Variance_Firm
Beta_Firm
Idiosynchr_Risk
12month_return_firm
# of 2-digit_SIC_codes
Median
31
Min
Max
Min
Obs
Table 2. Differences between high and low beta firms
This table reports differences in averages for CEO wealth and income variables for high vs low beta firms.
The two groups include firms whose beta is above (below) the sample median, respectively, and the
asterisks denote significance at the 1% (***), 5% (**) and 10% (*) levels in two-sided tests, respectively.
See Appendix A for variable definitions.
Variable
CEO_Age
CEO_Tenure
CashPay
CapitalIncome
TotalWealth
HoldingsOwnFirm
HoldingsOtherEquity
FlatsValue
RealEstateTotal
OtherWealth
Wealth_volatility
EquityPortf_Beta
EquityPortf_#Instrum
OptionBS_value
Firms with
below median beta
Mean
St.dev.
Firms with
above median beta
Obs Mean
St.dev.
50.48
7.31
246 471
7.20
7.10
321 352
236 293
1 527 386
657 552
57 406
26 261
46 070
804 115
0.02
0.12
950 381
9 285 661
8 814 280
226 285
167 899
193 857
1 974 839
0.11
0.36
2.25
59 690
4.92
232 689
32
255
252
257
Obs
t-test
for
diff.
49.55
5.89
513 260
6.80
6.61
1 129 263
274
271
274
-1.53
-2.37**
1.73*
257
366 739
258 3 526 908
261
674 928
261
66 601
259
20 079
259
51 529
258 2 782 672
211
0.05
261
0.28
1 866 725
16 600000
7 145 084
265 037
69 897
189 926
11 800000
0.29
0.45
274
274
275
275
272
272
274
226
275
0.02
0.43
0.03
0.43
-0.55
0.33
2.66***
1.02
4.59***
261
261
4.27
3 484 376
275
275
0.24
1.95*
2.35
480 698
Table 3. Firm beta and managerial wealth characteristics
This table reports coefficient estimates and t-values from regressing the beta of the firm (Beta_Firm) on
firm and ownership characteristics as well as variables related to the CEO’s personal wealth and its risk,
using robust and clustered standard errors. For variable definitions, see Appendix A. The variable
TotalWealth has been winsorized at the 95th percentile. The asterisks denote significance at the 1% (***), 5%
(**) and 10% levels (*), respectively.
Model
TotalWealth
Wealth%OwnFirm
(1)
(2)
(3)
(4)
0.0362**
(2.494)
-0.3591**
(-2.463)
0.0433***
(2.917)
-0.3661**
(-2.451)
0.2586
(1.447)
-0.0659**
(-2.340)
0.1869***
(3.471)
0.0103***
(2.804)
0.0227
(1.574)
-0.3232**
(-2.210)
0.0966
(0.537)
-0.0700**
(-2.580)
0.1404***
(2.680)
0.0031
(0.837)
0.0751***
(5.703)
-0.0687
(-0.708)
-0.1670*
(-1.819)
0.0504
(1.078)
-1.1972*
(-1.702)
yes
510
0.266
0.3604
(0.517)
yes
454
0.323
0.0239
(1.598)
-0.3162**
(-2.168)
0.0848
(0.470)
-0.0685**
(-2.497)
0.1337**
(2.571)
0.0014
(0.393)
0.0675***
(5.083)
-0.0608
(-0.626)
-0.1406
(-1.524)
0.0425
(0.894)
-0.2351*
(-1.923)
-0.3929
(-0.554)
yes
454
0.328
ln(CEO_Age)
ln(CEO_Tenure)
EquityPortf_Beta
0.2130***
(3.962)
ln(OptionBS_value)
ln(Sales)
SalesGrowth
Debt_to_Assets
Many_SIC
Top5_s.holders
Constant
Industry effects
Observations
Adj. R-squared
0.7263***
(3.552)
yes
515
0.244
33
Table 4. Firm beta, option incentives, and corporate governance
This table reports coefficient estimates and t-values from specification tests of the relationship between the
beta of the firm (Beta_Firm) and firm and ownership characteristics as well as variables related to the
CEO’s personal wealth and its risk, using robust and clustered standard errors. For variable definitions, see
Appendix A. The variable TotalWealth has been winsorized at the 95th percentile. The asterisks denote
significance at the 1% (***), 5% (**) and 10% levels (*), respectively.
Model
TotalWealth
Wealth%OwnFirm
ln(CEO_Age)
ln(CEO_Tenure)
EquityPortf_Beta
(1)
(2)
(3)
0.0257
(1.652)
-0.3711**
(-2.587)
0.0658
(0.367)
-0.0712**
(-2.539)
0.1592***
(2.811)
0.0808**
(2.543)
-0.2739*
(-1.839)
0.1118
(0.611)
-0.0685**
(-2.505)
0.1203**
(2.402)
0.0008
(0.226)
0.0241
(1.627)
0.0218
(0.041)
0.0967
(0.531)
-0.0702**
(-2.572)
0.1203**
(2.282)
0.0011
(0.305)
0.0631***
(4.624)
-0.0452
(-0.464)
-0.1347
(-1.435)
0.0366
(0.804)
0.0691***
(5.218)
-0.0629
(-0.644)
-0.1439
(-1.557)
0.0487
(1.047)
0.7565*
(1.909)
-0.0707**
(-2.148)
-0.1152*
(-1.874)
ln(OptionBS_value)
ln(OptionBS_ITM)
ln(OptionBS_not_ITM)
ln(Sales)
SalesGrowth
Debt_to_Assets
Many_SIC
5_largest_shareholders
-0.0039
(-0.987)
0.0106**
(2.225)
0.0663***
(4.992)
-0.0661
(-0.672)
-0.1659*
(-1.832)
0.0360
(0.796)
-0.2015
(-1.587)
Owner>30%
TotalWealth_* Owner>30%
Wealth%OwnFirm_*_Owner>30%
Constant
Industry effects
Observations
Adj. R-squared
0.6154
(0.913)
yes
433
0.330
-0.3379
(-0.433)
yes
454
0.338
34
-0.3464
(-0.640)
0.4490
(0.632)
yes
454
0.330
Table 5. Managerial income and firm risk taking
This table reports coefficient estimates and t-values from regressing beta of the firm (Firm_Beta) in
columns (1) & (2), and idiosynchratic firm risk (Idiosynchr_Risk) in columns (3) & (4) on firm and
ownership characteristics as well as variables related to the CEO’s personal wealth and its risk, using robust
and clustered standard errors. For variable definitions, see Appendix A. The asterisks denote significance at
the 1% (***), 5% (**) and 10% levels (*), respectively.
Model
RelSalary
Wealth%OwnFirm
ln(CEO_Age)
ln(CEO_Tenure)
EquityPortf_Beta
ln(OptionBS_value)
ln(Sales)
SalesGrowth
Debt_to_Assets
Many_SIC
5_largest_shareholders
Constant
Industry effects
Observations
Adj. R-squared
(1)
Beta_Firm
-0.0005**
(-2.559)
-0.2557*
(-1.742)
0.1088
(0.597)
-0.0556**
(-2.081)
0.1391***
(2.677)
0.0013
(0.354)
0.0717***
(5.607)
-0.0555
(-0.582)
-0.1549*
(-1.741)
0.0462
(0.989)
-0.2279*
(-1.828)
-0.2185
(-0.310)
yes
454
0.324
(2)
Beta_Firm
-0.0005**
(-2.325)
-0.3196**
(-2.054)
0.3898**
(2.033)
-0.0540*
(-1.799)
0.1831***
(3.323)
0.0109***
(2.710)
-1.1578
(-1.549)
yes
454
0.246
35
(3)
Idiosynchr_Risk
-0.0001***
(-3.057)
0.0026
(0.243)
-0.0078
(-0.863)
-0.0046*
(-1.851)
-0.0001
(-0.055)
0.0002
(0.802)
-0.0038***
(-4.280)
-0.0123
(-1.579)
0.0068
(0.561)
0.0072
(1.209)
0.0153
(1.584)
0.0731*
(1.955)
yes
454
0.304
(4)
Idiosynchr_Risk
-0.0001**
(-2.506)
0.0035
(0.344)
-0.0200**
(-2.199)
-0.0051*
(-1.912)
-0.0029
(-1.210)
-0.0002
(-0.733)
0.1138***
(3.056)
Yes
454
0.247
Table 6. ROA volatility and CEO wealth characteristics
This table reports coefficient estimates and t-values from regressing (ROA) on firm and ownership
characteristics as well as variables related to the CEO’s personal wealth and its risk, using robust and
clustered standard errors. For variable definitions, see Appendix A. The asterisks denote significance at the
1% (***), 5% (**) and 10% levels (*), respectively.
Model
TotalWealth
Wealth%OwnFirm
ln(CEO_Age)
ln(CEO_Tenure)
EquityPortf_Beta
ln(OptionBS_value)
ln(Sales)
SalesGrowth
Debt_to_Assets
Many_SIC
(1)
(2)
0.0045*
(1.748)
0.0282
(1.380)
-0.1071**
(-2.533)
-0.0121**
(-2.326)
0.0014
(0.234)
0.0013
(1.140)
-0.0111***
(-3.379)
0.0365
(1.136)
0.0761
(1.044)
-0.0123
(-0.959)
0.0045*
(1.747)
0.0284
(1.340)
-0.1069**
(-2.466)
-0.0121**
(-2.317)
0.0014
(0.229)
0.0013
(1.135)
-0.0112***
(-3.284)
0.0365
(1.134)
0.0764
(1.030)
-0.0123
(-0.955)
-0.0020
(-0.075)
0.4845***
(2.925)
yes
286
0.383
5_largest_shareholders
Constant
Industry effects
Observations
R-squared
0.4833***
(2.839)
yes
286
0.386
36