Role of CEOs’ Educational Background in Convertible Bond ... ANNUAL MEETINGS/201… · We find...
Transcript of Role of CEOs’ Educational Background in Convertible Bond ... ANNUAL MEETINGS/201… · We find...
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Role of CEOs’ Educational Background in Convertible Bond Issuance Decisions
Zainab Mehmooda, Marie Dutordoir
b, Amedeo De Cesari
c
November 13, 2017
Abstract
We examine the effect of U.S. CEOs’ education on their firms’ likelihood of issuing convertible
debt instead of straight debt and equity. We find that CEOs with a higher level of education are
more likely to issue convertible debt, particularly when such action is beneficial to their firms.
However, CEOs with an MBA degree are less likely to rely on convertibles, consistent with the
assumption that an MBA might dampen non-standard corporate finance choices. Our findings
are consistent with the upper echelon theory which suggests that better educated executives are
more innovative and make more optimal corporate finance choices. The findings withstand a
range of robustness tests.
Keywords: CEO education, MBA, convertible debt, security choice
a Alliance Manchester Business School at the University of Manchester, Manchester, M15 6PB Manchester, United
Kingdom. E-mail: [email protected] b Alliance Manchester Business School at the University of Manchester, Manchester, M15 6PB Manchester, United
Kingdom. E-mail: [email protected] c Alliance Manchester Business School at the University of Manchester, Manchester, M15 6PB Manchester, United
Kingdom. E-mail: [email protected]
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1. Introduction
Several papers analyze how the characteristics of top managers affect firms’ capital
structure, performance, international diversification, adoption of innovation, mergers and
acquisitions, and research and development (R&D) (Becker, 1970; Hambrick and Mason, 1984;
Russo et al., 2000; Herrmann and Datta, 2002; Hambrick, 2007 and 2014; Bennedsen et al.,
2008; Kaplan et al., 2012; Bhagat and Bolton, 2013; Nguyen et al., 2015; King et al., 2016).
In this study, we examine the impact of managerial education on firms’ choice to issue
convertible bonds. Convertible bonds are hybrid securities that have characteristics of both
straight debt and equity. Similar to holders of straight bonds, convertible bond holders receive
periodic interest payments alongside the principal payment. Similar to shareholders, convertible
bond holders have the choice to convert the bonds into stock at a specified conversion ratio
(Duca et al., 2012). The literature provides several theoretical justifications for the use of
convertible bonds: the risk shifting theory of Jensen and Meckling (1976) and Green (1984), the
risk uncertainty theory of Brennan and Kraus (1987) and Brennan and Schwartz (1988), the
backdoor equity theory of Stein (1992), and the sequential financing theory of Mayers (1998).
Empirical studies on convertible bond issuance motivations provide ambiguous evidence for
these theories (Lewis et al., 1999; Lewis et al., 2003; Bancel and Mittoo, 2004a and b; Chang et
al., 2004; Dutordoir et al., 2014; Dorion et al., 2014; Dong et al., 2017).
We use theories on CEO education and convertible debt issue motivations to investigate
the impact of CEOs’ educational background on their firms' decision to raise convertible bond
financing. The upper echelon theory on managerial characteristics implies that a stronger
educational background can be associated with a higher ability to understand and accept new and
complex strategic choices. The theory associates better education with the adoption of innovation
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(Hambrick and Mason, 1984; Hambrick, 2007). Apart from the upper echelon theory, the human
capital theory by Becker (1964) also views education of employees alongside their other
characteristics i.e. experience, intelligence, training, judgment, and wisdom to understand
complex and innovative concepts. Several empirical studies on human capital that explore the
role of skill decomposition suggest that primary or secondary education is more appropriate for
routine jobs, whereas higher levels of education are considered more suitable for innovation
(Nelson and Phelps, 1966; Acemoglu et al., 2002; Vandenbussche et al., 2006; Ciccone and
Papaioannou, 2009).
Given that convertibles are more complex and innovative securities than straight bonds
and equity in terms of design and functionality (Damodaran, 1999), we expect CEOs with a
stronger educational background to be more likely to issue convertibles instead of standard
securities.1 We measure education level in two different ways for robustness. We first use non-
MBA master’s degree and PhD as measures of education level against an undergraduate degree.
As a robustness check, we use the second measure of education level estimated on a point scale
ranging from 0 to 3, where zero represents the lowest level of education and three represents the
highest level of education. We separately examine the effect of an MBA on convertible bond
issuance decisions, as literature has shown particular interest in investigating the organizational
implications of having senior executives with formal education in business administration
(Grimm and Smith, 1991; Palmer and Barber, 2001; Barker and Mueller, 2002; Bertrand and
Schoar, 2003; Bhagat et al., 2010; King et al., 2016). Finkelstein et al. (2009) suggest that
executives with an MBA behave differently from executives without an MBA. Some studies
1 Compared to straight debt and equity, there has been and continues to be a significant amount of innovation in the
design of convertible securities (Lewis and Verwijmeren, 2011). An example is the payment of cash instead of
common stock to the convertible bondholders by the issuing firms. Out of all convertibles issued in 2007, 86%
include these cash settlement features.
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argue that an MBA might lead to more aggressive behavior (Bertrand and Schoar, 2003; Beber
and Fabbri, 2012), while others suggest that managers with an MBA are inclined to be risk-
averse and conventional (less innovative), as the curriculum of MBA education emphasizes and
motivates risk-aversion and a conformist mindset (Hambrick and Mason, 1984; Finkelstein et al.,
2009). The upper echelon theory also suggests that managers with an MBA are more educated
towards avoiding big losses and to pursue short-term performance at the expense of innovation.
We use a sample of 3,114 security offerings made by 1,035 U.S. firms over the period
2011-2015 for which we hand-collect CEO education data and estimate a multinomial probit
model for convertible bond, straight bond, and equity issues. We use the security choice
framework developed by Lewis et al. (1999) and extend this framework by including measures
for CEO education, and a range of CEO- and firm-specific control variables. As hypothesized,
our baseline results confirm that better CEO education (as measured by a non-MBA master’s
degree, PhD, and a point scale based on education level) is associated with a higher likelihood of
issuing convertibles. With regards to MBA, we find that CEOs with an MBA degree are less
likely to issue convertibles, consistent with the assumption that an MBA might dampen
innovative behavior. However, CEOs with better education may self-select into better quality
firms, resulting in a possible endogeneity bias in our results. We control for endogenous CEO-
firm matching by using a binary treatment model and industry averages of CEOs’ educational
qualifications as instruments for CEO education. Our results hold throughout.
We perform several additional tests to better understand the mechanisms through which
CEO education affects convertible bond issuance decisions. We examine whether CEOs with a
better educational background are more likely to issue convertibles when these hybrid
instruments are more suitable for the issuing firms, i.e., when the costs of raising standard
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financing instruments are higher. We analyze this research question by using interaction terms of
proxies for firm-specific financing costs, reflecting the benefits of convertibles, with variables
capturing CEO education. We find that better-educated CEOs issue convertible debt when
raising straight debt or equity would be more costly. Our findings thus confirm our expectations
and suggest that better education leads to a better understanding of the role of hybrid securities.
We also investigate the effect of education specialization in quantitative subjects, i.e.,
business and economics, accounting and finance, CA, CFA, CPA, mathematics, and engineering
versus qualitative subjects, i.e., art, science, history, geography, and psychology, on the security
choice decision. After controlling for endogeneity, we find that CEOs with an education
specialization in quantitative subjects are more likely to issue convertibles.
Educational qualifications differ not only with respect to the level of education, but also
with respect to the quality of the degree-awarding institutions (Jalbert et al., 2002; Bhagat et al.,
2010; Miller et al., 2015). Hence we also consider the effect of the quality of the degree-
awarding institution on the security issuance decision. Our security choice results indicate that
managers with a higher quality of education, i.e., a Master’s degree or PhD awarded by a top-20
institution, are more likely to opt for convertible debt instead of straight debt or equity.
We also investigate the relationship between CEOs’ education and market reactions to
convertible bond issues. Although existing studies report significantly negative stock price
reactions to convertible debt offerings (Lewis et al., 1999 and 2003; Krishnaswami and Yaman,
2008; Loncarski et al., 2008; Dutordoir and Van de Gucht, 2009; Duca et al., 2012), they do not
document the relation between CEOs’ education and announcement returns. Educational
qualifications encompass anticipations on the latent ability of CEOs (King et al., 2016).
Chevalier and Ellison (1999) report higher returns to mutual funds managed by fund managers
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who graduated from more prestigious universities. Bhagat et al. (2010) report a positive market
reaction to announcements of appointments of CEOs with stronger academic credentials.
Investors might expect CEOs with a high level and quality of education to issue convertibles
when it is beneficial to the firm, e.g., when the firm has high costs of issuing standard security
types, and to use the convertible bond proceeds in more value-creating ways. After controlling
for endogenous CEO-firm matching, we find a significantly positive market reaction to
convertible bond offer announcement made by CEOs with a high level of education. In
unreported tests, we do not find any significant effect of education quality on market reactions to
convertible bond offer announcements.
Our paper contributes to two strands of the literature. Firstly, it contributes to the
literature on CEO education by analyzing the impact of CEOs’ educational background on their
choice to issue a relatively complex hybrid security like convertible debt. Secondly, it
contributes to the literature on security offerings by providing new insights into the so far
ambiguous question of why firms choose convertible debt over straight debt or equity. Our study
answers this question from the perspective of the decision-makers, the managers, instead of the
issuing firms, as Hambrick (2014) says, “if we want to understand strategy we must understand
strategists." Our study also contributes to the literature on stock market reactions to security
issuance by analyzing the role of managerial education in market reactions.
The remainder of this paper is organized as follows: Section 2 presents the theoretical
background of the study. Section 3 describes the data. Section 4 explains the methodology and
results. Section 5 gives the conclusion of the paper.
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2. Literature Review
To examine our research questions, we draw upon two strands of literature: studies on the
impact of top managers’ educational background on their firms’ strategic decisions and
performance, and studies on convertible debt issuer motivations. In this section, we briefly
review studies from the two strands of literature.
2.1. CEOs’ educational background and firms’ strategic choices
CEOs’ educational background can impact their knowledge, perspective, and ability to
understand technical and abstract concepts (Bhagat et al., 2010). Several corporate finance
studies examine the impact of managers’ educational background on innovation, firm
performance, diversification, mergers and acquisitions, and research and development (R&D)
(Palmer and Barber, 2001; Malmendier and Tate, 2005; Frank and Goyal, 2007; Murphy and
Zabojnik, 2007; Bhagat et al., 2010; Malmendier et al., 2011; Kaplan et al., 2012; Fee et al.,
2013; Graham et al., 2013; Berger et al., 2014; King et al., 2016). Nelson and Phelps (1966)
argue that the more educated the managers are, the quicker they will be able to introduce new
techniques for production. To put it simply, educated managers make good innovators. Studies
have found that level of education is positively associated with the receptivity of innovation
(Becker 1970; Rogers and Shoemaker, 1971; Kimberly and Evanisko, 1981). Hambrick and
Mason (1984) present the upper echelon theory which suggests that top managers with high
education levels possess a stronger cognitive base, which enables them to learn and accept new
and complex strategic choices. Herrmann and Datta (2005) find that firms with top managers
with higher levels of education, lower average age, lower organizational tenure, and greater
international experience have a higher level of international diversification.
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Some studies argue that an MBA education reduces innovative tendencies in managers
since the analytical techniques that managers learn in an MBA program are primarily inclined
towards avoiding big losses (Hambrick and Mason, 1984; Hambrick, 2007 and 2014; Finkelstein
et al., 2009). Hence managers with an MBA might not be innovative or risk-prone. Bertrand and
Schoar (2003) on the other hand, suggest that executives with an MBA follow more aggressive
strategies on average.
Malmendier and Tate (2005) find that CEOs with engineering or science background
work in firms with larger investment–cash flow sensitivities compared with CEOs with a finance
education. Barker and Mueller (2002) on the other hand, find significant R&D spending
increases for firms where CEOs have advance science-related degrees. These findings support
the notion that CEOs’ educational specializations influence corporate decisions.
Table 1 contains a brief overview of the literature on the impact of managerial
characteristics on their strategic decision making.
<< Insert Table 1 about here >>
2.2. Evidence on convertible debt issuer motivations
Convertibles have become a chief source of raising capital for firms (Dutordoir et al.,
2014). There continues to be a significant amount of innovation in the design of convertible
securities compared to straight debt and equity (Lewis and Verwijmeren, 2011). This rapid rate
of innovation in the design of convertible securities provides insight into firms’ motivation for
issuing convertible debt. Convertible bond design can mitigate a variety of debt- and equity-
related financing costs arising from asset substitution (Green, 1984), financial distress and
information asymmetry (Stein, 1992), risk uncertainty (Brennan and Schwartz, 1988), and over-
investment (Mayers, 1998).
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Billingsley and Smith (1996) find that firms use convertible debt primarily as an
alternative to straight debt to lower the coupon rate and thus preserve cash flows. Lewis et al.
(1999) develop a security choice model to investigate the choice of convertible debt over
standard financing instruments. They find convincing empirical evidence in support of both the
"risk-shifting hypothesis" and the "backdoor equity hypothesis." Graham and Harvey (2001) use
survey data and find that equity undervaluation is a popular reason for issuing convertible debt as
backdoor equity. However, they find little evidence that firms issue convertible debt as
sweetened debt to mitigate the incentives of asset substitution by stockholders. Dutordoir and
Van de Gucht (2009) use a dual step security choice model similar to Lewis et al. (1999) and
show that firms issue convertible debt mainly as sweetened debt and not as delayed equity.
Dorian et al. (2014) use a contingent claim framework to measure the stockholders’ risk-shifting
incentive (RSI). They compute the RSI with and without convertible debt and find that the RSI
of shareholders’ decreases with convertible debt. Hence, they conclude that firms issue
convertible debt instead of straight debt when the risk-shifting incentives are high.
Table 2 contains a brief overview of the theoretical and empirical literature on
motivations to issue convertible debt.
<< Insert Table 2 about here >>
3. Data
3.1. Sample construction
We use a sample of convertible debt, straight debt, and common equity offerings made by
U.S. exchange-listed firms for the period 2011-2015. We exclude issues made by utilities (SIC
codes 4900-4999) and financial firms (SIC codes 6000-6999) due to regulatory aspects which
constrain the capital structure policy of these companies. We also exclude privately placed non-
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Rule 144A offerings. The motivation for excluding these offerings, as well as bank loans, is that
the literature has uncovered fundamental differences in market timing and announcement effects
of public and private security issues (Fields and Mais, 1991; Gomes and Phillips, 2007).
However, we include Rule 144A offerings in the sample, as these issues are more like public
than private offerings, in terms of information and liquidity (Gomes and Phillips, 2007). After
applying these filters to the Securities Data Company (SDC) Global New Issues Database, we
obtain a raw dataset of 452 convertible debt issues made by 348 firms, 2,945 straight debt issues
made by 1,048 firms, and 3,900 equity issues made by 2,106 firms. This makes up a total of
7,297 security offerings made by 3,502 firms.
Issues that meet the following criteria are retained:
Stock return data for the issuing firm is available on Center for Research in Security
Prices (CRSP).
Data on firm-specific variables is available for the fiscal year end before issue date for
shelf and rule 144a issues and for the fiscal year end before filing date for non-shelf
issues. For the non-shelf issues for which no filing date is available, we use the issue
date. We obtain all this firm-specific data from CRSP and Compustat database.
After applying the above-mentioned criteria, we obtain a final dataset of 191 convertible
debt issues made by 149 firms (134 Rule 144a; 45 shelf issues; 12 non-shelf issues), 1,948
straight debt issues made by 672 firms (448 Rule 144a; 422 shelf issues; 1,078 non-shelf issues),
and 975 equity issues made by 590 firms (786 shelf issues; 189 non-shelf issues). This gives us a
sample of 3,114 security offerings for which we collect data on CEOs’ educational background
from BoardEx, Compustat ExecuComp, Bloomberg, and Capital IQ. Information provided on
educational background varies across CEOs in the sample. We collect information in a thorough
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and systematic manner. We obtain the names of the issuing firms’ CEOs, for the years of issues,
from Compustat ExecuComp using company identifiers, i.e., PERMNO and CUSIP. We then use
the annual reports of the issuing firms to obtain the names of the CEOs which we do not find in
Compustat ExecuComp. Next, we use these names and collect CEOs’ education data from
BoardEx. We use Bloomberg to extract education data of CEOs which is not found in BoardEx.
We use Capital IQ to collect education specialization data. We collect information on degree
types (undergraduate, MBA, non-MBA master or doctoral), degree specialization (i.e. art,
science, law, engineering, accounting and finance, business and economics, CA, CFA, CPA
etc.), year of degree awarded, and record the degree awarding institute by name, in line with
Bhagat et al. (2010). We classify the degrees into two categories depending on whether they are
obtained from a Top-20 U.S. educational institution as per the U.S. News and World Report 2015
(King et al., 2016). We also collect data on CEOs’ age and tenure, as of the issue date, in line
with Bhagat et al. (2010).
3.2. Explanatory variables
3.2.1. Measures for CEOs’ educational background
Better CEO education is represented by a higher level of academic degree. A CEO with a
doctorate or a master's degree would, therefore, be considered better educated than a CEO with
an undergraduate degree. We use several dummy variables. Undergraduate equals one if the
CEO has attained only an undergraduate degree and zero otherwise. Non-MBA Master equals
one if the CEO has a non-MBA master’s degree but does not have a PhD and zero otherwise.
MBA equals one if the CEO holds an MBA and zero otherwise. PhD equals one if the CEO has a
doctorate and zero otherwise. For robustness, we use the second measure of better education,
Education level, measured using a point scale methodology following Datta and Rajagopalan
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(1998) and Herrmann and Datta (2005). We use a four-point scale based on the highest level of
degree earned by the CEO to build the variable of Education level which equals zero if the CEO
only attended a university, i.e., CEO is a dropout from an undergraduate programme (the CEO
did not complete his/her undergraduate) or merely holds a certificate, one if the CEO has an
undergraduate degree, two if the CEO has a master’s degree, and three if the CEO has a
doctorate. We construct a dummy variable, Quantitative, to distinguish between quantitative (i.e.
business and economics, accounting and finance, CA, CFA, CPA, mathematics, and engineering)
and qualitative (i.e. art, science, history, geography, and psychology) degrees, which equals one
if the CEO has a quantitative degree and zero otherwise. We use the quality of the degree
awarding institutions to proxy for education quality. We measure education quality using dummy
variables, which equal one if the CEO has attained his/her education from one of the Top-20
U.S. universities and zero otherwise; Non-MBA Master Top-20 equals one if the CEO has a non-
MBA master degree from one of the Top-20 U.S. universities and PhD Top-20 equals one if the
CEO has a doctorate from one of the Top-20 U.S. universities.
Table 3 shows summary statistics for the education variables. Approximately 30% of the
CEOs in our sample have an undergraduate degree only, with far fewer CEOs who hold a non-
MBA master’s degree but not an MBA or a doctorate (10.60%), and approximately 33% of
CEOs who hold an MBA but not a non-MBA master’s degree or a doctorate . Only 5.20% of the
CEOs hold an MBA as well as a non-MBA master’s degree (excluding the CEOs who hold a
PhD). Approximately 18% of the CEOs have a PhD and 48.94% of the CEOs have a degree in a
quantitative subject. Top 20 U.S. institutions awarded an undergraduate degree to 53.20% of the
CEOs who hold an undergraduate degree only, a non-MBA master’s degree to around 35% of
the CEOs who hold a non-MBA master’s degree but not an MBA or a doctorate, an MBA to
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47.16% of the CEOs who hold an MBA but not a non-MBA master’s degree or a doctorate, and
a PhD to nearly 5% of the CEOs.
<< Insert Table 3 about here >>
3.2.2. Control variables
We control for CEO- and firm-specific characteristics that are likely to influence the
choice between convertible debt, straight debt, and common equity.
3.2.2.1. CEO-specific measures
As suggested by the upper echelon theory, past literature shows that CEO characteristics
are an important element affecting the adoption of innovation and inclination to pursue risky
strategies (Barker and Mueller, 2002; Bertrand and Schoar, 2003; Hambrick, 2007; Marcati et
al., 2008; Beber and Fabbri, 2012; Ahn et al., 2017). We use two commonly used measures of
CEO characteristics: Age and Tenure. We measure CEO Age as of the issue year. As managers
grow older, they become more risk averse given their limited horizon and weakened career
concerns (Hambrick and Mason 1984; Barker and Muller, 2002; Matta and Beamish, 2008).
Research suggests that older employees are less innovative in their work behavior as they have
the tendency to be set in their ways already (Carlsson and Karlsson, 1970; Janssen, 2004). We
measure Tenure as the time of the CEO in office as of the year of issue. With increased time
spent in the top position, a CEO’s experience and task knowledge increases (Hambrick and
Fukutomi, 1991; O’Sullivan, 2000). Firms with longer tenured CEOs are more likely to
emphasize innovation in their firm’s strategies (Musteen et al, 2010).
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3.2.2.2. Firm-specific measures
Following Dutordoir and Van de Gucht (2009) we group firm-specific characteristics into
three categories: i) proxies of debt related financing costs, ii) proxies of equity related adverse
selection costs, and iii) proxies of external general financing costs. We measure all control
variables as of the fiscal year end date before the security issue date unless mentioned otherwise.
(i) Debt-related financing cost measures: A considerable amount of literature shows that
convertible debt is used as sweetened debt to alleviate debt related financing costs (Green, 1984;
Brennan and Kraus, 1987; Schwartz, 1988; Mayers, 1998). We use three debt related financing
costs proxies: Volatility, Leverage, and Tax. We measure stock return Volatility over trading days
240 through 40 relative to issue date. As the volatility of a firm increases its risk of adverse
selection and asset substitution, the associated costs of financial distress increase. Hence, firms
with high volatility are more likely to choose convertible debt as a financing instrument over
straight debt. We measure Leverage as the ratio of long-term debt to total assets. The higher the
leverage is in a firm’s capital structure, the greater the adverse selection costs and possibility of
asset substitution, making convertible debt a more attractive financing instrument. We measure
Tax as the ratio of taxes paid to total assets. This variable acts as an inverse debt related
financing cost proxy. Firms with high tax payments have high tax benefits of debt because the
interest payments on debt are tax deductible.
(ii) Equity-related financing cost measures: We use two equity related financing costs
proxies: pre-announcement Stock Run-up and Relative Issue Size. We measure pre-
announcement Stock Run-up over ̶ 75 trading days relative to the issue date, in line with Lewis
et al. (1999, 2003). A large increase in firms’ stock price reduces their equity related adverse
selection costs (Lucas and McDonald, 1990). Hence, firms with a large stock run-up are
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expected to choose more equity-like securities. We measure the Relative Issue Size as offering
proceeds divided by the market value of equity. An increase in issue size can increase the
potential wealth losses to the shareholders of the company. Hence, it can increase the adverse
selection costs associated with equity financing (Krasker, 1986).
(iii) General external financing cost measures: We use three issuer-specific external
financing cost proxies: Firm Size, return on assets (ROA), and market to book ratio (M to B). We
calculate Firm Size as the natural logarithm of total assets. Firm Size acts as a proxy for both
debt and equity related financing costs as it affects the information asymmetry related to firm
value and risk. The larger the firm size is the lower the information asymmetry, hence lower
costs of debt and equity financing. We measure ROA as the ratio of net income before interest
and taxes to total assets. Firms with a higher return on assets are more likely to opt for straight
debt rather than convertible debt, as high profitability before the issue makes it easier for the firm
to pay interest on debt securities (Lewis et al., 1999; Dutordoir et al., 2014). On the other hand,
when a firm with high return on assets issues securities, stockholders are more likely to infer that
this firm is overvalued, since undervalued firms would rather resort to internal financing.
Therefore, firms with high return on assets are expected to incur higher equity related adverse
selection costs (Myers and Majluf, 1984). We use ROA as one of the variables for our interaction
terms as well since this variable proxies not only for the debt capacity of the firm but also acts as
a measure of firm performance, both of which can effect a firm’s cost of capital. We calculate M
to B as the market value of equity divided by total assets. A higher market-to-book ratio is
associated with higher information asymmetry about firm value and risk, increasing the costs of
debt and equity financing (Brennan and Schwartz, 1988; Lewis et al., 1999; Dutordoir and Van
de Gucht, 2009). We use the M to B ratio as our second variable for the interaction term as it
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affects the firm in two ways. Firstly, firms' M to B ratio represents their growth opportunities and
firms with more growth opportunities are more likely to issue convertible debt (Mayers, 1998).
Second, like firm size, the M to B ratio also affects a firm's information asymmetry about its
(future) value and risk.
Table 4 shows the descriptive statistics for our control variables. Columns (1), (2), and
(3) contain the mean values of the control variables across convertible debt, straight debt, and
common equity issues. Columns (4), (5), and (6) display the t-stats for pairwise mean
comparisons.
<< Insert Table 4 about here >>
Looking at the CEO characteristics across the three security samples, the average CEO
Age is 55 years for convertible debt issuers. The average Age of CEOs issuing straight debt is
slightly higher, while that of CEOs issuing common equity is slightly lower. The average CEO
tenure is 10.40 years for convertible debt issuers, slightly longer for straight debt issuers and
slightly shorter for common equity issuers. Consistent with the sweetened debt view point,
convertible debt issuers have higher Volatility compared to straight debt issuers. Leverage is high
for convertible debt issuers compared to straight debt issuers. The higher the leverage is in firms’
capital structures, the greater the risk related to adverse selection costs and the possibility of asset
substitution, making convertible debt a more attractive financing instrument. Firms issuing
straight debt securities have the highest Tax ratio. Firms with higher tax payments have higher
tax benefits of debt because interest payments on debt are tax deductible. Hence debt issuers'
average values of Tax ratio are higher compared to convertible debt issuers and equity issuers.
Next, in line with Dutordoir and Van de Gucht (2009), convertible debt issuers have a higher
average Tax ratio compared to equity issuers. Stock Run-up is highest for the equity issuers
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followed by convertible debt issuers. A large increase in firms’ stock price reduces their equity
related adverse selection costs (Lucas and McDonald, 1990). Hence, the equity issuers have the
highest average Stock Run-up. Convertible debt issuers have higher Relative Issue Size compared
to equity issuers. An increase in issue size can increase the potential wealth losses to the
shareholders of the company. Hence, it can increase the adverse selection costs associated with
equity financing (Krasker, 1986). ROA of convertible debt issuers is higher compared to equity
issuers. When a firm with a high return on assets issues equity, stockholders are more likely to
infer that this firm is overvalued since undervalued firms would rather resort to internal
financing. Therefore, firms with a high return on assets are expected to incur higher equity
related adverse selection costs (Myers and Majluf, 1984). The statistics also show that firms
which issue straight debt have the highest ROA. High profitability before the issue makes it
easier for a company to pay interest on debt securities (Lewis et al., 1999; Dutordoir and Van de
Gucht, 2009). Straight debt issuers have the largest Firm Size. Firm’s size represents the extent
of information asymmetry about firm risk and value. The statistics show that straight debt issuers
face the least information asymmetry and convertible debt issuers face the most information
asymmetry constraints. Convertible debt issuers have a higher M to B ratio compared to straight
debt issuers. High growth firms have a higher information asymmetry related to firms’ risk,
increasing the cost of debt. Therefore, high growth firms are more likely to issue convertible debt
rather than straight debt (Dutordoir and Van de Gucht, 2009). Columns (4), (5), and (6) present
the t-stats for pairwise mean comparisons. These tests compare the mean values for the three
types of issuers. We find that Leverage ratio of convertible debt issuers is significantly larger,
whereas the Firm size of convertible debt issuers is significantly smaller than straight debt
issuers, in line with the sweetened debt viewpoint. Convertible and straight debt issuers also
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differ in their level of equity related financing costs. The t-stat for the Relative Issue Size shows
that convertible debt issuers have significantly lower equity related financing costs than straight
debt issuers as expected based on the equity-linked nature of convertible debt. The convertible
debt issuers have a significantly higher pre-announcement Stock Run-up and lower ROA than
straight debt issuers, as expected. The results presented in Column (5) are consistent with the
delayed equity viewpoint, as they indicate that convertible issuers have a significantly smaller
Stock Run-up and Firm Size and significantly higher ROA than equity issuers. Column (6) shows
that straight debt issuers have significantly higher Tax ratio, larger Firm Size and higher ROA
than equity issuers as expected. Equity issuers on the other hand, have larger M to B ratio than
straight debt issuers.
4. Methodology and results
We estimate a multinomial probit regression model to examine the relation between CEO
educational background and the choice of convertible debt. Although we are primarily interested
in the impact of CEO educational background on the choice of convertible debt, the model of the
security issue decision also includes straight debt and common equity issues. We include these
standard financing instruments in the security choice model estimation because managers choose
to issue convertible debt over these more basic securities. The effect of CEOs’ education on the
choice of convertible debt is conditional on their decision not to issue straight debt or common
equity (Lewis et al., 1999).
The dependent variable is a categorical variable, where category one equals convertible
debt, category two equals straight debt, and category three equals equity. With the dependent
variable being categorical, we can use either a multinomial logit or a multinomial probit model.
The multinomial logit model assumes that the odds of choosing an alternative (i.e., convertible
19
debt) over other alternatives (i.e., straight debt and common equity) are independent of the
choice set for all pairs (convertible debt versus straight debt, convertible debt versus common
equity, and straight debt versus common equity). This property is often referred to as the axiom
of independence from irrelevant alternatives (IIA). We conduct the Hausman test and find that
the assumption of independence from irrelevant alternatives is violated. We, therefore, use the
multinomial probit model which does not require the IIA property (Dong et al., 2017).
We begin by investigating the impact of better CEO education and an MBA on the
likelihood to issue convertible bonds. Table 5 reports estimates from the baseline regressions.
Regression results in Table 5 show that CEO education is a strong determinant of security choice
while controlling for all other CEO- and firm-specific determinants of security choice. In
Column (1), the coefficient of Undergraduate, compare against higher levels of education, i.e., a
master’s degree or doctorate, is significantly negative for convertible debt versus equity. This
finding indicates that a CEO with only an undergraduate degree is less likely to choose
convertible debt over a standard financing instrument compared to a CEO with a higher level of
education, i.e., a master's degree or doctorate. In Column (2), the coefficient for Non-MBA
Master compared against an undergraduate education is significantly positive for both,
convertible debt versus straight debt and equity. The coefficient for PhD compared against an
undergraduate education is also significantly positive for convertible debt versus equity, whereas
only marginally insignificant at 11% for convertible debt versus debt. Our results thus indicate
that CEOs with a non-MBA master or a doctorate are more likely to issue convertible debt rather
than straight debt or equity. The results are in line with the upper echelon perspective that high
levels of education are associated with innovation and the ability to evaluate complex choices.
20
We then separately examine the effect of having an MBA education on the decision to
issue convertible debt over standard financing instruments, while controlling for other CEO
characteristics and financing cost measures. In Column (3), the coefficient of MBA is
significantly negative for convertible debt versus straight debt, indicating that a CEO with an
MBA is more likely to opt for a standard financing instrument rather than a relatively complex
and innovative security, i.e., convertible debt. Since non-MBA master and MBA have the same
level of education, in Column (4), we examine Undergraduate, MBA, and PhD against the non-
MBA master level of education, to disentangle the effect of MBA from Non-MBA Master. The
coefficient of MBA is significantly negative for convertible debt versus straight debt and only
marginally insignificant (at 12%) for convertible debt verses equity. Also, the coefficient is
significantly negative for Undergraduate and positive for PhD at a p-value of 0.12, given the
choice of convertible debt versus equity. The results of the regression in Column (4), thus
confirm our earlier findings that CEOs with higher levels of education are more likely to choose
convertible debt over standard financing instruments, whereas CEOs with an MBA are less likely
to choose convertible debt. These findings are in line with the upper echelon perspective which
states that (i) managers with higher levels of education are more perceptive of innovation and
that (ii) managers with an MBA degree are educated to avoid big losses and to pursue short term
performance at the expense of innovation (Hambrick and Mason, 1984). Finkelstein et al. (2009)
also suggest that managers with an MBA are inclined to be risk-averse and conventional, as the
curriculum of an MBA emphasizes and stimulates risk-aversion and a conservative mindset, thus
providing support to our results that managers with an MBA are more likely to issue standard
financing instruments.
<< Insert Table 5 about here >>
21
4.1. Robustness checks
4.1.1. Alternative education measure
In table 6 we provide regression results using our second measure of better CEO
education termed as Education level. In Column (1), we show that Education level is
significantly positive for convertible debt versus equity, indicating that CEOs with higher levels
of education are more innovative and have a higher ability to evaluate complex choices
(Hambrick and Mason, 1984; Hambrick, 2007 and 2014; Finkelstein et al., 2009). Hence CEOs
with higher levels of education are more likely to issue a relatively innovative and complex
security like convertible debt. Since an MBA and a non-MBA master's degree are both treated at
the same level (i.e., 2) in Education level, we examine the effect of Education level together with
MBA in Column (2). Results in Column (2) show that MBA is significantly negative for both
convertible debt versus straight debt and equity as shown in Table 5, whereas Education level
remains significantly positive, thus confirming that managers with an MBA are less likely to
issue convertible debt. In Column (3), we make a further attempt to disentangle the effect of
MBA from Non-MBA Master in the education level variable by adding the interaction term of
MBA with Master2 along with Education level. The interaction term ensures that the effect of
Education level on security choice decision is not biased downwards because of the possible
opposite effect of an MBA and a non-MBA master on the security choice decision. The
coefficient of Education level in Column (3) remains significantly positive. The results in Table
6 are, therefore, in line with the upper echelon perspective that managers with higher levels of
education are open-minded, tolerant of change, and have a greater ability to evaluate complex
2 Master is a dummy variable which equals one if the CEO has a master level education, i.e., MBA or non-MBA
master.
22
choices, whereas managers with an MBA are educated to avoid big losses and to pursue short
term performance at the expense of innovation.
<< Insert Table 6 about here >>
4.1.2. Endogenous CEO-firm matching
We anticipate that the matching of a CEO to a firm is not the result of a random
assignment. We thus need to control for the endogenous CEO-firm matching. According to the
matching theory3, a two-sided matching process exists in which CEOs and firms select one
another, leading to strong relationships between firms’ and CEOs’ characteristics (Allgood and
Farrell, 2003; Li and Ueda, 2006). This implies that the CEOs with a stronger educational
background are likely to command greater value in the labor market and be in a better position to
‘self-select’ into the most viable firms. Similarly, better firms are more likely to attract and
appoint better-educated CEOs since firms perceive that education signals unknown or latent
talent (King et al., 2016). To account for this endogenous CEO-firm matching, we use a binary
treatment model in line with Cerulli (2014). The model provides a consistent estimation of
average treatment effects (i.e., CEO education) under the assumption of "selection on
unobservables” by using instrumental variables (IV) and two-step selection-model (Heckman).
The model requires the dependent variable to be either continuous or binary, whereas the
dependent variable in our study is categorical (security choice variable with three categories;
convertible debt, straight debt, and common equity). We thus divide our sample into two sub-
samples; sub-sample one contains convertible debt and straight debt issues, and sub-sample two
contains convertible debt and common equity issues. In these two sub-samples, our dependent
3 In economics, the matching theory by Mortensen is a mathematical framework attempting to describe the
formation of mutually beneficial relationships over time.
23
variable is binary with the choice of security being either convertible debt versus debt or
convertible debt versus equity. For each sub-sample we individually examine the effect of CEO
education variables (Undergraduate, Non-MBA Master, MBA, and PhD) on the choice of
convertible debt and use industry averages of CEO education as instruments for the relevant
education variables4.
In Table 7 we show results after controlling for the endogenous CEO-firm matching.
These results reaffirm our key findings by showing that firms which employ CEOs with better
education are more likely to issue convertible debt over standard financing instruments and that
managers with an MBA are more likely to opt for standard financing instruments rather than a
relatively complex and innovative instrument, i.e., convertible debt.
<< Insert Table 7 about here >>
4.1.3. Interaction terms
We add interaction terms of two financing cost measures, ROA and M to B, with CEO
education measures to examine whether a better educated CEO issues convertible debt only
when it is more beneficial to the firm, e.g., when the costs of issuing standard securities are high.
High profitability before the issue makes it easier for the firm to pay interest on debt securities
(Lewis et al., 1999; Dutordoir et al., 2014) making debt a relatively cheaper source of financing.
We thus expect the coefficients for the interaction terms of better CEO education (i.e., Non-MBA
Master and PhD) with ROA to be significantly negative for convertible debt versus debt, as
better-educated CEOs should realize that convertibles are more suitable (relative to straight debt)
for low-ROA firms. On the other hand, firms with a high ROA are expected to incur high equity-
4 We use the industry average of Undergraduate as an instrument for Undergraduate, industry average of Non-MBA
Master as an instrument for Non-MBA Master, industry average of MBA as an instrument for MBA, and industry
average of PhD as an instrument for PhD.
24
related adverse selection costs (Myers and Majluf, 1984). Hence we expect the coefficients for
the interaction terms to be significantly positive for convertible debt versus equity. Firms with a
larger M to B ratio tend to have higher levels of information asymmetry about their value and
risk and thus incur higher costs of obtaining both straight debt and equity (Brennan and
Schwartz, 1988; Lewis et al., 1999). We, therefore, expect the coefficients for the interaction
terms of CEO education measures (i.e., Non-MBA Master and PhD) with the M to B ratio to be
significantly positive for both convertible debt versus straight debt and equity. On the other hand,
we expect significantly negative coefficients of the interaction terms of MBA with ROA and M to
B since in our baseline tests we find support for the notion that managers with an MBA are risk
averse and conservative and hence less likely to issue a relatively complex security, i.e.,
convertible debt.
Table 8 shows the regression results after adding the interaction terms of CEO education
measures with ROA and M to B. In Column (1), we examine whether a CEO with a Non-MBA
Master or a PhD, as compared to a CEO with a mere undergraduate degree, is more likely to
issue convertible debt only when it is more beneficial for the firm, i.e., when costs of issuing
standard financing instruments like straight debt and equity are high. The coefficient of the
interaction term of Non-MBA Master with ROA is significantly negative for convertible debt
versus straight debt, whereas the coefficient of the interaction term of PhD with ROA is
significantly positive for convertible debt versus equity, indicating that a CEO with better
education will issue convertible debt only when it is more beneficial for the firm. In Column (2),
we show regression results for the interaction term of MBA with ROA. The coefficient of the
interaction term of ROA with MBA is significantly negative for convertible debt versus straight
debt and negative with a p-value of 0.13 for convertible debt versus equity, indicating that
25
managers with an MBA are more likely to opt for standard financing instruments rather than
convertible debt even when issuing convertible debt is more beneficial for the firm, i.e., costs of
debt and equity are high. This behavior of managers with an MBA could be due to their risk-
averse and conservative mindset stimulated by an MBA education (Finkelstein et al., 2009). The
coefficients of the interaction terms of CEO education measures with M to B are insignificant.
<< Insert Table 8 about here >>
4.1.4. Education specialization and quality
We also analyze the impact of education specialization (having a quantitative or
qualitative degree) on the choice of security. In Table 9, Column (1) shows that coefficient of
Quantitative is insignificant. However, after controlling for endogenous CEO-firm matching in
Column (2), the coefficient of Quantitative becomes significantly positive for convertible debt
versus equity, indicating that CEOs with a specialization in quantitative subjects are more likely
to opt for convertible debt.
We then examine the impact of CEOs’ education quality on the security choice decision.
Graduates from more prestigious institutes perform better because they are more intelligent and
skillful (Chevalier and Ellison, 1999). Literature suggests that both education level and quality
affect firm performance which results from the strategic choices made by the managers (Jalbert
et al., 2002; Bhagat et al., 2010; Miller et al., 2015; King et al., 2016). We thus examine whether
both the level and quality of CEO education (Non-MBA Master Top-20 and PhD Top-20) affect
the choice to issue convertible debt. In Table 9, Column (3) shows that Non-MBA Master Top-20
is significantly positive for both convertible debt versus debt and equity at 1%. After controlling
for endogeneity in Columns (4) and (5), we find that Non-MBA Master Top-20 remains
significantly positive for convertible debt versus equity and PhD Top 20 becomes significantly
26
positive for convertible debt versus debt. Hence, indicating that CEOs with better quality of
education are more likely to go for a relatively complex security like convertible debt because
they are cannier.
<< Insert Table 9 about here >>
4.2. Effect of CEOs’ education on market reactions
We also investigate the relationship between CEO education and stock market reaction to
convertible debt offer announcement. Bhagat et al. (2010) report a positive market reaction to
announcements of appointments of CEOs with stronger academic credentials. If the stock market
believes that CEOs with a higher level and quality of education are more likely to issue
convertibles when this is suitable for the firm, Non-MBA Master, PhD, Education Level, Non-
MBA Top 20, and PhD Top 20 should have a positive impact on their reaction to the convertible
bond announcement. We use data on cumulative abnormal returns for an event window of 1 to
1 and 2 to 2, obtained using Eventus. Table 10, Columns (1) and (2) present the results for
models analyzing the effect of CEO education level (i.e., Non-MBA Master, PhD and Education
Level) on market reaction to convertible bonds offer announcement. The coefficients of
education variables are insignificant in Columns (1) and (2). The coefficients of Non-MBA
Master and PhD remain insignificant after controlling for endogeneity in Columns (3) and (4). In
Column (5) the coefficient of Education Level becomes significantly positive for the five day
event window (-2, 2) after controlling for endogeneity, indicating that the stock market believes
that better educated CEOs are more likely to issue convertibles when this is beneficial for the
firm. In unreported tests, we do not find any significant effect of education quality on market
reaction to convertible bonds offer announcement.
27
<< Insert Table 10 about here >>
5. Conclusion
We analyze the impact of CEO education on the choice to issue convertible debt rather
than straight debt or equity. In line with the upper echelon theory which suggests that better
educated CEOs are less risk averse and more open to change, our results show that CEOs with a
higher level of education are more likely to choose convertible debt over straight debt and equity.
However, we find that CEOs with an MBA are less likely to choose convertible debt over
straight debt and equity. This finding confirms the notion that the curriculum of an MBA
emphasizes and stimulates risk-aversion and a conservative mindset. Moreover, we also examine
whether CEOs with a higher level of education issue convertible debt only when it is more
beneficial to the firm, which is when the costs of raising standard securities are high. We indeed
find that CEOs with higher levels of education are more likely to issue convertible debt when
costs of rising debt and equity are high. Our results are robust to controlling for potential
endogenous CEO-firm matching. Further investigation into the effect of education on security
choice reveals that not only the level but also the quality of education (as evidenced by the
ranking of the awarding institution) affects the choice of security, with a higher-quality
managerial education associated with a higher likelihood of choosing convertibles over standard
financing instruments. We also find evidence of positive market reaction to convertible bond
issues by better educated CEOs, indicating market’s belief that managers with high levels of
education are more likely to issue convertibles when this is suitable for the firm.
28
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Table 1
Overview of empirical evidence regarding the impact of managers’ educational background on firms
This table displays the results of prior empirical studies on the impact of managers’ educational background on firm
performance, strategic choices, diversification, mergers and acquisitions, and innovation. Yes (No) indicates
whether the evidence supports (does not support) the research question or hypothesis mentioned in the previous
column.
Paper Scope Tested Rationales Evidence
Nelson and Phelps (1966) US Human capital theory Yes
Bertrand and Schoar
(2003) US
Positive impact of MBA
on firm performance Yes
Malmendier and Tate
(2005) US
Positive impact of
educational background
on firm performance
Yes
King et al. (2016) US
Positive impact of CEO
education level and MBA
on bank performance
Yes
Wiersema and Bental
(1992) US
Positive impact of top
managements’ education
level and specialization on
strategic change
Yes
Herrmann and Datta
(2005) US
Positive impact of top
management teams’
education on international
diversification.
Yes
Palmer and Barber (2001) US
Positive impact of MBA
education on diversifying
acquisitions.
Yes
Barker and Mueller (2002) Global Upper echelon theory. Yes
Beber and Fabbri (2012) US
Positive impact of MBA
on the use of time varying
Foreign Exchange
derivatives.
Yes
34
Table 2
Overview of empirical evidence on the motivations for convertible debt issues
In line with Dutordoir and Van de Gucht (2009) the table presents the results of previous empirical research on the
motivations for convertible debt issues. Yes (No) indicates whether the evidence supports (does not support) the
rationale or hypothesis mentioned in the preceding column.
Paper Scope Tested Rationales Evidence
Green (1984) US Agency cost theory (for
debt-like conv.) Yes
Brigham (1966) US
Delayed equity viewpoint Yes
Sweetened debt
viewpoint No
(no specific theories
mentioned)
Brennan and Schwartz
(1988)
Adverse selection costs
(for debt-like conv.) Yes
Billingsley and Smith
(1996) US
Delayed equity viewpoint Yes
Sweetened debt
viewpoint Yes
(no specific theories
mentioned)
Mayers (1998) US
Sequential-financing
hypothesis (for debt-like
conv.)
Yes
Lewis et al. (1999) US
Green (1984) (for debt-
like Yes
conv.) Yes
Stein (1992) (for equity-
like
conv.)
Graham and Harvey
(2001) US
Green (1984) No
Brennan and Kraus
(1987) Yes
Brennan and Schwartz
(1988) Yes
Stein (1992) Yes
Mayers (1998) Yes
Dutordoir and Van de
Gucht (2009) Europe
Delayed equity viewpoint No
Sweetened debt
viewpoint Yes
Dorian et al. (2014) US Green (1984) Yes
35
Table 3
Descriptive statistics of CEO education variables
This table shows descriptive statistics for the education variables in the sample. The sample contains 3,114
observations comprised of convertible debt, straight debt and common equity issues. Undergraduate is a dummy
variable which equals one if the CEO has only an undergraduate degree. Non-MBA Master is a dummy variable
which equals one of if the CEO has a non-MBA master’s degree but does not hold a PhD at the same time. MBA is a
dummy variable which equals one if the CEO has an MBA. MBA*Master is a dummy variable which equals one if
the CEO holds an MBA as well as a non-MBA master degree. PhD is a dummy variable which equals one if the
CEO has a PhD. Quantitative is a dummy variable which equals one if the CEO has a degree in a quantitative
subject. UG Top-20 is a dummy variable equal to one if the CEO has obtained an undergraduate degree from one of
the top-20 universities. Master Top-20 is a dummy variable equal to one if the CEO has obtained a master’s degree
from one of the top-20 universities. MBA Top-20 is a dummy variable equal to one if the CEO has obtained an MBA
degree from one of the top-20 universities. PhD Top-20 is a dummy variable equal to one if the CEO has obtained a
PhD degree from one of the top-20 universities. Top-20 universities are as per U.S. News and World Report 2015.
Education Mean (%) 25th Percentile Median 75th Percentile
Undergraduate 29.57 0 0 1
Non-MBA Master 10.60 0 0 0
MBA 33.37 0 0 1
MBA*Master 5.20 0 0 0
PhD 17.73 0 0 0
Quantitative 48.94 0 0 1
UG Top-20 53.20 0 1 1
Non-MBA Master Top-20 35.45 0 0 1
MBA Top-20 47.16 0 0 1
PhD Top-20 5.23 0 0 0
36
Table 4
Descriptive statistics of Manager and Issuer Characteristics by Security
This table reports descriptive firm-specific statistics for samples of straight debt, convertible debt and equity
offerings made by U.S. exchange-listed firms from 2011 to 2015 excluding utility firms (SIC: 4900-4999) and
financial firms (SIC: 6000-6999). The security samples are obtained from SDC Platinum. The sample consists of
191 of convertible debt issues, 1948 straight debt issues, and 975 equity issues. Data on firm-specific characteristics
is retrieved from CRSP and Compustat and measured at fiscal year-end prior to the security issue date unless
otherwise indicated. Age is the measure of the age of CEO of the firm issuing security at the time of issue. Tenure is
the measure of the duration of CEO in office. Leverage is total debt divided by total assets. Volatility denotes the
daily stock return standard deviation measured over trading days -240 through -40 relative to the announcement
date. Tax is taxes paid divided by total assets. ROA is net income before interest and taxes divided by total assets.
Stock Run-up is the cumulative stock return, measured over the trading days -75 to -1 relative to the issue date. M to
B is the market-to-book ratio, measured as total assets plus the market value of equity measured one week prior to
the announcement date minus the book value of equity divide by total assets. TA is the book value of total assets. *,
** and *** denote significance at the 0.10, 0.05 and 0.01 levels, respectively.
(1) (2) (3) (4) (5) (6)
Convertible
Debt
Straight
Debt
Common
Equity t-stat for pairwise differences in mean values
Mean Mean Mean Convertible
Debt Vs Debt
Convertible
Debt Vs equity Debt Vs Equity
Age 55.424 57.527 54.314 -4.24*** 2.15** 12.53***
Tenure 10.401 11.191 7.777 -1.50 4.77*** 12.52***
Volatility 0.030 0.018 0.036 14.09*** -7.31*** -41.97***
Leverage 0.964 0.499 0.370 7.52*** 9.20*** 4.03***
Tax 0.010 0.024 0.005 -10.64*** 3.24*** 27.10***
Stock Run-up 0.001 0.0001 0.002 2.29*** -4.40*** 2.29**
Issue Size 391.916 1806.165 163.5459 -12.64*** 1.96* 28.38***
ROA 0.025 0.141 -0.181 -8.37*** 13.44*** 43.28***
Firm Size 7.020 9.586 5.465 -20.97*** 12.15*** 65.04***
M to B 4.265 3.576 4.660 2.53** -1.39 -7.71***
37
Table 5
Analysis of the effect of CEO education (Undergraduate, Non-MBA Master, MBA, and PhD) on the choice to issue convertible bonds.
CEO education data is obtained from Compustat ExecuComp, BoardEx, Bloomberg, and S&P Capital IQ. The sample contains 3,114 observations comprised of
convertible debt, straight debt, and common equity issues. Undergraduate is a dummy variable which equals one if the CEO has an undergraduate degree only.
Non-MBA Master is a dummy variable which equals one of if the CEO has a non-MBA master’s degree but does not hold a PhD at the same time. MBA is a
dummy variable which equals one if the CEO has an MBA. PhD is a dummy variable which equals one if the CEO has a PhD. Data on firm-specific
characteristics is retrieved from CRSP and Compustat, and measured at fiscal year-end prior to the security issue date unless otherwise indicated. CB in the
columns headings stands for convertible bond. Results are with firm-level clustered standard errors. See Table 4 for a description of the control variables.*, **,
and *** denote significance at the 0.10, 0.05, and 0.01 levels, respectively.
(1) (2) (3) (4)
CB Vs. Debt CB Vs. Equity CB Vs. Debt CB Vs. Equity CB Vs. Debt CB Vs. Equity CB Vs. Debt CB Vs. Equity
Undergraduate -0.021 -0.251*
-0.123 -0.295*
(0.125) (0.129)
(0.163) (0.165)
Non-MBA Master
0.513*** 0.627***
(0.175) (0.180)
MBA
-0.232* -0.130 -0.269* -0.239
(0.123) (0.126) (0.156) (0.156)
PhD
0.264 0.552***
0.0723 0.276
(0.166) (0.154)
(0.192) (0.178)
Age -0.032*** -0.002 -0.035*** -0.006 -0.032*** -0.001 -0.033*** -0.003
(0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010)
Tenure 0.015 0.047*** 0.012 0.041*** 0.014 0.047*** 0.012 0.043***
(0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010)
Volatility 28.070*** 1.735 27.700*** 1.272 28.440*** 2.322 27.620*** 1.145
(6.364) (6.295) (6.387) (6.329) (6.402) (6.289) (6.391) (6.289)
Leverage 0.103* 0.324*** 0.102* 0.322*** 0.095* 0.314*** 0.100* 0.321***
(0.058) (0.058) (0.058) (0.060) (0.058) (0.058) (0.058) (0.059)
Tax -10.910*** -0.685 -10.720*** -0.852 -10.85*** -0.729 -11.180*** -1.127
(3.688) (3.664) (3.665) (3.667) (3.682) (3.660) (3.664) (3.650)
38
Stock Run-up 9.433 -80.210*** 2.904 -84.490*** 10.330 -79.060*** 7.796 -82.380***
(23.830) (19.700) (23.880) (19.740) (23.940) (19.680) (23.930) (19.840)
Issue size -3.962*** 2.856*** -3.909*** 2.934*** -4.004*** 2.770*** -3.948*** 2.883***
(0.535) (0.495) (0.533) (0.497) (0.533) (0.488) (0.532) (0.499)
ROA -1.896*** 0.795** -1.840** 0.929*** -1.916*** 0.776** -1.856*** 0.887**
(0.724) (0.341) (0.731) (0.355) (0.725) (0.337) (0.721) (0.354)
Firm size -0.744*** 0.350*** -0.751*** 0.357*** -0.742*** 0.348*** -0.737*** 0.360***
(0.052) (0.047) (0.052) (0.048) (0.053) (0.047) (0.053) (0.048)
M to B 0.014 0.047*** 0.012 0.045*** 0.014* 0.047*** 0.013* 0.045***
(0.018) (0.017) (0.017) (0.017) (0.017) (0.017) (0.017) (0.017)
39
Table 6
Analysis of the impact of CEO Education level, and MBA on the choice to issue convertible bonds.
CEO education data is obtained from Compustat ExecuComp, BoardEx, Bloomberg, and S&P Capital IQ. The
sample contains 3,114 observations comprised of convertible debt, straight debt, and common equity issues.
Education level is constructed using a point scale ranging from 0 – 3: where 0 equals a CEO who is a dropout from
an undergraduate programme, 1 equals a CEO who has an undergraduate degree only, 2 equals a CEO who has a
master’s degree but does not hold a PhD at the same time, and 3 equals a CEO who has a PhD. MBA is a dummy
variable which equals one if the CEO has an MBA. MBA*Master is an interaction term of MBA with Master, which
equals one if the CEO holds both, a non-MBA master’s degree, and an MBA degree. Data on Firm-specific
characteristics is retrieved from CRSP and Compustat, and measured at fiscal year-end prior to the security issue
date unless otherwise indicated. CB in the columns headings stands for convertible bond. Results are with firm-level
clustered standard errors. See Table 4 for a description of the control variables. *, **, and *** denote significance at
the 0.10, 0.05, and 0.01 levels, respectively
(1) (2) (3)
CB Vs. Debt CB Vs. Equity CB Vs. Debt CB Vs. Equity CB Vs. Debt CB Vs. Equity
Education Level 0.064 0.261*** 0.107 0.297*** 0.0532 0.266***
(0.090) (0.090) (0.090) (0.090) (0.090) (0.090)
MBA
-0.259** -0.245*
(0.127) (0.129)
MBA*MA
0.370 -0.115
(0.255) (0.282)
Age -0.033*** -0.003 -0.033*** -0.004 -0.032*** -0.004
(0.010) (0.010) (0.010) (0.010) (0.010) (0.010)
Tenure 0.015 0.045*** 0.012 0.043*** 0.016 0.045***
(0.010) (0.010) (0.010) (0.010) (0.010) (0.010)
Volatility 27.730*** 1.342 27.660*** 1.198 27.900*** 1.297
(6.354) (6.292) (6.392) (6.295) (6.350) (6.290)
Leverage 0.107* 0.328*** 0.101* 0.322*** 0.107* 0.329***
(0.058) (0.059) (0.058) (0.059) (0.058) (0.059)
Tax -11.070*** -0.927 -11.220*** -1.138 -11.010*** -1.064
(3.664) (3.652) (3.653) (3.645) (3.680) (3.659)
Stock Run-up 7.710 -81.95*** 7.529 -82.77*** 6.732 -82.17***
(23.900) (19.850) (23.940) (19.870) (23.990) (19.890)
Issue size -3.924*** 2.942*** -3.929*** 2.925*** -3.929*** 2.951***
(0.535) (0.502) (0.534) (0.502) (0.536) (0.502)
ROA -1.855** 0.874** -1.842*** 0.900** -1.905*** 0.878**
(0.722) (0.351) (0.718) (0.353) (0.728) (0.352)
Firm size -0.742*** 0.358*** -0.736*** 0.361*** -0.746*** 0.360***
(0.053) (0.048) (0.053) (0.048) (0.053) (0.048)
M to B 0.014 0.046*** 0.014 0.046*** 0.013 0.046***
(0.018) (0.017) (0.018) (0.017) (0.018) (0.017)
40
Table 7
Analysis of the impact of CEO education (Undergraduate, Non-MBA Master, MBA, and PhD)on the choice to issue convertible bonds. – After
controlling for endogeneity.
CEO education data is obtained from Compustat ExecuComp, BoardEx, Bloomberg, and S&P Capital IQ. The sub-sample one contains 2,139 observations
comprised of convertible debt and straight debt issues, and sub-sample two contains 1,166 observations comprised of convertible debt and common equity issues.
Undergraduate is a dummy variable which equals one if the CEO has an undergraduate degree only. Non-MBA Master is a dummy variable which equals one of
if the CEO has a non-MBA master’s degree but does not hold a PhD at the same time. MBA is a dummy variable which equals one if the CEO has an MBA. PhD
is a dummy variable which equals one if the CEO has a PhD. Data on firm-specific characteristics is retrieved from CRSP and Compustat, and measured at fiscal
year-end prior to the security issue date unless otherwise indicated. Results are with firm-level clustered standard errors. See Table 4 for a description of the
control variables. CB in the columns headings stands for convertible bond. *, **, and *** denote significance at the 0.10, 0.05, and 0.01 levels, respectively.
(1) (2) (3) (4)
CB Vs. Debt CB Vs. Equity CB Vs. Debt CB Vs. Equity CB Vs. Debt CB Vs. Equity CB Vs. Debt CB Vs. Equity
Undergraduate -0.048** -0.004
(0.021) (0.072)
Non-MBA Master
0.106*** 0.172*
(0.028) (0.098)
MBA
-0.034* -0.179
(0.019) (0.110)
PhD
0.065*** -0.092
(0.024) (0.055)
Age -0.003*** 0.0001 -0.003*** -0.001 -0.003*** 0.0001 -0.004*** 0.0005
(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.002)
Tenure 0.003*** 0.008*** 0.003*** 0.008*** 0.002*** 0.007*** 0.002** 0.009***
(0.001) (0.002) (0.001) (0.002) (0.001) (0.002) (0.001) (0.002)
Volatility 4.216*** 1.328 4.220*** 1.187 4.099*** 1.540 3.991*** 1.113
(0.676) (1.00) (0.675) (0.979) (0.673) (1.005) (0.665) (0.977)
Leverage 0.012* 0.0778*** 0.010 0.077*** 0.012* 0.071*** 0.011* 0.072***
(0.007) (0.01) (0.007) (0.01) (0.007) (0.01) (0.007) (0.01)
Tax 0.010 -0.311 0.227 -0.386 -0.087 -0.783 -0.204 -0.275
(0.333) (0.752) (0.340) (0.749) (0.331) (0.817) (0.332) (0.750)
41
Stock Run-up -3.767 -10.790*** -4.414* -11.020*** -3.313 -10.780*** -2.954 -10.240***
(2.513) (2.794) (2.522) (2.788) (2.501) (2.847) (2.495) (2.816)
Issue size -0.436*** 0.371*** -0.421*** 0.369*** -0.430*** 0.328*** -0.427*** 0.374***
(0.051) (0.084) (0.051) (0.084) (0.051) (0.090) (0.051) (0.085)
ROA -0.692*** 0.049 -0.714*** 0.055 -0.697*** 0.071 -0.675*** 0.019
(0.070) (0.050) (0.070) (0.050) (0.070) (0.052) (0.070) (0.051)
Firm size -0.068*** 0.059*** -0.067*** 0.056*** -0.065*** 0.057*** -0.066*** 0.056***
(0.005) (0.008) (0.005) (0.008) (0.005) (0.008) (0.005) (0.008)
M to B 0.007*** 0.007*** 0.006*** 0.006** 0.007*** 0.005** 0.006*** 0.006***
(0.002) (0.003) (0.002) (0.003) (0.002) (0.003) (0.002) (0.003)
42
Table 8
Analysis of the impact of CEO education (Undergraduate, Non-MBA Master, and PhD) on the choice to issue convertible bonds. –including interaction terms of the
education variables with return on assets and market to book ratio.
CEO education data is obtained from Compustat ExecuComp, BoardEx, Bloomberg, and S&P Capital IQ. The sample contains 3,114 observations comprised of convertible
debt, straight debt, and common equity issues. Non-MBA Master is a dummy variable which equals one of if the CEO has a non-MBA master’s degree but does not hold a
PhD at the same time. MBA is a dummy variable which equals one if the CEO has an MBA. PhD is a dummy variable which equals one if the CEO has a PhD. Non-MBA
Master*ROA is the interaction term of Non-MBA Master with return on assets. Non-MBA Master *M to B is the interaction term of Non-MBA Master with the market to book
ratio. MBA*ROA is the interaction term of MBA with return on assets. MBA*M to B is the interaction term of MBA with the market to book ratio. PhD*ROA is the interaction
term of PhD with return on assets. PhD*M to B is the interaction term of PhD with the market to book ratio. Data on firm-specific characteristics is retrieved from CRSP and
Compustat, and measured at fiscal year-end prior to the security issue date unless otherwise indicated. CB in the columns headings stands for convertible bond. Results are
with firm-level clustered standard errors. See Table 4 for a description of the control variables. *, **, and *** denote significance at the 0.10, 0.05, and 0.01 levels,
respectively.
(1) (2) (3) (4)
CB Vs. Debt CB Vs. Equity CB Vs. Debt CB Vs. Equity CB Vs. Debt CB Vs. Equity CB Vs. Debt CB Vs. Equity
Non-MBA Master 1.914*** 0.605**
0.912** 1.019**
(0.506) (0.278)
(0.425) (0.448)
MBA
0.015 -0.036
-0.091 -0.305
(0.218) (0.141)
(0.216) (0.217)
PhD 0.537** 0.634***
0.616** 0.578**
(0.222) (0.160)
(0.291) (0.263)
Non-MBA*ROA -13.580*** 0.077
(4.582) (3.090)
MBA*ROA
-2.741* -1.654
(1.607) (1.106)
PhD*ROA -1.171 2.906***
(1.690) (0.918)
Non-MBA*M to B
-0.207 -0.203
(0.191) (0.200)
MBA*M to B
-0.053 0.052
43
(0.052) (0.050)
PhD*M to B
-0.126 -0.006
(0.081) (0.062)
Age -0.037*** -0.007 -0.031*** -0.0001 -0.035*** -0.006 -0.031*** -0.001
(0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010)
Tenure 0.014 0.042*** 0.013 0.046*** 0.012 0.041*** 0.014 0.047***
(0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010)
Volatility 27.060*** 0.802 28.400*** 2.173 27.920*** 1.343 28.260*** 2.351
(6.392) (6.358) (6.435) (6.341) (6.376) (6.347) (6.418) (6.302)
Leverage 0.089 0.310*** 0.103* 0.319*** 0.096 0.319*** 0.095 0.313***
(0.059) (0.060) (0.060) (0.060) (0.060) (0.060) (0.060) (0.060)
Tax -11.030*** -0.617 -10.340*** -0.314 -10.500*** -0.746 -10.610*** -0.866
(3.607) (3.625) (3.682) (3.668) (3.658) (3.660) (3.682) (3.657)
Stock Run-up -2.075 -84.040*** 11.700 -77.250*** 3.229 -85.280*** 10.250 -78.680***
(23.950) (19.670) (23.850) (19.530) (23.960) (19.680) (23.900) (19.660)
Issue size -1.379* 0.453 -1.488** 0.941* -1.891*** 0.940*** -1.925*** 0.770**
(0.773) (0.381) (0.728) (0.372) (0.727) (0.357) (0.733) (0.337)
ROA -3.956*** 2.868*** -3.978*** 2.778*** -4.019*** 2.894*** -4.031*** 2.814***
(0.524) (0.489) (0.533) (0.491) (0.535) (0.498) (0.531) (0.487)
Firm size -0.757*** 0.355*** -0.737*** 0.352*** -0.752*** 0.360*** -0.747*** 0.353***
(0.052) (0.047) (0.053) (0.047) (0.052) (0.048) (0.053) (0.047)
M to B 0.009 0.048*** 0.015 0.047** 0.026 0.051*** 0.024 0.037*
(0.017) (0.017) (0.017) (0.017) (0.018) (0.018) (0.020) (0.019)
44
Table 9
Analysis of the effect of CEO education quality (Non-MBA Master Top-20, PhD Top-20, and Education Level Top-20) on the choice to issue
convertible bonds.
CEO education data is obtained from Compustat ExecuComp, BoardEx, Bloomberg, and S&P Capital IQ. The sample used in columns (1) and (2) contains 3,114
observations comprised of convertible debt, straight debt, and common equity issues. Columns (2), (4), and (5) use the sub-sample one contains 2,139
observations comprised of convertible debt and straight debt issues, and sub-sample two contains 1,166 observations comprised of convertible debt and common
equity issues. Non-MBA Master Top-20 is a dummy variable which equals one of if the CEO has a non-MBA master’s degree from one of the Top-20 U.S.
universities. PhD Top-20 is a dummy variable which equals one of if the CEO has a PhD from one of the Top-20 U.S. universities. Quantitative is a dummy
variable which equals one if the CEO has a quantitative degree. Data on firm-specific characteristics is retrieved from CRSP and Compustat, and measured at
fiscal year-end prior to the security issue date unless otherwise indicated. CB in the columns headings stands for convertible bond. Results are with firm-level
clustered standard errors. See Table 4 for a description of the control variables.*, **, and *** denote significance at the 0.10, 0.05, and 0.01 levels, respectively.
(1) (2) (3) (4) (5)
CB Vs.
Debt
CB Vs.
Equity
CB Vs.
Debt
CB Vs.
Equity
CB Vs.
Debt
CB Vs.
Equity
CB Vs.
Debt
CB Vs.
Equity
CB Vs.
Debt
CB Vs.
Equity
Quantitative 0.045 0.044 0.005 0.097*
(0.120) (0.121) (0.018) (0.057)
Non-MBA Master Top 20
0.810*** 0.829*** 0.0659 0.916***
(0.259) (0.287) (0.0420) (0.170)
PhD Top 20
0.444 0.366
0.109*** -0.199
(0.275) (0.235)
(0.0414) (0.182)
Age -0.031** -0.001 -0.003*** 0.0004 -0.031*** -0.0002 -0.003*** -0.0002 -0.004*** -0.0001
(0.010) (0.010) (0.001) (0.002) (0.00969) (0.00919) (0.001) (0.002) (0.001) (0.002)
Tenure 0.016 0.048*** 0.002*** 0.009*** 0.012 0.044*** 0.002*** 0.006*** 0.002** 0.009***
(0.010) (0.010) (0.001) (0.002) (0.010) (0.011) (0.001) (0.002) (0.001) (0.002)
Volatility 28.470*** 2.443 3.978*** 1.188 28.230*** 2.137 4.118*** 1.071 4.085*** 1.456
(6.374) (6.298) (0.671) (0.974) (6.402) (6.315) (0.672) (1.026) (0.673) (0.993)
Leverage 0.101* 0.318*** 0.011 0.074*** 0.099* 0.313*** 0.012* 0.064*** 0.013* 0.076***
(0.058) (0.058) (0.007) (0.012) (0.059) (0.059) (0.007) (0.013) (0.007) (0.012)
Tax -10.79*** -0.714 -0.058 -0.623 -10.68*** -0.649 0.017 -0.354 -0.229 -0.305
(3.697) (3.666) (0.334) (0.767) (3.683) (3.678) (0.334) (0.786) (0.337) (0.757)
45
Stock Run-
up 9.427 -78.57*** -3.091 -10.010*** 3.415 -83.050*** -3.843 -12.960*** -3.266 -10.670***
(24.020) (19.770) (2.508) (2.810) (23.870) (19.740) (2.518) (2.956) (2.501) (2.820)
Issue size -3.971*** 2.803*** -0.690*** 0.025 -3.956*** 2.815*** -0.426*** 0.333*** -0.443*** 0.371***
(0.533) (0.488) (0.069) (0.050) (0.531) (0.486) (0.052) (0.089) (0.052) (0.085)
ROA -1.914*** 0.766** -0.067*** 0.058*** -1.936** 0.773** -0.703*** 0.050 -0.693*** 0.043
(0.732) (0.339) (0.005) (0.008) (0.738) (0.339) (0.070) (0.052) (0.070) (0.050)
Firm size -0.749*** 0.348*** -0.429*** 0.393*** -0.764*** 0.338*** -0.066*** 0.049*** -0.067*** 0.057***
(0.053) (0.047) (0.051) (0.085) (0.053) (0.047) (0.005) (0.009) (0.005) (0.008)
M to B 0.015 0.047** 0.007*** 0.006** 0.011 0.044*** 0.006*** 0.004 0.006*** 0.007***
(0.018) (0.017) (0.002) (0.003) (0.018) (0.017) (0.002) (0.003) (0.002) (0.003)
46
Table 10
Analysis of the effect of CEO education on market reactions to convertible debt offer announcement.
CEO education data is obtained from Compustat ExecuComp, BoardEx, Bloomberg, and S&P Capital IQ. The sample contains 115 observations of convertible
bond issues. Undergraduate is a dummy variable which equals one if the CEO has an undergraduate degree only. Non-MBA Master is a dummy variable which
equals one of if the CEO has a non-MBA master’s degree but does not hold a PhD at the same time. PhD is a dummy variable which equals one if the CEO has a
PhD. Education level is constructed using a point scale ranging from 0 – 3: where 0 equals a CEO who is a dropout from an undergraduate programme, 1 equals
a CEO who has an undergraduate degree only, 2 equals a CEO who has a master’s degree but does not hold a PhD at the same time, and 3 equals a CEO who has
a PhD. Data on firm-specific characteristics is retrieved from CRSP and Compustat, and measured at fiscal year-end prior to the security issue date unless
otherwise indicated. Results are with firm-level clustered standard errors. See Table 4 for a description of the control variables. CAR (-2, 2) in the columns
headings stands for cumulative abnormal returns over the time window of -2 to 2 days relative to the event date and CAR (-1, 1) in the columns headings stands
for cumulative abnormal returns over the time window of -1 to 1 days relative to the event date. *, **, and *** denote significance at the 0.10, 0.05, and 0.01
levels, respectively.
(1) (2) (3) (4) (5)
CAR (-1, 1) CAR (-2, 2) CAR (-1, 1) CAR (-2, 2) CAR (-1, 1) CAR (-2, 2) CAR (-1, 1) CAR (-2, 2) CAR (-1, 1) CAR (-2, 2)
Non-MBA Master -0.009 -0.021
0.011 0.017
(0.016) (0.019)
(0.046) (0.042)
PhD -0.011 -0.021
-0.030 -0.015
(0.016) (0.018)
(0.044) (0.041)
Education Level
-0.013 -0.008
0.024 0.112*
(0.009) (0.009)
(0.061) (0.067)
Age -0.0002 -0.00003 -0.0001 -0.0002 -0.0005 -0.001 -0.0001 -0.0002 -0.001 -0.003
(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.002) (0.002)
Tenure 0.002** 0.002** 0.002* 0.002* 0.002* 0.003* 0.002** 0.002** 0.001 0.001
(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.002)
Volatility 0.153 -0.00250 0.106 0.207 0.0414 0.197 0.0698 0.183 1.390 0.090
(0.714) (0.795) (0.805) (0.718) (0.725) (0.672) (0.717) (0.664) (1.408) (1.582)
Leverage 0.002 0.001 0.001 0.002 0.001 0.002 0.001 0.002 0.002 -0.003
(0.001) (0.002) (0.002) (0.001) (0.002) (0.002) (0.002) (0.002) (0.002) (0.003)
Tax -0.155 -0.258 -0.191 -0.120 -0.267 -0.147 -0.194 -0.129 -0.148 -0.658
(0.362) (0.401) (0.403) (0.364) (0.377) (0.349) (0.392) (0.363) (0.678) (0.761)
47
Stock Run-up -0.904 -0.547 -0.844 -0.999 -1.446 -1.583 -0.980 -1.090 -7.527** -9.839**
(2.140) (2.278) (2.232) (2.090) (2.247) (2.082) (1.986) (1.839) (3.540) (3.977)
Issue size -0.023 0.020 0.007 -0.029 0.011 -0.034 0.001 -0.031 0.017 0.026
(0.063) (0.063) (0.061) (0.061) (0.066) (0.061) (0.064) (0.059) (0.087) (0.099)
ROA 0.026 0.018 0.019 0.026 0.022 0.029 0.016 0.025 0.099 0.085
(0.049) (0.053) (0.053) (0.049) (0.050) (0.046) (0.050) (0.047) (0.100) (0.112)
Firm size -0.001 0.003 0.001 -0.001 0.001 -0.002 -0.0002 -0.002 -0.013 -0.020
(0.006) (0.006) (0.005) (0.005) (0.007) (0.007) (0.006) (0.006) (0.010) (0.011)
M to B 0.000004 -0.001 -0.001 -0.00001 -0.001 -0.0003 -0.0009 -0.0002 -0.003 -0.003
(0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.003) (0.003)