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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: zainab.mehmood@manchester.ac.uk b Alliance Manchester Business School at the University of Manchester, Manchester, M15 6PB Manchester, United

Kingdom. E-mail: Marie.Dutordoir@manchester.ac.uk c Alliance Manchester Business School at the University of Manchester, Manchester, M15 6PB Manchester, United

Kingdom. E-mail: amedeo.decesari@manchester.ac.uk

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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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33

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)