Is a finance-educational-background CEO more aggressive in company leverage? : an empirical study on companies in the U.S.

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Amsterdam Business School

MSc Business Economics, Finance track

Master Thesis

Is a Finance-Educational-Background CEO more

Aggressive in Company Leverage?

An empirical study on companies in the U.S.

Author: Xikai Chen

Student ID: 11084820

Supervisor: Florian Peters

Date: July 2016

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Statement of Originality

This document is written by Xikai Chen who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Abstract

It is widely debated that the personal characteristics of CEO may have an impact on the company decision making process. This paper examines the relationship between the CEO educational background in finance and the leverage ratio of the company using the data of US companies from 2007 to 2015. The result shows that there is no statistically significant relation between CEOs with a finance educational background and company leverage ratio. The findings also suggest the turnover of CEOs from non-finance to finance educational does not have a statistically significant impact on company leverage ratio.

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Acknowledgement

First of all I would like to show my gratitude to all my friends who have been giving me great support through my writing process. My appreciation also goes to my supervisor Florian Peters who has offered me insightful suggestions and kind assistance. In the end I want to say thank you to myself, you have made your first step in your academic life. May you good luck in the future.

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Chapter 1: Introduction

Introduction: This chapter gives an overview of the research. It starts with the background of the research problem, from which the research question is stated. And then it goes to the purpose of the study and some practical implication this research can give.

1.1 Background of the research problem

The connection between CEO characteristics and company performance is always a hot topic in academic research. This popularity mainly comes from the assumption that CEO is a core player in company performance given his great power on affecting decision making process. CEO educational background is crucial for a company given that the background may affect the way how business problems are perceived and the mental process during decision making (Fligstein, 1990, p.4). The educational background exerts an effect on the CEO’s conception of company governance, which shapes company’s strategy, long term objectives and how these objectives would be achieved (Koyuncu, 2010, p. 872).

Even though other top management members also participants in the overall decision making process, CEO is viewed as the final decision maker in terms of the future development direction (Alice et al., 2000, p.95). CEO decision making process also mirrors the cognitive behavior of CEO himself, the way CEO interprets and decodes data he perceives (Daellenbach et al., 1999, p. 200). Although it is widely agreed that CEOs’ heterogeneous talents affect the company performance, scholars remain divergent and provide little evidence on which behavioral characteristics, educational background or CEO properties are important for company performance. This leaves us the question of whether finance-education background will affect company performance.

While the topic of CEO educational background and company leverage performance are not completely novel, several aspects distinguish this thesis from the others. To start with, most

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related literature focus on how the CEO educational background affects the profit-related indexes of the company such as ROA. This paper looks into another aspect of the company performance-- the financing decision, especially the leverage ratio, and there is little research on this topic. Also most of the prior similar researches are relatively outdated because their studies were carried out before 2008 financial crisis. It is crucial because the financial crisis has transformed a lot of aspects of company business, strategy and governance. The Changes of business competition and economic environment and the arising of e-commerce also make company funding problems more unpredictable but more crucial. Taking these into account, it would be crucial to exam whether and how a finance-education-background CEO affects the financing performance of the company. While concentrated on CEO educational background, this paper acknowledge and control for variables such as CEO age, CEO gender, company size, profitability, tangibility of asset and market-to-book ratio, which have been found to affect company performance.

1.2 Research question

Based on the objective to explore the connection between CEO educational background and the company financing performance, the whole thesis will be guided by the research question above.

 Does CEO educational background affect company leverage performance?

1.3 Purpose of study

The priority of this paper is to examine the connection between CEO educational background and company leverage performance. Specifically I narrow down the topic to check whether companies led by CEOs with an educational background in finance have a higher leverage ratio than other companies led by CEOs with a non-finance educational background. This study may give some ideas to investors and shareholders to understand how the corporate decision making is biased by CEO educational orientation. Even though this study does not check or answer the decision making process of CEO selection, it gives some hints on which types of CEO (in terms of educational background) are more likely to preferred. High leverage ratio brings additional risk to company, if our research proves that CEOs with an

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educational background prefer debt funding, then our study also serve as an alarming call for business school teaching that concentrates on equipping students with financial knowledge and skills.

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Chapter 2: Theoretical Framework

Introduction: This chapter concentrates on the literature review. It will start with the review of the importance of the role of CEO, followed by the discussion of the impact from the educational background on company performance, and then it goes to three important theories in this study—the echelon theory, new institutional theory and agency theory. The factors that affect company leverage and the discussion on cash hold level are also included. Finally based on the literature review the hypotheses are proposed.

2.1 Literature Review

2.1.1 The importance of CEO

CEO leadership as a topic has vast literature; the problem with such vast literature is that it has been written from various perspectives from sociological, ecological, institutional, conventional management theories and efficiency theories. In the business world and academic word, CEOs are always under spotlight because of their great power on the company management and future development, they are viewed as captain of the Titanic in the roiling business ocean. In the quickly changeable business world, the tasks of CEOs have also changed with the expectations from the board and employees. As Jim (Jim, 2009.p. 42) points out, with the declining of the world economics and the recession brought by the financial crisis, the focus of the CEOs has been shifted from the business expansion and some “sweet” exciting deals to the debt recovery procedures and maintenance or restructuring of the whole company system against the economic downturn. Also, CEOs are under pressure to deal with the employee satisfaction with the popularity of the theory of employee friendly policies. Generally speaking, CEOs today are facing challenges that never expected before; the importance of this position is becoming more significant with a more complex contemporary environment.

However, the dominance of CEO is not always good for the company performance. Haleblian et al. (1993, p. 847) argues to the fact that when the uncertainty in the environment increases,

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the decision made solely by CEO is more likely to be biased and incomplete, therefore hindering company development. Haleblian et al. draws the data from 47 firms in the field of computer and natural gas during 1978-1982 and uses the variation of company’s return on assets as the indicator of turbulence from 1978 to 1982. Firm performance is measured by three indexes—return on assets, return on sales and return on equity. The results show that in turbulent environment, companies with a larger management team perform better than those with a dominant CEO and the opposite is true when the environment is stable. They suggest that the dominant CEO is preferred when the information procession in the environment is slow, the dominant CEO may reduce the time loss wasted on information procession. The similar argument is also made byHambrick (2007, p. 334) who agrees to the fact that a large team, including CEO, CFO and other board members may be beneficial for firm performance in a uncertain environment because more talents and skills are gathered, which may result in a wiser strategy more adaptive to the business environment. However, he also admits that even in a larger team, the CEO’s saying has a large weight and may guide the direction of the group discussion. In his paper, he reviews his original paper co-authored with Mason in 1984, then he tracks back to the development and refinement of the upper echelons theory in the recent decades and discusses the challenges and opportunities that remain to be explored, which includes CEO narcissism.

Some other researchers, especially the organizational ecological school, try to argue that the role of CEO may not be that important. Wasserman et al. (2001) argues that the CEO decision making is greatly constrained by the environmental factors. In other words, the decisions are not out of CEO’s willing, the decisions are made only because under that specific circumstances, CEO has to make that specific actions. Especially under the influence of industry culture and history, CEO has to make decisions in line with tradition and therefore has little impact on firm performance. In this study, the data are drawn from Compustat on 531 companies from 42 different industries during the period from 1979-1997, the dependent variable, firm performance, is measured by ROA (return on assets).The hypothesis that leadership varies across different industries and that CEO leadership effect is lower in high-development industries than in low-growth industries are tested and the outcomes are

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significant. Therefore the industry type is not negligible in my study, otherwise the outcome will be biased. The review and discussion will be extended in the section 2.1.5.

Even though the significance of CEO is still under debate, especially compared to other senior managers, most of the researchers and scholars have reached a common ground that CEO exerts a great influence on the decision making of the company development and therefore is vital to the film performance.

2.1.2 The educational background of CEO

A review of the literature on CEO educational background and firm performance provides evidence to support divergent results. Theses opinions are contrasted by varied schools of thoughts, some based on conventional management theories and others inclined to the organizational ecological school.

Some studies show at the decision making is a reflection of the CEO educational background; it acts as a channel through which the knowledge of CEO is applied. Stone et al. (2005) suggest that it would be very important for firms to link the CEO educational background to the strategy of the firm, given the survey result from 58 CEOs from 282 public traded firms. Stone et al. (2005) point out that the educational background and experience of the CEO is often reflected on firm strategy through the way the CEO cognitive attitude and interpretation of events in the business environment. Also in this study, they test the impact of CEO industry experience on ROA (return on assets) and the result is significant, indicating that rich industry experience is helpful for company performance.

The trend of CEO educational background has aged in the past decades, which may be explained by the changes in the business environment. Fligstein (1987, p. 44) has noticed that given the shift of the company strategy, the turnover of CEOs may happen due to their different educational background. The company wants to find the CEO whose educational background suits the objectives the company pursues. For example, in the early stage on 20th

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century in the US, with the booming demand for ammunitions, CEOs with an educational background in operations and manufacturing were preferred. This preference was shifted to CEOs specializing in marketing and sales during 1930-1950s. In the 1960-1970s, with the waves of large conglomerate mergers and acquisitions and the invention of new financial tools, the CEOs with a financial educational background become popular. When it stepped into 1980s, however, many companies realized the detriment of investment in technology and other long-run investments in exchange for concentration on portfolio management and short-run profitability is not appropriate (Hayes et al., 1990) because the film’s sustainability is hindered. This led to the dominance of CEOs with an educational background in operation and technology. Nowadays, with the development of financial innovation and the inter-field business of the company, the educational background of CEOs becomes more diversified.

Among so many different educational backgrounds, the educational background tracking in finance is always the hot topic, especially when it comes to the study of company performance. Alice et al. (2000) investigate the effects of the CEO financial orientation on the people/performance balance for IPO firms. Among other hypotheses, they hypothesized that firms that were controlled by a finance oriented CEO were more likely to have a better market potential (firm performance) than other firms managed by CEOs who had a non-financial background. Using data collected from IPO prospectus for firms that went public in 1988 and 1993 in the United States, they had a total of 126 firms for 1988 and 261 for 1993 that filed and went public. Alice et al., 2000, p. 98) used the Tobin’s Q as a measure of the market value (market price/book value per share) of the firm. Using CEO educational background in finance as the independent variable and human resource value as the dependent variable, the results of the study found out no evidence to support the hypothesis that firms headed by a CEO having a financial background performed better than firms headed by CEOs with a non-financial background. One interesting aspects which the study also found was the fact that firms controlled by younger CEOs recorded higher value of the Tobin’s Q. Supporting the results of the study, Alice et al. (2000, p.96) argues that finance oriented CEOs by paying less attention to human resource value and a suitable job environment, they somehow failed to lay out an appropriate foundation for the long term strategic survival of the firms.

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Shane et al. (1999) found evidence in support that the survival and performance of new firms could be better explained by using both institutional theory factors and economic efficient theories. Shane e al. examines the survival rate of 1292 new franchisors set up in the United States during 1979 to 1996. The results of their study also provided some support for the earlier work of Shane (1996) who argued that in the early stage of firm development, survival was far more important than any other goal, but he failed to show the connection of the company survival and the CEO educational background. These studies confirm the impact of different CEO educational backgrounds on companies in different fields, especially the financial educational background, and therefore may provide some hints for this paper.

2.1.3 The CEO personality and leverage

In the recent years, with the development of behavioral finance, some attentions are cast on the link between CEO personality and company leverage. Leverage is always an important issue for company. Too few debts may not fully pump up the company profit capacity, while too many debts will put the company and investors in danger. It is always a crux to find an appropriate leverage level for different companies. As one of the aspects that reflects and affects the characteristics of CEO, education is therefore examined by some researchers. The study carried out by Irene Wei Kiong Ting et al. (2015) found evidence to support that there is a positive and significant relationship between CEO education level and leverage, which may indicate that companies owned by CEO with a higher educational background may prefer higher leverage compared to the companies that have a CEO with a lower educational background. Irene Wei Kiong Ting et al. (2015) also shows that younger CEOs and female CEOs and longer-serving CEOs are risk-takers and more aggressive. The reason may come to the over confidence of younger and long-serving CEOs. The companies with young CEOs usually have a short history and are still in the stage of expansion, this requires more money to fuel the business growth. The long-serving CEOs usually had good performance in the past, and those companies are in a stable and good state, which can take more leverage for funding. But this study doesn’t point out the specific field of the CEO educational background; it only focuses on the level of the education. And another limitation is that the sample used in the

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study is from the publicly listed companies in Bursa Malaysia, thus the conclusion may vary if the same research applied to other countries. Even so, the study is crucial to my paper because it demonstrates that the educational background does have an impact on corporate financing.

There are other studies that directly check the link between over confidence and leverage. Po-HsinHo et al. (2016) examines the connection between CEO overconfidence and leverage in the bank. They use a stock option based proxy to measure the CEO overconfidence, the data are drawn from Standard & Poor’s ExecuComp database from 1992 to 2009. The results of their study showed evidence in support of the hypothesis that banks managed by an overconfident CEO have a higher leverage ratio compared to banks headed by non-overconfident CEOs. Even though the research doesn’t exam the source of the overconfidence, it is highly likely that it has something to do with the CEO personality, including age, gender and educational background. Another research by Henrik Cronqvist et al. (2012) finds that firms behave remarkably similarly to how their CEOs behave personally when it comes to leverage choices. When the CEO is more aggressive on his personal finance including housing or car mortgages, his company will also show a higher level of leverage. This research once again confirms that the CEO preference and personality have a huge impact on the corporate financing. In this study, another interesting finding about the determinant of CEO’s personal leverage is that age is negatively related to home mortgage. The reason may come to the fact that for an older CEO, he has accumulated more money and less capital constrained compared to a younger CEO therefore his personal preference is different from the young CEO. This sheds some light on my study in the way that age should be considered as the CEO financing preference may change with age.

2.1.4 The echelon theory and CEO overconfidence

This model largely comes from the works of Hambrick &Mason (1984) who argue that the CEO personal understanding of the world will guide the direction and the way he makes strategy for company. The theory assumes that the perception of problem and decision

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making process are subjective to CEO personal experience, educational background and other demographic factors. The theory also assumes the inattention of the CEOs, which makes them unable to process all the information around them. Given these assumptions, they argue that CEOs are more likely to be attracted by the things which are in line with their personal experience, age, gender, educational background an values. The advocators of this theory agree that CEO actions, which are highly related to their personality, have a huge impact on the film performance. This theory is highly related to this paper because it admits two important assumptions this paper is based on. First, the CEO personality shapes the way he makes decisions. And second, CEO’s action has an effect on company performance. As intuitively we acknowledge what we do reflects what we think and what we are, the echelon theory gives powerful support to the hypothesis of this paper.

The increasingly popular theory of overconfidence in behavioral finance can be viewed as a supplement to the echelon theory. Vast amount of researches have shown that the CEO overconfidence will bias the decision making of business forecast and corporate finance. For example, given the result from the survey, Harvey et al. (2013) points out that some CEOs’ behavioral traits such as optimism and risk tolerance are highly related to corporate financial policies. Other researches show that the overconfidence makes CEO overreact to the market information (Daniel et al. 1998). Even though the impact of CEO overconfidence on company performance is widely confirmed by many studies, the puzzle on the sources of the overconfidence remains unsolved. So far there are some explanations for why overconfidence happens, the environmental factors and CEO personality are the two popular ones. In the study by Catherine & Sarah (2012), overconfident CEOs are more likely to manipulate financial reports out of the public expectation and external pressure. They also mention that the personal stake is one of the sources for CEO overconfidence and the overconfident CEOs tend to attribute their success to ability blame the bad luck for their failure. Their detailed analysis is based on the data of 49 companies under the supervision of SEC Accounting and Auditing Enforcement Releases (AAERs) during 1990 to 2009. CEOs are more likely to be overconfident when they made a successful forecast in the last round, and this overconfidence will reduce the accuracy of the next prediction (Gilles & Charles, 2011). Gilles and Charles

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draw the data from 1994 to 2007 from the FirstCall database and check the quarterly predictions. The result shows that the overconfidence is a dynamic process that adjusts to the former predictions. Therefore the overconfidence also comes from the personal experience in the past. Even though there is no study on the direct connection between educational background and overconfidence, as mentioned above the study by Irene Wei Kiong Tinget al. (2015) shows a positive and significant relation between CEO educational level and leverage level, the CEO educational level may affect the corporate leverage through overconfidence. This logic may also apply to CEOs with a financial educational background because the superior financial expertise may give CEOs better access to the privileged information and thus lead to CEO overconfidence, which results in a higher leverage ratio.

2.1.5 The new institutional theory

In this theory, there is no universal definition for the concept of institution or institutionalization; some definitions are approached from a specific angle while the others are more ambiguous. The main idea of this theory is to view company as a flexible business unit shaped by the influences from external environment and the reactions to these constrains (Scott, 1987, p. 494). The advocators of this theory agree that there is a social reality with firms and business units and institutions play in an institutional environment which is bounded and defined by the social reality. The primary concentration of the institutional theory is on how regulatory framework constrains business units when they pursue the long-run objective of business success (Scott, 2007; Bruton et al., 2010, p. 422). Especially for the entrepreneurs, the survival and success of their companies heavily rely on the institutional environment. The inadequate institutional development gives new ventures growth opportunities (Baumol et al., 2007), while a well-developed institutional environment is a hindrance to company’s funding (Soto, 2000). This means that the company performance is not only relied on its internal resources, but also is constrained by some environmental factors which are beyond the capabilities and skills of CEOs.

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success and company performance. Meyer & Rowan (1977) point out that most of the CEOs and company just take this social reality for granted and do not notice the constraints exerted by it, they make a further argument that company performance has little to do with CEO educational background and actions. Given the enormous constraints that CEO faces, their decision making options are very limited and they have to follow the direction in that case, no matter what kind of work he does or which educational background he is from. Sometime the constraints not only come from the external factors such as entry barrier or competition pressure, they also generate from internal factors including existing control system and previous fixed asset investment (Hannan & Freeman, 1989:22). Hannan and Freeman argue that these internal pressures, also called inertial pressures, are in the most case the main obstacles to the radical change of the business strategies and company structure. The same argument is made by Martin (1992) who finds that the subcultures and countercultures within the company somehow hamper the CEO’s leadership effect on the company performance. The importance of this theory to this paper is that it argues against the echelon theory, stating that the bounding effect of the external environment is more conclusive on the company performance rather than the company internal sources.

2.1.6 The agency theory

Even though CEOs are expected to behave for the best interest of shareholders, this theory argues that when the ownership is widely dispersed, CEOs may turn to behave for their own best interest (Donaldson & Davis, 1991, p. 49-50). This theory needs to be taken into consideration in this paper because it gives a hint of the motivations behind CEO action. This theory assumes that humans or agents are self-centered and selfish, when there is no proper company supervision mechanism, the agents will exploit the opportunities to better pursue their own personal interest with their privileges and superior knowledge about the company internal sources and market activities. Therefore this theory believes that with the access to privileged information, CEOs may make different strategies compared to the case where this privileged information is absent, and those strategies will cast direct or indirect effects on firm performance.

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The problems happen due to the information asymmetry between better informed agents and more diverse shareholders (Fama & Jensen, 1983). This situation will be worse if the agents have dual important positions in the company, for example, a CEO also serves as a CFO at the same time, as he has more power to manipulate the decision making and a wider access to gain more privileged information. The leverage is a blade with two blades; proper use of leverage will fuel the business growth while too much leverage may jeopardize the investors and the company, leading to serious results including risk shifting and debt overhang. The study by Crutchley et al. (1989) suggests that the agency problem will lead to a higher corporate leverage. In this study, a CEO from a financial educational background is assumed to be better informed about the privileged information given their financial expertise compared to CEOs from other educational backgrounds. Therefore the agency theory cannot be neglected when we discuss the link between corporate leverage and CEO educational background.

On the other hand, there is another theory named stewardship theory that argues the opposite of agency theory. This theory believes that the CEO will serve as a good steward to behave in the best interest of the investors and therefore the improvement of the company performance should be attributed to the effective action taken by CEO. Assuming that there is no problem in the employee motivation, stewardship theory believes that granting CEO more power, such as the dual positions of CEO and CFO, will be beneficial for company performance (Miller & Sardais, 2011).

2.1.7 Measurement of leverage and the factors that affect it

Leverage is a knife with two blades; the proper use of leverage will fuel the growth of business, too much leverage, however, may put company into the danger of debt overhang and harm the interests of investors and company. There are different measurements for corporate leverage, the most widely used and the most traditional one is the calculated as the

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ratio of total liabilities to total assets. This measurement provides a proxy for the shareholders to check what will be left for them once the liquidation happens. The drawback of this measurement is that it fails to show whether the company will face any risk of default in the close future. Also, as total liabilities contains some items such as accounts payable, which can be applied to transactions purposes instead of to financing, therefore the level of leverage may be overestimated (Rajan et al., 1995). Another measurement of leverage is called net leverage ratio, which is defined as the book value of total debt minus net short-term assets all divided by book value of assets (Sharpe, 1994). The net short-term assets include items such as cash and other short-term investments. The advantage of net leverage ratio is that by excluding the net short-term asset a more comprehensive picture of the company’s balance sheet is shown with an overall tightness.

Except for the measurement of the total debts, it is also important to check the components of the total debts, mainly the short term debt and long term debt, as Berglöf (1994) points out, most of the companies will issued more than one type of financial claims. Therefore the ratio of short-term leverage, which is defined as the amount of short-term divided by the total assets, is used to measure the proportion of the short-term debt in the financing. The ratio of long-term leverage is defined in the same way. Compared to the stability of long-term debt, short term debt is less expensive but has a higher risk in terms of rolling over. The companies with more short-term debt usually faces more frequent renegotiation and thus the credit supply shock and other financial constraints remain a challenge for them. According to the study of Custódio et al. (2012), the debt maturity in US industrial companies experiences a decrease from 1976 to 2008, which means that companies were becoming more favor in short-term debt, especially the companies with a higher agency costs. This study provides a hint that when the corporate governance is weak, the CEO may take more short-term debts. Even though there is no study on the CEO characteristics and the debt maturity of the companies, it is worthwhile to check this connection. The different definitions of the leverage ratio above will all be included in the analysis in this paper so that to double check the impacts of the CEO educational background on company leverage performance.

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There are several factors that are highly correlated with leverage, according to the study of Rajan et al. (1995), they are firm size, tangibility of assets (the ratio of fixed assets to total assets), profitability and the market-to-book ratio. The study is echoed by Harris et al. (1991), who tried to interpret this correlation from the view of capital structure theories. As the fixed assets can be viewed as collateral, the loan lenders prefer companies with higher asset tangibility. Even though the effects of company size and profitability on leverage vary a lot according to the different corporate controls and external environment, both are significant in the related studies. As for the market-to-book ratio, it acts as a proxy for growth opportunities and mispricing, casting an indirect but important influence on company leverage. The detailed discussion on the effect of these factors on company leverage will be extended in the next section.

2.1.8 Corporate cash holdings

Cash holding level to some degree reflects the risk tolerance and preference of the companies, as cash is used to pay for the incoming mature short-term debt. The corporate cash holding ratio is defined as total cash divided by total assets (Tong, 2010); an increase in the corporate cash holding usually signals that the company cash flow becomes riskier. According to the study by Bates et al. (2009), the average cash holding level among industrial companies in US has doubled from 1980 to 2006. At the end of the sample period, the average companies were holding so much cash that they could pay off the entire debt obligation, indicating the connection between cash holding level and corporate leverage. Consistent with the agency theory, the study of Tong (2010) points out that a more risk-taking CEO will lead to a higher cash holding level, while a risk-averse CEO results in a lower cash-to-assets ratio. Another study carried out by Orens & Reheul (2013) also shows that the CEO demographics have an impact on corporate cash holdings. They used a sample of Belgian privately held SMEs and found that older CEOs and CEOs who have no experience in other industries will lead to a higher cash holding ratio compared to younger CEOs and CEOs who have experience in other industries. In spite of the fact that there is no research on the CEO educational background and corporate cash holdings, the previous studies have proved the CEO characteristics and

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corporate cash holdings. Given that the level of cash holdings to some degree also reflects the leverage, especially the short-term leverage preference and risk aversion, in this paper we will also test the relation between cash holdings and CEO educational background.

2.2 Propositions

Hypothesis 1

Companies led by a CEO with an educational background in finance have a higher leverage ratio than companies led by a CEO with an educational background in other fields.

Hypothesis 2

Companies led by a CEO with an educational background in finance have a higher short-term leverage ratio than companies led by a CEO with an educational background in other fields.

Hypothesis 3

Companies led by a CEO with an educational background in finance have a lower long-term leverage ratio than companies led by a CEO with an educational background in other fields.

Hypothesis 4

Companies led by a CEO with an educational background in finance have a higher net leverage ratio than companies led by a CEO with an educational background in other fields.

Hypothesis 5

Companies led by a CEO with an educational background in finance have a higher cash holding ratio than companies led by a CEO with an educational background in other fields.

Hypothesis 6

The turnover of CEO from no-finance educational background to financial educational background has a positive impact on the leverage ratio.

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Hypothesis 7

The turnover of CEO from no-finance educational background to financial educational background has a positive impact on the short-term leverage ratio.

Hypothesis 8

The turnover of CEO from no-finance educational background to financial educational background has a positive impact on the long-term leverage ratio.

Hypothesis 9

The turnover of CEO from no-finance educational background to financial educational background has a positive impact on the net leverage ratio.

Hypothesis 10

The turnover of CEO from no-finance educational background to financial educational background has a positive impact on the cash holding level.

Given the overconfidence theory, I expect CEO with an educational background in finance is more likely to be overconfidence on financing decision due to their superior knowledge on finance and therefore more accesses to privileged information, which results in a higher leverage ratio, the same results are expected to be applied to short-term leverage ratio, long-term leverage ratio, net leverage ratio and cash holding level. According to the study by Po-HsinHoet al. (2016), banks managed by an overconfident CEO have a higher leverage ratio compared to banks headed by non-overconfident CEOs. Even though this study doesn’t exam the source of the overconfidence, it is highly likely that it has something to do with the CEO personality, including age, gender and educational background. Combined with the research by Kiong Tinget al. (2015) who points out that CEO educational level has an impact on company leverage, I expect that the same logics applies to CEO educational background and company leverage and thus CEOs with a finance-education background are more overconfident due to their familiarity of financial knowledge and skills, and therefore are more likely to take aggressive funding strategy which leads to a higher leverage ratio. The

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pinpoint of CEO turnover from non-finance educational background to financial educational background is also taken into consideration, and I expect this turnover will have a positive influence on leverage ratio, short-term leverage ratio, long-term leverage ratio, net leverage ratio and cash holding level.

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Chapter 3: Research Methodology

Introduction: This chapter concentrates on the research methodology that employed in this thesis. It starts with the selection of the research topic and the preconceptions. Then it goes into the discussion of the research approach guiding this research. The general discussion of research methods is given and the concentration is put on the deductive and quantitative research method adopted in this study, followed by the sources of the data and the data clearing process. The later part shows the definition of the variables and the regressions.

3.1 Selection of research topic and preconceptions

My great interest to pursue a paper on this research topic comes from my previous knowledge on corporate finance and behavioral finance. This interest was inspired during my study in Amsterdam Business School, University of Amsterdam. When I was in the class of behavioral finance, I was greatly impressed how CEO overconfidence biased the company decision making process. Then I realized the classical assumption of rational economic agents was difficult to be held in the real life, we are always under the influence of our history and the external environment. Therefore I devoted a great deal of time in reading relative literatures and I was sparked by the idea of testing the connection between CEO educational background and company performance. My initial idea was to explore the connection between CEO educational background and company profit performance. However, with some insightful advice from my supervisor Florian Peters who is also an expert in the field of corporate finance, I decided to shift my focus on the company leverage ratio instead of profit performance after a long period of reflection on the relative literatures.

As Bryman & Bell (2007) points out, preconceptions would affect the researcher’s analysis, research design and interpretation of the results. Therefore during my research process, I try with great effort to stay objective, value free and open-minded.

3.2 Research Approaches

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data and give a clear picture of the research. With quantitative research strategy it is also easy to use software to deal with the data. Compared to qualitative method, quantitative method gives the research more objectivity with scientific model and clear data resources. Multiple regressions are used during the study; the data are processed on the software called Stata.

The quantitative methods are largely based on the econometrics. Several types of regressions are applied to cross check the outcome and make sure the correctness of the conclusions. With control variables, the OLS regressions are used in the first place to give a rough picture on the relationship between the interested variables. Then the entity fixed effects and time fixed effects are taken into account to control for the omitted variables. In the end, the method of difference-in-difference is employed to pinpoint the change of CEO’s educational background on the company leverage performance.

3.3 Truth criteria

3.3.1 Validity

The validity is the first crucial truth criteria as it focuses on the integrity of the study. It questions whether the indicators genuinely reflect the measurement that they claim to do (Bryman & Bell, 2007). The variables applied in this paper are based on different schools of theories and coding is backed up with vast number of relative literatures and researches. I tried to duplicate the data coding in the way the prior researches do so that the measurement are consistent with the supporting theories, especially when it comes to the measurement of company size and leverage ratio. All the data used in this study come from the public available databases on the famous data website WRDS so that the data meet the requirement of high quality under the supervision of U.S information law. To further check the correctness of the data, I used multiple channels, including company annual reports, to cross examine the CEO personal information such as gender, age, educational experience, company core financial indexes. When the information confliction occurs, the contradictory data will be removed from the sample so that the data uniformity is ensured. With the measurement taken above, I believe that the validity criteria are met in this thesis.

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3.3.2 Reliability

Reliability is the second important truth criteria as it concerns whether the result of the study is repeatable. Therefore, reliability requires the researchers to keep a close eye on the measurement and the processing of the data so that the consistency is guaranteed. Also the coding part should be checked carefully so that other coders are able to replicate the study and get the same result with the same data and coding. The data measurement of the variables used in this study is all cited from other famous paper so that the data consistency is ensured. Even though some slight modifications are made to the definition of the variables, the definitions are mainly based on the previous studies and the explanations are given on why these modifications are necessary, and usually it is due to the fact the some data resources are not accessible to me and therefore I have to turn to another similar measurement of a specific variable. These modifications, to some degree, ensure that the measurements in my paper are in line with the previous researches as much as possible. As the software Stata is applied to the data processing and regressions in my thesis, the coding part, which is the so-called do file will be revealed for the other researchers to replicate and check this study. With these actions taken above, I believe that the reliability criteria are met in this thesis.

3.4 Sources of data

The data used in this study is from secondary sources. The data were collected in the library of Amsterdam Business School, University of Amsterdam. The data come from the database of Compustat in WRDS, which is a credible and well-known database website so that the reliability and validity of the data can be guaranteed. The data of the CEO personal information including age, gender and financial expertise come from the sub-database Directors in Institutional Shareholder Services (formerly RiskMetrics) database. The information of the companies including total debts, total assets, total shareholders’ equity and industry code come from Capital IQ database on WRDS.

3.5 Definition of independent variable

In my study applies both dependent and independent variables. The dependent variable is focused on company leverage ratio. The independent variables are included to help explain

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the variance of the dependent variable. Some dummy variables are used because some independent variables are qualitative. The dummy variables are assigned with the value of 1 or 0. Some control variables are also used to prevent the risk from omitted variables.

3.5.1 Independent variable

CEO educational characteristics (Education)

The CEO educational characteristics are shown as Education in the regression. It is a dummy variable that equals 1 if the CEO has an educational background in finance, it equals 0 otherwise. Due to the limitation of the data access to any database on the detailed CEO education background information, in this study the variable named financial expertise in ISS database is used to measure the CEO educational background in finance, I assume that the CEOs with financial expertise are given senior knowledge on finance and this variable can be used as a substitute for the CEO educational background in finance. The data on this variable are available from 2007 to 2015 on ISS database, so I collected all the data on all US companies in this period. The following demographic variables including CEO age and CEO gender also come from the ISS database with the same period from 2007 to 2015.

The change of CEO educational background (Treat)

The change of CEO educational background is expressed as Treat in the regression. It is a dummy variable which equals to 1 if in a given year the former in the last year CEO is from non-finance educational background and the current CEO in this year has a finance educational background, it equals to 0 otherwise. This variable measures the impact of the change of CEO educational background on the company leverage performance.

The turnover of CEO (Post)

The turnover of CEO is expressed as Post in the regression. It is a dummy variable which equals to 1 if in a given year the CEO’s ID is different from that in the last year, it equals to 0 if the CEO is the same one as the last year. This variable measures the turnover of CEO; the identity of CEO is based on the ID number.

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This variable is a dummy variable which equals to 1 if in a given year the CEO turnover happens and a non-finance-educational-background CEO is replaced by a CEO from finance educational background, it equals to 0 otherwise. This variable pinpoints the effect of CEO turnover from no financial expertise to financial expertise.

3.5.2 Dependent variables Leverage ratio (Leverage)

The company’s leverage ratio is shown as Leverage in the regression. It is stated as the total debt divided by total assets. The measurements of leverage vary a lot as the purpose of the study changes, in this paper we adapt the most widely used definition of stock leverage, which is the ratio of total liabilities divided by total assets. This definition provides a proxy for the value left for shareholders once the liquidation happens (Rajan et al., 1995). The data on total debt and total assets are available from 1995 to 2016 on Compustat database. In order to match the company data to the CEO data, I only pick the data from 2007 to 2016. The following company variables including firm’s size, market-to-book ratio, tangibility and profitability also come from or are calculated based on the data from the Compustat database with the same period from 2007 to 2015.

Short term leverage (ShortLev)

The company’s short term leverage ratio is expressed as ShortLev in the regression. It is defined as the short term debt divided by the total assets. The variable debt in current liabilities total from Compustat database is used as an approximation for the amount of the short term debt. The short term leverage can reflect the aggressiveness of the financing, as it is cheaper but casts a risk of credit supply shock and financial constraints.

Long term leverage (LongLev)

The company’s long term leverage ratio is expressed as LongLev in the regression. It is defined as the long term debt divided by the total assets. The variable long term debt total from Compustat database is used as an approximation for the amount of the long term debt.

Net leverage (NetLev)

The company’s net leverage ratio is stated as NetLev in the regression. It is defined as the total debt minus cash and then all divided by total assets. The netting out of cash will better

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capture the overall tightness of the debt on the company’s balance sheet al.so this variable provides an alternative to leverage ratio, acting as a control and checking the robustness of the study.

Cash hold ratio (Cashhold)

The company’s cash hold ratio is expressed as Cashhold in the regression. It is calculated as the amount of cash divided by total assets. The variable cash and short term investments from Compustat database is used as an approximation for the amount of cash. The cash holding level to some degree indicates the riskiness of the company cash flows, as the cash is usually used for the retirement of the maturing debts, especially the short-term debts.

3.5.3 Control variables CEO age (Age)

The CEO age is presented as Age in the regression. The previous studies have used CEO age as an indicator for the job experience and even the risk preference. The study of Hermann & Deepak (2006) shows that the younger CEOs are bolder and more willing to take more risks, they are more active in risky project investment. Meanwhile, the older CEOs are pro for the maintenance of the stability of company performance and therefore are less risk tolerant. The study also points out the age is an important determinant of the information processing simply because younger CEOs have physical advantages and more open mind to deal with the rapid changing business world. Therefore the CEO age should be taken into consideration when we are discussing company leverage and CEO personal characteristics. The CEOs are divided into 6 groups based on their age, group 1 contains the CEOs aged from 29 to 39 (as the minimum CEO age in the data is 29), group 2 contains the CEOs aged from 40 to 49, group 3 contains the CEOs aged from 50 to 59, group 4 contains the CEOs aged from 60 to 69, group 5 contains the CEOs aged from 70 to 79, group 6 contains the CEOs aged from 80 to 89, group 7 contains the CEOs aged from 90 to 97 (as the maximum CEO ague in the data is 97).

CEO gender (Gender)

The CEO gender is a dummy variable presented as Gender in the regression. Since most of the CEOs in our data are male, this variable equals 1 if the CEO is a male and 0 if the CEO is a female. With the increasing popularity to board diversity, a lot of studies have found the

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gender of CEO may have an impact on company performance. Singh & Vinnicombe (2004, p. 481) find that female CEO has a positive impact on company performance as they are more sensitive and are able to notice some aspects which the male CEOs ignore. Even though no previous researches check the connection between CEO gender and company leverage, it is harm free to incorporate this important demographic factor to prevent the risk of omitted variables.

Firm size (Size)

The company’s size is presented as Size in the regression. It measures the natural logarithm of the annual sales, the same measurement used in studies by (Koyuncu et al, 2010; Rajan et al., 1995). It is an important variable to be controlled for even though its effect on leverage is more ambiguous. As the big companies tend to have more diversified business and are less likely to fail, usually they enjoy a good credit reputation and are more likely to use more debt for financing due to its low cost. However, the size also serves as an indicator for the information held by outside investors, their preference for equity will be higher with the increase of the size. Therefore it will be worthwhile to include this variable to avoid possible bias risk.

Tangibility of assets (Tangibility)

The tangibility of assets is presented as Tangibility in the regression. The tangibility of assets is calculated as the fixed assets divided by the total assets (Rajan et al., 1995). I use the variable of PPE-grossed (property, plant and equipment) as an approximation for the value of the fixed assets. The tangibility of assets is highly related to company leverage because the fixed assets are deemed as collateral, decreasing the risk of agency costs for the lender. Once the default happens, the tangible assets retain more value during liquidation, so the proportion of fixed assets acts as a safety proxy to lenders. Therefore the lenders are more willing to render loans to companies whose tangibility of assets is higher.

Profitability

The profitability is presented as Profitability in the regression. The profitability is defined as the cash flow generated from operation activities divided by the book value of assets (Rajan et al., 1995). The book value of assets equals the total asset minus the amount of intangible asset and liabilities. For the cash flow variable, I use the cash flow and short term investment as an

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approximation for it. The effect of profitability is still under debate according to the previous studies. In the study of Myers and Majluf (1984), they found that the relation between company leverage and profitability is negative. The reason may come to the fact that the companies with a higher profitability prefer the internal funds rather than debt. Other researchers, such as Jensen (1986), however, points out the opposite situation. In his study, he found that the market for corporate control is crucial. If this market is effective, the firms are forces to paying out with cash by leveraging up. Once the market is ineffective, the leaders of the profitable companies will tend to avoid the disciplinary role of debt and therefore decrease the leverage. On the other hand, the loan suppliers usually have preference for companies with current cash flows. Therefore, the profitability is an important control variable in our study.

Market-to-book ratio (Market-to-Book)

The market-to-book ratio is presented as Market-to-Book in the regression. It is calculated as the market value of assets divided by the book value of assets. The book value of assets equals the total asset minus the amount of intangible asset and liabilities. The market value of asset equals the book value of assets minus the book value of equity plus the market value of equity (Rajan et al., 1995). The market-to-book ratio should be considered for two reasons. First, the market-to-book ratio can be used as a proxy for growth opportunities (Myers, 1977). Companies that forecast a great growth opportunities in the future will use a great deal of equity finance. On the other hand, according to the study by Myers (1977), the highly leveraged firms are more likely to miss the profitable investment opportunities. Secondly, market-to-book ratio also measures the mispricing of the company. When the stock price is higher than the book value, the firms are more likely to issue stock instead of debt for financing as it is cheaper (Korajczk et al., 1991). This behavior will lead to a negative relation between firm leverage and market-to-book ratio. Based on the discussion above, market-to-book ratio plays a crucial role in this study.

3.6 Regression analysis

In this paper the multi-regression analysis is used given that there are more than two explanatory variables. The dummy variables are created from educational background, gender,

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treat and post. The dependent variables are various leverage ratios of the company. The data are in the form of panel data.

In the following regressions, Leverage is the leverage ratio (total debt divided by total assets) of the company, ShortLev is the short-term leverage ratio (short term debt divided by total assets), LongLev is the long-term leverage ratio (long term debt divided by total assets), NetLev is the net leverage ratio (total debt minus cash and then all divided by total assets), Cashhold is the cash held by the company(cash divided by total assets), Education is a dummy that refers to the financial educational background, Age is the CEO age, Gender is a dummy showing the CEO gender, Size measures the company size (natural logarithm of annual sales), Tangibility refers to the tangibility of the company assets (fixed assets divided by total assets), Market-to-Book is the market-to-book ratio (the market value of assets divided by the book value of assets) of the company, Profitability is the profitability (cash flows generated from operation activity divided by the book value of assets) of the company. What is more, it stands for the data of company i in year t, αi refers to the entity fixed effect

and τt stands for the time fixed effect.

To study the connection between company leverage performance and CEO educational background, the first general regression is given as following:

𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡 = α + 𝛽1𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝑖𝑡+ 𝛽2𝑆𝑖𝑧𝑒𝑖𝑡+ 𝛽3𝑀𝑎𝑟𝑘𝑒𝑡𝑡𝑜𝐵𝑜𝑜𝑘𝑖𝑡+𝛽4𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡

+ 𝛽5𝑃𝑟𝑜𝑓𝑖𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡+ 𝛽6𝐺𝑒𝑛𝑑𝑒𝑟𝑖𝑡+ 𝛽7𝐴𝑔𝑒𝑖𝑡+ 𝛼𝑖+ 𝜏𝑡+ 𝜇𝑖𝑡

(1) Several regressions will be run based on this general regression. The first regression is only a simple OLS regression on company leverage and its CEO educational background, no control variables are included. In the second regression the four most important control variables, firm’s size, market-to-book ratio, tangibility of the assets and company profitability will be taken into account. In the third and fourth regression, the time fixed effect and entity fixed effect are added in the analysis respectively. In the fifth regression both time fixed effect and entity fixed effect are considered. Then in the final regression the demographic factors are included as well.

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Then it comes to the analysis on the company short term leverage performance and CEO educational background, the general regression is given as following:

𝑆ℎ𝑜𝑟𝑡𝐿𝑒𝑣𝑖𝑡= α + 𝛽1𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝑖𝑡+ 𝛽2𝑆𝑖𝑧𝑒𝑖𝑡+ 𝛽3𝑀𝑎𝑟𝑘𝑒𝑡𝑡𝑜𝐵𝑜𝑜𝑘𝑖𝑡+𝛽4𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡

(2) Several regressions will be run based on this general one. The first regression is only a simple OLS regression on company short term leverage and its CEO educational background. Then the rest of the other regressions are run based on the same principles stated in the regression (1).

The third general regression is on the company long term leverage and CEO educational background, the general regression is shown as following:

𝐿𝑜𝑛𝑔𝐿𝑒𝑣𝑖𝑡= α + 𝛽1𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝑖𝑡+ 𝛽2𝑆𝑖𝑧𝑒𝑖𝑡+ 𝛽3𝑀𝑎𝑟𝑘𝑒𝑡𝑡𝑜𝐵𝑜𝑜𝑘𝑖𝑡+𝛽4𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡

(3) Several regressions will be run based on this general one. The first regression is only a simple OLS regression on company long term leverage and its CEO educational background. Then the rest of the other regressions are run based on the same principles stated in the regression (1).

Then it will come to the discussion on the company net leverage and CEO educational background, and the cash held by the company and CEO educational background. The general regressions are given below:

𝑁𝑒𝑡𝐿𝑒𝑣𝑖𝑡= α + 𝛽1𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝑖𝑡+ 𝛽2𝑆𝑖𝑧𝑒𝑖𝑡+ 𝛽3𝑀𝑎𝑟𝑘𝑒𝑡𝑡𝑜𝐵𝑜𝑜𝑘𝑖𝑡+𝛽4𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡 + 𝛽5𝑃𝑟𝑜𝑓𝑖𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡+ 𝛽6𝐺𝑒𝑛𝑑𝑒𝑟𝑖𝑡+ 𝛽7𝐴𝑔𝑒𝑖𝑡+ 𝛼𝑖+ 𝜏𝑡+ 𝜇𝑖𝑡 (4) 𝐶𝑎𝑠ℎℎ𝑜𝑙𝑑𝑖𝑡= α + 𝛽1𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝑖𝑡+ 𝛽2𝑆𝑖𝑧𝑒𝑖𝑡+ 𝛽3𝑀𝑎𝑟𝑘𝑒𝑡𝑡𝑜𝐵𝑜𝑜𝑘𝑖𝑡+𝛽4𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡 + 𝛽5𝑃𝑟𝑜𝑓𝑖𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡+ 𝛽6𝐺𝑒𝑛𝑑𝑒𝑟𝑖𝑡+ 𝛽7𝐴𝑔𝑒𝑖𝑡+ 𝛼𝑖+ 𝜏𝑡+ 𝜇𝑖𝑡 (5) Several regressions will be run based on these two general regressions in the same way applied to the regression (1).

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Aspect from the multi-regressions analysis stated above, the method of difference-in-difference is also used to pinpoint the change of CEO and the change of CEO educational background on company leverage performance. The five general regressions are expressed as the following:

𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡= α + ρ𝑇𝑟𝑒𝑎𝑡𝑖𝑡+ δ𝑃𝑜𝑠𝑡𝑖𝑡+ 𝛽1𝑇𝑟𝑒𝑎𝑡𝑖𝑡∗ 𝑃𝑜𝑠𝑡𝑖𝑡+ 𝛽2𝑆𝑖𝑧𝑒𝑖𝑡+ 𝛽3𝑀𝑎𝑟𝑘𝑒𝑡𝑡𝑜𝐵𝑜𝑜𝑘𝑖𝑡 +𝛽4𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡+ 𝛽5𝑃𝑟𝑜𝑓𝑖𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡+ 𝛽6𝐺𝑒𝑛𝑑𝑒𝑟𝑖𝑡+ 𝛽7𝐴𝑔𝑒𝑖𝑡+ 𝛼𝑖+ 𝜏𝑡+ 𝜇𝑖𝑡 (6) 𝑆ℎ𝑜𝑟𝑡𝐿𝑒𝑣𝑖𝑡= α + ρ𝑇𝑟𝑒𝑎𝑡𝑖𝑡+ δ𝑃𝑜𝑠𝑡𝑖𝑡+ 𝛽1𝑇𝑟𝑒𝑎𝑡𝑖𝑡∗ 𝑃𝑜𝑠𝑡𝑖𝑡+ 𝛽2𝑆𝑖𝑧𝑒𝑖𝑡+ 𝛽3𝑀𝑎𝑟𝑘𝑒𝑡𝑡𝑜𝐵𝑜𝑜𝑘𝑖𝑡 +𝛽4𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡+ 𝛽5𝑃𝑟𝑜𝑓𝑖𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡+ 𝛽6𝐺𝑒𝑛𝑑𝑒𝑟𝑖𝑡+ 𝛽7𝐴𝑔𝑒𝑖𝑡+ 𝛼𝑖+ 𝜏𝑡+ 𝜇𝑖𝑡 (7) 𝐿𝑜𝑛𝑔𝐿𝑒𝑣𝑖𝑡= α + ρ𝑇𝑟𝑒𝑎𝑡𝑖𝑡+ δ𝑃𝑜𝑠𝑡𝑖𝑡+ 𝛽1𝑇𝑟𝑒𝑎𝑡𝑖𝑡∗ 𝑃𝑜𝑠𝑡𝑖𝑡+ 𝛽2𝑆𝑖𝑧𝑒𝑖𝑡+ 𝛽3𝑀𝑎𝑟𝑘𝑒𝑡𝑡𝑜𝐵𝑜𝑜𝑘𝑖𝑡 +𝛽4𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡+ 𝛽5𝑃𝑟𝑜𝑓𝑖𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡+ 𝛽6𝐺𝑒𝑛𝑑𝑒𝑟𝑖𝑡+ 𝛽7𝐴𝑔𝑒𝑖𝑡+ 𝛼𝑖+ 𝜏𝑡+ 𝜇𝑖𝑡 (8) 𝑁𝑒𝑡𝐿𝑒𝑣𝑖𝑡= α + ρ𝑇𝑟𝑒𝑎𝑡𝑖𝑡+ δ𝑃𝑜𝑠𝑡𝑖𝑡+ 𝛽1𝑇𝑟𝑒𝑎𝑡𝑖𝑡∗ 𝑃𝑜𝑠𝑡𝑖𝑡+ 𝛽2𝑆𝑖𝑧𝑒𝑖𝑡+ 𝛽3𝑀𝑎𝑟𝑘𝑒𝑡𝑡𝑜𝐵𝑜𝑜𝑘𝑖𝑡 +𝛽4𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡+ 𝛽5𝑃𝑟𝑜𝑓𝑖𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡+ 𝛽6𝐺𝑒𝑛𝑑𝑒𝑟𝑖𝑡+ 𝛽7𝐴𝑔𝑒𝑖𝑡+ 𝛼𝑖+ 𝜏𝑡+ 𝜇𝑖𝑡 (9) 𝐶𝑎𝑠ℎℎ𝑜𝑙𝑑𝑖𝑡= α + ρ𝑇𝑟𝑒𝑎𝑡𝑖𝑡+ δ𝑃𝑜𝑠𝑡𝑖𝑡+ 𝛽1𝑇𝑟𝑒𝑎𝑡𝑖𝑡∗ 𝑃𝑜𝑠𝑡𝑖𝑡+ 𝛽2𝑆𝑖𝑧𝑒𝑖𝑡+ 𝛽3𝑀𝑎𝑟𝑘𝑒𝑡𝑡𝑜𝐵𝑜𝑜𝑘𝑖𝑡 +𝛽4𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡+ 𝛽5𝑃𝑟𝑜𝑓𝑖𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝑖𝑡+ 𝛽6𝐺𝑒𝑛𝑑𝑒𝑟𝑖𝑡+ 𝛽7𝐴𝑔𝑒𝑖𝑡+ 𝛼𝑖+ 𝜏𝑡+ 𝜇𝑖𝑡 (10) Where, Treat is a dummy which indicates the change of the CEO educational background from non-finance to finance, Post is a dummy referring to the turnover of the CEO and Treat*Post is the multiple of Treat and Post.