The impact of chief financial officer/chief executive officer : gender on financial risk-taking

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Amsterdam Business School

The impact of Chief Financial Officer/Chief Executive Officer

Gender on Financial Risk-taking

Master Thesis, final version Name: Jerphia van der Drift Student number: 10868208 Date: 18th_{June 2016}

Word count: 12.229

MSc Accountancy & Control, variant Accountancy

Faculty of Economics and Business, University of Amsterdam Supervisor: DR. A.K. Sikalidis

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Statement of Originality

This document is written by student Jerphia van der Drift who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is 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

There has been a significant increase over the past decade in the number of women occupying positions in the highest echelon of the business community. With this increase, researchers have begun to investigate the impact of female executives on various decisions, such as financing, investment, and mergers and acquisitions (e.g., Mohan and Chen 2004; Levi, Li, and Zhang 2008; Huang and Kisgen 2013). In general, their studies find that decisions made by female executives are significantly different from those made by their male counterparts. In this study I examine the relationship between Chief Financial Officer (CFO)/Chief Executive Officer (CEO) gender and financial risk-taking. I therefore hypothesize that firms with a female CFO are associated with a more conservative Debt-to-Capital Ratio and a higher-level of Interest Coverage Ratio than firms with a male CFO. The first hypothesis is assessed by empirical data, based on 2643 observations from 2008 until 2014. Subsequently no significant confirmation existed that firms with a female CEO are associated with a more conservative Debt-to-Capital ratio or a higher-level Interest Coverage Ratio than firms with a male CEO. I was not able to find Substantial evidence to support my hypothesis in my regressions. A sensitivity analysis also discovered no significant differences between CEO gender and financial risk.

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Summary

There has been a significant increase over the past decade in the number of women occupying positions in the highest echelon of the business community. With this increase, researchers have begun to investigate the impact of female executives on various decisions, such as financing, investment, and mergers and acquisitions (e.g., Mohan and Chen 2004; Levi, Li, and Zhang 2008; Huang and Kisgen 2013). In general, their studies find that decisions made by female executives are significantly different from those made by male executives. Gender differences in behaviour towards risk and in risk-related behaviour have long been studied in sociology and psychology literature. These studies support the belief that women are more risk averse than men.

However, evidence as to whether there exists a Chief Financial Officer/Chief Executive Officer (CFO/CEO) gender effect on financial risk-taking is limited and the results are mixed. In general, they find that female fund managers are more risk averse than male fund managers in their investment decisions.

Given these results, and although accounting research has shown greater awareness of gender issues in recent years, the issue of whether there exists gender differences in recent years in terms of financial risk-taking is still an open question, and calls for more research in this area. Additionally, this study is also relevant in the current discussion of the need for female executives. Thus I want to determine the impact of CFO/CEO gender on financial risk-taking. This leads to the following research question:

What is the impact of CFO/CEO gender on financial risk-taking?

This study contributes to both the scientific point of view and social point of view. From a scientific point of view, this research question and the research itself will enhance the existing literature. This research question will especially enhance the existing literature on the impact of CFO/CEO gender on financial risk-taking.

My sample covers the period from 2008 to 2014 and was selected from firms of the S&P 500. The data for name, gender and rank of the executive for all firms is collected from COMPUSTAT and COMPUSTAT executive. I merged the data from COMPUSTAT with the data from COMPUSTAT executive. After that I deleted the incomplete and or insufficient data. Lastly, I checked the normal distributions of the variables and winsorized where necessary.

I empirically examine whether firms with a female CFO/CEO are associated with a more conservative level of Debt-to-Capital Ratio and a higher-level Interest Coverage Ratio than firms with a male CFO/CEO. My analysis is motivated by well-researched and documented ethical,

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and other, differences between female and male. Given that females are generally more conservative and less motivated to take more numerous or extreme financial risks, I assume that female CFO/CEOs assess risks more conservatively, and may thereby hold a more conservative level of Debt-to-Capital Ratio and a higher-level Interest Coverage Ratio.

The empirical findings reported in this study, by two different regressions, demonstrate that firms with a female CFO are positively associated with less debt and properly higher borrowing capacity. Also, firms with a female CFO are positively associated with a higher Interest Coverage Ratio and properly higher borrowing capacity. However, on the other hand, I found no evidence that female CEOs are associated with less financial risk-taking in comparison with their male counterparts; the data relating to the female CEO had a p-value of 0.087, not significant but not that strong. Due to the doubts regarding the significance of the p-value, I performed a sensitivity analysis. The empirical findings of the sensitivity regression are as follows: Firms with a female CFO are positively associated with a lower Expense Ratio and properly higher borrowing capacity, however, I found no evidence that female CEOs are associated with less financial risk-taking compared to their male counterparts.

Therefore, the overall results deduced from my study provide support for H1, that Female CFO are positively associated with less financial risk-taking. Subsequently, I reject H2. In the news article of Lagarde (2010) it is stated that “if Lehman Brothers had been Lehman sisters today’s economic crisis clearly would look different”. My study indicates that in regard to this quote of Lagarde (2010) there may actually be some truth.

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1 Introduction

There has been a significant increase over the past decade in the number of women occupying positions in the highest echelon of the business community. With this increase, researchers have begun to investigate the impact of female executives on various decisions, such as financing, investment, and mergers and acquisitions (e.g., Mohan and Chen 2004; Levi, Li, and Zhang 2008; Huang and Kisgen 2013). In general, their studies find that decisions made by female executives are significantly different from those made by male executives. Gender differences in behaviour toward risk and in risk-related behaviour have long been studied in the sociology and psychology literature. These studies support the belief that female are more risk averse than male. For example, Johnson and Powell (1994) find that women are more risk averse than men in regard to betting. Jianakoplos and Bernasek (1998) find that single women are more risk averse than single men in investment decisions. Sundén and Surette’s (1998) research in gender differences in the conferring of defined contribution plan assets, reports that women are less likely to hold most of their assets in stocks. And in combination with Bernasek and Shwiff (2001) they also find that women allocate their pension more conservatively than men.

However evidence as to whether there exists a Chief Financial Officer/Chief Executive Officer (CFO/CEO) gender effect on financial risk-taking is limited and the results are mixed. Of the existing studies, only one of the elements in measuring financial risk is utilised, and not all of them contained in one study. For instance, Niessen and Ruenzi (2007) focus their study on mutual fund managers and compare the investment behaviour of male and female fund managers. In short, they find that female fund managers are more risk averse than male fund managers in their investment decisions. Dyreng, Hanlon, and Maydew (2010) do not find that executive gender affects corporate tax avoidance. Additionally, Ge, Matsumoto, and Zhang (2011)do not find that CFO gender affects discretionary accruals.

Over the past years a lot has happened in relation to financial risk-taking. Firstly, in 2008 the credit crisis started, during, and after which, laws and regulations changed aiming to reduce risk. You may question whether after the credit crisis men also became more risk averse than before. Secondly, the last years have seen a significant increase in the number of women belonging to the top echelon of the business community.

Given these results, and although accounting research has shown greater awareness of gender issues in recent years, the issue of whether there exists gender differences in recent years in terms of financial risk-taking is still an open question, and calls for more research in this area.. Additionally, this study is also relevant in the current discussion of the need for female

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executives.

Thus, I want to determine the impact of CFO/CEO gender on financial risk-taking, and in fact whether there is an impact of CFO/CEO gender on financial risk-taking at all.

This leads to the following research question:

What is the impact of CFO/CEO gender on financial risk-taking?

This study contributes to both the scientific point of view and social point of view. From a scientific point of view, this research question and the research itself will enhance the existing literature. This research question will especially enhance the existing literature on the impact of CFO/CEO gender on financial risk-taking.

This literature can possibly add new scientific findings on the topic that I want to investigate. Existing literature may be given more ability or alternatively this study could decrease the credibility of existing literature, because, to my knowledge, what I want to investigate has not been undertaken before. Lastly, I hope to arrive at useful conclusions. The results of this research will provide a foundation for future research and contribute inspiration for future studies on this topic. This is a ethically good reason to attempt attitude research.

There are also societal contributions to be made from this research. The first potential societal contribution is the usefulness of this research for investors. Investors may want to know, for instance, if female executives are more risk adverse to financial decision making than male counterparts. Accordingly, each investor could then choose their own preferences for risk adversity or not. Females, in high business positions such as CFO/CEO, could also use this research. If this study finds that females are not more risk adverse than males, then this notes that a female or male CFO/CEO in relation to financial risk-taking doesn’t matter. In the same way it is also relevant to the current discussion of the need for female executives in the top echelon of the business community. This study may also prove relevant to a board of directors, as they lead the organization.

The rest of this thesis is structured as follows: In chapter 2 I discuss prior literature to provide background information on the main topics. I will also formulate my hypotheses in this section. The objective of chapter 3 is to discuss the research methodology. In this chapter the sample selection process will be explained, as well as the proxies for financial risk-taking and the regression models. The results of the main analysis are discussed in chapter 4. Finally, chapter 5 contains the conclusion and discussion, in which I provide a brief summary, the answer on the research question, the limitations of this study and some directions for future research.

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2 Literature review and hypothesis development

In this chapter the main theoretical constructs used in this study will be explained. The first paragraph I will cover the term financial risk-taking. I will point out what factors affect financial risk-taking and give in depth explanations of those factors. I also point out how CFOs/CEOs can influence financial decision-making. Second, I discuss the ethical differences between men and women. Based on the ethical differences between male and female addressed in the first paragraph and the other differences between male and female highlighted in the second paragraph, in the last paragraph I will develop my hypotheses for my study on the effect of CFO/CEO gender in financial risk-taking.

2.1 Financial risk-taking

Risk is not an easy element to give in one definition. There are different types of risk (e.g. corporate risk, financial risk and personal risk). Risk is part of a lager idea of choice as affected by an expected return. Alternatively, most of the time, is risk seen as a possible loss and not as a possible profit. March and Shapira, (1987) stated in the classical decision theory, that risk is most commonly conceived as reflecting variation in the distribution of possible outcomes, their likelihoods and their subjective values. According to the paper of Arrow (1965) all theories assume that decisions makers prefer larger than expected returns on their investments than smaller ones. In general, they also assume that decision makers prefer smaller risks to larger ones. Subsequently, they infer that expected value is positively associated and that risk is negatively associated.

Classical decision-making theory defines that choice involves a trade-off between risk and expected return. There are two types of risk decision makers: risk averse decision makers and risk seeking decision makers. The risk averse decision makers favour relatively low risks and are willing to sacrifice some expected return in order to reduce the difference in possible outcomes. Risk seeking decision makers are the opposite of risk averse decision makers, they prefer relatively high risks and are willing to sacrifice some expected return in order to increase the variation March and Shapira, (1987).

The theory assumes that decision makers first calculate the alternative risk-return combinations that are available before choosing how they will deal with risks, however the paper of March and Shapira, (1987) concludes that it is not clear if decision makers actually treat risk in that way. The article of March and Shapira, (1987) describes an example of the decision theory in the paper of Lanir and Shapira (1984), the defence that decision makers seem to have dealt with

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the topic of shelter structure in a way that ignored a decision theory definition of risk. There are suggestions that decision makers from time to time deny risk, saying that there is no risk or that it is so minor that it can be ignored. The paper of Weinstein (1980) describes a joint form of denial, which involves the acceptance of the actuarial reality of the risk joined with a refusal to associate that reality with one’s self. The definition of denial proposes a psychological pathology. It may be a more philosophical denial of the relevance of probabilistic managerial perspectives on risk and risk taking., that it is just a one event or a belief in the causal basis of events. The inclination for individuals to observe chance events to be causal and under control has been recognized in different experiments by Langer (1975), as has the inclinations to develop causal theories of events even when the relations among events are recognized to be only incidental (Tversky and Kahneman 1982).

According to the theory described above, I can conclude that managers fail to follow the standards of the decision theory. Furthermore, the way that managers think about risk does not easily fit into classical theoretical ideas of risk. Managers are especially looking for alternatives that can be managed to meet the targets, rather than assess or accept risks. All these described factors are embedded in a managerial belief system that highlights the importance of risk and risk taking for being a manager.

In the paper of Anderson (2011) it is stated that, “Risk appetite today is a core consideration in any enterprise risk management approach”. Nowadays the requirements imposed by corporate governance standards and increasingly asked by stakeholders, investors, analysts and the public, is to clearly enounce the extent of management’s willingness to take risk in order to meet their strategic objectives.

The framework of risk appetite will help to manage their firms better and to realise their corporate governance responsibilities more effectively. Risk appetite, risk tolerance and risk universe are linked to firm performance. Risk universe is all the risk that a firm might face. Risk tolerance is about how much risk a firm can deal with. Risk appetite deals with the level of risk the organization wants to take and how this can be guaranteed. It is the responsibility of management to determine the risk appetite and ensure that risk management throughout the organization is consistent with this risk appetite. However, according to Anderson it is difficult because there are different boards in different circumstances that will have different views on significance of risk appetite and risk tolerance. Implementing an effective system of internal controls can reduce the impact of risk. Transferring the risk to someone else or sharing the risk is also a way to reduce the likelihood and impact of risk. Once the risk appetite and risk tolerance is identified, the management has to determine how to respond on the identified risk.

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According to Romney and Steinbart, (2012), management can respond to risk in different ways. Firstly, by implementing an effective system of internal controls it can reduce the likelihood and impact of risk. Secondly, simply accepting the likelihood and impact of the risk. Eventually, sharing the risk or transferring it to someone else is also an alternative. This can be done for example by buying insurance or outsourcing the activity that produces the risk. This may forced the company to sell a division, quit a product line or not enlarge as anticipated.

As in the paper of Anderson (2011), it is also stated that the risk appetite, risk tolerance and the level of risk-taking is influenced by several factors. In my study the effect of CFO/CEO gender on financial risk-taking is researched. The influence of CFO/CEO gender on financial decision-making is explained in paragraph 2.2.

In the paper of Plavia, Vähämaa and Vähämaa (2015) they find that behavioural differences between males and females have important implications for corporate financial decisions and outcomes. They specially find strong evidence that female executives in the banking sector hold higher levels of capital and are therefore more conservative than male executives.

The paper of Ross (2014) found that a firm with a neutral risk should be looking for risk in developing a competitive advantage in rivalry with other firms. The reason for this is that the payoff from favourable achievements more than counterbalances the cost of unfavourable achievements. Also, the paper of Coles et al. (2006) found that compensation maximization was another reason is to take risk.

The following part of this paragraph focuses on the influence of institutional factors of financial risk-taking, according to the study of Bargeron et al. (2010) on the influence of financial risk with the coming of the Sarbanes-Oxley Act (SOX),to be a signed into law in 2002 for publicly traded US companies. Their research examined the influence of SOX on risk-taking behaviour. Their findings show that several measures of risk-taking significantly decreased for US firms versus non-US firms. The decrease in risk-taking in US firms compared with non-US firms after the implementation of SOX is visible in stock price volatility and the investment decisions.

Risk-taking can be measured in several ways. For example an experience relating to gambling behaviour between females and males, or a questionnaire with dealing with the subject of risky choices. According to MacGrimmon and Wehrung (1990), questionnaires utilize

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hypothetical choices, so the understanding and motivations of the responses is sometimes dubious. Therefore my study selected database research as a more accurate research device. This study focuses on financial risk-taking and has chosen to use two financial variables to measure the level of financial risk-taking. Prior studies, e.g. Graham et al (2013) and Griffin et al. (2013), also used financial measures to measure financial risk-taking. Every variable of the two stands for a risk category will be assessed; financial risk, liquidity risk and solvency risk. Further elaboration into the measurement of these risks will be covered in paragraph 3, research methodology.

2.2 The influence of CFO/CEO on financial decision-making

CFO stands for Chief Financial Officer and CEO stands for Chief Executive Officer. They are the head of management for an organization and report to the board of directors. This study builds on previous literature, to examine the impact of gender executive on financial risk-taking and focuses on CFO/CEO gender rather than gender diversity among the top management team. According to Ge et al. (2011), the CFO oversees the firm’s financial reporting process and therefore he/she likely has the most direct impact of all financial decisions and the risks that are taken with these decisions. Crucial to this study is whether a CFO/CEO has an influence on financial decision making.

If there was no influence, then the gender of the CFO/CEO would not have a great impact on financial risk-taking. However, recent studies from Graham et al. (2013) suggest that CFO/CEO’s have an integral impact on financial decision-making. In their research they studied the characteristics of executives; information related to education, demographics and other information affecting their career path. They found evidence that psychological characteristics, such as risk aversion and optimism linked to decision-making policies, and subsequently this influences CFO/CEO financial decision-making. Similarly, evidence was identified showing differences in financial decision making between CFOs and CEOs, and between US-based and based CFO/CEOs. Graham et al. (2013) highlight differences between US and non-US-based CEOs, primarily that US-non-US-based CEOs tend to be more optimistic and less risk averse in comparison with those based outside the United States.

Research by Bertrand and Schoar (2003) provides evidence that individual managers affect decision-making behaviour and performance. Bertrand and Schoar (2003) tracked individual top managers across different firms and found that, managers fixed effects mattered for a wide range of corporate decisions. Managers influence diversity in investments, as well as

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financial and organizational practise. Graham, Harvey, and Puri (2013) found that managerial attitudes, such as risk aversion and optimism, were related to corporate financial policies. Thus overall, I conclude that CFO/CEO gender certainly has an impact on financial risk-taking, if indeed gender impacts the position of CFO/CEO. This study will contribute to previous literature by explicitly examining the impact of CFO/CEO gender on financial risk-taking.

2.3 Difference between male and female

In this paragraph I address the gender-based behavioural differences that exist between male and female, which will play a role in this study. I first focus on ethical differences that could lead to an effect on financial risk-taking. After that I discuss other differences that could contribute to an effect on financial risk-taking. This will culminate in a general statement to represent the difference between male and female.

2.3.1! Ethical differences

Gender differences in behaviour toward risk and in risk-related behaviour have long been studied in the sociology and psychology literature, but specifically the effects of gender-based behavioural differences for financial decision-making have received increased attention over the last decades. The literature in experimental and empirical studies on financial decision-making related to investment portfolios and acquisitions. The studies of Levin et al. (1988), Johnson and Powell (1994), Powell and Anisic (1997), Eckel and Grossman (2002) and Fehr-Duda et al. (2006) stand as experimental studies, whereas the empirical studies are that of Jianakoplos and Bernasek (1998), Sundén and Surette’s (1998), Barber and Odean (2001), Dwyer et al. (2002), Agnew et al. (2003) and Watson and McNaughton (2007).

From these studies a consensus exists that females are more conservative and risk adverse than men and show less risky behaviour in personal financial decision-making. There are no studies to suggest that male behave was more ethical than that of a female. For example, Johnson and Powell (1994) find that women are more risk averse regarding betting risk behaviour than men. Jianakoplos and Bernasek (1998) find that single women are more risk averse than single men in investment decisions. Sundén and Surette’s (1998) research, in gender differences in the conferring of defined contribution plan assets, reports that women are less likely to hold most of their assets in stocks. Additionally, in combination with Bernasek and Shwiff (2001) they also find that women allocate their pension more conservatively than men.

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According to the paper of Betz, O’ Connell and Shepard (2009) there are two approaches that explain the ethical behaviour of male and female: The structural approach, and the gender socialization approach.

Firstly, the structural approach assumes that ethical differences between male and female, arise in early socialization development and will be excluded by rewards and the cost of their jobs. Alternatively, the supporters of the structural approach, to the difference between male and female, believe that behaviour of male and female is formed by their job and how they structure their rewards related to their job. The primary difference between male and female becomes ever diminishing when they work together for the same employer for an extended period. The prediction of the supporters of the structuration theory is therefore that there are, in fact, no differences between male and female.

The gender socialization approach supposes, on the other hand, that male and female certainly bring in different norms and values to their job. As a result of this assumption there are also differences in interests and practices believed to relate to decisions within their workplace. These lead to different responses to rewards and costs by male and female. So according to this approach, male and female are more motivated by success, money and promotion. Betz, O’ Connell and Shepard (2009) conclude in their paper that supporters of the gender socialization approach expect that there are ethical differences between male and female.

The study of Betz, O’ Connell and Shepard (2009) examined whether there are ethical differences between male and female. Significant ethical differences were identified, for example, they found that males are more likely to be involved in unethical actions in comparison with females.

Nguyen et al. (2008) also researched the ethical differences between male and female and found that female judges were more ethically than male. This evidence supports the gender socialization approach in the study of Betz, O’ Connell and Shepard (2009). In summation, there are various studies, which researched the ethical differences between male and female, however the results are mixed.

2.3.2! Other differences between male and female

In this section other differences between male and female behaviour is discussed. There are not only ethical differences between male and female behaviour that could impact financial risk-taking. The most important differences between male and female behaviour in relation to financial risk-taking is that females are more risk-averse or careful than males. According to

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paper of Brynes, Miller and Schafer (1999), carefulness and aversion is predominant in decision and business judgment contexts. Barua, Davidson, Rama and Thiruvadi (2010), found that females require more evidence before recognizing revenues and therefore are less likely to recognize revenues aggressively. They also found that the less aggressive judgment related to recognition of revenues could result from females being less likely to recognize accruals. The study of Barua, Davidson, Rama and Thiruvadi (2010) also found another difference between male and female, in the fact that a female CEO/CFO is more in compliance with accounting regulations than a male CEO/CFO. The result of this difference would probably be small, because females are less likely to recognize revenue and accruals.

2.4 Hypotheses

Based on the above-mentioned literature and theories, I developed hypotheses to test. An overwhelming majority of the literature and or theories suggest that females are more risk adverse than males. Thus it could be assumed that females are more risk adverse than males in general. However, this has not been specifically examined in the context of financial risk. Therefore, I come up with the following hypothesis:

Hypothesis 1: Firms with female Chief Financial Officers are associated with more conservative

Debt-to-Capital Ratio and a higher-level Interest Coverage Ratio than firms with male Chief Financial Officers.

I consider whether firms with male Chief Financial Officers have a higher level of Debt-to-Capital Ratio. The Debt-to-Debt-to-Capital Ratio of a firm measures how much of the assets are financed by debt, long-term versus short-term. To measure financial risk-taking I will be utilizing the Debt-to-Capital Ratio. Niessen and Ruenzi (2007) focus their study on mutual fund managers and compare the investment behaviour of male and female fund managers. In general, they find that female fund managers are more risk averse than male fund managers in their investment decisions. The study of Graham et al. (2013) also uses the debt to capital ratio to measure financial risk.

Additionally, to measure risk-taking, I incorporate the Interest Coverage Ratio. This variable is a debt and profitability ratio, which determines to what extent a company can pay the interest on outstanding debt. I expect that firms with a female Chief Financial Officer have a higher-level Interest Coverage Ratio. This expectation is based on gender differences in

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behaviour toward risk and in risk-related behaviour, which have long been studied in sociology and psychology literature supporting women as more risk averse than men. For a specific example, Johnson and Powell (1994) find that women are more risk averse in regard to betting risk behaviour than men.

Since not only Chief Financial Officers have influence on financial risk-taking, I also want to measure the influence of Chief Executive Officers on financial risk-taking. This inherently leads to my second hypothesis:

Hypothesis 2: Firms with female Chief Executive Officers are associated with a more

conservative Debt-to-Capital ratio and a higher-level Interest Coverage Ratio than firms with male Chief Executive Officers.

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3 Data and method

The research methodology for this study will be a quantitative archival study (database research) to answer my research question. This research is focused on United States based CFOs and CEOs for a listed firm. The financial measures will be derived from the COMPUSTAT database, in which financial data of S&P 500 firms is collected over the years. When in the COMPUSTAT database, the sample of financial data of S&P 500 will only include United States based firms, thus it is not necessary to control for the impact of institutional and cross-country factors on these firms. The research will take place in an industry with female executives. To test if a CFO/CEO gender has impact on financial risk-taking CFO/CEO data and financial data is collected. The different variables used will be explained in this section. Also, in this chapter I will point out what sample will be employed in this research. After that I will discuss the design of my study. I will detail how financial risk is measured and discuss the regression model.

3.1 Sample selection

For this empirical research study I will use data from the United States. I focus on CFOs and CEOs because the sample of CFOs alone is too small for meaningful analysis. CEOs have an important role in major decisions of the firm, but several studies, for instance Business Trend Quarterly (2007), indicate that CEOs also play a significant role in acquisitions and capital structure decisions.

I collect my data set for female CFO/CEO using executive information on the ExecuComp database (this database only contains the largest firms) and I also require that the firm be a NYSE, Amex, or Nasdaq-listed firm in COMPUSTAT, from which I obtain firm financial data. The COMPUSTAT executive database is available from 1992 and electronic filings in the Securities and Exchange Commission (SEC) and Electronic Data Gathering, Analysis and Retrieval (EDGAR) system became active in 1993. My sample covers the period from 2008 to 2014. The data for name, gender and rank of the executive for all firms was collected. When COMPUSTAT executive reports two executives with the same title (CFO or CEO), within one firm in the same year, I chose the one with the highest rank. This identifies CFOs or CEOs for firm years. In the next step of the sample selection I merge the data from COMPUSTAT with the data from COMPUSTAT executive. After that I delete the incomplete and or insufficient data. Lastly, I checked the normal distributions of the variables and winsorized where it was necessary.

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Summarizing of the sample selection:

1.! I collect data from COMPUSTAT for financial data

2.! I collect data from COMPUSTAT executive for the gender of CFO/CEO 3.! I merged data from COMPUSTAT with the data of COMPUSTAT executive 4.! Deleting incomplete and/or insufficient data

5.! Checked for normal distribution of variables 6.! Winsiorized the variables where necessary Table 1: Sample selection

Observations

COMPUSTAT data 5208

Merged data COMPUSTAT and COMPUSTAT executive 4644

After deleting incomplete and/or insufficient data 2643

Table 2: Industry distribution

Division SIC Codes Observations

Agriculture, Forestry & Fishing (0100-0999) 7

Mining (1000-1499) 215

Construction (1500-1799) 36

Manufacturing (2000-3999) 1183

Transportation, Communications, Electric, Gas and Sanitary service (4000-4999) 231 Wholesale Trade (5000-5199) 42 Retail Trade (5200-5999) 242 Services (7000-8999) 264 Public Administration (9100-9729) - Not classifiable (9900-9999) 13

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3.2 Research design

To answer the research question, I designed a regression model, which is a combination of the models by Huang and Kisgen (2013) and Palvia et al. (2015). To measure financial risk-taking, I used two different proxies. The two proxies are calculated by separate regressions per industry year. Those proxies are discussed in paragraph 3.2.1. In my regression model, I used the Debt-to-Capital Ratio and the Interest Coverage ratio as dependent variables.

3.2.1 Proxies for financial risk-taking

There are different elements to measure financial risk-taking. According to the literature to develop my hypothesis, financial risk-taking will be researched by using the Debt-to-Capital Ratio and the Interest Coverage Ratio. In particular, I use a similar method of the study of Claessens (2000) of categorizing risk measures and the studies of Huang and Kisgen (2013) and Palvia et al. (2015). Financial risk-taking takes place in different ways and is dependent of multiple variables. To measure the level of financial risk in the paper of Graham et al. (2013) they use different financial variables, each of these variables stands for one risk category. The following categories: financial risk and solvency risk.

3.2.1.1 Debt-to-Capital Ratio

In the first category of financial risk, debt is the amount borrowed by one party to another party. The amount of debt that a firm uses is the subject of many discussions on financial risk and leads to an important point for my study. When a firm uses more debt by “levers up”, the firm is creating more risk and higher expected returns. The paper of Graham et al. (2013) mentions that when a firm uses more debt, the firm produces greater risk and expects increased returns.

Secondly, the paper of Gramham et al (2013) finds evidence that male CEOs are more likely to have higher levels of debt ratio, and specifically have higher short-term debt ratios in comparison with female CEOs. The paper by Hackbarth (2008) underlines that a high financial leverage and related large interest payments will decrease the ability of a firm to deal with financial shocks. Furthermore, Hackbarth (2008) highlights that the characteristics of a manager, including gender, can influence the debt ratio. A manager who is more overconfident takes more risks and chooses higher debt levels and often accumulates more new debt.

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For the firm, debt is risky as they may not be able to repay the particular amount of debt. The Debt-to-Capital Ratio is defined as the ratio of a company’s financial leverage, calculated as the company’s total debt relative to its shareholder equity, plus total debt, expressed as a percentage. The Debt-to-Capital Ratio gives readers of financial statements an idea of the firm’s financial structure and how the firm is financing its operations. As mentioned in the studies of Gramham et al (2013) and Hackbarth (2008), the higher the Debt-to-Capital Ratio, the more debt the firm has in comparison to its equity. The Debt-to-Capital Ratio can tell the readers of financial statements whether the firm is using debt financing or equity financing.

So overall, we can conclude that, when a firm has high Debt-to-Capital Ratios this may show a weak financial situation, thus creating more financial risk and higher expected returns, and subsequently, the cost of these debts may weigh on the firm and increase its default risk. This assertion is in line with the results of Palvia et al. (2015).

3.2.1.2 Interest Coverage Ratio

Credit Risk will be measured by the Interest Coverage Ratio. The Interest Coverage Ratio measures to what extent the company can pay interest on outstanding debt. The paper of Claessens et al. (2000) used the Interest Coverage Ratio as a solvency measure. The Interest Coverage Ratio is measured by the earnings before interest and taxes (EBIT), relative to interest expenses, expressed as a ratio.

When the firms Interest Coverage ratio is 1.5 or lower, the ability for the firm to meet its interest expenses may be doubtful. When a firm has an Interest Coverage Ratio lower than 1, it may indicate that the firm is experiencing problems generating cash to pay its interest expenses on their obligations. The higher the cash flow relative to interest payments for debt services, the less likely that the company has default risk on its debt service. So a higher Interest Coverage Ratio indicates a better financial health of the firm; they are more capable of meeting interest expenses on obligations and possess greater borrowing capacity.

3.2.2 Regression models

As mentioned earlier, I conducted a regression analysis to test my hypothesis whether CFO/CEO gender has an impact on financial risk-taking. I designed my models with variables informed by the models of Huang and Kisgen (2013) and Palvia et al. (2015). In particular, I will make use of two relatively similar models.

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In my analysis, I conduct a regression analysis to test my hypothesis of whether female CFO/CEOs have more conservative capital ratios and lower default risk. I start the analysis by examining the association between CFO/CEO and firms capital buffers. It is widely recognized that the amount of equity capital is a major factor in reducing insolvency risk. Higher capital buffers help firms to survive during a financial crisis. I designed my model with variables from the models of Claessens (2000) and Graham et al. (2013) to categorize risk measures, and the studies of Huang and Kisgen (2013) and Palvia et al. (2015) to inform the scheme of my regression model. Consequently, I assume that if gender based difference in risk aversion affects firm-level decisions, then firms with a female CFO/CEO should hold higher levels of equity capital, and thus the holding firm’s asset risk and other aspects at a constant. The models are stated below. Under each model, the variables used in the model are discussed individually. Regression model:

!"#$%"&'("%$)_*,, ='∝ ' +'0₁234"&3!56_*,,+'0₇234"&3!26_*,,' ''''''''''''''''''''''''''''''+''0₈'9:;_*,,'''''+''0_<=$>3_*,,'''''+''0_?'=@A_*,,

'''''+''0_B(6C_*,,''+''0_D!2_*,,

'''''''''''''''''''''''+''0_∗F∗(H3"I'JK44$3L)_*,,+ N_*,,

The dependent variable !"#$%"&'("%$)*,, stands for the Debt-to-Capital ratio and measures

firms j at time t. The Debt-to-Capital Ratio measures the firm’s total debt relative to shareholders’ equity, plus total debt. I controlled for fixed effect (difference in years) by introducing year dummies (years).

Where:

!"#$%"&'("%$)_*,,= Financial risk proxies: Debt-to-Capital for firm j in year t.

234"&3!56_*,,= 1 when the CEO of firm j is a female in year t and 0 otherwise. This is one of the two most important variables, which actually measures the main effect of this study.

234"&3!26_*,,= 1 when the CFO of firm j is a female in year t and 0 otherwise. This is also one of the two most important variables, which measures the main effect of this study.

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=$>3_*,,= Size of the firm; natural logarithm of the market value of equity for firm j in year t. =@A_*,,= Sales growth: Change in sales, measured as (S it – S it-1 )/S it-1.

(6C_*,,= Return on assets of firm j in year t calculated by: net income relative to total assets. !2_*,,= Cash flow from operations of firm j in year t, relative to assets for the year t-1.

Secondly, I exploit firms’ Interest Coverage Ratio. This measures borrowing capacity as a measure of financial risk-taking. To empirically examine the association between female CFO/CEO and the level of the Interest Coverage ratio, I run a multivariate regression of the following form.

Regression model:

OP%3I3L%'!)Q3I"R3'("%$)_*,, ='∝ ' +'0₁234"&3!56_*,,+'0₇234"&3!26_*,,' ''''''''''''''''''''''''''''''+''0₈'9:;_*,,'''''+''0_<=$>3_*,,'''''+''0_?'=@A_*,,

'''''+''0_B(6C_*,,''+''0_D!2_*,,

'''''''''''''''''''''''+''0_∗F∗(H3"I'JK44$3L)_*,,+ N_*,,

The dependent variable, OP%3I3L%'!)Q3I"R3'("%$)*,, measures firms j at time t. The Interest

Coverage Ratio measures the earnings before interest and taxes (EBIT) relatively to interest expenses, expressed as a ratio. I controlled for fixed effect (difference in years) by introducing year dummies (years).

Where:

OP%3I3L%'!)Q3I"R3'("%$)*,,= Financial risk proxies: Interest Coverage Ratio for firm j in year

t.

234"&3!56_*,,= 1 when the CEO of firm j is a female in year t and 0 otherwise. This is one of the two most important variables, which actually measures the main effect of this study.

234"&3!26_*,,= 1 when the CFO of firm j is a female in year t and 0 otherwise. This is one of the two most important variables, which measures the main effect of this study.

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9:;_*,,= Market to book value: book value to market value of equity ratio for firm j in year t. =$>3_*,,= Size of the firm; natural logarithm of the market value of equity for firm j in year t. =@A_*,,= Sales growth: Change in sales, measured as (S it – S it-1 )/S it-1.

(6C_*,,= Return on assets of firm j in year t calculated by: net income relative to total assets. !2_*,,= Cash flow from operations of firm j in year t, relative to assets for the year t-1.

The first two independent variables, 234"&3!56_*,, and 234"&3!26_*,, are the most important variables of my study. These variables capture the main effect, namely the effect of CFO/CEO gender on financial risk-taking. In paragraph 2.4 I explained my hypotheses. I expect that firms with a female CFO/CEO are associated with a more conservative Debt-to-Capital Ratio and higher-level Interest Coverage Ratio than firms with a male CFO/CEO.

I include several firm-specific control variables in my regression. In particular, I control for size, growth, and organizational characteristics of the firm. Furthermore, I also attempt to control for the financial conditions and riskiness of the firms, by including proxies for borrowing capacity, liquidity and profitability in the regressions. The control variables used in the regression are selected based on the prior literature of Huang and Kisgen (2013) and Palvia et al. (2015).

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4 Results

In this section I will discuss the results of my regression models for this study. Firstly, I will evaluate the descriptive statistics of the sample. Secondly, I will discuss the results of my main analysis, the effect of CFO/CEO gender on financial risk-taking. Finally, I will discuss a sensitivity analysis for measuring financial risk.

4.1 Descriptive statistics

The variables used in the regression, their definition and source, are shown in table 3. The descriptive statistics of the used sample are displayed in table 4. Table 4 contains a summary of the statistics of the full sample. After that, table 5 contains the distribution of CFO/CEO by gender and transition year. I winsorized all continuous variables at 5% to control for outliers. Table 3: Variable overview, definition and sources

Variable Definition Source

STUVWTX'YTWVZ_[,W Debt-to-Capital Ratio: is defined as the

ratio of a company’s financial leverage, calculated as the company’s total debt relatively to its shareholder equity plus total debt. A proxy for financial risk-taking.

COMPUSTAT

\]W^_^`W'SZa^_Tb^'YTWVZ_[,W Interest Coverage Ratio: measures to

what extent the company can pay interest on outstanding debt, calculated as, the earnings before interest and taxes (EBIT) relatively to interest expenses expressed in a ratio. A proxy for financial risk-taking.

COMPUSTAT

c^dTX^Sef_[,W A dummy variable which equals one for firms that have a female Chief executive officer.

COMPUSTAT executive

c^dTX^Scf_[,W A dummy variable which equals one for firms that have a female Chief executive officer.

COMPUSTAT executive

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ghi_[,W Market to book value: book value to

market value of equity ratio for firm j in year t.

COMPUSTAT

jVk^_[,W Size of the firm: natural logarithm of the

market value of equity.

COMPUSTAT

jlm_[,W Sales growth: Change in sales, measured

as (S it – S it-1 )/S it-1.

COMPUSTAT

Yfn[,W Return on assets of firm j in year t

calculated by: net income relative to total assets.

COMPUSTAT

Sc[,W Cash flow from operations of firm j in year

t, relative to assets for the year t-1.

COMPUSTAT

Table 4: Summary statistics

Variable Freq. Mean Median Minimum Maximum Std. Dev.

Dep. Variables !"#$%"&'("%$)_*,, 2643 0.3918068 0.3672184 0.0648935 0.8443465 0.2093077 OP%3I3L%'!)Q3I"R3'("%$)_*,, 2643 15.4115 8.79 0.9371738 79.71343 19.34567 Independent Variables 234"&3!56_*,, 2643 0.0461597 0 0 1 0.2098705 234"&3!26_*,, 2643 0.0817253 0 0 1 0.2739976 Control Variables 9:;_*,, 2643 3.312976 2.544384 0.7230638 10.26634 2.465704 =$>3_*,, 2643 9.512106 9.397883 2.724215 13.2896 1.116821 =@A_*,, 2643 0.0769283 0.0486715 -0.807276 11.064 0.3494448 (6C_*,, 2643 0.0598556 0.058164 -2.283249 0.3981721 0.0873597 !2_*,, 2643 0.1081506 0.1034803 -0.368277 0.4782209 0.0679547

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Table 5: Distribution of CFO and CEO by gender and transition year. Gender 2008 2009 2010 2011 2012 2013 2014 Total Male 246 284 352 357 366 365 337 2307 10.6% 12.3% 15.3% 15.5% 15.9% 15.8% 14.6% Female 26 32 41 41 56 68 72 336 7.8% 9.5% 12.2% 12.2% 16.7% 20.2% 21.4%

A summary of the statistics for the sample of CFO/CEO and the distribution of CFO and CEO by gender and transition year are shown in table 4 and 5. Table 5, the distribution of CFO and CEO by gender and transition year, indicates that 58.3% more females have been hired as CFO/CEO, with 46.3 % male, in my sample within the last three years, from the year 2012 to 2014. Due to this evidence, we can conclude that there has been a significant increase over the past decade in the number of women belonging to highest echelon of the business community. With this increase, researchers have begun to investigate the impact of female executives on various decisions, such as financing, investment, and mergers and acquisitions (e.g., Mohan and Chen 2004; Levi, Li, and Zhang 2008; Huang and Kisgen 2013). As an example, the paper of Huang and Kisgen (2013) stated that this increase over time might represent a growth in the amount of highly qualified women over the years 2012 to 2014 or decreases in discriminatory attitudes.

The pairwise correlation results between the variables are reported in table 6. The coefficients of the variables are relatively low in the sample, mitigating the concerns connected with the fact that multicollinearity could affect the regression outcomes.

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Table 6: Pairwise correlation matrix Variable Interest Goverage Ratio Debt-to-Capital ratio Female CEO Female CFO Market to book ratio _Size Sales growth rate Return on

investment _{Cash flow}

!"#$%$&#'()*$%+,$'-+#.)/,1 1.0000 (+2.#+3'-+#.)/,1 -0.5317* 1.0000 0.0000 4$5+3$(67_/,1 -0.0345 0.0834* 1.0000 0.0761 0.0000 4$5+3$(47_/,1 0.0756* -0.0375 0.0134 1.0000 0.0001 0.0539 0.4924 89:_/,1 0.1976* 0.2279* 0.0550* 0.0671* 1.0000 0.0000 0.0000 0.0047 0.0006 ;.<$_/,1 0.1448* -0.1044* -0.0077 0.0301 0.7184* 1.0000 0.0000 0.0000 0.6910 0.1213 0.0000 ;=>_/,1 0.0532* -0.0485* -0.0141 -0.0071 0.0657* 0.0227 1.0000 0.0062 0.0127 0.4683 0.7166 0.0007 0.2431 -7?/,1 0.3563* -.01853* -0.0229 0.0388* 0.2398* 0.2164* 0.0629* 1.0000

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0.0000 0.0000 0.2392 0.0463 0.0000 0.0000 0.0012

(4/,1 0.4087* -0.1233* -0.0094 0.0359 0.3074* 0.1590* 0.0123 0.4983* 1.0000

0.0000 0.0000 0.6276 0.0648 0.0000 0.0000 0.5264

(Table 6: pairwise correlation matrix continuous) *= P<0.05= significantly correlated, the number in parentheses in the left column reflects the same variable in the top row. Capital ratio is defined as the ratio of a company’s financial leverage, calculated as the company’s total debt relative to its shareholder equity, plus total debt. Interest Coverage Ratio is defined as the ratio of a company’s solvency risk; it measures the earnings before interest and taxes (EBIT) relative to interest expenses, expressed as a ratio. These ratios are both proxies for financial risk-taking. The female variables in the regressions are defined as follows: Female CEO is a dummy variable which equals one for firms that have a female CEO; Female CFO equals one for firms that have a female CFO. The control variables are defined as follows: market to book ratio is the book value to market value of equity ratio for firm j in year t, Size is natural logarithm of the market value of equity, Sales growth as the change in sales, measured as (S it – S it-1 )/S it-1, Return on assets of firm j in year t calculated by: net income relative to total assets and Cash flow from operations of firm j in year t is relative to assets for the year t-1.

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4.2 Regression Results

Regression analyses have been performed to test the hypothesis of the effect of CFO/CEO gender on financial risk-taking. As previously stated, two proxies for the measuring of financial risk are the Debt-to-Capital ratio and the Interest Coverage ratio. The indicator variables (CapitalratioW and ICRW) have been analysed by using a multivariate regression model.

To test my hypotheses I run two different regressions. The first regression is shown in table 6 and contains the dependent variable, Debt-to-Capital Ratio. The second hypothesis with the dependent variable, Interest Coverage Ratio, is shown in table 7. The two tables giving the coefficients and significance levels are presented.

This study begins with empirical analysis by conducting t-tests for differences in the mean level of Debt-to-Capital Ratio for female-led and male-led firms. Second, the empirical analysis by conducting t-tests for differences in the mean level of Interest Coverage Ratio for female-led and male-led firms.

Table 6: Regression Results Debt-to-Capital Ratio

Variables Coef. Std. Err. t P>|t|

!"#$%"&'(_),+ 0.0587002 0.0180341 3.25 0.001 !"#$%"&!(),+ -0.078195 0.0138627 -2.73 0.006 ,-.),+ 0.0274647 0.0016595 16.55 0.000 /01"_),+ -0.0203697 0.0035741 -5.70 0.000 /23_),+ -0.0340144 0.0109704 -3.10 0.002 4(5),+ -0.4252588 0.051133 -8.32 0.000 &!_),+ -0.3474169 0.0664867 -5.23 0.000

Year effects Yes

N 2643

Adj. R-squared 0.1406 F-statistic 34.26

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Notes: Capital ratio is defined as the ratio of a company’s financial leverage, calculated as the company’s total debt relative to its shareholder equity plus total debt; a proxy for financial risk-taking. The female variables in the regressions are defined as follows: Female CEO is a dummy variable which equals one for firms that have a female CEO; Female CFO equals one for firms that have a female CFO. The control variables are defined as follows: market to book ratio is the book value to market value of equity ratio for firm j in year t, Size is natural logarithm of the market value of equity, Sales growth as the change in sales, measured as (S it – S it-1)/S it-1, Return on assets of firm j in year t calculated by: net income relative to total assets and Cash flow from operations of firm j in year t is relative to assets for the year t-1.

For the dependent variable Debt-to-Capital Ratio, the regression analyses p-values are above significance levels (p<0.05), as shown in table 6. The variables of interest are female CEO and female CFO. A lower level of Debt-to-Capital Ratio is positively associated with less financial risk-taking. I therefore expect that the female CEO and female CFO coefficient should be negative.

The results related to female CEO, presented in table 7, depict a positive coefficient of 0.0587002, with a significance level of 0.001. So the coefficient is not in line with my expectations. However, the results related to female CFO, presented in table 6, shows a negative coefficient of -0.078195, with a significance level of 0.006. So the coefficient in this case is in line with my expectations. In total all my five control variables in the model are significant. Thus the Interest Coverage Ratio is influenced by those variables. Only the control variable market to book ratio is a positive coefficient therefore it is not in line with my expectations. Based in this regression there is an indication that female CFOs are positively associated with less financial risk-taking in comparison with male CFOs. This could indicate that firms with a female CFO are positively associated with less debt and a properly higher borrowing capacity However, on the other hand, I found no evidence that female CEOs are associated with less financial risk-taking in comparison with male counterparts.

I can now evaluate these results by comparing them with the results of the study by Palvia et al. (2015). In their study, they found a negative association between female CEOs and the default risk. The default risk indicates whether a firm will be unable to make the required payments on their debt obligations.

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Table 7: Regression Results Interest Coverage Ratio

!"#$%"&'(_),+ -2.733664 1.594297 -1.71 0.087 !"#$%"&!(),+ 3.939849 1.225525 3.21 0.001 ,-.),+ 0.4217847 0.1467058 2.88 0.004 /01"_),+ 0.9334502 0.3159703 2.95 0.003 /23),+ 1.647545 0.9698368 1.70 0.089 4(5),+ 41.48769 4.520398 9.18 0.00 &!_),+ 84.1468 5.877733 14.32 0.00

N 2643

Adj. R-squared 0.2138

F-statistic 56.27

P(f) 0.0000

Notes: Interest Coverage Ratio is defined as the ratio of a company’s solvency risk. It measures the earnings before interest and taxes (EBIT) relative to interest expenses, expressed as a ratio; a proxy for financial risk-taking. The female variables in the regressions are defined as follows: Female CEO is a dummy variable which equals one for firms that have a female CEO; Female CFO equals one for firms that have a female CFO. The control variables are defined as follows: market to book ratio is the book value to market value of equity ratio for firm j in year t, Size is natural logarithm of the market value of equity, Sales growth as the change in sales, measured as (S it – S it-1 )/S it-1, Return on assets of firm j in year t calculated by: net income relative to total assets and Cash flow from operations of firm j in year t is relative to assets for the year t-1.

The second empirical analysis is undertaken by conducting t-tests for the differences in the mean level of the Interest Coverage Ratio for female-led and male-led firms. The observed differences between female-led firms are statistically significant at the 5% level.

The variables of interest are female CEO and female CFO. A higher-level Interest Coverage Ratio is positively associated with less financial risk-taking. I therefore expect that the female CEO and female CFO coefficient should be positive.

The results related to female CEO, presented in table 7, depict a negative coefficient of -2.733664, with no significance level of 0.087. So the coefficient is not in line with my expectations. But, the results related to female CFO, presented in table 7, show a positive

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coefficient of 3.939849, with a significance level of 0.001. So the coefficient in this case is in line with my expectations. In total, each of the five control variables in the model is significant. Thus, the Interest Coverage ratio is influenced by those variables. Based in this regression there is an indication that female CFOs are positively associated with less financial risk-taking in comparison with male CFOs. This could indicate that firms with a female CFO are positively associated with a higher Interest Coverage Ratio and a properly higher borrowing capacity. However, on the other hand, I found no evidence that female CEOs are associated with less financial risk-taking in comparison with male counterparts.

A p-value of 0.087, it is significant for the 10% significance level it is not significant for the 5% significance level so not that strong. Due to the doubt about the significance of the p-value, I performed a sensitivity analysis.

4.3 Sensitivity analysis

In the main analysis, I was not able to find a significant effect of CEO gender on the Interest Coverage Ratio, the proxy for financial risk. I therefore performed a sensitivity analysis. I tested whether CFO/CEO gender affects the Expense Ratio as a proxy for financial risk. The Expense Ratio is measured by financial expense as (interest) relative to assets. Table 8 shows the regression results of the sensitivity analysis.

Regression model for sensitivity analysis:

6789:;9<=>?@A_B,C =<∝ < +<G_HI9J>K9L6M_B,C+<G_NI9J>K9LIM_B,C< <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<+<<G_O<PQR_B,C<<<<<+<<G_ST@U9_B,C<<<<<+<<G_V<TWX_B,C

<<<<<+<<G_Y=MZ_B,C<<+<<G_[LI_B,C

<<<<<<<<<<<<<<<<<<<<<<<+<<G_∗]∗(_9>`<abJJ@9;)_B,C+ d_B,C

The dependent variable 6789:;9<=>?@AB,C measures firms j at time t. The Expense Ratio

measures the financial expenses as interest relative to assets, expressed as a ratio. I controlled for fixed effect (difference in years) by introducing year dummies (years).

Where:

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I9J>K9L6M_B,C= 1 when the CEO of firm j is a female in year t and 0 otherwise. This is one of the two most important variables, which actually measures the main effect of this study.

I9J>K9LIM_B,C= 1 when the CFO of firm j is a female in year t and 0 otherwise. This is one of the two most important variables, which measures the main effect of this study.

PQR_B,C= Market to book value: book value to market value of equity ratio for firm j in year t. T@U9_B,C= Size of the firm; natural logarithm of the market value of equity for firm j in year t. TWX_B,C= Sales growth: Change in sales, measured as (S it – S it-1 )/S it-1.

=MZ_B,C= Return on assets of firm j in year t calculated by: net income relative to total assets. LI_B,C= Cash flow from operations of firm j in year t, relative to assets for the year t-1. Table 8: Regression Results sensitivity analysis

!"#$%"&'(),+ 0.0012372 0.0009534 1.30 0.194 !"#$%"&!(_),+ -0.0014263 0.0007328 -1.95 0.052 ,-.),+ 0.0005526 0.0000877 6.30 0.000 /01"_),+ -0.0026095 0.0001889 -13.82 0.000 /23_),+ -0.0012078 0.0005799 -2.08 0.037 4(5),+ -0.0236963 0.0027031 -8.77 0.000 &!),+ 0.0091122 0.0035148 2.59 0.010

N 2643

Adj. R-squared 0.1159

F-statistic 27.65

P(f) 0.0000

Notes: Expense ratio is defined as the ratio of a company’s financial leverage, calculated as financial expense as (interest) relative to assets; a proxy for financial risk-taking. The female variables in the regressions are defined as follows: Female CEO is a dummy variable which equals one for firms that have a female CEO; Female CFO equals one for firms that have a female CFO. The control variables are defined as follows: market to book ratio is the book

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value to market value of equity ratio for firm j in year t, Size is natural logarithm of the market value of equity, Sales growth as the change in sales, measured as (S it – S it-1 )/S it-1, Return on assets of firm j in year t calculated by: net income relative to total assets and Cash flow from operations of firm j in year t is relative to assets for the year t-1.

The variables of interest are female CEO and female CFO. A lower level of the expense ratio is positively associated with less financial risk-taking. I therefore expect that the female CEO and female CFO coefficient should be negative.

The results related to female CEO, presented in table 8, depict a positive coefficient of 0.0012372, with no significance level of 0.194.This no significance level, however it is much stronger than with my main analysis of the Interest Coverage Ratio (p-value of 0.087). Ultimately, the coefficient is not in line with my expectations. Alternately, the results related to female CFO, presented in table 8, show a negative coefficient of -0.0014263 with a significance level of 0.052. So the coefficient in this case is in line with my expectations. In total, each of the five control variables in the model are significant. Thus, the Expense Ratio is influenced by those variables. Based on this sensitivity regression there is an indication that female CFOs are positively associated with less financial risk-taking in comparison with male CFOs. This could indicate that firms with a female CFO are positively associated with a lower Expense Ratio and a properly higher borrowing capacity. However, on the other hand, I found no evidence that female CEOs are associated with less financial risk-taking in comparison with male counterparts.

The article of Sikalidis, A., & Leventis, S. (2016) states that they measure the borrowing capacity by employing the financial expenses coverage ratio, measured as the operating profit relative to financial expenses. They measure the financial expenses coverage ratio in this way because the prior literature of Dichev and Skinner (2002) proposes that the leverage variable is a relatively noisy proxy for measuring debt. In addition, further prior literature (e.g. Christensen, Lee & Walker 2009; Citron 1992; Day & Taylor 1996 and Moir & Sudarsanam 2007) proposes that the leverage variable is a relatively noisy proxy for debt. This considered, there is a compelling argument that the Debt-to-Capital Ratio is the prefect measure to examine financial risk-taking. It is a good measure for financial risk-taking but through prior literature it is may be not a prefect measure.

Therefore, my overall results revealed in this study provide support for H1: Female CFOs are positively associated with less financial risk-taking. In the news article of Lagarde (2010) it is stated that “if Lehman Brothers had been Lehman sisters today’s economic crisis clearly would look different”. My study indicates that in regard to this quote of Lagarde (2010) there may actually be some truth.

The impact of chief financial officer/chief executive officer : gender on financial risk-taking