Female CEO s. A study of their appointment performance and market reaction. Department of Business Administration

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Msc. Finance and International Business

Author: Elisabeth Gosvig Andersen Academic Supervisor: Steffen Korsgaard

Female CEO’s

A study of their appointment performance and market reaction

Department of Business Administration

Aarhus School of Business and Social Sciences, Aarhus

University

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Abstract

Females are becoming higher educated, but this does not show in the CEO positions in the companies. In Sweden only 1 percent of the Swedish CEOs are appointed by females, which can indicate that females are facing what literature is calling the “glass ceiling”. Therefore the purpose of this thesis research the appointment of female CEOs compared to their male counterparts.

The first research question is to investigate how the market is reacting to the announcement of a female CEO based on the event study. The theory for the study is not conclusive; some studies are suggesting that females are facing different stereotypes, which cause that the investors may be more skeptical about an announcement of a female executive. Others studies find no significant difference in the appointment of a female- or male CEO, indicating that the investors are not looking at females as less competent than males. The results from this theses is not able to give any conclusion for the tests, because of a small sample size, which mean that the tests is not significant, and is therefore not able to reject the stated hypotheses that the abnormal return is zero for the companies.

The second research question investigates whether there is a different in the company performance prior- and after the appointment of a female- and male CEO. Theory states that females are likely to find themselves on a “glass cliff”, which means it seems that they are appointed to leadership positions which is associated with a greater level of risk of failure and criticism. Because this thesis tests is not significant it is not able to give a final conclusion, but the tests indicates that companies appointing females to CEO positions are facing a higher level of poor performance prior to the appointment, than companies appointing males.

When looking at the performance after the appointment of the new CEO, theory states that companies appointing female CEOs were experiencing an increase in their share price. Another study found that females underperform the first year. This thesis test gives an indication that females are performing, about the same as their male counterparts, but is still performing a negative result, but not as negative as prior to the appointment.

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1. Introduction

Despite that females today have the same or a higher education level than men; the proportion of women reaching the top levels of the corporate hierarchy is still very low. According to Henrekson and Stenkula (2009), only 1 percent of the Swedish CEOs are women. There can be many reasons why this is the case; does females not want to occupy these positions, or are they facing what literature is calling “glass ceiling” and glass-cliffs”, meaning that it is the cultural rooted assumptions and beliefs in women’s skills and competencies, that prevent them from moving up to the top as chief executives or other positions in the top ranks within the corporation.

Because of the increasing pressure around the world to choose female directors to company boards, the situation can change (Adams & Ferreira, 2009). As a consequence, some governments are using legislations to regulate the number of females on company board of directors. This kind of legislation exists in Norway, where it is a policy that at least 40 percent of all board members within large private companies must be females (Bell, Smith, Smith, & Verner, 2008). But it does not seem that these legislations provide increasing opportunities for the females to move up the hierarchy within the corporation to top positions, and the question therefore is what the challenge to do this is.

As stated, Norway is the first Scandinavian country to implement legislation on the number of females on the boards, and maybe more countries will follow this statement, but according to this thesis’ data on company female CEO’s, the Norwegian companies do not take the next step and are implementing more females into CEO positions within the companies. And this is surprising, because companies have the chance to observe the female performance within the boards. This could have an effect on the chance to move up or get CEO positions in other companies, and thereby the chance to break down the glass ceiling.

Literature state that companies that are incorporating females into the organization are facing a competitive advantage, because males and females are thinking differently and these differences can give new ideas and therefore help corporations become more innovative and competitive. This factor is important in the future competitive environment.

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5 There may be a number of reasons for why the compositions for companies and boards may change in the future; females are developing the necessary experience and therefore are able to compete with males on top positions and board memberships. But also the fact that CEOs do not have the required time to serve all boards they are invited to join, and therefore the board may be forced to choose other relevant candidates. Several studies finds that females are often higher educated than males, and can therefore increase the abilities and skills of the company or the board, meaning that having a diverse corporation will result in more innovation and creativity (Burke, 1994).

1.1 Problem statement

Females have difficulties reaching the top positions in the company, and therefore facing what the literature is calling the “glass ceiling”, which could be because of different stereotypes.

Therefore the purpose of this thesis is to look at how the perception of females is from the markets perspective. The overall research question is:

How does the market react to the announcement of a female CEO?

The study want to investigate whether the market are reacting differently to the announcement of a female CEO than a male. The market reaction will be based on the announcement of the shift of a new company CEO.

The question on how the market is reacting to a female CEO is important, because studies suggest that females are being subject to stereotypes. Some of these stereotypes may exist because of other female executive’s performance, and because there is so few of them, it is difficult to get a picture of how they are performing compared to males in the same position.

Literature states that females are facing the “glass cliff” because they are appointed to CEO in companies that already have a poor performance, and thereby the female performance can be viewed as more negative than if they were appointed to companies that were facing a positive performance.

Therefore another research question for this thesis is;

Are females being appointed to companies that are facing poor performance and are they performing in a different way than their male counterpart?

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6 The thesis purpose will be based on the stock prices for companies based in Nordic countries, and the stock prices will be compared with a selected marked index for each country for the first part of the problem statement.

1.2 Delimitation

When trying to answer the questions stated in the problem statement for this thesis, the data will focus only at the Nordic countries; Denmark, Sweden, Norway, Finland and Iceland. These countries, excluding Iceland is all members of the Scandinavian countries, and the reason to choose these countries is because they in many ways are identical in terms of culture, public laws and legislations and contains the same similarities due to being a welfare state referred to as the Scandinavian Model. The Scandinavian model will be elaborated in section 6.1 Data selection.

The analysis will be limited to only examine the market reaction and company performance by only looking at the stock price performance. Therefore the thesis will not take other methods when examining company performance like return on equity, return on assets etc. into account.

1.3 Methodology

The event study methodology, which has become a standard method to examine how the stock prices are reacting to an announcement and events, which is described later in this study, is used to examine how the financial market is reacting to the announcement of a new female CEO, compared to the reaction of a male.

The tests for the event study will be performed in SAS and Excel.

The company performance will be examined by examining the stock price, the days before- and after the appointment. The tests for the performance will be performed in SPSS.

1.4 Thesis structure

The thesis is initially started by the introduction and the problem statement, which presents the thesis subject, examination and purpose.

The thesis will proceed with section 2 by the literature review which will present the existing literature that will result in and be the basis for the stated hypotheses in section 3. Section 4 will present the two different methodologies selected and used to test the

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7 hypotheses. What the data encompasses will be described in section 5 and include how the companies are selected and matched in pairs of females and males. Section 6 contains the analysis and the empirical evidence for the thesis. Section 7 will try to speculate in the reasons for not promoting females into chief executive positions. The thesis will summarize the results and close the thesis in the conclusion in section 8.

2. Literature review

The following section will give a review of the literature on how the market is reacting to the announcement of a female CEO compared to a male CEO, but also about which companies females are appointed into in terms of performance, and how they are performing after the appointment compared to their male counterpart.

Literature suggests that the appointment of a new CEO have an impact on how the financial market reacts on the change. Not only is there an opportunity to earn an abnormal return1 at the day of the announcement of the new CEO, the risk may also change with the change of CEO. Studies suggest that appointing a female CEO may reduce the risk of the company, because females has a more risk-averse profile and have a different leadership style.

Females are still facing what literature is calling the “glass-ceiling” and the “glass-cliff” when being appointed to the top ranks of the company, and this can be due to the fact that that the top positions of the company have the “think management – think male” idea, of who should be seated in the top-positions within the corporation. Females are under-represented in the top ranks of the management within companies, saying that women are facing the glass-ceiling; “a transparent barrier that allows females to advance only to certain levels in the corporation” (Lee & James, 2007), but this glass ceiling, can also be that females are facing some stereotypes which means that they are being ranked and evaluated differently than males.

The literature has been found in different databases like Google Scholar, Business Source Complete etc. and through searches in the Aarhus University’ online library.

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8 2.1 Theory on market reaction to gender change in CEO

The theory on how the market reacts to the announcement of a female CEO is not conclusive. Several studies points in different directions on how the female-leaded companies’ investors are reacting to the announcement of a female CEO.

The study by Wolfers (2006) finds no differences in the stock returns in the female-headed companies, and therefore concludes that the financial market neither do under- or overestimate the female CEOs, when being announced as new head of the company. Also Martin, Nishikawa & Williams (2009) finds no significant difference in the appointment of female- or male CEOs. The market reacts positively to the news of the change in the CEO positions whether the CEO is female or male. Thereby the financial market do not look at females as a stereotype as less competent than males (Martin, Nishikawa, & Williams, 2009).

In contrast the study by Lee & James (2007) finds that the stakeholders and the investors may be more skeptical, about the announcement of a female executive than a male. The female executives did not only generate a negative valuation, but the female CEO appointments did generate a more negative valuation effect than the male CEO appointment. Studies finds that females are underperforming relatives to males, however the underperformance is much weaker in larger companies and do not exist in one-employee-companies. Rietz & Henrekson (2000) found the female underperformance is for sales variables only, and when it comes to profitability, there are no gender differences. The study states that it is because female and male entrepreneurs are focusing on different things; females are focusing on pursuing other goals than the goals of the company (Rietz & Henrekson, 2000). Another study by Kolev (2012) supports the findings by the other studies that females have a tendency to underperform. In fact female executives tend to underperform by 0.35 % per month, and are facing a higher negative price reaction than males in the event window (Kolev, 2012).

Kolev (2012) do though suggest that the test should be redone in about 20 years, so there is a possibility that there is enough data to say whether the female CEOs is actually underperforming male CEOs.

The above theories are stating that the announcement of a female CEO will have a negative response on the stock market, because females tend to underperform. But an

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9 event study done for companies listed on the Singapore Exchange, Kang, Ding & Charoenwong (2010) found a positive investor reaction to the appointment of women directors on the boards. The investors did though react least positively when the appointed director also where occupying the CEO position within the company (Kang, Ding, & Charoenwong, 2010). Another study by Gondhalekar & Dalmia (2007) finds a weakly positively reaction in terms of abnormal return at the announcement date of a new female CEO. But there wasn’t any response on the announcement of a male. They also found that those companies appointing females in the CEO positions often are smaller, are more profitable (in terms of return of equity, profit margin etc.), but have lower price/book ratios (Gondhalekar & Dalmia, 2007). Also Davis et al. (2010) finds that female led service SME2s performs significantly better compared with their male counterparts, because female led companies have a stronger market orientation. These findings indicate that there is no difference in the actual performance between female- and male CEOs.

Despite the mixed conclusions that are listed above, the study by Niño & Romero (2007) finds when the company are making a CEO change in times that are associated with poor performance, they will experience significant positive abnormal returns. This statement can be the reason why some studies are experiencing positive abnormal returns and others don’t. Especially if it is correct that females are only appointed to the CEO positions in times where the company are experiencing poor performance, which will be elaborated on in the section “Theory on why females are appointed to CEO”. However there can be many reasons why the stock market reacts at a specific way. According to Niño & Romero (2007) the stock price return can be negative, if a CEO, that has a history with good results and performance, leaves the company or when a CEO that has performed negative result leaves the company, the stock price reactions are expected to be positive, because of the new and maybe better expectations for the future of the company (Niño & Romero, 2007).

2.1.1 The Glass Ceiling

As mentioned in the intro to this section, females in the corporation are facing the glass-ceiling, which mean that they are facing some difficulties when trying to reach the top within the corporation and therefore the glass-ceiling is evidence on why females are

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10 underrepresented among the senior management. The probability for females to get promoted to top executive positions within a company is less likely than for males. Females are forced to work harder than males, while their male counterparts with the same qualifications are the one receiving the promotions (Cobb-Clark, 2001). According to Tuck School of Business in 2006, only 7.2 percent of the largest American companies had females appointing the top positions of the company, which also suggest that the females are facing the glass ceiling (Coxbill, 2008).

The “glass ceiling” is defined as: “a transparent barrier that allow women to advance only to certain levels in the corporation” (Lee & James, 2007).

The reason why there are so few females occupying the top positions in the company is, according to Oakley (2000) because females are less committed to a carrier and is less willing to take risk. Therefore there is a judgment that females do not fit into the traditional CEO leadership style. Also Smith, Verner & Smith (2011) finds that females are not interested in taking the risk and those responsibilities that are related to the top executive jobs. Thereby it can be argued that women are still facing the glass-ceiling, because investors and others have a stereotype of how executives and other top management jobs within a company should act and lead.

Females are more risk averse, especially in the financial decision making, than males (Jianakoplos & Bernasek, 1998). Because females do not like to take high levels of risks, and are showing a more risk-averse profile, this can help the company to reduce the likelihood of excessive risk-taking in the decisions, when females are appointed to the company board (Kang, Ding, & Charoenwong, 2010). According to Martin, Nishikawa & Williams (2009) when changing the CEO position, the risk within the company changes. When appointing a female CEO rather than a male, the risk of the company is significant lower (Martin, Nishikawa, & Williams, 2009). Also Beatty & Zajac (1987) found that the risk may change with the change of CEO.

If there is already a female CEO in the company and in the board chair, this has an effect on the number of females within the company. This is because female’s that is already occupied in the company tends to bring more top women into the company (Bell L. A., 2005). Other studies states that female executives are playing a key role for females in the lower part of the organization in terms of motivation and seeing that moving up in ranks within the organization is possible (Singh & Vinnicombe, The 2002

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11 female FTSE index and women directors, 2003). This does conflicts with another study by Nina Smith that states that women leaders are acting like men in the private sector. Their view of other females is like males, and therefore women in the top have no effect on the probability of having more females on top positions within the company (Stræde, 2013).

2.2 Theory on why females are appointed CEO

Theories conclude that females are more attractive for companies where the performance is poor. Females are more risk averse than their male counterpart, and therefore the change in the CEO may change the risk of the company.

According to the study by Judge (2003), there is a negative correlation between the number of female on UK boards and the low company share price performance and conclude that “corporate Britain may be better off without women on the board” (Judge, 2003). In contrast Ryan & Haslam (2009) state that the negative share price performance is not caused by the number of females, but it is the share price drop that have caused the females to be appointed to the leadership positions within the companies. The results of the study conclude that females are being chosen over males in the context where there is an increased risk of organizational failure (Ryan & Haslam, 2009). Also Ashby, Ryan & Haslam (2007) finds that in fact it is not that the boards contain females that are leading the company into crisis, but it is the circumstances of company crisis that is causing females to being appointed to the company board.

An experiment done by Bruckmüller & Brandscombe (2010) found that the participants also chose female candidates over the males, when the company was in crisis and in contrast chose the male over the female for the successful company. They found that when the company was in downtimes, the female skills like communication and the ability to motivate was ranked high, but the participants did not see the female candidate as better suited in time of crisis (Bruckmüller & Branscombe, 2010).

2.2.1 Female executives performance after the appointment to CEO

Theory more or less conclude that females are appointed into positions that are risky and precarious and thereby finding themselves on a glass cliff. As Ryan et al (2011) describes, the companies do not put females on the top positions, because they think the females can turn things around. But maybe they should think about taking this strategy,

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12 because Ryan & Haslam (2005) found in times of crisis, those companies that appoints females actually did experience an increase in their share price after the appointment. The appointments made in times that were not so troubling, the companies were experiencing share price stability.

Strelcova (2005) did though, in contrast, find in her study that companies appointing female CEOs are experiencing that the females are underperforming in contrast to the males, at least in the first year following the appointment. In the second year after the appointment there is no documentation of a difference in the stock price performance (Strelcova, 2005).

Results on the study by Jalbert, Jalbert & Furumo (2013) suggests that females are appointed to CEO positions in some industries more often than others. They also conclude that female CEO’s are financing their companies differently than males, and thereby the perception from the financial market will be different. This may be explained by, that females are more risk averse than males. Females are more likely to be working in the public sector rather than the private section, because the private sector may be more risky – this may also be explained by females being more risk-averse (Eckel & Grossman, 2008).

2.2.2 The Glass Cliff

Females are more likely than men to find themselves on a “glass cliff”. The “glass cliff” is defined as; “the tendency for women’s leadership positions to be more precarious than those occupied by men and to be associated with greater risk of failure and criticism” (Ryan & Haslam, 2009). This states that females, like other studies also conclude, are appointed to positions that contains a high level of risk to fail and to get criticized on how the company are performing. According to Ryan & Haslam (2007) companies, in times of crisis, think of females, rather than male, for the leadership position, because females have a different leadership style that may fit better in times of crisis like being understanding, helpful and aware of other people’s feelings. But the fact that females are chosen for risky positions evolves a downward spiral for the females in future carriers. This only helps the stereotypes evolve to the negative side, because they do not prove themselves, that they may be as good as their male counterparts. For this reason females are likely to find themselves on a glass cliff.

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13 An article by Johnson & Powell (1994) argues that the decisions made by managers is associated with taking risk and can have negative consequences. But their study concludes that the quality of the decision making made by males and females do not differ.

The study by Haslam & Ryan (2008) finds a significant impact on how the female candidates are ranked in terms of being appointed to a leadership position. They conclude that females are ranked higher, when the company is facing a decline in their performance, even though the females are not seen as being better leaders or more appropriate for leading the company in risky positions. They argue that males that are asked to lead a company in times of crisis may decline the invitation, because they have a higher probability for something better to come along. Women don’t have this type of luxury and therefore take “whatever comes around”.

Ashby, Haslam & Ryan (2007) points out that men have more to lose than females in high-risk positions. Females are therefore seen to have less to lose and more to win. They state that this can be seen as companies are more willing to place females in high risk positions when the company are in times of crisis, possibly because females are seen to represent a “less valuable and more expendable resource than male leaders, and one less worthy of protection” (Haslam & Ryan, 2008).

Ryan, Haslam & Kulich (2010) also finds that females are selected for leadership positions when there is a high degree of risk and organizational failure. They sat up a study where groups had to compete; women were selected to compete on those seats that where less winnable and to run for those seat that was hard to win, whereas the other more likeable seats were given to the males. Former democratic national committee chair, John Bailey stated; “the only time to run a woman is when things look so bad that your only chance is to do something dramatic” (Ryan, Haslam, & Kulich, 2010).

The females are not only put into the leadership position, because the company thinks they can improve the situation or turn the company around, but because they are good people manager and also can take the responsibility for company disaster (Ryan, Haslam, Hersby, & Bongiorno, 2011). So because females are appointed to positions in companies that are already performing poorly, the females are likely to attract unfair criticism, because of the more risky and precarious leadership roles (Ashby, Ryan, &

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14 Haslam, 2007). Again the female are facing the stereotype, because they are not seen as the right leader. Females that are ranked higher in times of crisis will often be seen as a leader that cannot manage the leadership position and lead the company to good results and will therefore be blamed as being “part of the problem” (Ryan, Haslam, Hersby, & Bongiorno, 2011).

Not only are females looked at as being more risk averse than males, but also the risk of the company’s total risk are changing with the appointment of a new CEO. As mentioned, the appointment of a female CEO is reducing the risk of company. Because of this fact, the probability for females being appointed to CEO positions is much larger for companies with a relative high amount of risk, and when the company can see that they can benefit from the change and the appointment of a female CEO (Martin, Nishikawa, & Williams, 2009).

2.2.2.1 Competition – females versus males

Evidence states that females have a tendency to underestimate their own ability in comparison to males. If the females are of that perception that their career prospects are poor – these perceptions are likely to continue even though the environment and the surroundings are changing (Henrekson & Stenkula, 2009).

If females are of the perception that they don’t have a chance in getting the promotion, the females may be less effective in the competitive environment. When increasing the competitive environment in the experiment by Gneezy, Niederle & Rustichini (2003), the male performance was increasing, but was not the case for the females.

The results from Niederle & Vesterlund (2007) show no differences in the performance between males and females. Therefore concludes that females simply don’t like the competition and not because they feel that they are less able.

2.3 Stereotypes

One of the reasons why the “glass ceiling” and the “glass cliffs” still exists is because of the different, often negative, perceptions of females as leaders. The negative perception can be seen in the many different studies that states that company investors are reacting negative to the announcement of a female leader. But not only is it the investors that has a negative response – it can be difficult for the females to get to the top, because of

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15 stereotypes of how females are as leaders; they are not fulfilling the criteria that fits to the “perfect leader”.

One reason why females are not appointed into the executive jobs within the company can be because managers have the stereotype that females are more risk averse than males. For example in some industries is it important to be able to take some risk (Smeets, 2012). According to a study by Eckel & Grossman (2008) that finds in their experiments on insurance valuation, that male has a tendency to underestimate the effect for females.

There can be different reasons why females continue to get the low-end jobs within the company, rather than getting/taking the top-position within the corporation. These reasons can be divided into a classic economic argument as supply and demand in the economy. On the supply side; the traditional family roles comes into play. The females are often the ones that are taking care of the kids and the household, and therefore they will have a lower human capital investment and thereby a lower labor market attachment. This is because they will have to leave the labor market for a while when going on maternity leave (Smeets, 2012). The demand side is what this section is about; discrimination – which can be divided into to two subsections; 1) pure discrimination and 2) statistical discrimination. Especially the statistical discrimination is about stereotyping – the average expectation of the group of people, and because of this expectation, the group will be treated all the same (Smeets, 2012).

Henrekson & Stenkula (2009) do support, that the reason why so few females are striving for the management positions, can be due to the fact that they have to take care of the children and the household, and the fact that female do consider it important to take care of their children, and therefore priorities this rather than getting to the top of their carriers. They suggest that due to the time profile that is required by executive positions is likely to suit males better than females (Henrekson & Stenkula, 2009). The CEO is very important for the company’s wellbeing, and therefore the CEO overconfidence and other personal characteristics can have an effect on the company’ investment decisions (Bennedsen, Perez-Gonzalez, & Wolfenzon, 2006). Female are more risk-averse than males, and this can be viewed why females are chosen for the executives jobs in times where the company are performing poorly. But it can also be the reason why so few females are taking these types of jobs. They are not willing to

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16 take up the amount of risk and the responsibilities that are included in the job description (Smith, Verner, & Smith, 2011). For example there are stereotypes concerning women in management; as females are not “tough” enough to the job and are easily diverted from their job because of family concerns (Shrader, Blackburn, & Iles, 1997).

According to Lee & James (2007) the reason why investors and stakeholders are looking at female executives with a skeptical view and thereby a higher negative valuation can be due to the rarity of female appointments and gender stereotypes. It can be difficult for investors to judge, whether the appointment of the female executive is a good idea, because female CEOs are less common and therefore there are fewer companies with female executives to compare with, and whether they are doing well or not. Another stereotype is that the role of managers is defined as a masculine role and is employed by men – the “think manager – think male”-stereotype

Lee & James (2007) states that females, that are promoted within the company tends to have a more positively view on other females from outside the company. This statement is in contrast to a study from Nina Smith (Stræde, 2013) that finds that females on the top behave and look at other females, in the same way as males. Thereby she states that it’s not only males that are the ones discriminating. One of the reasons to this discrimination can be found in how people look at females and leadership – people looks at females as not being as interesting in positions that include responsibility as males. And females are not willing to sacrifice what is necessary (Stræde, 2013).

3. Hypothesis

Now there has been given a review of the existing literature on the topic on how the market is reacting to the announcement of a female CEO and the company performance prior- and after the appointment of the new CEO. From the literature there will be drawn up some hypothesis that has background from the stated theory from the last section.

The theory in the previous section does not give any conclusive answer on how the financial market is reacting to the announcement of a new CEO, whether the CEO is male or female.

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17 Therefore it could be interesting to find out how the financial market will react on the announcement of a new CEO in the Nordic countries, because one of the countries have a legislation stating that there should be a specific number of females on the company boards. Because of this legislation one should think that this will give a more positive- or neutral reaction if the announcement of the new CEO is a female. A negative response to the announcement of a female CEO can be due to the fact that there are a lot of stereotypes surrounding on how a CEO should act, and for these skills many will say that males are more fitted for the position. Not only do the stereotype surrounding females, containing that they are not fitted for the position, but also the fact that females are more risk averse than males can have an impact. Some will be of the opinion that the CEO position should be able to make some risky decisions.

Because of the many stereotypes that are planted on females it will, as mentioned, be interesting to see how the financial market is reacting to the announcement. Therefore the hypotheses for these statements will be as following;

H1: Abnormal returns earned by a male CEO around the announcement date = 0 H2: Abnormal returns earned by a female CEO around the announcement date = 0 H3: Abnormal returns difference between male and female CEOs around the announcement date = 0

This means that a rejection of each of the three hypotheses indicates that there is abnormal return with a certain percentage probability, for either the females or the males. When rejecting the H3 hypothesis indicates that there is a difference in the abnormal return between the female- and male companies.

The appointment of females to the CEO position is a bit different than those for males. The theory is not conclusive, and there are therefore different theories on why and when females are appointed to the CEO position in the company.

Some theory are stating that it is not the number of females in the company, that are causing the poor performance in the company, but it is the company that are appointing females that are already performing poorly. In times of crisis it seems that females are ranked higher; maybe because of their different skills than males; for example being understanding, helpful etc., but it can also be because companies are not able to attract

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18 males. Males will rather wait for a better position that is not too risky and can have a negative effect on their reputation. Or maybe the companies are just trying to do something drastic and trying other options.

To test whether females are more attractive for those companies that are experiencing poor performance the hypotheses will be looking at the time before the appointment of the new CEO. Therefore the hypothesis is testing whether the companies appointing females are performing a lower stock price performance than companies appointing males;

: The difference in company performance prior to the appointment of a female- or a male CEO = 0

A rejection of the hypothesis means that there is a difference in the company performance for both the female- and male companies for the days prior to the appointment of the new CEO, and thereby the study is able to tell whether the theory is true that females are appointed to companies with a poor performance.

How the financial market is reacting at the time after the appointment and the performance of the company after the appointment of the female CEO is not conclusively. Some studies found that the performance of the company is increasing after the appointment of the female CEO. Others are experiencing poor performance in the first year.

Therefore the next hypothesis will test whether there is a difference in the performance between female- and male companies after appointing the new CEO.

The difference in company performance between females and males after the appointment to CEO = 0

When rejecting this hypothesis means that there is a difference in performance for the female- and male companies after the appointment. This means that female- and male companies are not performing the same stock price results.

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4. Methodology

The literature and the hypothesis for the study of this thesis have now been stated in the previous sections. This section will look at the methodologies that have been chosen to test the five stated hypotheses.

4.1 Event study

To be able to test the first three of the stated hypothesis, H1, H2 and H3, the event study techniques have been chosen. The overall aim of an event study is to measure the effect of the event.

An event study is widely used to examine the price behavior around an event that contains announcements. The event study methodology has become the standard method to examine the security price reaction to announcements or events (Binder, 1998). An event study uses financial market data to measure whether or not a specific event has an impact on the value of the company (MacKinlay, 1997). The event study has the advantage of measuring the stock price effect immediately after the event has taken place, where for example measuring with accounting measures it would not be able to measure the exact effect.

In this case, the paper wants to measure whether the appointment of a female CEO and a male CEO has an impact on the value of the firm – is there a reaction in the share price – due to the new information that arises, have any impact on the markets expectations, using financial market data (MacKinlay, 1997). The event study, for this thesis will be based on the article of MacKinlay (MacKinlay, 1997). The study is using the financial market data, which is collected from Orbis and Datastream.

MacKinlay (1997) explains the importance of defining the event of interest and an event window. The event of interest is as stated in the hypotheses whether there is a difference in how the market reacts to the announcement of a female CEO and a male CEO.

4.1.1 The event window

To conduct an event study it is important to define the event of interest. As MacKinlay (MacKinlay, 1997) describes, besides the event of interest, it is also important to define and choose an event window.

In this case, the event window will be the day before the announcement date, τ-1, the day of the announcement, τ, and the day after the announcement date, τ+1.

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20 Figure 1: The time line of an event study

The reason why the day before the announcement day (τ-1) is chosen, is because the market may contain some information about the event, before the actually announcement is happened. This can be due to rumors or if information has gone out before the day of the announcement. The day after the day of the announcement (τ+1) is chosen due to the time difference around the world. There can be some late trades on the announcement, which can be due to the time difference and thereby the world market will react on the announcement on different times. Therefore the event window has been chosen to contain three days and this will meet the assumption of the efficient market.

4.1.2 The estimation period

When looking for an effect in the event window, this cannot be done without choosing an estimation period. Therefore an estimation period has to be chosen (MacKinlay, 1997). It is important that the estimation does not overlap the event period, because an overlap will have a consequence for the event. The overlap will stop the event from influencing the parameters estimates in the normal return model (MacKinlay, 1997). For this study the estimation period have chosen to be 200 days before the event window. Bartholdy, Olsen & Peare (2007) argues that the standard estimation period should be between 200 and 250 observation (Bartholdy, Olson, & Peare, 2007). Taking 200 days before the day of the announcement gives a clear picture of how the stock prices are moving, and therefore is helpful when looking at the event window and the reaction to the announcement.

4.1.3 Abnormal returns

Because the hypotheses want to determine whether the market reacts differently to the announcement of a change in gender of the new CEO, the focus will be at the stock price reaction. The market reaction will be measured as value created for the company.

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21 The stock price reaction will be measured as an abnormal return within the event window. To test whether this new CEO announcement will create value for the company, there are two different models that can be used;

- The market model: assumes that there is a linear relation between the market return and the stocks returns.

- The constant mean return model: assumes that mean return of the stock is constant over time

In this case the market model has been chosen, because this study is looking at daily stock data. The constant mean return model is deselected because it does not allow the general market movements. According to MacKinlay (1997), the market model gives the most accurate results.

The abnormal returns can be expressed as the actual return subtracted from the expected return of the company. The expected return does not include the return possibilities for the announcement. So the expected return is based on if the event has not occurred. There exists an abnormal return, if the number is above zero. So the abnormal return for the company i, at the event day t, is;

( )

The formula describes the differences between the actual return in the event window and the normal return. The normal return is defined as the expected return that is expected without the event taking place (MacKinlay, 1997). So the formula describes the relation between the expected return ( ( )) and the actual return ( ), which gives the abnormal return ( ) for company i. The expected return is based on the return without the information given in the event window, where the actual return is including the new information that gives the event window.

To be capable of using the formula, the normal stock process for the different companies, has been collected from the database “Datastream”. The collected data is used to calculate and thereby see the fluctuations and if there is any abnormal returns. These calculated abnormal returns are used in the different parametric- and non-parametric tests that will be discussed in the section “Statistical tests”.

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22 Apart from the calculation for the abnormal returns, there also has to be made calculations of the cumulative abnormal returns (CAR). The CAR is an expression of the sum of the abnormal return within the three days in the event window (MacKinlay, 1997). The cumulative abnormal return gives the possibility to make an overall conclusion on whether or not the event have an impact on the company value within the event window in total.

4.1.4 Statistical tests

When testing the hypotheses for the event study; H1, H2, and H3, different tests are used. There will be performed a series of tests with both parametric and non-parametric. The reason for using both the parametric and the non-parametric tests is that there is no test that is greater than others (Bartholdy, Olson, & Peare, 2007).

When having series of test, it will give the most valid and strong results and will be more robust. The argument for using both the parametric- and the non-parametric tests is because the non-parametric tests cannot be used in isolation, but are more or less a check of robustness based on the conclusion of the parametric tests (MacKinlay, 1997).

4.1.4.1 Parametric test

The parametric tests for abnormal returns are based on standard t-tests for the differences between the two means. Where the numerator of the t-test measures the absolute impact from the event relative to the expected value, the denominator scales the number by some measure of estimated variance (Bartholdy, Olson, & Peare, 2007). There are some general assumptions for the parametric tests to be efficiency. These general assumptions containing the observations should be drawn from the normal distribution, the observations needs to be independent, and have the same variance and the variables need to have been measured in at least an interval scale (Bartholdy, Olson, & Peare, 2007).

The different parametric tests differ from their way of adjusting for problems encountered in the data (Bartholdy, Olson, & Peare, 2007) and include;

T1: t-test with adjusted cross sectional dependence T2: t-test with adjusted cross sectional independence T3: t-test with standardized abnormal return

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23 T4: t-test with adjusted standardized abnormal return

In the case the assumptions are violated, the non-parametric tests are being used instead. There can be some difficulties fulfilling the assumptions, because of the daily stock prices. Because of these daily stock prices, it can be difficult to fulfill the assumptions of normal distribution.

4.1.4.2 Non-parametric test

The non-parametric tests are the opposite of the parametric tests. There are no assumptions to fulfill and therefore these tests should be more robust and more reliable. The main difference between the two tests is that the non-parametric tests are not based on the assumptions of normal distribution (Bartholdy, Olson, & Peare, 2007).

The non-parametric tests include; T5: Sign test

T6: Rank test

If the situation is that the data don’t follow a perfectly normal distribution, the non-parametric tests will be the best and the most usable. The non-non-parametric tests cannot be used in isolation, but are used as a check-up for the robustness based on the conclusion from the parametric tests (MacKinlay, 1997).

As mentioned in the “parametric test”-section, is can be difficult to fulfill the assumption of normal distribution, because of the daily stock returns, and it can therefore be necessary to rely more on the non-parametric tests.

4.2 Performance

Theory by Ryan and Haslam (2009) state, that companies that are facing a drop in their share price are more likely to appoint females to leadership positions. They conclude that with an increase in risk of organizational failure, it will increase the probability of appointing a female for a leadership position. Therefore this section will take a look at the methodology for testing the hypothesis H4 and H5. These states that the reason why females are appointed to CEO, is because the company has a poor performance and that females are not performing as good as their male counterparts.

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24

4.2.1 The estimation period

To be able to test whether or not there is a difference in the stock price performance for the companies, the study will look at an estimation period of 200, 100 and 50 days before- and after the appointment date. The reason for this is to be sure that it is not only for a specific time period that the company is performing in a specific way. In contrast to the event study, where the announcement day was used, the appointment date is now used, because for this test we are interested in how the company is performing in the time period before and after the day they are appointing the new person as new company CEO.

Not for all companies it was possible to have 200 days after the appointment date, because some of the appointments are so new, that they are not able to have 200 days after. But it is not desirable to delete more companies, so therefore they are included in the tests anyway. So the number of days after the appointment date that was available was included into the tests.

When looking at how the stock prices are performing within the estimation period, it is necessary to look at the how the stock prices have been moving within the estimation period, whether there has been a negative or a positive price movement. Therefore it will be a logical thing to look at the percentagewise movement from the day before/after the appointment date and the last day within the estimation period, and thereby are able to see whether the differences are positive or negative. The reason why it is most logical to look at the percentage development is because the companies are very different in sizes. Small companies can have an increase by 20 DKK where a large corporation can have had an increase by 100 DKK. But the 20 DKK increase for the small company can mean a large progress, where it for the large corporation can mean it is only a small increase. So when looking at the percentage development it will give a more precise indication of the company development.

The tests will be performed in the statistical program SPSS.

5. Data

Now there has been a description on the methodology that will be used to test the stated hypotheses and tests that there will be used for each area.

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25 This section will contain a description of the data collection process and how the different companies have been chosen and the matching process.

5.1 Data selection

The data contain stock returns from the Nordic countries, which is Denmark, Sweden, Norway, Finland and Iceland. Iceland is not included in the Scandinavian countries, but to be able to get a larger sample size, Iceland is included in the data. Furthermore Iceland is not that different from the other Scandinavian countries. The Nordic- or Scandinavian countries are often referred to countries as a whole, and this is not only because of the geography, but also because these countries do share a number of characteristics, which is not as similar in other countries.

There are a lot of good reasons why choosing these countries collectively, because in many ways they are identical in terms of culture, public laws and legislation. Besides these similarities, they are also characterized by the large public sector, which also is known as the welfare state (Andersen, 2011). They generally follow, what is called the Scandinavian Model, which has the overall purpose to promote economic efficiency, to improve the society’s ability to handle its problems, and to increase the living conditions for the individual (Sharpe, 1987).

According to MacKinlay (MacKinlay, 1997), there has to be determined some selection criteria after the event has been identified. The data is collected through Orbis and Datastream. Orbis is a financial database that contains company information on 65 million companies worldwide (Copenhagen Business School). Companies were first collected through Orbis and because Orbis doesn’t contain any financial information about the companies, Datastream have been used for this purpose. Datastream is a financial tool that is able to obtain financial data from those companies selected from Orbis (Aarhus University).

5.1.1 Selection criteria

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26 Table 1: Searching criteria

Searching criteria

World regions: Denmark, Finland, Iceland, Norway, Sweden Directors: Chief Executive Officer (CEO)

Branch: Public

Listed/Unlisted

companies: Publicly listed

Using these restrictions for the data search, the data was narrowed down and contained 926 companies.

To be able to conduct the financial data for the companies from Datastream, the companies ISIN code were also needed, so these were being added when extracting the data from Orbis. The ISIN code is the code that is used to identify specific securities, and is therefore used as security identification (International Securities Identification Numbers Organisation ).

5.1.2 Matching

Companies are being matched with each other – a company appointing a female CEO with a company appointing a male CEO. The companies are matched separately based on one criterion, the SIC-code3. The SIC-code is a four digit code and is used to identify which industry a certain company is operating in (Worldwide Business Directory).

Table 2: Matching criteria

Matching criterion

1. SIC

The reason why the companies are only being matched by a single criterion, the SIC-code, is because the sample is very small and contains very few companies with female CEOs. If the population where containing a higher number of female CEOs, other matching criteria could be used; like the age of the CEO, the CEO’s work experience, may come into play when looking at how the market reacts to the announcement, but

3

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27 also how they are performing after the appointment. So to be able to get the largest sample as possible, only the SIC-code is used as a matching criterion.

The SIC-code is used as matching criteria, because this will give a clean effect – this means that matching the companies by the industry-code (SIC) the sample is cleared for differences in business occurrences and fluctuations.

Not all companies were possible to make a clean match, meaning that all four digits in the SIC-code where not possible to fulfill, which means that there will be a trade-off. In order not to have to delete companies, the companies where matched as close to their original SIC-code as possible. They has to fulfill the first two digits before they will be matched, because the SIC-code’s overall industry is based on the first two digits in the code. By not matching the companies with a clean SIC-code matched, there will occur a tradeoff, because there can be some fluctuations on some sub-industries that are not affected in others. Those that were not able to be matched were deleted.

After the matching process the sample size now contains 66 companies; 33 with a female CEO and 33 with a male CEO. The sample size contains companies from Norway, Sweden, Denmark and Finland. Iceland is left out of the population, because there are no companies having female CEOs in Iceland according to the Orbis database. Thereby companies with a female CEO are representing 3.5 percent of the total sample size.

5.1.3 Datastream

To be able to find the different companies in Datastream, the ISIN numbers, which was added in Orbis, was used. When conducting the company information in Datastream, the companies had to be listed on an index. To get the return index for the different companies, the individual company had to be traded on an exchange in their own country respectively.

The stock exchanges that were chosen for the individual company depends on the country they were from; so for example the Swedish companies had to be listed on the Swedish stock exchange. By choosing that the company has to be listed in it owns country’s stock exchange gives a more clean effect, so that there isn’t any differences in one country that is not existing in other countries. This can for example be the macroeconomic fluctuations etc. The situation were that some countries are affected by

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28 some events and others not, can for example be the financial crisis were most of the European countries are affected, except for Norway. This is due to the membership of the European Union, and since Norway is not a member and thereby not affected in the same way as the members. When only looking at the countries individual, these differences are being excluded and thereby the effect will be as clean as possible. The announcement- and appointment dates were also extracted, because these dates are used for both the event study where the announcement dates is used as the date of the event, and for the performance analysis the appointment date will be the basis for this analysis. The announcement date is due to the assumption of an efficient market and that the information around the data of the event will be reflected in the stock prices immediately after the announcement day. The returns indexes that are conducted and used to calculate the log-return, is done in Excel, and is used for the event study.

The chosen indexes used to the matched companies are, as mentioned, different for each company in different countries. It is important that the chosen index reflects the business that the companies are occupied within, so the sectors are not skewed in a direction that is due to the occupied sector;

- For the Norwegian companies, the “Oslo Exchange all share” was used.

Choosing this “all around” index can result in some problems in terms of skewness. There can be an explanation on why the index is behaving in the special way, at a specific point of time, and not at other times of an event for example in terms of change in taxes. This can mean that the abnormal return can be smaller/bigger at a certain time, and not the case at other times.

- For the Swedish companies – this is the largest pool of companies from one country, so there is not specific industry that the companies are following. To be able to have an index that contains all the industries, the “OMX Stockholm” was chosen. Choosing an overall index can give the same problem as described for the Norwegian companies, but the problem will probably be smaller, in terms of skewness, because there are countries in different types of industries.

- For the Danish companies, the two companies is within the pharmaceutical industry, and therefore the index for Denmark is “FTSE Denmark Pharm & Bio – Price index” has been chosen. Because the index is for the given industry, the effect will be cleaner than for, for example the effect for the Norwegian

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29 companies, because the index will only look at those events that have happened in the pharmaceutical industry and therefore is cleared for events happened in industries outside this.

- For the Finish companies, the “OMX HELSINKI” is chosen. Even though the two companies have been matched as equally as possible, there is still a large difference in the two industries. The first company deals with paper and the other is within the industry of textile. Therefore a more general index will be the best in this case.

As mentioned in the methodology section, the estimation period is chosen to be 200 days for the event study. Because the event window contains three days, the data from Datastream contain 203 days. For the performance analysis the data will as mentioned, contain 50, 100 and 200 days before and after the day of the appointment of the new CEO.

There are a number of companies where the observations were not available because, the available data does not go back to the time of the announcement. If it was a female company, the company was deleted. If it was a male, the company was replaced with another that met the matching requirement, if possible.

After this process, the sample is now containing 42 companies; 21 companies with a female CEO and 21 companies with a male CEO.

According to Bartholdy, Olson & Peare (2007) the portfolio has to contain 50 stocks before it will deliver a good size and power for the test statistics, but having a portfolio containing above 25 stocks will deliver an acceptable size and power for the test statistics. The sample for this thesis of 21 companies is below the acceptable 25 stocks, so the tests can be insignificant when doing the test and for the interpretation. Stating this disadvantage for the sample, the sample is still covering all the Nordic listed companies. This indicates that the companies are not that ahead when it comes to appoint females into the CEO position. As some literature is stating, this research should maybe be redone again in about 20 years, where there can be a chance for females are seated in more CEO positions.

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30 5.2 Thin trading

Whatever the stock is thickly, medium or thinly traded depends on the measure of how often the stock is being traded. Before a stock is thinly traded, it has to be traded less than 40 percent of the trading days (Bartholdy, Olson, & Peare, 2007). Because of the assumption of an efficient market, there cannot be a high degree of thin trading, because having a thin traded market indicates that the market is not efficient. Having a thin traded data does also have an effect on the power of the tests – thin traded data have little explanatory value. Therefore it is important to adjust for this thin trading in the study.

There are four different methods to deal with thin trading (Bartholdy, Olson, & Peare, 2007):

- Calculate simple returns for each stock, only on the days where prices are available, and subtracts them from the market returns – according to Bartholdy, Olsen & Peare (2007), this is the least acceptable method of dealing with thin trading, because it gives unbiased estimates of the abnormal returns on the calculated days, but fails to use the return information on the other days. Therefore this method is not efficient.

- Lumped return method are calculating return from the last recorded price, and therefore it gives a zero return on the nontrading days and therefore only a relatively large positive or negative return on the trading days. The negative of this method is that it underestimates the variance of the returns and therefore also biases the test statistics.

- The uniform method calculates the total return between the trades and then allocates the average return to each day over the period. This is done for all of the nontrading days, as well as for trading days. This way of handling thin trading gives the same bias as for the lumped return method. According to Maynes and Rumsey (1993) the uniform method performs the same as the lumped return method.

- Trade-to-trade returns are using the information about the total stock and the market returns over time.

In this case to adjust for thin trading, the Lumped return method is used. This method gives more observations than if it was not adjusted for this, because it will give a zero

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31 return for no trading days. It is easier to handle and calculate than the uniform method, and gives about the same performance. The Lumped return is the most commonly used method when dealing with thin trading (Bartholdy, Olson, & Peare, 2007).

If companies are traded less than the 40 percent, they are either deleted or replaced by other companies. They are only replaced in cases where it is a company with a male CEO.

There are some companies that needs to be deleted, because they have more than 80 days that is not traded within the estimation period.

The list below, contain those companies that were deleted due to thin trading and do only contain those companies with a female CEO. The male companies that were matched with these are of course also deleted.

- NGS Next Generation Sys Sweden (Sweden) – was not traded 125 days of the 203 days

- Amhult 2 (Sweden) – was not traded 149 days of the 203 days - Senzime (Sweden) – was not traded 124 days of the 203 days - GC Rieber (Norway) – was not traded 170 days of the 203 days - Tide ASA (Norway) – was not traded 101 days of the 203 days - Medistim (Norway) – was not traded 146 days of the 203 days - Context Vision (Sweden) – was not traded 129 days of the 203 days

So now the sample contains 28 companies; 14 companies with a female CEO and 14 companies, with a male CEO.

5.3 Data processing

When the data was collected from Datastream, it was transferred to Excel and treated in a way so it is ready to be used in the SAS program. The return index, also collected from Datastream, was used to calculate the log-return in Excel.

All regressions are made in SAS and tests are done in both the SAS program and in Excel.

5.4 Data descriptive

To be able to describe the basic structure of the data, the descriptive statistics are made in the statistics program SAS. The descriptive statistics are made separately; for

Female CEO s. A study of their appointment performance and market reaction. Department of Business Administration

Female CEO’s

A study of their appointment performance and market reaction

Department of Business Administration

Aarhus School of Business and Social Sciences, Aarhus

University

Abstract

Table of contents

1.

Introduction

2.

Literature review

3.

Hypothesis

4.

Methodology

5.

Data