Master Thesis
Long term wealth effects of corporate spin-offs The influence of focus and board structure
July 2016
Name: Yves van den Berk Student number: 6063608 MSc Business Economics, Finance track
Supervisor: Dr T. Jochem
Abstract
In this thesis the wealth effects of corporate spin-offs are analyzed. Using a sample of 143 completed spin-offs in the US, positive abnormal buy-and-hold returns are found for 1 year and 3 years after the initial trading day of the spin-off. Furthermore, no significant influence of the dummy variable focus is found. The first model, with 1 year abnormal buy-and-hold returns as a dependent variable, suggests that gender diversity has a significant positive influence. The second model, with 3 year abnormal buy-and-hold returns as a dependent variable, suggests that CEO duality has a significant positive influence and board size has a significant negative influence on stock price performance.
2
Statement of originality
This document is written by Student Yves van den Berk 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.
3
Table of Contents
1. Introduction ... 4
2. Literature review ... 6
2.1 Definition of a corporate spin-off ... 6
2.2 Motives for corporate spin-offs ... 6
2.3 Long term wealth effects of spin-offs ... 7
2.4 Focus increasing spin-offs ... 9
2.5.1 Board structure ... 9
2.5.2 Board Size ... 10
2.5.3 Gender diversity ... 10
2.5.4 Average age of board members ... 11
2.5.5 CEO Duality ... 11
2.6 Hypotheses ... 12
3. Methodology ... 13
3.1 Buy and hold returns ... 13
3.2 Variable construction ... 14
3.3 Regression model ... 15
4. Data and descriptive statistics ... 16
4.1 Sources and sample selection ... 16
4.2 Descriptive statistics for variables ... 17
5. Results ... 19
5.1 Long term effects ... 19
5.2 Regressions ... 20
6. Robustness checks ... 24
7. Conclusion and discussion ... 25
8. References ... 27
4
1. Introduction
On December 16th 2015 Google announced to spin-off its self-driving car project into a separate business. This is part of the bigger plan to spin-off several of its advanced-technology units into stand-alone companies within the total business portfolio (Bloomberg, 2015). In order to serve their stakeholders, companies may decide to spin-off several of its business units just like in this example. According to Rajan et al (2000) keeping the business units may lead to a diversification discount of the total business value. Spinning of a business unit may increase focus on core activities and lead to efficiency improvements when managers don’t have the skills to properly manage the non-core assets (Daley, Mehrotra and Sivakumar, 1997). The influence of focus increasing spin-offs on parent companies has been extensively examined in the finance literature, focus increasing spin-offs lead to positive abnormal short term stock returns of the parent companies (Desai & Jain, 1999). When looking at the influence of focus on the spin-off subsidiaries there seem to be contradicting results. Burch and Nanda (2003) conclude that the aggregate value of firms undertaking a spin-off increases as a result of reductions in diversity. Wruck and Wruck (2002) however find contradicting results: spin-offs might be structured in order to allocate weak assets to the subsidiary. Furthermore they find that corporate spin-offs can be seen as an event to restructure top management. Denis, Denis and Walker (2015) find that boards of corporate spin-offs are smaller and include more directors with relevant industry experience. Since the board of directors of a corporate spin-off has to be formed from scratch and these companies are comparable to newly established companies the question raises whether board structure is an important driver of the long term wealth effects of spin-offs. This leads to the following research question:
What is the influence of focus and board structure on the long term performance of corporate spin-offs?
This paper contributes to existing literature because it examines the long term effects of corporate spin-offs by looking at the focus and board structure. This could be a relevant subject when appointing new board members or when investing in corporate spin-offs. Furthermore this research question is part of the bigger question about the optimal board formation. In order to answer the research question, first the abnormal
5
buy-and-hold returns of the spin-offs will be calculated using the method of Barber and Lyon (1997). Firms will be matched on industry, market-to-book-ratio and size. The average abnormal buy-and-hold returns will be calculated for 1 year, 2 years and 3 years. Then, several regressions will be done with the abnormal buy-and-hold returns as dependent variable in order to test the different hypotheses. The independent variable which are used in the analyses are focus, board size, gender diversity, average age of board members, and CEO duality. Existing literature finds a negative influence of board size and CEO duality on firm performance in general and a positive influence of gender diversity (Jensen, 1993, Carter et al., 2003). This thesis will examine whether the same effect is found when examining spin-offs.
The reminder of this thesis is structured as follows: in section two the different existing theories on corporate spin-offs together with an overview of existing literature on long term wealth effects will be explained. Furthermore, the theories of board structure and general firm performance will be discussed. In section three the used methodology will be explained. Section four covers the data selection process and the descriptive statistics of all variables. In section 5 the results will be discussed and compared to existing literature. The robustness checks will be explained in the sixth section. In the final section the conclusion is presented and limitations and suggestions for further research will be discussed.
6
2. Literature review
2.1 Definition of a corporate spin-off
A corporate spin-off refers to an original company, the parent, which is separated into two or more independent companies. Or stated differently: “A spin-off refers to the separation of the management of some assets of a firm into a separate entity” (Chemmanur, Krishnan and Nandy, 2014). The spun-off company is referred to as the subsidiary. After the event the subsidiary operates separately from its parent, is publicly listed and has its own board of directors.
2.2 Motives for corporate spin-offs
Although under the Modigliani-Miller (1958) capital market conditions a corporate spin-off wouldn’t lead to an increase in overall business value, several reasons are found in the finance literature on why firms may decide to spin-off a subsidiary. Among them, three are prominent: the asymmetric information hypothesis, the managerial incentives hypothesis and the restructuring motives.
Asymmetric information
One motive on why firms may undertake corporate spin-offs is to lower principal agent costs. Schipper and Smith (1983) state that creating a new company which is publicly traded gives shareholders the opportunity to better monitor the agent, since more information is available. This information could be the financial statements or the stock price of the subsidiary. Gilson et al (2001) found that a spin-off may also increase analyst coverage, this could lead to more information accuracy which leads to less information asymmetry.
Managerial incentives
The decision to spin-off a subsidiary may also be driven by improved efficiency as a result of more accurate managerial incentives. Aron’s (1991) model states that spin-offs can increase efficiency because new incentive contracts can be designed for divisional managers. Pyo (2006) finds that new compensation packages may be an important reason for spin-offs, as a result of this efficiency may increase. Chemmanur and Yan (2004) created a model which describes the effect of a corporate spin-off on
7
management. By undertaking a spin-off, firms get more exposed to a potential takeover, which could discipline the management.
Restructuring motives
Miles and Rosenveld (1983) state that a spin-off may increase value because of investment decisions. The spin-off creates a better environment for the managers of the unit firm which are consistent with the firm’s competitive priorities. In other words: unit firms may generate positive NPV projects which they normally wouldn’t. Another restructuring motive is that firms may decide to spin-off a subsidiary when this unit operates in an unrelated business compared to the parent company. For this reason the parent is traded at a diversification discount (Rajan et al, 2000).
2.3 Long term wealth effects of spin-offs
Corporate spin-offs are generally associated with positive long term abnormal returns for both the spin-offs and their parents. There is a relative small amount of studies that look at the long term effects of spin-offs due to the small number of spin-offs that occur each year. Table 1 highlights some studies that examine the long term wealth effects of corporate spin-offs. Only the results of the subsidiaries are shown, often it’s the case that also the parents and the combined firms show long term excess returns. It’s clear from table 1 that all studies use the buy-and-hold approach in order to calculate abnormal returns. In order to match firms different variables are used, the most common method is based on asset size and two digit SIC code. Another common method is based on asset size and market-to-book ratio.
8
Table 1. Literature overview of long term wealth effects of corporate spin-offs
Study Period Region Sample size Method Matching t+ 12 months t+ 24 months t+ 36 months Cusatis et al (1993) 1965-1988
US 146 BHAR Size, SIC code 4.5 25.0** 33.6** Desai and Jain
(1999)
1975-1991
US 162 BHAR Size, SIC code 15.69*** 36.19*** 32.31*** McConnell and
Ovtchinnikov (2004)
1965-2000
US 311 BHAR Size, SIC code Size, market-to-book 19.40*** 16.80*** 24.37*** 24.55*** 26.32*** 20.75** Harris and Madura (2010) 1981-2006
US 246 BHAR Size, market-to-book ratio
17.32*** 24.58*** 26.15*** Feng, Nandy and
Tang (2015)
1993-2006
US 91 BHAR Size, market-to-book ratio
5.8*** 5.9*** 4.1*** Veld and
Veld-Merkoulova (2004)
1987-2000
EU 70 BHAR Size, market-to-book ratio
11.95 13.72 15.14
This table shows an overview of studies that examine the long term wealth effects of corporate spin-offs in the US and Europe. All studies use the buy-and-hold portfolio approach in order to measure excess returns, after 12 months, 24 months and 36 months. Matching is based on firms with similar size, same two digit SIC code or similar market-to-book ratio. Significance levels at 1%, 5% and 10% presented by ***, **, *, respectively.
One of the first studies on long term wealth effects of corporate spin-offs was done by Cusatis et al (1993). They find positive abnormal buy and hold returns for 2 and 3 years after the initial trading date of the spin-off in a total sample of 146 spin-offs. Furthermore they found that these results are limited to firms which are involved in takeover activity. Desai and Jain (1999) also found positive abnormal buy and hold returns. A driver behind these results is corporate focus. Only firms with a different two digit SIC code compared to their parent show significant positive buy and hold returns. McConnell and Ovtchinnikov (2004) use both firm size and SIC code and firm size and market-to-book ratio in order to construct portfolios of matched firms and spin-offs. In their sample of 311 firms they find positive significant abnormal returns for spin-offs in both portfolios. Feng, Nandy and Tang (2015) find lower positive abnormal buy and hold returns than previous studies. Furthermore they find that CEO’s with a high level of stock payments are more likely to undertake a spin-off. After the spin-off, companies have a greater operating growth compared to their industry- and size-matched firms. A
9
study performed in Europe by Veld and Veld-Merkoulova (2004) doesn’t find any significant abnormal buy and hold returns. This would suggest that spin-offs in Europe are different compared to US spin-offs.
2.4 Focus increasing spin-offs
A source of the long term wealth effects of corporate spin-offs which is often mentioned is corporate focus. In a focus increasing spin-off, the subsidiary operates in a different industry compared to the parent company. According to Daley, Mehrotra and Sivakumar (1997) focus increasing spin-offs can generate value when managers don’t have the skills to properly manage the non-core assets. After the spin-off, management has more time to manage the core assets, therefore the total business value of the parent company can increase. No evidence is found that the focus increasing subsidiaries generate abnormal long term results. Desai and Jain (1999) do find long term abnormal buy-and-hold returns for both the focus increasing parents as well as their subsidiaries compared to the non-focus increasing spin-offs. A possible explanation for these results is that in case of the non-focus increasing spin-offs, parents are more likely to spin-off an underperforming subsidiary. Burch and Nanda (2003) conclude that the aggregate value of firms undertaking a spin-off increases as a result of reductions in diversity. Wruck and Wruck (2002) however find contradicting results. The elimination of the valuation discount for parents following the spin-off, results in a discount for subsidiaries. Spin-offs might be structured in order to allocate weak assets to the subsidiary.
It’s clear that there is a contradicting literature on corporate focus. On one hand the increase in focus can reduce the diversification discount of the entire business, both the spin-off and the parent. On the other hand it’s possible that only the parent will benefit from focus-increasing spin-offs, the spin-off will be discounted because weak assets have been allocated.
2.5.1 Board structure
An important part of a corporate spin-off is the formation of a board of directors for the spun-off unit firm. Denis, Denis and Walker (2015) find that boards of corporate spin-offs are more independent, smaller and include more directors with relevant industry experience. Wruck and Wruck (2002) examine corporate spin-offs as an event to restructure top management. They find that top management experience of a parent company and governance expertise affect the management composition of spun-off firm.
10
Furthermore the top management structure is related to the value created with the spin-off. The influence of board structure on firm performance in general has been studied frequently in the finance literature. In these papers, the most prominent board structure components are: board size, board diversity, average age of board members and CEO duality.
2.5.2 Board Size
One of the first studies of board size and firm performance was done by Lipton and Lorsch (1992). The primary task of directors is to monitor top managers and to criticize their policies. They argue that normally directors don’t actually do this, furthermore they argue that this problem increases with larger boards because the cost of an individual director to monitor management decreases with the total number of board members. Similarly Jensen (1993) finds that boards are less likely to function effectively when there are more than seven or eight directors. When boards are larger it becomes more difficult to arrange board meetings or to reach consensus. As a result of this larger boards are slower in decision making and less efficient. Cheng (2008) finds that board size is negatively associated with the variability of return on assets, stock returns and R&D expenditures. In order to reach consensus it takes more compromises in the case of larger boards. Contradicting to earlier research Bhagat and Bolton (2008) don’t find any significant influence of board size on firm performance. They use a sample of US firms between 1990 and 2004.
2.5.3 Gender diversity
Board diversity relates to a combination of characteristics, attributes, and expertise which is contributed by each board member. It’s related to decision making and the corporate governance process (Walt and Ingley, 2013). Examples of observable diversity measures are age, race or gender. Examples of less visible diversity measures are education, functional background or values. The underlying theories of board diversity are the agency theory and the resource dependence theory. The agency theory states that more diverse boards are able to monitor the management in a more efficient way due to the fact that board members have a different background. The resource dependence theory states that diverse board increase the sources inserted by each individual board member which increases the total business value (Carter et al., 2003). The relationship between board gender diversity and firm performance was examined by Shrader et al. (1997). In a sample of 200 US firms they found no significant results
11
between the fraction of women on the board and firm performance. Studies performed by Carter et al. (2003), Erhard et al. (2003) and Lückerath-Rovers (2012) however do find a significant positive results. Board gender diversity has a positive influence on firm value and firm performance, measured as Tobin’s Q, ROA or ROI. A study performed by Adams & Ferreira (2009) shows that women on boards have better attendance records than men. This would suggest that gender-diverse boards are more effective in monitoring management.
2.5.4 Average age of board members
The influence of average age of board members on firm performance has been examined less often in the finance literature. According to Baysinger & Butler (1985) younger board members could bring new ideas and insights, but the most important factor that has to been taken into consideration is experience. Besides new ideas and insights Carter et al. (2003) also argue that younger board members are less risk averse.
2.5.5 CEO Duality
CEO duality is defined as a person who is serving both as a CEO and chairman of the board of directors. A study performed by Pi and Timme (1993) shows that firms where both titles are separated show higher return on assets and lower costs compared to firms where a dual CEO is present. Combining two titles can lead to suboptimal leadership, however this varies across firms and industries. The influence of powerful CEO’s on stock returns has also been examined by Adams, Almeida & Ferraira (2005). Firms with CEO’s who have power over the board show more variable stock returns. CEO duality could also have a positive side. According to Finkelstein & D’aveni (1994) CEO duality can establish strong leadership.
It’s clear that board structure has been extensively examined in the finance literature. However, little has been written about the influence of board characteristics on the long term performance of corporate spin-offs. This creates an interesting gap in the finance literature. Corporate spin-offs are comparable to newly established firms and boards have to be formed from scratch, this makes it a particular interesting field for research. The aim of this paper is to test whether board characteristics are an important driver of the long term wealth effects of corporate spin-offs.
12 2.6 Hypotheses
The objective of this is to analyze whether corporate focus and board structure have an influence on the long term performance of corporate spin-offs. Matched firms buy-and-hold returns and several regressions will be used to test the following hypotheses: Hypothesis 1: Spun-off subsidiaries have positive abnormal buy-and-hold returns
This is based on earlier research done by Desai and Jain (1999), McConnell and Ovtchinnikov (2004) and Harris and Madura (2010). In all these studies corporate spin-offs show long term positive abnormal returns after 1 year, 2 years and 3 years using different samples and different time periods.
Hypothesis 2: Spin-offs that increase corporate focus show higher long term abnormal
returns compared to non-focus increasing spin-offs
Papers by Desai and Jain (1999) and Burch and Nanda (2003) show that focus increasing spin-offs show higher long term abnormal results compared to non-focus increasing spin-off.
Hypothesis 3: Board size has a negative influence on the long term performance of
spun-off subsidiaries
This is based on general research of the influence of board size on firm performance (Lipton and Lorsch (1992), Jensen (1993) and Cheng (2008)). Since in case of corporate spin-offs boards have to be formed from scratch it could be the case that an even bigger influence will be found compared to existing literature.
Hypothesis 4: Board gender diversity has a positive influence on the long term
performance of corporate spin-offs
According to Carter (2003) a diverse board could increase the sources inserted by each individual board members, this can increase total business value. Papers by Erhard et al. (2003) and Lückerath-Rovers (2012) finds a significant positive influence of gender diversity on firm performance.
13
Hypothesis 5: Firms with a higher average board member age show higher abnormal
buy-and-hold returns
Research done by Baysinger & Butler (1985) states experience is the single most important factor that has to be taken into consideration in case of board members. Age could a proxy for experience and thus have a positive influence on performance.
Hypothesis 6: Firms where CEO duality is present show lower
This is based on research done by Pi and Timme (1993) and Adams, Almeida & Ferraira (2005), firms with a dual CEO show lower return on assets and more variable stock returns.
3. Methodology
In this section the methodology used to test the different hypotheses in this thesis will be discussed. First, the method to calculate the buy-and-hold returns will be explained. Then the construction of the different (independent) variables will be explained. Lastly, the regression model will be introduced.
3.1 Buy and hold returns
To estimate the long term effects of corporate spin-offs, the matching firm approach of Barber and Lyon (1997) is used. In order to calculate buy-and-hold returns, each spin-off is matched to a firm based on industry, market-to-book ratio and total assets (size). First for each spin-off, firms are matched on similar 2 digit SIC code. From the remaining firms only companies with a market-to-book ratio within the 10% range compared to the subsidiaries are selected. The last step is to keep firms which are closest to the spin-offs measured as total assets. The return of each spin-off will then be compared to the return of the matched firm for holding periods 12 months, 24 months and 36 months using equation 1. (1) 𝐵𝐻𝐴𝑅𝑖,𝑇 = ∏(1 + 𝑅𝑖𝑡) 𝑇 𝑡=1 − ∏(1 + 𝐸(𝑅𝑖𝑡)) 𝑇 𝑡=1
Where 𝐵𝐻𝐴𝑅𝑖,𝑡:𝑇 presents the abnormal buy-and-hold return for firm i for period t:T
∏𝑇𝑡=1(1 + 𝑅𝑖𝑡) presents the actual return of the spin-off
∏𝑇𝑡=1(1 + 𝐸(𝑅𝑖𝑡)) presents the expected return of the spin-off, in this case the return of the
14 t=1 is the initial trading date of the spin-off
In order to calculate the average value weighted buy-and-hold return equation 2 is used.
(2) 𝐴𝐵𝐻𝐴𝑅𝑇 = 1
𝑁∑ 𝐵𝐻𝐴𝑅𝑖,𝑇 𝑁
𝑖=1
Where 𝐴𝐵𝐻𝐴𝑅𝑡:𝑇presents the average buy-and-hold return for period t:T 1
𝑁∑ 𝐵𝐻𝐴𝑅𝑖;𝑡:𝑇 𝑁
𝑖=1 presents the total buy-and-hold returns in period t:T divided by the number of firms
The t-statistic (equation 3) is used to whether the ABHAR’s are different from zero Barber and Lyon (1997)
(3) 𝑡 − 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐 = 𝐴𝐵𝐻𝐴𝑅𝑇 𝑆𝐷 (𝐵𝐻𝐴𝑅𝑇)/√𝑁 3.2 Variable construction
The different variables explained in this section are used to test the different hypotheses described in section 2.
Corporate focus
In order to measure corporate focus, the two digit SIC codes of all spin-offs are gathered from the Thomson One SDC database. The two digit codes of the parent companies and the spin-off are compared, when the codes are different the dummy equals 1. The dummy equals 0 when the codes are equal, thus not focus increasing.
Board size
Board size equals the total number of directors on the board for each firm. Data is gathered from the ISS, former RiskMetrics database and the BoardEx database.
Average board age
Average board age is the average board age of each firm. Calculated by taking the total age of each firm and dividing it by the total number of board members. This data is also gathered from the ISS database and the BoardEx database
Gender diversity
In order to measure gender diversity the Blau (1977) index on heterogeneity will be used. This is calculated as 1 − ∑ 𝜌𝑖2, where ρ equals the fraction of women and the fraction of men on the board. An index above 0.25 would reflect a heterogeneous board.
15 CEO duality
CEO duality is a dummy which equals 1 when the CEO of a company is the chairman of the board as well. The dummy is 0 when nu dual CEO is present. Data is gathered from the ISS database and the BoardEx database.
Industry dummies
In order to create industry dummies, the Thomson One/ SDC database is used. All spin-offs are categorized in 12 different industries. The dummy equals 1 if a company belongs to specific industry and equals 0 otherwise.
Control variables
The first control variable which is used is market capitalization which is calculated by taking the total number of shares outstanding times the share price in the first month after the initial trading date. The second control variable is market-to-book ratio, this is calculated by taking the number of shares outstanding times the share price, adding total assets and subtracting book value of common equity and deferred taxes, this value is divided by total assets.
3.3 Regression model
The different hypotheses will be tested by performing an OLS regression (equation 4):
(4) 𝐵𝐻𝐴𝑅𝑖,𝑡= 𝛽0+ 𝛽1𝐹𝑜𝑐𝑢𝑠𝑖,𝑡+ 𝛽2𝐵𝑜𝑎𝑟𝑑𝑠𝑖𝑧𝑒𝑖,𝑡+ 𝛽3𝐺𝑒𝑛𝑑𝑒𝑟𝐷𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦𝑖,𝑡+
𝛽4𝐵𝑜𝑎𝑟𝑑𝐴𝑔𝑒𝑖,𝑡+ 𝛽5𝐶𝐸𝑂𝐷𝑢𝑎𝑙𝑖𝑡𝑦𝑖,𝑡+ 𝛽6𝐹𝑖𝑟𝑚𝑆𝑖𝑧𝑒𝑖,𝑡+ 𝛽7𝑀𝑎𝑟𝑘𝑒𝑡𝑇𝑜𝐵𝑜𝑜𝑘𝑖,𝑡+ 𝛽8−20𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦𝐷𝑢𝑚𝑚𝑖𝑒𝑠𝑖,𝑡+ 𝜀𝑖,𝑡
Where 𝐵𝐻𝐴𝑅𝑖,𝑡 presents the buy and hold return for firm i
Focus is a dummy which equals 1 if a spin-off is focus increasing, measured as a different two digit SIC code compared to its parent
Boardsize is the total number of board members of firms i in year t GenderDiversity equals the Blau index of firm i in year t
BoardAge is the average board member age of firm i in year t
CEODuality is a dummy which equals 1 when a dual CEO is present for firm i in year t FirmSize is the market capitalization of firm i in year t
MarketToBook equals the market-to-book ratio for firm i in year t IndustryDummies are industry dummies for firm i in year t
16
4. Data and descriptive statistics
In this section the data and descriptive statistics used for the analyses will be discussed. First the different sources, the sample selection and some descriptive statistics of the sample will be explained. Then, the descriptive statistics and correlation of all used variables will be presented.
4.1 Sources and sample selection
In order to get the data for the analyses, the Thomson One SDC Platinum database is used. Only completed US spin-offs that occurred between 1990 and 2012 are selected When dropping duplicates, 817 spin-offs remain. Furthermore the CRSP database is used for stock prices. Only firms that have that have available stock price data are selected, 178 companies remain. The Compustat database is used to get relevant accounting data and SIC industry codes. Based on these criteria the final sample consists of 143 completed US spin-offs that occurred between 1990 and 2012 and of which 3 years of stock price data is available. In order to get relevant corporate governance data the ISS, former RiskMetrics database is used. This database gives the name, age, gender and title of the board members. Merging this database with the existing database leads to 48 spin-offs with corporate governance data. In order to find more corporate governance data, the BoardEx database is used. This results in a final sample of 78 spin-offs with relevant data on board members. Especially for spin-spin-offs that occurred before 1999 it’s difficult to find governance data because this is the time when the BoardEx database started to collect information.
Table 2 provides summary statistics on the final dataset. It’s clear from table 1 that a lot of spin-offs occurred between 1993 and 2002. Furthermore it can be noticed that a lot of spin-offs occur in the High Technology, Industrial and Material industry. Lastly, it can be concluded that there is a lot of variation in size of the spin-offs, measured as the stock price times the number of shares outstanding in the first month after the spin-off.
17
Table 2. Descriptive statistics for spin-offs in the US
Panel A. Spin-offs per year Panel B. Spin-offs per industry
Year # Spin-offs % Industry Type # Spin-offs %
1990 6 4.2 Consumer Products and Services 4 2.8
1991 4 2.8 Consumer Staples 11 7.69
1992 4 2.8 Energy and Power 11 7.69
1993 9 6.29 Financials 15 10.49
1994 10 6.99 Healthcare 14 9.79
1995 12 8.39 High Technology 23 16.08
1996 11 7.69 Industrials 19 13.29
1997 13 9.09 Materials 17 11.89
1998 11 7.69 Media and Entertainment 10 6.99
1999 10 6.99 Real Estate 5 3.5 2000 12 8.39 Retail 4 2.8 2001 9 6.29 Telecommunications 10 6.99 2002 6 4.2 Total 143 100 2003 7 4.9 2004 4 2.8
2005 2 1.4 Panel C. Market Capitalization
2006 3 2.1 Mean 1583.40 2007 2 1.4 Standard Deviation 4389.42 2008 3 2.1 Minimum 3.12 2009 1 0.7 Maximum 11000.29 2011 1 0.7 2012 3 2.1 Total 143 100
This table presents 143 completed spin-offs in the US in het period 1990- 2012. Spin-offs announcement dates and completion dates were gathered from the Thomson One SDC database. Panel B presents the different spin-offs per industry, based on the SIC code. Panel C presents the Market Capitalization in million dollars, compounded by taking the number of shares outstanding times the stock price in the first month after the spin-off.
4.2 Descriptive statistics for variables
Table 3 presents descriptive statistics for all variables used in this thesis. The buy-and-hold returns reported in row 1 varies between -3.76 and 4.36. It’s clear that there is a lot of variation, this can also be seen when looking at the standard deviation of 1.23. However, both the median and the mean are positive. It’s clear from the second row that around 60% of the spin-offs are focus-increasing. When looking at board size, similar results as the Denis, Denis and Walker (2015) paper are found, they also find a median number of 7 board members. This means that in this sample the spin-offs also have a relative small number of board members. The gender diversity coefficient, or the Blau index, has a mean of 0.1414. However the median tells us that more than 50% of the boards have 0 female board members. The maximum fraction of female board members
18
is 50% (Blau index of .5 equals the same number of men and women). A lot of variation is found when looking at the average age of board members (minimum 42.5, maximum 77). Furthermore it’s clear that the majority of the firms, around 68%, have a CEO who is also chairman of the board.
Table 3. Descriptive statistics for variables
This table presents the mean, median, standard error, minimum, maximum and number of observations for each variable used in the analyses.
Table 4 provides correlation coefficients of the different variables which are used in this thesis. Analyses of the correlation shows that most of the correlations are low. No correlation is above 0.3041 or below 0.3545, that’s why no bias because of multicollinearity is expected. When looking at the different coefficients it becomes clear that larger firms are correlated with larger boards (coefficient of 0.1779), larger boards are positively correlated with average age of board members (0.2693) and firms with a high market-to-book-ratio are associated with lower average age of board members (0.3545)
Table 4. Correlation matrix BHAR Focus Board
size BLAU Age CEO Duality Market Cap Market-to-Book
BHAR 1 Focus -0.0309 1 Board size -0.1167 -0.0265 1 BLAU 0.1103 0.1109 0.0061 1 Age 0.1552 0.1688 0.2693 -0.0247 1 CEODuality 0.1044 0.0217 0.2444 0.1326 0.3041 1 Market Cap 0.028 -0.1356 0.1779 0.0529 0.0048 0.1607 1 Market-to-book -0.063 -0.1433 -0.1898 0.021 -0.3545 -0.2754 -0.0141 1 This table presents the correlation coefficients of the different variables which are used in this thesis.
Variable Mean Median SD Minimum Maximum Obs.
BHAR 0.2052 0.1533 1.2347 -3.7599 4.3596 143 Focus 0.6013 1 0.4913 0 1 143 Board size 6.8461 7 2.6189 3 15 78 Blau Index .1441 0 0.1690 0 0.5 78 Average Age 60.4532 60.2539 5.7886 42.5 77 78 CEO Duality .6794 1 0.4696 0 1 78 Market Cap 1583.40 3000.65 4389.42 3.12 11000.29 143 Market-to-Book 2.1121 1.4388 2.1475 .5122 15.1122 143
19
5. Results
5.1 Long term effects
The raw returns of the spin-off portfolios, measured as the equal weighted average buy-and-hold returns after 12, 24 and 36 months are presented in table 5. It’s clear that all buy-and-hold returns are significantly different from zero. The results are similar to earlier research performed by Cusatis, Miles and Woolridge (1993) who found raw returns 19.9%, 52.0% and 76.0% for 12, 24 and 36 months. Differences may be due to a different chosen time frame. It’s hard to draw any conclusions based on raw returns because no benchmark is chosen.
Table 5. Raw returns of spin-off subsidiaries
Holding period 12 months 24 months 36 months
Mean return 23.8% 42.4% 63.8%
t-statistic 4.64*** 5.68*** 6.95***
% positive 52% 52% 53%
This table presents the raw buy and hold returns for 143 US listed spin-offs in the period 1990-2012. Returns are calculated using monthly stock prices. Significance levels at 1%, 5% and 10% presented by ***, **, *, respectively.
In order to calculate abnormal buy-and-hold returns the spin-offs are matched to firms based on industry, size and market-to-book ratio. The average abnormal buy-and-hold returns after 12, 24 and 36 months are presented in table 6. It’s clear that significant abnormal returns are found after 1 year and 3 year. The mean return after 1 year of 13.1% is similar to earlier research (Desai and Jain, 1999, McConnell and Ovtchinnikov, 2004, Harris and Madura, 2010, Feng, Nandy and Tang, 2015), in these studies average buy-and-hold returns between 5.8% and 19.4% are found. Similar for year 3 where abnormal returns between 4.1% and 32.3% are found (in this study 21.0%).
Table 6. Matched firm average buy and hold returns (equal weighted)
Holding period 12 months 24 months 36 months
Mean return 13.1% 13.3% 21.0%
t-statistic 2.07*** 1.54 1,99**
% positive 52% 51% 51%
This table presents the firm matched buy and hold returns for 143 US listed spin-offs in the period 1990-2012. Firms are matched on the basis of SIC code, book to market value and total asset size. Significance levels at 1%, 5% and 10% presented by ***, **, *, respectively.
20 5.2 Regressions
In this section the different regressions are presented. The first model, table 7, uses the 1 year buy-and-hold returns as dependent variable.
Table 7. Regressions with 1 year buy-and-hold returns as dependent variable
This table presents several OLS regressions with 1 year abnormal buy-and-hold returns as dependent variable. The independent variables used is the regression are focus, board size, gender diversity measured with the Blau index, average age of board members and CEO Duality. The control variables which are used are market-to-book ratio and market capitalization. Significance levels at 1%, 5% and 10% presented by ***, **, *, respectively, standard errors are between brackets.
Focus
In the first regression, only focus is taken as independent variable. Focus has a negative coefficient which is contradicting earlier research done by Desai and Jain (1999) and Burch and Nanda (2003) but is in line with the paper from Wruck and Wruck (2002). Focus increasing spin-offs may have negative influence on the 1 year stock price performance because weak assets have been allocated by the parent. However, the results are insignificant so nu conclusion can be drawn from this result. After adding control variables in the second regression still no significant result is found.
Board size
In regression 3 only the board structure components are taken as independent variable. In regression 4 the control variables are added, lastly in regression 5 all variables are included. When comparing regression 2 and 3, the adjusted r squared increases from 0.015 to 0.093. This means that in the third regression 9.3% of the variance is explained
(1) (2) (3) (4) (5) Focus -0.0426 (-0.32) -0.0273 (-0.20) (0.63) 0.110 Board size -0.0546 (-1.52) -0.0496 (-1.40) -0.0468 (-1.30) Gender diversity 0.940 (1.66) 1.131** (2.00) 1.105* (1.94) Average age 0.0165 (1.03) 0.0187 (1.14) 0.0179 (1.08) CEO Duality -0.00769 (-0.04) (0.50) 0.105 (0.50) 0.106 Market_to_book ratio 0.0417 (1.13) 0.0756 (1.55) 0.0784 (1.60) Market Cap -0.0794** (-2.06) -0.104* (-1.80) -0.101* (-1.74) Constant 0.192 (0.50) 1.173* (1.86) -0.359 (-0.38) 0.672 (0.52) 0.620 (0.48) Observations 143 143 78 78 78 R2 0.079 0.112 0.234 0.283 0.287 Adjusted R2 -0.006 0.015 0.093 0.123 0.115
21
by the model, this is a large increase compared to the second regression. In all three regressions board size has a negative coefficient, this is in line with earlier research of board size and firm performance in general (Lipton and Lorsch, 1992, Jensen, 1999, Cheng, 2008). However, the results are insignificant again so nu conclusion can be drawn from this result
Board diversity
Without adding control variables (regression 3) board diversity (the Blau index) has a positive but insignificant coefficient. When adding control variables board diversity becomes significant at the 5% level. Gender diversity has a positive influence on the 1 year abnormal returns of corporate spin-offs. This is in line with existing theory on gender diversity (Carter et al., 2003, Erhard et al., 2003 and Lückerath-Rovers, 2012) and may be the result of better monitoring or the sources inserted by each individual board member. When using all the different independent variables in regression 5, the coefficient gender diversity remains significant (10%) and positive.
Average age
Average age of board members has a positive coefficient in all three variables. This could mean that age is a proxy for experience which has a positive influence on performance, however these results are not significant.
CEO Duality
CEO duality has a positive coefficient in the third regression (insignificant), but a positive coefficient in the fourth and fifth regression (insignificant).
Control variables
The control variable market-to-book ratio is positive in all three regressions, this would suggest that firms with high market-to-book ratio perform better. However no conclusions can be drawn because all results are insignificant. The control variable market capitalization is significantly negative in all three regressions. This would mean that in the last case that a 1 million dollar increase in market capitalization would lead to a 10.1% lower buy-and-hold return.
22
Table 8. Regressions with 3 year buy-and-hold returns as dependent variable
(1) (2) (3) (4) (5) Focus -0.211 (-0.97) -0.186 (-0.89) -0.207 (-0.71) Board size -0.117* (-1.81) -0.111* (-1.86) -0.116* (-1.92) Gender diversity 0.912 (0.90) (1.35) 1.286 (1.39) 1.334 Average age 0.0418 (1.45) 0.0354 (1.28) 0.0369 (1.32) CEO Duality 0.247 (0.67) 0.606* (1.70) 0.604* (1.69) Market_to_book ratio 0.0152 (0.26) (1.34) 0.109 (1.26) 0.104 Market Cap -0.219*** (-3.64) -0.349*** (-3.59) -0.354*** (-3.62) Constant 0.912 (1.46) 3.799*** (3.85) -1.163 (-0.69) 3.531 (1.64) 3.630* (1.67)
Industry fixed effects Yes Yes Yes Yes Yes
Observations 143 143 78 78 78
R2 0.091 0.178 0.177 0.317 0.322
Adjusted R2 0.007 0.088 0.025 0.165 0.159
This table presents several OLS regressions with 3 year abnormal buy-and-hold returns as dependent variable. The independent variables used is the regression are focus, board size, gender diversity measured with the Blau index, average age of board members and CEO Duality. The control variables which are used are market-to-book ratio and market capitalization. Significance levels at 1%, 5% and 10% presented by ***, **, *, respectively, standard errors are between brackets.
Focus
In all three regressions (1, 2 and 5) the dummy variable focus is positive but insignificant.
Board size
In the all three regressions with 3 year abnormal buy-and-hold returns as dependent variable board size has a negative significant (10%) coefficient. In the last regression this means that an additional board member would lead an 11.6% decline in abnormal buy-and-hold returns. This is in line with earlier research of board size and firm performance in general (Lipton and Lorsch, 1992, Jensen, 1999, Cheng, 2008). This may be the results of less efficient monitoring, more difficulty in arranging board meetings or more problems with reaching consensus.
23 Board diversity
Without adding any control variables de term gender diversity, measured with the Blau index is positive but insignificant. When looking at the r squared of this regression only 2.5% of the variance is explained by the model. When adding control variables the r squared increases to 0.165. However the gender diversity term remains insignificant, the same applies for the fifth regression when the term focus is included.
Average age
In all three regressions, average age of board members has a positive coefficient. However no result is significantly different from zero.
CEO Duality
In the third regression the dummy variable CEO duality is positive but insignificant. When adding control variables the dummy becomes significant. In this case having a CEO who’s also the chairman of the board of directors would lead a 60.4% increase in buy-and-hold returns compared to firms where no dual CEO is present. This contradicts earlier research on CEO duality in general done by Pi and Timme (1993). A possible explanation for the positive coefficient is that CEO duality can establish strong leadership (Finkelstein & D’aveni, 1994). Because corporate spin-offs can be seen as newly established firms, strong leadership might be an important characteristic of CEO’s. When adding the term focus to the regression, CEO duality remains significantly positive.
Control variables
The control variable market-to-book ratio is insignificant in all three regressions. The variable market capitalization however is significantly (1%) negative in all three regressions.
24
6. Robustness checks
In order to check for robustness, all regressions in the two models are repeated using robust errors.
1 year buy-and-hold returns
When looking at regressions with 1 year buy-and-hold returns as dependent variable, using robust errors (Appendix I) it becomes clear that the term gender diversity becomes significant in the third regression (10%) and more significant in the fourth and fifth regression (from 10% to 5%). Furthermore the term board size becomes significant in the third and fourth regression (10%). The control variable market-to-book ratio also becomes significant in the fifth regression (10%).
3 year buy-and-hold returns
When looking at regressions with 3 year buy-and-hold returns as dependent variable, using robust errors (Appendix II) it’s clear that the variable board size becomes more significant in the third and fifth regression (from 10% to 5%). The term CEO duality is insignificant in the regression with robust errors. The control variable market capitalization remains significant at the 1% level.
To summarize, the term gender diversity in the first model and the term board size in the second model remain significant or become more significant when using robust errors. The term CEO duality in the second model becomes insignificant and the term board size becomes significant in the first model.
25
7. Conclusion and discussion
This study examined the wealth effects of corporate spin-offs in the US that occurred between 1990 and 2012. The study started with a literature research where different explanations for the long term abnormal returns of corporate spin-offs were discussed. Furthermore, the general board structure theories were explained. It became clear that the most prominent board structure components are board size, gender diversity, average age of board members and CEO duality. Besides board structure another important wealth driver for spin-offs is corporate focus, it became clear that there is no consensus in the existing literature about whether focus increasing spin-offs are an important driver of the wealth effects. Now that all models have been tested the general research question can be answered:
What is the influence of focus and board structure on the long term performance of corporate spin-offs?
The results suggest that corporate focus, which means that the spun-off subsidiary doesn’t have the same two digit SIC code compared to its parent, doesn’t have a significant influence on the long term buy-and-hold returns. No proof is found that the subsidiary shows higher or lower stock price performance as a result of the dismissal of the diversification discount (Rajan et al, 2000). Different components of board structure suggest that there is significant influence on the long term abnormal buy-and-hold returns. Board size, measured as total board members of each firm has a significant negative influence of 11.6% (10% significant) on the 3 year abnormal buy-and-hold returns. This would suggest that extra board members reduce performance. This may be the results of less efficient monitoring, more difficulty in arranging board meetings or more problems with reaching consensus (Lipton and Lorsch, 1992, Jensen, 1999, Cheng, 2008). Gender diversity, measured as the Blau index (Blau, 1977) has a positive significant influence (10%) on the 1 year abnormal buy-and-hold returns. This is in line with earlier general research (Carter et al., 2003, Erhard et al., 2003 and Lückerath-Rovers, 2012) and may be the result of better monitoring or the sources inserted by each individual board member. This may also be the result of better attendance records from female board members (Adams & Ferreira, 2009). The average age of board members doesn’t seem to have a significant influence on the performance of spin-offs.
26
There is no solid basis to assume that board experience, measured as age, is an import driver of the wealth effects. Spin-offs where a dual CEO is present show significant (10%) higher 3 year buy-and-hold returns compared to spin-offs where such a CEO is absent. This may the result of a need for strong leadership (Finkelstein & D’aveni, 1994). When performing robustness checks all results remain significant or become more significant, except for CEO duality.
Limitations
One important limitation of this research is the number of observations. A total sample of 143 spin-offs and a total of 78 firms with governance data which is accessible through public databases is small. Therefore it’s more difficult to draw conclusions or to find significant results compared to a much larger sample. Another limitation is the fact that there was limited time to perform the analyses, as a result of this no extra governance data could be found or checked in firm proxy statements. The small number of observations is also the result of merging and dropping duplicates or dropping firms with missing information, the initial sample consisted of 817 spin-offs. There might also be a selection bias, spin-offs with no stock price data because they are for example listed on a foreign exchange are excluded. This might drive the buy-and-hold returns upwards. Lastly, there might be some endogeneity issues, good performing firms might choose small diverse boards. In that case there is a reverse causality bias. The last limitation is the limited number of control variables.
Implications and further research
An important implication of this study is part of the bigger corporate governance question about optimal board formation and monitoring. It can be concluded that smaller boards are an important driver of the long term performance of spin-offs. Furthermore, one could argue that gender diversity quota on boards is not only good from a social justice perspective but also from an economic point of view. Further research on this subject could focus more on the background of board members. Is relevant industry experience of board members for example an important driver of the long term performance of focus increasing spin-offs? Another interesting subject may be to look at whether the board members of the subsidiary were also part of the parent
27
company. Another interesting research is perhaps to look at more CEO characteristics besides duality.
8. References
Adams, R. B., Almeida, H., & Ferreira, D. (2005). Powerful CEOs and their impact on corporate performance. Review of financial studies, 18(4), 1403-1432.
Adams, R. B., & Ferreira, D. (2009). Women in the boardroom and their impact on governance and performance. Journal of financial economics, 94(2), 291-309.
Ahn, Seoungpil, and Mark D. Walker. (2007): "Corporate governance and the spinoff decision." Journal of Corporate Finance 13.1: 76-93.
Aron, D. J. (1991). Using the capital market as a monitor: corporate spinoffs in an agency framework. The Rand Journal of Economics, 505-518.
Barber, B., & Lyon, J. (1997). Detecting long-run abnormal stock returns: The empirical power and specification of test statistics. Journal of Financial Economics 43, 341-372.
Baysinger, B., Butler, H.N. (1985). Corporate governance and the board of the directors: Performance effects of changes in board composition.
Journal of Law, Economics and Organization, 1(1), 101–124.
Bhagat, S., & Bolton, B. (2008). Corporate governance and firm performance. Journal of
corporate finance, 14(3), 257-273.
Blau, P. M. (1977). Inequality and heterogeneity. New York: Free Press
Bloomberg, Google to Make Driverless Cars and Alphabet Company in 2016 (2015, December 16) Retrieved March 14, 2016, from
http://www.bloomberg.com/news/articles/2015-12-16/google-said-to-make- driverless-cars-an-alphabet-company-in-2016
Burch, T., & Nanda, V. (2003). Divisional diversity and the conglomerate discount: evidence from spinoffs. Journal of Financial Economics 70, 69-98.
Carter, David A. , Betty J. Simkins, and W. Gary Simpson. (2003) "Corporate governance, board diversity, and firm value." Financial Review 38.1 : 33-53.
Chemmanur,T.J., K Krishnan and DK Nandy (2014), The effects of corporate spin-offs on productivity, Journal of Finance 27, 72-98.
Chemmanur, T. J., & Yan, A. (2004). A theory of corporate spin-offs. Journal of Financial
Economics, 72(2), 259-290.
Cheng, S. (2008). Board size and the variability of corporate performance .Journal of
28
Cusatis, P. J., Miles, J. A., & Woolridge, J. R. (1993). Restructuring through spinoffs: The stock market evidence. Journal of Financial Economics, 33(3), 293-311.
Daley, L., Mehrotra, V., Sivakumar, R., (1997). Corporate focus and value creation: evidence from spinoffs. Journal of Financial Economics 45, 257 – 281.
Dasilas, Apostolos, et al. (2011) Wealth Effects and Operating Performance of Spin-Offs: International Evidence. Working Paper,
Denis, D., D. Denis, and M. Walker (2015), CEO Assessment and the Structure of Newly Formed Boards, Review of Financial Studies.
Desai, H., & Jain, P. C. (1999). Firm performance and focus: long-run stock market performance following spinoffs. Journal of financial economics, 54(1), 75-101. Erhardt, N. L., Werbel, J. D., & Shrader, C. B. (2003). Board of director diversity and firm
financial performance. Corporate governance: An international review, 11(2), 102-111.
Feng, Y., Nandy, D. K., & Tian, Y. S. (2015). Executive compensation and the corporate spin-off decision. Journal of Economics and Business, 77, 94-117.
Finkelstein, S., & D'aveni, R. A. (1994). CEO duality as a double-edged sword: How boards of directors balance entrenchment avoidance and unity of
command. Academy of Management journal, 37(5), 1079-1108.
Gilson, S. C., Healy, P. M., Noe, C. F., & Palepu, K. G. (2001). Analyst specialization and conglomerate stock breakups. Journal of Accounting Research, 39(3), 565-582. Harris, O., & Madura, J. (2010). Cause and effects of poison pill adoptions by spinoff
units. Journal of Economics and Business, 62(4), 307-330.
Jensen, M. C. (1993). The modern industrial revolution, exit, and the failure of internal control systems. The Journal of Finance, 48(3), 831-880.
Lipton, M., & Lorsch, J. W. (1992). A modest proposal for improved corporate governance. The business lawyer, 59-77.
Lückerath-Rovers, M. (2013). Women on boards and firm performance. Journal of
Management & Governance, 17(2), 491-509.
McConnell, J., & Ovtchinnikov, A. (2004). Predictability of long-term spinoff returns. Journal of Investment Management, 2(3), 35-44.
Miles, J.A. and Rosenfeld, J.D. (1983) The effect of voluntary spin-off announcements on hareholder wealth, Journal of Finance, Vol. 38 (No. 5, December 1983), pp. 1597-1606.
Modigliani, F., & Miller, M. H. (1958). The cost of capital, corporation finance and the theory of investment. The American economic review, 48(3), 261-297.
29
Pi, L. & Timme, S. (1993). ‘Corporate control and bank efficiency’, Journal of Banking and
Finance, 17, pp. 515–530.
Pyo, U. (2007). Enhancing managerial incentives and value creation: Evidence from corporate spinoffs. Journal of Economics and Finance, 31(3), 341-358.
Rajan, R.G., Servaes, H. and Zingales, L. (2000) The cost of diversity: The diversification discount and inefficient investment, Journal of Finance, Vol. 55, pp. 35–80. Schipper, K. and Smith, A. (1983) Effects of recontracting on shareholder wealth: the
case of voluntary spin-offs, Journal of Financial Economics, Vol. 12, pp. 437-467.
Shrader, C. B., Blackburn, V. B., & Iles, P. (1997). Women in management and firm financial performance: An exploratory study. Journal of managerial issues, 355-372.
Veld, C. and Veld-Merkoulova, Y.V. (2004) Do spin-offs really create value? The European case, Journal of Banking & Finance, Vol. 28 (No. 5), pp. 1111-1135. Walt, Nicholas, and Coral Ingley. (2003): "Board dynamics and the influence of
professional background, gender and ethnic diversity of directors." Corporate
Governance: An International Review 11.3: 218-234.
Wruck, E. G., & Wruck, K. H. (2001). Restructuring top management: Evidence from corporate spinoffs. Journal of Labor Economics, Vol .20 (No. 2)
30
Appendix
Appendix I. Regressions with 1 year abnormal buy-and-hold returns, using robust errors
Appendix II. Regressions with 3 year abnormal buy-and-hold returns, using robust errors
(1) (2) (3) (4) (5) Focus -0.211 (-0.99) (-0.91) -0.186 (-0.84) -0.207 Boardsize -0.117** (-2.03) -0.111* (-1.97) -0.116** (-2.03) Gender diversity 0.912 (0.98) (1.53) 1.286 (1.58) 1.334 Average age 0.0418 (1.31) 0.0354 (1.12) 0.0369 (1.15) CEO Duality 0.247 (0.58) (1.52) 0.606 (1.49) 0.604 Market_to_book ratio 0.0152 (0.26) 0.109 (1.36) 0.104 (1.29) MarketCap -0.219*** (-3.15) -0.349*** (-2.73) -0.354*** (-2.68) Constant 0.912*** (2.75) 3.799*** (3.86) (-0.56) -1.163 (1.16) 3.531 (1.16) 3.630 R2 0.091 0.178 0.177 0.317 0.322 Observations 143 143 78 78 78 R2 0.091 0.178 0.177 0.317 0.322 Adjusted R2 0.007 0.088 0.025 0.165 0.159 (1) (2) (3) (4) (5) Focus -0.0426 (-0.35) -0.0273 (-0.22) (0.84) 0.110 Boardsize -0.0546* (-1.83) -0.0496* (-1.69) -0.0468 (-1.56) Gender diversity 0.940* (1.84) 1.131** (2.31) 1.105** (2.24) Average age 0.0165 (0.82) 0.0187 (1.02) 0.0179 (0.98) CEO Duality -0.00769 (-0.04) (0.57) 0.105 (0.58) 0.106 Market_to_book ratio 0.0417 (0.79) 0.0756 (1.56) 0.0784* (1.67) MarketCap -0.0794* (-1.81) -0.104* (-1.79) -0.101* (-1.77) Constant 0.192** (2.49) 1.173** (2.11) (-0.27) -0.359 (0.48) 0.672 (0.44) 0.620
Industry fixed effects Yes Yes Yes Yes Yes
Observations 143 143 78 78 78
R2 0.079 0.112 0.234 0.283 0.287
