The Influence of Institutional Investors on Acquisition Returns in Western Europe

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The Influence of Institutional Investors on Acquisition Returns

in Western Europe

In this paper, I find that the share of institutional ownership has a positive influence on acquisition returns therefore supporting the theory that institutional investors are taking up the role of corporate monitor to block shareholder-value destroying acquisitions. However, I do not find evidence that the presence of banks and insurance companies has a negative influence on acquisition returns as they could be conflicted in their role of a corporate monitor due to a business relationship with the company. Foundations and funds, which are seen as independent in their role as monitor and therefore expected to have a positive influence, also does not show significant results in their influence on acquisitions returns. The sample consists of 840 acquisitions done between 2000 and 2009 by listed companies domiciled in Western Europe.

Sven Bozuwa

Supervisor: Nanne Brunia

University of Groningen / University of Uppsala

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Table of Content

1. Introduction………...………3

2. Literature………...………...6

2.1 Institutional investors as monitor...7

2.2 The influence of institutional investors...7

2.3 Determinants of acquisition returns...9

3. Data...11

3.1 Data and selection criteria...11

3.2 Control variables...12

3.2 Descriptives...12

4. Methodology...14

4.1 Event study...14

4.2 Parametric and non-parametric...17

4.3 Regression analysis...17

5. Results...18

5.1 Parametric and non-parametric results...18

5.2 Regression results...22

5.3 Robustness tests...24

6. Conclusion...24

References...27

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1. Introduction

Acquisitions have been a widely used managerial tool for corporate growth and CEOs have seen their compensation packages increase significantly following an acquisition. As an illustration for direct M&A compensation packages, in acquisitions done by Healthsouth, Travelers, Exxon and Bankers’ Trust top managers have received between $5 million and $14 million as reported by Grinstein and Hribar (2004). However, when analyzing shareholder returns, results show not everybody has benefitted from these corporate expansions. The acquiring-firm shareholders have lost more than $240 billion in the period 1998 until 2001 (Moeller, Schlingemann and Stulz, 2003). This non-alignment of interests between managers and shareholders has been widely discussed in literature beginning with the article by Berle and Means (1932) where they state that the deviation between the interests of managers and owners is due to information asymmetries and risk preferences.

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are lower compared to smaller shareholders (Grossman and Hart, 1980). The rise of institutional investors since the 1990’s is an interesting phenomenon. Given their significant shareholdings they are well positioned to influence envious managers. Large institutional investors like TIAA-CREF, CalPERS and Hermes are on the forefront of shareholder activism and use proxy voting to pressurize management (Carleton, Nelson and Weisbach 1998; Gillian and Starks, 2007; Becht et al., 2009). However, institutional investors might also have a conflict of interest as a corporate monitor (Duggal and Millar, 1999). This conflict of interest arises if the investor has a business relationship with the firm, which reduces their incentive to vote against management as it could damage the business relationship. Therefore, one can classify institutional investors based on their sensitivity to managerial pressure (Brickley, Lease and Smith, 1988). Pressure-sensitive institutional investors, which also have a business relationship, are therefore likely to be more sensitive to managerial pressure. For example, banks who could also earn significant fees on investment banking services and financing are likely to be more sensitive to management pressure, while insurance companies often have corporate insurance contracts. On the other hand, pressure-resistant institutions such as public pension funds do not have other relationships with the firm besides their equity investment. Pressure resistant institutional investors are therefore better positioned to discipline managers. In this sample, banks and insurance companies have been classified as pressure sensitive while foundations, private equity funds and pension funds (“funds”) have been classified as pressure resistant.

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founder William Hewlett, sued HP for threatening Deutsche Bank with a loss of future business if they did not vote in favor of the proposed acquisition of Compaq. It was claimed just before the final shareholder vote Deutsche Bank’s Asset Management division switched their 17 million votes in favor of HP’s management1. A phone message has been leaked out in the press suggesting management needed to do something “extraordinary” to convince Deutsche Asset Management to vote in favor of the acquisition2. Although the proxies of Deutsche Bank were not voided as demanded by Walter Hewlett, the investment bank was fined by the U.S. Securities and Exchange Commission (“SEC”) for not disclosing their conflict of interest as it made $2 million in investment banking fees. The above example shows the potential conflict sensitivity of investors for managerial pressure. Therefore I want to assess the influence of institutional investors on acquisitions returns based on their sensitivity to this pressure.

This paper will contribute to the literature in several ways. There is no research available that takes into account both types of institutional investors when assessing their impact on acquisition returns. But as shown in the takeover of Compaq by HP some institutional investors are more sensitive to management pressure than others. Secondly, the focus of this paper will be on Western Europe as most research has focused on the U.S. Little is yet known about Europe and results can not be generalized as ownership structures differ significantly (December 2005 listed corporations in the United States had an average of 65.7% of total institutional ownership compared to only 19.5% in Europe3). As shown by Chen at al. (2004), ownership concentration is an important factor in the influence of institutional investors on acquisition returns.

1 http://www.sec.gov/news/press/2003-100.htm 2 http://www.nytimes.com/2002/04/09/technology/09HEWL.html?pagewanted=all 3

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In this research I will use parametric, non-parametric test and an ordinary least squares regression (“OLS”), in which the control variables used are based on literature, to examine the influence of institutional shareholders on acquisition returns. The dataset will contain out of 840 acquisitions which occurred between 2000 and 2009 where the target and acquiror resided in Western Europe. Acquisition returns are measured by the cumulative abnormal returns (“CAR”) of four days around the acquisition announcement.

The results show that there is a positive relationship between the presence of institutional ownership and acquisition returns. However, I do not find evidence that the presence of banks and insurance companies leads to negative acquisition returns. Also, for foundations and funds I do not find significant results even though they are seen as independent monitors and should be better positioned to influence acquisition returns.

2. Literature

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2.1 Institutional Investors as Monitor

Institutional investors are well positioned to monitor corporate management for several reasons. Institutional shareholders have relatively more capital invested in the company than the board of directors and are therefore more concerned with the financial performance (Schleifer and Vishny, 1986). As the returns of monitoring are positively related to the size of the shareholdings, larger investors have traditionally shown more interest in taking up the role of corporate monitor. In addition, institutional shareholders have better resources to analyze the gathered information than smaller investors (Coval and Moskowitz, 2001). Since institutional investors experience considerable share price drops when they sell their shares, they are more focused on long term stock performance instead of quick trading gains (O`Barr and Conley, 1992).

2.2 The Influence of Institutional Investors

Not only are institutional investors better positioned than other investors, they are also better positioned to influence corporate management decisions. Furthermore, large shareholders, like institutional investors, have better access to boardrooms and top managers (Carleton, Nelson and Weisbach, 1998). When looking at their influence on corporate performance there is a positive relationship between firm value and large shareholdings of institutional investors (Gillian and Starks, 2003). Large institutional ownership holdings lead to higher operating cash flow returns (Saunders, 2007). Furthermore, following the purchase of a block of shares by an active investor operating performance increases (Bethel, Liebeskind and Opler, 1998).

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are getting more favorable votes from institutional investors in comparison to individual investors (Gillian and Starks, 2005).

When looking at the relationship between institutional investors and acquisition performance, companies with a higher institutional shareholder base result in higher acquisition returns (Chen et al., 2004). However, block holdings are an important criteria as the influence on management is more powerful with a higher concentration of institutional ownership. In addition, institutional investors decrease their holding in advance of acquisitions that yield lower returns which indicates they have better information (Chen et al., 2004). Based on theory and empirical results I therefore will look at the following hypothesis

Hypothesis 1: The presence of institutional investors has a positive influence on acquisition

returns

There are disparities in the willingness of the different types of institutional investors to discipline management (Brickley, Lease and Smith, 1988), as found in more recent research, an increase in business relationships results in less votes against top management in case of proxy contests (Davis and Kim, 2006). Pressure sensitive institutional investors remain on the side of management to secure prospective business relationships (Black, 1990), while public pension funds, classified as pressure resistant, are able to reduce ex ante bad acquisitions more often than other institutional investors (Qiu, 2008).

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Hypothesis 2: The presence of banks and insurance companies has a negative influence on

acquisition returns

Hypothesis 3: The presence of funds and foundations has a positive influence on

acquisition returns

2.3 Determinants of Acquisition Returns

The following selected literature on acquisitions returns has used control variables as shown in Table 1 in relation to acquisition performance.

Table 1: Variables influencing CAR

Control variables CAR Sign. Author

Focus - *** 3, 5 Focus Dummy, if target is in the same 4-digit SIC industry code + 4, 10 Experience Dummy, if acquirer had any acquisition experience Experience - ** 4, 13 Cross border Dummy, if acquisition is cross border

+ 5 Cash Dummy, payment with at least some cash

Cross border + *** 12 Common Law Dummy, common law in target country - ** 11 Leverage Total liabilities to total assets

Cash + ** 2, 5 MtB Total assets to market capitalization of the bidder Common Law + 7, 11 Relvalue Ratio deal value to market value bidder

Leverage + 6 Size Bidders market capitalization

MtB - 1, 2

- * 6

Relvalue - 1, 2, 8 * 10% significance level

+ 3 ** 5% significance level

Size + 1, 8 *** 1% significance level

1=Duggal and Millar, 1999 8=Asquith et al., 1983 2=Servaes, 1991 9=Draper and Paudya, 2006 3=Eun et al., 1996 10= Eun at al., 2010

4=Eije et al., 2010 11=Moeller and Schligemann, 2005 5=Johnson et al., 2010 12= Chakrabarti et al., 2009 6=Masulis et al., 2007 13= Johnson et al., 1993 7=La Porta et al., 2000

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returns are lower in cross-border acquisitions compared to domestic acquisitions in the period of 1985 to 1995 (Moeller and Schlingemann, 2005). Paying in cash increases bidder returns (Servaes, 1991). Including cash as a payment method for the transaction is a strong and confident signal to shareholders, while paying in shares can indicate that the bidder’s shares are overvalued. However, cash payments to the shareholders of the target are taxed immediately therefore a combination of different modes of payments is used for tax purposes. Investor protection and effective corporate governance enhance shareholder returns which are attributable to efficient allocation of capital across firms, broad financial markets and dispersed ownership of shares (La Porta et al., 2000). Acquisitions returns are therefore, higher in common-law systems in which the law is amended based on new cases compared to civil law where judges are bound by the statures. Bidder’s gains are lower in cases where the target is domiciled in a country with a civil law origin as found by Moeller and Schlingemann (2005). High leverage reduces free cash flow due to the recurring interest payments therefore limiting managers’ ability to use these funds to engage in acquisitions (Masulis et al., 2007). Higher leverage also increases management focus on operating performance as breaching of debt covenants leads to creditor involvement in the company’s operations and a potential loss of their jobs as a consequence. In addition, most bank debt covenants tend to have an excess cash flow sweep covenant which reduces cash on the balance sheet. Concluding, leverage is an important control variable as it is disciplining managers who otherwise would be driven by engaging in acquisitions due to excess free cash flow.

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are therefore better positioned to increase their returns (Asquith et al., 1983). Secondly, the large size of a company could hold smaller competitors back from entering into the bidding process, leading to a smaller premium being paid by the acquirer (Moeller and Schlingemann, 2005).

3. Data and Methodology

3.1 Data Source and Selection Criteria

In this research, I will examine the impact of institutional investors on acquisition performance around the announcement date based on a sample of 840 acquisitions from 2000 to 2009. The dataset is obtained from Zephyr, a product of Bureau van Dijk, which contains company and acquisition data which will be used in this research to find deal characteristics and information on companies involved in M&A transactions which occurred between 2000 and 2009. The search criteria of the sample are presented in Table 2. The acquirer and target are both domiciled in Western Europe, (Austria, Belgium, Switzerland, Cyprus, Germany, Denmark, Spain, Finland, France, United Kingdom, Greece, Ireland, Italy, Luxembourg, Netherlands, Norway, Portugal, and Sweden). Only acquisitions done by listed companies are included so that I can find the cumulative abnormal stock returns for the bidder. Also, the ISIN (International Securities Identifying Number) is used to retrieve daily stock returns so the absence of the number will exclude the transaction from the sample. The most common explanation for the absence of an ISIN number is delisting, although the exclusion could lead to the survivorship bias but Higson and Elliot (1998) find that the exclusion of delisted firms does not statistically change the results. Additionally, multiple acquisitions in a 5 day period are excluded as the announcement effect of these clustered acquisitions can not be separated.

Table 2: Sample Selection Criteria

Selection Criteria Specification Time Period 2000-2009

Domicile Acquirer in Western Europe Listing Acquirer

ISIN Included

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Daily stock returns are retrieved from Bloomberg together with the MSCI market index. The MSCI Europe index, a free float adjusted market capitalization index, s used to find the abnormal returns around the announcement date as it contains all geographical market indices of the targets countries used in this research.

3.2 Control Variables

Based on literature, I will use the following independent variables. Firstly, a dummy variable Focus is created for related acquisitions which will be one if the acquisition is in the same 4 digit Standard Industrial Classification (“SIC”) code. Secondly, a dummy variable Experience is created if the company has done an acquisition 2 years preceding the event date. The dummy variable Cross-border is created to reflect acquisitions done outside the acquirer’s country of domicile. A dummy variable Cash is created to account for that at least some of the payment for the target has been done in cash. The dummy variable Common-law is created to reflect if the target’s country of domicile has a common-Common-law legal structure. Leverage is also added as an independent variable, although leverage is normally calculated as net debt over normalized EBITDA the availability of the this data was limited and so for consistency over the whole dataset I use total liabilities over total assets. Market to Book will be used as a proxy for company performance and is measured by total assets over market capitalization of the bidder. Relative value is added to reflect the value of the acquisition compared to the bidders total asset size. Finally, Size is added to take the bidders total assets value into account. All data used has been obtained from Zephyr.

3.3 Descriptives

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Table 3: Sample distribution by dominance of investor type

% of Ownership N

Banks and Insurance > Foundations and Funds 464 Foundations and Funds > Banks and Insurance 142 No Instutional Investors 234 840

Descriptive statistics in Tables 5, 6 and 7 show the different characteristics of the overall dataset and subsets for banks and insurance controlled companies and by foundations and funds. When looking at the median deal size between the different groups we can see that the median of market capitalization of the bidder is higher when the acquirer is primarily owned by banks and insurance companies. The average performance, measured by the Market to Book ratio, is above 2.0x for companies that are controlled by banks and insurance companies. Overall, the performance seems to be higher than samples used by Cho (1998) and Demsetz and Villalonga (2011) who reported averages of 1.10 and 1.26 versus 1.81 in the sample group used in this research. When looking at ownership we can see that the mean of institutional ownership is lower in the sample in which there are more banks and insurance companies present, banks are the dominant shareholder with 13.96% in this group. While in the group in which foundations and funds are the most dominant shareholder we see that funds hold on average 10.36%

Table 4: Descriptive Statistics – Total Sample

Variables Mean Median St. Dev. N

Focus 0.28 n.a. n.a. 237

Experience 0.44 n.a. n.a. 368

Cross-border 0.51 n.a. n.a. 426

Cash 0.37 n.a. n.a. 310

Common-law 0.29 n.a. n.a. 241

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Table 5: Descriptive Statistics – Banks and Insurance

Focus 0.26 n.a. n.a. 178

Cash 0.32 n.a. n.a. 219

Market to Book ratio 1.52 0.72 3.66 418 Deal value ($m) 71.68 9.30 264.19 464 Relative Value 0.35 0.06 0.48 455 Mkt Cap Bidder ($m) 3,998.93 91.07 14,732.26 464 Leverage ratio 0.52 0.61 0.82 464 Bank Own. (%) 13.96 11.50 12.02 464 Foundation Own. (%) 0.15 0.00 2.61 464 Insurance Own. (%) 6.60 2.26 8.46 464 Funds Own.(%) 7.92 4.95 8.93 464 Institutional Own. (%) 28.64 20.79 23.52 464 Blockholders (> 5%) 7.14 0.00 12.20 464

Table 6: Descriptive Statistics – Foundations and Funds

Focus 0.35 n.a. n.a. 59

Cash 0.57 n.a. n.a. 62

Market to Book ratio 2.09 0.81 8.31 142 Deal value ($m) 127.66 10.03 510.08 142 Relative Value 0.33 0.09 0.47 142 Mkt Cap Bidder ($m) 3,295.38 106.11 11,495.54 142 Leverage ratio 0.49 0.54 0.58 142 Bank Own. (%) 7.78 4.71 8.59 142 Foundation Own. (%) 0.48 0.00 5.33 142 Insurance Own. (%) 2.84 0.11 4.98 142 Funds Own.(%) 10.36 4.26 16.56 142 Institutional Own. (%) 21.47 10.94 23.82 142 Blockholders (> 5%) 7.74 0.00 12.41 142 4. Methodology 4.1 Event Study

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to t=-10 days preceding the acquisition as used by Brown and Warner (1985). The event window will be shorter and will be t=-1 and t=2. However, several event windows will be tested to control for possible information leakage prior to the announcement and post announcement effects. Figure 1 lays out graphically the main window used in this research.

Figure 1: Event Study Time Line

3.4.1 Abnormal Returns

To calculate the abnormal returns I will use the market model based on MacKinley (1997). The market model has an advantage over the constant return model as it takes out the variance of the market return. The reduction of variance will lead to a higher R2, a measure of the variability explained by the model.

Abnormal returns will be calculated in the way following:

In which is the abnormal return, , is the actual return and is expected normal return based on the market model for the company i for the time t.

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In which is the expected normal the return on security i ; = the return on the market index; and

β

are the parameters for the security and = the residual for security i.

The abnormal returns will be accumulated and will lead to my dependent variable in the following way:

Comparing the mean CAR between samples that include institutional shareholdings versus those that do not will test the validity of my first hypothesis. The same approach will be taken for samples with the presence of banks and insurance companies and the presence of foundations and funds in order to reach to conclusions on my second and third hypotheses.

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4.2 Parametric and Non-Parametric Tests

Following the assumption of a normal distribution and uncorrelated residuals, the t-test will be used to verify the null hypothesis that the sample mean is not statistically different from zero. The approach follows Brown and Warner (1985) to determine the significance of the CAR’s, which will be tested on a 10%, 5% and 1% significance level. Then the t-test will be used to verify the hypothesis that the sample means differ significantly from each other.

A non-parametric test will also be applied as a non-normal distribution of the variables could lead to distorted results in the parametric test. Following Kolari and Pynnonen (2010) and MacKinley (1997), I will use the Corrado rank test. The rank test used is based on Corrado (1989) in which the abnormal returns of each company are ranked over the selected period and result in the cumulative sum of the ranks4. The Corrado test has proven to be robust against cross correlation in relation with event day clustering’s (Kolari and Pynnonen, 2010).

4.3 Regression Analysis

The hypotheses will also be tested with the following OLS regression as specified below:

CAR will be the dependent variable and the following control variables are used: industry focus, experience of management, cross-border acquisitions, cash payment, common-law origin, leverage, market to book ratio, size of the bidder compared to acquisition price, logarithm of firm size and finally the share of institutional investors. Also, I will include dummy variables for the presence of institutional investors in general, presence of banks and

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insurance companies as shareholder and presence of foundations and funds as shareholder to test my hypotheses.

Institutional presence will be measured in two ways to capture its influence on acquisition returns. The first method will be the total share of institutional investors which will be found by taking the part of the company that is owned by institutional investors and dividing it by the total number of shares outstanding at the time of the acquisition. This method has been used widely in the literature. The second method will be the percentage of block holders, owning a voting share of 5% or more, is a measure that captures the concentrated equity ownership.

5. Results

Using the Jarque Bera test for normality I do not find any non-normal distributions besides total size of the bidder which will be transformed in its logarithm to control for this effect. Therefore, in this research I will mainly focus on the parametric test to confirm my hypotheses.

5.1 Parametric and Non-Parametric Results

Table 7 contains the results of the t-test on the significant of the Cumulative Average Abnormal Returns (CAAR) in the different event windows.

Table 7: CAAR for different event windows

CAAR t-test % of Positive Rank Test (-1;+1) 13.2% 2.16 ** 56.9% 0.80 ** (-10;-2) (0.3%) 0.16 49.3% 2.19 (-1;+2) 10.2% 3.56 *** 56.1% 3.24 ***

***,**,* for a significance level of 1%,5% and 10%

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From the t-test in the table we can see that the event window of (-1; +2) seems to generate a positive CAR of 0.1% and within the 1% significance threshold. Therefore, based on this result, I will use the event window of t=-1 and t=2 after the event date which indicate that some investors are slow to react and movements in the share price are also seen on the second day after the event. Also, after testing for price run-ups before the acquisition, I do not find that these occur and therefore the chosen window of t=-1 and t=2 is justified.

In Table 8, we can see that the presence of institutional investors results in a higher cumulative abnormal return when compared to when there are no institutional investors present in the ownership structure. Also, I find the same result in the other two samples in which the presence of banks and insurance as for the other sample of foundations and funds presence yields higher returns. However none of these differences are significant.

Table 8: CAAR: Presence of Institutional Investors

CAAR t-test % of Positive Rank Test(1) Insti 12.4% 3.30 *** 57.3% 1.39 None 4.3% 1.41 54.2% Difference 8.1% 1.27 Banks&Insur. 13.0% 3.30 *** 53.9% 1.62 None 4.0% 1.40 51.2% Difference 9.1% 1.47 Found.&Funds 12.5% 3.06 *** 58.9% 0.98 None 5.6% 2.07 ** 55.4% Difference 7.0% 1.14

(1) Z-value at sample with highest mean

***,**,* for a significance level of 1%, 5% and 10%

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corresponds to the findings of Chen et al. (2004) where they also highlight that the concentration of institutional ownership is a critical factor.

Table 9: CAAR: Institutional Investors by % of ownership

Total CAAR t-test

% of Positive Rank Test 0-5% 4.6% 1.83 * 52.9% 1.48 5-15% 10.7% 2.15 ** 56.3% 0.41 15-40% 10.6% 2.10 ** 58.7% 0.49 40%+ 21.1% 1.79 * 59.4% 0.94

Table 10: CAAR: Banks and Insurance companies by % of ownership

Sensitive CAAR t-test

% of Positive Rank Test 0-5% 5.3% 2.20 ** 53.4% 1.19 5-15% 8.4% 0.45 55.2% 0.10 15-40% 13.2% 0.05 61.6% 1.15 40%+ 7.9% 5.19 *** 58.5% 0.53

Table 11: CAAR: Foundations and Funds companies by % of ownership

Resistant CAAR t-test

% of Positive Rank Test 0-5% 9.6% 0.01 55.0% 1.02 5-15% 11.9% 0.04 55.1% 0.39 15-40% 11.4% 0.01 64.0% 1.10 40%+ 10.4% 0.12 52.9% 0.21

When looking at the differences between the various types of owners we see this relationship is less clear with almost no significant returns except for banks and insurance companies in situations in which they own between 0%-5% or more than 40%.

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Table 12: CAAR: Subsamples by control variables

Panel Variable N CAAR t-test

% of Positive Rank Test(1) Diversifying 237 10.4% 3.54 ** 60.8% 1.29 Non-Diversifying 622 10.1% 2.65 *** 54.2% Difference 0.3% 0.54 Experienced in M&A 368 9.9% 3.58 *** 58.7% 0.75 No prior Experience 471 10.4% 2.26 ** 54.0% Difference (0.5%) 0.09 Cross-border 426 13.1% 2.72 *** 56.3% 1.22 Domestic 413 7.1% 2.39 ** 55.8% Difference 6.0% 1.05

Some Cash payment 329 6.8% 1.94 * 55.2%

No payment in Cash 532 12.2% 3.21 *** 56.6% 0.91 Difference (5.4%) 0.90 Common-law 242 6.1% 1.94 60.2% 0.27 Civil-law 599 11.8% 3.11 *** 54.4% Difference (5.7%) 0.90 Leverage: Low 425 9.2% 3.11 *** 55.3% 0.84 Leverage: High 415 6.9% 2.94 *** 60.2% Difference 2.3%

High Market to Book Ratio 298 15.3% 2.33 ** 55.6% 1.28 Low Market to Book Ratio 463 7.8% 2.92 *** 56.1%

Difference 7.6%

Size bidder: Small 282 21.7% 2.93 *** 56.1% 2.48 ** Size bidder: Big 558 7.9% 1.22 55.0%

Difference 13.7% 1.78 *

(1) Z-value at sample with highest mean

H F G A B C D E

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acquisitions are paid by at least some cash generate lower returns, but not significant, of 6.8% compared to 12.2% where there was no cash payment involved. Common-law countries, in this sample only the United Kingdom, have seen historically higher returns as found by Moeller and Schlingemann (2005) which not is confirmed in my research with the negative difference being insignificant. Excess cash flow has been seen as a stimulant for managers to engage in acquisitions and therefore it it’s interesting to note that actually lower leveraged companies seem to generate better returns with 9.2% than higher leveraged companies with 6.9%, although the difference is also insignificant. Market to book ratio has been used to show the company’s performance but literature shows that companies with high ratio’s “Glamour Bidders” have shown negative returns historically. In this paper, I find that companies with a higher market to book ratio deliver better, but with the difference being insignificant, returns for shareholders. Finally the last subsample is the size of the acquiror, in which it shows that smaller companies generate better returns, this goes against the theory of Dugal and Miller (1999) and Laporta at al (2000) that larger bidders have more resources and therefore better able to select profitable targets.

5.2 Regression Results

First of all, I do not find any signs of autocorrelation or multicollinearity in the models as measured by the VIF and Durbin-Watson statistics.

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Table 13: Results of OLS Analysis for the influence of Institutional Investors

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Variable Coef. T-Value Coef. T-Value Coef. T-Value Coef. T-Value Coef. T-Value Coef. T-Value Coef. T-Value Coef. T-Value (Constant) 0.09 1.11 0.12 1.32 0.07 0.88 0.09 1.14 0.09 1.15 0.15 1.31 0.15 1.42 0.10 1.19 Focus 0.06 1.48 0.06 2.43 ** 0.06 2.49 ** 0.06 1.50 0.06 2.49 ** 0.05 1.12 0.05 1.13 0.06 1.50 Experience 0.07 2.80 *** 0.06 1.77 * 0.06 1.80 * 0.07 1.82 * 0.07 1.80 * 0.08 1.76 *** 0.08 2.75 *** 0.07 3.20 *** Cross-border 0.03 0.92 0.04 0.97 0.04 0.99 0.03 0.92 0.03 0.92 0.03 0.68 0.03 0.68 0.04 0.94 Cash -0.03 -0.88 -0.03 -0.88 -0.03 -0.93 -0.03 -0.91 -0.03 -0.91 0.00 -0.02 0.00 -0.02 -0.03 -0.91 Common-law 0.01 0.19 0.00 0.13 0.01 0.17 0.01 0.22 0.01 0.21 0.03 0.55 0.03 0.54 0.01 0.18 Leverage -0.04 -0.42 -0.03 -0.36 -0.03 -0.30 -0.04 -0.41 -0.04 -0.42 0.01 0.11 0.01 0.11 -0.04 -0.43 Market to Book 0.00 0.76 0.00 0.77 0.00 0.79 0.00 0.77 0.00 0.77 0.00 0.44 0.00 0.44 0.00 0.73 Relative Value 0.02 2.14 ** 0.02 2.12 ** 0.02 1.12 0.02 2.15 ** 0.02 1.16 0.03 1.15 ** 0.03 1.15 0.02 1.17 Size Bidder -0.01 -1.73 * -0.01 -1.73 * -0.01 -2.75 *** -0.01 -1.72 * -0.01 -1.70 * -0.02 -1.79 ** -0.02 -1.80 * -0.01 -1.72 * Total Inst. Ownership .050 2.21 **

0-5% Ownership -0.04 -0.81

5-15% Ownership 0.05 1.08

15-40% Ownership -0.02 -0.51

>40% Ownership 0.05 2.56 **

Banks and Insurance Dominance -0.06 -2.28

Foundation and Funds Dominance -0.06 -1.32

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5.3 Robustness Tests

As a robustness test, I also look at the different ownership thresholds of the various types of shareholders makes a difference as shown in Table A.5 and A.6. In Model 9 to 14 there are no significant returns of the split between several ownership shareholding of foundations and funds in general which seems in line with what is found in model 7.

Also, we can not make the assumption that all institutional investors will put the same amount of time in monitoring their assets and therefore I will look at their separate influence. In model 16, we can see that private equity funds are able to make a positive difference on acquisition returns. Private equity firms are known in the market as being pro-active in managing their investments, for example with holding board positions, and are therefore more easily able to influence acquisition returns. Foundation and pension funds however do not show significant positive returns. Regarding banks and insurance companies, I look at their individual influence in Model 23 and 24 in which I do not find any significant results for both. What is more, on the influence of insurance companies separately we can not find any significant results as seen in Model 24.

6. Conclusion

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Furthermore, I do not find evidence that funds and foundations have a significant positive effect on acquisition returns. However, I do find that the presence of private equity funds as a shareholder increases acquisition returns. Private equity funds are pro-actively involved in their investments and tend to have board positions so are therefore better positioned to influence management. When looking at pension funds I do not find any significant results. This outcome is therefore not in line with Qiu (2008) where she finds that public pension funds are better able to reduce bad acquisitions than other institutional investors.

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Appendix A

Table A.1: Sample distribution per country

Country (N) Austria 5 Belgium 19 Switzerland 25 Cyprus 4 Germany 52 Denmark 26 Spain 36 Finland 38 France 61 United Kingdom 242 Greece 38 Hungary 0 Ireland 21 Isreal 0 Italy 51 Luxembourg 2 Malta 0 Netherlands 24 Norway 34 Portugal 9 Sweden 153 840

Table A.2: Sample distribution per sector

Sector (N) Agriculture 89 Mining/Construction 38 Manufacturing sic 2 94 Manufacturing sic 3 121 Transportation 77 Trade 81 Financial 48 Services 213 Health Services 79 840

Table A.3: Sample distribution per year

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Table A.5 Results of OLS Analysis for the influence of Foundation and Fund Ownership

Model 9 Model 10 Model 11 Model 12 Model 13 Model 14 Model 15 Model 16 Model 17 Variable Coef. T-Value Coef. T-Value Coef. T-Value Coef. T-Value Coef. T-Value Coef. T-Value Coef. T-Value Coef. T-Value Coef. T-Value (Constant) 0.10 1.25 0.12 1.40 0.10 1.20 0.09 1.12 -0.02 -0.15 0.10 1.26 0.09 1.12 0.12 1.43 0.10 1.24 Focus 0.06 1.52 0.06 1.47 0.06 1.48 0.06 1.53 0.06 1.57 0.06 1.52 0.06 1.41 0.06 1.54 0.06 1.51 Experience 0.07 2.22 ** 0.07 1.81 * 0.06 2.79 *** 0.07 3.20 *** 0.07 2.84 *** 0.07 1.80 * 0.06 1.79 * 0.06 1.76 * 0.06 1.80 * Cross-border 0.03 0.93 0.03 0.91 0.03 0.93 0.03 0.90 0.03 0.94 0.03 0.94 0.03 0.87 0.04 0.95 0.03 0.93 Cash -0.03 -0.88 -0.03 -0.89 -0.03 -0.89 -0.03 -0.84 -0.03 -0.82 -0.03 -0.88 -0.03 -0.87 -0.03 -0.78 -0.03 -0.89 Common-law 0.01 0.17 0.01 0.17 0.01 0.15 0.01 0.22 0.01 0.16 0.01 0.17 0.01 0.18 0.00 -0.01 0.01 0.17 Leverage -0.04 -0.43 -0.04 -0.42 -0.04 -0.43 -0.04 -0.41 -0.04 -0.43 -0.04 -0.44 -0.04 -0.40 -0.05 -0.54 -0.04 -0.43 Market to Book 0.00 0.75 0.00 0.75 0.00 0.74 0.00 0.78 0.00 0.76 0.00 0.75 0.00 0.76 0.00 0.76 0.00 0.75 Relative Value 0.02 2.14 ** 0.02 2.14 ** 0.02 1.13 0.02 1.16 0.02 1.11 0.02 1.14 0.02 1.16 0.02 2.11 ** 0.02 2.14 ** Size Bidder -0.01 -1.72 * -0.01 -1.75 -0.01 -1.74 * -0.01 -1.74 * -0.01 -1.76 -0.01 -1.72 * -0.01 -1.74 * -0.01 -1.75 * -0.01 -1.72 * Foundation and Fund Ownership 0.00 -0.19

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Table A.6: Results of OLS Analysis for the influence of Banks and Insurance Companies

Model 18 Model 19 Model 20 Model 21 Model 22 Model 23 Model 24

Variable Coef. T-Value Coef. T-Value Coef. T-Value Coef. T-Value Coef. T-Value Coef. T-Value Coef. T-Value

(Constant) 0.09 1.06 0.12 1.43 0.10 1.17 0.10 1.22 0.15 1.55 0.08 1.03 0.10 1.18 Focus 0.06 1.49 0.06 1.45 0.06 1.50 0.06 1.48 0.06 1.54 0.06 1.49 0.06 1.50 Experience 0.07 2.82 *** 0.06 2.77 *** 0.06 3.11 *** 0.06 2.79 *** 0.07 3.20 *** 0.07 1.80 * 0.07 1.81 Cross-border 0.03 0.93 0.04 0.98 0.04 0.95 0.03 0.92 0.03 0.93 0.03 0.91 0.03 0.93 Cash -0.03 -0.86 -0.03 -0.88 -0.03 -0.89 -0.03 -0.88 -0.03 -0.84 -0.03 -0.88 -0.03 -0.86 Common-law 0.01 0.18 0.01 0.14 0.01 0.16 0.01 0.17 0.01 0.20 0.01 0.16 0.01 0.19 Leverage -0.04 -0.44 -0.04 -0.40 -0.04 -0.39 -0.04 -0.45 -0.04 -0.42 -0.04 -0.44 -0.04 -0.43 Market to Book 0.00 0.77 0.00 0.77 0.00 0.76 0.00 0.74 0.00 0.79 0.00 0.78 0.00 0.76 Relative Value 0.02 2.13 ** 0.02 2.12 0.02 2.14 ** 0.02 2.13 ** 0.02 2.15 ** 0.02 1.12 0.02 1.14 Size Bidder -0.01 -1.72 * -0.01 -1.72 * -0.01 -1.73 * -0.01 -1.71 * -0.01 -1.74 * -0.01 -1.73 * -0.01 -2.13 **

Bank and Insurance Ownership 0.00 0.93