A RE STRONG OPERATORS MORE SUCCESSFUL ACQUIRERS ?

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A

RE STRONG OPERATORS MORE SUCCESSFUL ACQUIRERS

?

T

HE RELATION BETWEEN ACQUIRER

’

S OPERATING PERFORMANCE AND ANNOUNCEMENT RETURNS IN

E

UROPEAN TAKEOVERS

Author: P.E. Mebius

University of Groningen Faculty of Economics and Business MSc Business Administration - Finance

Supervisor: Dr. Ing. N. Brunia

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A

RE STRONG OPERATORS MORE SUCCESSFUL ACQUIRERS

?

T

HE RELATION BETWEEN ACQUIRER

’

S OPERATING PERFORMANCE AND ANNOUNCEMENT RETURNS IN

E

UROPEAN TAKEOVERS

ABSTRACT

This paper seeks to prove that a relationship exists between the abnormal announcement returns to acquirers’ shareholders and the acquirers’ operating performance prior to the acquisition announcement. The acquirers’ operating performance is measured in three ways based on work by Morck et al. (1990) and Servaes (1991) as the industry adjusted total stock returns, EBIT growth and M/B ratio prior to the announcement year. The relation between operating performance and abnormal announcement returns is tested for a sample of 766 announced European acquisitions between 2001 and 2006. The results have been adjusted for the influence of the method of payment, the relatedness of the takeover and the legal status of the target using OLS regressions. The results show that all acquirers earn significantly positive abnormal returns around the announcement day and superior performing acquirers earn significantly higher abnormal announcement returns than inferior performing acquirers.

JEL classification: G14; G34

Keywords: Acquisitions; Bidder; Operating performance; Announcement returns; Event study; European Union

P.E. Mebius

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ACKNOWLEDGEMENTS

This thesis is the final work of my MSc Business Administration, specializing in Corporate Financial Management at the University of Groningen. The aim of this thesis is to test the relation between acquirers’ operating performance and abnormal announcement returns in European acquisitions.

During the course of writing my thesis I have unexpectedly lost my grandfather with whom I have discussed the content and progress of my thesis very frequently. I would therefore like to dedicate this work to him. Furthermore, I would like to thank the following people who have supported me throughout writing my thesis. Firstly, I would like to express thanks to my thesis supervisor Dr. Ing. Nanne Brunia, who committed his time to giving me valuable feedback and extensive criticism during the entire process. Secondly, I would like to thank my family, girlfriend and friends who have also supported me throughout the process of writing my thesis.

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1 INTRODUCTION

Mergers and acquisitions (M&As) are one of the most important events in corporate finance. From the vast amount of research on the effects of M&As, we know that the target’s shareholders on average earn positive returns around the announcement of a takeover, whereas the announcement returns to the bidder’s shareholders are less clear (see for example Bruner (2002) or Tuch and O’Sullivan (2007)). Announcement returns to acquirers vary from significantly negative to slightly positive depending on several deal characteristics, leaving the acquirer’s shareholders zero returns on average around the announcement.

Koller et al. (2005) suggest one of the key results from academic research on M&As is that stronger performing acquirers earn higher abnormal returns around the acquisition announcement. Their conclusion is based on research by Morck et al. (1990) and Servaes (1991), which is outdated and may no longer be relevant. Morck et al. (1990) and Servaes (1991) use different measures of performance to test the relation between the acquirer’s operating performance and announcement returns. Both papers interpret the acquirer’s operating performance prior to the announcement of an acquisition as a measure of the quality of management. Morck et al. (1990) use two measures of operating performance to determine the quality of management. They calculate the acquirer’s total stock returns and growth of earnings before interest and taxes (EBIT) during three years prior to the year of the acquisition announcement. Superior acquirers are those with an above industry average stock return or EBIT growth during this period. Servaes (1991) uses Tobin’s Q, which can be approximated by the ratio of the market value to the replacement costs of a firm’s assets, as a measure of operating performance. The acquirer’s Q is calculated for the year prior to the announcement year and superior performing acquirers are those with a Q that is larger than 1 and higher than the acquirer’s industry average Q.

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superior acquisition decisions, on which the market should react positively in the sense of higher abnormal announcement returns to the acquirers’ shareholders.

Koller et al. (2005) suggest that results from M&A research are in line with this expected relation and claim that: “acquirers whose earnings and share price grow at a rate above industry average for three years before the acquisition earn statistically significant returns on announcement” and report similar results when the market-to-book (M/B) ratio is used as a measure of operating performance. Morck et al. (1990) indeed find that inferior acquirers do much worse in making acquisitions than firms with superior management. And furthermore, Servaes (1991) finds that acquirers with superior management earn higher abnormal returns around the announcement than acquirers with inferior managers. These results do suggest that superior industry adjusted operating performance indeed leads to higher abnormal announcement returns, but is this relation indeed as significant as it seems? And are the performance measures that Morck et al. (1990) and Servaes (1991) use valid measurements of the acquirer’s managerial quality? To answer these questions this paper uses their methods to test the hypothesis that superior operating performance leads to higher abnormal announcement returns for a large sample of 766 European acquisition announcements. Thus, the main research question of this thesis is:

“Do European acquirers that outperform their industry prior to the announcement of an acquisition earn higher abnormal announcement returns than acquirers that underperform compared to their industry?”

This paper will test for the existence of this relation for a sample of all announced acquisitions of private and public European targets by publicly quoted non-financial European acquirers between January 2001 and December 2006. After calculating the acquirers’ industry adjusted operating performance based on the measures employed by Morck et al. (1990) and Servaes (1991), the sample of 766 deals will be divided into sub-samples of superior and inferior performing acquirers. To measure the effect of the announcement on the acquirer’s shareholder’s returns, a market and risk adjusted event study as described by MacKinlay (1997) will be used for all sub-samples. Finally, OLS regression analyses will control for the possible impact of deal characteristics on the results.

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market. Most M&A research is focused US or UK based firms. The evolution of the single European market has had a vast impact on M&A activity in this region. Since the burst of the internet bubble in 2000, European firms made a great number and Euro value of acquisitions as illustrated in Figure I, cumulating to a total deal value of over € 450 billion in 2006 compared to around € 150 billion in 2001. Secondly, Koller et al.’s (2005) assertion that academic research found a positive relation between the acquirer’s relative performance and announcement returns is based on research that might not be relevant anymore. The lack of recent research, especially for the European M&A market is the most important consideration for the topic of this paper and filling this gap is its contribution.

Figure I

Announced deals and deal values in the European Union

Figure I shows the number and cumulative value (in billion €) of announced M&As per year for the period from 2001 to 2006 by quoted European companies.

0 200 400 600 800 1000 1200 2001 2002 2003 2004 2005 2006 Year Nu m be r o f d ea ls 0 50 100 150 200 250 300 350 400 450 500 De al v al ue

Number of deals Deal value (billion €)

Source: Zephyr database of Bureau van Dijk

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presents the results of the data analysis and investigates the relation between the acquirer’s performance and announcement returns, in the light of the results. In the final section, the conclusions drawn from these results are summarized and discussed and recommendations for further research are made. All evidence from the data analyses, such as figures and tables, which have not been discussed in the main text, can be found in the appendices.

2 THEORETICAL

BACKGROUND

In this section the theoretical rationale behind the relation between the acquirer’s industry adjusted operating performance prior to the acquisition announcement and its shareholders’ abnormal announcement returns is presented. First, the reason why there should be a relation between performance and announcement returns based on new considerations and existing theories is discussed. Subsequently, the theoretical influence of some important deal characteristics on the acquirers’ announcement returns is discussed.

2.1 The relation between operating performance and abnormal announcement returns

Why would there be a relation between the acquirer’s abnormal announcement returns and its industry adjusted operating performance prior to the announcement? To answer this question it should first be clear how operating performance is interpreted. The industry adjusted operating performance of a company, as measured by the industry adjusted total stock returns, EBIT growth (Morck et al. (1990)) or M/B ratio (Lang et al. (1989), Servaes (1991)), can be interpreted as a measurement of the quality of the acquirer’s management. Doing so, it is important to consider operating performance relative to the acquirer’s industry. This controls for factors that can not be controlled by management such as the overall market conditions, which weakens the relation between the acquirer’s operating performance and the quality of its management.

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the result of superior management. Industry adjusted stock returns are representing the underlying business risk of a company and higher returns should therefore indicate higher underlying risk and not higher quality of management. Furthermore, stock prices might be kept artificially high by the management, due to management reward systems that increase when stock prices are higher. Managers can artificially manipulate stock prices by actions that change the actual or perceived value of a company’s assets or by releasing false information or spreading false rumors (Allen and Gale (1992)). In addition, high EBIT growth might be the result of a combination of good luck in high-risk investment decisions and bad management. EBIT growth might come from negative net present value investments, which is definitely not a correct indicator of superior management. This form of EBIT growth might be the result of managerial hubris or CEO empire building as discussed in Roll (1986).

A more pressing relation between performance and management quality is presented by Lang et al. (1989) and Servaes (1991). They use an industry adjusted Tobin’s Q, approximated by the ratio of firm market value to book value (the M/B ratio), as a measure of the quality of management. Lang et al. (1989) define Tobin’s Q as an increasing function of the quality of a firm's current and anticipated projects under existing management, which makes Tobin’s Q a suitable measure of management quality. Similarly, Servaes (1991) also interprets Tobin’s Q as a measure of managerial performance. Dong et al. (2006) also suggest that the acquirer’s Q is an appropriate measure of the quality of corporate growth opportunities and the degree of managerial discipline. Tobin’s Q or the M/B ratio do not require risk adjustment or normalization, in contrast with comparisons of stock returns and accounting measures between companies (Lang and Stulz (1994)). And because Tobin’s Q is the present value of all future cash flows divided by the replacement cost of capital, the M/B ratio is expected to be the best measure of the quality of management. High positive future cash flows are often the result of positive NPV investments and high growth, which is dependent on the decisions made by a company’s managers.

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superior acquisition decisions. The Q hypothesis of takeovers, as discussed by Lang et al. (1989) and Servaes (1991), suggests that takeovers reallocate the target’s assets to different uses. These different uses generate higher or lower payoffs depending on the quality of the acquirer’s management (Dong et al. (2006)).

Additionally, the results of Lang et al. (1989), Morck et al (1990) and Servaes (1991) show that the acquirer’s stock returns around the acquisition announcement are highly dependent on its operating performance. Their findings imply that better performing acquirers, which might be interpreted as acquirers with superior management, make better acquisitions leading to higher announcement returns. Acquirers that perform above the industry average prior to the announcement are expected to be able to realize higher abnormal announcement returns than inferior performing acquirers because they are able to make better acquisition decisions. Superior operating performance (due to high quality of management) leads to better investment decisions, which the market reacts positively to, which in turn translates into higher abnormal returns around the announcement. Thus, the hypothesis tested in this paper will be:

“Acquirers that outperform their industry prior to the announcement of an acquisition earn higher abnormal returns around the announcement than acquirers that underperform compared to their industry”

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not introduce new theories explaining the relation between operating performance and abnormal announcement returns.

Whereas the proposed positive theoretical relation between the acquirers’ industry adjusted operating performance and abnormal announcement returns is in line with the previous theories, another much cited theory has been introduced by Roll (1986). Roll’s hubris hypothesis (1986) suggests that superior performance of acquirers leads to overconfidence of its managers who, infected by hubris, make bad acquisitions. This would mean that superior performance of an acquirer does not lead to better decisions, because managers might be blinded by their own successes in the past when making decisions for the future. The hubris hypothesis also suggests that overconfident managers overpay for their targets because they overestimate their own ability to run the targets in the future. In light of this, Roll (1986) expects superior performing acquirers to realize lower announcement returns. An important assumption of Roll (1986) is that financial markets are rational and management is not. In line with Lang et al. (1989), Morck et al. (1990) and Servaes (1991) this paper expect markets to be inefficient. If that prediction is correct, superior industry adjusted operating performance is indeed a good indicator of the quality of management. Superior performing acquirers should then be able to realize higher abnormal announcement returns compared to inferior performing acquirers because they are able to make better acquisitions.

2.2 The relation between deal characteristic and abnormal announcement returns

Most research on shareholders’ returns from acquisitions considers the impact of deal characteristics on the announcement returns. To determine the significance of the relation between performance and announcement returns it is essential to control for the impact of these deal characteristics on the announcement returns. Therefore, this section focuses on the impact of the method of payment, the relatedness of the acquisition and the legal status of the target on the acquirer’s abnormal announcement returns.

Method of payment

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shares as the method of payment is an indication of the acquirer’s intrinsic value relative to its market value. For instance, the acquirer’s managers will want to use shares as the method of payment if the acquirer’s market value is higher than its intrinsic value. Since the acquirer’s managers have superior information compared to its shareholders, the market is expected to react negatively on an announcement of an acquisition when the method of payment is in shares. Many papers on the announcement returns to acquirers’ shareholders, such as Myers and Majluf (1984), Travlos (1987), Morck et al. (1990), Yook (2003) or Martynova and Renneboog (2006) find significant proof for this hypothesis.

Industry related acquisitions

Another well documented deal characteristic is the relatedness of the acquirer’s and target’s industry. Tuch and O’Sullivan (2007) report that results from M&A research on average find higher announcement returns to the acquirers’ shareholders if the industries of the acquirer and target are related. Tuch and O’Sullivan (2007) state that one of the most important motives for acquisitions is synergy. Furthermore, Singh and Montgomery (1987) expect that related companies should be able to realize larger economies of scope and thus benefit more from those synergies. Results from research are consistent with this proposed relation between relatedness and the ability to create value. On average the results show higher announcement returns to the acquirers’ shareholders for related acquisitions compared to unrelated acquisitions. For more details see for example: Singh and Montgomery (1987), Morck et al. (1990), Hubbard and Palia (1999), Walker (2000) and Tuch and O’Sullivan (2007).

Legal status of the target

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auctions, whereas private targets have less bargaining power and according to Gräbner and Eisenhardt (2004) often opt for a preferred bidder above a higher price. A reason for this lower competition is the quality and quantity of information about the target company that is available to bidding companies, making it harder to correctly valuate the target. Results of the impact of the target’s legal status on the acquirer’s announcement returns are in line with these theories and on average show higher returns to the acquirer’s shareholders if the target is a private firm.

Other deal characteristics

Other deal characteristics that have been frequently included in M&A literature are the geographic location of the target, the relative size of the target and the mood of the acquisition. The results of M&A research provide little consensus about the expected impact of these deal characteristics on the acquirer’s announcement returns. Additionally, a significant relation between these deal characteristics and the acquirers’ operating performance prior to the acquisition announcement is not expected. These deal characteristics are excluded from the research because of the limited relevance for this topic. For a discussion on the impact of the target’s geographic location, relative size and mood of the acquisition on the acquirer’s announcement returns, refer to Walker (2000), Cosh and Guest (2001), Moeller et al. (2004), Conn et al. (2005), Sudarsanam and Mahate (2006) or Tuch and O’Sullivan (2007).

3 DATA AND METHODOLOGY

In this section the data and methodology are presented. First the data is described, dealing with the sample selection, summarizing the sample characteristics and describing the data construction. Subsequently, the methods used to analyze the data are discussed.

3.1 Data

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3.1.1 Sample selection

A sample of announced acquisitions between January 2001 and December 2006 was constructed using the Zephyr M&A database of Bureau van Dijk. Only announced domestic and cross-border acquisitions with both a European Union based acquirer and target were used. Since market capitalizations and return data are not readily available for private firms, only publicly quoted acquirers were included, but both private and public targets. In line with Campa and Hernardo (2004) and Martynova and Renneboog (2006), acquirers from the financial sector were excluded, because of special regulations that apply for these companies that will bias the results of the research. Finally, only acquisition announcements with a minimal deal value of € 10 Million and a minimal acquired stake of 50% of the target’s equity were included. The resulting initial sample consisted of 1503 acquisition announcements.

A closer examination of the acquirers’ sample revealed that for a number of deals important information about deal characteristics was missing, or not in line with the criteria set to construct the sample. Deals with non-listed or financial sector acquirers, a non-European target or acquirer, a deal value below € 10 Million, an acquired stake below 50% or missing daily stock return data (Source: Thompson Financials Datastream) were excluded from the sample. Excluding these deals reduced the sample to 908 deals. Based on Martynova and Renneboog’s (2006) research, deals that were announced within 200 trading days after a previous acquisition announcement by the same acquirer were excluded from the sample. This is done to prevent biases when estimating the model’s parameters. The final sample consists of 766 acquisition announcements. Table I provides an overview of the criteria used to construct the acquirers sample.

Table I

Selection criteria and sample size

Table I presents an overview of the number of deals based on the selection criteria to construct the final sample of acquisition announcements and the corresponding numbers of deals after each criterion.

Criterion Number of deals left

Announcement between January 2001 and December 2006 335,928

European Union based target and acquirer 97,912

Publicly quoted acquirers only 41,941

Acquisition of at least 50% of the targets equity 11,553

Non-financial acquirer based on US SIC classification 6,844

Minimal deal value of € 10 Million 1,503

Exclude deals with missing or incorrect information 908

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3.1.2 Sample characteristics

Table II shows the distribution of the sample based on several deal characteristics. The distribution of deals is comparable with Martynova and Renneboog’s (2006) 1990s intra-European M&A sample for the percentage of domestic deals and successful deals. Furthermore, as in Martynova and Renneboog’s (2006) sample, the percentage of payments in shares is declining between 2001 and 2006. Remarkable is the high percentage of private targets and industry related acquisitions of respectively 87% and 73%. These percentages are substantially higher than in Martynova and Renneboog’s (2006) sample that only uses 2-digit US SIC codes to classify a deal as related, whereas this paper uses 4-digit US SIC codes. Appendix A graphically presents the sample distributions based on the deal characteristics.

Table II Sample characteristics

Table II shows the distribution of all takeover announcements per year and is based on the following characteristics: (i) the geographic location of the target and acquirer, (ii) the acquired stake, (iii) the final status of the bid, (iv) the legal status of the target and acquirer, (v) the relatedness of the acquisition and (vi) the method of payment.

2001 2002 2003 2004 2005 2006 2001-2006 Percentage

Total sample 140 127 138 128 117 116 766 100.0%

Domestic deal 93 86 91 80 74 72 496 64.8%

Cross-border deal 47 41 47 48 43 44 270 35.2%

Acquisition of 100% 115 104 113 108 95 99 634 82.8%

Acquisition of voting majority 25 23 25 20 22 17 132 17.2%

Completed bid 121 114 117 114 105 96 667 87.1% Withdrawn bid 19 13 21 14 12 20 99 12.9% Private target 121 109 124 108 109 100 671 87.6% Public target 19 18 14 20 8 16 95 12.4% Focused deal 101 94 101 97 85 84 562 73.4% Diversifying deal 39 33 37 31 32 32 204 26.6% Cash payment 47 47 49 52 46 42 283 36.9% Shares payment 15 10 7 14 7 9 62 8.1% Mixed payment 78 70 82 62 64 65 421 55.0%

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Table III shows that the UK accounts for the major part of all acquisition announcements in the European Union with around 50% of all acquirers and targets being UK based firms. This is in line with other research on European M&As. The second and third largest markets for corporate control are Italy and France followed by Sweden, Spain and the Netherlands.

Table III

Geographic distribution of deals

Table III is an overview of the geographic distribution of the deals. The number of deals as well as the percentage of the total sample is given for the targets and the acquirers per country.

Acquirers Targets

Country Number of deals Percentage Number of deals Percentage

Austria 6 0.8% 5 0.7% Belgium 7 0.9% 14 1.8% Denmark 11 1.4% 14 1.8% Finland 28 3.7% 18 2.3% France 69 9.0% 69 9.0% Germany 22 2.9% 62 8.1% Greece 6 0.8% 3 0.4% Ireland 23 3.0% 18 2.3% Italy 71 9.3% 66 8.6% Luxemburg 1 0.1% 2 0.3% Netherlands 35 4.6% 40 5.2% Portugal 6 0.8% 12 1.6% Spain 37 4.8% 43 5.6% Sweden 49 6.4% 37 4.8% UK 395 51.6% 363 47.4% Total 766 100.0% 766 100.0%

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Table IV Annual deal values

Table IV shows the deal values per year, as well as the deal values of the total sample (in millions €). Subsequently the mean, median, minimum value, maximum value and total deal values per year are presented. 2001 2002 2003 2004 2005 2006 2001-2006 Mean 282.2 456.1 388.1 328.5 197.2 319.5 330.5 Median 30.2 42.7 37.0 46.0 50.4 37.2 40.3 Minimum 10.0 10.0 10.0 10.1 10.5 10.0 10.0 Maximum 11,114.1 25,793.5 15,384.0 9,313.4 5,338.1 6,400.0 25,793.5 Total 39,511.3 57,926.2 53,562.5 42,049.2 23,074.9 37,065.3 253,189.4

3.1.3 Data construction

The required data to test the relation between operating performance and announcement returns were extracted from several databases. All information on deal characteristics such as the method of payment, acquirer’s and target’s industry, legal status of the target and country information was extracted from Bureau van Dijk’s Zephyr database. Daily total (including dividends) stock return index (RI) data calculated in Euros are extracted from Thompson Financial Datastream for the sample of 766 acquirers. This data was also extracted for a sample of all non-financial European quoted firms to calculate industry returns as well as for the broad based S&P Europe Index. Furthermore, annual EBIT data, market capitalizations and shareholder’s funds are extracted from the Amadeus database by Bureau van Dijk for the sample of all acquirers as well as the sample of all European quoted firms. The data of all quoted European firms is used to calculate industry EBIT growth and M/B ratios.

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industry when firms have multiple US SIC codes, the primary US SIC code as provided by Bureau van Dijk’s Amadeus database is used. This was done for the acquirers’ sample, as well as for the sample of all quoted European firms. After excluding all European financial services firms, eight industries were constructed.

The first performance measure used is the acquirers’ industry adjusted stock return. This is the difference between the acquirers’ daily total stock returns and that of its industry over a three-year period ending in the three-year prior to the three-year of the acquisition announcement. The industry’s total stock return was calculated as the simple arithmetical average total stock return over the three years prior to the announcement year of all European quoted companies in the specific industry.

The second performance measure used is the acquirer’s industry adjusted EBIT growth over two years prior to the announcement year. Morck et al. (1990) calculate this measure for three years prior to the announcement year, but due to limited earnings data availability for 1998 (for calculating the EBIT growth of acquisitions that were announced in 2001) the EBIT growth over two years prior to the announcement year was calculated. Using three years as Morck et al. do would decrease the sample of acquisition announcements by 126 deals. Based on Morck et al. (1990) the EBIT growth was calculated as: log (EBITt-1) – log (EBITt-3), where t is the year of the acquisition. The industry’s EBIT growth was calculated using a similar procedure as employed to calculate the industry’s total stock returns.

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industry as the acquirer. The M/B ratios were calculated in the year prior to the announcement year.

The sample of acquirers is subsequently divided in sub-samples of superior and inferior performing acquirers for each performance measure, constructing three sub-samples of inferior performing acquirers and three sub-samples of superior performing acquirers. Whether or not the acquirer’s abnormal announcement returns were significantly different from zero for all sub-samples was tested. Finally, the hypothesis that superior performing acquirers earn significantly higher announcement returns compared to inferior performing acquirers for each performance measure was tested. The methodology used to do so will be discussed in the following sub-section.

Table V presents the percentages of superior acquirers based on one performance measure that also belongs to the sub-samples of superior acquirers based on the other performance measures. The percentage of acquirers that perform in a superior fashion compared to their industry based on any combination of two performance measures is just over 50% at the highest.

Table V

Correlation between sub-samples of superior acquirers

Table V represents the correlation between the different sub-samples of superior performing acquirers based on the three performance measures. The numbers in this table are the percentages of superior acquirers based on the performance measure in the first column, that are also superior acquirers based on the other performance measures. The numbers between brackets are the percentages of superior acquirers based on the performance measure in the first column that do not have a calculated value based on the other performance measures because of missing data.

Stock returns EBIT growth M/B ratio

Stock returns - 39.1% (27.2% N/A) 37.6% (7.5% N/A)

EBIT growth 52.8% (12.1% N/A) - 45.2% (3.9% N/A) M/B ratio 51.8% (15.1% N/A) 46.2% (15.1% N/A) -

Source: own calculations

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Table VI

Descriptive statistics of the performance measures

Table VI elucidates the descriptive statistics of the distribution of the performance measures. For each performance measure, i.e. the average stock returns including dividends during the three years prior to the year of the acquisition announcement, the EBIT growth in the two years before the year of the acquisition announcement and the M/B ratio in the year prior to the year of the acquisition announcement, the statistics are presented for (i) the industry, (ii) all acquirers, (iii) the superior acquirers and (iv) the inferior acquirers. Subsequently, the mean, median, minimal value, maximum value, variance, Skewness and the Kurtosis are presented and are denominated in fractions. The sample size n varies because some deals where excluded from the sample based on missing data to calculate the performance measure. The last column presents the difference in the mean value per performance measure between the sub-sample of superior acquirers and that of inferior acquirers. The values are expressed in fractions.

Panel A: Stock returns

Industry All acquirers Superior acquirers Inferior acquirers Difference

Mean 0.0005 0.0006 0.0011 0.000 0.0011 Median 0.0006 0.0006 0.0010 0.000 Minimum -0.0007 -0.0037 -0.0005 -0.004 Maximum 0.0015 0.0043 0.0043 0.001 Variance 0.0000 0.0000 0.0000 0.000 Skewness -0.3577 -0.0419 0.8620 -1.197 Kurtosis -0.3749 1.9229 1.5729 2.787 n 639 639 345 294

Panel B: EBIT growth

Mean 0.0722 0.1111 0.2423 -0.0667 0.3090 Median 0.0839 0.1051 0.2065 -0.0212 Minimum -0.1186 -0.9173 -0.1055 -0.9173 Maximum 0.3282 1.0761 1.0761 0.2616 Variance 0.0098 0.0604 0.0372 0.0369 Skewness 0.2714 0.0689 1.5582 -1.4650 Kurtosis 0.1522 2.7192 3.4352 3.0388 n 530 530 305 225 Panel C: M/B ratio

Mean 2.3083 2.4347 3.7641 1.4484 2.3157 Median 2.2260 1.9538 3.2446 1.3573 Minimum 1.3694 0.0086 1.5332 0.0086 Maximum 4.7486 12.4071 12.4071 4.2123 Variance 0.5396 3.1202 3.6895 0.4162 Skewness 1.7809 2.0730 1.7655 0.7199 Kurtosis 3.2161 5.7648 3.4487 1.2847 n 702 702 299 403

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3.2 Methodology

In this part the methodologies used to analyze the data are discussed. First, the market and risk adjusted event study methodology used to measure the acquirer’s abnormal announcement returns are described. Following which, the methods to test the results from event study are presented. A test is used to determine the normality of the acquirer’s returns around the announcement and subsequently, the parametric and non-parametric tests, to determine the significance of the results. Finally, the regression analyses used to test for the impact of deal characteristics on the results is described.

3.2.1 Event study methodology

An event study is used to measure the effect of the acquisition announcement on the acquirer’s returns. The event study is used to measure the difference between the acquirer’s normal returns and abnormal returns caused by the event. The event is the acquisition announcement, which is defined by Bureau van Dijk as “the date that a formal bid is made public by any source”. Assuming market rationality, share prices should immediately reflect all available information in the market and the acquirers’ share prices are thus affected by the announcement of an acquisition. The event study methodology was extensively discussed by Brown and Warner (1980; 1985) and MacKinlay (1997) presents a thorough review of the literature on event studies. It has proven to be a suitable methodology to measure the short-term impact of an unexpected event on a firm’s returns.

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Figure II

Event study time windows

Figure II graphically depicts the estimation window and the event window used to calculate the effect of the acquisition announcement on the acquirers’ returns.

Estimation window Event window

t = -200 t = -20 t = 0 t = +20 To measure the impact of the announcement on the acquirers’ returns around the announcement, the expected normal returns should first be calculated during the estimation and event windows. The acquirer’s abnormal returns (ARit) are the ex-post realized returns (Rit) over the event window, subtracted by the acquirer’s expected normal returns (E(Rit)) in case there would not have been an announcement:

(1)

AR

_it

=

R

_it

−

E

(

R

_it

)

.

The total returns of firm i at date t were calculated using the following formula (Martynova and Renneboog (2006)). RIit is the firm’s total return index as extracted from Thompson Financial Datastream: (2) 1 1 − −

−

=

it it it it

RI

R

.

In order to calculate the expected normal returns (E(Rit)) a market and risk adjusted model was used. MacKinlay (1997) finds that using the market and risk adjusted model improves the results, compared to using the mean adjusted model. The market and risk adjusted model controls for the influence of systematic risk on the acquirer’s return and is therefore preferred for detecting the effect of the announcement on the acquirer’s returns. MacKinlay (1997) specifies the market and risk adjusted model as follows:

(3)

R

_it

=

_α

_i

+

_β

_i

(

R

_mt

)

+

_ε

_it.

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date t (with E(εit) = 0) are estimated using an OLS regression analysis of Rit on Rmt over the estimation period (MacKinlay (1997)). The last step to before calculating the ARit as described in formula (1) is to estimate E(Rit) as follows:

(4) E R_it i _iR_mt ^ ^

)

( =

_α

+

_β

.

From the ARit for each acquirer, the average abnormal return for period t (AARt) for the sample of N acquirers is calculated as follows:

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∑

=

N i it t

AR

N

AAR

1

and the variance is:

(6)

∑

=

N i t

_N

i

AAR

1 2 2

1 )

var(

_σ

_ε .

The AARt shows the average abnormal return for the sample N on a specific day in the event window. When the AARs around or on the announcement date significantly differ from zero, one might conclude that the acquirers’ stock price reacts to the acquisition announcement. MacKinlay (1997) calculates the cumulative average abnormal returns (CAAR) over any event window from t1 to t2 using the following formula:

(7)

∑

= = 2 1 ) , (₁ ₂ t t t t AAR t t CAAR

which has a variance of:

(8)

∑

= = 2 1 ) var( )) , ( var( ₁ ₂ t t t t AAR t t CAAR .

(24)

the event windows used by Morck et al. (1990) and Servaes (1991). Using different event windows also provides information about possible price run-ups prior to the announcement and past announcement drifts. The tests to measure the significance of this impact are described in the next sub-section.

3.2.2 Tests

To test whether the AARs and CAARs are statistically different from zero a two-sided t-test based on Brown and Warner (1980; 1985) and MacKinlay (1997) was used in this study. A two-sided t-test is used because research on M&As have shown results varying from significantly positive to significantly negative returns around the announcement, providing little consensus about the sign of this study’s results. Whether the AARs in the estimation window are normally distributed was also accounted for. Without assuming normality, results for sample would be asymptotic. In general, this is not problematic for event studies, since for the test statistic, convergence to the asymptotic distributions is rather quick (Brown and Warner (1980; 1985), MacKinlay (1997)). The Jarque-Bera test was used to determine the normality of the AARs in the estimation window.

Test for normality

Based on Thadewald and Büning (2007), the Jarque-Bera test was used to determine whether the AARs in the estimation window are normally distributed. The normality of the estimation window’s AAR is important, because if these returns are not normally distributed, the parametric test’s validity decreases. The Jarque-Bera test is based on the Skewness and Kurtosis of a sample and tests the null hypothesis of a normally distributed sample. Thadewald and Büning (2007) define the Jarque-Bera test as follows:

(9)

_











₋

+

=

4 )

3 (

2 2

K

S

n

JB

(25)

Table VII Tests of normality

Panel A divides the sample of acquirers in sub-samples of superior and inferior performing acquirers based on the three performance measures. Furthermore, Panel B divides the sample based on the following deal characteristics: the method of payment, industry relatedness and the target’s legal status. To determine the normality of the AARs in the estimation window the Skewness, Kurtosis and the Jarque-Bera value with the corresponding probability are presented. The Skewness, Kurtosis and Jarque-Bera determine whether the null hypothesis of normally distributed AARt in the estimation window has to be rejected. Subsequently, the mean, median, minimum, maximum and variance of the AARs are presented. The sample size n varies per sub-sample due to missing data needed to calculate the performance measures for several acquirers or based on the deal characteristics. The values are expressed in fractions.

Panel A: Performance measures

All acquirers Superior Inferior Superior Inferior Superior Inferior

Mean 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Median 0.0000 0.0000 0.0001 0.0001 0.0000 0.0000 0.0001 Minimum -0.0022 -0.0040 -0.0039 -0.0037 -0.0040 -0.0034 -0.0039 Maximum 0.0024 0.0053 0.0039 0.0029 0.0037 0.0045 0.0037 Variance 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Skewness 0.0640 0.1459 -0.0363 -0.0604 -0.2218 0.1055 -0.0654 Kurtosis 2.6668 3.8784 2.8693 3.0084 3.0746 3.6031 4.0060 Jarque-Bera 0.9553 6.4250 0.1677 0.1100 1.5182 3.0619 7.7187 Probability 0.6202 0.0403 0.9196 0.9465 0.4681 0.2163 0.0211 n 180 180 180 180 180 180 180

Panel B: Deal characteristics

Method of payment Industry relatedness Target's legal status All acquirers Cash Shares Focussed Diversifying Private Public

Mean 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Median 0.0000 -0.0002 0.0004 0.0000 -0.0002 0.0000 -0.0002 Minimum -0.0022 -0.0035 -0.0067 -0.0026 -0.0047 -0.0026 -0.0061 Maximum 0.0024 0.0061 0.0068 0.0027 0.0083 0.0026 0.0120 Variance 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Skewness 0.0640 0.7092 -0.1063 0.0676 0.6164 0.0796 0.6646 Kurtosis 2.6668 4.2492 2.8921 2.8802 4.4387 2.8355 5.0238 Jarque-Bera 0.9553 2.6793 0.4265 0.2448 2.6923 0.3932 4.3966 Probability 0.6202 0.0000 0.8080 0.8848 0.0000 0.8215 0.0000 n 180 180 180 180 180 180 180

Source: own calculations

Parametric tests

(26)

(10)

(

)

~

(

0 ,

1 )

var

AAR

N

AAR

t t

=

θ

and the test statistic for the CAAR(t1, t2) over any event window ranging from t1 to t2 as:

(11)

~

(

0 ,

1 )

)

,

(

var(

)

,

(

2 1 2 1

_N

t

CAAR

t

CAAR

=

θ

.

The test statistics are tested for N-1 degrees of freedom, depending on the sample tested at levels of 1%, 5% and 10% significance. If the null hypothesis of zero AAR and CAAR is rejected, it can be concluded that the acquisition announcement has a significant impact on the acquirer’s equity value. A significantly positive CAAR indicates that the equity market expects the acquisition to create value, whereas a significantly negative CAAR implies that the acquisition is expected to destroy value (Markides and Ittner (1994)).

To test whether the CAARs are significantly different for the sub-samples of superior performing acquirers and inferior performing acquirers, this study uses the z-test as described by Dodd and Warner (1983). The test statistic is defined as follows:

(12) 2 2 1 1 2 1

n

L

n

L

CAAR

z

+

−

=

where n1 and n2 are respectively the number of observations in the two sub-samples and L1 and L2 are the CAARs’ variations of the two sub-samples.

Non-parametric test

(27)

(13)

∑

=

+

−

=

n t it

K

s

L

K

n

t

1

(

)

2

1 (

1

where s(K) is specified as:

(14) 2 1

))

2

1 (

1 )

(

2 1

+

−

=

∑ ∑

= =

L

K

n

L

K

s

t t t n i it .

L is the number of days in the event window, n is the number of events, Kit is the rank of the ARit of acquirer i for the event window ranging from t1 to t2. The procedure of the Corrado rank test converts the distribution of abnormal returns in a uniform distribution of rank value. This distribution is independent of the non-normality of the original distribution. It can therefore be assumed that the distribution is normal and the null hypothesis of zero abnormal returns can be tested.

3.2.3 Regression analyses

To identify what explains the variation in the abnormal returns this study used OLS regression analyses. Using OLS regressions controls for the impact of deal characteristics on the results. To test the impact of the acquirers’ industry adjusted operating performance on the results a regression analysis for each performance measure was conducted. Based on Servaes (1991) it was estimated that two OLS regression models per performance measure were needed. Both regressions measure the relation between the acquirers’ past operating performance and the CARi(t1, t2). The second regression includes dummy variables for the several deal characteristics, the first regression only includes the acquirer’s past performance variable. This is done to test the impact of the deal characteristics on the relation between the acquirers’ past performance and the announcement returns. Based on the results of the AARs as presented in the next section, the OLS regression analyses for the event window from t1 = 0 to t2 = +1 were performed. The OLS regressions are calculated for each performance measure as follows:

(15)

CAR

_i

(

t

₁

,

t

₂

)

=

_β

₀

+

_β

₁

SUP

(28)

The variables are defined as:

CARi(t1, t2) = the CAR of acquirer i between t1 = 0 and t2 = +1,

SUP = 1 if the acquirer belongs to the sub-sample of superior acquirers based on the performance measure, 0 if the acquirer belongs to the sub-sample of inferior acquirers,

CSH = 1 of the method of payment is cash, 0 if the method of payment is shares or other,

SHR = 1 of the method of payment is shares, 0 if the method of payment is cash or other,

REL = 1 if the target and the acquirer have the same 4-digit US SIC code, 0 otherwise,

LEG = 1 if the target is a private firm, 0 if the target is publicly quoted,

εi = the error term.

One of the assumptions of the OLS regression is a constant variance of the error terms. However, a constant variance of the error terms is very unlikely with high frequency financial data, such as daily stock returns. In the presence of heteroscedasticity, the OLS regression coefficients are no longer efficient and the conclusions drawn from the results could be wrong.

4 RESULTS

(29)

The impact of deal characteristics on the acquirers’ announcement returns in general will be discussed first, following which the impact of these deal characteristics on the relation between operating performance and announcement returns will be addressed.

4.1 The relation between operating performance and announcement returns

Table VIII presents the daily AARs for the sample of all acquirers and sub-samples of superior and inferior acquirers, based on the three performance measures for five days around the announcement day. All acquirers as well as all samples of superior and inferior acquirers earn significantly positive returns at the announcement day and the day after the announcement day. Superior performing acquirers furthermore earn significantly higher AARs at the announcement day compared to inferior performing acquirers for all three performance measures. These results support the hypothesis that superior performing acquirers earn higher abnormal announcement returns than inferior performing acquirers. Appendix B presents a more detailed overview of the samples’ AARs including test statistics.

Table IX presents the CAARs for the sample of all acquirers and sub-samples of superior and inferior acquirers based on the three performance measures for different event windows. The CAARs for different event windows directly around the announcement day are significantly positive for the sample of all acquirers, all sub-samples of superior performing acquirers and most sub-samples of inferior performing acquirers. Furthermore, superior performing acquirers earn significantly higher CAARs directly around the announcement day compared to inferior performing acquirers and the results are strongest when the M/B ratio is used as the performance measure. This might confirm the expectation that the M/B ratio is the best measure of the quality of management. Additionally, Table IX shows no significant price run-up before the announcement day for all samples of acquirers except inferior performing acquirers based on the industry adjusted EBIT growth. Finally, only some samples of superior and inferior performing acquirers earn a significantly positive post announcement CAAR, but the differences between the sub-samples are not significant. The results once more support the hypothesis that superior performing acquirers earn higher abnormal announcement returns than inferior performing acquirers.

(30)

(31)

Table VIII

AAR to acquirers’ shareholders from day -2 to day +2 for different sub-samples based on

the three performance measures

Table VIII presents the AARs to the acquirers’ shareholders for (a) all acquirers, (b) superior performing acquirers and (c) inferior performing acquirers. Panel A presents the results for the sub-samples based on the industry adjusted stock returns cum dividends during three years prior to the year of the acquisition announcement. Panel B presents the results for the sub-samples based on the industry adjusted EBIT growth during two years prior to the year of the acquisition announcement. Panel C presents the results for the sub-samples based on the absolute and industry adjusted M/B ratio in the year prior to the year of the acquisition announcement. The last column presents the difference between the daily AARs of the two sub-samples per performance measure. All values are expressed in fractions.

All acquirers Superior acquirers Inferior acquirers Difference

(n = 766) (n = 347) (n = 292)

Day AAR AAR AAR ∆AAR

-2 0.0013 0.0021 0.0004 0.0017

-1 0.0001 -0.0004 0.0004 -0.0008

0 0.0054 *** 0.0081 *** 0.0029 ** 0.0052 ***

1 0.0056 *** 0.0077 *** 0.0085 *** -0.0007

2 0.0002 0.0007 -0.0006 0.0013

(n = 766) (n = 305) (n = 225)

-2 0.0013 0.0006 0.0019 -0.0013 -1 0.0001 0.0000 -0.0018 0.0018 0 0.0054 *** 0.0081 *** 0.0039 *** 0.0042 ** 1 0.0056 *** 0.0045 *** 0.0059 *** -0.0014 2 0.0002 0.0012 -0.0001 0.0013 Panel C: M/B ratio

(n = 766) (n = 299) (n = 403)

-2 0.0013 0.0011 0.0011 0.0000

-1 0.0001 0.0001 -0.0002 0.0002

0 0.0054 *** 0.0084 *** 0.0021 * 0.0063 ***

1 0.0056 *** 0.0064 *** 0.0063 *** 0.0001

2 0.0002 0.0018 -0.0015 0.0033 *

(32)

Table IX

CAAR to acquirers’ shareholders for different event windows and for different sub-samples

based on the three performance measures

Table IX presents the CAARs to the acquirers’ shareholders for different event windows for (a) all acquirers, (b) superior performing acquirers and (c) inferior performing acquirers. Panel A presents the results for the sub-samples based on the industry adjusted stock returns cum dividends during three years prior to the year of the acquisition announcement. Panel B presents the results for the sub-samples based on the industry adjusted EBIT growth during two years prior to the year of the acquisition announcement. Panel C presents the results for the sub-samples based on the absolute and industry adjusted M/B ratio in the year prior to the year of the acquisition announcement. The last column presents the difference between the different CAARs of the two sub-samples per performance measure. All values are expressed in fractions.

(n = 766) (n = 347) (n = 292)

Event window CAAR CAAR CAAR ∆ CAAR

0, +1 0.0110 *** 0.0158 *** 0.0113 *** 0.0045 *

-1, 0 0.0054 *** 0.0077 *** 0.0033 * 0.0044 *

-1, +1 0.0111 *** 0.0154 *** 0.0117 *** 0.0037

-10, -1 0.0020 0.0031 0.0048 -0.0016

+1, +10 0.0047 0.0086 ** 0.0081 ** 0.0005

(n = 766) (n = 305) (n = 225)

0, +1 0.0110 *** 0.0126 *** 0.0098 *** 0.0028 -1, 0 0.0054 *** 0.0081 *** 0.0021 0.0060 ** -1, +1 0.0111 *** 0.0126 *** 0.0080 *** 0.0046 -10, -1 0.0020 0.0005 0.0101 ** -0.0095 * +1, +10 0.0047 0.0027 0.0118 *** -0.0092 Panel C: M/B ratio

(n = 766) (n = 299) (n = 403)

0, +1 0.0110 *** 0.0148 *** 0.0084 *** 0.0064 **

-1, 0 0.0054 *** 0.0085 *** 0.0019 0.0065 **

-1, +1 0.0111 *** 0.0149 *** 0.0083 *** 0.0066 **

-10, -1 0.0020 0.0024 0.0018 0.0006

+1, +10 0.0047 0.0095 ** 0.0025 0.0070

(33)

Figure III

CAAR to acquirers’ shareholders for superior and inferior performing acquirers

Figure III graphically shows the CAARs to the acquirers’ shareholders for superior and inferior performing acquirers based on the three performance measures over the -20, +20 event window.

Stock returns -0.01 0 0.01 0.02 0.03 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 C AAR EBIT growth -0.01 0 0.01 0.02 0.03 0.04 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 CA AR M/B ratio -0.01 0 0.01 0.02 0.03 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 Day CAAR

Superior acquirers Inferior acquirers

(34)

4.2 The impact of deal characteristics on the results

This sub-section first describes the impact of deal characteristics on the acquirers’ abnormal announcement returns and then deals with the impact of deal characteristics on the relation between operating performance and announcement returns.

The impact of deal characteristics on the announcement returns

Table X presents the daily AARs for the sample of all acquirers and sub-samples based on the method of payment, industry relatedness of the deal and the target’s legal status. The AARs are again presented for five days around the announcement day. All acquirers, as well as all sub-samples based on the deal characteristics, earn significantly positive returns at the announcement day. Furthermore, acquirers earn significantly higher AARs at the announcement day when the target is a public firm and significantly higher AARs at the day after the announcement day when the target’s and acquirer’s industries are unrelated. The method of payment has no significant effect on the acquirers’ announcement day returns. These results are not in line with general results from M&A research as discussed in Section 2, which on average find higher announcement returns to the acquirer when the payment is in cash, the target’s and acquirer’s industries are related or the target is a private firm. Appendix B presents a more detailed overview of the samples’ AARs, including test statistics.

Table XI presents the CAARs for the sample of all acquirers and sub-samples based on the deal characteristics for different event windows. The CAARs for different event windows directly around the announcement day are significantly positive for all but one sub-samples based on the deal characteristics. Furthermore, acquirers of public targets earn significantly higher CAARs around the announcement day than acquirers of private targets. Differences based on other deal characteristics are not significant. Additionally, Table XI shows no significant price run-up before the announcement day for all samples of acquirers and only acquirers that use shares as the method of payment earn a significantly higher post-announcement CAAR. As with the AARs, these results are not in line with general results from M&A research. Appendix C presents a more detailed overview of the samples’ CAARs, including test statistics

(35)

show no significant relation between the acquirers’ CARs around the announcement day and the deal characteristics for the sample of all acquirers. Once again these results are not in line with previous findings in M&A research.

Table X

AAR to acquirers’ shareholders from day -2 to day +2 for different sub-samples based on

deal characteristics

Table X presents the AARs to the acquirers’ shareholders for the sample of all acquirers and sub-samples based on several deal characteristics. Panel A presents the results for the sub-samples based on the method of payment. Panel B presents the results for the sub-samples based on the industry relatedness of the acquisition. Panel C presents the results for the sub-samples based on the legal status of the target. The last column presents the difference between the daily AARs of the two sub-samples per deal characteristic. All values are expressed in fractions.

Panel A: Method of payment

All acquirers Cash Shares Difference

(n = 766) (n = 283) (n = 62)

-2 0.0013 0.0037 ** 0.0025 -0.0013

-1 0.0001 0.0007 0.0013 0.0007

0 0.0054 *** 0.0088 *** 0.0088 *** 0.0000

1 0.0056 *** 0.0059 *** 0.0059 ** 0.0000

2 0.0002 0.0007 0.0031 0.0025

Panel B: Industry relatedness

All acquirers Focused Diversifying Difference

(n = 766) (n = 562) (n = 204)

-2 0.0013 0.0024 ** -0.0018 0.0042 *

-1 0.0001 0.0003 -0.0005 0.0007

0 0.0054 *** 0.0055 *** 0.0052 ** 0.0003

1 0.0056 *** 0.0044 *** 0.0092 *** -0.0048 **

2 0.0002 0.0006 -0.0008 0.0014

Panel C: Target's legal status

All acquirers Private Public Difference

(n = 766) (n = 671) (n = 95)

-2 0.0013 0.0009 0.0042 * -0.0033

-1 0.0001 0.0002 -0.0009 0.0011

0 0.0054 *** 0.0044 *** 0.0126 *** -0.0082 ***

1 0.0056 *** 0.0056 *** 0.0057 ** -0.0001

2 0.0002 -0.0001 0.0023 -0.0024

(36)

Table XI

CAAR to acquirers’ shareholders for different event windows and for different sub-samples

based on deal characteristics

Table XI presents the CAARs to the acquirers’ shareholders for different event windows for the sample of all acquirers and samples based on several deal characteristics. Panel A presents the results for the sub-samples based on the method of payment. Panel B presents the results for the sub-sub-samples based on the industry relatedness of the acquisition. Panel C presents the results for the sub-samples based on the legal status of the target. The last column presents the difference between the different CAARs of the two sub-samples per deal characteristic. All values are expressed in fractions.

Panel A: Method of payment

All acquirers Cash Shares Difference

(n = 766) (n = 283) (n = 62)

0, +1 0.0110 *** 0.0147 *** 0.0148 *** 0.0000

-1, 0 0.0054 *** 0.0095 *** 0.0102 *** -0.0007

-1, +1 0.0111 *** 0.0154 *** 0.0161 *** -0.0007

-10, -1 0.0020 0.0068 -0.0046 0.0114

+1, +10 0.0047 0.0038 0.0214 *** -0.0176 *

Panel B: Industry relatedness

All acquirers Focused Diversifying Difference

(n = 766) (n = 562) (n = 204)

0, +1 0.0110 *** 0.0098 *** 0.0143 *** -0.0045

-1, 0 0.0054 *** 0.0057 *** 0.0047 0.0010

-1, +1 0.0111 *** 0.0101 *** 0.0139 *** -0.0038

-10, -1 0.0020 0.0040 -0.0036 0.0076

+1, +10 0.0047 0.0037 0.0075 -0.0038

Panel C: Target's legal status

All acquirers Private Public Difference

(n = 766) (n = 671) (n = 95)

0, +1 0.0110 *** 0.0100 *** 0.0183 *** -0.0083 **

-1, 0 0.0054 *** 0.0046 *** 0.0117 *** -0.0071 *

-1, +1 0.0111 *** 0.0102 *** 0.0174 *** -0.0072

-10, -1 0.0020 0.0013 0.0069 -0.0103

+1, +10 0.0047 0.0034 0.0137 * -0.0172

***, ** and * are statistically significant at 1%, 5% and 10% levels Source: own calculations

The impact of deal characteristics on the relation between performance and returns

(37)

expected that deal characteristics that are associated with higher returns more often involve superior acquirers. Superior performing acquirers use cash as the method of payment more often and also more frequently announce industry focused deals than inferior performing acquirers, when EBIT growth is used as the performance measure. Although these results are significant, the differences are insignificant for the other performance measures and deal characteristics. These results indicate that the deal characteristics have no significant impact on the relation between the acquirer’s operating performance and announcement returns.

Table XII

Percentages of deals per deal characteristic for sub-samples of superior and inferior acquirers

Table XII shows the percentages of deals for each deal characteristic per sub-sample of superior and inferior acquirers based on the three performance measures. Both cash and shares payments are included because for some deals the method of payment is other than cash or shares. For the other two deal characteristics, i.e. industry focused and the private targets, the percentages quoted are the deals belonging to these deal characteristics while the remaining percentage of deals for each sub-sample belongs to respectively; industry diversifying deals and deals that involve a public target. The last column presents the difference between the percentage of the superior sub-sample and that of the inferior sub-sample.

Superior Inferior Difference

Cash payment 36.6% 36.0% 0.6%

Shares payment 8.6% 9.6% -0.9%

Industry focused 74.4% 71.2% 3.1%

Private target 86.7% 89.4% -2.6%

n 347 292

Cash payment 40.3% 32.9% 7.4% * Shares payment 7.9% 10.2% -2.4% Industry focused 77.7% 69.8% 7.9% ** Private target 85.9% 89.8% -3.9% n 305 225 Panel C: M/B ratio

Cash payment 35.8% 35.7% 0.1%

Shares payment 8.0% 8.2% -0.2%

Industry focused 73.6% 72.5% 1.1%

Private target 87.3% 88.6% -1.3%

n 299 403

(38)

Table XIII presents the results from the OLS regressions for the three performance measures and deal characteristics. The results show that the acquirer’s operating performance has no significant impact on the acquirer’s CAR around the announcement day for all three performance measures and controlling for deal characteristics has no impact on these results. Acquirers furthermore earn a significantly higher CAR around the announcement day when the deal is diversifying for the sample based on EBIT growth and when the target is a public firm, for the sample based on the M/B ratio. These results are in line with the result that acquirers earn higher returns if the acquisition is industry diversifying or the target is a public firm (see Tables X and XI). Altogether, these results do not support the hypothesis that superior operating performance of acquirers prior to the acquisition announcement leads to higher announcement returns around the announcement day. Servaes (1991) does find a significant relation between the acquirers’ operating performance and CAR around the announcement day when the control variables for deal characteristics are included. Morck et al. (1990) and Servaes (1991) also find significantly higher returns if the payment is in cash. They do not control for the industry relatedness and target’s legal status.

(39)

Table XIII

OLS regression analyses of superior and inferior acquirers’ CAR on acquirers’ industry

adjusted performance prior to the announcement year and deal characteristics

Table XIII presents the acquirers’ CAR around the announcement day from 0 to +1 as a dependent variable of its industry adjusted operating performance prior to the announcement. The variable SUP stands for the superior performing acquirers based on the three performance measures presented above the three columns. The regressions are estimated using OLS regression. Panel A presents the regressions’ coefficients without control variables and Panel B includes the control variables for the method of payment, industry relatedness of the acquisition and legal status of the target. The independent variables are described in Section 3.2.4. The values and coefficients are expressed in fractions.

Panel A: Regressions without control variables

Variable Coefficient Coefficient Coefficient

Intercept 0.0113 *** 0.0098 *** 0.0084 **

SUP 0.0045 0.0028 0.0064

Adjusted R² -0.0003 -0.0011 0.0007

n 639 530 702

Panel B: Regressions with control variables

Variable Coefficient Coefficient Coefficient

Intercept 0.0303 *** 0.0226 *** 0.0201 ** SUP 0.0044 0.0032 0.0063 CSH 0.0026 0.0025 0.0071 SHR -0.0005 -0.0027 0.0069 REL -0.0104 -0.0112 ** -0.0034 LEG -0.0139 -0.0062 -0.0138 * Adjusted R² 0.0042 0.0032 0.0030 n 639 530 702

***, ** and * are statistically significant at 1%, 5% and 10% levels Source: own calculations

5 SUMMARY AND CONCLUSIONS

This final section presents the main conclusions that are drawn from the results and tries to answer the research question that was defined in Section 1. Section 5.1 discusses the conclusions and Section 5.2 gives limitations of this thesis and recommendations for further research.

5.1 Conclusions

A RE STRONG OPERATORS MORE SUCCESSFUL ACQUIRERS ?

A

RE STRONG OPERATORS MORE SUCCESSFUL ACQUIRERS

?

T

’

E

A

RE STRONG OPERATORS MORE SUCCESSFUL ACQUIRERS

?

T

’

E

ABSTRACT

ACKNOWLEDGEMENTS

TABLE OF CONTENTS

1 INTRODUCTION

2 THEORETICAL

BACKGROUND

3

DATA AND METHODOLOGY

AR

=

R

−

E

(

R

)

−

=

RI

RI

RI

R

R

=

α

+

β

(

R

)

+

ε

α

β

∑

=

AR

N

AAR

1

∑

=

N

AAR

1

)

var(

σ

∑

∑













−

+

=

4

)

3

(

K

S

S

n

_α

_β

_ε

_α

_β

_N

_σ

_

₋

_N