ECONOMIC GAINS AROUND MERGERS AND ACQUISITIONS IN THE EUROPEAN CONSTRUCTION INDUSTRY

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ECONOMIC GAINS AROUND MERGERS AND ACQUISITIONS IN THE

EUROPEAN CONSTRUCTION INDUSTRY

JAN VAN DER HEUL

S1296345

University of Groningen

Faculty of Management and Organization

Landleven 5

Groningen, 9747 AD

E-mail: janvanderheul@hotmail.com

January 24, 2007

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ECONOMIC GAINS AROUND MERGERS AND ACQUISITIONS IN THE

EUROPEAN CONSTRUCTION INDUSTRY

Abstract

Conventional wisdom says that mergers and acquisitions (M&As) do not add value for their shareholders. This research shows the opposite by focusing on one industry and region only: the construction industry of the European Union. This research is both a pledge for more industry specific research concerning M&As and evidence of value creating M&As for the shareholder.

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INTRODUCTION

Most business organizations are seeking to grow. This can be supported by the empirical observation of Freier’s (1990) who found that “over the past 20 years, the minimum company size that is required to compete successfully in most industry segments has been steadily increasing”. Knowing that growth is a requirement for business survival, a firm can either grow internally, by investing in in-firm resources or externally by acquiring another firm. Buying a business unit or an entire firm from another party, a business transaction between two independent organizations, is called an acquisition. Another way to grow is to merge with another company. A merger involves a combination of two or more entities, which may or may not be equal participants. Sometimes a merger is really an acquisition financed by common stock. Mergers are typically more expensive than acquisitions as the parties incur higher legal costs. There are many reasons for parties to decide to merge rather than treat the combination as an acquisition. Some of the more frequently encountered reasons are that mergers do not require cash and mergers may be accomplished tax free for both parties. An advantage of acquisitions on the other hand is that the acquirer does not experience the dilution of ownership that occurs in a merger. As mergers and acquisitions are somewhat related and intertwined with each other, both are taken into account in this research and further in this paper the term M&A (mergers and acquisition) will be used to point out both

transactions.

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However conventional research does not provide clear evidence on whether or not firms should engage in M&As. Some studies have concluded that M&A transactions weaken a firm’s position in the market and destroy shareholder value, while others have found that M&As do generate significant economic returns. Most of this research was focused on M&As in general, or on a specific industry. However, not much has been written about the construction industry. Choi and Russell (1994) present an article in which they research the economic gains around mergers and acquisitions in the construction industry of the USA. This research concerns domestic M&As only. They found that the performance of construction M&As was positive for the acquirer at an insignificant level.

As the international construction industry is currently experiencing high growth (ENR, 2006), large construction firms increasingly move operations overseas and doing so, many of them engage in M&A transactions abroad. There is no research available that assesses these international diversifications by construction firms. Though, research on the construction industry is scarce, there is certainly hardly any research on the construction industry in the European Union (EU) as a region. This is especially disappointing as with its harmonization of economies, the EU promotes cross border activity and much M&A activity concerning the construction industry has already taken place.

Research Objective

This study aims to research the economic gains (or losses) for the acquiring firms in the construction industry of the EU around M&As. The main research question that can be formulated is:

Main Research Question

To what extent do transactions of M&As contribute to the economic performance of acquiring construction firms based in the European Union?

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THEORY

Literature on M&A transactions and the concomitant economic gains an losses around them is numerous. Researchers far from agree on whether M&As increase or destroy shareholder value. Before entering the discussion on the impact of M&As on shareholder value, it is wise pay some attention to the terms corporate diversification and M&A.

Corporate diversification is the entering into a new business by a firm and it can occur in three ways: industrial diversification, geographical diversification or a combination of both. Industrial diversification means that a firm from a certain industry is starting businesses in another industry, different from the core business. Geographical diversification involves moving operations to a new geographical area. In countries such as the USA this could mean that a West Coast based firm is seeking moving operations to the East Coast, while it could also mean that a Dutch company is starting operations in, for example, Spain.

Methods of achieving diversification include internal development, acquisitions, strategic alliances and joint ventures. Each method has its own set of issues, benefits and limitations and therefore various forms and means of diversification can be mixed and matched to create a range of options.

Much research has been done on the topic of diversification and different results on the benefits of it have been found.

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Other authors such as Comment and Jarrell (1995) find that greater corporate focus is consistent with shareholder wealth maximization. For the 1980s they found an increasingly positive relation between stock returns and focus, a trend toward specialization and the failure of diversified firms to exploit economies of scope. During these 1980s the conventional wisdom held that economies of scope had been reversed. Managers were advised to avoid diversification and to shrink their rapidly grown enterprises that resulted from past diversification strategies. This view of concentrating on a core business marks a change from the theoretical justifications for diversification that were advanced previously. Arguments for diversification include managerial economies of scale, economies of scope in production and marketing, financial synergies such as earnings smoothing and the efficiencies that arise with an internal capital market. This new focus on specialization is consistent with Jensen (1986), who argues that corporate diversification programs exemplify the theory that managers of firms with unused borrowing power and large free cash flows are more likely to undertake low-benefit or even value destroying investments. Similarly, Meyer, Milgrom and Roberts (1992) argue that failing businesses can have too ready access to cross subsidies when they are part of a diversified firm. In their research, Jarrell and Comment (1995) also show that diversified firms do not take advantage of some of the underlying efficiencies that were thought to motivate diversification, such as internal capital markets and the allowing of a greater use of debt.

However, research is not only negative about the effects of diversification on firm value and stock returns. Walker (2000) investigates the strategic objectives and stock price performance of acquiring firms. The results of his research show that acquiring firm shareholders earn higher returns following takeovers that expand the firm’s operations geographically or increase its market share. Also Walker (2000) finds that acquiring firm shareholders earn higher returns following cash offers. Various studies identify five wealth increasing motivations for corporate takeovers. First, acquisitions can increase efficiency by creating economies of scale or by disciplining inefficient managers. Second, takeovers can exploit asymmetric information between acquiring firm managers and acquiring- or target firm shareholders. Third, acquisitions can diminish agency problems associated with a firm’s free cash flow. Fourth, takeovers can enhance the firm’s market power. Fifth, acquisitions can utilize tax credits. (Walker, 2000)

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industries might benefit more from a M&A growth strategy than others and therefore using a sample of firms from several industries and drawing conclusions on the stock performance around M&As is a bit risky. It is like blending apples and pears and expecting orange juice. This research will focus also for this reason, on M&As in one particular industry only: construction. Moreover, M&As are the most logical growth strategy for construction firms that wish to diversify, especially in case of international expansion. In running their business, construction firms rely heavily on a network of subcontractors, suppliers, local governments and personnel. Therefore if a construction firm wishes to grow and expand its operations, a strategy of M&As rather than internal growth, will be preferred in order to reap the benefits of these network relationships. As mentioned before, M&As are a quicker and cheaper method of diversification for construction firms compared to internal growth or strategic alliance (Choi & Russell, 2004). From this perspective, shareholders are expected to value M&As in the construction industry higher than M&As in industries where it is a less favorable method of growth, and therefore the cumulative abnormal stock returns around the announcement of a M&A in the construction industry are expected to be positive for the acquirer. This results in hypothesis 1:

H1: Cumulative abnormal stock returns around the announcement of a M&A in the construction industry are positive.

In addition to this the different time periods in intervals of years will be analyzed to see if there are differences in the abnormal stock returns for different years in which the M&As have taken place. No assumptions are being made for this and therefore no hypothesis if formulated for this variable.

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to be an even more suitable way to grow and diversify internationally . The following two hypotheses can be derived from this:

H2: Cumulative abnormal stock returns around the announcement of a M&A in the construction industry are expected to be higher for international M&As than for domestic M&As.

H3: Cumulative abnormal stock returns (CAR) around the announcement of a M&A are expected to be higher for M&As outside the EU than within the EU.

Within the construction industry, different lines of business exist. From real estate to commercial construction, from infrastructure to industrial construction. Between these lines of business many synergies can be created through M&As because of the relatedness of business However, when a firm chooses to undertake business in an unrelated industry, synergies are less likely to be created because of the differences in core business. Therefore M&As of construction firms are more likely to be valued higher when it involves a target company in the same industry than from a different industry.

H4: Cumulative abnormal stock returns around the announcement of a M&A are expected to be higher for related M&As than for unrelated M&As.

M&As are being paid for in different ways. Methods of payment include cash, shares or a mix of both. The payment method has been regarded as an important variable in M&A analysis and in general paying in cash is favoured over paying with shares for the acquirer, unless the stock price is overvalued (Rau and Vermaelen, 1998). Stock over- or undervaluation is not taken into account in this research and therefore it is expected that the cumulative abnormal stock returns around M&As that are being paid for in cash are higher than those paid for in shares.

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worth of the acquirer result in greater wealth gains. From this, the following two hypotheses can be derived.

H6: Cumulative abnormal stock returns (CAR) around the announcement of a M&A are expected to be higher for M&As where the deal value as a percentage of the acquirer’s net sales is higher.

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DATA AND METHODOLOGY

Variables and Measures

Dependent Variable

The dependent variable in this research is the success of the M&As. The success of a M&A can be measured by the valuation of the acquiring company by the shareholder. The stock price is, according to financial theory, a representation of all information available at a certain moment in time. The shareholder is, according to the same financial theory, aware of this information and can therefore make a rational valuation of a firm’s stocks. A frequently used measure of the success of the M&A is the cumulative abnormal return (CAR) To measure the cumulative abnormal returns, several alternatives are available. Sirower (1997) comes up with four alternatives suitable for event studies: market model returns, market adjusted returns, mean adjusted returns and raw returns. Similar research on M&As has used the market adjusted returns method (MAR), and this approach will be used here as well.

The market adjusted returns are the stock returns of the sample firms adjusted for the index for the construction industry in the EU consisting of numerous construction firms. In this study a time window of 41 days will be used: the MARs will be calculated for the announcement date of the M&A and 20 days before and after this date. To determine CAR, it is necessary to define the abnormal return (Ai) for each firm’s stock i for day t. This can be defined as follows:

Ai,t=Ri,t−Rm,t

where Ri,t is the actual return for the European construction firm’s stock i on day t and Rm,t is the market return on the EU construction industry index for day t.

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and is simply the total (cumulative) of all the abnormal returns over the 41 day time window. In the multiple regression analysis, the total of 41 day CAR will be used.

Independent Variables

There are several independent variables that have an impact on a firm’s stock performance around the announcement of a M&A. Variables that will be included in this research are whether the M&As are international or domestic and in case of international whether they take place within or outside the EU. Thirdly the relatedness of the M&As will be assessed, M&As are considered related when the target firm is in the same industry (construction) as the acquirer and unrelated if not. Another variable is the method of payment, whether the M&A is paid in cash or in shares or a mix of both. Finally the relative size of the transaction will be assessed using the percentage stake that is being acquired and the relative deal value as a proportion of the net sales of the acquiring firm. A precise description of these variables can be found in appendix A.

Besides these variables, the year in which the M&A is announced and the total absolute deal value will be taken into account. However, considering the explorative nature, no specific hypothesis will be formulated around this variable.

Methodology

The CARs will be calculated for every day of the 41 day time period. The results of this will be presented in a graph showing the average CAR of all EU M&A transactions. Further, each variable will be analyzed individually, calculating the average 41 day CAR of, for instance, all domestic M&As and all international M&As. For each variable, a graph will be presented and the differences between the two (or more) groups of each variable will be analyzed. Then a t-test will be used in order to determine whether the 41 day CAR significantly deviates from zero.

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Finally a multiple regression analysis will be performed in order to determine what impact the different variables have on the total 41 day CAR.

Sample

This research will make use of the data on M&A transactions that is available on the Zephyr database of Bureau van Dijk Electronic Publishing. Furthermore, stock price information is available at Thompson Datastream, a database that contains historical stock data on most publicly traded firms.

In composing the sample, the following criteria were used. First of all, the acquiring firm had to be a construction company according to the Zephus industry classification of Zephyr. This resulted in 3059 deals for the analysis period (2000-2005). Second, the acquiring firm had to be based in the European Union, shrinking the sample to 1799 M&As. Further the acquiring firm had to be publicly traded leaving 574 deals. Only M&As with payment methods of cash, shares or a mix and a known deal value were allowed in the sample leaving 287 M&As. Due to incomplete information and M&As taking place on the same day by the same acquirer (these were considered as one M&A: the deal values were combined), 207 M&As were considered for analysis, involving a total of 85 different acquiring companies (some companies have struck more than one deal in the analysis period (see appendix B)).

Sample Characteristics

Table 1 shows the number of M&As that took place in each year of the analysis period. M&A activity reached a peak in 2005.

Table 1: The Number of M&As During the Analysis Period (2000-2005)

Period 2000 2001 2002 2003 2004 2005 Total Number of M&A transactions 29 33 37 29 34 45 207

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Table 2: Summary of Statistics Dummy Variables

Method of Payment Number of

deals Related Unrelated Domestic International

Within EU

Outside

EU Cash Shares Mix 207 37,0% 62,8% 72,9% 27,0% 64,3% 36,0% 57,0% 5,8% 37,2%

Table 3 Shows the summary of the statistics of the continuous variables. Interesting to see in this table is that on average the total 41 day CAR is positive at 4%.

Table 3: Summary of Statistics Continuous Variables

n=207 Mean median St Dev Min Max Deal values (mln EUR)

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RESULTS

The results of the analysis of the data will be organized around each variable, starting with a general analysis based on averages after which the regression analysis will follow.

Primary results

Figure 1 shows that on average the M&As in the EU construction industry deliver a positive cumulative return for the shareholder: the 41 day average CAR is 3,8%. Also, 62% of all M&As delivered a positive CAR on day 41 as can be seen in table 4. The pre-event CAR reflects the period prior to the announcement, while the post-event CAR reflects the period after the announcement.

Figure 1: Average CAR for all EU Construction M&As

Average CAR, n=207 0% 1% 1% 2% 2% 3% 3% 4% 4% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 Day CA R EU Construction M&A

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Figure 2 shows the average CAR of the M&As that took place in the different years of the period researched. It clearly shows the ‘last good year’ before the recession after 9/11 in 2001 wherein the CAR reached a peak after 41 days of 11%. In all the other years the 41 day CAR is lower, however still positive.

Figure 2: M&As by Year

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When the M&As are split up in domestic and international a distinction arises based on the average CAR of both categories as is shown in figure 3. This graph clearly shows that on average international M&As are valued higher by shareholders than domestic M&As. The CAR for day 41 for international M&As is 6% while the average CAR for the same interval for domestic M&As is ‘only’ 3%, though still positive.

Figure 3: Domestic and International

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When in turn the international M&As are split up in M&As that have taken place in- and outside the EU another distinction can be made. Figure 4 clearly shows the difference between the M&As that have taken place outside the EU and within the EU. The average 41-day CAR for M&As outside the EU more than double (10%) the average CAR of M&As that have taken place within the EU (3,6%). This shows that on average the ‘more’ international the M&A is, the higher this is valued by the shareholder.

Figure 4: Within or Outside the EU

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When the aspect of relatedness, or the industry effect is taken into account it is clearly visible that the average 41-day CAR of related M&As is higher than the unrelated M&As as can be seen in figure 5. Related M&As deliver a 41-day CAR of 5,3% while unrelated M&As deliver little more than half of that: 2,7% CAR.

Figure 5: Related and Unrelated M&As

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Looking at the method of payment of the M&As in figure 6, there is a clear trend towards the favouring of cash instead of shares, which is in accordance to theory. Surprising is the high performance of the M&As that are being paid for by a mix of cash and shares, although intertwining along the 41 day period, the 41 day CAR for M&As paid for in cash is on overage 3,7% while those M&As that are paid for by a mix of cash and shares have a 41 day CAR of on average 5%. The M&As that are paid in shares perform the worst with a negative 41-day CAR of on average -3,4%.

Figure 6: Method of Payment

Method of Payment -6% -4% -2% 0% 2% 4% 6% 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 Day CA R Shares Cash

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In the next graph, figure 7, the averages of the CARs are split up in two groups. One group consists of M&As where the acquiring construction firm acquires less than a 50% stake of the target firm and the other group consists of M&As where the construction firms acquire more than 50% of the target firm. It was expected, according to theory, that the relative size, thus also the % stake acquired, had a positive influence on the performance of the M&A. The M&As of smaller stakes seem to have a slightly higher 41 day CAR, however the trend along the time window is in the same direction.

Figure 7: % Stake Acquired

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In order to be more precise, the relative size of the transaction as a proportion of the net sales of the acquirer has been calculated resulting in the following graph (figure 8). The M&As have been split into groups of more or less equal size resulting in a group of M&As of which the deal value as a proportion of the acquirers’ sales is between 0%-10% and a group of 10%-100%. This graph shows that on average the CAR is higher where the deal value as a proportion of the acquirer’s net sales is higher with a difference of almost 3%: a CAR of 3,6% for the group with lower proportionate deal values and 6,3% for the group with higher proportionate deal values. Notable is that the effects for the latter group arise only after the deal.

Figure 8: Deal Value as a Proportion of the Acquirer’s Net Sales

Deal Value as % of Net Sales

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Testing CAR for Significance

To find out whether the total 41 day CAR significantly deviates from zero, the total 41-day CAR was tested for significance using a one sample t-test and was found to be significant with an average of 3,8% at the 1% significance level (see appendix C). Figure 9 shows the distribution of the CARs.

Figure 9: CAR distribution

CAR Total (day 41)

50. 0 40. 0 30. 0 20. 0 10. 0 0. 0 -10. 0 -20. 0 -30 .0 -40. 0 -50. 0 -60. 0 40 30 20 10 0 Std. Dev = 14.46 Mean = 3.8 N = 207.00 Correlation

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less likely it is that the transactions are being paid for in cash and the more likely it is that they are being paid for in shares. This makes sense as the larger deal values the more likely it is that they are being paid for in shares instead of cash. Finally the variable ´yearhype´, the transactions that took place in the years 2000, and 2001 (*also one transaction from 1999 is included in this group), the years prior to the attacks of September 11, shows a significantly positive relationship with the total CAR and the international and outside EU variables suggesting that M&As that took place in those years are very successful and at the same time taking place mostly international and outside the EU.

Table 5: Correlations Pearson Correlation Sig. (2-tailed) Total CAR International Outside

EU Related Cash Shares

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Multiple regression

For the multiple regression analysis, the mean of every variable was subtracted from the value of these different variables to get a so-called clean effect and ascribe economic value to the regression results ((minus mean method) (see appendix E for all results)).

First of all, all hypothesized variables were included in the regression. Table 6 shows the different regression results and the variable ‘constant’, representing the total 41 day CAR, is highly significant (1% level) at an average of 3,9%. Furthermore, the variable shares shows to be significantly negative related to the total CAR. Those M&As paid for in shares deliver an average CAR of –5,8% (3,9%-9,7%) and is significant at a 5% level. Also, the M&As that took place during the ‘hype’ years are significantly positive at an average of 8,9% (3,9%+5%). The variables cash appears to be insignificant as well as the variables for the relative size of the transaction: percentage deal value to sales and % stake.

Table 6: Multiple Regression Results (1)

Variable 1 B t-values Significance

(Constant) Total 41 day CAR _.039 _3.772 _.000

International (mm) _-.006 _-.204 _.839

Outside EU (mm) _.036 _.840 _.402

Related (mm) _.029 _1.355 _.177

Cash (mm) _-.018 _-.780 _.436

Shares (mm) _-.097 _-2.118 _.035

Percentage deal value to sales (mm) _.031 _.355 _.723

% Stake (mm) _-.013 _-.376 _.708 Yearhype (mm) _.050 _2.133 _.034 N ₂₀₄ Adjusted R Square _.030 F Value _1.747 Significance _.090 1

Definition of variables can be found in appendix A

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both take place outside the EU and are related at the same time. The results of this regression can be found in table 7.

Variable B t-values Significance

(Constant) _.039 _3.839 _.000

Shares (mm) _-.078 _-1.811 _.072

% Stake (mm) _-.001 _-.023 _.982

Yearhype (mm) _.051 _2.297 _.023

Outside EU & Related (mm) _.143 _2.774 _.006

N ₂₀₁ Adjusted R Square _.067 F Value _3.792 Significance _.003

The variable outside EU and related together show to be highly significant as a determinant of the 41 day total CAR at an average of 18,2% (3,9%+14,3%) at a 1% significance level. Paying in shares remains a significant determinant of the 41 day total CAR in this model at an average of -3,9% (3,9%-7,8%), however, the significance decreases to a 10% level. Also, those M&As that took place during the ‘hype years’ of 2000 and 2001 are still a significant determinant of the 41 day total CAR at an average of 9,0% (3,9%+5,1%). The relative size variables remain insignificant as in the previous model.

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Variable B t-values Significance

(Constant) _.039 _3.862 _.000

% Stake (mm) _.008 _.256 _.798

Yearhype (mm) _.059 _2.621 _.009

International & Related (mm) _.063 _1.979 _.049

Shares (mm) _-.081 _-1.866 _.064 N ₂₀₁ Adjusted R Square _.049 F Value _2.993 Significance _.013

However, it is lower than the combined variable of outside EU and related. This could be a result of the limited number of observations of M&As that have taken place outside the EU and are related (see frequency table in appendix F). There are 25 observations of international related M&As and only 8 observations of M&As that are related and have taken place outside the EU.

Furthermore, it is notable that as an additional effect, the average 41 day CAR of M&As that have taken place during the ‘hype’ years increases to 9,8% (3,9%+5,9%) at an increased significance level of 1%.

Summarizing, the multiple regression shows statistically significant results on four main fronts:

1. In general EU construction M&As deliver a positive 41 day CAR of 3,9% and this is significant at a 1% level

2. M&As that are being paid for in shares deliver a negative 41 day CAR of -5,8%. This is significant at a 5% level.

3. The M&As that have taken place in the ‘hype’ years deliver a positive 41 day CAR of 9,8% at a 5% significance level.

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DISCUSSION

In this chapter the hypotheses will be revisited one by one.

H1: Cumulative abnormal stock returns around the announcement of a M&A in the construction industry are positive.

Hypothesis 1 can be accepted based on the significantly positive 41 day CAR of 3,9% . Accordingly, it is concluded that shareholders of EU construction firms on average realize significant gains around M&A transactions. This finding is different from most previous research that the CAR of acquiring firms is either positive at an insignificant level or negative at a significant or non significant level. It is however consistent with more recent research by Moeller et al. (2006) who found that the M&As in the current cycle are creating short term shareholder value. Most importantly it is evidence that M&As in the construction industry in particular do create shareholder value and that M&A is one of the ways to grow in the construction industry.

H2: Cumulative abnormal stock returns around the announcement of a M&A in the construction industry are expected to be higher for international M&As than for domestic M&As.

Hypothesis 2 is being rejected as no significant relationship was found between the 41 day CAR and the fact whether or not a deal is international. However, combining this variable with the variable of relatedness does deliver a significant result. International M&As that are related at the same time create significant gains for the shareholder: the average total CAR after 41 days that is achieved is 10,2% (3,9% + 6,3%). This is consistent with previous research that showed that related diversification pays off for the acquiring company’s shareholder. It is also consistent with the claim of other researchers that international M&As usually perform better than domestic ones. Though, both claims do not work separately, they work in combination.

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Although on average it shows that deals that take place outside the EU and thus are ‘more’ international deliver a higher 41 day-total CAR than those that take place within the EU (see figure 4), it is not significant in a multiple regression analysis. For this reason, hypothesis 3 can be rejected as no significant relationship was found between the CAR and the M&As taking place within or outside the EU. However, those M&As that concern a target outside the EU and are related at the same time deliver a highly significant 41 day CAR of 18,2% at a 1% significance level. However, one should note that this is only the case with 8 M&As.

H4: Cumulative abnormal stock returns around the announcement of a M&A are expected to be higher for related M&As than for unrelated M&As.

Hypothesis 4 by itself can be rejected as no significant relationship was found between the relatedness of the M&As and any of the four CAR time intervals. However, related M&As that are international create significant gains for the shareholder. The average 41 day CAR for related international M&As is 10,2%, as indicated before for hypothesis 2.

H5: Cumulative abnormal stock returns around the announcement of a M&A are expected to be higher for M&As paid for in cash than those paid for with shares.

Hypothesis 5 can partially be accepted based on the statistical result that M&As that are being paid for in shares create a significant loss for the shareholder looking at the 41 day total CAR. The average loss for shareholders of construction firms that pay the M&A in shares is -5,8% (3,9%-9,7%). The average gains for shareholders of construction firms that pay M&A in cash is positive, although insignificant.

H6: Cumulative abnormal stock returns (CAR) around the announcement of a M&A are expected to be higher for M&A where the deal value as a percentage of the acquirer’s net sales is higher.

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deal size, the larger the cumulative abnormal return for the acquiring firm’s shareholders (see figure 8).

H7: Cumulative abnormal stock returns (CAR) around the announcement of a M&A are expected to be higher for M&As of which the percentage stake acquired is higher.

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CONCLUSION

The aim of this research was to research the economic gains (or losses) around the announcement of a M&A for the acquiring firms in the construction industry of the EU.

This research has shown that studying M&As is useful when looking at one particular industry at a time. Some industries are more suitable for certain growth strategies than others. The way to grow for the construction industry, especially when going international, is through mergers and acquisitions. Shareholders appreciate M&As in the construction industry significantly, showing a general agreement among them that merging and acquiring is a good method for construction companies to grow. Reasons for the approval of shareholders are likely to be the acquisition of important networks of subcontractors, local governments and skilled employees that come with the other firm domestically or abroad.

This study fits in recent research findings that M&As pay off for the shareholder. Recent research by Moeller et al. (2006) already showed that the current merger cycle, as opposed to the previous two, is showing a creation of shareholder value through M&As. This study supports this claim and furthermore pleads for a different way of researching firms’ performance around M&As, namely per industry.

Of course, there is never one best way of doing things, and therefore this research should be reviewed critically as well. In order to strengthen the point made about doing research on M&As, the performance of M&As and other growth strategies should be researched for different industries, one by one.

Future Research Agenda

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Accessed November 21st_{, 2006.}

Thompson Datastream accessed October 30, 2006.

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APPENDIXES

Appendix A: Definition variables

Domestic: An M&A transaction is considered domestic when it concerns a target that is in the same country as the acquirer.

International: An M&A transaction is considered international when it concerns a target that is outside the country of the acquirer

Within EU: An M&A transaction is considered within the EU when it concerns a target that is based within the EU.

Outside EU: An M&A transaction is considered outside the EU when it concerns a target that is based outside the EU.

Related: An M&A transaction is considered to be related when it concerns a target that is in the same industry (construction), according to the Zephus Industry Classification by Zephyr, as the acquirer.

Unrelated: An M&A transaction is considered to be related when it concerns a target that is not in the construction industry, according to the Zephus Industry Classification by Zephyr, as the acquirer. The Zephus Industry Classification can be found on the Zephyr database online. Method of Payment:

Shares: The total deal amount is being paid by shares only Cash: The total deal amount is being paid in cash only Mix: A mix of cash and shares is used to pay the transaction

Relative size of the transaction:

Sales (revenue): also defined as operating revenue or turnover, is the revenue realized from day-to-day operations of the enitity

Stake acquired: the percentage of the shares of the target firm that the buyer acquires with the M&A transaction

Announcement date: the day on which the M&A is announced to the public Yearhype: All transactions that have taken place before September 11th, 2001

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Appendix B: List of M&A deals in the European Union

Acquiror name Target name

Announcement Date

Abbot Group plc Deutsche Tiefbohr AG 31-8-2001

Abengoa SA Befesa Medio Ambiente SA 22-3-2000

Abengoa SA Befesa Medio Ambiente SA 11-6-2002

Abertis Infraestructuras SA Retevisión SA 4-12-2003

Acesa Infraestructuras SA Tradia 29-12-2001

Acesa Infraestructuras SA Iberica de Autopistas SA 20-5-2002

Acesa Infraestructuras SA Sociedad de Aparcamientos Barcelona SA 23-9-2002

Acesa Infraestructuras SA Tradia 1-10-2002

Acesa Infraestructuras SA Áurea Concesiones de Infrastructuras SA 20-5-2002

Acesa Infraestructuras SA Sociedad de Aparcamientos Barcelona SA 1-4-2003

Actividades de Construcción y Servicios SA Grupo Dragados SA 15-1-2003 Actividades de Construcción y Servicios SA Grupo Dragados SA 3-7-2003 AEGEK SA Domitor SA 13-2-2002

Alfred McAlpine plc Kennedy Utility Management Ltd 22-3-2001

Alfred McAlpine plc Stiell Ltd 1-3-2002

Alfred McAlpine plc Eastern Contracting Holdings BV 1-9-2002

Alfred McAlpine plc Inframan Ltd 20-12-2002

Alfred McAlpine plc AIMS Ltd 6-6-2003

Alfred McAlpine plc UK Power Construction Ltd 6-2-2004

Amec plc Agra Inc. 16-2-2000

Amec plc Spie SA 5-12-2002

Amec plc Socoa 12-7-2004

Amec plc Generale Maintenance Services 11-8-2004

Amec plc Paragon Engineering Services Inc. 20-1-2005

Amec plc Environmental Advice Centre Ltd 7-3-2005

Amec plc NNC Holdings Ltd 21-6-2005

Amec plc PK SAS 4-7-2005

Amec plc Domec SA 16-12-2005

Arcadis NV FC International SA 13-8-2002

Artisan (UK) plc Bickerton Group plc 19-1-2000

Artisan (UK) plc EC Rippon Ltd 5-12-2000

Attikat SA Atemke SA 6-6-2002 Attikat SA Sigalas S SA 6-6-2002 Áurea Concesiones de Infrastructuras SA Codad 28-9-2001 Áurea Concesiones de Infrastructuras SA Autopista Trados 45 SA 27-12-2002

Avenir Numeric's SA Simatel SARL 26-3-2004

Avenir Numeric's SA Multe-pass 5-10-2005

Balfour Beatty plc Metroplex Corporation 14-4-2000

Balfour Beatty plc Integral Technologies Inc. 3-7-2000

Balfour Beatty plc John Kennedy (Holdings) Ltd 10-10-2001

Balfour Beatty plc ABB Ltd's rail electrification business 21-12-2001

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Bami SA Inmobiliaria De Construcciones y Terrenos

Inmobiliaria Zabalburu SA 28-6-2000

Promociones Coto de los Ferranes SL 1-2-2002

Metrovacesa SA 31-7-2003

Bau Holding Strabag AG Roads and Bridges 26-8-2005

BBA Group plc Oxford Aviation Holdings Ltd 1-3-2000

BBA Group plc Lynton Aviation Inc. 5-6-2000

BBA Group plc Osprey Aviation 5-6-2000

BBA Group plc Snow Filtration Company LLC 16-6-2000

BBA Group plc Gulfstream Aerospace Corporation's engine overhaul

and repair business

16-1-2001

BBA Group plc Aircraft Service International Group Inc. 11-7-2001

Bilfinger Berger AG Rheinhold & Mahla AG 6-6-2002

Bilfinger Berger AG Abigroup Ltd 23-10-2003

Blick plc Alfia Services Ltd 9-5-2001

Bouygues SA Colas SA 4-7-2000

Bouygues SA BDT 13-2-2002

Bouygues SA TF1 SA 26-4-2005

Bowater Windows Ltd Wickrather Bauelemente AG 7-9-2000

BRISA - Auto Estradas de Portugal SA

Nutrend Engenharia SA 22-2-2005

Budimex SA Dromex SA 30-8-2000

Carillion plc GT Railway Maintenance Ltd 27-9-2001

Carillion plc Citex Professional Services Ltd's management services

division

19-8-2002

Carillion plc Planned Maintenance Group Ltd 9-3-2005

Chicago Bridge & Iron Co NV

Howe-Baker International LLC 30-7-2000

Chicago Bridge & Iron Co NV

Pitt-Des Moines Inc's engineered construction and water storage units

7-2-2001

Colas Ltd Aram Resources plc 25-11-2000

Colas SA SCCF Iasi SA 12-12-2001

Compañía Levantina de Edificación y Obras Públicas SA

Sturm 2000 SL 18-12-2003

Diekat SA Ikaros Ekmetalleysi Pigon Energeias SA 21-3-2005

Doprastav AS Metrostav AS 19-8-2000

Dürr AG Carl Schenck AG 1-11-1999

Edrasis - C Psallidas SA VES SA 23-4-2002

Edrasis - C Psallidas SA Lykodimos SA 24-9-2002

Edrasis - C Psallidas SA Enviprosystems SA 7-4-2003

Edrasis - C Psallidas SA Redra Construct Grup SA 11-10-2005

Eiffage Construction Mitex SA 17-4-2002

Eiffage Construction Mitex SA 29-5-2002

Enterprise plc ARM Group Ltd, The 31-7-2000

Enterprise plc Thomas Bermingham Contractors Ltd 12-2-2001

Enterprise plc Dewsbury Civil Engineering Company Ltd 27-4-2001

Enterprise plc First Claims Response (Manchester) Ltd 9-7-2001

Enterprise plc Brophy Grounds Maintenance Ltd 8-11-2001

Enterprise plc Lloyd & Scotter Lighting Ltd 29-4-2002

Enterprise plc JMPC Ltd 27-7-2004

Enterprise plc MRS Environmental Services Ltd 8-9-2004

Enterprise plc Heating and Building Maintenance Co. Ltd 7-2-2005

Enterprise plc CCMR Ltd 17-3-2005

Enterprise plc JJ McGinley Ltd 24-3-2005

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Enterprise plc Strategem Ltd 3-5-2005

Enterprise plc CRW Maintenance Ltd 29-7-2005

Enterprise plc Trinity Group Holdings Ltd 3-8-2005

Envesta plc Findstar plc 29-3-2001

Envesta plc Seven Telecom Ltd 19-7-2002

Envesta plc Seven Telecom Ltd 18-7-2001

Exbud Skanska SA Rzeszowskie Przedsiebiorstwo Robot Drogowych SA 3-7-2002

Fayat SA Charlatte SA 7-11-2003

Ferrovial Servicios SA Amey plc 16-4-2003

Fomento de Construcciones y Contratas SA Portland Valderrivas SA 16-4-2002 Fomento de Construcciones y Contratas SA Servia Canto SA 1-11-2002 Fomento de Construcciones y Contratas SA Grucycsa SA 31-7-2003 Fomento de Construcciones y Contratas SA La Montañesa SL 25-6-2004 Fomento de Construcciones y Contratas SA

Cementos Portland Valderrivas SA 3-10-2005

Fomento de Construcciones y Contratas SA

Grupo Hernández Cerrajero-Marepa 10-10-2005

Fomento de Construcciones y Contratas SA

Entemanser SA 13-10-2005

Galliford plc Try Group plc 22-8-2000

Galliford Try plc Gerald Wood Homes Ltd 27-3-2001

Galliford Try plc Burton Communications Ltd 30-3-2001

Galliford Try plc Knapp Group Ltd 27-7-2001

Galliford Try plc Pentland Ltd 3-10-2005

General Construction Company SA

Terna SA 3-9-2004

Generale Mobiliare Interessenze Azionarie SpA

Leonardo Finanziaria Srl 10-9-2005

George Wimpey plc Laing Homes Ltd 16-10-2002

George Wimpey plc Alfred McAlpine Homes Holdings Ltd 14-8-2001

Gnomon Construction Company SA GEKAT SA 19-4-2002 Gnomon Construction Company SA Koronis SA 19-4-2002 Gnomon Construction Company SA TEGK SA 19-4-2002 Gnomon Construction Company SA Ergokat SA 19-4-2002 Gnomon Construction Company SA Kastat SA 19-4-2002 Gnomon Construction Company SA Metrik SA 19-4-2002

Groupe GTM SA L'Entreprise Industrielle 17-5-2000

Grupo Dragados SA HBG Hollandsche Beton Groep NV 5-2-2002

Grupo Dragados SA Tratamiento de Aceites y Marploes SL 17-6-2003

Grupo Ferrovial SA Dromex SA 20-9-2000

Grupo Ferrovial SA Belfast City Airport Ltd 27-5-2003

Grupo Ferrovial SA Compania Espanola de Servicios Publico Auxiliares SA 29-8-2003

Grupo Ferrovial SA Swissport International AG 22-8-2005

Havelock Europa plc TeacherBoards (1985) Ltd 30-6-2004

Havelock Europa plc Clean Air Ltd 15-7-2004

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Impresa Pizzarotti & C. SpA Garboli-Conicos SpA 25-7-2005

Imtech NV Landis Public Networks' Belgian and German operations 27-6-2002

Imtech NV Farnest Engineering BV 29-8-2002

Imtech NV ASPark SA's certain car park maintenance activities 28-9-2004

Imtech NV Goodmarriott & Hursthouse Ltd 21-6-2005

INEC SA Compagnie Financiere Exploitation Electriques et

Industrielles

13-7-2001

Isotron plc EBIS Iotron Ltd 16-9-2005

Isotron plc Drug Test Ltd 23-11-2005

Jarvis plc Braddons International Ltd 2-8-2002

John Laing Homes plc Beechcroft plc 14-1-2000

John Mowlem & Company plc

Bower Group plc 23-6-2000

John Mowlem & Company plc

Pall Mall Holdings Ltd 31-7-2001

KBC International Property Fund SICAV 19-5-2000

KBC Peel Hunt 18-12-2000

Keller Group plc Allied Mechanical Services Ltd 24-3-2000

Keller Group plc TCDI Inc 1-8-2000

Keller Group plc Minages et Traveaux Souterrains SA 13-12-2000

Keller Group plc Catoh Inc. 22-11-2001

Keller Group plc Lime Column Markteknik AB 15-1-2004

Kier Group plc Tudor Homes (East Anglia) Ltd 3-2-2004

Koninklijke BAM NBM NV HBG Hollandsche Beton Groep NV 11-6-2002

Kythreotis Holdings Ltd Super Beton Ltd 5-11-2002

Macquarie European Infrastructure plc

Bristol International Airport plc 20-12-2000

McInerney Holdings plc Charlton Group (UK) Ltd 21-1-2002

McInerney Holdings plc Alexander Developments (North East) Ltd 21-5-2004

Mercury Group plc Smith Melzack Pepper Angliss Ltd 1-3-2005

MITIE Group plc McCartney Group Ltd, The 5-6-2000

MITIE Group plc MITIE Engineering Services (North) Ltd 16-8-2000

MITIE Group plc MITIE Engineering Services (Scotland) Ltd 16-8-2000

MITIE Group plc MITIE Roofing (South East) Ltd 16-8-2000

MITIE Group plc Mitie Managed Services Ltd 25-4-2002

MITIE Group plc Trident Safeguards Ltd 2-7-2003

MITIE Group plc Eagle Pest Control Services UK Ltd 3-7-2003

MITIE Group plc Executive Holdings Ltd 5-11-2003

MITIE Group plc Intruder International Ltd 4-5-2005

MITIE Group plc MITIE Air Conditioning (North) Ltd 24-8-2004

MITIE Group plc MITIE Engineering Services (Retail) Ltd 24-8-2004

MITIE Group plc MITIE Roofing Services Ltd 24-8-2004

MITIE Group plc MITIE Security (Scotland) Ltd 24-8-2004

MITIE Group plc MITIE Greencote Ltd 24-8-2004

MITIE Group plc Watch Security Ltd, The 30-6-2005

Mochlos SA Alpha Technical SA 29-4-2002

Mochlos SA Skordalos SA 29-4-2002

Mochlos SA Hellenic Constructions SA 29-4-2002

Montpellier Group plc Britannia Group plc 23-3-2000

Montpellier Group plc Allen plc's Building Contracting business 4-4-2001

Montpellier Group plc VHE Holdings plc 17-8-2001

Montpellier Group plc Jarvis Porter Group plc 22-1-2002

Morgan Sindall plc Carillion Housing 16-5-2001

Morgan Sindall plc Pipeline Constructors Group plc 20-12-2001

Mostostal Siedlce SA Hotel Warszawa 28-10-2005

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Mota-Engil SGPS SA Companhia Portuguesa de Trabalhos Portuarios e Construcoes SA

5-7-2002

NCC AB Hydrobudowa SA 1-5-2001

NCC AB Hydrobudowa SA 5-3-2003

Obrascón Huarte Lain SA Inmobiliaria Fumisa SA de CV 2-4-2003

Obrascón Huarte Lain SA Infraestructura 2000 24-6-2003

Obrascón Huarte Lain SA ZPSV Uhersky Ostroh AS 15-1-2004

Pantechniki SA LMN SA 13-1-2005

Permasteelisa SpA Josef Gartner GmbH 12-12-2000

Permasteelisa SpA Allied Bronze 7-1-2002

Permasteelisa SpA FCC-Planterm Srl 23-7-2003

Persimmon plc Tilbury Douglas Homes Ltd 22-2-2000

Persimmon plc Beazer Group plc 24-1-2001

Persimmon plc Merewood Group Ltd 5-6-2003

Persimmon plc Senator Homes Ltd 14-12-2005

Pfleiderer AG Pfleiderer Grajewo SA 16-2-2000

Proodeftiki SA Ypodomi SA 12-1-2004

Propan Homes plc Pinemount Trading Ltd 19-3-2003

Propan Homes plc Honeygrove Holdings Ltd 10-4-2003

Raven Mount plc Swan Hill Group plc 12-11-2003

Raven Mount plc Raven Group Ltd 26-11-2004

Redrow plc Tay Homes plc 29-11-2001

ROK Property Solutions plc Rockeagle Land Ltd 20-3-2001

ROK Property Solutions plc Retail Maintenance Services Ltd 6-8-2001

ROK Property Solutions plc Llewellyn Management Services Ltd 20-8-2002

ROK Property Solutions plc John Dickie Construction Ltd 13-7-2004

ROK Property Solutions plc Team Building Maintenance Ltd 18-10-2004

ROK Property Solutions plc Morrow Holdings Ltd 28-4-2005

ROK Property Solutions plc Topcon (Builders & Contractors) Ltd 6-7-2005

ROK Property Solutions plc Lemmeleg Building & Contracting Ltd 6-7-2005

Sacyr Vallehermoso SA Somague SGPS SA 12-12-2003

Sacyr Vallehermoso SA Somague SGPS SA 20-8-2004

Sacyr Vallehermoso SA Sufi SA 21-4-2005

Skanska AB Selmer AS 13-4-2000

Skanska AB Kvaerner Construction Group Limited 29-8-2000

Somague SGPS SA Somague Concessões e Serviços SA 5-10-2002

Sonae SGPS SA Sonae Imobiliaria SGPS SA 17-10-2001

Sonae SGPS SA Sonae Produtos e Derivados Florestais SGPS SA 30-4-2003

Sonae SGPS SA Modelo Continente SGPS SA 16-11-2004

Sonae SGPS SA Imocapital SGPS SA 16-12-2004

Sonae SGPS SA Modelo Continente SGPS SA 19-5-2005

South Staffordshire Group plc

TradeSource Ltd. 12-4-2000

Smith Business Forms Ltd 13-10-2000

Regency Financial Holdings plc 15-5-2002

Speedy Hire plc Kingfisher Hire and Sales Ltd 3-7-2003

Speedy Hire plc Ashtead Plant Hire Company Ltd's Big Air division 28-7-2003

Speedy Hire plc St Vincent Plant Ltd 1-9-2003

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Speedy Hire plc Delyn Hire Centres Ltd 3-10-2005

Spie Batignolles SA Laurent Bouillet SARL 19-11-1999

St Mark Homes II plc St Mark Homes Capital plc 24-9-2004

Stenoak Associated Services plc

Allen Surfacing Ltd 21-5-2001

T Clarke plc H & C Moore Ltd. 19-7-2000

T Clarke plc JJ Cross Ltd 27-6-2001

T Clarke plc GDI Electrical Company Ltd 14-1-2002

T Clarke plc AG Aylward EMS (Maintenance and Minor Works) Ltd 3-7-2003

T Clarke plc Mitchell & Hewitt Ltd 12-3-2004

T Clarke plc Anglia Electrical Services Ltd 15-9-2004

T Clarke plc Smith Contracting Services Ltd 10-1-2005

T Clarke plc Waldon Electrical Contractors Ltd 6-5-2005

Taylor Woodrow plc Monarch Development Corporation 7-4-2000

Taylor Woodrow plc Bryant Group plc 22-1-2001

Taylor Woodrow plc Wilson Connolly Holdings plc 1-9-2003

Technip SA Coflexip Stena Offshore 3-7-2001

Technip SA Isis 26-7-2001

Teesland plc Property Fund Management plc 27-9-2004

Teixeira Duarte Engenharia e Construçoes SA

Compania General de Servicios y Construcciones SA 1-7-2003

Telephone Maintenance Group plc

Westcom Technical Services Ltd 28-10-2005

Telindus Group NV Arche Communications SAS 21-5-2005

Themeliodomi SA Nestos SA 25-1-2002

United Utilities plc National Grid Transco plc's north-of-England gas network 31-8-2004

Vinci SA Groupe GTM SA 12-7-2000

Vinci SA Worldwide Flight Services Inc. 10-9-2001

Vivendi Universal SA MP3.com Inc. 21-5-2001

Vivendi Universal SA Elektrim Telekomunikacja Sp zoo 5-9-2001

Vivendi Universal SA USA Networks Inc.'s entertainment assets 17-12-2001

Vivendi Universal SA Union Generale Cinematographique SA 30-9-2002

VT Group plc Guidance Enterprises Group Ltd 25-10-2004

Watermark Group plc Globe Audio Ltd 24-5-2002

Watermark Group plc M'n'H Recycling Ltd 27-3-2003

Watermark Group plc Media On The Move Ltd 21-7-2003

Watermark Group plc Air Fayre Ltd 21-1-2004

Westbury plc Prowting plc 17-5-2002

Wigmore Group plc, The Speymill Contracts Ltd 26-3-2002

Wigmore Group plc, The DF Blanchard (Salisbury) Ltd 30-6-2003

Wilson Bowden plc Ward Homes Group Ltd 14-11-2003

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Appendix C: T-test total 41-day CAR One-Sample Statistics N Mean Std. Deviation Std. Error Mean total ₂₀₇ _.03797 _.144629 _.010052 One-Sample Test Test Value = 0 95% Confidence Interval of the Difference t df Sig. (2-tailed) Mean

Difference Lower Upper

total 3.777 206 .000 .037969 .01815 .05779

CAR Total (day 41)

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Appendix D: Correlations Pearson Correlation Sig. (2-tailed) Total CAR International Outside

EU Related Cash Shares

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Appendix E: Multiple regression analysis

Regression 1

Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .264(a) .070 .030 .143215

a Predictors: (Constant), Yearhypemm, sharesmm, stakemm, relatedmm, internmm, percmm, cashmm, outsideeumm

ANOVA(b)

Model

Sum of

Squares df Mean Square F Sig.

Regressio

n .287 8 .036 1.747 .090(a)

Residual 3.835 187 .021

1

Total 4.122 195

a Predictors: (Constant), Yearhypemm, sharesmm, stakemm, relatedmm, internmm, percmm, cashmm, outsideeumm

b Dependent Variable: total

Coefficients(a) Unstandardized

Coefficients

Standardized Coefficients

Model B Std. Error Beta t Sig.

(Constant) _.039 _.010 _3.772 _.000 internmm -.006 .029 -.018 -.204 .839 outsideeum m .036 .043 .074 .840 .402 relatedmm .029 .022 .098 1.355 .177 cashmm _-.018 _.023 _-.062 _-.780 _.436 sharesmm -.097 .046 -.161 -2.118 .035 percmm .031 .087 .026 .355 .723 stakemm -.013 .034 -.028 -.376 .708 1 Yearhypem m .050 .024 .158 2.133 .034

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Regression 2

Variables Entered/Removed(b) Model Variables Entered Variables Removed Method 1 Yearhypem m, sharesmm, stakemm, relatedmm, percmm, outsideeum m, cashmm(a) . Enter

a All requested variables entered. b Dependent Variable: total

a Predictors: (Constant), Yearhypemm, sharesmm, stakemm, relatedmm, percmm, outsideeumm, cashmm

ANOVA(b)

Model

Sum of

Regressio

n .286 7 .041 2.001 .057(a)

Residual _3.836 ₁₈₈ _.020

1

Total 4.122 195

a Predictors: (Constant), Yearhypemm, sharesmm, stakemm, relatedmm, percmm, outsideeumm, cashmm b Dependent Variable: total

Coefficients

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Regression 3

Variables Entered/Removed(b) Model Variables Entered Variables Removed Method 1 Yearhypem m, sharesmm, stakemm, relatedmm, percmm, outsideeum m(a) . Enter

Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 _.257(a) _.066 _.037 _.142715

a Predictors: (Constant), Yearhypemm, sharesmm, stakemm, relatedmm, percmm, outsideeumm

ANOVA(b)

Model

Sum of

Regressio

n .273 6 .045 2.232 .042(a)

Residual 3.849 189 .020

1

Total 4.122 195

a Predictors: (Constant), Yearhypemm, sharesmm, stakemm, relatedmm, percmm, outsideeumm b Dependent Variable: total

Coefficients

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Regression 4

Variables Entered/Removed(b) Model Variables Entered Variables Removed Method 1 outsideeurel mm, stakemm, percmm, Yearhypem m, sharesmm( a) . Enter

a Predictors: (Constant), outsideeurelmm, stakemm, percmm, Yearhypemm, sharesmm

ANOVA(b)

Model

Sum of

Regressio

n .374 5 .075 3.792 .003(a)

Residual 3.748 190 .020

1

Total _4.122 ₁₉₅

a Predictors: (Constant), outsideeurelmm, stakemm, percmm, Yearhypemm, sharesmm b Dependent Variable: total

Coefficients

Model _B _{Std. Error} _Beta t Sig.

(Constant) _.039 _.010 _3.839 _.000 sharesmm -.078 .043 -.128 -1.811 .072 percmm .040 .085 .033 .472 .638 stakemm -.001 .032 -.002 -.023 .982 Yearhypem m .051 .022 .161 2.297 .023 1 outsideeurel mm .143 .051 .195 2.774 .006

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Regression 5

Variables Entered/Removed(b) Model Variables Entered Variables Removed Method 1 cashmm, outsideeurel mm, percmm, Yearhypem m, stakemm(a) . Enter

a Predictors: (Constant), cashmm, outsideeurelmm, percmm, Yearhypemm, stakemm

ANOVA(b)

Model

Sum of

Regressio

n .311 5 .062 3.097 .010(a)

Residual _3.812 ₁₉₀ _.020

1

Total 4.122 195

a Predictors: (Constant), cashmm, outsideeurelmm, percmm, Yearhypemm, stakemm b Dependent Variable: total

Coefficients

(Constant) .038 .010 3.775 .000 percmm .006 .085 .005 .075 .940 stakemm -.002 .033 -.004 -.052 .958 Yearhypem m .050 .023 .156 2.196 .029 outsideeurel mm .148 .052 .202 2.850 .005 1 cashmm -.006 .022 -.019 -.258 .797

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Regression 6

Variables Entered/Removed(b) Model Variables Entered Variables Removed Method 1 sharesmm, stakemm, Yearhypem m, internrelmm , percmm(a) . Enter

a Predictors: (Constant), sharesmm, stakemm, Yearhypemm, internrelmm, percmm

ANOVA(b)

Model

Sum of

Regressio

n .301 5 .060 2.993 .013(a)

Residual 3.821 190 .020

1

Total _4.122 ₁₉₅

a Predictors: (Constant), sharesmm, stakemm, Yearhypemm, internrelmm, percmm b Dependent Variable: total

Coefficients

(Constant) _.039 _.010 _3.862 _.000 percmm .031 .086 .025 .356 .722 stakemm .008 .032 .018 .256 .798 Yearhype mm .059 .022 .184 2.621 .009 internrelm m .063 .032 .140 1.979 .049 1 sharesmm -.081 .043 -.133 -1.866 .064

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Appendix F: Frequencies

Statistics

outsideeu

Internation

al(1) outsideeurel internrel

Valid ₂₀₇ ₂₀₇ ₂₀₇ ₂₀₇ N Missin g 0 0 0 0

Frequency Table

Outside EU

Frequency Percent Valid Percent

Cumulative Percent 0 187 90.3 90.3 90.3 1 20 9.7 9.7 100.0 Valid Total ₂₀₇ _100.0 _100.0 International

Cumulative Percent 0 151 72.9 72.9 72.9 1 ₅₆ _27.1 _27.1 _100.0 Valid Total ₂₀₇ _100.0 _100.0

Outside EU and Related

Cumulative Percent .00 199 96.1 96.1 96.1 1.00 ₈ _3.9 _3.9 _100.0 Valid Total 207 100.0 100.0

International and Related