Automotive mergers and acquisitions and the influence on the acquirers stock prices

Automotive mergers and acquisitions and the

influence on the acquirers stock prices.

Abstract

This thesis examines the factors that determine the success of M&As within the automotive industry. 46 automotive transactions in the period between January 1997 and December 2013 were analyzed on stock returns in a five day window around the announcement day. An insignificant cumulative average abnormal return of 0.15% was found. Several variables were tested on their influence on the cumulative abnormal return. The coefficient of relative size has a negative and significant effect on the cumulative abnormal return. Furthermore a significant negative relation was found between Chinese- and Portuguese companies on the cumulative abnormal return, indicating that acquirers from these countries under perform compared to the reference point.

Author:

Joris Meens

Student ID: 6118887 Supervisor: N. Martynova

Universiteit van Amsterdam

BSC Economics and Business Track Finance and Organization Amsterdam, 20 February 2014

Contents

Introduction ... 3

Literature review ... 4

M&A’s in general, and the determinants of success: ... 4

Automotive M&As ... 7

Determinants in success M&As automotive industry ... 8

Hypotheses ... 10

Methodology ... 11

Event study and time period of interest: ... 11

Explanatory variables: ... 12 Data ... 13 Results ... 14 Univariate regression: ... 15 Multivariate regression: ... 15 Correlations: ... 17 Discussion results ... 18 Conclusion ... 20 Literature List: ... 22 Appendix ... 25 2

Introduction

During 2013, a record number of 80 million cars and trucks were sold worldwide (LeBeau, 2012). Although being a record, expectations are that this number will increase to over a 100 million in 2018, making it one of the largest industries in terms of sales number and revenues.

Since the early 90’s the automotive landscape has changed drastically. Changes in environmental thinking, economic circumstances, more advanced and costly technology and increasing globalization are factors that have had an impact on the way automobile

manufacturers operate nowadays. One of the often seen outcomes of these challenges is that automotive companies merge or acquire with other companies. The following widely accepted expectations are that the future of the automotive industry will be determined by even more mergers, creating a small amount of massive manufacturers serving the world (Blake et al., 2003).

One of the leading drivers for mergers and acquisitions in general are synergy benefits in all forms and shapes. In almost all investigated cases the target shareholders earn

significant benefits during an overtake. This image is less one-sided when it comes to the acquiring party. Loughran and Vijh (1997) concluded that the acquiring shareholders are not rarely left with a near zero or negative return. Overpricing and disappointing levels of synergy are given as two of the most important contributors of these outcomes. Cummins and Weiss (2004) somewhat softened this conclusion by stating that M&A’s are more likely to have beneficial outcomes for the acquirer if the transaction is inter-industrial. This statement is underlined by Kohers and Kohers (2000), who investigated M&A’s in the high-tech industry and found positive returns for the acquirers.

Today only limited research about M&As within the automotive industry has been done. The recent history has seen both successes and failures, and with more expected M&As coming further investigation could make a significant contribution. The leading question during this thesis will therefore be:

What drives the success of mergers and acquisitions in the automotive industry?

This research is done by investigating automotive mergers and acquisition during 1997 until 2013 registered in the Zephyr database. One of the targets is to investigate if any abnormal returns were shown round the announcement day of the M&As. Furthermore a close look will be taken in the potential determinants of making the M&A a success/failure.

The following part contains a literature review about the specific issues of M&As in general and within the automotive industry. The third part contains the data and the

methodology used during this thesis. Thereafter the results are described and discussed, followed by the conclusion.

Literature review

This chapter aims to answer the posed question and to find the determinants to success/failure in M&As within the automotive industry based on existing literature. To do so, the first paragraph will look at M&As in general and the determinants of success/failure. The second paragraph will investigate the specific drivers behind automotive M&As. The final paragraph will conclude if the determinants found in the first paragraph are applicable for the

automotive industry

M&A’s in general, and the determinants of success:

Today mergers and acquisitions are a widely known and investigated topic in economics. A merger or acquisition is described as a group of companies that share their activities and resources. This can be achieved by one company taking over the other, but also by companies cooperatively sharing resources on a more horizontal level. According to Martynova and Renneboog (2006, P.9), the key motives for companies to give presence in M&As are potential synergies and the correction of managerial failure.

Looking deeper in to what this actually means, the term synergy is described as: the

interaction or cooperation of two or more organizations, substances, or other agents to produce a combined effect greater than the sum of their separate effects

(oxforddictionaries.com). Typically, takeovers create synergies. These can be found both in the operational-, (economies of scale, eliminating duplicate activities, etc.) as in the financial field (expended capital, access to assets, etc.) and create a new business unit that operates and uses its resources more efficient. The correction of managerial failure is explained as the benefits that can arise through implementing better management, in the widest sense of the word. This implies that the acquirer thinks it has the managerial resources to improve the targeted companies performance.

Looking at why M&As in specific are a common way of investment Dickerson et al. (1997) investigated the drivers and impact of M&As on company performance. During this research 3 advantages in favor for M&As compared to other investment methods were pointed out. First of them is the fast materialization of the investment. When one company

takes over another, the corresponding investment can be materialized on the financial

statements in a relatively limited amount of time. This normally is faster than the time needed for other investments to payoff. The second motivation for M&As is that companies can gain access to new markets and knowledge. This can lead to diversification of the existing market operations and to access of intangible assets of the acquired party. The last given motivation in the article is that by acquiring a rival company instead of initiating a self-made investment this rival is removed of the market, potentially increasing the market power of the acquirer. Although the potential benefits are numerous and plausible, reality has shown very mixed outcomes on the actual results. During history several methods were developed in determining the success of a company takeover. An often picked method, that will also be used during this research, is the short term stock price movements round the announcement date (Andrade et al. (2001), Kiymaz & Baker (2008)). This method checks whether any abnormal returns are seen around the announcement date of a merger or acquisition in a short time window.

As mentioned before, literature is almost unanimous in concluding that target

companies significantly benefit from a takeover in terms of share price reactions. This image is less one sided when it comes to the acquirer. Although a positive abnormal return round the announcement day is possible, many researches find a zero or negative abnormal return. Martynova and Renneboog (2006) investigated what is known as the fifth takeover wave starting at the second half of the 90’s and onwards, and found a significant positive but small abnormal return . During their research several trends regarding modern day M&A’s were investigated and checked for their impact on the acquirers share prices round the

announcement day. The outcomes of their results could be used to create characteristics that could help explain the chance of an overtake to become successful for the acquirer. Two trends that were found that could be especially feasible for the automotive industry and are described below:

Cross-border vs. Domestic

Martynova and Renneboog saw an increased number of cross border transactions since the mid-nineties in almost every industry. Despite this increased popularity results indicated that cross border transactions generate less abnormal return for the acquirer compared to domestic transactions, namely 0.4% versus 0.6%. This conclusion corresponds to the findings of Mentz and Schiereck (2008, p12.), who investigated the effect of cross-border M&A’s on share

prices within the automotive supply industry. The main argument used to explain this outcome is that domestic M&As may lead to managerial difficulties due to cultural and regulatory differences. Stock markets might anticipate for these difficulties which in turn will reduce the abnormal return.

Method of Payment

The method of payment is a widely discussed and important part of a merger or acquisition. Companies generally have 3 payment options when it wants to take over another firm, namely cash, shares or a combination (Faccio & Masulis, 2005). Martynova and Renneboog (2006, p. 21) stated that the method of payment chosen by the acquirer signals something about the foreseen quality of the targeted firm. The idea is that when a company offers cash it wants to pay off the target shareholders to prevent potential future benefits to leave the entity. A share based offer on the other hand, signals that the acquirer wants the existing target shareholders to remain involved to share risk. If the overtaking party believes in underpricing of the target, it prefers to offer a cash bid based on the idea of keeping the profit inside. In line with this idea Martynova and Renneboog found a higher abnormal return for the acquirer when the bid is offered involving cash, compared to full stock offers. This is conclusion is confirmed by Heron and Lie (2002), who also found a larger abnormal return for the acquirer if the offer is made in cash.

Relative Size

Another frequently used characteristic in the field of M&As is relative size. Although Martinova en Renneboog don’t separately discuss this item, numerous literature show the impact of the relative size of the transaction on the successes of the takeover. The conclusions of these studies are not consentient though. Relative size is explained as the ratio of the targets assets or deal value to the assets of the acquirer. Moeller et al. (2004) found a negative

correlation between relative size of the transaction and the short-term returns on stock prices of the acquirer. One of their explanations is that the larger the transaction at stake the more time it takes to finalize it. This could delay the potential benefits reaching the acquirers shareholders. Haleblian and Finkelstein (1999) on the other hand, found that production companies do benefit from increased relative size transactions. The main explanation for this conclusion is that the larger the companies at stake, the more potential benefits of economies of scale arise through combining producing processes.

Automotive M&As

The automotive industry has seen lots of mergers and acquisitions in the last 20 years and the expectations are that this trend will continue (Dannenberg & Kleinhans (2007), Blake et al., (2003)). Looking at the industry specific drivers for this expectations, both Elie (2012) and Campbell (2012) investigated the automotive landscape and found diverse motivations for automotive M&As which are listed below

Geographic outlook

Cross border investments in the automotive industry are now more important than ever in the pursue of new customer markets. With new fast growing economies and increasing

globalization, upcoming markets attract many investments from foreign countries. These investors typically are the well-known developed companies that are seeking for entrances in new geographical markets.

On the other hand local (less developed) companies are increasingly pursuing M&As as well, with the main purpose of acquiring knowledge and resources that decrease the gap with established (foreign) competitors. In 2011 39% of all automotive transactions were cross border and the future expectations are that this number will increase (Campbell, 2012).

Import tariffs and factories

Beside taking over automotive corporations, another frequently used way of getting entrance to new geographical markets is by taking over existing assembly plants. Apart from the advantages of producing close to the market, this method can give import tax benefits (Francois & Spinanger (2004), P.6). Numerous countries have high import tariffs on motor vehicles. A strategy to bypass these import tariffs is to produce the vehicles within the

countries themselves. This strategy is being used by an increasing number of companies as the countries with the high tariffs are not rarely the upcoming markets with new potential

customers (Muller, 2012).

Increasingly complex technology

The automobile is becoming a more and more complex product. New materials, more strict rules on safety and environment and the increasing role of infotainment are the most important factors of this increased complexity (Kalmbach et al. (2011)). Reduced product development time also plays a role in this context. To acquire the needed resources and share

the initial investments for this increasing technological challenges, companies might collaborate with or overtake an existing party.

According to Campbell (2012) the weights given in the table below describe the weights attached to the described drivers.

Determinants in success M&As automotive industry

Although literature describes a large amount of possible motives for automotive companies to overtake/collaborate with other manufacturers, only very little has been written about the specific determinants of success. Looking at the automotive specific drivers for M&As and the influence of these characteristics on the announcement effect described by literature, the following expectations are made.

Cross-border and New Geographical Markets

Literature predicts a slightly smaller return for cross-border transactions compared to

domestic Mentz and Schiereck (2008, p12.). The main given motivation is that differences in culture and regulatory requirements lead to managerial imperfection. Despite this negative tendency, geographical expanding is at the top list of automotive priorities. It seems plausible

0% 5% 10% 15% 20% 25% 30% 35% 40% 45%

M&A drivers

that distinction should be made regarding this topic between cross-borders that do or do not give access to new geographical markets. Companies that enter new markets through a merger or acquisition are therefore expected to outperform cross-border M&As that will not do so.

Import Tariffs

In expansion of the cross-border vs. domestic item, avoiding import tariffs is an important motive for automotive companies to produce products in the foreign country at stake. As explained before, foreign companies acquiring factories/companies in these countries is the most common way to avoid the tariffs. The expectations therefore are that these transactions have a positive effect on the abnormal return (Muller, 2012). For this research, import tariffs above 15% are considered high.

Firm Size

Although literature is not cohere regarding the influence of relative firm size on short-term M&A performance, the argument given by Haleblian and Finkelstein (1999) regarding production companies seems plausible. Cars are high-tech complicated products that could be suitable for component and production sharing. Especially as this is the main motive for car manufacturers to seek for cooperation with other manufacturers (Campbell (2012), Elie (2012)). So relative firm size is expected to have a positive effect.

Percentage Stake Purchased

Although literature doesn’t indicate any influence of the percentage stake acquired on the abnormal returns of the acquirer, reasons are there to believe that this relation does exist. Due to the importance of operational synergies in automotive M&As, integration of operational activities is at the top list of importance during a takeover (Blake et al., 2003). An increased stake enlarges the direct control over the target, which can shorten the time needed to implement the combined activities. Expectations therefore are that there is a positive correlation between the percentage stake acquired and the acquirers abnormal return.

Method of Payment

Literature is coherent regarding the influence of payment method on the acquirers share price performance round the announcement date. (Martynova & Renneboog (2006), Heron & Lie

(2002)). Cash financed transactions outperform equity financed transactions. Because of this widely confirmed conclusion, the same is expected for the automotive industry.

Acquirers Nationality

To check whether the nationality of the acquirer has any influence on the short-term success of a M&A, the irelation of the acquirers home country on CAR will be tested. Special attention regarding this subject will be paid to China. The automotive world is known for a limit amount of old and traditional players (Bernhart et al (2011)). One of the new upcoming automotive producing economies that is included in this research is China (Elie, 2012). At the moment China is investing heavily on R&D within the automotive industry. Despite this China still legs experience and specific knowledge on both the automotive industry as M&A transactions (Zhao & Anand, (2009)). It is therefore expected that China will have a negative effect on the cumulative abnormal return.

Hypotheses

In order to answer the questions posed in the introduction of this thesis and to see which factors might influence the probability of success three hypothesis will be tested.

The first part of the empirical research examines if any abnormal returns are seen round the announcement day of an automotive merger or acquisition. The first hypothesis regarding this part is given below.

Hypothesis I: Automotive acquirers will realize a positive stock price effect round de announcement date of an automotive merger or acquisition.

Because of the increasing importance of cross-border transactions on the automotive industry special attention regarding this topic will be paid. The hypothesis regarding this topic that will be tested is described below.

Hypothesis II: Automotive M&As that give entrance to new geographical markets lead to a higher cumulative abnormal return.

As stated in the literature part, China is a relatively new mass-producer of motor vehicles. Due to leg in experience Chinese acquirers are expected to underperform.

The third hypothesis therefore is:

Hypothesis III: Chinese acquirers will realize a lower abnormal return.

Methodology

The empirical analysis of this thesis contains two parts. The first part is a standard event study to check on abnormal returns, the second part tries to find explanatory variables that

contribute to this abnormal return.

Event study and time period of interest:

To calculate the abnormal return round the announcement day, the standard event study methodology will be applied. According to de Jong (2007), a solid fundament for any event study should contain the following steps; first identify the event of interest, secondly specify a benchmark model to calculate the normally expected returns and finally calculate and

interpret the found abnormal returns. These steps and their implications on the final model will be described in more detail.

The event of interest during this research is the initial announcement day of the merger or acquisition. The base model that will be used to determine the (ab)normal return during this study is known as the Market Model. The Market Model is explained by the following regression:

𝑅 = 𝛼 + 𝛽 ∗ 𝑅𝑚𝑘𝑡

In this model α is a constant and β is an estimate that indicates the responsiveness of a stock to the market. This market is the index of the stock exchange on which the acquirer’s stock is traded. The time window that will be used for the estimation of the expected normal return is one calendar year starting at a month and one year prior to the announcement day. The abnormal return then is calculated by comparing the actual returns of the stock with what it should be according to the expected normal return model. A 5 day window around the announcement day is used, as suggested by Georgen and Renneboog (2004).

𝑅 = 𝛼 + 𝛽 ∗ 𝑅𝑚𝑘𝑡 + 𝜀

The abnormal return is captured in the ‘ℇ’ symbol in the equation above. Now the cumulative abnormal returns per company can be calculated.

After all the companies separate returns are calculated, the Cumulative Average Abnormal Return (CAAR) will be determined. The CAAR is an event cross sectional descriptive which is formulated by:

CAAR =1_𝑛∑𝑛 CAR 𝑖=1

With this formula portfolio wide characteristics can be calculated. This makes it possible to create more general statements about abnormal returns during the M&A announcement period. To check whether the found values are significant, a t-test will be completed. This test is expressed in the following formula:

𝑡 = 𝐶𝐴𝐴𝑅𝑝𝑡/_√𝑁𝑠𝑡

In which:

• CAARpt = cumulative average abnormal return for time interval t (5 days) • CARnt= cumulative abnormal return for company n and time interval t (5 days) • St = standard deviation for time interval t (5 days)

• √𝑁 = square route of number of firms

The standard deviation is calculated by the following formula:

𝑆 = √_(𝑛−1)1 �𝑛_𝑘=0(𝐶𝐴𝑅𝑛𝑡 − 𝐶𝐴𝐴𝑅𝑝𝑡)²

Explanatory variables:

As found in the literature, stock prices during M&As in general are affected by a wide amount of different factors. From literature several variables were found that could explain any potential abnormal returns during an automotive M&A. To test the effect of these

characteristics on abnormal returns several regressions were run. The variables of interest that were included in the regressions are described in the following table:

Table 1: model variables:

Variable Type Description

CAR continues Cumulative Abnormal Return

PM dummy 1 if Cash, 0 if equity

RS continues Ratio deal value/acquirer’s assets

CB dummy 1 if cross-border, 0 if domestic

NE dummy 1 if new geographical entrance, 0 if not

SI continues Percentage stake increase

HT* dummy 1 = High tariff, 0 ≠ high tarrif

China dummy 1 = China, 0 ≠ China

Germany dummy 1 = Germany, 0 ≠ Germany

US dummy 1 = US, 0 ≠ US

Italy dummy 1 = Italy, 0 ≠ Italy

France dummy 1 = France, 0 ≠ France

Sweden dummy 1 = Sweden, 0 ≠ Sweden

Portugal dummy 1 = Portugal, 0 ≠ Portugal

Russia dummy 1 = Russia, 0 ≠ Russia

Japan dummy 1 = Japan, 0 ≠ Japan

India is used as country dummy reference point and is therefore not included

*import tariffs above 15% are seen as high tariff countries

The regressions are performed in two steps. First a univariate regression of all the separate variables, excluding countries, on CAR is performed. This shows the separate effect of the variables on CAR. The model has the following shape.

𝐶𝐴𝑅 = 𝛼 + 𝛽1𝐸𝑥𝑝𝑙𝑎𝑛𝑎𝑡𝑜𝑟𝑦𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒 + 𝜀

Secondly, a multivariate regression of the following shape will be performed to determine the effects of the variables on CAR.

𝐶𝐴𝑅 = 𝛼 + 𝛽1𝑃𝑀 + 𝛽𝑅𝑆 + 𝛽3𝐶𝐵 + 𝛽4𝑁𝐸+ 𝛽5𝑆𝐼 + 𝛽6𝐻𝑇 + 𝛽7𝐶ℎ𝑖𝑛𝑎 + 𝛽8𝐺𝑒𝑟𝑚𝑎𝑛𝑦 + 𝛽9𝑈𝑆 + 𝛽10𝐼𝑡𝑎𝑙𝑦 + 𝛽11𝐹𝑟𝑎𝑛𝑐𝑒 + 𝛽12𝑆𝑤𝑒𝑑𝑒𝑛 + 𝛽13𝑅𝑢𝑠𝑠𝑖𝑎 + 𝛽14𝐽𝑎𝑝𝑎𝑛 + 𝜀

Data

This research aims to investigate if any abnormal returns are detected round the

announcement date of a merger or acquisition in the automobile manufacturer industry. A number of automotive mergers is added into a portfolio and used for statistical research. This portfolio contains M&As registered in the Zephyr database starting from the 1st of January 1997 (the first recorded date in the database), until the 31th of December 2013. Because this

study investigates abnormal stock returns only transactions of listed acquirers were included. This restriction doesn’t follow for the target company. These requirements resulted in 46 M&A deals, listed in the table in the appendix. More details on the specific characteristics of the transactions can be found in the appendix.

The daily stock returns of the both the manufacturers as well as daily market returns from the indices on which the acquirers stocks are traded are downloaded from the

DataStream database.

Results

This part describes and interprets the results found. First the abnormal return round the announcement date will be discussed. Thereafter the described variables and their influence on the cumulative abnormal returns will be treated.

Abnormal returns:

To test whether any abnormal returns were found round the announcement date financial data of 46 automotive transactions were analyzed on abnormal returns. The chosen time interval is 5 days (-2,2) surrounding the announcement day. As mentioned above the abnormal returns are calculated using the market model.

The CAAR that is found for the given time window is given in the table below: Table 2: CAAR

CAAR

return 0.1463%

t-statistic t = 0.251

As expected in the first hypothesis there is a positive abnormal return round the

announcement day which has a value of 0.15%. In order to test whether this abnormal returns significantly differ from zero, the test for CAARs is performed. As shown in the table the t-value is 0.251 and is not significant at the 10% level. Due to this insignificance no general statements about the cumulative average abnormal return can be made, despite the positive return found for the investigated sample.

Univariate regression:

In order to test what variables influence the cumulative abnormal returns, several OLS-regressions were performed. To express the separate effect of the variables on the cumulative abnormal returns a univariate regression was performed of all variables separately on CAR (by OLS). The results are given in the table below.

Table 3: univariate regression

CAR = Dep. Var. Coefficient (Std err) P-Value R²

Relative Size -2.780 (1.667) 0.099* 0.063

Payment Method 0.501 (1.338) 0.710 0.004

Cross Border 0.342 (1.439) 0.813 0.001

New Geog. Entrance 0.747 (1.313) 0.572 0.008

Stake Increase 0.409 (0.717) 0.572 0.008

Import Tariffs 1.348 (1.688) 0.429 0.016

N=46

*significant at 10% level

As shown in the table the regressed variables have barely any effect on the cumulative abnormal return. The only variable that is significant (at the 10% level) is Relative Size. The coefficient of relative size has a negative value of -2.780, which indicates that when the deal value increases compared to the acquirers assets a smaller abnormal return can be expected. Expectations based on the literature were that relative size would have a positive effect on the cumulative abnormal return. The R² value of 0.063 indicates that relative size alone explains 6.3% of the cumulative abnormal return.

Looking at the effects of the other variables on CAR it is interesting to see that all variables, except for cross-border, have an effect on CAR in the direction as expected. Although this suggests a certain influence on the abnormal returns, due to the insignificance of the variables no general statements about the influence of the variables on CAR can be made.

Multivariate regression:

To test how much of the cumulative abnormal return can be explained by the described variables, an OLS regression of all the variables on CAR was performed. The following results were found.

Table 4: multivariate regression:

CAR = Dep. Var. Coefficient (Std err) P-Value

Relative Size -3.217 (1.882) 0.098*

Payment Method 0.0480 (1.762) 0.978

Cross Border -4.126 (2.876) 0.163

New Geog. Entrance -0.239 (2.217) 0.915

Stake Increase 0.526 (1.012) 0.607 Import Tariffs 0.922 (2.132) 0.669 China -6.780 (3.823) 0.087* Germany -2.216 (3.372) 0.517 US -4.263 (3.634) 0.251 Italy -2.324 (4.822) 0.634 France 0.052 (3.861) 0.989 Sweden 0.623 (3.390) 0.856 Russia -3.913 (5.40) 0.486 Portugal -12.705 (5.946) 0.042** Japan -3.207 (3.310) 0.341 constant 6.159 (4.288) R² = 0.3372 N = 46

*significant at 10% level, **significant at 5% level

The model has a R² value of 0.3372, indicating that the tested variables explain CAR for 33.72%. Corresponding to the univariate regression, relative size has a negative coefficient and is the only significant (non-country) variable at the 10% level. In contrast to the univariate regression, the coefficients of cross-border and new geographical entrance have become negative but remain insignificant. Due to this insignificance no general statements about the non-country variables, apart from relative size, can me made.

In order to check the influence of the acquirer’s nationality on the cumulative abnormal returns, country dummy variables were included in the regression. Including the countries leads to two significant variables, namely China and Portugal. Confirming

expectations, the Chinese acquirers earn a lower abnormal return compared to the acquirers from India, which were used as the country dummy reference point. The coefficient has a

value of 6.78 which is significant at the 10% level. Portugal has a negative coefficient of -12.71. This negative relation on CAR is significant at the 5% level.

The other country coefficients all are insignificant and therefore no general statements can be made.

Correlations:

To check the relations between the different variables Pearson r correlation coefficients were calculated. The coefficients are values that vary between -1 and 1 and express the relationship between two variables. For the interpretation of the strengths of the correlations a Pearson’s r between +.30 to +.39 is considered as a moderate positive relationship and a Pearson’s r between +.40 to +.69 as a strong positive relationship. Correlations between -.30 to -.39 were considered as a moderate negative relationship and correlations between -.40 to -.69 are considered as a strong negative relationship. The table below shows the strongest correlations for the investigated variables.

Table 5: correlations

Cross-border New Geographical Entrance

New Geographical Entrance 0.4291

High Import Tariffs 0.4703

China -0.6785

A correlation coefficient of 0,4291 indicates a strong positive correlation between cross-border transactions and new geographical entrances. This indicates that cross-cross-border transactions are often used for new geographical entrances. As explained in the literature review, one of the most important motives for automotive cross-border transactions is the seek for new geographical markets. This expectation is underlined by the high correlation.

Another high positive correlation is found for high import tariffs and new geographical entrances. This shows that the new geographical markets that automotive companies seek to enter often have a high import tariff. As mentioned in the literature, the upcoming economies that attract many foreign automotive companies not rarely have high import tariffs on motor

vehicles (Muller, 2012). Therefore the high correlation is explainable and in line with the expectations.

A high negative correlation was found between China and cross-border transactions, indicating that Chinese companies barely merge with or acquirer foreign companies. Kung & Chang (2004) investigated the Chinese automobile market and found several possible

explanations that could explain this outcome. The first argument given is that China is one of the quickest developing countries. Its automotive industry is far less developed compared to the established foreign companies. The Chinese customer market is booming and demanding higher quantities than the Chinese automakers can deliver. Therefore the Chinese

manufacturers have less incentives to expand their geographical footprint.

Another argument given by Kung & Chang is that the Chinese government has a big influence on China’s larger companies and uses this power to enhance companies to invest in local activities. This again reduces incentives to invest abroad.

Discussion results

This section discusses the found results for this research. The first part comments on the found abnormal returns. The second part describes the effect of the variables on the cumulative abnormal returns.

Abnormal returns:

One of the questions posed at the beginning of this research is whether any abnormal returns are found on the acquirers stock prices during an automotive merger or acquisition. The expectations prior to the regressions were that automotive M&As would lead to positive abnormal returns for the acquirers. The found cumulative average abnormal return found for the 5 day time period round the announcement day is 0.1463%. Although this number suggests a positive abnormal return, the resulted t-value is 0.251 and is therefore not significant.

Due to this insignificance the first hypothesis will be rejected. As mentioned before, many literature find a near zero or negative abnormal return for the acquirer during M&As (Loughran and Vijh (1997), Cummins and Weiss (2004)). An actual result of 0.15% therefore can be plausible, but is not large enough to be considered as a positive abnormal return.

Succes factors

The second part of this research aims at explaining what factors determine the cumulative abnormal return. A univariate- as well as a multivariate regression were run to check the influence of the variables on CAR. The multivariate regression has a R² value of 33.72%, so the model is explained to only a limited extend by the chosen variables. Looking closer at the individual variables, the control variable relative size is significant at the 10% level and has a negative contribution to CAR in both the univariate- as the multivariate regression. This negative coefficient is against the expectations prior to the research.

A possible explanation for this outcome is that the larger the transaction is, the more difficult and time consuming it is to implement. This could delay the acquirer taking full benefit of the transaction and therefore have a negative effect on the stock prices. This explanation is in line with the outcomes of Moeller et al (2004) as described in the literature part.

The payment method and stake increase variables both have a coefficient on CAR in the direction as expected but are not significant at the 10% level. Therefore no statements about the influence of the variables on CAR can be made.

During this research special attention is paid to cross-border transactions as they contribute to a large part of automotive M&As. The expectations were that cross-border transactions themselves would have a negative influence on CAR, but that new geographical entrances and transactions that avoided import tariffs would have a positive influence. A negative, but insignificant, coefficient for cross-border- and new geographical entrance transactions was found. The coefficient for high tariff driven transactions is positive but again insignificant. This insignificance could be caused by the limited size of transactions used for this research. Because of the insignificance of the cross-border related transactions no conclusions regarding this topic can be made, and therefore the second hypothesis cannot be accepted.

To check whether the acquirer’s nationality has any influence on the cumulative abnormal returns, country variable dummies are added to the multivariate regression model. Of the 9 included country dummies 2 resulted in a significant result, namely China and

Portugal. The China variable has a negative coefficient of -6.80, which is significant at the 10% level. This variable indicates that Chinese acquirers significantly underperform compared to the reference country. This outcome confirms expectations, despite China being one of the most promising growth markets for automotive companies(Elie, 2013). A possible

explanation for this result can be found in the inexperience of Chinese companies. As stated by Zhao & Anand (2009), Chinese companies are often still behind in terms of knowledge and experience compared to the industry leaders. The high negative correlation between the China– and the cross-border variable suggests that the Chinese automotive M&As are mostly domestic. It is possible that lag in knowledge and experience leads to poorer results and therefore lower stock price returns. This outcome is confirmed by Roll (1996) and Fuller et al.(2002), who investigated the effect of M&A experience on the company performance and found a positive relation. The third hypothesis can therefore be accepted.

Like China, Portugal also has a negative dummy coefficient which has a value of -12.71 and is significant at the 5% level. Possible explanations for this outcome are harder to find. In comparison to most countries included in this research, Portugal is only a quite small automotive manufacturer. Pike & Vale (1996) investigated the Portuguese vehicle

manufacturing industry and came up with several conclusions. Until recent, the Portuguese government applied strict rules and boundaries to vehicle production. This implied that basically all investments had to come from domestic parties. Because of this, the Portuguese automotive industry never really developed and suffered under less competitive prices and quality. Although the Portuguese governments dropped many of the applied rules in the mid- nineties, the effects still exists. Portuguese vehicle manufacturers are still considered as unexperienced parties. It therefore seems possible that due to this image shareholders do not believe in the skills of the acquirer and the contribution of takeovers on company performance.

Conclusion

This thesis studies mergers and acquisitions within the automotive industry. The cumulative abnormal returns of 46 automotive mergers and acquisitions are calculated in the time window of 5 days (-2,2) surrounding the announcement date. Thereafter several variables were tested on their influence on the found cumulative abnormal returns.

During the five day window a cumulative average abnormal return of 0.1463% was found. Although this outcome suggest a positive abnormal return, the corresponding significance test shows a t-value of 0.251, making the outcome insignificant. General statements about the cumulative average abnormal return can therefore not be given

In both the univariate- as the multivariate regression, the variable relative size has a negative coefficient that is significant at the 10% level. This indicates, against expectations, that the larger the deal size compared to the acquirer’s assets the lower the expected abnormal return.

During this research lots of attention has been paid on cross-border transactions. To check whether the acquirer’s nationality has any influence on the cumulative abnormal return, country dummies were added to the multivariate model. This resulted in a negative and significant coefficient for China and Portugal. This indicates that both Chinese and Portuguese acquirers earn a significant lower cumulative abnormal return.

Literature List:

Andrade, G., Mitchell, M., Stafford, E., (2001) New Evidence and Perspectives on Mergers,

Harvard Business School Working Paper No. 01-070

Asquith, P., Bruner, R.F., Mullins, D.W. (1983) The gains to bidding firms from merger

Journal of Financial Economics, 11: 121-139

Bernhart, W., Kleimann, P., Hoffmann, M. Kalmbach, R. (2011) Automotive landscape 2025, Roland Berger strategy consulatants.

Blake, D., Cucuzza, T., Rishi, S., (2003) Now or never: the automotive collaboration imperative, Strategy & Leadership, Vol. 31 Issue: 4, pp. 9-16

Campbell (2012) Automotive M&A Outlook: Why Build When You Can Buy? M&A International inc.

http://mergers.net/fileadmin/MAI/public_documents/MAI_Automotive_Sector_Report_2012_r ev1_02.pdf

Cummins D., Weiss M., (2004) Consolidation in the European insurance industry: do mergers and acquisitions create value for shareholders? pp 1–28

http://fic.wharton.upenn.edu/fic/papers/04/p0402.html

Dannenberg, J., Kleinhans, C., (2007) The Coming Age of Collaboration in the Automotive Industry, Mercer management journal 17

Dickerson, A., H. Gibson and E. Tsakalotos (1997) The impact of acquisitions on company performance: evidence from a large panel of UK firms, Oxford Economic Papers 49, pp. 344-361.

Elie, P., (2013) Driving Value: 2012 Automotive M&A Insights, PWC case published online:

http://www.pwc.com/gx/en/automotive/industry-publications-and-thought-leadership/driving-value-2012-automotive-manda-insights.jhtml

Elie, P., (2012) Driving Value: 2011 Automotive M&A Insights, PWC case published online:

https://www.pwc.com/en_GX/gx/automotive/pdf/automotive-ma.pdf

Francois, J.F., Spinanger, D. (2004) Regulated Efficiency, World Trade Organization

Accession, and the Motor Vehicle Sector in China The world bank economic review, vol. 18. No.1

Faccio, M. Masulis, R.W.,(2005) The Choice of Payment Method in European Mergers and Acquisitions, The journal of finance • vol. LX, no. 3 • June 2005

Fuller, K., Netter, J., Stegemoller, (2002), M. What do returns to acquiring firms tell us? Evidence from firms that make many acquisitions, Journal of Finance, no 57, pp 1763–1793.

Günter K. Stahl, Andreas Voigt, (2008) Do Cultural Differences Matter in Mergers and Acquisitions? A Tentative Model and Examination. Organization Science 19(1):160-176?

Goergen, M., Renneboog, L., (2004) Shareholder Wealth Effects of European Domestic

and Cross‐border Takeover Bids. European Financial Management, Vol 10(1), pp 9-45

Haleblian, J., Finkelstein, S., (1999) The Influence of Organizational Acquisition Experience on Acquisition Performance: A Behavioral Learning Perspective Administrative Science Quarterly 1999 44: 29

Heron, R. and E. Lie (2002) Operating performance and the method of payment in takeovers,

Journal of Financial and Quantitative Analysis, 37, (1), 137-155.

Jong, de F (2007) Event Studies Methodology. Tilburg University

Kiymaz, H., and Baker, H., (2008) AdministrationShort-Term Performance, Industry Effects, and Motives: Evidence from Large M&As, Quarterly Journal of Finance and Accounting, Vol. 47, No. 2 (Spring, 2008), pp. 17-44

Kohers, N., Kohers, T., (2000) The value creation potential of high-tech mergers, Financial

Analysts Journal, Vol. 65, number 4, pp. 40-50

Kung, C.Y., Chang, C.P. (2004) Application of grey prediction model on China automobile industry. Journal of Grey System, 2004, Vol. 16, Issue 2, pp 147-154

LeBeau, P., (2014) Global auto sales hit record high of 82.8 million, CBNC online article,

http://www.cnbc.com/id/101321938

Loughran T, Vijh A (1997) Do long-term shareholders benefit from corporate acquisitions?

Journal of Finance 52: pp.1765–1790

Martynova, M. and Renneboog, L.D.R., (2006). Mergers and Acquisitions in Europe. Discussion paper 2006-003, Tilburg University, Tilburg Law and Economic Centr et al.

Mentz, M., Schiereck, D. (2008) Cross-border mergers and the cross-border effect:

the case of the automotive supply industry. Review of Managerial Science, no. 2, pp 199-218

Moeller, S., F. Schlingemann and R. Stulz (2004), Firm size and the gains from acquisitions,

Journal of Financial Economics, 73, (2), pp 201-228.

Muller, J. (2012) Why the world’s automakers love Brazil. Forbes Magazine October 22 2012, http://www.forbes.com/sites/joannmuller/2012/10/05/why-the-worlds-automakers-are-loving-brazil/

Oxford dictionaries database

http://www.oxforddictionaries.com/definition/english/synergy?q=synergy,10-02-2014

Pike, A., Vale, M. (1996) ‘Greenfields and Brownfields’: Automotive industrial development in the UK an Portugal. Finisterra, XXXI, Vol. 62, pp 97- 119

Roll, R., (1996) The Hubris Hypothesis of Corporate Takeover, The Journal of Business, Vol. 59, No. 2 pp 197-216

Zhao, Z. J., Anand, J., (2009) A Mutlilevel perspective on knowledge transfer: evidence from the Chinese automotive industry. Strategic Management Journal 30: pp 959–983

Appendix Name Acquirer (country)

Name Target (country)

Announcement date Acquired Stake (%)

Anhui Ankai (CN) CHONGQING ANKAI (CN) 14-02-2008 51.0

AUDI AG (DE) LAMBORGHINI (IT) 10-06-1998 100.0

AUDI AG (DE) AUDI SENNA LTDA (BR) 25-02-2005 49.0

BEIQI FUTIAN (CN) BEIJING FOTON (CN) 04-06-2008 100.0

BEIQI FUTIAN (CN) NIHON FOTON JID. (JP) 28-07-2009 100.0

DAIMLERCHRYS (GE) FIAT'S CAR DIVISION (IT) 02-03-2000 22.0

DAIMLERCHRYS (GE) CHRYSLER Electric (US) 23-10-2000 100.0

DAIMLERCHRYS (GE) MITSUBISHI FUSO (JP) 15-01-2004 100.0

DONGAN HEIBAO(CN) SHANDONG HEIBAO (CN) 31-12-2004 100.0

DONGFENG auto(CN) ZHENGZHOUNISSAN (CN) 18-10-2004 16.0

DONGFENG moto(CN) DONGFENG MOTOR (CN) 25-01-2013 100.0

FIAT spa (IT) ZASTAVA (RS) 01-10-2008 67.0

FIAT spa (IT) CHRYSLER GROUP (US) 03-06-2011 6.0

FORD motor (US) LAND ROVER (GB) 16-03-2000 100.0

FORD motor (US) TROLLER VEICULOS (BR) 04-01-2007 100.0

FORD motor (US) VOLVO PERSONVAG. (SE) 30-01-1999 100.0

GM Company (US) ISUZU POLSKA (PL) 23-04-2013 40.0

GUANGZHOU (CN) GAC CHANGFENG (CN) 22-03-2011 71.0

ISUZU MOTORS (JP) SOLLERSISUZU ZAO (RU) 31-05-2012 16.0

MAN AG (DE) SCANIA AB (SE) 18-09-2006 100.0

MAZDA MOTOR (JP) CHANG'AN FORD (CN) 02-04-2004 15.0

MITSUBISHI (JP) NETHERLANDSCAR BV(NL) 04-04-2001 50.0

MITSUBISHI (JP) FUJIAN (CN) 30-11-2005 25.0

NISSAN MOTORS (JP) SIAM NISSAN (TH) 05-04-2004 50.0

OSKOSH-TRUCKS (US) UNNAMED PLANT (US) 25-05-2005 100.0

PSA (FR) SEVEL ARGENTINA (AR) 27-05-1998 50.0

PSA (FR) DONGFENG PSA (CN) 27-05-2004 35.0

PSA (FR) MITSUBISHI MOTO (JP) 03-03-2010 18.0

SEVERSTAL (RU) ZAVOD (RU) 20-05-2005 99.6

SAIC (CN) JINBEI GM (CN) 26-02-2004 25.0

SAIC (CN) CNAI (CN) 09-06-2004 100.0

TATA MOTORS (IN) DAEWOO OMMERCIAL(KR) 18-02-2004 100.0

TATA MOTORS (IN) JAGUAR CARS LTD (GB) 26-03-2008 100.0

TOYOTA PT (PT) CAETANOBUS – (PT) 12-11-2009 26.0

VW AG (GE) BUGATTI INTERNAT (LU) 30-07-1998 50.1

VW AG (GE) LAMBORGHINI SPA (IT) 01-07-1998 30.0

VW AG (GE) AUTOEUROPA (PT) 20-01-1999 100.0

VW AG (GE) SKODA AUTO AS (CZ) 22-05-2000 100.0

VW AG (GE) ROLLSROYCE MOTORS (GB) 06-06-1998 50.0

VW AG (GE) PORSCHE AG (DE) 04-07-2012 100.0

VOLVO AB (SE) VOLVO GM TRUCKS (SE) 30-04-1998 100.0

VOLVO AB (SE) NISSAN DIESEL MOTOR (JP) 25-09-2006 45.0

VOLVO AB (SE) DONGFENG MOTOR (CN) 26-01-2013 13.0

VOLVO AB (SE) MACK (US) 25-04-2000 50.0

VOLVO AB (SE) PREVOST CAR INC. (CA) 18-10-2004 13.0

YANGZHOU MOTOR (CN) YANGZHOU YAXING (CN) 12-06-2008 10.0