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The effect of the announcement of mergers and

acquisitions on acquiring firms in the high-technology

industry of the United States between 2009 - 2018

Abstract

Using 133 mergers and acquisitions where the acquirer and the target firm is considered as a high-technology firm located in the United States, this study examines the effect of the announcement of mergers and acquisition on the stock price of the acquiring firm in the short-run. An event study methodology is used to calculate the abnormal returns (AR) and the cumulative average abnormal returns (CAAR). This study aims to measure the abnormal returns using both the capital asset pricing model and the Fama-French three factor model. For the event study the event windows ±5, ±3 and ±1 are used. I find that within the given event windows acquiring firms experience a positive abnormal return that do not differ from zero when both the capital asset pricing model and the Fama-French three factor model are used. Furthermore, at the day of the announcement itself I find that acquiring companies incur a positive abnormal return that is significant at a 1% level when the capital asset pricing. The results are similar as found by previous researchers.

Author: Matthijs Uytewaal, 10797831 Supervisor: Ilko Naaborg

Bachelor Thesis

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Statement of Originality

This document is written by Student Matthijs Uytewaal who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.

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Table of Contents

1. Introduction 4

2. Literature review 6

2.1 Economic theory of mergers and acquisitions 6

2.2 Literature on mergers and acquisitions, empirical results 7

2.3 Hypothesis 10

3. Methodology and data description 10

3.1 Methodology 10

3.1.1 Captial Asset Pricing Model 10

3.1.2 Fama-French three factor model 12

3.1.3 Event study 13

3.2 Data 15

4. Results 16

5. Conclusion 19

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1. Introduction

Mergers and acquisitions are long and widely used as an expansion strategy through inorganic growth by firms in the developed and emerging economies (Rani et. al, 2013). On the one hand, mergers and acquisitions diversify risks, give opportunities to growth and can create unexploited synergies. On the other hand, mergers and acquisitions come at a cost and require a large amount of capital. Furthermore, the acquiring company takes the risk of not having the right or enough information about the target company. This can lead to the diminishing of value through mergers and acquisitions.

Through job creation, contribution to efficiency gains of the production process and technological advancements, high-technology firms have established themselves as leaders in the economy. More than half of the total GDP of the developed economies are based on the high-technology industry according to a report of the OECD (Organization for Economic Cooperation and Development). Because of the productivity gains and cost savings, the high-technology sector in the United States lowered the inflation by half percentage point (Kohers and Kohers, 2000) Within the high-technology industry, learning and innovation are

important to the survival and competitiveness of the organization. High-technology firms are constantly seeking for ways to promote new products and innovate different departments. In the high-technology industry, where extraordinary turbulence exists and quick changes in regulations and technology is standard, firms frequently turn to external sources to get new ideas, products and patents (Kennedy et. Al, 2002). High-technology firms are knowledge-driven. When a company is exposed to new ideas based on the difference in knowledge between the target firm and the acquiring firm, opportunities for organizational learning will increase (Cloodt, Hagedoorn, & Van Kranenburg, 2006).

For the past 18 years, the high-technology industry had the highest number of transactions of mergers and acquisitions within the United States. The high-technology industry represents 19.9% of the total number of transactions in the time period 2000 to 2018. Furthermore, the high-technology industry is the third largest industry in terms of transaction value and is one of the fastest growing industries (PwC, 2017). High premiums are paid by the bidding firms to acquirer another firm. Using Microsoft as example, Microsoft paid a premium of 50% to LinkedIn’s closing stock price and paid $26.2 billion in total in December 2016. Up to now, the deal between Microsoft and LinkedIn is considered as one of the largest deals within the high-technology industry of all time.

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The main motivation for a merger or acquisition is the improvement of the firm’s performance in the market. However, the past shows that mergers and acquisitions do not give the desired improvement of the firm performance (Mariana, 2015). This fuels the interest to investigate the motives behind mergers and acquisitions and if mergers and acquisitions do create value for shareholders of the acquiring company. In this paper, I will investigate whether mergers and acquisitions create value for the acquiring company within the high-technology industry of the United States. Numerous researchers have investigated mergers and acquisitions. Researchers such as Dobb and Ruback (1997) and Huang and Walking (1987) conclude that target firms incur a positive abnormal return from mergers and acquisitions, but the effect of mergers and acquisitions for the acquiring company remains unclear. Kohers and Kohers (2000) and Franks et al. (1991) argue that shareholders of

acquiring companies generally experience a negative abnormal return. Other researchers such as Dobb and Ruback (1997) find that shareholders of the acquiring company incur positive abnormal returns. Therefore, the research question of this paper is:

to what extent does the announcement of mergers and acquisitions give abnormal returns to bidding firms within the US high-technology industry?

This paper will investigate the research question within a period ranging from 2009 to 2018 to get the most recent results. Furthermore, this paper will focus only on companies who are listed on an exchange in the US and the company needs to be geographically located within the United States. At last, the companies must be considered as a high-technology company. Following Rani et. Al (2013) an event study is used to test whether a company incur any abnormal returns from the announcement of a merger or acquisition. Most researchers use CAPM to predict the expected returns and calculate the abnormal returns. The objective of this thesis is to calculate the expected returns and the abnormal returns with the CAPM model and the Fama-French three factor model. This Fama-French three factor model includes the size and value risk factors that are left out by the CAPM model.

The research of this paper is interesting for managers because in the short-run a profit or loss can be made by announcing a merger or an acquisition. Furthermore, it is interesting for shareholders of the bidding company since the value of the stock can be lowered due to the announcement of a merger or acquisition. Moreover, shareholders can participate on this drop or increase in the stock price.

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The remainder of this paper is structured as follows: section 2 discusses the review of the existing literature, provides definitions of mergers and acquisitions and give the motives behind mergers and acquisitions. Section 3 covers the methodology and data description used to test the research question. Section 4 analyzes the results about the tested hypothesis. Finally, section 5 covers the conclusion and the limitation of this thesis and recommendations for further research.

2. Literature review

This section provides the definition of a merger and acquisition together with the motives behind mergers and acquisitions. Furthermore, this section gives an overview of research about the effect of the announcement of mergers and acquisitions on stock prices done in the past and mentions the testable hypothesis.

2.1 Economic theory of mergers and acquisitions

An acquisition is defined as a purchase by a company known as ‘the acquirer’, who gains interest in another company known as ‘the target’. The bidding firm offers the target firm cash, stock or a combination of cash and stock in exchange for the shares of the target company. Acquisitions are often called takeovers because after the completion of the acquisition, the acquiring company controls both tangible and intangible assets of the target company. On the other hand, a merger is a combination of two firms who were previous to the merger two separate companies. In a merger, two companies combine all of their assets into one new legal entity. Most of the time the target firm and the bidding firm have

approximately the same size. Shareholders of both the target and the bidding firm, become joint owners of the new legal entity (Schoenberg, 2006). Pooling the tangible and intangible assets of the target and the acquiring company lead to an increase in economic efficiency through synergies according to the efficiency theory (Mariana, 2015).

The motives behind mergers and acquisitions can be internal and external. The managers do not have any influence about the external motives, such as the globalization of the market, changes in regulation or changes in technology. On the other hand, internal motives can be influenced by managers.

Berkovitch and Narayanan (1993) argue that there are three internal motives for mergers and acquisitions: the synergy motive, the agency motive and the hubris motive. The synergy motive assumes that managers of the acquiring and target company maximize the

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value of the shareholders and only engage in the takeover if it gains value for the shareholders. Moreover, the gains in value for the target and acquiring firms would be positive. The target company gains increase from a takeover when it has bargaining power over the acquiring firm. When the target firm can resist the offer of the acquirer or there is some competition between different acquirers, the target firm has bargaining power over the acquiring firm. If the sum of two companies together is larger than when the two companies operate separately, a synergy is created. According to Sirower (1997) synergies can be categorized into four categories: cost synergy, market synergy, financial synergy and revenue synergy. The cost synergy assumes that when two companies merge, costs such as

administration costs and overhead costs will be lowered. Furthermore, with market synergy the acquiring company has more bargaining power over its suppliers because size of the company is larger. The financial synergy assumes that a merger reduces risk and therefore the cost of capital. As last, the revenue synergy is an advantage in the form of economies of scope.

The agency motive assumes that the management of the acquiring firm act in their own interest and do not act in the interest of the shareholders. This suggests that managers maximize their own utility instead of the utility of the shareholders. Moreover, the agency motive suggests that the welfare of the manager increases at the cost of the shareholders of the acquiring firm.

The hubris theory explains why biddings by acquiring companies are made, even when there is a valuation error. Acquiring firms infected by hubris pay too much for the target firms (Roll, 1986). The hubris theory assumes that the acquisition or a merger is made due to a manager’s mistake. There are no synergies gained according to the hubris theory, for

example if the bidding company offers the target company a very large premium (Berkovitch & Narayanan, 1993).

The agency theory and hubris theory can lead to a destruction of value for the

shareholders since these kind of mergers and acquisitions do not contribute in the interest of the shareholders. Whereas, the efficiency theory can lead to a creation of value for the existing shareholders.

2.2 Literature on mergers and acquisitions, empirical results

Many researchers investigated the wealth effect of mergers and acquisitions on the

shareholders of both the acquiring and the bidding company. The evidence for target firms is clear. Researchers such as, Dobb and Ruback (1997), Huang and Walking (1987) and

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Chkraborty (2010) conclude that target firms incur significant positive abnormal returns around the announcement of a merger or acquisition. The evidence on returns to the bidding firm is not conclusive. Some researchers find negative abnormal returns to shareholder, where others find positive or zero abnormal returns.

Dodd and Ruback (1983) investigate the results of other empirical researchers ranging from 1958 to 1975 and restricted the dataset of the pervious researchers to investigate 172 bidding firms. They conclude that stockholders of successful bidding firms earn positive abnormal returns of 2.83%. Whereas shareholders of unsuccessful bidding firms neither gain or lose.

Campa and Hernando (2004) study the effect of mergers and acquisitions in Europe during the period of 1998-2000. They find that the mean cumulative abnormal return to shareholders of acquiring firms differs not significantly form zero. In their study, 55% of the returns to the acquiring firms were negative. The results found are similar as the results found by Bruner (2002) who investigated 130 studies from 1971 to 2001 and summarizes their findings.

On the contrary, the paper of Walker (2000) shows that acquiring firms incur a significant negative abnormal return of -0.84% around the announcement of mergers and acquisitions. An event window of -2 to 2 and a sample of 278 mergers and acquisitions was used. This study is followed by DeLong (2001) who study the wealth effect when at least one party is a bank. His study shows that within the event window of -10 to 1, acquiring firms incur a significant negative abnormal return of -1.68%.

Recent papers like, Tanriverdi and Bülent Uysal (2015) investigate the wealth effect of the announcement of mergers and acquisitions when the IT capabilities of the acquiring company are superior relative to the capabilities of the target company. Companies with superior IT capabilities are signaling to replace or remove the IT resources of the target company. This increases the risk of revenue growth. The market takes such risk into consideration and thereby lower the stock price of the acquirer. Therefore, Tanriverdi and Bülent Uysal argue that acquiring companies experience a negative abnormal return around the announcement of mergers and acquisitions. Furthermore, Wong and Cheung (2009) examine the effect of mergers and acquisitions on the stock prices in Asia from 2000 till 2007. Wong and Cheung find that acquiring firms incur negative abnormal returns at the announcement of mergers and

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acquisitions and a positive abnormal return before the announcement of a merger or acquisition.

Mitchell and Stafford (2000) argue that acquiring firms incur negative abnormal returns of -0,14, which is statistically significant. The research is performed by calculating the abnormal returns (AR) with the Fama-French three factor model instead of the capital asset pricing model. Mitchell and Stafford investigated 366 firms within an event window of 0 to -1.

There is also evidence from the high-technology industry. Kohers and Kohers (2001) and Gerpott (1995) conclude both that target firms, within the high-technology industry, incur significant positive abnormal returns around the announcement of mergers and acquisitions. They argue that the success of mergers and acquisitions are based on the effects of both the economies of scale and scope of the research and development. Furthermore, the researchers conclude that the effect of the announcement of mergers and acquisitions on the acquiring firm is less clear. The effect of the announcement of mergers and acquisitions on acquiring firms depends on difference factors, such as the chosen time window, estimation window, the model to calculate the abnormal returns and data selection.

Furthermore, Davis and Madura (2017) conclude that high growth to book acquirers within the high-technology industry pay higher premiums to acquirer another firm without experience positive or negative abnormal returns. Whereas, low growth to book acquirers within the high-technology industry pay large premiums and receive significant negative abnormal returns around the announcement of mergers and acquisitions. Davis and Madura argue that higher synergies are expected by managers and shareholders when the target firms have low growth expectations and the acquiring firms have high growth expectations.

Moreover, Davis and Madura argue that there is evidence that some mergers and acquisitions are executed by desperation as some acquirers have low growth opportunities. These

acquirers pay the highest premium resulting is very negative abnormal returns at the

announcement day. In opposition, Lusyana and Sherif (2016) investigate the performance of acquiring firms when they acquirer high-technology US firms during the dotcom bubble. Lusyana and Sherif conclude that in the short run, acquiring firms incur positive abnormal returns. They use an event window of 5 days before and 5 days after the announcement or a merger or acquisition.

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2.3 Hypothesis

As concluded by previous researchers, the wealth effect of mergers and acquisitions on the acquiring firms remains contradictory. The conclusions are distributed from slightly positive abnormal returns to negative or insignificant different from zero abnormal returns. Therefore, this study tests if acquiring firms incur significant abnormal returns different from zero. The statistical hypothesis using to test the research question is:

H0: Acquiring firms within the high-technology industry do not experience abnormal returns around the announcement of mergers and acquisitions. CAAR = 0

H1: Acquiring firms within the high-technology industry do experience abnormal returns around the announcement of mergers and acquisitions. CAAR  0

This hypothesis will be tested with a t-test at a significance level of 1%, 5% and 10%.

The results of the test are evaluated with a two-sided test, because previous researchers do not conclude that the abnormal returns are strictly negative or strictly positive.

3. Methodology and data description

Previous researchers use the capital asset pricing model in their empirical evidence section to calculate the predicted returns and compared them with the actual returns for different time windows. This thesis investigates if acquiring companies incur abnormal returns when the predicted returns are calculated according to the capital asset pricing model, but also if the predicted returns are calculated following the French three factor model. The Fama-French three factor model adjusts the returns for size and value. Moreover, an event study is used to test if the cumulative abnormal returns differ from zero.

3.1 Methodology

3.1.1 Captial Asset Pricing Model

The capital asset pricing model (CAPM) is widely used to estimate the cost of capital for firms and evaluate the performance of portfolios. Furthermore, it is considered as one of the most important models to measure the relationship between returns and risk in practice. The

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capital asset pricing model measures the level of risk and the relation between systematic risk and the expected returns (Fama and French, 2004)

The capital asset pricing model is based on crucial assumptions. These assumptions are divided into two different categories, individual behavior assumptions and market structure assumptions. The first category assumes that investors are rational and mean-variance optimizers. This means that an investor only cares about the mean-variance and the mean of their investment. Investors invest in the portfolio that minimizes the variance given an expected return and maximize the return given a certain variance (Fama and French, 2004). Furthermore, the planning horizon of the investors is a single period and investors have homogeneous expectations. The second category assumes that all assets are publicly held and traded on public exchanges, short positions are allowed and investors can borrow or lend at a common risk-free rate. Moreover, there are no taxes or transaction costs included and all information is publicly available (Bodie et. al, 2014, p.340).

The capital asset pricing model regresses the excess return of a security on the market risk premium. By doing this the model captures the security’s sensitivity to systematic risk, also known as 𝛽 (beta). This beta is the percentage change by which the security return tends to increase or decrease for every 1% increase or decrease in the return on the market portfolio. Beta is calculated by the following formula:

𝛽𝑖

=

𝑆𝐷(𝑅𝑖)∗ 𝐶𝑜𝑟𝑟 (𝑅𝑖,𝑅𝑚𝑘𝑡)

𝑆𝐷(𝑅𝑚𝑘𝑡)

=

𝐶𝑜𝑣 (𝑅𝑖,𝑅𝑚𝑘𝑡)

𝑉𝑎𝑟(𝑅𝑚𝑘𝑡

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The alpha is the intercept of the equation (denoted by, 𝛼). This alpha is the expected return of the security if the market excess return (𝑟𝑚𝑘𝑡 − 𝑟𝑓) turns out to be zero. The error term

(denoted by, 𝜀) captures the unsystematic risk and other factors not captured by the

regression. The error term is mean-zero. This error term is called the residual (Bodie et. al, 2014, p 249). The complete regression is:

(𝑟𝑖 − 𝑟𝑓) = 𝛼 + 𝛽(𝐸 (𝑟𝑚𝑘𝑡) − 𝑟𝑓) + 𝜀 (2)

Where (𝑟𝑖 − 𝑟𝑓) is the excess return on the firm’s security. With this regression, the 𝛽 and the 𝛼 of a company are estimated.

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3.1.2 Fama-French three factor model

Fama and French (2004) argue that the systematic risk captured by the beta in equation (2) is not the only risk factor that influences the return of the firm’s security. Fama and French (1992) added two factors to the equation (2) that helps explaining the return of the firm’s security. The first factor is the size factor and is measured by the market capitalization of the company. The second factor added by Fama and French (1992) is the value factor and is measured by the book-to-market ratio of the firm. This leads to the following regression:

(𝑟𝑖 − 𝑟𝑓) = 𝛼 + 𝛽1(𝐸 (𝑟𝑚𝑘𝑡) − 𝑟𝑓) + 𝛽2(𝑆𝑀𝐵) + 𝛽3(𝐻𝑀𝐿) + 𝜀 (3)

Where SMB (small minus big) is the size factor. This size factor is the return of a portfolio of small stocks in excess of the return on a portfolio with large stocks. Furthermore, the HML (High Minus Low) is the value factor. This value factor is the return of a portfolio of stocks with a high market ratio in excess of the return on a portfolio with low a book-to-market ratio. In total, this model (3) assumes that the excess return on a stocks security is explained by the sensitivity to three factors: (i) the return of the market portfolio in excess of the risk-free rate, captured by (𝑟𝑚𝑘𝑡 − 𝑟𝑓), (ii) the return of a portfolio with small stocks in excess of the return of a portfolio with large stocks (SMB), (iii) the return of a portfolio with high book-to-market ratio stocks in excess of a portfolio with small book-to-market ratio stocks (Fama & French, 1996).

The size and value factors are constructed by using six value-weighted portfolios. These six portfolios are based on size and book-to-market value. SMB is the average return on three small portfolios minus the average return on three big portfolios (4) (Fama & French, Common risk factors in the returns on stocks and bonds, 1993).

𝑆𝑀𝐵 =1

3(𝑠𝑚𝑎𝑙𝑙 𝑣𝑎𝑙𝑢𝑒 + 𝑠𝑚𝑎𝑙𝑙 𝑛𝑒𝑢𝑡𝑟𝑎𝑙 + 𝑠𝑚𝑎𝑙𝑙 𝑔𝑟𝑜𝑤𝑡ℎ) − 1

3(𝑏𝑖𝑔 𝑣𝑎𝑙𝑢𝑒 + 𝑏𝑖𝑔 𝑛𝑒𝑢𝑡𝑟𝑎𝑙 + 𝑏𝑖𝑔 𝑔𝑟𝑜𝑤𝑡ℎ) (4)

The HML is the average return on the two value portfolios minus the average return of the two growth portfolios (5) (Fama & French, Common risk factors in the returns on stocks and bonds, 1993).

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𝐻𝑀𝐿 =1

2(𝑠𝑚𝑎𝑙𝑙 𝑣𝑎𝑙𝑢𝑒 + 𝑏𝑖𝑔 𝑣𝑎𝑙𝑢𝑒) − 1

2(𝑠𝑚𝑎𝑙𝑙 𝑔𝑟𝑜𝑤𝑡ℎ + 𝑠𝑚𝑎𝑙𝑙 𝑔𝑟𝑜𝑤𝑡ℎ) (5)

3.1.3 Event study

Based on previous studies about the effect of the announcement of mergers and acquisitions on the stock prices an event study is applied. Researchers such as Huang and Walking (1987), Dobb and Ruback (1983), Chkraborty (2010) and Lusyana and Sherif (2016) use an event study to test their hypothesis. Event studies are an important tool in the world of finance. An event study calculates the abnormal return on a stock within a certain event window. The abnormal return can be defined as the difference between the actual return of a stock and the return of the stock if there was no announcement of a merger or acquisition, (𝑅𝑎,𝑡− 𝑅𝑖,𝑡). Furthermore, an event study calculates the cumulative average abnormal return (CAAR). The CAAR can be calculated by first measuring the betas of the capital asset pricing model (6) and the Fama-French three factor model (7). The regressions to measure the betas are as followed:

(𝑟𝑖 − 𝑟𝑓) = 𝛼 + 𝛽(𝐸 (𝑟𝑚𝑘𝑡) − 𝑟𝑓) + 𝜀 (6)

(𝑟𝑖 − 𝑟𝑓) = 𝛼 + 𝛽1(𝐸 (𝑟𝑚𝑘𝑡) − 𝑟𝑓) + 𝛽2(𝑆𝑀𝐵) + 𝛽3(𝐻𝑀𝐿) + 𝜀 (7)

These regressions are used to calculate the normal returns for each firm. To measure the beta an estimation window of 200 trading days and 26 trading days prior the announcement of a merger or acquisition is chosen (figure 1). This time frame is chosen to not have the bias from the rumors of the announcement of a merger or acquisition captured in the betas and to have a long-time period to get representative betas. The return on the market portfolio of equation (6) and (7) are collected from the Center for Research in Security Prices (CRSP). The US one month Treasury bill rate is used as the risk-free rate, which is assumed to be appropriate for the United States. Furthermore, the SMB and HML factors from equation (7) are collected from the CRPS database.

Figure 1:

Estimation window Event window

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The second step to calculate the cumulative average abnormal returns is to calculate the abnormal returns for each chosen time period 𝑡1 to 𝑡2. This paper analyses the effect of the announcement of mergers and acquisitions for three different event windows, [-5, +5], [-3, +3] and [-1, +1]. The abnormal returns are calculated by subtracting the normal return of firm i on time t, calculated by the capital asset pricing model and the Fama-French three factor model, from the actual returns from firm i on time t within the given event window.

𝐴𝑅 = 𝑅

𝑎,𝑡

− 𝐸(𝑅

𝑖,𝑡

)

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The third step to calculate the cumulative average abnormal return, is to calculate the CAR (cumulative abnormal return). The cumulative abnormal return with both the capital asset pricing model and Fama-French three factor model is calculated by summing up the abnormal return (8) for each firm within the event windows (9).

𝐶𝐴𝑅 = ∑

𝑡𝑡=𝑡2

𝐴𝑅

𝑖

1 (9)

The fourth step is to calculate the cumulative average abnormal return (CAAR). The CAAR is calculated by taking the average of the summation of the 𝐶𝐴𝑅𝑖 (10).

𝐶𝐴𝐴𝑅 = 1

𝑁

𝐶𝐴𝑅

𝑖

𝑁

𝑖=1 (10)

As last the hypothesis can be tested by using a t-test. The t-test is performed as followed:

Standard deviation:

𝑠 = √

1 𝑁−1

(𝐶𝐴𝑅

𝑖

− 𝐶𝐴𝐴𝑅

𝑖

)

2 𝑁 𝑖=1 (11) The t-test:

𝐺 = √𝑁

𝐶𝐴𝐴𝑅 𝑠

~ 𝑁(0,1) (12)

Assuming the cumulative abnormal returns (CAR’s) are uncorrelated and the sample size is sufficient large, the t-test is approximately normally distributed with mean zero and standard deviation one (De Jong, 2007).

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3.2 Data

In order to collect data to test the hypothesis if acquiring firms within the high-technology industry do experience abnormal returns around the announcement of mergers and

acquisitions, the Thomson ONE database is used. Thomson ONE provides company information from different sources of Thomson Reuters. The Thomson ONE database contains specific information about mergers and acquisitions transactions. Thomson ONE provides also detailed information about the announcement date of the mergers and

acquisitions, the deal value, percentage of shares owned and the industry of the acquiring and target firm. After using Thomas ONE to get the data on the companies, DataStream is used to get the financial data containing stock prices. Several criteria are added to the Thomson ONE database, which leads to a sample of 137 mergers and acquisitions within the high-technology industry of the US. First the criterion is that both the acquiring and the bidding firm needs to be considered as a high technology firm and geographically located in the United States. Secondly, the acquirer and the bidding company needs to be publicly listed on an exchange and the merger or acquisition needs to be announced between 01/01/2009 to 01/01/2018. Thirdly, the deal status needs to be completed and the percentage of shares owned after the transaction needs to be between 51 and 100 percent.

The argument to include the criteria that the acquirer and target firms need to be publicly listed is since a not publicly listed firm is not included in DataStream. The next step is to request from Thomson ONE the names of the acquiring and target firms, the DataStream codes, the announcement dates of the mergers and acquisitions and the deal values. After this, the DataStream option in Excel is executed. Within this option the DataStream codes for each company and the announcement dates are used to extract the stock prices 200 days prior and 5 days after the announcement day. This results in a table including company names and stock prices for the estimation window and event window.

After removing deals where the date of the announcement was not a trading day and deals where the deal value was zero, there are 134 deals left. As last, one deal is removed because DataStream give inconclusive information about the stock prices of the acquiring company. In the end, 133 events are left to examine.

After collecting the daily stock prices, market return, risk-free rate, size factor and value factor from DataStream and the CRPS database, the daily stock returns are calculated for each company. Furthermore, a variable 𝑡 = 0 is created on the day the merger or

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announcement date, i.e. 20 days before the announcement date is 𝑡 = −20. Finally, the

capital asset pricing model and the Fama-French three factor model are estimated as described in the methodology section and together with the actual returns the abnormal returns are calculated for the 133 events.

4. Results

This section provides discusses the empirical results found from the short-term event study. The short-term event study is performed on 133 acquiring high-technology firms located in the United States. The aim is to calculate the cumulative average abnormal return (CAAR) by calculating the abnormal returns (AR) using the capital asset pricing model and the Fama-French three factor model, summing the abnormal returns up and averaging them. The event study is executed for different event windows namely, [-5,+5], [-3+3] and [-1,+1]. The results of the CAAR are split up into the abnormal average return (AAR) for each day within the even window. Furthermore, the test is conducted as stated in the methodology section of this paper. To answer the research question, the statistical hypothesis described in the hypothesis section is used.

Tabel 1: CAAR acquirer – Capital asset pricing model

Event day AAR Std. dev t-value p-value 5 0.0629% 0.0241 0.3009 0.763 4 -0.2406% 0.0255 -1.0881 0.279 3 -0.1242% 0.0220 -0.6510 0.516 2 -0.0529% 0.0270 -0.2259 0.822 1 -0.0463% 0.0359 -0.1487 0.882 0 0.9583% 0.0243 4.5480*** 0.000 -1 0.0714% 0.0222 0.3709 0.713 -2 0.0662% 0.0259 0.2948 0.769 -3 -0.0502% 0.0191 -0.3031 0.762 -4 -0.0183% 0.0210 -0.0100 0.992 -5 0.1590% 0.0478 0.3832 0.702 CAAR [-5,+5] 0.7853% 0.0964 0.9395 0.349

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From table 1 we conclude that the acquiring firms experience an abnormal return of zero for the eleven day period ranging from 5 days before and 5 days after the announcement of a merger or acquisition. The CAAR for this period is 0.7853% and t-value corresponding to this percentage is 0.9395, which is not significant at a 10% significance level. Furthermore, the p-value is larger than 0.10.

Table 2: CAAR acquirer – Capital asset pricing model

Event window CAAR Std. dev t-vaule p-value

[-3.+3] 0.8223% 0.0826 1.1481 0.253 [-1,+1] 0.9834% 0.0722 1.5708 0.119

*10%, **5% and ***1% significance level

For the event window of three days before and three days after the announcement of a merger or acquisition an insignificant positive abnormal return of 0.8223% is found. This percentage is not significant at a 10%, 5% or 1% level. For the event window of one day before and one day after the announcement of a merger or acquisition an insignificant positive percentage of 0.9834% is found. This means that within this event window, acquiring firms experience an abnormal return not different from zero.

Intuitively, the acquiring firms within the high-technology industry experience no abnormal returns at the given event windows. Moreover, at the day of the announcement itself acquiring companies experience a significant positive abnormal return of 0.9583%, which is significant at a 1% level. Furthermore, the AAR’s before the announcement of a merger or acquisition are all insignificant. This suggests that there are no rumors about the

announcement.

The results found for the cumulative average abnormal returns using the capital asset pricing model are in line with the existing literature. Campa and Hernando (2004) and Bruner (2002) also find that acquiring firms do not incur any abnormal returns different from zero around the announcement of a merger or acquisition. Furthermore, Dobb and Ruback (1977) also find that on the announcement day itself the acquiring firm incur a positive abnormal return which is significant.

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Table 3: CAAR acquirer – Fama-French three factor model

Event day AAR Std. dev t-value p-value

5 0.0270% 0.0242 0.1287 0.898 4 -0.2397% 0.0243 -1.1375 0.254 3 -0.1198% 0.0214 -0.6456 0.519 2 -0.0827% 0.0275 -0.3468 0.730 1 -0.0831% 0.0359 -0.2670 0.790 0 0.6143% 0.0276 2.5668** 0.013 -1 0.0766% 0.0219 0.4034 0.687 -2 0.1038% 0.0269 0.4450 0.657 -3 0.0059% 0.0194 0.0351 0.972 -4 -0.0237% 0.0194 -0.1409 0.888 -5 0.1143% 0.0476 0.2792 0.781 CAAR [-5, +5] 0.3929% 0.0941 0.4815 0.631

*10%, **5% and ***1% significance level

For the event window of five days before and five days after the announcement of a merger or acquisition an abnormal return is expected that is not different from zero when the Fama-French three factor model is used. From table (3) can be concluded that insignificant results are found at a 10% significance level, because the p-value is larger than 0.10.

Table 4: CAAR acquirer – Fama-French three factor model

Event window CAAR Std. dev t-value p-value

[-3,+3] 0.5150% 0.0830 0.7156 0.475 [-1,+1] 0.6078% 0.0652 1.1992 0.233

*10%, **5% and ***1% significance level

From table (4) can be concluded that if the Fama-French three factor model is used to calculate the abnormal returns, the cumulative average abnormal returns for the given event windows are insignificant at a 10% significance level.

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Furthermore, from table (3) is seen that at the announcement day acquiring firms experience a positive abnormal return of 0.6143%. This percentage is significant at a 10% and 5% significance level.

5. Conclusion

This study investigates the effect of the announcement of mergers and acquisitions on the acquiring firm within the high-technology industry of the United States using a sample of 133 completed mergers and acquisitions between 2009 and 2018. To investigates this effect the following research question will be answered: to what extent does the announcement of

mergers and acquisitions give abnormal returns to bidding firms within the US high-technology industry. Based on the literature, an event study is used to measure the effect of

the announcement. For the event study, the betas are calculated using an estimation window of 200 up to 26 days prior the announcement of a merger or acquisition. Furthermore, the event windows used to see if acquiring firms experience any abnormal returns are [-5.+5], [-3,+3] and [-1,+1]. The names of the acquirer, deal value, announcement day of the mergers and acquisitions, the stock prices, the risk-free rate, the market returns and the variables to calculate the SMB and HML factors are collected through the Thomson ONE database and DataStream. Moreover, data from the CRSP database is collected to calculate the expected returns following the capital asset pricing model and the Fama-French three factor model. A two-sided t-test is used to test whether acquiring firms incur any abnormal returns within the given event windows and the t-test is performed at a 10%, 5% and 1% significance level. The results show that acquiring firms do not incur any abnormal returns at the given event windows when both, the capital asset pricing model and the Fama-French three factor model are applied. Furthermore, at the day of the announcement of the merger or acquisition a positive abnormal return is realized of 0.9583% when the capital asset pricing model is used to calculate the expected returns and 0.6143% when the Fama-French three factor model is used. Moreover, the percentages of abnormal returns are lower when the Fama-French three factor model is applied for all event-windows. This result is consistent with the excising literature since Fama and French (1992) argue that the three-factor model is a better predictor of the returns than the capital asset pricing model.

The study in this paper has some limitations. This study investigates the announcement effect using both the capital asset pricing model and the Fama-French three factor model. Fama and French (2015) argue that a five-factor model including size, value, profitability and

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investment patterns predicts on average the stock returns better than the Fama-French three factor model. Therefore, more research can be done by calculating the abnormal returns using the Fama-Fench five factor model instead of using the Fama-French three factor model. Furthermore, this study investigated the short-term effect of the announcement of mergers and acquisitions, while the long-term effect remains unclear. The long-term effect can be useful for managers, since the effect of efficiency improvement and synergies can be shown in the long-term. Moreover, on the announcement day a significant positive abnormal return is found. Further investigation can test what factors makes this abnormal return significantly positive. Possible factors could be the payment method, reputation, the size of the acquiring company or the size of the target company. To include these deals characteristics managers of companies can explain the abnormal returns and the large premiums paid.

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