Mergers and Acquisitions in the Chinese High-Technology Industry

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Mergers and Acquisitions in the Chinese High-Technology Industry


The high-technology industry is an interesting sector for the corporate finance literature. Firms in this industry are characterized as high-risk firms, but with high growth prospects, possibly leading to extreme outcomes for the acquirers of high-tech companies. Results of research on the short-term performance of acquiring firms after mergers and acquisitions (M&A) are ambiguous. In this research, the event study methodology is used to see if high-tech M&As create short-term shareholder value.

By analyzing cumulative average abnormal returns (CAAR) of 702 domestic M&A in the Chinese high- technology sector, I have found that takeovers of high-tech targets create positive short-term shareholder value. Variables examined in this study are relatedness and payment method, both showing a negative effect on the cumulative abnormal returns (CAR). In my research I also find that the main driving factor for abnormal returns turns out to be the relative size of firms, which shows a significant positive effect on the CAR.

BSc Economics and Business

Specialization: Finance and Organisation Author: Rick Mocking

Student number: 11062800

Thesis supervisor: dhr. dr. M.R.C. Bersem


Statement of Originality

This document is written by Rick Mocking who declares to take full responsibility for the contents of this document.

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

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.


List of tables

Table 1: SIC codes high-technology industry ... 27

Table 2: Descriptive statistics variables ... 17

Table 3: Descriptive statistics Cumulative Average Abnormal Returns ... 18

Table 4: Pairwise correlation matrix ... 27

Table 5: Variance inflation factor ... 27

Table 6: Breusch-Pagan test for heteroskedasticity ... 27

Table 7: Multivariate regression analysis ... 20

Table 8: Robustness check estimation period ... 21

Table 9: Robustness check market index ... 21

List of Figures Figure 1: M&A announcements per year ... 28

Figure 2: Event study timeline ... 14


Table of Contents


1. Introduction ...5

2. Literature and hypothesis development ...6

2.1 Mergers and Acquisitions ...6

2.2 Empirical evidence and hypotheses ...7

2.2.1 M&A activity in China...7

2.2.2 M&A in the high-technology industry ...8

3. Data and methodology ...12

3.1 Data sampling...12

3.2 Event Study Methodology ...13

3.3 Regression...16

4. Results ...16

4.1. Descriptive Statistics ...16

4.2. Multivariate analysis regression...18

4.3. Robustness check ...21

5. Conclusion...22

References ...24

Appendix ...27


1. Introduction

The worlds demand for continuing technology innovation is driving the global competition in the high-technology industry. The United States is currently the leader in this sector, but China is not far behind.1 With an investment plan amounting $1.3 trillion spread out over six years, China is planning to take-over the US as global leaders. One objective in the so-called ‘Made in China 2025’ plan is to be independent of foreign technology.2 This so-called ‘tech nationalism’ of China increased the demand for technological innovation. However, to keep up with this global demand for innovation and increasing competition, managers might prefer outsourcing their technological innovations instead of increasing research & development (R&D) costs. Reason for this is that in-house development could be time-consuming and more expensive (Dutta & Kumar, 2009). One type of outsourcing that could be used for increasing technology innovation, while keeping R&D expenses moderate, are mergers and acquisitions (M&A).

There are several incentives to participate in M&A, including the most important; synergy motives. However, takeovers might not be so beneficial when managers participate in M&A because of hubris or managerial incentives. The question how investors perceive takeovers is an interesting topic in the corporate finance literature, with ambiguous results shown by e.g., Jarrel and Poulsen (1989) who provided evidence for the claim that abnormal returns for acquirers change over time from positive to negative.

These ambiguous results are also expected in the high-technology industry, because it is characterized by a high-growth, high-risk nature (Kohers & Kohers, 2000). Consequently, shareholders’ expected returns following from a merger or acquisition could be extreme. Some research has already been done for acquisitions of high-technology firms, by for example Kohers and Kohers (2000), who found positive significant cumulative abnormal returns (CAR) of 1.26% for takeovers of high-tech firms. However, most of these studies are focused on the more developed economies in the United States and Europe, and despite being the second largest economy in the world, research on M&A in China remains behind. With this study, I am contributing to the scarcity of M&A literature in the emerging market of China, by answering the following research question:

Do Mergers and Acquisitions create positive short-term shareholder value for publicly listed acquirers in the Chinese High-Technology sector?

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The research question will be answered by analyzing the cumulative average abnormal returns (CAAR) of a sample of 702 domestic M&A in China, in the period 2010 – 2020. The short-term performance will be measured with an event study methodology, after which the different explanatory variables will be examined to see if they affect the CAR, by using a multivariate regression analysis.

Results of the event study show a 1.92%, 3.06% and 3.5% CAAR for three different event windows. The multivariate regression analysis shows that the relative size of the target has a significant positive effect, whereas the results for the cash payment method and relatedness show a negative effect. State-ownership and leverage show both no significant effect in the regression analysis. More on the dependent variables in the literature section.

This research is structured as follows: Section 1 includes the introduction of this research;

section 2 includes the literature and hypothesis development; in section 3, the methodology is explained; in section 4, the descriptive statistics are given and the results are discussed; section 5 includes the final conclusion, as well as a brief summary of this study.

2. Literature and hypothesis development

This section starts with an introduction and definition of M&A, followed by the different M&A incentives and its role in the high-technology industry. Thereafter, empirical evidence on the short- term stock performance and the influencing variables will be reviewed and summarized to create a clear overview of the existing literature. The hypotheses that will be tested to answer the research question will be constructed based on the literature and empirical evidence.

2.1 Mergers and Acquisitions

In this section, a brief explanation will be given on the definition of mergers & acquisitions, the incentives of M&A and of the short- and long-term performance.

M&A activity is commonly used for increasing corporate value, which makes it one of the most researched topics in corporate finance. The term merger indicates two companies that merge their assets and become one entity, and an acquisition is a transfer of ownership from one company to another. There are different varieties of M&A e.g., horizontal, vertical and conglomeration. The terms mergers and acquisitions are regularly used interchangeably because of their similar motives.

Berkovitch and Narayanan (1993) elaborate on the incentives to participate in a takeover by acknowledging three major motives: the Agency motive, Hubris and the Synergy motive. The Agency motive, or better known as the principal-agent problem, suggests that the manager, who is acting in behalf of the shareholders, enhances the welfare of the management at the expense of the


overconfident managers pursue mergers with a low probability of value creating. Shareholders are being damaged by managers who are paying high premiums for overvalued firms. Finally the Synergy motive, which appears to be the most important motive, says that the financial benefits of merging two companies is bigger than the sum of two separate companies (Mukherjee, Kiymaz, & Baker, 2004;

Berkovitch & Narayanan, 1993).

Synergies can be divided in two types: revenue enhancing synergies, created by e.g., cross-selling accounts and access of patents; and, cost saving synergies, which are mainly created through economies of scale (Berkovitch & Narayanan, 1993). R&D seems to have a positive effect on revenue enhancing synergies as well as cost saving synergies, through new product introduction and sharing technology information. One industry that is characterized by high R&D expenses is the high- technology industry.

To examine the value effects of M&A, a distinction has to be made between the short- and long- term performance. Most M&A research is done on the short-term effects around the so-called event date, which is the announcement date in this research. The short-term effects are usually measured for a couple of days around the event date, whereas the long-term effects can go up to a few years.

Short-term stocks are influenced by the expectations of the public about specific events, in this case mergers or acquisitions. M&A announcements are generally received positively by investors. Most of the time, the target firm shareholders are the ones benefiting the most from M&A transactions, whereas acquiring firm shareholders see little to none short-term wealth increase (Kiymaz & Baker, 2008).

Long-term performance is a different subject. In this case, the actual integration of the target firm is more important, compared to short-term performance.

2.2 Empirical evidence and hypotheses

Numerous research has been conducted on the different effects of mergers on stock returns. In this section, I will be looking at the different outcomes resulting from past research, the different determinants of the abnormal returns and the hypotheses tested to answer the research question.

2.2.1 M&A activity in China

China’s stock exchanges, the Shenzhen Stock exchange and the Shanghai Stock exchange are both relatively young and characterized by their high market-volatility. Despite its earlier reputation, the stock price informativeness has increased and is comparable to US levels (Carpenter, Lu & Whitelaw, 2015). Theoretical literature suggests that managers can learn, through stock price information, about their own firms’ prospects and fundamentals, which subsequently leads to an increase in merger


synergies and post-merger performance (Ouyang & Szewczyk, 2017). Thus, it will be interesting to see how this relates to the actual short-term performance of Chinese M&As.

Another typical characteristic of the Chinese stock market is the segmentation system, containing three types of shares: state-owned shares, legal person shares and tradable shares (Chi, Sun & Young, 2011).

Wong and Cheung (2009) looked at the M&A announcement effects on bidding and target firms in Asia. They report the following results for bidding firms: a significant positive CAAR (2,72%) at the pre-announcement period (-50, -2); for the announcement period (-1,0) a negative, but insignificant CAAR (-0.38%); and for the post announcement period (+1,+50) a positive (9.2%) CAAR, at the 1%

significance level. The results for target firms, contrary to most M&A studies, seem less promising (- 2.5%, -0.24%, -5.2%), which might be due to poor pre-announcement performance. The results for bidding firms in China are as follows: -3.3%, -1.5%, 11%, and are all insignificant.

Similar to Wong and Cheung (2009), Chi et al. (2011) investigated the effect of M&As on acquiring firms in the Chinese stock markets and their results show positive short-term CARs at the 1 percent significance level, expect for (-2,+2) which is significant at 10% level. Besides these results, they make a distinction between the different ownership structures (state-owned; legal Person; and tradable) and show that ownership structure does impact the acquirers’ performance. Their results indicate that state ownership has a significant positive effect on the performance, whereas the legal person ownership has a significant negative effect.

Besides Chi et al. (2011) and Wong and Cheung (2009), very little research has been conducted on the short-term effects of an M&A announcement, while the market is actually experiencing rapid growth.

2.2.2 M&A in the high-technology industry

Technology is more and more integrated in our lives, which consequently leads to an increasing demand for technological resources and innovation. Instead of increasing R&D expenditures, managers favor outsourcing the production of technological capabilities (Howells, James & Malik, 2003). Outsourcing, by for example M&A, might be favorable because managers might experience strong competitive pressure. Besides that, development of technologies in-house might be time- consuming and more expensive (Dutta & Kumar, 2009). The high-tech industry is an interesting topic for M&A research because of its growth prospect. This increases potential shareholder wealth benefits and their riskiness, following from an increase in uncertainty because it depends on future successes.

Given the risk tech firms are bringing, acquirers of these firms must be very certain about the acquisitions.


Kohers and Kohers (2000) answer certain questions in their study on high-tech mergers, to see if M&As of these high-tech targets create value for the acquiring firm shareholders. Their results show significant positive abnormal returns (1.26 percent in the two-day event period), regardless of what payment method is used. This indicates that investors perceive these high-growth acquisitions as positive, regardless of the risk that is associated with these acquisitions. Another finding from Kohers and Kohers (2000) is that targets in the high-tech industry receive higher premiums than other industries.

Dutta and Kumar (2009) conducted a similar study in which they focus specifically on the effect of R&D expenditure on abnormal performance in the Canadian M&A market. Their results show that R&D expenditure has a significant positive effect on abnormal returns of the acquirer and find proof for their growth hypothesis, which states that investors of R&D intensive firms react positive to a takeover.

Based on the results from Kohers and Kohers (2000) and Dutta and Kumar (2009), I do expect to find short-term abnormal returns during a M&A announcement. Based on the beforementioned literature and evidence, the following hypothesis will be tested in this research:

H0: Acquisitions of Chinese High-Tech firms do not affect short-term abnormal returns: CAAR = 0 H1: Acquisitions of Chinese High-Tech firms do affect short-term abnormal returns: CAAR 0

2.3. Determinants

There are numerous factors that could possibly affect the short-term performance of M&A. In this section, I will be discussing the factors that are incorporated in this research.

Several studies have shown that industry relatedness is an important factor in the research on short-term performance after an M&A announcement. Eckbo (1983) shows that both the acquirer and the target performing in the same industry (horizontal) perform better than unrelated (non- horizontal) mergers, after the merger proposal announcement. Kiymaz and Baker (2008) did research on short-term performance of large M&As. They included an industry relatedness variable in their regression. Their results show a significant positive effect on the dependent variable, which confirms the thought that related mergers create value through synergy.

Hagedoorn and Duysters (2010) carried out a similar study specifically for the high-tech industry.

Their results suggest that the strategic fit between related companies improves the tech-performance of firms. This is not the same as economic performance of the firm, but it does translate to higher economic benefits through economies of scale and scope. However, they do emphasize the fact that these effects relate to long-term effects of M&A. These results are supported by Cloodt, Hagedoorn


and Van Kranenburg (2006). These studies focus primarily on western countries, whereas this thesis focuses on the emerging market China.

One study that focuses on relatedness in an emerging country is conducted by Barai and Mohanty (2014), who carried out a research on industry relatedness in India. Both related acquisitions and related mergers show positive CAAR for the long- and short-term. These results do suggest that investors in India do welcome related M&As and supports the widely held assumption that synergy gains through economies of scale and scope are larger for related M&As.

Taking into account the beforementioned studies, one would expect that the industry relatedness has a positive effect on the short-term performance after a M&A announcement. Therefore, the following hypothesis will be tested for the high-tech industry:

H2: Relatedness in the high-tech industry has a positive effect on bidder performance upon announcement.

The payment method bidders use to acquire companies could reveal something about managers’

expectations of the M&A. Where a cash payment seems straightforward, involvement of stock payments are a bit more complicated. When stocks are being used, managers do believe that the shares will increase in value because of synergy gains. However, stock payments could also mean that managers want to share the risk with target firms’ shareholders. The payment method could give a specific signal to investors and is therefore expected to affect abnormal returns.

Yook (2003) states the following: “Market participants interpret a stock-financed (cash-financed) acquisition as a negative (positive) signal of the value of the acquiring firm” (p. 478). The choice of management to use a specific payment method can be used as a signaling technique (Yook, 2003).

This effect of payment method on acquirers’ abnormal returns is confirmed by Asquith, Bruner and Mullins (1990); Wansley, Lane and Yang (1983); and Travlos and Papaioannou (1991). Chi et al. (2011) state that managers have a preference for stock payments when the firms’ stocks are over-valued.

Contrary to these results, Chang (1998) shows that cash bidders earn an insignificant 0.09 percent average abnormal return on the acquisition of privately held targets, and a significant 2.64 percent abnormal return for stock bidders. Stock payments of private targets tend to create large blockholders, which subsequently increases monitoring of managerial performance.

As for high-tech firms, Dutta and Kumar (2009) find that R&D intensive firms prefer to use stock payments over cash payments, compared to non-R&D firms. Their results show an insignificant negative effect on abnormal returns.


Specifically for Chinese acquirers, Chi et al. (2011) find that the dominant payment method is cash. Based on prior findings, I suspect that cash payments do positively affect abnormal returns.

Therefore, the following hypothesis will be tested:

H3: The cash payment method has a positive effect on bidder performance upon announcement.

There are a number of studies that have shown the importance of firm size. Moeller, Schlingemann and Stulz (2004) examined the effect of firm size on the gains of bidders from acquisitions. They tested 12,023 deals made and their results show that small acquirers perform significantly better than large acquirers after an M&A announcement. One possible explanation for this is that managers of large firms suffer from the hubris effect, that is reflected in paying excessive premiums for firms without increasing synergy.

Asquith, Bruner and Mullins (1983) test the size effect on bidders’ abnormal returns looking at the relative size of the target. By doing a pairwise t-test, they show that the CAR are significantly greater for larger target firms. This is in line with findings from Kohers and Kohers (2000), showing a positive relationship between relative size and bidder excess returns. The driving factor for this result is that investors believe that larger targets are better able to add synergies. Interesting is the finding from Travlos (1987), showing a negative coefficient for the effect of relative size on abnormal returns.

All in all, it may be concluded that relative size has a substantial effect on the bidders’ abnormal returns and is therefore included as control variable.

The second control variable is leverage, which is measured by the debt-to-equity ratio. The effect of leverage on bidder returns is tested in multiple studies e.g., Maloney, McCormick and Mitchell (1993); Kang (1993), and both studies show a positive effect on the bidder performance. One theory that can explain these results is the Free Cash Flow hypothesis by Jensen (1986), stating that an increase of debt decreases the waste of free cash flows, which will incentivize managers to invest in profitable investments (Kang, 1993). Based on these results, I expect a positive correlation between leverage and abnormal returns.

The last control variable is the state ownership of firms, which, according to several studies, affects the short-term shareholder value after M&A. Megginson and Netter (2001) summarized different studies on public versus private ownership, where most results show significant superior results for private enterprises.

Zhou, Guo and Hua (2015) and Ma, Sun, Waisman and Zhu (2016) tested the effect of state ownership on the post-merger performance of Chinese acquirers. Ma et al. (2016) give several reasons how state ownership could positively or negatively affect M&As. State Owned Enterprises (SOE) could enjoy government privileges, such as exceptional funding by Chinese banks and less strict lending


requirements, among other benefits. They are, however, restricted by social, political and economic objectives. Following these objectives are large labor surplus, heavy debt and weak corporate governance, which decreases M&A performance. Their results show a negative stock price reaction for SOE and positive for private owned firms, consistent with most findings in Megginson and Netter (2001).

On the contrary, Zhou et al. (2015) find that SOE acquirers show an overall insignificant positive abnormal return of 0.52%. This is, however, less than private owned firms (0.87%). But after splitting the targets in private-owned firms and SOE the results change and the announcement effects for private-owned firms are greater (1.13%). One factor that influences the performance of SOE is the political period, where hot political periods show higher announcement abnormal returns for SOE acquirers than during cold political periods (1.03% versus 0.47%). Given the various results, it can be concluded that state-ownership of firms could affect the acquirer performance, so this will be included as a control variable.

This research focuses only on private, domestic targets. Therefore, differences between public and private companies, and domestic and cross-border acquisitions cannot be determined. The reason for this is that the sample of public and cross-border targets will be too small to give reasonable conclusions on this topic. However, I will give a brief elaboration of these topics since these are important determinants of the short-term performance.

Conn, Cosh, Guest and Huges (2005) examined the impact on UK acquirers and found a mean CAR of -0.99 at the 1 percent significance level for public domestic M&As and -0.09 for cross-border mergers. However, the results turn significantly when looking at private acquisitions, where the domestic mean CAR is 1.05 against 0.38 for cross-border deals. They find three factors that explain the higher bidder returns of private M&As: increased monitoring; absence of hubris; and a private company discount.

As for cross-border deals, they state that the bidder gains depend on the amount of offsetting by cultural dissonance. These results indicate that investors do favor private domestic deals (Conn et al., 2005). These results are not entirely true for emerging markets like China, where investors react positive of the announcement of cross-border M&As, according to Chen and Young (2010) and Ning, Kuo, Strange and Wang (2014).

This is just a glimpse of the many studies done on the differences between domestic and cross- border M&As, and public - private targets. As can be seen, both factors have significant influence, but due to lack of data these topics remain subject for further investigation.


3. Data and methodology

The data and methodology section will explain how the data is collected and filtered to get to the final sample and what methodology is being used to test the hypotheses.

3.1 Data sampling

This study analyzes M&A deals in the Chinese high-tech industry for a ten-year period, from 2010 to 2020. The database used to collect the sample is called Zephyr, which is a comprehensive database with deal information. The following data are collected from Zephyr: acquirer ISIN codes;

announcement date; date of completion; acquirer primary SIC code; payment method; and ownership of the firm. This is done by setting the following requirements in Zephyr:

- Acquiring firm has to be public;

- Date of completion between 01/01/2010 – 31/12/2020;

- Deals have to be completed;

- Both target and acquirer are located in China;

- Final stake of the acquirer is 100%;

- Deal value has a minimum of $1M;

- The target firm is operational in the high-tech industry, classified following the SIC codes for the high- technology industry in table 1, which can be found in the Appendix (Phillips & Kile, 2009). The Standard Industrial Codes (SIC) is a system for classifying firms based on their primary business description.

These requirements create a preliminary sample of 1051 M&A. The firm-specific data (daily stock prices, total assets acquirer and debt-to-equity ratio) is downloaded from the Factset database, as well as daily returns of the market indices. After adjusting the preliminary sample for missing data, the final sample consists of 702 events. Figure 1 in the appendix shows the number of M&A announcements per year.

3.2 Event Study Methodology

The Event Study methodology is an empirical investigation to test the impact of specific events on the value of firm, which is in this case M&A. The impact of specific events is relevant for understanding corporate policy decisions (Kothari & Warner, 2006). The investigation on the effect of these events is done by examining the abnormal returns (i.e. actual returns minus expected returns).

The Event Study methodology is supported by the Efficient Market Hypothesis. Market efficiency implies that the current asset prices correctly reflect all public information and future information will be reflected immediately in security prices (MacKinlay, 1997).


In this research, an event study will be executed on the basis of MacKinlay (1997), who summarized and reviewed different methods e.g., Brown and Warner (1985). The first step in this process is to determine the event date, the estimation window and the event windows.

The announcement date of the takeover is chosen as trigger event, instead of the completion of the deal itself. Dodd (1980) found that the information release is the highest on the first official announcement and the expected price effects will therefore happen before the actual date of completion.

The choice of estimation window length is a trade-off between increasing statistical accuracy (larger window) and avoiding other interfering events. To avoid possible biases arising from interfering estimation windows, I will be using the Alpha and Beta of the first event to calculate the abnormal returns for subsequent events if a single firm has multiple interfering events. This way, biases will be avoided while keeping the sample as large as possible. Since there are no specific guidelines for the estimation window, this research will use t-150 to t-21 prior to the event date, which is t=0, following Boateng et al. (2008). A second estimation window (t-90, t-21) will be used to see check if differences arise from a shorter estimation window. The purpose of the small gap between the estimation window and the event window is to exclude any biases that follow from possible information leakages or insider trading. These leakages could affect the normal return used to calculate abnormal returns.

The main event window in this thesis is the three-day event window (-1, +1), based on Moeller et al. (2004). The abnormal returns on t-1 are examined to see if the event is anticipated by investors and the abnormal returns on t+1 are tested to see if the information is immediately reflected in security prices and is therefore a test on market efficiency (Kothari & Warner, 2006). A seven-day (-3, +3) and an eleven-day event window (-5, +5) will also be tested. See figure 2 for a graphical representation of the event study timeline.

Figure 2: Event study timeline


The next step is to choose a benchmark for the normal returns (i.e. the expected returns if the event would not have taken place). This step is crucial for the execution of the event study, because if this is not done with caution, false inferences can result from subsequent statistical tests (Strong, 1992). There are multiple approaches to calculate the normal returns e.g., Constant Mean Return Model; Market Adjusted Model; Fama & French 3- and 5-factor model. In this case, the Market Model is chosen since it appears to be a simple, reliable and powerful estimator under certain conditions (Brown & Warner, 1985):

𝑅𝑖𝑡 = 𝛼𝑖 + 𝛽𝑖𝑅𝑚𝑡 + 𝜀𝑖𝑡 (1)

Rit is the stock return of a firm on day t,


i and


i are the parameters of the market model,



is the return on the market index on day t, εitis the error term with an expectation of zero (𝐸(𝜀𝑖𝑡)=0, ).

𝑣𝑎𝑟(𝜀𝑖𝑡) =



The Shenzhen Stock Exchange (SZSE) Component Index is a market-cap weighted index, which will be used as proxy for the market portfolio. The exchange in China that focuses on high-tech enterprises is the Chinext, a subsidiary of the Shenzhen Stock Exchange. Since this index is fairly new (2009), there will be missing data when this index is being used in this event study. Therefore, the SZSE component index is being used to keep the sample as large as possible. I will, however, include this index as a robustness test by examining if different results arise when the Chinext index is being used as market index. This will be tested for the period between June 2010 – January 2020, which correspond to the first data available for the Chinext index.

There is however, some criticism on the Market Model. Coutts, Mills and Roberts (1994) performed several econometric tests on the use of a single index market model. They find that the Ordinary Least Squares (OLS) method is unsuitable to use as an estimation technique and researchers using this model should be aware of possible biases resulting from the event study. Despite the criticism given on the market model, it seems to be sufficient since it is still a commonly used model in M&A scholars (e.g., Kohers & Kohers, 2000; Moeller, Schlingemann, & Stulz, 2004). One reason for this is given by Strong (1992), who states the following “Although the OLS market model abnormal return may be biased for an individual security, in an event study, the bias in conditional abnormal returns may average out to zero in the sample” (p. 544).

After obtaining the expected normal returns and the actual returns, the abnormal returns (AR) will be calculated by subtracting the expected normal returns from the actual returns:


ARit = Rit― 𝛼i ―βiRmt (3)

Where 𝐴𝑅𝑖𝑡is the abnormal return for stock

𝑖 on date , 𝑡 𝑅𝑖𝑡is the actual return of stock on date, is the expected return based on the estimation window, and are the estimated

𝐸(𝑅𝑖𝑡) 𝛼𝑖 𝛽𝑖

parameters estimated using OLS and daily data from the estimation window.

The next step in this process is to cumulate the abnormal returns (CAR) in order to fully capture the price effect of the event on the shares (Strong, 1992). The CAR is averaged out, resulting in the CAAR, to determine the results for the sample as a whole.

CARi= ∑𝐴𝑅𝑖𝑡 (4)


N∑CAR (5)

3.3 Regression

Different variables that can possibly affect the CAR have been introduced in section 2.2. In this section, the measurements of the variables will be explained, and the regression equation is given.

To see what variables have explanatory value, multivariate analysis is conducted for the different time intervals. the following regression equation will be used:

CARi= β0 + β1∗ Related + β2∗ Cash Deal + β3∗ Relative Size + β4∗ Leverage + β5∗ State Ownership + ε



- Related is a dummy variable with value 1 if the acquirer is classified as high-tech firm (based on the SIC code) and 0 if otherwise;

- Cash Deal is a dummy variable indicating the payment method with the value 1 if the payment method is Cash and 0 if another payment method is used;

- Relative Size is measured by the ratio of deal value to total assets of the acquirer;

- Leverage is measured with the debt-to-equity ratio;

- State Ownership is a dummy variable with the value 1 if the acquirer is owned by the state and 0 if otherwise;

- 𝜀 is the error term of the regression.


4. Results

In this section the results of the event study and the multivariate regression analysis, as explained in the previous section, will be discussed. This section concludes with two robustness tests of the market index and the estimation period.

4.1. Descriptive Statistics

This section contains a brief discussion of the descriptive statistics of the variables and the CAAR resulting from the event study.

Table 2 shows the descriptive statistics of the independent variables, gathered from Zephyr and Factset. The descriptive statistics are broken down into the number of observations, the mean, standard deviation, minimum and maximum. It is worth noticing that the mean for the dummy variables explain the frequency of state-ownership, relatedness and cash payments, over the sample (e.g., 5% of the sample is state-owned).

Another remarkable observation is the large spread for the relative size and leverage. Skewness in the variables, as a result of outliers, could affect the returns. To tackle this problem, the variables will be transformed using the natural logarithm of the variables. It can be seen that the spread of the two variables has decreased, which prevents possible biases.

Table 2: Descriptive statistics variables

(1) (2) (3) (4) (5)

Variable Obs Mean Std.Dev. Min Max

State-Owned (1=Yes) 702 0.0513 0.221 0 1

Related (1=Yes) 702 0.707 0.456 0 1

Cash Payment (1=Yes) 702 0.564 0.496 0 1

Relative Size 702 0.474 1.3 0.0001 19.59

Leverage 702 0.485 1.099 0 16.46

ln(Relative Size) 702 -2.237 1.857 -9.146 2.975

ln(Leverage) 702 0.292 0.38 0 2.86

To answer the research question, I tested the hypothesis if the CAAR differs from zero. Model (6) in table 3 shows the t-statistics for the three different event windows. From this table, it can be inferred that the CAARs (1.92%, 3.06% and 3.5%) differ from zero at the 1% significance level. Based on the 1% significance levels, the null hypotheses can be rejected and the conclusion can be drawn that the M&A of high-technology firms (positively) affect the short-term performance of the acquirer.

These results go against the general assumption that the targets are the ones benefiting the most


with the findings of Kohers and Kohers (2000), who show a 1.26% CAR at 1% significance level, as mentioned in the literature section of this paper. Another interesting result is that the CAAR increases with the event window, which could indicate the presence of information leakage and insider trading.

Table 3: Descriptive statistics Cumulative Average Abnormal Returns

(1) (2) (3) (4) (5) (6)

Variable Obs Mean Std.Dev. Min Max t-statistic

CAAR (-1,1) 702 0.0192 0.0718 -0.474 0.612 0.0192***

CAAR (-3,3) 702 0.0306 0.121 -0.409 0.527 0.0306***

CAAR (-5,5) 702 0.035 0.1561 -0.264 0.248 0.0350***

To be able to test whether the independent variables explain the CAR, the variables have to be tested on multicollinearity. If there is multicollinearity between the variables, the independent effects of the variables cannot be separated. Table 4 in the appendix shows the degree of collinearity between the variables and no significant collinearity is observed. The presence of multicollinearity is formally tested using the Variance Inflation Factor in table 5 in the appendix, where a value higher than 5 indicates multicollinearity. The VIF table shows no values higher than 5, so no multicollinearity is observed in this model.

The second assumption that is tested is for heteroskedasticity, which is done with the Breusch- Pagan test shown in table 6 in the appendix. The results show a low p-value (0.0000) and the null hypothesis of constant variance is rejected, which means that heteroskedasticity is assumed in the model. To deal with heteroskedasticity, robust standard errors will be used in the multivariate regression analysis (section 4.2).

4.2. Multivariate analysis regression

This section contains the results of the regression analysis that examines if, and to what extent, the independent variables from the regression equation in section 3.3 affect the CAR. The results are shown in table 7.

The degree to which the independent variables predict the dependent variable CAR is tested by running a number of regressions for each event window, which gives a total of 12 models. The first model only looks at the control variables state ownership, relative size and leverage and does not take into account the independent variables. The second model uses the related dummy variable together with the control variables, the third model adds the payment dummy to the control variables and the fourth and final model includes all variables. Robust standard errors are used for all regressions to


There are multiple things worth noticing. First, the highly insignificant control variable state- ownership is quite remarkable. Given the literature in section 2.3 one would expect that state- ownership affects the short-term performance. The second variable in the analysis, relative size, shows a positive effect for all event windows. One explanation for this is that investors favor large targets because they believe that they bring larger synergies. This result is in line with findings from Kohers and Kohers (2000) and Asquith et al. (1983). The third control variable, leverage, does not show a significant effect on the dependent variable. A possible explanation is that most high-technology firms hold a large amount of cash and consequently, a low leverage ratio.

The effect of relatedness is ambiguous, with a negative significant effect in model 6, 10, 12 and to a smaller extent in model 8. For the other models, model 2 and model 4 the effect is also negative, but insignificant. These results oppose the findings shown in section 2.3, which state that the synergy gains are bigger for industry related firms.

The significant negative results for the payment dummy, indicating that the payment method is cash, is also in contrast with most studies shown in section 2.3. Thus, from these results it can be suggested that investors do favor other payment methods than cash (e.g., stock or mixed).


Table 7: Multivariate regression analysis

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

VARIABLES CAR(-1,1) CAR(-1,1) CAR(-1,1) CAR(-1,1) CAR(-3,3) CAR(-3,3) CAR(-3,3) CAR(-3,3) CAR(-5,5) CAR(-5,5) CAR(-5,5) CAR(-5,5)

State_Owned_Dummy 0.002 0.002 -0.000 -0.000 -0.004 -0.004 -0.008 -0.007 -0.007 -0.006 -0.013 -0.012

(0.009) (0.009) (0.009) (0.009) (0.015) (0.014) (0.015) (0.014) (0.019) (0.019) (0.019) (0.019)

Relative Size 0.008*** 0.008*** 0.007*** 0.007*** 0.013*** 0.012*** 0.011*** 0.011*** 0.017*** 0.017*** 0.015*** 0.014***

(0.001) (0.001) (0.001) (0.001) (0.003) (0.002) (0.002) (0.002) (0.003) (0.003) (0.003) (0.003)

Leverage 0.005 0.003 0.004 0.003 0.005 -0.001 0.003 -0.002 0.016 0.008 0.013 0.006

(0.007) (0.007) (0.007) (0.007) (0.012) (0.013) (0.012) (0.013) (0.018) (0.018) (0.017) (0.018)

Related_Dummy -0.007 -0.006 -0.022** -0.020* -0.031** -0.028**

(0.006) (0.006) (0.011) (0.011) (0.014) (0.014)

Payment_Dummy -0.011** -0.011* -0.022** -0.020** -0.035*** -0.032***

(0.006) (0.006) (0.009) (0.009) (0.012) (0.012)

Constant 0.035*** 0.040*** 0.040*** 0.044*** 0.058*** 0.074*** 0.067*** 0.082*** 0.068*** 0.092*** 0.084*** 0.104***

(0.005) (0.007) (0.006) (0.008) (0.009) (0.013) (0.011) (0.014) (0.012) (0.017) (0.014) (0.018)

Observations 702 702 702 702 702 702 702 702 702 702 702 702

R-squared 0.039 0.041 0.045 0.046 0.038 0.044 0.045 0.051 0.041 0.049 0.053 0.059

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1


4.3. Robustness check

To see whether or not the main results of the CAAR change under certain conditions, a robustness check is applied.

The first robustness check is for the estimation period, shown in table 8. To see if the results change for a shorter estimation period, a t-test for differences in means is done on two different estimation periods. Model (1) in table 8 shows the estimation period (-150, -20), which is used to test the hypotheses. Model (2) calculates the abnormal returns based on a shorter estimation window, namely (-90, -20). The results of the t-test for differences in means in the last column show no significant changes, therefore it can be concluded that the estimation period (-150, -20) is an effective and robust estimation period.

Table 8: Robustness check estimation period

(1) (2) (1)-(2)

Event Window (-150,-20) (-90,-20) t-statistic

CAAR(-1,1) 0.0192*** 0.02009*** -0.3299

CAAR(-3,3) 0.0306*** 0.03272*** -0.4667

CAAR(-5,5) 0.0350*** 0.03823*** -0.5476

*** p<0.01, ** p<0.05, * p<0.1

A second robustness test is done to see if different results follow from the use of another market index (Chinext). As previously indicated in section 3.2, I have used the Shenzhen Stock Exchange Composite Index as a market proxy to calculate the abnormal returns. However, the Shenzhen Stock Exchange is not focused on high-tech enterprises, like the Chinext. Because the Chinext started in 2010, there is limited data available compared to the Shenzhen index. To make sure the samples are equal, a correction has been made for the SZSE Index. Results in table 9 show no significant differences between the two market indices, which indicates that the use of the Shenzhen Stock Exchange Composite index is a robust and effective market index.

Table 9: Robustness check market index

(1) (2) (1)-(2)

Event Window SZSE Index Chinext Index t-statistic

CAAR(-1,1) 0.0185389*** .0184856*** 0.018 CAAR(-3,3) 0.0300646*** 0.0300411*** 0.0047 CAAR(-5,5) 0.0343644*** 0.0342852*** 0.0122

Obs. 681 681

*** p<0.01, ** p<0.05, * p<0.1


5. Conclusion

China is currently on a quest to take over the United States as leader in the high-technology industry. To keep up with the global demand for technological resources, companies have to increase R&D expenditures. Outsourcing in the form of M&A might be a cheaper and less time-consuming way compared to an increase in R&D expenses, to keep up with the competition.

The question how investors react to M&A is a hot topic in the corporate finance literature.

Voluminous research on M&A has been developed over the years, however research in China is still relatively scarce. The high-technology industry is particularly interesting for M&A research since it is characterized by a high-growth, high-risk nature. With this study, I am contributing to the existing corporate finance literature by looking at the emerging market of China by answering the following research question:

Do Mergers and Acquisitions create positive short-term shareholder value for publicly listed acquirers in the Chinese High-Technology sector?

To answer this research question, I have analyzed the short-term announcement performance of 702 domestic M&A in the Chinese high-tech industry. To determine what variables could possibly explain this short-term announcement performance, a literature study has been done and different hypotheses were formulated.

First, the high-technology industry is characterized by a high-growth, high-risk nature. Mainly due to these characteristics, abnormal returns are suspected and this is also the first hypothesis. Acquirers performing in the same industry as their target are expected to generate larger synergies through a takeover. Based on these findings, it can be concluded that related takeover announcements to be higher than non-related takeovers. Previous literature has shown that the method of payment has a significant effect on the short-term shareholder value during a takeover announcement, where cash payments are expected to have a positive influence and stock payments negative. Based on this, I do expect cash payments to have a positive effect in this research and is included as hypothesis.

The most common way to measure firm performance is by testing the stock returns, which reflect investors’ expectations of the takeover. The event study methodology is used to test the stock returns, by calculating the CAR, based on the market model.

In this research, I have found significant 1.92%, 3.06% and 3.5% CAARs for the (-1,1), (-3,3) and (- 5,5) event windows and therefore, the null hypothesis is rejected, which is consistent with related literature (Kohers & Kohers, 2000). Based on these results, we can answer the research question by concluding that M&A do actually create positive short-term shareholder value for publicly listed acquirers in the Chinese High-Technology sector.


In the multivariate regression analysis, I have examined the individual effects of the independent variables on the stock returns. For both the relatedness variable, as well as payment method, no results are found to accept our hypothesis. On the contrary, results in this study show a negative effect for both variables, which is the opposite of what we expected, which means that H2 and H3 are rejected.

The overall conclusion that can be drawn from this study is that high-technology M&A show positive wealth effects in China. Based on these results, the findings from Kohers and Kohers (2000) can be generalized to emerging markets.

This study does have a few limitations. The first one is the fact that only a few independent variables have been examined in this study, which translates to a very modest R-squared in the multivariate regression analysis. One reason for this is the limited daily availability for private target firms. In addition to this, no distinction has been made between public/private targets. Secondly, to determine the effect of the payment method, this study only looked at cash payments versus other payment methods. This could be split up between e.g., mixed and stock payments, better view of how the payment method affects the performance. A third limit is that the Chinese stock market has a very distinctive segmentation system, which could possibly explain the contrary results found for the related and payment variable.

Future research could be done by including more explanatory variables, so a more comprehensive model can be built.



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Table 1: SIC codes high-technology industry

Table 4: Pairwise correlation matrix

Variables CAR(-5,5) CAR(-3,3) CAR(-1,1) Related_D ummy



State_Own ed_Dumy

Relative Size


CAR(-5,5) 1.000

CAR(-3,3) 0.934 1.000

CAR(-1,1) 0.783 0.872 1.000

Related_Dummy -0.101 -0.088 -0.056 1.000

Payment_Dummy -0.153 -0.131 -0.121 0.109 1.000

State_Owned_Du mmy

-0.030 -0.029 -0.016 -0.006 -0.056 1.000

Relative Size 0.200 0.194 0.195 -0.029 -0.240 -0.120 1.000

Leverage 0.025 0.003 0.016 -0.217 -0.051 0.098 -0.064 1.000

Table 5: Variance inflation factor Table 6: Breusch-Pagan test for heteroskedasticity

283 Drugs

357 Computer and Office equipment 366 Communication equipment

367 Electronic Components and Accessories

382 Laboratory, Optic, Measure, Control Instruments 384 Surgical, Medical, Dental Instruments

481 Telephone Communications

482 Miscellaneous Communication Services 489 Communication services, NEC

737 Computer Programming, Data Processing, etc 873 Research, Development, Testing Services

5045 Wholesale-Computers & Peripheral Equipment &


386 Photographic and Equipment Design


ln(Relative Size) 1.087 .92

Payment Dummy 1.083 .923

ln(Leverage) 1.065 .939

Related Dummy 1.061 .943

State Owned Dummy

1.03 .971

Mean VIF 1.065

Ho Constant variance Variables Fitted values of

CAR(5,5) Chi2(1) 114.08 Prob>chi2 0.0000


Figure 1: M&A announcements per year




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