Mergers and Acquisitions in US
High-Tech Industry
Measuring the Impact on Wealth Performance of
Firms
Author:
Siyun Han
10598006
Supervisor:
Zhivotova, Evgenia
Bachelor Thesis Finance
University of Amsterdam, Faculty of Economics and Business
Finance and Organization
Statement of Originality
This document is written by Siyun Han, 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.
Abstract
This paper investigates in the M&A transactions happened in US high-tech industry during the period between 2011 and 2015. It studies on three event windows (i.e. ±1 days, ±3 days and ±5 days of the merger announcement date) and presents evidence that shareholders of targets always gain positive abnormal return while shareholders of bidders sometimes receive negative abnormal return. By analyzing several relevant factors, we find out that it is the choose of payment method that leads to the loss of bidders, but overall M&A is a good strategy for firms to grow as the total market return of bidder and target is larger than zero.
1. Introduction
Mergers and acquisition (M&A) has long been widely used as an efficient strategy for firms to grow and expand, it attracts firms in both developed economies and emerging economies (Rani et al., 2013, p.1). Managers aim at increasing sales, sharing resources, saving taxes and gaining market power through M&As. As synergy is the primary motive for takeovers (Berkeovtith and Narayanan, 1993, p.347), the activity only takes place when both bidder and target involved worth more than itself apart. However, according to collated research and a recent Harvard Business Review report (2016), around 70% to 90% M&A deals are regarded as a failure and 60% deals destroy the value of shareholders in bidding firm. This remarkable number raise the interest to investigate if M&A is a useful strategy and find out causes of the loss. Before going into details, this thesis will initially test if M&A indeed lead to significantly negative return on bidders’ shareholders. To make the topic more specific, the focus of this thesis will be only on high-tech industry.
The reason why choose high-tech industry as the focus is based on the conclusion of OECD (1997) from a theoretical perspective. Firstly, high-tech industries are primarily knowledge-driven, hence firms operating in this high-tech environment combine aggressive behavior and extensive knowledge in order to improve their performance and survive in the rapidly changing and technologically complicated environment (Bierly and Chakrabarti, 1996, p.123). Secondly, firms operating in high-tech industry innovate more and expend more in international trades, so the changes in the business environment stimulate us to investigate the impact M&As from an international aspect, and their dynamism inspires as well as improves performance in other sectors. (OECD, 1997).
Furthermore, evidence shows that over the past 15 years, the technology industry has experienced the highest volume of M&As when compared to any other industry. The high-growth nature of technology-based industries makes them distinct from other types of industries and become one of the main drivers of the resurgence in global M&A
activity (PwC, 2015). In 2015, the high-tech sector was characterized as the busiest sector with 243 completed deals and in total $9,432.68 million deal value, representing 16.3% of the market share and ranked first (Thomson One Statistics) (For more information on 2015 M&A, see appendix A). Many firms merge frequently to seek opportunities even if the transaction turns out to be a failure in the end. Taking Microsoft corporation as an example, in December 2016, Microsoft paid $196 per share and in total $26.2 billion, a 50% premium to the social network’s closing price around that time to acquire LinkedIn which is its biggest Merger and Acquisition (M&A) transaction up to now (Wall Street Journal, 2016). Even though Microsoft just wrote off $7.6 million and admitted its next biggest acquisition transaction of Nokia a failure in 2015 (Wall Street Journal, 2015). Therefore, research in high-tech industry is full of interests and illumination. Therefore, it is worth investing to see if M&A is a value-added strategy.
There is numerous academic press studying on M&As, but the impact of M&As on both firm’s performance improvement remains unclear (King, Dalton, Daily and Covin, 2014). Empirical evidence from Kohers and Kohers (2000, p.37), Langetieg (1978, p.377) agreed that shareholders of high-tech targets generally enjoy positive abnormal return while investors in bidding firms experience negative abnormal return. However, other scholars stated that M&As also lead to positive abnormal return for bidders. Therefore, the research question of this thesis is what is the impact of M&A on wealth creation of merged firms in high-tech industry. This study will investigate M&A deals happened in the period between 2011 and 2015 to get the latest result, and the geographical focus of this study will be on United States, in which locates the concentration of high-tech businesses.
The remainder of the thesis is organized as follows. Section 2 covers literature review. Section 3 reports the data description and methodology used to answer the research question, also hypothesis is put forward in this section. Section 4 analyses the results
of dataset used. Finally, sections 5 present the conclusions and limitations of this study together with some recommendations for future research.
2. Literature review
In this section, M&A is introduced, and conclusion of current studies on how M&A affect wealth creation are referred to establish the hypothesis of our research question. Moreover, several relevant factors to be used in the afterwards test are mentioned in this sector as well.
2.1 Definition and motives of Merger and Acquisition
Wheelen and Hunger (2009, p.190) define the M&A activity as a transaction involving two or more corporations in which stock is exchanged but in which only one corporation survives. In other words, the two companies become one and the name for the corporation becomes composite and is derived from the two original names. M&A are divided into three types: horizontal merger, vertical merger and conglomerate merger. (Berk, J. et al., 2013, p.933).
According to the study of Berkeovtith and Narayanan (1993, p.1), synergy is the primary motive in takeovers with positive gain. Firms aim to make profits by achieving economies of scale, combining complementary resources, obtaining tax advantages, and eliminating inefficiencies through M&A activities. They also expect to expand into a new market by garnering proprietary rights to launch products or services, increase market power by purchasing competitors, short up weaknesses in key business areas, penetrate new geographic regions, or provide managers with new opportunities for career growth and advancement (Berk, J. et al., 2013, p.935). In fact, a successful merger between companies increases benefits for the entire corporation.
Numerous literatures studied the wealth effect of M&A on shareholders of bidders and targets. For instance, Langetieg (1978, p.377), Dennis and McConnell (1986, p.140), Lang et al. (1991, p.320) all resulted in positive CAR for acquired firm and negative CAR for acquiring firm around the announcement date. As above literatures study the whole market, there are also several studies only focus on high-tech M&As. Gerpott, (1995, p.163); Kohers and Kohers, (2000, p.37); Loughran and Vijh, (1997, p.1777) also stated that technological-related firms generate positive returns for targets and the success of M&As in high-tech industry is based on the effects of economies of scale and scope of R&Ds. However, wealth effects on bidders remain unclear.
Therefore, this study is going to test the value effect of M&A on shareholders of both bidder and targets, i.e. the abnormal return is significantly different from zero or not, to see if the result is consistent with conclusions from literature reviewed above. So, the hypothesis of this study is as followed:
H0: M&A do not affect shareholders’ value of firms involved in the announcement, CAR=0
H1: M&A significantly affect shareholder’s value of firms involved in the announcement, CAR≠0
The null Hypothesis will be tested at 1%, 5%, 10% significance level, respectively.
2.3 Relevant factors
Based on current literature, our expectation is that shareholders of targets enjoy positive abnormal return while shareholders of bidders experience negative abnormal return. If our results tested using event study give evidence to support our expectation, the following four relevant factors will be introduced to investigate which is responsible for the negative outcome: market size of bidders, size of premium, reputation of bidders and payment method.
According to Moeller (2004, p.202), the abnormal return can differ in sign for large and small firms. Numerous literature stated that shareholders of large firms have higher possibility to gain negative abnormal return comparing to shareholders of small firms, this can be show from the overpayment aspect and hubris aspect. Overpayment problem is explained by Dong et al. (2002) that firms with higher valuations are undertaking efforts to acquire less overvalued assets with more overvalued equity, and they have pressure on the stock price for acquisition paid because of the activities of arbitrageur. Besides, managers of large firms are considered to be more prone to hubris, as they are more confident to succeed and large firms obtain more resources (Moeller, 2004, p.204). Therefore, we use market value of bidders to measure firm size and data are collected from DataStream, which is a global financial and economic database covering time series information on different indexes, to see the relationship between market value and abnormal return of bidders and test whether this factor is significant to responsible for the negative outcome.
H2: firm size is negatively correlated to cumulative abnormal return.
2.3.2 Premium size
The success of an M&A is based not only on the future profits expected to be obtained, but also on the capacity to complete the operation at a price that is not greater than the profits. In this regard, the premium which is bid price above the market value of the target’s shares has awakened considerable interest as an explanatory factor for the returns obtained by the shareholders of the bidders and targets (Mueller and Sirower, 2003; Moeller et al., 2005). Therefore, the second relevant factor to be discussed is the premium size which equals to the ratio of deal value to market value of target. Premium initially is regarded to have positive influence on abnormal return of bidders, however, too high premium may lead to overpayment and thus turn shareholders’ abnormal return to negative (Kaon et al., 2009, p.6). In this thesis, the relation between premium size and abnormal return is tested to see if the negative abnormal return is caused by overpaid premium.
2.3.3 Payment method
Chang (1998, p.773) showed the evidence that method of payment plays an important role in explaining bidding firms’ negative abnormal return. Results showed that firms financing a takeover with cash-related payment method have a better outcome than firms financing a takeover with common stock, which tend to receive negative abnormal return (Kaon et al., 2009, p.14). This situation can be explained as the consequence of asymmetry information, which is based on the premise that managers have private information about their firms that they do not share with investors (Hansen, 1987). There are three alternative methods of payment: all cash, all stock and a combination of both. We regard the payment method as a dummy variable in this thesis. In other words, if the deal is paid in cash, then variable ‘Cash’ equals to 1 while other two variables, ‘Stock’ and ‘Cash and Stock’ are 0. All the information of payment method is collected from the official websites of acquiring firms.
H4a: ‘Cash’ is positively correlated to cumulative abnormal return. H4b: ‘Stock’ is negatively correlated to cumulative abnormal return.
H4c: ‘Cash and Stock’ is negatively correlated to cumulative abnormal return.
2.3.4 Reputation
The fourth factor being expected to have impact on negative abnormal return of bidders is reputation. Brammer et al. (2004, p.1) holds the argument that it may be possible that firms with strong reputations earn negative abnormal returns since the euphoria surrounding them has caused investors to be willing to pay too much. Similarly, Hecker et al. (2002, p.27) found that damage on reputation have significantly negative impact on abnormal return. However, no evidence showed that reputation and negative abnormal return to shareholders involved in M&A activities are correlated. Therefore, it is interesting to investigate the effect of reputation in M&A-related cases. Criterion that firm to be considered with reputation is that if it is listed on top 500 of Fortune’s annual survey of ‘America’s Most Admired Companies’. Reputation is also a dummy variable, value 1 when firm is reputational.
H5: Reputation is negatively correlated with abnormal return.
3. Data Descriptive and Methodology
The objective of this thesis is to measure whether M&As create positive abnormal return on both bidder and target firms in US high-tech market, if so, then M&A have positive impact on wealth effect, otherwise negative effect. Hence in this section, model relating to abnormal return and the description of data being used are introduced.
3.1 Sample selection
To measure the price reaction of shareholders of both bidders and targets taking M&A activities by analyzing totally 77 deals collected from Thomson Financial’s SDC Platinum database (see Appendix B). SDC contains information on merger and acquisition transactions. For each transaction, SDC provides detail data for the target and acquiring firms including the name, announcement date, deal value and major industry of both firm. SDC also reports a variety of detailed information about the transaction, including the dates the merger was announced and completed, the share of the target that was purchased and the share owned after completion of the merger. We selected all the public M&As happened in US high-tech industry during the period of 2011 and 2015. Initially, there are 684 deals in total. To measure the premium size, we skip deals which are incomplete or without deal value and then narrow down to 141 deals. Finally, we eliminate 64 deals which has same bidder and target and left 77 targets and bidders in the end.
3.2 Event Studies
Based on the evidence given by current studies, we expect that shareholders of target firms earn positive abnormal return while investors from bidding firms experience a negative abnormal return around the M&A announcement date. To test the expectation, event studies will be applied, which is an approach showed that stock market reactions to merger announcements could help to predict mergers’ future profitability if financial
markets are efficient so that the information is processed is unbiased in nature (Fama, Fisher, Jensen, & Roll, 1969, p.10). Event study methodology relies on capturing any abnormal return to a particular security in a given period, which is simply the difference between actual return (Ra), and that which would have been expected in the absence of the event, the ‘normal’ return (Ri). The standard procedure can be divided into several steps:
First step: collect daily stock prices and calculate parameter
To study the stock price reaction to the merger’s announcements, we first estimate the “normal return” for each firm. We choose the estimation window of 180 trading days, starting from 240 trading days prior to the M&A announcement date. All the daily stock prices are collected from the Center for Research in Security Prices (CRSP).
Second step: calculate parameters and expected return
According to Cable and Holland (1999), there are four main models to calculate return: Capital Asset Pricing Model (CAPM), Market Model (MM), Main Adjusted Returns Model (MAR) and the Market Adjusted Returns or Index Model (IM). Among these four approaches, MM was found to be an acceptable simplification of the general model in all cases.
Ri,t =𝛼 + 𝛽*Rmkt,t+𝜀, t=-240,-239,…,-61
Where Ri,t is the particular return of firm i on date t, and Rmkt,t is market return on date t.
Therefore, we first use MM to calculate parameters alpha and beta by doing the ordinary least square (OLS) regression and afterwards predict the expected return of event windows. It is stated that the impact of announcement can be significantly different if the different window periods are chosen for calculating abnormal returns (Andrade, Mitchell, & Stafford 2001, p.105). So, this study analyses the reaction of the stock prices of the firms involved in the selected M&A activities from three different event windows, i.e. ±1 days, ±3 days and ±5 days of the merger announcement date.
Third step: calculate AR, CAR and CAAR
Abnormal return is the difference between actual return and expected return of firms: ARi,t=Ra,t- E[RI,T]
where E[Ri,t] is the expected return of firm i on date t and Ra,t is the actual return of firm i on date t.
According to Duso et al. (2007), to reduce information leakage and make results more reliable, the cumulative abnormal return (CAR) which is the sum of the daily abnormal return (i.e. CAR=Σ𝑅𝑎 − 𝑅𝑖) is referred to present total firm valuation effect of the merger announcement within the event windows. We use t-test to see if CAR is significantly different from 0. If so, M&As have impact on firm performance. Both abnormal return for bidders and targets will be tested.
3.2 Market return
To test the synergy effect, market return model is applied which is computed by multiplying the average of CAR by the market value of the firm's equity at the announcement date for the M&A activity (Shah and Arora, 2014, p.174). Both market return for bidders and targets within three different event windows will be presented in the result part. And the total gain will also be shown as the sum of the target and acquirer gains.
3.3 OLS regression model
To see how four relevant factors correlated to cumulative abnormal return, we establish the following model:
CARB= 𝛽0+ 𝛽1*FIRMSIZEi+ 𝛽2*PREMIUMSIZEi+ 𝛽3*CASHi+ 𝛽4*STOCKi+
𝛽5*CASHSTOCKi+ 𝛽6*REPUTATIONi+ 𝜀
where, FIRMSIZE is measured by market value of each bidding firm (in millions of US dollars)
PREMIUMSIZE is measured by the ratio of deal value to market value of targets (in millions of US dollars)
CASH is the dummy variable and value 1 if the deal is all cash-paid STOCK is the dummy variable and value 1 if the deal is all equity-paid
CASHSTOCK is the dummy variable and value 1 if the deal is paid in cash and stock
REPUTATION is the dummy variable and value 1 if the bidder is listed on top 500 of Fortune’s annual survey of ‘America’s Most Admired Companies’
We regress CAR from three different event windows on four separate factors following the ordinary least square (OLS) regression.
4 Results
In this section, the main results in regard to M&As are presented. First we discuss the statistics of abnormal return and significance of CAR within different period of event windows for targets, following the relating results for bidders. Then we calculate the market return for bidder, target and a combination of both to see the synergy effect. Finally, the results from multiple regression are shown to present the effect of each factor. For the robustness test, normal return calculated by CAPM is consistent with the results from market model.
4.1 Cumulative Abnormal Return
Table 1 summarize the statistics of CARs (i.e. number of observations, mean, t-statistics, p-value and significant or not). In table 1.1, we find that the means of CAR are all larger than 0 for each event windows with an observation of 77 targets. Then we apply t-test to examine if the null hypothesis is significant to see the results are consistent by using different event windows. Results show that p-values testing if mean is larger than zero are all equal to zero, therefore, the null hypothesis is rejected whatever 1%, 5% or 10% significance level is being used, implying that the target firms’ shareholders always earn positive abnormal returns due to the announcement of an M&A. More detailed, results indicate that three-day event window receive the biggest CAR while eleven-day event window receive the smallest CAR.
Table 1. Summarize statistics Table1.1 Targets
CAR Obs. Mean Std. Dev. t-statistic p-value
(mean>0) Significant or not (-5,5) 77 0.2627884 0.2603496 8.8572 0.0000 *Yes (-3,3) 77 0.3221589 0.2488908 10.0262 0.0000 *Yes (-1,1) 77 0.0237419 0.0957022 9.5142 0.0000 *Yes
*at 1%,5%,10% level of significance
For bidders, we take the same way as we done to targets. We first summarize statistics of CARs within different event windows and find that all the means of CAR are positive with a sample of 77 bidders, as presented in Table 1.2. We then use t-test to see if the null hypothesis is supported under different event window. P-value equals to 0.3242 for the eleven-day event window and equals to 0.1655 for the seven-day event window, hence the null hypothesis is not rejected at 1%, 5% and 10% level of significance. However, for a three-day event window, p-value is 0.0363, which imply that the null hypothesis is reject at either 5% or 10% significance level but accepted at 1% significance level. Therefore, we can find that the shorter the event window, the more significant for bidders to experience negative abnormal return with the announcement. And 42 out of 77 bidding firms gain better post-merger abnormal return than pre-merger abnormal return while on average 44 bidding firms gain positive abnormal return within different event windows and t-test showed that the mean is not significantly different from zero at any level of significance.
Hence, our finding is that targets gain positive abnormal return while bidders experience negative abnormal return through M&As, which is consistent with the expectation mentioned above.
Table 1.2 Bidders
CAR Obs. Mean Std. Dev. t-statistic p-value(mean<0)
Significant or not
(-5,5) 77 0.01121 0.093895 0.9925 0.3242 No
(-3,3) 77 0.01370 0.0840264 1.4312 0.1655 *Yes
(-1,1) 77 0.02374 0.0957022 2.1769 0.0363 **Yes
Post-merger 77 0.00124 0.053184 0.2025 0.8401 No
*at 10% level of significance **at 5%, 10% level of significance
4.2 Market Return
Secondly, we calculate the market return within different windows. By multiplying the abnormal return of each independent firm with its market value, we find that targets always get positive market return while bidders only experience positive market return within the three-day event window. However, three total market returns are larger than zero and climb dramatically when event window become smaller. These outputs support the conclusion that M&As create altogether synergy and leave positive impact on both targets and bidders.
Table 2. Market Return Table 2.1
(-5,5) Obs Mean Min Max
Bidder 77 -25.04087 -1203.403 1453.675 Target 77 47.59871 -15.13557 621.0461 Total 77 22.55784 -1218.53857 2074.7211
Table 2.2
(-3,3) Obs Mean Min Max
Bidder 77 -21.18097 -1623.914 2194.905 Target 77 68.86033 -29.20998 975.4242 Total 77 47.67936 -1653.12398 3170.3292
Table 2.3
(-1,1) Obs Mean Min Max
Bidder 77 105.6486 -878.0485 3362.615 Target 77 153.189 -8.849505 2597.995 Total 77 258.8376 -886.898005 5960.61
4.3 Multiple regression analysis
As we find that M&As cause negative abnormal return for bidders, we further investigate the effect of each factor on cumulative abnormal return within different event windows to see the reason of this negative circumstance. OLS multiple regression is applied. As presented in Table 3, coefficients imply that all variables are negatively correlated to CAR except the variable ‘marketsize’. Therefore, we preliminary conclude that high-reputation of firms, and large premium paid for M&A deals regardless of payment method will lead to lower abnormal returns. However, p-values indicated that ‘marketsize’,’premium size’ and ‘reputation’ are insignificantly affecting CAR at 1%, 5% and 10% level of significance, regardless of the choice of event window. But in terms of the method of payment, effects of paying in all cash and paying in cash and stock are continuously significant at 10% level of significance and sometimes significant at 5% and 1% significance level. And stock-paid method has effect on CAR within three-day event window at 95% and 90% confidence level. Overall, this study find that method of payment could be responsible for the negative abnormal return received by the shareholders of bidders.
Table 3. OLS regression Table 3.1 CAR (-5,5)
Variable coefficient t-statistic p-value Significant or not
marketsize 7.16E-08 0.37 0.716 No
stock -0.0850992 -0.78 0.438 No
cash -0.1497283 -1.57 0.124 *Yes
stockcash -0.1841355 -1.86 0.069 **Yes reputation -0.0475201 -1.08 0.287 No
_cons 0.1779576 1.9 0.064
*at 10% level of significance **at 5%,10% level of significance
Table 3.2 CAR (-3,3)
Variable coefficient t-statistic p-value Significant or not
marketsize 2.17E-08 0.13 0.901 No premiumsize -0.0103192 -0.63 0.533 No stock -0.0849137 -0.88 0.382 No cash -0.1690284 -2 0.052 *Yes stockcash -0.20396 -2.33 0.024 *Yes reputation -0.0325133 -0.83 0.409 No _cons 0.201196 2.43 0.019
*at 5%,10% level of significance Table 3.3 CAR (-1,1)
Variable coefficient t-statistic p-value Significant or not.
marketsize 6.86E-08 0.48 0.634 No premiumsize -0.0084758 -0.62 0.535 No stock -0.1885453 -2.37 0.022 *Yes cash -0.1824132 -2.61 0.012 **Yes stockcash -0.2079344 -2.88 0.006 **Yes reputation -0.0358473 -1.11 0.272 No _cons 0.2162268 3.16 0.003
**at 1%, 5%,10% level of significance
4.4 Robustness check
To check results and evaluate the robustness, this study will also apply the CAPM which is the second preferable model to calculate the normal return(Cable and Holland, 1999):
Ri,t =Rf + 𝛽 ∗ (RMKT,t - Rf)
Where Ri,t is the particular return of firm i on date t, Rmkt,t is market return on date t and Rf is the risk-free rate, usually a three-month US treasury rate is used as rf for US-based investments.
All the data are collected from CRSP. Using the same steps to calculate CAR, it is shown that results analyzing by CAPM is consistent with that using the market model. Therefore, the afterward outcomes are robust as well.
Table 4 Bidders
CAR Obs. Mean Std. Dev. t-statistic p-value(mean<0) Significant or not (-5,5) 77 0.01321 0.095588 0.9928 0.3355 No (-3,3) 77 0.02013 0.0792582 1.4352 0.1628 *Yes (-1,1) 77 0.02592 0.0925024 2.1928 0.0295 **Yes Post-merger 77 0.00142 0.060125 0.2201 0.7902 No
*at 10% level of significance **at 5%, 10% level of significance
5 Conclusion and Limitation
This study analyzes the impact of Mergers and Acquisitions on wealth performance of both bidders and targets operating in US high-tech industry, taking a sample of 77 completed M&A deals between 2011 and 2015. Following the current literatures, we
use event study as the methodology and refer the concept of abnormal return, cumulative abnormal return to see the wealth impact. Using three different event windows, i.e. ±1 days, ±3 days and ±5 days of the merger announcement date, we collected data from CRSP and calculated the normal return following the market model. T-test is applied to test whether CAR is significantly different from 0 at 1%, 5% and 10% significance level, respectively. Results showed that for bidders, negative abnormal return is received and this result is due to the payment method. But for targets involved in M&As, they always enjoy positive abnormal return. The similarity between targets and bidders is that they both gain higher abnormal return within the shorter event window. And the results of market return show that M&A is overall a good strategy to create value as the total market return is positive.
Although current conclusion of how M&A affect wealth creation of shareholders is clear, further researches should be analyzed to find out more facts. Firstly, this study only focus on US high-tech industry, the results from cross-section M&As could have difference as the global environment provides more opportunities for firms to share resources and expand itself. Moreover, although shareholders of bidders get negative abnormal return from the short-run perspective, it remains unknown what the long-term effect of M&As is. Also, method of payment should not be the only factor leading to negative abnormal return, more relevant factors should be found out and investigated. Therefore, more research should be done on studying cross-sectional M&As and analyzing its long-term effects and the reason behind it, so that M&As could be improved to be a better strategy for firms to take in the future.
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Appendix A
Figure 1. Numbers of M&A deals in 2015
Source: Thomson Financial’s SDC Platinum database (2016)
Figure 2. Ranking value of M&As in 2015
Source: Thomson Financial’s SDC Platinum database (2016)
0 50 100 150 200 250 NU M B ER INDUSTRY 0 2000 4000 6000 8000 10000 12000 AM O U N T ($ ) INDUSTRY
Appendix B List of firms
Acquiror Name Target Name
1 ON Semiconductor Corp Fairchild Semiconductor Intl 2 Western Digital Corp SanDisk Corp
3 Microsemi Corp PMC-Sierra Inc
4 Diodes Inc Pericom Semiconductor Corp 5 Seagate Technology PLC Dot Hill Systems Corp
6 IBM Corp Merge Healthcare Inc
7 Intel Corp Altera Corp
8 Avago Technologies Ltd Broadcom Corp 9 Fortinet Inc Meru Networks Inc 10 Microchip Technology Inc Micrel Inc
11 Knowles Corp Audience Inc 12 Lexmark International Inc Kofax Ltd
13 Microsemi Corp Vitesse Semiconductor Corp 14 Hewlett Packard Co Aruba Networks Inc
15 NXP Semiconductors NV Freescale Semiconductor Ltd 16 MaxLinear Inc Entropic Communications Inc 17 SS&C Technologies Holdings Inc Advent Software Inc
18 Lattice Semiconductor Corp Silicon Image Inc
19 Intel Corp Vuzix Corp
20 Cypress Semiconductor Corp Spansion Inc
21 EnerNOC Inc World Energy Solutions Inc 22 TTM Technologies Inc Viasystems Group Inc* 23 Peerless Systems Corp Deer Valley Corp 24 Teledyne Technologies Inc Bolt Technology Corp 25 Scientific Games Corp Bally Technologies Inc
27 Oracle Corp MICROS Systems Inc 28 Avago Technologies Ltd PLX Technology Inc 29 SanDisk Corp Fusion-io Inc
30 Analog Devices Inc Hittite Microwave Corp 31 EXAR Corp Integrated Memory Logic Ltd 32 Microchip Technology Inc Supertex Inc
33 Oracle Corp Responsys Inc
34 Sonus Networks Inc Performance Technologies Inc 35 Nielsen Holdings NV Harris Interactive Inc
36 MACOM Technology Solutions Hol Mindspeed Technologies Inc 37 Microsemi Corp Symmetricom Inc
38 ACI Worldwide Inc Official Payments Holdings Inc 39 Microsemi Corp AML Communications Inc 40 Maxim Integrated Products Inc Volterra Semiconductor Corp 41 Cisco Systems Inc Sourcefire Inc
42 Bally Technologies Inc SHFL entertainment Inc
43 IHS Inc RL Polk & Co
44 Salesforce.com Inc ExactTarget Inc 45 Oracle Corp Acme Packet Inc 46 ACI Worldwide Inc Online Resources Corp 47 Scientific Games Corp WMS Industries Inc 48 athenahealth Inc Epocrates Inc
49 Oracle Corp Eloqua Inc
50 Nielsen Holdings NV Arbitron Inc
51 Riverbed Technology Inc OPNET Technologies Inc 52 Nuance Communications Inc Ditech Networks Inc
53 IBM Corp Kenexa Corp
54 Verint Systems Inc Comverse Technology Inc
56 Thomson Reuters Corp FX Alliance Inc
57 Dell Inc Quest Software Inc
58 Ingram Micro Inc Brightpoint Inc
59 Sonus Networks Inc Network Equip Technologies Inc 60 Cypress Semiconductor Corp Ramtron International Corp 61 Mercury Computer Systems Inc Micronetics Inc
62 Teledyne Technologies Inc LeCroy Corp
63 IHS Inc XeDAR Corp
64 Microchip Technology Inc Standard Microsystems Corp 65 SXC Health Solutions Corp Catalyst Health Solutions Inc
66 DTS Inc SRS Labs Inc
67 Viasystems Group Inc DDi Corp
68 Oclaro Inc Opnext Inc
69 Oracle Corp Taleo Corp
70 Blackbaud Inc Convio Inc
71 Synopsys Inc Magma Design Automation Inc
72 CTS Corp Valpey-Fisher Corp
73 Google Inc Motorola Mobility Holdings Inc 74 ACI Worldwide Inc S1 Corp
75 CACI International Inc Paradigm Holdings Inc
76 Merge Healthcare Inc Ophthalmic Imaging Systems Inc 77 Skyworks Solutions Inc Advanced Analogic Tech Inc
