Short-term target shareholder wealth creation in mergers and acquisitions of advanced IT firms

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Master Thesis

Short-Term Target Shareholder Wealth Creation in Mergers and

Acquisitions of Advanced IT Firms

23rd_{June 2017}

Ivana Kohutiarova, 11371110

MSc. in Business Administration – Strategy Track

Amsterdam Business School, University van Amsterdam Thesis Supervisor: Andreas Alexiou

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1 Statement of Origin

Statement of Originality

This document is written by Student Ivana Kohutiarova 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.

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2 Abstract

This research investigates the short-term reaction of capital markets on the M&A announcement of targets with advanced IT capabilities. The sample of this study consists of 2,141 M&A transactions, which were announced during the period of 1999-2016 between US publicly listed companies. Previously conducted research has shown that acquirers tend to earn negative or slightly flat post-M&A announcement cumulative abnormal returns, while targets tend to provide a positive abnormal return. This research focuses on the value-weighted short-term cumulative abnormal returns with use of the 3-day event study methodology of the targets. The research provides empirical evidence that aimed to prove that advanced IT capabilities of companies involved in M&A lead to positive abnormal return of the targets. On the contrary, the research concludes that the concept of industry similarities across the sample does not intensify returns. Nevertheless, the rationale behind the M&A financing decision represents a significant impact on the direction and size of the cumulative abnormal returns. However, the research concludes that cash-financing is the only moderating factor influencing cumulative abnormal returns of the target. One of the major findings suggests that both acquiring and acquired firms with advanced IT capabilities achieve on average higher cumulative abnormal returns of the target.

Keywords: Mergers and Acquisitions; cumulative abnormal returns; event study methodology;

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Short-Term Target Shareholder Wealth Creation in Mergers and Acquisitions of Advanced IT Firms 4

3 Table of Contents

1 Statement of Originality ... 2 2 Abstract ... 3 3 Table of Contents ... 4 4 Introduction ... 6

5 Literature and Hypotheses Development ... 10

5.1 M&A Strategies ... 10

5.1.1 Reasons for M&A ... 10

5.2 Value Creation for Shareholders – Abnormal return ... 12

5.3 Technology driven M&A ... 16

5.4 Literature Gap ... 18

5.5 Hypothesis Development ... 18

6 Methodology ... 22

6.1 Sample Selection and Sampling Strategy ... 23

6.2 Variables ... 24

6.2.1 Dependent Variables ... 24

6.2.2 Independent Variables... 25

6.2.3 Moderating Variables ... 26

6.2.4 Control Variables ... 27

6.3 Descriptive Statistics about the Sample ... 29

6.4 Statistical Model ... 32

7 Results ... 34

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7.2 Regression Analysis ... 38

8 Discussion... 42

8.1 Major Findings ... 42

8.2 Contributions of the Study ... 45

8.3 Limitations and Future Research ... 46

9 Conclusion ... 47

10 Acknowledgement ... 48

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4 Introduction

M&A activity reached its peak in 2015 with a global deal value of 4.7 trillion USD, surpassing the previously 2007 highest activity. The following year, 2016, the global M&A activity dropped significantly to 3.9 trillion USD; ranking as the third highest year (J.P.Morgan, 2017). According to J.P. Morgan (2017), a leading US investment bank, companies in past years were complementing their organic growth by acquiring access to primarily innovation and new geographic reach, while taking advantage of the low-cost funding. Nowadays, Mergers & Acquisitions (M&A) are increasingly driven by the firms’ increased need for new and enhanced information systems (IS) and information technology (IT). M&A transactions occur either in the form of mergers or acquisitions. The merger combines the two or more companies by crossing their stocks and creating one legally consolidated entity (Giacomazzi, Panella, Pernici, & Sansoni, 1997), whereas acquisition is defined as an act of exchange in which a company uses cash, equity, debt or any combination to acquire ownership of the target company. M&A is chosen by companies to strengthen their competitive advantage, reduce costs or get access to new markets and customers. In order to maximise the created value and achieve high synergies via M&A, companies need to successfully transform their technologies and maximise their value-enhancing synergies. This transfer consists of different factors such as the capacity to absorb new technology, knowledge, expertise, skills and languages within the companies. It is assumed that the horizontal integration is less risky, and therefore enables greater level of exploitation of the proposed synergies. Additionally, academic research proves that managers are better at exploiting the areas of their own expertise. Therefore, commonalities between involved firms are perceived as synergy-creating. A successful M&A transaction creates greater value for

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shareholders than aggregating the values of the two stand-alone companies. The most recent research focuses (Canace & Mann, 2014) on the impact of long-term abnormal returns of the IT and non-IT firm. Additionally, a study by Tanriverdi & Bülent Uysal, (2015) investigated the impact on the abnormal cumulative returns of the acquirers with advanced IT capabilities. The study concludes that IT asymmetry between companies involved in the M&A results in a negative abnormal return for the acquirer. Furthermore, the focal point of the recent managerial research has increasingly been on technology-oriented M&A as a strategic decision, enabling companies to thrive innovation in a highly competitive environment, unlocking access to new markets and providing the ability to better adapt to market needs. The strategic rationale of managers behind M&A has changed considerably over the last decade, as managers are decreasingly engaging in M&A to protect market share or eliminate competitors, but mainly to gain access to capabilities to drive the future growth. A survey conducted by Mckinsey & Company (2015), a leading management consulting firm, concludes that companies which perceive themselves to be successful in the integration of the M&A are significantly more likely to seek access to expertise and capabilities externally. However, the key success of the M&A transactions is reflected in the integration process of technologies and operations. According to Sarrazin & West (2014) technology has a dual effect on the value created by acquisition; it can either be a value-driver or a value-drainer. It depends on the success of integration delivery. Therefore, it is important for the companies to critically evaluate, own, and target IT capabilities and then assign them appropriate importance.

This research paper is focused on the reaction of capital markets towards the announcement of M&A transactions driven by companies’ business technology strategies to extend and exploit

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their technological capabilities. The study investigates whether capital-markets discount the risks associated with IT integration and the replacement of IT operations in the valuation of the target’s share price. The research assumes that the share price of the acquired company with the increased IT capability will rise as a consequence of the expectation of the higher IT efficiency. Following prior studies that highlighted various challenges and obstacles related to IT integration, the main focus of this study is to provide more insight into the short-term value-creation after the M&A announcement driven by access to information technology capabilities. Additionally, the research analyses the impact of the focus of the take-over strategy on the value creation and the rationale behind the transaction financing.

The research contributes to current literature by enriching it with up-to-date empirical analysis of the value-creation as a consequence of the M&A announcement on the abnormal returns of the acquired company. Furthermore, the research explains the important impact of IT capabilities on the value creation of the merging companies. Consequently, the research reinforces the existing knowledge that diversification is beneficial for the companies. Lastly, the research includes the rationale behind transaction payment method as a mean of valuation of companies and the consequent reaction of the capital markets to value proposition. This research is investigating the impact and the means through which the companies are looking for external resources to help them grow. This research is based on the assumption that capital markets are efficient and free of information asymmetry.

The most noteworthy contribution of this study is in its methodological approach, which quantitatively measures IT capabilities across a broad sample of transactions. Current literature is

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lacking in quantitative empirical research about the companies’ IT capabilities, which is inevitable due to the complexity and the difficulty of its measurement. However, it is increasingly necessary to find relevant proxy for measurement of the IT competence as IT has become an important strategic driver of success. IT is no longer perceived as a support function but rather as an essential factor of competitive advantage driving business value. This study uses IW (InformationWeek) annual ranking as proxy of advanced IT capabilities, which ranks companies with outstanding IT capabilities across all industries with above average IT capabilities and their application in business technology within their industry. The use of such a measure is a new proposal, as the previous research mostly focused on the impact on patented development, innovation, industry relatedness and knowledge transfer in technology driven M&A.

Additionally, this research contributes to the strategic management field by exploring the rationale behind the strategic decision of increasing technology driven M&A, which reached its all-time high in 2015. This informs the managers’ decision regarding a possible diversification and allows them to evaluate the impact of the transaction financed on the target shareholders. At the present time, it is essential for the companies to know how to adequately value and integrate IT capabilities of the acquiring companies. Thus, this research bridges the gap in the academic literature that lacks appropriate measurement of the IT capabilities and managerial need to evaluate this impact of advanced IT capabilities on acquired value.

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5 Literature and Hypotheses Development

The following chapter presents the main research and findings of the existing literature with focus on the rationale behind M&A strategies and value-enhancement for the shareholders. The last section outlines the tested hypotheses of this research.

5.1 M&A Strategies

Paralleling the ever-increasing global investment into merger and acquisitions (M&A) by real-world firms, using M&A as a source of future value creation has been a central topic of research in finance and management science wishing to understand M&A. Initially, scholars focused on the assessment of the added value of acquisition to firm performance and its consequence on shareholders (Haleblian, Devers, & McNamara, 2009). While there are many paths towards M&A value-creation - such as increasing firm efficiency, exercising market power, market discipline or resource redeployment to support turnaround, etc. - the research paper is focused on the role of efficiency, resource deployment, and short-term value creation for the shareholders one day after the M&A announcement.

5.1.1 Reasons for M&A

A number of scholars have shown that there are various ways to create value via M&A, although the significance of value-creating factors differs amongst studies. One of the early studies in the field suggests market power as a motive for acquisitions, but their results were inconclusive given a sample of firms with high capitalization and diversified portfolios (Eckbo, 1983; Stillman, 1983). Traditionally, increased market power leads to excess returns as the companies have the power to influence price, quantity and the nature of the product on the market (Singh & Montgomery, 1987). Under this hypothesis, the firm would appropriate more value from the

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customers and thus increase its pricing strategy. However, there is very limited academic evidence to support this hypothesis.

Another stream of research suggested efficiency as an acquisitive motive, where a number of studies investigated cost improvements through M&A as a source of value-creation. For example, M&A could show improvements in long-term plant-productivity post acquisitions (McGuckin & Nguyen, 1995), while according to Banerjee and Eckard (1998) there is some evidence for increased economies of scale through horizontal mergers. Devos (2009) concludes that post-M&A efficiencies are the result of operating synergies rather than tax advantages or market power characteristics for financial strategies. Industry related mergers are better in realizing operating synergies and benefiting from the economy of scale than unrelated mergers. On the contrary, diversified take-over strategy leads to higher financial synergies including increased cash flow stability, cheaper access to capital and lower bankruptcy probability (Martynova, Oosting, & Renneboog, 2006).

Moreover, finance literature has particularly investigated market discipline, arguing that acquisitions could be value-enhancing to discipline ineffective managers, and that firms with weak governance are more likely to become takeover targets (Agrawal & Walkling, 1994; Jensen, 1986; Jensen & Ruback, 1983).

And finally, most closely related to the role of new technologies in the M&A value creation is the topic of resource redeployment that suggests acquisitions as a means to redeploy assets, to gain access to better or combined resources or to transfer knowledge and benefit from economies

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of scope via horizontal acquisitions (Capron & Pistre, 2002; Karim & Mitchell, 2000). Additionally, research claims that companies that engage in horizontal M&A gain innovation from the newly established companies. For example, Uhlenbruck et al. (2006) found that some firms used M&A to access scarce resources held by internet-firms leading to a positive stock-market response.

5.2 Value Creation for Shareholders – Abnormal return

Several major studies have analysed the impact of the M&A activities on the shareholder value-creation. The most common outcome of the research is that the target shareholders are in a victorious position, while acquirers are not favoured by capital markets (Goergen & Renneboog, 2004; Hazelkorn, Zenner, & Shivdasani, 2004). Nevertheless, the value of the cumulative abnormal returns (CAR) is affected by various factors of the transactions. There are three perspectives on how to evaluate the success of the M&A; the success of the target, the success of the acquirer or a combination of both (Schief, Buxmann, & Schiereck, 2013). The most extensive study was conducted by Andrade, Mitchell, & Stafford, (2001) analysing 4,256 completed mergers, including private and public companies across the period of 1973-1998, divided into four M&A waves. The research concludes that targets are clear winners of the M&A, as the target shareholder enjoy cumulative abnormal returns of 16 percent on average, while acquirers have slightly negative CAR varying from -0.3 percent to -1 percent. The research concludes that the overall combined value created results in positive CAR varying from 1.4 percent to 2.6 percent depending on the M&A wave.

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The table below summarizes the previous empirical academic research on the M&A activities and their impacts on the abnormal returns. The summary focuses primarily on studies, which tested the short-term reaction of the capital markets on the M&A announcement for the target shareholders of diversified industries across the globe. A majority of the studies proves that target shareholders enjoy a significant share-price enhancement as a response to the M&A announcement. The study by Ang & Cheng (2006) reports increase in targets shareholders’ wealth of 26,16 percent with the use of the one-day event methodology across an 18 year time-frame (1986-2001), while the sample studied earlier by Eckbo, (1983) found significantly lower CAR in the target of 6,24 percent using the same event-window across a different period (1963-1978). Thus, it is important to note that M&A activity is clustered into cyclical patterns influenced by market conditions. These waves are impacted by the technological or industrial shocks and arise in favourable political and economic situations as a consequence of credit expansion and market booms (Martynova & Renneboog, 2008).

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Table 1: Evidence of the short-term event studies Author (s) and Year Period of

Research

Sample Scope Event Window (days)

Industry Main Findings

1963-1978 196 industry related deals US (-1, +1) (-20, +10) Non-financing Diversified

• Targets earn positive abnormal significant returns for event window (-1, +1) 6,24% and for (-20, +10) 14,08%

Mitchell & Lehn, (1990) 1980-1988 232 bidders, 240 friendly acquisitions, 228 hostile acquisitions

US (-1, +1) Diversified • Abnormal returns of -1.66% for targets, which restructured following the bid and 0.7% for the targets, which were not restructured (both significant)

Mulherin & Boone (2000)

1990-1999 376 Public Targets

US (-1, +1) Non-financial Diversified

• Targets earn positive abnormal significant returns1,2% Walker (2000) 1980-1996 278 acquisitions, 230 mergers, 48 tender offers US (-2, + 2) Non-financial and Non-utilities

• No significant abnormal returns due to industry relatedness and firm size • Negative abnormal returns of

-0,84% (significant) Andrade, Mitchell,

& Stafford (2001)

1973-1998 596 transactions US (-1, +1) Non-financial Diversified

• Results based on the M&A waves target’s CAR always significant across all waves around 16%

Capron & Pistre (2002)

1988-1992 101 Horizontal acquisitions

World (-20, +1) Diversified • Relatedness is not sufficient condition to benefit from synergies • Abnormal cumulative return of 34% Sudarsanam &

Mahate, (2003)

1983-1995 519 listed acquirers

UK (-1, +1) Diversified • Acquirer earns abnormal return varying from -1,39% to -1,47% (significant)

Raj & Forsyth, (2003)

1990-1998 112 M&A deals UK (-20, +5) Non-financial Diversified

• Targets earn abnormal returns of with hubris 29,22% % compare to others 27,82%

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Gupta & Misra (2004)

1980-1998 285 M&A deals US (-10, +10) Banks • Acquirer abnormal returns over -1 to 0-day period is -1,57% (significant)

• Other day-windows are insignificant Schaik, Dimitri, &

Steenbeek, (2004)

1993-2003 136 M&A deals Japan (-1, +1) Diversified • Combined abnormal returns of 0,57%

Goergen & Renneboog(2004)

1993-2000 123 M&A deals Europe (-1, 0) Diversified • Targets earn abnormal returns of 9,01 % Moeller, Schlingemann, & Stulz (2004) 1980-2001 12 023 acquisitions of public firms

US (-1, +1) Diversified • Announcement returns for the targets are 1,1%

Ben-Amar & André (2006)

1998-2000 238 M&A deals by 138 Canadian firms

Canada (-1, +1) Diversified • Targets earn abnormal returns of 1,6%

Ang & Cheng, (2006)

1984-2001 848 deals US (-1, 0) Non-financials Diversified

• Targets earn abnormal returns of 26,11%

Tanriverdi & Bülent Uysal (2015)

1998-2007 549 M&A Deals US (-1, +1) Diversified • Focus on the technology superiority of the firms

• Acquirers with superior technology capability has significant negative returns

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5.3 Technology driven M&A

Investment into Information Technology (IT) capabilities and resources has become one of the leading subjects of the executives’ agenda and a focal research subject. The successful implementation of IT within companies is known to be one of the critical factors for a firm’s survival and growth (Bharadwaj, 2000). Strategic IT resources account for more than 50 percent of the capital investment and 4.2 percent of the annual revenue within the U.S. corporations (Tanriverdi, 2006). As defined by Bharadwaj (2000), the IT capability refers to “the firm’s

capacity to leverage the potential of information technology by effectively deploying IT resources in combination or co-present with other resources in the organization”. Additionally, IT

capabilities of a firm has emerged as the source of competitive advantage. The successful implementation and transfer of technology within and between firms are important research topics for scholars, consultants and research institutions (Buck-Lew, Wardle, & Pliskin, 1992; Buono, 1997; Stylianou, Jeffries, & Robbins, 1996).

Companies are using two strategies in order to obtain new technologies either externally, via acquisition or internally, through in-house research and development (R&D) (Xue, 2007). According to the accounting literature, despite its risks, R&D costs offer several benefits to the firm such as a positive impact on the firm’s current and future market value and its future profitability (Kallunki, Pyykkö, & Laamanen, 2009; Xu, Magnan, & André, 2007). Nonetheless, firms seek M&A deals to fill technological or R&D gaps via the acquisition of external technologies that promise future growth and innovation (Canace & Mann, 2014; Singh & Montgomery, 1987). This is also reflected in the current market environment where, due to the on-going market disruption by digital technologies, tech and non-tech companies are

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increasingly looking for solutions via M&A (EY, 2016). Therefore, companies perceive small technological advanced firms as a vital source of the technological input in the rapidly technologically changing environment (Greenstein, 2002). This strategic approach eliminates the time- consuming, path-dependent and uncertain process of the internally developed technological capabilities and resources (Steensma & Fairbank, 1999). Moreover, some studies suggest efficiency gains given from more advanced technology firm acquisition either do not increase or marginally increase their R&D spending after acquiring a high-tech firm compared to non-technology firms (Kallunki 2009). Puranam & Sirkanth (2007) suggested that acquirers re-deploy innovative resources of targets either through corporate integration or by keeping innovative capabilities in a separate unit. Additionally, research proved that exploitation and integration of the target resources are higher if the merging companies operate within the same industry (Uhlenbruck, Hitt, & Semadeni, 2006). However, the integration of IT systems post acquisition also imposes a significant risk on firms, resulting in both short and long-term problems. Failure to adequately evaluate the IT capabilities of a potential partner could result in a missed exploitative opportunity (Lohrke, Frownfelter-Lohrke, & Ketchen Jr., 2016). Therefore, when considering acquisitive risks, the sole focus shall lay not only on the financial, legal and operational issues but also on key challenges related to the successful integration of IT, to avoid damaging a firm’s competitive advantage (Stylianou et al., 1996). Nevertheless, prior research supports the argument that the value created does not guarantee captured value. Notably, only 30 percent of managers involved in M&A achieve successful IT integration according to a survey conducted by Accenture (2007). It is estimated that IT synergies account for 50-55 percent of the total post-acquisition benefits. The proportion varies across the industries. For example in financial services industry 55% from the total synergies are related to IT, these synergies decrease headcount costs, IT infrastructure, procurement and route optimization (Sarrazin &

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West, 2014). Despite the growing challenges of post-merger IT integration, the academic community has been unable to provide sufficient empirical evidence and factors demonstrating the effective integration of IT and its financial benefits to the firm.

5.4 Literature Gap

There has been a tremendous amount of literature studying M&A and its impact for the target shareholders and company itself. The previous research covers all dimensions of the M&A success factors and its impact on the numerous stakeholders involved in either prior or post-M&A process. However, the IT competences are increasingly becoming strategic factors of the companies and at the same time a driver of the future success. As currently IT is embodied in all departments, it is losing its back-office support function; rather, it is now identified as the driver of competitive advantage. Academic literature is lacking relevant quantitative measure, which would proxy IT competencies of the firms. Instead of it, academic literature identified technology driven M&A according to patent development, R&D spending or cluster companies as high-tech and low-tech companies.

The most recent academic research does not analyse the impact of IT capabilities on the short-term abnormal returns across the 18-year period and a majority of the samples have less than 600 deals. There is no research focusing on the target abnormal returns, which could be influenced by the IT competencies of the firm.

5.5 Hypothesis Development

As described above, IT competencies are a critical asset to the firm’s future growth and long-term value creation. According to Tanriverdi & Bülent Uysal (2015) firms with inferior IT competencies are facing higher risk to be acquired by companies with superior IT competencies. Following this logic, acquiring companies screen for targets, where they can re-deploy their

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resources, exploit synergies and provide a higher combined value for the shareholders of one entity. The short-term reaction of the capital markets upon the M&A announcement is a forward-looking estimate of the combined value of the newly proposed entity (Moeller et al., 2004). The immediate movement of the share price reflects the proposed premium paid by the bidding firm and also the future synergic value (Martynova et al., 2006). A number of studies have shown that target companies exhibit positive abnormal returns at the announcement of M&A transactions, whereas acquiring firms exhibit slightly negative or flat abnormal returns (Andrade et al., 2001; King, Dalton, Daily, & Covin, 2004; Moeller et al., 2004) (see more details in Table 1.). Based on the previously conducted research, this research assumes acquirers are willing to pay a higher premium for superior IT targets compared to non-IT leaders, target shareholders are expected to earn higher abnormal returns. On the flipside acquirers who are already IT leaders maybe less likely to overpay leading to lower target abnormal returns. Therefore, this research paper is testing the following hypothesis:

Hypothesis 1a (H1a): Target shareholders are likely to earn higher abnormal returns at

M&A announcement when the target’s IT capabilities are superior compared to the acquirer.

Hypothesis 1b (H1b): Target shareholders are likely to earn lower abnormal returns at

M&A announcement when the acquirer’s IT capabilities are superior compared to the target.

According to Singh & Montgomery (1987) industry relatedness reflects the transfer of functional skills including research and development, production, marketing and distribution between firms, while the value created from the related acquisitions consists of three sources (1) economies of scale, (2) economies of scope and (3) market power. The nature of the take-over strategy can be classified either as focus or diversified. The focus strategy exploits operating synergies, which

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are maximized in industry-related M&A, while diversified strategy focuses on financial synergies leading to increase financial efficiencies and conglomerate building. Studies that investigate the effect of horizontal acquisitions on the value creation of shareholders find that positive abnormal returns for target and acquiring shareholders, whereas the positive effect is even more pronounced for target shareholders (Huyghebaert & Luypaert, 2013). One explanation is that industry relatedness enables the acquirer to better realize and exploit synergies while improving the capital structure of the target (Pennings, Barkema, & Douma, 1994). On the contrary, one of the leading study in the academic M&A literature written by Eckbo (1986) argues that companies are engaging in the inorganic growth in order to diversify and gain access to complementary resources and reduce exposure risks. Therefore, diversification leads to the over-valuation of the target company and the willingness to pay higher premium to the target shareholders due to information asymmetry and industry related knowledge. In the particular case of high-tech M&A deals, horizontal acquisitions enable increased economies of scale due to the common R&D input of both firms leading to greater value-creation (Ahuja & Katila, 2001). This argument is also supported by studies suggesting technology acquirers decrease their R&D spending after acquisitions (Kallunki et al., 2009). On the other hand, other studies argue that while knowledge relatedness between merging high-tech firms may facilitate incremental renewal, complementarity mergers result in higher likeliness of discontinuous strategic transformation and better post-merger invention performance (Makri, Hitt, & Lane, 2010). Nonetheless, research that investigated the moderating impact of industry relatedness on IT leadership and capital market returns has not been abundant. This research assumes that advanced IT capabilities would result in increased operating synergies which are fully exploited in industry related M&A. Thus, the following statement is hypothesized:

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Hypothesis 2 (H2): Upon industry related M&A announcements, target shareholders are

expected to earn higher abnormal returns when the acquirer’s IT capabilities are superior compared to the target

Furthermore, academic research has been testing the rationale behind the choice of the M&A transaction financing and its impact on value creation. According to data from J.P Morgan (2017), cash transaction in 2016 represented 62 percent in comparison to 54 percent in 2015. However, the increased trend in the recent years to finance the M&A deal with cash might be caused by the cheap funding possibilities, which are the outcome of post-financial crisis; the low interest rates. It is a common belief that the choice of the deal payment method maybe a signal of the quality of the firms involved: In general, cash deals are assumed to be conducted by acquirers that perceive the target firm to be undervalued, while stock deals signal to the market that the acquirer is attempting to use its overvalued equity as a cheap currency to pay for its acquisitions (Goergen and Renneboog 2004). Martynova & Renneboog, (2008) and Goergen & Renneboog (2004) show both that target shareholders are substantially benefiting more from cash-financed transaction in comparison to equity payment, however both payment methods result in significantly positive abnormal returns. This difference derives from the information asymmetry about the future financial prospects of a firm between involved managers and outside investors. For a cash deal the market anticipates the higher quality of the target firm that positions the target shareholders for a higher premium during negotiations, whereas a deal stock may signal uncertainty about the quality of the target firm; thus acquirer may intend to keep target shareholders involved to share the risk (King et al., 2004, Martynova and Renneboog, 2006). Numerous researches documented that besides the rationale behind the payment methods, cash-financed transactions are more beneficial to acquiring firms’ shareholders (Carow, Heron, &

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Saxton, 2004; Loughran & Vijh, 1997; Huang & Walkling, 1987; Travlos, 1987). Therefore, the rationale behind the payment method is perceived as an indicator of the quality of the acquiring firm and its proposed future synergies. However, the academic literature is absent about the direct impact of the deal financing on wealth creation of target shareholders in regard to IT capabilities. Since both payment methods are associated with positive abnormal returns for target shareholders this study aims to investigate the moderating effect of the payment means on the companies with superior IT capabilities. Thus, the following statement is hypothesized:

Hypothesis 3a (H3a): Upon announcement of cash-financed deals, target shareholders

are expected to earn higher abnormal returns when the acquirer’s IT capabilities are superior compared to the target.

Hypothesis 3b (H3b): Upon announcement of stock-financed deals, target shareholders

are expected to earn higher abnormal returns when the acquirer’s IT capabilities are superior compared to the target.

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6 Methodology

This chapter explains the research design and approach of the thesis. The first section focuses on the sampling strategy. Then, the following section describes operationalization of the dependent, independent, moderating and control variables. The final part of the chapter explains the models implemented for the data analysis.

6.1 Sample Selection and Sampling Strategy

The research consists of the analysis of the secondary data obtained from Wharton CRSP – Compustat Merged, M&A Bloomberg, Eventus, Thomson One and Information Week (IW) databases. The sample is composed of US publicly traded companies, involved in M&A transactions between 1999 and 2016 with a deal size above 50 million USD. Data related to the IT capabilities were obtained from the Information week (IW) database, which identifies the most innovative leaders with advanced business technology across all industries every year. IW ranking focuses on the applied technology, which is practical and measureable, and results in a business value. The sample derived from the ranking of 6434 entities including non-profit organization, universities, hospitals, professional firms, private and public companies in the past 18 years (1999-2016). From the list, only publicly listed targets were selected for the further analyses. Company codes were retrieved individually for every company and matched with the M&A deals within researched period. The complete list of the M&A transactions of public companies with transactions details were retrieved from the Thomson One M&A database. Accounting data about the companies involved in the M&A were retrieved from M&A Bloomberg and Wharton CRSP Compustat Merged databases. Cumulative abnormal returns were available via Eventus and data were merged based on CUSIP codes with the transaction details. After dropping deals with missing values, the final sample included 2,141 M&A deals in

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which 391 are acquirers, 51 targets have advanced IT capabilities and 26 deals include target and acquirer with advanced IT capabilities. Almost 20 percent of the companies from the total sample have superior IT capabilities identified by the IW.

6.2 Variables

6.2.1 Dependent Variables

In order to measure capital markets’ responsiveness towards technology-driven acquisitions, an event study was conducted to determine firms’ abnormal returns at the event date (t) defined as the announcement date of an M&A transaction. Abnormal returns enable the measurement of the unanticipated impact of a news-event on a firm’s stock price (Kothari, Lewellen, & Warner, 2006) As argued by Andrade, Mitchell and Stafford (2001) the best way to examine the impact of the M&A transaction on the value creation or destruction of a firm’s value is to analyse capital market reactions around the announcement date. However, it is crucial to distinguish the effect of the event-driven performance from the market movement. Hence, an event window of 3 days [-1,+1] has been selected to measure the actual return realized around t and an estimation window of 255 days to measure the parameters used for calculating the expected- or normal returns. Normal returns are approximated by the market model, which assumes a constant and linear relation between returns of individual assets and the market index resulting in excess returns:

where ri,t refers to the return of stock i at time t, RM refers to the risk premium measured by the

excess return of a market portfolio over a risk-free security that can be proxied by a broad-based index such as the S&P 500, and αi and βi refer to the parameters of the market model

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The difference between the actual return and the expected return is defined as the abnormal return, which are aggregated through the event window to calculate the cumulative abnormal

returns (CAR):

This measure represents the dependent variable and is widely used for evaluation of the value created via M&A (Hayward & Hambrick, 1997; Lubatkin, 1987; Singh & Montgomery, 1987; Uhlenbruck et al., 2006).

6.2.2 Independent Variables

Advanced IT Capabilities. Publicly available data were used to measure the IT competencies of

the firms engaged in the M&A. Information Week (IW) annually publishes the list of Elite 100 (previous IW500) organization including non-profit, public, private and educational organizations. The organizations are ranked and evaluated by the IW experts and industry peers as the companies with above average IT and applied business technology within their industry. The assessment is not looking for the investment and development of IT, but rather sees technology as the driver of business value and innovation. Therefore, if the company is included in the list 5 years prior and 1-year post M&A announcement, the company is considered to be IT and technology advanced amongst its peers. This timeframe was chosen because the list, at the beginning of 1999 included 500 companies, later dropped to 250 and currently rank only 100 companies annually. There has been a decreasing number of companies ranked in the list and therefore there could be marginal differences explaining why the company did not make it into the list. In addition to this logic, the R&D innovation cycle is rapidly decreasing from the initial 8-year in 1970 to 2-year in 2010. However, the innovation cycle is to a great extent industry-dependent (Boutellier, Gassmann, & Zedtwitz, 2013). Thus, implementation and use of the

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innovation and technology is also taken into consideration.

IT capability is measured as a binary dummy variable (0,1). 1 is assigned to companies included in the list and therefore considered IT leaders while if a company is not included in the list, it is considered IT lagger and is assigned 0. Previous studies (Bharadwaj, 2000; Li Wang & Alam, 2007; Muhanna & Stoel, 2010; Santhanam & Hartono, 2003; Tanriverdi & Bülent Uysal, 2015) included IW ranking as the equivalent proxy for the IT competence and business technology competencies of the company.

6.2.3 Moderating Variables

Industry Relatedness. As previous research indicates, horizontal acquisitions have a positive

impact on the value creation (Singh & Montgomery, 1987) and increased post-acquisition performance (Haleblian, Devers, McNamara, Carpenter, & Davison, 2009; Puranam & Srikanth, 2007). Thus, industry relatedness is used as the moderator positively affecting mainly operating synergies of merged or acquired companies (Tanriverdi & Uysal, 2011). Industry relatedness measure is obtained from the primary four-digit Standard Industry Classification (SIC) codes of the acquirers and targets. Study by Capron & Pistre (2002) examined 101 horizontal acquisitions across 11 industries and defined industry relatedness of the involved companies by the exact matching of the primary four-digit SIC codes. Nevertheless, the research concludes that cumulative abnormal returns are not earned resources when only resources are transferred to the acquirers. Another, study by Walker (2000) and Andrade et al., (2001) considers industry relatedness in the M&A when the first two-digits of the SIC codes are identical. As researched by Walker (2000) the industry relatedness is one of the strategic objectives during M&A processes which aims to expand the firm’s operations geographically, broaden the product line or increase market share based on the first two digits of the SIC codes being identical. Moeller et

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al., (2004) identified the diverse transaction if the first two digits of the target and acquirer are different. Therefore, this research measures industry relatedness as exact matching of the first two digits of the SIC codes of merging or acquiring firms. The aim of the research is to analyse relatedness M&A in terms of product or product-market similarities, where information technology capabilities can be transferred, and operating synergy maximised. This approach was proven to have a great level of commonality and correspondence (Kusewitt, 1985) within the value chain (Hayward & Hambrick, 1997).

Financing. The financing is used as another moderating variable influencing the intensity of

cumulative abnormal returns of target (Haleblian, Devers, McNamara, et al., 2009). The payment of the transaction can be settled in purely cash, purely stock or a combination of any other financing instruments including cash, stock and/or debt. This research focuses only on purely cash and purely equity financed deals. Research excludes analyses of the impact of the combination of different financial instruments used for the financing of the M&A. The payment method is measured as 2 separate dummy variables mapping deals financed purely by cash (1 or 0), purely by the stock (1 or 0).

6.2.4 Control Variables

The following sector outlines control variables used in the correlation matrix and regression model. Control variables are divided into categories based on their characteristics focusing on (a) target, (b) acquirer and (c) deal.

6.2.4.1 Target Characteristics

Return of Equity (ROE). ROE is used as a proxy for the profitability. ROE is measures the

level of return of a firm or its effectiveness in generating profit for the equity owners (Hitt, Harrison, & Ireland, 2001; Mueller, 1980). The ROE is measured at the end of the fiscal year

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prior to the M&A announcement. The measures are directly obtained from the Bloomberg database and are calculated as net income available to common shareholders over the average total common equity and multiplied by a hundred.

6.2.4.2 Acquirer Characteristics

Size. Market value is used as a proxy for the firm size. The firm size influences the created value

for the shareholders. According to research conducted by Moeller et al., (2004) acquisition by the small firms tends to be profitable for shareholders but results in small gains. On the contrary, a large acquirer creates large dollar losses. At the same time, Malmendier & Tate, (2005) find that managers of large companies are overconfident and make more acquisitions resulting in the lower abnormal returns. Market value is measured as the total market capitalization at the end of the fiscal year prior to the M&A announcement.

6.2.4.3 Deal Characteristics

Deal Method. The transaction method has a statistically significant influence on the

post-announcement abnormal return (Bruner, 2004; Tuch & O’Sullivan, 2007). In order to successfully integrate target and ensure effective cooperation between the firms engaged in the M&A, the deal approach might be a key factor for the collaboration with the target management. There are two recognized approaches; either it is a friendly or a hostile approach. It is expected that the hostile method has a negative impact on the CAR (Goergen & Renneboog, 2004) as the acquirer might face challenges from the target with the integration. The hostile approach might result in reduced willingness to co-operate and realize proposed synergies (Tanriverdi & Bülent Uysal, 2015). Therefore, the dummy variable was created and 1 is assigned to hostile and 0 to friendly bid.

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Deal Status. The researched sample includes all the deals regardless of their outcome. The

research controls the final deal status and whether market is able to anticipate success or failure of the transaction. Therefore, the dummy variable was constructed with 1 assigned to completed deals and 0 to terminated, proposed and pending transactions.

6.3 Descriptive Statistics about the Sample

The total sample consists of 2,141 deals between 1999 and 2016. The yearly overview indicates that most of the M&A deals happened in 1999, representing slightly more than 13 percent and the least M&A activity was in 2011 with slightly more than 3 percent of the total sample. The most targets with advanced IT capabilities were 2001. However, this might be explained by a large number of ranked companies included in 1999-2002 in the IW list, which dropped from an initial 500 to 250 in 2002. The acquisition of the target with high IT capability within the sample was highest in 1999 and 2011. In the transactions in 2002, 2013 and 2016 there was no target included with advanced IT skills. Table 2 below documents the annual distribution and IT ranking among the M&A transaction into greater detail.

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Table 2: Sample distribution by announcement year and IW Ranking

The sample contains all transactions (completed, proposed, pending and terminated) between 1999 and 2016 with a deal value above 50 million USD, which were publicly traded. The superior IT capabilities of target and acquirer are classified as being present on IW list (-5 to +1 year) prior to M&A deal.

Announcement

Year IT Superior Capability

IT Inferior Capability

Acquirer Target Both Both All

1999 34 7 0 248 289 2000 28 2 0 157 187 2001 ₅₀ ₇ ₃ ₁₄₃ 203 2002 16 1 0 77 94 2003 14 2 1 96 113 2004 23 5 1 95 124 2005 34 4 2 82 122 2006 35 2 3 88 128 2007 ₄₁ ₄ ₂ ₉₆ 143 2008 17 2 4 52 75 2009 16 1 4 45 66 2010 ₂₅ ₃ ₀ ₆₃ 91 2011 11 7 2 46 66 2012 15 1 1 51 68 2013 7 0 0 62 69 2014 12 1 2 79 94 2015 7 0 1 106 114 2016 6 2 0 87 95 Total 391 51 26 1673 2141 Proportion _18,3% _2,4% _1,2% _78,1%

Additionally, the research is testing, the impact of industry relatedness on CAR. All the deals across the sample are clustered into two categories; industry related deals which account for 64,22% and industry unrelated which account for the 35,78% left. From this it can be concluded that companies are primarily interested in the exploitation of operating synergies and focused

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take-over strategies. Table 3 below documents the industry distribution and IT ranking amongst the M&A transaction into greater detail.

Table 3: Industry related

The sample is divided into two categories; industry related or industry unrelated deals. The industry related across the sample is determined according to the first two digits of the SIC codes. If the first two-digits are matching the deal is considered to be industry related if not, it is marked as industry unrelated. The industry relatedness is further analysed according to IT capability of the involved firms.

Advanced IT capability IT Inferior

Capability

Acquirer Target Both Both All Proportion

Industry Related 207 40 19 1096 1375 64,22%

Industry Unrelated 184 11 7 577 766 35,78%

Furthermore, an important feature of the analysed sample is the moderation effect of the deal financing on the CAR. The tested hypotheses focus on the impact of cash and equity financing. Table 4 summarises yearly proportion of the deals according to their financing method. The highest proportion has the combined financed transaction, which accounts for more than half (52,12%). The combination can include financial instruments such as cash, stock or debt. Overall purely cash financed transactions count for 51.33% and purely equity make up 16,16% of the sample. It can be observed from the distribution that 2years prior to the financial crisis in 2008, companies were primarily financing deals with cash and decreasingly with stock.

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Table 4: Payment Method Sample Summary

Table 4 summaries distribution of the M&A deals within the sample and the financing mean between 1999 and 2016. The table also provides proportion of each financing method per year and per total sample of 2,141 deals.

Announcement Cash Stock Combination

Year Deal % Deals % Deals %

1999 128 44,29% 65 22,49% 96 33,22% 2000 82 43,85% 42 22,46% 63 33,69% 2001 92 45,32% 58 28,57% 53 26,11% 2002 41 43,62% 16 17,02% 37 39,36% 2003 55 48,67% 16 14,16% 42 37,17% 2004 58 46,77% 20 16,13% 46 37,10% 2005 63 51,64% 13 10,66% 46 37,70% 2006 76 59,38% 20 15,63% 32 25,00% 2007 86 60,14% 13 9,09% 44 30,77% 2008 44 58,67% 15 20,00% 16 21,33% 2009 27 40,91% 11 16,67% 28 42,42% 2010 67 73,63% 11 12,09% 13 14,29% 2011 45 68,18% 8 12,12% 13 19,70% 2012 42 61,76% 3 4,41% 23 33,82% 2013 39 56,52% 6 8,70% 24 34,78% 2014 50 53,19% 10 10,64% 34 36,17% 2015 52 45,61% 12 10,53% 50 43,86% 2016 52 54,74% 7 7,37% 36 37,89% All 1099 51,33% 346 16,16% 696 32,51% Total 2141 6.4 Statistical Model

The research is conducted based on hierarchical linear modelling, which is a complex form of the ordinary least square (OLS) regression that explores the statistical significance of independent and interaction variables after accounting for control variables. Variables are grouped based on three levels: 1. control variables 2. independent variables and 3. interaction variables. Several regression models are measured where with each step a new explanatory variable will be added to determine whether an improvement in the model’s explanatory power (R2_{) can be observed.} The following OLS regression model will be investigated:

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where, CAR i,(-1,1) refers to the 3-day cumulative abnormal return measured one day prior and one day after the announcement day t for stock i.

The independent variables high IT (HIT) are dummy variables that equal 1 if company i has been listed as IT leader by the InformationWeek within the timeframe of 5 years prior and 1 year after the announcement date. We include three HIT dummy variables measuring 1 when either the acquirer, target or both firms together are listed as IT leaders. In addition, a number of control variables that are frequently used by the M&A literature have been added: Size refers to the market capitalization of an acquirer measured by the natural log of the total dollar market value of its outstanding shares at the announcement date t-1. ROE refers to the ratio of net income available to common shareholders over the average total common equity at t-1. Deal Completion is a dummy variable that equals 1 if the deal has been successfully completed. Hostility is a dummy variable that equals 1, when a deal was announced without consent of the target firm’s management. At last, two variables were selected to measure their interaction effect with the independent variables: Cash and Stock are dummy variables that equal 1 when the deal’s underlying payment method has been identified as cash or stock. Industry relatedness is a dummy variable that equals 1 if the acquirer’s and target’s two-digits SIC codes are the same.

Given that the underlying nature of the data in this study is fully quantitative, the most preferred prediction technique - to explore the relationship between firm IT capabilities and stock price returns - is an OLS regression. Using a hierarchical modelling approach by adding independent variables after controls is supposed to increase the robustness of the results.

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7 Results

The results chapter describes the statistical outcome of the conducted research. Firstly, the descriptive statistics of the variables are presented to provide an overview of the data. Then, significant correlations are marked in the correlation matrix. Lastly, the chapter is concluded with several regression models carried to test the formulated hypotheses in the “hypotheses in development” section of this paper.

The results are based on the cleaned data from the outliers. The outliers are cleaned according to their Z-score results and all measures with Z-score above 3 and below -3 were winsorized. This technique improves the normal distribution of data and significantly scales skewness. The reliability and the scaling of the data are not performed as these measures are not suitable for the database research.

7.1 Descriptive Statistics and Correlation Analysis

Table 5 provides descriptive statistics and the bivariate correlation analysis matrix of the final sample. The linear correlations were measured by the Pearson product-moment correlation coefficient. These correlations are analysed in the following order; firstly, the correlations between dependent and independent variables are defined. Secondly, the correlations between dependent variables and control variables are presented. Thirdly, the table outlines the correlations between moderators and dependent variables, and lastly the correlation between moderating variables and independent variables are included. The correlations matrix indicates that data is free of multicollinearity as there are no correlation above 0.9.

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The sample is comprised of 2,141 M&A deal observations announced during the period 1999-2016 with a transaction value higher than 50 million USD. Looking at the correlations between IT rankings and the dependent variables expressed as CAR over the three-day window shows a significantly positive relationship (r= 0,067) between abnormal cumulative return of the target and advanced IT capabilities of the target. Similarly, bivariate analysis finds a modestly positive significant evidence (r= 0.053) for the correlation of the target’s CAR when only the acquirer possesses advanced IT capabilities, in contrast to a slightly negative significant correlation (r=

-0,043) with CAR of the target, where none of the involved companies has advanced IT

capabilities.

Secondly, looking at the dependent variables and control deal characteristics variables, we observe an influence on the direction of these controls on the target’s CAR. With regards to the correlation between CAR of the target control variables, there is a strong positive significant correlation with deal status (r= 0,149) and a strong negative significant correlation with ROE of the target (r=- 0,057).

Thirdly, the correlations between moderator variables and target’s CAR are predominantly significant. Mainly, target’s CAR has a negative significant correlation with industry relatedness. (r= - 0,078) this contradicts previously conducted research by Ben-Amar & André (2006) and L. Lang (1994), who argued that diversification can lead to value destruction. The CAR has a significantly positive relationship with cash financing (r= 0,135) and a strongly negative significant relationship with stock financing (r= - 0,109). The correlations result proves that targets which are perceived to be undervalued by the acquirer tend to be financed by equity,

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which results in lower returns than those financed purely by cash (Martynova & Renneboog, 2008). The full scope of the correlations is included in the following or table below.

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Table 5: Sample Size, Mean, Standard Deviations and Correlations Matrix of Variables The table 5 analyses the total sample of the M&A transaction across time horizon of 1999-2016.

(1a) (1b) (1c) (1d) 2 3 4 5 6 7 (8a) (8b)

(1a) IT Superior Target 1

(1b) IT Superior Acquirer -0.058** 1

(1c) IT Superior Target & Acquirer -0,017 -0,041 1 (1d) Both-Non-IT -0.198** -0.903** -0.215** 1 (2) CAR (-1, +1) Target 0.067** 0.053* -0,022 -0.043* 1 (3) Deal Method 0,015 0,002 0,037 -0,021 -0,025 1 (4) Deal Status -0.054* 0.066** -0,023 -0,039 0.149** _-0.206** ₁ (5) Size Acquirer -0,018 -0.178** -0,011 0.178** -0,027 -0,035 -0,014 1 (6) ROE Target 0.057** 0,023 0,031 -0.057** -0.116** 0,034 -0,024 -0,015 1 (7) Industry Relatedness 0.048* -0.104** 0,022 0.080** _-0.078** _0,033 _-0.047* _0,038 _0,012 ₁

(8a) Cash Financing 0,017 0,025 -0.046* -0,006 0.135** 0,023 0,009 0.043* -0.061** -0.065** 1

(8b) Stock Financing -0,019 0.100** 0.067** -0.119** -0.109** 0,020 0,006 -0.080** -0,010 0,001 -0.452** 1

N 2141 2141 2141 2141 2141 2141 2141 2141 2141 2141 2141 2141

Mean 0,024 0,183 0,012 0,790 0,240 0,025 0,877 8,024 3,402 0,636 0,513 0,162

Std Dev 0,153 0,386 0,110 0,407 0,238 0,155 0,328 2,009 27,821 0,481 0,500 0,369

All control variables are measure as fiscal year prior to the M&A. Heteroscedasticity consistent t-statistics are reported in parentheses (Hsieh, 1983). *. Correlation is significant at the 0.05 level (2-tailed).

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7.2 Regression Analysis

In order to test the hypothesis, a hierarchical linear modelling regression was conducted. The missing data were excluded pair-wise and the total sample for the regression consisted of 2,141 cases. The regression analyses examined the linear relationship between the independent variables (IT capability), the dependent variables (abnormal cumulative returns of the target), and moderate variables. Table 6 reports coefficient estimates and White’s heteroscedasticity-consistent standard errors of the ordinary least squares regression for the target CARs. Model 1 shows a foundation model consisting only of the four control variables to make sure that these variables do not explain the entire relationship between superior IT capability and the cumulative abnormal returns. In Model 2 the main effects of independent variables are introduced one by one, reflecting all scenarios of the advanced IT capabilities across the transactions, while the models control for the scenario when both companies are not included in the IW list. Model 3 and Model 4 include the moderating effect of industry relatedness and deal financing on the transaction. The final (Model 5) combines control variables, main effect and interaction effects. Across all models, variance inflation factors (VIFs) are below suggested cut-off value of 10 (min=1,00 max=2,71) proving the lack of multicollinearity across the researched sample.

There is consistent significantly strong evidence for the deal status (ß=0,11; P> 0,001), and the return of the equity of the target (ß= -0,001; P >0,001), on the abnormal cumulative returns of the target across all tested models. Therefore, it can be concluded that the market is able to anticipate successful bids. The first hypothesis tests the impact of the advanced IT capability on the CAR of the target. It is assumed that the higher IT advanced capabilities increase the CAR. As shown in Model 2, the coefficient of the superior IT capabilities of the acquirer is positive and significant (ß=0,03; P<0,05), however, in Models 2-4 they are insignificant. Targets with

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the advanced capabilities are consistently strongly significant across all models (ß=0,13; P>

0,001) with CAR. The final (Model 5) model finds a significant positive relationship between

CAR and targets with strong IT capabilities (ß=0,24; P> 0,001) and slightly less significant when a company has lower IT capability than its target (ß=0,03; P<0,05). The magnitude of the significance of the coefficient is independent of controls variable or interaction effects. Therefore, it is concluded that if the companies have dissimilarity across their IT capabilities, it leads to a positive impact on the CAR of the target. If the target possesses advanced IT capabilities, capital markets will reward target shareholders in reaction to M&A announcement. It is clear that complementarities in IT domain between target and acquirer have a positive impact on the cumulative abnormal returns.

In order to test the interaction effects of the third and fourth hypotheses, the interaction variables were constructed. Looking at the second hypothesis, there is a strong negative significant evidence of the impact of the industry relatedness across the analysed sample, but weaker evidence for the interaction effect on the CAR in Model 3. Both Models 4 and 5 do not provide significant evidence of the moderating effect. Therefore, it is concluded that the industry relatedness and thus focus take-over strategy between target and acquirer does not intensify abnormal cumulative returns of the target. However, the regression finds a negative significant relationship (ß= -0,03; P> 0,001) with CARs of the target.

At last, the third hypothesis tests the moderating effect of the payment method on the abnormal returns of the target. Financing of the deal purely by cash is perceived to indicate that bidding managers believe in a greater exploitation of the synergies, while managers of the undervalued firms prefer to acquire target by the stock. Model 4indicates a significant negative interaction

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effect on the abnormal returns of the target (ß=-0,22; P<0,01) and it is consistent with the final model, Model 5, as there is a significant coefficient (ß=-0,05; P<0,01).

The overall strength and robustness of the model measured by the R2_{, coefficient of}

determination, is relatively low varying from 3.6% to 7.5%, being the highest in the final model (Model 5). Therefore, it is concluded that more than 90% of the variation in target’s CAR remains unexplained by the model. Looking at the R2_{of the previous studies researching CAR}

of the targets, the results are in line with the academic study of 130 targets in Japan with the same even –window results in R2_{varying from 2,4% to 5,4% (Schaik et al., 2004). The similar}

results of R2_{are for the larger sample study, 12 023 transactions, of Moeller et al.,(2004)}

varying from 4% to 5.5%. Other studies (Walker, 2000) reported similar R2_{results, therefore it}

is concluded that despite a relatively high percentage of unexplained variations in the model, the strength of the model is still in line with previous studies.