Do Technological Mergers and Acquisitions Create Long-term Value for
European Acquirers? Evidence From Operating Performance
Ana Paula Viveiros Franco
Dissertation Master in Finance
Supervised by
Miguel Augusto Gomes Sousa, Phd
Biographical Note
Ana Paula Viveiros Franco was born in Madeira Island, on the 2nd of May, 1995. She
completed the BSc in Management in 2016 of the University of Beira Interior.
After completing the bachelor degree, Ana enrolled in the Master in Finance program of the School of Economics and Management of the University of Porto. The present dissertation is regarded as the last stage to complete the Master Program.
Acknowledgements
Firstly, I would like to express my sincere gratitude to my supervisor Prof. Miguel Augusto Gomes Sousa, for the continuous support of my study and related research, for his patience, motivation, and immense knowledge. His guidance helped me in the research and in the writing process of this dissertation.
I thank my fellow classmates and friends for all the support and memories created during the last two years. You truly made my time in Porto unforgettable.
Last but not the least, I would like to thank my family for supporting me throughout writing this dissertation and in life in general.
Abstract
Theoretically, technology acquisitions can benefit companies by providing valuable resources, increasing market power, and initiating strategic renewal. Yet despite these opportunities, they often present a significant challenge for both buyers and sellers. The purpose of the present dissertation is to extend the research on technology acquisitions, hence the following question will be answered: “Do Technological Mergers and Acquisitions Create Long-term Value for European Acquirers?”
Using the Return on Assets, the EBIT margin and the Asset Turnover ratio as measures of operating performance, the results of this study suggest that Mergers and Acquisitions completed in Europe over the period 2007 to 2014 result in poor improvements for the acquiring firm. Using a matching procedure similar to that employed by Healy et al. (1992) and Martynova et al. (2006), in which post-merger performance is regressed on a combined target and acquirer pre-takeover performance, our results suggest a negative impact on the long-term operating performance of acquiring technological firms in both the univariate and multivariate analysis performed.
The evidence implies that there are two main factors that contribute to a failed acquisition: the first is the lack of understanding of the management team of the value drivers for generating a higher operational performance; the second is the inadequate planning for post-merger integration (Goergen and Renneboog, 2004). As Puranam and Srikanth (2007) stated:
“Simply acquiring a technologically sophisticated target does not automatically guarantee that a firm will attain proficiency at the new technology and will be able to use it as a platform for future product development.”
Sumário
Teoricamente, as aquisições de empresas tecnológicas beneficiam as empresas, fornecendo recursos valiosos, aumentando o poder de mercado e possibilitam a renovação estratégica. No entanto, apesar dessas oportunidades, este tipo de acquisições geralmente representam ser um desafio para adquirentes e adquiridos. O objetivo da presente dissertação é ampliar a pesquisa sobre aquisições de tecnologia, sendo assim a seguinte pergunta será respondida: “As fusões e aquisições tecnológicas criam valor de longo prazo para os adquirentes europeus?”
Usando o Retorno sobre Ativos, a Margem EBIT e o Giro sobre Ativos como medidas de desempenho operacional, os resultados deste estudo sugerem que as fusões e aquisições concluídas na Europa no período de 2007 a 2014 resultaram em melhorias precárias na empresa adquirente. Usando um procedimento de correspondência semelhante ao empregado por Healy et al. (1992) e Martynova et al. (2006), no qual o desempenho pós-fusão é regredido em um desempenho pré-aquisição combinada de adquiridos e adquirentes, os nossos resultados sugerem um impacto negativo no desempenho operacional de longo prazo de aquisições de empresas tecnológicas na análise univariada e multivariada que foi realizada.
A evidência implica que há dois fatores principais que contribuem para uma aquisição falhada: o primeiro é a falta de compreensão por parte da equipa de gestão dos fatores que geram um maior desempenho operacional; o segundo é o planeamento inadequado na integração pós-fusão (Goergen e Renneboog, 2004). Como Puranam e Srikanth (2007) afirmaram: “A simples aquisição de uma meta tecnologicamente sofisticada não garante automaticamente que uma empresa atinja a proficiência na nova tecnologia e seja capaz de usá-la como plataforma para o desenvolvimento futuro de produtos”.
Content
1. Introduction ... 1
2. Literature Review ... 2
2.1. Mergers and Acquisitions ... 2
2.2. Main Theories and Models Regarding M&A ... 3
2.2.1. Overpayment Hypothesis ... 3
2.2.2. Information Asymmetry Theory ... 3
2.2.3. Size Effect ... 4
2.2.4. Hubris and Herding Hypothesis ... 4
2.3. Mergers and Acquisitions in the Technology Industry ... 5
2.4. Motivations for buyers... 6
2.4.1. Adding Strategically Valuable Resources ... 6
2.4.2. Enhancing Market Power ... 6
2.4.3. Achieving Strategic Renewal ... 7
2.5. When Not To Acquire ... 7
2.6. Similar Studies ... 8
2.6.1. Technological M&A Studies: Short-term ... 8
2.6.2. Technological M&A Studies: Long-term ... 9
2.6.3. Operational Performance Studies: Long Term ... 10
2.7. Open Issues in Technology Acquisitions Literature ... 12
3. Methodology ... 14 3.1. Univariate Analysis ... 14 3.2. Multivariate Analysis ... 16 4. Data Collection ... 19 4.1. Control Group ... 21 5. Descriptive Analysis ... 22
5.1. Descriptive Variables Before the Deal ... 22
5.2. Operating Performance Before the Deal ... 23
6. Results ... 25 6.1. Univariate Analysis ... 25 6.1.2. Main Variables ... 25 6.1.3. Performance Measures ... 26 6.2. Multivariate Analysis ... 28 6.2.1. ROA ... 28 6.2.1. EBIT Margin ... 31
6.2.2. Asset Turnover ... 33
7. Conclusions, Limitations and Future Research ... 35
8. References ... 37
Annexes ... 42
List of Tables Table 1. DID estimator ... 18
Table 2. Observations per year ... 20
Table 3. Number of Transactions per Country ... 20
Table 4. Operating Performance ... 23
Table 5. Operating Performance before the deal ... 24
Table 6. Main Variables Change ... 26
Table 7. Main Ratio Changes ... 27
Table 8. Summary of Results using the ROA ratio. ... 30
Table 9. Summary of Results using the EBIT Margin ratio. ... 32
1. Introduction
Large, established firms acquire small, technology-based firms so both can exploit their capabilities in a coordinated way, as well as foster their exploration capacity. These high technology deals, that focus mainly on acquisitions of firms operating in areas like telecommunications, computers, the Internet and biotechnology, are triggered by impressively high market valuations and anticipated value gains.
“Today, one out of every five transactions has a clear link to some form of technology, and the value of these deals as a percentage of the overall market is even greater”.1 According with several sources, the
technology mergers and acquisitions sector2 has been on rise for the past years and, in 2016,
this deals represented almost 30% of the total $2.5 trillion of completed transactions3, setting
new post-dotcom-bubble highs for both quarterly value and volume.4 Because this has been
such a high-demand sector for M&A it is important to study its value creation, especially since it is mainly based on expectations of revenue enhancements.
While a large literature has addressed other types of M&A activity, we place special emphasis on deals involving large technological firms, where both target and acquirer belong to a firm technology related. Our value creation is measured by the accounting performance of the acquirer firm. Although this measure has been used in the past management literature to evaluate the value created from an acquisition, we did not find any studies that used the firms operating performance as a measure of long-term value creation in technological acquisitions. This study pretends to specifically answer the question “Do Technological Mergers and Acquisitions Create Long-term Value for European Acquirers?” and understand the impact after an acquisition in terms of long-term value creation for the shareholders of the acquiring firms. The focus is on deals between 2007 and 2014 in Continental Europe and the UK. This dissertation is organized as follow. In Section 2 we review the literature, which includes the main definitions, motives and studies related with M&A. In Section 3, we explain the methodology used, in Sections 4 and 5 we describe the sample selection process and the descriptive analysis of the sample, respectively. Finally, in Section 6 we present our results and in Section 7 we present our conclusions and limitations.
1 The 2017 M&A Report: The Technology Takeover, BCG 2 M&A hereafter
3 The 2017 M&A Report: The Technology Takeover, BCG 4 Financier Worldwide (2015)
2. Literature Review
2.1. Mergers and Acquisitions
Through the decades, M&A have been a common strategy for companies seeking rapid growth. Brealey et al. (2003) state that a merger can be defined as combining two firms into one, with the acquirer assuming assets and liabilities of the target firm, whereas an acquisition refers to a takeover of a firm by purchase of that firms’ common stock or asset.
Still according with the same authors, mergers are often categorized as horizontal, vertical, or conglomerate. A horizontal merger is defined as taking place between two firms in the same line of business, combining two companies that offer similar products or services to the same market under a single ownership. A vertical merger takes place when two companies, that previously sold to or bought from each other, combine under one single ownership in which the buyer expands backward towards the source of raw material or forward in the direction of the ultimate consumer. A conglomerate merger involves companies in unrelated lines of business.
Economic theory has provided many possible reasons to explain why mergers occur: (1) efficiency-related reasons that often involve economies of scale or other “synergies” (Roll, 1986; Goergen and Renneboog, 2004); (2) attempts to create market power or market discipline (Ahuja and Katila, 2001); (3) self-centered attempts to “over-expand” (Shleifer and Vishny, 1997); (4) to take advantage of opportunities for diversification, such as exploiting internal capital markets and managing risk for undiversified managers (Andrade et al., 2001; Agarwal and Helfat, 2009). These reasons explain the occurrence of many of the mergers that occurred over the last century, and thus are clearly relevant to a comprehensive understanding of what drives acquisitions.
There have been many studies testing whether M&A activity increases shareholders’ wealth around the announcement day and on the long-term. There is a unanimous consensus that shareholders of the target companies generally benefit in the short-term from M&A, mainly due to the premium received by selling their shares (Asquith et al., 1983; Franks and Harris, 1989; Limmack, 1991). On the other hand, the results obtained regarding acquirers’ long-term post-acquisition returns do not reach to a consensus. Prior studies typically reported significantly negative long-term results or found no significant difference on the long-term.
2.2. Main Theories and Models Regarding M&A
2.2.1. Overpayment Hypothesis
The study of the winners’ curse by Varaiya and Ferris (1987) serves as theoretical basis for the Overpayment Hypothesis. This study reports that in a competitive bidding situation, the winner is very likely to overestimate the value of the target. The overpayment hypothesis proves that when the expected gains that come from acquiring targets are lower than the premiums that are paid by the acquiring companies, it may lead to a negative association between premiums and returns.
Shleifer and Vishny (1997) elaborate furthermore, stating that the Overpayment hypothesis is the origin of the winners’ curse phenomenon, which can be a sign of agency problems, as bidders’ managers exploit the acquisitions to their own benefits without considering the profits derived from the acquisitions.
Overpayment hypothesis shows that it is very likely for targets to accept deals that offer highest premium and finance with cash, mainly because cash financing will bring higher premiums to the targets than stock payments due to some factors such as capital gains on taxes, competition and the popularity of financing takeovers with cash (Franks et al.,1989).
2.2.2. Information Asymmetry Theory
The theoretical background for this theory was first developed by Myers and Majluf (1984) in their pecking order theory. This theory defends that companies prioritize their sources of financing by using internal funds first, then issue debt, and finally raise equity as a last resort. Information asymmetry is often classified as a “signaling model” because it explains how the choice of payment method by bidders’ managers bears valuable information about the true value of the firm to the market participants.
Jensen (1986) elaborates furthermore, stating that acquirers will likely choose stock payment when the value of the company is overvalued, and opt for cash payment when the value of the firm is undervalued. When managers act in the best interest of their previous shareholders and the company is overvalued, managers will opt for stock payment that allows sharing the risk of losses with the new target shareholders.
2.2.3. Size Effect
The study by Moeller et al. (2004) confirms that smaller acquirers are likely to earn higher positive abnormal returns than larger acquirers. As the relative size ratio increases when the acquirers are smaller, the abnormal returns for the smaller acquirers also increase.
There are various ways to measure relative size. Based on a research by Fuller and Stegemoller (2002), the relative size defines as the target market deal value divided by bidders’ market capitalization. Moreover, other academic researches that focus on the target market deal value confirm that there is a significantly positive relationship between the transaction value and the bidders’ return (Benou and Madura, 2005).
2.2.4. Hubris and Herding Hypothesis
Although most acquiring firms make statements about the potential synergies from M&A, frequently the forecasted benefits are not obtained (Goergen and Renneboog, 2004). This may be the result of over-optimistic synergy forecasts by the bidding management or the fact that the M&A was initiated for entirely different reasons, such as managerial hubris or other agency problems.
The managements’ hubris theory hinges on the assumption that the management of the acquiring firm makes mistakes in evaluating potential targets (Roll, 1986). The same author states that managerial hubris is the main element for explanation of value-destroying takeovers: overconfident managers overestimate the creation of synergetic value. This study explains why bidders’ managers are willing to pay a deal value above the current market price. Empirical studies in bidders’ managers’ overconfidence show that the irrational managers who pay high premium tend to use cash, while the usage of stock payment is negatively related with high premium. (Goergen and Renneboog, 2004).
The hubris hypothesis in combination with herding is also able to explain the cyclical patterns in M&A activity. Herding predicts that there is an equal probability that managers are over- and underestimating the synergies of potential mergers or acquisitions (Graham, 1999). The same author affirms that companies tend to imitate the actions of a leader, which makes the first successful takeover to encourage other companies to undertake similar transactions. Since the main purpose of the other companies is to follow the actions of the leader, rather than take action based on a clear economic rationale, most of their takeovers suffer from managerial hubris. Thus, the combination of herding and hubris predicts that inefficient takeovers follow efficient ones.
2.3. Mergers and Acquisitions in the Technology Industry
Technological acquisitions involves the absorption of technological inputs of target firms to the acquiring firms’ knowledge base. In result, the acquirers will expand its technological competitive advantage by increasing their innovation output (Ahuja and Katila, 2001). Analyzing the existing literature, established firms are increasingly turning to tech M&A to increase their market power, achieve strategic renewal and modernize their technical capabilities and products. Agarwal and Helfat (2009) state that for established firms, tech acquisitions are an apparent avenue for strategic renewal. Like all strategic issues, strategic renewal presents both opportunities and challenges for organizations. The authors also mention that if a firm develops a dynamic capability for acquisitions, this will help to institutionalize renewal within the organization and enable renewal activities to function more effectively on a continuing basis.
Eisenhardt and Martin (2000) mention that acquisition routines bring new resources to the firm from external sources, because this kind of M&A endow managers with a shifting selection of products and engineering know-how that is prone to drive to superior performance. Tech firms very often rely on a strong alliancing process for accessing outside knowledge achieve superior performance.
For Santos and Eisenhardt (2009) an acquisition of a technological firm is a way to control the market by overlapping the boundaries of the firm and eliminate entrepreneurial rivals. This way, companies shape their organizational boundaries and construct new markets. Acquisition (and often destruction) of the resources of entrepreneurial rivals makes acquiring firm to try to control the market by overlapping their organizational boundary with the market boundary in such a way that their firm occupies as much of the market space as possible.
Buyers often pursue technology acquisitions to tap the innovative potential of young, entrepreneurial firms, which are an increasingly important engine of new technical knowledge. Graebner and Eisenhardt (2010) give the example of several tech M&A, including Cisco, Oracle and Dell. Technology acquisitions have helped Cisco to strengthen its videoconferencing products and drive demand for networking equipment, Oracle to broaden its business software offerings and Dell to gain expertise in computer services. Yet despite their strategic potential, it is still not clear if this kind of acquisition create shareholder value for the acquiring firm, with much of the existing literature review affirming that many technology acquisitions fail in the terms of value creation. (King et al. 2008).
2.4. Motivations for buyers
As noted above, many technology firms pursue M&A despite mixed evidence of their success, raising the question of what firm leaders hope to accomplish from these deals. In the following chapter we will discuss the motivations of buyers to merge with another technological firm.
2.4.1. Adding Strategically Valuable Resources
Amongst the existing literature, the most widely accepted and commonly acknowledged reason why buyers pursue technology acquisitions is to obtain specific products or technologies that are owned or under development by the target firm (Birkinshaw et al., 2000; Graebner, 2004; Ranft and Lord, 2000). This way, buyers hope to create value by combining targets’ technologies with buyers’ own technical, achieving competitive advantage and superior performance (Graebner, 2004).
Ranft and Lord (2000) found that while 35% of acquirers of technology firms named obtaining specific product-related technologies as their primary motive for engaging in acquisitions, obtaining product innovation and engineering capabilities came in a close second, representing 32% of the total responses. This means that not only buyers are concerned with obtaining existing technologies, but also accessing the knowledge required to develop future product generations and other related innovations.
Additionally, Zenger and Lazzarini (2004) highlight that large, established firms, whose payment systems rely more heavily on seniority than on skill or performance, may choose acquisition over internal development to build technology resources because smaller, younger firms are often more innovative.
2.4.2. Enhancing Market Power
A less explored but also important motivation for buyers is the increase of market power by providing customer relationships that rapidly expand the buyers’ presence into new geographic areas or customer segments (Santos and Eisenhardt, 2009; Birkinshaw et al., 2000; Graebner, 2004).
Birkinshaw et al., (2000) found that buyers making cross-border acquisitions of R&D units sought to create or expand their presence in more international markets. Ranft and Lord (2000) found that buyers consider market and sales relationships as one of the main
motivations in 18% of technology acquisitions. While firms can also reach new markets through alliances, acquisition provides exclusive and permanent access to the targets’ knowledge and customer relationships, providing the buyer with resources that are unique and therefore more competitively valuable (Kale and Puranam, 2004).
In addition, acquisitions can enhance a buyers’ power by completely eliminating one or more potential rivals. In their study of constructing and dominating nascent markets, Santos and Eisenhardt (2009) observed that some firms acquired targets with technologies that posed competitive threats to the buyers' market control.
2.4.3. Achieving Strategic Renewal
Strategic renewal can be defined as significant and disruptive change in a firms’ approach to achieving superior performance (Agarwal and Helfat, 2009). Strategic renewal can be especially valuable for technology firms, since they are constantly facing dynamic competitive environments.
Acquisitions renew acquiring firms by challenging redeployment and reconfiguration of resources, as well as by exposing buyers to new, more dynamic practices and routines (Capron and Mitchell, 1998; Karim and Mitchell, 2000). Resource reconfiguration is particularly beneficial for the acquiring firms because new technologies often arise from combinations of existing knowledge. This way, acquisitions can rejuvenate buyers, preventing them from becoming rigid and inert. (Kogut and Zander, 1992).
Moreover, acquisitions can improve technology firms’ ability to innovate by enhancing their absorptive capacity, or ability to recognize, assimilate, and apply new information (Cohen and Levinthal, 1990). While organizations can also achieve renewal through other means such as alliances or joint ventures, still according with the same authors, acquisitions allow great flexibility, an important advantage in choppy high-technology industries.
2.5. When Not To Acquire
While the preceding discussion outlined the advantages of technology acquisitions, it is also important to highlight that these strategies are not always best choice for the buyer (Capron and Mitchell, 2009; Kale and Puranam, 2004; Santos and Eisenhardt, 2009). Sometimes relative alternatives such as internal development and alliances might be the most viable option.
activities (Capron and Mitchell, 2009). As a result, most technology firms will combine acquisitions with a portfolio of other strategies that sometimes are too different and unrelatable with the strategies the buyer had in first place. Firms may rely on internal development for technologies that are close to their existing expertise and may ally rather than acquire when the desired resources are important but not central to the firms’ success, (Kale and Puranam, 2004). Firms may also turn to alliances when acquisition is unrealistic, perhaps because companies with the needed resources are very large, possess too many unwanted resources, or are simply not for sale (Santos and Eisenhardt, 2009).
2.6. Similar Studies
The impact of M&A on shareholders’ wealth effect has been a subject of some discussion and empirical analysis in both short-term and long-term. In the next session, we will discuss different approaches that have generated a considerable amount of empirical and theoretical conclusions, resulting in high fragmentation of points of view.
2.6.1. Technological M&A Studies: Short-term5
It is argued that M&A related with technological companies receive positive market perception. As a matter of fact, most studies regarding this subject (Kohers and Kohers, 2001; Benou and Madura, 2005; Lusyana and Sherif, 2016) report empirical evidences of positive abnormal returns around announcement date.
Kohers and Kohers (2000) examined the value creation potential of 1634 high-tech mergers between 1987 and 1996 and found that upon the announcement date acquirers of high-tech targets experience significantly positive abnormal returns with an average CAR of 1.26% for the three-day event window. The authors conclude their study suggesting that the market is optimistic about tech mergers and expects that acquisitions of high-tech companies will provide future growth benefits for acquiring companies.
In their following study, Kohers and Kohers (2001) used an initial sample of 304 mergers involving both US acquirers and foreign acquirers, occurring over the period from January 1984 until December 1995. The average two-day CAR for bidders is 0.70%, and the two-day bidder CAR for the 1990-1995 period is 1.21%. That led them to conclude, once again, that
the market tends to exhibit excessive enthusiasm toward the expected benefits of certain high-tech mergers.
Benou and Madura (2005) analyzed 3816 listed US high-tech acquisitions from 1980 to 2001.The results of their study showcased that the acquirers of the sample experienced positive but insignificant results around the announcement period, with CARs averaging 0.35% for the 2-day event period. The authors noticed a positive relationship between the targets’ media exposure prior to the acquisition announcement and the valuation effects of the bidder. It appears that the more media attention the tech targets receive prior to the acquisition, the more enthusiastic bidder shareholders become about the prospects of these companies and the more favorably they react to news about their acquisition.
The recent study of Lusyana and Sherif (2016) used a sample composed by 1078 high-tech target deals between 2007 and 2014 to examine the performance of bidders acquiring high-tech US targets in the short run and long run. Regarding the short run results, they found positive and significant abnormal returns over the 11-day event window for the domestic bidders, with CARs averaging 0.23%. According with the authors, these results highlight the unrealistic expectations regarding the potential of many high-tech firms and market psychology associated with technology-driven areas tech stocks.
However, Sears and Hoetkers (2014) found contradicting results. Using evidence from the US technological acquisitions obtained from SDC database from 1995 to 2004, their results reported a negative abnormal return averaging -0.4% during the 3-day event window for the technological M&A. The authors justify this results with the high uncertainty nature and higher degree of information asymmetry present in high-tech acquisitions.
2.6.2. Technological M&A Studies: Long-term6
In Kohers and Kohers (2000), the authors state that the examination of the long-term shareholder value creation after an high-tech merger would be an interesting area for future research, since it possibly would help to understand if the positive reaction from the acquirer company shareholders is an unbiased forecast of the future long-term performance of high-tech mergers. As stated, “the nature of high-growth high-technology-based industries distinguishes them from
other types of industries. In addition to their high-growth potential, however, another distinctive feature of high-tech industries is the inherent uncertainty associated with companies whose values rely on future or
developments in unproven, unchartered fields”, (Kohers and Kohers, 2000).
Therefore, in the following year, Kohers and Kohers (2001) used an initial sample of 304 mergers to measure the 36-Month CAR following the merger, noticing that the acquirers significantly underperform both industry-matched and book to market matched control portfolios, reporting an average negative cumulative abnormal return of -18.68%. According with Kohers and Kohers (2001), this negative finding provides evidence of the costly consequences of agency problems in high-tech acquisitions and conclude that the high expectations regarding the future merits of these investments do not seem to be justified.
Lusyana and Sherif (2016) also studied the abnormal returns of high-tech US acquirers in the long run between 2007 and 2014. Overall, their results reported a negative cumulative average return of -0.09% for the three years analyzed, suggesting that investors and bidders are overoptimistic about the future performance of high-tech mergers.
2.6.3. Operational Performance Studies: Long Term7
While some research papers have been written on the stock price performance following high-tech M&A, the empirical evidence of the changes in post-acquisition accounting performance is, to our knowledge, non-existent. Because the differences are significant if comparing both methodologies, we decided to include in our literature review studies that, in spite of not being related exclusively with high-tech M&A, use the same accounting methodology that we are going to apply in this dissertation.
Martynova et al. (2006) investigate the long-term profitability of 155 European corporate takeovers completed between 1997 and 2001, where the acquiring and target companies are from Continental Europe and the UK. The authors employ four different variables8 of
operating performance and selected a group of peer companies which were chosen in order to control for industry, size and pre-event performance. While several of the studied measures indicate a decrease in operational performance (-0.01% for measure EBITDA/ BVassets and – 0.62% for measure (EBITDA – ∆WC)/BVassets)), this reduction becomes insignificant after the control for the performance of the peer companies was done. The authors also conclude that merger characteristics such as means of payment, geographical scope, and industry relatedness do not have significant explanatory power for the obtained
7 See annex 2
results.
Dickerson et al. (1997) compared the performance of acquiring companies with non-acquiring firms for a large cross-section of UK firms observed between 1948 and 1971. The authors conclude that there is no evidence that acquisitions have a beneficial effect on company performance as measured by profitability. In fact, their results report that acquisitions have a systematic detrimental impact on acquirers’ performance: the average return on assets of acquiring companies showed a significant negative impact of acquiring amounting to 2.03 percentage points in the years in which subsequent acquisitions are made. Thus this papers’ results suggest that takeovers do not lead to enhanced performance as measured by profitability.
Healy et al. (1992) used post-merger accounting data in a sample of the 50 largest US firms to test directly for changes in operating performance that result from mergers the occurred between 1979 to 1984. The authors also used a control group matched by same industry peers as a benchmark to evaluate post-merger performance. They conclude that the merger acquirers have significant improvements in operating performance after registering a median industry-adjusted increase of 0.2 percentage points in the cash-flow margin and on asset turnover.
Gugler et al. (2003) examined the effects of 45000 worldwide completed mergers from 1981 to 1998. The study was carried out to determine the effects of mergers on corporate performance across national, international and sector levels (split by type of merger: horizontal, vertical and conglomerate). The effects of mergers were analyzed using profitability –profit divided by assets– and sales –sales divided by assets– then, the results were compared with the performance of control groups of non-merging firms. The authors obtained mixed results, with about 56.7% of all mergers result in higher than projected profits, but almost the same percentage of mergers results in lower than projected sales after 5 years.
Rau and Vermaelen (1998) focus their research on mergers and tender offers that occurred worldwide between 1980 and 1991. They splited their sample of 3500 companies into glamour and value firms, where first are companies with low book to market ratio and value bidders are firms with high book to market ratio. As a result, bidder underperforms while in tender offers, bidder over performs in the three years after the acquisition. Their conclusion is that the downward of performance is related to the fact that managers and market overestimate the ability of glamour bidder to manage other companies, which results in an
over valuation of the past performance.
2.7. Open Issues in Technology Acquisitions Literature
While researchers have made substantial progress in understanding technology acquisitions, we have noted several gaps in the current literature. There is a consensus that studying the acquirers’ shareholder value creation in this particular industry has been a neglected variable (Graebner et al., 2010; Sears and Hoetker, 2013). The present study aims to address this shortcoming.
Kohers and Kohers (2001), Benou and Madura (2005), Sears and Hoetkers (2014) and Lusyana and Sherif, (2016) have begun to elaborate results regarding the cumulative abnormal returns of tech M&A. However, these results have as base the stock market returns around the announcement date and do not consider accounting data to measure the long-term return of M&A. Authors like Healy et al. (1992) argue that price performance studies are not able to determine whether takeovers create real economic gains because from the stock price perspective, the anticipation of real economic gains is observationally equivalent to market mispricing .The authors also state that stock price studies are also unable to provide evidence on the sources of any merger-related gains and to identify the sources of such gains. In response to this literature gap, our study focus is to perform a univariate and multivariate analysis in the acquirers’ operational performance. Although this measure has been used to evaluate the value created from an acquisition in the existing M&A literature, as far as we are concerned, it has not been used in the study of technological acquisitions.
Another open issue we identified is the lack of M&A studies centered in Continental Europe. During the last decade, M&A involving European companies have occurred in unprecedented numbers. In 2017 alone, the total value of European M&A activity has pushed past $200 billion for the first time in a decade9. Despite these developments, empirical
research on M&A activity remains mostly confined to the UK and US and there is little known about the effect of Continental European mergers on the operating performance of acquiring and target firms. Our study contributes to this literature by updating their evidence for the sample of the most recent European M&A and by checking the robustness of the results using an adequate methodology of measuring the improvement or decreases in post-merger operating performance.
Lastly, considering the mixed information available concerning post-acquisition performance of the technological industry, to our best knowledge none of them focused on the recent wave of technology M&A. It is appropriate to investigate it with newly available data, with the proper stringent methodology that strives to capture the true effects of M&A, particularly on the long-term performance.
3. Methodology
The majority of the studies on post-acquisition operating performance rely on different measures as well as approaches. In this dissertation, we will use the methodology developed by Healy et al. (1992) and Martynova et al. (2006). This methodology consists in the use of accounting statements as the base for the financial data, as well as in the selection of a benchmark formed by companies that did not engaged in M&A in the same time period to eliminate possible variations caused by external factors.
Our study aims tom look to the post-merger period, 3 years after the deal, and compare it with the period before the deal.
3.1. Univariate Analysis
As mentioned before, to study the long-term performance of M&A, we will apply a methodology that uses accounting performance measure as benchmark for the success/failure of the acquisition. The operating performance approach allows the investigator to concentrate on costs and efficiency, which has the advantage of focusing on real observed operating effects rather than the expectations over the announcement period. However, this methodology is far from perfect since this method generally analyses operating performance over long periods after the M&A deal and through all the period, many factors may impact the firms’ efficiency or general performance that is not related to the merger itself.
Following the work of Martynova et al. (2006) and Healy et al. (1992), we will consider the accounting data before and after the deal and examine the changes in the acquiring firm performance. To analyse the impact in the operational performance of the acquiring firms, we need to measure the change of the operational performance indicators between the year before the transaction [-1] and the three following years [-1, +3]. We will use the year before the transaction, instead of the event year, as a reference year since data are more reliable and do not contain the effects of the transaction itself, such as one-time cost, commissions and other transaction related costs.
According with Healy et al. (1992), the most used measures to capture the operating performance in previous post-acquisition studies are the return on assets (ROA), the EBIT margin and the Asset Turnover, and are calculated as it follows:
𝑅𝑜𝑎 = 𝐸𝐵𝐼𝑇 𝑎𝑠𝑠𝑒𝑡𝑠 (1) 𝐴𝑠𝑠𝑒𝑡 𝑇𝑢𝑟𝑛𝑜𝑣𝑒𝑟 = 𝑠𝑎𝑙𝑒𝑠 𝑎𝑠𝑠𝑒𝑡𝑠 (2) 𝐸𝐵𝐼𝑇 𝑀𝑎𝑟𝑔𝑖𝑛 = 𝐸𝐵𝐼𝑇 𝑠𝑎𝑙𝑒𝑠 (3)
The first ratio measures how effectively a firm is using its assets to generate cash. Thereby, higher values of return on assets show that business is more profitable. The asset turnover ratio is useful because it conveys the idea of how productive the assets of the company are. Lastly, the EBIT margin calculates how much cash is generated for every dollar of sales, which is how much profit a firm makes after paying for variable costs of production such as wages, raw materials but prior to interests and taxes.
The variables considered in this analysis are going to be the total assets, revenue and EBIT. In order to compute the changes on these variables in the first, second and third year, we will compare the year itself to the previous year of the transaction. Given this, it is possible to establish a comparison among them and verify the changes occurred. Thus, we will do so using the following formula:
𝑥𝑖𝑡+𝑗−𝑥𝑖𝑡−1
𝑥𝑖𝑡−1 (4)
where:
x - operational performance variable; i - acquiring firm;
t-1 - previous year of the deal;
j - year after the acquisition for which we want to calculate the change of the performance
measure (year 1, 3 and 3).
Noteworthy, the observations whose EBIT is negative in the year -1 will be excluded from this specific calculation. Even thought by doing this we are skewing the results by excluding those observations that were most likely to improve, we cannot calculate variations of EBIT starting with a negative value.
In the case of the operational performance measures calculated as ratios, such as ROA, EBIT margin and Asset turnover will be estimated in changes in percentage points using the following formula:
𝑦𝑖𝑡+𝑗− 𝑦𝑖𝑡−1 (5)
Afterwards, in order to eliminate the effects that some variations could have occurred due to changes in the overall sector and not necessarily due to the acquisition itself, we will adjust the variation in our sample by subtracted the same variation occurred in the sector during the same period. To compute the evolution in sector, median will be preferred instead of mean, owing to its robustness since it is not affect by extreme cases.
To test whether the average difference before and after the acquisition is statistically different form zero, once again we will use the t-student test (parametric test) and the Wilcoxon signed-rank test (non-parametric test) that, as referred before, is not affected by the outliers making the results more robust and consistent.
3.2. Multivariate Analysis
To further investigate the long-term impact of an acquisition, a multivariate analysis is going to be performed. Although the univariate tests performed before show some changes in the performance of technological companies after a merger, a multivariate regression allows the control of macroeconomic factors that may explain those changes. This helps to estimate what would have been the technological firms’ performance if it had not been involved in an acquisition.
In order to analyse this effect, the difference-in-difference10 approach is going to be used to
estimate the effect of an acquisition on the performance of tech firms by comparing the changes in outcomes over time between a group of firms that were enrolled in a tech acquisition and a group of firm that were not (Martynova et al., 2006).
This approach removes biases in post-intervention period comparisons between the acquiring and control group that could be the result from permanent differences between those groups, as well as biases from comparisons over time in the acquiring group that could be the result of trends due to other causes of the outcome (Lechner, 2010). However, some authors such as Bertrand et al. (2004) criticized the potential bias in DID error terms citing some limitations such as it requires baseline data and a non-intervention group.
Therefore, between the firms’ outcome when involved in an tech acquisition and the outcome that this firm would have if it had not been involved in an acquisition will be estimated using the following formula:
𝑦𝑖𝑡1 − 𝑦𝑖𝑡0 (6) where:
- 𝑦𝑖𝑡1 is the outcome for a technological firm 𝑖 which has been involved in an acquisition
in period 𝑡;
- 𝑦𝑖𝑡0 is the outcome for the same technological firm 𝑖 if it was not subject to an acquisition,
in the same period 𝑡.
To regress the data collected across the two groups of firms (acquirers and non-acquirers), we will use to the following formula:
𝑌𝑖𝑡 = 𝛽0+ 𝛽1 𝐷𝑎𝑐𝑞𝑢𝑖𝑟𝑖𝑛𝑔𝑖 + 𝛽2 𝐷𝑝𝑜𝑠𝑡1 + 𝛽3 𝐷𝑎𝑐𝑞𝑢𝑖𝑟𝑖𝑛𝑔𝑖 ∗ 𝐷𝑝𝑜𝑠𝑡𝑡+
𝛽4 log(𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠) + 𝜀𝑖𝑡 (7)
The performance of a tech merger firm will be determined by the ROA, EBIT Margin and Asset Turnover. As explanatory variables the regression includes the natural logarithm of total assets in the three years before the merger, to control for the size of the company, and two dummy variables: one that distinguishes observations from the pre-merger period (DPOST = 0) from observations from the post-merger period (DPOST = 1) and another that distinguishes acquiring technological firms companies (Dacquiring = 1) from the control companies (Dacquiring = 0). The interaction of both dummies (DACQUIRING x DPOST) is the target-variable as a significant test coefficient would mean a significant difference in the performance of technological companies, after the merger, when compared with both the same company before the merger and control companies that were not involved in mergers in the chosen time period. Its coefficient 𝛽3 represents the DID estimator of the effect of acquiring on the group of the acquiring firms.
Table 1- DID estimator
In each variable, the model is going to be first estimated considering all three years after the operation, and then we will compare the period after the operation with the period before and then each year after the operation will be considered individually. The selection process of the control group will be explained in the next chapter.
Before After Difference
Acquiring Firms 𝛽0 + 𝛽1 + 𝛽4 𝛽0 + 𝛽1 + 𝛽2 + 𝛽3 + 𝛽4 𝛽2 + 𝛽3
Control Group 𝛽0 + 𝛽4 𝛽0 + 𝛽2 + 𝛽4 𝛽2
4. Data Collection
Data on European acquisitions – involving both a European bidder and target – was collected from the period January 1, 2007–December 31, 2014.
The sample consists of high-tech bidders whose targets are high-tech industries. Technology-driven sectors, according to the Securities Data Corporation (SDC) and siccode.com, are classified into biotechnology, ICT, electronics, telecommunications, software and computer-related services, high-tech manufacturing, communication services, patenting activity, and miscellaneous publishing and biotechnology industries, SIC codes 26-27, 59–62.
We only use complete acquisition deals to investigate the effect of M&A announcements on bidders’ shareholder wealth. The high-tech M&A bidders’ criteria are all European firms and the deal value that is worth more than €50 million, since small deals may have no effect on the acquirers’ share price (King et al., 2008; Sears and Hoetkers,2014 ; Lusyana and Sherif, 2016). We eliminated acquisitions in which, at the time of the deal, the acquirer was not public.
A sample containing all 170 successful M&A that both the acquirer and the target were technological or similar companies was obtained from Zephyr database, a comprehensive M&A database for all deals occurred in the world.
To analyse the impact in the operational performance of the acquiring firms, we need to measure the change of the operational performance indicators between the year before the transaction [-1] and the three following years [-1, +3] and so deals where accounting data for the acquiring company was not available for the year before the transaction and for at least one year after the transactions were dropped. In the case a firm acquired more than one company during one-year period only the first deal was considered.
After excluding deals where the targets’ total assets represent less than 10% of the acquirings’ total assets (in order to guarantee the significance of the transaction to the acquiring company), our sample was reduced to a total of 61 observations.
Focusing on the main sample, Table 2 shows that M&A were quite well distributed among all eight years considered, with the exception of the year 2009, which is 10% lower if compared with the previous year, totalizing only 3% of the total number of mergers. The 2009 low is perhaps reflecting the result of the 2008 World Financial Crisis. However, from 2010 until 2014, the number of M&A per year have been in continuous growth, with 2014 being the peak year of M&A in our study.
Table 2- Observations per year
The acquisitions included in our sample occurred in a total of fifteen countries as it can be seen in Table 3. It is noticeable that the majority of deals were concentrated in the United Kingdom, France, Sweden and Germany, representing 54% of all sample. In the opposite side, Finland, Ireland, Portugal and Ukraine were the countries where only one M&A occurred.
Furthermore, to measure the changes in operating performance following a takeover, we will compare this sample with a benchmark of technology companies in order will be used as a proxy for the expected performance has the takeover bid not have taken place.
Table 3- Number of Transactions per Country
Country Nº of deals % Country Nº of deals %
UK 10 16% Poland 3 5% France 9 15% Belgium 3 5% Sweden 8 13% Netherlands 3 5% Germany 6 10% Norway 2 3% Switzerland 5 8% Others11 4 7% Spain 4 7% Total 61 Italy 4 7%
11 It was classified as “other”, countries where it was registered one deal, namely: Finland, Ireland, Portugal and Ukraine
Year Nº of deals % 2007 7 11% 2008 8 13% 2009 2 3% 2010 9 15% 2011 8 13% 2012 8 13% 2013 8 13% 2014 11 18% Total 61
4.1. Control Group
The control group was selected through the application of a matching method similar to that employed by Healy et al. (1992). The objective of this method is to select a group of companies similar to the acquiring companies in the year before the acquisition.
Each company of our sample was paired with another that is the most alike as possible, but which have not been involved in an acquisition. This control group allow us to analyze the results of both acquiring and non-acquiring firms and eliminate the variations caused by external factors such as the economic situation which is assumed to affect all tech firms in an identical way. Whereas, without a control group, the possible external factors outcomes could be wrongly attributed to the result of an acquisition.
To compare with each firm of our sample, we choose European firms from the technology industry that were not involved in any M&A transactions and that have a similar size (measured by the total assets) and operating performance. Those firms had an amount of total assets between 80% and 120% of the total assets of the firms in our sample, in the year before the acquisition. From those companies, we selected the company with the closest ROA.
In the end, after selecting 61 non-acquiring firms, together with the 61 acquiring firms involved in an M&A operation, our final sample of the accounting-based measure methodology is composed by 122 firms in total.
5. Descriptive Analysis
In this chapter, the descriptive statistics for both the acquiring companies (acquiring group) and the control companies (control group) are going to be presented in the year before the acquisition (year -1).
5.1. Descriptive Variables Before the Deal
Table 4 exhibits the differences in Assets, Revenue and EBIT between the acquiring group and the control group in the year before the acquisition.
In terms of assets, the acquiring companies held assets worth in median 4,969 million euros while all the other companies in the sector held assets worth a median of 3,975.7 million euros. Therefore, it is noticeable that acquiring companies were the largest companies operating in the market, with a median asset worth more 994.3 million euros than the market median asset.
However, regarding revenue, the market median was 7.1 million of euros superior to the acquiring firms, with market companies reporting a revenue median of 3,111.1 million euros and acquiring firms 3,104 million euros. Finally, in the EBIT median values, the operational flows of acquiring firms where higher, reporting a median of 338.5 million euros while the control group reported a median of 274.5 million euros.
We tested if any of the differences between the main variables of the sample and the control group were significant. The results showed that none of these differences are statistically significant before the acquisition and so we can conclude that the companies from the acquiring group and from the control group are similar.
Consequently, the analysis on this dissertation will be centered mainly on the median since, in presence of outliers, it reflects a more realistic idea of a “central measure” than the average.
Table 4. Descriptive variables before the deal
This table reports the summary statistics for the sample of 61 firms for the acquiring group plus 61 firms for the control group. The sample period begins in January 2007 and ends in December 2014. The variables Total Assets, Revenue and EBIT are referred to the year before the acquisition. All values presented are in millions of Euros. The differences between the acquiring group and the control group are not statistical significant.
5.2. Operating Performance Before the Deal
Table 5 displays the differences between the acquiring group and the control group for the firms’ operating performance before the acquisition.
The acquiring group registered a median ROA of 11%, while the control groups’ median ROA was 8%. As it can be seen, there is a difference in the ROA median results with a significance level of 5%, indicating that the acquiring group was more profitable when using their assets to generate earnings than the control group in the year before the acquisition. The median EBIT Margin was also higher in the acquiring group: 14%, compared with the 9% registered by the control group. The higher median EBIT margin from the acquiring group indicates that the acquiring firms have higher earnings ability that could be mainly due to more efficient cost management or better revenue management. However, the control group registered a higher median Asset Turnover ratio of 89%, bigger than the 86% median registered by the acquiring group, suggesting that the companies from the acquiring group are using their assets less efficiently if compared by the market.
In sum, even though the acquiring firms and the control group firms are similar (by construction), before the acquisition, in terms of EBIT Margin and ROA, the acquiring firms were more profitable in that year. This higher profitability may have led to the decision of
Acquiring Group Control Group Difference
Panel A: Assets Mean (€ Millions) 11,031.1 9,852.6 1,179.5 Median (€ Millions) 4,969.0 3,975.7 994.3 Panel B: Revenue Mean (€ Millions) 8,640.0 6,698.2 1,941.8 Median (€ Millions) 3,104.0 3,111.1 -7.1 Panel C: EBIT Mean (€ Millions) 902.8 681.1 221.7 Median (€ Millions) 338.5 274.5 64.0
acquiring another company from the same industry since higher profitability means more cash flow and more confidence in the management competence.
Table 5. Operating Performance Before the Deal
This table reports the summary statistics for the sample of 61 firms for the acquiring group plus 61 firms for the control group. The sample period begins in January 2007 and ends in December 2014. The variables ROA, EBIT Margin and Asset Turnover are referred to the year before the acquisition. All values presented are in millions of Euros. The acquiring group, the control group and the difference columns are presented in percentage. The difference column is presented in percentage points. The classification ***, **, * denotes for 1%, 5% and 10% significance level.
Acquiring Group Control Group Difference
ROA Mean (%) 12% 9% 3%* Median (%) 11% 8% 3%** EBIT Margin Mean (%) 14% 12% 2% Median (%) 14% 9% 5% Asset Turnover Mean (%) 108% 92% 16% Median (%) 86% 89% -3%
6. Results
6.1. Univariate Analysis
In the univariate analysis, the change of each individual variable and ratio will be examined in order to understand the impact of acquisitions in the technologic industry regarding operational performance.
As before, we will focus mainly on the median results in order to exclude the effects of outliers presented in our sample.
The raw and adjusted change for the different variables will be computed from the period before the acquisition to the average of the three years after plus the post years individually. The adjusted change will be computed by subtracting the sector median change in the performance measure to the change in the performance measure of the acquiring firms. This procedure removes the macroeconomic factors that may affect the results of both groups equally.
6.1.2. Main Variables
Observing the Table 6, it is possible to see that all of our three performance measures reveal significant changes: in Panel A, the total number of assets escalated an average increase of 36.45%, significant with a 99% confidence level. This positive effect in the total assets variable is expected since when a firm acquires another it also acquires some of the target firms’ assets. When market-adjusted, this value is drops to 26.78% in the three-year average, but it is still significant in the first and third year after the acquisition.
The change observed in the total assets after the acquisition is very similar to the change observed in the revenue (Panel B). This variable also records a significant increase (the aggregated three years after the acquisition positive median change 18.32% significant at 1% level). When market-adjusted, this value drops to 11.30%, but it is still significant in the first year and in the 3-year average.
Regarding the EBIT, Panel C in Table 6 shows that the raw change after the acquisition is positive but not statistically significant. This trend continues when the values are market-adjusted with the exception of the first year, whose change is statistically significant at 5%. Overall, the comparison of the “raw” performance reveals that M&A deals in tech industry contribute to the positive change in the three years analyzed in the three main variables, and this effect tens to increase over time.
Table 6. Main Variables Change
This table displays the change of three variables analyzed in this study. The mean results are not presented since they are affected by the presence of outliers. The adjusted median change is given by the median change subtracted by the industry change that may affect the results of both groups equally. In the first column, the change of the three years after the acquisition in relation to the year before is presented. In the following columns, the change of the first, second and third years after the acquisition in relation to the year before is displayed. The classification *, **, ***, correspond to the statistical significance at the 10%, 5%, and 1% level for Wilcoxon Signed Rank Test in the median results.
6.1.3. Performance Measures
Table 7 presents the change after the acquisition of the performance ratios: Return on Assets, EBIT Margin and Asset Turnover.
Analyzing the ROA ratio, table 7 shows that the median changes are negative and statistically significant for the first (decreased 1.82%), second (decreased 1.94%) and third (decreased 2.20%) year after the acquisition, with a statistical significance at the 1% level. For the three-year average after the acquisition we obtained similar results, with a 2.02% decrease on a 99% confidence level. When comparing these results to the adjusted median change, the ROA variation displays almost identical results: in the three years after the acquisition, the ROA is on average 1.75% lower than before with a 95% confidence level and so the results indicate that mergers have a declining effect on the performance of technological firms.
Regarding the change of the EBIT margin, the average for the three years after the acquisition is negative (decreased 0.66%) and statistically significant at 1% confidence level. When adjusting for the change in the control group, the results also show a negative average decrease for the three years after the acquisition with a 99% confidence level.
From -1 to 3y From -1 to +1 From -1 to +2 From -1 to +3
Panel A: Total Assets
Median Change (%) 36.45 *** 27.43 *** 32.50*** 44.59 ***
Adjusted Median Change (%) 26.78 24.25 *** 21.58 33.77 **
Panel B: Revenue
Median Change (%) 18.32 *** 11.16 *** 18.99 *** 30.22 ***
Adjusted Median Change (%) 11.30 * 9.70 *** 10.83 20.81
Panel C: EBIT
Median Change (%) 9.98 1.24 10.76 13.65
According with table 7, there was also a negative average variation in the asset turnover ratio of 4.27%, which indicates that the acquiring firms, after the acquisition, are not using their assets in an efficiently manner, even though this variation is not statistical significant.
Table 7. Main ratio change.
This table displays the change of three variables analyzed in this study. In order to know if the change of the variables is significant or not, we did the Wilcoxon Signed Rank Test for the median results. The mean results are not presented since they are affected by the presence of outliers. The adjusted median change is given by the median change subtracted by the industry change that may affect the results of both groups equally. In the first column, the change of the three years after the acquisition in relation to the year before is presented. In the following columns, the change of the first, second and third years after the acquisition in relation to the year before is displayed. The classification *, **, ***, correspond to the statistical significance at the 10%, 5%, and 1% level
Our results are in line the previous empirical studies that documented deterioration in operational performance after the merger such as Dickerson et al. (1997) and Gugler et al. (2003). In the other hand, our results contradict the findings that reported a significant improvement in post-acquisition performance such as Healy et al. (1992). Also Kohers and Kohers, (2001) and Lusyana and Sherif (2016) reported negative cumulative abnormal returns when studying the long-term performance of technological M&A, suggesting that investors and bidders are overoptimistic about the future performance of high-tech mergers.
From -1 to 3y From -1 to +1 From -1 to +2 From -1 to +3
Panel A: ROA
Median Change (%) -2.02 *** -1.82 ** -1.94 *** -2.20 ***
Adjusted Median Change (%) -1.43 ** -0.94 ** -2.23 *** -1.75**
Panel B: EBIT Margin
Median Change (%) -0.66*** -0.54 *** -0.60 * 0.23 ***
Adjusted Median Change (%) -0.48*** -0.02 *** -0.76 * -0.64 ***
Panel C: Asset Turnover
Median Change (%) -4.27 -9.36 3.07 -5.89
6.2. Multivariate Analysis
As stated before, the long-term effects of acquiring a tech firm was also studied through a OLS estimation of a DID model.
This model includes a variable dummy, Dacquiring, that takes the value 1 for acquiring firms and 0 otherwise, another variable dummy, Dpost, that takes the value 1 in the post-acquisition years and 0 otherwise and an interactive dummy (Dacquiring*Dpost) that captures the effect of the acquisition.
For each operating performance variable, the model was first estimated considering the three years after the acquisition, i.e., just comparing the period after the acquisition with the period before (columns (1) to (4)) and then considering each year after the acquisition individually (columns (5) to (8)). Therefore, in models (5) to (8), the variable Dpost and the interactive dummy Dacquiring*Dpost were replaced by different dummy variables for each year after the acquisition: the variables Dpost1, Dpost2 and Dpost3 and the variables Dacquiring*Dpost1,
Dacquiring*Dpost2 and Dacquiring*Dpost3, respectively.
The model was also estimated with and without the controlling variable log(Total Assets) to verify the robustness of the conclusions.
6.2.1. ROA
Table 8 displays the results obtained by the regression of our DID model using as dependent variable the Return on Assets ratio.
Overall, the acquisition has a negative significant impact. Therefore, the coefficients associated to the variables Dacquiring*Dpost (that represents the effect of the acquisition) are negative and are statistically different from zero, which suggests that the profitability changed negatively after the acquisition. The same is true for the coefficients associated to the dummy variables for each year after the acquisition (with the exception of the first year).
The addition of the control variable log (total assets) in columns (4) and (8) has positive and
statistically significant coefficients at a 1% level, indicating that the performance of a firm can be influenced by its size, i.e. bigger firms tend to present higher margins.
The regression results are consistent with our univariate analysis findings which indicates that the profitability is negative and statistically significant, that is, the technological acquirers record a decline in their operating performance after the merger. This results are similar with the ones obtained by Dickerson et al. (1997) report that acquisitions have a damaging impact on the acquirers’ average return on assets and Martynova et al. (2006), who report a decrease
on the ROA ratio, even though this reduction was statistically insignificant after the control for the performance of the peer companies.
Table 8. Summary of Results using the ROA ratio.
This table reports results of regressions using as dependent variable the ROA ratio for the three years after the acquisition. A set of control variables are used, including the natural logarithm of real assets to control the influence of the firms’ size (Log(assets)), a dummy variable distinguishing the performance before and after the merger (Dpost) another dummy variable distinguishing the acquiring group firms from control group firms (Dacquiring) and a variable distinguishing the post-merger performance of acquiring firms ( Dacquiring*Dpost). Columns (2) to (4) represent the three years after the merger aggregated, and columns (5) to (8) represent the three years after the acquisition separately. Standard errors are reported under the coefficient in parenthesis.
***, **, * Significant at the 1, 5, and 10 percent levels, respectively
Variables (1) (2) (3) (4) (5) (6) (7) (8) 0.003 0.003 0.031** 0.031** 0.003 0.031** 0.031** (0.008) (0.008) (0.015) (0.015) (0.008) (0.015) (0.015) -0.019** 0.005 0.006 (0.009) (0.012) (0.012) -0.017 -0.017 -0.004 -0.005 (0.011) (0.011) (0.015) 0.014 -0.013 -0.013 0.010 0.012 (0.011) (0.011) (0.016) (0.016) -0.024** -0.024** -0.005 -0.006 (0.011) (0.011) (0.015) (0.014) -0.037** -0.039** (0.018) (0.017) -0.026 -0.028 (0.022) (0.021) -0.048** -0.049** (0.022) (0.020) -0.037* -0.037* (0.022) (0.021) 0.020*** 0.021*** (0.007) (0.005) 0.088*** 0.101*** 0.088*** -0.023 0.103*** 0.101 *** 0.088*** -0.023 (0.005) (0.009) (0.011) (0.022) (0.008) (0.009) (0.011) (0.016) Adjusted R2 0.002 0.005 0.011 0.011 0.004 0.003 0.007 0.007 N 488 488 488 488 488 488 488 488 Constant Dacquiring*Dpost Dacquiring*Dpost1 Dacquiring*Dpost2 Dacquiring*Dpost3 Log(Total Assets) Dacquiring Dpost Dpost1 Dpost2 Dpost3
6.2.1. EBIT Margin
Table 9 shows the results of our DID model with the EBIT margin as proxy for operating performance, i.e., dependent variable.
As shown previously in the descriptive statistic, our model confirms that the acquiring firms achieved before the acquisition higher EBIT margins than non-acquiring firms because coefficient associated to Dacquiring is always positive and statistically different from zero. However, the acquisitions have no effect on EBIT margin as, even though the coefficients associated to the variables Dacquiring*Dpost are always negative, they are not statistically different from zero.
The coefficients associated with log (total assets) are positive and statistically significant, suggesting that larger firms tend to have higher margins.
The results contrast when compared with the univariate analysis, where the differences where mostly negative and statistically significant.
Comparing with similar studies, our findings are different from Martynova and Renneboog (2006) who conclude that EBITDA margin increase after M&A.
