MSc International Financial Management Faculty of Economics and Business
University of Groningen
MSc Economics & Business Department of Business Studies
Uppsala University
M&A post-acquisition performance in OECD countries;
A study of the telecom industry
Nout Slinkman Student number: 1899147 Thesis Supervisor: W. Westerman 10-01-2014 Abstract
Studying M&A’s in the telecom industry listed on stock exchanges in various OECD countries, this paper finds post-acquisition underperformance of acquirers. Applying a calendar-time portfolio approach, various determinants are identified
which impact the performance of acquirers in the telecom industry. Additionally, by identifying four roles of capabilities in M&A’s, this paper argues that the winners in the next M&A wave are firms which capture benefits by accessing capabilities,
knowledge, talent, resources and markets outside their traditional core business using a capability-based M&A strategy. Moreover, acquirers which enhance their capabilities in M&A deals outperform acquirers which leverage their capabilities on
newly acquired products or services.
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Table of content
1. Introduction ... 4
2. Literature Review ... 5
2.1 Post-acquisition performance ... 5
2.2 Understanding variance in M&A post-acquisition performance ... 7
2.3 Strategic motives in the telecom industry... 9
2.4 The role of capabilities ... 14
3. Methodology... 15
3.1 Sample ... 16
3.2 Measuring long-term performance ... 18
3.3 Calendar time portfolio approach ... 19
3.4 Construction of risk factors ... 20
3.5 Measuring determinants of post-acquisition performance ... 21
4. Post-acquisition performance ... 22
5. Determinants of long-term performance ... 25
5.1 Results ... 25
5.2 The role of capabilities... 29
6. Conclusion ... 34
6.1 Limitations ... 37
6.2 Implications and future research ... 37
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1. Introduction
Over the last decades, many firms have incorporated acquisitions to a key aspect of their strategy, causing worldwide merger and acquisition value (M&A) to reach trillions of dollars each year. Prior research shows several motives for firms to engage in acquisitions, such as consolidation, acquiring new products and services, benefiting from economies of scale and scope, geographical and industrial diversification, and benefiting from superior technological, managerial or business skills. On average, empirical research on M&A performance concludes that acquisitions have a neutral or negative effect on the market value of acquiring firms (Agrawal & Jaffe’s, 2002). Moreover, scholars have shown that the vast majority of M&A’s fail to deliver the promised expected value and synergies. There are numerous reasons for failures in M&A’s, including unrealistic expectations, cultural misfits, high deal premiums and complex
integration (Kummer and Steger, 2008; Teerikangas and Very, 2006). So, why do firms continue to transact M&A deals, in massive number and dollar terms, when so many are shown to fail? And what determines a successful M&A strategy?
Focusing on firms in OECD countries, this paper studies the performance of acquirers in the
telecommunication industry, and hopes to further pin point the sources of underperformance. Following the trends in the telecom industry of political reforms, industrial consolidation, globalization of businesses and technological innovations, telecom firms keep trying to create value through M&A’s. “The
telecommunications industry was again the most active in the global M&A market, with deals
representing 20% of the world’s total in 1999” (Miyake and Sass, 2000). By distinguishing four broad types of the role of capabilities in acquisitions, this paper tries to create a quantifiable measurement for the resource-based view (RBV). The RBV has gained in attention and acknowledgement in strategic literature, as it suggests that firms create value based upon it’s unique set of intangible, knowledge-based resources and capabilities. The RBV implies that in an industry known by recent exogenous shocks such as technological advances and political reforms, which are known to increase M&A activity, firm-specific capabilities will be a large determinants of corporate success in the long-run. Therefore, acquirers with a capabilities-driven strategy will have a higher post-acquisition performance than those who pursue other inorganic growth strategies. Even though there has been a lot of research concerning post-acquisition performance, the combination with the resource-based view has been limited (Uhlenbruck et al., 2006).
5 year period following acquisitions. Additionally, as sources of underperformance are yet to fully be determined, understanding the variance in post-acquisition performance lies at the heart of much M&A research (Cartwright and Schoenberg, 2006).
Therefore, the main research question of this paper is twofold; (1) Are mergers and acquisitions value-creating or value destroying in the telecommunication industry? and (2) do acquirers which follow capabilities-based strategy outperform firms that do not?
2. Literature Review
M&A’s have been researched for over 30 years, with the paradox of increased amount of M&A deals and underperformance of M&A’s as one of the most remarkable and researched areas. Even with the recent financial crisis, global M&A activity has increased over the last decade. The main cause of increased activity is that companies see acquisitions as a key strategy achieve inorganic growth. In 2012, worldwide M&A value accounted for a staggering US$ 2.6 trillion (ThomsonReuters). However, empirical studies show that acquisitions often fail to deliver the expected value, or even destroy value for the shareholder of the acquirer.
2.1 Post-acquisition performance
There has been extensive research examining long-run stock returns following acquisitions. Many finance scholars focused their research on the issue of whether acquisitions create wealth for shareholder or reduce shareholder wealth (Cartwright and Schoenberg, 2006). Empirical research shows that on average
acquisitions have a neutral or modest negative effect on an acquiring firm’s financial performance in the post-announcement period, while takeovers bring positive short-term returns for shareholders of target firms. While some studies focus solely on the long-term financial performance, others examine the impact of variables on post-acquisition performance (Loughran & Vijh,1997).
Jensen and Ruback’s (1983) paper is one of the fundamental and widely referenced papers concerning acquirer’s underperformance. The authors conclude with the statement that “corporate takeovers generate positive gains, that target shareholders benefit, and that bidding firm shareholders do not loose” (Jensen & Ruback; 1983, pp. 47). Moreover, Roll (1986) concludes that the null hypothesis of zero abnormal
performance to acquirers should not be rejected. Following up on Roll’s work (1986), Loderer and Martin ‘s (1992) findings show significantly negative abnormal returns over the three-year
6 pounds. Gregory (1997) finds two-year statistically significant cumulative annual abnormal returns
(CAARs) between -0.1192 to -0.1801 under six different models, thereby showing negative excess returns for acquirers.
Mitchell and Stafford (2000) review the methods used in previous literature regarding abnormal returns. The authors analyse 2,068 M&A transactions in the period between 1961 and 1993, reporting negative mean abnormal monthly returns over three years of -0.04% and -0.03% for equal-weighted and value-weighted M&A portfolios. Carow et al. (2004) find, consistent with the general notion in literature of acquirers’ returns, on average negative acquirers returns, while most target shareholder gain from the acquisition. Furthermore, the authors explore the advantages of early-movers in industry acquisition waves, as they assume that having superior information experiences would enhance the acquisition returns and would outperform other acquirers in term of long-term performance. The study shows that especially acquirers that acquire in an early-mover position, conduct acquisitions in related industries and during industry expansion phases realize significantly superior returns. Concluding, Carow et al. (2004, pp. 563) find that “combined abnormal returns are higher for acquisitions that occur at the beginning of acquisition waves”.
As empirical research has not reached a consensus regarding the prediction of post-acquisition
performance, King et al. (2004) conducted a meta-analysis to assess the impact of the most commonly researched variables on post-acquisition performance. The authors conclude from their meta-analysis that M&A activity does not create “superior post-acquisition performance for acquiring firms and is consistent with the non-value maximizing arguments often advanced to explain M&A activity” (King et al., 2004; pp. 192). However, they find that unidentified variables still hold significant variance in post-acquisition performance, showing the need for additional theory and changes in the selection of proper variables. Zollo & Meier (2008) state that the difficulty in measuring M&A performance lies in the fact that it is a multifaceted construct, as there is not one factor which can capture all the different ways used to proxy it. Additionally, the negative post-acquisition performance found in the literature can also be related to the division of activity in waves. Acquisitions towards the end of each wave are often driven by non-rational, frequently self-interested managerial decision-making, which are believed not to be in the best interest of the firm, leading to lower post-acquisition performance (Martynova & Renneboog, 2008; Yildiz & Fey, 2010).
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2.2 Understanding variance in M&A post-acquisition performance
“The desire to understand the variance in post-acquisition performance lies at the heart of much M&A research” (Cartwright and Schoenberg, 2006, pp. 6). Justifications for the long-term underperformance of acquiring firms can be found in various research areas. Explanations for the performance variance of acquisitions in strategic management research in the M&A field have mostly been on the identification of strategic and process factors (Cartwright and Schoenberg, 2006). Overall, the main factors examined in the literature involve the negotiation process, the level of financial, strategic and cultural fit, and the integration following the acquisition. Recently, an emerging field of enquiry has been the cultural, emotional and behavioral aspects of M&A’s in an organization context. Moreover, the wider integration process adds additional explanations for the M&A underperformance. Cartwright et al. (2012) show that 32% of the published papers between 1963 and 2009 involves the acquisition process, including pre-acquisition management, post-pre-acquisition integration, cultural issues, the process of knowledge transfer and the human side of M&A’s. The process perspective recognizes that the acquisition process is an important determinant of acquisition activities and outcomes (Jemison & Sitkin, 1986). Inappropriate decision-making, negotiation and integration processes under the process perspective can be possible explanations for underperformance of mergers and acquisitions. As different stakeholder groups and employees of both firms are involved at different stages of the acquisition/integration process, explaining variance in key broad explanations is difficult. Consequently, research concerning the process perspective on acquisitions is highly varied and broad. Literature on the pre-acquisition process contains topics as the method of payment, type of bid, negotiation process, influence of investment bankers, number of bidders and management interests (e.g. Loughran and Vijh, 1997; Sudarsanam & Mahate, 2006; Sitkin et al., 1996).
This paper will initially focus on the factors most researched by finance scholars to explain
8 book-to-market ratios. Rau & Vermaelen (1988) argue that in firms with high book-to-market ratios, managers and shareholders are more likely to overestimate the ability of the firm to manage an acquisition. In contrast, in firms with low market-to-book ratios are argued to be more careful when considering a major acquisition, as the firm could face severe problems when the acquisitions fails. Andre et al. (2004) confirm the findings of Fama & French (1992), as acquirers with high market-to-book ratios significantly underperformed in a 36-month post acquisition period, while firms with low market-to-book ratios had insignificant positive abnormal returns. Recent acquirer performance, also referred to as the momentum factor, has also gained acknowledgement by the financial economics literature as an important determinant of post-acquisition performance. Carhart’s (1997) four factor pricing model controls for the momentum factor, as scholars are not fully conclusive in how it determines
post-acquisition performance, although it is proven to affect post-post-acquisition performance (King et al., 2004).
Additionally, scholars have shown differences in excess returns between diverse methods of payments (Loughran and Vijh, 1997; Mitchell and Stafford, 2000; Andre et al, 2004). Loughran and Vijh (1997) argue that equity acquirers underperform in the post-acquisition period, as they find that stock acquirers earn 24 percent less than matching firms whereas cash acquirers earn 18 percent more than matching firms. As integration issues often destroy value and synergy effects for the acquirers, the degree of relatedness between the acquirer and target firm is assumed to impact the post-acquisition performance of acquiring firms. Specifically, M&A literature suggests that acquiring related firms leads to increased post-acquisition performance (King et al., 2004).
9 The stake acquired in an acquisition could potentially affect the performance of acquirers. Partial acquisition is argued to provide a lower cost to enter the market and/or reduce risks substantially. However, it is also often viewed as a lower degree of commitment or a lack of financial resources, thus harder to fully integrate the businesses. Therefore, it is argued that acquirers which acquire the target firm partially would perform worse than firms which fully acquire the target firm.
Finally, literature concerning deal value and post-acquisition performance shows acquirers in large deals are worse off even though the acquirer often pays lower premiums. Managerial incentives and overconfidence are argued to lead lower performance for large acquirers. Alexandridis et al. (2011) conclude that the complexity of large deals makes it harder to integrate both businesses, making it unlikely that they offer any economic benefits to the acquirer.
As the RBV implies that acquirers with a capability-based strategy outperform firm which do not, a deeper insight in the good of strategic fit, as well as the M&A process itself, is needed to create a better
understanding of the capabilities involved in M&A’s. As the finance literature barely focuses on the goodness of fit, as it is hard to quantify, the next section of this paper focuses on the strategic fit literature and different motives to identify the different capabilities involved in M&A deals in the telecom industry.
2.3 Strategic motives in the telecom industry
Telecommunications refers to the movement of data, voice, graphics, images, text, and video over electrical or optical media (Wilcox et al., 2001). Since the mid-1990s, the telecom industry is known for its uncertain and ever changing nature. This uncertainty is clearly shown by the stock price activity, and can attributed to several factors. As most M&A’s occur in waves, which are often caused by
technological or regulatory shocks, an industry with continues innovation and change in the market in the market forms an interesting basis for this paper. These exogenous shocks set off a new era for M&A’s in the telecom industry.
10 technologically innovative and globalized sectors of the world economy (Harper, 1997; Lehn, 2002), these changes leaded to an steady increase in M&A activity in preparation for the telecom industry as an
integrated global information marketplace. Due to technological process and globalized world, new products, services and technologies need to quickly be assessed to determine their value for the firm. A clear example is the introduction of the 4G networks, which induced bidding wars across Europe. Following the trends of political reforms, industrial consolidation, globalization of businesses and technological innovations, firms try to create value through M&A’s. These trends caused the traditional core business of telecom firms to shrink, leading this paper to argue that firms should enhance their focus on capturing benefits by accessing capabilities, knowledge, talent, resources and markets outside their shrinking traditional core business.
The exogenous shocks determine the strategic motives behind mergers and acquisitions. The “strategic fit” literature focuses on the strategic attributes and performance of two firms, involving the extent to which the target firm’s business is related with, and can be incorporated by, the acquirer’s business and strategy. The strategic reason behind the merger or acquisition is the most researched area in the strategic fit literature. This paper argues that firms which acquire to enhance their knowledge-based capabilities outperform firms that do not. In the long run, the decision to engage in mergers and acquisitions based on capabilities may be an important vehicle to build capacity and improve organizational performance of the firm. Derived from the literature, four broad types of strategic motives in M&A’s are
distinguished.
Capability enhancement deals
11 bidder” (Capron & Pistre; 2002, pp. 781). However, acquirers are found to earn abnormal returns when they transfer their own resources to the acquired company. Moreover, a recent growing body of literature in M&A’s concerning the transfer of knowledge and capabilities between and within organizations sheds more light on transfer of knowledge-based capabilities. Yildiz & Fey (2010) focus on two main areas in the knowledge transfer literature, namely compatibility of new knowledge and organizational unlearning. The authors researched the compatibility of new knowledge and organizational unlearning, stating that “the success of knowledge transfer depends on the extent to which these practices are compatible with recipients units’ needs, interpretations of prior experiences and existing values”. If firms lack this compatibility, Yildiz & Fey (2010) argue that firms should unlearn those parts which cause incompatibility. As new technology and capabilities become increasingly important for firms (Ranft & Lord, 2002), this compatibility and organizational unlearning could get increasingly important. Shimizu et al. (2004) use an organizational learning perspective and the RBV to show potential benefits of cross-border M&A’s. They suggest further RBV-related research concerning the wealth creation potential of cross-border M&A’s. Furthermore, they conclude that “acquisitions of firms headquartered in other countries present an especially good opportunity for the acquiring firm to learn knowledge and acquire new capabilities” (Shimizu et al., 2004). In sum, these studies suggest that some M&As are motivated by the acquisition of new capabilities and learn new knowledge.
Diversification deals
Diversification has been suggested as one of the dominant reasons for M&A’s. Firms are known to diversify through mergers and acquisitions either across multiple lines of business (industrial diversification) and across different national markets (geographic diversification).
Firms diversify across different markets to either seek new customers, obtain efficiency gains, gain access to (new) resources and to exploit their firm-specific capabilities. Sources of potential value through geographic diversification could be associated with the ability to arbitrage tax regimes,
exchange rate differences and market power on an international level. Following basic finance theories, since economic activities are less than perfectly correlated in different markets, international
12 manage, M&A’s provide the advantages of better learning possibilities, increased market shares and efficiency, and possibility of obtaining resources. These high-control governance structures also have the advantage of protection of knowledge, known as a critical factor of entry modes dynamics. Denis et al. (2002) conclude in their paper on geographic diversification and firm value that an increase in global diversification reduces excess value, while reductions in global diversification increase access value. The relatively high failure rate of geographic diversification M&A’s could be attributable to
underestimation of complexities of foreign acquisitions, agency problems and the importance of prior experience.
Industrial diversification through M&A’s involves acquiring firms increasing their involvement in a wider range of revenue producing activities. Research in this area focusing on the impact of industrial diversification on firm value has mostly negative outcomes, while economic theory would suggest both positive and negative outcomes of this type of diversification. Scholars conclude that, on average, the costs of industrial diversification outweigh the benefits (Kim et al., 2008; Denis et al., 2002). Benefits of industrial diversification include better financing deals and more stable cash flow, as firms with multiple activities are imperfectly correlated.
Consolidation deals
A well-known feature of the telecom industry is the consolidation trend, as operators continuously are pushing to buy or sell telecommunications assets and gaining economies of scale or scope.
Efficiency theory suggests that firms engage in mergers and acquisition to generate enough realizable synergies to make deals profitable, that is, to increase firm value. Synergy is a broad concept and can entail many different ways of value gains, such as increasing market share and power, lower cost by economies of scale and scope, combining business units and pooling resources. However, three types of synergies are generally distinguished, namely financial synergies, operational synergies and managerial synergies (Trautwein, 1990). Synergies can be achieved by combining operations and activities that are performed by both firms separately. Well-known examples of these activities and operations are
13 It must be noted that a high number of M&A’s fail to deliver the expected synergies, mostly since, by definition, synergies are possibilities, not certainties (Ficery et al., 2007). Synergies are usually hard to measure, and often entail intangible benefits such as skills, access to markets or even culture. Managers often tend to overestimate the value of these benefits, as they include these synergies in their
calculations, while these cannot be easily transferred into cash.
Conglomerate deals
Conglomerate mergers or acquisitions are deals which involve completely unrelated companies,
companies in different geographic markets, or companies whose product are not comparable with those of the acquiring firm. Several advantages and disadvantages of conglomerate deals can be identified. The main argument for conglomerate deals stems from a finance perspective, arguing that investors want firms in their portfolio with lower correlation to decrease risk. As conglomerate deals enable investors to diversify either across different industries or countries, the costs of capital will go down. In sum, conglomerate deals will in theory provide the firm with cheaper access to capital. Similarly, firms will also have lower risk of fluctuations in income, leading to improved income stability. Other factors which are in favour of conglomerate acquisitions are the lower bankruptcy probabilities and the increased market value of debt after the acquisition. However, on the whole, scholars argue that acquisitions in related industries create more wealth for shareholders for both bidders and targets in comparison with
conglomerate acquisitions. The value creation of acquisitions in related industries in general can be found in synergies. As firms are comparable in those acquisitions, firms can combine activities and operations, which lead to lower cost. Moreover, lower costs of scale and scope, as well as knowledge and resource sharing, will in theory enhance the performance of the firms in the merger or acquisition.
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2.4 The role of capabilities
The RBV suggests that the primary way to create value is based on the firm’s unique sets of intangible, knowledge-based resources (Wernerfelt, 1984; Amit & Schoemaker, 1993). Firms differ since each firm possess a different and unique bundle of resources, capabilities and routines. Amit & Schoemaker (1993) were the first to make a distinction between resources and capabilities, stating resources are tradable objects and non-specific to a firm, while capabilities are firm specific and are used to engage the resources within the firm. “Competitive capabilities are the set of organizing processes and
principles a firm uses to deploy its resources to achieve strategic objectives” (Kogut and Zander, 1992). The dynamic capabilities theory focuses on the acquisition, adaptation and integration of internal and external resources, competences and skills which can be valuable to the firm. Firms need to
continuously develop new capabilities in response to new opportunities. Furthermore, as these
capabilities involve tacit knowledge, they cannot be bought, but need to be developed in organizations. The main argument of the RBV concerning the (non) tradability of capabilities in markets, is the role of causal ambiguity. Capabilities are be hard to assess and identify from the outside, which might be the cause for the high failure rate in acquisitions. As a firm’s choice of strategy is dependent on the internal resources and capabilities, as well as the speed they can acquire or accumulate new ones, it is needless to say that resources and capabilities hugely affect the strategy. However, acquisitions can provide a short-cut to tacit knowledge. This upcoming trend can particularly be seen in dynamic and rapid changing environments, in which acquisitions are shown to be a fast way to obtain knowledge.
Needless to say, for identification of the role of capabilities in a transaction, a clear definition of capabilities needs to be given. Capabilities need to drive the company’s strategy and innovation, by integrating processes, people and technologies to add value to the customer and ultimately the
shareholders of the acquiring company. Thus, capabilities such as tax, operations, facilities, assets are hereby excluded, as they are viewed as necessary, but not as value enhancing or necessities to be competitive. As the telecom industry is one in which advantages are achieved by continuous innovation and knowledge, capabilities need to create value in the market. However, these are ultimately also the most complex capabilities for firms, and take a lot of effort to complete integrate and coincide. Based upon the different motives previously discussed, this paper distinguishes several roles of capabilities in M&A.
15 the primary reasons for the acquisition, this is also viewed as a capability enhancement deal, since tacit knowledge typically goes hand in hand with technological patents. Second, when acquirers apply their existing capabilities on newly acquired products or services, the acquisition serves a “capability
leverage (CL)” role. This happens mainly when a company follows a product diversification strategy.
Third, when telecom companies expand into different markets to employ their existing capabilities in a new market, the “geographical capability deployment (CD)”. Geographical expansions happen often when firms search for a new customer base. This could either be domestically or internationally, as long as the acquirer is not be present in the specific geographical region at the time of the acquisition. Finally, when the acquirer largely ignores capabilities, it is called “limited capabilities (LC)”. This role of capabilities is primarily being identified when acquirers either take over assets on the cheap
following a bankruptcy of the target firm, “buy market share”, take over rivals or increase their
customer base. More, limited capability deals are also classified as such when private equity firms try to generate revenue by diversifying their portfolio, as there is no crucial knowledge passed through in the acquisition.
Figure 1. Role of capabilities in M&A’s
Geographical Capability
Deployment Capability Enhancement
Limited Capabilities Capability Leverage
Low Degree of use capabilities High
3. Methodology
The data and methodology used to conduct this researched are discussed in this chapter. Paragraph 3.1 discusses the sample and database, while paragraph 3.2 explains different measurements for long-term performance. In paragraph 3.3 the applied method for measuring post-acquisition performance is
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3.1 Sample
This paper focuses on M&A deals between 1998 and 2011. M&A deals have been restricted to include mergers or acquisitions where control is transferred from one firm to another, thus excluding minority purchases, purchase of remaining interest, LBOs, spin-offs, recapitalization, self-tenders, exchange offers, repurchases, and privatization. The deal data is retrieved from Zyphyr, a database containing extensive information regarding mergers and acquisitions. The stock returns stem from Datastream, using the ISIN numbers retrieved from the Zephyr Database of Bureau Van Dijk, which covers M&A, IPO and venture deals on a globe scale.
A acquisition sample of 223 acquisitions from the Zephyr database is extracted in the time period between January 1, 2000 and December 31, 2011. This sample meets the following criteria:
1. The acquisition is completed. 2. The acquirer is a public listed firm
3. The acquirer controls less than 50% of the target’s shares prior to the announcement and owns 100% of the target’s shares after the transaction.
4. The target may be public, private or a subsidiary. 5. The deal was either paid fully in cash or stock.
5. Either the acquirer or the target is a firm in the telecom industry (US SIC, 481)
6. The acquirer has not acquired other firms in the following 2 years (overlapping cases). 7. Companies have market data and financial statements over the sample period.
When measuring the influence of specific capabilities on the performance of M&A’s, it seems
17 Table 1 present the descriptive statistics. The sample includes 62 (28%) cash payment and 161 (72%) stock payment. Further, out of the 223 acquisitions, 141 (63%) are acquirers and targets are related based on a 2-digit SEC code, whereas 82 (27%) are not related. In 197 (88%) acquisitions the acquirer took over 100 percent of the target firm, and 26 (12%) acquirers took over between 50 and 100 percent of the target firm. Finally, most of the transactions where non-cross border (77%), whereas 52 (23%) transactions were cross-border. On average, acquirers show a small positive recent acquirer
performance over the previous year, while the average book-to-market ratio is 0,48. The average deal value is over $2.054 billion, and the average market capitalization of acquirers over $11 billion. Additionally, figure 1 shows the number of acquisitions and deal value by year.
Table 1. The frequency distribution of various variables over the whole sample
Frequency distribution and averages 223 100%
Main stock index NASDAQ National Market 71 32%
New York Stock Exchange 35 16%
Euronext Paris 19 9%
Australian Stock Exchange 41 18%
London Stock Exchange 40 18%
Toronto Stock Exchange 17 8%
Related (SEC 2digit) Yes 141 63%
No 82 37%
Cross-border Yes 52 23%
No 171 77%
Stake 100% 197 88%
50% > x < 100% 26 12%
Method of payment Cash 62 28%
Stock 161 72%
Recent acquirer performance (N=12) 2,81%
Book-to-market ratio 0,48
Size (in million US$) 11,06
Deal value (in million US$) 2,054
Fig 2. Number of acquisitions over the sample by year.
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3.2 Measuring long-term performance
Studies of long-term returns are sensitive to the way tests are done (Fama,1998). As stated by Mitchell and Stafford (2000), the measurement of long-term abnormal performance is complex.
Recent literature distinguishes two main approaches of measuring long-term performance: (1) the Buy and Hold Abnormal Return (BHAR) approach and (2) the calendar-time approach (also called Jensen-Alpha approach). Mitchell & Stanford (2000) argue against the BHAR’s approach and advocate a methodology that accounts for the dependence of event-firm abnormal returns, such as the calendar-time portfolio approach. Cumulative abnormal returns (CAR’s) pose fewer statistical problems than long-term BHAR’s (Fama, 1998). The main problems concerning BHAR’s are a skewness bias, a rebalancing bias and a new listings bias (Barber & Lyon, 1997). Concluding, the calendar-time portfolio approach holds several advantages over the BHAR methodology, which deal with some of these issues. First of all, all cross-correlations of event firms are taken into account, as portfolios with monthly calendar-time returns are created. Second, the distribution of the monthly returns present a better approximation for the normal distribution (Dutta & Jog, 2009). Additionally, the calendar-time portfolio approach also reduces, but does not eliminate, misspecification when samples are drawn from a single industry (Barber et al. 1997).
The construction of monthly portfolios in calendar time to measure the average abnormal long-term performance has three main advantages; (1) monthly returns are less sensitive to “the bad model problem”, (2) monthly portfolio returns allow taking into consideration the
cross-correlation between firms in the sample and (3) better statistical inferences are allowed by portfolio returns (Fama, 1998). Following this line of reasoning, this paper makes use of a monthly calendar-time portfolio approach to measure CAR’s as proposed by Fama & French (1992).
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3.3 Calendar time portfolio approach
The Calendar Time portfolio approach was introduced by Jaffe (1974) and Madelker (1974). Since then, this method has gained popularity among financial economics scholars and has been advocated by many. The calendar time portfolio approach is mostly used to calculate returns for firms experiencing an event, such as IPO’s, M&A’s and SEO’s, and estimate whether they are abnormal in a multifactor regression (Andre et al., 2004).
To estimate price performance over two years (24 months) following the event, a portfolio is constructing including all firms which experienced an acquisition within the previous 24 months. Note that the number of firms included in a portfolio is not consistent each month, as the number of firms is not equally
dispersed over the sample period. The portfolio is first constructed in the month following the first acquisition (s) of our sample, while the last month is the last monthly returns of the firms. The portfolio composition is done at the beginning of each months where firms that completed an acquisition in the previous month are included and the firms that completed an acquisition 24 months ago are excluded. Thus, the portfolios are recomposed each month and an equally-weighted portfolio excess return is calculated. The time series of monthly excess returns is regressed on three prominent assets pricing models to ensure the robustness of the inferences, namely the Capital Asset Pricing Model (CAPM), the Fama-French three factor model (TFPM) and the Four Factor Pricing Model (FFPM) by Carhart (1997). The weighed least squares (WLS) method is used for the regressions rather than the Ordinary Least Squares (OLS) method for two main reasons. First, “the WLS procedure allows to weight months with more acquiring firms more heavily; second, it deals with potential heteroskedastic residuals induced by calendar clustering” (Andre et al, 2004). The weight factor is based on the number of securities in each specific month.
CAPM : rp,t – rf,t =αp +βp RMRFt + ept (1)
TFPM : rp,t – rf,t = αp + βp RMRFt + sp SMBt + hp HMLt + ept (2)
FFPM : rp,t – rf,t = αp + βp RMRFt + sp SMBt + hp HMLt + mp UMDt+ ept (3)
20 portfolio of past twelve month “up” and “down” (Kothari et al., 2006). The “Alpha” value (αp) reported in the regression model shows the monthly average abnormal return of the sample. The loadings for each factor ( p, sp, hp mp) are estimated using a time-series method, and indicate the sensitivity (beta’s) of the event portfolio to the factors. Note that if a particular calendar month has no firms in the portfolio, that month is dropped when estimating the equation.
3.4 Construction of risk factors
The monthly excess returns are regressed on four factors, namely RM-RF, SMB, HML and UMD. The market risk premium (RM - Rf) is determined by the value-weighted return of all the securities listed on a stock index in excess of the risk free rate (Andre et al., 2004). As shown by Fama and French (1992), the factors SMB, HML and UMD can be estimated using firms within a specific region. As this paper has multiple stock indexes falling in the same geographic region, some factors are constructed
combining the firms in specific regions. For the North-American region, the NASDAQ, NYSE and Toronto Stock Exchange are used to estimate the size factor, the market-to-book factor and the
momentum factor. Similarly, for the European Region, firms from the Euronext Paris and London Stock Exchange are used to construct the three additional variables used in the three and four factor pricing models. Due to limited data, the factors SMB, HML and UMD on the Australian market are derived from Kenneth French’s website (http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/). It must be noted that the market risk-premium is not constructed combining firms from different stock exchanges. By increasing the sample size of securities to calculate the factors, the reliability and accuracy of the factors is enhanced.
21 “Small minus Big” (SMB) is the equal-weight average of the returns on the small stock portfolios minus the returns on the big stock portfolios, whereas “High minus Low” (HML)is the equal-weight average of the returns on the value stock portfolios minus the returns on the growth stock portfolios.
SMB = ((S/L – B/L) + (S/M – B/M) + (S/H – B/H))/3 (4)
HML = ((S/H – S/L) + (B/H – B/L))/2 (5)
UMD is constructed in a similar fashion. For each firms in the portfolio, we calculate the performance between twelve months and two months before the acquisition. Subsequently, we use the prior
performance ranking and the previously calculated size ranking to calculate a 50 percent breakpoint for size, and a 30 and 70 percent breakpoint for prior performance. The stocks are sorted into six portfolios; the stocks above the 50 percent size breakpoint are designated “B” and the remaining 50 percent “S”. Finally, the stocks above the 70 percent prior performance breakpoint are designated “Up” (U), the middle 40 percent are designated Medium and the firms below the 30 percent prior performance breakpoint are designated “Down” (D). Once more six value-weight portfolios are formed using the intersections of the size and prior performance breakpoints, namely S/D, S/M, S/U, B/D, B/M and B/U. “Up minus Down” (UMD) is the equal-weight average of the returns on the “Up” stock portfolios minus the returns on the “Down” stock portfolios:
UMD = ((S/U-S/D) + (B/U – B/D))/2 (6)
3.5 Measuring determinants of post-acquisition performance
22 For the role of capabilities, deals are classified by their role of capabilities based on corporate
announcements/filing, management opinions and new articles, which is verified by a third-party to ensure the accuracy and faithfulness. The time series of monthly excess returns is regressed on the four factor pricing model, except for size, book-to-market and recent acquirer performance, in which case the corresponding factor controlling for that factor is left out of the pricing model.
4. Post-acquisition performance
Table 2 shows the results for the CAPM, three factor and four factor models of the various indexes. The analysis of the long-term abnormal return results of the acquiring firms focuses on the alpha coefficients from the pricing models. The “Alpha” value (αp) reported in the regression model shows the monthly average abnormal return of the sample. Overall, we can conclude that acquisitions in the telecom industry are value-destroying, as on average securities on the various stock exchanges show negative abnormal returns in the 24 months after acquisitions. Using equally-weighted returns, this paper finds significant underperformance ranging from -0.14% to -1.53% per month. Both the New York Stock Exchange and the Euronext Paris show significant monthly average abnormal returns of -0,92% and -1,51% respectively (when looking at the pricing model with the highest R2). This monthly average abnormal return is measured over a 24-month post-acquisition period, leading to a cumulative annual average abnormal return of -11% (-0,92% * 12) for acquirers on the NYSE and -18,12% (-1,51% * 12) on the Euronext Paris. Although overall acquirers on the other indexes show negative monthly abnormal returns, these are insignificant. Acquirers on the Australian Securities exchange showed insignificant average monthly abnormal returns of -0.63%, on the NASDAQ of -0.48%,on the Toronto Stock Exchange of -0.41% and on the London Stock Exchange of -0.12%. Although slightly less, abnormal average monthly returns are still negative when excluding M&A deals during the peak of the crisis (2008).
23 acquirers. Concluding, results on the post-acquisition performance of acquirers are in line with previous literature, and focusing on a single industry, the telecom industry, does not result in large differences.
As a rule of thumb, papers normally include models if they add explanatory power to the regression. However, as there is still no conclusive consensus in the literature regarding the methodology for long-term performance, the explanatory power of the different pricing models is shown in the results. As previously mentioned, the beta is left free to change over time, as a specific industry and an event happened over the course of time, which could change the sensitivity to the market. The beta for all models across all indexes is significant. In theory, the higher the sample size, number of industries and the variation explained in the model, the closer the beta would get to 1. Except for the London Stock Exchange, extensions on the CAPM model seem to improve the explanatory power of the regressions. Although limited, the adjusted R2is higher with the use of the three and four factor pricing models. The limited increase of explanatory power of the models could explain why the results do not uniformly show the beta getting closer to 1.
24 Table 2. Results regarding the long-term performance of acquirers, look at other papers what they put above these tables in the first place. WLS Andre at al. Shown in parentheses, the t-statistics relate to the ratio of the coefficient to its standard error.
London Stock Exchange New York Stock Exchange
Factor loading CAPM TFPM FFPM Factor loading CAPM TFPM FFPM Equally-weighted Equally-weighted αp -0.14% -0.13% -0.12% αp -0.99% -0.95% -0.92% (-0.32) (-0.30) (-0.26) (-1.99)** (-1.90)* (-1.84)* Bp 1.14 1.14 1.13 Bp 0.56 0.54 0.54 (16.22)** (15.20)** (14.87)** (9.78)** (9.07)** (9.07)** Sp 0.10 0.13 Sp 0.51 0.42 (0.37) (0.47) (2.18)** (1.78)* Hp 0.02 0.01 Hp -0.11 -0.02 (0.08) (0.06) (-0.63) (-0.13) Up -0.07 Up -0.19 (-0.52) (-1.48) Adjusted R2 61.6% 61.2% 61.1% Adjusted R2 36.7% 38.8% 39.2%
NASDAQ Toronto Stock Exchange
Factor loading CAPM TFPM FFPM Factor loading CAPM TFPM FFPM Equally-weighted Equally-weighted αp -0.44% -0.43% -0.48% αp -0.54% -0.46% -0.41% (-1.06) (-0.08) (-1.14) (-1.17) (-0.99) (-0.86) Bp 1.00 1.00 0.98 Bp 0.80 0.81 0.81 (18.21)** (17.01)** (16.73)** (10.44)** (10.53)** (10.45)** Sp 0.03 -0.12 Sp 0.29 0.30 (0.13) (-0.61) (1.38) (1.39) Hp -0.03 -0.10 Hp -0.27 -0.27 (-0.17) (-0.59) (-1.31) (-1.32) Up -0.20 Up -0.05 (-2.30)** (-0.58) Adjusted R2 66.9% 66.5% 67.3% Adjusted R2 44.8% 45.9% 45.6%
Australian Securities Exchange Euronext Paris Factor loading CAPM TFPM FFPM Factor
loading CAPM TFPM FFPM Equally-weighted Equally-weighted αp -0.37% -0.63% -0.73% αp -1.80% -1.51% -1.53% (-0.60) (-0.98) (-1.07) (-3.79)** (-3.15)** (-3.15)** Bp 1.10 1.10 1.10 Bp 0.82 0.82 0.83 (16.50)** (16.36)** (16.29)** (19.26)** (18.82)** (17.87)** Sp -0.04 -0.06 Sp -0.02 -0.06 (-0.53) (-0.67) (-0.11) (-0.32) Hp 0.37 0.08 Hp -0.39 -0.41 (1.36) (-0.53) (-2.75)** (-2.80)** Up 0.37 Up 0.06 (1.36) (0.59) Adjusted R2 62.1% 62.3% 62.1% Adjusted R2 55.8% 57.1% 56.7%
25
5. Determinants of long-term performance
The results on the post-acquisition performance of acquirers in the telecom industry show that the regressions on the time-series data of acquirer excess returns listed on the Australian Securities exchange, London stock exchange and NASDAQ have the highest explanatory power. Additionally, these indexes have the highest sample size in terms of firms included in the calendar-time portfolio approach. Therefore, acquirers on these three indexes are used to test for the determinants of post-acquisition performance. As these indexes are geographically dispersed, sufficient data to draw conclusions about acquisition in the telecom industry throughout the world. A variety of deal-specific and firm-specific variables are known to influence long-term post-acquisition performance. The factors in the three and four capital pricing models control for (1) size, (2) market-to-book ratio and (3) recent acquirer performance. As these models are accepted in the financial economics literature, it can be assumed that these are one of the most influential determinants of long-term performance. Besides these three variables, various papers have shown other variables which affect the post-acquisition performance. Based on this literature, this paper examines the impact of the following variables on post-acquisition performance; (4) method of payment, (5) degree of relatedness, (6) geographical scope of the acquisition, (7) stake acquired and (8) the deal value.
5.1 Results
Acquirers listed on the NASDAQ, Australian Securities Exchange and the London Stock Exchange showed on average to have insignificant monthly abnormal returns of -0.48%, -0,63% and -0,12%
respectively. Tables 3a, 3b and 3c present the results for the determinants of post-acquisition performance of acquirers listed on the various stock exchanges. Conclusions regarding the influence of the variables on the long-term performance of acquirers are drawn by comparing the results of the different indexes.
26 However, acquirers with a low BM ratio on the Australian Securities exchange show significant positive abnormal returns, while acquirers with a high BM ratio show insignificant negative results.
Acquirers in small deals seem to outperform acquirers in large deals, as small acquirers on all three stock indexes have higher average monthly abnormal returns compared to acquirers involved in acquisitions with a high deal value. Acquirers with high deal values on the NASDAQ and Australian Securities exchange show significant negative abnormal returns of -0,71 and -1,81 respectively. Acquirers with a negative prior performance show negative significant abnormal returns for all three stock indexes. Corresponding, acquirers with a high recent performance over the twelve months prior to the acquisition outperform acquirers with a low recent performance, as the abnormal returns are higher for acquirers with a high recent performance. Even though it is argued that firms are often pushed towards M&A to send a positive signal the market, the market generally in turn does not respond positively to an acquisition. The method of payment shows significant results for both the portfolio of acquisition in cash and stock for two out of the three indexes. On the Australian Securities Exchange and the London Stock exchange, acquirers paying in stock significantly outperform acquirers which paid for the acquisition in cash. This is inconsistent with the consensus in the literature that equity acquirers underperform in the post-acquisition period compared to cash acquirers (Loughran and Vijh, 1997; Mitchell and Stafford, 2000). Loughran and Vijh (1997) argue that as managers possess private information that shareholders do not have, acquirers will only pay with stock when it overvalued. A possible explanation to the contradictory results could be the fact that tech-firms are known to keep cash overseas to reduce tax bill. This stuck cash is often used in acquisitions, as if the acquirers pay in cash, they acquire the target at a considerable “discount”. This discount could be argued to seduce firms to engage in acquisitions faster, while not being fully committed or without a clear strategic motive, leading to a lower performance. However, further research is recommended to pin point the cause behind this result.
cross-27 border acquisitions underperform compared to non cross-border, this would underperformance would decrease in time.
Surprisingly, results indicate that acquirers on two indexes perform worse in related deals than acquirers in unrelated deals. On the London Stock Exchange and the NASDAQ, acquirers in related deals show negative abnormal returns, while acquirers in unrelated deals show positive abnormal returns. However, the literature concerning related and unrelated M&A deals does not solely focus on the telecom
industry, which has some unique features. The regulatory changes, technological advances, rapid product and service changes and the fact that the telecom industry is merging with computer and internet industries are argued to affect the performance. This indicates that firms which try to capitalize the technological advances and acquire new capabilities, outperform firms which consolidate or take over rival firms.Consistent across all three indexes, partial acquirers perform much worse than acquirers which fully acquire a target firm. Even though the sample size for partial acquirers is much lower than full acquisitions, the Australian Securities index shows highly significant negative abnormal returns for partial acquirers. These results are consistent with the literature, leading this paper to argue that due to a lower degree of commitment companies which acquire the target firm partially perform worse than companies which fully acquire and thereby commit to the acquisition.
In sum, the results fully correspond with previous literature for three variables, in which case similar results are found for all three stock indexes. These variables are deal value, prior performance and stake. Thus, acquirers in small deals seem to outperform acquirers in large deals, acquirers with a positive prior performance outperform acquirers with negative prior performance and acquirers which acquire a full stake in the target firm outperform partial acquirers. Several variables show mixed results, although these do not show an indication to question the results drawn in prior literature. These
28 Table 3a. Results of the determinants for acquirers listed on the NASDAQ.
Variable Alpha's (over 2 years) t-statistics Adjusted R2 Months
Method of payment (1) Cash -0.45% (-1.04) 0.53 152 (2) Stock -0.67% (-0.89) 0.39 149 Geographical scope (1) Cross-border 1.07% (-1.37) 0.40 160 (2) Non Cross-border -0.91% (-2.58)** 0.59 163 Degree of relatedness (1) Related -1.00% (-3.09)** 0.62 162 (2) Unrelated 0.49% (-0.58) 0.32 163 Stake (1) Partially -0.90% (-0.65) 0.13 52 (2) Fully -0.46% (-1.29) 0.62 163 Size (1) Big -0.38% (-1.00) 0.55 163 (2) Small -0.83% (-1.67)** 0.51 163 Book-to-market (1) High -0.32% (-0.62)** 0.50 162 (2) Low -1.34% (-2.98) 0.49 160 Recent performance (1) Good 0.48% (-0.84) 0.48 159 (2) Bad -1.43% (-2.72)** 0.44 157 Deal Value (1) High -0.71% (-1.79)* 0.56 158 (2) Low -0.59% (-1.00) 0.43 163
Table 3b. Results of the determinants for acquirers listed on the Australian Securities Exchange.
Variable Alpha's (over 2 years) t-statistics Adjusted R2 Months
Method of payment (1) Cash -1.96% (-2.40)** 0.47 163 (2) Stock 4.49% (2.78)** 0.39 119 Geographical scope (1) Cross-border -2.18% (-1.01) 0.47 100 (2) Non Cross-border -0.82% (-1.21) 0.63 151 Degree of relatedness (1) Related -0.47% (-0.49) 0.47 162 (2) Unrelated -1.34% (-1.29) 0.48 129 Stake (1) Partially -3.90% (0.04)** 0.10 99 (2) Fully -0.26% (-0.35) 0.61 163 Size (1) Big -0.35% (-0.55) 0.56 158 (2) Small 0.44% (0.29) 0.37 163 Book-to-market (1) High -1.89% (-2.51) 0.64 102 (2) Low 0.75% (0.65)** 0.44 162 Recent performance (1) Good -0.02% (-0.02) 0.42 125 (2) Bad -1.87% (-1.87)* 0.50 162 Deal Value (1) High -1.81% (-2.52)** 0.48 160 (2) Low 0.45% (0.35) 0.39 163
29 Table 3c. Results of the determinants for acquirers listed on the London Stock Exchange.
Variable Alpha's (over 2 years) t-statistics Adjusted R2 Months
Method of payment (1) Cash -1.84% (-3.58)** 0.47 151 (2) Stock 2.34% (2.26)** 0.49 148 Geographical scope (1) Cross-border -0.72% (-0.80)* 0.38 140 (2) Non Cross-border 0.06% (0.10) 0.54 157 Degree of relatedness (1) Related -0.83% (-1.33) 0.51 148 (2) Unrelated 0.64% (0.74) 0.34 159 Stake (1) Partially -0.39% (-0.94) 0.60 164 (2) Fully 4.34% (1.51) 0.09 59 Size (1) Big 0.06% (0.10) 0.51 152 (2) Small 0.75% (0.88) 0.41 147 Book-to-market (1) High -0.69% (-1.08) 0.44 159 (2) Low 0.13% (0.16) 0.38 138 Recent performance (1) Good 0.35% (0.53) 0.45 148 (2) Bad -0.74% (-1.09)* 0.44 149 Deal Value (1) High -0.70% (-1.34) 0.38 157 (2) Low 0.64% (0.82) 0.24 146
*Significant at the 0.90 level **Significant at the 0.95 level
5.2 The role of capabilities
The final part of the paper explores the role of capabilities in M&A’s. As previously stated, the telecom industry is characterized by trends of political reforms, industrial consolidation, globalization of
30 1,1%
0,7% 0,5%
-0,6% Monthly excess return
Role of Capabilities
Capability enhancement (37) Capability leverage (23)
Geographic Capability deployment (18) Limited Capabilities (64)
the shareholders of the acquiring company. This paper distinguished four broad types of the role of capabilities in acquisitions; (1) capability enhancement, in which the acquirer fills in the gap in its current capabilities by acquiring new ones, (2) capability leverage, when acquirers apply its existing capabilities on newly acquired products or services, (3) geographical capability deployment, when the acquirer employs its existing capability base in a new market and when the acquirer largely ignores capabilities, it is classified as a (4) limited capability deal. Table 4 shows examples of acquisition in the telecom industry with different roles of capabilities.
Similarly to the method used for other determinants of post-acquisition performance, due to sample size and explanatory power, acquirers on the NASDAQ, Australian Securities Exchange and London stock exchange are used to test for the relation between post-acquisition performance and the role of
capabilities in a transaction. Based upon corporate announcements, management opinions and news articles, 144 deals in the telecom industry are classified by their role of capabilities. Seven acquisitions are not classified as the role of capabilities could not be clearly distinguished. The classification of the M&A deals is verified by a third-party to ensure the accuracy and faithfulness. Based on the literature review, the RBV implies that successful acquirers engage in M&A deals that either enhance
capabilities, leverage those, or ideally do both. Deals made with a capabilities perspective are more likely to generate value over time. Acquirers on the NASDAQ are more engaged in capability enhancement and capability leverage deals (56%) than acquirers on the London Stock Exchange (28%) and Australian Securities Exchange (16%). Figure 3 show the monthly average excess returns for the different capability roles for acquirers listed on the three stock exchanges.
31 Table 4. Examples of deals with acquirers listed on the NASDAQ based on the different types capabilities involved. The four main roles of capabilities are capability enhancement deals, capability leverage deals, geographical deployment deals and limited capability deals.
Deals that enhanced capabilities Nuance acquired Spinvox Year: 2009 Deal value: $102.5 million
In 2009, Nuance Communications acquired SpinVox, a leading provider of voice-to-text services to telecommunications companies across five continents. By integrating SpinVox’s carrier services with Nuance’s advanced speech recognition platform, Nuance will further accelerate the growth of its voice-to-text business and scale to meet the needs of a growing, global customer base. “With SpinVox’s robust infrastructure, language support and operational experience, we will broaden the reach and capabilities of our platform.” The acquisition creates a voice-to-text platform – comprising full and partial speech automation, Web services integration and advanced features – that is state-of-the-art today, and offers customers and partners the assurance of technological leadership through its robust product and services roadmap.
Deals that leverage capabilities Yahoo acquired eGroups
Year: 2008 Deal value: $432 million
In 2000, Web giant Yahoo acquired to acquire privately held eGroups, an email list service, for $432 million in stock. Yahoo has continuously acquired companies to expand its range of services, particularly Web 2.0 services. Yahoo applied its capabilities in the innovative internet business to further complement its existing services and enhance it advertisement profits. eGroups sends out two billion e-mails a month. Because ee-mails of Egroups are targeted to groups of people who share a common interest, its service is an attractive medium for advertisers. By applying Yahoo’s network of advertisers and in-house capabilities in the web industry, it will be able to leverage its existing knowledge and capabilities on an incoming service.
Deals that geographically deploy capabilities VimpelCom acquired Wind
Telecom Year: 2011
Deal value: $6.5 billion
The Russian mobile operator VimpelCom acquired WIND Telecom in 2011. The deal covers all of WIND Telecom’s assets, leading VimpelCom to add nine new markets including Pakistan, Bangladesh, Burundi, Italy and Canada to the eleven it already covers. The $6.5 billion deal will double VimpelCom’s mobile customer base to about 173 million, making VimpelCom the world’s fifth largest mobile Telecom operator. “It’s a massive step, propelling the company into such a fast geographic expansion from essentially being a regional player,” said VimpelCom CEO Alexander Izosimov.
Deals with limited capabilities NetSol International Inc. acquired SuperNet AG
Year: 2000 Deal value: $20.5 million
32 Table 5a, 5b and 5c show the results using a calendar-time portfolio approach. Some results are as expected, while others show signs contradictory to prior expectations. Implications regarding the relation between the role of capabilities and post-acquisition can be drawn by comparing the results of acquirers listed on different stock indexes. First, acquirers involved in a “capability enhancement” deals, in which the acquirer fills in the gap in its current capabilities by acquiring new ones, show positive abnormal monthly returns for all three indexes, which are significant for acquirers on the London Stock Exchange. “Competitive capabilities are the set of organizing processes and principles a firm uses to deploy its resources to achieve strategic objectives” (Kogut and Zander, 1992). The capabilities enhanced in M&A deals show to enhance the performance and deployment of the acquirers resources, creating value for investors. Contradictory to prior expectations, acquirers in capability leverage deals do not show positive abnormal returns. Acquirers who leverage their capabilities in the telecom industry on the NASDAQ and London Stock exchange show significant negative abnormal returns, while the Australian Securities Exchange shows insignificant abnormal returns. This could potentially be caused by the difficulty of transferring and integrating specific capabilities to the target firm, and the time that it takes to apply the capabilities on the newly acquired product or services. In capability enhancement deals, the firm specifically requires particular capabilities which the firm needs, while in capability leverage deals it applies its existing capabilities on new products. Thus, the need for capabilities seems lower. Additionally, the telecom industry has been known for its innovative products and services stemming from related industries like the internet industry. These products and services are relatively unknown for even the most experienced personnel active in the telecom industry for decades. Therefore, the applicability of existing knowledge and capabilities on new products and services may be harder to assess and identify from the outside.
33 Moreover, in a technologically advanced industry like the telecom industry, innovation seems to be even more important.
Concluding, acquirers which enhance their capabilities in M&A deals outperform acquirers which do not follow a capability-driven strategy. This is in line with the notion that technological convergence inherently outpaces regulations, leading acquirers which focus on capabilities to be more successful in M&A’s than acquirers which do not, especially in the telecom industry. As the core business in the telecom industry is shrinking and this trend is expected to continue in the long run, capability-driven M&A’s strategies provide a way to succeed in scale when incorporating non-traditional activities in the business model of telecom firms. However, leveraging the existing capabilities on newly acquired products and services shows negative abnormal returns for acquirers. This could be explained by the difficulty of assessing and identifying the applicability of existing knowledge and capabilities on new products and services and the merging of the services and products from industries traditionally different from the telecom industry, such as the internet industry. Results for geographic capability deployment deals, which in essence is not a capability-driven strategy, show no clear direction.
Table 5a. Results for acquirers listed on the NASDAQ. Portfolios are created for the different roles of capabilities, each portfolio containing all firms which are classified to a specific role.
Variable Alpha's (over 2 years) t-statistics Adj. R2 Months (N)
CE 0.341% (0.43) 0.44 164
CL -1.822% (-2.63)** 0.48 126
GCD 1.235% (-1.02) 0.02 94
LC -0.965% (-2.23)** 0.50 159
Table 5b. Time-series regression on the excess returns on acquirers listed on the Australian Securities Exchange. Variable Alpha's (over 2 years) t-statistics Adj. R2 Months (N)
CE 1.911% (1.38) 0.42 86
CL 2.207% (0.86) 0.28 118
GCD -0.307% (-1.06) 0.18 88
LC -2.288% (-2.24)** 0.41 140
Table 5c. Time-series regression on the excess returns on acquirers listed on the London Stock Exchange
Variable Alpha's (over 2 years) t-statistics Adj. R2 Months (N)
CE 1.724% (2.06)** 0.18 74
CL -2.544% (-2.99)* 0.32 129
GCD -0.166% (-0.09) 0.25 81
LC 0.998% (1.17) 0.45 136
34
6. Conclusion
This paper studies the long-run performance of acquirers in the telecom industry. The telecom industry is one of the most active in the global M&A market (Miyake and Sass, 2000), mainly caused by
exogenous shocks, such as political reforms, industrial consolidation, globalization of business and technological innovation. Using a calendar-time portfolio approach controlling for various risk factors, advocated by the most influential financial economic scholars, time-series excess returns are regressed on three prominent asset pricing models, namely the Capital Asset Pricing Model (CAPM), the Fama-French three factor model and Carhart’s (1997) four factor model. On average, acquirers across six stock indexes located in OECD countries show negative monthly average abnormal returns over a 24 month post-acquisition period. Using equally-weighted returns, this paper finds underperformance ranging from -0.14% to -1.53% per month for acquirers. Acquirers on the NYSE and Euronext Paris show significant monthly average abnormal returns, whereas acquirers on the Australian Securities Exchange, NASDAQ and Toronto Stock Exchange and London Stock Exchange show insignificant average monthly abnormal returns. Even though acquirers on indexes perform a bit worse than others, no clear direction can be found in terms of post-acquisition performance between telecom acquirers in different countries or regions. This can be explained by the global and fast changing nature in the telecom industry in which trends are picked up fairly quickly. Thus, this paper finds that on average, M&A’s are value-destroying for acquirers in the telecommunication industry.
As the methods for post-acquisition performance using an event study are mostly examined, several scholars state the need for a better understanding of the determinants of long term post-acquisition performance (Loughran & Vijh, 1997; Zotto & Meier, 2008). Therefore, portfolios are created measuring the influence of several determinants of acquirers post-acquisition performance on the NASDAQ, Australian Securities Exchange and London Stock Exchange. Based on the literature, the following determinants are used to test the impact on post-acquisition performance; (1) size, (2) market-to-book ratio and (3) recent acquirer performance, 4) method of payment, (5) degree of relatedness, (6) geographical scope of the acquisition, (7) form of the acquisition and (8) the deal value. Results clearly show that acquirers in small deals seem to outperform acquirers in large deals, acquirers with a positive prior performance outperform acquirers with negative prior performance and acquirers which acquire a full stake in the target firm outperform partial acquirers. Two variables show to be inconsistent with prior literature, namely the degree of relatedness and the method of payment. Regulatory changes,
35 inconsistent with the consensus in the literature that equity acquirers underperform in the
post-acquisition period compared to cash acquirers. A possible explanation could be the incentive to engage in M&A’s due to cash stored abroad to reduce taxes, which offer firms a “discount” in the acquisition. Size, book-to-market ratio and the geographical scope of the acquisition show mixed results, while showing no indication to question the results drawn in prior literature.
The RBV implies that acquirers which follow capabilities-based strategy outperform firms that do not. Capabilities are defined as “drivers behind the company’s strategy and innovation, by integrating processes, people and technologies to add value to the customer and thereby the shareholders of the acquiring company”. By distinguishing four main roles of capabilities in M&A deals, this paper is able to quantify the resource-based view and empirically test the influence of the type of capabilities on the post-acquisition performance. Based on the trends and strategic M&A motives in the telecom industry, the four mail roles of capabilities in M&A’s are capability enhancement, in which the acquirer fills in the gap in its current capabilities by acquiring new ones, capability leverage, when acquirers apply its existing capabilities on newly acquired products or services, geographical capability deployment, when the acquirer employs its existing capability base in a new market and when the acquirer largely ignores capabilities the acquisition is classified as a limited capability deal. The results show that acquirers which enhance their capabilities in M&A deals outperform acquirers which do not follow a capability-driven strategy. This is in line with the RBV and the notion that technological convergence inherently outpaces regulations, leading acquirers which focus on capabilities to be more successful in M&A’s than acquirers which do not, especially in the telecom industry.
