M&As during a takeover wave: Value creation or destruction
for the shareholders? An event study of UK acquirers
Loïc Marius Le Grand Student number: s1767941 Contact: l.m.le.grand@student.rug.nl
University of Groningen Faculty of Economics and Business MSc International Financial Management
Uppsala University Department of Business Studies
MSc Business and Economics
Supervisor: Dr. R.O.S. (Raymond) Zaal
Abstract: This paper examines the effects of acquisitions on the performance of bidding firms during and prior to the sixth takeover wave (2003-2007). Using UK acquiring firm-level
and country-level data, the performance of the company is examined by studying the abnormal returns surrounding the announcement of the acquisition. Cross-border acquisitions seem to be outperformed by domestic acquisitions, whereas M&As (mergers and acquisitions)
that are announced during an M&A wave perform better than acquisitions announced before an M&A wave. There is no evidence supporting the hypothesis that diversifying M&As
destroy more value than related M&As.
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Table of Contents
1. Introduction ... 3
2 Literature ... 5
2.1 Merger waves ... 5
2.2 Motives for M&As ... 7
2.3 Development of hypotheses ... 8
2.3.1 Merger wave versus non-merger wave performance ... 8
2.3.2 Corporate diversification ... 10
2.3.3 Global diversification / Cross-border acquisitions ... 11
3 Methodology ... 13
3.1 Event studies ... 13
3.2 Data sources and Sample ... 16
3.3 Dependent variable ... Error! Bookmark not defined. 3.4 Independent variables ... Error! Bookmark not defined. 3.5 Control variables ... Error! Bookmark not defined. 4 Results ... 21
5 Conclusion ... 27
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1. Introduction
The topic of mergers and acquisitions (M&As)1 has been extensively researched throughout the past few decades (for an overview, see Jensen and Ruback, 1983; Bruner, 2002; Martynova and Renneboog, 2008). The subject has received so much attention, because it is a vital part of international business (Moeller and Brady, 2014). Deals can be worth several billions of dollars; from 2000 until 2010, M&As at the total cost of $18.72 trillion were completed (McCarthy and Dolfsma, 2013), which underlines the relevance and importance of M&A research. A popular way by which the performance of M&As is measured, is by examining the performance of the firms around the announcement date to study the effect on the wealth of shareholders (for example, see Eckbo and Thorburn, 2000; Goergen and Renneboog, 2004; Conn, Cosh, Guest, and Hughes, 2005). The results demonstrate that the shareholders of the target firm benefit from the increase in share prices surrounding the announcement of a bid for the firm (Eckbo, 1983; Servaes, 1991; Martynova and Renneboog, 2011). Different studies look at the results for acquiring firm’s shareholders, and these findings are more ambiguous. Several scholars find negative abnormal returns for shareholders of bidding firms (Servaes, 1991; Kaplan and Weisbach, 1992; Sudarsanam and Mahate, 2003), while others provide evidence of positive abnormal returns accruing to the shareholders of the bidding firms (Chatterjee, 1992; Kang, Shivdasani, and Yamada, 2000; Martynova and Renneboog, 2011). This indicates a lack of consensus on this topic.
One of the characteristics regarding M&A activity is that they appear to occur in waves (Martynova and Renneboog, 2008). Bhagat, Dong, Hirshleifer, and Noah (2005) and Harford (2003) find that total announcement wealth effects of M&As from periods outside the takeover waves are significantly lower than gains earned during waves. This explains why researchers have looked into the issue of firm performance during a takeover wave. A so-called takeover wave is a reflection of a wave-like pattern in M&A activity. M&As have a tendency to cluster over time, as has been examined by academic literature (Andrade, Mitchell, and Stafford, 2001; Harford, 2005). This leads to high levels of M&As in one time period, and lower levels in another. Duchin and Schmidt (2013) classify “periods of high merger activity” as merger waves, and this is definition we will maintain. Five waves have been identified so far, with the fifth wave occurring from the mid-1990s until 2000 (Moeller, Schlingemann, and Stulz, 2005; Martynova and Renneboog, 2008; 2011. Moeller et al. (2005)
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find that the returns to acquiring shareholders during the fifth takeover wave are positive when measured as the returns around the announcement date. Martynova and Renneboog (2011) also document significant positive returns surrounding the announcement date which accrue to the shareholders of the acquiring firm. A sixth wave has been identified more recently, which occurred between 2003 and the end of 2007 (Alexandridis, Mavrovitis, and Travlos, 2012; McCarthy and Dolfsma, 2013; Ahern and Harford, 2014). Where the fifth wave was noticeable for its size and geographical distribution (Martynova and Renneboog, 2011), the sixth wave is characterized by an abundance of available liquidity, more payments in cash and not as much overvaluation of acquirers compared with target firms (Alexandridis, et al., 2012). The sixth merger wave has not yet been thoroughly researched, and the aim of this paper is to address this gap in the existing M&A literature. It may be useful to research M&A activity in waves, learn from the findings, and use this knowledge when considering an M&A in the future.
The majority of the research in M&A literature has been focused on US domestic deals (see Martynova and Renneboog, 2008), although there is a growing body of literature dedicated to other areas, such as Europe (Aw and Chatterjee, 2004; Danbolt and Maciver, 2012; Gregory and O’Donohoe, 2014) and Japan (Higgins and Beckman, 2006; Higgins, 2013) . The UK is an interesting country to examine because of the large impact that the country has in terms of value and amount of deals (Gregoriou and Renneboog, 2007; World Investment Report, 2014), where it is usually ranked second behind the United States. Another example of its large influence is that it topped the rankings in terms of target region for cross-border M&As in 2006, edging the US (Saigol, L. and Politi, J., 2006).
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2 Literature
As mentioned in the introduction, the topic of mergers and acquisitions has been given a substantial amount of attention in the academic literature. One of the reasons why this happens is the potential influence that deals can have on the economy. Values of the largest deals usually are in the billions of pounds. Table A1 in Appendix A represents the 20 largest acquisitions made by a UK acquirer to illustrate the magnitude of acquisitions. The impact that M&As have on both of the participating firms and the countries that are involved, explains the importance and relevance of the studies that are being done on this subject. This section gives a historical overview of the previous five waves, and examines the sixth wave. Subsequently, motives for engaging in an M&A are discussed, followed by the development of the hypotheses.
2.1 Merger waves
As can be seen from Figure 1, M&A activity seem to materialize in waves. The five waves that occurred prior to the sixth will now be summarized, as well as the sixth wave itself. The summary that is presented below is based on information from prior research (Martynova and Renneboog, 2008; McCarthy and Dolfsma, 2013; Moeller and Brady, 2014).
Figure 1. US Merger wave since 1897 (total number of deals). Taken from: Martynova and Renneboog, 2008.
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which was dominated by horizontal mergers(mergers among competitors / firms that operate in the same industry), came to an end when the equity market crashed in 1903, after cheap credit had been used too extensively.
The second wave (circa 1918-1929) started after the First World War. During this period of war, M&A activity was normal. The recovery of economies and spare capacity that was left from the production for the war drove this wave. The second wave was characterized by mergers with the purpose of forming oligopolies. Numerous smaller firms merged in order to achieve economies of scale and be able to withstand the top firms in its respective industries, which had created a monopoly position during the first wave. Black Thursday (24 October 1929) and the concurrent crash in the stock market ended the second wave. Another notable aspect of the first two waves is that they were mainly confined to the United States.
The third wave (circa 1950-1973) did not start until after the Second World War. Academics agree that the beginning of this wave was around the same time that antitrust regulations in the US were made more strict. This wave is characterized by a large amount of diversifying M&As (M&As between firms active in different businesses), and is also known as the conglomerate wave. Companies tried to profit from possibilities in markets where they were not yet active in. Increases in value, and the opportunity to surmount inefficiencies in capital markets were some of the potential benefits. The oil crisis in 1973 caused a global recession, ending the third wave. The wave occurred in the US, UK and Europe.
The fourth wave (1981-1989) started after the recovery of the second oil crisis in 1979, and was driven by deregulation in the financial systems, relaxing of antitrust laws in the US and UK and technological innovation. Leveraged buyouts (LBOs), a specific technique where a public company with a significant amount of debt was acquired, developed and finally taken public again, was a popular means of acquiring. The number of hostile takeovers (takeovers where the bids were not recommended by the target firm’s management, and advises its shareholders to reject the offer) increased spectacularly during this wave. The motive for the M&As during this wave comes from the desire to eliminate the conglomerates’ inefficiencies, and restructure the business. The stock market crash on Black Monday in 1987 (19 October 1987) is often seen as the beginning of the end for the wave. The wave transpired in the US, UK, Europe and Asia.
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wave. Europe and the US were almost at the same level in terms of M&A deals, and there was a significant amount of activity in Asia as well. Additionally, this wave is characterized by a large percentage increase in cross-border M&As, which was proof of the ongoing globalization (Gregoriou & Renneboog, 2007). The wave ended abruptly because of corporate scandals and a collapse of the stock markets in 2000.
Since mid-2003, M&A activity has been increasing. The sixth wave began with the recovery of global markets after the bubble of the fifth wave burst. M&A volume experienced an increase of 71% in 2004 compared with 2002, growing from $500 billion to $1 trillion. In Europe, comparable developments were visible. In order to counter the looming recession that the US faced after the collapse of the global stock markets, the Federal Reserve System lowered the interest rates. This gave way to a new bubble in the housing market, which spread across the globe as investors from around the world were interested in buying securities backed by mortgages, and other types of debt obligations. As credit was now inexpensive, interest rates were low, firms became interested in M&As once again. An lavishness of liquidity seems to be the main rationale for this wave (Harford, 2005). The sixth wave differs from the preceding waves in terms of speculations, which was one of its main drivers. Companies were bought with the prospect of selling them off after markets had increased the value of the M&As. According to Moeller and Brady (2014), the performance of deals outperformed the market during this wave, which differentiates it from the previous waves. Another stock market crash terminated the sixth wave, the sub-prime crisis, which begun in August 2007.
2.2 Motives for M&As
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shareholders of the acquiring firm. It posits that management only makes an acquisition when they can extract value, which is at the expense of acquirer shareholders (Berkovitch and Narayanan, 1993).
2.3 Development of hypotheses
2.3.1 Merger wave versus non-merger wave performance
The timing of a merger is one of the factors which can affect the performance of the merger and the extent to which it creates or destroys wealth for the shareholders in terms of stock returns (Harford, 2003). The performance of a merger can be measured in multiple ways. One of the most popular ways to examine performance is by studying the returns surrounding the announcement date of the M&A (Martynova and Renneboog, 2008). The manner in which these returns are calculated will be elaborated upon in the methodology section. There are various theories which have been put forth regarding merger waves, which will be summarized here.
The economic disturbance theory was developed by Gort (1969). He suggests that when an economic disturbance or shock has taken place, for example when capital markets are unstable, the difference in values for different types of companies increases and can lead to M&As. The disturbance theory is supported by Lambrecht (2004), who finds a positive relation between mergers in waves and value for the firm. Another possible explanation for merger waves is that managers of a firm undertake actions which are in the best interest of the shareholder, known as the efficiency motive (Coase,1937). The efficiency motive states that after a shock occurs (either an economic, a regulatory, or a technological shock), the firms react jointly by reallocating assets. The crowding of the mergers is a result of the managers who are contending for the optimal division of assets, who all respond at the same time (Harford, 2003). Moreover, Harford (2003) compares mergers within waves and outside of waves, and his results validate the findings that mergers in a wave result in higher returns than off-wave mergers. This leads to the following hypothesis:
H1a: Mergers by UK firms during the sixth merger wave will perform better than UK firm mergers that occurred prior to the merger wave.
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years 2001-2002) versus conditions of intense M&A activity (2003-2007), rather than two unstable scenarios (the M&A wave and the financial crisis).
There are three major theories suggesting that M&As are the result of distortions rather than the search for efficiency. The first is a theory that notes that managers want their firm to remain independent rather than being acquired, and therefore engage in acquisitions themselves, as a defence mechanism (Gorton, Kahl, and Rosen, 2009). This builds on the agency cost theory as presented by Jensen (1986), who posits that managers who have excess cash flows at their disposal will engage in M&As that are in their own best interest, as opposed to the interest of the shareholder. The empirical literature finds that this results in value destruction for the shareholders in the form of negative acquirer returns (Lang, Stulz and Walkling, 1991; Schlingemann, 2004).
The second theory is known as the hubris theory. Introduced by Roll (1986), the theory focuses on the overconfidence, or hubris, of the manager. According to the idea of hubris, there are no gains from an M&A, and they only occur because the manager of the acquiring firm makes a positive valuation error. The managers of a firm may be erroneously confident with regard to the extent to which synergies can be created with the acquisition, resulting in poor performance. Berkovitch and Narayanan (1993) and Goergen and Renneboog (2004) find evidence for this theory. The theory can be applied to merger waves as well. The variability of estimates regarding target firm values increases during waves, resulting in bids from managers who are falsely assuming that these M&As will create value. Duchin and Schmidt (2013) find proof for this, noting that during merger waves the quality of forecasts and analyses are of inferior quality compared with periods outside merger waves. This is a result, they say, from an increase in demand for firm analyses, and a limited supply of analysts in the short-run. Hence, the analysts have to perform more analyses in the same amount of time, which leads to a degradation in quality of the forecasts and analyses.
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by this phenomenon (Milbourn, Boot, and Thakor, 1999). The theories and findings lead to the following hypothesis:
H1b: Mergers by UK firms during the sixth merger wave will perform worse than UK firm mergers that occurred prior to the merger wave.
2.3.2 Corporate diversification
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H2a: UK acquirers engaging in diversifying mergers during the sixth merger wave perform better than UK acquirers engaging in related mergers.
However, another strand of literature notes that the diversification of companies is caused by difficulties between shareholders and the managers of the firm, also known as agency problems. There are various motives for managers to diversify. The first is that it is either expected to increase their reputation, power, and compensation (Jensen, 1986). The notion of reallocation of capital can be a potential source of losses for a diversified firm vice versa a focused / related firm, if this reorganization is not efficient (Martin and Sayrak, 2003). Secondly, Amihud and Lev (1981) suggest that managers engage in diversifying M&As in order to lower the earnings risk, thereby ensuring the survival of the firm and their own position. Managerial entrenchment, whereby managers ensure themselves, is a theory based on the idea of Amihud and Lev (1981). This theory, introduced by Shleifer and Vishny (1989), notes that the failure of M&As is the result of managers who pursue M&As to make sure that they are not replaced, rather than creating wealth of the shareholders. Investments that increase the costs of replacement are a consequence of this notion. Recent empirical literature has found support for this strand of literature (Morck, Shleifer, and Vishny, 1990; Matsusaka, 1993; Andrade et al., 2001). The resulting hypothesis is therefore:
H2b: UK acquirers engaging in diversifying mergers during the sixth merger wave perform worse than UK acquirers engaging in related mergers.
2.3.3 Global diversification / Cross-border acquisitions
The percentage of acquisitions which involves two firms from different countries, also known as cross-border acquisitions, is increasing. Where less than a quarter of the mergers in 1998 was cross-border (measured in terms of volume), it account for almost half of total merger volume in 2007 (Erel, Liao, and Weisbach, 2012). During the fifth M&A wave, the M&As were mostly across national boundaries (Shimizu, 2004). These are a few examples that illustrate the growing importance of cross-borders M&As.
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in cross-border M&As with the intention of diversification of the portfolio of their shareholders, or benefitting from mispriced production factors in other countries. Another theory that emphasizes the potential benefits of cross-border M&As is the internalization theory. Caves (1971) was one of the first to discuss this. The internalization theory notes that firms with intangible assets (e.g. goodwill, patents) engage in FDI through an M&A to increase the scale in which they exploit these assets. To avoid that the intangible assets of the firm are imitated or misused, the firm can perform a cross-border M&A. In this way, the firm can maintain full control of its assets. These theories propose that cross-border acquisitions can lead to a gain for the acquiring firm. There is a growing body of literature that reports cross-border M&As outperforming domestic M&As. Goergen and Renneboog (2004) find superior returns to bidding firms’ shareholders in a cross-border M&A compared with a domestic one; Danbolt and Maciver (2012) look at cross-border M&As out of and into the UK, and report that bidding firms experience greater wealth increases when engaging in a cross-border takeover than in domestic acquisitions. Gregory and O’Donohoe (2014) report losses for acquirers of UK targets, where domestic acquirers are found to perform worse than cross-border bidders. Based on the prior evidence, we formulate the subsequent hypothesis:
H3a: Cross-border M&As during the sixth merger wave with a UK acquirer will perform better than UK domestic M&As.
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prospective gains of the acquisition in the form of lower share prices. Conn et al. (2005) and Moeller and Schlingemann (2005) find evidence for this hypothesis.
There are several authors that find evidence that suggests that the managerial motives are a driver for cross-border acquisitions rather than synergistic motives. Eckbo and Thorburn (2000) find lower returns for US bidders compared with Canadian bidders in acquiring Canadian firms; Moeller and Schlingemann (2005) report lower returns for US firms engaging in cross-border M&As than for domestic takeovers. Aw and Chatterjee (2004) also report negative returns and inferior performance of cross-border acquisitions from UK bidders next to domestic M&As, while Conn et al. (2005) finds similar results for the UK firms. Martynova and Renneboog (2008) also finds worse returns from acquisitions resulting in geographical diversification in contrast with domestic acquisitions. These findings lead to the following hypothesis:
H3b: Cross-border M&As during the sixth merger wave with a UK acquirer will perform worse than UK domestic M&As.
3 Methodology
This section describes the event study methodology, followed by the sample description and data sources used for the study. Consequently, the variables will be described, how they are collected and defined.
3.1 Event studies
In this study I adopt a shareholders’ approach when examining the effects that an acquisition have on the wealth of the stakeholders. Since shareholders are the residual claimants of the firm, this approach is sensible. The most common method used to measure the effects of takeovers on shareholder wealth in the short run is the event study. This method assumes that there is an efficient market.
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that may be important. The semi-strong form is assumed to hold for event studies (Martynova and Renneboog, 2008). The effect of an unexpected announcement of an M&A is presumably measurable by examining the differences between the returns that occur around an event (the announcement of the M&A), and the return that was expected in case this announcement had not occurred. This difference is also known as the abnormal return.
Various models can be used to measure the abnormal return. The model that will be used here is the market model. There are other models that can be used, such as the mean adjusted return model, or a multi-factor model; yet, this study will use the market model as this has been the dominant model in empirical literature examining short-run M&A announcement effects (for an overview, see Martynova and Renneboog, 2008). Fama, Fisher, Jensen and Roll (1969) were the first to use event study methodology; they state that for a stock i an Ordinary Least Squares (OLS) regression can be executed:
R𝑖,𝑡 = α𝑖 + β𝑖R𝑚,𝑡+ ε𝑖,𝑡, (1)
where R𝑖,𝑡 represents the return for stock i at time t, R𝑚,𝑡 is the return of the benchmark, the return of the local market index at time t. Since we look at UK acquirers, this market index is the UK market index, retrieved from Thomson Reuters DataStream. α𝑖 and β𝑖
are parameters that differ per firm, and are estimated via an OLS regression using information from the estimation window. The estimation window spans the 200 days before the event window [201,-2] and is used to estimate the parameters α𝑖 and β𝑖. α𝑖 represents the risk free rate, β𝑖 is the sensitivity of firm i to the market portfolio. The event window is the time window in which the abnormal returns will be examined [-5,+5], where t = 0 is the announcement date of the acquisition. The selection of the estimation window and event window is in line with previous literature (Moeller and Schlingemann, 2005; Humphery-Jenner and Powell, 2014), and ensures that the two windows do not conflict to avoid the two windows from influencing each other (MacKinlay, 1997). ε𝑖,𝑡 is the random error with a mean of zero. The abnormal return AR𝑖,𝑡for stock i at time t is then calculated as follows:
AR𝑖,𝑡 = ε𝑖,𝑡 = R𝑖,𝑡 – (α̂𝑖 + β̂𝑖R𝑚,𝑡) (2)
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the estimated parameters for firm i as estimated by OLS. In order to assess the effect of stock movements, the abnormal returns surrounding the event (the announcement of the M&A) are examined. Adding the abnormal returns for a period surrounding the event window, creates the cumulative abnormal returns (CAR), which can be calculated by the following equation:
CAR𝑖 = ∑𝑡=1𝑡=−1AR𝑖,𝑡 (3)
As mentioned above, an estimation window, event window and event date have to be formed in order to compute the abnormal returns. The event date is the date at which the M&A is announced, according to Zephyr. When there is no trading on the day of the announcement, the first day of trading after the announcement is selected as the day of the announcement. The event date is defined as t = 0 for firm i, which is the announcement date. The event window is a time frame which surrounds the event date. For this window, the CARs will be tested. If a perfect efficient market exists, the day of the announcement would be sufficient as event window. However, it is common in empirical literature that the event window is larger than merely the event date. There are multiple motives for this; firstly, information can be leaked prior to the announcement date (Danbolt and Maciver, 2012). Additionally, even though the day of the announcement is defined, it is not clear whether or not this announcement is made when the market is open or after closing (Masulis, 1980). Based on these two assumptions, the strong form of the efficient market hypothesis is unlikely, and therefore an event window surrounding the announcement is opted for. The three days surrounding the event date [-1, +1] is selected as event window, which is frequently opted for in literature (Moeller and Schlingemann, 2005; Danbolt and Maciver, 2012; Humphery-Jenner and Powell, 2014). Lastly, the estimation window runs 200 days prior to the event window. This is in line with previous literature (Moeller and Schlingemann, 2005; Humphery-Jenner and Powell, 2014), and ensures that it does not conflict with the event window in order to avoid that the two windows affect each other (MacKinlay, 1997). While the traditional market model has been popular over the past decades, there is another model, the market adjusted abnormal returns model (Brown and Warner, 1985), which can be viewed as a modified version of the market model (MacKinlay, 1997). Several other studies have found the market adjusted returns model to work well (Fuller, Netter, and Stegemoller, 2002; Conn et al. 2005) .
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AR𝑖,𝑡 = R𝑖,𝑡− R𝑚,𝑡 (4)
in which AR𝑖,𝑡 represents the market adjusted abnormal return of stock i on day t, R𝑖,𝑡 is the log return of the stock i at day t, and R𝑚,𝑡 is the return of the local market on day t, for which all the data is retrieved from Thomson Datastream. The market model has difficulties with potential endogeneity, which the market adjusted returns model avoids. In order to increase the power of the tests, the market model will be tested, after which the market adjusted model will be used to check the results.
Non-normality has been checked for and found (see Appendix Table A2); therefore, a nonparametric test is used, the Wilcoxon signed rank test. According to Brooks (2013) this is one of the two ways to deal with non-normality in an event study.
3.2 Data sources and Sample
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3.3 Variables
The cumulative abnormal returns (CAR) are calculated using the market model and used as dependent variable. Eq. (3) shows how the CAR is calculated.
The variables of interest are generated dummy variables. The first independent variable is a time dummy, which splits the sample into two time periods, the period which precedes the sixth takeover wave (2001-2002) and the sixth wave itself (2003-2007). Furthermore, a dummy variable will be created to identify whether an M&A is diversifying or related, based on the SIC code. Akbulut and Matsusaka (2010), who give an extensive overview of the corporate diversification literature of the past 50 years, note that the most commonly used measure for diversification is the SIC code. By examining the SIC codes of the acquirer and the target, the assessment can be made of whether the two firms are active in the same
industry, which would be a related merger, or not, which would be a diversifying merger. The third dummy will divide the sample into a sub-sample with cross-border acquisitions and a sub-sample with domestic acquisitions by UK firms. This is defined as when the acquirer and target country code are the same in Zephyr, or when the two codes differ.
Several control variables are included that have been standard in literature concerning short-term wealth effects resulting from M&A announcements (see e.g. Moeller et al., 2004; Humphery-Jenner and Powell, 2014). These control variables are included to ensure that the results of the independent variables introduced in the previous section are not influenced by external factors.
Run-up: The pre-announcement run-up of share price may influence the market reaction following the announcement of the M&A (Martynova and Renneboog, 2011). The run-up is measured as the abnormal returns calculated by the market model in the two months before the event window (the window consists of the 40 trading days prior to the start of the event window; [-41,-2].
Relative size: To account for the size of the transaction compared with the acquiring firm, this measure is included. This is measured as the transaction value scaled by the market value of assets year prior to the announcement (Moeller and Schlingemann, 2005; Humphery-Jenner and Powell, 2014).
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Tobin’s Q: The Tobin’s Q functions as a proxy for the quality of the corporate
governance within the bidding firm as well as the potential that the acquiring firm has to grow (Martynova and Renneboog, 2011). This is calculated by the following equation: (market value of equity + book value of debt) / (book value of assets); all values are measured the year before the announcement of the M&A, data collected from DataStream.
Leverage: This variable measures the effect that higher levels of debt have on the
abnormal returns of an acquiring firm. It is measured as the book value of debt scaled by total assets, both values taken year prior to M&A announcement, data collected from DataStream.
Firm size: The size of the firm can also influence the returns of a firm, since larger firms have a tendency to overpay for their targets (Moeller, Schlingemann, and Stulz, 2004). In order to control for this, we include the variable firm size, defined as the natural logarithm of the book value of assets the year prior to the acquisition announcement. Data is collected from DataStream.
Intangible Assets: The assets of a firm that are intangible (e.g. trademarks, patents, copyrights) can also have an impact on the abnormal returns of the firm. This variable controls for its effect. It is defined as the ratio of the acquirer’s intangible assets divided by the book value of assets of the firm, both taken the year before the announcement, and collected from DataStream..
Target Size: As the size of the target increases, it can become more difficult for an acquiring firm to merge its operations with those of the target firm. Therefore we control for the target size by way of the natural log of the transaction price, which is retrieved from Zephyr.
World Bank Governance Index: The World Bank Governance index is based the equally-weighted score of six governance variables. These variables are percentile ranks for
corruption, the rule of law, accountability, government effectiveness, regulation and political stability. The reason for using this variable is that target firms originating from countries with lower scores on these governance variables can benefit from the higher quality of governance in the UK. In case the level of governance is higher in the target country, to opposite
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GDP Growth: In order to account for the differences in country-level growth this measure is included. It is retrieved from the World Bank database, and expresses the annual percentage growth rate in GDP at market prices based on a constant local currency.
Antidirector Rights: This is a variable which is used to proxy for the quality of corporate governance in the country of the target firm. The values that are assigned to each country range from 0 to 6 (La Porta et al., 1998). These values also serve as a proxy for shareholder rights (Moeller and Schlingemann, 2005), where a higher score implies a better protection for shareholders.
In accordance with prior literature (Danbolt and Maciver, 2012; Humphery-Jenner and Powell, 2014), M&As which are financed by cash only perform better than other forms of payment. We control for the method of payment by creating two dummy variables, which are the cash dummy, which is a dummy that takes value 1 if the deal was financed with cash only, and the shares dummy; a dummy that takes value 1 if the deal was financed with stock only.
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Descriptive statistics
The sample contains all completed domestic and cross-border M&As of UK listed firms between 2003 and 2007 with a transaction value of at least £1 million as listed on the Zephyr database by Bureau van Dijk, where control of the target shifts to the listed acquirer and values for all variables are available on either Zephyr or DataStream. Dependent variable CAR represents the (-1,+1) abnormal returns around the announcement period. Pre-wave is a dummy variable that takes value 1 if the M&A was announced before the sixth takeover wave (2001-2002), and 0 if it was announced during the takeover wave (2003-2007). Firm size is defined as the natural logarithm of the book value of assets the year before the announcement. Leverage denotes the book value of debt scaled by book value of assets, both values measured the year prior to the
announcement; data collected from DataStream. Tobin’s Q represents the market value of equity plus book value of debt over the book value of assets; all values measured the year before the announcement, data collected from DataStream. ROA denotes the return on assets, which is the net income scaled by book value of assets; values measured the year before the announcement; data collected from DataStream. Intangible Assets is the ratio of intangible assets and book value of assets; values measured the year before the announcement; data collected from DataStream. Run-up is the CAR of the 40 days before the start of the event window (-41,-2). Cash and Shares are dummy variables with information derived from Zephyr. Cash is defined as a deal which is financed by cash, according to Zephyr. Shares is defined as a deal which is financed by shares, according to Zephyr. Relative Size represents the ratio of the transaction value and the market value of assets; transaction price is retrieved from Zephyr, market value of assets from DataStream. Target Size is defined as the natural logarithm of the transaction price, retrieved from Zephyr. Antidirector Rights is a value ranging from 0 to 6, defined by (La Porta et al., 1998). World Bank Gov. Index denotes the equally-weighted average percentile score of six country governance variables as specified by the World Bank. GDP Growth represents the percentage of growth in GDP at market prices based on 2005 US$. International Experience is defined as the ratio of foreign sales and total sales.
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4 Results
The cumulative abnormal returns for 757 M&A announcements are examined in this sample. The values of the CAR for event window [-1,+1] are presented; however, 3
alternative event windows are used as a robustness check to see whether the outcome of the values for CAR [-1,+1] are correct. In addition, CAR calculated with the market-adjusted model are used as a second robustness check. The results are robust for these checks. Table 2
Wilcoxon Signed Rank Test Results
Entire sample contains all completed domestic and cross-border M&As of UK listed firms between 2001 and 2007 with a transaction value of at least £1 million as listed on the Zephyr database by Bureau van Dijk, where control of the target shifts to the listed acquirer and values for all variables are available on either Zephyr or DataStream. CARt>0 is the % of values of
the CAR during the [-1,+1] event window that was larger than 0. Pre-Wave includes all M&As that are announced between 2001 and 2002, Wave is defined as all M&As during the sixth takeover wave between 2003 and 2007. Wave-Dom. And Wave-CB are the domestic and cross-border M&As announced during the wave. Wave-Div and Wave-Rel are M&As announced during the wave that are either defined as diversifying (acquirer and target firm have different 2-digit SIC code) or related (acquirer and target firm have similar 2-digit SIC code). *,** and *** denote statistical significance at 10%, 5% and 1%. % CARt>0 P-value n Entire Sample 59.1 0.000*** 757 Pre-Wave 52.7 0.381 207 Wave 61.5 0.000*** 550 Wave-Dom 64.0 0.000*** 381 Wave-Cross 55.6 0.052* 169 Wave-Div 58.4 0.000*** 262 Wave-Rel 63.2 0.000*** 288
The outcomes for the Wilcoxon signed rank test are presented in Table 2. For the entire sample consisting of UK acquiring firm M&As from 2001 until 2007, nearly 60% of the observations report a positive CAR during the event window [-1,+1]. Comparing the M&A announcements before and during the wave, the results for the CAR during the wave are more positive than those for the pre-wave. In addition, the pre-wave median does not differ
statistically from 0, whereas the difference for M&As announced during the wave is
22
The first hypothesis that this study examines look at is the hypothesis that cumulative returns that accrue to shareholders of acquiring firms during a takeover wave differ from returns that are reported before a takeover wave. We have opted for a gradual implementation of the variables, starting with the time dummy which separates the M&As that were
announced before the sixth takeover wave from those that were announced during the wave. When examining column [1] in table 3, the dummy implies that there seems to be a significant negative relation between the differentiation of pre-wave M&As and the
cumulative abnormal returns. The pre-wave acquisitions seem to perform worse than the wave acquisitions. This is statistically significant at a 10% level, and confirms the validity of
hypothesis H1a.When examining this after adding the bidder characteristics in column [2], the deal characteristics in column [3], the sign of the wave dummy does not change, although the results of column [2] and [4] are not significant. These findings seem to lend support to the idea that M&As that are announced during an M&A wave report higher CARs than M&As that occur before a wave.
Furthermore, several variables display notable signs. Firm size has a negative relation with the cumulative abnormal returns, which is significant throughout the regressions. A larger acquiring firm is more likely to overpay for its targets, as was stated in the section describing the control variables, and the regression seems to support this notion. Target size has a positive and significant influence on the returns of the acquiring firm. This is surprising, because prior literature suggests that there is a negative relation between the size of the target and the returns of the acquiring firm. The country-level controls all have insignificant
23 Table 3
Cross-sectional regression analysis of cumulative abnormal returns (CAR)
The sample contains all completed domestic and cross-border M&As of UK listed firms between 2001 and 2007 with a transaction value of at least £1 million as listed on the Zephyr database by Bureau van Dijk, where control of the target shifts to the listed acquirer and values for all variables are available on either Zephyr or DataStream.
Dependent variable CAR represents the (-1,+1) abnormal returns around the announcement period. Pre-wave is a dummy variable that takes value 1 if the M&A was announced before the sixth takeover wave (2001-2002), and 0 if it was announced during the takeover wave (2003-2007). Firm size is defined as the natural logarithm of the book value of assets the year before the announcement. Leverage denotes the book value of debt scaled by book value of assets, both values measured the year prior to the announcement; data collected from DataStream. Tobin’s Q represents the market value of equity plus book value of debt over the book value of assets; all values measured the year before the announcement, data collected from DataStream. ROA denotes the return on assets, which is the net income scaled by book value of assets; values measured the year before the announcement; data collected from DataStream. Intangible Assets is the ratio of intangible assets and book value of assets; values measured the year before the announcement; data collected from DataStream. Run-up is the CAR of the 40 days before the start of the event window (-41,-2). Cash and Shares are dummy variables with information derived from Zephyr. Cash is defined as a deal which is financed by cash, according to Zephyr. Shares is defined as a deal which is financed by shares, according to Zephyr. Relative Size represents the ratio of the transaction value and the market value of assets; transaction price is retrieved from Zephyr, market value of assets from DataStream. Target Size is defined as the natural logarithm of the transaction price, retrieved from Zephyr. *, ** and *** denote a 10%, 5% and 1% significance level.
Dependent variable 3-day CAR
[1] [2] [3] Constant 0.013 0.072 0.079 (0.003) (0.019) (0.022) Pre-Wave -0.002* -0.005* -0.006* -(0.006) (0.006) (0.006) Firm Size -0.004*** -0.007*** (0.001) (0.002) Leverage 0.033** 0.034** (0.015) (0.015) Tobin's Q -0.001 -0.001 (0.002) (0.002) ROA -0.039*** -0.041*** (0.014) (0.014) Intangible Assets -0.039 -0.035 (0.012) (0.012) Run-up -0.070*** (0.023) Cash -0.007 (0.008) Shares -0.024 (0.011) Relative Size -0.006* (0.004) Target Size 0.003* (0.002) Observations 757 757 757 Adjusted R² 0.004 0.016 0.033
The second hypothesis that the study examines is whether diversifying M&As report significantly higher / lower returns when compared with related M&As. The results of the regression are reported in table 4. Again, I apply a similar approach as during the first hypothesis. A univariate regression of the diversification dummy is reported in column [1], and variables are gradually added to the regression. The diversification dummy is
24 Table 4
Cross sectional regression of related and diversifying M&As of UK listed firms during the sixth takeover wave The sample contains all completed domestic and cross-border M&As of UK listed firms between 2003 and 2007 with a transaction value of at least £1 million as listed on the Zephyr database by Bureau van Dijk, where control of the target shifts to the listed acquirer and values for all variables are available on either Zephyr or DataStream. Dependent variable CAR represents the (-1,+1) abnormal returns around the announcement period. Pre-wave is a dummy variable that takes value 1 if the M&A was announced before the sixth takeover wave (2001-2002), and 0 if it was announced during the takeover wave (2003-2007). Firm size is defined as the natural logarithm of the book value of assets the year before the announcement. Leverage denotes the book value of debt scaled by book value of assets, both values measured the year prior to the
25
Table 5 presents the regression performed on the cross-border dummy and consequent variables. Column [1] is only a regression with the dependent variable, the cumulative
abnormal return, and the cross-border dummy. This suggests that the CARs for cross-border acquisitions are lower than domestic acquisitions, a finding which is significant at a 5% level. In column [2], the bidder firm characteristics are added to the regression, and the cross-border effect remains negative; however, it becomes insignificant, so no conclusions can be drawn with regard to this variable. Tobin’s Q has a significantly negative effect on CARs, implying that if the prospect growth rate of the firm is higher, this has a negative influence on the abnormal returns which accrue to the shareholders of the acquiring firm. The run-up has a positive and significant effect on the CAR, which is in line with prior literature, suggesting that the cumulative abnormal returns are highly affected by the performance of the share prior to the announcement. Firm size, leverage, ROA and Intangible Assets all have insignificant coefficients, so no inferences can be made based with regard to these variables.
Examining column [3], the cross-border dummy is significant at a 10% level, and the sign is similar to that which we found in column [1]. These findings confirm Hypothesis 3b, that cross-border acquisitions done by UK firms perform worse than domestic UK
acquisitions during the sixth takeover wave. All variables from column [2] have the same sign and remain either significant or insignificant, except for firm size, which is now highly
26 Table 4
Cross sectional regression of cross-border and domestic M&As of UK listed firms during the sixth takeover wave
The sample contains all completed domestic and cross-border M&As of UK listed firms between 2003 and 2007 with a transaction value of at least £1 million as listed on the Zephyr database by Bureau van Dijk, where control of the target shifts to the listed acquirer and values for all variables are available on either Zephyr or DataStream. Dependent variable CAR represents the (-1,+1) abnormal returns around the announcement period. Pre-wave is a dummy variable that takes value 1 if the M&A was announced before the sixth takeover wave (2001-2002), and 0 if it was announced during the takeover wave (2003-2007). Firm size is defined as the natural logarithm of the book value of assets the year before the announcement. Leverage denotes the book value of debt scaled by book value of assets, both values measured the year prior to the announcement; data collected from DataStream. Tobin’s Q represents the market value of equity plus book value of debt over the book value of assets; all values measured the year before the announcement, data collected from DataStream. ROA denotes the return on assets, which is the net income scaled by book value of assets; values measured the year before the announcement; data collected from DataStream. Intangible Assets is the ratio of intangible assets and book value of assets; values measured the year before the announcement; data collected from DataStream. Run-up is the CAR of the 40 days before the start of the event window (-41,-2). Cash and Shares are dummy variables with information derived from Zephyr. Cash is defined as a deal which is financed by cash, according to Zephyr. Shares is defined as a deal which is financed by shares, according to Zephyr. Relative Size represents the ratio of the transaction value and the market value of assets; transaction price is retrieved from Zephyr, market value of assets from DataStream. Target Size is defined as the natural logarithm of the transaction price, retrieved from Zephyr. Antidirector Rights is a value ranging from 0 to 6, defined by (La Porta et al., 1998). World Bank Gov. Index denotes the equally-weighted average percentile score of six country governance variables as specified by the World Bank. GDP Growth represents the percentage of growth in GDP at market prices based on 2005 US$. International Experience is defined as the ratio of foreign sales and total sales. *, ** and *** denote a 10%, 5% and 1% significance level. Dependent
variable
Market Model CAR
27
5 Conclusion
This study examines the effects that M&As, which are done during a takeover wave, have on acquiring firm shareholder wealth. Prior literature has mostly focused on M&As during the fifth takeover wave and before, whereas this study examines the effects of the sixth takeover wave. By reviewing a sample consisting of M&As which are executed by UK firms during and shortly before the sixth takeover wave, 2001 up to and including 2007, the study provides evidence for several strands in M&A literature.
Firstly, M&As that are announced before an M&A wave seem to perform worse than M&As that are announced during a takeover wave. This finding is consistent with Harford (2003), who finds that on-wave mergers perform better than off-wave mergers. With regard to the second hypothesis, the significance of the diversification variable is too low; therefore, we cannot make any inferences with regard to this hypothesis. The third and final hypothesis, however, is provided with significant results. UK acquiring firms engaging in cross-border M&As perform worse than domestic M&As. These results are similar to the ones reported by Goergen and Renneboog (2004), Moeller and Schlingemann (2005) and Martynova and Renneboog (2008).
The results of the study imply that when managers are considering a takeover, they should consider and examine whether or not there is an ongoing takeover wave, since this can have an influence on the subsequent returns of the firm. Moreover, the boards of firms that are considering to engage in cross-border acquisitions should prioritize finding a suitable
candidate within their borders, as the results demonstrate that domestic acquisitions outperform cross-border acquisitions.
There are several limitations that have to be recognized. Since the UK is selected as the acquiring firm-country, the findings of this study are only generalizable for countries which are similar to the UK, such as the US. In addition, there is a strong presence of US target firms in the cross-border sample. This can bias the findings of where the target firm comes from towards the US. Thirdly, the semi-strong form of the efficient market hypothesis is assumed to hold, implying that the market responds directly, and the share prices fully reflects this response. Therefore, caution is advised when drawing inferences from these results.
28
29
Appendix:
Table A1Jarque-Bera. This table represents the skewness, kurtosis, Jarque-Bera (JB) scores and corresponding p-values for the values samples
that are applied throughout the study. Entire sample contains all completed domestic and cross-border M&As of UK listed firms
between 2001 and 2007 with a transaction value of at least £1 million as listed on the Zephyr database by Bureau van Dijk, where
control of the target shifts to the listed acquirer and values for all variables are available on either Zephyr or DataStream. Pre-Wave
includes all M&As that are announced between 2001 and 2002, Wave is defined as all M&As during the sixth takeover wave
between 2003 and 2007. Dom. And CB are the domestic and cross-border M&As announced during the wave.
Wave-Div and Wave-Rel are M&As announced during the wave that are either defined as diversifying (acquirer and target firm have
different 2-digit SIC code) or related (acquirer and target firm have similar 2-digitSIC code).
N Skewness Kurtosis JB-score p-value
Entire sample 757 CAR (-1,+1) 2.77 44.71 55839.9 0.0000
30 Table A2
Value of largest M&As by a UK firm (announced year). This table represents the 20 largest M&A deals by a UK acquiring firm. Year is defined as the announcement year. All data is retrieved from the Zephyr database.
Firm and Country Information
Year Value (billion £) Acquiring Target
Firm Country Firm Country
2000 129.24 Vodafone Airtouch PLC UK Mannesmann AG Germany
1998 29.1 British Petroleum Co.
PLC
UK Amoco Corp. USA
1998 20.89 Zeneca Group PLC UK Astra AB Sweden
1999 17.19 BP Amoco PLC UK Atlantic Richfield
Company
USA
2008 15.01 HM Treasury UK Royal Bank of
Scotland Group PLC, The
UK
2000 12.45 Granada Group PLC UK Compass Group
PLC UK 2007 9.56 Lehigh UK LTD UK Hanson PLC UK 2002 8.99 HSBC Holdings PLC UK Household International Inc. USA 2000 7.08 British Telecommunications PLC UK Viag Interkom GMBH & Co Germany 2000 7.02 HSBC Holdings PLC UK CCF - Crédit Commercial de France SA France
2002 7 Network Raild LTD UK Railtrack PLC UK
1999 7 Lloyds TSB Group PLC UK Scottish Widdows UK
1999 6.98 British Aerospace PLC UK Marconi
Electronics Systems LTD UK 1998 6.55 Commercial Union Group PLC, The UK General Accident PLC UK
2000 6.28 National Grid Group
PLC, The
UK Niagara Mohawk
Holdings Inc.
USA
1999 6.06 Vodafone Group PLC UK Airtouch
Communications Inc.
USA
2000 5.9 Vodafone Group PLC UK Hutchison Essar
Telecom
India
2007 5.47 Vodafone Group PLC UK Woolwich PLC UK
2000 5.4 Barclays PLC UK Canary Wharf
Group PLC
UK
2004 5.38 Songbird Acquisitions
LTD
31
Table A3
This table presents the sample from 2001-2007 of cross-border and domestic acquisitions, retrieved from the Zephyr database provided by Bureau van Dijk. The sample contains all completed domestic and
cross-border M&As of UK listed firms between 2001 and 2007 with a transaction value of at least £1 million as listed on the Zephyr database by Bureau van Dijk, where control of the target shifts to the
listed acquirer and values for all variables are available on either Zephyr or DataStream. Panel A lists the top 5 target countries for cross-border acquisitions. Panel B shows the division of acquisitions
across the different industries.
32
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