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The Causes And Motives Of Merger Wave Finance Essay

The topic of merger and acquisition has attracted attention of academics all over the world in the last decades. This topic is of significant importance for economy as there are large numbers of merger deals which involve billions of money around the world. For example, in 2011, 25,988 merger and acquisition deals happened all over the world and accounted for a total value of 1,634,874.76 million dollars [1] . The conduction of mergers and acquisitions may help companies to gain access to new resources, lower cost and increase revenues. The main motives for merger and acquisition come from economies of scale, economics of vertical integration, improvement of operating efficiency, combination of complementary resources, and redistribution of capital (Brealey, Myers and Allen, 2006).

The merger and acquisition activities present upward or downwards trends and cluster in merger waves. The causes of these merger waves are economic, regulatory, technological shocks (Mitchell and Mulherin, 1996) or market misvaluations (hodes-Kropf, Robinson, and Viswanathan, 2005). The most representative and influential merger waves are US and UK merger waves (Weston et al., 1996). Even though the starting date and duration of each merger waves are not specific, there are many merger waves in the US and UK history. In United States, the merger wave stated in 1893 and ended in 1904 because of the panics of antitrust laws applicable to the prevailing horizontal mergers in that period and the beginning of First World War. After that, another merger wave occurred in the US from 1919 and ended in 1929 stock market crash and the Great Depression. At the same time, in the UK, merger wave began owing to the changing structure of British industry and the need of larger companies. In 1970s, the feature of merger wave in the UK is that UK companies acquired US firms based on their expectation of the rising of United States. Next merger wave in the US is from 1984 to 1989. This merger wave is characterized with leverage buyout and large numbers of hostile bids and ended with stock market and junk bond market crash. Also at the same period in the UK, merger wave with similar characteristics started because of confidence with market deregulation and privation of state-own companies. New merger wave from the period of 1993 to 2000 was the era of the mega deal (Lipton, 2006). This merger wave ended with the collapse of dotcom bubble and Enron scandals.The most recent merger wave is from 2003 to 2007, the driver of this wave is the availability of abundant liquidity and the end of this merger wave is 2007 financial crisis. In this merger wave, merger proposals contain higher cash elements and acquirers are more rational and less acquisitive, and the market for corporate control is less competitive (Alexandridis et.al, 2010).

From the merger wave history described above, it is not difficult to find that the ending dates for these merger waves are often in wars or financial crisis (Pazarskis et.al, 2006) and that merger waves in the UK and US are also driven or ended by some similar factors such as regulation or the demand of larger capacity. Even through the US and UK market share similar characteristics, difference between the US and UK market also exists. For example, the shareholder power is stronger in the UK than in the US. The reason is as follows: the institutional investors in the UK located closely in the city of London and act actively to intervene in company decisions at lower cost (Crespi and Renneboog, 2010). The UK regulation also imposes much fewer restrictions on both institutional and individual shareholders (Black and Coffee, 1994). Shareholders can replace all managers with majority votes by calling for a special purpose meeting (Gao and Mohamed, 2012). The stronger shareholder power may make managers in the UK think more when make takeover decisions, and thus, merger activities may show different characteristics in UK.

To sum up, there are different characteristics in the sixth merger wave, and there are also differences in merger activities between US and UK. The purpose of this dissertation is to investigate the sixth merger wave between 2003 and 2007 in the UK to check whether there is an early wave effect [2] and if it did exist in this merger wave, to find the potential explanations for the merger wave effect.

As suggested by Mitchell and Mulherin (1996) and Harford (2005) that merger waves strongly clustered by industry, so merger deals are first divided into group based on the acquirer industries. In this dissertation, merger announcements that took place between 2003 and 2007 are first divided based on the acquirer macro industry provided by Thomson one Banker database [3] . Then, the samples are spitted into early and late acquisitions based on the merger announcement date. I alternatively classified early acquisitions as the first 10%, 20%, 30%, 40%, or 50% of all deals announced during merger wave, and the remaining deals are classified as late acquisitions. For example, if the first 10% of deals are classified as early acquisitions, the remaining 90% of deals in the merger wave are defined as late acquisitions. At last, all the samples defined as early acquisitions from different macro industries are put together as early acquisitions while all the samples classified as late acquisitions from different industries are put together as late acquisitions.

After defining the early and late acquisitions, I conduct event study to these early and late acquisition samples. The event day is the merger announcement day, and there are three event windows to be studied: they are [-1, 1], [-2, 2] and [-3, 3] around the announcement date respectively. The three-day event window consists of 196 sample deals, and five-day and seven-day event window contains 193 sample deals. Modified market model is used to calculate the cumulative abnormal returns for the three-day, five-day and seven-day event windows.

The main findings of this study are as follows. First, the relative deal size [4] during early wave is smaller than the relative deal size during late wave, and for private targets, early bidders often earn more return than late bidders do. Second, the regression results indicate that there is a significant early wave effect in the three-day event window if the first 10% or 20% of deals announced in merger wave is defined as early wave. There is no significant early wave effect in the three-day event window when early wave is defined as first 30%, 40% or 50% of deals announced in merger wave. In five-day and seven-day event window, no early wave effect is detected. Therefore, this early wave effect is highly sensitive to the definition of the early stage of merger wave and the number of days included in the event window. Moreover, from the summary statistics of cumulative abnormal return (CAR) for three-day window, it is easy to find that the standard deviation of CARs is largest for the first 10% and 20% of deals announced in merger wave among all other percentage of deals announced in merger wave. Therefore, if the first 10% or 20% of deals announced in merger wave is defined as early wave, the returns of acquiring firms in early wave vary a great deal.

Third, regression analyses are conducted to test two hypotheses. The first one is the hubris hypothesis that managerial hubris in merger and acquisition may result in overpaying for target firms or overoptimistic about the merger and thus result in negative returns on the stock price of acquirers in late stage of merger wave (Floegel et.al, 2005). The second one is the free cash flow hypothesis that managers make use of cash holdings to make merger bids to benefit themselves (Jensen, 1986). However, the regression results show that both the hubris hypothesis and the free cash flow hypothesis fail to explain the lower return in late stage of merger wave. This result is consistent with finding by Alexandridis, Mavrovitis and Travlos (2010) that in the sixth merger wave managers tend to be less overconfident and more rational when making takeover bids but is different from previous findings by Floegel, Gebken and Johanninga (2005) that overconfidence accounts for lower return in late stage of merger wave.

Fourth, I find a significant positive relationship between cash holding and merger announcement return for the UK data sample, contrast to the negative relationship found based on the US data in other research [5] . Finally, I find that market overvaluation may have more explanation power than the overconfidence hypothesis and the free cash flow theory for the lower return of bidders in late acquisitions.

There are three contributions of my dissertation. Firstly, I study abnormal return in different stages of merger wave between 2003 and 2007 based on the UK data. Secondly, I find a significant positive relationship between cash holding and merger announcement return for the UK data sample, contrast to the negative relationship found based on the US data in other research. Thirdly, I find that market overvaluation may have more explanation power for the lower return of bidders in late acquisitions than the overconfidence hypothesis and the free cash flow theory for the UK samples between 2003 and 2007.

The structure of this dissertation is as follows: Chapter 1 is the introduction presenting the background, the purpose and the main findings and contributions of the dissertation. Chapter 2 gives a literature review that summarizes and discusses the theories and previous empirical evidences. Chapter 3 presents the hypotheses tested in the study. Chapter 4 describes the sample selection and methodology used in the study. Chapter 5 is the summary statistics of data between early and late stages of merger wave. Chapter 6 presents study results, which include univariate analysis and the hypothesis test results. Chapter 7 draws the final conclusion.

Chapter 2: Literature review

2.1 Introduction

There is a variety of literature about the merger and merger wave. This literature review focuses on those theories which suggest that abnormal returns of the bidders are higher in a certain stage of the merger wave and the explanation for this effect. The structure of this chapter is as follows: the second section discusses the definition of merger and the empirical evidence about the merger wave. The third section reviews the causes and motives of merger wave. The return of merger wave is discussed in the fourth section. The abnormal return at different stages of merger wave and its explanation are described in the fifth and sixth section, respectively. The seventh section discusses previous study and research on returns to shareholders of acquirers.

2.2 Merger wave

Merger is one of the basic legal acquisition procedures that a firm can use. According to Agorastos and Pazarskis (2003), there are three possible ways to form merger and acquisition activities. Firstly, merger by absorption, where the acquiring firm retains its name and its identity, and it acquires all of the assets and liabilities of the acquired company. After the merger, the acquired firm ceases to exist as a separate business entity. Secondly, merger by consolidation, both the acquiring firm and the acquired firm terminate their previous legal entity and an entirely new corporation is created. Thirdly, merger by acquisition, where one company buy another company’s voting stock for cash, shares of stock, or other securities and there are two types: the acquisition of shares and the acquisition of assets.

Merger activity is always recognized to happen and cluster in merger waves. Many researches provide evidence that mergers often occur and cluster in waves. Mitchell and Mulherin (1996) studied industry-level patterns in merger and acquisition activity across 51 industries between 1982 and 1989 and found that there are significant differences in both the rate and time-series cluster of these activities. Andrade et al (2001) observed that merger activities in the 1990s also strongly cluster by industry. Moreover, Alexandridis et.al (2010) and Goel and Thakor (2010) provided further evidence on the merger wave, updating the database of facts for the 2000s.

2.3 The causes and motives of merger wave

There are various discussions and theories about the cause and motive of the merger waves. Gort (1969) argued that changing economic conditions have a different impact on opinions of company's interested parties for their company value and mergers are more likely to occur in upward rather than in downturns of the stock market. Banerjee and Eckard (1998) found that the merger wave of 1897–1903 results from firms merging to improve their operational efficiency rather than monopoly power. They concluded that operational efficiency is the probable source of value gains for companies to participate in merger and acquisition. Mitchell and Mulherin (1996) provided evidence that the causes of these industry-specific merger waves are economic, regulatory, or technological shocks. Rhodes-Kropf and Viswanathan (2004) provided evidence on the theory that valuation errors affect merger activity. Acquirers whose stock price has high firm-specific error use stock to buy targets whose stock price has lower firm-specific error and by doing so, both firms benefit from positive time series sector error. Shleifer and Vishny (2003) also found that merger waves are more likely to occur because overvalued firms seeking to acquire less overvalued assets. Moreover, Rhodes-Kropf, Robinson, and Viswanathan (2005) also developed decomposition to support the theory of Rhodes-Kropf and Viswanathan (2004) and Shleifer and Vishny (2003) that misevaluation drives mergers. They also argued that even though neoclassical explanations are crucial to understand merger activity at sector level, misvaluation is critical to understand who buys whom, no matter whether the merger occurs during a period when productivity shocks could have resulted in a spike in merger activity.

However, on the other hand, despite the various reasons and motives provided by researchers, Trautwein (1990) surveyed several different theories of merger and acquisitions, and argued that there is no one approach that can be a single explanation of the merger motivation. Some explanations are used to provide some evidence and tend to be more relevant in the exact merger wave or the examined marketplace. Brealey and Myers (1996) even regarded merger waves as one of the ten most important but unresolved questions in financial economics and suggested that better theories are needed to help explain merger wave. Bruner (2004) also supported that merger waves in United States over last hundred years all have a number of unique characteristics, and thus merger waves cannot be explained by a certain or specific set of factors. In a word, the motives and causes of merger waves are still on debate in contrast to the consistent findings of return for acquirer in merger wave.

2.4 The return of acquirer in merger wave

When it comes to the returns of the acquirer in the merger wave, according to research conducted by Moller et.al (2005), acquiring-firm shareholders lost 12 cents around acquisition announcements per dollar spent on acquisitions for a total loss of $240 billion from 1998 through 2001, whereas they lost $7 billion in all of the 1980s. Alexandridis et.al (2010) also found that in the recent sixth merger wave (2003-2007) deals continue to destroy at least as much value for acquiring shareholders as in the 1990s.

2.5 The abnormal return at different stages of merger wave

Floegel, Gebken and Johanninga (2005) studied merger waves in 1990s and found that there is a significant wave effect defined as the difference in abnormal returns at early and late wave stages. They also found that the wave effect is significant regardless of some other variables like the method of payment and the public status of the target firms. Goel and Thakor (2010) also found strong empirical evidence to support that earlier acquisitions produce higher bidder returns, involve smaller targets, and result in higher compensation gains for the acquirer’s top management team than the later acquisitions in the wave using data between 1 January 1979 and 31 December 2006.

2.6 The explanation of the abnormal return at different stages of merger wave

As to the explanations to the abnormal return at different stages of the merger waves, there are three explanations provided by Floegel et.al (2005). First, CEO’s overconfidence: Roll’s (1986) hubris hypothesis says that managers who engage in merger and acquisitions are overly optimistic about their ability to create value. Billett and Qian(2008) also found evidence that CEO are more likely to acquire again following positive experience from past acquisitions even if these future deals may destroy the wealth. Managers of late wave bidders might tend to be overconfidence as the result of the fact that they made successful acquisition decisions at early wave stages. Thus, overconfidence which comes from the success of previous mergers might lead to higher prices provided for targets at late wave stages, or a less careful choice of those targets, which may result in lower return at late wave stages. Floegel et.al (2005) did empirical test about the hubris hypothesis using the 1990’s data and discovered that the more successful the bidders have been at the early stage of a merger wave, the lower are the cumulative abnormal returns they experience around the bid announcement date in late acquisitions.

Second, free cash flow hypothesis: Jensen (1986) asserts that managers of firms that have a large amount of cash often do not pay out the cash to their shareholders but to increase their own private benefits. Acquisitions are one of ways for managers to spend cash instead of paying the dividend to shareholders. Consequently, the free cash flow theory implies that managers of firms with substantial free cash flows are more likely to undertake low-return or even value-destroying mergers. Harford’s (1999) study confirms the agency theory using the data in United States from 1950 to 1994. He found that cash-rich firms are more acquisitive than other firms. He also provided stock return evidence to show that cash-rich acquirers are value decreasing and there is negative stock price reaction to the merger announcement. Therefore, the low return at late wave stage may be motivated by managers’ self-interest.

Third, competitive advantage theory: Akdogu (2003) developed a model which posits that it might be expensive for bidding firms to lose a target to a competitor, which suggests that in order to maintain competitive advantage, the firm overpaid at late stage of wave where fewer targets are available and thus incurs a loss. In this dissertation, I focus on the first two explanations of early wave effect: the managerial overconfidence and the free cash free theory and undertake empirical analyses of these two explanations.

2.7 Previous study and research on returns to shareholders of acquirers

2.7.1 Previous study and research on returns to shareholders of acquirers: Method of Payment

There are a large number of studies and research examining the impact of the method of payment on the wealth of shareholders of bidding firms. Asquith, Bruner and Mullins (1990) investigated the effect of merger bids on stock returns and found that the bidding firm’s returns are positive for cash bid while negative for stock payment bids. The bids financed with a combination of common stock and cash have a return between equity alone and cash alone and are significant different from both. In contrast, Moeller, Schlingemann, and Stultz (2003) and Fuller, Netter, and Stegemoller (2002) found significant abnormal returns for their samples regardless of how the bids are financed. Travlos (1987) reported that takeovers financed with cash offer considerably more value to bidders than those financed with stock issues for the US domestic acquisition. For Japanese bids in US, Pettway et al. (1993) discovered similar results. On the other hand, for takeovers in Canada, Eckbo et al. (2000) presented evidence that bidders gain the greatest using mixed payment method.

2.7.2 Previous study and research on returns to shareholders of acquirers: Relative size

Asquith et al. (1983) found that there is significant positive relationship between the cumulative excess return and the relative size of the target firm to the acquiring firm from 1955 to 1979. In contrast, Moeller et al. (2004) documented a negative relationship between acquirer returns and the target-to-bidders relative size using data from 1980 to 2001. However, some researchers have not found a significant relationship between relative size and returns of shareholders of bidders (Travlos, 1987; Franks, Harris and Titman 1991). In addition, Fuller, Netter, and Stegemoller (2002) discovered that, on the one hand, for public acquisitions, the CARs of the bidders become smaller as relative size of targets increase. On the other hand, for private merger and acquisition deals, the larger the target size, the larger the CARs of bidders will be.

2.7.3 Previous study and research on returns to shareholders of acquirers: Public Status of the Target

Chang (1998) examined bidder returns at the announcement of a takeover when the targets are private firms and found positive abnormal returns for stock offers while no abnormal returns in cash offers. The reasons are perhaps as follows: takeovers of private firms create large block holders due to the highly concentrated ownership of the private firms. Large shareholders are usually effective monitors of the managerial performs, which may improve the future performance of the firm. In addition, Allen and Sirmans (1987) also reported similar positive bidder return in real estate investment trust mergers. On the other hand, Fuller, Netter, and Stegemoller (2002) studied 3,135 samples of takeovers from 1990 to 2000. They found that bidder shareholders gain when the bidding firm buys a private firm or a subsidiary of a public firm and lose when the bidder buys a public firm. They suggested that besides the monitoring benefits, liquidity discount and tax consideration also result in higher return for bidders’ shareholders. Moeller, Schlingemann and Stulz (2003) found that bidders earn positive abnormal returns when the target is privately held and incur negative returns when the target is publicly traded. In short, empirical evidence shows that acquirers gain higher returns when targets are private firms than when targets are public firms.

Merger waves’ characteristics have changed over time, for example, compared to the 1980s, mergers in the 1990s are generally stock swaps, and hostile takeovers virtually disappear (Andrade, Mitchell and Stafford, 2001). Alexandridis, Mavrovitis and Travlos (2010) who have examined the sixth merger wave found that the drivers of the sixth wave lie primarily in the availability of abundant liquidity and merger proposals contain higher cash elements relative to merger wave in the 1990s. Moreover, acquirers are less overvalued relative to targets and acquisition decisions are more cautious and rational in the sixth merger wave.

Furthermore, there are also differences of the merger waves between the US and the UK capital markets in the type of deals, the methods of payment, and the behavior of the involved companies (Pazarskis et.al, 2006). According to Gao and Mohamed (2012), in the UK shareholders have stronger power and managerial power is much weaker than that in the US. Management has less power in takeover decisions, due to the fact that institutional investors in the UK are more closely located and more active to take part in company decisions than counterparties in the US (Crespi and Renneboog, 2010). Regulation also imposes less restriction on shareholders to influence the board (Black and Coffee, 1994). Therefore, different results might be expected from analysis of the merger wave for the UK samples for the recent period.

There are different characteristics in the sixth merger wave, and there is also difference in merger activities between US and UK. This dissertation examine the abnormal returns on different phrase of the merger wave and try to explain the difference based on the updating database of facts for the sixth merger wave occurred between 2003 and 2007 in the UK. Since most of the previous research on early wave effect is conducted on a three-day event window, I expand the event window to see whether there are different results for the larger event window.

Chapter 3: Hypotheses

From previous literature of merger wave and wave effect, we know that takeover bids in early stage of merger wave often earn higher cumulative abnormal returns around the announcement date than bids in late stage of merger wave do (Floegel, Gebken and Johanninga, 2005; Goel and Thakor, 2010). To check whether there is early wave effect for the sixth merger wave in the UK, the hypothesis is as follows:

Hypothesis 1: there is early wave effect in the sixth merger wave between 2003 and 2007 in the UK.

After identifying the early wave effect, the potential explanation for the wave effect is explored:

Billett and Qian (2008) argue that frequent bidders have been associated with managerial overconfidence. The bidder’s manager may become overconfident in late stage of merger wave for success in early stage of merger wave or even other’s success in early acquisitions. The hubris hypothesis by Roll (1986) stipulates that merger and acquisition may be driven by managerial hubris rather than the potential gains of the deals. The managerial hubris in merger and acquisition may result in overpaying for target firms and thus show a negative effect on the stock price of acquirers. Malmendier and Tate (2006) study about 400 companies in United States during the period of 1980 to 1994 and find that the market reaction to these takeover bids by overconfident CEOs is significantly negative. Based on the literature review, I develop the hypothesis :

Hypothesis 2: the lower return in late stage of merger wave is because the late bidders are overconfident.

Another explanation for the decreasing returns in late acquisitions is the agency theory that bidder’s manager is perusing for his own interest. Agency theory by Jensen( 1986) analyzes the conflict of interest between corporate managers and shareholders and suggests that companies with large numbers of free cash flows often do not pay out these free cash flows to their shareholders but use these cash flow to increase their private benefits. Moreover, these free cash flows which give freedom to managers from monitoring by external capital providers like debtors may be used to finance merger and acquisition for the manager’s own interest. Harford’s (1999) study confirms the agency theory using the data in United States from 1950 to 1994. He finds that cash-rich firms are more acquisitive than other firms. He also provides stock return evidence to show that cash-rich acquirers are value decreasing and there is negative stock price reaction to the merger and acquisition announcement. Moreover, the low interest rates and abundant liquidity before the financial crisis may lead to excessive free cash flow in late stage of merger wave. Therefore, to test whether the free cash flow theory can explain the early wave effect, the hypothesis developed is:

Hypothesis 3: the lower return in late stage of merger wave is because late acquirers use the free cash flow to increase private benefits.

The foundation of these hypotheses test is that the market is efficient: shareholders react quickly to the merger announcement, and stock price reflects at least shareholders’ opinion about the merger announcement.

Chapter 4: Data selection and Methodology

4.1 Introduction

This chapter discusses the data selection and the methodology used in this dissertation. The second section describes the source of the data and the criteria of selecting the sample. The third section proposes the methodology employed in this study. The definition of merger wave and the classification of early and late phase of merger wave are provided first. Then, event study methodology is discussed to identify and analyze the merger wave effect.

4.2 Data selection

Data for the merger bid information and bidders’ accounting information is collected from Thomson Financial Securities Data Corporation (SDC) Worldwide Mergers and Acquisitions database, and the following data requirements are imposed:

i. Both acquiring firms and target firms’ nations are United Kingdom.

ii. Acquirers are public traded companies with stock price data available on Thomson Reuters Datastream Database or Thomson Financial Securities Data Corporation (SDC) Worldwide Mergers and Acquisitions database at the time of the merger announcement date from 01/01/2003 to 31/12/2007.

iii. The deal value is at least 1 million dollars, and the size of the target is at least 1% of the size of the acquirer [6] .

iv. The acquirer gained control of the target company through the merger: acquirer had a minority ownership of less than 50% before the deal and a majority ownership of more than 50% after the merger.

v. The deal is completed.

vi. Clustered acquisitions where the acquirer is involved in more than one acquisition proposals within a three-day, five-day or seven-day window are omitted from the analysis.

The bidders’ stock price data [-4, 3] days around the merger announcement date is collected on Thomson Reuters Datastream database.

The original deal number collected from the Thomson one Banker based on the requirements described above is 299. However, when I collect the stock price data on the Thomson Reuters Datastream database, several problems occurred. First, some firms’ stock price data or trading volume data is not available on the Thomson Reuters Datastream database. Second, for some firms, there is no trading volume around the announcement date and the stock price has not changed during the three-day, five-day or seven-day event windows. These deals which have the problems described above are also eliminated from the sample. Therefore, the final sample of the study consists of 196 transactions for the three-day event window and 193 transactions for the five-day and seven-day event windows.

4.3 Methodology

4.3.1 Time definition of sixth merger wave

According to the various research literature, the sixth merger wave started in 2003, peaked in 2006, and ended approximately in late 2007. Goel and Thakor (2010) use a detrended market P/E ratio measure as in Bouwman et al. (2009) study to identify merger waves. They identify a month as a merger wave month if that month’s detrended market P/E or M/B is above its past five-year average. Based on this method, majority of months between 2003 and 2007 are classified as merger wave months. On the other hand, Alexandridis, Mavrovitis and Travlos (2010) follow Harford’s (2005) method to identify 24-month merger wave peaks by taking the total number of bids for each industry, and randomly assign each occurrence to one month to determine the highest 24-month concentration from each of the draws. If 99% of the stimulation is lower than the actual peak concentration, that 24-month period is classified as a wave. This method also confirmed that the sixth merger wave peaked between 2005 and 2006. Thus, the merger wave analyzed in this article is the sixth merger wave occurred between 2003 and 2007.

4.3.2 Classification of early and late acquisitions

As suggested by Mitchell and Mulherin (1996) and Harford (2005) that merger waves strongly clustered by industry, so merger deals are first divided into group based on the acquirer industries. In this dissertation, merger announcements that took place between 2003 and 2007 are first divided based on the acquirer macro industry provided by Thomson one Banker database. Then, the samples are spitted into early and late acquisitions based on the merger announcement date. I alternatively classified early acquisitions as the first 10%, 20%, 30%, 40%, or 50% of all deals announced during merger wave, and the remaining deals are classified as late acquisitions. For example, if the first 10% of deals are classified as early acquisitions, the remaining 90% of deals are defined as late acquisitions. At last, all the samples defined as early acquisitions from different macro industries are put together as early acquisitions while all the samples classified as late acquisitions from different industries are put together as late acquisitions. Table 1 show summary statistics on the number of early and late acquisitions announced during merger waves using the five alternative definitions of early acquisitions (the first 10%, 20%, 30%, 40%, or 50% of all deals announced during the merger wave).

Table 1

Summary statistics on early versus late acquisition in merger waves

Panels A and B show the number of early and late acquisitions announced during merger waves using the five alternative definitions of early acquisitions. Early acquisitions are the first 10%, 20%, 30%, 40%, or 50% of deals announced in each merger wave. The remaining deals are classified as late acquisitions. Panel A shows the number of acquisitions for a three-day event window which includes one day prior to the announcement day, the day of the announcement, and one day after the announcement day. Panel B shows the number of acquisitions for five-day and seven-day event window which includes the announcement day, two or three days prior to the announcement day and two or three days after the announcement day. The sample includes all UK, completed, domestic, public merger and acquisitions reported in Thomson one Banker between 2003 and 2007. Acquirers are listed on London stock exchange and have data available on Thomson Reuters Datastream database. The deal value is at least 1 million dollars, and the size of the target is at least 1% of the size of the acquirer. Acquirer had a minority stake of less than 50% before the deal and a majority stake of more than 50% after the merger. Clustered acquisitions where the acquirer is involved in more than one acquisition proposals within a three-day or five-day or seven-day window are omitted.

Percentage of deals classified as early acquisitions

10%

20%

30%

40%

50%

Panel A: Number of acquisitions for [-1,1] event window

 

Number of deals

Early acquisitions

21

39

59

78

101

Late acquisitions

175

157

137

118

95

All acquisitions

196

196

196

196

196

Panel B: Number of acquisitions for [-2,2] and [-3,3] event window

Number of deals

Early acquisitions

20

39

58

76

100

Late acquisitions

173

154

135

117

93

All acquisitions

193

193

193

193

193

4.3.3 Event study

In order to capture the abnormal return of acquiring firms’ shareholders from the merger announcement, event-study methodology is used. Event study is based on the efficient market hypothesis (Fama et al. 1969) that investors react quickly on available information and their reaction results in stock price changes that reflect the value of firm current and future performance. There are three steps in my event study:

Identify the event date

Define the event window

Calculate cumulative abnormal return

Identify the event date

Merger and acquisition deals are often negotiated between acquirer and target before the announcement. There may be information leakage before the merger announcement, so investors may expect the deal before announcement date. However, usually the market’s expectations are fully formed on the announcement date, whereas there is no significant wealth effect around the deal completion date (Asquith 1983; Dodd 1980). Therefore, the event date in this dissertation is defined as the merger and acquisition announcement date.

Define the event window

The efficient markets hypothesis (EMH) states that financial markets are informationally efficient and that stock prices reflect all known information. In a capital market that is efficient with respect to public information, stock prices quickly adjust following a merger announcement, incorporating any expected value changes (Andrade et.al, 2001). When it comes to choosing the event window, the event will be the merger announcement, and the event window will include the day of the announcement. Days after the announcement day are usually added to the event window because they will capture the market reaction of the announcement. Days prior to the announcement day can also be added to the event window because they will capture the market reaction to possible information leakages before the official deal announcement (Ma, Pagán and Chu, 2009). However, accuracy (predictive power) will be lower when more days are included in the event window due to the possibility of confounding effects from other market events (MacKinlay, 1997). According to Seiler (2004), event window should cover the entire effect of the event but be as short as possible. Therefore, a three-day, five-day and seven-day event windows around the announcement date are used. They are [-1, 1], [-2, 2] and [-3, 3] around the announcement date respectively.

Calculate the cumulative abnormal return

There are a variety of models to calculate the cumulative abnormal returns. Market model is chosen to measure the abnormal return for the following reasons. First, it reflects the viewpoint of the common shareholder without having leverage effects imbedded in it (Weston and Mansingkha, 1971). Second, the model also takes the bidder’s systematic risk into account. Third, by aggregating the abnormal returns cross-sectionally and over time relative to a merger event with large samples, the event may be systematically captured (Lubatkin, 1983).

As to the market model, in general, most studies apply an event-study framework. Acquired firms’ performance prior to and after the event date is studied. The cumulative abnormal return is calculated by summing up the average residuals over a period of time to study the impact of the merger on the shareholders’ wealth. The procedure begins with an adjustment of the stock risk by using modern portfolio theory such as capital asset pricing model (CAPM) or Fama French three-factor model to calculate the expected return. The modern portfolio theory suggests that the expected return on a security is the risk free interest rate plus a risk premium (Michel and Shaked, 1985). Abnormal return is the difference between the actual return and the expected return predicted by the model and then, the abnormal returns are accumulated to get the cumulative abnormal return. As suggested by the literature of Yang (2008), Jensen Measure (Jensen, 1968), expressed as the intercept of the regression of the excess return of the acquiring firms on the excess return of the market index, is a reasonable measure of merger performance for acquirers for the long term cumulative abnormal returns. Alexandridis, Mavrovitis and Travlos (2010) also chose similar method to estimate monthly abnormal returns for a period of 3-years following the acquisition announcement by using the independent variables from the Fama and French (1993) and Carhart (1997) models.

However, in this dissertation, modified market model is used to estimate the abnormal returns to bidders due to the fact that there are many bidders who take bids frequently during the sample period. If market model like CAPM measure is used, the acquiring firms included in the sample must be relatively inactive in the acquisition market (Lubatkin, 1983), but for frequent bidders, other merger announcement may occur in the estimation period, which may bias the estimation. As a consequence, the cumulative abnormal returns for the event window [-1, 1], [-2, 2] and [-3, 3] around the announcement date are calculated as follows:

The returns are calculated as:

Where is bidder i’s daily stock return on date t and is the return for the value weighted FTSE index on date t.

Chapter 5: Summary statistics of data between early and late stages of merger wave

In this chapter, the summary statistics of the cumulative abnormal returns (CAR) to bidders in different stages of merger waves are shown and discussed. Means, medians and differences in mean CAR between early and late stage of merger wave are presented, and difference tests for means are conducted based on t-test for equality in means.

Table 2

Summary statistics of the cumulative abnormal returns to bidders in different stages of merger waves

The sample of acquisition deals meets the criteria described in Table 1. Panel A, B and C show the summary statistics of the cumulative abnormal returns to bidders in different stages of merger waves for event window [-1, 1], [-2, 2] and [-3, 3] around the announcement date respectively. Early acquisitions are the first 10%, 20%, 30%, 40%, or 50% of deals announced in each merger wave. The remaining deals are classified as late acquisitions. Standard deviations are in brackets. The difference tests for means are based on t-test for equality in means. * denotes statistical significant at the 5% level.

Percentage of deals classified as early acquisitions

10%

20%

30%

40%

50%

Panel A: Cumulative abnormal returns for three-day event window

 

 

Early acquisitions

mean

0.4577

0.2166

0.1519

0.1224

0.0979

median

0.0225

0.0225

0.0134

0.0134

0.0120

(1.9175)

(1.4262)

(1.1584)

(1.0138)

(0.8858)

Late acquisitions

mean

0.0098*

0.0184*

0.0173*

0.0170*

0.0153*

median

0.0103

0.0096

0.0089

0.0077

0.0049

(0.1057)

(0.0678)

(0.0700)

(0.0711)

(0.0680)

Difference (Early-Late)

mean

0.4479

0.1982

0.1346

0.1054

0.0826

median

0.0122

0.0129

0.0045

0.0057

0.0070

All acquisitions

mean

0.0578

0.0578

0.0578

0.0578

0.0578

median

0.0104

0.0104

0.0104

0.0104

0.0104

Panel B: Cumulative abnormal returns for five-day event window

Early acquisitions

mean

0.0469

0.0365*

0.0310*

0.0250*

0.0272*

median

0.0183

0.0287

0.0231

0.0164

0.0155

(0.0208)

(0.0183)

(0.0131)

(0.0111)

(0.0099)

Late acquisitions

mean

0.0215*

0.0210*

0.0211*

0.0235*

0.0208*

median

0.0113

0.0063

0.0062

0.0063

-0.0001

(0.0845)

(0.0781)

(0.0813)

(0.0833)

(0.0843)

Difference (Early-Late)

mean

0.0255

0.0155

0.0098

0.0014

0.0064

median

0.0070

0.0224

0.0169

0.0101

0.0156

All acquisitions

mean

0.0241

0.0241

0.0241

0.0241

0.0241

median

0.0118

0.0118

0.0118

0.0118

0.0118

Panel C: Cumulative abnormal returns for seven-day event window

Early acquisitions

mean

0.0484

0.0400*

0.0325*

0.0258*

0.0288*

median

0.0273

0.0284

0.0217

0.0202

0.0176

(0.1194)

(0.1307)

(0.1128)

(0.1060)

(0.1014)

Late acquisitions

mean

0.0187*

0.0171*

0.0171*

0.0191*

0.0142*

median

0.0042

0.0037

0.0038

0.0036

-0.0007

(0.1020)

(0.0960)

(0.1001)

(0.1030)

(0.1067)

Difference (Early-Late)

mean

0.0297

0.0229

0.0154

0.0067

0.0146

median

0.0231

0.0247

0.0179

0.0167

0.0183

All acquisitions

mean

0.0218

0.0218

0.0218

0.0218

0.0218

 

median

0.0078

0.0078

0.0078

0.0078

0.0078

Table 2 shows the mean, median and the difference of the cumulative abnormal returns (CAR) to bidding firm shareholder for event window [-1, 1], [-2, 2] and [-3, 3] around the announcement date respectively. As can be seen in the table, for the three-day event window, the average abnormal return within the wave is 0.0578 with a corresponding median return of 0.0104. When splitting the bids into early and late phase of merger wave, a different picture occurs. For the first 10%, 20%, 30%, 40%, or 50% of deals announced in merger wave, the mean cumulative abnormal return (CAR) is 0.4577 0.2166, 0.1519, 0.1224 and 0.0979 respectively which is 0.4479, 0.1982, 0.1346, 0.1054 and 0.0826 higher than the corresponding late stage returns. As for the median returns, the stockholders of the bidders at early stage still received a higher median cumulative abnormal return than the counterparties of bidders at late stage, even though the difference for medians is smaller than for means.

For the cumulative abnormal returns for event window [-2, 2] and [-3, 3] around the announcement, the mean of CAR is 0.0241 for five-day event window and 0.0218 for seven-day event window. In addition, the median is 0.0118 for five-day event window and 0.0078 for seven-day event window. It is worth noting that the mean cumulative abnormal return is the largest for the three-day event window while smallest for the seven-day event window. Thus, the mean cumulative abnormal returns are decreasing as time goes by. The market seems to act quickly to the merger announcement news.

Moreover, for the samples which are classified as early wave, the average CARs for five-day event window are 0.0469, 0.0365, 0.0310, 0.0250 and 0.0272 for the first 10%, 20%, 30%, 40%, or 50% of deals announced in merger wave and all is higher than the ones for the late stage of the merger wave. Mean CARs for seven-day event window defined as early stage are also 0.0297, 0.0229, 0.0154, 0.0067 and 0.0146 higher than the counterparties classified as late stage. As for the median of CARs, the same results hold for both the five-day and the seven-day event window.

However, when compared with the difference of mean cumulative abnormal return between early and late acquisition for the three-day event window, the differences for both the five-day and the seven-day event window are smaller; whereas the difference of median return between early and late acquisition is larger for both the five-day and the seven-day event window than for the three-day event window. The reason may be that the standard deviations for both the five-day and seven-day event window data are smaller than the corresponding three-day event window data. The return for longer period event windows is more concentrated than the return for the short three-day event window.

Another fascinating phenomenon can be found in table 2 is that the standard deviations for three-day event window for the first 10%, 20%, 30%, 40%, or 50% of deals announced in merger wave are the largest in all the subsamples. This result indicates that in the early stage of merger wave for event window [-1, 1] around the announcement date, the cumulative abnormal returns received by the shareholders vary a great deal.

Looking through the first 10%, 20%, 30%, 40%, and 50% of deals announced in merger wave, we can observe that the mean cumulative abnormal returns tend to decrease as more deals are included in the early stage for all the event window. However, when it comes to the medians, there is no such case. The t-test for equality in means with unequal variance is conducted, but the results are not significant at 5% level. However, other factors such as size of the deals, method of payment and public status of the target, may have influence on the cumulative abnormal return and introduce noises. Therefore, regression analysis is needed to see whether there is early wave effect in the merger wave.

Chapter 6 Results

6.1 Introduction

This chapter reports The empirical results of the analysis. The second section discusses the characteristics of bidders and deals in different stages of merger wave. The third section presents the results of univariate analysis. The fourth section discusses the results of hypothesis test and the fifth section presents other findings in the regression analysis. The samples used in regression analyses are different for different event windows, but they are all the UK domestic deals between 2003 and 2007. The sample for three-day event window consists of 196 transactions, and the sample for the five-day and seven-day event windows contains 193 transactions.

6.2 Characteristics of bidders and deals in different stages

I first look at the size difference between early acquisitions and late acquisitions in the merger wave. The differences-in-means test is conducted. I test both the actual size [7] and the relative deal size [8] of early and late acquisitions announced in the sixth merger wave. Early acquisitions are the first 10%, 20%, 30%, 40%, or 50% of deals announced in merger wave. The remaining deals are classified as late acquisitions. The numbers in parentheses in table 3 are t-statistics. In the part of actual size, mixed results can be seen in table 3 for all event windows. If the first 10%, 20% or 50% of deals announced in merger wave is classified as early acquisition, the mean actual size is bigger in early wave than that in late wave. In contrast, if first 30% or 40 of deals announced in merger wave is defined as early wave, the opposite results hold.

When it comes to the relative deal size, the outcomes are much more consistent: in general, the average relative deal size during the early acquisitions is smaller than the average relative deal size during the late acquisitions. For example, if the first 10% of all acquisitions announced during merger wave is defined as early acquisitions and the remaining one as late acquisitions, the average relative deal size of late deals is 31.7% larger than that of early deals for three-day event window [9] , and their differences are significant different from zero at 10% significant level. Except the last columns in table 3, for all the event window around the announcement date, all the other columns show that the mean relative deal size during early acquisitions is smaller than the mean relative deal size during the late acquisitions. This result is consistent with the finding of Asquith et al. (1983) that there is significant positive relationship between the cumulative excess return and the relative size of the target firm but opposite to the finding of Moeller et al. (2004) that there is a negative relation between acquirer returns and the target-to-bidders relative size. In addition, relative size is one of the independent variables in the multivariate regressions to test hypotheses instead of actual size.

Table 3

The relative deal sizes during early acquisitions are smaller than that during late acquisitions in merger wave

The sample of acquisition deals meets the criteria described in Table 1. Panel A and B show the difference in mean actual size and mean relative deal size of late and early acquisitions for event window [-1, 1], [-2, 2] and [-3, 3] around the announcement date respectively. In each panel, the first result shows the difference in mean actual size of late and early acquisitions. The actual size is the transaction value [10] measured in $million. The second result shows the difference in mean relative deal size of late versus early acquisitions, that is, the size of a late acquisition minus that of an early acquisition. Relative size is defined as the transaction value divided by acquirer market value [11] four weeks prior to the announcement date. Early acquisitions are the first 10%, 20%, 30%, 40%, or 50% of deals announced in each merger wave. The remaining deals are classified as late acquisitions. The difference tests are based on t-test for equality in means. Numbers in parentheses are t statistics. ** and * indicate significance at the 5% and 10% levels, respectively.

Percentage of deals classified as early acquisitions

10%

20%

30%

40%

50%

Panel A: Difference in mean size of late and early acquisitions for three-day event window

Size definitions

Actual size

-182.9

-32.3

39.1

138.1

-132.0

(-0.74)

(-0.23)

(0.38)

(2.17)**

(-1.47)*

Relative size

31.7%

25.2%

34.6%

34.0%

-2.0%

(1.45)*

(1.04)

(1.31)*

(1.13)

(-0.06)

Panel B: Difference in mean size of late and early acquisitions for five- and seven-day event window

Size definitions

Actual size

-192.6

-30.0

36.5

68.6

-133.4

(-0.74)

(-0.21)

(0.35)

(0.74)

(-1.47)*

Relative size

29.8%

25.8%

35.2%

34.2%

-16.5%

 

 

 

(1.35)*

(1.04)

(1.32)*

(1.13)

(-0.45)

 

Then I look at three bidder characteristics in different stages of merger wave: the cash holding of bidders, the financial leverage of bidders and the market overvaluation of bidders.

Firms that have excess cash may take part in the merger and acquisition. The agency cost of free cash flow theory by Jensen (1986) says that managers of firms that have large numbers of free cash flows often do not pay out these cash flows to their shareholders but to increase their own private benefits. Harford (1999) also argues that cash-rich firms are more likely than others to attempt acquisitions, and he also finds stock return evidence that these acquisitions are decreasing the value. Therefore, I first look at cash holding of bidders in different stages of merger wave. To control the size effect, I use the variable cash divided by book value of asset to represent the cash holding of bidders. From table 4, we can see that for the three-day event window [12] , if early acquisition is defined as the first 10% or 20% of deals announced in merger wave, the cash holding of early bidders is about 2% higher than that of late bidders. However, the situation reverse if early acquisition is defined as the first 30%, 40% or 50% of deals announced in merger wave for all event windows. In addition, if early acquisition is defined as first 30% of deals announced in merger wave, the cash holding is significantly lower for early bidders than for late bidders in the three-day event window.

Then, I look at the financial leverage of bidders. In contrast to cash holding, debt plays a role to alleviate the agency problems. Grossman and Hart (1982) suggest that issuing debt makes managers work hard. Agency cost theory also argues that debt disciplines managers. Maloney, McCormick and Mitchell (2001) look at 428 mergers over the period of 1962 to 1982 and discover positive relationship between the leverage of the bidding firm and the abnormal returns of the bidders at merger announcement. To control the size effect, I use the variable debt divided by book value of asset as a proxy of the financial leverage of bidders. Table 4 indicates that, for three-day event window [13] , if early acquisition is defined as first 10% or 20% of deals announced in merger wave, the financial leverage is higher for early bidders than for late bidders. For example, for three-day event window, if early acquisition is defined as first 20% of deals announced in merger wave, the ratio of debt to asset is about 8% higher for early bidders than for late bidders and this difference is significantly different from zero. However, the situation also reverses if early acquisition is defined as the first 30%, 40% or 50% of deals announced in merger wave for all event windows. This difference between early and late acquisitions in merger wave is not significant from zero.

At last, I look at the market overvaluation of bidders. Shleifer and Vishny (2003) argue that the sixth merger wave is initiated as a result of overvalued firms seeking to acquire less overvalued assets. Firm, whose equity is overvalued, can make acquisitions with stock. Dong, Hirsheifer, Richardson and Teoh (2006) examine the takeover bids between 1978 and 2000 and find that the valuation of the bidder has a significantly negative impact on the abnormal return around the merger announcement date. Tobin’s Q is used as a proxy of market overvaluation of bidders. Tobin’s Q is defined as the market value of assets divided by the book value of assets, whereas the market value of assets is total book assets minus the book value of equity plus market value of equity. From table 4, we can see that, for all event windows, if early acquisition is defined as first 10%, 20%, 40% or 50% of deals announced in merger wave, tobin’s Q is higher for early bidders than for late bidders. For instance, for three-day event window, if early acquisition is defined as first 10% of deals announced, tobin’s Q is about 80% higher for early bidders than for late bidders and this difference is significant different from zero at 5% level.

Table 4

The bidder characteristics difference between early acquisitions and late acquisitions in merger waves

The sample of acquisition deals meets the criteria described in Table 1. Panel A and B show the mean difference in bidder characteristics of early and late acquisitions for event window [-1, 1], [-2, 2] and [-3, 3] around the announcement date respectively. In each panel, CASH includes cash and marketable securities; DEBT is the net debt; ASSET is acquirer’s total asset; TOBINQ is defined as the market value of assets divided by the book value of assets, whereas the market value of assets is total book assets minus the book value of equity plus market value of equity. Early acquisitions are the first 10%, 20%, 30%, 40%, or 50% of deals announced in each merger wave. The remaining deals are classified as late acquisitions. The difference tests are based on t-test for equality in means. Numbers in parentheses are t statistics. ** and * indicate significance at the 5% and 10% levels, respectively.

Percentage of deals classified as early acquisitions

10%

20%

30%

40%

50%

Panel A: Difference in bidder characteristics of early and late acquisitions for three-day event window

Bidder characteristics

Cash/Assets

0.0274

0.0156

-0.0370

-0.0120

-0.0073

(0.775)

(0.470)

(-1.216)*

(-0.421)

(-0.256)

Debt/Assets

0.0030

0.0720

-0.0491

-0.0809

-0.0449

(0.046)

(1.211)*

(-0.563)

(-1.127)

(-0.702)

Tobin Q

0.8021

0.1864

-0.3570

0.1908

0.5177

(1.789)**

(0.232)

(-0.495)

(0.294)

(0.779)

Panel B: Difference in bidder characteristics of early and late acquisitions for five- and seven-day event window

Bidder characteristics

Cash/Assets

0.0245

0.0100

-0.0339

-0.0129

-0.0112

(0.652)

(0.298)

(-1.108)

(-0.447)

(-0.391)

Debt/Assets

0.0237

0.0849

-0.0317

-0.0735

-0.0398

(0.346)

(1.417)*

(-0.360)

(-1.004)

(-0.617)

Tobin Q

0.9149

0.2230

-0.2470

0.2345

0.4186

 

 

 

 

(2.114)**

(0.278)

(-0.338)

(0.358)

(0.628)

In short, the relative deal size during early wave is smaller than relative deal size during late wave and market overvaluation of early acquisitions is larger than that of late acquisitions. The differences of bidders characteristics between early and late acquisitions are also quite different based on definitions of early acquisitions.

6.3 Univariate analysis

In this section, all samples are divided into subsamples based on whether there is a tender offer or not, payment methods and the public status of targets so as to further examine the factors that influence the cumulative abnormal returns (CAR) around merger announcement date.

6.3.1 Univariate analysis- CAR by whether there is a tender offer or not

Mandelker (1974) reports that acquiring firms’ shareholders earn abnormal positive returns if the mergers in his sample are preceded by a tender offer. Dodd and Ruback (1977) analyze both successful and unsuccessful deals and also find that the shareholders of successful bidders earn significant positive abnormal return. In table 5, all samples are divided into subsamples based on whether there is a tender offer or not. The definition of tender offer given by Thomson one Banker is a formal offer of determined duration to acquire a public company's shares made to equity holders. The offer is often conditioned upon certain requirements such as a minimum number of shares being tendered. From table 5, we can see that, for three day event window, if there is tender offer, early bidders earn much more cumulative abnormal return around merger announcement than late bidder do. For example, if early acquisition is defined as first 10% of deals announced in merger wave, early bidders earn 176.78% more CAR than late bidders do in the three-day event window. However, the situations are quite different in both five-day and seven-day event window. Early bidders earn slightly less CAR than late bidders if there is tender offer. For instance, in the five-day event window, if early acquisition is defined as first 40% of deals announced, early bidders earn about 4% less return CAR than late bidders do and this difference is significantly different from zero at 10% level.

Under the subsample that no tender offer is made, a different picture shows up. In three-day event window, early bidders earn slightly less CAR than late bidders do for most definition of early acquisitions. However, this situation also reverses in five-day and seven-day event window. That is, for most definition of early acquisitions, early bidders earn about 2% more CAR than late bidders do and the difference between early and late acquisitions is significantly different from zero at 10% level.

Table 5

The difference of mean CAR between early and late acquisitions by tender offer

The sample of acquisition deals meets the criteria described in Table 1. Panel A, B and C show the difference in mean of cumulative abnormal returns to bidders in merger wave by whether there is a tender offer or not for event window [-1, 1], [-2, 2] and [-3, 3] around the announcement date respectively. Early acquisitions are the first 10%, 20%, 30%, 40%, or 50% of deals announced in each merger wave. The remaining deals are classified as late acquisitions. Tender means that a tender offer is launched for the target. The difference tests are based on t-test for equality in means. ** and * indicate significance at the 5% and 10% levels, respectively.

Percentage of deals classified as early acquisitions

10%

20%

30%

40%

50%

Panel A: Difference in mean CAR of early and late acquisitions for three-day event window

Tender

1.7678

1.0785

0.7219

0.5683

0.4401

No Tender

0.0401

-0.0261

-0.0122

-0.0061

-0.0016

Panel B: Difference in mean CAR of early and late acquisitions for five-day event window

Tender

-0.0385

-0.0433

-0.0369

-0.0405*

-0.0145

No Tender

0.0362

0.0294*

0.0224*

0.0123

0.0140

Panel C: Difference in mean CAR of early and late acquisitions for seven-day event window

Tender

-0.0182

-0.0351

-0.0385

-0.0260

-0.0002

No Tender

 

0.0375

0.0366*

0.0295*

0.0156

0.0210*

6.3.2 Univariate analysis- CAR by method of payment

In table 6, all samples are divided into subsamples based on payment method. For those bids financed with 100% stock, in the three-day event window, early bidders earn much more CAR than late bidders do. For example, if early acquisition is defined as first 10% of deals announced in merger wave, early bidders earn 105.28% more return than late bidders do. As for five-day and seven-day event window, early bidders still early more than late bidders but the magnitude is much smaller. For instance, in seven-day event window, if early acquisition is defined as first 40% of deals announced, early bidders earn 4.7% more return than late bidders and this difference between early and late bidders is significantly different from zero at 10% level.

When looking at deals financed with a combination of stock and cash, a different result occurs. In three-day event window, if early acquisition is defined as first 10% of deals announced, early bidders earn 7.34% more return than late bidders and this difference is significant. However, for other definition of early acquisitions, early bidders earn slightly less than late bidders do. When it comes to five-day and seven-day event window, situations reverse. For all definition of early acquisitions, early bidders earn slightly more than late bidders.

Table 6

The difference of mean CAR between early and late acquisitions by method of payment

The sample of acquisition deals meets the criteria described in Table 1. Panel A, B and C show the difference in mean of cumulative abnormal returns to bidders in merger wave by method of payment for event window [-1, 1], [-2, 2] and [-3, 3] around the announcement date respectively. Early acquisitions are the first 10%, 20%, 30%, 40%, or 50% of deals announced in each merger wave. The remaining deals are classified as late acquisitions. Stock offers include all bids where 100% of the consideration offered is stock. Mixed offers include all bids where the method of payment is a mix of cash and stock. The difference tests are based on t-test for equality in means. * indicate significance at the 10% levels.

Percentage of deals classified as early acquisitions

10%

20%

30%

40%

50%

Panel A: Difference in mean CAR of early and late acquisitions for three-day event window

Stock

1.0528

0.6992

0.6432

0.4370

0.3215

Mixed payment

0.0734*

-0.0324

-0.0163

-0.0117

-0.0079

Panel B: Difference in mean CAR of early and late acquisitions for five-day event window

Stock

0.0204

0.0311

0.0308

0.0195

-0.0034

Mixed payment

0.0481

0.0191

0.0131

0.0044

0.0042

Panel C: Difference in mean CAR of early and late acquisitions for seven-day event window

Stock

0.0268

0.0531

0.0512

0.0470*

0.0330

Mixed payment

 

 

0.0478

0.0192

0.0118

0.0043

0.0064

6.3.3 Univariate analysis- CAR by public status of targets

First, we look at the bids whose targets are public firms. In three-day event window, early bidders earn much more than late bidders do, and the magnitude of difference in CAR between early and late bidders is quite large. However, in five-day and seven-day event window, the results are different. Early bidders earn less return than late bidders and this difference is statistically significant. For instance, in the five-day event window, if early acquisition is defined as first 40% of deals announced in merger wave, early bidders earn 3.76% less than late bidders do, and this result is statistically significant at 5% level.

Then, we look at private targets. Results are consistent in all event windows. Early bidders earn more cumulative abnormal returns than late bidders do. For example, in the three-day event window, if early acquisition is defined as first 50% of deals announced in merger wave, early bidders earn 2.39% more return than late bidders and the difference is statistically significant at 5% level. In the five-day event window, if early acquisition is defined as first 30% of deals announced, early bidders earn 3.47% more return than late bidders and the result is also significant at 5% level.

Finally, we look at targets that are subsidiaries. In three-day event windows, early bidders earn less return than late bidders do. For example, if early acquisition is defined as first 50% of deals announced in merger wave, early bidders earn 11.37% less return than late bidders and this difference is statistically significant at 10% level. However, different pictures show up in five-day and seven-day event window. Early bidders earn more return than late bidders do. For instance, if early acquisition is defined as first 20% of deals announced, early bidders earn 11.79% more return than late bidders do and this result is significant at 10% level.

Table 7

The difference of mean CAR between early and late acquisitions by public status of targets

The sample of acquisition deals meets the criteria described in Table 1. In this sample, the publicly traded bidder gains control of a public, private or subsidiary target. Panel A, B and C show the difference in mean of cumulative abnormal returns to bidders in merger wave by public status of target for event window [-1, 1], [-2, 2] and [-3, 3] around the announcement date respectively. Early acquisitions are the first 10%, 20%, 30%, 40%, or 50% of deals announced in each merger wave. The remaining deals are classified as late acquisitions. The difference tests are based on t-test for equality in means. ** and * indicate significance at the 5% and 10% levels, respectively.

percentage of deals classified as early acquisitions

10%

20%

30%

40%

50%

Panel A: Difference in mean CAR of early and late acquisitions for three-day event window

Public

1.2558

0.7674

0.5630

0.4602

0.3378

Private

0.0582

0.0195

0.0198

0.0153

0.0239**

Subsidiary

0.0618

-0.2297

-0.1576

-0.0874

-0.1137*

Panel B: Difference in mean CAR of early and late acquisitions for five-day event window

Public

-0.0290

-0.0437*

-0.0367*

-0.0376**

-0.0194

Private

0.0267

0.0210

0.0230

0.0121

0.0215*

Subsidiary

0.1122

0.1179*

0.0238

0.0022

-0.0039

Panel C: Difference in mean CAR of early and late acquisitions for seven-day event window

Public

-0.0148

-0.0353

-0.0364*

-0.0250

-0.0068

Private

0.0358

0.0356*

0.0347**

0.0186

0.0282*

Subsidiary

 

0.0790

0.0845

0.0059

-0.0015

0.0191

To sum up the result of univariate analysis, there are three main findings. Firstly, results are extremely sensitive to the definition of early acquisitions and the type of event window. For different event window, opposite results may show up. Secondly, the magnitude of difference in CAR between early and late acquisition is relative large in the three-day event window under most univariate analysis. Thirdly, for private targets, early bidders often earn more return than late bidders do.

6.4 Results of hypothesis test

In this section, the results of hypotheses test are presented and discussed. The dependent variables are cumulative abnormal returns around merger announcement date. The explanatory variables have been described in the previous chapter. I use the dummy variable EARLY that takes the value of one if acquisitions are the first 10%, 20%, 30%, 40%, or 50% of deals announced in merger wave. In order to control the presence of frequent acquirers that have been associate with managerial overconfidence (Billett and Qian, 2008), dummy variable SERIAL is introduced. SERIAL equals to one if the acquiring firm has made two or more acquisitions within two consecutive years, and zero otherwise. I also include dummy variable INTER that takes the value of one when target and acquirer have a different 2-digit SIC code.

6.4.1 Hypothesis 1: there is early wave effect in the sixth merger wave between 2004 and 2007 in the UK

In order to identify early wave effect, I use the dummy variable EARLY that takes the value of one if acquisitions are the first 10%, 20%, 30%, 40%, or 50% of deals announced in merger wave. If there is early wave effect, the coefficient of dummy variable EARLY should be significantly positive. In table 8, for the three-day event window, we can find that the coefficient of EARLY is significantly positive if early acquisition is defined as first 10% or 20% of deals announced in merger wave. The coefficient is 0.4056 (significant at 5% level) and 0.2177 (significant at 10% level) respectively. That is, controlling for acquirer and deal characteristics, there is a significant early wave effect independent on the variables used to capture it if the first 10% or 20% of deals announced in merger wave is defined as early wave. On the other hand, for the five-day and seven-day event window, the early wave effect is not significant for all definitions of early wave. [14] 

However, this situation may not be a surprise: in the first place, according to the efficient market hypothesis, the abnormal return cannot last long and this result, to some degree, shows that the UK stock market is efficient. In the second place, large event window may introduce more noises rather than useful information. The predictive power will be lower when more days are included in the event window due to the possibility of confounding effects from other market events (MacKinlay, 1997). In the third place, the early wave effect detected by some previous studies is also for the three-day event window. [15] In addition, I have to admit that the insignificant early merger wave effect may be due to the imperfect way to identify merger wave in the first place. That is, the identification of the merger wave is using the research results of others.

As for other explanatory variables, none of them shows significant influence on the cumulative abnormal return for all event windows. But the coefficient of variable TENDER and CASH/ASSET is large: 0.3742 and 0.4377 respectively. These two variables show large positive impact on the cumulative abnormal returns. In addition, univariate analyses for explanatory variables are conducted in the previous chapter.

Above all, controlling for several deal and acquirer characteristics, there is early wave effect if the first 10% or 20% of deals in merger wave is defined as early wave. The early wave effect is very sensitive to the definition of the early stage of merger wave and the number of days included in the event window.

Table 8

Acquirer cumulative abnormal return regressions for three-day event window

This table shows regressions results of the acquirer cumulative abnormal return regressions for the event window [-1, 1] around the announcement date. The sample meets the criteria described in Table 1. The dependent variable is the three-day acquirer cumulative abnormal return (CAR) and is regressed on early acquisition dummy and control variables. Early acquisitions are the first 10%, 20%, 30%, 40%, or 50% of deals announced in merger wave. The remaining deals are classified as late acquisitions. Control variables include INTER—a dummy variable equal to 1 if the acquiring and the target firms have a different 2-digit SIC code [16] ; SERIAL—a dummy variable equal to 1 if the acquiring firm has made 2 or more acquisitions within 2 consecutive years, and zero otherwise; TENDER—a dummy variable equal to 1 if a tender was made to target shareholders and zero otherwise; PRIVATE—a dummy variable takes 1 if the targets are private firms; PUBLIC—a dummy variable takes 1 if the targets are public firms; STOCK—a dummy variable equal to 1 if 100% of the consideration offer is stock; CASH includes cash and marketable securities; DEBT is the net debt; ASSET is acquirer’s total asset; RSIZE is the relative deal size defined as the transaction value divided by acquirer market value 4 weeks prior to the announcement date; TOBINQ is defined as the market value of assets divided by the book value of assets, whereas the market value of assets is total book assets minus the book value of equity plus market value of equity. N is the number of observations in each regression and Adj. R2 is the adjusted R-squared. P-values are reported in parentheses below the coefficients. ** and * indicate significance at the 5% and 10% levels, respectively.

Percentage of deals classified as early acquisitions

Dependent variable: CAR

10%

20%

30%

40%

50%

CONSTANT

-0.1429

-0.1158

-0.1070

-0.1071

-0.0952

(0.513)

(0.601)

(0.632)

(0.635)

(0.683)

EARLY

0.4056

0.2177

0.1564

0.1238

0.0725

(0.014)**

(0.099)*

(0.183)

(0.265)

(0.513)

INTER

-0.0761

-0.0796

-0.0944

-0.1048

-0.0970

(0.497)

(0.483)

(0.404)

(0.355)

(0.394)

SERIAL

-0.0432

-0.0489

-0.0387

-0.0627

-0.0538

(0.729)

(0.698)

(0.761)

(0.621)

(0.672)

TENDER

0.3742

0.3438

0.3405

0.3318

0.3461

(0.200)

(0.244)

(0.250)

(0.264)

(0.245)

PRIVATE

0.1391

0.1165

0.1190

0.1269

0.1195

(0.478)

(0.556)

(0.549)

(0.525)

(0.552)

PUBLIC

-0.0426

-0.0246

-0.0136

0.0064

-0.0180

(0.893)

(0.939)

(0.966)

(0.984)

(0.956)

STOCK

0.1545

0.1856

0.2091

0.1969

0.1995

(0.289)

(0.205)

(0.154)

(0.180)

(0.176)

CASH/ASSETS

0.4377

0.4407

0.3288

0.3473

0.3696

(0.228)

(0.231)

(0.372)

(0.346)

(0.316)

DEBT/ASSETS

-0.0283

-0.0069

-0.0430

-0.0475

-0.0349

(0.843)

(0.962)

(0.767)

(0.745)

(0.811)

RSIZE

-0.0361

-0.0467

-0.0427

-0.0476

-0.0491

(0.623)

(0.528)

(0.566)

(0.523)

(0.512)

TOBINQ

-0.0141

-0.0162

-0.0149

-0.0143

-0.0146

(0.322)

(0.260)

(0.301)

(0.325)

(0.316)

N

166

166

166

166

166

Adj. R2

 

 

0.0305

0.0091

0.0030

-0.0005

-0.0058

After identify early wave effect, I try to find potential explanations for the early wave effect. However, due to the fact that the number of samples which are classified as early wave is small, I mainly focus on the samples classified as late acquisitions. That is, I analyze the hypotheses developed to explain negative abnormal returns to bidders in the late stage of merger wave so as to see which of these hypotheses can help to explain the wave effect.

6.4.2 Hypothesis 2: the lower return in late stage of merger wave is because the late bidders are overconfident

Billett and Qian (2008) argue that frequent bidders have been associated with managerial overconfidence. The bidders’ managers may become overconfident in late stage of merger wave for success in early stage of merger wave or even other’s success in early acquisitions. Therefore, in order to identify the overconfident acquirers, a dummy variable SERIAL which takes the value of one when the acquiring firm has made two or more acquisitions within two consecutive years is used. If the hubris hypothesis can explain the wave effect, the coefficient of the dummy variable SERIAL should be significantly negative. However, the regression result in table 9 shows that the coefficients of the dummy variable SERIAL are insignificant positive for both last 80% and last 90% of deals in merger wave. This result is inconsistent with the managerial hubris hypothesis, as the hypothesis would predict a significantly negative coefficient. Thus, my finding indicates that the wave effect is not due to managerial overconfidence. This insignificant result is, to some degree, consistent with the finding by Alexandridis, Mavrovitis and Travlos (2010). They study the sixth merger wave between 2003 and 2007 and find that acquirers are less acquisitive and acquirer CEOs displayed less overconfidence about their ability to create value through merger and acquisition. Alexandridis, Mavrovitis and Travlos (2010) also argue that the significantly lower premiums paid during the sixth merger wave implies more rational and promising acquisition decisions. Thanks to more rational CEOs, the frequent bidders seem to experience less negative market reaction on the merger announcement.

6.4.3 Hypothesis 3: the lower return in late stage of merger wave is because late acquirers use the free cash flows to increase private benefits

The low interest rates and abundant liquidity before the financial crisis may lead to excessive free cash flow in late stage of merger wave. According to the literature review in chapter 2 and 3, a significantly negative relationship between cash holdings and the cumulative abnormal return should be found. However, in table 9, the totally opposite results are discovered: the coefficients for variable CASH/ASSETS are 0.0959 (significant at 5% level) for last 80% of bids and 0.0931 (significant at 10% level) for last 90% of bids. That is, there is significantly positive relationship between the cash reserves and the cumulative abnormal return for event window [-1, 1] around the announcement date. This result is inconsistent with the free cash flow theory and the finding of Harford (1999). The cash holdings by acquirers make significantly positive contribution to acquirers’ cumulative abnormal return in the three-day event window. The reason may be that cash holding can ease the financial stress of the company or cash can help companies grow well after merger and acquisition. One of the persuading facts is that cash is valuable when market experience credit squeeze that occurs in recent financial crisis. Moreover, there are economic theories that predict positive bidder cash holding effect. A liquid balance sheet allows firms to undertake valuable projects when they occur (Keynes, 1936). Comparing to internal funds like cash, external funds are expensive because there are agency costs (Myers, 1977). Furthermore, Gao and Mohamed (2012) investigate cash-rich bidders in the UK from 1984 to 2007. They find that contrasting to US bidders, cash-rich bidders in the UK have better announcement abnormal returns than cash-poor ones. They argue that cash reserve allows bidders to capture growth opportunities that arise from acquisitions and defence negative shocks. In this case, there is indeed difference between US and UK. The reason behind this difference may be the fact that shareholder power is stronger in the UK than in the US. In addition, there is also no evidence that cash rich firms waste their cash on acquisitions (Pinkowitz, J Sturgess, and Williamson, 2011). As a result, the free cash flow theory also fails to explain the early wave effect, but at least a different market reaction on bidders’ cash holding is identified.

To sum up the hypothesis test result, we can observe that controlling for several deal and acquirer characteristics, there is early wave effect if the first 10% or 20% of deals in merger wave is defined as early wave. However, both the hubris hypothesis and free cash flow theory fail to explain this wave effect.

6.5 Other findings

There is a seemingly unexpected factor that can account for the lower returns of bidders in the late stage of merger wave. As indicated in table 9, the coefficient of the variable Tobin’s Q [17] is significantly negative. The coefficients are -0.0033 (significant at 5% level) for last 80% of bids and -0.0027 (significant at 10% level) for last 90% of bids. Tobin’s Q represents market overvaluation of acquirers. That is, market overvaluation has shown statistically significant negative effect on the cumulative abnormal return for three-day event window in late stage of merger wave. Market overvaluation plays a vital role in merger and acquisition activities. If the company is overvalued by market and its manager hold insider information of the company, then he can take advantage of this overvaluation to make acquisitions. Looking back to the history of last six merger wave, we can find the fact in common that each of these merger waves ends with the decline in the stock market (Pazarskis et.al, 2006). Rhodes-Kropf and Viswanathan (2004) provide evidence on the theory that valuation errors affect merger activity. Acquirers whose stock price has high firm-specific error use stock to buy targets whose stock price has lower firm-specific error and by doing so, both firms benefit from positive time series sector error. Shleifer and Vishny (2003) also discover that merger waves are more likely to occur because overvalued firms seek to acquire less overvalued assets. The market overvaluation seems to affect the merger wave. Moreover, according to research by Rhodes-Kropf, Robinson, and Viswanathan (2005), roughly 40% of the total dollar amount of merger activity occurs during these merger waves, highly overvalued bidders are responsible for the bulk of these mergers. Stock market is efficient to information and reacts to information quickly and rationally. The market may adjust and correct for part of the overvaluation at the merger announcement and thus market overvaluation exerts a negative influence on the cumulative abnormal return of the bidders. Dong, Hirsheifer, Richardson and Teoh (2006) investigate the takeover bids between 1978 and 2000 and find that the valuation of the bidder has a significantly negative impact on the abnormal return around the merger announcement date. Akbulut (2005) also finds similar result that overvalued companies are more acquisitive and receive negative market reaction to the merger and acquisition announcement. Just as said by Rhodes-Kropf, Robinson, and Viswanathan (2005), even though economic shocks could well be the primary drivers of merger activity, market misvaluation affects who buys whom, shapes merger wave and affects the cumulative abnormal returns of bidders around the merger and acquisition announcement. All in all, the regression result indicates that the overvaluation of bidders leads to lower return in late stage of merger wave.

Table 9

Regression analysis of cumulative abnormal returns to late wave bidders

This table shows the results of regression analysis of cumulative abnormal return to late wave bidders. The sample meets the criteria described in Table 1. The dependent variable is the three-day cumulative abnormal return to bidders in late acquisitions. Late acquisitions are the last 80%, or 90% of deals announced in merger wave. INTER is a dummy variable equal to 1 if the acquiring and the target firms have a different 2-digit SIC code, and zero otherwise. SERIAL is a dummy variable equal to 1 if the acquiring firm has made 2 or more acquisitions within 2 consecutive years, and zero otherwise. TENDER is a dummy equal to 1 if a tender offer was made to target shareholders and zero otherwise. PRIVATE and PUBLIC are dummy variables that take the value of one for bids for private firms and for public firms. STOCK is a dummy variable that take the value of one if 100% of the consideration offer is stock. CASH includes cash and marketable securities; DEBT is the net debt; ASSET is acquirer’s total asset; RSIZE is the relative deal size defined as the transaction value divided by acquirer market value 4 weeks prior to the announcement date; TOBINQ is defined as the market value of assets divided by the book value of assets, whereas the market value of assets is total book assets minus the book value of equity plus market value of equity. N is the number of observations in each regression and Adj. R2 is the adjusted R-squared. P-values are reported in parentheses below the coefficients. ** and * indicate significance at the 5% and 10% levels, respectively.

Percentage of deals classified as late acquisitions

Dependent variable: CAR

 

80%

 

90%

 

CONSTANT

0.0131

0.0125

(0.541)

(0.572)

INTER

0.0121

0.0091

(0.284)

(0.426)

SERIAL

0.0023

0.0028

(0.856)

(0.826)

TENDER

-0.0168

-0.0253

(0.591)

(0.439)

PRIVATE

-0.0022

-0.0022

(0.915)

(0.913)

PUBLIC

-0.0169

-0.0180

(0.616)

(0.611)

STOCK

-0.0114

-0.0054

(0.462)

(0.720)

CASH/ASSETS

0.0959

0.0931

(0.009)**

(0.010)*

DEBT/ASSETS

0.0166

0.0167

(0.216)

(0.227)

RSIZE

-0.0014

-0.0036

(0.847)

(0.615)

TOBINQ

-0.0033

-0.0027

(0.026)**

(0.051)*

N

130

145

Adj. R2

 

 

 

0.0438

 

0.0489

 

Chapter 7: Conclusion

The objective of this dissertation is to examine whether there is early wave effect in the sixth merger wave between 2003 and 2007 in United Kingdom and to explore the potential explanations for the early wave effect identified. The main findings of this dissertation are as follows: in the first place, the relative deal size of early wave is smaller than relative deal size during late wave and for private targets, early bidders often earn more return than late bidders do.

In the second place, the regression results indicate that there is a significant early wave effect in the three-day event window if the first 10% or 20% of deals announced in merger wave is defined as early wave. However, this early wave effect is very sensitive to the definition of the early stage of merger wave and the number of days included in the event window. That is, there is no significant early wave effect in the three-day event window when early wave is defined as first 30%, 40% or 50% of deals announced in merger wave and in five-day and seven-day event window, no early wave effect is detected. Moreover, from the summary statistics of cumulative abnormal return (CAR) for three-day window, it is easy to find that the standard deviation of CARs is largest for the first 10% and 20% of deals announced in merger wave among all other percentage of deals announced in merger wave. Therefore, if the first 10% or 20% of deals announced in merger wave is defined as early wave, the returns of acquiring firms in early wave vary a great deal.

In the third place, regression analyses are conducted to test two hypotheses. The first one is the hubris hypothesis that managerial hubris in merger and acquisition may result in overpaying for target firms or overoptimistic about the merger and thus result in negative returns on the stock price of acquirers in late stage of merger wave (Floegel et.al, 2005). The second one is the free cash flow hypothesis that managers make use of cash holdings to make merger bids to benefit themselves (Jensen, 1986). However, the regression results show that both the hubris hypothesis and the free cash flow hypothesis fail to explain the lower return in late stage of merger wave. This result is consistent with finding by Alexandridis, Mavrovitis and Travlos (2010) that in the sixth merger wave managers tend to be less overconfident and more rational when making takeover bids but different from previous findings by Floegel, Gebken and Johanninga (2005) that overconfidence accounts for lower return in late stage of merger wave.

In the fourth place, I find a significant positive relationship between cash holding and merger announcement return for the UK data sample, contrast to the negative relationship found based on the US data in other research [18] . Finally, I discover that market overvaluation may have more explanation power for the lower return of bidders in late acquisitions than the overconfidence hypothesis and the free cash flow theory.

When it comes to the limitation of this dissertation, there are at least two points. The first one is imperfect way to identify merger wave and research results from previous studies are used instead of using methods to capture merger wave. The second point is about the number of samples: due to the fact that a number of bidders in the sample have missing data for the stock price or the trading volume around the announcement date, this kind of bids are not included in the dissertation sample and as a result, information may be missing for the incomplete data samples.

In addition, there is also the suggestion for further research. Other explanation for early wave effect such as the herding approach that late bidders may just follow other previous successful bidders to take part in merger and acquisition activities and thus result in a loss in the late stage of merger wave or hypothesis that bidders in late stage rationally pay more to maintain their competitive advantage can be studied or test for further research.


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