The short run performance of Dutch
Mergers and Acquisitions:
A study of acquiring-firm returns after
the recent merger wave
Thesis: MSc. Business Administration, Finance Student: R.M. Voortman, 1172425
Abstract
The existing literature on the merger and acquisition performance of acquiring firms is divided, in this research we re-examine this issue for the Dutch M&A market. We would like to answer the question whether Dutch mergers and acquisitions create value in the short-run for acquiring companies. We empirically test how stock returns of Dutch acquisitive firms in the period 2000-2005 perform compared to a
benchmark. In our research we use the mean-adjusted-, the CAPM-, and the Fama and French three-factor model to compute ‘normal’ returns. We find that stockholders of acquiring firms suffer a loss of about 0,20% over the event window [-10,+10]. The statistical significance of this result is too small to strongly reject our null hypothesis that the event has no impact on the behaviour of returns. We find a statistical
significant evidence of underperformance for public target companies, on the contrary the acquisition of a private target company results in significant positive returns.
When we decompose our sample further we find positive abnormal returns for relative small transactions and significant negative abnormal returns for relative big
transactions. Furthermore our portfolio shows a size effect in acquisition
Index
1. Int roduction... 4
2. Theoretical Framework ... 10
2.1 Introduction... 10
2.2 Types of M&A... 10
2.3 Theories of the Value Effects ... 11
2.3.1. Value Creation... 12
2.3.2. Value Reduction ... 12
2.3.3. Value Neutral... 12
2.4 Market Efficiency... 13
2.5. Measurement of M&A Profitability ... 14
2.5.1. Event Studies ... 14 2.5.2. Accounting Studies ... 15 2.5.3. Surveys of Executives... 15 2.5.4. Clinical Studies ... 16 3. Literature Review... 17 3.1 Introduction... 17
3.2. Acquiring Shareholder Returns ... 17
3.3. Transaction Characteristics... 18
3.3.1. Size Effect... 18
3.3.2. Explaining the Size Effect ... 19
3.3.3. Private vs. Public Targets ... 20
3.3.4. Book-to-Market Value ... 21
3.3.5. Method of Payment ... 21
3.4. Overview... 22
4. Review of Models ... 24
4.1. Defining Abnormal Performance ... 24
4.4. Three-Factor Model Fama and French ... 27
4.5. Measurement and Analysis of Abnormal Returns ... 29
5. Methodology and Data Selection... 32
5.1. Introduction... 32
5.2 Data Collection... 33
5.3. Fama and French Portfolio Construction... 35
6. Results ... 37
6.1. Introduction... 37
6.2. Split up by Method ... 38
6.3. Yearly Sample CAR and Number of Transactions ... 40
6.4. Private vs Public Targets ... 41
6.5. Relative Deal Value ... 44
6.6 Size ... 46
7. Conclusion... 49
8. Recommendations for Future Research ... 52
References... 53
Appendix 1a Sample Construction... 58
Appendix 1b Descriptive Statistics ... 59
Appendix 2a (Cumulative) Abnormal Returns; Mean-adjusted... 60
Appendix 2b (Cumulative) Abnormal Returns; CAPM ... 61
1. Introduction
‘The sobering reality is that only about 20 percent of all mergers really succeed. Most mergers typically erode shareholder wealth…’ Grubb and Lamb (2000)
The number of mergers and acquisitions (M&A) has increased enormously since the past decades. During the late 1990s the size, volume and frequency of M&A surpassed anything the world had ever seen before. The booming world economy in combination with a high level of deregulation and high equity prices formed the basis for many managers to restructure organisations and form large highly diversified conglomerates. The most recent wave of mergers and acquisitions was remarkable compared to its predecessors. For the first time, M&A activity in Europe hit levels similar to those experienced in the US. There was an enormous increase in M&A activity in Continental Europe perceptible. As Martynova and Renneboog (2006) state ‘while most M&A transactions in Europe during the 1990s were still in the UK, M&As in Continental Europe have risen substantially both in number of deals and total transaction value compared to the previous decades’. The Dutch M&A market grew from a total value of 8 billion Euro in 1997 towards a value of more than 120 billion Euro in 2000, representing a market increase with factor 15. M&A transactions formed the basis for growing the company and reached record levels in 2000. Managers were extraordinary confident about the future, and mainly lead by managerial hubris and market irrationality in their judgement of M&A transactions, Bouwman, Fuller and Nain (2003).
transactions financed with private equity2 in Europe. Total transaction value in the Netherlands totalled 112 billion Euro in 2005, representing more than 300 transactions. € -€ 200,0 € 400,0 € 600,0 € 800,0 € 1.000,0 € 1.200,0 € 1.400,0 € 1.600,0 2000 2001 2002 2003 2004 2005 0 2.000 4.000 6.000 8.000 10.000 12.000 14.000 16.000
Number of transactions Total deal value M&A activity Europe 2000-2005 Total deal value in billion Euro
€ -€ 20,0 € 40,0 € 60,0 € 80,0 € 100,0 € 120,0 € 140,0 2000 2001 2002 2003 2004 2005 0 50 100 150 200 250 300 350 400
Number of transactions Total deal value M&A activity Netherlands 2000-2005 Total deal value in billion Euro
Figure 1.1 M&A activity Europe 2000-2005 Figure 1.2 M&A activity Netherlands 2000-2005
The enormous growth in M&A activity has been the basis for a large extent of economic research in the past decades, many researchers have sought to find out whether M&A transactions create or destroy value and how potential gains or losses are distributed among transaction participants. Many participants can be identified in a M&A transaction; to identify the value effects of a M&A transaction the literature focuses on the shareholders of both parties. Through short and long term event studies abnormal returns are analysed to identify possible wealth effects. Event studies examine the abnormal returns to shareholders in the period surrounding the announcement of a transaction. The raw return for one day is simply the change in share price and any dividends paid, divided by the closing share price the day before. The abnormal return is the raw return less a benchmark of what investors required that day, i.e. the opportunity cost of capital. Typical benchmarks are the return dictated by the Capital Asset Pricing Model (CAPM) or the return on a large market index. Bruner (2001) used investors required returns, defined as the return investors could have earned on other investment opportunities of similar risk, as the benchmark for measuring performance. Against this benchmark value can be conserved, created or destroyed.
2
Event studies focus on the impact of particular types of firm-specific events on the prices of the affected firms’ securities. Stock price reactions are analysed in a pre-defined event window surrounding the announcement date of a M&A transaction. The announcement date is defined as the first day information regarding the specific event becomes publicly available, often surrounding this announcement date rumours are in the market and arbitrage opportunities exist. Event studies focus typically on the short-term or the long- term wealth effects surrounding the announcement of an event. Short- term event studies are based on the assumption that all information about future cash flows of the M&A transaction is incorporated within a short event window typically one, three or five days surrounding the announcement. Share price reactions are analysed before (pre) and after (post) the announcement date to identify clearly
the whole effect surrounding the event. In order to eradicate possible arbitrage effects
in the market a longer post-horizon is taken quite often. Long- term event studies examine the behaviour of stock returns typically one, three or five years around the announcement of M&A (Bruner, 2001). Whether long term wealth effects can be attributed totally to such a specific event as a M&A transaction remains questionable, since it is hard to correct risk effects completely over one, three or five years.
The mass of short-term event studies suggests that target shareholders earn sizeable positive market returns, that bidders earn zero or negative adjusted returns, and that
combined returns are positive3. In contrast long-term event studies show
underperformance4 of the acquirer during the event. Recent research have been
impact on the value effect in a M&A transaction. Stock based deals are associated with significantly negative returns at deal announcements.
The central question in this report is whether mergers and acquisitions are value-neutral activities in the short run for the acquirer, as identified by previous research. Through analyses of short-term stock price reactions surrounding announcements we will identify possible abnormal stock returns. In our research we’ll follow the null hypothesis as formulated by MacKinlay (1997):
H0: The event has no impact on the behaviour of returns (mean or variance), and
results in neutral value effects to acquiring shareholders
To evaluate bidder returns we will follow the standard event study methodology, as suggested by Brown and Warner (1980). We will take an event window of one day before (pre), and one day after (post) announcement [-1,1]. To correct for possible arbitrage effects in the market and to identify possible pre- run up share price developments we’ll use next to the three-day event window an event window of ten days pre, and ten days post announcement [-10,+10]. Brown and Warner (1980) indicate that in case of abnormal performance the differences between methodologies based on Mean Adjusted Returns, Market Adjusted Returns, and Market and Risk Adjusted Returns are quite small. ‘The mean-adjusted methodology picked up abnormal returns no less frequently than did the other methodologies’. The use of risk adjustment procedures in the other methodologies did not enhance the power of the tests. Though the finding of Brown and Warner we will use next to a Mean Adjusted Return model, the Capital Asset Pricing Model (CAPM) and the three-factor model of Fama and French to correct for market, size and book-to-market effects. The methodology of the asset-pricing models will be explained in detail in chapter four.
characterized by weaker investor protection and less developed capital markets. Furthermore Faccio and Lang (2002) indicate Continental European companies are typified by a more concentrated ownership structure.
We will empirically test whether M&A activity shows any underperformance in our sample of Dutch public firms form January 2000 to December 2005. Given our time frame we directly test whether managerial hubris and market irrationality might have been gone after 2000. We will re-examine the stated wealth and size- effects by Moeller, Schlingemann and Stulz (2002) by making size distinctions in our sample. Through a split up in bidder- and deal-size characteristics, possible size effects in M&A transactions will be identified. We’ll explicitly subdivide our sample based on market capitalizations, book-to-market values and relative deal value, defined as the deal value divided by the bidders market value of equity. Based on the findings of Moeller, Schlingemann and Stulz (2002) we formulate the the following hypotheses concerning the size-effects:
H1: The announcement return for acquiring-firm shareholders is higher for small
acquirers than large acquirers.
H2: The announcement return for acquiring-firm shareholders is higher for relative
small transactions than relative big transactions.
Furthermore, we will make a distinction in our sample between public and private targets to test whether the acquisition of a private target leads to significant higher returns for bidder shareholders, as stated by Fuller, Netter and Stegemoller (2002).
Based on the research of Fuller, Netter and Stegemoller (2002) we formulate the following hypothesis concerning the type of target:
H3: Acquiring shareholders gain when buying a private firm or subsidiary but lose
when purchasing a public firm
period after the last M&A wave, has not been subject for research before although it is interesting to notice whether there has been a correction in M&A behavior after the enormous downturn in 2000. As Bouwman, Fuller and Nain (2003) state significantly more acquisitions occur when stock markets are booming than when markets are depressed. Acquirers buying during periods of low stock-market valuation appear to be making better acquisitions than those buying during high stock-market-valuation periods. The market welcomes the announcements of M&A transaction during high market- valuation periods, however shareholders earn significant negative abnormal returns during high market- valuation periods. During high market-valuation period’s managers are subject to managerial hubris and the market shows high forms of irrationality. By analyzing abnormal returns after the economic downturn in 2000 we directly test whether the downturn put back managerial focus and took away irrationality in the market. Additionally, we combine the studies of Moeller, Schlingemann, Stulz (2002) and Fuller, Netter and Stegemoller (2002) in our research to identify whether target characteristics and size effects can explain abnormal returns. Finally we combine the three-factor asset-pricing model of Fama and French, the CAPM and a mean adjusted return model to identify possible abnormal returns surrounding announcements.
2. Theoretical Framework
2.1 Introduction
Due to the enormous growth in M&A transactions during the latest and largest merger wave in history (1992-2000) many researchers started to focus on the implications and wealth effects of M&A transactions. Extensive research has been done about the profitability of M&A activity over the past 30 years, resulting in a huge number of diverging studies. The central question is whether M&A transactions create value and how the total wealth effect is distributed between the different parties. There are two primary parties identifiable in a M&A transaction, the buyer and the seller of the target company. The enormous amount of research done in the past resulted in many diverging theories about the wealth distribution between these two parties. After all these years of research it still isn’t unambiguous identifiable whether M&A create or destroy value and how these effects differ between parties and over time.
2.2 Types of M&A
acquisitions involve firms with no apparent potential for operating synergies, and are motivated by financial synergies, tax implications and incentives. Risk reduction by diversification is a often called wrong rationale for conglomerate acquisitions, because investors are personally able to combine resources to achieve a reduction in variance through portfolio effects. Berger and Ofek (1995) studied the degree of relatedness between the business of the buyer and seller and associated returns. They found an average loss in value from diversification of between 13 and 15 percent. In particular, conglomerate deals are associated with the poorest returns. Houston, James and Ryngaert (2001) identified expected synergies as important drivers of wealth creation through mergers in their study of bank mergers. Strategic acquisitions are the most valuable rationale for M&A transactions, because of operating synergies and efficiency improvements.
2.3 Theories of the Value Effects
2.3.1. Value Creation
Coase (1937) argued that the organization of a given firm responds to the appropriate balance between the costs of using the market and the costs of operating internally. M&A transactions are a response to exchanging environmental forces such as technological change that alters the balance between the transaction costs of the markets and internal production. In case of M&A transactions the transaction costs are lower than the cost of internal change and M&A transactions can be seen as value creating activities. Bradley, Desai and Kim (1988) identify synergies as the base for value creation, they include in their definition of synergies economies of scale, more effective management, improved production techniques, and the combination of complementary resources. M&A transactions are value-increasing transactions because of synergy effects between both parties
2.3.2. Value Reduction
Jensen (1986) states that M&A transactions are value reducing activities because of the agency costs of free cash flow. A firm with high free cash flow is one where internal funds are in excess of the investments required to fund positive NPV projects. Excessive free cash flows leads to investments in value reducing negative net present value projects. The interest and incentives of managers and shareholders conflicts over such issues as the optimal size of the firm and the payment of cash to shareholders. Another value- reducing theory is the Shleifer and Vishny (1989) model of managerial entrenchment. Managers make investments that increase the managers’ value to shareholders, these management-specific investments do not enhance value to the shareholders themselves. Consistent with the model of Jensen, managers in the entrenchment model are hesitant to pay out cash to shareholders.
2.3.3. Value Neutral
bidding company and positive gains for the target company. In general a M&A transaction can be seen as a re-distribution of money resulting in neutral combined value effects. Often the value neutrality theory of Roll forms the null- hypothesis in event-study research, in our research we’ll follow this principle.
2.4 Market Efficiency
The efficient market hypothesis (EMH) has been the central proposition of finance for years, explaining the behaviour of capital markets. Fama (1970) defined an efficient financial market as ‘one in which security prices always fully reflect the available information’, there are no arbitrage opportunities and investors are assumed to be rational. In case of irrationality, random trades and rational arbitrageurs will bring back security prices to their fundamental value. According to the EMH there should be no under or over-reaction to particular news announcements, such as M&A transactions. Prices quickly and fully adjusted after information becomes available in the market and there are no arbitrage opportunities surrounding announcement, assuming a neutral value effect inside M&A transactions supporting the Hubris theory of Bruner (2001). Fama (1970) distinguishes between three forms of the EMH. First, there is a weak form EMH, relevant information is based on past prices and returns and there are no opportunities to earn superior risk-adjusted returns. Second, the
semi-strong form EMH states that investors cannot earn superior risk-adjusted returns
2.5. Measurement of M&A Profitability
The measurement of M&A profitability forms a critical issue in drawing conclusions about empirical data. There are a lot of research methods, all suggesting different ways to measure profitability and value creation. First, there are measurement studies that determine profitability by testing a null hypothesis, for example event studies and accounting studies. As mentioned before the value-neutrality hypothesis of Bruner (2001) is often used as the null hypothesis. Second, there is a group of studies that describe rather than test value effects inside M&A transactions. Through qualitative research insights into value creation that may not be known in the stock market are collected and analysed. No research approach is fault-free, and the different methods need to be seen as complementary. In our research we primarily focus on the wealth effects for the shareholders, which as assumed will also be beneficial to other stakeholders. Because we focus on short-term abnormal returns to shareholders surrounding the announcement date the best way to measure profitability is by an event-study.
2.5.1 Event Studies
In addition, Bruner states ‘to the extent that the event is unanticipated, the magnitude of abnormal performance at the time the event actually occurs is a measure of the impact of that type of event on the wealth of the firm’s shareholders’. Any such abnormal performance is consistent with market efficiency, however, since abnormal returns would only have been attainable by an investor if the occurrence of the event could have been predicted with certainty. Because, in theory, stock prices are the
present value of expected future cash flows, market based event studies are athorough
direct measure of the value created for investors. Event studies are vulnerable to confounding events, which could skew the returns for specific companies at specific events; the law of large number can deal with this.
2.5.2. Accounting Studies
Accounting studies measure profitability by analysing financial performance indicators before and after the announcement of an event. Reported financial results of acquiring companies are compared to non-acquiring peer companies to measure value effects. Typical financial measures are net income, return on equity or assets, earnings per share, leverage, and liquidity of the firm, Bruner (2001). Accounting studies are credible because used statements have been certified and accounts have been audited. Reported financial statements are used by investors in judging corporate performance and form hereby an indirect measure of economic value creation. After the accounting scandals of Enron and Ahold the use of accounting studies seriously comes into play.
2.5.3. Surveys of Executives
2.5.4. Clinical Studies
3. Literature Review
3.1. Introduction
In the past decades a lot of research has been done to figure out the wealth effects of M&A transactions. In measuring the total value effect researchers mostly take into account a semi strong form of the EMH, assuming that public available information is immediately incorporated in security prices. In general, most researchers take a shareholder perspective to identify possible value effects. The total value effect is distributed between bidder and target shareholders. On the whole can be stated that target firm shareholders enjoy significant positive returns. Bruner (2001) summarizes the findings of 21 studies, concluding M&A transactions deliver a premium to target firm shareholders. According to the returns to bidder firm shareholders the wealth effect is more complex to figure out. Bruner (2001) concludes in his summary of 41 studies ‘in the aggregate, abnormal (or market-adjusted) returns to buyer shareholders from M&A activity are essentially zero’. Bidder shareholders typically earn the opportunity cost of the investment, not generating any value at all.
3.2. Acquiring Shareholder Returns
acquisition behaviour, they identified a negative cumulative abnormal return of 0,70% to acquiring firm shareholders. They measured the return by comparing the change in the market value of the bidder to the market value of the target’s equity. In their research of mergers and tender offers Kaplan and Weisbach (1992) reported a statistical significant loss for bidding shareholders of 1.49%, ten days surrounding the announcement of the event. Healy, Palepu and Ruback (1992) analysed the 50 largest US mergers and stated a negative cumulative abnormal return of 2.2% to acquiring shareholders. Lang, Stulz and Walking (1989) reported a zero cumulative abnormal return for acquiring shareholders ten days surrounding announcement, in their study of tender offers between 1968 and 1986. Finally Bradley, Desai and Kim (1988) indicated a statistical significant positive short-term abnormal return of 1% to acquirers, in their sample of tender offers.
3.3. Transaction Characteristics
Next to the overall wealth effect, research focuses particularly on the characteristics of the transactions in order to identify possible value drivers. Every deal can be typified by underlying characteristics, ranging from acquirer, target and deal characteristics. Often these characteristics contribute to the explanation of the wealth effects. Past research clearly signals the existence of a size effect in acquisition announcement returns. Furthermore, acquirers significantly earn higher returns when acquiring a private target. Other explaining characteristics are the book-to-market values and the method of payment.
3.3.1. Size Effect
benchmark model that corrects for size risks. Based on market capitalization our sample will be split up in two groups, one group above the median value and one group below the median value. This in order to make a small minus big abnormal return correction in our sample, as dictated by the Fama and French three factor model. Moeller, Schlingemann and Stulz (2005) also stated that acquisition announcements in the 1990s are profitable in the aggregate for acquiring- firm shareholders until 1997, but that losses of acquiring- firm shareholders from 1998 through 2001 wiped out all the gains made earlier. A small number of acquisitions with negative synergy gains by firms with extremely high valuations made the total wealth effect for acquiring shareholders negative. Managerial hubris, large amounts of excess cash and high demanding shareholders formed the basis for excessive value reducing acquisitions. After the enormous economic downturn in the end of 2000, M&A activity decreased tremendously. Glamour- buying, empire-building managers got replaced; investment opportunities decreased and M&A activity only could be justified through identifiable synergies. In our research we’ll identify possible extraordinary transaction impacts on acquisition announcement returns by making a yearly split up of our sample.
3.3.2. Explaining the Size Effect
has growth opportunities the agency cost of free cash flow as stated by Jensen occurs resulting in value reducing acquisitions. Finally, there’s an arbitrage explanation. When an acquirer is a small firm, arbitrageurs are unlikely to use their resources for a merger, because it will be too difficult to establish large short position.
3.3.3. Private vs. Public Targets
3.3.4. Book-to-Market Value
Rau and Vermaelen (1998) argue that book-to-market value ratios can be a good indicator of possible wealth effects. In their research they distinct between glamour acquiring companies and value acquiring companies. Glamour acquirers are highly valued firms; based on their prior stock market performance their stocks receive premium ratings in the form of high price to earnings ratio or market-to-book value ratio. In contrast value-acquiring firms are undervalued but have the potential for subsequent value gains. Glamour acquirers are high-growth firms, structural looking for ways to grow the business; in contrast value acquirers are low growth firms. Rau and Vermaelen (1998) report that glamour acquirers enjoy significantly higher announcement period returns but much lower post-acquisition returns over 3 years than for value acquirers irrespective of the payment method used. Bidders
significantly over extrapolate past performance of companies, as reflected in the book-to-market value ratio, when stating the desirability of an acquisition. Finally Rau and Vermaelen (1998) report a significant tendency for glamour acquirers to finance their acquisitions with their own, overvalued, stock.
3.3.5. Method of Payment
The method of payment is a further driver of profitability analyzed by many
researchers. Asquith, Bruner and Mullins (1987), Huang and Walking (1987) found that deals where stock was the method of payment, bidder shareholders earned significant negative returns at deal announcements, while on the other hand paying with cash results in a neutral value effect. Paying with stock signals overvaluation of the bidder’s stock, for which the market corrects. Contrary to the acquiring
shareholder return, the market's average response to a merger bid is always positive for target firms. This response is significantly more positive when the offer is
3.4. Overview
The literature offers a wide range of research methods for the analysis of M&A performance ranging from quantitative to qualitative research. The measurement of M&A profitability is mainly based on the analysis of market-based returns. Through quantitative event studies the distribution of the value effect between parties is analyzed. Event studies focus either on the short-term or on the long-term value implications of the transaction. Short-term event studies have typical event windows of one, three or five days surrounding announcement, while long-term studies focus on one, three or five years surrounding the announcement of a M&A transaction. There has been a long discussion going on during the last decade whether short-term event studies really incorporated all information about future cash flows, stating the total value effect. Assuming the conditions of the semi-strong efficient market hypotheses we can state that all available information in the market is incorporated in the short-term, especially when taking a longer short-term event window.
In his extensive survey about the drivers and determinants of M&A profitability Bruner (2001) projects some interesting insights from former studies. M&A transactions are not suitable to build market power, possible gains do not derive from anticompetitive considerations. Furthermore, M&A transactions generally result in value reduction when used from the excess cash perspective. This corresponds with the agency cost theory of free cash flows from Jensen. M&A transactions are more profitable when managers have more at stake. Healey, Palepu and Ruback (1997) state ‘the transaction characteristics that were under management control substantially influenced the ultimate payoffs from takeovers’. Finally, the character of the bid is a main determinant of profitability. Tender offers create value for bidders whether more friendly negotiated M&A transactions typically result in lower shareholder returns.
4. Review of Models
4.1. Defining Abnormal Performance
4.2. Capital Asset Pricing Model
The Capital Asset Pricing Model (CAPM) of Sharpe (1964) and Lintner (1965) is one of the oldest and most used models in asset pricing theory. The model is widely used in many applications and states how investors should measure risk and the relation between risk and expected return. The CAPM states the variance of a stock by itself is not an important determinant of the stock’s expected return. To measure expected returns one should take into account the market beta of the stock, which measures the covariance of the stock’s return with the return on a market index, divided by the variance of that index. The market beta of a stock indicates the risk a stock is subject to and measures the sensitivity of the asset’s return to variation in the market return. The CAPM is based on the assumption that investors only care about the mean and variance of their portfolio. Furthermore, markets are frictionless and investors have homogeneous beliefs. Investors are risk- averse and only care about the mean and variance of their investment return.
( )
Rit rf i(
E( )
Rmt rf)
it E = +β − +ε (1)( )
it=0 E ε( )
2 i it Var ε =σε (2)Equation 1 states the expected return on asset i,E
( )
Rit , is a function of the risk freeinterest rate rf and a risk premium βi
(
E( )
Rmt −rf)
. The risk premium is defined as the market beta of asset i times the premium per unit of beta, which is the expected market return minus the risk free interest rate. For every security i Rit and Rmt are theperiod t returns on security i and the market portfolio, ε is the zero mean disturbance it term. In our research we’ll take the three-month US Treasury bill rate as the risk free rate, as suggested by Grinblatt and Titman. Because we need the daily risk free interest rate we divide the three-month rate by 360 days. The market model parameters will be estimated over the 120 days prior to the event, similar to the
research of MacKinlay (1997). The relevance of the CAPM will depend on the R2 of
the market model regression. MacKinlay states ‘the higher the R2 the greater is the
Sharpe (1964) and Lintner (1965) predict the market portfolio is mean-variance efficient, this implies that differences in expected return across securities are entirely explained by differences in market beta. Other variables do not add anything to the explanation of expected asset returns. The CAPM has been subject to extensive research throughout the past decades. In their research Fama and MacBeth (1973), Gibbons (1982) and Stambaugh (1982) consistently reject the prediction that the premium per unit of beta is the expected market return minus the risk free rate, as stated by Sharpe (1964) and Lintner (1965). Furthermore, Fama and French (1992) confirmed in their research using the cross-section regression approach that size, earnings-price, debt-equity, and book-to-market ratios add to the explanation of expected stock returns. The market beta doesn’t explain the entire difference in expected returns across securities and is an insufficient variable to explain the entire market risk. Based on this evidence Fama and French propose a three-factor model for expected returns, which takes into account similar size and book-to-market patterns in the covariation of fundamentals. Though the CAPM has never been an empirical success because it’s limited ability to explain behavioral influences in the market, the model is a good way to start analyzing portfolio theory and asset pricing. Furthermore, the model is frequently used to build on by more complicated models like Fama and French three-factor asset-pricing model.
4.3. Mean Adjusted Return Model
The Mean Adjusted Return model is consistent with the CAPM, which assumes that a security has constant systematic risk and that the efficient frontier is a constant combination of the means and standard deviations of the mean-variance efficient portfolio. The efficient frontier represents the most efficient trade-off between mean and variance. The Mean Adjusted Return model assumes that the ex ante expected return for a given security i is equal to a constant K , which represents the predicted i
return on security i in time period t.
( )
Ri Ki itE = +ε (3)
( )
it=0This constant K can differ across securities and function as a benchmark against i
which share price performance is projected to indicate possible abnormal performance. The security’s mean return is estimated from a time series of the security’s return, excluding the announcement date, over a representative period, for example six months or a year before announcement. In our research the securities mean return is calculated as the mean return 120 days before the first day in our event window, [-10], consistent with the research of MacKinlay (1997).
The abnormal return ε is equal to the difference between the observed share price it
return R and the benchmark return it K . i
i it it
i R K
K :ε = − (5)
The Mean Adjusted Return model is a quick and easy method to measure abnormal performance. Although it’s based on the assumptions of the CAPM, the Mean Adjusted Return model performs well under a wide variety of conditions as stated by Brown and Warner (1980). Main shortcoming of the model is the fact it does not fully capture all of the relevant risk factors in the economy. Variables as size, market-to-book ratios and firm characteristics are highly correlated with the sensitivities of securities to risk factors. Further, as stated in the introduction, the model underperforms risk-adjusted models in case of clustering of events. The model is based on the assumption of efficient markets and assumes full rationality of investors. Behavioral biases aren’t taken into account when just taking the mean and marginal risk of the return on stocks. Masulis (1978) introduced the Mean Adjusted Return model with his alternative Comparison Return Period approach for measuring security price performance in his research about the effect of capital structure change on security prices.
4.4. Three-Factor Model Fama and French
multifactor efficient’. There are optimal efficient investor portfolio’s, which have the largest expected return given the return variances and covariance of their returns with other variables. In contradiction to the CAPM the three-factor model is based on multifactor efficiency, along with a market beta additional betas are required to explain expected returns. In their research focusing on the reflections of the behaviour of stock prices on the behaviour of earnings, Fama and French (1995) concluded that market and size factors in earnings help to explain those in returns. High book-to-market ratios signals persistent poor earnings whether low book-to-book-to-market ratios signals strong earnings. Furthermore they state, ‘stock prices forecast the reversion of earnings growth observed after firms are ranked on size and book-to-market ratios’. Size is strongly related to profitability, small firms tend to have lower earnings on assets than big firms. As Fama and French state ‘small firms can suffer a long earning depression that bypasses big firms suggest that size is associated with a common risk factor that might explain the negative relation between size and average return’. Based on this evidence, Fama and French introduced a three-factor model for expected returns.
( )
Rit Rft im(
E( )
Rmt Rft)
isE(
SMBt)
ihE(
HMLt)
E − =β − +β +β (6)
In this model expected returns are not only based on a market beta β , but are also im
based on the additional betas βis and βih. The model explicitly makes a size
correction through the SMB equation, which is the difference between the return on t
diversified portfolios of small and big stocks. Additional, the model corrects for
book-to-market ratios through the HMLt equation, defined as the difference between the
stated mean variance efficient portfolio. The building blocks of the Fama and French model will be further discussed in chapter five.
4.5. Measureme nt and Analysis of Abnormal Returns
Before we can measure and analyze the abnormal returns it’s important to give a clear definition of our event window. In our measurement we’ll follow the method as suggested by MacKinlay (1997). Returns will be indexed in event time using t, in our research t = 0 is defined as the announcement date of the transaction or the event date.
The event window is represented by t = T1 + 1 to t = T2, and t = T0 + 1 to t = T1
constitutes the estimation window for the parameters in our normal return models. The length of the estimation window and the event window respectively are defined as L1 = T1 -T0 and L2 = T2 -T1. In our research we use two event windows next to each other T1 and T2 are respectively [-1,+1] and [-10,+10]. To estimate our parameters we use an estimation window of 120 days prior to the event. Because including the event window in the estimation of our parameters could lead to a large influence of our event returns on our normal return measure we take care of the fact that the estimation window and the event window don’t overlap.
The sample abnormal return is defined as the difference between the security return and the benchmark return as dictated by one of our models for normal performance. In our sample the abnormal return is defined as:
( )
λ λλ i i
i R E R
AR = − (7)
The abnormal return is the disturbance factor of the benchmark model, in our research
we’ll follow the null hypothesis from MacKinlay, H0, ‘that the event has no impact on
In order to formulate conclusions about the impact of M&A transactions during our event window we must aggregate our abnormal return observations. The cumulative abnormal return (CAR) is the sum of the included abnormal returns in our sample.
The CAR and its variance are defined as:
(
λ1,λ2)
= CAR∑
= 2 1 λ λ λ λ ARi (8) 2(
1, 2) (
1 2 1)
2 i i λ λ λ λ σε σ = − + (9)Because we want to formulate conclusions about our whole sample we also have to aggregate across observations of the event. Given N events our sample aggregated abnormal returns and it’s variance are:
∑
= = N i i AR N AR 1 1 λ (10)( )
∑
= = N i i N AR 1 2 2 1 var λ σε (11)These average abnormal returns can be aggregated over the whole sample to calculate the average CAR and it’s variance:
(
)
∑
= = 2 1 2 1, λ λ λ λ λ λ AR CAR (12)(
(
))
∑
( )
= = 2 1 2 1, var var λ λ λ λ λ λ AR CAR (13)The average CAR gives an overview of the sample abnormal return during our event window and gives an impression of the overall sample performance. To draw conclusions about these numbers we have to test the statistical significance of our null hypothesis. We’ll follow MacKinlay (1997) and use θ to test H1 0:
)
(
(
(
))
2 1 2 1 2 1 1 , var , λ λ λ λ θ CAR CAR = ~N( )
0,1 (14) 1θ provides insights in the number of standard deviations our average sample CAR is
5. Methodology and Data Selection
5.1. Introduction
We choose to research the short-run performance of Dutch companies involved in M&A transactions, during the time period 2000-2005, for several reasons. During the nineties M&A activity in the Netherlands, consistent with the latest M&A wave,
increased tremendously. Figures5 5.1 and 5.2 show the enormous growth in the
number and value of M&A transactions until the bubble in 2000. M&A transactions formed the basis for growing the company during this period and often were based on
managerial hubris6. This value reducing acquisition behaviour often resulted in
negative short-run shareholder returns. This research forms a direct test whether the period after 2000 put back focus on shareholder value creation. Our sample consist of all M&A transactions of public Dutch bidding companies in the time period just before and after the bubble burst, 2000-2005. As mentioned in the introduction this research directly tests whether corporate restructuring can best be typified as an efficient response to economic shocks or instead is better described as an imperfect reaction to management entrenchment and hubris.
0 50 100 150 200 250 300 350 400 1997 1998 1999 2000 2001 2002 2003 2004 2005 M&A activity Netherlands 1997-2005
Number of transactions 0 20 40 60 80 100 120 140 1997 1998 1999 2000 2001 2002 2003 2004 2005 M&A activity Netherlands 1997-2005
Total deal value in EUR billion
Figure 5.1 M&A activity Netherlands 1997-2005 Figure 5.2 M&A activity Netherlands 1997-2005
the Netherlands, but no mutual funds. The CBS index is representative for all Dutch stocks; any observed behaviour within the CBS index is therefore representative for the behaviour of all Dutch stocks. The shares within the CBS index are traded in liquid markets with complete information, stock prices of the firms within the CBS index are therefore believed to represent fundamental values. The CBS index consists of firms with diverging market caps, implicating a size effect for which we need to correct.
5.2 Data Collection
To form our sample of M&A transactions we use the Mergermarket database.
Mergermarket is an unrivalled source of deal history, the database covers among others European deals greater than EUR 5 million since 1998 and includes summary information for every transaction like fully-sourced financials, exit multiples, and deal characteristics. The database explicit mentions the announcement date for every transaction, which we need to calculate short-term stock price reactions.
Because we are interested in Dutch M&A behaviour, we make a search for Dutch companies that completed a merger or acquisition between 2000 and 2005.
divide the total deal value by the bidders market value of equity to get the relative deal value. We use the median relative deal value to split up our sample in two deal groups; small transactions and big transactions. We also use the acquirers market value of equity to make size distinctions in our sample to identify possible size effects in acquisition announcement returns. Based upon the median market value of equity we split up our sample in two groups; small acquirers and large acquirers. For the Fama-French three-factor model we need to obtain four financial variables; daily total return, book-to-market ratios, market capitalization and the daily risk-free rate. To obtain the stock returns and financial data we make use of the Thompson’s DataStream database. As the risk free rate we took the 3 month US treasury bill rate, as suggested by Grinblatt and Titman (2002).
5.3. Fama and French Portfolio Construction
To study the economic fundamentals in the Fama and French model we’ll use six portfolios formed from sorts of stocks on size and book-to-market values. Every year the CBS index stocks are ranked on size, as stated by the daily market capitalization, based on the yearly average market capitalization the stocks are clustered. The median CBS index size is then used to split up our sample into two groups, small and big. There’s a big variance in the total market capitalization values of our sample, ranging from 2 million Euro to 133 billion Euro in 2000. Furthermore we’ll break up the CBS index stocks into three book-to-market equity groups based on the yearly average book-to-market values as downloaded from DataStream. After ranking the total CBS index every year on book-to-market value we break up the sample based on the breakpoints for the bottom 30% (low), middle 40% (medium) and top 30% (high), as stated by Fama and French. In our research we’ll filter for negative book-to-market values when calculating the breakpoints and forming the clusters. Our decision to split up our benchmark index into three groups on book-to-market values and only two groups on size is in line with the evidence in Fama and French (1992), where they state that book-to-market values has a stronger role in average stock returns than size. For every year we construct six portfolios based upon the intersections of the three book-to-market groups and the two size groups.
After forming the six portfolios we construct the portfolio SMB (small minus big), which mimics the risk factor in returns related to size. For every day between 20 July
19997 and 31 December 2005 we compute the daily SMB values defined as the
difference between the daily returns on the three small-stock portfolios and the daily returns on the three big-stock portfolios. The SMB value can be seen as the difference between the returns on small- and big-stock portfolios with about the same weighted-average book-to-market equity, as stated by Fama and French. Because the SMB values completely correct for the three book-to-market portfolios the values are largely free of the influence of book-to-market values. The SMB focuses on the different return behaviour of small and big stocks. The HML (high minus low) portfolio mimics the risk factor in returns related to book-to-market equity and is
7
6. Results
6.1. Introduction
The results from our research are summarised in table 6.1, before taking a close look at these numbers we can draw some general conclusions about the short-term wealth effects in our sample. To support our analysis we added appendices 2a, 2b and 2c, which show the average announcement (cumulative) abnormal returns and the corresponding significance levels for the different benchmark models. First of all when we take a look at our [-1,+1] event window we can see a positive average CAR which holds under as well the CAPM as the Fama and French benchmark model.
However, the low corresponding values of ?1 don’t provide enough evidence to reject
our null hypothesis of zero abnormal return. The [-10,+10] event window shows an average CAR which is negative under all three research methods. The event announcement results in a negative value development for bidding shareholders. Nevertheless this underperformance is only significant, at a 99% significance level, under the mean-adjusted method. Overall, when looking at the different research methods, we can conclude we only find little evidence to reject our null hypothesis. The announcement of a Dutch M&A transaction isn’t a significant value-reducing activity in the short run for the acquirer. In the next sections of this chapter we’ll take a further look at the short-term distribution of the value effect.
-0,252 -0,15% -1,048 -0,74% -2,407 ** -2,94% large acquirer -1,141 -0,67% 0,063 -0,12% -1,470 -1,19% small acquirer -2,394 ** -0,92% -0,368 -0,18% 0,015 0,06% big transactions 1,248 0,64% -0,075 -0,05% -2,018 ** -3,55% small transactions 2,335 ** 2,12% 2,367 ** 2,72% 2,181 ** 2,29% private -2,360 ** -1,52% -2,429 ** -1,76% -3,060 * -3,82% public -0,779 -0,20% -0,590 -0,12% -2,766 * -1,62% average CAR [-10,10] -1,312 -0,76% -1,431 -1,01% -0,974 -1,19% large acquirer 1,805 *** 1,06% 1,717 *** 1,15% 1,591 1,06% small acquirer 0,718 0,28% 1,097 0,62% 1,113 0,85% big transactions -0,074 -0,04% -0,912 -0,61% -0,647 -1,14% small transactions 2,510 ** 2,28% 2,245 ** 2,60% 2,761 * 2,82% private -1,703 *** -1,10% -1,955 *** -1,41% -1,387 -1,73% public 0,517 0,13% 0,228 0,05% -0,131 -0,08% average CAR [-1,1] ?1
Fama & French ?1 CAPM ?1 Mean-adjusted -0,252 -0,15% -1,048 -0,74% -2,407 ** -2,94% large acquirer -1,141 -0,67% 0,063 -0,12% -1,470 -1,19% small acquirer -2,394 ** -0,92% -0,368 -0,18% 0,015 0,06% big transactions 1,248 0,64% -0,075 -0,05% -2,018 ** -3,55% small transactions 2,335 ** 2,12% 2,367 ** 2,72% 2,181 ** 2,29% private -2,360 ** -1,52% -2,429 ** -1,76% -3,060 * -3,82% public -0,779 -0,20% -0,590 -0,12% -2,766 * -1,62% average CAR [-10,10] -1,312 -0,76% -1,431 -1,01% -0,974 -1,19% large acquirer 1,805 *** 1,06% 1,717 *** 1,15% 1,591 1,06% small acquirer 0,718 0,28% 1,097 0,62% 1,113 0,85% big transactions -0,074 -0,04% -0,912 -0,61% -0,647 -1,14% small transactions 2,510 ** 2,28% 2,245 ** 2,60% 2,761 * 2,82% private -1,703 *** -1,10% -1,955 *** -1,41% -1,387 -1,73% public 0,517 0,13% 0,228 0,05% -0,131 -0,08% average CAR [-1,1] ?1
Fama & French ?1
CAPM ?1
Mean-adjusted
6.2. Split up by Method
When we make a distinction between the three research methods we can see some slightly different results. The CAPM model and the Fama and French model are closely related to each other, both models show an equivalent development of the direction the average CAR moves. This holds under as well the different event windows as under all research variables. The differences between the models can be explained by the amount of adjustment for market- and systematic risk. As discussed in chapter 4 the Fama and French model is the most accurate model, that’s why we’ll follow this model in drawing further conclusions about our research. Contrary to the mean-adjusted method, the CAPM model and the Fama and French model show positive average CAR’s in the [-1,+1] event window. Around the event announcement there’s a positive value effect in the market. When we broaden our event window we can see the CAR develops downwards, resulting in negative average CAR values for all three methods. This negative sample CAR is only significant under the mean-adjusted method.
The CAR development for public targets is significantly different from the development of private targets. All three methods show a negative sample average abnormal return for public target companies and a positive sample average abnormal return for private targets, this result holds under both event-windows. In the [-10,+10] event window this effect is significant for all the benchmark models. When we take a look at the relative deal values it appears that in our three-day event window all three methods show a negative sample CAR in case of a small transaction and a positive
sample CAR in case of a big transaction. However the low corresponding ?1 values
is the opposite and also holds under the CAPM model. Around the event announcement the market reacts negative to a relative small transactions. The market corrects for this effect over a longer period. All three models show the same value effects to large and small acquirers. Surrounding announcement the average CAR for small acquirers shows a positive value, which is statistical significant at the 10% level under the CAPM- and the Fma and French- model. This positive CAR becomes negative when widening our event window. This negative effect is significant under the mean-adjusted method.
Figures 6.1 and 6.2 show the development of the sample average abnormal return around the event announcement. To identify possible different daily movements between the research methods, the abnormal return is calculated for both event windows. -1,5% -1,0% -0,5% 0,0% 0,5% 1,0% 1,5% -1 0 1
Fama & French Mean-adjusted CAPM Sample CAR development
[-1,+1] -2,5% -2,0% -1,5% -1,0% -0,5% 0,0% 0,5% 1,0% 1,5% 2,0% 2,5% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10
Fama & French Mean-adjusted CAPM Sample CAR development
[-10,+10]
Figure 6.1 Sample CAR development [-1,+1] Figure 6.2 Sample CAR development [-10,+10]
there’s a negative CAR development towards the announcement date and a positive CAR development after the announcement date. These findings hold under all research methods. When the M&A transaction is announced there’s a positive correction one day after the announcement. At the announcement date all three methods show a insignificant negative abnormal return. Overall can be stated that when a transaction is announced, bidding shareholders experience slightly positive returns. The statistic evidence for the short term value effects around announcement is small. In the event window [-1,+1] the Fama and French model shows values in the region 1< ?1>-1 implying little evidence.
If we split up the [-10,+10] event window we can draw conclusions about the possible pré- and post-announcement value effects. For all three methods the CAR shows a very diverging development in the pre- announcement period, [-10, 0]. According to the Fama and French model the sample CAR is negative for every day in the event window [-10,0]. There’s a relative large decline visible in the period [-1,0], which can point to possible information in the market. Focusing on the announcement date the sample cumulative average abnormal return for the Fama and French model is
-0,62%, given the standard error of 0,25% the value of ?1 is -2,44, implying a
significant negative effect at announcement. The post period, [0,+10] shows zero to slightly positive CAR developments for the CAPM and the Fama and French model. The stock price adjusts little for the negative pré announcement value effects.
6.3. Yearly Sample CAR and Number of Transactions
-6,0% -5,0% -4,0% -3,0% -2,0% -1,0% 0,0% 1,0% 2,0% 3,0% 2000 2001 2002 2003 2004 2005 0 5 10 15 20 25 30 35 40 45
Number of transactions CAR
Yearly CAR and number of transactions
CAR based on yearly average
Figure 6.3 Yearly sample CAR and number of transactions
From figure 6.3 we can identify a change in investment behaviour after the bubble in 2000. The activity on the Dutch M&A market shows an enormous downturn in volume after 2000. This downward movement is noticeable until 2003 for as well the number of transactions as the yearly average CAR. Especially in 2003 there was a small number of negative value deals which brought down our sample CAR over the total research period. From the year 2004 we can see a recovery of the yearly CAR and an increase in the number of transactions again. Confidence in the markets increases, resulting in a larger number of deals and a positive CAR development. Finally, we can state that the development of the Dutch M&A market reaches it’s highest returns in 2005. Compared to the period before the bubble the number of transactions is smaller, but the total CAR reaches a higher level. This can point to a possible managerial focus on a smaller number of transactions which offers more space for synergy effects and other value increasing variables.
6.4. Private vs Public Targets
We identified a significant difference between the cumulative abnormal returns of private and public target companies. As mentioned in chapter 6.2. we identified a statistical significant negative sample CAR when acquiring a public target company, and a significant positive CAR when the target concerns a private company. These results hold under all three methods. At the announcement date the Fama and French model reports a positive average CAR of 1,84% for private targets and a negative
average CAR of -2,02% for public targets. Given the corresponding ?1 values in
between January 2000 and December 2005. The data concerns the [-10,+10] event windows and is based upon the Fama and French three factor model. Every bubble in the graph represents a transaction in our portfolio and the bubble size in the pictures expresses the relative deal value.
-20% -15% -10% -5% 0% 5% 10% 15% 20%
jan-00 jan-01 jan-02 jan-03 jan-04 jan-05 CAR for private target company Event window [-10,+10] -30% -25% -20% -15% -10% -5% 0% 5% 10% 15% 20% 25% 30%
jan-00 jan-01 jan-02 jan-03 jan-04 jan-05 CAR for public target company Event window [-10,+10]
Figure 6.4 CAR for private target company Figure 6.5 CAR for public target company
private to public companies. The positive economic developments in the markets brought back managerial confidence and put back managerial focus on public target companies. The relative deal value in case of public targets increases and the number of transactions concerning public targets shows values equivalent to the year 2000 again.
Managerial confidence and M&A activity complement each other. More confident managers want to make more and larger acquisitions. This could be a reason for the shift in M&A behaviour in 2005 towards ‘large’ public targets. The CAR in 2005 for public targets shows diverging values, resulting in an average yearly CAR of +1,7%. In 2005 the shift in M&A activity didn’t resulted in negative CAR’s. Managers stayed focused in choosing their acquisition targets and investments are a result of an extended decision framework. Nevertheless there’s always the danger of overconfidence showing up in an increasing number of large loss deals as mentioned by Moeller, Schlingemann and Stulz (2005).
After mentioning the total value effects regarding public and private target companies, it’s interesting to analyse the daily CAR development for both target groups. Figure 6.6 and 6.7 show the daily CAR development for public and private target companies. The numbers are based upon the Fama and French benchmark model.
-2,5% -2,0% -1,5% -1,0% -0,5% 0,0% 0,5% 1,0% 1,5% 2,0% 2,5% -1 0 1 Public Private
CAR development public vs. private [-1,+1] -3,0% -2,5% -2,0% -1,5% -1,0% -0,5% 0,0% 0,5% 1,0% 1,5% 2,0% 2,5% 3,0% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 Public Private
CAR development public vs. private Event window [-10,+10]
Figure 6.6 CAR development public vs. private [-1,+1] Figure 6.7 CAR development public vs. private [-10,+10]
companies. This positive effect is visible for the announcement date as well as one day post announcement. In contrast to this, public target companies suffer negative CAR adjustments after announcement. The CAR for public targets is negative for as
well the announcement date as the day after announcement. Given the ?1 value in
appendix 2c of +2,5 we can state the value effect around the transaction announcement of a private target is significant at 95%. When a public company is acquired shareholders suffer a significant abnormal return of 1,19% at the announcement date. Shareholders identify the acquisition of a public target company with negative value effects and share price corrections are visible after announcement. If we take a wider look we can see that the CAR development is negative for public target companies for all twenty days surrounding announcement. This negative effect is largest during the three days surrounding the announcement. The CAR development for private target companies is mostly positive, acquirers especially experience positive value effects in the event period [-1,+1]. After this period there’s a little negative market correction visible but the sample average abnormal return for private target companies remains positive.
6.5. Relative Deal Value
Based upon the Fama and French model, figure 6.8 and 6.9 plot the daily sample average abnormal return for small and big transactions.
-0,5% -0,4% -0,3% -0,2% -0,1% 0,0% 0,1% 0,2% 0,3% 0,4% 0,5% -1 0 1 Small Big
CAR development small vs. big [-1,+1] -3,0% -2,5% -2,0% -1,5% -1,0% -0,5% 0,0% 0,5% 1,0% 1,5% 2,0% 2,5% 3,0% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 Small Big
CAR development small vs. big Event window [-10,+10]
Figure 6.8 CAR development small vs. big [-1,+1] Figure 6.9 CAR development small vs. big [-10,+10]
Figure 6.8 shows a negative CAR at the announcement date for both small and big transactions, irrespective of the relative transaction value the market reacts negative on the announcement of a M&A transaction. The pré announcement effect is positive for both kind of transactions, the correction at the announcement date is larger when it concerns a small transaction. After the announcement date there follows a positive correction in the sample average abnormal return for as well small as large transactions. From figure 6.8 we can’t identify a different market behaviour towards small and big transactions, we can only conclude the daily developments are larger in case of a small transactions. The daily sample CAR in our [-10,+10] event window shows a capricious development. In case of small transactions the average CAR gets negative four days before announcement, the market corrects for this effect nine days after announcement. Contrary to small transactions, big transactions show a negative average CAR value during almost whole the event. The market doesn’t correct for the negative return over time. The development of small and big transactions show the same pattern, there’s no typical different behaviour identifiable during our event window.
Because of the small number of transactions in the years 2003 and 2004 these number are hard to interpret. Peak values in these years provide irrelevant CAR values, nevertheless it remains interesting to identify typical CAR movements.
-7,00% -5,00% -3,00% -1,00% 1,00% 3,00% 5,00% 7,00% 2000 2001 2002 2003 2004 2005 Small Big
Yearly CAR Small vs. Big Event Window [-1,+1] -7,00% -5,00% -3,00% -1,00% 1,00% 3,00% 5,00% 7,00% 2000 2001 2002 2003 2004 2005 Small Big
Yearly CAR Small vs. Big Event Window [-10,+10]
Figure 6.10 Yearly CAR small vs. big [-1,+1] Figure 6.11 Yearly CAR small vs. big [-10,+10]
If we take a look at the [-1,+1] event window we can see that the short term impact of a big transaction is always bigger than a small transaction, irrelevant whether it concerns a positive or a negative CAR movement. This is related with the relative high deal value or the relative small market value of the bidder. On the one side a big transaction is harder to integrate in the management of a company, but on the other side it creates more possibilities for synergy benefits. This explains the larger impact of a big transactions. Furthermore, if we exclude the low 2003 value for big transactions we can identify a short-term positive CAR development from the year 2002, in our [-10,+10] event window. This development can point to positive post ‘boom’ behaviour in the market. Overall can be stated that for as well small as big transactions we can identify only small positive or negative yearly CAR values, excluded the big transaction value in 2003. Our average sample CAR is obviously effected by a small number of large negative value deals which wiped out gains made earlier. This finding is consistent with the research of Moeller, Schlingemann and Stulz (2005).
6.6 Size
is consistent with the research of Moeller, Schlingemann and Stulz (2005). This announcement effect is corrected if we take a look at the cumulative abnormal returns in our [-10,+10] event window. The Fama and French model even reports a negative cumulative abnormal return for small acquirers over the longer event window. Figure 6.12 and 6.13 show the daily CAR developments for as well small as large acquirers. The numbers are based on the Fama and French benchmark model.
-1,5% -1,0% -0,5% 0,0% 0,5% 1,0% 1,5% -1 0 1
Small acquirer Large acquirer CAR development small vs. large [-1,+1] -3,0% -2,5% -2,0% -1,5% -1,0% -0,5% 0,0% 0,5% 1,0% 1,5% 2,0% 2,5% 3,0% -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10
Small acquirer Large acquirer CAR development small vs. large Event window [-10,+10]
Figure 6.12 Yearly CAR small vs. large [-1,+1] Figure 6.13 Yearly CAR small vs. large [-10,+10]
From figure 6.12 we can conclude the average CAR development for both target groups especially diverges at the announcement date of a M&A transaction. The market reacts negative on M&A announcements of large acquirers. The Fama and French model shows a significant negative reaction of almost 1%, on the announcement day for large acquirers. Acquiring shareholders gain when the announcement is made by a small acquirer. Figure 6.13 shows an enormous reaction for the six days surrounding announcement, this reaction holds for as well small as large acquirers. In our event window [-10,+10], the Fama and French model reports sample CAR’ s of respectively +0,69% and -2,24% at the announcement date. Both
numbers show a high level of ?1, implying a significant different announcement effect
for both transaction types. We can see a post- announcement correction for the sample CAR of both small and large acquirers. Eight days after announcement the total positive announcements effect of small acquirers disappears and becomes negative.
relative deal size we can identify a possible shift towards relative smaller or larger transactions for both groups.
-30% -25% -20% -15% -10% -5% 0% 5% 10% 15% 20% 25% 30%
jan-00 jan-01 jan-02 jan-03 jan-04 jan-05 CAR for small acquirer
Event window [-10,+10] -20% -15% -10% -5% 0% 5% 10% 15% 20%
jan-00 jan-01 jan-02 jan-03 jan-04 jan-05 CAR for large acquirer
Event window [-10,+10]
Figure 6.14 Car development small acquirers [-10,+10] Figure 6.15 Car development large acquirers [-10,+10]
7. Conclusion
Our research deals with the short-run performance of mergers and acquisitions. Past research of short-run M&A performance has for a long time been using event studies in determining the value effects between both parties. Whether results obtained with event studies are statistically reliable and unbiased remains questionable. The returns to acquiring shareholders show diverging results and it remains hard to formulate unambiguous conclusions about the short-run wealth effects. Past research has been focusing mainly on the period before the last merger wave, in our research we shift this focus to the period after this last merger wave. To compare the results of our study of the recent M&A behaviour in the Netherlands we make use of different benchmark models. We explicitly correct for market, size and book-to-market risk effects.
In our research we don’t find enough evidence to reject our null hypothesis of value neutrality. In the Dutch M&A market the announcement of a M&A transaction results in neutral value effects to acquiring shareholders. The Fama and French model shows a insignificant negative sample average abnormal return of 0,20% over our [-10,+10] event window. The corresponding ?1 value of -0,78 shows there is some evidence to reject our null hypothesis, however this evidence is too small. Only the mean-adjusted method shows a significant underperformance of 1,62% in the short-run for Dutch acquirers. In our study of abnormal performance the differences between the methodologies based on Mean Adjusted Returns, Market Adjusted Returns, and Market and Risk Adjusted Returns are quite small, as already indicated by Brown & Warner (1980). The Fama and French model and the CAPM model show
complementary value developments. Over a longer period the mean-adjusted model slightly diverges and becomes less reliable.
