Download Are abnormal buy and holding returns in mergers subject to value

Are abnormal buy and holding returns in mergers subject to value
investing variables?
An empirical study into acquiring-firms to discover which factors effect
long-term abnormal returns
Master thesis
Tilburg School of Economics and Management
Student name: Joeri Moerman
Administration number: 357652
Supervisor: dr. D.A. Hollanders
Date: 24-11-2014
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Abstract
This report looks at the long-term abnormal returns for 169 mergers that took place in the U.S. in
the period from 1995-2007. The results suggest that mergers and acquisitions of these companies
were value destructing. The abnormal returns ranged from -2% and insignificant for a 1 year
period to -8% and significant for a 3 year period.
As was expected private target status, tender offer status and deal size at the time of the
announcement all have a positive and significant impact on the buy and holding returns. An
acquisition offer that is paid for with stock was shown to have negative and significant effects on
buy and holding returns. Whether the merger was with a company in a related or different study
did not have a significant effect on the long-term abnormal returns.
The regressions in this paper showed that long-term value investors should focus on
mergers of acquiring companies with higher net debt to assets than industry average and higher
return on equity than industry average. Additionally, results on profit margin and liabilities to
shareholders´ equity showed too insignificant and conflicting results to really indicate future
positive buy and holding returns by selecting on these parameters at the announcement date.
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
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TABLE OF CONTENTS
ABSTRACT……..………………………………………………………………………….………..………..2
1. INTRODUCTION………............................................................................................................................5
2. LITERATURE………..................................................................................................................................8
2.1
Motivations for mergers………………......…………………………..….…...………………….......8
2.2
Previous research on long-term stock returns......................................……………..…………..…... 8
2.3
Explanations for long-term abnormal returns..................................................................................... 9
2.4
Long-term stock returns for offers concluded through a tender offer..……...……………… …..… 10
3. HYPOTHESIS FORMATION .……………………….…………………................................................. 11
3.1
Long-term abnormal stock returns........................................................................................................ 11
3.2
The influence of different deal characteristics on abnormal returns......................................................12
3.3
Value investment variables ...................................................................................................................13
4. DATA ……..………………….………………….……………………………………………………..16
4.1
Sample design ……………………….……………………………………………………………16
4.2
Data and sources ……………..…………………………………………………………..….…….16
4.3
Deal characteristics..…..………………..……………………………….…………………..……..17
5. METHODOLOGY…...………………………………………………………………………….….……..20
5.1
Calculation of short-term abnormal stock returns ………………………………….........................20
5.2
Method of long-term abnormal returns analysis...............................................................................21
6. RESULTS……………………….…………………………….……..………………………….….…….24
6.1
Descriptive statics CAR short-term abnormal returns…...………………………………………….24
6.2
Descriptive statics average buy and hold abnormal returns (ABHAR)....………...……………….24
6.3
T-test of equality of means of long-term abnormal stock returns (ABHAR) regarding the value
investment variables...……………………………………………...………………………….…………...27
6.4
Regression analysis long-term ABHAR ……………..………………...……….………………….29
7. CONCLUSION………………………………………………………………………………….….…….35
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8. DISCUSSION…………………………..……………………………………………………….….…….36
REFERENCES……………………………………………………………………………………………….37
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1.
Introduction
The impact of measuring the effect of a merger event on the acquiring firm can be done
in two ways. The first approach is to measure whether changes in intrinsic value for the
combined firms are seen after the merger as compared to both of the firms individually combined
before the merger. The second approach is to measure stock returns surrounding announcement
dates and the change of these stock returns after the merger (abnormal returns). In this study the
focus of the research will be on examining long-run abnormal buy and holding returns. This
approach is used to simulate the actual investment experience (e.g. in abnormal results) of the
shareholder as most accurate as possible. A large group of studies on long-term performance
concludes that acquiring firms experience significantly negative abnormal returns following
M&A’s (mergers and acquisitions). Agrawal, Jaffe and Mandelker (1992) reported a statistically
significant five-year abnormal return of - 10.3 % and Anderson and Mandelker (1993) spoke of a
five-year abnormal return of - 9.3 %. Recent studies of Betton, Eckbo, and Thorburn (2008)
reported a negative five-year abnormal return of - 21.9 %. Rau and Vermaelen (1998) reported a
3-year abnormal return of - 4.04 %. They used the control portfolio approach.
The results of short-term performance studies (≤ 3 days) on M&A’s show that there is a
big gain for target shareholders and only slight losses to acquiring shareholders (Andrade,
Mitchell and Stafford, 2001). According to the efficient market hypothesis (EMH) there should
be no abnormal returns in the long run because all available information should be directly
incorporated in the stock price around the event date. However the above quoted papers report
significant abnormal long-term returns and thus end up challenging the EMH.
A large field of research claims that the persistent underperformance of acquiring firms
after M&A deals is more likely due to analysis method errors and benchmark errors, than due to
market mispricing at the announcement date of the acquisition. The statistical power of event
studies on these long-term abnormal returns is still a big topic of discussion in the current
corporate finance literature (Fama (1998) and Mitchell and Stafford (2000). Researchers who test
whether the long-run abnormal returns anomaly does exist, use different return benchmarks and
different analysis methods in order to accommodate for this problem. Their reasoning is that if
the long-run anomaly does exist in all methods, according to them the EMH should be rejected.
Mitchell and Stafford (2000) stated that this is the wrong approach because abnormal returns are
positively cross-correlated among merger events. The assumption of independence between
merger events that these studies that report these significant abnormal returns use, is therefore
not appropriate to use. In order to correct for cross-correlation among merger events in this study,
an analysis program named Event Study Metrics was used, that used a more sophisticated
version of the BMP test-statistic (introduced by Boehmer, Musumeci, and Poulsen (1991)) with a
cross-correlation adjustment as was proposed by Kolari and Pynnönen (2010).
The purpose of this thesis is to find evidence on the question whether shareholders of
acquiring firms gain or lose in a long post-acquisition event period (1, 2, or 3 years). In this
thesis, the long-term abnormal stock returns of acquiring firms in the US market during the
period from 1995 to 2007 will be researched.
Figure 1 shows the number of total M&A deals in the public US market from 1995-2007.
Shown is that the number of deals peaked in the year of 1998, declined drastically after the
technology and internet bubble burst in 2000, and reached its lowest level in the year 2002. The
housing bubble caused by low-quality subprime mortgage reached its maximum in 2006 and
started to have its effect on the financial world in 2007, which led to the global financial crisis in
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the late 2000s. The subprime crisis did not seem to have any effect on the number of merger
deals in our sample up until 2007, although there is a small decrease in the total value of
transactions in 2007.
Figure 1: the merger deal activity and the total value of all transactions in that year from 1995
to 2007 for the U.S. only
This thesis will analyze various variables in order to test for the possible reasons for postmerger abnormal stock returns. The second purpose of this paper is to determine what factors
(deal characteristics and financial ratios) influence the abnormal returns of an M&A deal. In this
study we will research whether long-term abnormal stock returns are affected by: the means of
payment (cash vs. equity), the relative deal size of the merger, the organizational form of the
target (public vs. target) and whether it was a tender offer merger or not.
The second chapter will summarize the literature on M&A’s. In the third chapter the
hypotheses of this thesis will be announced and in the fourth chapter the criteria for data
selection from the Mergers & Acquisitions SDC Platinum Database will be described. In the fifth
chapter the methodology of calculating the abnormal returns will be discussed and in the sixth
chapter, the results on the long-term abnormal stock returns of acquiring firms will be interpreted
and shown. The conclusions regarding the results of the main hypothesis and several other
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hypotheses testing will be made in the seventh chapter and this thesis will end with a discussion
of the results and recommendations for future research/research methods.
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2.
Literature
2.1 Motivations for mergers
The main reason for mergers and acquisitions is that acquiring firms can create economic
value through synergies such as economies of scale. Merging companies can decrease the unit
production cost by using their production capacity more efficiently and can create economies of
scope through combining overhead costs from activities, like marketing or logistics. A third
reason is to create market discipline on the target by removing lacking target management
(Trautwein, 1990). This needs to be done because managers systematically make mistakes in
evaluating M&A opportunities because of overconfidence and too much optimism. A fourth
reason is the diversification strategy theory which states that two corporations from two different
industries merge with the goal to increase borrowing capacity and decrease risk (Berk and
DeMarzo, 2007). A fifth reason for mergers would be creation of market power through the
formation of monopolies which enables the merged company to set consumer prices indifferent
of market conditions.
2.2 Previous research on long-term stock returns
2.2.1 Efficient market hypothesis (EMH)
EMH theory means that all public available information on the merger is directly
included into the share price of the acquiring firm on the announcement date and that no after
effects should be present. A lot of reports however in the field of long term abnormal return
research come up with significant abnormal stock returns after the announcement date. As a
possible explanation Mitchell and Stafford (2001) mentioned that the results on long term
abnormal returns in most papers are questionable due to insufficient and false methodologies and
benchmarks used. Fama (1998) stated that these abnormal returns would disappear by using a
multifactor asset pricing model that corrects for size, beta and book-to-market value.
2.2.2 Long-term stock returns following mergers
The most recent report of 11483 mergers from Betton, Eckbo and Thorburn (2008)
reported five-year BHARs of -22% for bidding companies. Agrawal, Jaffe and Mandelker (1992)
showed a significant -10.3% of CAAR (cumulative average abnormal returns) over a five-year
post event period and also reported significant and negative CAARs for shorter holding periods.
Loderer, Martin and Blum (1992) found a five year CAAR that was negative, but not significant.
Loughran and Vijh (1997) found a five-year buy-and-hold return of -15.9% for a total sample of
788 mergers. Rau and Vermaelen (1998) researched 2823 mergers and reported a statistical
significant 3 year CAAR of -4.04% for an portfolio with a control for size and book-to-market
ratio.
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2.3 Explanations for long-term abnormal returns
2.3.1 Public vs. private target
There has been a lot of research on whether abnormal returns from a private or public
target differ from each other. There are quite a lot of differences between private and public
targets. An accessible market valuation (e.g. in the form of the share price traded on an New
York Stock Exchange) is not available to private targets compared to public targets. Also the fact
that private firm owners are not well diversified and over-invested in their own firms has an
impact on the valuation of private targets (Fuller, Netter and Stegemoller, (2002)). This means
that a merger provides them with a chance to cash out on their private investments in the firm
and put their investment in a more diversified, safer portfolio. Furthermore, private targets do not
have the lawful obligations to disclose all accounting information to the public, thereby
increasing the risk of information-asymmetry between acquirers and private targets. This results
in acquiring firms asking a discount for the acquiring price of a private target, because they are
somewhat unsure about the actual, intrinsic value of the private target (Fuller, Netter and
Stegemoller, (2002)). Moeller, Schlingemann and Stulz (2003) reported a CAAR of 1.5 % and 1.0 % for the acquisition of respectively private targets and public targets for a 3 day window
surrounding the announcement date. Antoniou, Petmezas and Zhao (2007) found a significant
and positive abnormal return of 1.6 % for private targets and an insignificant abnormal return of
-0.6 % for public targets.
2.3.2 Diversifying or related mergers
Diversification is an often stated reason for mergers at the announcement date of the
merger. Diversification happens when an acquiring company merges with a company that
operates in another industry than the target company. This study uses the industrial SIC-code of
the target and acquiring company to research whether a merger has a diversifying or related
industries purpose. A merger in a related industry happens when the first two numbers of the
SIC-code are similar for both companies (e.g. 2722 and 2756). A reason for diversification is
increased debt capacity and risk reduction (Berk and DeMarzo (2007)). Another reason for a
diversifying merger is that it creates more diversified companies that have a reduced company
risk and decreased chances of default (Berk and DeMarzo, 2007).
Martynova, Oosting and Renneboog (2006) examined the operating performance of
combined firms following diversifying and related mergers and found no difference between
related or diversifying mergers in increase or decrease of operating performance. Akbulut and
Matsusaka (2010) however reported an abnormal return of diversifying acquisitions of 0.6% in a
3 day time window surrounding the announcement date (at 1% significance level).
2.3.3 Payment method (cash vs. equity)
The method of payment for the acquisition is chosen by the acquiring managers using
their own information about the actual (intrinsic) value vs. the market share price. This theory
was called the “equity signaling hypothesis” and was introduced by Myers and Majluf (1984). If
the acquiring managers thinks that their acquiring firm stocks´ are overvalued (e.g. price ›
intrinsic value), then the management will use stock to pay for the acquisition most likely.
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
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Whenever the acquiring management thinks the firm´s stock is undervalued, management is
more likely to use cash to pay for the acquisition. This means that acquiring shareholders will
react to this payment information by upping the stock´s share price in case of a cash payment and
will decrease the stock´s share price in case of a stock payment.
Moeller, Schlingemann and Stulz (2003) reported a 3 day abnormal return of - 2.3% and
- 1.2 % for stock paid and cash paid acquisitions. Loughran and Vijh (1997) found five year
abnormal returns of -24.2 % and 18.2 % for respectively equity paid mergers and cash paid
mergers. Dutta and Jog (2009) reported three year abnormal returns of -10 % for stock paid deals.
2.4 Long-term stock returns for offers concluded through a tender offer
Tender offers are mergers in which price offers are done directly to the target´s public
shares after negotiations with target´s management on the market value of the target´s company
have failed. A tender offer seems to increase the value of the merger as can be seen in the
research of Loughran and Vijh (1997), they reported a significant five-year abnormal return of
43% for a sample of tender offers. Rau and Vermaelen (1998) found that acquiring firms
(controlling for size and market-to-book value) in tender offers had a three year abnormal return
of 8.85%. Agrawal, Jaffe and Mandelker (1992) found small, but insignificant CAARs following
tender offers. Loderer and Martin (1992) reported on five-year abnormal returns that were
positive, but insignificant. In conclusion, there is some evidence of positive abnormal returns for
acquirers in case of tender offers. Tender offers seem to benefit the acquiring shareholders
because of the aggressive technique of acquisition (e.g by paying less than target´s management
quoted value).
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3.
Hypothesis formation
The first paragraph will introduce the main hypothesis on the long term abnormal results
performance of acquiring firm. In the second paragraph, the influence of the relative deal size
characteristic on the abnormal buy and holding returns will be hypothesized. Finally, four other
hypotheses on the long-term abnormal returns of mergers are formulated based on a (Warren
Buffett) value approach of investing.
3.1 Long-term abnormal stock returns
Several papers have argued that the methodologies and benchmarks used in long-term
event studies are wrong. Mitchell and Stafford (2000) stated that long-term abnormal returns are
found (seen as an anomaly), because the returns around multiple mergers events are positively
cross-correlated. The assumption of independence among these mergers is false and is
inappropriate to make. That is why this paper will use methodologies for calculating long-run
abnormal stock returns that are corrected for industry, size, book-to-market ratio values and
cross-correlation among mergers. This methodology is called the matched firms approach and
the matched and original returns abnormal returns are calculated using a Fama French 3-factor
regression (correcting for beta, size and book-to-market).
Both Franks, Harris and Titman (1991) and Dutta and Jog (2009) did not find any strong
support for significant, long-term abnormal performance for acquiring firms over the post-event
period although both use the Fama French 3-factor regression, but apply totally different
benchmark methods.
However, Betton, Eckbo and Thorburn (2008) reported five-year BHARs of - 22% for
bidding firms in mergers using the matched firm approach. Gregory (1997) calculated abnormal
returns using six different analysis methods and the Fama-French 3-factor regression. They
stated that the 2 year abnormal returns of UK acquiring firms are negative and significant and
range from - 11.8 to - 18 % using several benchmark models. Sudarsanam and Mahate (2003)
reported 3 year negative abnormal returns as well of -15% using various benchmark models.
The results from the reports above suggest a high level of market inefficiency for some studies
and its seems that targets are overpaid for at the time of the events. Some of the above mentioned
studies are summarized in Table 1:
Table 1: BHARs as reported in several papers
Author(s)
Sample
Sample size Firm/deal
period
type
Dutta and
Jog (2009)
Betton,
Eckbo, and
Thorburn
(2008)
Mitchell and
Stafford
(2000)
Holding
period
19932002
19802003
1300
Acquisitions 3 years
11483
All
19611993
2767
Acquisitions 3 years
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
5 years
ABHAR
(Equal
weighted)
0.4 % per
month
-21.9 %***
No
significant
abnormal
12
Rau and
Vermaelen
(1998)
Rau and
Vermaelen
(1998)
Gregory
(1997)
19801991
316
Tender
offers
3 years
return
8.85 %
19801991
2823
Mergers
3 years
-4.04 %
Mergers
2 years
Loughran
and Vijh
(1997)
19701989
Cash and/or
stock
acquistions
5 years
-11.8%* to –
18%**
depending on
methods used
-6.5 %, 9.6%, 18.2%
and -24.2%
Agrawal,
Jaffe, and
Mandelker
(1992)
1955 1987
Franks,
Harris, and
Titman
(1991)
19751984.
19841992
788 total,
207
cash+equity,
196 cash
only and
385 equity
only
acquistions
937 mergers
and 227
tender offers
399
Mergers +
5 years
tender offers
from
NYSEAMEX
3 years
YSE/AMEX
acquisitions
10.26% *,
no significant
abnormal
return for
tender offers
No
significant
abnormal
returns
The papers above show that long run abnormal returns are often quite negative or have
not significant abnormal returns Only the paper of Agrawal, Jaffe and Mandelker (1992) reported
a positive 5 year abnormal return of 10.3 %. The question whether mergers create value for the
shareholders of acquiring firms is still unanswered and depends on the research method
(regression) and benchmark models used. Therefore, the following hypothesis will be tested in
this study:
H1: A merger will create long-term negative abnormal returns for the shareholders of the
acquiring firm over the one, two and three-year time window.
In this thesis, the Fama-French three-factor regression model and the matched firms
approach will be used to examine whether the acquirer’s shareholders gain or lose from the
M&A deals in the long run. This benchmark model and this analysis method will be thoroughly
described in section 5.
3.2 The influence of different deal characteristics on abnormal returns
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There are multiple long-term studies that have shown that the deal characteristics and the
status of the target have a significant effect on abnormal returns. These characteristics are: 1. the
mode of acquisition (tender offer or normal offer); 2. related or diversifying deals; 3. method of
payment (cash vs. equity); 4. organizational form of the target (private vs. public). All of these
characteristics will be included in this study. The following paragraph will summarize the current
research on the proven influence of relative deal size on the abnormal returns of a merger or
acquisition. In the last paragraphs the Warren Buffett value investment variables will be
introduced and four (value) factors will be hypothesized that could affect the abnormal returns of
acquirers in case of mergers.
3.2.1 Relative deal size
The relative deal size of a merger is the market value of the target divided by the market
value of the acquirer. The higher the proportion of this ratio, the more important the economic
event becomes for the acquirer (Eckbo et al., 1990). Eckbo et al. (1990) stated several reasons why
a higher relative deal size could return both positive and negative abnormal returns. Higher relative
deal size could bring in more synergy effects (positive effect), but it could also be more difficult to
manage a larger target company (negative effect). On the other hand, when the size of target
increases, the target´s bargaining power increases as well and the acquisition will become more
expensive for the acquirer (negative effect). The results from several studies are mixed.
Agrawal, Jaffe and Mandelker (1992) ranked all their mergers in ten quintiles according
to relative deal size and found that the quintile portfolio of largest relative deal size had a
significant five year CAAR of -16.3% and that the quintile portfolio with the smallest relative
deal size had a five year CAAR of -3.4% , but insignificant. Faccio, McConnell and Stolin
(2006) found that deals with a small relative deal size have negative CAARs and that deals with
a larger relative deal size have positive CAARs. Their reasoning behind these results is that
bigger bidders (and thus having a smaller relative deal size) tend to buy public targets and
consequently have negative CAARs, whereas smaller bidders (and thus having a larger relative
deal size) tend to buy private, unlisted targets and therefore have positive CAARs. Martynova,
Oosting and Renneboog (2006) found that the acquisition of larger targets (larger relative deal
size) resulted in better performance profitability of the combined firm, while acquisitions of
relatively a smaller target (smaller relative deal size) lead to a profitability decline in
performance. This study proposes the following hypothesis which will be tested for truthfulness:
H2: A merger with a larger relative deal size generates larger abnormal stock returns than a
merger with a smaller relative deal size. This is because deals with relative larger deal size are
more likely to involve private targets, whereas deals with smaller deal size are more likely to be
with public targets.
3.3. Value investment variables
Warren Buffett is one of the best known long-term investors in the investor´s world and
Berkshire Hathaway’s equity portfolio (managed by Buffett) has outperformed the benchmarks
in 27 out of 31 years, on average exceeding the S&P 500 Index by 11.1% and a Fama and French
characteristic-based portfolio by 8.6% per year. This long-term outperformance of the S&P 500
Index is in contradiction with the strong-form efficiency theory, which states that share prices
should reflect all the public information that can be acquired by analysis of the company and the
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
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economy. Therefore strong-form market efficiency theorists state that it is not possible for
investors to earn a return greater than the general market return over a longer period of time.
Buffett is often referred to by the academic world as a statistic anomaly. Buffett always counters
this statement with the argument that among all these year-in-year-out outperformers an
uncharacteristically large group of outperforming investors comes from the Graham-andDoddsville school of value investing (following the intrinsic value-philosophies of Benjamin
Graham).
However in a recent paper from Frazzini, Kabiller and Pedersen (2013), the authors
mentioned that the remaining alpha (of Buffett’s successful way of investing) can be accounted
for by controlling for a quality QMJ factor and a beta BAB factor. QMJ in Clifford, Frazzini and
Pedersen (2014) stands for the difference in return between a portfolio of high-quality stocks
(excellent credit rating) and a portfolio of junk companies (miserable credit rating). BAB in
Frazzini and Pedersen (2011) stands for the difference in return between a safe portfolio with
low-beta stocks and a riskier portfolio of high-beta stocks. If these two factors are included in the
regressions of Bershire´s portfolio, the alpha of Berkshire’s public stock portfolio drops down to
a statistically insignificant annualized 0.3%. These two factors explain the outperformance of
Buffett’s public portfolio over the years almost completely and a significant reason behind
Buffett’s success is the fact that he buys safe, high-quality value stocks. This paper will use
Buffett´s investment strategy to discover whether abnormal returns can be found around mergers
for this stock picking strategy as well. In this part of the thesis I will look at whether more
financially sound companies (companies that Buffett would buy), also make more sound
acquisition decisions (stockholder-orientated). This should be apparent in that companies that
possess these 4 below mentioned financial ratios, will only make the acquisition decision if
they´re certain that they will increase shareholder value and thus long-term abnormal returns.
3.3.1 Net debt/total assets (N/A)
Buffett believes that a business should be able to achieve excellent returns on equity
while employing little or no debt. He states that good investment decisions will produce
satisfactory economic results and that companies can make these investments regardless of the
level of leverage. Furthermore, highly leveraged companies are vulnerable during economic
slowdowns and investors should stay away from firms that can only earn excellent returns on
equity by employing significant debt (Hagstrom, 2005, page 136)
H3: An acquiring firm with a smaller 3 year net debt to total assets-ratio than the industry
average, has less financial distress costs and will make more shareholder-orientated acquisition
decisions. This results in higher abnormal buy and holding returns for firms with a lower than
industry average net debt to total assets-ratio.
3.3.2 Return on equity (ROE)
Instead of focusing on earnings per share, which can be easily manipulated in the
company´s accounting reports, Buffett likes to focus on measuring a company’s annual
performance by calculating the return on equity (the ratio of operating earnings to shareholders’
equity). Buffett believes that the perfect test of economic performance is whether a company
achieves a high earnings rate on equity capital employed. He states that companies that have the
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
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ability to re-use their existing and excess capital at above – market rates of return for a longer
period of time are a good investment. An ability to earn excess returns on equity is an indication
that the company has certain competitive advantages that are not easily reproduced by its
competitors.
(Hagstrom, 2005, page 136).
H4: An acquiring firm with a bigger 3 year average return on equity than the industry average,
shows greater economic growth and will make more shareholder-orientated acquisition decisions.
This results in higher abnormal buy and holding returns for firms with a higher than industry
average return on equity.
3.3.3 Net profit margin (PM)
Buffett has the opinion that managers of high-cost operations tend to have a way of
managing costs that continually add to overhead, whereas managers of low-cost overhead firms
are always succeeding in finding ways to cut expenses. Buffett mentions that he doesn´t invest in
companies in which managers allow the unnecessary escalation of costs and then as a result of
this escalation have to initiate a restructuring program to bring down costs. The announcement of
a cost-cutting program is for Buffett an indication that the company is not in the full known as to
what expenses can do to a company’s owner’s earnings. For example, Berkshire Hathaway´s
after-tax overhead corporate expense is less than 1 % of their earnings, whereas other companies
have an overhead of 10 % of corporate expenses. This results in a shareholders loss on ROE of 9
% simply because of corporate overhead in these companies (Hagstrom, 2005, page 140).
H5: An acquiring firm with a bigger 3 year average net profit margin than the industry average,
has less overhead from sales/marketing/administration and will make more shareholderorientated acquisition decisions. This results in higher abnormal buy and holding returns for
firms with a higher than industry average profit margin.
3.3.4 Liabilities-to-shareholders´ equity
Buffett also looks among other things at the financial solvability of the company and the
pressure short and long-term liabilities place on the operational activities of the company. A
company that has a lot of short-term and long-term debt obligations, but not a lot of cash on hand
(thus a small equity buffer) will have a more difficult time in hard financial turmoil to renew
debt covenants or issue new debt than a company that has more financial room-to-move (James,
2005).
H6: An acquiring firm with a smaller 3 year average liabilities to total assets-ratio than the
industry average, has more financial solvability and will make more long-term and shareholderorientated acquisition decisions. This results in higher abnormal buy and holding returns for
firms with a lower than industry average liabilities to shareholders ´equity-ratio.
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
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4.
Data
In this chapter the sample design will be provided and then the data sources will be
introduced and finally an overview of the researched deal characteristics and financial ratios will
be given.
4.1 Sample design
This thesis will study the merger and acquisition deals during the time period from Jan.
1st 1995 to Dec. 31st 2007. To be included in the sample, the acquiring firm must meet the
following standards:
1. The deal value is at least one billion U.S. Dollars.
2. The deal has to be marked as completed and unconditional in the SDC M&A Database
3. Only mergers and acquisitions from in the United States of America based firms are included
4. Only firms in the sample are included that acquire a 100 % share of the target’s stock stake in
the M&A deal.
5. The acquiring firm should be listed on NYSE stock exchange and has to have data on the daily
stock returns for CRSP and annual data on the business fundamentals from Compustat.
6. The M&A deal has to have a period of 3 year merger free activities before the announcement
date and 3 year merger free activities after the announcement date to be included in the sample
7. The target should be a public or private firm.
8. The acquirer should be a public firm.
9. Only acquisitions that are paid for with either cash or stock or a combination of those 2 are
included.
10. Only acquiring firms that have matched firms in the same industry (first 2, preferably 4 digits
of the SIC-code), with market value of more than 50 % and less than 200 % of the acquirer´s
market value and with book-to market value of more than 80 % and less than 125 % of the
acquirer´s book-to-market value are included in the sample.
11. Finally, the CRSP data for the matched firm has to be available for at least two years
following the announcement date of the merger.
The next paragraph will cover the data and the sources from which the data was obtained
4.2 Data and Sources
In this study, the SDC Platinum database is used to identify the deal characteristics of the
M&A deal sample. The CRSP database is used to obtain the daily stock returns of the acquiring
and matched firms. The Kenneth French´s website is used for the Fama-French market, size and
book-to-market ratio factors, and market risk premium. Compustat is used for the financial ratios
of the acquirer and the matched firm.
This results in a sample of 169 M&A deals. The deal size ranges from 1 billion US
dollars to 26 billion US dollars, with an average of 3.179 billion US dollars. The average market
capitalization of the acquirers equals 12.763 billion USD. More descriptive summary of
acquiring firms values such as book-to-market values at the announcement data of the
acquisitions in the sample are presented in Table 1.
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
17
Table 2: Sample distribution of means at announcement date of book-to-market value, stock
payment-dummy, target is private-dummy, merger into same industry-dummy, merger through
tender offer-dummy, value of the merger transaction and market capitalization of acquirer.
B/M stands for the book-to-market value of the acquiring company at the announcement date. Pure stock
dummy represents 1 if the transaction was completed paid for by equity and 0 otherwise and can be found
in the SDC database. Private target dummy is also found in the SDC database and is identical to 1 if the
target is private. Same industry dummy is 1 for targets with a 2-digit SIC code identical to the one of the
bidder and 0 otherwise. Tender offer dummy is 1 if the deal was made through a public tender offer and 0
otherwise and can also be found in SDC database. Transaction value ($ billion) is the total value paid by
the acquirer. Market cap is the market capitalization of the acquirer at the announcement date of the
merger.
Merger
year
B/M
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
,473
,457
,457
,532
,465
,397
,473
,702
,586
,400
,483
,337
,340
Pure
stock
dummy
,00
,75
,56
,42
,37
,50
,40
,00
,25
,38
,29
,29
,36
Private
target
dummy
,50
,13
,33
,11
,21
,14
,13
,00
,25
,08
,29
,18
,08
Same
industry
dummy
,00
,50
,67
,58
,79
,50
,87
1,00
1,00
,69
,62
,76
,72
Tender
offer
dummy
1,00
,13
,22
,11
,21
,14
,07
,33
,00
,08
,24
,00
,16
Transaction
value (in $
billions)
2,569
2,874
3,254
2,615
3,160
3,268
4,233
2,044
2,218
2,972
3,156
3,729
3,102
Market cap
(in $
billions)
29,173
8,361
4,459
5,404
9,113
23,461
5,938
8,935
7,065
3,579
22,337
15,199
17,130
4.3 Deal characteristics
4.3.1 Organization form of the target
The sample is split in subsets of public and private targets and this information is directly
obtained from the SDC Platinum database. The average deal value of deals with public targets is
3.34 billion USD, while the average deal value of deals with private targets is almost the same
with 3.29 billion USD.
4.3.2 Related or Diversifying M&A deals
The first two numbers of the SIC code are used to identify the industry in which both the
acquiring and target companies operate. In this thesis the M&A deals are identified as related or
diversifying by using this two-digit US SIC code. The SDC Platinum database was used to
obtain the SIC code for both the acquirers and the targets companies. In case the first two digits
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
18
are the same for both the bidder´s and the target´s SIC code, the deal will be referred to as a
related deal. In the sample of 169 companies there is no noticeable difference between related
and diversifying M&A deals for both deal size and acquirers’ market capitalization.
4.3.3 Relative deal size
The relative deal size is the market value of the target relative to the market value of the
acquiring firm and is used to determine whether the relative deal size matters for the long-term
abnormal returns. The deal value of the deals is retrieved from the SDC Platinum database as
well as the acquirer’s market capitalization.
4.3.4 Method of payment
The method of payment for the M&A deals was retrieved from the SDC Platinum
database and the whole sample is divided into two sets of deals based on the methods of
payment:
(A) All-stock deals are all mergers where the acquirers’ stock was used to pay for the
deal value. (B) All-cash deals are all mergers where the method of payment in the merger was
only cash. The equity deals have a mean deal value of 3.52 billion USD. The cash deals have a
mean deal value of 2.52 billion USD. The average market capitalization of the acquirer for the
equity bidder’s sample is 12.61 billion USD and the average acquirer´s market capitalization for
the cash bidder’s sample is 13.45 billion USD.
4.3.5 Direct offer or tender offer?
The acquiring company has the option to go directly to the shareholders of the targeted
company to buy their shares if management of the targeted company does not agree on the price
of the offer for the company. This is called a tender offer. In this dataset 14.8 % of the deals were
made through a tender offer and there were no noticeable differences in deal value and market
value of the acquirer between both groups.
4.3.6 Value investment variables
In table 3 the difference of the value investment variables of the merging stock with the
industry average is depicted for every merger year.
Table 3: Sample distribution of value investment variables and relative deal size as a function of
year of the merger
This table shows the number of acquisitions in the sample per merger year. The relative deal size
is calculated by dividing the market equity of the target by the market equity of the acquirer at
the announcement date. The net debt/assets ( N/A) of the acquirer is calculated 2 years prior to
the announcement date and these 3 values give an average N/A that is compared to the industry´s
average at the announcement date. The same is done for respectively the acquirer´s return on
equity (ROE), net profit margin (PM) and liabilities/shareholders equity (L/SE). Net debt is
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
19
calculated by adding together debt in current liabilities and long-term debt and subtracting cash
and short-term investments. This number is then divided by the total assets the company has.
Return on equity is calculated by dividing net income on shareholders’ equity. Net profit margin
is calculated by dividing net income on revenues. Liabilities/shareholders equity is calculated by
adding together short and long-term liabilities and dividing by shareholders equity. All of these
numbers for the acquiring firm and the matching industry were retrieved from Compustat.
Merger
year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
N
2
8
9
19
19
14
15
3
4
13
21
17
25
Relative
deal size
0,405
0,363
0,673
0,319
0,463
0,304
0,413
0,442
0,121
0,531
0,369
0,569
0,306
∆N/Ai∆N/Aindustry
0,249
0,107
0,078
0,051
-0,031
-0,058
-0,073
0,094
0,027
0,140
0,072
0,072
0,050
∆ROEi∆ROEindustry
0,139
0,028
0,028
0,040
0,030
0,048
-0,005
-0,018
0,183
0,059
0,067
0,050
0,095
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
∆PMi∆PMindustry
0,030
0,012
0,028
0,014
0,009
0,057
-0,022
-0,010
0,004
0,031
-0,001
0,065
0,050
∆L/SEi∆L/SEindustry
0,115
-0,003
0,168
-0,010
-0,002
0,053
0,013
0,198
0,054
0,024
0,130
0,079
-0,008
20
5.
Methodology
The estimation window in this study is the period from 240 to 40 days prior to the
announcement date. In the estimation window of 200 days the Fama-French 3 factor model
factors will be estimated. The announcement date is defined at t=0 (namely the event date) and
an event window of either 1, 2 or 3 years was used to calculate the abnormal buy and holding
returns for the event and matched firms. In this study I will follow the suggestions made by
Peterson (1989) who stated that an estimation window of 100-300 days is appropriate to
calculate the parameters of the used benchmark model (in this case the Fama-French factors).
The event window and the estimation window are separated to prevent cross-correlation
(MacKinley (1997)).
5.1 Calculation of short-term abnormal stock returns
A lot of papers analyze short-term abnormal returns by summing daily abnormal returns
over time. First of all this study will evaluate the short-term abnormal returns (CAAR) by
cumulating the daily abnormal return for a 20 day time window surrounding the announcement
date. A simple market model will be used as a benchmark for this short-term abnormal returns
estimation.
The abnormal returns, using the 𝛼𝑖 𝑎𝑛𝑑 𝛽𝑖 calculated from the estimation window for
each company, can be found using formula 1A:
𝐴𝑅𝑖𝑡 = 𝜀𝑖𝑡 = 𝑅𝑖𝑡 − (𝛼𝑖 + 𝛽𝑖 ∙ 𝑅𝑚𝑡 )
[1𝐴]
In this formula Rit is the actual stock return for merging firm i at day t following the
announcement day, E(Rit)= 𝛼𝑖 + 𝛽𝑖 ∙ 𝑅𝑚𝑡 is the expected stock return for merging firm i at
trading day t, and Rmt is the actual market return (return on the S&P 500) at day t following the
announcement day.
The average abnormal return t days after the announcement day is then calculated using
formula 1B:
𝑁
𝐴𝐴𝑅𝑖𝑡 = ∑
𝑖=0
𝐴𝑅𝑖𝑡
𝑁
[1𝐵]
In this equation, N is the number of acquiring firms in the sample. To evaluate the longrun abnormal stock return the AARt during the post-event period are culminated, which yields a
cumulative average abnormal return (CAAR) on every trading day t following the announcement
date (t=0). This is done in formula 1C.
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
21
𝜏
𝐶𝐴𝐴𝑅𝑡 = ∑ 𝐴𝐴𝑅𝑡
[1𝐶]
𝑡=0
However, this approach results in over-estimated and negative abnormal returns for τ≥250 that
are not realistically and therefore the method described in the following two sections will be used
in case of a long-term event study.
5.2 Method of long-term abnormal returns analysis
There is a lot of evidence that the choice of benchmark can have an important impact on
the range of abnormal returns found in long-term event studies (French, 1998). Therefore the
most commonly used benchmark found in long-term studies will be used, namely the FamaFrench (1993) three-factor model.
5.2.1 Choice of benchmark model: Fama-French Three-Factor Model (FF)
The three-factor model was developed by Fama and French (1993). This model regresses
the daily excess returns for acquiring firm i on a market factor, a size factor, and a book-tomarket factor. In other words, this Fama-French Three-Factor approach evaluates the benchmark
stock returns after adjusting for the beta risk, size, and the book-to-market factors, and thus this
regression controls for the abnormal influences of the size effect, the book-to-market ratio effect
and the market risk effect (β). The Fama French regression equation is shown below:
𝑅𝑖,𝑡 = α + β(R m,t − R f,t ) + si ∙ SMBt + hi ∙ HMLt + εit
[2]
If α is positive in this equation then this in an indication that, after controlling for market
risk, size, and book-to-market factors in returns, an acquirer has performed better than expected,
which means that the merger was a value-building operation for the acquirer. SMB is defined as
the return on a market value-weighted portfolio of small stocks minus the return on a market
value-weighted portfolio of big stocks at every day t following the announcement date. HML is
defined as the difference between the return on a market value-weighted portfolio of high bookto-market value stocks with a market value-weighted portfolio of low book-to-market value
stocks at every day t following the announcement date.
The values for the SMB and HML portfolios in equation 2 above are collected from
Fama French´s website and are calculated every day from all NYSE, Amex and Nasdaq listed
firms with respect to different market capitalization and book-to-market ratio portfolios.
The problem however is that while analyzing abnormal returns, realized returns have to
be compared to a benchmark model of normal returns. This limitation is what was named by
Fama (1998) a “bad model problem”. The imperfect calculation of normal benchmark returns is
not so relevant over short horizons such as a few days, but the choice of benchmark can have a
significant impact over long-term horizons (e.g. 1 year and longer) and this might result in
skewed right-sided abnormal returns in long-term return studies (Fama (1998)).
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
22
5.2.2 Analysis method
The approach used in most long-term event studies nowadays for estimating long-term
abnormal returns is known as calculating buy-and-hold abnormal returns (BHARs). BHARs are
defined as the difference between long run buy-and-hold returns to event firms as compared to
control firms (matched firms). Advocates of this approach argue that BHAR depicts the actual
investment results for a long-term investor in a better way. The buy-and-hold abnormal return
(BHAR) is simply the compounded return on a buy-and-hold investment in a matched firm with a
normal return less the compounded return on a buy-and-hold investment in the event firm with an
expected return. This is shown in equation 3
𝜏
𝜏
𝐵𝐻𝐴𝑅𝑖𝑡 = ∏[1 + 𝑅𝑖𝑡 ] − ∏ [1 + 𝐸(𝑅𝑖𝑡 )]
𝑡=1
[3]
𝑡=1
Return on the matched firm
Expected return on the event firm
The most commonly used method of assessing abnormal returns when implementing the
BHAR method is to match event firms to control firms on the basis of firm characteristics, in
particular market size, market beta and Book-to-Market (BM) ratio. Loughran and Ritter (2000)
stated that while looking for the anomaly of long-term negative abnormal returns; a distinction
has to be made from other cross-sectional patterns such as size and book-to-market in order to
make sure that the anomaly is not just a manifestation of those patterns. Barber and Lyon (1997)
stated as the main reason for using this approach in long term event studies that the statistical
properties of this method were favorable.
To test the null hypothesis that the mean BHAR is equal to zero for a number of N merging
firms, the following equation will be used:
𝑡𝐵𝐻𝐴𝑅 =
̅̅̅̅̅̅̅̅̅̅
𝐵𝐻𝐴𝑅
𝑖𝑡
𝜎(𝐵𝐻𝐴𝑅𝑖𝑡 )
√𝑁
[4]
̅̅̅̅̅̅̅̅̅̅
In this equation 𝐵𝐻𝐴𝑅
𝑖𝑡 is the sample average and 𝜎(𝐵𝐻𝐴𝑅𝑖𝑡 ) is the cross-sectional sample
standard deviation of abnormal returns for the sample of N acquiring firms. The ABHAR are
calculated over different event time periods (e.g. one, two and three year holding periods postacquisition) and this gives insight on whether long-term investors in these acquiring firms could
benefit or lose from the merger deal during the specific time period (Bessembinder and Zang,
2012).
Several papers in the past have shown that ABHARs are very sensitive to the choice of
benchmarks (e.g. Ikenberry, Lakonishok and Vermaelen (1995), Barber and Lyon (1997), and
Lyon, Barber and Tsai (1999)). Nowadays it is common to use a single matched firm benchmark
as was recommended by Fama and French (1992). The same approach will be used in this paper.
Barber and Lyon (1997) stated that the firm control approach eliminates the new listing bias, the
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
23
rebalancing bias, and the skewness problem and returns well specified test statistics in all
situations. In this paper event firms will be matched with other firms based on having a similar
industry, market size and book-to-market value. There are however some problems with the
matched firms approach.
Ang and Zhang (2004) found that most testing procedures that were used in studies
analyzing abnormal returns before 2004 have very low power for samples of medium size (≤500
companies) over long event horizons (three or five years). Another issue with using the matched
firms approach is that it suffers from the before mentioned cross-correlation problem and the bad
model problem (Fama (1998), Mitchell and Stafford (2000)). The cross-correlation surfaces
because matching on firm specific characteristics (such as size and book-to-market ratio) fails to
completely remove the correlation between event firms’ returns over time. The bad model
problem is created because there is no benchmark out there that gives a perfect estimate of the
event firm´s return in case there was no event. In order to circumvent this problem somewhat
bootstrapping-based tests are used in the analysis program Event Study Metrics that rely on
repeated random sampling to measure the significance of relevant test statistics more precisely.
As was mentioned earlier, this study will use t-statistics that are corrected for cross-correlation
by using a sophisticated version of the BMP test-statistic (introduced by Boehmer, Musumeci,
and Poulsen (1991).
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
24
6.
Results
6.1 Descriptive statics CAR short-term abnormal returns
Figure 2 below shows the short-term results of the event firms in a 20 day window
surrounding the announcement date (event date). Shown is that there is a sharp decrease in the
abnormal return on the event day (~1.5 %) and the day following the event day (~1%).
Figure 2: The cumulative abnormal average return in a 20 day window surrounding the event
The same negative abnormal results can be seen in table 4. The same small run-up of 0.3
% in the abnormal returns 3 days prior to the announcement date can also be found in the figure
above.
Table 4: The abnormal short-term results over different time windows surrounding the
announcement date
Date
(-10...10) (-5...5) (-3...0) (0...0)
(0...1)
(0...2)
(0...3)
(0...5)
CAAR
-0,0236 -0,0242 0,0031 -0,0146 -0,024 -0,0275 -0,0275 -0,0258
Significance
0,0444 0,0015 0,0741 0,0014
0
0
0 0,0001
6.2 Descriptive statics average buy and hold abnormal returns (ABHAR)
As can be seen in the table 5 below the ABHARs over the sample of 169 firms shows a
significant negative ABHAR of -7.85 % over a 3 year buy and holding period and also negative,
but nonetheless not significant ABHARs of -2.14% and -5.08% over a 1 and 2 year holding
period. The results of a 3 year holding period are in line with hypothesis 1. For significant
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
25
confirmation of hypothesis 1 in relation to a 1 year and 2 holding period, a larger sample size
will be needed, but for now hypothesis 1 is rejected for the 1 and 2 year holding period.
Table 5: The abnormal buy and holding results over different time windows after the
announcement date
Time window
ABHAR
Significance
3 years (755 days)
-0,0785
0,0888
1 year (251 days)
-0,0214
0,4738
2 years (503 days)
-0,0508
0,1912
In figure 3 the average buy and hold return is shown for the first three years and as can be
seen there is a significant increase in ABHAR from day 7 until the end of the first year. After
that a downward trend is shown in which the ABHAR decreases until midway into the third year
and then a slight recovery towards the end of that same year.
Figure 3: The average abnormal buy and holding returns (ABHAR) for the whole sample over a
period of 3 years
In table 6 the ABHARS per year are shown. 2005 and 2006 produced positive ABHARS
over a somewhat larger sample and 1998 to 2000 produced negative ABHARs over a somewhat
larger sample.
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
26
Table 6: summary statistics of abnormal buy and holding returns per year for all 169
M&A deals presented in the sample
This table shows a sample of acquisitions done by publicly listed U.S. firms in the period 1995-2007. The
acquisitions are collected from the SDC Merger and Acquisition Database of U.S. The U.S. targets are
either public or private firms. The columns show the abnormal buy and holding returns of the acquiring
firms in the year the merger took place. ABHAR 3 years is calculated by comparing the predicted returns
of the acquiring firm in the period with the return in the same period of a matching firm in the same
industry and with similar book to market value and market capitalization. This is done daily from trading
day 0 to trading day 755 after the announcement date. ABHAR 2 and ABHAR 1 year after event are
calculated by compounding returns from trading day 0 to day 503 and respectively from trading day 0 to
251. Shown are the mean, minimum, maximum and standard deviations of these ABHARs.
Merger year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
ABHAR 3 years after event
ABHAR 2 years after event
ABHAR 1 year after event
ABHAR 3 years after event
ABHAR 2 years after event
ABHAR 1 year after event
ABHAR 3 years after event
ABHAR 2 years after event
ABHAR 1 year after event
ABHAR 3 years after event
ABHAR 2 years after event
ABHAR 1 year after event
ABHAR 3 years after event
ABHAR 2 years after event
ABHAR 1 year after event
ABHAR 3 years after event
ABHAR 2 years after event
ABHAR 1 year after event
ABHAR 3 years after event
ABHAR 2 years after event
ABHAR 1 year after event
ABHAR 3 years after event
ABHAR 2 years after event
ABHAR 1 year after event
ABHAR 3 years after event
ABHAR 2 years after event
ABHAR 1 year after event
ABHAR 3 years after event
ABHAR 2 years after event
ABHAR 1 year after event
ABHAR 3 years after event
Minimum
0,287
-0,125
-0,010
-0,731
-0,940
-0,523
-0,571
-1,025
-0,550
-2,149
-1,123
-0,839
-1,368
-1,282
-1,424
-0,976
-1,670
-0,935
-1,665
-1,364
-0,952
0,171
0,243
0,032
0,039
-0,079
-0,049
-1,807
-1,046
-0,602
-1,009
Maximum
1,942
1,485
0,525
0,735
0,857
0,611
1,355
0,910
0,790
1,084
1,115
0,357
0,807
0,815
0,644
0,946
0,856
0,353
0,314
0,373
0,329
0,761
1,355
0,438
0,362
0,383
0,134
1,245
0,932
0,980
1,719
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
Mean
1,115
0,680
0,258
-0,063
-0,085
-0,067
0,391
0,081
0,050
-0,215
-0,169
-0,112
-0,136
-0,173
-0,067
-0,170
-0,314
-0,146
-0,301
-0,234
-0,185
0,416
0,699
0,205
0,224
0,173
0,059
-0,271
-0,102
-0,022
0,164
Std. Deviation
N
1,170
1,138
0,379
0,485
0,623
0,344
0,653
0,657
0,363
0,661
0,475
0,317
0,562
0,628
0,474
0,647
0,759
0,350
0,516
0,515
0,323
0,307
0,582
0,209
0,167
0,219
0,077
0,882
0,567
0,405
0,720
2
2
2
8
8
8
9
9
8
18
17
17
19
18
18
14
14
14
15
14
14
3
3
3
4
4
4
13
12
12
21
27
2006
2007
ABHAR 2 years after event
ABHAR 1 year after event
ABHAR 3 years after event
ABHAR 2 years after event
ABHAR 1 year after event
ABHAR 3 years after event
ABHAR 2 years after event
ABHAR 1 year after event
-0,504
-0,735
-0,812
-0,717
-0,938
-0,715
-0,583
-0,662
1,427
0,794
1,390
0,670
2,230
0,774
0,709
0,643
0,205
0,063
-0,012
-0,053
0,165
0,006
-0,028
0,001
0,493
0,346
0,504
0,337
0,607
0,367
0,364
0,341
20
19
17
17
17
25
25
25
6.3 T-test of equality of means of average abnormal buy returns regarding the value
investment variables
6.3.1 General results
In the table below the results for several T-tests for the equality of means regarding the
ABHARs are shown for the variables used in the hypotheses in section 3.3 of this paper. The
difference of the ABHARs is tested for the groups (quartiles) with the highest value vs. the
lowest value of the value investing variable in the sample.
Table 7: T-test for equality of means of ABHARs regarding value investment variables
3 year-ABHAR
2-year ABHAR
Variable used
Mean lowest Mean
Diff.
T-stat
Mean
Mean
Diff.
quartile
highest
lowest highest
quartile
quartile quartile
Net debt/assets
0,002
-0,207
-0,197
0,073
-0,270*
-1,963
-0,205
(ND/A)
Return on equity
-0,021
-0,037
-0,268
-0,049
-0,050
-0,0574
-0,100
(ROE)
Profit
-0,069
0,0065
0,049
-0,062
-0,070 -0,116
0,045
margin(PM)
Liabilities/Share
0,143
-0,252* -1,843
-0,171
holders equity
-0,110
-0,117
0,054
(L/SE)
Deal size
0,0570
--,177
-1,382 -0,132
-0,182
-0,120
0,050
1-year ABHAR
Variable used
Mean lowest Mean
Diff.
T-stat
quartile
highest
quartile
ND/A
0,067
-0,181* -1,791
-0,114
ROE
0,0129
-0,056
-0,560
-0,043
PM
-0,035
-0,011
-0,025
-0,264
L/SE
-0,106
-1,093
-0,044
0,063
Deal size
-,152*
-1,704
-0,075
0,078
6.3.2 Return on equity (ROE)
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
T-stat
-1,619
-0,389
0,365
-1,355
-1,485
28
The difference between the two groups is negative for all three holding periods, meaning
that the group with the smallest ROE also scored the smallest ABHAR. However, the difference
is not significant, so the statement that there is no difference between the two groups can be
affirmed for this sample and hypothesis 4 is rejected based on this analysis and sample size. It
seems that a larger sample size (10 times as big) will not decrease the standard deviation within
the two groups enough (1/√N) to make this difference more significant. A large sample size will
likely not lead to any other conclusions.
6.3.3 Profit margin (PM)
The difference between these two groups is both positive for a 2 and 3 year holding
period and negative for a 1 year holding period. Again, the difference is not significant, so the
statement that there is no difference between the two groups can be affirmed for this sample and
hypothesis 5 is rejected based on the results from this analysis and sample size. As the
significance of the test results is even smaller than for ROE, it seems likely that a larger sample
size might not decrease the standard deviation within the two groups enough to make this
difference more significant.
6.3.4 Net debt divided by assets
Surprisingly, the difference between the two groups is negative and significant for a 1
and 3 year holding period, meaning that the group with the smallest net debt to assets ratio also
scored the smallest ABHAR, contradictory to hypothesis 3. The difference for a 2 year holding
period is also negative, but just not significant enough. A larger sample size will be needed for a
2 year holding period to derive to any more conclusive implications, but using this sample size
and analysis method hypothesis 3 will be rejected.
6.3.5 Liabilities divided by shareholders’ equity
The difference between the two groups is negative for all three holding periods, but only
significant for a 3 year holding period, in contradiction with hypothesis 6. It seems that a larger
sample (10 times as big) size might decrease the standard deviation within the two groups
somewhat and thereby make this difference more significant. A large sample size for the 1 and 2
year holding period will be needed to derive to any more conclusive implication, but using this
sample size and analysis method hypothesis 6 will be rejected
6.3.6 Deal size
In line with previous studies and hypothesis 2, the difference between the two groups is
negative for all three holding periods, but only significant for a 1 year holding period. It seems
that a larger sample (10 times as big) size might decrease the standard deviation within the two
groups somewhat and thereby make this difference more significant. A larger sample size for the
2 and 3 year holding period will be needed to derive to any more conclusive implications, but for
now hypothesis 2 is rejected for a 2 and 3 year holding period.
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
29
6.4 Regression analysis long-term ABHAR
The following regression was done to analyze the significance of the various deal
characteristics and the value investment variables with regards to the abnormal buy and holding
returns (BHAR):
BHAR it = α + β1 ∙ 𝑆𝑡𝑜𝑐𝑘 𝑝𝑎𝑦𝑚𝑒𝑛𝑡 𝑑𝑢𝑚𝑚𝑦i + β2 ∙ 𝑃𝑟𝑖𝑣𝑎𝑡𝑒 𝑡𝑎𝑟𝑔𝑒𝑡 𝑑𝑢𝑚𝑚𝑦𝑖 + β3 ∙
𝑁
𝑁
𝐷𝑒𝑎𝑙 𝑠𝑖𝑧𝑒 + β4 ∙ 𝑇𝑒𝑛𝑑𝑒𝑟 𝑜𝑓𝑓𝑒𝑟 𝑑𝑢𝑚𝑚𝑦 + β5 ∙ 3 year av. [∆( 𝐴𝑖 − 𝐴 𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦 )] + β6 ∙
𝑖
𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦
3 year av. [∆(𝑅𝑂𝐸𝑖 − 𝑅𝑂𝐸 𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦 )] + β7 ∙ 3 year av. [∆(𝑃𝑀𝑖 − 𝑃𝑀 𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦 )] + β8 ∙
𝐿
𝐿
3 year av. [∆(𝑆𝐸𝑖 − 𝑆𝐸𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦 )] + εit
𝑖
𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑦
[5]
The relative deal size is determined by the formula: Relative size = deal value/acquirer’s
market value. The private target dummy equals 0 if the acquirer merges with a publicly held firm
and is 1 if the acquirer merges with a privately held firm. The diversifying deal dummy is 0 if the
acquisition deal occurs between two related industries and is 1 if the acquisition deal occurs
between two diversifying industries, which is based on the two-digit US SIC code. The stock
payment dummy equals 0 if the acquirer did not pay the merger with all stock and equals 1 if the
acquirer paid with all stock. The method for calculation of the rest of these variables can be
found in the explanation of table 2 and 3. εit are the residuals after the regressions of the buy and
holding returns on all variables.
A total of 4 regressions per holding period were done with these variables to test if there
was a difference in the power of the regression based on the inclusion of those variables. The
same industry-related merger dummy was excluded from these regressions because of its
insignificance and because it did not improve the overall testing power of the regressions. On the
next page the results of these regressions on respectively the 3, 2 and 1 year BHAR are shown in
table 8, 9 and 10:
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
30
Table 8: Regression analysis of 3 year BHAR on different deal characteristics and value
investment variables
In all tables *,** and *** stand for a 10 % significance, 5 % significance and 1 % significance
level based on the t-statistics. Ave. stands for average
(Constant)
Stock payment
dummy
Private target
dummy
Deal size dummy
Tender offer
dummy
Reg.1
T-stat
Reg. 2
T-stat
Reg. 3
T-stat
Reg. 4
T-stat
-,072
-1,044
-0,083
-1,203
-0,125*
-1,744
-0,134*
-1,872
-0,376***
-4,192
-0,357***
-4,003
-0,357***
-4,030
-0,356***
-4,045
0,326***
2,628
,305**
2,473
,302**
2,473
0,295**
2,434
,190**
2,165
0,183**
2,096
0,217**
2,459
,202**
2,295
,320**
2,564
0,292**
2,349
0,280**
2,268
,287**
2,344
,421**
2,120
,310
1,618
,387
,920
,439
1,106
Difference of
L/SE with
industry ave.
Difference of
N/A with
,382*
1,989
,366*
1,922
industry ave.
Difference of
PM with
industry ave.
Difference of
ROE with
,630*
1,935
industry ave.
R2 adjusted
0,200
0,214
0,228
0,222
1,944
1,927
1,888
1,699
coefficient
Durbin-Watson
coefficient
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
31
Table 9: Regression analysis of 2 year BHAR on different deal characteristics and value
investment variables
(Constant)
Reg.1
T-stat
Reg. 2
T-stat
Reg. 3
T-stat
Reg. 4
T-stat
-0,086
-1,378
-0,099
-1,596
-0,138**
-2,112
-0,136**
-2,054
-0,242***
-2,952
-0,222***
-2,711
-0,219***
-2,692
-0,222***
-2,717
0,243**
2,166
0,219*
1,963
0,216*
1,951
0,218***
1,949
,111
1,365
0,104
1,290
0,134
1,644
0,127
1,542
0,283**
2,460
0,258**
2,257
0,250**
2,202
0,253**
2,216
0,053
0,289
0,363**
2,042
0,306
,794
0,375
1,061
Stock
payment
dummy
Private target
dummy
Deal size
dummy
Tender offer
dummy
Difference of
L/SE with
industry ave.
Difference of
N/A with
,381**
2,173
0,367**
2,108
industry ave.
Difference of
PM with
industry ave.
Difference of
ROE with
0,529*
1,817
industry ave.
R2 adjusted
0,121
coefficient
0,141
0,152
0,145
2,030
1,839
DurbinWatson
2,063
2,040
coefficient
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
32
Table 10: Regression analysis of 1 year BHAR on different deal characteristics and value
investment variables
(Constant)
Stock payment
dummy
Private target
dummy
Deal size dummy
Tender offer
dummy
Reg.1
T-stat
Reg. 2
T-stat
Reg. 3
T-stat
-2,441
Reg. 4
-0,104**
T-stat
-2,350
-0,088**
-2,114
-0,094**
-2,280
-0,107**
-0,225***
-4,130
-0,214***
-3,927
-0,213***
-3,905
-0,216***
-3,932
0,189**
2,532
0,176**
2,364
0,175**
2,350
0,177**
2,363
0,254***
4,723
0,250***
4,676
0,260***
4,753
0,256***
4,627
0,143*
1,879
0,130*
1,710
0,128*
1,674
,129*
1,683
0,002
,019
0,197*
1,652
0,205
,791
0,066
,279
Difference of
L/SE with
industry ave.
Difference of N/A
0,198*
with industry ave.
1,699
0,194*
1,659
Difference of PM
with industry ave.
Difference of
ROE with
0,172
,880
industry ave.
R2 adjusted
0,245
0,253
0,252
0,246
1,747
1,759
1,768
1,809
coefficient
Durbin-Watson
coefficient
6.4.1 Power of the regressions
Including the liabilities-to-shareholders´ equity ratio and the profit margin in the
regressions, decreases the R2 adjusted somewhat and lowers the value of the Durbin-Watson
coefficient from its ideal value of 2. Also, the significance of the return on equity, one of the
most important factors used by value investors, decreases if these two variables are included in
the regression. Therefore regression model 3 will be used mostly in the analysis of the factors
that best predict the BHARS.
6.4.2 Tender offer
The effect of the tender offer dummy in these regressions is significant and positive and
increases from 0.128 to 0.250 to 0.280 with increasing holding period. This means that a tender
offer increases the value of the buy and holding return anywhere from 12.9% to 28.7 % over a 1
to 3 year holding period.
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
33
6.4.3 Relative deal size
The effect of the relative deal size in these regressions is significant in the 1 and 3 year
holding period and varies from 0.260 to 0.134 to 0.217 with increasing holding period. This
means that if the relative deal size increases by 0.5 the value of the buy and holding return
increases anywhere from 6.7 % to 13.0 % over a 1 to 3 year holding period. This result confirms
hypothesis 2 for a 1 and a 3 year holding period. The sample size might need to be bigger to
confirm hypothesis 2 for a 2 year holding period as well.
6.4.4 The effect of the method of payment: the stock payment dummy
The effect of paying the acquisition with stock only in these regressions is significant and
decrease from -0.213 to -0.219 to -0.357 with increasing holding period. This means that a stock
offer decreases the value of the buy and holding return anywhere from 21.3% to 35.7 % over a 1
to 3 year holding period
6.4.5 Organization form of the target
The effect of private target dummy in these regressions is significant and increases from
0.175 to 0.216 to 0.307 with increasing holding period. This means that an offer to a private
target increases the value of the buy and holding return anywhere from 17.5% to 30.7 % over a 1
to 3 year holding period.
6.4.6 Return on equity (ROE)
The effect on the return on equity in these regressions is 0.172 and not significant for a 1
year holding period and is positive and significant for 0.529 to 0.630 for a respectively 2 and 3
year holding period. The 2 and 3 year holding period regression returns affirm hypothesis 4,
however the 1 year holding period rejects this hypothesis because of the low level of significance,
a larger sample size would be needed for the 1 year holding period to confirm this hypothesis for
all holding periods. In number this translates to that a return on equity of over 10 % the industry
average (0.1), increases the value of the buy and holding return anywhere from 1.72 (notsignificant) % to 6.3 % over a 1 to 3 year holding period
6.4.7 Profit margin (PM)
The effect of profit margin in these regressions is positive, but not-significant and
increases from 0.205 to 0.306 to 0.387 with increasing holding period. The results are not in
accordance with hypothesis 5 and therefore this hypothesis is rejected.
6.4.8 Net debt divided by total assets (N/A)
Surprisingly, the effect of the net debt-to-assets ratio in these regressions is positive and
significant and increases from 0.194 to 0.367 to 0.366 with increasing holding period. This
means that a net debt-to-assets ratio that is 10 % bigger than the industry average (0.1) increases
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
34
the value of the buy and holding return anywhere from 1.94 % to 3.66 % over a 1 to 3 year
holding period. The results are not in accordance with hypothesis 3 and therefore this hypothesis
is rejected.
6.4.9 Liabilities divided by shareholders’ equity (L/SE)
The effect of the liabilities-to-shareholders’ equity ratio in these regressions is positive,
but not-significant for the 1 and 2 year holding periods and positive and significant with 0.421
for the 3 year holding period. For the 3 year holding period the result is the opposite of
hypothesis 6 and for the 1 and 2 year holding period this hypothesis is rejected based on a lack of
significance. Overall hypothesis 6 is rejected.
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
35
7.
Conclusion
Abnormal returns for the event firms were estimated using the Fama French 3 factor
model and using the matched firm approach. Abnormal returns were calculated by subtracting
the forecasted return for the event firm from actual return of the event firm for a 20 day time
window. The short-term results indicated that the acquiring firms gained an immediate and
statistical significant loss at the day of the announcement of -1.46 % and a total loss of -2.75 %
in a subsequent 4 day window (including the announcement day).
Secondly, the returns over a longer period are compounded for both the event and
matched firm to estimate the effect of the mergers on the acquiring shareholders ‘return.
Abnormal returns are again calculated by calculating the difference between both. The results
suggest that mergers and acquisitions of these M&A companies were value destructing. The
abnormal returns were -2.14 %, -5.08 % and -7.85 % in a 1, 2 and 3 year period. Only the last
number is significant at the 10 % level. The results also show that mergers paid for with
stock/equity on average had long-term abnormal results that were 35.6 % lower than those of
cash paid mergers after a 3 year period. Mergers that were done through a tender offer returned
on average a 28.7 % higher abnormal return and mergers of private targets increased the
abnormal return with 29.5 % compared to public targets. An increase of the relative deal size of
0.5 increased the abnormal return on average with 10.1 %. All these figures are for a 3 year
holding period. This study found no significant effect on abnormal returns of same industry
mergers.
Several value investment variables were proposed in this study, namely: higher return on
equity, higher profit margin, lower net debt/total assets and lower liabilities/shareholders’ equity,
all in relation to their industry averages. These variables are usually found in companies that
have excellent normal returns and using a Fama French 3 factor model the aim of this study was
to find out if these companies also make better shareholder-orientated acquistions decisions,
resulting in positive abnormal returns. This report found for a 2 and a 3 year holding period that
both a higher net debt/total assets and a higher return on equity gave significant and positive
abnormal returns (but only) if these 2 variables alone were included in the regression model.
The power of this regression was also bigger than that of a regression with all 4 value variables
included. A change in the return on equity (ROE) has a slighly higher effect on abnormal returns
than a change in net debt/assets (N/A). A 0.1 absolute increase in the value of ROE or N/A
results on average in a 6.3 % and 3.66 % higher abnormal return for a three year holdig period.
Contratry to belief a higher net debt/total assets ratio increased the abnormal returns instead of
decreasing it. A higher return on equity did however increase the abnormal return and is in line
with what was expected beforehand. The regressions on abnormal buy and holding return of the
other 2 value variables gave no significant results for the 1 and 2 year holding period. Only the
liabilities/shareholders equity variable was significant in the 3 year, but this seems to be an
outlier that is not consistent with the value of this variable in the first two years.
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
36
8.
Discussion
First of all due to the small sample size (169 companies) chosen, the results of this study
are sometimes not clear regarding the absolute significance of these value investing variables.
Especially with respect to the equality of means comparison made in this paper, a larger sample
should lead to a more significant difference between both groups. This report has used numerous
deal characteristics (e.g. relative deal size, organizational status target) that were successful in
explaining variation in abnormal stock returns as well as using a well know benchmark model
(e.g. Fama-French’s 3 factor mode). In this study event and matched firms were paired based
based on size and book-to-market. However, this still leaves open the possibility that the size and
book-to-market matched firms differ systematically in non-matched characteristics. These nonmatched characteristics might affect the results of the analysis on abnormal returns as well. Also,
size and book-to-market values of both the event and matched firm may change in opposite
directions after corporate events, resulting in pairs of matched firms and event firms that were
well-matched in terms of selected characteristics at the event date, but may not remain so in the
1,2 and 3 year buy and holding period after the event date(Bessembindeer and Zang, 2012).
Suggestions for future research would be to include more complex matching algorithms
to select event and control firms (Bessembindeer and Zhang, 2012). Bessembindeer and Zhang
reported that matched firms differed significantly in terms of illiquidity, idiosyncratic volatility,
return momentum, and market beta. Bessembindeer and Zhang stated that either abnormal
returns may be directly associated with the characteristics of the event being studied or may be
the direct effect of other not-included differences in firm characteristics across event and control
firms. Bessembindeer and Zhang stated that a wide range of firm characteristics such as firm
size, book-to-market ratio, illiquidity, return momentum, market beta, and idiosyncratic volatility
all have explanatory power for stock market returns as has been extensively proven in several
scientific studies. They found that variation in illiquidity and idosyncratic volatility
characteristics between firms explained a substantial portion of the observed BHARs.
Bessmbindeer and Zhang used the following Fama-MacBeth regression to correct for
differences in important firm characteristics between matched and event firm over time:
ln(1 + 𝑟𝑒𝑡 ) − ln(1 + 𝑟𝑚𝑡 ) = 𝛼 + 𝛽1 ∆𝐵𝑒𝑡𝑎𝑒𝑡 + 𝛽2 ∆𝑆𝑖𝑧𝑒𝑒𝑡 + 𝛽3 ∆𝐵𝑀𝑒𝑡 + 𝛽4 ∆𝑀𝑜𝑚𝑒𝑡
+ 𝛽5 ∆𝐼𝑙𝑙𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝑒𝑡 + 𝛽6 ∆𝐼𝑑𝑖𝑜𝑉𝑜𝑙𝑒𝑡
ret stands for the return of the event firm at day or month t, rmt stands for the market return at day
or month 6, ∆𝐵𝑒𝑡𝑎𝑒𝑡 , ∆𝑀𝑜𝑚𝑒𝑡 , ∆𝑆𝑖𝑧𝑒𝑒𝑡 , ∆𝐵𝑀𝑒𝑡 , ∆𝐼𝑙𝑙𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦𝑒𝑡 , ∆𝐼𝑑𝑖𝑜𝑉𝑜𝑙𝑒𝑡 are respecitvely
the difference in beta between the event and matched firm with regards to beta, momentum,
market size book-to-market value, illiquidity and idiosyncratic volatility. In a follow up-study
monthly values on illiquidity, idiosyncratic volatiliy, book-to-market value and momentum could
be included and controlled for in the regressions to get more accurate results on the significance
of the value investement variables and the deal characteristics in the regressions.
Master thesis – Joeri Moerman 357652 – Mergers & Acquisitions
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