Research on "MERGERS and ACQUISITIONS"

IBS-Pune Prepared by:
JAGDISH PADAKI (Faculty BRM) Gourav Ranjan

Advisor:
Dilip Sharma

An empirical study of the value creation in
Mergers & Acquisitions

Focusing on acquiring companies located in the UK or Scandinavia

4th January 2012

ACKNOWLEDGEMENT

I would like to express my special thanks of gratitude to my PROF. JAGDISH PADAKI who gave me the golden
opportunity to work on this wonderful live research project on the topic “MERGER & ACQUISITION” which
also helped me in enhancing my practical skills by applying all the theoretical and analytical skills and also
came to know about so many new things.
Also I would like to give my special thanks to “MR. DILIP SHARMA”, “V.P. of M&A” in “HU Consultancy Pvt.
Ltd.” for availing me authenticated sources relevant to this topic.
At the end I would also thankful to my friends who had helped me while completing this report.

I worked on this project not only for marks but to enhance my knowledge and analytical skills in this field.

Thanks to all who had helped me.

Abstract

This thesis evaluates the effects of mergers and acquisitions upon the acquiring
company based on the underlying strategic rationale for the deal. The analysis is based on
both daily stock prices (analysis up to 1 year after the event) as well as on operating measures
(analysis up to 3 years after the event) in relation to UK and Scandinavian based acquiring
companies. An event study approach is taken to evaluate the abnormal performance
following a merger or an acquisition. Both a parametric t-test as well as different non-
parametric tests are conducted and the empirical results indicate that the highest amount of
power and reliability is attached to the non-parametric tests.

The main empirical results from the thesis in relation to the performance of the
acquiring firms are that in most cases it is not possible to detect any abnormal
performance. This indicates that it is not possible to find support in favour of a positive
reaction or effect of a merger. However, in cases when an abnormal performance is
detected it is mostly negative indicating that the acquiring company looses value. In the
shortest period of time around the event day the stock prices show some evidence in
favour of a positive reaction in the stock prices in response to the deal.

In relation to the investigation of differences between the different strategies it appears that
it is not possible to conclude that all of the strategies differ. However, some indication
of one strategy outperforming the remaining strategies as well as one strategy being
outperformed by the others was detected from the analyses of differences.

An empirical study of the value creation in M&A – in relation to the strategic
rationale

Table of contents

1. Introduction ................................................................................................................ 1
1.1. Research question ................................................................................................ 2
1.2. Definitions and clarifications .............................................................................. 3
1.3. Delimitation .......................................................................................................... 3
1.4. Evaluation of sources........................................................................................... 4
2. Research method ........................................................................................................ 5
2.1. Measuring performance based on stock prices................................................. 5
2.2. Measuring performance based on operating measures ................................... 8
2.3. Preparation and selection of data..................................................................... 10
3. Results from previous empirical studies on value creation in M&A ................... 13
4. Event studies ............................................................................................................. 15
5. Measuring abnormal performance based on stock prices .................................... 16
5.1. Estimation period, event day and event window ............................................ 16
5.2. Creating a benchmark for the ‘normal’ performance ................................... 17
5.3. Choice of tests – Parametric or Non-parametric ............................................ 18
5.3.1. The parametric t-test ..................................................................................... 19
5.3.2. Non-parametric tests..................................................................................... 20
6. Measuring abnormal operating performance........................................................ 24
6.1. Definition and determination of the selected operating figures .................... 24
6.1.1. Return on assets – based on EBIT and EBITDA ......................................... 25
6.1.2. Return on sales – based on EBIT and EBITDA ........................................... 26
6.1.3. Cash flow return on assets ............................................................................ 26
6.1.4. Tobin’s Q ...................................................................................................... 27
6.2. Event day, event window and estimation period ............................................ 27
6.3. Creating a benchmark - performance based .................................................. 28
6.4. Specification of statistical tests – Parametric and non-parametric tests ...... 29
6.4.1. Parametric t-test for abnormal operating performance................................. 29
6.4.2. Wilcoxon Signed Rank – test for abnormal operating performance ............ 30
7. Test for differences between the strategies............................................................. 31
8. Overview and discussion of the research approach and the selected tests.......... 32
8.1. Overview of the research approach ................................................................. 32
8.2. Discussion of choice of research approach ...................................................... 33

An empirical study of the value creation in M&A – in relation to the strategic
rationale

9. Descriptive statistics ................................................................................................. 37
9.1. Description of the strategies.............................................................................. 38
9.2. Descriptive statistics – Daily stock prices ........................................................ 39
9.3. Descriptive statistics – Accounting figures ...................................................... 40
10. Hypotheses and expectation in relation to the empirical results........................ 41
11. Empirical evidence – abnormal stock price performance .................................. 45
11.1. Empirical results – parametric t-test ............................................................. 45
11.1.1. Robustness and power of the parametric t-test ........................................... 48
11.2. Empirical results – the non-parametric tests ................................................ 51
11.2.1. Empirical results of the Rank test............................................................... 51
11.2.2. Empirical results of the Sign test ................................................................ 52
11.2.3. Empirical results of the Generalized Sign test ........................................... 54
11.3. Examining differences between strategies..................................................... 55
11.4. Summery of the empirical results in relation to stock prices ...................... 58
12. Empirical evidence – abnormal operating performance .................................... 60
12.1. Presentation of empirical results.................................................................... 60
12.1.1. Return on assets – EBIT ............................................................................. 60
12.1.2. Return on assets – EBITDA ....................................................................... 64
12.1.3. Return on sales - EBIT ............................................................................... 68
12.1.4. Return on sales – EBITDA ......................................................................... 71
12.1.5. Cash Flow Return on assets........................................................................ 75
12.1.6. Tobin’s Q .................................................................................................... 78
12.2. Examination of differences between strategies based on operating performance
.............................................................................................................. 82
12.3. Summary of the empirical results in relation to operating performance... 86
13. Evaluation of the data, the research approach, and the empirical results........ 88
14. Conclusion ............................................................................................................... 90
15. A final perspective on value creation in M&A..................................................... 94
16. Bibliography............................................................................................................ 96

An empirical study of the value creation in M&A – in relation to the strategic rationale

1. Introduction

Due to increased competition and increased globalization the economic environment has
changed in recent years and in light of this, the challenges a company faces have become
larger and more demanding. One result of this has been an increase in the number of
acquisitions both within and across borders. For companies to stay at pace with competitors
growth through acquisitions has become increasingly important and has at least partly
replaced organic growth. The number of mergers and acquisitions as well as the value of
these transactions has risen significantly through time. However, the increase in the M&A
activity is not linear, in fact it moves up and down over time with an overall rising tendency.
1
This phenomenon is referred to as ‘merger waves’ in fact research documents periods
with obvious increases in activity. These ‘merger waves’ influence the business
environment and in conducting analysis of M&A activity possible merger waves within the
period in question should be kept in mind.

In research literature attention has been paid to M&A transactions and several studies have
been conducted in order to understand and determine the trends and the
characteristics in this field. The overall motive for the acquirer is value creation and in the
light of increasing M&A activity it is relevant to examine whether or not value is created.
One branch of research has focused on whether or not value is created for the acquiring
firm in relation to mergers and acquisitions. No overall consensus exists that unanimously
documents whether or not value is created for the acquiring firm. Moeller et al. (2005) have
concluded that value is actually destroyed when engaging in acquisitions. The reason for
this conclusion is imputed to the fact that the largest M&As are the ones experiencing
massive losses. A part from the ‘large loss deals’ the remaining companies actually
experience gains from M&A. The conclusions from this paper are based on US companies
and have not yet been fully explored on European companies.

Companies engaging in mergers and acquisitions can be motivated by several different
objectives, some of the most obvious as presented by Sudarsanam (2003) are synergies,
increased growth, cost savings and increased efficiency. Apart from these more

1 Several research studies have focused upon M&A activity and have identified periods
of increased activity, which is referred to as ‘merger waves’ (see Mulherin and Boone
(2000) and Andrade et al. (2001))

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apparent motives might also include decreased transaction costs, increased knowledge or so
forth. Besides the motive for a company to engage in mergers or acquisitions the type of
industry in which the company operates also affect the type of merger. The motive for the
acquisition in the case of a mature industry could be far different from the motive
dominating an immature industry. In paying attention to the motive that drives the
acquisition and the industry in which the acquirer operates emphasize the importance of
the strategic rationale for a merger or an acquisition.

It is widely recognized that it is decisive for a company to set up a strategy in order to meet
the challenges it faces due to a fierce competition and a quickly changing environment.
The strategy a company chooses to follow should be in line with an overall goal of value
creation. A decision to expand through acquisitions has to correspond to the underlying
strategy of the company. In line with this, the strategy or the motivation behind an
acquisition is according to Bower (2001) an important factor in determining whether or not
an acquisition becomes a success - meaning whether or not value is created. In his study he
distinguishes between five overall strategies, which are examined based on US companies.
The five strategies are referred to as: ‘the Overcapacity M&A’, ‘the Geographic Roll-up
M&A’, ‘the Product or Market Extension M&A’, ‘the M&A as R&D’, and ‘the Industry
Convergence M&A’.

1.1. Research question

The findings from the two mentioned articles are the main motivators for this thesis and the
ambition of the thesis is thus to combine the two observations and apply them to
European companies in an analysis of value creation. In order to conduct this analysis the
following research question is posed:

Is it possible to detect abnormal performance in relation to a Merger or an Acquisition?
- And does abnormal performance differ between the different strategies/motives behind
the deal?

In order to ensure the debt of the thesis the overall research question is analyzed in
connection to the following sub questions:

• Is value created in general based on the entire sample of firms?

• When looking at each strategy separately is value created within the strategies?

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• Is it possible to document a significant difference between the strategies?

Throughout the thesis the analyses are conducted in relation to both stock prices and
operating figures, which enables a focus upon short-term as well as long-term value
creation.

The purpose of this thesis is to detect value creation in mergers and acquisitions based on
the strategy motivating the transaction. Seeing that the thesis is mainly inspired by the two
observations mentioned above, it aims to document if value is created in mergers and
acquisitions in cases where the acquiring company is located in the UK or Scandinavia.

1.2. Definitions and clarifications

The vocabulary and the research approach in this thesis are in line with existing
research in the field. Therefore no separate definitions are presented here except for a
clarification of what is meant by mergers and acquisitions.

The sample included in the thesis consists of both mergers and acquisitions, but no
distinction between the two types of deals is made in the analyses of value creation. A
specific merger or acquisition is referred to as a deal.

1.3. Delimitation

To conduct the analysis some delimitation has been made partly due to the scope of the
thesis, but also in relation to the available sources of information. The companies
included in the analysis were chosen based on some overall criteria in which the starting point
was a sample of 959 companies. In preparing this data and conducting the analysis the sample
was reduced somewhat. A specific deal was excluded in cases where no information is
available to document the strategy behind the deal. This is a necessary exclusion due to the
fact that the entire analysis is based on the strategy behind the deal and thus it is a necessity
to distinguish the motive for each deal.

To determine the underlying strategy some further delimitation have been made. In cases
where a subsidiary was acquired the deal was excluded. This was done in order to maintain
focus upon acquisitions of another company. In addition, some deals included in the original
sample of 959 companies were located outside the UK and Scandinavia and these deals were
also excluded in order to stay in line with the overall characteristic

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of the data sampling – focusing exclusively on acquiring companies situated in either the UK
or Scandinavia. Furthermore, the original sample included some deals that were announced in
1999 or completed in 2005. These deals were also excluded from the sample.

In order to perform the analyses stock prices or operating figures needed to be available. To
conduct the analysis based on stock prices at least 75 observations prior to the
announcement day were needed for a deal to be included. Furthermore, the sample only
consists of companies that in the relevant period were listed on a stock exchange in the UK or
Scandinavia, therefore, companies that was only listed elsewhere was excluded. In the case
of operating figures, deals were excluded if changes in the accounting year were made
during the period of analysis or if accounting figures in the year prior to the event were
unavailable. For a specific deal to be included all operating figures needed not be available.
Finally the benchmark was constructed based only upon those deals included in the analysis
of operating performance. This final delimitation is in particular due to the scope and time
available for the paper seeing that the data collection was a very large and demanding task.

1.4. Evaluation of sources

The sources used in this thesis are primarily research papers, which are considered
appropriate for this type of thesis. In setting up the event study inspiration was sought in
previous literature, and it was attempted to choose methods that were well documented and
evaluated in previous work. In doing so the research approach is valid without having to
perform a simulation study of the approach.

Seeing that the approach is chosen in accordance with recommendations from previous
literature the different alternative approaches and models are not discussed in debt. The
reason for not including this type of discussion is primarily the limited scope of the thesis
as well as the consideration that basing the event study on the work of primarily Brown and
Warner (1985), Barber and Lyon (1996), MacKinlay (2001), and Bartholdy et al. (2007)
ensures a valid and reliable approach. Furthermore, the thesis will focus more on the
analysis and the actual results that can be drawn. Since the approach is well specified and valid
a comparison with other empirical work is possible.

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2. Research method

As mentioned above, the starting point of this thesis is the two observations – first, value
is destroyed in mergers and acquisitions based on a US sample of firms, and second, the
underlying strategy is a determinant for the success of a deal. These two observations have
served as inspiration in designing the research question for this thesis. In order to
examine if any value is created the event study methodology is chosen as the research
approach.

The event study methodology is applied to the analysis of both stock prices and
operating figures and both parametric and non-parametric tests are included. In the
following an elaboration of the research method is presented.

2.1. Measuring performance based on stock prices

In the analysis of performance based on stock prices, daily stock quotes are applied. The
analysis of stock prices is performed on five different time intervals (the 11 day event
window, 1 month, 3 months, 6 months and 1 year). The method chosen and the analysis
conducted are inspired by those methods applied in other research papers in particular
MacKinlay (1997) and Bartholdy et al. (2007), which in large also corresponds to Brown
and Warner (1985). Both parametric and non-parametric tests are conducted in order to
examine if value is created.

To perform an event study the first thing to do is to determine the event day, the event
window and the estimation period. In this thesis the event day is set to the day that the
announcement of the deal is published. The reason why the announcement day is
chosen as the event day instead of the completion day is the fact that stock prices are
expected to adjust immediately to new information - in this case news of an upcoming
merger or acquisition. The event window is the event day and five days before and after the
event day, thus 11 days in total or ± 5 days of the event. In determining the normal
performance of the companies an estimation period needs to be chosen. In this case the
estimation period consists of 250 days prior to the first day in the event window. The
estimation period is in line with the recommendations from Bartholdy et al. (2007). Due
to the fact that not all stocks trade multiple times a day or even daily, an adjustment for

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thin trading is recommended. This is done by applying trade-to-trade returns to the entire
sample as recommended by Bartholdy et al. (2007).

To conduct the event study the abnormal return at each day in the estimation period and the
event window is calculated. The abnormal performance or in this case the abnormal return
(AR) is the difference between the actual return and the ‘normal’ return. The
‘normal’ return is estimated by means of the market model. This model assumes that all stocks
perform equally to a market index adjusted by the risk associated with the stock. In
estimating the ‘normal’ return the actual return of the company is regressed on the market
index and the parameters α and β are estimated. The main market index in the country of
the acquirer is chosen as the market index, these are FTSE 100, OMXS30, OMXC20,
OMXH25, OSEBX and OMXI15 for the UK, Sweden, Denmark, Finland, Norway, and Iceland
respectively. Since the aim of the thesis is to detect value creation based on a sample of firms
over a period of time an aggregation of the daily abnormal returns is necessary. For this
purpose the cumulative abnormal return (CAR) is calculated both over time and across
securities.

In relation to testing for abnormal performance by means of stock prices the following
statistical tests are performed:

• Parametric t-test
• Non-parametric tests:
o Rank test
o Sign test
o Generalized Sign test

In the parametric t-test the null hypothesis of no abnormal performance is tested by
means of whether or not the average CAR is significantly different from zero. This
parametric t-test is conducted for the entire sample as a whole and also for each of the
strategies. A parametric test is subject to some assumptions that might influence the
robustness of the results. The assumptions are presented in section 5.3.1.

The main advantage of non-parametric tests is the fact that no assumptions are attached and
the results are robust regardless of possible problems in relation to the parametric test.
According to Cowan (1992) another important motive for including non-parametric tests is
the fact that when thin trading is present in the sample, violation of the

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assumptions that characterize the parametric test is more likely and thus non-parametric tests
are justified.

The Rank test conducted in this thesis is based upon Corrado (1989). In the Rank test all
abnormal returns for each security in the estimation period and in the period of analysis are
ranked with the lowest rank corresponding to the lowest abnormal return and the highest

2
rank the highest abnormal return. The expected rank of the event day is the median rank
plus 0.5. In order to test for abnormal performance, under the null hypothesis it is
tested if the expected rank is equal to the rank at the event day.

The Sign test is conducted in line with Corrado and Zivney (1992). The Sign test is based
upon the assumption that the probability of observing a negative or a positive abnormal
return is the same - that is 0.5. In performing the test the sign for each daily abnormal
return is traced which is done by first obtaining the median abnormal return for each
security and afterwards for each day in the sample the sign is obtained by subtracting the
median abnormal return from the actual abnormal return. If obtaining a positive sign the
observation is given the value 1, a negative sign is given the value -1 and finally in cases
where the actual abnormal return is equal to the median abnormal return the observation is
given the value 0. Under the null hypothesis the probability of observing a positive
cumulative abnormal return is the same as observing a negative abnormal return.

The Generalized Sign test is based on Cowan (1992). The main difference from the Sign test
included in this thesis is that in the Generalized Sign test, the probability of observing a
negative or a positive abnormal return is estimated based on the actual returns observed
in the estimation period. Thus in conducting the Generalized Sign test, the probability of
observing a positive abnormal return around the event day is compared to the
probability of a positive abnormal return in a period unaffected by the event (the estimation
period). Under the null hypothesis of no abnormal performance the number of securities
with positive abnormal returns in the event window is equal to the number that is expected in
the absence of an event.

After having performed the parametric and non-parametric tests for abnormal
performance a F-test is performed to determine if any differences between the strategies

2 In case of the event period being analyzed the estimation period and the event window
consist of 261 abnormal returns. These are ranked from 1 to 261.

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can be traced. The F-test indicates if a difference is detected, however, it does not
determine where the difference is observed. Therefore, three pair wise difference tests are
also concluded, that is the Fisher’s Least Significant Difference (LSD) method, the Bonferroni
adjustments to LSD method, and the Tukey Multiple Comparison Method.

All tests for abnormal stock price performance are conducted for each strategy as well as for
the total sample. A significance level of 5% is applied; however, tests that yield significant
results with a 10% significance level are also commented.

2.2. Measuring performance based on operating measures

The analysis of operating performance is conducted in line with the method specified in Barber
and Lyon (1996). In the analysis of performance based on accounting figures six operating
measures – Return on assets (ROA) based on both EBIT and EBITDA, return on sales (ROS)
based on both EBIT and EBITDA, cash flow return on assets and Tobin’s Q - are tested in
order to detect any abnormal performance in the sample. To test for abnormal operating
performance the following tests are applied:

• Non-parametric test
o Wilcoxon Signed Rank test

To observe any abnormal performance a measure for the normal performance is
constructed - in this case a benchmark for each security is created as the measure for
normal performance. The benchmark is constructed on the basis of the ROA in the year prior
to the event. For each deal within each year the ROA is observed and a benchmark group is
established based on all securities with a ROA of ±10% of the ROA in question. The
median observation from the benchmark group is observed and applied as the benchmark.
The benchmark group is kept constant throughout the analysis and a benchmark for each
security, for each operating figure and in each year is observed as the median observation in
the benchmark group. If no observations fall inside the ±10% of the ROA a benchmark is
obtained as the median between the observation itself and the security with an ROA the
closest to observation in question.

After constructing the benchmark any abnormal performance is detected in the
following way. The performance of each company is measured as the difference
between accounting figures in two years and in the same way, the performance of the

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benchmark is the difference between the benchmark corresponding to the accounting
figures in question in the same two years. Finally the abnormal performance is the
difference between these two differences. The tests for abnormal operating performance are
performed in the event year and the three years following the event. Abnormal
performance is in all cases the difference between the performance in the year prior to the
event and the year in question. The event year is determined as the accounting year in which
the transaction is completed.

The parametric t-test that is performed to detect abnormal operating performance is in line
with the parametric test described above regarding stock prices. The main difference is
the construction of the normal performance, which in this case is based on a benchmark as
described above. The null hypothesis in this t-test is zero abnormal performance, which
corresponds to the average cumulative abnormal performance being equal to zero. In
analysing abnormal performance based on operating figures a potential problem with
extreme observations (outliers) may influence the test. According to Barber and Lyon
(1996) this potential problem can be overcome by applying winsorized data in which
st th st
extreme observations that fall outside the 1 and 99 percentiles are replaced by the 1
th
and 99 percentile respectively.

The non-parametric test of performance – Wilcoxon Signed Rank test - is constructed in line
with the method applied in Wilcoxon (1945) and Lowry (1999-2008). First, the sign of the
abnormal performance for each security is determined. Second, the observations are
ranked regardless of their sign as the absolute values of the abnormal performance. The
signs are giving back to the ranks in order to test if the sum of the positive ranks is the
same as the sum of the negative ranks. In this case the null hypothesis is that the sum of
both positive and negative ranks is zero in which case no abnormal performance is
detected. The null hypothesis is tested by means of the standard normal distribution.

The tests specified above are conducted upon the entire sample of companies as a whole as
well as upon each strategy. A significance level of 5% is applied; however, tests that yield
significant result with a 10% significance level are also commented.

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2.3. Preparation and selection of data

When performing an event study, the selection and preparation of the data is extremely
important, therefore, the next section is an elaboration on the collection of the sample on
which the analysis is performed. The database ‘Zephyr’ was used to select the overall
sample. It is a database that contains information about deals in relation to company
transactions. By means of the information provided by ‘Zephyr’ a crude sample based on
the following criteria was selected:

1) The acquiring company was based in either the UK or Scandinavia (Denmark, Finland,
Iceland, Norway, and Sweden) – a criteria set up in order for the thesis to possibly serve as
a counterpart to analysis performed on US data.

2) For a deal to be included the announcement date and the completion date should be in
an interval from the beginning of 2000 to the end of 2004 – this criteria was set up to ensure
the most recent data in which a three year post event period was available.

3) A deal value larger than EUR 75mio. (Only deals with an available actual deal value are
included). – The smallest deals were excluded by this criterion which was set up as a response
to a presumption that with respect to the smallest deals it would be difficult to obtain
adequate documentation for the chosen strategy.

4) Only Mergers and Acquisitions were included in the sample.

5) The current deal status should be completed.

The crude selection above yielded a sample of 1194 deals. The deals in which the
acquiring company after completion had an ownership share of the target of less than
97,5% or an ownership share of more than 20% of the target prior to the deal were
excluded from the sample which was reduced to 959 deals.

The next step in preparing for the event study analysis was to sort the 959 deals based on
the strategic rationale. The sample is divided into the following six strategies:

• The Overcapacity M&A (Overcapacity)
• The Geographic Roll-up M&A (Geographic)
• The Market Extension M&A (Market)
• The Product Extension M&A (Product)

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• The M&A as R&D (R&D)
• The Industry Convergence M&A (Convergence)

The motivation for this division is Bower (2001), however, he only operates with five
different strategies where ‘The Product Extension M&A’ and ‘The Market Extension M&A’
are one strategy ‘Product or Market Extension M&A’ (Product + Market). The rationale for
dividing this strategy is a presumption that the result from ‘Product’ is potentially different
than that of ‘Market’. In order to keep this research in line with previous research the
tests are also conducted for the combined strategy. The characteristics for each strategy
are in line with those described in Bower (2001) and the overall criteria will be presented later
on in section 9.1.

In determining the underlying strategy behind each transaction the rationale or the
motivation for the transaction were determined based on different sources of
information – web-pages, articles, annual reports, press releases etc. If documentation for
the strategy of a specific deal was not available the transaction was excluded from the
sample. For each deal with a documented strategy the stock prices and the
accounting figures was collected from ‘Datastream’ and ‘Amadeus’/’Orbis’ In order for a
transaction to be included either stock prices or accounting figures had to be available, thus in
cases when neither was available the particular deal was excluded from the sample. Seeing
that only stock prices or accounting figures are a requirement the final sample for each of
the two analyses are not the same. In case of the analyses performed based on stock prices
the final sample consists of 410 deals and the sample for the analyses of operating
performance consists of 389 deals. The documentation for the chosen strategy is submitted
in appendix A.

From ‘Datastream’ a time series of daily stock prices is collected for each deal
containing quotes in the interval ranging from 250 days before and until one year after the
announcement day. The time series is adjusted in order to exclude holidays from the sample in
thereby only actual possible trading days are included. Due to thin trading a stock need to be
traded at least 30 days of the 250 days in the estimation period in order to be included. The
choice of a minimum of 30 days is made to ensure the power of the estimation model. To
perform the analyses the data for the estimation period needs to be available; however, in
some cases the acquiring companies stock is not available in the

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entire period of analysis. The deal is included anyway and the analyses are performed only
on the period available.

From ‘Amadeus’/‘Orbis’ accounting figures for each deal is collected from a year before
the transaction and until three years after the transaction, and in addition ‘Total Assets’ are
collected two years before the event due to the construction of the performance
measures. The accounting figures need not be available in all three years after the
transaction in order to be included.

All tests that are conducted in this thesis are carried out in Excel or SAS. The
preparation of the data is performed in Excel and the actual tests are conducted in SAS. The
reason for applying SAS in this thesis is the programs ability to process extensive data. The
analyses are performed by means of the IML procedure – a matrix procedure
– that facilitates work with complex data, where a procedure is repeated numerous times.
Throughout the thesis references to the relevant spreadsheets and codes is present in the
beginning of each section.

The remainder of this thesis is structured as follows. Section 3 is a description of the main
conclusion from previous studies in relation to value creation and M&A activity. In section 4
to 8 the event study method is presented for both stock prices and operating figures, and
furthermore, the choice of research approach is discussion and evaluated. A description of the
data that is included in the analysis is presented in section 9. Section
10 presents the main hypotheses in relation to the analysis performed in this thesis. The
empirical results in relation to detecting abnormal stock price performance are present in
section 11, while section 12 presents the empirical results corresponding to the abnormal
operating performance. Finally, before the concluding remarks, section 13 consists of an
evaluation of the research approach and an attempt to detect possible
pitfalls in relation to the empirical work in this thesis.

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3. Results from previous empirical studies on value creation in M&A

The aim for the companies when engaging in a merger or an acquisition is primarily value
creation and according to Jensen and Ruback (1983), managers also compete to gain control
over as many company resources as possible and thereby create a better market position for
their company.

Harford (2005) concludes that economic, regulatory and technological factors have an
impact on the creation of a ‘merger wave’, which is defined as a cluster within the M&A
activity. One of the factors, which had a significant impact within the European market, was
the introduction of the Single Market in 1992 as well as the single currency in 1999. These
actions were followed by deregulations and privatization, which increased the
competition between European companies and lead to a prolonged bull effect within the
stock market. All these aspects affect the willingness of European companies to engage in
mergers and acquisitions. Two ‘merger waves’ have been detected, concerning the
European market - a small one during 1987-1992 and another one during 1995 to 2001.
(Sudarsanam, 2003)

When examining the impact that a merger or an acquisition has on a company the
general result, according to MacKinlay (1997), is that the shareholders of the target
company gain large positive abnormal returns whereas the shareholders of the acquiring
company gain close to zero abnormal return. This result if found by use of either the daily
stock prices or the accounting figures. Jensen and Ruback (1983) used the stock data and
divided the shareholders into two groups and concluded that the shareholders of the target
company gained abnormal performance and as well did the shareholders of the acquiring
company when analyzing the impact in the short-run – one month around the
announcement. Loderer and Martin (1990) supported this conclusion. They examined
the abnormal returns within an interval of 6 days around the announcement day and did
also conclude that the overall result was a minor positive abnormal return. Loughran and
Vijh (1997) examined the long-term abnormal return and concluded a loss in value seen
from the perspective of the acquiring company. Both Agrawal et al. (1992) and Loderer and
Martin (1992) also documented negative abnormal performance in relation to the acquiring
company concerning the long-term abnormal performance.

13


The studies mentioned are all conducted based on US companies and within the period of
1962 to 1987. However, Franks & Harris (1989) and Goergen and Renneboog (2004)
documented that also in the UK, positive abnormal returns are present in the short-run. The
expectation of negative abnormal returns in the long-run in the UK market was supported
by Franks & Harris (1989) and Baker and Limmack (2002).

The analyses conducted by means of analyzing the abnormal operating performance are
divided into two groups those using the earning based measures and those using cash flow
based figures. Ravenscraft and Scherer (1987) used both approaches and concluded
that when measuring the performance by use of accounting profitability a decline in wealth
was detected, whereas when basing the analysis on cash flow no decline was found
which was supported by Ghosh (2001). Meeks (1977) also documented a decline in the
UK company value when measuring the performance by means of accounting profitability
figures. Manson et al. (1994) use the cash flow performance measure and document
an improved performance within the UK companies after a merger or an acquisition.

Investments in R&D are considered to be a management or an investment decision and not a
decision to raise capital and are often not publicly announced as a repurchase of stocks or a
merger, whereas it often is not detected before the financial reports are published. Daniel
and Timan (2001) argue that due to the fact, that R&D investments are intangible assets,
investors find it hard to process such information whereas the market takes time to
incorporate the value of such an investment, which supports the statement that the stock
market does not incorporate the correct value of an R&D investment in the short run
presented by Eberhard et al. (2004). Furthermore, they argue that the positive abnormal
operational performance, due to the increase in R&D, can be detected in the long run.

Based on the studies presented above it seems likely to detect the same tendencies
within the British and the Scandinavian companies included in this thesis.

14


4. Event studies

Ball and Brown (1968) and Fama et al. (1969) introduced the event study methodology that is
essentially used today and it has become the preferred method when measuring
performance induced by an event. The notation ‘event study methodology’ has come to refer
to different procedures for estimating abnormal returns. Examples of such events are
earnings announcements, issues of new debt, macroeconomic announcements or
acquisitions. In an event study the objective is to measure the effect of a specific event upon
the value of the firm - this can be measured by the change in stock prices. One advantage
of event studies is that the methodology is applicable for various purposes and it is fairly
simple to implement. Since the early literature about event studies was published by in
particular Brown and Warner (1980, 1985) the application of the event study methodology
have extended and these papers have inspired the main part of the research literature that
have been published in recent years.

Seeing that an event study is an examination of the effects of a certain event upon the value
of the firm, the first thing to do is to determine what is meant by an event – in this thesis the
event is defined as the time of the announcement of a merger or an acquisition. In
order to detect if any value is created as a result of the event it is necessary to detect
abnormal performance. To detect abnormal performance a measure for the normal
performance needs to be constructed. The normal performance is the performance that
would be expected in the absence of an event. In order to perform a test trying to detect
abnormal performance by means of an event study attention must be paid to the ‘Efficient
Markets Hypothesis’ (EMH). According to Fama (1970) the EMH is available in three forms -
Weak, semi-strong and strong - depending on how information is incorporated into
stock prices. When assuming the weak form, it is expected that all historical information
be reflected in the stock prices. The semi-strong form assumes that all public available
information is incorporated into the stock prices and lastly, the strong form expects all
information – public as well as private – to be incorporated into the stock prices.

Elton et al. (2003) as well as Fama (1991) conclude that the financial markets are efficient
and it is not possible to consistently earn an abnormal return, because all available
information is incorporated into the security prices immediately.

15


5. Measuring abnormal performance based on stock prices

The description of the event study procedure for stock prices is outlined in the following way:
First, a definition and a determination of the estimation period, the event day and the event
window are presented. Second, the expected or ‘normal’ return and afterwards the abnormal
return is defined and estimated, and finally the tests for abnormal performance are
presented.

5.1. Estimation period, event day and event window

When setting up an event study it is important to consider the choice of estimation
period, and event window (presented in figure 5.1). The estimation window is the period
over which the market model is estimated. The estimation of the market model is applied to
determine the ‘normal’ or expected return in the absence of an event. In order for the market
model to represent the ‘normal’ return the estimation period has to be set to a period that
is ideally unaffected by any abnormality. In this event study the estimation period is set
to a 250-day period immediately prior to the first day in the event window – that is about
a year of trading prior to the event window. Consistent with MacKinlay (1997) it is generally
recommended that the event window is excluded from the estimation period in order to
make sure that the event does not influence the estimation of the market model. In prior
research literature the length of the estimation window varies but according to Armitage
(1995), an estimation period of about 100- days prior to the event window is sufficient
though it is common to choose an estimation period between 200 and 300 days.

In accordance to Dodd & Ruback (1977) the event day is set to the day of the
announcement of the deal and not the day the deal is completed. According to the
efficient market hypothesis new information will be incorporated in the stock prices

16


3
immediately and therefore the announcement day is the day in which the stock prices will
react. The announcement day is registered from Zephyr, which is considered a reliable
source of information in relation to correctly determining the announcement day. In fact
the determination of the announcement day is critical in conducting an event study, since this
day is the reference day against which abnormal performance is detected.

In expecting that the efficient markets work perfectly it would have been sufficient to
restrain the event window to include only the event day. However, according to Elton et al.
(2003) the stock prices might react over time and not just on the event day. When
examining the time interval around an announcement it is common to detect abnormal
returns on both sides of the event day. The reason for abnormal returns appearing after the
announcement day can be due to either the fact that the announcement took place too
late in the day for the market to fully react or because it took time for the
information to be reflected in the stock price. Explanations for the abnormal returns being
present prior to the announcement day could be that before an announcement is made a
news release is posted to notify the public about the upcoming event. This action would
in an efficient market be reflected in the stock price prior to the actual announcement.
Another explanation could be that information about the announcement is leaked to the
market.

The above mentioned, supports the assumption of a semi-strong form of the efficient
market hypothesis and therefore, the event window in this thesis is set to ± 5 days of the event
day. Another reason for choosing an event window of 11 days is the fact when thin trading
occurs a security might not be traded on the announcement day and therefore, if only
including the event day it is likely that an effect will not be detected.

5.2. Creating a benchmark for the ‘normal’ performance

Different alternatives are available when creating the benchmark to be used as a
measure for the normal performance (expected return) – both statistical and economic
methods. In this thesis the measure for the normal return is created on the basis of the
market model. This choice of benchmark model is in line with recommendations from

3 The day a deal becomes publicly
known.

17


several research papers – in particular Brown and Warner (1985), Barber and Lyon
(1997), and Bartholdy et al. (2007).

The market model is a statistical model that relates the return of a given security to the
return of a market portfolio and in estimating the normal return a linear regression is
estimated based on ordinary least squares (OLS). In this thesis the market portfolio is
chosen as the market index in the acquirer’s home country that is, as mentioned, FTSE
100, OMXS30, OMXH25, OMXC20, OMXI15, and OSEBX. The market model assumes a
linear relation between the security return and the market return and under the
presumption that a particular security correlates with the market index the expected return is
derived from the market index for the country in question. The market model is estimated by
use of the estimation period, and the parameters α and β from the OLS estimation, are
derived for each security. The abnormal return is afterwards calculated as the difference
between the actual return for the security and the expected return based on the market
model. These abnormal returns are calculated for each security at each point in time over
the period of analysis.

5.3. Choice of tests – Parametric or Non-parametric

Event studies can as mentioned earlier be conducted by means of both parametric and non-
parametric tests. As concluded by MacKinlay (1997) and in accordance with most research
literature a test for abnormal returns induced by an event should consists of both types of
tests. A parametric test is a statistical test, which is subject to certain assumptions in
relation to the distribution. It is assumed that the abnormal returns are normally distributed
and if the assumptions hold the power of the parametric test is large and outperforms the
power of the non-parametric tests. Nevertheless, in cases when the assumptions are
violated the non-parametric test should be used instead of a parametric test because the
main advantage of non-parametric is that the distribution of returns is not required to be
normal. In addressing the potential problem of violation of assumptions the use of both
parametric and non-parametric tests allow the researcher to
verify the robustness of the parametric test.

18


Abnormal performance based on stock prices is tested by means of the following four tests:

• Rank test (Corrado, 1989)
• Sign test (Corrado and Zivney, 1992)
• Generalized Sign test (Cowan, 1992)

These four tests are presented afterwards as well as the motivation for including the
specific test. Focus will be upon the most essential aspects of each test and not a
complete presentation of each aspect. The research approach in this thesis is in full
agreement with tests performed in other event studies trying to detect abnormal
performance in stock prices.

5.3.1. The parametric t-test

Overall parametric t-tests for abnormal performance are based on calculating the
difference between two means and if transferred to this specific research problem the
question is whether or not the abnormal returns are significantly different than zero. Under
the null hypothesis of no abnormal performance no difference between means can be
detected. In accordance with the research question the purpose of this thesis is not an
examination of the effects of M&A on a specific acquiring company, instead the purpose is to
examine whether or not an effect can be detected based on a group of companies
motivated by the same strategy. In order to examine the general effects of mergers and
acquisitions the abnormal returns calculated for each company at each point in time need
to be aggregated both through time and across companies. As mentioned above, the
abnormal return is calculated for each security at each point in time by subtracting the
estimated ‘normal’ return from the actual return. In MacKinlay (1997) this is calculated as:

4
ARi,t = Ri,t + αi - βiRm,t

In aggregating the abnormal returns it becomes possible to observe overall inferences for
the event. The aggregation of daily abnormal returns is measured by the cumulative abnormal
return (CAR). CAR is measured for each security as well as across securities as an average
cumulative abnormal return by summing the daily abnormal returns for each security in the
case of CAR and by summing the average daily abnormal returns

4 α and β are the parameters estimated in the
market model

19


across securities in the case of the average CAR. The null hypothesis of zero abnormal
returns, which corresponds to a CAR equal to zero, is tested based on a test statistic in which
the average CAR is divided by a measure for the variance of the average CAR. The exact
variance cannot be observed and so the variance needs to be estimated before calculating the
test statistic. Rejection of the null-hypothesis indicates that an abnormal return is created as a
response to the merger or the acquisition.

The parametric t-test is conducted for the entire sample as a whole as well as for the
individual strategies in an attempt to detect if value is created overall in the sample
chosen. Besides testing for value creation it is also of interest to detect differences
between the strategy performances and so an F-test is performed. The specification of the
test for differences is presented in section 7.

When performing a t-test four assumptions concerning the probability distribution of the
abnormal returns must hold in order to wholly rely on the results of the test. These
assumptions are: normally distributed and independently and identically distributed
abnormal returns, an expected value of the abnormal returns of zero, and a constant
variance (homoscedasticity). The normality assumption is critical in order to rely upon the
results of the parametric test. The assumptions are discussed and verified in the analyses.
If these assumptions are violated a non-parametric test should be use instead in order to
verify the robustness of the parametric test. In addition, another motive for the non-
parametric test stems from the fact that the parametric test is conducted on accumulated
average abnormal returns and it is possible that certain observations will have had a large
affect on the test statistic and therefore might have influenced the conclusions.

5.3.2. Non-parametric tests

The main advantage of non-parametric tests is the fact that they are not subject to
restrictions and assumptions in relation to the distribution of returns and so the results are
reliable even on data where one or more assumptions are violated. A common
characteristic of these tests is the fact that the distribution is symmetric by construction.
Another important motive for including non-parametric tests is according to Maynes and
Rumsey (1993) that when thin trading is present in the sample, violation of the
assumptions underlying the parametric test is more likely and thus non-parametric tests are
justified.

20


In performing the non-parametric test the procedure is the same as that of the parametric test
– at least part of the way. The choice of event day, event window and estimation period is
exactly the same, which is also the case for the estimation of the market model used as the
measure of the normal return. However, the remainder of the tests are performed
differently, which will be elaborated in the following. The null hypothesis of zero abnormal
performance from the parametric test is also the one applied to the non- parametric tests,
even though the null hypothesis is specified in a different way to comply with test.

5.3.2.1. The Rank test

The Rank test is conducted in line with Corrado (1989). The main advantage of the Rank
test is that it is not restricted by any assumptions in relation to symmetry in the
distribution. To obtain the test statistic the Rank test is constructed in the following way.
All abnormal returns for each security in the estimation period and the period of analysis are
ranked with the rank 1 corresponding to the lowest abnormal return and the highest rank
corresponding to the highest abnormal return. According to Corrado and Zivney (1992) the
adjustment for thin trading is made by standardizing each rank by the number of non-missing
returns in the sample. The expected rank of the event day is the median rank plus 0.5,
however, after standardizing the expected or average rank is 0.5. In order to test for
abnormal performance the null hypothesis tests whether the expected rank is equal to the
rank at the event day – meaning to test whether or not it equals 0.5. In case of positive
abnormal performance the rank of the event day would be larger than the expected rank.
The test statistic in the Rank test is calculated by dividing the difference between the
rank on the event day and the average rank with the standard deviation of differences
between actual ranks and average rank over the period of interest.

The Rank test is considered to be more powerful than both the parametric t-test as well as
other non-parametric tests, which in particular is due to non-normality in the
distributions of return and misspecifications of the market model (abnormal returns).
Another important aspect or potential problem that is widely discussed in research is the
possibility of variance changes around the event day. In case of variance changes the
parametric t-test becomes less powerful and credible if these misspecifications are not

21


addressed. One advantage of the Rank test presented by Corrado and Zivney (1992) is the
fact that it is immune to these misspecifications in relation to variance changes.

5.3.2.2. The Sign test

The Sign test is conducted in line with Corrado and Zivney (1992) and is based upon the
assumption that the probability of observing a negative or a positive abnormal return is the
same - that is 0.5. In performing the test the sign of the abnormal return on each day is
traced, which is done by first obtaining the median abnormal return for each security in
the sample and afterwards subtracting the median abnormal return from the actual
abnormal return and obtaining the sign of the difference. Subtracting the median is in line
with the assumption that the probability of observing a positive abnormal return is 0.5. If
obtaining a positive sign the observation is given the value 1, a negative sign is given the value
-1 and finally in cases where the actual abnormal return is equal to the median abnormal
return the observation is given the value 0. Under the null hypothesis the probability of
observing a positive cumulative abnormal return is the same as observing a negative
abnormal return. Under the null hypothesis of no abnormal return the Sign test assesses
the probability of a positive abnormal return – assuming that this probability is 50%. The
test statistic of zero abnormal return on the event day is calculated by means of the signs
across the sample on the event day divided by the standard deviation. If the test is correctly
specified the amount of positive and negative signs are according to Cowan (1992) the same
in the absence of any abnormal reaction to the event.

One advantage of this test is the fact that it is not restricted by any requirements in
relation to symmetry in the distribution in order for the test to be correctly specified.
However, assuming that the median of abnormal return is equal to zero might lead to
misspecification. This is overcome by calculating the sample median of abnormal return as
described above. In a simulation study Corrado and Zivney (1992) illustrate that the power of
the Sign test is larger than that of the parametric t-test – due to lack of symmetry
requirements in the distributions. Furthermore, they illustrate that the Sign test is
dominated by Rank test.

22


5.3.2.3. The Generalized Sign test

The Generalized Sign test is based on Cowan (1992). The main difference from the Sign test
described above is that in the Generalized Sign test the probability of observing a negative
or a positive abnormal return is based on the actual returns observed in the estimation
period across time and between securities. Thus in conducting the Generalized Sign
test the probability of observing a positive abnormal return around the event day is compared
to the probability of detecting a positive abnormal return in a period unaffected by an
event. By applying this estimated probability of a positive abnormal return the possibility
of asymmetry in the abnormal return distribution under the null hypothesis is addressed.
The composition of the Generalized Sign test is in correspondence with the Sign test except
for in creation of the median. This represents the main improvement compared to the Sign
test mentioned above. Under the null hypothesis of no abnormal performance the number
of securities with positive abnormal returns in the event window is equal to the number that
is expected in the absence of abnormal performance.

The main advantage of the Generalized Sign test is that it takes into account the
possibility of asymmetry in the distributions of return. It becomes more powerful and better
specified than the Sign test. In comparing the Generalized Sign test to the Rank test, the
Generalized Sign becomes relatively more powerful as the event window increases.
According to Cowan (1992) the Generalized Sign test has more power than the Rank test in
relation to event windows of 11 days as is the case in this thesis. However, in general and
under ideal conditions the Rank test is the more powerful of the two, while under less
ideal conditions, e.g. in cases of thinly traded stocks, the Generalized Sign becomes the
most powerful.

In order to detect abnormal performance on a longer term this thesis also includes
analyses of operating performance. Again the event study methodology is applied,
however, in a different way than was the case of stock prices. In the following the
methodology for a study of operating performance is presented, as well as an
elaboration of the tests that will be conducted.

23


6. Measuring abnormal operating performance

The basis for the evaluation of abnormal operating performance is the work by Barber and
Lyon (1996) in which they evaluate methods used in event studies based on operating
figures. In order to evaluate the effects of an event based on operating figures
– in this case a merger or an acquisition - the actual performance of the acquiring
company needs to be compared to a measure of the performance in the absence of an
event. In case of stock prices the market model was used to estimate the normal return, in
case of operating performance the normal performance is represented by a
benchmark. In relation to operating performance the choice of performance measures,
benchmark and statistical test vary.

The presentation of the event study procedure in relation to operating figures is
organized as follows. First focus will be on the operating figures selected for the study. Then
the event day and the estimation period are defined. Afterwards the benchmark is
determined and finally the statistical tests are specified.

6.1. Definition and determination of the selected operating figures

In trying to detect the effects of an event upon the performance of the company the first thing
to do is to decide upon the operating figures to include in the event study. In this case six
performance measures have been selected:

• Return on assets based on EBIT - ROA (EBIT)
• Return on assets based on EBITDA – ROA (EBITDA)
• Return on sales based on EBIT – ROS (EBIT)
• Return on sales based on EBITDA – ROS (EBITDA)
• Cash Flow return on assets – CF ROA
• Tobin’s Q

The reason for including more than one operating figure is an attempt to provide more debt
to the conclusions of the event study and at the same time most performance measures
have some drawbacks and including more than one will help overcome these potential
drawbacks. In the following the selected operating figures will be presented in more details.

24


6.1.1. Return on assets – based on EBIT and EBITDA

Return on assets (ROA) is the most widely used measure for detecting abnormal
operating performance. ROA is a profitability measure that represents a company’s ability
to convert invested funds into earnings. The higher the ROA the more efficient is the company
in generating earnings. The smaller the ROA the more asset intense is the company and so
the more funds must be reinvested in order to continue to generate earnings. In this thesis
ROA is calculated as operating income divided by the average of beginning and end of year
book value of total assets.

Return on assets can be calculated by means of both EBIT and EBITDA, as is done in this
thesis. The most widely applied of the two is EBIT. Earnings before interest and tax (EBIT) also
known as operating income or operating profit is a measure for all profits within the
company before taking interest payments and income taxes into account. The fact that ROA is
unaffected by the capital structure of the company makes it possible to compare ROA across
industries and countries. This is why ROA is one of the most used performance measures.
Earnings before interest tax depreciation and amortization (EBITDA) takes it a step further
than EBIT by excluding the two non-cash items. In this way EBITDA is made indifferent of
choices of financing and of accounting decisions and thus it is possible to compare across
industries and across countries. However, neither EBIT nor EBITDA is without drawbacks and
thus focusing exclusively on either of the two is not adequately.

According to Barber and Lyon (1996) the drawbacks in relation to ROA are: ‘historic cost’,
‘nonoperating assets’, and ‘earnings manipulation’. ‘Historic cost’ refers to the fact that total
assets is measured in historic costs while operating profit is measured in current currency, a
better measure would be to apply current cost or replacement cost of total assets. Calculating
ROA based on the total assets of the firm when in fact only the operating assets should have
been applied might understate the true profitability of the operating assets, which is
referred to as the ‘nonoperating assets’ drawback. And finally, since operating profit is an
accrual-based accounting figure the managers might over- or understate the reality in the
direction most suitable to meet the goals. This is referred to as ‘earnings manipulation’.
The remaining performance measures in the thesis are chosen to overcome these three
potential drawbacks.

25


6.1.2. Return on sales – based on EBIT and EBITDA

Return on sales (ROS) is a measure intended to evaluate the operational efficiency of a
company and the margin expresses the profit that is produced per unit of sales. An
increasing ROS indicates that the company is becoming more efficient and at the same time
better prepared to resist periods of e.g. falling prices or increased competition, which
might result in deceasing sales. Return on sales is calculated as the operating income
divided by the sales of the year. The return on sales measure is constructed based on both
EBIT and EBITDA, and is thus made independent of both financing and accounting decisions.

By including ROS, two of the three drawbacks in relation to ROA are addressed, that are
‘historic cost’ and ‘nonoperating assets’. The main advantage of return on sales is the fact
that it is constructed based on figures from the income statement, and so the ratio is in
current currency. As mentioned, the operating figures have drawbacks and as such ROS is
not free of disadvantages since it does not directly measure the productivity of assets.
It is possible to increase sales and operating income without increasing the assets within
the company, this is clearly an improvement of productivity. However, if sales and
operating income increase proportionally the return on sales will not reflect increased
productivity. (Barber and Lyon, 1996)

6.1.3. Cash flow return on assets

Cash flow return on assets measures the operating efficiency of a company in terms of cash
generated from operations per unit of total assets. It is calculated as the operating cash flow
divided by the average of beginning and end year book value of total assets. Thus it shows
how well the company generates cash from the investments in assets. A higher cash flow
return on assets is an indication of greater efficiency in generating cash from investments in
assets.

The main reason for including this operating measure in the analysis is the fact that it
overcomes the potential problem of earnings manipulation. In cases when managers
manipulate earnings to correspond better to their intentions a ROA measure will
become biased and less reliable. A cash flow return measure overcomes this problem.
However, this is a measure that fluctuates greatly with changes in cash flow. This is a
significant disadvantage seeing the cash flow changes greatly in relation to a merger or

26


an acquisition and therefore it becomes difficult to compare the acquiring company to
companies unaffected by the acquisition.

6.1.4. Tobin’s Q

The final operating measure in this thesis is Tobin’s Q, a performance measure that serves
as a proxy for the growth opportunities within the company. Tobin (1969) defined
Tobin’s Q as the market value of assets divided by the replacement cost of assets. The
market value of assets is the market capitalisation defined as the number of outstanding
shares times the stock price on the last trading day of the year. The replacement cost of
assets is represented by the book value of total assets. If the value of a company given by the
financial markets equals the book value of the assets Tobin’s Q is equal to 1. A Tobin’s Q
between 0 and 1 indicates that the particular stock is undervalued and a ratio higher than
1 is an indication of an overvalued stock. Lang et al. (1989) interpret the Tobin’s Q ratios as
an indication of the company’s management skills where a ratio above 1 implies good
management skills and a ratio below 1 is an indication of poor management skills.

Tobin’s Q is a simple measure that is easy to calculate and interpret, however, in order for
the Tobin’s Q to yield meaningful results an accurate measure of both market
capitalisation and replacement cost is needed. In fact detecting a Tobin’s Q higher than
1 could be an indication of an upward bias due to a less accurate measure or it could
indicate a price above the competitive levels, thus conducting a test with Tobin’s Q it is
important to keep the potential bias in mind.

The null hypothesis used in relation to the Tobin’s Q is different from the ones used to
analyses the other performance measures. When analysing the Tobin’s Q ratios they must
be compared to the value 1 because that is the ratio the companies are striving to obtain.

6.2. Event day, event window and estimation period

Measuring abnormal performance on operating figures, the definition of the event day and
the estimation period are different from an event study on stock prices. In case of operating
figures the event study is not based on a single event day instead it is based on the accounting
year in which the merger or the acquisition is completed. It is important to observe the event
year as the year in which the deal is completed since the accounting

27


figures do not respond to rumours or announcements seeing that the book value of the
company do not change until the deal is actually completed.

The estimation period is used to obtain a measure for the ‘normal’ or expected
performance in the absence of an event. To be sure that this measure is in fact
unaffected by the event the estimation period is the accounting year prior to the event
year. According to Barber and Lyon (1996) it is important to base the measure of
expected performance on past performance and so the estimation period should be prior to
the event year.

6.3. Creating a benchmark - performance based

According to Barber and Lyon (1996) the best way to set up a benchmark is to
benchmark against past performance and industry. However, benchmarking against past
performance is the most important. Due to the limited scope of this paper the data
collected for the thesis was in some ways limited, and it was considered that the data was
not adequate for benchmarking against both performance and industry. Therefore, in this
thesis benchmarking is made exclusively on past performance. Furthermore, their work also
concluded that in order to obtain powerful test statistics it was important to maintain a
constant comparison group throughout the analyses for which reason the benchmark group
is kept constant over time as well as over operating figures.

The approach for creating a benchmark based on the past performance is in line with the
recommendations of Barber and Lyon (1996). The comparison group is based on the ROA in
the year prior to the event year. This ensures that the benchmark is created on past
performance unaffected by the event itself. The comparison group includes all companies
with a ROA of ± 10 % of the company’s ROA. In most cases this yielded a comparison group
consisting of more than the company itself, however, in some cases no companies fall within
the comparison group and in these cases the benchmark is created based on the company
that had an ROA closest to the company in question. The benchmark was finally calculated as
the median ROA of the comparison group.

To calculate benchmarks for the remaining years of interest to the analysis and to
calculate the benchmark for the remaining operating figures, the comparison group is kept
constant and the median is calculated for each operating figure in each of the years
-1, event year, +1, +2 and +3.

28


6.4. Specification of statistical tests – Parametric and non-parametric tests

As with the event study based on stock prices both parametric and non-parametric tests are
applied to detect abnormal operating performance. Following Barber and Lyon (1996) the
reason for including both tests is the fact that a sample which consists of extreme
observations makes the parametric test less powerful and in these cases a non- parametric
test is preferred.

Both the parametric and the non-parametric tests are based on the change in a
company’s operating performance relative to the change in the appropriate benchmark.
According to Barber and Lyon (1996), testing for abnormal operating performance based
on changes yield more powerful tests than if applying the level of the performance.
To calculate the abnormal performance the first thing to do is to calculate the actual
5
differences as well as the benchmark differences between the periods of analysis. The
abnormal performance is then calculated by subtracting the benchmark
difference from the actual difference, that is APit = ΔPit - ΔE(Pit). The calculation of
abnormal performance is repeated for each company, each year and each operating
figure. Having determined the abnormal performance it is possible to perform analysis of
abnormal operating performance, which in this case is tested by means of the following
two tests:

• Wilcoxon Signed Rank test

In the following section the two tests will be presented. Focus will be upon the null
hypothesis and the most important aspects of each test. It will thus not be a
comprehensive elaboration of each test.

6.4.1. Parametric t-test for abnormal operating performance

As with the parametric test with respect to stock prices the t-test focuses on the
cumulative abnormal performance (CAR). To calculate CAR with respect to operating figures
the aggregation takes place over the yearly abnormal performance measures. That is when
calculating CAR for the event year it only consists of the abnormal performance measured
as the difference between the pre-event year and the event year

5 The difference in the performance measures and the corresponding benchmarks are
calculated over the periods: year -1 to event year, event year to year 1, year 1 to year 2, and
year 2 to year 3.

29


and so forth. Again it is necessary to aggregate over time for each company and across
companies at each point in time. Finally the average CAR is calculated and applied to test
the null hypothesis of no abnormal operating performance, here that the average CAR is
equal to zero.

As with the t-test performed on stock prices this particular t-test is submitted to the same
assumption in relation to the distribution of abnormal performance. It is likely that these
assumptions are violated making the results less powerful. Furthermore, when the objective is
to analyse operating figures it is likely that a few extreme observations will affect the average
CAR and then affect the conclusions that can be drawn from the test. A t-test based on
unwinsorized data will result in conservative tests compared to non- parametric ones due to
the extreme observations in the data. To overcome this problem
st
winsorized data is applied. In this way the extreme observations that fall outside the 1
th st th
and 99 percentiles are replaced by respectively the 1 and 99 percentile. By applying
winsorized data the conservatism disappears. (Barber and Lyon, 1996)

6.4.2. Wilcoxon Signed Rank – test for abnormal operating performance

To overcome the potential problems with respect to the assumptions a non-parametric test
is also performed - in this case the Wilcoxon Signed Rank test. The test is based on the
assumption that the probability of observing a positive difference is the same as the
probability of observing a negative difference, which leads to the null hypothesis of a
median abnormal operating performance of zero. To obtain the test statistic the starting
point is the abnormal performance, which is calculated by subtracting the benchmark (the
median in the benchmark group) from the actual performance – based on
differences. First, the absolute values of the abnormal performance is ranked, the lowest
abnormal performance is given rank 1. Afterwards, the original sign is reassigned to the ranks.
Third, the ranks with a positive sign are summed and the ranks with a negative sign are
summed. Finally, the sum of both positive and negative ranks is calculated and tested against
the null hypothesis, expecting the sum of ranks to be zero, which corresponds to an
equal probability of observing a positive or a negative sign.

30


7. Test for differences between the strategies

When comparing the average CARs for the six original strategies and the two combined
strategies an F-test is applied. There are two reasons why computing multiple t-tests
instead of the F-test does not work. The first reason is that one would have to perform the t-
test multiple times, which leads to the second reason, concerning the increasing risk of
making a Type I error. Making a Type I error means rejecting the null hypothesis when it in
fact is true and by computing multiple t-test the probability of making the Type I error
increases proportionally by the number of t-tests. A method to circumvent this problem is by
decreasing the significance level alpha, however; this will increase the risk of making a Type
II, where the null hypothesis is not rejected even though it is false. Therefore, when
comparing the means of more than two populations the analysis of variance must be used.

As for other parametric tests, the analysis of variance has some assumptions that need to be
fulfilled before the conclusion is reliable. In the case of the analysis of variance
independence between cases must exists, the random variables must be normal
distributed and the population variances must be equal. The assumption most frequently
violated is the normal distribution of the random variables. This problem can be
resolved by the use of transformation. The methods used in most cases are squaring the
variables, taking the logarithm, the square root or the reciprocal value of the random
variables and afterwards conducting the F-test as well as the confidence intervals again. The
conclusions based on the transformed variables will now be reliable because the
assumptions will be fulfilled.

In this thesis the transformations are conducted by raising the CARs to the power of 0.5
because this transformation diminishes the impact of extreme outliers and the histogram of
the abnormal returns becomes more bell shaped.

The result of the F-test gives an indication of whether or not a difference between the
population means exists. However, the test does not indicate between which means the
difference exists and therefore, to be able to examine the pair wise differences three
methods are available.

31


The first method used to create the simultaneous confidence intervals is the Fisher’s Least
Significant Difference (LSD) which uses the Student t distribution to test whether differences
exists by use of an adjusted variance. One drawback in relation to this method is, that it
becomes more and more unreliable as the number of comparisons increases due to the
problems regarding the Type I errors as mentioned above. This is the reason for employing
the second method called the Bonferroni Adjustments to LSD Method which modify the alpha
level according to the number of pair wise comparisons included in the test. This adjustment
increases the risk of conducting the Type II error. The most powerful test named Tukey
Multiple Comparison Method addresses both the Type I and the Type II problem. The test
determines if any pairs of the sample means have a greater difference than the critical
value. In theory this method requires the sample sizes to be equal, nevertheless, in most
cases this is not the case and adjustments can be made to overcome this requirement.

In some cases, the F-test rejects the null hypothesis concluding a difference exists,
however, at the same time the Tukey test does not reject the null hypothesis and thereby
concludes that no difference exists. This can happen because Tukey is a more
conservative approach by only looking at the pair wise differences. Under the null
hypothesis for the F-test all the population means are expected to be equal and the
alternative hypothesis expects at least one of the means to differ. In reality, what the F- test
does is to look at linear contrasts whereas pair wise comparisons are only one of many
linear contrasts. Therefore, when two opposing conclusions occur the final conclusion
must be that no significant difference is found between the means.

8. Overview and discussion of the research approach and the selected tests

Before focusing on the empirical results a short overview of the selected tests is
presented here as well as a discussion of the appropriateness of the research approach.

8.1. Overview of the research approach

With respect to examining abnormal returns based on stock prices the following four tests
are conducted:

• Non-parametric tests – Rank test, Sign test and Generalized Sign test

32


These tests are performed for test of short-term abnormal performance and are
conducted on different periods of time - the event window, 1 month, 3 months, 6
months and 1 year. First, the performance of the entire sample is tested in order to
detect abnormal returns on an overall scale. Afterwards, the tests are conducted on each
strategy in order to detect possible differences in performance in relation to the choice of
strategy. The reason for applying stock prices exclusively to the analysis of shorter time
intervals is an attempt to avoid possible macroeconomic tendencies from influencing
the stock prices and the conclusions.

Abnormal operating performance is measured by means of:

• Non-parametric test – Wilcoxon Signed Rank test

With respect to abnormal operating performance focus is upon the long run abnormal
performance and these are also conducted on different periods of time – event year, 1 year,
2 years and 3 years after the event year. Again the tests are performed on the total sample
and afterwards on each strategy separately.

Finally, in relation to both stock price performance and operating performance a test for
differences between the strategies is conducted by means of an F-test as well as pair wise
difference methods.

8.2. Discussion of choice of research approach

Consensus is as mentioned to apply the event study methodology when testing the
effects of an event upon the performance of the company and in this aspect the method
chosen is appropriate. Furthermore, the parametric test and the non-parametric tests are all
conducted in line with recommendations from other research papers in the field, in
particular Brown and Warner (1985) and Bartholdy et al. (2007) and so the method and the
tests that are chosen are considered to be appropriate. Brown and Warner (1985),
Bartholdy et al. (2007), MacKinlay (1997), and Barber and Lyon (1996) have been the main
literature in determining the method for evaluating performance and the included tests are
in line with their recommendations. In defining and performing the various statistical tests
the original authors have been applied in order to perform the tests correctly. This
approach of relying on previous empirical work in deciding upon the most relevant tests,
and afterwards constructing the tests in line with what is

33


recommended by the professor - who actually designed the test - is considered to be an
appropriate and relevant way to determine how to conduct an event study. In
conclusion, the overall research method and the statistical tests are appropriate for this type
of research and furthermore, in line with previous empirical work.

Most empirical work in this field of research is agreeing that the best choice is to
perform both parametric and non-parametric tests to support the conclusions and not only
rely upon one test. Most of the statistical tests have some drawbacks or at least some
potential problems and by applying more than one statistical test, the results will support
each other and it is possible to address the drawbacks.

In choosing which tests to apply to a certain research problem it is necessary to keep in mind
the data that is to be analyzed and in many cases it is appropriate to adjust the data to
overcome potential problems, and thereby improving the power and specification of the
tests. Conducting analysis upon stock prices a potential problem is thin trading, which is
common on smaller stock exchanges. The data in this thesis shows evidence of thin trading
and so trade-to-trade returns have been applied in order to overcome the problems with
thin trading. According to Bartholdy et al. (2007) thin trading can be adjusted in several
ways – Missing data, lumped returns, uniform returns and trade-to- trade – however, the
recommendation from this work is to adjust for thin trading by means of the trade-to-trade
adjustment seeing that the power and specification of the test is improved the most. In
analyzing the operating performance winsorized data has been applied in order to avoid
outliers that would influence mean and variance and in that way make the parametric t-test
less powerful. The choice of winsorizing the data is equally in line with what is recommended
in research literature.

In the thesis both parametric and non-parametric tests are included. This is considered to be
an important and correct decision. The parametric t-test is under ideal conditions the most
powerful test. However, as mentioned, the t-test is submitted to stringent assumptions in
relation to the distributions of returns and in those cases when the assumptions are
violated the power of the test is reduced. The main advantage of including non-
parametric test is the fact that they are not restricted by the same assumptions and
thus under less ideal conditions the non-parametric tests becomes more powerful than the
parametric tests. In relation to the analysis of abnormal stock price

34


performance three non-parametric tests are performed – the Rank test, the Sign test and the
Generalized Sign test.

In comparing the power of the non-parametric tests the Rank test is the most powerful of
the three under ideal conditions. The Generalized Sign test has more power than the Sign
test, and in the case of an increasing event window it also outperforms the Rank test.
(Cowan 1992)

In relation to measuring abnormal performance based on either stock prices or operating
figures it is recommended to apply non-parametric tests instead of parametric tests due to
the non-parametric tests having more power. The conclusion in favour of the point of view
that the non-parametric tests are more powerful than the parametric one is an indication
of the fact the assumptions are violated seeing that otherwise the power of the t-test should
have dominated. In relation to stock price performance Bartholdy et al. (2007) emphasizes
non-parametric tests compared to parametric ones and in the case of operating performance
Barber and Lyon (1996) concludes that the Wilcoxon Signed Rank test outperforms the
parametric test.

Prior to performing the analyses and based upon previous research it is expected that in order
for the parametric t-test to be well specified a quite large sample is needed in order for the
assumptions not to be violated. This poses potential problems in relation to the smallest of the
six strategies in this thesis (namely ‘R&D’ and ‘Convergence’) and the result might be that
the returns are not normally distributed and the conclusions are less reliable. The larger the
sample the more likely it is that the assumptions are fulfilled. In these cases with
violations of assumptions the conclusions drawn from the parametric t-test must be
compared to the conclusions drawn from the non-parametric tests to verify the power. In
relation to the non-parametric tests it is expected that the tests are well specified and in
detecting abnormal performance these conclusions should have higher reliability than the
corresponding parametric ones.

Despite the fact that the selected tests in this thesis are designed in correspondence with
recommendations from previous research literature, there might still be some potential
problems or biases that might influence the results, but which have not yet been
addressed.

35

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