The Impact of Finance Mergers and Acquisitions on Short-Term Performance of Acquiring Companies An Event Study Focused on the British Isles

MASTER THESIS WITHIN BUSINESS ADMINISTRATION

THESIS WITHIN: Finance

NUMBER OF CREDITS: 15 ECTS

PROGRAMME OF STUDY: International Financial Anaylsis

AUTHOR: Kreshnik Elshani

Juan José Ramos Nogales

TUTOR: Haoyong Zhou

JÖNKÖPING May 2020

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Acknowledgements We would like to thank those who have contributed to the writing of this paper. Firstly, we

acknowledge the help of our thesis tutor Haoyong Zhou, who has helped us with expertise, great

ideas and advice along the way. Secondly, the colleagues Tingxuan Li, Zichun Huang, Eric

Orianwo, and Oliver Frey from the seminars who have provided us with useful feedback. Finally,

we would like to thank Toni Duras for help with using the STATA software and analysing the

code, and Albin Blomberg for the input along the way. Thank you all for your support, without

you this thesis would not have been the same.

________________________ ________________________

Kreshnik Elshani Juan José Ramos Nogales

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Master Thesis in Business Administration

Title: The Impact of Finance Mergers and Acquisitions on Acquiring Company’s in the British Isles Authors: Kreshnik Elshani and Juan José Ramos Nogales Tutor: Haoyong Zhou Date: 2020-05-22

Key terms: Event Study, Mergers and Acquisitions, Abnormal Returns, Cumulative Abnormal

Returns, Market Model, Finance, British Isles.

Abstract

Background: Mergers and acquisitions (M&A’s) are common ways for businesses to expand,

compete, and maintain in competitive business environments. A strongly debated question in

literature is whether or not these M&A’s provide measurable benefits, as factors such as industry,

geographic location, and regulations play key roles in the impacts of the M&A’s. In this paper, we

investigate the short-term effects of M&A’s based on stock returns of acquiring companies, with

a focus on finance industries in the British Isles.

Purpose: The purpose is to study whether or not there are significant short-term abnormal

returns for acquiring companies when M&As of financial services target enterprises take place.

Further, the study examines factors which can affect the impact of M&A’s, such as size of

transaction, whether it is domestic or cross-border, whether or not the acquiring company is in a

finance industry, and whether there is evidence of merger waves related to finance M&A’s in the

British Isles.

Method: An event study methodology is applied and focused on calculating the cumulative

abnormal returns, as well as verifying whether those are statistically significant. The study analyses

100 M&A’s conducted on target companies from the UK and Ireland between the years 2000

and 2019. The event study is performed using the STATA statistical software, which is used to

analyse the stock return performance in comparison to the domestic market index for each

acquiring company.

Conclusion: The study finds statistically insignificant results, concluding that M&A events do

not generate significant abnormal returns for acquiring companies. This is in line with majority of

previous research done, showing that M&A deals are not deemed significantly value creating nor

value destroying. M&A’s within finance industry where the acquiring companies were domestic,

in a finance industry, where the deals were smaller, were all shown to have less negative, albeit

still insignificant results. This study also presents evidence for merger waves. Moreover, this

thesis adds a clear geographic and industry component which is often missing in previous

research, showing that within finance industry in the British Isles the impacts of M&A deals are

unlikely to be statistically significant in causing abnormal returns.

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Contents

1. Introduction and Background ............................................................ 1 1.1 Introduction to M&A’s ............................................................................. 1 1.2 Background and Information on M&A’s .................................................. 3

2. Research Problem ............................................................................... 5

3. Thesis Purpose .................................................................................... 5

4. Delimitations ........................................................................................ 6

5. Ethical Considerations ........................................................................ 6

6. Definitions ............................................................................................ 7

7. Literature Review ................................................................................. 8 7.1 Efficient Market Hypothesis.................................................................... 8 7.2 Event Study Methodology and Abnormal Returns ............................... 10 7.3 Previous Research on Short-Term Performance After M&A’s ............. 12 7.4 Acquisitions: Causes ............................................................................ 14 7.5 Acquisitions: Consequences ................................................................ 15 7.6 British Isles M&A’s - Cross Border versus Domestic M&As ................. 16 7.7 Waves in Mergers and Acquisitions ..................................................... 18 7.8 Conclusion literature review ................................................................. 19

8. Methodology ...................................................................................... 20 8.1 Data ..................................................................................................... 20 8.1.1 Data Gathering ..................................................................................... 20 8.1.2 Research Data ........................................................................................ 20 8.1.3 STATA ................................................................................................. 22 8.2 Event Study Methodology .................................................................... 23 8.3 Event Window ...................................................................................... 24 8.4 Estimation Window .............................................................................. 24 8.5 Normal Returns .................................................................................... 25 8.6 Abnormal Returns ................................................................................ 26 8.7 Cumulative Abnormal Returns ............................................................. 27 8.8 Tests of Significance ............................................................................ 27

9. Empirical Results and Discussions ................................................. 28 9.1 Research Question 1 ........................................................................... 28 9.2 Research Question 2 ........................................................................... 31 9.3 Research Question 3 ........................................................................... 32 9.4 Research Question 4 ........................................................................... 34 9.5 Research Question 5 ........................................................................... 35

10. Conclusions and Discussion ............................................................ 36 10.1 Conclusions ......................................................................................... 36 10.2 Discussion............................................................................................ 37

11. Reference list ..................................................................................... 39

12. Appendix ............................................................................................ 47

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1. Introduction and Background

_____________________________________________________________________________________

In this section we present M&A’s and the current situation M&A activity as well as background.

This serves as the introduction to both the thesis topic and thesis focus along with what is aimed

to be achieved.

______________________________________________________________________

1.1 Introduction to M&A’s

The worldwide volume of Mergers and Acquisitions (M&A’s) has been increasing lately, and the

mergers and acquisitions activity is likely to be driven by companies looking to strengthen their

businesses, especially during periods of prolonged uncertainty (Cristerna and Ventresca, 2020).

With the multiple benefits possible it is easy to understand why companies seek to combine

resources or accumulate as much as they can, but considering the literature there are doubts on

whether or not M&As pay off. Depending on different geographic regions and industries, the

actual benefits of acquiring other companies can vary significantly (DeYoung, Evanoff and

Molyneux, 2009).

There has been significant increase in transformation deals in the United States (+45%). However,

there has been a significant decrease in the number of deals in Europe (-27%) and Asia (-29%).

When uncertainty arises, companies prefer to stick to the businesses they know very well (related

businesses or sector) instead of getting into different markets (Cristerna and Ventresca, 2020),

2020). As uncertainty dampens in 2020, not only are companies expected to gain more strength

and liquidity which lead to a more aggressive M&A strategy by the end of 2020, but also this will

lead to more M&A activity worldwide (2020 Global M&A Outlook, 2020).

Despite this uncertainty in the markets, 2019 has recorded the third strongest year for global M&A

even though they have had a weaker performance compared to 2018 (Data.bloomberglp.com,

2018). The bouncy year in the US was mainly due to the tax cuts and the country’s strong

economy. However, 2019 was the lowest in terms of GDP growth since 2008, reporting a roughly

2,5% global growth. While there are many factors which contribute to this result, the predominant

effect is the political uncertainty which has spread out across the globe and the trade disputes

between major economies are the main causes of the slowdown (Global M&A 2020 Outlook,

2019). There are European countries (excluding the UK, Germany, France, Italy and Spain) where

M&As activity is expected to pick up. On the flip side, their interest in the UK remains as fourth

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among potential foreign investors even though the uncertainty caused by Brexit (Mergers &

Acquisitions Review, 2019).

The UK’s financial sector remains the leading industry in terms of M&As in 2019, accounting

for 28% of all transactions which a value of £62bn. Debt funding continues to be accessible and

cheap and new bank debt was issued in support of over 511 deals during 2019 by mainly UK and

Ireland financial institutions. Aside from major banking groups, new parties have been involved

in these debt funding (Experian.co.uk, 2019). The number of M&A projects funded by private

equity has significantly increased in the global perspective (Global M&A 2020 Outlook, 2019).

Even though it shows a slight down in 2019, this trend is expected to continue in 2020 (Global

M&A 2020 Outlook, 2019).

The aim of this research is to analyze the impact of M&A’s of strictly financial services companies

which are based in the British Isles. Since “new” finance companies are critical in creating new

value in the finance sector, and large companies and funds continue regularly acquiring them in

order to gain competitive advantages, it is therefore important to know if those acquisitions pay

off according to expectations, and to what extent. Ireland and UK present good research countries

as they have a large amount of finance companies, as well as, London being a financial center for

the European continent and Dublin becoming a bigger potential financial hub after Brexit

(Mergers & Acquisitions Review, 2019).

This paper will focus on recent M&As in the British Isles. More specifically, the impact that a

merger or acquisition as an “event” has on the stock prices, and thus returns of the shareholders

of the acquiring company. The goal is to contribute to existing research with a focus on finance

companies in the British Isles, by researching whether the M&As are profitable in the short-term

or not.

The thesis will be relevant for researchers, managers and CEOs. As it contributes with a unique

focus in short-term event study research, it provides new knowledge on M&A research focusing

on finance industry in the British Isles. Managers and CEOs active in the British Isles can view

the results as a guideline for future decision-making in terms of M&A’s. Unlike prior studies

which tend to have broader perspectives, this thesis has a narrow focus which provides specific

geographic and industry-based results. These results may therefore be regarded as differentiated

from other research and suitable for those who are interested in a more detailed and concise report.

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1.2 Background and Information on M&A’s

Finance firms have a history of consolidating since the 1980s (DeYoung, Evanoff and Molyneux,

2009), and it is a natural competitive process that could see a future increase as the world becomes

more interconnected. The reasoning being similar to that of old family businesses, which used to

prevail in every town quarter due to a combination of close relationships with the communities

and experience in their areas, nowadays suffering issues with long-term longevity as larger

corporations take over (Kuruppuge and Gregar, 2018). In essence, co-operation and inter-

connectedness are factors which play a key role in businesses surviving and prospering in today’s

business environments, this is what has been the fundamental reasoning behind the merging and

acquiring process of companies.

For businesses the benefits and reasons of acquiring another company could be a method of

market entry, a way to gain access to tangible and intangible assets and resources, acquiring lower

cost processes, lowering competition as well as increasing the market share (Achim, 2015). These

plentiful opportunities continue to constitute the main drivers of acquisitions as presented by

current research literature. This is especially relevant with new acquisitions due to the notorious

rapid technological advancement, which has seen the introduction of the merging between

technology and finance. This has increased the demands of many larger corporations who seek to

acquire companies with competitive advantages before they become more serious existential

threats; primary examples of this are amongst so called fintech companies (Dranev, Frolova,

Ochinova, 2019).

The potential for a business to rapidly improve their position on a competitive market through a

combination of company capabilities or an acquisition is naturally attractive to business leaders,

but it is not without risk. Acquisitions have been shown to be very sensitive to integration

processes which can be made difficult by cultural, corporate, and interpersonal reasons (Dranev,

Frolova, Ochinova, 2019). Other issues related to M&As are related to technical aspects as well

as the underlying motives for the processes. Managers may have what is known in literature as

managerial hubris, which causes them to overestimate the benefits of mergers and acquisitions

and initiate projects which in turn end up decreasing firm value (Jiang et al., 2011).

Considering both the benefits and the risks, researchers are not in agreement on what the impacts

of M&As are on the acquiring firms’ performances, as there is no consistent evidence on whether

or not the firms on average benefit from acquisitions (DeYoung, Evanoff and Molyneux, 2009).

Hudgins and Seifert (1996) researched stock price reactions of acquisitions within the specific

finance section of banking in the US, and found that abnormal returns were not earned by the

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acquiring firms, but only by the acquired firms, over the short-term. On the other hand, other

researchers found proof of abnormal positive returns in the short-run (Mallikarjunappa, 2018;

Rosen, 2006).

DeYoung, Evanoff and Molyneux (2009) further claimed that pre-2000s literature stated that

bidder companies (acquiring companies) would face slightly negative returns, while post-2000s

literature on the topic stated that acquiring companies could face positive returns as case for

European bank mergers. That was not the case for US bank mergers on the other hand which had

yielded mixed results (Deyoung, Evanoff & Molyneux, 2009). The mixed results of the impacts

of acquisition are also demonstrated in UK government data which shows high fluctuations in

amount of acquisitions in recent years.

Although as mentioned earlier there has been a reduction in M&As in Europe overall, which could

possibly correlate with the reduction the UK faced in the years post-2008 financial crisis, there

has been a dramatic increase since the year 2018. This could be related to recent economic growth

in the EU and abroad (Reuters, 2018), as well as a weaker sterling due to the Brexit political

process (The In-House-Lawyer, 2020).

Graph 1 – M&A’s of UK Companies by UK Companies Years 2003-2018

Source: Ons.gov.uk, 2020.

The UK rise in M&A’s in 2018 is reflective of the general trend which is similar in Ireland as

well (The Irish Times, 2019). The region being examined in the study is therefore highly relevant

in terms of M&A activity, and of which impacts we analyze.

0

200

400

600

800

1,000

1,200

2003 2004 2005 2006 2007 2008 2009 2010¹ 2011 2012 2013 2014 2015 2016 2017 2018

Number of M&A's of UK Firms by UK Firms

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2. Research Problem

This thesis will investigate the stock returns of companies which have acquired other firms within

the finance industries of banking, asset management and alternative financial investments, in

order to empirically research and present whether or not acquiring firms experience abnormal

returns in the short-run due to the event of an M&A. Further, to measure whether the returns of

those companies post-M&A are positive or negative. If the data shows positive abnormal returns

from finance M&A’s, the invisible hand of the market interprets these acquisitions as value

creating possibly due to ‘’greater than the sum of parts’’ economies of scale or consolidating

market.

The problem is that currently the research literature has been inconclusive, with large variations

between studies and depending on industry. It is also difficult to predict if the acquisition will add

value to the company in the form of synergies. There has yet to be a specific study focusing on

the British Isles and the finance industry connected to it. As there are two international financial

centers in the British Isles (London and Dublin) it is an excellent area of studying the impacts of

M&A’s due to their clusters of businesses. Thus, both researchers and managers in charge of

M&A projects in the region and elsewhere could benefit from this study.

3. Thesis Purpose

The purpose of this thesis is as presented in the introduction to answer the following research

questions:

Research Question 1: Are there significant short-term abnormal returns associated with M&As in

the finance industry of the British Isles?

Null Hypothesis: There are no significant abnormal returns related to M&A’s in the finance

industry in the British Isles.

Research Question 2: Is there a difference in the effects in the short-term between domestic and

cross-border M&As within the British Isles finance industry?

Research Question 3: Is there a difference in the nature of the abnormal returns between large

deals and small deals of M&As?

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Research Question 4: Is there a difference in the nature of the abnormal returns between deals

within finance industry companies, and deals where the acquiring company has been of a different

industry?

Research Question 5: Is there a presence of merger waves in M&A’s conducted in the finance

industry in the British Isles?

Further, we aim to contribute with new research knowledge on mergers and acquisitions with a

specific regional and industry focus of British Isles and finance, analyzing whether they are value-

creating or not.

4. Delimitations

The scope of this thesis is limited to the geographic areas of Great Britain and Ireland as well as

the finance industry. Further, the number of company mergers and acquisitions examined are

limited to 100 because of data limitations as they have to qualify a number of criteria such as

having stock prices available and having historical stock prices of relevant indexes available. In

this study. Due to these limitations, the results should be understood as reflecting the specific

geographic and industry areas and may not necessarily be transferable to other areas.

5. Ethical Considerations

Regarding the data gathering for this research, we ensure following of general guidelines provided

by Jönköping University on ethical data collection and processing methods. Further, we avoid

any form of plagiarism and unethical information-sharing which ensures that our research

conclusions become unique related to our research, and have their own implications.

Any implications from the findings of this study regarding the movements of stock prices after

acquisitions and the actions based upon it by managers have been considered from an ethical

perspective. The agency problems of short-term stock price movements benefitting managers

through short term stock-based performance compensation have for the most part been handled

internally by the firms through long term stock option incentives and other policies that have been

integrated into the firms as historical scandals have shown that short term incentives might hurt

the firm and society as a whole.

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6. Definitions

M&A “Mergers and acquisitions (M&A) are defined as consolidation of companies.

Differentiating the two terms, Mergers is the combination of two companies to

form one, while Acquisitions is one company taken over by the other.”

(EduPristine, 2015).

Event

Study

Event study is a method of studying how an event changes a firm’s prospects by

quantifying the impact of the event on the firm’s stock (MacKinlay, 1997).

Abnormal

Returns

Abnormal returns are the difference between the returns that would have been

reached if event had not occurred (normal returns), and the actual reached returns.

They are calculated by deducting the former from the latter. (MacKinlay, 1997).

CAR CAR refers to Cumulative Abnormal Returns - The sum of all abnormal returns.

(MacKinlay, 1997).

Test

Statistic

Statistic used to determine if there is a significant difference between the means

of two groups, which may be related in certain features (Everitt, 1998).

Null

Hypothesis

A general statement or default position that there is nothing significantly different

happening, or that there is no relationship between two measured phenomena

(Everitt, 1998).

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7. Literature Review

_____________________________________________________________________________________

In this section we describe the theoretical framework which underlines this thesis, with specific

subsections of relevant theoretical concepts and history in order to get an understanding of the

extensive research background which provides a clearer understanding of the thesis topic.

______________________________________________________________________

7.1 Efficient Market Hypothesis

The efficient market hypothesis is a well-supported financial theory which states that in an

efficient market the stock prices reflect all available information (Cardona, Gutierrez and

Agudelo, 2015). It is a foundation for event studies as it provides the assumption that the market

is somehow efficient, which then is the basis of a hypothesis to an analysis of abnormal returns

(Collings, Wood and Caliguri, 2016, p. 222-223).

The development of the efficient market hypothesis is generally attributed to Fama (1965),

although earlier writers such as Cootner (1964) who claimed that future stock price changes could

not be predicted, and Samuelson (1965) who stated that “there is no way of making an expected

profit by extrapolating past changes in the future prices”, are also noted as contributing to the

original establishment of the theory.

Nevertheless, Eugene Fama in his seminal paper “The Behavior of Stock Market Prices” who not

only empirically confirmed the randomness of stock prices by testing the so called “random walk

model” (model of assumption that stock prices are followed through a random process), but also

defined the concept of an “efficient market”. This was not without elements of critique, as

Grossman (1976) claimed that there was a kind of market efficiency paradox, as he stated

“informationally efficient price systems aggregate diverse information perfectly, but in doing this

the price system eliminates the private incentive for collecting the information”, that is that once

traders assume the market is perfectly efficient, they cease to collect information with the same

incentive and thus it leads to less efficiency of the market.

Thus, it was already near its conception a theory that did not have full support amongst researchers

and academics. Grossman and Stieglitz (1980) further argued that there was a cost of information

which made it impossible for the market to be efficient, as informed traders would seek out return

on investment higher than cost of information, and the incentive to invest would no longer be

there. That was a continuation of the original market efficiency paradox presented by Grossman

in 1976. Still in the 1980s, De Bondt and Thaler (1985) conducted research based on both

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experimental psychology and studying stock price developments, which found evidence for so

called “excess volatility”, that is that people tended to overreact to dramatic or new events, which

would oppose the efficient market hypothesis as it could not be explained by publicly available

information, or fundamental information, only.

Then came the 90s with Eugene Fama, who also referred to the research done by Grossman and

Stieglitz in 1980 when re-presenting the efficient market hypothesis, but with the assumption that

cost of information would be zero (Fama, 1991). He presented research on event studies which

stated that as early as one day post-announcement of an event, the stock prices would adjust

against abnormal returns and thus the market would be efficient. With that said, he also brought

up issues with the theory such as event studies, due to focusing on the average adjustment of

prices to information, they do not tell how much of the residual variance is rational. Another

issue was that event studies had a “joint hypothesis” problem and cannot be tested for market

efficiency, as stock prices could react (drift) due to bias in calculating abnormal returns or slow

market reactions assuming acquiring firms overpay for their acquisitions (Fama, 1991).

For the purpose of this review it is important to consider the three forms of the efficient market

hypothesis which explain how quickly the market would react to publicly announced information

which are weak-form, semi-strong-form, and strong-form (Fama, 1965). These three forms are

created due to considering the hypothetical information reflected in market prices. Weak-form

market efficiency implies that technical analysis of past data would impossibly lead to abnormal

positive returns, as the current stock price would reflect all past changes. Weak-form market

efficiency has had mixed results in literature with some empirical research proving it (Mobarek

and Fiorante, 2014; Alexeev and Tapon, 2011; Chen and Metghalchi, 2012), and other research

disproving it (Abushammala, 2011; Ntim et al., 2011).

Strong-form market efficiency implies that stock prices reflect all information, public or not

public (Fama, 1965). This is highly unlikely and not supported by literature (Malkiel, 2011). That

is as the only realistic way for strong-market-form efficiency to be true is when assuming that

insider information is illegal and thus not included in the analysis. Therefore, one can conclude

market inefficiencies under this form.

Finally, there is semi-strong-form market efficiency, which implies that only all public

information is incorporated into the stock price (Fama, 1965). This form of market efficiency

appears to have mixed empirical backing, as Kashiramka and Rao (2014) researched the effects

of mergers and acquisitions on stock price returns and found evidence for weak-form market

efficiency in the Indian IT sector, but not for semi-strong-form market efficiency. On the other

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hand, Jackson (2015) also researched the effects of M&A’s and found significant positive

abnormal returns related to the announcement date of an M&A, but since it the total standard

abnormal returns returned post-day of announcement date, it was concluded that the market was

semi-strong efficient. Research done by Gersdoff and Bacon (2009), also M&A based event

study, concluded that one could outperform the market, but found semi-market efficiency in

specific days post-event day where stock prices would return towards equilibrium, those days

being day 5 and 25.

Despite the criticism towards it, and the research disputing to what degree the hypothesis can be

verified, the efficient market hypothesis still serves as a highly relevant research theory with

significant empirical support even in new research literature. and which is continuously

researched and tested in different settings. It is clear from the research on the topic, going all the

way from its academic inception to the current state, that there are critical factors such as

geography, industry, scale of research data, and event window periods which play a role in how

well one regards the hypothesis to fit reality. In this thesis it will be possible to test the hypothesis

through examining the returns related to the event window, which can determine whether

acquiring companies outperformed the market or not with the M&A process.

7.2 Event Study Methodology and Abnormal Returns

A common methodology in measuring impacts of certain events is event study, where gains and

losses of certain firm events such as M&A’s, corporate announcements, leadership behavior, and

other scenarios which are regarded as being able to influence the value of a firm are measured. A

key aspect of any event study in finance is the measurement of normal and abnormal returns,

which are estimated in order to measure the effect of an event in a certain time period (MacKinlay,

1997).

Beyond the general use of event studies, as has been shown to be extensively used for a large

amount of different cases, there is significant use of event studies in specifically M&A research.

In fact, event studies are the dominant way of measuring impacts of M&A’s (Collings, Wood and

Caliguri, 2016, p. 221-225). The reasons for that are stated as ease of use and precision. With that

said, there has also been critique for it being too simplistic, that it fails to include variables such

as organizational, social, and market complexities.

The methodology known as an event study originated in 1933 in a paper called "Characteristics

and Procedure of Common Stock Split-Ups” by James Dolley, according to MacKinley (1997),

but it is widely attributed to either Ball and Brown (1968), who studied the usefulness of

accounting income numbers regarding annual reports in estimating stock price returns, or the

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well-known paper of Fama et al., (1969), where the researchers measured how quickly stock

prices would adjust to new information. More specifically, Fama et al., (1969) researched the

effect of a stock split announcement, on the stock prices. They applied the so called “market

model” in their event study, where the stock returns were compared to market returns in order to

detect abnormal returns. The market model is the most regularly used model for estimating returns

in event studies due to it resulting in smaller variances of abnormal returns which leads to greater

statistical tests (Strong, 1992), With that said, there are also some other methods for measuring

returns in event studies are capital asset pricing model and the mean return model (Cable and

Holland, 1999; Fama, 1998).

The importance of understanding event study models is not merely for theoretical purposes, as

model misspecification may cause bias in the study (Binder, 1998). This is primarily a problem

when relevant variables are omitted or irrelevant are included, but with enough of a sample size

and assuming event dates are not clustered in calendar time with the market model the average

abnormal returns tend to be unbiased in research studies.

There is discussion on whether market model tends to be the most appropriate, as although some

consider it superior to the CAPM model due to doubts on the relationship between beta and stock

returns (Cable and Holland, 1999), therefore casting doubts on the CAPM model. Others, like

Jagannathan and Wang (1996), defend it by stating that if expanding the market portfolio to

include human capital and letting the beta vary over time it performs well in explaining returns.

With that said, it is generally accepted that although CAPM and the market model are the main

models applied, the market model of event studies dominates the CAPM model in all but a few

cases (Cable and Holland). Fama (1998), states that the market model is suitable for estimating

the effect of company specific events, such as M&A’s, because the estimation of abnormal returns

does not constrain the cross-section of expected returns. This is because expected returns

estimated using the market model are conditional because they are given by the market return.

Returning to event studies in general, they have also been criticized by McWilliams and Siegel

(1997) when it comes to M&A research for not being enough to explain complex processes such

as acquisitions. They argued that accounting measures need to be performed when measuring

M&A event impacts. This is on the other hand not fully supported by Collings et al., (2016), who

argue that increased measures can cause issues if they do not assess the same theoretical construct.

Therefore, based on recent literature which tends to separate the measures in order to more

distinctly separate between different measures of gains or losses, for example the difference

between profits and abnormal returns, this thesis should focus on one specific measure.

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7.3 Previous Research on Short-Term Performance After M&A’s

Measuring the impacts of M&A on stock performance of firms is a relatively common research

topic with varying results depending on study method and area. Factors such as whether the

acquiring firm or the target firm are analyzed, and which industry is analyzed and during what

period are all able to influence the results (Kiymaz and Baker,2008). A key aspect which

influences the impact of specifically M&A transactions in the short-term is listed as whether the

target company is foreign, while being in the same industry only made a difference in studies with

a long-term focus (Hazelkorn et al., 2004).

What research seems to have established is that in most cases, M&A creates value for the

shareholders of the acquired firm, but not for the acquiring firm, using stock returns as a

measurement for value (Hazelkorn et al., 2004). Although that is disputed by Kiymaz and Baker

(2008) who argue that although for the most part the acquiring firm may face negative abnormal

returns in the short-term, there are cases of them gaining on it as well, leading to the average

benchmark return of M&As being equal to zero. Yuce and Ng (2009), who researched short-term

performance of 1361 Canadian M&A without any specific industry target, found that acquiring

firms made significant positive cumulative abnormal returns. They did however find that the

target firms gained the most from the M&A’s as is stated in most, if not all, of research literature

on this topic regardless of geographic location and industry.

A fundamental aspect of just why M&A stock performances for acquiring firms may so often be

negative is stated as the “Hubris Hypothesis” (Kiymaz and Baker, 2008; Lin et al., 2008; Raj and

Forsyth, 2003). This is the subjective managerial aspect which plays into the decision-making

process of an M&A and affects results, and thus research literature. This is important to note as

there is not always a quantitative error which by design causes acquiring companies to accumulate

negative abnormal returns post-M&A, rather, human faults are consistently brought up as playing

a critical role.

With that in mind, Hazelkorn et al., (2004) went on to conduct their own research which showed

that in the short-term, the cumulative average abnormal returns were negative for the acquiring

company when studying only non-financial industries. Similarly, Kiymaz and Baker (2008) had

confirmed the case for non-financial industries, in that stockholders of acquiring firms did not

gain as positive abnormal returns (as theirs were negative, if just slightly) as stockholders of

acquired firms who got positive abnormal returns.

In the case of firms in finance industry, of which most current literature focuses on banking related

industries, literature states that results can vary significantly between the US and Europe. In the

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US, most studies find negative abnormal returns associated with acquiring companies, while in

the EU there are more studies which point towards positive abnormal returns, at least in the short-

term, for acquiring companies (Altunbas and Marqués, 2008). Research done by Cybo-Ottone

and Murgia (2000), focusing on the European banking sector, found that there were significant

cumulative abnormal returns for both the acquiring firms and acquired firms. The reason for

acquiring firms in the EU making positive abnormal returns in the short-term, while not often the

case in the US, was stated as regulation differences in the banking sectors (Cybo-Ottone and

Murgia, 2000). With that said, it is not necessary that acquiring firms in Europe make positive

abnormal returns as more literature shows. Drymbetas and Kyriazopolous (2014) conducted

research on 40 European cross-border M&A’s for the period 1998-2009, finding negative

abnormal short for the acquiring companies in the banking industry.

Narrowing the focus further down to the British Isles reveals a significant lack in recent literature

which focuses on finance and short-term performance of M&A’s. Nevertheless, Uddin and

Boateng (2009) analyzed 373 acquisitions in the UK over the period 1994 to 2003 in order to find

the impact of M&A on acquiring firms in the short-term. They found that UK firms’ acquisitions

did not lead to positive abnormal returns, unless the target company was from the US, which gave

support to the idea that geographic locations play a role in the impacts of M&A’s based on stock

performance. Similarly, Sudarsanam and Mahate (2003) also found that mergers did not create

positive value for acquiring firms’ shareholders when looking at so called “glamour firms” who

were highly valued due to past stock performance.

Positive short-term results in the UK were seen by Chinese companies who completed

acquisitions of companies in the UK (Zhu and Moeller, 2016). They analyzed a sample of 44

completed M&A using the event study methodology, to find that Chinese cross-border acquiring

firms into the UK earned positive abnormal returns on the announcement of an M&A deal. This

points towards another key aspect of M&A literature, which is that M&A’s tend to lead to positive

abnormal returns to a higher degree when they are Asian companies, or conducted in Asia (Ma et

al., 2009). This is theorized as being due to more structured corporate ownership which decreases

the risk of M&A related agency problems.

To conclude, there have been lots of studies on the topic of short-term performance after M&A’s.

The problem is that there are significant differences in the results depending on geographic region

as well as industry, and there is a great lack of literature from the British Isles which focuses on

finance industry specifically. As the British Isles are separate from the continental Europe in many

cultural aspects as well as in terms of regulations and law as the UK and Ireland use common law,

there are aspects which could influence the results of post-M&A performances similar to the claim

14

for the difference between Asia and Europe. The theory in this case would be based on the

difference between the results in China and in Europe, as we would consider if the same kind of

law structure differences could apply to differences between Common Law and Civil Law in

Europe, and thus also influence the results.

7.4 Acquisitions: Causes

Causes and consequences of M&A have been a topic of discussion for researchers for several

decades. Theories which describe the aims for acquisitions sometimes estimates their

consequences. Many researchers agree that acquisitions are bundled through time. These

clustered acquisitions define the plots of the merger’s activity. Previous studies examined the

reasons why mergers occurred. These drivers could be split into 4 groups (Yaghoubi et al., 2016).

Firstly, managers of acquiring companies hoping to generate value to themselves and

shareholders due to their overconfidence. Secondly, the decision is driven by industry-level

factors, such as industry shocks or size redistribution within an industry. Thirdly, the acquisition

is connected with economic conditions and finally the takeover involves behavioral theories

(Yaghoubi et al., 2016).

The neoclassical approach advocates that managers undertake acquisitions to maximize

shareholders’ wealth. Managers look for value-creating opportunities and possible synergies

which could be generated by economies of scale, inefficiency improvements, involving managers

on companies’ performance etcetera (Jensen and Ruback,1983).

Non-value adding trades are associated with mistaken decisions. The hypothesis states that

overconfident managers have a higher company valuation of the target company and the market

does not emulate the full value of the merged company. Moreover, empirical evidence from

previous research demonstrates that there are no overall gains from acquisition deals due to

managers’ valuation bias (Yaghoubi et al., 2016). This thesis will cover whether or not short-term

stock returns reflects the long-term economic evidence.

Contrary to the neoclassical approach, the agency costs hypothesis proposes that managers

undertake M&A deals out of self-interest. For example, conflicts over the payout ratio may arise.

If the company presents an excess of cash flows and managers decide not to pay dividends,

managers might spend this non-profitable cash in value-destroying acquisitions. Handling

dividends decreases the resources under management's control, therefore it reduces their power.

Preceding research shows that increasing the level of debt compensates the agency cost and as a

result, managers wouldn’t have the incentive to take unprofitable acquisitions (Yaghoubi et al.,

15

2016). Other theories show that managers might tackle conglomerate merger to diversify their

portfolio and exposure to the firm. Under this type of acquisition, the target company is

completely unrelated, therefore, no synergies are expected. (Amihud and Lev ,1981).

Acquisition activities differ between industries. Previous studies illustrate that M&A might come

in waves, which means that if there are industry-level shocks, that would affect the number of

acquisitions. This thesis will be focused on the financial services industry in the UK and Ireland.

In order to be more precise, we have considered 100 companies and 100 events. Economic

industry shocks create discrepancies on company’s valuation causing merger waves altering the

individual’s expectations making the future less predictable. In that case, historical data is less

reliable to predict future returns, therefore, the number of alternative estimations increase

(Yaghoubi et al., 2016). In this study we will complete a deeper analysis of the abnormal returns

in order to correct this data misinterpretation.

New technology breakthroughs allow companies to offer new products and services using the

latest technology. For example, the acquisition of a Fintech company might change completely

how a particular bank processes transactions. The aim of this research is also to analyze the impact

of the acquisition of small financial services companies by larger financial institutions. Industry-

related acquisitions are considered to be a positive response to industry shocks. Examples of these

acquisitions might lead to the emergence of new technologies, innovating financial methods and

deregulation.

7.5 Acquisitions: Consequences

The consequences of M&As could be classified into macroeconomic or microeconomic effects.

Among the macroeconomic factors, it is obvious that M&As have increased the overall

productivity gains, especially in the financial sector industry where companies are highly

interdependent. M&A not only transfer and redistribute wealth, they also transfer the know-how

and expertise to the acquirer companies. Takeovers are means to increase the capital base of

acquiring companies to expand their business and also improves efficiency (Mueller, 1985).

In terms of microeconomic research, M&A have significant consequences in the acquiring

company in terms profits, risks growth, leverage and taxes etcetera the thesis will cover mainly

the changes in the stocks returns.

Firstly, it might be observed that there are wealth effects on the acquiring firm as target firms do

not exist after the acquisitions deal. Previous studies have demonstrated that the announcement

of an acquisition causes positive abnormal results for the targeted firm, that’s the reason why

16

acquirer normally offer premiums to the target company shareholders (Roll, 1986). The synergies

achieved can be classified into two types. Operational synergies, such as, distribution and

production costs and Financial synergies, such as leverage increase, tax benefits

There is a discrepancy whether abnormal returns are significant or not in terms of the acquirer

companies. Some investigations agree to the point that abnormal returns in the short-term as

acquirer firms are likely to obtain positive abnormal returns if recent mergers by other firms have

been well-accepted or the stock market is performing well. However, there might be a long-term

reversal in the acquirers’ returns if the deal was made during the bull market period. (Rosen,

2006). In this research we are going to investigate the impact of these abnormal returns in acquirer

firms in the short-term to test this evidence and identify the reasons why M&As in financial keep

increasing.

7.6 British Isles M&A’s - Cross Border versus Domestic M&As

As we have previously discussed in the introduction, even though the total value of acquisitions

has increased worldwide, the number of transactions has marginally decreased. The global

takeovers have slowed this year due to the increase of geopolitical and economic tensions. In this

thesis we are going restrict the geographical location to the British Isles (United Kingdom &

Republic of Ireland).

In a global sense, firms have multiple motivations as to why they want to conduct an M&A

abroad. The possibility to gain from better exchange rates is one example, although investors may

be wary of new markets due to different regulatory systems which include different accounting

and taxation laws (Rose et al., 2018; Kang 1993). Despite the potential risks, Kang (1993)

believed that cross-border M&A’s would lead to higher benefits than domestic M&A’s. This was

supported by Goergen and Renneboog (2004) who stated that cross-border operations should be

superior according to literature, due to the possibility for acquiring companies to gain from capital

and factor imperfections. Ironically, their research results showed that domestic M&A’s resulted

in better results compared to cross-border M&A’s.

Further support for domestic M&A’s being generally more positive in their results are given by

Eckbo and Thorburn (2000) who researched the gains of acquiring firms from both a cross-border

and domestic point of view, and found that domestic M&A’s garnered statistically significant

positive results, while cross-border M&A’s would lead to insignificant results. This was

supported Rose et al., (2018) and Cybo-Cottone and Murgia (2000), who all argued for domestic

M&A’s in favor of cross-border M&A’s, as they would be more value creating and result in higher

announcement effects.

17

Hazelkorn et al. (2004) on the other hand, stated that cross-border M&A’s were more profitable

when they researched US companies acquiring other US firms or other firms abroad. This was

due to the ability to enhance the geographic coverage, gain access to local technological expertise,

acquire lower labor and production costs as well as experience potential for accelerated growth.

The idea that cross-border M&A’s are more successful was also supported by Dutta, Saadi, and

Zhu (2013), although it is stated that it comes with multiple integration issues as well as a higher

complexity.

Regarding the UK, the domestic viewpoint remains unsettled. The UK M&A market has been

relatively lower than previous years. A total of 4,998 has been recorded in 2019 which comprises

a decline of 12% compared to 2018 (Experian.co.uk, 2019). It might be noticed that the figures

are dramatically lower due to the Brexit deadline, for example, just under 1,216 transactions were

announced in Q3 almost 700 deals less than Q2. The number of domestic M&As is still

remarkably greater than Cross-Border M&As as it happened in the previous years. In terms of

allocation London remains by far the main location. In relation to the value (£m), the financial

services industry remains as the first sector for M&As (Experian.co.uk, 2019), this is one of the

reasons why we have decided to analyze this sector in this report.

Despite Brexit and other geopolitical factors, the outlook for M&A activity in the UK financial

services remains positive driven mainly by Fintech innovations. (check data for examples) Long

term interest rates keep the cost of debt cheap and central banks are reluctant to increase rates.

These factors will continue facilitating to increase the M&As activity (Enterprise Times, 2019)

Financial firms are increasing the number of takeovers of Fintech while it has been noticed a

consolidation among mid-sized assets managers and mid-sized brokers largely as result of

regulation MiFID II. It is likely that future M&As will be targeted by sub-sectors such as smaller

investment banks, asset managers, insurance companies and global payments firms (Enterprise

Times, 2019).

Regarding the ROI, economic uncertainty, especially Brexit, has a significant impact reducing

the M&A activities. EU buyers are waiting to see the “post- Brexit agreement” in order to

complete their takeovers. Companies are looking to Ireland as a post-Brexit European base this is

due to the fact that the number of inbound transactions from the UK (including large internal

cross-Border transfers) has rocked notably in the financial service sector (The Irish Times, 2019).

As the post-Brexit scenario still remains uncertain, it's difficult to predict an M&A forecasting.

Contrary to the UK market, the Irish market presents higher Cross-border than domestic

acquisitions (Statista, 2019) Regarding sectors, technology and Pharmaceutical comprised the

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higher M&A activity. Financial services industry accounts for approximately 8% (Assets. KPMG,

2019) However, some reports have suggested that this percentage will increase under a positive

post-Brexit scenario.

7.7 Waves in Mergers and Acquisitions

As it is known, M&As are one of the most popular business investments. There are some previous

studies which state that M&As come in waves as M&A transactions have a significant impact on

the market especially in related companies (Institute for Mergers Acquisitions and Alliances

IMAA, 2020). A merger wage is an intense period of M&A activities in a particular sector or

industry and lasts from a short period to a long period of time depending on the companies

involved. The beginning of these waves is not defined but the ending could be related in some

cases with political and economic factors (Uddin and Boateng, 2009), for example the 1929 crash

or the dotcom bubble. It could be argued that M&As play an important role in market capitalism

and there is a continuous wave of M&As: (Institute for Mergers, Acquisitions and Alliances

IMAA, 2020).

Based on past experience, it has been illustrated that any M&A wave is triggered by multiple

factors. Firstly, all waves happen in periods of economic recovery (following an economic

downturn). Secondly, the waves concur with the period of rapid credit expansion, which is the

current situation now where interest rates are low and a bullish market period. It might be

observed that past waves ended with the collapse of stock markets, as a result, thriving capital

markets contribute to waves in M&A.

Thirdly, M&A waves occur before an industrial or technological shock, such as, technology and

financial innovations, deregulation, increase of foreign competition etc. M&A deals also increase

when regulatory changes related to anti-trust or takeover regulation takes place (Pure.uvt.nl, 2005)

In our case, it is obvious that the financial service sector presents waves of takeovers. With

London being the financial center in Europe and the thrive of FinTech, acquiring companies are

seeking to take over targeted companies which threatens their business model using disruptive

technology or revolutionary products in order to remain dominant in the sector.

Previous research suggested that takeovers in the early stages of the wave happens due to industry

shocks. These acquisitions tend to generate short-term profits to their shareholders and create

synergies. However, unprofitable acquisitions happen due to overvaluations and managers’

overconfidence (Uddin and Boateng, 2009). Finally, it is important to mention that takeover

causes differ across M&As waves and sectors.

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7.8 Conclusion literature review

The literature on mergers and acquisitions is broad. M&As are among the most important

corporate activities with a significant impact on stakeholders. The main findings from previous

studies show that: M&A activity presents a wavy pattern (e.g. mergers are clustered in industries

through time).

The main factors which cause this fluctuation include industry an economy level shocks, miss-

valuation and managerial conflicts. Market response to announcement of acquisition is generally

negative for acquirer companies and positive to target companies. Previous research also states

several factors related to performance of takeovers, such as, acquirer and target company

characteristics, deal offer, industry and macro-environment aspects (Yaghoubi et al., 2016).

We can also conclude that previous studies are heavily biased towards gains to acquirers and

elements that affect that earnings. It is also biased in finding sources of value creation of M&As

or value-destroying. After multiple studies, the question of what the sources of value in M&As

are, has not been answered yet.

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8. Methodology

_____________________________________________________________________________________

In this section we describe the methodological approach to this thesis, with descriptions and

explanations behind methods chosen. Further, we will also present the data used for the research

and how it was gathered. This research is primarily done through the statistical analysis software

STATA, and thus much of the methodology explained below is literature based, in order to

understand the meaning and reasoning behind the different research steps.

______________________________________________________________________

8.1 Data

8.1.1 Data Gathering

The secondary data for the thesis was gathered using primarily PRIMO university database along

with IDEAS Repec. This was done to find the most relevant reviewed papers which could serve

as a theoretical foundation for the thesis with scientific background. Google Scholar was also

applied. As for finding specific papers we made use of keywords such as “Impacts of M&A’s”,

“M&A Short-term effects”, and “Mergers and Acquisitions consequences”, which would then

bring us the related literature which could serve as a base to develop upon and find other literature

in such a way.

Research papers were chosen for their research significance, relevance to the thesis, as well as

date of publishing in order to include the most relevant information available. Papers who are

decades old are reviewed due to their status as critical within the topic, which serve as foundations

of many other papers and is thus widely regarded as reliable in the more technical aspects of this

literature review. Other secondary data was gathered through public publications of companies

and firms, as well as government organizations which could provide statistics on M&A’s.

8.1.2 Research Data

The data regarding M&A investigated in this research is exported from the database Thomson

Reuter Datastream which is a macroeconomic and financial database that provides reliable and

comprehensive data. The observational research complies two separate data sets, the M&As list

of events and the daily closing price of target companies from the British Isles (United Kingdom

& Republic of Ireland) before and after events from 2000 to 2019. The initial data set exported

from Thomson contains 286 observations companies taking part in acquisition.

21

After filtering the data by announcement date, we gathered a sample population of 100 finance

companies and 100 events. Besides making sure that each firm chosen satisfies our selection

criteria in terms of sector (alternative financial investments, banks and other financials) and target

company’s location. A list of our M&A deals is presented in appendix B.

As has been discussed previously in literature review. The sample has been filtered in order to

avoid any possible clash with any other events that could affect the acquiring company’s stocks

return. The initial sample extracted from data stream contain 2932 mergers and acquisitions for

financial services (defined as Asset management, Alternative Financial Investments and Banks

in data stream) in UK and Ireland. Companies with multiple events studies have been remove

avoiding noise in the data sample (Aktas, de Bodt and Cousin, 2003).

Acquiring companies which have presented events such as, dividends announcements, annual

reports releases, profit warning, changes in the members of the board during the estimation and

observation window have been excluded, ending up with a sample of 100 companies with 100

M&As.

To conduct this research, daily closing prices of the following indexes have been exported.

Acquiring companies’ stock's returns have been compared according to their benchmark in order

to present a foundation for measurement of stock price returns which are then matched to the firm

specific stock prices.

Historical stock prices have been extracted from Thomson Reuters Datastream. Only the closing

price of the stock has been considered to calculate the stock's returns. The data set is split into 100

target firms and 100 acquiring firms according to their benchmark.

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Table 1 - Indexes Used for Generating Market Returns

Acquirer Nation Index

United Kingdom FTSE 100, FTSE 250, FTSE SmallCap

Spain IBEX 35

Denmark OMX Copenhagen 20

United States S&P 500

Canada S&P/TSX 60

Australia AXJO

Switzerland SMI

India Nifty 50

Germany DAX 30

South Africa MSCI South Africa

Hong Kong Hang Seng

Qatar QSE

Japan Nikkei 225

Ireland ISEQ

France CAC 40

Norway OMX Oslo 20

Sweden OMX Stockholm 30

8.1.3 STATA

Stata is a statistical software package used for data management, statistical analysis, graphics,

simulations, regression and custom programming. Further in this method section we will describe

the theoretical aspects of the methods applied, as foundational understanding of the statistical

processes involved assists in understanding the results. For the results, we apply the commonly

used software tool for event studies which is STATA (Blossfeld et al., 2019).

The code applied will be listed in Appendix C, and is derived from Princeton University’s guide

for event studies using STATA. The steps on the program are similar to what would otherwise be

done manually on excel, with the benefit that it can more efficiently handle a larger amount of

sample data which would otherwise be inefficient in excel.

23

8.2 Event Study Methodology

This research investigated the effect of an economic event, namely announcement date, using the

event study methodology (Arc.hhs.se, 2010). Event studies are created to measure the impact of

an economic event on a security’s return. This is one of the most popular methods to measure the

impact of M&As on acquiring companies’ stock price therefore, event study method fits to carry

out the purpose of this research (Perepeczo, 2007).

The methodology, normally called the abnormal returns methodology, as stated in the literature

review is employed to measure the change of stock prices related to certain announcements or

events. Resting on the market figures, it might be observed that shareholders benefit from M&As

as the stock price tends to rise (Perepeczo, 2007). The purpose of this abnormal return procedure

is to define if shareholders’ additional gains or losses depend on the economic event. The

abnormal returns quote the excess of returns from a post-acquisition event (Perepeczo, 2007).

Here will be the presentation of the general structure of the methodology, which is followed by

more in-depth analysis and argumentation. The event study methodology follows that presented

by MacKinlay (1997), which includes 7 steps:

1. Event Definition - We define it in our methodology as the event window, and set it at (-

5,+5).

2. Selection Criteria - Our data is restricted to M&A of companies in the finance industry,

which is in Thomson Reuters defined as industries Asset Management, Banks, and

Alternative Financials. Further, it is also restricted to data availability on the indexes.

Indexes used are: FTSE 100, FTSE 250, FTSE SmallCap, CAC 40, S&P/TSX 60, SMI,

ISEQ, DAX 30, QSE, Nifty 50, Hang Seng, MSCI South Africa, AXJO, S&P 500,

OMX 20, IBEX 35, Nikkei 225, OMX Stockholm 30, OMX Oslo 20.

3. Normal and Abnormal Returns - Measured as the returns of the estimation window and

the event window, respectively. Calculated using the market model.

4. Estimation Procedure - The estimation window of this event study is 200 days, from -

200 days to -20 days prior to the M&A announcement.

5. Testing Procedure - STATA event study procedure following market model structure.

6. Empirical Results - Empirical results will follow the methodology and data sections of

this thesis.

7. Interpretation and Conclusions - Author interpretation of how M&A impact stock

prices, and what the effect implies for acquiring firms.

24

8.3 Event Window

There is no clear literature consensus on what event window to apply and depending on whether

it is long-term or short-term studies they can range in research from only the day of the event to

over one thousand days post-event. The only technical requirement is that the event window

ensures the actual abnormal returns are captured (Sethi and Krishnakumar, 2010). Typical short-

term event windows used in literature as 5 or 7 days before and after the event, while long-term

event windows range from 50 days post-event and beyond (Sethi and Krishnakumar, 2010).

Andrade et al., (2001) use a shorter estimate of the average short-term event window at one day

before and one day after the event. Further, they state that an event window that is too long reduces

the statistical precision of the abnormal returns calculated. Another research benefit is that the

shorter the event window the easier it is to identify abnormal returns present (Armitage, 1995). A

final argument for using short event windows is stated by Konchitchki and O’Leary (2011), as a

short window is less likely to clash with confounding events which also might affect the stock

prices.

The event window is the period of the event of interest, in our case it is the period of the

announcement dates for the M&As, along with 5 days before, and 5 days after. As there is no

strict benchmark on the setting of event windows for short-term analysis, we estimated the period

based on earlier research along with literature recommendations in gaining accurate results.

Panayides and Gong (2002) consider 5 days a good time period for a short-term event window as

it allows one to fully capture the event being examined. It is chosen as (-5,+5) instead of (-1,+1)

in order to gain extra data on the returns as well as accounting for a minor error or misplacement

in the event date (MacKinlay, 1997). Finally, in this study, the event date, or announcement date,

is referred to as day zero as is common in literature (Konchitchki and O’Leary, 2011).

Graph 2. Graphical representation of our event window.

8.4 Estimation Window

Estimation windows refer to a period before the event where the stock returns can be assumed to

be normal. They are calculated in order to get a benchmark with which to compare normal and

25

abnormal returns. The standard procedure is to use a period before the event as the estimation

window (Campbell et al., 1997).

Sethi and Krishnakumar (2010) stated that estimation periods are typically a period of 200 days

around 250 or 50 days before the event window. We choose to have a period of 220 to 20 in this

thesis, as it would be a period completely unrelated to the event period, and thus give us returns

which may be assumed to be normal for the company’s without colliding with any other events.

8.5 Normal Returns

The normal returns are the returns we measure which happen during a period before the event

window referred to as the estimation window. Its purpose is to give us data of the ordinary returns

of the stocks prior to the event. As mentioned in the literature review, there are multiple models

to estimate normal returns, such as CAPM, market model, and the mean return model

(MacKinlay, 1997). The market model relates the returns of any given security or stock to the

return of the market portfolio or index (MacKinlay, 1997), and is by far the most widely used

model. For any stock, the estimation of normal returns according to the market model is:

𝑅𝑖𝑡 = 𝛼𝑖 + 𝛽𝑖𝑅𝑚𝑡 + 𝜀𝑖𝑡,

𝐸(𝜀𝑖𝑡 = 0) 𝑣𝑎𝑟(𝜀𝑖𝑡) = 𝜎𝜀2

where 𝑅𝑖𝑡 and 𝑅𝑚𝑡 are the period-𝑡 returns on stock 𝑖, and the market portfolio, respectively. 𝜀𝑖𝑡

is the zero mean disturbance term. 𝛼𝑖, 𝛽𝑖 and 𝜎𝜀2 are the parameters (MacKinlay, 1997).

We use the fitting country indexes based on size and industry of companies as the market

portfolios with which we compare the stock prices. Since in literature the market model is

considered an improvement from other models such as the constant mean return model and the

CAPM (MacKinlay 1997; Cable and Holland, 1999), we choose to use it in this thesis.

This is due to the market model being more accurate by removing the part of the returns that is

related to variation in the market’s (index) returns, and therefore the variance of the abnormal

return is reduced (MacKinlay, 1997). The market model also proved the most successful in a

model selection hypothesis testing study conducted by Cable and Holland (1999), as it accounted

for the most dependent variables along with being the most accurate. As it can spot abnormal

returns more accurately and remove “noise”, it means only the abnormal returns associated with

our M&A’s will be highlighted.

26

Hendersson (1990) states that a majority of event studies use continuously compounded returns,

which improve the return distribution normality, eliminating negative values and making it easier

to convert daily returns if needed. Thus, the returns are applied in log form as:

𝑅𝑗𝑡 = ln(1 + 𝑅𝑒𝑡𝑢𝑟𝑛),

where 𝑅𝑗𝑡 is the continuously compounded return on stock 𝑗 in period 𝑡.

8.6 Abnormal Returns

In order to calculate the abnormal returns, as the event returns may be also affected by external

factors (systematic and market risk), it is needed to adjust the returns by subtracting the normal

returns. These returns are known as adjusted or abnormal returns (University of Groningen, 2018).

The normal return is the expected stock return as if the event didn’t occur. To calculate this

expected return, we have taken the stocks prices from the estimation window before the event. It

is important to set the estimation window around seven days before the event takes place. The

dates from the event window should be excluded as those might be affected by the event in the

coming days, otherwise, data might be biased.

In conclusion, the abnormal returns can be calculated following the formula below (Haleblian and

Finkelstein, 1999):

𝐴𝑅𝑖𝑡 = 𝑅𝑖𝑡 − 𝐸(𝑅𝑖𝑡)

Where:

𝐴𝑅𝑖𝑡 - Abnormal return for company i over period t.

𝑅𝑖𝑡 – Return for company i over period t.

𝐸(𝑅𝑖𝑡) – Expected return for company i over period t.

t - Day or month, depending on the data accepted for calculations and unit of the event window.

If the abnormal returns 𝐴𝑅𝑖𝑡 is greater than zero, the takeover generates gains for shareholders.

When the abnormal returns 𝐴𝑅𝑖𝑡 is equal to zero, the takeover doesn’t affect shareholders’ wealth.

However, if abnormal returns 𝐴𝑅𝑖𝑡 is below zero, the takeover makes a loss for shareholders.

27

8.7 Cumulative Abnormal Returns

Once the abnormal returns are measured, we aggregate them in order to measure the effect of the

M&A announcements on the stock prices. Since we apply continuous compounding in measuring

our returns, the CAR represents the abnormal return on a portfolio that is rebalanced every time

period to give equal weighting in each stock (Strong, 1992). An important assumption of

cumulative abnormal returns as a measure of M&A performance is based on market efficiency,

as both market risk and market performance are considered when using CAR in the performance

measurement (Haleblian and Finkelstein, 1999).

Aggregating the AR’s is important because tests with single events are not as useful to draw

overall inferences (MacKinley, 1997). Thus, by aggregating the abnormal returns over the event

we get the cumulative abnormal returns as:

𝐶𝐴𝑅𝑖 (𝑡1,𝑡2) = ∑ 𝐴𝑅𝑖𝑡

𝑡2

𝑡=𝑡1

Where 𝑡1, and 𝑡2 are time periods in the event window.

8.8 Tests of Significance

We conduct a test statistic of significance, to see whether the average abnormal returns for each

stock is statistically different from zero. As the standard deviation may be a result of pure chance.

Following general guidelines derived from research and academic institutions we derive our null

hypothesis as there being no abnormal returns during the event window, whereas the alternative

hypothesis suggests presence of abnormal returns. The test is based on 95% significance level, to

derive whether or not the average abnormal return for the stock is significantly different from

zero at the 5% level (Sorescu, Warren & Ertekin, 2017). This hypothesis test is expressed as:

𝐻0: 𝐴𝐴𝑅𝑡 = 0

𝐻1: 𝐴𝐴𝑅𝑡 ≠ 0

If the average abnormal returns are greater than zero, it means that the M&A announcements have

increased the wealth of shareholders. If the AAR’s are lower than zero it means the opposite, that

the M&A announcements have decreased the wealth of the shareholders. There are multiple ways

to test these null hypotheses under event studies, our method is based on the Princeton guide for

event studies using Stata, with the test statistic expressed as:

28

𝐴𝐴𝑅𝑡𝑡 − 𝑠𝑡𝑎𝑡 =𝐶𝐴𝐴𝑅𝑡

(𝜎𝑁,𝑝𝑟𝑒√𝑁𝑡)

For the cumulative average residual, the t-test formula is expressed as:

𝐶𝐴𝐴𝑅𝑡𝑡 − 𝑠𝑡𝑎𝑡 =𝐶𝐴𝐴𝑅𝑡

(𝜎𝑁,𝑝𝑟𝑒√𝑁𝑡)

Where 𝑁𝑡 = the absolute value of event day, 𝑡, plus 1.

9. Empirical Results and Discussions

_____________________________________________________________________________________

In this section we present the research results along with an analysis which includes the impacts

as well as the research results in relation to other research and our own literature review, to gain

a research- as well as theoretical perspectives on the tests done. The results and analysis are

divided separately for each research question and theoretical concept.

______________________________________________________________________

9.1 Research Question 1

RQ1: Are there significant short-term abnormal returns associated with M&As in the finance

industry of the British Isles?

Null Hypothesis: There are no significant short-term abnormal returns related to M&As in the

finance industry in the British Isles.

Graph 3 - Depicts returns of acquiring firms for a period of 5 days before and after M&A event

29

Table 2 - Regression results with event windows (-5,+5), (-1,+1), and (-2,+2).

Linear Regression Number of obs = 100

𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒~𝑛

𝑅𝑜𝑏𝑢𝑠𝑡

𝐶𝑜𝑒𝑓. 𝑆𝑡𝑑. 𝐸𝑟𝑟. 𝑡 𝑃 >｜t｜ [95% 𝐶𝑜𝑛𝑓. 𝐼𝑛𝑡𝑒𝑟𝑣𝑎𝑙]

[−5, +5] _𝑐𝑜𝑛𝑠 -.009368 .0087261 -1.07 0.286 -.0266826 .0079465

[−2, +2] _𝑐𝑜𝑛𝑠 .0440029 .04679 0.94 0.349 -.0488387 .1368444

[−1, +1] _𝑐𝑜𝑛𝑠 -.003828 .0047607 -0.71 0.479 -.0128291 .0060634

Our research showed that when looking at the total of all the 100 acquiring companies regressed,

the CAR (cumulated abnormal returns) for all companies treated as a group was -0,009368, i.e. -

0.94% as shown in table 1 and Appendix A. This means that the firms on average made a loss

related to the M&A event window short-term period. The results were insignificant for all

companies except 4 with the significance level 0.05, which means that only for those 5 companies

were the impacts of the M&A on the stock prices enough to create abnormal returns with 95%

confidence.

These results do not dramatically change depending on event window and estimation window

changes. Multiple version differing from the main periods established in the methodology section

were attempted in order to fully exclude the possibility that there were significant results in

different estimation or event windows. Examples of tested periods are shorter and different

estimation windows and event windows, as well as event windows which did not have equal days

before the event as after the event. In the end it was concluded that factors such as asymmetric

time windows did not have a significant effect on the research results.

Amongst the 4 companies which had significant CAR’s, 3 of them had positive CAR’s and 1 had

negative CAR’s, which leads to inconclusive results on whether or not firms in the finance

industry gain or lose as an aggregate when looking at CAR’s. With that said, as stated earlier, our

research shows that there is an insignificant effect of M&A announcements on the stock prices of

the acquiring companies.

Further, there was a notable difference when assuming different event windows. As shown in

table 1, not only do the amount of significant results increase the smaller the event window, but

the coefficients (average) abnormal returns turn from the negative as shown in the (-5,+5)

example, into slightly insignificantly positive. The (-1,+1) window has been selected as it is the

first trading day after the announcement date, where the information regarding the corporate

activity is publicly available. In this case, even though the figures show slightly positive abnormal

30

returns, the results are highly insignificant. This thesis also tests the (-2,+2) as this is the time

where analysts normally release their target price about the M&A after the announcement date.

In this case results are also insignificant presenting positive abnormal returns.

These results are in line with earlier literature on M&A performance which often finds

insignificant returns for the acquiring firms (Fuller, Netter and Stegemoller, 2002; Bruner, 2004).

In our case, the returns are negatively insignificant. As they are negative, it is similar to literature

stated in the earlier review, such as Kiymaz and Baker (2008) who also highlighted that acquiring

firms would likely face slight negative returns.

Our research lends support to the idea that judicial and geographic factors when regarding M&A

performance results. As Asian and European M&As more often had positive abnormal returns,

and US M&A’s more often had negative returns abnormal returns (Ma et al., 2009; Altunbas and

Marqués, 2008). As the UK and US both have different judicial systems from continental Europe,

that is common law instead of civil law, it could be one of the factors which have influenced why

the returns in our research lean towards negative, albeit being insignificant. As mentioned in the

literature review on research done by Ma et al. (2009) as well as Cybo-Ottone and Murgia (2000),

country regulations are theorized to affect the post M&A performance of acquiring companies.

Thus, although it is beyond the scope of this research, the fact that the UK differs in law structure

from continental Europe could be what affected the results into being insignificantly negative

rather than positive. The reasons for this are likely different corporate structures which are not as

resilient towards agency problems (Ma et al., 2009).

There is plenty of other research, such as that done by (Campa and Hernando, 2004; Jarrell and

Poulsen, 2004), which shows insignificant negative abnormal returns for acquiring companies.

Jarrell and Poulsen (2004) mention three widely acknowledged research explanations for why

returns to acquiring companies are often either close to zero or negative. Two of those which are

relevant for our empirical research are that the full wealth effects may not be observed in acquiring

firm stock prices at the time of the bid because they are a relatively small component of the total

acquiring firms’ wealth, and that acquisitions could be poor investment projects and the effects

on stock prices accurately reflect this.

In our case, it is clear that many of our acquiring firms are large banks and financial institutions,

and therefore a large M&A still might not be large enough to make a significant difference on

their stock prices. Secondly, it is largely theorized as mentioned in the literature earlier in this

31

thesis, that acquisitions are often non-beneficial and can even be costly, which our research points

towards.

With all this in mind, it is not possible to reject the null hypothesis and to confirm whether or not

M&As of finance industries in the British Isles between 2000-2019 have created or destroyed

value to acquiring shareholders. The results show that there are cases of significant positive and

negative abnormal returns, but for the vast majority of cases the results were insignificant and

inconclusive as they differed between insignificantly positive and insignificantly negative. Our

findings using the market model are thus in agreement with other research which has shown

insignificant abnormal stock performance post-M&A.

9.2 Research Question 2

RQ2: Is there a difference in the effects in the short-term between domestic and cross-border

M&As within the British Isles finance industry?

Graph 4 - Depicts the returns of acquiring firms from the British Isles and acquiring firms from

abroad.

Table 3 – Cumulative Abnormal Returns for Cross-Border and Domestic M&A’s.

𝑁𝑢𝑚 𝑜𝑓 𝑜𝑏𝑠 = 100

𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒~𝑛

𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 0.173%

𝐶𝑟𝑜𝑠𝑠 − 𝐵𝑜𝑟𝑑𝑒𝑟 −2.83%

32

According to the graph above, the returns from Domestic M&As comprises 0,173%, in

comparison with – 2.83% from Cross-Border. This result might be due to the allocation factor.

Domestic transactions tend to present greater performance as the market’s response is less

volatile. On the other hand, Cross-Border transactions tend to be perceived as riskier, therefore

the returns tend to be lower.

Further, as Eckbo and Thorburn (2000) had theorized, there are most likely certain advantages

which domestic acquiring firms have which cross-border acquiring firms do not. Firstly, foreign

direct investment controls could increase acquisition costs for cross-border acquiring firms. As

M&A’s are often very large deals, they require a significant amount of bureaucracy, which is

made worse when it is a cross-border deal. Dutta, Saadi, and Zhu (2013) believed that despite the

increased complexity and integration issues, cross-border deals would be more successful, while

we see in our results that it is not the case. It is instead likely that the increased complexity leads

to increased effort and time required, as well as increased costs associated with the projects, which

is widely viewed as more likely to lead to an unsuccessful M&A.

And secondly, horizontal market relationships between target firms and acquiring firms within

similar geographic locations could provide another benefit for domestic acquiring firms (Eckbo

and Thorburn, 2000). Here, similarly to Oh, Peters and Johnston (2014), synergy in the domestic

markets would provide for a more efficient M&A process and thus be regarded as a more

profitable event, as displayed in the results.

Finally, this result is similar to that reached by Aw and Chatterjee (2004), who also found that the

performance of UK firms which acquired other UK firms saw higher performance compared to

cross-border deals. Most common theory as reason for this is synergy in the internal market, where

it is not only more efficient to acquire a domestic firm, but also regarded by the market as being

more efficient and less risky as managerial hubris could play a role in those M&A’s (Aw and

Chatterjee, 2004). Managerial hubris in this case could play a role through the decisions to initiate

an M&A being made by managers under a state of hubris, without fully considering the risks and

downsides, which leads to the event not being viewed as a beneficial one from the outside, and

simply not being profitable.

9.3 Research Question 3

RQ3: Is there a difference in the nature of the abnormal returns between large deals and small

deals of M&As?

33

Graph 5 - Depicts the returns of acquiring firms from deals of 20 million and over in value and

returns of acquiring firms from deals of 20 million and under in value.

Table 4 - Cumulative Abnormal Returns for Small and Large M&A’s.

𝑁𝑢𝑚 𝑜𝑓 𝑜𝑏𝑠 = 100

𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒~𝑛

𝑆𝑚𝑎𝑙𝑙 𝐷𝑒𝑎𝑙𝑠 −0.53%

𝐿𝑎𝑟𝑔𝑒 𝐷𝑒𝑎𝑙𝑠 −1.40%

As the graph and table above illustrate, the negative returns from Small deals (-0,53%) are

significantly lower than the returns from Big deals (-1,40%), as a result, the size of the deal size

might have an impact on the returns. 20 million and under in terms of value M&A’s are referred

to as small deals as they represent roughly the lowest 50% of our data sample, while above 20

million represents the upper 50% of our data sample. It is thus a measurement chosen by authors

as there is no literature consensus regarding what constitutes a large M&A and a small M&A, as

those are relative concepts which entirely depend on the larger context.

The fact that larger deals appear to result in more negative results compared to smaller deals is

supported by Alexandridis et al., (2012). The reason for this is likely the inherent complexity in

a larger M&A that is not included in smaller deals which might be more easily conducted. Further,

the expectations from the market of high costs during a large M&A could contribute to a negative

development. Our results here are in line with research done by Oh, Peters and Johnston (2014),

who argues that integration problems are a large hinder in performance results. We can thus

conclude that there is a difference in the nature of the abnormal returns.

34

9.4 Research Question 4

RQ4: Is there a difference in the nature of the abnormal returns between deals within finance

industry companies, and deals where the acquiring company has been of a different industry?

Graph 6 - Depicts returns of acquiring firms in finance industries (Asset Management, Banks,

Alternative Financial Investments) and acquiring firms in non-finance industries.

Table 5 - Cumulative Abnormal Returns for Finance industry and Non-Financy Industry

Acquiring Company M&A’s.

𝑁𝑢𝑚 𝑜𝑓 𝑜𝑏𝑠 = 100

𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒~𝑛

𝐹𝑖𝑛𝑎𝑛𝑐𝑒 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 −0.434%

𝑁𝑜𝑛 − 𝐹𝑖𝑛𝑎𝑛𝑐𝑒 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 −1.439%

We see in the graph and table above that there is a difference in the nature of the abnormal returns

depending on the industry of acquiring company, as the cumulative average abnormal returns for

M&As within the finance industry are -0.435%, while for differing industries it is -1.439%. It can

then be seen that M&A’s within the same industries have a lower but statistically insignificant

negative effect on the stock prices surrounding the event of an M&A announcement. However,

these findings are as the other ones insignificant.

The differences between the results for M&A’s within finance industry and those from acquiring

companies outside finance industry could be related to synergy, similarly to the difference

between domestic and cross-border M&A’s. Essentially, having more knowledge within an

industry or a market region simplifies business processes which includes M&A’s. Being a finance

35

firm that acquires another finance firm is widely regarded as being an easier process of integration

compared to acquiring a company from another industry which will come with unique challenges,

of which many might be unexpected. Oh, Peters and Johnston (2014) also argued that integration

challenges presented a large problem for performance, and thus these results are not very

surprising overall and are in line with earlier research conclusions.

9.5 Research Question 5

RQ5: Research Question 5: Is there a presence of merger waves in M&A’s conducted in the

finance industry in the British Isles?

Graph 7 - Depicts activity of M&As in monthly periods over 19 years.

As it has been discussed in the Literature review, M&As normally come in waves triggered by

multiple factors. The number of M&A deals have been classified monthly from 2000 to 2019.

According to the graph above, this theory has been tested in this event study to show the influence

of this effect in the abnormal returns. The number of M&A deals have been classified monthly

from 2000 to 2019.

Following the previous conclusion, which states that abnormal returns are statistically

insignificant, the presence of waves reinforces this statement. According to (Gugler, Mueller,

Weichselbaumer and Burcin Yurtoglu, 2012) waves are followed normally by lower insignificant

abnormal returns for acquiring companies, in comparison with M&A deal executed during

periods of low merger activity. The amount of M&A deals is related to the market cycles. When

markets are booming the presence of waves increases as there is more activity on the markets, on

36

the contrary, during recession times the presence of waves is significantly lower. As it might be

observed in the graph above, the events for this study follows a wave pattern.

The existence of abnormal returns doesn’t necessarily mean that those are caused only by that

particular event, other M&As and other macroeconomic factors (such as, economic growth,

interest rates, inflation and fiscal and monetary policies) might impact the significance of

abnormal returns (Uddin and Boateng, 2009).

10. Conclusions and Discussion

_____________________________________________________________________________________

In this section we present the conclusions based on our research results as well as theoretical

foundation. Here the thesis is summarized and analyzed in terms of context and implications, as

well as authors’ own reflections and ideas for future research.

______________________________________________________________________

10.1 Conclusions

This research focused on analyzing whether the impacts of M&As within finance industries have

a significant positive on acquiring company’s stock returns. After concluding the empirical

research, it has been found that in the British Isles, the effect of M&As was statistically

insignificant on the stock returns of the firm’s analyzed, therefore there is no destruction or value

creation for acquiring firm shareholders.

This provides further support to earlier research done in other areas which have also found

statistically insignificant results of M&A on stock returns, and provides opposition to literature

within the British Isles which has found statistically significant abnormal returns. The causes for

the results are likely that the acquiring companies have market capitalizations so high as to render

the M&As insignificant. With that said, although the total results were statistically insignificant

negative returns, there were cases with significant positive and negative results.

We also found statistically insignificant differences for acquiring firms depending on geographic

area of acquiring company, size of acquisitions, and industry of acquiring company. Acquiring

firms within the British Isles appear to have better stock returns than acquiring firms from outside

the British Isles. Acquiring firms who conduct small deals appear to have better stock returns than

acquiring firms who conduct large deals (above 20 million value). Acquiring firms within finance

37

industries who conduct M&A’s of other finance industry firms appear to have better stock returns

than acquiring firms outside of finance industries.

Further, the shorter the event window, the higher the number of acquiring companies with

significant abnormal returns. However, since our data sample is 100 firms, it is still not a relevant

percentage which could be extrapolated to a larger sample. This is tied to the efficient market

hypothesis, as the likelihood of outperforming the market is reduced over time, as earlier research

mentioned had concluded that the market adapts quite rapidly. Thus, it would not be a wise

decision to conduct an M&A in the British Isles for the purpose of creating value in the short-

term.

10.2 Discussion

This thesis has implications which are both practical and theoretical. It adds to literature a specific

sub-section of M&A research with a focus on the British Isles and finance, which is otherwise

quite an uncommon focus in research. Further, managers interested in results of M&As in the UK

or Ireland, or those who are interested more specifically in the finance industry sectors of asset

management, banks, and alternative financials may benefit in their decision-making from the

results acquired.

As the impacts of M&A’s according to literature seem to differ within a single country, it is

doubtful if these results could be extrapolated to other sectors or regions with similar results. It is

instead quite likely that this area of research is very dependent on industry and geographic area

in order to gain similar results. Therefore, this research should only be viewed in the proper

context of its geographic and industry focus, as different research has had both similar and widely

different results elsewhere.

Further, results can also depend on multiple other factors. Our research was limited to a relatively

small amount of companies at 100, as more conclusive results could possibly be gathered with a

larger sample size. This study could also be extended to more related or unrelated target industries

to get the larger sample size.

Due to time constraints, this thesis does not include a distinction between size of acquisitions and

size of the acquiring companies, this is something that most likely has affected the results due to

large corporations or bank having acquired firms at prices which would not affect their stock price

returns in the short-run.

38

For future research, a broader event window could be considered, including times of crisis times.

A long-term event study could be conducted to test if there is any significant impact for acquiring

companies in terms of synergies, market share, cost-efficiency and value creation for

shareholders. A long-term study could disclose more significant factors which affect the abnormal

return in M&As to complete a deeper M&A analysis. It could be that short-term results are

insignificant, while long-term gains or losses are different and an effect of the M&A’s can be

noticed there.

As mentioned in the empirical results section based on research question 1, a final future research

plan could focus on the law differences between the British Isles and continental Europe, and thus

do a similar study on finance companies but with a focus on continental Europe. This would

enable a clear view on whether or not the results differ based on those geographic focuses the

same way they differ between for example Europe and China.

39

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47

12. Appendix

Appendix 1 – Cumulative Abnormal Returns (CAR) of all 100 Companies

48

Appendix 2 – Company Information

Company_ID

Announcement

Date Target Name Acquirer Name

1 2008-09-17 HBOS PLC Lloyds TSB Group PLC

2 2004-07-23 Abbey National PLC Santander Central Hispano SA

3 2017-03-04 Aberdeen Asset Management PLC Standard Life PLC

4 2015-03-12 TSB Banking Group plc Banco de Sabadell SA

5 2004-12-14 Northern Bank Ltd Danske Bank A/S

6 2005-12-20 Goldfish Bank Ltd Morgan Stanley

7 2004-10-26 Australia & New Zealand Banking Group Ltd-Project Finance Bus Standard Chartered PLC

8 2004-07-12 Marks & Spencer Retail Financial Services Holdings Ltd HSBC Holdings PLC{HSBC}

9 2010-10-18 BlueBay Asset Management PLC Royal Bank Of Canada

10 2017-06-23 Capita Asset Services (UK) Ltd Link Administration Holdings Ltd

11 2007-01-29 Egg PLC Citigroup Inc

12 2016-06-06 Winton Capital Management Ltd Affiliated Managers Group Inc

13 2013-03-25 Cazenove Capital Holdings Ltd Schroders PLC

14 2017-05-23 Touchstone Innovations PLC IP Group PLC

15 2004-11-22 Baring Asset Management Ltd- Financial Services Group Unit Northern Trust Corp

16 2009-08-12 Insight Investment Management Global Ltd Bank of New York Mellon Corp

17 2013-05-05 Sainsbury's Bank PLC J Sainsbury PLC

18 2005-12-01 Egg PLC Prudential PLC

19 2014-06-20 The Mall Ltd Partnership Capital & Regional PLC

20 2000-12-05 ICC Bank PLC Bank of Scotland PLC

21 2011-05-27 King Sturge International LLP Jones Lang LaSalle Inc

22 2017-04-18 Key Retirement Solutions Ltd Partners Group Holding AG

23 2014-10-23 Equitix Holdings Ltd Tetragon Financial Group Ltd

24 2000-07-28 Chase de Vere Investments PLC Bristol & West PLC

25 2003-11-25 BlueCrest Capital Management (UK) LLP Man Group PLC

26 2018-04-09 Speirs & Jeffrey Ltd Rathbone Brothers PLC

27 2018-12-21 Stonehage Fleming Family & Partners Ltd Caledonia Investments PLC

28 2011-06-07 Lexicon Partners Ltd Evercore Partners Inc

29 2012-10-03 Investment Property Databank Ltd MSCI Inc

30 2015-11-12 Oaknorth Bank Ltd Indiabulls Housing Finance Ltd

31 2012-12-27 The Industrial Trust- Industrial Estates Portfolio (32) Hansteen Holdings PLC

32 2011-02-04 Anglo & Overseas PLC EP Global Opportunities Trust Plc

33 2011-12-16 Barrie & Hibbert Ltd Moody's Corp

34 2005-07-26 Broadcastle PLC Siemens AG

35 2019-01-22 Nutmeg Saving & Investment Ltd Goldman Sachs Group Inc

36 2015-07-28 Rowan Dartington & Co Ltd St. James's Place PLC

37 2012-04-28 X-Leisure Unit Trust Land Securities Group PLC

38 2010-03-29 Sovereign Reversions PLC Grainger PLC

39 2019-07-29 Neptune Investment Management Ltd Liontrust Asset Management PLC

49

40 2013-01-14

Carillion Private Finance Defence Ltd-Northwood MoD HQ PFI

Project HICL Infrastructure Co Ltd

41 2013-12-20 Levitas Investment Management Services Ltd Brooks Macdonald Group PLC

42 2016-12-19 Duncan Lawrie Asset Management Ltd Brewin Dolphin Ltd

43 2019-07-08 Investec Argo Property Fund Investec Property Fund Ltd

44 2019-02-04 Capital Step Holdings Ltd Duke Royalty Ltd

45 2015-12-23 Graphite Capital Management LLP-Private Equity Funds Business Intermediate Capital Group PLC

46 2010-10-26 Asset Management Investment Co PLC GLI Finance Ltd

47 2014-01-31 Boost ETP LLP WisdomTree Investments Inc

48 2019-05-29 Volvere PLC Volvere PLC

49 2014-10-29 Japaninvest Group PLC

Haitong International Securities Group

Ltd

50 2019-09-04 WFI Financial LLP Kingswood Holdings Ltd

51 2014-09-19 BlackRock Greater Europe Investment Trust PLC

BlackRock Greater Europe Investment

Trust PLC

52 2010-11-16 Financial Services Net Ltd Moneysupermarket.com

53 2011-08-30 TCF Global Independent Financial Services Ltd Mattioli Woods PLC

54 2014-04-17 European Wealth Management Group PLC EW Group Ltd

55 2007-06-14 Copal Partners Ltd Merrill Lynch & Co Inc

56 2011-08-22 Investment Platforms Ltd Centaur Media PLC

57 2007-08-20 UK Australasia Ltd APN Property Group Ltd

58 2018-10-01 Core Financial Holdings Ltd AFH Financial Group PLC

59 2019-09-30 Speedloan Finance Ltd-Pledge Books(113) H&T Group PLC

60 2017-11-30 Coex Partners Ltd Tp Icap PLC

61 2018-07-27 Acorn To Oaks Financial Services Ltd City of London Group PLC

62 2004-10-19 Irevna Crisil Ltd

63 2011-03-16 Islamic Bank of Britain PLC Qatar International Islamic Bank QSC

64 2018-07-04 Perfectcard DAC EML Payments Ltd

65 2015-03-09 Barker Poland Asset Management LLP Walker Crips Group PLC

66 2019-05-14 Life Settlement Assets PLC Life Settlement Assets PLC

67 2016-01-29 Kypera Holdings Ltd Castleton Technology PLC

68 2010-05-04 Grantfinder Ltd Idox PLC

69 2010-01-18 Troy Income & Growth Trust PLC (WAS 37679D)

Troy Income & Growth Trust PLC (WAS

37679D)

70 2008-08-05 Kaupthing Singer & Friedland Premium Finance Ltd Close Brothers Group PLC

71 2018-09-18 Kier Business Services Ltd-Kier Pensions Unit XPS Pensions Group PLC

72 2018-05-09 FundAssist Ltd Broadridge Financial Solutions Inc

73 2015-05-13 Amentum Capital Ltd (NOW 8C4330) Financial Products Group Co Ltd

74 2015-04-01 WK Nowlan REIT Management Ltd Hibernia REIT PLC

75 2014-04-14

Bank of America Merrill Lynch- International Wealth Management

Business Julius Baer Gruppe AG

76 2013-01-10 Kleinwort Benson Investors International Ltd Virtus Investment Partners Inc

77 2003-08-05 Carr Sheppards Crosthwaite Ltd-Cardiff Business Wh Ireland Group PLC

78 2003-02-28 Thomson Financial-Portfolio Solutions Assets Linedata Services SA

50

79 2002-07-09

NatWest Personal Financial Management Ltd(Royal Bank of

Scotland) Toronto-Dominion Bank

80 2003-07-21 EBS Pensioneer Trustees Ltd Charles Stanley Group PLC

81 2018-08-17 Corinthian Group Ltd Just Group PLC

82 2018-12-11 Hargreave Hale Ltd-Sipp Business Curtis Banks Group PLC

83 2014-05-15 County Life & Pensions Ltd Tavistock Investments PLC

84 2014-11-19 New Century AIM VCT PLC New Century AIM VCT PLC

85 2019-09-02 Sure Valley Ventures Pires Investments PLC

86 2018-08-01

Smith & Williamson Corporate Finance Ltd-Nominated Adviser &

Corporate Broker business Cenkos Securities PLC

87 2010-08-18 Envestors Ltd Braveheart Investment Group PLC

88 2017-05-19 Hazel Capital LLP Gresham House PLC

89 2019-10-17 Sinfonia Asset Management Ltd Tatton Asset Management PLC

90 2006-07-11 Meridian Informed Purchasing Ltd Accenture LTD

91 2007-09-03 Handelsbanken Life & Pensions Ltd Storebrand ASA

92 2007-11-26 Key Asset Management Group Ltd Skandinaviska Enskilda Banken AB

93 2013-02-05 Heartwood Wealth Group Ltd Svenska Handelsbanken AB

94 2019-11-04 Intuitus Ltd Endava PLC

95 2018-06-27 Franchise Finance Ltd Hitachi Capital (UK) PLC

96 2014-03-17 Lorica Consulting Ltd Aon PLC

97 2013-11-11 Newbridge Credit Union Ltd Permanent TSB Group Holdings PLC

98 2013-10-02 Cooper Williamson Ltd Begbies Traynor Group PLC

99 2017-02-06 Avox Ltd Thomson Reuters Corp

100 2005-11-02 Quorum Holdings Ltd-Jersey Trust Business Investec PLC

Company_ID Target Nation Acquirer Nation

1 United Kingdom United Kingdom

2 United Kingdom Spain

3 United Kingdom United Kingdom

4 United Kingdom Spain

5 United Kingdom Denmark

6 United Kingdom United States

7 United Kingdom United Kingdom

8 United Kingdom United Kingdom

9 United Kingdom Canada

10 United Kingdom Australia

11 United Kingdom United States

12 United Kingdom United States

13 United Kingdom United Kingdom

14 United Kingdom United Kingdom

15 United Kingdom United States

51

16 United Kingdom United States

17 United Kingdom United Kingdom

18 United Kingdom United Kingdom

19 United Kingdom United Kingdom

20 Ireland United Kingdom

21 United Kingdom United States

22 United Kingdom Switzerland

23 United Kingdom Guernsey

24 United Kingdom United Kingdom

25 United Kingdom United Kingdom

26 United Kingdom United Kingdom

27 United Kingdom United Kingdom

28 United Kingdom United States

29 United Kingdom United States

30 United Kingdom India

31 United Kingdom United Kingdom

32 United Kingdom United Kingdom

33 United Kingdom United States

34 United Kingdom Germany

35 United Kingdom United States

36 United Kingdom United Kingdom

37 United Kingdom United Kingdom

38 United Kingdom United Kingdom

39 United Kingdom United Kingdom

40 United Kingdom Guernsey

41 United Kingdom United Kingdom

42 United Kingdom United Kingdom

43 United Kingdom South Africa

44 United Kingdom Guernsey

45 United Kingdom United Kingdom

46 United Kingdom Guernsey

47 United Kingdom United States

48 United Kingdom United Kingdom

49 United Kingdom Hong Kong

50 United Kingdom United Kingdom

51 United Kingdom United Kingdom

52 United Kingdom United Kingdom

53 United Kingdom United Kingdom

54 United Kingdom United Kingdom

55 United Kingdom United States

56 United Kingdom United Kingdom

57 United Kingdom Australia

58 United Kingdom United Kingdom

59 United Kingdom United Kingdom

60 United Kingdom United Kingdom

52

61 United Kingdom United Kingdom

62 United Kingdom India

63 United Kingdom Qatar

64 Ireland Australia

65 United Kingdom United Kingdom

66 United Kingdom United Kingdom

67 United Kingdom United Kingdom

68 United Kingdom United Kingdom

69 United Kingdom United Kingdom

70 United Kingdom United Kingdom

71 United Kingdom United Kingdom

72 Ireland United States

73 Ireland Japan

74 Ireland Ireland

75 Ireland Switzerland

76 Ireland United States

77 United Kingdom United Kingdom

78 United Kingdom France

79 United Kingdom Canada

80 United Kingdom United Kingdom

81 United Kingdom United Kingdom

82 United Kingdom United Kingdom

83 United Kingdom United Kingdom

84 United Kingdom United Kingdom

85 United Kingdom United Kingdom

86 United Kingdom United Kingdom

87 United Kingdom United Kingdom

88 United Kingdom United Kingdom

89 United Kingdom United Kingdom

90 United Kingdom Bermuda

91 Ireland Norway

92 United Kingdom Sweden

93 United Kingdom Sweden

94 United Kingdom United Kingdom

95 United Kingdom United Kingdom

96 United Kingdom United Kingdom

97 Ireland Ireland

98 United Kingdom United Kingdom

99 United Kingdom United States

100 United Kingdom United Kingdom

53

Company_ID Deal Size (M USD) Target Industry Acquirer Industry

1 25 439,45 Banks Banks

2 15 787,49 Banks Banks

3 4 642,40 Asset Management Asset Management

4 2 529,77 Banks Banks

5 1 863,51 Banks Banks

6 1 718,73 Banks Brokerage

7 1 500,00 Banks Banks

8 1 418,69 Banks Banks

9 1 382,96 Asset Management Banks

10 1 126,07 Asset Management Professional Services

11 1 091,34 Banks Banks

12 800,00 Asset Management Asset Management

13 643,95 Asset Management Asset Management

14 639,38

Alternative Financial

Investments Alternative Financial Investments

15 483,18 Asset Management Banks

16 387,19 Asset Management Banks

17 385,24 Banks Food & Beverage Retailing

18 365,75 Banks Insurance

19 362,31

Alternative Financial

Investments REITs

20 308,82 Banks Banks

21 295,55 Asset Management Real Estate Management

22 261,33 Asset Management Alternative Financial Investments

23 255,66 Asset Management Asset Management

24 199,16 Asset Management Asset Management

25 178,66 Asset Management Asset Management

26 159,19 Asset Management Asset Management

27 142,50 Asset Management Other Financials

28 141,38 Asset Management Asset Management

29 125,00 Asset Management Professional Services

30 99,92 Banks Brokerage

31 96,60 Asset Management REITs

32 92,08 Asset Management Asset Management

33 77,71 Asset Management Professional Services

34 72,55 Asset Management Telecommunications Equipment

35 58,01 Asset Management Asset Management

36 53,38 Asset Management Asset Management

37 49,61

Alternative Financial

Investments REITs

38 49,52 Asset Management Other Real Estate

39 49,52 Asset Management Asset Management

54

40 49,17 Asset Management Alternative Financial Investments

41 39,20 Asset Management Asset Management

42 31,82 Asset Management Asset Management

43 31,31

Alternative Financial

Investments REITs

44 30,29 Asset Management Other Financials

45 29,65

Alternative Financial

Investments Alternative Financial Investments

46 20,62 Asset Management Credit Institutions

47 20,00 Asset Management Brokerage

48 19,81

Alternative Financial

Investments Alternative Financial Investments

49 16,99 Asset Management Brokerage

50 16,92 Asset Management Asset Management

51 15,61 Asset Management Asset Management

52 14,45 Asset Management Internet Software

53 14,16 Asset Management Asset Management

54 14,02 Asset Management Other Financials

55 11,00 Asset Management Brokerage

56 10,37 Asset Management Publishing

57 10,32 Asset Management REITs

58 10,0316 Asset Management Asset Management

59 9,8304 Diversified Financials Diversified Financials

60 9,5183 Asset Management Brokerage

61 8,3885 Asset Management Other Financials

62 7,8388 Asset Management Professional Services

63 7,7613 Banks Banks

64 7,021 Asset Management Other Financials

65 5,9076 Asset Management Brokerage

66 5,4333 Asset Management Asset Management

67 5,025 Asset Management IT Consulting & Services

68 4,99 Asset Management Software

69 4,8533 Asset Management Asset Management

70 4,6874 Asset Management Brokerage

71 4,6046 Asset Management Professional Services

72 0 Asset Management Professional Services

73 0 Asset Management Professional Services

74 0 Asset Management REITs

75 0 Asset Management Banks

76 0 Asset Management Asset Management

77 0 Asset Management Brokerage

78 0 Asset Management Software

79 0 Asset Management Banks

80 0 Asset Management Asset Management

81 0 Asset Management Other Financials

55

82 0 Asset Management Insurance

83 0,1142 Asset Management Other Financials

84 0,5552 Asset Management Asset Management

85 1,3372 Asset Management Other Financials

86 2,624 Asset Management Brokerage

87 2,6478 Asset Management Asset Management

88 3,4336 Asset Management Asset Management

89 3,4641 Asset Management Other Financials

90 0,00 Asset Management IT Consulting & Services

91 0,00 Asset Management Insurance

92 0,00 Asset Management Banks

93 0,00 Asset Management Banks

94 0 Asset Management Software

95 0 Asset Management Credit Institutions

96 0 Asset Management Insurance

97 0 Banks Banks

98 3,985 Asset Management Asset Management

99 0,00 Asset Management Internet Software

100 0,00 Asset Management Asset Management

56

Appendix 3 – STATA Code

sort company_id date

by company_id: gen target=date if Event_dummy==1

egen td=min(target), by(company_id)

drop target

gen dif=date-td

by company_id: gen event_window=1 if dif>=-5 & dif<=5

egen count_event_obs=count(event_window), by(company_id)

by company_id: gen estimation_window=1 if dif<-19 & dif>=-220

egen count_est_obs=count(estimation_window), by(company_id)

replace event_window=0 if event_window==.

replace estimation_window=0 if estimation_window==.

tab company_id if count_event_obs<11

tab company_id if count_est_obs<200

gen predicted_return=.

egen id=group(company_id)

forvalues i=1(1)100 {

l id company_id if id==`i' & dif==0

reg ret market_return if id==`i' & estimation_window==1

predict p if id==`i'

replace predicted_return = p if id==`i' & event_window==1

drop p

}

sort id date

gen abnormal_return=ret-predicted_return if event_window==1

by id: gen cumulative_abnormal_return = sum(abnormal_return)

sort id date

by id: egen ar_sd = sd(abnormal_return)

gen test =(1/sqrt(count_event_obs)) * ( cumulative_abnormal_return /ar_sd)

list company_id cumulative_abnormal_return test if dif==0

reg cumulative_abnormal_return if dif==0, robust