“Mergers and Acquisitions in the UK Banking Industry and ...

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“Mergers and Acquisitions in the UK Banking Industry and their

Impact on the Shareholders’ Wealth.”

Submitted by

Sofia Zisi

ANR: 645086

December 2014

Tilburg University

School of Economics and Management

Program: MSc. Finance 2014

Supervisor: Dr. M. Da Rin

Second Reader: Dr. F. Castiglionesi

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ABSTRACT In this study I will discuss thoroughly the mergers and acquisitions in the UK banking industry and their impact

on the shareholders’ wealth. Mergers and acquisitions (M&A) are vital instruments in financial industry.

Moreover, I decided to deal with the banking industry as it is one of the most energetic markets.

The main measurement procedure is the event study methodology, using the market model. However, I use

additional two models- the market adjusted model and constant mean model- for confirmation. The

cumulative abnormal return for the period [-1,+1] is equal to 5,936% for the targets, -0,2172% for the

biddersand 0,2096% for the combine entities.

Moreover, the target firms present a high positive abnormal return due to the M&A event, implying gains for

these firms. On the other hand, we notice a negative reaction for the bidders and an overall gain for the

combine entities that is created by the fact that the bidders’ loss is compensated by the targets’ gains.

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Contents ABSTRACT ...................................................................................................................................................... 2

Chapter: 1...................................................................................................................................................... 4

Introduction .............................................................................................................................................. 4

Chapter: 2...................................................................................................................................................... 5

2.1 Basic Concepts of Mergers & Acquisitions .......................................................................................... 5

2.2 Common theories of what causes mergers and acquisitions ............................................................. 6

2.3 UK Specific Motives ........................................................................................................................... 14

2.4 Real Effects of M&A .......................................................................................................................... 14

Operating performance .......................................................................................................................... 15

EVENT STUDIES ........................................................................................................................................... 17

Chapter: 3.................................................................................................................................................... 19

UK Banking Evolution .............................................................................................................................. 19

Chapter: 4.................................................................................................................................................... 20

Event Study Methodology....................................................................................................................... 20

4.1 Identify the Event .............................................................................................................................. 21

4.2 Identify The Benchmark Model ........................................................................................................ 21

4.3 Define The Measurement Of The Abnormal Return ......................................................................... 26

4.4 SAMPLE SELECTION & DATA SOURCES ............................................................................................. 32

Chapter: 5 ................................................................................................................................................... 33

RESULTS .................................................................................................................................................. 33

Targets .................................................................................................................................................... 33

Target abnormal returns (AR) and cumulative abnormal returns (CAR) ................................................ 34

Bidders .................................................................................................................................................... 37

Combine .................................................................................................................................................. 40

Summarize of Empirical Results .............................................................................................................. 42

Chapter: 6.................................................................................................................................................... 43

CONCLUSION ........................................................................................................................................... 43

Appendix ..................................................................................................................................................... 45

Bibliography ................................................................................................................................................ 65

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Chapter: 1

Introduction Mergers and acquisitions are a vital part of the corporate finance world. Every day markets are changing and

only the most innovative businesses survive. Using various forms of corporate restructuring - such as merger,

acquisition, bankruptcy and many other forms - the companies struggle to survive and defeat the competition.

Moreover, the speed that businesses cease to exist depends mainly on their size, the influence that these

firms have on the economy and the national security.

The banking industry is one of the most energetic markets for mergers and acquisitions. Many financial

institutions decide to merge because of the changes in regulation and technology. In some other cases, these

institutions decide to merge in order to achieve greater efficiency or to create a more competitive company or

to demand market share.

The literature does not offer any clear indication on whether, on average, the participating financial

institutions yield profit from M&A. In short, previous papers are undecided if an M&A has positive impact on

the shareholders’ wealth and the efficiency of the firm. In particular, a large part of literature state that the

financial institutions made a merger or acquisition so as to develop their efficiency, but they found diminutive

signals of optimistic shareholders’ wealth effects. On the other hand, other papers have come to the

conclusion that mergers and acquisitions have been helpful to develop both efficiency improvements and

enhanced shareholders’ value.

Thus, my research question will be to determine the influents of the mergers and acquisitions on the

shareholders’ wealth in the banking industry. The biggest part of the literature examined M&A at early stages

in the industry consolidation process, mainly for the period mid-1980s through the mid-1990s. So, my original

contribution would be to focus on the 90s-00s. Also, in my study I will use UK firms, due to the position of the

country as one of the largest international financial center, holding one-fifth of all European banking assets.

(Davies R., Richardson P., Katinaite & Manninig M., 2010).

After the introduction, I will continue my study with the chapter two to six. More specific, at chapter two I will

display a literature review, which starts with the basic concepts on mergers and acquisitions and continues

with some common theories about what causes a merger or acquisition in the banking industry on an

international level. Subsequent by chapter three, this chapter is a briefing about UK industry and how it has

changed through M&A. Chapter four is dedicated on the event study methodology which is embraced in this

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paper to carry out the research. At chapter five I will present my empirical results, while at chapter six I finish

the study with the conclusion along with some ideas for future developments on the subject.

Chapter: 2

2.1 Basic Concepts of Mergers & Acquisitions At chapter two, I will present the basic concepts of the merger and acquisition. First of all, at any M&A

transaction there are two parts: the acquirer (or the Bidder) and the acquired (or the Target) firm. Thus, I

display some definitions so as to explain the meaning of “target” and “bidder” firms:

“acquirer” or “bidder” firm is that one that attempts to obtain or merge with another company

“acquired” or “target” company is that one which is bought by the acquiring company (Donald,

DePamphilis, 2011)

The basic types are:

1. Merger

A (forward) merger is a corporate activity under which the target company’s assets and obligations are

absorbed by the acquiring company. So, after the deal is completed, the target company legally ceases to exist

as a separate business entity (Becher, David A, 2000). Also, a few times we can see the “reverse merger”,

which means that the target firm absorbs the bidder one. (Giuliano, Iannotta, 2010). In addition, we use the

definition “consolidation” which means that the firms, which participate in a merger, cease to exist in order to

create a new one.

Acquisition of stock

The first step is to buy stock openly from the target’s shareholders in order to obtain the majority control. The

acquisitions are conducted through private negotiations, if they fail then a tender offer is used. A tender offer

is a public offer made by the acquirer company towards all shareholders of a target company in order to

tender their stock for sale. Usually, this stock is sold at a specified price and during a specified time period.

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(Stowell, David P., 2013)

2. Acquisition of assets

This definition implies the purchase of the target’s company’s assets and the distribution of them proceeds to

the target’s company’s shareholders.

Another classification is that M&A transactions can be described either friendly - when target’s managers

accepted the deal - or hostile - when target’s management does not want to be acquired. (Giuliano, Iannotta,

2010).

At this study, we will not come across hostile M&A as it is focusing on the banking industry which is heavily

regulated and so leaves little room for hostile takeovers (Becher, David A, 2000)

Regardless of the deal arrangements, M&A transactions are also grouped as follow:

Horizontal: in which the target firm is a member of the same sector as the bidder firm,

Vertical: in which the target firm is a member of the same production procedure as the

bidder firm but an altered stage of the line, and

Conglomerate: in which both target and bidder firms are part of unrelated industries.

(Giuliano, Iannotta, 2010)

2.2 Common theories of what causes mergers and acquisitions

The banking industry in an International Level

Previous studies (Bruner, 2002) about M&A focus mainly on the non-financial firms and that has as a result,

the motives not to apply to the regulated financial sector. Thus, I used a literature review constricted to the

M&A of banks.

Based on the literature, I am going to present the main motives of M&A in the financial industry. Each and

every firm has one or more reasons to proceed to M&A, but all of them have as a common purpose the value

maximization (Allen N Berger, Rebecca S Demsetz, Philip E Strahan, 1999). Furthermore, in the financial

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sector, the M&A participants expect profits- through decreasing costs and aggregate revenue- after the

corporate activity.

1. Economies of Scale

The cost reduction that arises with increased output of a product. Also, the economies of scale formed

because of the inversion of the relationship between the quantity produced and per unit fixed costs. It is

important to state that Economies of Scale may reduce the average variable cost, due to the operational

synergies.

So, in the M&A framework the combinations of two or more financial services firms will potentially reduce the

cost and increase the value of shareholders. According to the report of “The Group Ten”, the economies of

scale could be considered as a motivation, only if it is negative correlated with the size of the bank. Thus,

when the firms which participate in an M&A are small or medium size firms, then the researchers cannot

consider the “economies of scale” related also for large banking organizations.

In an algebraic aspect, the research portrays that the scale economies exist in the retail commercial banking

sector and finds that the cost curve is usually a flat U-shaped with a lowest point under $10billion dollars

subject to the country that we study (Dean Amel, Colleen Barnes, Fabio Panetta, Carmelo Salleo, 2004).

A more recent study of Goisis et al(2008), also confirms the previous remark(Gianandrea Goisis, Maria Letizia

Giorgetti, Paola Parravicini, Francesco Salsano, Giovanna Tagliabue, 2009)

However, Hughes and his colleagues believe that scale economies are present in the large banking deals as

well. But then again larger banks confront higher risks and so increase the cost making off any economies of

scale indications. So studies that do not take into account the “differences in banks capital structure and risk

taking” (Bartholdy, Jan; Riding, Allan, 1994)cannot perceive them.

2. Economies of scope

Economies of scope are conceptually similar to economies of scale. The economies of scope refer to reducing

the average cost for a firm, producing two or more products. There are two types of these economies: the

first type is associated with the cost and the second one with the revenue (Dean Amel, Colleen Barnes, Fabio

Panetta, Carmelo Salleo, 2004)

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Cost scope economies derive from the offering of more than two products which can be created from the

same fix costs incurred in gathering information database or computer equipment.

Revenue scope economies, on the other hand, are based on "cross selling to an existing customer base" (Dean

Amel, Colleen Barnes, Fabio Panetta, Carmelo Salleo, 2004). The works on economies of scope in the banking

industry M&A are narrow. Nevertheless, a big portion of studies have not detected a strong association

among scope economies and banking products or among “on-balance sheet and off-balance sheet bank

products” (Mester, 1996). The main reason for the lack of proof is the challenging task of measuring the

economies of scope seeing that we require a benchmark, which should involve only one product financial

institution. The absence of such institutions in the real world generates reservations on the reliability of

outcomes (Dean Amel, Colleen Barnes, Fabio Panetta, Carmelo Salleo, 2004)

However, the findings depend on the country we are studying (Belén Dıaz Dıaz, Myriam Garcıa Olalla, Sergio

Sanfilippo Azofra, 2004). If the sample of our paper is based on the American market, such as (Humphrey,

Lawrence B. Pulley and David B., 1993), (Allen N. Berger, Gerald A. Hanweck, David B. Humphrey,

1987)and(Allen N. Berger, David B. Humphrey, Lawrence B. Pulley, 1996), thenthe studies have not found

evidence of any connection. Nonetheless those studies took place before Gramm-leach Billey act that means

that the future results might be different (Mester, Loretta J., 2005).

On the other hand, studying European bank mergers we observe the present of the scope. But it is more

statistical significant for the superior European banks due to the second European banking directive (Laura

Cavallo, Stefania P.S. Rossi,, 2001)

3. Efficiency of management

As with any other company, banks have their shareholders and their managers. The main objective of the

shareholders is to maximize their wealth; however the wealth depends on the value of shares. The value of

shares fails in the hands of the managers.

This happens either due to the agency problems, which are created (Donald, DePamphilis, 2011) since the

objective of the managers contradicts with the shareholders objective, or due to the lack of experience, which

leads to the failure of share value maximization.

Shareholders believe that a merger can treat these problematic points of management either by threatening

the manager’s stability of employment creating additional motives to keep high standards or by importing

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more experiences managers from the acquiring company that will cut cost, increase sales and pursue

unexploited prospects. Once more we find mix reviews of the validity of this motive. Under this theory poor

management can reflect as low efficiency and so based on this assumption we are expecting a larger more

efficient institution to tend to acquire a smaller less efficient (All991)most studies do agree (i.e. (Wilson, David

C. Wheelock and Paul W., 2000)but we can find exceptions like the paper of (Rhoades, Timothy H. Hannan and

Stephen A., 1987)that testing fail to support the theory.

4. Market power

An additional motivation is the impact that a combination of financial institutions could have on their market

power. In general, we tend to see that an M&A will increase the market power which provides pricing control

and elimination of competition (LOWINSKI, SCHIERECK, THOMAS, 2004)

The market power has three sources:

• Product diversity

• Obstacles to entry the sector

• Market segment

So the simple increase market share from M&A is not enough since new corporations will try to enter and

push “prices towards marginal costs”.

But the nature of the banking sector is highly regulated and working in a national oligopoly (Vennet, Rudi

Vander, 1996)provides the opportunity to take advantages of the market power.

An increase in market power will give the firm the control of the pricing and move their profits upwards. More

specific the market power allows the bank to decrease their costs by renegotiate the conditions of their

interest cost of their liabilities and at the same time rise their revenues generated by depository services fees

(Dymski, Cary, 1999).The renegotiation of interest costs is due to the changes made to their creditworthiness.

The banks (or any financial firm) aim to gain the additional credentials through the market extensions so as to

receive or increase their credit rating. The higher the credit rating, the more access the firms gains in the

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capital markets and more important at a lower cost (Zingales, Steven N. Kaplan and Luigi, 1997).

We can understand the M&A can provide relative high gains to the financial firm but it is difficult to achieve

and sustain them. As mention the banking industry is highly regulated and so any M&A has to be approved by

the authorities’ organization in each country. In many cases the national organizations delay the requests in

order to protect the health of the market. And even if an M&A is approved and the firms get their gains they

will always be a next corporate activity that will change the oligopoly dynamic.

Overall studies like Gilbert’s survey (1984) that review 45 readings found that 27 of them provide evidence of

the advantages of the market power, but naturally the levels of market power gain is linked to what other

features will be consider stable (Berger, 1995).

5. Risk diversification

Financial firms create M&A in order to achieve risk diversification. M&A can provide a lower level of risk under

the assumption that the cash flows of each participant into the M&A is designed to be negatively correlated.

This means that if one party faces financial problems the other one’s cash flow should not be influenced and

ideally could be able to compensate the losses. It can be achieved either by product diversification or

geographical diversification. (Ittner, Constantinos C. Markides and Christopher D., 1994)

The theory states that the risk that the bank faces should be able to shrink if it (and any firm in this case)

undertakes new product lines whose gains have none or lacking correlation to the banks’ existing product

gains (Dymski, Cary, 1999). This is the product diversification, the geographical diversification is conducted

under the assumption the gains from financial instruments issued in different locations possibly will have

comparatively smaller or adverse correlation (Dymski, Cary, 1999)

The diminution of risk is not always a guarantee outcome and it could have narrow prospects, as it is vastly

related to the nature of the newly chosen actions. This happens due to the fact that the financial firms could

enter into segments having little skills and know-how(Dymski, Cary, 1999). Simultaneously, it is necessity the

new activities be highly observed thus creating further expenses (Dymski, Cary, 1999). Overall the absence of

experience and the supplementary costs would have the reverse impact on the firm.

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6. Reductions tax obligation

Another motivation for an M&A is the reduction of tax obligation.

When the M&A is completed successfully we will see a formation of wealth if the combine’s entity tax

obligation is lower than the summation of the individual firm’s tax obligation (Rezaee, Zabihollah, 2001)

The lower level of tax obligation can be created through the accounting procedures as follow(Donald,

DePamphilis, 2011):

a) Buyers have the ability to counterbalance any future profits that were produced by the

combined firms with the accumulated losses of the target firm.

b) Future tax obligations may be reduced with the assistance of the acquired firm’s unused tax

credits.

c) Under the accounting rules, the consolidated statements must present the targets assets in

their market value and not in their book value. In this way, it will be most likely to reduce the

value of the assets by lowering the taxable income.

Lastly, the corporate activity transaction may be categorized as tax-free reorganization,ifit is planned properly.

As for example in the United States, the Internal Revenue Code offers a tax exception for the trade of stocks

(in a stock-for-stock transaction) that has the purpose of restructuring the company.

7. Impact on Revenue

Most of the above have the ability not only to affect the cost of the firm but also could potentially increase

the revenue. The revenue of a financial firm comes from the customers and so a larger number of customers

will generate higher revenue. The product diversification will give the ability to the financial firm to serve a

larger selection of product lines and have the ability to present “a one-stop shopping” experience to the

customers which is a popular demand as seen from the below figure:

Thus, there are two ways to increase the revenue: either by offering more products to the same customers or

by attracting new ones. In the same way, geographic diversification and the increase in the market share will

also attract a larger group of customers due to the larger exposure of the financial firm(Ten, 2001).If the

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customers of a bank are paying high fees again, it positively affects the revenue. The market power as we

mention earlier can provide the opportunity to adjust the fees of the customers.

Overall the larger size of the firm will provide the ability to service a wealthier class of customers which will

provide more revenue.(Ten, 2001).

(Dymski, Cary, 1999)literature review suggests “that bank takeovers may increase banks franchise value for

their shareholders”. Franchise value is practical the incomes from the financial firm’s business actions adapted

for a leverage factor and for the worth added form public provisions. The public provision can be divided in to

three categories:

a) The deposit insurance, which in severe circumstances of bankruptcy of the bank, the funds of

the customers are up to one point insured.

b) The “too-big-to-fail” guarantee that is applied for large commercial banks. Because large banks

cannot be tolerated to fail as this will create a domino effect that will destabilize the market

(Dymski, Cary, 1999)(Resent domino affect can be seen after the collapse of Leman Brothers )

c) The assurances delivered by the government

(Boyd, John H., Graham, Stanley L., 1991)present the option that financial firms seek the TBTF statues in order

to grant them self a larger access to the government safety net. A bank forecasting to take advantage from

the too-big-to-fail protection would display risky positions in their portfolios. (George J. Benston, William C.

Hunter and Larry D. Wall, 1995).

8. Non-maximization motives

As we see all the above elements aim to the shareholders gains. However, for many researchers, there is

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evidence that these elements do not apply or their affect is not as strong as we thought. That is why

researchers refocus their search and introduce the non-maximization motives.

One of the key player of those motives is the distinction of the managers and shareholders objectives (the

agency problem). The main objective of managers is to increase their own personal wealth, which is tied up to

his salary and his position. We also need to remember that managers are humanbeingsthat means they want

to accomplish human needs and strive for prestige (empire-building objective). All of those objectives during

an M&A are in risk.

More extensively, during an M&A the impact of a manager’s reward is of a great importance. A manager

might be forced to accept less responsibility or even leave, as his services are not needed. So a manager

should make a choice between his own and his shareholders’ objectives (Charles Hadlock, Joel Houston,

Michael Ryngaert, 1999). One manager might be redundant butat the same time another one gains more

responsibility and so greater rewards mainly as an increase in his salary.In his study,(MURPHY, KEVIN J.,

1999)approved a positive connection between the manager’s wage and the size of the company. The size of

the company also positively stimulates the exposure of the firm to the media. Being in the head of a firm with

this level of exposure most likely increases the status and power of the manager (Scherer, David Ravenscraft

&, 1987).

The market is under the impression that larger firms acquire smaller and therefore a large bank has less

chancesto be the target in a hostile takeover. Thus, an M&A might be designed by the management team in

order to increase their job security through the increase of size. Also, many times managers prefer a defensive

acquisition making the first step, so as to be protected from a potential hostile takeover, which could result in

the loss of their positions. This argument is supported by (Jeff Madura, Kenneth J. Wiant, 1994)whose findings

report that the shareholders’ wealth dropt,because of the negative impact the M&A had on the firm, as it was

motivated by the objectives that bidder’s management team had.

Government is another player that encourages an M&A and does not take into account the shareholders’

wealth. It is known that governments undertake the safety-net for banks. Besides that, during the crisis

governments deliver motives for the alliance of troubled institutions (Allen N Berger, Rebecca S Demsetz,

Philip E Strahan, 1999) or governments might themselves obtain a distressed institution .Resent example can

be the British government that announced: “The Government is making capital investments to RBS” (Treasury,

2008)on 13 October 2008, in order to recapitalize RBSdue to the crisis. The British Government would make

an investment of up to 58% in the Group. However, even if the goal was to make available new tier 1 capital

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to UK banking industry, in order to support the real economy and restructure the financial industry, at the end

the government possessed further than 57% of the bank's equity share capital.

But the government can disheartened an M&A as their in place regulations limitations. Within the UK legal

context mergers among banks can be obstructed when they are observed to border antagonism.

2.3 UK Specific Motives As my research is focused on the British Banking sector it would be helpful to observe the specific motives

that the financial industry has in each country. A survey was conducted for the Group Ten report in which 5

people, who held different position within the industry, were interviewed.

The overall results divided the motivations in two motives: firstly, for M&A classified by the sector and

secondly, for M&A outside the sector. Within the sector, they believe that the important motive was the cost

saving due to the growth of size. Second significant “key” was the rise in income due to increase in size.

Except of these two very important motives, there are also some others, such as the market power and

empire building. However, across sectors M&A produce alter rankings with revenue increase due to size and

product diversification leading followed by managerial empire building. Cost saving was characterized as

“slightly important”(Ten, 2001).

2.4 Real Effects of M&A We have examined the motives of the M&A and now we will follow with a presentation of the most important

results on the banks efficiency from the M&A that the literature has to offer.

It would be helpful to give a comprehensive definition of the efficiency of a bank.

Firstly, it’s an extensive notion that can be linked to various features of a company’s activities. So a firm would

be characterized as cost efficient if it was able to diminish costs for a certain amount of production, and a firm

would be characterized as profit –efficient if it exploits profits for a certain amount of input and production

(Ten, 2001).Financial gains ,generated by M&A, have been analyzed by comparing the before and after levels

of performances keeping track of the accounting ratios or more complicated frontier based on or using event

studies to calculate the abnormal return on the announcement day.

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Operating performance The operating performance methodology was primary used to research costs and efficiency. Operating

performance usually analyze the variations in accounting profit or cost ratios before and after the merger and

acquisition event (Robert DeYoung, Douglas D. Evanoff, Philip Molyneux, 2009), nevertheless in the literature

we come across papers like (Jane C. Linder, Dwight B. Crane, 1993) paper that their methodology is based on

comparing the company’s merger or acquisition results with a controlled group of non –merger companies.

The literature on the operating performance can be divided into the ones that practice univariate t-test and

into those that estimate efficiency measured by cost or profit efficiency frontier (Rhoades, Stephen A., 1994)

Univariate t-tests evaluate profitability ratios (e.g. return on assets (ROA) and return on equity (ROE)) and cost

ratios (e.g. cost per employee.).The flaw of the studies that use this approach is that due to the design of the

accounting ratios the results cannot differentiate the impact from the changes in market power and changes

in efficiency. A second weakness is created from the fact that there is some level of wrongly estimation

regarding various product mixes, since some charge more to produce that others (Allen N Berger, Rebecca S

Demsetz, Philip E Strahan, 1999). That is why researchers focused on the use of the efficient frontier.

The efficient frontier approach is a more complex methodology that compares individual financial firm’s level

of cost (or profit) to the level of cost (or profit) of the optimal practice of the industry. This benchmark point is

calculated with the assistants of statistical methods using the inputs, production and prices of each individual

institution as factors producing an efficient frontier. So the distance of the firms results from the frontier

would negatively relate to its efficiency (Ten, 2001). But this methodology has its weakness as in this method

the period chosen to be one to six years after the merger it is not realistic to assume the results are not

infected from other factors besides the M&A, a second weakness is that it does not use economic measures

but accounting (Rhoades, Stephen A., 1994).

The consequences of the consolidation is not all reported to be the clearly negative or positive. The impact of

the consolidation on cost efficiency differs by country (Ten, 2001)but also by the time period examined

(Berger et al., 1999).That is why I have tried to discuss the literature divided by country and in order

depending on the time period that I examine.

The discussion starts with studies on the United States market. It is noted that studies that as a data period

have M&A from the 80s find small or no cost efficient evidences. (Berger, Allen N.; Humphrey, David B,

1992)study the United States megamergers (both parties had more $1 billion in assets) in the 80s cause as

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they clam those where the firms that will emerge from this consolidations will in the future dominate the

sector. Their analysis finds that the mergers could potentially create cost efficiency. The analysis on the real

outcomes presents a different story that on average the was no great progress on the cost efficiency levels as

“The average X-efficiency improvement was less than 5 percentage points and was not statistically

significant”(Berger, Allen N.; Humphrey, David B, 1992). The X-efficiency is the effectiveness with which a set

of inputs is used to create outputs. The small X-efficiency gains in combination with the large diseconomies

produced a slight deterioration in cost efficiency

(PERISTIANI, 1997)analyzed 4900 merger transaction that occurred between the 1980 and 1990 and this large

sample concluded that “during the 1980s, mergers were not beneficial to banks in terms of X-efficiency”.

Whereas the reports that obtain their data from the mergers of the 1990s deliver diverse results. A

representative example is Rhoades (1994) that examines one set of nine studies of M&As of large US

organizations, majority of being in-market merger.

Concluded that in all cases the merger resulted in significant cost reduction as expected in all cases, but out of

the nine mergers only four mergers were obviously improved in their cost efficiency.

An additional study establishes slight improvement in average cost X-effciency for merger of small or larger

financial institutions(Berger, Allen N., 1998). Some paper propose that the cost efficiency is subject to the type

of acquisition, the incentives behind it and the management team behavior (Berger et al., 1999).More resent

study of Kwan and Wilcox

(2002) found indications of important cost drops of U.S. bank consolidation (8032 bank mergers) throughout

the 1990s (1985- 1997), however only when adjusting the records for consolidated accounting rules.

Studies that focus is the American market using profit efficiency estimators present a positive reaction to the

consolidation. More specific the (JALAL D. Akhavein, Allen N. Berger, David B. Humphrey, 1997)paper

examines all United States banking firms mergers from 1981 to 1989 and derives that due to the increase in

size there is a significant profit efficiency improvement that cannot be detected by the studies that focus on

cost efficiency .Because the profit efficiency examines all the cost efficiency variations plus the variations

made to the production of the bank after the merger.

(Ken B. Cyree, James W. Wansley, Harold A. Black, 2000)robust this conclusion as through this research he

compares the different growth strategies and reports that the firms that choose not to grow or decided to

grow via branching or product expansion underperformed compare to financial firms that choose acquisition

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growing strategies. More specific “banks which include bank acquisition as a part of a growth strategy from

1987-1991 have positive and significant changes in performance in the 1993-1997”(Ken B. Cyree, James W.

Wansley, Harold A. Black, 2000)

The focus of the study turns to examine the merger affects in the European area.

One great event that affected the all the European countries (directly or indirectly) is the begging of the EMU.

That is why (Huizinga et al., 2001) choose as his sample period the 1994-1998. And they report for 53

European banks that the cost efficiency is positively touched by the merge whiles the profit efficiency was

only slightly enhanced. Other pan-European researches support his findings such as (Belén Dıaz Dıaz, Myriam

Garcıa Olalla, Sergio Sanfilippo Azofra, 2004). Díaz et al. (2004) also discuss other aspects of M&A as bank to

bank merges achieve higher levels of efficiency and (Yener Altunbaş, David Marqués, 2008)generalize the

thought by putting forward that merging banks which have parallel strategies achieve higher levels of

efficiency and profit performance. Díaz et al. (2004) in the same paper suggest that the impacts appear some

years after the merger. Studies based on individual European countries endorse the conclusions commencing

from the pan-European based studies (Robert DeYoung, Douglas D. Evanoff, Philip Molyneux, 2009)

Next we are presenting the relative literature review of the European county that is the subject of this paper

the United Kingdom.(John Ashton, Khac Pham, 2007) study of 61 UK bank mergers concerning 1988 to 2004

establish efficiency enhancements on average, but minor indications that cost savings were created by

decreases in retail deposit rates. Past academic literature arising from UK’s building society mergers such as

(Michelle Haynes, Steve Thompson, 1999) paper indicate both negative and positive performance impact.

EVENT STUDIES The event study methodology exploiting information from the financial markets is able to calculate the

influence of a definite event on the wealth of the financial institution. In case of efficient financial markets,

the stock market reactions to M&A announcements could help the prediction of mergers’ future profitability.

This approach, which is named as “the event study methodology”, was developed at the 1970s and is broadly

accepted, despite its limitations and some caveats on its applicability. (Tomaso Duso, Klaus Gugler, Burcin

Yurtoglu, 2010)

18 | P a g e

The overall conclusion of the event study method is that typical the combined shareholder’s wealth is

unaffected by the declaration of the M&A due to the fact that the buyer undergoes a loss that compensates

the gains of the target. Consequently, M&As denote an allocation of wealth to the targets bank shareholders

from the shareholders of the buyer. (Ten, 2001)

Furthermore, we can see papers like (Joel F Houston, Christopher M James, Michael D Ryngaert, 2001)who

examine major bank mergers concerning the time period 1985 to 1996 and were unsuccessful to discover if

mergers generate worth for large banks.

A reason for the various results could be the natures of the merger. As we can see in DeLong (2001) separates

his sample and reports different results for each sub-category. More specific,the mergers that where between

banks with alike strategies about their products and geography, enhanced the return of the stockholder by

3% .But mergers that are conducted for diversification and so the parties firms have no similarities do not

produce gains for the shareholders. Also, (J.Harold Mulherin, Audra L Boone, 2000)stated that deregulation

plays also an important role. In particular, they referred that in the 1990s, deregulation was directed toward

sectors like banking. Past research has often excluded banking industry due to heavy regulation, so the

removal of the regulatory burdens in banking industry allows it to become part of mainstream merger analysis.

Next, I noticed a restricted number of research that focus on the European bank M&A and their impact on the

shareholders wealth. Overall they present a positive impact.(Alberto Cybo-Ottone, Maurizio Murgia,

2000)using the event study method on 54 M&A deals whose assets where higher than 100billion and was

completed from 1989 to 1997 where able to note that the average M&A presents a growth in value, at the

period of the transaction’s announcement. Their study covers 13 European countries in addition to the Swiss

market and furthermore covers deals that the banks open out to the insurance market or investment banking.

This allowed them to report that domestic bank-to-bank deals and domestic banks with insurance companies’

deals create a higher level of positive abnormal return but at the same time mergers among banks and

securities firms and deals across different countries failed to report any gains.

The overall positive abnormal return for the shareholders is also proven form (Joel F. Houston, Michael D.

Ryngaert, 1994) . In their paper, they uncover a number of findings concerning acquisitions of publicly traded

banks during the period 1985-1991. First of all, there is no clear positive revaluation of the combined bidder

and target values at the time of the announcement of the event. A possible explanation is that positive returns

to targets are cancelled out by negative returns to bidders. However, they concluded that total returns display

19 | P a g e

an increase in recent years. Also, they stated that bidder banks use to be more profitable than target ones and

more lucrative than other banks in their industries. Last but not least, the market responds more beneficial to

M&A announcements by banks with a good operating performance in past years.

(Marcia Millon Cornett, Hassan Tehranian, 1992)examine the post-acquisition performance of large bank

mergers between 1982 and 1987. They find a significant correlation between announcement- period

abnormal stock returns and the various performance measures, showing that market participants are able to

identify in advance the improved performance associated with bank acquisitions.

Furthermore, (Alberto Cybo-Ottone, Maurizio Murgia, 2000) there are studies which are associated with the

stock market valuation of M&A in the European banking industry. These studies are based on a sample of

large deals which were observed for the time period 1988-1997. Thus, they noticed a positive and significant

increase in value for the average merger at the time of the event announcement. Moreover, their results were

different from those reported for US bank mergers. An explanation for these different results could stem from

the different structure and regulation of European banking markets, which have more similarities between

them than compared with the US markets.

Moreover, as (José Manuel Campa, Ignacio Hernando, 2006) who inspected 244 European banks M&As from

1998 to 2002 separate their results for the wealth of bidder and target firm. For the target firms, they note

that near the announcement bringan increase in their shareholders returns and two years after the deal they

improve their financial performance. For the bidder bank, on the other hand, they note zero abnormal returns

near the announcement. And finally a year after the deal the abnormal returns for the target and bidder firm

where around zero. The results can be explained from the fact that the target banks were cost efficient.

Chapter: 3

UK Banking Evolution In the United Kingdom 300 banks and building societies are permitted to accept deposits. Nevertheless, there

is a noticeable consternation of the supply of retail banking services. This is visible if ones thinks that the four

large UK banking Groups are now owners of the fifteen out of the sixteen cleaning banks existing in 1960.The

20 | P a g e

four big groups, alongside with Nationwide and Santander form near to the “80% of the stock of UK customer

lending and deposits”(Davies R., Richardson P., Katinaite & Manninig M., 2010)

Figure 3 M&A in the British Banking System from 1960 to 2010. Source Davies et al. (2010)

Chapter: 4

Event Study Methodology The main objective of the Event Study methodology will be to compare the level of returns of the stock around

the event date and the expected return if there had not been the event. The difference between the realized

return in the event period and the expected returns is referred to as the abnormal return(Halpern, 1983).This

21 | P a g e

is conducted under the notion that the impact of an event will be reflect on the price of the security, and the

level of the abnormal return will represent the level of impact that event had on the wealth of the firm.

The event study methodology can be simplified in to 3 steps:

1. Identify the event of interest and in particular the timing of the event.

2. Specify a "benchmark" model that will reflect the expected stock return behaviour.

3. Calculate and analyse abnormal returns around the event date.

4.1 Identify the Event Following the first step, the event that is of importance in my research are the announcements of M&A in the

banking industry. Identifying the actual date of the merger and acquisitions has been completed as the event

date would not yield meaningful results, as the takeover is usually announced a long time before and potential

changes in the value of the target and bidder firms should already be reflected in the stock price. It is much

more interesting to see what happens on the day that the takeover plans become public knowledge (the

announcement day)

4.2 Identify The Benchmark Model In the second step we must define as a "benchmark" model for the expected (normal) stock return

behaviour of the acquiring and target bank. In the literature there are many different models has

gathered some and I will presentment them shortly below:

a) Constant Mean Return Model

Under this model the expected return is based on:

𝑅𝑖𝑡=𝜇𝑖+𝜁𝑖𝑡

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𝐸(𝜁𝑖𝑡)=0

𝑣𝑎𝑟(𝜁𝑖𝑡)=𝜎2𝜁𝑖

Where

𝑅𝑖𝑡 ∶ is return of stock ifor the day t

𝜇𝑖: is the mean return of stock i.

𝜁𝑖𝑡: is the disturbance term for stock iwith an expectation of zero and variance 𝜎2𝜁𝑖on day t

b) Market Model

Market model is a statistical model which connects the returns of the stock to the market return. And

so for any stock ithe market model is:

𝑅𝑖𝑡=𝛼𝜄+𝛽𝑖𝑅𝑚𝑡+𝜀𝑖𝑡

𝐸(𝜀𝑖𝑡)=0

𝑣𝑎𝑟(𝜀𝑖𝑡)=𝜎2𝜀𝑖

Where

𝑅𝑖𝑡 ∶is return of security ifor the day t

𝑅𝑚𝑡 ∶is the market return for day t

𝜀𝑖𝑡 :is the zero mean disturbance term.

𝛼𝜄, 𝛽𝑖 : are the parameters of the market model

23 | P a g e

c) Other Statistical models

Other statistical models have been suggested to model the expected return. A general one is the factor model.

The market model introduced earlier is an example of a factor model. We can also have multifactor models by

introducing the industry index in the model.

d) Economic Models

Economic models can be seen as statistical models redesign to calculate more constrained returns. Two

commonly used economic models are the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory

(APT).

The CAPM presented bySharpe (1964)is an “equilibrium theory where the expected return of an asset is

determined by its covariance with the market portfolio”. The APT presented by is an “asset pricing theory

where the expected return of an asset is a linear combination of multiple risk factors”.

For this study the main model I will use is the market model as presented by(Stephen J. Brown, Jerold

B. Warner, 1985). This decision was made on the bases of advantages of this model and

disadvantages of other models. More specific the CAPM economic model was more used in the 70s

but due to the fact that the event study would also be subject to the assumptions of the economic

model the use of them has come to an end. The APT economic model has been compared to the

market model and has been found that since the main factor of the APT acts like the market factor

with the additional factors adding no important contribution there is little advantages in using the

APT compare to the market model. The advantage would be the elimination of bias in the testing.

But the statistical models similarly remove these bias and since the APT needs more data to be

conducted compare to the market model the advantages are insignificant. The constant mean return

24 | P a g e

is one of the simplest of models however as (Stephen J. Brown, Jerold B. Warner, 1985) discovery

does produce similar results to the more complicated models. The market model is an enhancement

of this model as it removes the part of the return that is connected to deviation in the market’s

return. Making the event study’s capability to notice the event disturbances to increase. Also most of

the studies examined in the literature review where conducted with the use of the market model so

it will be easier to compare.

The advantage to practice the market model will rest on the 𝑅2 (coefficient of determination) produced by the

regression. The greater R2, the greater variation of the abnormal return will derive from a higher volatility of

the slope coefficient. Cause of the claim for robustness reason I also calculate the Market adjusted returns

model and the Constant Mean Return Model.

I will continue with introducing in depth the three models that I will use. But first I will present the

event window that I will be studying. .

The above graph indicates the event window, which is the period that I am interested in seeing the fluctuation

of the abnormal return. The reason behind including days before the official announcement of the merger and

acquisition in the press, is due to the unofficial leak of the information prior to the official announcement that

has being recorded in previous studies.

First of all, so as to estimate the normal return of a stock (NRi), I need to define an estimation period, known

as “estimation window” [T0, T1] which proceeds the event period [T1, T2].An estimation period is usually

selected prior to the event window in order not to overlap. So, I can consider the stock return during the

estimation period as the normal stock return, but the estimation period should be long enough. The choice of

the estimation period is arbitrary. Brown and Warner have used 35 month as the estimation period, while

(Martynova, M, Renneboog,, 2006)used 240 days.

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So, I finalized my event window to be equal to 45 days (T = [-22,+22] ), where t=0 will be the announcement

day of the M&A provided from the transaction report on SDC. This is done in order for the parameters to be

unaffected by the event. For my study I will follow (Martynova, M, Renneboog,, 2006) and I will have the

estimation period -214 day to -44 day.

My main model will be the market model which is a statistical model where the return of the market is linked

to the return of any stock. The model assumes “linearity, homoscedasticity and independence in stock returns”

(HART, J. R. & APILADO, 2002). (Eckbo, B.Espen, 1983)also claims “the regression coefficients of the market

model reflect systematic co-movements of the share return with the return on the market portfolio while the

serially uncorrelated zero mean error term picks up the impact of non-market factors (such as firm- or industry

specific) information events and random fluctuations” and according to (Norman, Strong, 1992) is the most

commonly used.

So the market model examined will be:

𝑅𝑖𝑡= 𝛼𝜄+𝛽𝑖𝑅𝑚𝑡+𝜀𝑖𝑡

An OLS-regression model is used to estimate parameters 𝛼𝜄 and 𝛽𝑖 for each stock i. The parameters are

estimated during a period prior to the event window and are referred to as αiand𝛽i.

𝛽�� =∑ (R𝑖𝑡

𝑇1

𝑡=𝑇0+1− ��𝑖)(Rmt − 𝜇��)

∑ (Rmt − 𝜇��)𝑇1𝑡=𝑇0+1

2

αι=μi−βi𝜇��

σεi2 =

1

L1−2∑ (Rit

T1t=T0+1 − ai + βiRmt )

2

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Where

μi=1

L1∑ Rit

T1t=T0+1

μm =1

L1∑ Rmt

T1t=T0+1

Expected returns 𝑅𝑖𝑡 are calculated as follow:

��𝑖𝑡=𝛼��+𝛽��𝑅𝑚𝑡

4.3 Define The Measurement Of The Abnormal Return

In the third step I must define the measurement of the abnormal return. The abnormal return is the after the

event actual return of the security over the event window minus the expected return of the firm if the event

had not occurred over the event window. For a stock i on day t the abnormal return is

𝐴𝑅𝑖𝑡 = 𝑅𝑖𝑡 − 𝛼�� + 𝛽��𝑅𝑚𝑡

Where: 𝐴𝑅𝑖𝑡 is an abnormal return.

As recommended by (Steven J. Pilloff, Anthony M. Santomero, 1996) and by(Alberto Cybo-Ottone, Maurizio

Murgia, 2000)there will be no adjustments in the case for multiple bidders.

There are papers like (Pinches, Roger P. Bey and George E., 1980)that present the market modelhas

disadvantage which is that it has been misspecified. They show that heteroscedasticity appears to be

widespread when the market model specified in equation is employed. As a result, the market model presents

wrong figures for the abnormal return.

27 | P a g e

Under this sensitivity of the market model I applytwo additional models.

Additional Models:

1) The market adjusted returns model

(Stephen J. Brown, Jerold B. Warner, 1980)discussed that the market adjusted returns model “takes into

account market wide movements which occurred at the same time that the sample firms experienced events.”

Thus, the abnormal return is produced by the difference between market index return and each one security

return. Since the market adjustedreturns model can be observed as limiting the market model parameters

��𝜄to be zero and ��𝑖 to be one.

So the formula is as follow:

𝑅𝑖𝑡= 𝑅𝑚𝑡+𝜀𝑖𝑡

Expected returns Rit are calculated as follow:

𝑅𝑖��=𝑅𝑚𝑡

With the abnormal return is

A𝑅𝑖𝑡=𝑅𝑖𝑡−𝑅𝑚𝑡

However a disadvantage of this model is that it relies heavily on the choice of market index. Additionally the

use of the market index is unrealistic, as different stocks have different level of risk, adding to the creation of

miscalculated abnormal return.

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On the other hand, one of the advantages of the market adjusted model is that it recurs minor volume of data.

A second advantage is that using daily data it eliminates the biases of the market model parameters as it does

not use model parameters.

2) Constant Mean Return Model

According to the constant mean return model the expected returns of each security are equivalent to the

mean average of the returns in the estimation period

𝑅𝑖=1

𝑇∑ 𝑅𝑖𝑡

𝑇2𝑇1

And so the abnormal return is: ARit=Rit−𝑅𝑖

The constant mean return model is characterized by researchers as “naive model”, because the risk and

market wide factors are not accounted for and so the assumption is unrealistic. However, this model has the

advantagethat it needs less data than the market adjusted model, because we do not need any market index

data. And again we do not need to calculate any model parameters compare to the market model.

Cumulated abnormal returns (CAR)

(JOHN D. LYON, BRAD M. BARBER, and CHIH-LING TSAI, 1999)argue that cumulative abnormal returns are a

biased predictor of buy-and-hold abnormal returns. Nonetheless, cumulative abnormal returns have the

advantage that they are less skewed and therefore less problematic statistically.

Cumulated abnormal returns (CAR) for any period [𝑡1;𝑡2] during the event window are designed as follows:

CAR(𝑡1,𝑡2)=∑ 𝐴𝑅𝑡 𝑡2

𝑡=𝑡1

Where 𝐴𝑅𝑡 is the mean abnormal return on the day t

And it is calculated by the following formula:

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𝐴𝑅𝑡 =

1

𝑁∑ 𝐴𝑅𝑖𝑡

𝑁𝑖=1

N: volume of securities

All the above procedures will be followed for two groups of securities which are the targets’ firms’ securities

and the bidders’ firms’ securities. In case that a firm ismember to more than one merger I will contact the

processor for each time it appears as if therewere totally unrelated securities. This is followed in order not to

reduce my sample and create misleading results.

Combine Entities

To examine the joint firm we go along the suggestions by (Joel F. Houston, Michael D. Ryngaert, 1994), who

weight the abnormal returns of the bidder 𝐴𝑅𝑡𝐵 and the abnormal returns of the target 𝐴𝑅𝑡𝐺 by their market

value:

ARt,Transaction=𝐴𝑅𝑡𝐺∗𝑀𝑉𝑡𝐺+𝐴𝑅𝑡𝐵∗𝑀𝑉𝑡𝐵

𝑀𝑉𝑡𝐺+𝑀𝑉𝑡𝐵

As market value, we depend on those detected at the end of the estimation period (in t= -21)

Statistical tests are then invoked in order to test the hypothesis that on average, returns around the event

date are not different from their expected returns. The test that will be used are the t-test and sign test.

Statistical test procedure

I have applied deferent models to calculate the abnormal returns. The next step is to measure the significance

of my results. The key focus of this study is the effect of the M&A on the price of a security and on the wealth

of the shareholders. Due to the way the abnormal returns are calculated, if the event does not affect the price,

then the AR should be equal to zero.

30 | P a g e

So my null hypothesis to test is:

H0: the mean abnormal return is zero

H1:the mean abnormal return is different from zero.

I will test this hypothesis using a t-statistic. (Strong, 1992) assuming that the mean abnormal return is normally

distributed and independent

A t-statistic is found as below:

t=𝐴𝑅𝑡

𝑠(𝐴𝑅𝑡)/√𝑛~t(N-1)

Where

𝐴𝑅𝑡 =

1

𝑁∑ 𝐴𝑅𝑖𝑡

𝑁𝑖=1

And

S(ARt)=√1

𝑁−1∑ (𝐴𝑅𝑖𝑡

𝑁𝑖=1 − 𝐴𝑅𝑡

)2

S(ARt)is the standard deviation of the mean abnormal return across securities on day t

In order to continue I have made some necessary assumptions for the above equations.

Assumptions:

Firstly, the mean abnormal return is the same for all securities.

Secondly, the variance is identical for all securities.

Thirdly, there is no cross-correlation in abnormal returns.

Next, I will compare the absolute t-value that I have calculated with the critical t value at the 0.05 level of

significant for N-1 degrees of freedom and if the t-value is higher than the critical I will reject the null

hypothesis.

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This testing will be conducted for the targets and the bidders firms in each benchmark model. In order to see if

the event influents the targets and bidders companies differently as the literature has mention.

The t- test is a member of the parametric test and if the assumption about the normal distribution is corrupted

it provides weak results about the level of significance. This could result in the “When there is no abnormal

performance, for all of the performance measurement methods the t-tests reject the null hypothesis at

approximately the significance level of the test”(Stephen J. Brown, Jerold B. Warner, 1980).

This is why in the literature in the case of event studies we see the adoption of non- parametric test under

which the sample does not need to have normal distribution. So as to generate more powerful conclusions we

also conduct a sign test, so as to test the following null hypothesis:

𝐻0: the percentage of positive abnormal returns is equal to 50%

With an alternative hypothesis of:

𝐻1: the percentage of positive abnormal return is higher than 50%

Sign Test

(Charles Corrado, Terry Zivney, 1992)pinpointed the superiority of the sign test, if it is specified correctly. The

sign test is a plain version of the binomial test under which the percentage of abnormal returns matches 50%

(Stephen J. Brown, Jerold B. Warner, 1985).

Thus, we are going to compute z-score, using the formula:

Z = |𝑃−0.5|

√0.5(1−0.5)/𝑁

Where:

P: the percentage of positive abnormal return on day t

N: the volume of securities

Under the sign test, the Z-test has a unit normal distribution.

Since it will be a one tiled test, my interpretations on a 5% significant level will be according to a critical value

Z of 1.64.

32 | P a g e

4.4 Sample Selection & Data Sources

The aim of this dissertation is to investigate the effects of the banks merger and acquisition

announcements on the shareholders wealth in the recent years (1990-2013).

The sample selection contains various criteria so as to support the current study. I will be

examining the period 1990 until 2013. This specific period allows me to obtain sufficient

samples of bank mergers and notice any affects from the crisis of 2007. The country that I wish

to examine is the United Kingdom. For this reason, I have stated that the acquirer must be UK

national. Moreover, I have restricted the acquirers to be banks (SIC code: 6000) and the targets

to be members of the financial industry (SIC code: 6XXX). Last but not least, in order to obtain

historical data from reliable sources to conduct the tests, I have implemented that both the

targets and acquirers are public companies.

Also, I used SDC and Datastream program so as to obtain all the above criteria.

In the appendix, there is thoroughly a list with target- bidder firms and the announcement date

(Table 6)

To conduct the event study using the market model I need calculate the return for each and

every bank.

For this reason I used the Datastream to find out the RI total return index and also the stock

return. Also Total Return Index (TRI) has the big advantage that it is composed by the identical

factors for each country and so the estimated coefficient will not be affected by the differences

of the index composition and will allow the comparison among the firms (Alireza Tourani Rad,

Luuk Van Beek, 1999).

Also, I decided to take the daily data as using daily data is widely spread among the empirical

studies which conduct event study methodology and also is more accurate to detect the

abnormal return in contrast to weekly or monthly data.(Alberto Cybo-Ottone, Maurizio Murgia,

2000)

In order to calculate the return of a stock there are two methods:

a. Rit =𝑃𝑖𝑡−𝑃𝑖𝑡−1

𝑃𝑖𝑡−1∗ 100

b. Rit =log (𝑃𝑖𝑡

𝑃𝑖𝑡−1) ∗ 100

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Where Pit is the price of the stock

However, I decided to use the logarithmic approach, as covers both theoretical and empirical

reasons. (Norman, Strong, 1992)

Chapter: 5

RESULTS As I explained previously, I found 91 M&A events. So, at this point I will represent the data for

the targets and bidders extensively.

Targets This study was designed to explore the impact of an M&A event on to the price of the equity. I

will start with the companies that played the role of the target party and I am going to present

and comment the results.

In my sample I have 75 companies in financial sector, characterized as targets. The targets

companies and the announcement dates of event are represented in Table 7 in appendix.

The main research model is the market model. So the following table represents the beta

across securities:

Estimated Mean Maximum Minimum SD Negative Positive

Beta 0.7048 2.269154 -0.12076 0.564466 5.63% 94.36%

(In the appendix you will find the table from which the above results was extracted)

Beta is the slop coefficient of the regression line, stands for systematic risk and represents the

level of volatility of the price stock compare to the market. More specific, when the beta is

34 | P a g e

equal to 1, means that the price of security will move with the market. Also, when beta < 1

displays that the security will be less volatile than the market, while beta> 1 indicates that the

security’s price will be more than the market. Here, we see that the beta is equal to 0,7048. An

explanation for this value, which is lower than 1, could be the thin trading problem. The thin

trading problem appears when in the market there are few trading activities because of the

absence of buyers or sellers. This situation makes the market more volatile. Moreover, the thin

trading problem makes the estimated parameters of the model to be bias. (Jan Barthodly, Allan

Riding, 1994).

Furthermore, the maximum value of beta is 2,26915 and indicates that the prior levels of

pricing for the target firm is consisted with the market’s level of pricing. This applies for all

target firms that represented a positive level of beta. Thus, this higher level of beta could be

translated in to lower levels of abnormal returns for the targets. On the other hand, negative

beta is an indication that the prior levels of pricing have a reverse relationship to the market’s

index pricing.

However, as we can notice the minimum beta is only -0,12076 and the negative observations of

beta are only 5,63% of the sample. So, based to these elements we can clearly state that for

this sample the target securities do have positive relationship to the market performance.

Target abnormal returns (AR) and cumulative abnormal returns (CAR) I will continue my analysis by discussing the daily abnormal returns of target firms from -22 day

up to +22 day around the announcement date of the merger and acquisition (t=0) using the

Market Model, Market Adjusted Model and Constant Model.

Also, in order to find out if these results are statistical significant I calculated t-statistic.

In the following table we can see the AR and t-stat of the target firms over the entire event

windows and more specified periods:

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From the tables above, we can see that the abnormal return (AR) varies according to the day

we observe. This study is focused on the reaction of the shareholders’ wealth to a merger and

acquisition event the day 0 is the most important to notice, so as to conclude to a result.

Thus, from the table of AR, on day zero we have a high and positive abnormal return 3,16%

according to the market model result. This result is verified by the market adjusted model and

constant mean return model which give AR equal to 3,098% and 3,246% respectively.

It is important to state that looking at the days prior to the announcement day (-1 and -2 day)

we can notice a positive abnormal return higher than the previous days level. In the literature

this situation is called “leakage information affect”. The leakage information affect happens

when the certain people in the market know in advance the expected announcement about the

M&A and so their activities are accorded to the particular security.

In addition, as I stated above the statistical significance is extremely important factor. For this

reason we use the t-test, which is higher than 2,60at the announcement day (day 0). This

number indicates a statistical significant result on the 5% level and the null hypothesis is

rejected. However, the sign test reject the null hypothesis only if we use the market model.

Both the high volume of positive abnormal return and the statistical significance at the 0.05

level permit this study to reject the null hypothesis for the day 0 (announcement day). So, we

can state that a merger and acquisition event creates value to a bank in the UK on the day of

the announcement.

Market Model Market Adjusted Model Constant Mean Return Model

Days AR T- STAT SIGN TEST AR T- STAT SIGN TEST AR T- STAT SIGN TEST

-5 0,412576 1,026722 0,23904572 0,45260274 1,18521336 1,28745262 0,263369013 0,659252272 1,0533703

-4 0,533232 2,070551 0,95618289 0,585753425 2,317957878 0,81928803 0,363232026 1,285747574 0,1170411

-3 -0,60083 -0,88587 0,47809144 -0,53027397 -0,82384021 0,58520574 -0,578137837 -0,889815461 0,819288

-2 0,171215 0,424885 1,19522861 0,037808219 0,084770594 0,58520574 0,025834766 0,052528008 1,5215349

-1 0,284005 0,481015 0,95618289 0,138767123 0,232756348 1,52153491 0,092410108 0,143258721 0,3511234

0 3,161944 2,691775 1,91236577 3,098767123 2,711612298 0,81928803 3,246245725 2,761270133 1,5215349

1 2,490631 3,39838 1,19522861 2,511369863 3,725860974 1,05337032 2,369259424 3,41452835 0,3511234

2 0,252916 0,463302 0 0,458082192 0,81471485 1,75561721 0,543369013 0,780304135 1,0533703

3 -0,45998 -1,71751 2,62950294 -0,31068493 -1,04772598 3,16011097 -0,596357015 -2,156621202 3,160111

4 -0,23121 -0,692 0 -0,32328767 -0,88431465 1,52153491 -0,600192631 -1,444058928 0,819288

5 0,57656 1,887236 2,39045722 0,481917808 1,64110866 0,58520574 0,247204629 0,82641528 0,5852057

36 | P a g e

CAR

Period Market Model Market Adjusted Model Constant Mean Return Model [-20,0] 6.096273029 5.632246931 5.862174277

[-10,0] 6.222599714 6.024549606 5.620923812

[-1,0] 3.445949286 3.237534247 3.338655833

{0} 3.161944286 3.098767123 3.246245725

[-1,+1] 5.936580143 5.74890411 5.707915257

[-10,+10] 10.697432 10.76646741 9.245572842

[-20,+20] 9.424490114 9.049918164 7.121609323

We continue by introducing the cumulative abnormal returns. The CAR is the sum of the

movement of the return during a specific period.

The three tables above display the CAR for the three models. It is clear that the shareholders of

target firms earn in all analysed event windows as the CAR results are highly positive and

significant. This is consisted with the empirical results of the literature. Also, a similar study for

US banks leads to the same results. In particular, (Marcia Millon Cornett, Hassan Tehranian,

1992) reported an average of 8% for the period [-1,+1], while we found an average of 6% for a

two-day excess return. Our sample of UK bank mergers can be also compared with that

constructed by (Alberto Cybo-Ottone, Maurizio Murgia, 2000), who studied the European

banking Industry and reported an average market revaluation of target bank of about 12.93%.

Furthermore, we can observe that symmetric CARs – using the same number of days before

and after the announcement day – are close or almost equal to CARs computed before the

announcement. In other words, we note some information leakage for bank M&A deals.

Examining closer at the days around the announcement, it is obvious that the event has

affected the value of the target’s abnormal return. In addition, because of the large movement

in a short period of time, it is clear that the event, which occurred in those few days, was the

driving force.

Summarizing the analysis of the target’s abnormal return we understand that the

announcement of the M&A event has a positive effect on the wealth of the bank’s shareholder

in the UK. This result is in line with the results mention in the literature review.

37 | P a g e

Bidders

I will carry on a similar analysis for the firms that played the part of the bidder in the merger

and acquisition event. So, in the appendix there is also the table with the Bidders’ names and

the date announcement (Table 8).

Before starting the analysis we should anticipated different level of movements than of the

target group since the bidder group is formatted by larger financial institutions and the leaders

of the industry.

Also, the following table shows the results of beta:

Mean Maximum Minimum SD Negative Positive

Beta 1.1182 2.14333 0.010269 0.440461 0% 100%

(In the appendix you will find the table from which the above results was extracted)

Looking at the level of the average beta it is obvious that the abnormal return relay heavily on

the level of beta.

Another significant point to mention is that there are no negative betas in the table above. This

fact means that the bidders firms are positive related to the market index. This is highly

expected from such large firms and the leaders of the industry.

Also, comparing the mean beta of the target with the mean beta of the bidders we notice that

the latter is much higher. This is an indication that the performance of the bidders firms was

more correlated to the market index before the announcement than the performance of the

target firms.

Moving along the table we can see that the maximum value of beta is equal to 2.1433 and

there is no negative observation. These two elements are a hint that the levels of abnormal

returns will be lower than that of the targets, due to the fact that the expected returns will be

created under more correlated to the market conditions, making the difference with the

market return to be smaller.

38 | P a g e

Bidder Abnormal Returns and Cumulative Abnormal Returns

The next step of my analysis, is to present the abnormal returns and t-stat for the three models:

We can notice, at a first glance, that the bidders’ abnormal returns compared to targets’ abnormal

returns, confirm the original expectations discussed by studying the models parameters in the previous

section.

First of all, the most important day is the day zero, which is the announcement day of the M&A event.

From the table above we observe that the bidder AR on day 0 is -0.1791% according to the market

model and 0,02% and 0,1279% across the other two models. However, the t-test indicates that the

results are not statistical significant and the majority of them are negative numbers. This is verified also

by sign test.

The above remarks allow me to state that a merger and acquisition event impacts negatively on the

wealth of the shareholders of the bidder party when the bidder is a member of the banking industry in

the UK.

CAR

Period Market Model Market Adjusted Model Constant Mean Return Model [-20,0] 0.026071934 0.3632 0.449522546

[-10,0] 0.144281056 0.223066667 0.123521334

[-1,0] -0.212505796 -0.1336 -0.104426424

Market Model Market Adjusted Model Constant Mean Return Model

Days AR T- STAT SIGN TEST AR T- STAT SIGN TEST AR T- STAT SIGN TEST

-5 0,190514473 0,874142769 0,92998111 0,24706667 1,051289887 1,96299092 0,148320121 0,587230184 0,577350269

-4 -0,36947405 -1,69703524 1,162476387 -0,2625333 -1,199962972 2,88675135 -0,454613212 -1,732872738 1,039230485

-3 0,115329595 0,493582213 0,232495277 0,14293333 0,673133172 2,88675135 0,044186788 0,182266381 1,039230485

-2 0,074825946 0,313548532 0,92998111 -0,0133333 -0,053324751 3,57957167 0,141253455 0,459318062 0,346410162

-1 -0,03333568 -0,138914 0,232495277 -0,1602667 -0,698575763 2,88675135 -0,232346545 -1,013143792 0,577350269

0 -0,17917012 -0,60812048 0,232495277 0,02666667 0,103008933 2,88675135 0,127920121 0,424117929 0,346410162

1 -0,0047185 -0,01280526 0 0,0304 0,081497238 2,19393102 0,078320121 0,201193678 0,346410162

2 -0,12654432 -0,53153991 0,232495277 0,06013333 0,264298003 3,34863156 0,188053455 0,509838282 1,039230485

3 -0,42113108 -1,98658326 2,557448052 -0,44 -1,929928917 4,96521232 -0,578746545 -2,130773266 2,886751346

4 0,304737973 1,370332747 0,232495277 0,12293333 0,624884081 2,65581124 -0,109146545 -0,515383513 0,577350269

5 0,411467432 1,619158966 0,92998111 0,3536 1,984533111 1,5011107 0,054853455 0,323960939 0,808290377

39 | P a g e

{0} -0.17917012 0.026666667 0.127920121

[-1,+1] -0.217224296 -0.1032 -0.026106303

[-10,+10] 0.213017042 0.640533333 0.08685588

[-20,+20] -0.566681228 0.270533333 -0.586821574

The table above reports the results for the sample of acquiring banks. As we can notice, the

most results thought the three models are negative. It is important to state that our empirical

results for acquiring banks are significantly similar to several studies, which are related to M&A

in US banking industry, which have documented a significant negative price effect for acquiring

banks (e.g. (Marcia Millon Cornett, Hassan Tehranian, 1992), (Joel F. Houston, Michael D.

Ryngaert, 1994)). Also, Siems (1996) in his study of 19 US bank mega mergers announced in

1995, also found a significant negative market reaction to the mean acquiring bank.

Thus, contrary to most US studies there are no significant cumulative abnormal returns (CAR)

accruing to the bidder shareholders in any of the analyzed event window. Also, CARs are slightly

positive or negative, depending on the particular event window chosen. This result differs from

the majority of US studies, which report negative abnormal bidder returns.

The cumulative abnormal return (CAR) represents the overall movement of the abnormal

return on the period that it is calculated.

40 | P a g e

Looking the CAR results for the bidder firms we notice a downward movement the days after

the announcement. This situation can be interpreted from the fact that the information of the

announcement needs a couple of days to be reflected in the stock prices.

To sum up, the results of the abnormal returns and cumulative abnormal returns draw the

conclusion that the banks (bidders) are subject to a loss of wealth. This loss can be easily

explained by the “agency problem”, under which the bidder has paid more than the accrual

worth of the target.

This conclusion is in line with other studies that focus in the European market such as the study

of (DeLong, Gayle L., 2001) and (José Manuel Campa, Ignacio Hernando, 2006).

Combine At Table 9 in appendix we can see analytically the combine entities and the dates of event

announcement.

Another important element for this study is the use of the market value prior to the event

window. For this reason the following table indicates the market value of the bidders and

targets on day -23 (one day prior the event window [-22,+21])

See Table 10 at Appendix

Looking at the value of the market value of each firm, we can notice that the level of the

market value of the target firms seems to be lower than the level of the market value of the

bidder firms.

This reinforces the belief that the large firms acquire smaller ones, as it has been stated in the

literature.

41 | P a g e

Looking at the event day (day zero) we notice that the abnormal return is negative. This is

expected as seen by the equation of the abnormal return of the combine entities:

𝐴𝑅𝑡,𝑇𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛 =𝐴𝑅𝑡𝐺 ∗ 𝑀𝑉𝑡𝐺 + 𝐴𝑅𝑡𝐵 ∗ 𝑀𝑉𝑡𝐵

𝑀𝑉𝑡𝐺 + 𝑀𝑉𝑡𝐵

As the bidders that have negative abnormal returns have higher market value and so a larger

weighted percentage and a higher influent on the result. On the day of the announcement the

both t-statistic and sign test do not show any significant result.

Furthermore, looking the days -1 and +1 we notice slightly positive results that could be an

indication that the targets earnings overcome the bidder losses.

Market Model Market Adjusted Model Constant Mean Return Model

Days AR T- STAT SIGN TEST AR T- STAT SIGN TEST AR T- STAT SIGN TEST

-5 0,363928 1,5410905 0 -0,07950379 -0,35938453 0,25819889 -0,06962267 -0,29560913 0,77459667

-4 0,254847 1,3373835 0,2581989 0,250243323 1,02940115 0,77459667 0,239067288 1,088550962 0,51639778

-3 -0,10969 -0,558275 1,0327956 -0,05186955 -0,29281001 0,51639778 -0,297423 -1,17788748 1,54919334

-2 -0,36119 -1,269652 0,2581989 -0,25817401 -0,76939699 0,25819889 -0,35223697 -1,197269297 0,77459667

-1 0,104106 0,3698455 0,2581989 -0,03706317 -0,11361504 1,29099445 -0,01350047 -0,039976052 0

0 -0,05767 -0,218793 0,5163978 -0,0208752 -0,07310907 1,03279556 -0,25966154 -0,842366051 0,25819889

1 0,582495 1,0120766 0,5163978 0,750428341 1,392970215 0,77459667 0,771681582 1,353387916 0,25819889

2 0,516611 1,419997 1,5491933 0,43320506 1,002719091 1,03279556 0,41543851 1,013758612 2,06559112

3 -0,30546 -1,527575 1,2909944 0,183026233 0,590325773 1,29099445 -0,00546464 -0,014126427 2,5819889

4 -0,32515 -1,404473 1,8073922 -0,25859622 -1,02404297 1,03279556 -0,48302214 -1,596631473 0,25819889

5 0,305768 1,6110004 0,7745967 -0,05784998 -0,24899343 0 -0,26913111 -0,986031652 0,25819889

42 | P a g e

CAR

Period Market Model Market Adjusted Model Constant Mean Return Model

[-5,+5] 0,088053579 0,104680906 -0,023333114

[-3,+3] 0,052742738 0,113135864 0,010462047

[-1,+1] 0,209644306 0,387586066 0,30915285

The above tables give us the results for the combined entities of a bidder and a target. These

results show significantly positive CAR for the overall sample. This conclusion is in line with

other studies on European Bank M&A (Patrick Beitel, Dirk Schiereck, Mark Wahrenburg, 2004),

(Alberto Cybo-Ottone, Maurizio Murgia, 2000). Thus, M&A of UK banks for the examined period

can be considered as being clearly successful in respect to generating overall shareholder value.

Summarize of Empirical Results

Looking the findings we can underline some points, which are very important and verified

through the three models that I used for my analysis:

The sudden pick in the CAR of the target firms which is confirmed through all three

models.

The negative reaction of the bidder firms around the announcement day.

The higher levels of the abnormal return produced by the market model.

Last but not least, the combine entities are more affected by the bidder’s abnormal

return results.

43 | P a g e

Chapter: 6

CONCLUSION The first objective of this dissertation is to calculate the effect of merger and acquisition on the

wealth of financial institutions in the United Kingdom. Thus, for this purpose, I firstly discuss the

motives behind the M&A events. The main motives are the cost saving, market power, increase

in revenue, empire building and product diversification.

After that, I continued with the application of “event study methodology”, which is widely used

in the pre literature for mergers and acquisitions. So, after conducting the event study – using

three different models – I found interesting outcomes about the movement of target and

bidder shareholders’ wealth. More specific, for the target firms, the study concludes to a high

positive reaction to the event on the day of the announcement (day zero). Moreover, for the

event window period I also found a positive aggregate abnormal return. On the other hand, I

concluded to the opposite results for the bidder firms. Thus, on the announcement day we saw

a negative reaction, which means loss due to the M&A event. However, due to the fact that the

bidders are highly correlated with the market, we notice higher abnormal returns under the

market adjusted model. Last, about the combine entities, I observe an overall gain created from

the fact that the bidders’ loss is compensated by the targets’ gains.

All the above remarks are made on the financial system of the United Kingdom that is one of

the leader markets in the Europe. Thus, similar results could be expected if similar studies

would occur for other countries in the Eurozone.

Another issue is the capital market, which is characterized by efficiency in the literature.

Efficiency in the markets means that the performance of the market echoes all the available

information. The higher the level of efficiency, the faster the market response to any additional

information. My study confirms that the markets work in an efficient way when the news of the

announcement echoes on the announcement day. However, the fact that the abnormal returns

are also viewable before the announcement day might be an indication that the market either

overvalued or undervalued the securities. Or even that the market suffers from legged of

information.

In the conclusion, I would like to note the limitations of this study and future studies on this

subject of M&A. My results are calculated through three different models which are designed

to cover each other’s weakness, but my results in many observations are not statistical

significant.

I am able to confirm with this dissertation the changes in the shareholders’ wealth.

Furthermore, the future of this study could be to conduct the same study to a larger scale and

44 | P a g e

to examine if other external factors, such as deregulation and laws, technological changes,

globalization, shareholder pressures, introduction to the euro, Macroeconomic conditions

interact with the level of the abnormal returns due to the merger and acquisition in the UK

banking industry.

45 | P a g e

Appendix

BETA

0,49464135 0,202979167 0,931434599

0,35350211 1,60164557 0,226144068

0,068438819 0,15761097 1,085232068

0,345654008 0,957510549 1,221713136

0,411012658 0,926313559 1,36

1,224683544 0,935169492 0,300466102

2,269153846 0,935169492 0,9078827

0,405907173 0,549367089 -0,040217391

1,374767932 1,64558903 0,260720339

1,341308017 0,619152542 0,165949367

0,399957806 1,681097046 0,92384557

1,551603376 0,011008898 1,250970464

1,075223729 0,017242194 0,465822785

1,04701519 1,158262712 0,465847458

1,584135021 1,324279661 0,181653543

0,651518987 0,870337553 1,456624473

0,478559322 -0,070379747 1,627679325

0,506185654 0,164008439 1,627679325

1,327848101 0,152881356 1,171694915

-0,059268644 0,410464135 -0,120761905

0,312278481 0,228945148 0,076962025

0,50164557 0,226751055 0,6907173

0,058417722 0,457805907 0,724894515

1,449576271 0,155122363

Average: 0,704863767

(Table 1) (Beta for targets)

46 | P a g e

beta

0,720628019 1,693333333 1,277584746

1,011518987 1,449576271 1,092531646

0,987721519 1,547805907 1,095889831

0,345654008 1,60164557 1,277004219

0,124364407 0,15761097 1,217974684

1,479873418 0,957510549 1,092362869

1,494345992 0,926313559 1,205677966

1,215611814 1,470295359 0,742869198

0,354618644 0,010269198 0,737838983

1,15814346 1,72809322 0,728101266

1,156101695 1,482827004 0,745907173

0,327594937 1,737118644 1,188312236

1,331434599 1,366567797 1,250970464

1,374767932 1,01440678 0,714514768

1,551603376 1,283037975 0,714514768

1,143813559 1,004135021 0,706751055

1,205443038 0,986877637 0,990337553

1,584135021 0,164008439 1,380635593

2,143333333 1,349830508 0,930548523

0,111059322 0,937341772 1,315400844

1,486751055 1,435316456 1,777457627

1,327848101 0,789324895 1,256483051

1,682118644 0,78814346 1,655527426

1,406075949 0,787805907 1,399409283

1,605780591 1,34257384 1,22257384

0,931434599

1,120950421 :average (Table 2) (Beta for bidders)

47 | P a g e

Market Model Market Adjusted Model Constant Mean Return Model

Days AR T- STAT AR T- STAT AR T- STAT

-22 -0,1971 -0,845770711 -0,123972603 -0,572791238 0,000218328 0,000847921

-21 -0,11699 -0,389733017 -0,317945205 -1,009364382 -0,331836467 -0,986097195

-20 0,017371 0,057824616 -0,048356164 -0,163265773 -0,194987152 -0,557701703

-19 -0,42932 -1,310672824 -0,480410959 -1,632521387 -0,249370713 -0,839886857

-18 -0,16228 -0,30754245 -0,254794521 -0,495299406 -0,002247426 -0,004212954

-17 0,352184 1,083394219 0,442054795 1,338588873 0,433369013 1,414257947

-16 0,057028 0,322612311 0,029452055 0,152802177 -0,004439207 -0,019187903

-15 0,438496 1,060144985 0,300410959 0,760249942 0,56419093 1,38693109

-14 0,377615 0,775842215 0,379589041 0,794931556 0,164875862 0,319918177

-13 -0,09431 -0,207444086 -0,094246575 -0,213018123 0,008026547 0,017313233

-12 0,057616 0,212718895 -0,038219178 -0,144082995 -0,032110439 -0,108861218

-11 -0,74072 -1,961515345 -0,627782127 -1,754272846 -0,44605695 -1,657906783

-10 0,372266 0,689130399 0,310759058 0,588432209 0,283169167 0,525349632

-9 0,765452 1,92065106 0,617245449 1,609303746 0,756367886 1,767255402

-8 -0,13119 -0,37925667 -0,152432545 -0,452787458 -0,135501888 -0,391994632

-7 0,971103 2,577670662 1,12980727 2,959399436 1,250573543 2,659392893

-6 0,282818 1,188506604 0,335745715 1,4191385 0,053361303 0,22218618

-5 0,412576 1,026721834 0,45260274 1,18521336 0,263369013 0,659252272

-4 0,533232 2,070550605 0,585753425 2,317957878 0,363232026 1,285747574

-3 -0,60083 -0,885870078 -0,530273973 -0,823840209 -0,578137837 -0,889815461

-2 0,171215 0,424885352 0,037808219 0,084770594 0,025834766 0,052528008

-1 0,284005 0,481015065 0,138767123 0,232756348 0,092410108 0,143258721

0 3,161944 2,691774576 3,098767123 2,711612298 3,246245725 2,761270133

1 2,490631 3,398380454 2,511369863 3,725860974 2,369259424 3,41452835

2 0,252916 0,463301804 0,458082192 0,81471485 0,543369013 0,780304135

3 -0,45998 -1,717510463 -0,310684932 -1,047725981 -0,596357015 -2,156621202

4 -0,23121 -0,691997619 -0,323287671 -0,884314654 -0,600192631 -1,444058928

5 0,57656 1,887235706 0,481917808 1,64110866 0,247204629 0,82641528

6 -0,1318 -0,516887074 -0,301232877 -1,063041352 -0,118548796 -0,431217478

7 -0,22144 -0,783437493 0,008082192 0,032012106 -0,0995077 -0,351440379

8 0,812551 0,985210827 0,684657534 0,843152242 0,564464903 0,658174969

9 0,765365 0,827579409 0,850410959 0,951565976 0,720355314 0,847996978

10 0,62124 1,381388123 0,68260274 1,557456151 0,594601889 1,252505983

11 -0,42936 -0,523866148 -0,604109589 -0,782237396 -0,41800085 -0,533266602

12 0,199843 0,545579454 0,189726027 0,506060153 0,189259424 0,445152961

13 0,061979 0,173070153 -0,067534247 -0,16462363 -0,483069344 -0,933855462

14 -1,20646 -1,082342876 -1,215753425 -1,125707441 -1,277589892 -1,191780799

15 0,762927 1,172817774 0,570821918 1,043732039 0,465971752 0,976977831

16 0,895261 1,459848941 0,868493151 1,53486513 0,728163533 1,282349169

17 -0,05104 -0,181797748 -0,12630137 -0,352754835 -0,480740576 -1,002437868

18 -1,71956 -1,882164857 -1,386849315 -1,742886089 -1,322521398 -2,028861379

48 | P a g e

19 0,03282 0,094800526 0,193013699 0,599461778 0,091314218 0,309892199

20 0,306976 0,940935372 0,254246575 1,103823559 0,14199915 0,7158985

21 0,646088 1,266694333 0,502054795 1,081231702 0,435971752 0,917218119

(Table 3) (Targets’ AR)

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(Table 4) (Bidders’ AR)

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(Table 5) (Combine AR)

Date Announced

Target Name Acquirer Name

01-05-90 Fundinvest PLC Midland Bank PLC

07-24-90 Hambros Advanced Technology

Hambros PLC

02-25-91 CE Heath PLC TSB Group PLC

03-12-91 Standard Chartered Bank AU Ltd

Standard Chartered Bank PLC

05-01-91 Bank of Wales PLC Bank of Scotland PLC

02-10-92 Aitken Hume International PLC

Aitken Hume International PLC

03-06-92 Midland Bank SA Midland Bank PLC

04-27-92 Countrywide Banking Corp Ltd

Bank of Scotland PLC

05-12-92 Countrywide Banking Corp Ltd

Bank of Scotland PLC

06-02-92 TSB Bank Channel Islands Ltd TSB Group PLC

05-09-94 Banesto Royal Bank of Scotland Group

06-17-94 Tyndall Bank(Jupiter Tyndall) Cater Allen Holdings PLC

11-01-94 HMC Group PLC Abbey National PLC

11-03-94 Irish Permanent PLC Abbey National PLC

05-23-95 Allied Provincial PLC King &Shaxson Holdings

07-04-95 First National Finance Corp Abbey National PLC

08-08-95 Barclays PLC Barclays PLC

10-09-95 Lloyds Bank PLC TSB Group PLC

01-22-96 Standard Chartered Bank PLC

National Westminster Bank PLC

02-19-96 Gartmore PLC National Westminster Bank PLC

02-27-96 Barclays PLC Barclays PLC

03-01-96 Banco de Santander SA Royal Bank of Scotland Group

04-03-96 Banco de Santander SA Royal Bank of Scotland Group

07-30-96 National Westminster Bank National Westminster Bank

53 | P a g e

PLC PLC

08-06-96 Barclays PLC Barclays PLC

09-23-96 Lloyds Abbey Life PLC Lloyds TSB Group PLC

10-18-96 King &Shaxson Holdings Gerrard &Natl Holdings PLC

01-20-97 Dah Sing Financial Hldg Ltd Abbey National PLC

02-26-97 Barclays PLC Barclays PLC

05-22-97 Siparex Royal Bank of Scotland Group

06-25-97 Cater Allen Holdings PLC Abbey National PLC

06-30-97 EFT Group PLC Bank of Scotland PLC

07-01-97 Computershare Ltd Royal Bank of Scotland Group

08-22-97 Barclays PLC Barclays PLC

11-17-97 National Westminster Bank PLC

Barclays PLC

02-18-98 Barclays PLC Barclays PLC

04-21-98 National Westminster Bank PLC

National Westminster Bank PLC

08-05-98 Woolwich PLC Woolwich PLC

02-26-99 Alliance & Leicester PLC Alliance & Leicester PLC

04-22-99 Bank Bali Tbk PT Standard Chartered Bank PLC

04-22-99 Bank Bali Tbk PT Standard Chartered Bank PLC

04-28-99 Nakornthon Bank PLC Standard Chartered Bank PLC

05-18-99 Banco Santander Central Hispan

Royal Bank of Scotland Group

09-03-99 Nakornthon Bank PCL Standard Chartered Bank PLC

09-06-99 Legal & General Group PLC National Westminster Bank PLC

09-24-99 National Westminster Bank PLC

Bank of Scotland PLC

11-29-99 National Westminster Bank PLC

Royal Bank of Scotland Group

12-09-99 Banco Anglo Colombiano(Lloyds)

Lloyds TSB Group PLC

08-11-00 Woolwich PLC Barclays PLC

11-03-00 Bank of Scotland PLC Abbey National PLC

12-05-00 ICC Bank PLC Bank of Scotland PLC

01-31-01 Abbey National PLC Lloyds TSB Group PLC

54 | P a g e

08-14-01 Euro Sales Finance PLC Royal Bank of Scotland Group

11-04-02 e-primefinancial PLC e-primefinancial PLC

05-09-03 Bank of Western Australia HBOS PLC

08-06-03 Koram Bank Standard Chartered PLC

10-06-03 First Active PLC Royal Bank of Scotland Group

10-28-03 Bank of Bermuda Ltd HSBC

11-02-03 Korea First Bank HSBC

11-03-03 Woori Fin Hldgs Co Ltd HSBC

02-15-04 National Australia Bank Ltd HBOS PLC

02-21-04 Alliance & Leicester PLC Alliance & Leicester PLC

09-23-04 Absa Group Ltd Barclays PLC

11-22-04 Korea Exchange Bank HSBC

01-06-05 ABN-AMRO Holding NV Royal Bank of Scotland Group

01-10-05 Korea First Bank Standard Chartered PLC

03-30-05 Standard Chartered Nakornthon

Standard Chartered Bank PLC

06-06-05 Asia Commercial Bank Standard Chartered PLC

06-20-05 Bayerische Hypo- und Vereins

Royal Bank of Scotland Group

11-25-05 St George Bank Ltd HBOS PLC

07-04-06 Banco Bilbao Vizcaya HSBC

07-21-06 GrupoBanistmo SA HSBC

09-29-06 Hsinchu International Bank Standard Chartered Bank PLC

10-25-06 Far Eastern International Bank

HSBC

02-26-07 BancoSalvadoreno SA HSBC

03-19-07 ABN-AMRO Holding NV Barclays PLC

07-23-07 Barclays PLC Barclays PLC

09-03-07 Korea Exchange Bank HSBC

09-03-07 Korea Exchange Bank HSBC

10-10-07 Bank of Communications Co Ltd

HSBC

02-25-08 Alliance & Leicester PLC Lloyds TSB Group PLC

05-02-08 Asia Commercial Bank Standard Chartered PLC

09-17-08 HBOS PLC HSBC

09-17-08 HBOS PLC Lloyds TSB Group PLC

11-13-08 Storebrand ASA Royal Bank of Scotland Group

55 | P a g e

10-22-09 Bao Viet Holdings HSBC

02-03-10 Bank Victoria Intl Tbk PT Standard Chartered Bank PLC

05-12-10 Nedbank Group Ltd Standard Chartered PLC

08-23-10 Nedbank Group Ltd HSBC

08-22-11 Warka Bank for Investment Standard Chartered PLC

(Table 6)

Date Announced

Target Name

07-24-90 Hambros Advanced Technology

03-12-91 Standard Chartered Bank AU Ltd

05-01-91 Bank of Wales PLC

02-10-92 Aitken Hume International PLC

06-02-92 TSB Bank Channel Islands Ltd

05-09-94 Banesto

11-03-94 Irish Permanent PLC

07-04-95 First National Finance Corp

08-08-95 Barclays PLC

10-09-95 Lloyds Bank PLC

02-19-96 Gartmore PLC

02-27-96 Barclays PLC

03-01-96 Banco de Santander SA

04-03-96 Banco de Santander SA

07-30-96 National Westminster Bank PLC

08-06-96 Barclays PLC

09-23-96 Lloyds Abbey Life PLC

10-18-96 King &Shaxson Holdings

01-20-97 Dah Sing Financial Hldg Ltd

02-26-97 Barclays PLC

05-22-97 Siparex

06-25-97 Cater Allen Holdings PLC

06-30-97 EFT Group PLC

07-01-97 Computershare Ltd

08-22-97 Barclays PLC

56 | P a g e

11-17-97 National Westminster Bank PLC

02-18-98 Barclays PLC

04-21-98 National Westminster Bank PLC

08-05-98 Woolwich PLC

02-26-99 Alliance & Leicester PLC

04-22-99 Bank Bali Tbk PT

04-22-99 Bank Bali Tbk PT

04-28-99 Nakornthon Bank PLC

05-18-99 Banco Santander Central Hispan

09-03-99 Nakornthon Bank PCL

09-06-99 Legal & General Group PLC

09-24-99 National Westminster Bank PLC

11-29-99 National Westminster Bank PLC

08-11-00 Woolwich PLC

11-03-00 Bank of Scotland PLC

12-05-00 ICC Bank PLC

01-31-01 Abbey National PLC

08-14-01 Euro Sales Finance PLC

11-04-02 e-primefinancial PLC

05-09-03 Bank of Western Australia

08-06-03 Koram Bank

10-06-03 First Active PLC

10-28-03 Bank of Bermuda Ltd

11-03-03 Woori Fin Hldgs Co Ltd

02-15-04 National Australia Bank Ltd

02-21-04 Alliance & Leicester PLC

09-23-04 Absa Group Ltd

11-22-04 Korea Exchange Bank

01-06-05 ABN-AMRO Holding NV

03-30-05 Standard Chartered Nakornthon

06-20-05 Bayerische Hypo- und Vereins

11-25-05 St George Bank Ltd

07-04-06 Banco Bilbao Vizcaya

07-21-06 GrupoBanistmo SA

09-29-06 Hsinchu International Bank

10-25-06 Far Eastern International

57 | P a g e

Bank

03-19-07 ABN-AMRO Holding NV

07-23-07 Barclays PLC

09-03-07 Korea Exchange Bank

09-03-07 Korea Exchange Bank

10-10-07 Bank of Communications Co Ltd

02-25-08 Alliance & Leicester PLC

09-17-08 HBOS PLC

09-17-08 HBOS PLC

11-13-08 Storebrand ASA

10-22-09 Bao Viet Holdings

02-03-10 Bank Victoria Intl Tbk PT

05-12-10 Nedbank Group Ltd

08-23-10 Nedbank Group Ltd

(Table 7)

Date Announced

Acquirer Name

01-05-90 Midland Bank PLC

07-24-90 Hambros PLC

05-01-91 Bank of Scotland PLC

02-10-92 Aitken Hume International PLC

03-06-92 Midland Bank PLC

04-27-92 Bank of Scotland PLC

05-12-92 Bank of Scotland PLC

05-09-94 Royal Bank of Scotland Group

06-17-94 Cater Allen Holdings PLC

11-01-94 Abbey National PLC

11-03-94 Abbey National PLC

05-23-95 King &Shaxson Holdings

07-04-95 Abbey National PLC

08-08-95 Barclays PLC

01-22-96 National Westminster Bank PLC

02-19-96 National Westminster Bank PLC

02-27-96 Barclays PLC

03-01-96 Royal Bank of Scotland Group

04-03-96 Royal Bank of Scotland Group

07-30-96 National Westminster Bank PLC

08-06-96 Barclays PLC

58 | P a g e

09-23-96 Lloyds TSB Group PLC

10-18-96 Gerrard &Natl Holdings PLC

01-20-97 Abbey National PLC

02-26-97 Barclays PLC

05-22-97 Royal Bank of Scotland Group

06-25-97 Abbey National PLC

06-30-97 Bank of Scotland PLC

07-01-97 Royal Bank of Scotland Group

08-22-97 Barclays PLC

11-17-97 Barclays PLC

02-18-98 Barclays PLC

04-21-98 National Westminster Bank PLC

08-05-98 Woolwich PLC

02-26-99 Alliance & Leicester PLC

05-18-99 Royal Bank of Scotland Group

09-06-99 National Westminster Bank PLC

09-24-99 Bank of Scotland PLC

11-29-99 Royal Bank of Scotland Group

12-09-99 Lloyds TSB Group PLC

08-11-00 Barclays PLC

11-03-00 Abbey National PLC

12-05-00 Bank of Scotland PLC

01-31-01 Lloyds TSB Group PLC

08-14-01 Royal Bank of Scotland Group

11-04-02 e-primefinancial PLC

05-09-03 HBOS PLC

08-06-03 Standard Chartered PLC

10-06-03 Royal Bank of Scotland Group

10-28-03 HSBC

11-02-03 HSBC

11-03-03 HSBC

02-15-04 HBOS PLC

02-21-04 Alliance & Leicester PLC

09-23-04 Barclays PLC

11-22-04 HSBC

01-06-05 Royal Bank of Scotland Group

01-10-05 Standard Chartered PLC

06-06-05 Standard Chartered PLC

06-20-05 Royal Bank of Scotland Group

11-25-05 HBOS PLC

07-04-06 HSBC

59 | P a g e

07-21-06 HSBC

10-25-06 HSBC

02-26-07 HSBC

03-19-07 Barclays PLC

07-23-07 Barclays PLC

09-03-07 HSBC

09-03-07 HSBC

10-10-07 HSBC

02-25-08 Lloyds TSB Group PLC

05-02-08 Standard Chartered PLC

09-17-08 HSBC

09-17-08 Lloyds TSB Group PLC

11-13-08 Royal Bank of Scotland Group

10-22-09 HSBC

05-12-10 Standard Chartered PLC

08-23-10 HSBC

08-22-11 Standard Chartered PLC

(Table 8)

Date Announced

Target Name Acquirer Name

07-24-90 Hambros Advanced Technology

Hambros PLC

05-01-91 Bank of Wales PLC Bank of Scotland PLC

02-10-92 Aitken Hume International PLC

Aitken Hume International PLC

05-09-94 Banesto Royal Bank of Scotland Group

11-03-94 Irish Permanent PLC Abbey National PLC

07-04-95 First National Finance Corp

Abbey National PLC

08-08-95 Barclays PLC Barclays PLC

02-19-96 Gartmore PLC National Westminster Bank PLC

60 | P a g e

02-27-96 Barclays PLC Barclays PLC

03-01-96 Banco de Santander SA Royal Bank of Scotland Group

04-03-96 Banco de Santander SA Royal Bank of Scotland Group

07-30-96 National Westminster Bank PLC

National Westminster Bank PLC

08-06-96 Barclays PLC Barclays PLC

09-23-96 Lloyds Abbey Life PLC Lloyds TSB Group PLC

10-18-96 King &Shaxson Holdings Gerrard &Natl Holdings PLC

01-20-97 Dah Sing Financial Hldg Ltd

Abbey National PLC

02-26-97 Barclays PLC Barclays PLC

05-22-97 Siparex Royal Bank of Scotland Group

06-25-97 Cater Allen Holdings PLC Abbey National PLC

06-30-97 EFT Group PLC Bank of Scotland PLC

07-01-97 Computershare Ltd Royal Bank of Scotland Group

08-22-97 Barclays PLC Barclays PLC

11-17-97 National Westminster Bank PLC

Barclays PLC

02-18-98 Barclays PLC Barclays PLC

04-21-98 National Westminster Bank PLC

National Westminster Bank PLC

08-05-98 Woolwich PLC Woolwich PLC

02-26-99 Alliance & Leicester PLC Alliance & Leicester PLC

05-18-99 Banco Santander Central Hispan

Royal Bank of Scotland Group

09-06-99 Legal & General Group PLC

National Westminster Bank PLC

09-24-99 National Westminster Bank PLC

Bank of Scotland PLC

11-29-99 National Westminster Bank PLC

Royal Bank of Scotland Group

08-11-00 Woolwich PLC Barclays PLC

11-03-00 Bank of Scotland PLC Abbey National PLC

61 | P a g e

12-05-00 ICC Bank PLC Bank of Scotland PLC

01-31-01 Abbey National PLC Lloyds TSB Group PLC

08-14-01 Euro Sales Finance PLC Royal Bank of Scotland Group

11-04-02 e-primefinancial PLC e-primefinancial PLC

05-09-03 Bank of Western Australia HBOS PLC

08-06-03 Koram Bank Standard Chartered PLC

10-06-03 First Active PLC Royal Bank of Scotland Group

10-28-03 Bank of Bermuda Ltd HSBC

11-03-03 Woori Fin Hldgs Co Ltd HSBC

02-15-04 National Australia Bank Ltd

HBOS PLC

02-21-04 Alliance & Leicester PLC Alliance & Leicester PLC

09-23-04 Absa Group Ltd Barclays PLC

11-22-04 Korea Exchange Bank HSBC

01-06-05 ABN-AMRO Holding NV Royal Bank of Scotland Group

06-20-05 Bayerische Hypo- und Vereins

Royal Bank of Scotland Group

11-25-05 St George Bank Ltd HBOS PLC

07-04-06 Banco Bilbao Vizcaya HSBC

07-21-06 GrupoBanistmo SA HSBC

10-25-06 Far Eastern International Bank

HSBC

03-19-07 ABN-AMRO Holding NV Barclays PLC

07-23-07 Barclays PLC Barclays PLC

09-03-07 Korea Exchange Bank HSBC

09-03-07 Korea Exchange Bank HSBC

10-10-07 Bank of Communications Co Ltd

HSBC

02-25-08 Alliance & Leicester PLC Lloyds TSB Group PLC

09-17-08 HBOS PLC HSBC

62 | P a g e

(Table 9)

Targets Market Value on Day -23

Bidders Market Value on Day -23

Hambros Advanced Technology

17,5 Hambros PLC 463,43

Bank of Wales PLC 13,76 Bank of Scotland PLC 1055,43

Aitken Hume International PLC

21,57 Aitken Hume International PLC

21,57

Banesto 772,3 Royal Bank of Scotland Group

3263,6

Irish Permanent PLC 172,61 Abbey National PLC 5051,2

First National Finance Corp

143,51 Abbey National PLC 6276,14

Barclays PLC 11442,61 Barclays PLC 11442,61

Gartmore PLC 493,79 National Westminster Bank PLC

606,37

Barclays PLC 12736,47 Barclays PLC 12736,47

Banco de Santander SA

5740,24 Royal Bank of Scotland Group

4742,95

Banco de Santander SA

5778,63 Royal Bank of Scotland Group

4500,55

Barclays PLC 12569,05 Barclays PLC 12569,05

Lloyds Abbey Life PLC 3930,61 Lloyds TSB Group PLC 18475,97

King &Shaxson Holdings

44,47 Gerrard &Natl Holdings PLC

163,13

Dah Sing Financial Hldg Ltd

6625,68 Abbey National PLC 10362,55

Barclays PLC 17621,32 Barclays PLC 17621,32

Siparex 95,91 Royal Bank of Scotland Group

4460,79

Cater Allen Holdings PLC

142,4 Abbey National PLC 13129,59

EFT Group PLC 73,4 Bank of Scotland PLC 4817,92

09-17-08 HBOS PLC Lloyds TSB Group PLC

11-13-08 Storebrand ASA Royal Bank of Scotland Group

10-22-09 Bao Viet Holdings HSBC

05-12-10 Nedbank Group Ltd Standard Chartered PLC

08-23-10 Nedbank Group Ltd HSBC

63 | P a g e

Computershare Ltd 155,85 Royal Bank of Scotland Group

5055,29

Barclays PLC 18808,02 Barclays PLC 18808,02

National Westminster Bank PLC

312 Barclays PLC 24721,71

Barclays PLC 26678,18 Barclays PLC 26678,18

National Westminster Bank PLC

321,75 National Westminster Bank PLC

321,75

Woolwich PLC 5251,28 Woolwich PLC 5251,28

Alliance & Leicester PLC

4694,61 Alliance & Leicester PLC 4694,61

Banco Santander Central Hispan

25047,06 Royal Bank of Scotland Group

12922,93

Legal & General Group PLC

7884,8 National Westminster Bank PLC

297

National Westminster Bank PLC

303,75 Bank of Scotland PLC 9776,19

National Westminster Bank PLC

289,5 Royal Bank of Scotland Group

11617,26

Woolwich PLC 4048,52 Barclays PLC 21903,95

Bank of Scotland PLC 7481,95 Abbey National PLC 12721,23

ICC Bank PLC 41,75 Bank of Scotland PLC 8557,09

Abbey National PLC 17423,8 Lloyds TSB Group PLC 38933,37

Euro Sales Finance PLC

87,28 Royal Bank of Scotland Group

42425,45

e-primefinancial PLC 9,63 e-primefinancial PLC 9,63

Bank of Western Australia

2067,89 HBOS PLC 27192,73

Koram Bank 1644840 Standard Chartered PLC

8592,79

First Active PLC 683,62 Royal Bank of Scotland Group

46034,75

Bank of Bermuda Ltd 1191,65 HSBC 88384,5

Woori Fin Hldgs Co Ltd 4544457 HSBC 87788

National Australia Bank Ltd

45188,05 HBOS PLC 28685,14

Alliance & Leicester PLC

4163,73 Alliance & Leicester PLC 4163,73

Absa Group Ltd 34180,39 Barclays PLC 32741,25

Korea Exchange Bank 4772309 HSBC 98962

ABN-AMRO Holding NV

31656,68 Royal Bank of Scotland Group

52402,89

St George Bank Ltd 14008,84 HBOS PLC 32239,3

64 | P a g e

Banco Bilbao Vizcaya 14051,02 HSBC 106384,3

GrupoBanistmo SA 55067,36 HSBC 107990,1

Far Eastern International Bank

1074,53 HSBC 109709

ABN-AMRO Holding NV

22721,81 Barclays PLC 51017,5

Barclays PLC 49273,32 Barclays PLC 48756,13

Korea Exchange Bank 48756,13 HSBC 105827,8

Bank of Communications Co Ltd

8899712 HSBC 102769

Alliance & Leicester PLC

342274,6 Lloyds TSB Group PLC 23155,56

HBOS PLC 3118,96 HSBC 102471,9

HBOS PLC 15904,64 Lloyds TSB Group PLC 17741,81

Storebrand ASA 15904,64 Royal Bank of Scotland Group

10870,68

Bao Viet Holdings 9987,99 HSBC 125108,8

Nedbank Group Ltd 22004220 Standard Chartered PLC

36101,79

Nedbank Group Ltd 69462,94 HSBC 112260,9 (Table 10)

65 | P a g e

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