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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)
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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
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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
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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
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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.
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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.
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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.
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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)
49 | P a g e
50 | P a g e
(Table 4) (Bidders’ AR)
51 | P a g e
52 | P a g e
(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
Bibliography
(n.d.).
Alberto Cybo-Ottone, Maurizio Murgia. (2000). Mergers and shareholder wealth in European banking.
Journal of Banking & Finance .
Alireza Tourani Rad, Luuk Van Beek. (1999). Market valuation of European bank mergers. European
Management Journal .
Allen N Berger, Rebecca S Demsetz, Philip E Strahan. (1999). The consolidation of the financial services
industry: Causes, consequences, and implications for the future. Journal of Banking & Finance, ELSEVIER .
Allen N. Berger, David B. Humphrey, Lawrence B. Pulley. (1996). Do consumers pay for one-stop banking?
Evidence from an alternative revenue function. Journal of Banking & Finance .
Allen N. Berger, Gerald A. Hanweck, David B. Humphrey. (1987). Competitive viability in banking : Scale,
scope, and product mix economies. Journal of Monetary Economics .
Bartholdy, Jan; Riding, Allan. (1994). THIN TRADING AND THE ESTIMATION OF BETAS: THE EFFICACY OF
ALTERNATIVE TECHNIQUES. The Journal of Financial Research .
Becher, David A. (2000). The valuation effects of bank mergers. Journal of Corporate Finance, ELSEVIER .
Belén Dıaz Dıaz, Myriam Garcıa Olalla, Sergio Sanfilippo Azofra. (2004). Bank acquisitions and
performance: evidence from a panel of European credit entities. Journal of Economics and Business .
Berger, Allen N. (1998). The efficiency effects of bank mergers and acquisition: A preliminary look at the
1990s data. The New York University Salomon Center Series on Financial Markets and Institutions
Volume 3.
Berger, Allen N.; Humphrey, David B. (1992). Megamergers in Banking and the Use of Cost Efficiency as
an Antitrust Defense.
Boyd, John H., Graham, Stanley L. (1991). Investigating the banking consolidation trend. Quarterly
Review .
Bruner, R. (2002). Does M&A pay? A survey of evidence for the decision-maker. Financial Management
Association .
Charles Corrado, Terry Zivney. (1992). The Specification and Power of the Sign Test in Event Study
Hypothesis Tests Using Daily Stock Returns . Journal of Financial and Quantitative Analysis .
Charles Hadlock, Joel Houston, Michael Ryngaert. (1999). The role of managerial incentives in bank
acquisitions. Journal of Banking & Finance .
David, S. (2012). Investment Banks, Hedge Funds and Private Equity .
Davies R., Richardson P., Katinaite & Manninig M. (2010). Evolution of the UK banking system. Research
and analysis Evolution of the UK banking system .
66 | P a g e
Dean Amel, Colleen Barnes, Fabio Panetta, Carmelo Salleo. (2004). Consolidation and efficiency in the
financial sector: A review of the international evidence. Journal of Banking & Finance .
Dean Amel, Colleen Barnes, Fabio Panetta, Carmelo Salleo. (2004). Consolidation and efficiency in the
financial sector: A review of the international evidence. Journal of Banking & Finance, ELSEVIER .
DeLong, Gayle L. (2001). Stockholder gains from focusing versus diversifying bank mergers. Journal of
Financial Economics .
Donald, DePamphilis. (2011). Mergers and Acquisitions Basics- All you need to know.
Dymski, Cary. (1999). The Bank Merger Wave.
Eckbo, B.Espen. (1983). Horizontal mergers, collusion, and stockholder wealth. Journal of Financial
Economics .
George J. Benston, William C. Hunter and Larry D. Wall. (1995). Motivations for Bank Mergers and
Acquisitions: Enhancing the Deposit Insurance Put Option versus Earnings Diversification. Journal of
Money, Credit and Banking .
Gianandrea Goisis, Maria Letizia Giorgetti, Paola Parravicini, Francesco Salsano, Giovanna Tagliabue.
(2009). Economies of scale and scope in the European banking sector. International Review of
Economics .
Giuliano, Iannotta. (2010). Investment Banking: A guide to Underwriting and Advisory Services.
Halpern, P. (1983). Corporate Acquisitions: A Theory of Special Cases? A Review of Event Studies Applied
to Acquisitions. The Journal of Finance .
HART, J. R. & APILADO. (2002). Inexperienced banks and interstate mergers. Journal of Economics and
Business .
Humphrey, Lawrence B. Pulley and David B. (1993). The Role of Fixed Costs and Cost Complementarities
in Determining Scope Economies and the Cost of Narrow Banking Proposals. The Journal of Business ,
Chicago Journals .
Ittner, Constantinos C. Markides and Christopher D. (1994). Shareholder Benefits from Corporate
International Diversification: Evidence from U.S. International Acquisitions.
J.Harold Mulherin, Audra L Boone. (2000). Comparing acquisitions and divestitures. Journal of Corporate
Finance .
JALAL D. Akhavein, Allen N. Berger, David B. Humphrey. (1997). The Effects of Megamergers on
Efficiency and Prices: Evidence from a Bank Profit Function. Review of Industrial Organization .
Jan Barthodly, Allan Riding. (1994). Thin trading and the estimation of betas: the efficacy of alternative
techniques. Journal of Financial Research .
Jane C. Linder, Dwight B. Crane. (1993). Bank mergers: Integration and profitability. Journal of Financial
Services Research .
67 | P a g e
Jeff Madura, Kenneth J. Wiant. (1994). Long-term valuation effects of bank acquisitions. Journal of
Banking & Finance .
Joel F Houston, Christopher M James, Michael D Ryngaert. (2001). Where do merger gains come from?
Bank mergers from the perspective of insiders and outsiders. Journal of Financial Economics .
Joel F. Houston, Michael D. Ryngaert. (1994). The overall gains from large bank mergers. Journal of
Banking & Finance .
Joel F. Houston, Michael D. Ryngaert. (1994). The overall gains from large bank mergers. Journal of
Banking & Finance .
John Ashton, Khac Pham. (2007). Efficiency and Price Effects of Horizontal Bank Mergers. Journal of
Financial Services Research .
JOHN D. LYON, BRAD M. BARBER, and CHIH-LING TSAI. (1999). Improved Methods for Tests of Long-Run
Abnormal Stock Returns. THE JOURNAL OF FINANCE .
José Manuel Campa, Ignacio Hernando. (2006). M&As performance in the European financial industry.
Journal of Banking & Finance .
Ken B. Cyree, James W. Wansley, Harold A. Black. (2000). Bank Growth Choices and Changes in Market
Performance. The Financial Review .
Laura Cavallo, Stefania P.S. Rossi,. (2001). Scale and scope economies in the European banking systems.
Journal of Multinational Financial Management .
LOWINSKI, SCHIERECK, THOMAS. (2004). The Effect of Cross-Border Acquisitions on Shareholder
Wealth— Evidence from Switzerland. Review of Quantitative Finance and Accounting .
Marcia Millon Cornett, Hassan Tehranian. (1992). Changes in corporate performance associated with
bank acquisitions. Journal of Financial Economics .
Martynova, M, Renneboog,. (2006). Mergers and Acquisitions in Europe. CentER Discussion Paper .
Mester, L. J. (1996). A study of bank efficiency taking into account risk-preferences. Journal of Banking &
Finance .
Mester, Loretta J. (2005). OPTIMAL INDUSTRIAL STRUCTURE IN BANKING. Federal Reserve Bank of
Philadelphia .
Michelle Haynes, Steve Thompson. (1999). The productivity effects of bank mergers: Evidence from the
UK building societies. Journal of Banking & Finance .
MURPHY, KEVIN J. (1999). EXECUTIVE COMPENSATION. Handbook of Labor Economics, Volume 3, Edited
by O. Ashenfelter and D. Card.
Norman, Strong. (1992). Modelling Abnormal Returns: A Review article. Journal of Finance & Accounting .
Patrick Beitel, Dirk Schiereck, Mark Wahrenburg. (2004). Explaining M&A Success in European Banks.
European Financial Management .
68 | P a g e
PERISTIANI, S. (1997). Do Mergers Improve the X-Efficiency and Scale Efficiency of U.S. Banks? Evidence
from the 1980s. Journal of Money, Credit, and Banking .
Pinches, Roger P. Bey and George E. (1980). Additional Evidence of Heteroscedasticity in the Market
Model. The Journal of Financial and Quantitative Analysis .
Rezaee, Zabihollah. (2001). Financial Institutions, Valuations, Mergers, and Acquisitions: The Fair Value
Approach.
Rhoades, Stephen A. (1994). A Summary of Merger Performance Studies in Banking, 1980–93, and an
Assessment of the ‘‘Operating Performance’’ and ‘‘Event Study’’ Methodologies. Board of Governors of
the Federal Reserve System .
Rhoades, Timothy H. Hannan and Stephen A. (1987). Acquisition Targets and Motives: The Case of the
Banking Industry. The Review of Economics and Statistics .
Robert DeYoung, Douglas D. Evanoff, Philip Molyneux. (2009). Mergers and Acquisitions of Financial
Institutions: A Review of the Post-2000 Literature. Journal of Financial Services Research .
Scherer, David Ravenscraft &. (1987). Mergers, sell-offs, and economic efficiency.
Stephen J. Brown, Jerold B. Warner. (1980). Measuring security price performance. Journal of Financial
Economics .
Stephen J. Brown, Jerold B. Warner. (1985). Using daily stock returns: The case of event studies. Journal
of Financial Economics .
Steven J. Pilloff, Anthony M. Santomero. (1996). The Value Effects of Bank Mergers and Acquisitions.
Stowell, David P. (2013). Investment Banks, Hedge, Funds and Private Equity.
Ten, G. o. (2001). Report on Consolidation in the Financial Sector.
Tomaso Duso, Klaus Gugler, Burcin Yurtoglu. (2010). Is the event study methodology useful for merger
analysis? A comparison of stock market and accounting data. International Review of Law and
Economics .
Treasury. (2008, October 13). Treasury statement on financial support to the banking industry. The
Telegraph .
Vennet, Rudi Vander. (1996). The effect of mergers and acquisitions on the efficiency and profitability of
EC credit institutions. Journal of Banking & Finance .
Wilson, David C. Wheelock and Paul W. (2000). Why Do Banks Disappear? The Determinants of U.S.
Bank Failures and Acquisitions. The Review of Economics and Statistics .
Yener Altunbaş, David Marqués. (2008). Mergers and acquisitions and bank performance in Europe: The
role of strategic similarities. Journal of Economics and Business .
Zingales, Steven N. Kaplan and Luigi. (1997). Do Investment-Cash Flow Sensitivities Provide Useful
Measures of Financing Constraints? The Quarterly Journal of Economics .
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