economic analysis of stock exchange consolidation · 2015-06-06 · economic analysis of stock...

Economic Analysis of Stock Exchange

Consolidation

Master Thesis

submitted to

Prof. Dr. Peter Gomber

Chair of Business Administration, esp. e-Finance

Faculty of Economics and Business Administration

Goethe-University Frankfurt am Main

Supervisor:

Dr. Marco Lutat

Author:

Jędrzej Mazur, BA

Master of Science in Management

Major: Finance & Information Management

January 15, 2012

Economic Analysis of Stock Exchange Consolidation - 1 -

Table of contents

List of figures ............................................................................................................................. 3

List of tables .............................................................................................................................. 4

Abbreviations .............................................................................................................................. 5

Symbol directory ......................................................................................................................... 7

1 Introduction ........................................................................................................................... 8

2 Economic Rationality behind Stock Exchange Consolidation ........................................ 10

2.1 A brief history of stock exchange consolidation ........................................................... 10

2.2 Key drivers of stock exchange M&A ............................................................................ 13

2.2.1 External drivers ................................................................................................... 13

2.2.2 Internal drivers .................................................................................................... 17

3 Empirical Investigation of Shareholders Value Creation in Stock Exchange M&A .... 19

3.1 Methodology ................................................................................................................. 19

3.1.1 Outline of an event study .................................................................................... 19

3.1.2 Calculation of abnormal returns .......................................................................... 21

3.1.3 Aggregation of abnormal returns ........................................................................ 23

3.1.4 Buy-and-hold abnormal return (BHAR) ............................................................. 24

3.2 Data …… ...................................................................................................................... 26

3.3 Descriptive statistics ..................................................................................................... 26

3.4 Testable hypotheses ...................................................................................................... 33

3.5 Inferential statistics ....................................................................................................... 34

3.6 Discussion of the results of the event study .................................................................. 36

3.6.1 Hypotheses based on the non-wealth-maximizing management ........................ 36

3.6.2 Hypothesis based on the wealth-maximizing management ................................ 37

3.7 Comparison of the event study results with other empirical investigations on value

creation in M&A ........................................................................................................... 37

3.7.1 Abnormal returns to target companies ................................................................ 37

3.7.2 Abnormal returns to bidders ............................................................................... 38

3.8 Limitations of the event study ....................................................................................... 39

3.9 Areas for further research .............................................................................................. 40

4 Economic Impact of Stock Exchange Consolidation ....................................................... 43

4.1 The impact of stock exchange consolidation on transaction costs ................................ 43

4.1.1 Explicit costs ....................................................................................................... 43

4.1.2 Implicit costs ....................................................................................................... 45

4.2 Macroeconomic impact of stock exchange consolidation ............................................ 48

4.2.1 Cost of capital, investment and output ................................................................ 48

4.2.2 Market efficiency and economic growth ............................................................. 49

4.2.3 Financial stability and monetary policy .............................................................. 51


4.2.4 Domestic stock market ........................................................................................ 52

4.3 Fragmentation vs. consolidation: discussion and outlook ............................................ 55

4.3.1 Merits and drawbacks of market fragmentation.................................................. 55

4.3.2 Merits and drawbacks of market consolidation .................................................. 57

4.3.2 Outlook ……………… ...................................................................................... 57

5 Conclusions .......................................................................................................................... 61

6 References ............................................................................................................................ 63

Appendix .................................................................................................................................. 79

Non-plagiarism statement ...................................................................................................... 89


List of figures

Figure 1: Value of the global stock exchange M&A in years 2000-2010 ………..…………11

Figure 2: Expected revenue synergies arising from the merger between Deutsche Börse and

NYSE Euronext ………………………………………………………………..…18

Figure 3: Expected cost synergies arising from the merger between Deutsche Börse and

NYSE Euronext ………………………………………………………..…………18

Figure 4: The event study time line …………………………………………………………20

Figure 5: Consequences of stock exchange consolidation ………………………………….54


List of tables

Table 1: Timeline of M&A deals involving stock exchanges……………………..………...12

Table 2: Timeline of stock exchange demutualization ……..…………………………..…..16

Table 3: Cumulative abnormal returns of 15 bidders over three event windows using market

model for determining the normal return …………………………………………27

Table 4: Cumulative abnormal returns of 15 bidders over three event windows using market

and industry model for determining the normal return ………………...…………28

Table 5: Cumulative abnormal returns of 15 targets over three event windows using market

model for determining the normal return …………………………………...…….39

Table 6: Cumulative abnormal returns of 15 targets over three event windows using market

and industry model for determining the normal return ………………………...…30

Table 7: Buy-and-hold abnormal returns of 9 bidders over three event windows using market

index as a benchmark ………………………………………………………….….31

Table 8: Buy-and-hold abnormal returns of 9 bidders over three event windows using

industry index as a benchmark ………………...………………………………….33

Table 9: Cumulative average abnormal returns of 15 targets and bidders over three event

windows …………………………………………………………………………..35

Table 10: Average buy-and-hold abnormal returns of 9 bidders over three event windows

using industry index as a benchmark ……………………………………………...36


Abbreviations

ABHAR Average Buy-and-Hold Abnormal Return

AMEX American Stock Exchange

ASX Australian Securities Exchange

ATS Alternative Trading System

BHAR Buy-and-Hold Abnormal Return

BM&F Bolsa de Valores, Mercadorias & Futuros de São Paulo

BME Bolsas y Mercados Españoles

CAPM Capital Assets Pricing Model

CAAR Cumulative Average Abnormal Return

CAR Cumulative Abnormal Return

CBOE Chicago Board Options Exchange

CBOT Chicago Board of Trade

CCP Central Counterparty

CDAX Composite DAX

CDNX Canadian Venture Exchange

CF ROA Cash Flow Return on Assets

CM Collateral Management

ECB European Central Bank

ECN Electronic Communication Network

EMU Economic and Monetary Union

ETF Exchange Traded Fund

EU European Union

FSAP The Financial Services Action Plan

FTSE Financial Times Stock Exchange

GARCH Generalized Autoregressive Conditional Heteroscedasticity

GARCH-GED Generalised Autoregressive Conditional Heteroscedasticity with

Generalised Error Distribution

GDP Gross Domestic Product

IBEX Iberia Index

LIFFE London International Financial Futures and Options Exchange

LSE London Stock Exchange


M&A Mergers and acquisitions

MiFID The Markets in Financial Instruments Directive

NASDAQ National Association of Securities Dealers Automated Quotations

NPV Net Present Value

NYMEX New York Mercantile Exchange

NYSE New York Stock Exchange

NZX New Zealand Exchange

OTC Over the Counter

ROA Return on Assets

ROE Return on Equity

S&P Standard & Poor’s

SIMEX Singapore International Monetary Exchange

SME Small and medium enterprises

TSX Toronto Stock Exchange


Symbol directory

AARt Average Abnormal Return on all stocks in the sample, where t stand for the day

in the event window, t ℮ (T1; T2)

ABHARi Average Buy-and-Hold Returns on stock i

ARit Abnormal return on a share i on day t

BHARi,KL Buy-and-Hold Returns on stock i for a period (K, L)

CAARt Cumulative Average Abnormal Return on all stocks in the sample, where t

stand for the day in the event window, t ℮ (T1; T2)

CARi Cumulative Abnormal Return on stock i, i.e. sum of abnormal returns on share

i over the event period (T1, T2)

N Number of companies in the sample.

Rit Actual return on share i in day t

Re Normal return

RMt Return on the market index in day t

RINDUSTRY,t Return on the industry index in day t

Rbt Return on the benchmark index in day t

σCAAR Sample standard deviation of CAAR

υi,t Error term

Xi, Vector of firm i characteristics


1 Introduction

In the last two decades, the evolution and international integration of capital markets has

fundamentally altered the stock exchanges’ operating environment. Globalisation,

deregulation, demutualization and advances in technology were the key drivers of this change.

In order not to lose competitive advantages, stock exchanges started to consolidate, taking

advantage of economies of scale and network externalities. Stock exchange consolidation will

undoubtedly affect number of factors, such as: valuation of the acquiring and acquired firms,

market quality as well as macroeconomic performance.

The aim of this thesis is to analyse the motives behind stock exchanges consolidation and to

investigate its economic impact. The focus is placed on the horizontal dimension only, i.e.

consolidation at the same level of the value chain. Therefore, vertical integration of trading,

clearing and settlement is beyond the scope of this work. Due to the fact that mergers and

acquisitions are by far the most popular form of stock exchange consolidation, they will be

discussed in more detail. Following European Central Bank (2000, p. 3), this thesis defines

merger as a combination of two or more companies that results in the creation of a new entity

and acquisition as a purchase of shares in another firm in order to achieve a managerial

influence. Even though the terms consolidation and mergers & acquisitions are often used as

synonyms, they are not equivalent since consolidation also encompasses other forms of

cooperation, such as: alliances, implicit mergers, joint ventures and outsourcing (Schmiedel &

Schonenberger, 2005, p. 7). In this work, the others forms of stock exchange consolidation

will be touched upon only very briefly.

The analysis of economic consequences of stock exchange consolidation is performed in this

thesis from two perspectives. The first one is rooted in the shareholder value theory and treats

stock exchange as a typical corporation, whose aim is to maximize value to shareholders.

Therefore, the decision to merge with another stock exchange should be based on the cost-

benefit calculus and be approved only if the transaction leads to shareholder value generation.

In order to evaluate the impact of stock exchange consolidation on stock prices of the

acquiring and acquired companies, the following research question is posed:

RQ1: Are stock exchange mergers and acquisitions value-enhancing projects?

In this thesis, an event study approach is taken to answer this question and evaluate the

abnormal stock performance of acquirers and targets following the merger announcement.

Positive abnormal returns imply value creation, while negative - value destruction.


The second perspective, from which the economic impact of stock exchange consolidation is

analysed, relates to the micro- and macroeconomic consequences of stock exchange

consolidation. In particular, this thesis tries to answer the following research question:

RQ 2: What is the impact of stock exchange consolidation on transaction costs and the

macroeconomic performance?

Moreover, this work discusses both merits and drawbacks of market consolidation and

fragmentation. In particular, it tries to predict whether in the future stock trading will

consolidate into a single market or be dispersed across multiple trading venues. For this

reason, the following research question to be asked is:

RQ 3: Is it feasible to establish a single global stock exchange?

In order to answer research questions 2 and 3, a judgmental analysis of the related literature is

performed.

Overall, it is surprising that the impact of stock exchange consolidation on shareholder value

creation has been given so little attention in the literature. To my knowledge, this thesis is a

pioneering study in this field. Therefore, it will contribute to the market microstructure

literature by providing empirical evidence on the influence of M&A on stock prices and

returns of both the acquiring and acquired stock exchange. Moreover, it aims to provide a

comprehensive framework for a systematic analysis of both micro- and macroeconomic

implications of stock exchange consolidation.

The remainder of this thesis is structured as follows. The next chapter discusses the

motivation behind stock exchange mergers. It also investigates driving factors in the recent

wave of M&A activity. Chapter 3 deals with the first perspective of the economic impact of

stock exchange consolidation, i.e. shareholder value creation in stock exchange mergers.

Moreover, chapter 3 describes data, methodology and provides empirical results of the event

study. Chapter 4 reviews the implication of stock exchange consolidation on transaction costs

and macroeconomic performance as well as discusses advantages and drawbacks of market

consolidation and fragmentation. It also tries to predict the future structure of stock markets.

Concluding remarks are presented in the last chapter.


2 Economic Rationality behind Stock Exchange

Consolidation

This chapter deals with the economic motivation behind stock exchange consolidation. At

first, it provides a snapshot of the most significant stock exchange M&A in the last decade.

Then, it describes factors that have contributed to an increase in the level of stock exchange

consolidation. Finally, it discusses reasons for stock exchange consolidation.

2.1 A brief history of stock exchange consolidation

In the past, stock exchanges enjoyed a regional monopoly position due to considerable

geographical and technological limitations. As noted by Arnold et al. (1999, p. 1085) “without

telephones, telegraphs, or teletypes, face-to-face bargaining was essential in effecting

securities sales”. The necessity of physical presence is perceived as the most significant

barrier of consolidation, which led to the creation of multiple regional exchanges. In the XX

century, however, regional exchanges started to lose their quasi monopoly position, following

advances in telecommunications and legislative changes. In order to remain competitive,

exchanges needed to grow either organically (by attracting more trading in existing products

and developing new ones) or externally (through mergers and alliances) (cf. Committee of

Wise Men, 2001, p. 81). Due to highly homogenous products offered by stock exchanges

industry, further organic growth was hardly possible. Therefore, the only way for exchanges to

grow and move down the declining average cost curve was to merge with other stock

exchanges1.

In the last two decades, the environment in which stock exchanges operate has undergone

even more profound changes. Due to a number of factors 2 , stock exchanges lost their

privileged position and were forced to cooperate. As pointed out by Aggarwal (2002, p. 106),

in the early 1990s, the most popular form of cooperation between stock exchanges were

strategic alliances. For instance, several Bolsas formed alliances that aimed to coordinate

membership and listing requirements, order execution as well as trading technology

(Aggarwal, 2002, p. 106). The most significant difference between mergers and alliances is

1 This statement can be exemplified by the evidence from the U.S. market, where the number of

regional stock exchanges declined from 100 in 1900, to 35 in 1935 and 15 in 1965 (El Serafie &

Abdel Shahid, 2002, p. 11).

2 The next subchapter deals with these factors in detail.


that alliances do not aim at a complete unification of ownership, pricing and decision making

(Shy & Tarkka, 2002, p. 2). Another popular form of stock exchange collaboration were

implicit mergers, defined by Di Noia (2001, p. 3) as “agreements between two exchanges such

that the securities listed in one exchange are listed by the other and remote access is offered

to the traders of each exchange, with reciprocity and without further requirements”. A

prominent example of an implicit merger was the cooperation between the London

International Financial Futures and Option Exchange and the Chicago Board of Trade that

started in 1997. Even though strategic alliances and implicit mergers guaranteed that

exchanges will maintain their identities (Williamson, 1997, p. 410), they failed due to

regulatory hurdles and inability to achieve proportional benefits by all involved entities. The

failure of alliances was one of the reasons why stock exchanges started to merge in the late

1990s. Since 1999, when NASDAQ acquired American Stock Exchange, the number of M&A

transactions involving stock exchanges has exceeded 303. In years 2006-2008 there was a peak

in the M&A activity in terms of the transaction values, as shown in Figure 1.

Figure 1 Value of the global stock exchange M&A in years 2000-2010 (Erman et al., 2011)

3 A list of the most significant deals is presented in Table 1.


Year Acquirer Target

Deal

value

(bn)

2011 Deutsche Börse NYSE Euronext n.a.

2011 LSE Toronto Stock

Exchange n.a.

2010 Singapore Exchange Australian Securities Exchange $7.8

2008 Bovespa Bolsa de Mercadorias &

Futuros $9.0

2008 Chicago Mercantile Exchange Nymex $8.9

2008 NYSE Euronex American Stock Exchange $0.3

2007 Toronto Stock Exchange Montreal Stock Exchange $1.3

2007 LSE Borsa Italiana €1.5

2007 NASDAQ OMX Philadelphia Stock Exchange n.a.

2007 NASDAQ OMX $3.7

2007 Deutsche Börse International Securities

Exchange $2.8

2006 Chicago Mercantile

Exchange CBOT $11.9

2006 NYSE Euronext $11.0

2005 NYSE Archipelago n.a.

2005 NASDAQ Instinet n.a.

2004 Toronto Stock Exchange Natural Gas Exchange n.a.

2004 OMX Copenhagen Exchange n.a.

2004 NASDAQ BRUT ECN n.a.

2003 OMX Helsinki Exchange n.a.

2002 Euronext LIFFE n.a.

2002 Euronext Portuguese Exchange n.a.

2001 Toronto Stock Exchange CDNX (Canadian Venture

Exchange) n.a.

2001 BME Spanish Exchange

Madrid Stock Exchange

Valencia Stock Exchange

Barcelona Stock Exchange

Bilboa Stock Exchange

n.a

2000 CDNX Winnipeg Stock Exchange n.a


2000 Euronext

Paris Stock Exchange

Amsterdam Stock Exchange

Brussels Stock Exchange

n.a

2000 Hellenic Stock Exchange

Thessaloniki Stock Exchange

Athens Stock Exchange

Athens Derivative Exchange

n.a

2000

Hong Kong Exchanges &

Clearing

Stock Exchange of Hong Kong

Hong Kong Futures Exchange

Hong Kong Securities

Clearing Company

n.a

1999 Singapore Exchange Stock Exchange of Singapore

SIMEX n.a

1999 CDNX (Canadian Venture

Exchange)

Vancouver Stock Exchange

Alberta Stock Exchange n.a

1999 NASDAQ American Stock Exchange n.a

Table 1 Timeline of M&A deals involving stock exchanges. Source: author's compilation based on Grant (2011) and Aggarwal & Dahiya (2006, p. 100)

2.2 Key drivers of stock exchange M&A

In the corporate finance theory, there are two types of factors that influence corporate growth

(Thorwartl, 2005, p. 23). External drivers involve changes in the company’s operating envi-

ronment that are beyond its control. As emphasized by Levitt (1983, pp. 92-93), mergers &

acquisitions are an answer to the new conditions. Internal drivers, on the other hand, are de-

scribed as synergistic potential that can be realized through mergers and acquisitions.

2.2.1 External drivers

Technological change is considered one of the most significant external factors affecting

company’s operations. According to Ohmae (1993, pp. 36-40), firms consolidate in order to

stay competitive in the environment characterized by a rapid technological progress. The ad-

vent of the Internet as well as the emergence of electronic trading have revolutionized the

market microstructure, enabling the organization of central stock markets in a decentralized

form. As a result, the location of stock exchanges lost its former relevance. With the inception

of electronic communication networks (ECNs) and alternative trading systems (ATSs), the

competitive pressure in the market rose considerably, providing a strong incentive for consol-

idation (Domowitz & Steil, 2002). Moreover, the introduction of electronic order books ac-

celerated the trend towards disintermediation as investors with direct market access started to

play the role formerly reserved to brokers. This led to a cost-cutting pressure on intermediar-


ies. Again, in order to remain competitive, traditional trading floors started to consolidate. It

should be emphasized that electronic trading, contrary to traditional floor trading, is character-

ized by a very high level of scalability, which allows achieving substantial economies of scale.

The other external driver of mergers and acquisitions is globalisation, which refers to trade

liberalization, reduction of barriers to international capital flow as well as political and eco-

nomic integration. Growing global competition is forcing firms to become more efficient.

Hence, mergers and acquisitions are perceived as a way to achieve higher efficiency by utiliz-

ing economies of scale and scope. With respect to stock exchanges, the integration of interna-

tional capital markets, removal of regulatory restrictions on capital flows, harmonization of

regulation as well as creation of the political and economic union in Europe increased the lev-

el of cross-border trading. Stock exchanges took advantage of global opportunities by entering

foreign markets through mergers, acquisitions and alliances. Furthermore, it has been ob-

served that stock exchanges are converging and thus becoming more interdependent4, which

adds another argument for further consolidation.

The most significant legislative milestones in the process of the stock market integration were:

the enactment of OECD Code of Liberalisation of Capital Movements, The European Invest-

ment Services Directive, The Financial Services Action Plan (FSAP) and The Markets in Fi-

nancial Instruments Directive (MiFID). As pointed out by Nielsson (2009, p. 265), even

though MiFID aims at promoting inter-market competition, it might also result in further con-

solidation of stock exchanges. The reason is that trading venues would need to achieve critical

mass in face of an increased competitive pressure from new market entrants.

The introduction of a single currency in Europe added another integrative force. Williamson

(1997, p. 410) claims that due to the emergence of economic and monetary union (EMU), the

differences between stock exchanges from the point of view of investors have diminished.

Moreover, as argued by Gaspar (2000), the introduction of euro eliminated conversion costs

and exchange rate risk, which increased the level of cross-border investments in the EMU.

Abraham & Pirard (2002, p. 13) list the major consequences of the introduction of a single

currency in Europe. They believe that, among others, it would exercise pressure on small

stock exchanges to find niche markets or to consolidate. They also claim that the intense com-

4 See e.g. Fraser & Oyefeso (2005), Chelley-Steeley et al. (1998), Bessler et al. (2003), Kim et al.

(2005)


petition among trading venues, arising from the financial markets integration in Europe,

would eventually result in a wave of consolidation among European stock exchanges.

Furthermore, the emergence of an equity culture has increased the attractiveness of cross-

border investment in Europe (McAndrews & Stefanadis, 2002, p. 7). As pointed out by Allen

& Gale (2000), in the past European financial system was bank-oriented, with banks playing

the role of intermediaries between investors and companies. This has gradually changed and

today equity is becoming a popular method of corporate funding. The increased interest in

cross-border trading provides yet another incentive for stock exchanges to expand beyond

their national boundaries.

Global competition and the rise of automated trading gave stock exchanges a strong impetus

to reformulate their business strategy. Many exchanges had no choice but to adopt a new cor-

porate governance structure and start acting more entrepreneurially. Demutualization, i.e. a

conversion from member-owned, non-profit organizations into profit-driven, investor-owned

companies, was perceived as the best way to achieve these goals (Aggarwal, 2002, p. 113).

As emphasized by Aggarwal (2002, p. 106), the central concept of demutualisation is the sep-

aration of trading rights (membership) and ownership. The most significant advantage of de-

mutualization is that it provides stock exchanges with an access to capital that is necessary for

investment in state-of-the art technology. It also guarantees that exchanges, as shareholder

owned corporations, will need to achieve higher cost efficiency and revenue generation in

order to maximize value to shareholders. Hence, as argued by Lee (2002), demutualization

puts pressure on stock exchanges to consolidate in order to achieve revenue and cost syner-

gies.

Since 1993, when Stockholm Stock Exchange demutualized, the number of exchanges acting

as joint-stock companies has increased considerably5. Some stock exchanges went a step fur-

ther and conducted a public offering. Self-listing is perceived as a driving force of stock ex-

change consolidation as it enables financing of mergers and acquisitions through the exchange

of shares in a stock swap. Moreover, stock exchanges are no longer controlled by strategic

investors who could block transactions for fear of losing their privileges. Furthermore, public-

ly traded stock exchanges are characterized by higher transparency, which facilitates the ac-

quisition process (European Central Bank, 2007, p. 69).

5 For a complete list of stock exchanges that underwent demutualization, see Table 2.


Exchange Year of Demutualisation

OMX Group 1993

Helsinki Stock Exchange 1995

Copenhagen Stock Exchange 1996

Amsterdam Stock Exchange 1997

Borsa Italiana 1997

Australia Stock Exchange 1998

Hellenic Stock Exchange 1999

Iceland Stock Exchange 1999

Singapore Stock Exchange 1999

London Stock Exchange 2000

Euronext 2000

Deutsche Börse 2000

Toronto Stock Exchange 2000

Hong Kong Stock Exchange 2000

BME Spanish Exchanges 2001

Oslo Bors 2001

NASDAQ 2001

Tokyo Stock Exchange 2001

Osaka Stock Exchange 2001

Philippines Stock Exchange 2001

Swiss Exchange 2002

Chicago Mercantile Exchange 2002

International Stock Exchange 2002

New Zealand Stock Exchange 2003

Bursa Malaysia 2004

CBOT 2005

NYSE 2006

American Stock Exchange 2006

CBOE 2006

Table 2 Timeline of stock exchange demutualization (cf. Aggarwal & Dahiya, 2006, p. 98; Aggarwal 2002, p. 106)


2.2.2 Internal drivers

According to Salter & Weinhold (1994, p. 117), internal drivers (in other words – synergies)

are essential for the success of mergers and acquisitions. Synergies can be achieved through

economies of scale and scope, learning curve as well as coinsurance (Thorwartl, 2005, p. 31).

Grant (2011) claims that “exchanges are generally fixed-cost businesses”. Hence, an increase

in trading volume leads to a decrease in average operating costs. Likewise, Jensen & Natorp

(2000) hold that economies of scale in equity trading arise from significant costs associated

with setting up a trading infrastructure and negligible costs of increasing trading volume. The

existence of economies of scale in trading has been confirmed by many empirical studies6.

The recent emergence of electronic trading has triggered a shift in the average cost curve, al-

lowing for even greater economies of scale. McAndrews & Stefanadis (2002) claim that effi-

ciencies from sharing common trading platforms are the primary rationale for stock exchange

consolidation.

Scope economies, conceptually similar to economies of scale, refer to a decrease in the aver-

age cost that stems from common investment supporting multiple products. As noted by

Schmiedel & Schonenberger (2005, p. 8), due to the integration of user interfaces, merged

stock exchanges can develop new products at a lower unit cost than separately. Lee (2003, p.

7) holds that stock exchange consolidation enables sharing of multiple functions, such as:

marketing, listing, order routing, order execution, matching and information dissemination. It

should be emphasized, however, that in the equity trading industry economies of scope are far

less significant than economies of scale (Group of Ten 2001, p. 5).

The synergies arising from stock exchange mergers and acquisitions are profound. Deutsche

Börse, for instance, expects that the merger with NYSE Euronext will generate at least €100

million in revenue synergies and €400 million in annual costs savings (Deutsche Börse, 2011,

p. 37). The revenue synergies shall be realised through “cross-selling and distribution

opportunities, increased turnover from liquidity pool consolidation and new products, a

progressive introduction of Deutsche Börse Group’s clearing capabilities and expanded

scope for technology services and market data offerings” (Alpha Beta Netherlands, 2011, p.

A-90). Figure 2 describes anticipated sources for the revenue synergies. It is estimated that

approximately 50% of the projected revenue synergies will be realized in the clearing business

6 See e.g. Doede (1967), Schmiedel (2001), Hasan et al. (2002), Malkamäki (2000).


and approximately 50% in the derivatives and cash markets as well as in the technology

business.

Figure 2 Expected revenue synergies arising from the merger between Deutsche Börse and NYSE Euronext (Alpha Beta Netherlands, 2011, p. A-90)

The cost synergies shall stem from savings in information technology, clearing, market

operations as well as corporate administration and support functions (Alpha Beta Netherlands,

2011, p. A-88). Figure 3 sets forth the areas in which costs savings can be achieved.

Figure 3 Expected cost synergies arising from the merger between Deutsche Börse and NYSE Euronext (Alpha Beta Netherlands, 2011, p. A-88)

Similarly, NYSE Euronext expected significant cost synergies arising from the acquisition of

the Amex. Cost savings, estimated to approximately $100 million within the first two years,

should be achieved by integration of staff, technology, data centres as well as the

consolidation of professional and contract services and vendors (NYSE Euronext, 2008, p. 2).

It should be emphasized that post-merger integration involves significant expenditures. In the

case of the pending merger between Deutsche Börse and NYSE Euronext, long-run

implementation and restructuring costs are estimated to be 1.5 to 2 times as high as the

expected cost synergies and reach the level of €600 - €800 million (Alpha Beta Netherlands,

2011, p. 97). Furthermore, advisory fees and other direct transaction costs provide another

cost factor. The direct costs of the Euronext integration exceeded the amount of €28 million

(Euronext, 2001, p. 38).


3 Empirical Investigation of Shareholders Value

Creation in Stock Exchange M&A

This chapter focuses on the consequences of consolidation from the perspective of the owners

of the acquiring (bidder) and acquired (target) stock exchanges. At first, it provides an over-

view of the event study method that is used to evaluate the impact of the merger announce-

ment on the abnormal returns of bidders and targets. In the next step, both descriptive and

inferential statistics are calculated. Finally, the results of the event study are discussed and

compared with evidence from other industries.

3.1 Methodology

One of the most widely used methods of examining stock price reaction on a particular event

in the capital market or a company’s life is an event study, introduced by Ball & Brown

(1968) as well as Fama et al. (1969) and popularised by Brown and Warner (1980, 1985). The

main advantage of an event study is that it enables a separation of company-specific events

from industry- and market-specific. Events studies implicitly assume that markets are at least

semi-strong efficient, according to Fama’s (1970) classification. It means that stock prices

reflect all publicly available information on a particular asset. Therefore, stock prices should

automatically adjust to new information, e.g. announcement of a merger. Event study

determines whether such information leads to an abnormal movement of a share’s price. If

abnormal returns are positive and statistically significant, one can conclude that mergers

create shareholder value. Negative abnormal returns imply value destruction and insignificant

returns suggest that value is neither created nor destroyed in the process of a merger.

According to Fama (1991), capital markets are efficient and since all information is

immediately incorporated into stock prices, one cannot consistently earn abnormal returns.

3.1.1 Outline of an event study

Event study consists of three time periods:

Estimation window (also called control period) is used to estimate parameters of

market model;

Event window is used to determine whether the event resulted in abnormal returns and

if the event announcement was anticipated or leaked;

Post event window is used to examine the performance after the event.


Figure 4 presents these time frames:

Figure 4: The event study time line

It should be emphasized that the results of an event study are vulnerable to time intervals

chosen. Hence, it is critical to determine the length of estimation and event windows. The

usual length of the estimation period is one calendar year (or 252 days), as suggested by

Brown & Warner (1985, p. 6). Dyckman et al. (1984, p .3) argue that the parameter estimation

period should have a minimum of 120 days. With respect to the event window, Panayides &

Gong (2002, p.61) show that an 11 day interval fully captures the effects of an event.

It is also crucial to define an event day. According to Dodd & Ruback (1977, p. 352-353) and

Halpern (1983 p. 304), it is the merger announcement day and not the deal closing date that

should be taken as an event day. This statement is in line with the efficient market hypothesis.

However, as argued by Elton et al. (2003), the event window should not be restrained only to

the event day since share prices may react over time to the merger announcement. The reasons

for abnormal returns before the announcement day could be insider trading, information leaks

or even the market anticipation (Keown & Pinkerton, 1981, p. 855-857).

In this thesis, event window is set to five days before and five days after the merger

announcement day. As a robustness check, the event window is shortened to one day before

and one day after the event and prolonged to ten days before and ten days after the event day.

Furthermore, the estimation window is determined as 252 trading days preceding five days

before the merger announcement7.

7 In a case of a merger between Chicago Mercantile Exchange and Chicago Board of Trade, estima-

tion window comprises of only 244 days due to the lack of data in the Datastream database. Nev-

ertheless, this length of the estimation window meets the minimum requirement of 120 days.


3.1.2 Calculation of abnormal returns

Abnormal returns, also referred to as residual returns, are computed as a difference between

actual returns and normal returns, i.e. returns expected in the absence of the event (Campbell

et al., 1997, pp. 151-153). Abnormal returns are expressed as:

ARit = Rit - Re,

where

ARit – abnormal return on a share i on day t

Rit - actual return of share i in day t

Re – normal return

According to Parkin & Dobbins (1993, pp. 507-508), normal returns can be calculated using

three different models:

Market model: ordinary least-square regression of share returns on returns of the

market index RMt

ARit = Rit - αi - βi RMt

Market index should be a broad-based value-weighted index or a floating-weighted

index (Benninga, 2008, pp. 373-374).

Simplified market model: assuming αi = 0 and βi =1

CAPM model: βi is estimated from the market model and risk free rate (rft) is a 3-

month yield on treasury bills on a one month basis

ARit = Rit - rft - βi (RMt - rft)

Market model assumes that stock returns are a linear function of only one factor – market

index. However, stock returns can be driven by more variables. According to the so-called

two-factor model, stock returns are determined by both a market (RMt) and industry

(RINDUSTRY,t) factor (Halpern, 1973, p. 562-563). Hence, abnormal returns can be calculated as

follows:

ARit = Rit - αi - βi, MARKET RMt - βi, INDUSTRY RINDUSTRY,t


where

RMt - return of the market index on day t

RINDUSTRY,t - return of the industry index on day t

In the literature, even more sophisticated models can be found (cf. Schwert, 2000, p. 2617;

Gorgen and Renneboog, 2003, p.8-10; MacKinlay, 1997, p. 17-19). However, as showed by

Brown and Warner (1985, p.14), in large samples results are not very sensitive to models

applied.

In this thesis, normal returns is estimated based on the market model, which is congruent with

recommendations from Bartholdy et al. (2007), Barber & Lyon (1997) and Brown & Warner

(1985).

In order to measure the significance of abnormal returns for each company, both parametric

and non-parametric tests can be performed. As suggested by Benninga (2008, p. 379), the

most popular parametric test it the t-statistics, which can be obtained by dividing abnormal

return for each day in the event window by the standard error of regression8. While performing

the t-test, four assumptions regarding the probability distribution of abnormal returns must be

met:

regression residuals are normally distributed;

abnormal returns are independently and identically distributed;

variance of residuals is constant (homoscedasticity);

expected value of abnormal returns is zero.

If these assumptions are violated, non-parametric test should be performed instead. As argued

by Maynes & Rumsey (1993, p. 146), one should use non-parametric test when confronted

with thin trading in the sample. The most popular non-parametric tests are: rank test (cf.

Corrado, 1989), sign test (cf. Corrado & Zivney, 1992) and generalized sign test (cf. Cowan,

8 It should be noted that using standard error of regression in the denominator results in a slight unde-

restimation of the true variance of market model. However, as the sampling error approaches zero

with the increase in the length of estimation window, the effect of sampling error becomes mini-

mal and is hence often disregarded.


1992). In this thesis, parametric t-test is used to test the statistical significance of abnormal

return since there is no thin trading problem in the sample securities.

The next step is to calculate total, cumulated abnormal returns (CARi) accrued to shareholders

of firm i during the event window. CARi is simply the sum of abnormal returns of share i over

the event period (T1, T2).

3.1.3 Aggregation of abnormal returns

After calculating abnormal returns (ARit) for firm i, results are then aggregated across all

companies in order to compute the average abnormal returns for all firms in the sample. The

average abnormal return is estimated by the following equation:

AARt - average abnormal return of all stocks in the sample, where t stand for the day in the

event window, t ℮ (T1; T2);

N - number of companies in the sample.

The average abnormal returns can be then aggregated over the even window using an

analogical approach to that used to compute cumulative abnormal returns for each security i.

Cumulative average abnormal returns (CAARt) for any interval in the event window are

computed using the following formula:

CAARt – cumulative average abnormal return of all stocks in the sample, where t stand for the

day in the event window, t ℮ (T1; T2);

Alternatively, cumulative average abnormal returns (CAAR) can be calculated as follows:


The t-statistic for CAAR is computed using the following formula

where σCAAR stands for the sample standard deviation and N is the sample size.

σCAAR is calculated as:

The degree of freedom used in this test is n − 1.

3.1.4 Buy-and-hold abnormal return (BHAR)

Kothari & Warner (1997) as well as Barber & Lyon (1997) argue that there are many

problems in making statistical inferences using cumulative abnormal returns. They suggest

that market reactions over a longer period of time should be assessed by means of the buy-

and-hold abnormal return (BHAR) analysis, also known as characteristic-based matching

approach. Barber & Lyon (1997) point out that BHAR is the appropriate estimator since it

measures investor experience more precisely. Mitchell & Stafford (2000, p. 296) define

BHAR as a return from a strategy of investing in all firms that complete an event (e.g.

acquisition) and selling at the end of a prespecified holding period versus a comparable

strategy using otherwise similar nonevent firms. BHAR for a period (K, L) can be calculated

as a geometrically compounded return on a company (Rit) minus geometrically compounded

return for the benchmark market index (Rbt).

The average buy and hold abnormal return (ABHARKL) in case of equally weighted stocks can

be calculated as follows:

where N is the number of firms in the sample.


In order to test the significance of the average buy and hold abnormal return (ABHAR), the

following test statistic is calculated:

where

The degree of freedom used in this test is n − 1.

In this thesis, 1-year, 1-5 year and 2-year BHARs of bidders are calculated for each announced

deal9. Respective market portfolios are used as a benchmark.

It should be pointed out that the differences between CARs and BHARs result from the fact

that BHARs take into account the effect of monthly compounding while CARs ignore it.

3.2 Data

Share prices as well as benchmark indices were gathered from Datastream and Yahoo Finance

databases. The relatively modest sample size results from the fact that before demutualization

stock exchanges were private unlisted companies. To my knowledge, until 2011 there have

been only six accomplished mergers (and one pending) between listed stock exchanges.

However, several times information about possible mergers has been revealed to the public.

According to Pound & Zeckhauser (1990) as well as Clarkson et al. (2006), takeover rumours

may influence the abnormal returns of stock prices of the acquiring and acquired companies.

Therefore, eight rumoured mergers have been added to the sample. The announcement days

and rumour days were gathered from the ZEPHYR and Merger Market databases and

compared with data stored in the Lexis/Nexis database.

Furthermore, the normal return has been calculated using the following broad indices that

served as proxies for market indexes in the one factor model:

FTSE All shares – United Kingdom

CDAX – Germany

9 In the case of a recently announced Deutsche Börse – NYSE merger, long term BHARs cannot be

calculated.


Euronext 100 – France

S&P 500 – USA

Ibex 35 – Spain

OMX Nordic 40 – Nordic countries

S&P/ASX 200 – Australia

NZX 50 – New Zealand

S&P/TSX - Canada

In order to check the robustness of the model outcomes, abnormal returns have been also

regressed on the industry index, represented by the Dow Jones Global Exchanges Index.

Regression outputs presented in Appendix show that stock exchanges returns are jointly

determined by market and industry indices, as indicated by very low levels of p-values for the

F-statistics. Therefore, all descriptive and inferential statistics will be calculated separately for

the one-factor market model and the two–factor market and industry model10.

3.3 Descriptive statistics

The impact of acquisition announcements on bidders’ stock returns is presented in Table 3

(for the one-factor model) and Table 4 (for the two-factor model). Tables 5 and 6 show

targets’ stock returns for the one- and two-factor models, respectively.

It should be pointed out that cumulative abnormal returns calculated using one factor model

are on average twice as high as those based on two-factor model, irrespective of the event

window length and the transaction side. The reason for this discrepancy may stem from the

fact that in the period examined, the industry index (Dow Jones Global Exchanges Index)

constantly outperformed the market. Since multi-factor models, in general, better capture

determinants of stock returns, results based on two factor market and industry model seem to

be more plausible than those based on one factor only.

10 All data, tables and calculations are provided in the enclosed CD.


Table 3: Cumulative abnormal returns of 15 bidders over three event windows using market model for determining the normal return

Bidder Target

Announce-

ment /

Rumour day

Market model

Bidder

CAR

(-10;10)

Bidder

CAR

(-5;5)

Bidder

CAR

(-1;1)

Chicago

Mercantile

Exchange

NYMEX Holding 2008-03-17 -8.28% -5.10% -10.98%

Chicago

Mercantile

Exchange

CBOT Holdings

Inc. 2006-10-17 2.41% -5.06% 2.62%

Deutsch Börse NYSE Euronext 2011-02-15 1.09% 3.40% -1.93%

NASDAQ Instinet 2005-04-22 61.53% 58.89% 54.76%

NASDAQ OMX 2007-05-25 9.65% 11.49% 3.55%

Deutsche Börse

(via Eurex)

International

Securities

Exchange

2007-04-30 -13.31% -10.44% -6.20%

NYSE Euronext 2006-05-22 -5.87% -16.54% -2.75%

Euronext NV London Stock

Exchange 2004-05-28 0.01% -1.96% -0.13%

Deutsche Börse Euronext NV 2005-09-27 1.76% 4.94% 0.32%


NYSE Euronext

BME Bolsas

Mercados

Espanoles

2007-09-21 11.17% 13.19% 16.51%

ASX Ltd New Zealand

Exchange Ltd 2007-10-16 -1.88% -4.66% -4.94%

London Stock

Exchange

Toronto Stock

Exchange 2011-02-09 -0.22% 0.66% 2.01%

London Stock

Exchange NASDAQ OMX 2011-02-23 -7.89% -4.85% -1.28%

NASDAQ OMX NYSE Euronext 2011-04-01 18.03% 17.81% 14.89%

Average

(CAAR) 4.88% 4.18% 4.65%

Standard

deviation 17.63% 17.63% 15.58%

Coefficient of

variation 3.62 4.22 3.35

Skewness 2.59 2.29 2.65

Excess

kurtosis 8.19 6.78 8.20


Bidder Target

Announce-

ment /

Rumour day

Market and industry model

Bidder

CAR

(-10;10)

Bidder

CAR

(-5;5)

Bidder

CAR

(-1;1)

Chicago

Mercantile

Exchange

NYMEX Holding 2008-03-17 0.93% 0.05% -2.12%

Chicago

Mercantile

Exchange

CBOT Holdings Inc. 2006-10-17 -2.22% -1.59% 0.62%

Deutsche Börse NYSE Euronext 2011-02-15 -1.54% 0.49% -3.86%

NASDAQ Instinet 2005-04-22 31.59% 32.97% 30.70%

NASDAQ OMX 2007-05-25 1.51% 3.72% -1.82%

Deutsche

Börse (via Eurex)

International

Securities Exchange 2007-04-30 -5.37% -3.63% -3.73%

NYSE Euronext 2006-05-22 -2.12% -14.7% -4.92%


Exchange 2004-05-28 0.86% -1.15% -0.38%

Deutsche Börse Euronext NV 2005-09-27 1.99% -0.10% 1.32%

Deutsche Börse Euronext NV 2005-11-07 0.90% -1.91% 0.09%

NYSE Euronext BME Bolsas

Mercados Espanoles 2007-09-21 -3.04% 2.52% 7.12%

ASX Ltd New Zealand

Exchange Ltd 2007-10-16 1.05% -1.36% -1.65%

London Stock

Exchange

Toronto Stock

Exchange 2011-02-09 0.74% 1.65% 2.96%

London Stock

Exchange NASDAQ OMX 2011-02-23 -1.60% -0.23% 0.57%


Average

(CAAR) 2.01% 1.52% 1.96%

Standard

deviation 8.63% 9.86% 8.58%

Coefficient of

variation 4.29 6.48 4.37

Skewness 3.24 2.27 3.00

Excess

kurtosis 11.53 8.45 10.17

Table 4 Cumulative abnormal returns of 15 bidders over three event windows using market and industry model for determining the normal return


Bidder Target

Announce-

ment / Rumour

day

Market model

Target

CAR

(-10;10)

Target

CAR

(-5;5)

Target

CAR

(-1;1)

Chicago Mercantile

Exchange NYMEX Holding 2008-03-17 -6.71% -4.21% -7.98%

Chicago Mercantile

Exchange CBOT Holdings 2006-10-17 25.28% 20.73% 23.87%

Deutsch Börse NYSE Euronext 2011-02-15 9.95% 9.25% -5.12%

NASDAQ Instinet 2005-04-22 -9.60% -8.25% -10.25%

NASDAQ OMX 2007-05-25 31.87% 29.74% 21.43%

Deutsche Börse (via

Eurex)

International

Securities

Exchange

2007-04-30 46.66% 55.70% 67.05%

NYSE Euronext 2006-05-22 -1.73% -5.30% -0.42%


Exchange 2004-05-28 -1.95% -4.64% -3.28%

Deutsche Börse Euronext NV 2005-09-27 -1.79% 3.24% 0.75%


NYSE Euronext

BME Bolsas

Mercados

Espanoles

2007-09-21 7.24% 8.36% 12.60%

ASX Ltd New Zealand

Exchange Ltd 2007-10-16 -4.07% -4.89% -3.79%

London Stock

Exchange

Toronto Stock

Exchange 2011-02-09 4.88% 3.11% 4.58%

London Stock

Exchange NASDAQ OMX 2011-02-23 3.28% -3.77% 0.32%


Average

(CAAR) 7.44% 7.45% 7.52%

Standard

deviation 15.52% 16.88% 19.18%

Coefficient of

variation 2.09 2.27 2.55

Skewness 1.52 1.95 2.37

Excess

kurtosis 1.88 4.16 6.69

Table 5 Cumulative abnormal returns of 15 targets over three event windows using market model for determining the normal return


Bidder Target Announement/

Rumor day

Market and industry model

Target

CAR

(-10;10)

Target

CAR

(-5;5)

Target

CAR

(-1;1)

Chicago Mercantile

Exchange

NYMEX

Holding 2008-03-17 1.66% -3.64% 0.55%

Chicago Mercantile

Exchange

CBOT

Holdings 2006-10-17 12.57% 2.84% 9.73%

Deutsche Börse NYSE Euronext 2011-02-15 10.14% 9.10% -4.27%

NASDAQ Instinet 2005-04-22 -10.98% -8.18% -10.1%

NASDAQ OMX 2007-05-25 34.24% 29.36% 21.80%

Deutsche Börse (via

Eurex)

International

Securities

Exchange

2007-04-30 19.38% 28.07% 36.45%

NYSE Euronext 2006-05-22 4.79% 0.96% 6.12%


Exchange 2004-05-28 -0.86% -3.04% -1.87%


Deutsche Börse Euronext NV 2005-11-07 -0.94% 1.46% 0.12%

NYSE Euronext

BME Bolsas

Mercados

Espanoles

2007-09-21 -2.98% 0.98% 6.03%

ASX Ltd New Zealand

Exchange Ltd 2007-10-16 -5.09% -5.02% -3.66%

London Stock

Exchange

Toronto Stock

Exchange 2011-02-09 7.53% 4.26% 3.49%

London Stock

Exchange

NASDAQ

OMX 2011-02-23 1.38% -5.27% -0.87%


Average

(CAAR) 4.92% 4.01% 4.80%

Standard

deviation 10.99% 11.02% 11.44%

Coefficient of

variation 2.23 2.75 2.39

Skewness 1.41 1.61 1.73

Excess

kurtosis 2.76 2.14 3.64

Table 6 Cumulative abnormal returns of 15 targets over three event windows using market and industry model for determining the normal return


The results presented above indicate that, on average, both bidders and targets enjoy positive

cumulative abnormal returns. Interestingly, in all cases cumulative average abnormal returns

do not depend on the event window chosen - they are relatively stable regardless of the length

of the period studied. Nevertheless, there is much dispersion in the data, as indicated by high

values of coefficients of variation. It should be noted that bidders’ returns are much more

dispersed than targets’. Moreover, in all four cases, the distribution of cumulative abnormal

returns is positively skewed which indicate the many of values lie to the left of the mean.

Again, bidders’ returns are much more skewed than targets’. Finally, all four distributions are

leptokurtic, as showed by positive values of excess kurtosis. Bidders’ returns distribution has

fatter tails than targets’, which means that among bidders there is a higher probability of

extreme values of abnormal returns.

Tables 7 and 8 present buy-and-hold average abnormal returns of bidders in already

accomplished mergers. The sample used to calculate BHAR differs from that used to compute

CAR. Firstly, potential acquirers in the rumoured mergers were removed from the sample

because the scope of the analysis based on BHAR is to investigate whether stock exchanges

that have undergone a merger outperform the market in the long run. Moreover, the sample

has been enlarged by the addition of following mergers between:

Borsa Italiana and London Stock Exchange,

American Stock Exchange and New York Stock Exchange (NYSE)

São Paulo Stock Exchange (Bovespa) and Brazilian Mercantile and Futures

Exchange (BM&F).

The reason is that, in order to analyse bidder’s abnormal returns, the target company does not

have to be a listed entity. Similarly to the previous analysis of cumulative abnormal returns,

buy-and-hold average abnormal returns were calculated against two benchmarks: broad

market index (S&P 500, CDAX, FTSE All share, Índice Bovespa) and industry index (Dow

Jones Global Exchanges Index). Regardless of the benchmark chosen, long-term average

abnormal returns are negative. Interestingly, average buy-and-hold abnormal returns decrease

with an increase in the length of event window, from minus 20% to minus 30%. Dispersion of

data is also lower for larger event windows. Furthermore, due to the presence of an outlier

(NASDAQ), the distribution of 2-year BHAR calculated using industry index as a benchmark

is negatively skewed and has large kurtosis. It should be emphasized that taking a market

http://en.wikipedia.org/wiki/Positive_number

http://en.wikipedia.org/wiki/Extreme_value


index as a benchmark leads to a large sampling uncertainty (especially for a period of 12

months). Therefore, it seems reasonable to base further inferences about bidders’ long-term

performance on the industry index only.

Bidder Target Announcement

day

Benchmark Market Index

Bidder

BHAR

12 months

Bidder

BHAR

18 months

Bidder

BHAR

24 months

Chicago

Mercantile

Exchange

NYMEX Holding 2008-03-17 -16.37% -27.27% -34.31%

Chicago

Mercantile

Exchange

CBOT Holdings 2006-10-17 -2.26% -26.37% -25.08%

NASDAQ Instinet 2005-04-22 113.90% 88.94% 49.30%

NASDAQ OMX 2007-05-25 7.85% -5.89% -8.31%

Deutsche

Börse

International

Securities

Exchange

2007-04-30 -62.76% -43.04% -41.36%

NYSE Euronext 2006-05-22 5.25% 11.79% -17.07%

NYSE AMEX 2008-01-18 -4.42% -7.42% -20.45%

LSE Borsa Italiana 2007-06-23 -19.89% -14.60% -23.52%

BM&F Bovespa Holding 2008-03-27 -42.63% -57.23% -72.41%

Average

(ABHAR) -2.37% -9.01% -21.47%

Standard

deviation 49.32% 42.11% 32.36%

Coefficient of

variation 20.82 4.67 1.51

Skewness 1.71 1.69 1.02

Excess

kurtosis 4.43 3.87 3.26

Table 7 Buy-and-hold abnormal returns of 9 bidders over three event windows using market index as a benchmark


Bidder Target Announcement

day

Benchmark Industry Index

Bidder

BHAR

12 months

Bidder

BHAR

18 months

Bidder

BHAR

24 months

Chicago

Mercantile

Exchange

NYMEX

Holding 2008-03-17 0.21% -12.60% -9.09%

Chicago

Mercantile

Exchange

CBOT

Holdings 2006-10-17 -72.79% -59.45% -22.35%

NASDAQ Instinet 2005-04-22 18.80% -21.34% -105.20%

NASDAQ OMX 2007-05-25 -1.01% 9.01% -9.07%

Deutsche

Börse

International

Securities

Exchange

2007-04-30 -79.93% -34.89% -33.42%

NYSE Euronext 2006-05-22 -27.61% -89.48% -60.50%

NYSE AMEX 2008-01-18 22.90% 12.48% 8.07%

LSE Borsa Italiana 2007-06-23 -29.18% -15.11% -19.56%

BM&F Bovespa

Holding 2008-03-27 -21.75% -27.21% -27.10%

Average

(ABHAR) -13.57% -26.51% -30.91%

Standard

deviation 50.26% 32.12% 33.71%

Coefficient of

variation 3.70 1.21 1.09

Skewness 0.71 -0.86 -1.48

Excess

kurtosis 1.13 0.68 2.48

Table 8 Buy-and-hold abnormal returns of 9 bidders over three event windows using industry index as a benchmark

3.4 Testable hypotheses

The decision to merge with another company should be based on the desire to maximize the

value for shareholders of the acquiring firm. This could be achieved by increasing the market

value of the bidder. If the acquirer is not able to achieve statistically significant abnormal

returns, then the acquisition is viewed by the market as a zero NPV project. Merger can also

destroy shareholder value if the firm earns negative abnormal returns. In order to investigate


acquirers’ stock price reactions on a merger announcement, a null hypothesis that bidders on

average do not earn abnormal returns

H0: Bidders’ CAARs = 0

is tasted against an alternative one:

H1: Bidder’s CAARs ≠ 0 (Hypothesis A)

Although the target usually plays a passive role in any merger attempt, an acquisition

announcement always triggers a change in its stock price. If an increase in a target’s market

value is significant, it can be argued that target’s shareholders gain from the merger. Negative

CAARs imply value destruction. Statistically insignificant CAARs mean that information

about the merger does not have an influence on the average returns to target’s shareholders.

H0: Target’s CAARs = 0

H1: Target’s CAARs ≠ 0 (Hypothesis B)

One can also analyze the long-term performance of acquirers after a merger announcement. If

the bidding firm is able to outperform its benchmark (i.e. the difference between acquirer’s

average returns and index returns is significantly positive), it means that an investor who buys

bidder’s shares at the announcement day and sells them at the end of the holding period is

better off than if she had invested in a benchmark index. In order to examine long-run post-bid

performance of acquirers, the null hypothesis of no abnormal returns

H0: Bidder’s ABHAR = 0

is tasted against the alternative one:

H1: Bidder’s ABHAR ≠ 0 (Hypothesis C)

3.5 Inferential statistics

Table 9 presents cumulative average abnormal returns and respective t-statistics for targets

and bidders for the entire sample (N=15) of stock exchange M&A.

The findings indicate that on average mergers are a zero NPV projects for the shareholders of

the bidding company. Although the average cumulative abnormal returns for all three event

windows are positive, they are not significantly different from zero. This result is robust for


both one and two-factor models. Hence, I fail to reject the Hypothesis A that mergers neither

create nor destroy the acquirer’s shareholder value.

* indicates statistical significance at 10% level, 14 degrees of freedom

Table 9 Cumulative average abnormal returns of 15 targets and bidders over three event windows

The results for the acquired companies are not unanimous as they depend on the model

specification and the length of the event window. Only for the one-factor model and an event

window of (-10; 10), target CARs are positive and significant at the 10% level. In this case, I

reject the hypothesis of no abnormal returns to the target company at the 10% significance

level and conclude that the target’s shareholders earn abnormal returns. In all other cases

(two-factor model – all event windows, one-factor model – event windows (-5; 5) and (-1; 1)),

I fail to reject the hypothesis of no abnormal returns to shareholders of the acquired company.

Table 10 displays results of the average buy-and-hold abnormal returns and respective

t-statistics of a subsample of bidders in already accomplished mergers (N=9). They are

negative in all time intervals studied. The results are statistically significant at a 5% level in

two holding periods (18 and 24 months). Therefore, it can be argued that in the long run, stock

exchanges that have undergone a merger do not perform better than the benchmark. Hence, I

reject the Hypothesis C of no difference between the average long term return of bidders and

the benchmark index.

One factor model Two factor model

CAAR

(-10;10)

CAAR

(-5;5)

CAAR

(-1;1)

CAAR

(-10;10)

CAAR

(-5;5)

CAAR

(-1;1)

B

i

d

d

e

r

Abnormal

return 4.88% 4.18% 4.65% 2.01% 1.52% 1.96%

t-statistic 1.07 0.92 1.16 0.90 0.60 0.89

T

a

r

g

e

t

Abnormal

return 7.44% 7.45% 7.52% 4.92% 4.01% 4.80%

t-statistic 1.86* 1.71 1.52 1.73 1.41 1.62


Benchmark Industry Index

ABHAR

12 months

ABHAR

18 months

ABHAR

24 months

Bidders Abnormal Return -13.57% -26.51% -30.91%

t-statistic -0.81 -2.48 ** -2.75 **

** indicates statistical significance at 5% level, 8 degrees of freedom

Table 10 Average buy-and-hold abnormal returns of 9 bidders over three event windows using industry

index as a benchmark

3.6 Discussion of the results of the event study

The results of the event study suggest that stock exchanges M&A are not value-enhancing

decisions for shareholders of the acquiring company. Hence, the question arises why stock

exchanges engage in merger activities. The answer depends on whether management of the

acquiring company displays a wealth-maximizing behaviour.

3.6.1 Hypotheses based on the non-wealth maximizing behaviour of the

management

There are two hypotheses that assume non-wealth maximizing behaviour of the management

(Hawawini & Swary, 1990).

According to the manager-utility-maximization theory, interest of management and

shareholders (owners of the corporation) are not aligned. The aim of managers is to maximize

their own utility and not to serve the interest of owners (classical principal-agent problem).

Shleifer & Vishny (1989) argue that managers engage in mergers activities to increase their

remuneration and power, which are both a function of the firm size (this phenomenon is called

in the literature management entrenchment or empire building). According to many empirical

studies11, there is a strong relationship between the management remuneration and firm sales.

Another rationale for mergers is the risk reduction: large firms guarantee job security (Amhiud

& Lev, 1981).

The other behavioural hypothesis of mergers and acquisitions is the hubris theory, described

by Roll (1986). He suggests that managers of the acquiring firm are characterized by pride and

arrogance (hubris). Therefore, they persistently claim that their valuation of the target is

correct even though it is commonly believed that the true economic value of the acquired

11 See e.g. Baumol (1967), Penrose (1959)


company is lower. Managers are convinced that they can find “bargains”. They also fall victim

to the winner’s curse, as they take part in the bidding contest and pay too much for the target.

As a result, stock price of the target rises and its shareholders earn abnormal return.

3.6.2 Hypothesis based on the wealth maximizing behaviour of the

management

Even though management of the acquirer acts in the sole interest of its shareholders, it is not

able to earn abnormal returns in a merger process because of the fact that the market for

corporate control is efficient (Hawawini & Swary, 1990, p. 36). As a consequence, potential

bidders will drive the stock price of the target up to the level where target’s shareholders get

all the wealth generated by the acquisition. Therefore, shareholders of the acquired company

are able to earn abnormal returns.

3.7 Comparison of the event study results with other empirical

investigations on value creation in M&A

There is an extensive literature on the impact of takeovers on shareholder value creation and

the market for corporate control. It is, however, inconclusive on whether mergers are NPV

positive projects. In the review presented below, I follow Campa & Hernando (2004), Jensen

& Ruback (1983), Datta et al. (1992) as well as Bruner (2002).

3.7.1 Abnormal returns to target companies

The vast majority of empirical studies find that shareholders of target companies enjoy

considerable positive cumulative abnormal returns. The returns are statistically significant,

regardless of the industry, variation in time periods or type of the deal. According to Datta et

al. (1992), shareholders of target companies earn on average an abnormal return of 21.81%.

This finding is consistent with Jensen & Ruback (1983) who report a 29.10% abnormal gain

for the target’s shareholders. Campa and Hernando (2004) summarize the results of thirteen

empirical studies and conclude that shareholders of the target company enjoy on average an

abnormal return of 9%. This result is in line with Goerge & Renneboog (2003) who also

report an abnormal return of 9%. Some studies (Danbolt, 2002; Karceski et al., 2000) show

significantly negative abnormal returns, but these are rather exceptions.

According to Huang & Walking (1987) and Anrade et al. (2001), the method of payment has

the greatest impact on the returns of target companies. Cash transactions yield significantly

higher target returns than stock offers. Another factor that influences target returns is the


number of bidders. On average, targets gain in multiple-bidder contests compared to single

bidder offers (Servaes, 1991).

An interesting phenomenon, widely discussed in the literature, is a positive target’s stock

return run-up prior to the deal announcement. Weston et al. (2003, pp. 199-201) analyze ten

empirical studies on pre-announcement abnormal returns to target companies and conclude

that target returns increase, on average, by 11.8%, 20 to 50 days before the merger

announcement. Even though the majority of empirical studies indicate a significant positive

target’s share returns run-up, they differ in the explanation of the source of this phenomenon.

According to Keown & Pinkerton (1981, p. 866), one of the possible reasons for the run-up is

insider trading. Jarrel & Poulsen (1989), on the other hand, claim that the run-up is caused by

media rumours and the prebid share purchase by potential acquirers. Sanders & Zdanowicz

(1992) show that the run-up is not related to speculation but is rather due to the probability of

the deal taking place. Schwert (1996) concludes that the target’s stock returns run-up is the

additional cost incurred by the acquirer. Similarly, Meulbroek & Hart (1997) point out that

insider trading results in higher acquisition premiums.

3.7.2 Abnormal returns to bidders

Empirical studies report both negative, zero and slightly positive cumulative abnormal returns

to the shareholders of the acquiring company. There is, however, a big discrepancy between

significant abnormal returns to target companies and the negligible returns to bidders. Jensen

& Ruback (1983) conclude that acquisitions create value only for shareholders of the target

company and are break-even investment projects for the bidding firms. They also argue that in

case of successful deals, shareholders of the bidding company gain, but they lose if

transactions are unsuccessful. Datta et al. (1992) show a contrary evidence, pointing out that

the shareholders of the bidding company do not gain, irrespective of whether the deal is

successful or not. Bruner (2002) reviews results of 44 empirical studies, 17 of which report

value creation, 14 – value conservation and 13 – value destruction. Campa & Hernando

(2004) present the findings of 17 studies: ten papers show negative abnormal returns (in most

cases not statistically significant) and seven studies find that shareholders of the bidding firm

earn no or slightly positive abnormal returns.

As suggested by Martynova & Renneboog (2008, p. 14), the long-term performance of bidders

depends on a way the benchmark return is estimated. Using the market model as a benchmark

yields significantly negative CARs over the period of three years after the merger


announcement. The studies employing market-adjusted model, the capital asset pricing model

(CAPM) or a beta-decile matching portfolio report inconsistent findings about the long-run

abnormal returns. Martynova & Renneboog (2008) argue that the long-term performance of

bidders should be analysed in subsamples distinguished by the means of payment (cash versus

equity) and the type of the target firm (public versus private). As pointed out by Mitchell &

Stafford (2000), mergers fully financed by equity result in statistically significant negative

long-term abnormal returns, whereas all-cash bids yield positive returns. Bradley & Sundaram

(2004) suggest that long-term abnormal returns in acquisitions of a public target companies

are not significantly different from zero, whereas they are significantly negative when the

target is an unlisted company.

Nevertheless, the majority of empirical studies reports significant negative long-term

performance of bidders. One of the possible explanations for this phenomenon is that bidders’

shareholders tend to overestimate the benefits from the acquisition. The other reason could be

dissemination of new relevant information about the transaction (Caves, 1989).

It should be pointed out that there are several methodological drawbacks of the empirical

studies on long-performance of bidders (Jensen & Ruback, 1983) as it is difficult to isolate the

influence of the transaction from the effects of other events taking place one or two years after

the deal announcement.

3.8 Limitations of the event study

Even though event study is one of the most popular methodologies in empirical corporate

finance, there are some major limitations that one should bear in mind while analysing results

of the event study.

Firstly, assumptions used in the event study are not always valid due to market inefficiencies.

As a result, stock prices may not fully or immediately reflect all available information.

Moreover, stock performance may not be solely determined by the reaction to the merger

announcement in case of e.g. unforeseen coexisting events. But even if the event window is

free from any external news, a change in the bidder’s stock price cannot be fully attributed to

the fact that the company has announced a deal. As pointed out by Hawawini & Swary (1990,

p. 36), a merger announcement may reveal important information about the acquirer that may

be unrelated to the transaction (e.g. it may signal that the bidder is financially stronger than

the market has thought). This favourable information may lead to an increase in the bidder’s


share price, regardless of conditions of the acquisition. On the other hand, a merger can also

reveal that the acquirer has no opportunities to grow internally, which may drive down the

acquirer’s stock price regardless of merger conditions. Moreover, results of the event study are

very sensitive to estimation and test periods (length of the event window). With longer

estimation period there is a trade-off between estimation accuracy and possible parameter

shifts. Another problem may stem from the calendar time clustering of abnormal returns (cf.

Brown & Warner, 1980). An overlap of announcement days across stocks may trigger a

problem of a cross-correlation in abnormal returns.

Finally, there are some specific limitations to the event study carried out in this thesis. Firstly,

small sample size leads to inaccurate estimates of the statistics and results in the low power of

the test. Secondary, a problem of multicollinearity may arise since it is probable that the Dow

Jones Global Exchanges Index is correlated with each market index. It may lead to imprecise

estimation of regression coefficients which are used to determine stocks’ normal returns.

Furthermore, some results of the study are not robust as they show sensitivity to the model

chosen for calculating expected stock returns. Finally, it is difficult to choose announcement

days precisely as there are often contradicting rumours about the acquisition prior to the

official announcement.

3.9 Areas for further research

A number of areas for future research into value creation in stock exchange consolidation can

be identified.

Firstly, other proxies for normal return that those applied in this thesis could be used to test

for the outcomes robustness. In the literature, the normal return is often estimated using the

Fama-French three factor pricing model (cf. Loughran & Ritter, 1995; Mitchell & Stafford,

2000; Jegadeesh, 2000) or Carhart four factor pricing model (cf. Brav et al., 2000). Since the

path-breaking article by Fama & French (1992), it is commonly believed that the systematic

risk only is not a sufficient determinant for stock returns. Fama & French (1992) suggest

incorporating two further factors into the capital asset pricing model: size (measure by the

market capitalization) and book-to-market ratio. Carhart (1997) presents an extension of the

Fama-French model by adding a momentum factor.


Moreover, when measuring the long-run performance of bidders, it is recommended to take as

a benchmark the portfolio of companies matched by market-to-book ratio and size with the

target and bidder rather than the industry index, as indicated by Barber & Lyon (1997).

Furthermore, once abnormal returns are calculated, it would be interesting to determine

whether some economic variables have an influence on CAR. A common methodology is a

regression of firm and deal characteristics (e.g. method of payment) on CAR.

CARi,t = α + δ Xi,t + υi,t

Xi, – vector of firm i characteristics

υi,t – error term

The main drawback of this methodology is the probable violation of the ordinary least square

assumption E[Xi,t υi,t ] = 0, which results in inconsistent estimates.

One could also test for the existence of differences between cross-Atlantic and pan European

mergers by adding respective dummy variables. However, to run such a regression, more data

is needed than currently available.

Another area for further researches is the analysis of the impact of the merger on operating

performance of the acquirer. Barber & Lyon (1996) apply the event study methodology based

on accounting figures. They compare the actual performance of a bidder with its theoretical

performance in the absence of the deal (so called normal performance, analogical to the

normal return in case of the event study on stock prices). It should be pointed out that such an

event study is based on the accounting year when the deal is closed (accounting ratios do not

respond to deal announcements). The estimation period is selected in order to have a proxy for

the expected performance in the absence of the transaction. The most popular measures for the

operating performance are: ROA (return on assets), ROS (return on sales), CF ROA (cash

flow return on assets) or Tobin’s Q. The normal performance is estimated against a

benchmark represented by the past performance of the company and the performance of the

competitors (comparison group).

Finally, the impact of mergers and acquisitions can be evaluated not only from the standpoint

of the bidder’s or target’s shareholders. One can also analyze the combined performance and

asses total gains from the transaction. It would be interesting to compare the combined returns

of targets and bidders in mergers of stock exchanges with the empirical evidence from other


industries. Campa & Hernando (2004) analyze results of six empirical studies on combined

weighted return for bidders and targets and conclude that almost all papers report positive

combined returns. It should be pointed out, however, that the significant abnormal returns of

small targets are offset by moderate returns of highly capitalized bidders, resulting in

negligible combined abnormal returns.


4 Economic Impact of Stock Exchange Consolidation

In this chapter, micro- and macroeconomic implications of stock exchange consolidation are

analysed. They are separated into those affecting market participants (investors, intermediaries

as well as issuers) and the economy as a whole. The consequences for users of the

consolidated stock exchange are considered from the perspective of implicit and explicit trade

costs. Macroeconomic impact, on the other hand, focuses on financial stability, market

efficiency, economic growth, and monetary policy. Furthermore, voices in the discussion

whether market should consolidate or fragment are presented and critically evaluated. Lastly,

the chapter contains prospects for the future development of stock exchanges. In particular, it

assesses whether a vision of a global stock exchange is realistic.

4.1 The impact of stock exchange consolidation on transaction

costs

Trading costs are one of the most important factors taken into account by brokers and

investors when choosing a trading venue. They can be distinguished between easily

measurable explicit costs, such as commissions, fees, etc. and implicit costs, indirectly

incurred by market participants (e.g. liquidity or opportunity costs). The integration of stock

exchanges is likely to affect both explicit and implicit transaction costs.

4.1.1 Explicit costs

Pagano & Padilla (2005, p. 7) present four plausible arguments why stock exchange

consolidation could lead to a reduction in explicit costs.

Firstly, integration of stock exchanges’ operations will reduce their fixed costs, which in turn

will decrease the average cost of trade. The key question in whether stock exchanges will be

willing to pass on these synergies on market participants. Pagano & Padilla (2005, p. 7) argue

that the competitive pressure is likely to arise and induce a consolidated stock exchange to

pass through these savings to the end users via lower fees.

Secondly, as indicated by Schmiedel et al. (2002), there are significant economies of scale in

settlement. Therefore, efficiency gains due to consolidation are not limited to trading but also

encompass the post-trading phase: with an increase in trading volume, clearing and settlement

efficiency also increases (particularly by netting of larger trades). Again, Pagano & Padilla

(2005, p. 7) believe that this cost efficiencies can be passed on to market participants in the

form of reduced fees.


Thirdly, stock market consolidation results in market professionals accessing only a one single

trading platform. This can lead to substantial savings, especially in terms of human capital,

software and hardware. Final investors can benefit from it provided that these efficiencies will

be passed on to them via lower commissions. Another benefit for local users may stem from

the fact that members of one exchange gain a wider and cheaper access to all securities traded

on the integrated market, without incurring further costs of multiple exchange membership.

Furthermore, Pagano & Padilla (2005, p. 33) clam that the harmonization of policies, rules

and regulations leads to a significant reduction of expenses incurred for staff-training and

compliance.

Deutsche Börse and NYSE Euronext (2011, p. 6) name another benefit for market participants

resulting from consolidation: lower collateral requirements. This savings of approximately

USD 4 billion could be achieved by a reduction of margins due to lower clearing fund

contributions as well as the possibility to offset risky positions.

Similar expectations with respect to explicit costs were voiced by key market participants who

took part in a poll carried out by the London Economics think tank in collaboration with

PricewaterhouseCoopers. 73% of those surveyed claimed that a further integration of financial

markets would lead to lower brokerage commissions and other direct transactions costs

(London Economics, 2002, p. 4).

However, some institutions (e.g. European Central Bank, 2007, European Commission, 2011)

raise concern that too extreme consolidation may lead to disadvantages for market participants

in terms of higher transaction costs. They warn that a further consolidation may result in

monopoly rents for the combined entity since less competition may induce the merged

organization to raise listing fees and other explicit transaction costs (McInish, Wood, 1996).

The European Commission is particularly concerned that stock exchange mergers may have a

detrimental influence on innovation and technology solutions (European Commission, 2011).

It is commonly known that advances in technology are beneficial to market participants as

they ultimately lead to a decrease in transaction costs. Furthermore, because of high barriers to

entry in the stock exchange industry, incentives for fee competition between the key players

may diminish after stock exchange consolidation. According to the European Commission

(2011), mutual and pension funds, professional brokers, as well as retail and investment banks

could be negatively affected by a lower degree of competition. Last but not least, it is still

doubtful that merging stock exchanges will pass on synergy gains to end customers. As


reported by The Economist (2006, p. 14) “… more than a few investment bankers were furious

(…) when Euronext announced that it was returning €1 billion to shareholders - without

cutting trading fees”.

In order to empirically estimate the efficiency gains arising from stock exchange

consolidation, Pagano & Padilla (2005) carry out a natural experiment provided by the

inception of Euronext. They put a particular emphasis on a question whether efficiency gains

were passed on to final investors in the form of reduced trading fees. As suggested by the

Competition Commission (2003), such gains must benefit the customers and be achieved as a

clear result of the merger (i.e. they would not have materialize without the merger). To

analyze the influence of the Euronext creation on average trading fees charged in Paris,

Brussels and Amsterdam, Pagano & Padilla (2005) run several regressions with the

integration dummy as an independent variable. They control for any exogenous factors, such

as economic and politic events. Their finding is that the average trading fees in Paris and

Amsterdam fell by 15% and 30%, respectively, as a consequence of the Euronext integration.

This effect is statistically significant. Interestingly, there is no relation between trading fee

reduction in Brussels and the creation of Euronext. According to the authors, this may be due

to a small amount of observations for this trading venue.

4.1.2 Implicit costs

Implicit transaction costs are directly related to liquidity: higher liquidity translates into lower

implicit costs. It should be noted that liquidity is a multi-dimensional variable, which can be

examined from the perspective of market breath, depth, and resiliency (Kyle, 1985; Harris,

1990). Why is liquidity such an important decision-making criterion for investors? Black

(1971) points out that a liquid market guarantees: always quoted bid-ask price, small enough

spreads and immediate execution of small orders with minimal effect on price. According to

Pagano and Padilla (2005, pp. 7-8), there are many reasons why stock market consolidation

could lead to higher liquidity, which implies lower implicit costs.

Firstly, a direct consequence of stock exchange integration may be the bid-ask spreads

narrowing (increased market breadth). This can be achieved by:

- a reduction of adverse selection costs (when addition order flow due to the merger

comes from uninformed traders),


- a decrease in market makers’ inventory-holding costs (since an increased trading

activity makes the order flow more predictable and reduces inventory rebalancing

costs after executing large orders),

- enabling intermediaries to settle fixed order processing costs,

- creating incentives for market professional to enter the market (which may result in

competition in quote-setting).

Secondly, a provision of greater liquidity can reduce adverse price impact of large orders as

market depth increases. As far as the third dimension of liquidity: market resiliency is

concerned, greater liquidity may result in lower price volatility. Pagano & Padilla (2005, p. 8)

argue that a merger implies reduced noise generated by individual orders because the order

flow is becoming larger and more stable. They also claim that lower bid-ask spread reduce the

bid-ask bounce of transaction prices.

These predictions are congruent with the results of a survey carried out among European

financial market participants. 59% of interviewees expect bid-ask spreads to decrease and

45% hold that the price impact of transactions should decrease following the stock exchange

integration (London Economics, 2002, p. 4).

In order to empirically test their predictions, Pagano & Padilla (2005, p. 33-48) run

regressions which investigate the influence of the Euronext consolidation on different

measures of liquidity (bid-ask spreads, volumes traded and volatility). They find out a

statistically significant decrease in bid-ask spreads of the shares included in the France’s CAC

40 index, which cannot be linked to a downward trend in spreads on other European trading

venues. The spreads reduction in Paris due to the Euronext integration ranges from 38% to

48%. A similar effect can be observed in Brussels and Amsterdam, where spreads fell by a

statistically significant range of 23% - 30% (for Brussels) and 4% - 11.5% (for Amsterdam).

Interestingly, spreads increased in Lisbon following the Euronext integration. This effect is,

however, statistically significant under some model specifications only.

Pagano & Padilla (2005, p. 33-48) also report a statistically significant increase in trading

volume by approximately 40% in Paris, Brussels, and Amsterdam as a consequence of the

Euronext consolidation, after controlling for a general upward trend in the industry. They also

find that the inception of Euronext has led to a creation of thicker market as the volatility of


large-cap shares traded in Paris, Brussels, Amsterdam and Lisbon fell following the Euronext

merger. The reduction in volatility is statistically significant and ranges from 9% to 18%.

The findings of Pagano & Padilla (2005) are in line with other empirical studies on the impact

of stock exchange integration on various proxies for market liquidity. Arnold et al. (1999)

investigate how the merger of regional stock exchanges in the US affected trading volume and

execution costs. They find that the merging exchanges managed to attract additional order

flow and experienced bid-ask spreads tightening.

In a noteworthy study, Nielsson (2009) analyses the influence of Euronext integration on the

liquidity of listed firms. His main research question is: what types of companies (in terms of

size, industry, foreign expose, etc.) are the greatest beneficiaries of the merger in terms of

shares liquidity. The results show that the gains are asymmetrically distributed among

companies. Large corporations and companies with cross-border sales benefit the most from

the consolidation – they experience a statistically significant increase in turnover, decrease in

bid-ask spreads and increase in market depth, as measured by the Amivest liquidity ratio.

Small and medium enterprises as well as companies not engaged in foreign sales, on the other

hand, experience a statistically insignificant increase in liquidity. A plausible explanation for

this phenomenon is the fact that big companies with foreign exposures are more familiar and

visible to new investors who enter the market following the consolidation (Nielsson, 2009, p.

232).

Furthermore, the author examines how the Euronext merger has affected stock returns and

volatility. Even though there has been an overall decrease in volatility, only the reduction in

volatility among big companies is statistically significant. Nielsson (2009, p. 237) concludes

that stock exchange consolidation may not be in the interest of all companies since the

benefits of an increased liquidity are restricted to particular types of companies. However, as

there is no evidence that the Euronext integration has triggered a reduction in liquidity for any

types of companies, the merger can still be Pareto improving.

The relationship between the stock exchange integration and volatility of returns is also

analysed by Dorodnykh & Moneim (2011). They employ correlation and cluster analyses and

use the GARCH model to investigate the change in volatilities of national markets involved in

three mergers: Euronext, OMX, and BME. The results of this study show a gradual, but not

similar reduction of volatility in each market. The level of decrease is influenced by economic

fundamentals of each market and the degree of interdependency with other financial markets.


This finding is in line with Ben Slimane (2010) who also studies how the gains of reduced

volatility due to the Euronext merger are allocated among trading venues. He tests a change in

volatility of the Dutch, Belgian and Portuguese markets following the Euronext creation using

the GARCH GED model. Interestingly, he finds that only a Portuguese market has reported a

statistically significant decrease in volatility. It leads him to the conclusion that the benefit of

mergers in terms of reduced volatility depends on the market features and degree of

integration with other markets before the merger.

4.2 Macroeconomic impact of stock exchange consolidation

4.2.1 Cost of capital, investment and output

As noted in the previous section, stock exchange consolidation has a positive influence on

liquidity, which is an important decision making criterion for investors who care about

implicit transaction costs. It should be emphasized that liquidity is also central for

corporations, as it ultimately influences the cost of capital.

According to Amihud & Mendelson (1986), the most illiquid securities could gain even 50%

in value if, ceteris paribus, their liquidity would be as high as of that of the most liquid shares.

This implies the existence of liquidity premium - extra return demanded by investors for

holding less liquid shares. Similar relation between liquidity and stock returns is reported by

Datar et al. (1998) and Brennan & Subrahmanyam (1996): higher liquidity (measured as a

turnover rate) translates into lower stock returns.

There is a strong link between stock returns and the cost of capital as, in the equilibrium, the

required rate of return on the stock market is the company’s cost of capital. Domowitz & Steil

(2002) estimate that a 10% decrease in trading costs triggers a 1.5% reduction of the cost of

capital to blue-chip firms. The London Economics (2002, p. 2) think tank developed a model

which relates a company’s cost of equity capital to transaction costs of its equity on the

secondary market. The results show that a reduction in transaction costs arising from a full

integration of European financial markets would lead to a 40 basis points decrease in equity

cost of capital across Europe. Such a level of reduction in the cost of capital is also expected

by a majority of financial market participants taking part in the survey carried out by London

Economics (2002) in collaboration with PricewaterhouseCoopers. In another simulation,

London Economics (2002) estimates the effects of a decrease in clearing and settlement costs

(arising from the integration of financial markets) on the equity cost of capital. The results


indicate that the equity cost of capital should go down by further 10 basis points, implying a

total reduction of 0.5%. Furthermore, London Economics (2002) also expect the cost of bond

finance to fall by 40 basis points. The reason is that following a stock exchange consolidation,

credit spreads (one of the key determinants of the cost of debt financing) should tighten due to

an increase in market depth.

Since the cost of capital goes down, it can be expected that investment expenditures rise as

profit maximizing companies drive down the marginal product of capital to its new lower cost

(Henry, 2003, p. 3). A rise in capital investment (which is a component of the aggregated

demand) has a positive effect on both the demand and supply-side of the economy through a

multiplier effect on national income (Burda & Wyplosz, 2005, p. 320). Therefore, it can be

summarized that following a merger, both explicit and implicit transaction costs decrease,

which leads to a reduction of listed companies’ cost of capital. This, in turn, may result in an

investment boom that has a positive impact on the equilibrium level of gross domestic

product. In 2002, London Economics predicted that a deeper integration of European capital

markets would lead to a 1.1% increase in the EU-wide GDP in constant prices. This forecast

was reviewed in 2010, when it was empirically proven that financial market integration in

secondary equity trading resulting from a implementation of the MiFID directive had raised

the long-run level of gross domestic product (at constant prices) by approximately 0.7% -

0.8% (London Economics, 2010, p. 2).

4.2.2 Market efficiency and economic growth

The concept of market efficiency is central to financial markets and economic growth.

Primarily, this term is used to describe a market in which relevant information is incorporated

into the prices of capital assets12. The most common type of efficiency often referred to by

economists is the allocation efficiency, which means that funds are allocated from ultimate

lenders to ultimate borrowers in the most socially useful way (Markovits, 2008).

There is a strong link between market efficiency, allocation of resources and economic

growth. An efficient stock market promotes risk sharing for investors and issuers since it

enhances opportunities for economic agents to allocate capital across time, space and risk. As

indicated by the European Central Bank (2007, p. 61), an improved allocation of resources

spurs economic growth. Michelacci & Suarez (2004) argue that an efficient stock market

12 Bachelier (1900) was the first to recognize the informational efficiency of financial markets.


promotes innovation, business inception and a better allocation of resources by enabling firms

to go public at an earlier stage of the company’s lifecycle. Likewise, Levine (1991) shows that

efficient stock market may reduce liquidity risk – an impediment to invest in long term

investment projects. According to Levine (1991), it may encourage technological innovation

and economic growth. In another empirical research, Levine & Zervos (1998) investigate

aggregate data for a period of 1976–1993 and find that a more liquid and efficient stock

market contributes to the long-term growth.

It should be noted that one of the prerequisites for market efficiency is market liquidity13. As

suggested by Muranaga & Shimizu (1999, p. 2), deeper markets ease price discovery and

therefore reduce market price uncertainties (a situation when securities prices temporarily

diverge from market-clearing equilibrium prices). Muranaga & Shimizu (1999) therefore

claim that a decline in market price uncertainties improves market efficiency, which leads to

an efficient fund and risk allocation.

It can be argued that stock exchange consolidation may have a positive effect on market

efficiency and economic growth. As noted in the previous section, stock exchange merger, in

general, has a beneficial impact on market liquidity, which is an important factor affecting

market efficiency. Furthermore, Obstfeld (1994) as well as Devereux & Smith (1994)

emphasize the role of stock exchange integration in efficient fund allocation and economic

growth. They point out that due to the possibility of international risk sharing, investors

change their preferences and start to invest in risky, high-return projects, thereby spurring

economic growth.

Moreover, stock exchange merger provides investors with an opportunity to hold more

diversified portfolios and overcome the so called home bias in portfolio selection (a tendency

for investors to invest in domestic assets and relinquish the benefits of diversification by not

holding foreign securities). This argument was empirically investigated by Pagano & Padilla

(2005). They show that French investors have been holding more diversified portfolios since

the Euronext creation. It should be pointed out that the home bias reduction and a better

portfolio diversification lead to a more efficient fund allocation.

Last but not least, the pending merger between Deutsche Börse and NYSE may increase

market efficiency by the creation of a benchmark yield curve in interest rate derivatives.

13 This topic has been widely discussed by Brown & Zhang (1997) and Easley & O’Hara (1992).


According to Wooldridge (2001, p. 49), this will facilitate price discovery, because “no

factors other than expected future spot rates will systematically affect forward interest rates”.

The creation of a benchmark yield curve should lead to higher liquidity and market efficiency.

In sum, through an increased liquidity of a consolidated stock exchange market, the possibility

of international risk sharing and a wider portfolio diversification, market efficiency should

significantly improve, which positively affects the resource allocation and economic growth.

4.2.3 Financial stability and monetary policy

Financial stability is a concept that has gained prominence in the wake of the current turmoil

on financial markets. The crisis has clearly showed how instability of the financial system can

spread into the real economy, to the detriment of social welfare and economic growth.

According to Schinasi (2004, p. 8) “the financial system is in a range of stability whenever it

is capable of facilitating (rather than impeding) the performance of an economy, and of

dissipating financial imbalances that arise endogenously or as a result of significant adverse

and unanticipated events”. This definition emphasizes the fact that financial stability

contributes to an efficient allocation of real resources, spreads risks, withstands economic

shocks and accelerates the rate of growth of output.

Muranaga & Shimizu (1999, p. 3) argue that market liquidity is crucial for maintaining

financial stability. They suggests that the emergence of systemic risk or a collapse of the

financial system is triggered by the market coming to a halt or by the loss of the investors’

belief in the price discovery function of the market. Thus, from Muranaga’s & Shimizu’s

(1999) point of view, the more liquid the markets, the higher investors’ faith in market

sustainability. Therefore, it can be argued that an increase in market liquidity driven by the

stock exchange consolidation has a stabilizing impact on the financial system.

Enderlein (2001) presents further arguments why a pending merger between Deutsche Börse

and NYSE could enhance European and transatlantic financial stability. He claims that the

creation of a consolidated trading and post-trading platform (with a focus on the OTC

derivatives market) could improve the stability of financial system. It has been argued that

unregulated OTC derivatives markets have contributed to the financial instability during the

current financial crisis (Noyer, 2010). Therefore, regulatory bodies have emphasized the needs

to create more resilient financial infrastructure (International Monetary Fund, 2010), start

trading all standardized OTC derivative contracts on regulated markets and clear them through


central counterparties (G20, 2009). As suggested by Enderline (2011), a pending merger

between Deutsche Börse and NYSE might guarantee that these goals will be achieved. An

implementation of the above mentioned actions should lead to a better management of

systematic risk, provide markets with higher transparency and thereby reinforce financial

stability.

Furthermore, Enderlein (2001) holds that the prospective merger between Deutsche Börse and

NYSE could improve global financial stability by harmonizing and integrating regulation.

More specifically, due to the merger, traders will no longer have incentives to engage in the so

called regulatory arbitrage in order to exploit loopholes in regulations and omit unfavourable

regulation. Moreover, harmonization of rules and regulations resulting from the transatlantic

merger might put an end to the destabilizing “race to the bottom” - “(…) the possibility that

CCPs could compete with each other by lowering collateral thresholds and clearing fees and

adjusting the layers of protection in ways that expose CMs and their customers to greater

risks” (International Monetary Fund, 2010, p. 26). As a result, neither American nor European

stock exchanges will try to attract higher volumes by lowering standards, which will have a

positive impact on global financial stability.

It has been argued that financial stability and monetary stability overlap to a large extent

(Schinasi, 2007; Padoa-Schioppa, 2003). According to Issing (2003, p. 16), monetary stability

can be perceived as a synonym for price stability and implies low level of inflation. Some

economists claim that price stability is a conditio sine qua non for financial stability

(Schwartz, 1995). Others state that monetary stability promotes financial stability (Bordo &

Wheelock, 1998) or that this both concepts mutually reinforce each other (Issing, 2003).

Empirical studies show that many financial crises were triggered by shifts in the price level

(Bordo et al., 2000).

It is believed that more efficient financial markets promote transmission of monetary policy.

Therefore, as long as stock exchange consolidation contributes to improved market efficiency,

it will enhance the effective conduct of monetary policy (European Central Bank, 2007, p.

72). In sum, mergers between stock exchanges could have a positive impact on both financial

and monetary stability, which mutually reinforce each other.

4.2.4 Domestic stock market

Local stock market development is vital for companies and domestic economies. It has been

argued that stock markets perform a crucial role in the economy by channelling household


savings to corporations and providing means for investors to exchange flows (European

Central Bank, 2007). Some economists14 express concern about possible adverse influence of

accelerated internationalization (issuing American depositary receipts, cross-listing, or raising

capital in international markets) on the domestic stock market development. Therefore, it is of

an utmost importance to assure that stock market integration does not harm the development

of domestic financial markets, especially in emerging economies.

It has been argued in the previous sections that stock exchange consolidation has a favourable

influence on liquidity and the companies’ cost of capital. Yet, these benefits are concentrated

among largest companies with international exposure (Nielsson, 2007). Huyghebaert (2007,

pp. 109-110) fears that small, young companies which use stock market to finance their

expansion may be hurt by stock exchange consolidation. He argues that informationally

opaque companies actually benefit from investors’ home bias. A similar conclusion is drawn

by Cantale (1996). She develops a model under the assumption of information asymmetry and

establishes a signalling equilibrium in which good quality firms (that bond them to comply

with stricter disclosure requirements of foreign markets) can be easily distinguished from bad

quality companies (characterised by lower profitability). Therefore, a decision to enter

international capital market is perceived by investors as a credible signal of being a good

quality company. Yet, this reduction in agency costs is detrimental to liquidity of the

remaining small and medium companies in the domestic market (Stulz, 1999).

Likewise, Claessens et al. (2006, p. 342-343) predict that the agglomeration of liquidity in one

market (i.e. consolidated stock exchange) may have adverse spillover effects on domestic

stock markets since trading in companies that cross-list tends to migrate into the international

market. A decrease in aggregate domestic market liquidity can trigger a reduction in liquidity

of individual securities, as suggested by Chordia et al. (2000). This implies that small and

medium-sized companies that do not access international capital markets may have difficulties

in obtaining financing in situation when the aggregate domestic market liquidity decreases. In

sum, stock exchange consolidation may have a detrimental effect on small capitalization firms

and the development of domestic stock market as a whole since it is becoming more difficult

for firms and local markets to attract investor interest.

14 See e.g. Claessens et al. (2006), Karolyi (2004), Moel (2001)


Figure 5 systematically summarizes both micro- and macroeconomic implications of stock

exchange consolidation. The dark arrows symbolize ceteris paribus consequences, whether

the white ones – conditional implications, holding true only under specific assumptions.

Figure 5 Consequences of stock exchange consolidation


4.3 Fragmentation vs. consolidation: discussion and outlook

The question whether market should consolidate or fragment has been long studied in the

literature. In a pioneering study on this topic, Hamilton (1979) presents two opposite effects of

multi-market trading. On the one hand, it may induce competition between market makers,

resulting in narrower bid-ask spreads (competition effect). On the other hand, the so called

fragmentation effect may prevent economies of scale from being fully realized, leading to a

lower probability of execution, higher volatility and wider spreads.

An extensive number of empirical researches have been carried out to investigate the impact

of market consolidation and fragmentation on market quality. Yet the results are inconclusive

– while some authors argue that market consolidation leads to higher liquidity through the

network externality, others claim that fragmentation is more beneficial for competition. This

subsection reviews both arguments and presents empirical studies supporting each viewpoint.

4.3.1 Merits and drawbacks of market fragmentation

Markets are said to be fragmented “when trading simultaneously takes place at different

locations” (Gresse, 2010, p. 3). According to Harris (2002, pp. 530-533), markets fragment

due to the heterogeneity of traders. They differ considerably in terms of: quantities traded,

patience, market access, creditworthiness and information. Uninformed traders, for instance,

choose fully transparent lit markets, whereas informed traders prefer consolidated, anonymous

trading venues. Foucault & Parlour (2000) model two stock exchanges competing for listing

through: technology, listing fees and listing requirements. They conclude that both stock

exchanges may co-exist provided that they differentiate themselves through the factors

described above. In sum, the possibility to address different trading motives is one of the

biggest merits of market fragmentation.

As suggested by Stoll (2003), market fragmentation promotes innovation and efficiency. Even

though “the term «fragmentation» has a harmful connotation, (…) in fact, fragmentation is

just another word for competition” (Stoll, 2003, p. 594). Gresse (2010, pp. 5-6) gives

numerous examples on how competition between trading venues has fostered innovation. For

instance, due to the competitive pressure arising from ECNs, NASDAQ launched in 2002

SuperMontage - an electronic trading platform that aggregates quotes from liquidity providers

and ECNs.


Several authors have empirically proven that market fragmentation may also lead to an

increase in liquidity and consequently a decrease in implicit transaction costs15. Brown et al.

(2006) examine the competition between NYSE and the Consolidated Stock Exchange in the

period 1885–1926. They find that bid-ask spreads narrowed by more than 10% on NYSE after

Consolidated began trading NYSE securities. Boehmer & Boehmer (2002) investigate the

change in liquidity for 30 Amex-listed ETFs after the NYSE’s decision to start trading these

securities. They document that this event led to a significant reduction of effective, quoted and

realized spreads and an increase in quoted depth. The authors argue that before the NYSE

entry, ETF market makers earned substantial rents due to the insufficient competition.

Likewise, Lutat & Christalla (2011) investigate how the Chi-X market entry in CAC 40 stocks

influenced the liquidity of the incumbent Euronext Paris market. They report an increase in

liquidity of the most actively traded stocks in the home market. O’Hara & Ye (2009) examine

how order flow fragmentation affects the quality of trading in the US. They report that more

fragmented securities experience lower trading costs and faster execution speeds. Even though

fragmentation leads to higher short-term volatility, prices are more efficient as they are closer

to being a random walk. The authors conclude that, contrary to many theoretical predictions,

fragmentation does not harm market quality.

It should be noted, however, that the literature is inconclusive in determining consequences of

order flow fragmentation on market quality. Even though a majority of studies report a

positive influence16, some authors find a negative impact of decentralised trading on liquidity,

volatility and trading costs17.

The opponents of the view that market fragmentation fosters competitions and has a beneficial

impact on market quality name the post-MiFID environment as an example of unintended size

effects of market fragmentation18. Numerous studies have analysed the impact of MiFID on

market transparency, liquidity and volatility. Gresse (2011) finds that the implementation of

15 For a detailed review of studies investigating the influence of fragmentation on market quality, see

Levin (2003).

16 See de Fontnouvelle et al. (2003), Hengelbrock & Theissen (2009), Mayhew (2002), Nguyen et al.

(2007), Foucault & Menkveld (2008), Fong et al. (2001)

17 See Mendelson (1987), Bennett & Wei (2006), Cohen et al. (1985), Cohen et al. (1982), Porter &

Thatcher (1998).

18 One of the main objectives of MiFID was to increase competition, efficiency, market transparency

and investor protection.


pro-competition rules has increased market fragmentation. This, in turn, has had an adverse

effect on price quality by increasing short-term volatility. Gomber & Pierron (2010, p. 3) find

that a reduction of average transaction sizes (resulting, among others, from the introduction of

the MiFID directive) has induced investors to limit their own information leakage while trying

to capture as much information as possible about the trading strategy of their counterparts.

This has led to a diminished price transparency. In sum, as noted by Enderline (2011, p. 31-

32), more fragmented market structure results in liquidity dispersion among various trading

venues (regulated markets, multilateral trading facilities, dark pools as well as OTC markets)

and price inefficiencies.

Stoll (2003) names another drawback of order flow fragmentation: in fragmented markets it is

more difficult to maintain priority rules across trading venues. Price priority can be

maintained on consolidated markets because, with transparency, traders can send their orders

to the trading venue offering the best price. Furthermore, Chowdry & Nanda (1991) argue that

adverse selection costs increase with the number of trading venues listing a security.

Moreover, as shown by Easley et al. (1996) and Bessembinder & Kaufman (1997), the launch

of a new trading venue may skim off the valuable uninformed order flow from the primary

market.

4.3.2 Merits and drawbacks of market consolidation

According to Gresse (2010, p. 3) “security markets are often considered as natural

monopolies because the marginal cost of a trade decreases with the quantity of orders

executed in the market”. Indeed, the main arguments supporting stock exchange consolidation

are economies of scale and network externalities. As early noted by Stigler (1961) and Doede

(1967), the average operating costs of market infrastructure providers are a declining function

of trading volume, which supports the view of substantial scale economies in the securities

industry. Another reason for securities market to be perceived as natural monopolies is the

existence of the so-called virtuous circle of liquidity (Gresse, 2010, p. 1). Exchanges can be

seen as networks in which the greater number of investors, the higher the utility for every

participant (Economides, 1993). As suggested by Ramos (2003), due to network externalities,

order flow tends to consolidate in one market, in both space (to called spatial network

externality) and time (so called temporal network externality). Markets become more

attractive with an increase in number of traders because it is easier to find counterparty. In

addition, a greater number of traders leads to more accurate price information, which, in turn,


attracts even more traders. A notion that liquidity begets liquidity favours further order flow

consolidation (Sarkar et al., 2009). A positive impact of trading consolidation on liquidity has

been reported by a number of empirical studies19. Gajewski & Gresse (2007) also find that

trading costs tend to be lower in a centralized order book than in a hybrid market when orders

are equally split between an order book and competing dealers.

The main concern about market consolidation refers to monopoly rents earned by the

monopolist exchange. Economides (1996) theoretically argues that welfare losses due to

monopolistic position of the consolidated stock exchange may not be offset by positive

network externalities. Yet, it is not clear whether monopolist exchange would be able to

exploit opportunities arising from its dominant position since this could violate the

competition law. Furthermore, Foucault & Parlour (2004) claim that an increased competition

(being named as one of the key benefits of fragmentation) does not assure that stock

exchanges will choose welfare-maximizing trading rules. Therefore, it can be argued that

“even monopolist exchange may be welfare-enhancing” (Huyghebaert, 2007, p. 108).

4.3.3 Outlook

The question whether in the future stock trading will consolidate into a single market or will

be dispersed across multiple trading venues cannot be answered unambiguously. There is a

trade-off between scales economies and network externalities (arising from the order flow

consolidation) and the enhanced competition among trading venues (being a direct

consequence of market fragmentation).

According to Di Noia (1998, p. 4), in equilibrium, when stock exchanges are not

interconnected (implying no cooperation between trading venues), only one market will

survive the fierce competition. This market will offer the greatest liquidity, or the lowest

transaction costs, or the most state-of-the-art technology (Coffee, 2002). The premise is that

markets with network externalities are winner-takes-all-markets (Harris, 2002, p. 536).

Even though order flow externality creates substantial barriers to entry, it seems that the full

consolidation of stock exchanges is not achievable. Madhavan (2000) was the first to call this

phenomenon a network externality puzzle, which refers to the fact that “markets are

fragmented and remain so for a long period of time, despite strong economic arguments for

19 See e.g. Amihud et al. (2003), Barclay & Hendershott (2004)


consolidation” (Madhavan, 2000, p. 226). The main driving force behind this phenomenon is

trader heterogeneity and its direct consequence – market segmentation (Blume, 2002). Due to

the fact that there is a wide scope for differentiation, markets can coexist.

Another reason why a full integration of stock exchanges is hardly possible stems from the

fact that markets operate under different regulatory regimes and the unification of all trading

rules and regulations requires a wide political consensus. Tax treatment poses a further

obstacle as there are significant differences in tax laws and double-taxation treaties between

countries (Miskin & Clarke 2001).

A further force hindering full consolidation of stock exchanges is the home bias in portfolio

selection. Even though the scale of this phenomenon is gradually diminishing20, investors still

maintain a strong preference for their home assets. There are numerous behavioural

explanations on why home bias is likely to persist in the future. French & Poterba (1991) as

well as Strong & Xu (2002) suggest that investors and fund managers are more optimistic

about the domestic market development. Another explanation for the existence of home bias

refers to information frictions, as pointed out by Gehrig (1993), Tesar & Werner (1995), as

well as Hau (2001). Linguistic, cultural and geographical barriers often prevent investors from

correctly valuing foreign assets, which provides an incentive to invest in domestic securities.

Finally, many jurisdictions show a clear preference for home-country investments. For

instance, in some countries pension funds are legally obliged to invest a majority of their

assets in domestic government bonds. In other countries, investing in domestic equities

receives a favorable tax treatment (The Economist, 2001).

It seems that “market diversity does not necessarily imply inferior price formation and high

transaction costs” because “traders can obtain benefits of consolidation in fragmented

markets when information flows freely between market fragments and when some traders can

choose which fragment in which to trade” (Harris, 2002. p. 533). A similar point of view is

expressed by Huyghebaert (2007, p. 104) who claims that the creation of a fully consolidated

stock exchange with one price board is not achievable. He advocates another model of stock

market integration: a federation of sub-exchanges (subsidiaries) that would keep their own

identity. There are two successful examples of such a consolidation model: Euronext and

Norex. Federal stock market model at a global scale would lead to a reduction of transaction

20 See subsection 4.2.2


costs following the introduction of a one common trading platform. Furthermore, it could also

ensure that growth companies will have a further access to the local investor base. Therefore,

it seems that this stock exchange consolidation model will prevail in the future.


5 Conclusions

The competitive landscape in the stock exchange business has been shaped in the last twenty

years by the four main factors: demutualisation, technological progress, deregulation as well

as globalisation. In the wake of a fierce competition within the industry, stock exchanges were

forced to reformulate their business strategy and governance structure in order to operate more

efficiently. One way to achieve the above mentioned cost efficiencies was to merge with other

exchanges.

The synergistic potential in the stock exchanges industry is substantial, taking into account

large economies of scale and significant network externalities. Therefore, one would expect

that stock exchange mergers and acquisitions are value enhancing projects and contribute to

shareholder value generation (RQ 1). In order to empirically validate this hypothesis, an event

study has been carried out. The findings indicate that on average mergers are zero NPV

projects. Furthermore, in the long run, stock exchanges that underwent a merger underperform

the benchmark Dow Jones Global Exchanges Index. Hence, the question arises why

exchanges engage in non-value enhancing merger activities. One of the possible answers is

that managers do not act in the sole interest of shareholders. They prefer to lead larger

companies, which increases their power and remuneration (empire building). Another

explanation refers to the hubris theory, stating that managers are too confident about the

expected synergies arising from a merger, which leads to overpayment for the target firm.

According to the third hypothesis, the market for corporate control is efficient and therefore

potential bidders drive the stock price of the acquisition target up to the level where target

shareholders receive all the wealth generated by the acquisition.

In this thesis, the analysis of consequences of stock exchange consolidation has been

performed both from the viewpoint of the stock exchange shareholders as well as from the

micro- and macroeconomic perspective. More precisely, it has been investigated how the

market consolidation affects transaction costs and macroeconomic performance (RQ 2). Based

on numerous empirical studies, it can be argued that stock exchange consolidation may result

in a decrease in transaction costs as investors are expected to benefit from the enhanced

liquidity. Furthermore, higher liquidity translates into lower company’s cost of capital. This

may lead to an increase in the level of corporate investment expenditures, which directly

affects the gross domestic product. Furthermore, increased liquidity, bundled with other

consequences of stock exchange consolidation (e.g. the possibility of international risk sharing


and a wider portfolio diversification) should improve the market efficiency and spur economic

growth. Efficient markets, in turn, foster financial and monetary stability.

A main concern with respect to stock exchange consolidation refers to the monopolistic posi-

tion of the combined entity. A lack of competition may have a detrimental effect on innova-

tion and technology solutions. Furthermore, integration of stock exchanges could adversely

influence the domestic stock market development.

The question whether stock market consolidation benefits or hampers market quality has been

investigated in an extensive number of empirical studies. Yet the results are inconclusive. On

the one hand, market consolidation leads to higher liquidity (through the network externality)

and enables the most efficient execution of trades (by exploiting economies of scale). On the

other hand, fragmented markets foster competition, innovation and can better satisfy needs of

heterogeneous traders.

It is interesting to predict whether the establishment of a single global stock exchange is

feasible (RQ 3). Even though there is a clear rationale for order flow consolidation (network

effects and scale economies), it seems that in the future trading will be dispersed across a

small number of trading venues. These venues will address the desire for product

differentiation, existence of cross-country regulatory and legal differences as well as home

bias in portfolio selection. Following Huyghebaert (2007), I expect that exchanges will

consolidate according to the federal stock market model. It assumes the introduction of a

common trading platform with a simultaneous preservation of the identity of sub-exchanges.

This model could solve one of the main drawbacks of market consolidation, i.e. domestic

market development as it will ensure that growth companies will have a further access to the

local investor base. Furthermore, the existence of a few consolidated stock exchanges will

ensure that none of them will earn monopoly rents.


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Appendix – List of regressions:

Regression 1 Bidder's (Deutsche Börse) abnormal returns 2005-11-07

Regression 2 Bidder's (LSE) abnormal returns 2011-02-23

Regression 3 Bidder's (NASDAQ) abnormal returns 2011-04-01


Regression 5 Bidder's (ASX) abnormal returns 2007-10-16

Regression 6 Bidder's (NYSE) abnormal returns 2007-09-21

Regression 7 Bidder's (LSE) abnormal returns 2011-02-09

Regression 8 Bidder's (Euronext) abnormal returns 2004-05-28


Regression 10 Bidder's (CME) abnormal returns 2008-03-17

Regression 11 Bidder's (CME) abnormal returns 2006-10-17


Regression 13 Bidder's (NYSE) abnormal returns 2006-05-22



Regression 16 Target's (Instinet) abnormal returns 2005-04-22

Regression 17 Target's (NYSE) abnormal returns 2011-02-15

Regression 18 Target's (OMX) abnormal returns 2007-05-25

Regression 19 Target's (ISE) abnormal returns 2007-04-30

Regression 20 Target's (NYSE Euronext) abnormal returns 2011-04-01

Regression 21 Target's (Euronext) abnormal returns 2005-11-07


Regression 23 Target's (BME) abnormal returns 2007-09-21

Regression 24 Target's (NYMEX) abnormal returns 2008-03-17

Regression 25 Target's (CBOT) abnormal returns 2006-10-17

Regression 26 Target's (NZX) abnormal returns 2007-10-16

Regression 27 Target's (NASDAQ) abnormal returns 2011-02-23

Regression 28 Target's (TSX) abnormal returns 2011-02-09


Regression 30 Target's (LSE) abnormal returns 2004-05-28


Regression 1

Regression 2

Regression 3


Regression 4

Regression 5

Regression 6


Regression 7

Regression 8

Regression 9


Regression 10

Regression 11

Regression 12


Regression 13

Regression 14

Regression 15


Regression 16

Regression 17

Regression 18


Regression 19

Regression 20

Regression 21


Regression 22

Regression 23

Regression 24


Regression 25

Regression 26

Regression 27


Regression 28

Regression 29

Regression 30


Ehrenwörtliche Erklärung

Ich erkläre hiermit ehrenwörtlich, dass ich die vorliegende Arbeit selbständig und nur

unter Benutzung der angegebenen Literatur und Hilfsmittel angefertigt habe.

Wörtlich übernommene Sätze und Satzteile sind als Zitate belegt, andere

Anlehnungen hinsichtlich Aussage und Umfang unter Quellenangabe kenntlich

gemacht. Die Arbeit hat in gleicher oder ähnlicher Form noch keiner

Prüfungsbehörde vorgelegen und ist auch noch nicht veröffentlicht.

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