UNIVERSITEIT GENT

FACULTEIT ECONOMIE EN

BEDRIJFSKUNDE

ACADEMIEJAAR 2013 – 2014

Cross-delisting and the effects on

visibility, liquidity and market value

Masterproef voorgedragen tot het bekomen van de graad van

Master of Science in de Handelswetenschappen

Sylvia Duson

Kimberley Lampaert

onder leiding van

Koen Inghelbrecht

Acknowledgments

This thesis would not have been made without the help and support of many. We

would like to extend our sincere gratitude to all those who made it possible.

First of all, we want to focus a word of thanks to our promotor Professor Inghelbrecht.

We would like to thank him for all his help and unrelenting support. Also his classes

were a source of information and inspiration.

Further, we would also like to thank family, friends and classmates who supported us

during the painstaking process of data collection and data processing, which without

their support would not have been possible to realize.

A final word of thanks goes to our proofreaders.

Cross-delisting and the effects on visibility, liquidity and market value

Abstract

This study tests whether a firm’s decision to cross-delist has a negative

influence on visibility, liquidity and market value. We examine a voluntary cross-

delisting from the NYSE, as this is seen as the most prestigious exchange, by companies

originating from Germany, the U.K. and France. Our research consists of three

hypotheses. First, we want to examine whether a cross-delisting has an influence on

the company's visibility using two proxies: analyst coverage and number of newspaper

references. By doing several empirical tests, we cannot find any significant proof that

the cross-delisting event has an influence on the visibility of a company. Furthermore,

we examine the effect of cross-delisting on the liquidity of the domestic market. The

delisting event seems to have little or no consequences on liquidity. Finally, we

investigate the impact of cross-delisting on the value of the company. For the effect on

the company's value, as expressed by the Tobin q ratio, we can conclude that the

delisting event has a negative and significant impact. Our findings for this last

hypothesis are consistent with the conventional wisdom on cross-listing, which claims

that a cross-listing leads to an increase of the company's value. Our research leads us

to conclude that once a company has been cross-listed on the NYSE, a subsequent

delisting does not have a negative result on the visibility and liquidity of the company.

There are however indications that this delisting may lead to a decrease of the Tobin q

ratio by about 4 %.

TABLE OF CONTENTS

1 Introduction .............................................................................................................. 1

2 Literature review....................................................................................................... 4

2.1 Depositary receipts as a means to cross-listing ................................................. 4

2.1.1 An historical view on cross-listing ............................................................... 4

2.1.2 Depository receipts ..................................................................................... 5

2.1.3 Recent evolutions in cross-listing .................................................................... 9

2.2 A review of conventional wisdom on cross-listing .......................................... 10

2.2.1 Visibility, or investor recognition .............................................................. 11

2.2.2 Liquidity ..................................................................................................... 12

2.2.3 Effects on the company's value and cost of capital.................................. 13

2.2.4 Corporate governance .............................................................................. 15

2.3 Cross-delisting: a study of the effects on visibility, liquidity and market value

17

3 Research .................................................................................................................. 25

3.1 Selection of the data sample............................................................................ 25

3.2 Hypothesis development ................................................................................. 29

4 Visibility ................................................................................................................... 31

4.1 Methodology and data ..................................................................................... 31

4.2 Results .............................................................................................................. 33

4.2.1 Number of newspaper references ............................................................ 33

4.2.2 Analyst coverage ....................................................................................... 36

5 Liquidity ................................................................................................................... 42

5.1 Methodology and data ..................................................................................... 42

5.2 Results .............................................................................................................. 44

6 Company’s value ..................................................................................................... 48

6.1 Methodology and data ..................................................................................... 48

6.2 Results ............................................................................................................... 50

7 Conclusion ............................................................................................................... 56

8 References ............................................................................................................... 58

9 Appendix.................................................................................................................. 62

LIST OF FIGURES

Figure 1. Market share of sponsored DR Programs ......................................................... 6

Figure 2. Capital raised by structure during 2004 and 2005: Shift to Global Depositary

Receipts ............................................................................................................................. 8

Figure 3. OTC Depositary Receipt Trading ...................................................................... 10

Figure 4. Total sponsored and unsponsored DR programs ............................................ 18

Figure 5. Visibility - Number of newspaper references .................................................. 33

Figure 6. Visibility - Analyst coverage ............................................................................. 37

Figure 7. Liquidity - Trading volume ............................................................................... 44

Figure 8. Company's value - Tobin q ratio ...................................................................... 50

LIST OF TABLES

Table 1. Summary of the different types of ADR .............................................................. 7

Table 2. Possible combinations of regulation S and ADR.................................................. 8

Table 3. Delisted sample ................................................................................................. 27

Table 4. Matching sample ............................................................................................... 28

Table 5. Visibility - Number of American newspaper references ................................... 34

Table 6. Visibility newspaper references - Delisted sample ........................................... 35

Table 7. Visibility newspaper references - Delisted and matching sample .................... 36

Table 8. Visibility - American analyst coverage ............................................................... 38

Table 9. Visibility analyst coverage - Delisted sample .................................................... 39

Table 10. Visibility analyst coverage - Delisted and matching sample ........................... 40

Table 11. Wilcoxon rank-sum test - Number of newspaper references - Delisted sample

......................................................................................................................................... 41

Table 12. Wilcoxon rank-sum test - Analyst coverage - Delisted sample ....................... 41

Table 13. Liquidity - Delisted sample .............................................................................. 45

Table 14. Liquidity - Delisted and matching sample ....................................................... 46

Table 15. Company's value - Delisted sample ................................................................. 52

Table 16. Company's value - Delisted and matching sample .......................................... 54

Table 17. Business summary delisted and matching companies .................................... 62

Table 18. Newspapers used in the analysis..................................................................... 69

Table 19. Correlation matrix visibility ............................................................................. 70

Table 20. Correlation matrix liquidity ............................................................................. 70

Table 21. Correlation matrix company's value ............................................................... 71

1

1 INTRODUCTION

Cross-listing occurs when a company's equity is traded not only on the domestic

capital market, but also on at least one foreign capital market. If a company chooses to

cross-list, this implies certain additional costs such as the cost for listing on the foreign stock

exchange, the cost of adapting their accounting and reporting standards to the foreign

market, additional publication and legal demands imposed by foreign legislation, and others.

For many years, cross-listing has known a rising popularity. So, there should be a profit, a

clear advantage for a company to counterbalance these costs. These advantages can be

summarized in what literature calls the conventional wisdom. The most important element

here is visibility, which can also be defined as investor's recognition. Investor recognition is

usually measured in the number of articles about a firm in the financial newspapers, and the

number of analysts following this firm. A larger degree of investor recognition allows a firm

to expand its shareholder base more easily. Other elements are liquidity, the effects of cross-

listing on the company's value, and finally corporate governance as an additional motive for

cross-listing.

Recently, academic literature has expressed some doubts on the value of these

traditional elements. Dobbs and Goedhart asked in their 2008 article whether the

advantages of cross-listing still exists, and also Karolyi in his 2006 paper indicated that recent

evidence challenged the conventional wisdom on cross-listing. At least for companies from

developed markets, there seems to be an understanding that there are no advantages to

cross listing, as opposed to firms from emerging markets where the advantages may still be

relevant. in 2011, there were 2289 firms cross listing. (Bank of New York-Mellon, Yearbook

2011). However, this already implied a reduction from a historical high of 4700 companies.

As the world's economy grows more global, one might expect otherwise and this reduction

can be a source for study.

In my literature review, I have found many studies discussing advantages of cross-

listing as well as some studies on the reasons for terminating a cross-listing. However, I have

found almost no study about the effects of terminating a cross listing. Since many firms have

ended their cross listing recently, one might wonder what the effects are. After a regulation

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change in 2007 made it more easy to leave the U.S. market, a number of European

companies terminated their cross-listing in the period between May 2007 and May 2008

(Dobbs and Goedhart, 2008) and the exodus did not end there. Witmer (2005) asked why

firms cross-delist, but as his study contains a large number of involuntary delistings as well

as a large number of Canadian firms, his study does not allow to draw definitive conclusions.

Marosi and Massoud (2006) focus on the legal bonding hypothesis and see the associated

costs to be a reason for delisting. Chaplinsky and Ramchand (2008) consider a lack of quality

at the "supply side" as a delisting motive, meaning that a lot of cross-listed firms did not

have the basic quality to attract investors. Bessler et al. (2011) concluded that the benefits

of cross-listing did not materialize and that the managerial decision to cut costs by delisting

was justified. However his study is limited to German firms only.

The aim of our study is to investigate what happened to the visibility, liquidity and

company's value of a sample of European firms, who were listed on the NYSE and delisted

from 2007 to 2010. Our sample includes but is not limited to German firms, as we also

investigate companies from the U.K. and France. We do limit our study to those companies

where the delisting is a result of a managerial decision, excluding the involuntary delistings.

We only include companies that are in the same going concern conditions, leaving out those

who went through corporate restructuring. This gives us a sample of nineteen companies,

for which we will research whether delisting has a negative impact.

In Section 2, we will review the history of cross-listing and the types of depositary

receipts. We will also look at the motives for cross-listing, as identified by economic

research. These include company's value , cost of capital, visibility, liquidity, and the

bonding hypothesis. In a second part of this section, we will devote some attention to the

more recent development of cross-delisting: companies reversing on their decision to cross-

list are delisting in the foreign market, keeping only their domestic listing active. We ask

ourselves, if cross-listing has so many advantages, then why do firms cross-delist? We will

research whether the cross-delisting decision has real impact on these benefits that

conventional wisdom predicts that should flow from the cross-listing event. We will study

some of the literature that has been written about this phenomenon, in a search for the

reasons to delist.

3

In section 3 we will introduce our own research, and point out in which aspect this

research offers a contribution to this domain. Our own research will focus on companies

from mature western European markets, terminating their cross-listing in the U.S..

In the next three sections then we will search whether or not the decision to actually

delist has had any influence on the firms' equity. In Section 4, we will talk extensively about

the "visibility" aspect of cross-delisting, both in terms of analyst coverage as well as

newspaper coverage. In Section 5, we will take a look at the "liquidity" item and in Section 6

we will study the effects of delisting on the company's value. Section 7 will offer a

comprehensive conclusion of our study.

4

2 LITERATURE REVIEW

2.1 Depositary receipts as a means to cross-listing

2.1.1 An historical view on cross-listing

The first cross-listing events took place in the interwar period. As in these times

communication speed was slower and currency exchange rates and regulatory issues made

it difficult for the individual investor to invest in markets overseas, depositary receipts were

introduced as a way to overcome these problems. In 1927, the first ADR (American

Depositary Receipt) was created by J.P. Morgan, to allow American citizens to invest in the

British retailer Selfridge's. The stock market crash of 1929 and the Glass-Steagall Act of 1933

forced the American banks to separate their investment banking operations from the

commercial bank. The disrepute of investment banking and the effects of the financial crisis

put a temporary halt on the issuance of depositary receipts.

After World War II, these activities took a new start. In the 1950s, American and

South African firms came to the European markets, with the British, French, Belgian and

Dutch exchanges as principal host markets. The leading cross-listing industry was mining. In

the 1980s, many firms started to list on the Tokyo exchange, and this was followed by a

reversal of foreign listings from Tokyo to the Western markets in the 1990s. From 2000 on,

Canadian and Indian firms were the most important source of cross-listings, and the U.S.

attracted more than 50% of all new foreign shares, followed by the United Kingdom and

Luxemburg. All industries were represented, but the electronics industry was the most

important. (Sarkissian and Schill, 2011)

Due to the ever-growing globalization, the situation is now even more diffuse, with

firms from emerging markets wanting to be listed on major stock exchanges in the

traditional markets. Most academic studies however focus on cross-listing on the U.S. capital

market or on the London Stock Exchange, other markets are often only mentioned to

demonstrate the superiority of the U.S. market. Taking the large number of recent cross-

listings on continental European exchanges, such as Luxembourg, into account, there seems

to be something missing in this literature. The Bank of New York-Mellon yearbooks clearly

5

lists Luxembourg as a stock market on the rise for trading GDR's reaching an almost equal

level to London. Sarkissian and Schill (2011) also conclude that, for cross-listings after 2000,

Luxembourg is the second host market after the U.S. but before the U.K.

European firms have a long history of cross-listing on other European exchanges, but

Daimler-Benz was the first German firm to list in the USA, in 1993. From 1993 to 1998

European firms were the largest segment of foreign firms on the New York Stock Exchange,

as 133 European firms were listed there. (Bancel and Mittoo, 2001). However, from the late

1990's on, international cross-listing started to lose its appeal. All major exchanges were

confronted with a wave of delistings and by the end of 2002, the number of internationally

cross-listed firms was reduced to less than 50 % of the 1997 maximum (Karolyi, 2006).

Regulations made it difficult to leave the U.S. stock markets, but between may 2007 and may

2008 however, 35 European firms ended their cross-listing on the NYSE, which led some

authors to conclude that the advantages of cross-listing now are non-existent. (Dobbs and

Goedhart, 2008)

2.1.2 Depository receipts

If an investor wishes to invest in shares of a foreign company, he will almost always

buy Depositary Receipts (DRs). This is for a number of practical reasons which include

amongst others (J.P. Morgan, 2005):

- DRs trade and settle in the same manner as any other security on the investor's home

market.

- DRs pay dividends and deliver company action notification in the currency and language of

the investor's home market.

- DRs are easy to purchase and hold.

- DRs enable comparison with other investments thanks to accessible price and cost

information.

6

Figure 1. Market share of sponsored DR Programs

There are only four banks that issue depositary receipts, namely:

Bank of New York Mellon,

JP Morgan,

Citibank,

Deutsche Bank.

(Source: BNY Mellon, yearbook 2011)

Depositary receipts can be made on the initiative of the company whose shares they

represent, we talk about sponsored DRs in this case, but they can also be unsponsored. The

Bank of New York - Mellon 's website says : "Depositary Receipts are issued or created when

investors decide to invest in a non-U.S. company and contact their brokers to make a

purchase. These brokers, through their international offices or through a local broker in the

company's home market, purchase the underlying ordinary shares and request that the

shares be delivered to the depositary bank's custodian in that country."(BNY Mellon,

10/03/2013)

These depositary receipts exist in different forms.

For American investors, the following forms are available :

level 1 ADR : This permits only over the counter trading, no listing on stock

exchanges, no possibility to raise capital, but the company is not subjected to

American accounting or disclosure regulations. This form is open to all companies

with a listing on their domestic stock market. An unsponsored DR is always level 1, as

the other types require the active cooperation of the company to submit to the

regulations.

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level 2 ADR : For listing on an American Stock Exchange, requires registration with

U.S. SEC and accounting by U.S. GAAP, annual reports have to be published by

American standards.

level 3 ADR : This is based on level 2, but allows for raising capital on the American

market through the issuance of new shares in the U.S. The company has to follow all

publication and disclosure regulations as an American company.

SEC rule 144A : These shares can not be listed, but can only be traded over the

counter. This form allows for the issuance of new capital but it may not be traded

freely by investors, trading is limited to Qualified Institutional Buyers only. A

Qualified Institutional Buyer is legally recognized by security market regulators to

need less protection than normal investors. A minimum requirement for example is

that the institution should manage at least $ 100 million.

There exists a fifth form which may not be traded in the U.S. at all :

- SEC regulation S, which is also called the offshore regulation : these shares can not be

registered in the U.S., and they can not be held nor traded by a U.S. person. They are

registered to offshore non-U.S. residents. I mention this form here because this is how the

U.S. sees the GDRs (Global Depositary Receipts).

Table 1. Summary of the different types of ADR

(Source: JP Morgan, 2005)

Apart from the ADR (American depositary receipt) which allow US investors to invest

in non-American firms, we have seen the rise of GDR (Global Depositary Receipts). Currently,

8

GDRs are traded on the following stock exchanges: the London Stock Exchange, the

Luxembourg Stock Exchange, NASDAQ Dubai, Singapore Stock Exchange, and the Hong Kong

Stock Exchange. A GDR can not be traded in the US. The typical GDR structure combines a

depositary receipt offered in Europe which is a regulation S (offshore receipt) with a

depositary receipt offered in the U.S. as an ADR. The following table gives the possible

combinations:

Table 2. Possible combinations of regulation S and ADR

(Source: JP Morgan, 2005)

Originally, GDRs were not important, as they accounted for only 1 % of all capital raised

through Depositary Receipts in 2000, but they have been growing rapidly to a market share

of 40 % in 2004-2005.

Figure 2. Capital raised by structure during 2004 and 2005: Shift to Global Depositary Receipts

(Source: J.P. Morgan, 2005)

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2.1.3 Recent evolutions in cross-listing

Traditionally a firm chooses to cross-list in order to gain more investor recognition.

However, in two recent evolutions, investor recognition seems to be not so important. A

first evolution is the "involuntary cross-listing". As we have seen, recently, depositary

receipts can be created on demand, when an investor decides to invest in shares of a foreign

company and passes the order to his bank or broker. This may seem relatively harmless, but

a recent change in regulations by the SEC (2008) allowed US depositary banks to create

unsponsored ADRs without notifying the companies or obtaining their consent. An example

of this is the French company l'Oréal. A recent study by Iliev, Miller and Roth (2010) shows

that this involuntary cross-listing can have positive effects on the firm's value for small

companies, but can have negative effects for companies who meet the criteria to be listed

on the NYSE. They state: "we find that the net effect of the regulation change was a

significant destruction of firm value" (Iliev, Miller and Roth, 2010, p.3). Besides this effect on

the value of the firm, this involuntary cross-listing also exposes the firm to legal risks in the

U.S. Apart from this, it limits also their possible access to the U.S. capital market if they

might choose to do so in the future, as they would have to obtain the consent of each bank

who runs an unsponsored ADR on their shares. The authors document that this may be a

real problem to many firms, as 748 unsponsored ADR programs were created in the six

months following the amendment, as opposed to only 69 ADR programs in the decade

before the amendment.

A second evolution is the choice for level I or rule 144 A programs, which do not

allow for listing on a U.S. stock exchange. A study by Korczak and Bohl (2005) shows that

between 1995 and 2001, 33 companies from Central and Eastern Europe came to the U.S.

capital market. However, only one of them used ADR level II, while fourteen choose for ADR

level I permitting only over the counter transactions and another eighteen choose for rule

144 A allowing only institutional investors. This finding is not consistent with the traditional

wisdom which says that visibility is the most important factor.

As a result of these two evolutions, the Over The Counter market becomes more

important, as the following figure indicates.

10

Figure 3. OTC Depositary Receipt Trading

(Source: BNY Mellon, yearbook 2010)

2.2 A review of conventional wisdom on cross-listing

There can be no doubt that cross-listing causes additional costs for a firm. Economic

wisdom has searched for reasons to justify this additional costs. The conventional wisdom

can almost be summarized in one single word: 'visibility' or 'investor recognition'. Merton

refers to "the degree of investor recognition" as "the number of investors that know about a

security" (Lehavy and Sloan, 2008, p. 2). He states that investors use only securities that they

know about in constructing their optimal portfolio. A firm's visibility is greatly increased by

cross-listing, leading to greater analyst coverage and media attention. Out of this improved

visibility, other, more substantial, advantages are derived, such as a lesser cost of capital,

more liquidity on the stock market, and a higher appreciation of the firm's value. I will now

look at these arguments in closer detail.

11

2.2.1 Visibility, or investor recognition

One of the dominant factors in the decision of the destination market is enhancing

the visibility of the company. The choice of cross-listing implies an increase of publications in

the financial press and a closer monitoring by securities analysts, this way the investor

awareness of the securities increases. By cross-listing its stocks a firm could expand its

shareholder base more easily than if it would be traded only at his domestic market.

However, the benefits go beyond this expected increase in shareholder base. The company

also gains more credibility on the target market, thus allowing it to call on the financial

markets in order to raise capital or issue bonds. (Licht, 2003)

Baker, Nofsinger and Weaver (1999) have demonstrated that the number of analysts

keeping track of a certain firm rises sharply at the decision of seeking a cross-listing. They

have studied the effects of cross-listing on either the New York Stock Exchange or on the

London Stock Exchange on the firm's visibility, and found that the number of analysts rises

by an average of 6.18 analysts for companies listing on the NYSE which is an increase by 128

%, and by 3.4 analysts for firms listing on the LSE, an increase of 48 %. The number of articles

published with regard to these firms also takes a steep rise for those listed on the NYSE, and

this not only for articles in the Wall Street Journal, but also for articles in the Financial Times

and publications in their home market. For companies listing in London on the other hand,

the results are mixed, they only found an increase in publications in the Financial Times but a

decrease for publications in the Wall Street Journal as well as the domestic publications.

These findings were also confirmed by Lang, Lins and Miller (2003) who demonstrated that

non-US firms cross-listing on US exchange have greater analyst coverage, as well as

increased forecast accuracy, when compared to other non-US firms. They also show that this

improvement in analyst coverage and accuracy occurs around the cross-listing decision, and

that it has a positive effect on firm value.

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2.2.2 Liquidity

Liquidity can be defined as the degree to which an asset can be sold or bought

without affecting the price. The most important element to liquidity is a high level of trading

activity. Liquidity, or illiquidity, reflects itself in two ways.

First, there is an additional return investors demand, Amihud and Mendelson (1986)

have shown in a study of the expected returns over stocks that illiquidity is a risk factor for

which a price has to be paid in the return generating process. Expected stock returns are an

increasing function of expected illiquidity. As investors are aware that they may occur a loss

when selling a less liquid asset, they demand a higher return to compensate for this loss. In

another study by Amihud (2002), he has examined the effect over time by calculating

'market excess stock returns', this is the excess return generated by a stock as compared to

the Treasury bill rate. In this study, he finds there is also an effect over time. These expected

stock excess returns are not constant but vary over time as a function of changes in market

illiquidity.

Second, there is a direct effect on the prices of stock. Illiquidity reflects in the impact

of orders on price. If you sell a illiquid share, you push the price down and vice versa.

Therefore, companies from relatively illiquid stock markets should be inclined to cross-list on

more liquid exchanges (Pagano, Randl, Roëll, Zechnerl, 2001). Their study, which

concentrates on European companies, indeed confirmed that it is more likely for a company

to cross-list in more liquid and larger markets. Liquidity is here measured in terms of trading

costs. The most important element of these is the bid-ask spread, this spread becomes

smaller for more liquid stocks. They demonstrate that by cross-listing the trading cost of an

equity is reduced by over 40 % of average trading cost before cross-listing.

Recent literature (Karolyi 2006, Halling et al. 2006) has identified some new points of

interest, such as the trading volume and price finding. The answers to these questions can

differ depending on the characteristics of the companies and their domestic markets. Halling

et al. (2006) have shown that trading volume on the domestic market increases for

companies from developed markets in the wake of the cross-listing event and continues on a

higher level thereafter. For companies from emerging markets however, trading volume on

the domestic market decreases after being cross-listed. They have also demonstrated this

13

effect in relation to the degree of protection against insider trading on the domestic market.

For companies from markets with weak enforcement of insider-trading protection, trading

volumes drop significantly.

2.2.3 Effects on the company's value and cost of capital

Conventional wisdom holds that the company's value increases through cross-listing.

Several theories have been advanced to explain this phenomenon. Starting in 1987, Merton

described how investor recognition can explain an increase in valuation. He shows that if all

other elements remain constant, the value of the firm will increase as investor recognition of

the firm increases. If only few investors know about a certain equity, the market can only

clear if these investors take a large and undiversified position in this equity. These investors

then require a higher expected return, to compensate for this specific risk. When a company

lists on a stock exchange, they have to provide a full set of information. If a firm provides full

information, then this excess return is reduced, thus lowering the capital cost of the firm.

(Merton, 1987)

Lehavy and Sloan (2008) have shown in their article on "investor recognition and

stock returns" that stock returns are more dependent on investor recognition than on

accounting information. Lev (1989) already showed that earnings can explain no more than

10 % of the variation in stock returns, concluding that earnings are of limited usefulness to

investors. An additional 30 % of this variation can be traced back to changes in expectations

of future abnormal earnings, as demonstrated by Liu and Thomas (2000). This still leaves 60

% of the variations to be explained by other elements. Lehavy and Sloan (2008) explain these

remaining variations by using Merton's model of capital market equilibrium under

incomplete information. Merton (1987) shows that the value of the firm increases as the

degree of investor recognition rises.

If only few investors know about a certain equity, then the market can only clear if

these investors take a large and undiversified position in this equity. They then require a

higher expected return, to compensate for this specific risk. When a company lists on a stock

14

exchange, they have to provide a full set of information. If a firm provides full information,

then this excess return is reduced, thus lowering the capital cost of the firm. (Merton, 1987)

A NYSE staff report by Cetorelli and Peristiani (2010) indicates that there is a relation

between cross-listing on a certain market, and the value of the firm as expressed by Tobin's

Q ratio. They have proven that the value of a firm, as expressed by Tobin's Q ratio, raises by

a second listing on a market with more prestige than their domestic market. They consider

the U.S. market to be the most prestigious. They have also proven the inverse effect: a

cross-listing on a less prestigious market leads to a decline in valuation.

Roosenboom and Van Dijk (2009) published a study in the Journal of Banking and

Finance on "The market reaction to cross-listings: Does the destination market matter?".

They consider four possible explanations for valuation gains around cross-listings: market

segmentation, market liquidity, information disclosure, and investor protection by bonding

on a stock exchange with higher standards of investor protection. The results of their study

show a clear gain of 1,3 % for cross-listing on the US market, which can be attributed to

bonding and information disclosure effects. There is a somewhat lesser gain of 1,1 % for

cross-listing on the London exchange, where this gain is attributed to market segmentation

and information disclosure. There is almost no gain (0,6 or 0,5 %) for cross-listing on

continental European markets or on the Japanese market. These findings are consistent with

the findings of Cetorelli and Peristiani (2010), and lead us to the conclusion we should focus

on firms leaving the U.S. market, as there are no valuation benefits for other, less

prestigious, markets.

Other authors have focused on different reasons for cross-listing, where the cross-

listing in itself leads to an increase in valuation. Gozzi, Levine and Schmuckler (2005) have

studied the bonding hypothesis, which states that there is a positive effect on a company's

value due to bonding to a better corporate governance system than their domestic system.

They have found however that the valuation effects as a result of bonding are only

transitory.

Most studies do conclude to the existence of valuation benefits due to a cross-listing

on the U.S. market, but other studies challenge this. A study by Glaum et al. (2006) focusing

on German firms, shows that CFO's mention non-financial goals such as reputation, positive

15

influence on the acquisition position, or attracting workforce, are more important than the

financial goals as studied by the finance literature. Another study by Bessler et al. (2011)

bluntly asks "were there any benefits" as he studies the listing and delisting of German firms

on NYSE and Nasdaq. He concludes that the advantages associated with cross-listing were

only temporary, and that no significant valuation benefit was associated with cross-listing.

His study as well as the study by Glaum et al. (2006) however focuses only on German

companies.

2.2.4 Corporate governance

As investors buy shares of a company, they trust the managment of the company to

act in their best interest. Sadly, this is not always the case. Often, managment acts in its own

best interest. This is called the agency problem: the agents should look after the interest of

their principals, but use their power for their own benefit instead. In a publicly held

corporation, the people who run the company (managers) are not the owners

(shareholders), and also other parties can have a stake in its success (stakeholders) (Financial

times Lexicon, 20/03/2014). Often, managment is rewarded on achieving short-term targets.

Vis-a-vis the shareholders, they display a satisfying behavior, aiming for targets that are

acceptable to the shareholders, and not a maximizing behavior. Therefore, sets of rules and

systems of control have to be worked out. This is called "corporate governance" and should

minimally include a board of directors as supervisors to the managment, and an external

auditor to check the financial statements. Well known examples of bad corporate

governance are Lernout & Hauspie, as well as Enron. Both companies went into bankruptcy,

resulting in a total loss for the shareholders. After the Enron scandal, corporate governance

came on top of the economic policy agenda with the Sarbanes-Oxley act being passed in

2002. Under this law, top management now has to individually certify the financial

information, and faces criminal penalties in case of fraud.

Legislation on corporate governance has often been lacking in some countries,

leading authors to see a cross-listing in the US, and the subsequent compliance with

American rules, as an important advantage of the cross-listed companies. These should offer

a far superior minority shareholder protection, and as a result be able to attract more capital

16

from minority shareholders. Wojcik, Clarck and Bauer (2005) have studied the relationship

between cross-listing and corporate governance between 2000 and 2003, showing that

companies with a US cross-listing have higher corporate governance ratings than companies

without a US cross-listing. This advantage becomes more consistent in 2003 suggesting a

possible impact of the Sarbanes-Oxley act (2002). At the same time, they find that in

contrast to the importance of cross-listing in the US, there is no significant relationship

between corporate governance and cross-listing within Europe.

On the other hand however, these rules on corporate governance may deter

companies from cross-listing. In their study on "Private benefits of control, ownership and

the cross-listing decision", Doidge, Karolyi, Lins, Miller and Stulz (2005) have shown that

shareholders who can extract private benefits from their company are less likely to cross-list

on a US stock exchange, as the higher standards on transparency and disclosure, and the

increased monitoring, limits their ability to do so.

Witmer (2005) has also shown in his paper "why do firms cross-(de)list? An

examination of the determinants and effects of cross-delisting." that especially firms who

come from countries with poorer investor protection are more likely to cross-delist, which is

contrary to what might be expected. Indeed, the cross-listing benefits through bonding are

thought to be higher for firms originating in countries with lesser investor protection. You et

al. (2008) provides an explanation for this contradiction: for countries with poor investor

protection, the investors in the home country don't trust the firms' motives and become

even more doubtful about a foreign listing. You et al. (2008, p.24) also conclude "Our

findings do not support the bonding hypothesis". They find their results to be in conflict

with the results of prior studies, but attribute this finding to the fact that earlier studies

concentrated on listings in the US-market, while their dataset is dominated by cross-listings

in European countries. The level of investor protection of the home country seems to be the

key variable, more than the level of investor protection of the listing country. This is not

supportive of the bonding hypothesis. They offer "investor sophistication" as a possible

explanation, stating that only more sophisticated investors (from more developed countries)

recognize the benefits of cross-listing, inviting further research on this topic.

17

O'Connor (2007) comes to a similar conclusion. His research focuses on a sample of

538 firms originating in the emerging markets. He finds that only firms from countries with a

high disclosure legislation gain from listing as a level II or III ADR, and this valuation benefit

occurs only after the firms are listed for four or five years in the US. For companies from

countries with low disclosure legislation, there is no valuation gain. Therefore , the decision

of the majority of firms from low disclosure regimes not to list as exchange-traded ADR's is

justified.

Yet another aspect of the Sarbanes Oxley act has been shown by Dobbs and

Goedhart (2008) as they attribute the decline of cross-listing companies on the NYSE in part

to the effect of this legislation being applied on foreign firms. It is not really clear whether

the Sarbanes-Oxley act has indeed improved corporate governance, but it is clear that

companies are spending millions of dollars to comply with the requirements of this act. BP

plc has estimated the cost to be around $ 100 million. They also state that companies from

countries with an equivalent legislation on corporate governance, when cross-listing on the

NYSE, face higher costs, as imposed by this act, without deriving any advantage.

2.3 Cross-delisting: a study of the effects on visibility, liquidity and

market value

As we have seen in the previous sections, cross-listing once was a growing

phenomenon, and scholars have provided a number of motivational factors for this

behavior. However, it is a simple fact that the total number of cross-listed firms, including

the "over the counter" market, has dimished from 4700 in 1997 to 2289 in 2002, a decline by

over 50 % (Karolyi, 2006). This decline comes not only from "natural" causes such as

bankruptcy, mergers or takeovers, but also from voluntary delisting decisions. Dobbs and

Goedhart asked in their 2008 article whether the advantages of cross-listing still exist, and

also Karolyi in his 2006 paper indicated that recent evidence challenged the conventional

wisdom on cross-listing. Further important elements in the decline of the US capital market

are the passage of the Sarbanes-Oxley act (in 2002) and the regulation change in 2007

allowing foreign firms to deregister more easily with the S.E.C. As a result, 35 European

18

firms such as Ahold, Bayer, Hellenic Telecom and others terminated their cross-listing in the

period between May 2007 and May 2008. There seems to be an understanding that, for

companies from developed markets, there are no advantages to cross-listing, as opposed to

firms from emerging markets where the advantages may still be relevant.

This leads to a worldwide dynamic in cross-listing, rising back to "Over 3,000 such

international cross-listings were distributed across many of the major markets around the

world as of the end of 2008, according to the World Federation of Exchanges." (Gagnon and

Karolyi, 2010, p.2). This tendency continues until today, as confirmed by Bank of New York -

Mellon's 2013 yearbook. We also see a continuing trend toward more unsponsored DR

programs. This indicates that the demand for DR programs has turned into a buyers' market,

where investors seek to invest in foreign companies, often without knowledge or consent of

the companies themselves. These unsponsored DR programs however are not relevant in

our study, as they do not involve a management decision to list or delist.

(source: BNY Mellon, yearbook 2013)

Figure 4. Total sponsored and unsponsored DR programs

19

When considering the number of delistings, these facts have led to the rise of

academic literature trying to find determinants, causes, for the delisting phenomenon.

Some literature also looks at the consequences. Over the period 1961-2004, a total of 1330

foreign firms cross-listed in the US, and 728 cross-delisted. These delistings comprise

involuntary delistings, delistings due to mergers and acquisitions, as well as voluntary

delistings. It is important to notice that only 48 of these are true voluntary delistings

(Chaplinsky and Ramchand, 2008). Furthermore, they point out that firms cross-delisting as a

result of the Sarbanes-Oxley act usually have low average profitability, median assets, and

market capitalization of less than $ 230 million. In their study, 60 % of these firms had not

one single analyst following them one year after the cross-listing event. They see this as an

indication that some cross-listed firms didn't have the basic qualities required for a

successful listing right from the start, and never were able to attract investor's attention.

We have to point out that these firms also didn't have a single analyst covering them before

the cross-listing. This may explain the difference in findings with Baker, Nofsinger and

Weaver (1999), who concluded that the number of analysts rose after cross-listing.

Contrary to Chaplinsky and Ramchand (2008), most of our firms are sufficiently large

and should be appealing to investors, to conclude that their delisting is not a result of a lack

of basic quality. On the other hand, many of them cite low liquidity in the US market as one

of the reasons to delist, thus confirming Witmer's findings (2005) that firms with a low

percentage of trading in the US are more likely to delist. The low level of US trading makes it

hard for management to justify the costs of compliance with US regulations, especially with

the Sarbanes-Oxley act, adding a further incentive to delisting in the US.

In their 2006 paper "You can enter but you cannot leave - US securities markets and

foreign firms", Marosi and Massoud draw attention to the fact that delisting from the US

markets had been delayed by regulation making it difficult for foreign firms to voluntarily

deregister from the US market. As an example, a company is allowed to deregister with the

SEC if it has less than 300 "shareholders of record". The definition of "shareholder of record"

however is different for domestic and foreign firms: Domestic issuers are allowed to count

"street names", counting institutions as Merrill Lynch holding the shares on behalf of

investors as a single shareholder, while foreign issuers have to count all individual

shareholders, making it a greater hurdle to tackle. However, deregistration is more difficult,

20

but not impossible. They also made a first study into the motivations for foreign firms to exit,

focusing on the bonding hypothesis of Coffee (1999, 2002) and Stulz (1999). This hypothesis

states that by listing on a US market, a company commits itself to enhanced minority

shareholder protection as found in the US. It would also mean that firms from countries with

weaker corporate governance regulations would benefit more, and thus should be less likely

to deregister. However, the costs of compliance with US regulations, which have increased

due to the Sarbanes-Oxley act, may create incentives for foreign firms to delist and

deregister, even as this might lead to the loss of bonding benefits.

Marosi and Massoud (2006) have concluded that the importance of legal bonding as

a benefit gained from US regulations has declined, and that the increased regulations and

government controls have inspired an increasing number of firms to exit the US security

markets. Firms from countries with weaker corporate governance rules were even more

likely to deregister, which is contrary to what might have been expected, as these firms were

to benefit more from the stricter US governance regime. A second finding is that firms with

greater insider ownership are also more likely to deregister. Increased insider control helps

managers to avoid governance restrictions, which in this case means deregistering to avoid

higher post-SOX compliance costs. An interesting element comes from an event study

analysis, which shows that local (non-US) market response is more negative, with relation to

the deregistration announcement, in markets with better home country governance and less

negative in markets with weaker home country governance rules. According to the legal

bonding hypothesis, investors in firms from good governance countries should lose the least,

not the most, from US deregistration.

Jon Witmer (2005) tries to find determinants, factors which allow to predict which

firms are most likely to deregister. He also studies the negative return of the delisting

announcement. In his study, which spans the time period from 1990 to 2003, he examines a

sample of 140 foreign firms that delist in the US, 49 of them are voluntarily delisting,

whereas an additional 91 are involuntarily delisted. He finds an abnormal return of - 5 %

upon announcement of cross-delisting, which is many times larger than the positive effect of

the cross-listing announcement as found in the studies by Foerster and Karolyi (1999) and

Miller (1999). However, he indicates that his results here are driven by the large number of

involuntarily delists in his sample. His results are further biased by the large presence of

21

Canadian firms in his sample: 73 firms are originally Canadian, and the rest of the world

counts only 67 delistings. This has certainly an effect on his results where he examines the

juridical system of the country of origin, as well as the corporate governance system in the

country of origin.

However, his results confirm the conclusion of Marosi and Massoud (2006) that firms

from countries with poorer investor protection are more likely to deregister, which is the

opposite of what one might expect based on the bonding hypothesis. He calls it the

"avoiding hypothesis", stating that these firms want to avoid the added costs of US

regulations, suggesting however an alternative approach : after the corporate governance

scandals such as Enron, foreign firms may re-evaluate their estimate of the benefits by

bonding to a US market. His other conclusions however align with the predictions of the

traditional wisdom on cross-listing. Witmer (2005) finds that smaller firms, with a low

percentage of trading in the US, are more likely to delist; which is confirmed by Chaplinsky

and Ramchand (2008). He interprets this as consistent with the liquidity hypothesis and

Merton's awareness hypothesis. He also finds that Nasdaq-firms are more likely to delist

than NYSE-firms, which he considers consistent with the bonding and signaling hypothesis,

as the Nasdaq is less regulated. Furthermore, he examines the effect on home stock return,

and he finds that this return will hardly be negatively affected if the relative amount of

trading in the US market is very low in relation to the home market.

In his search for determinants, Witmer (2005) studies the traditional accounting

variables, such as the Market to Book ratio, which is a proxy for growth opportunities, but

his findings are that accounting variables are not significant in determining whether or not a

firm will choose to cross-delist. A significant determinant however is market value,

indicating that larger firms are less likely to cross-delist. This is also consistent with the

finding that smaller firms are less able to absorb the increased costs of cross-listing. It seems

that avoiding costs is an important factor in the decision, in relation to the firm's size.

Another significant variable is the share of turnover in the home market, and this is not

surprising as many of the sample firms cite low US trading volume as a reason for delisting in

the US. This supports the liquidity hypothesis: a low trading volume in the US indicates a

poor liquidity and a lacking ability to raise capital in the US.

22

With regard to German firms, Bessler et al. (2011) examined their listing and delisting

on the US market, in the context of market segmentation and bonding. Their starting point is

the fact that during the 1990's eighteen German firms were cross-listed in the US: sixteen on

the NYSE and two on the NASDAQ. Since 2000, thirteen of the initial eighteen have

deregistered, thus ending their cross-listing. This prompts them to ask why these firms

reversed on their decision and delisted. "Was it because the expected cross-listing benefits

never materialized or was it because the expected benefits disappeared with the integration

of financial markets, the emergence of alternative trading platforms and changes in

corporate governance rules and regulations in the US and Germany?" (Bessler et al., 2011,

p.2). Their study concentrates on market segmentation and bonding theories as the decisive

factor for cross-listing. These theories state that cross-listing should lead to lower costs of

capital and higher market valuations, and this is thoroughly tested. Their findings are that no

significant valuation benefits came from the cross-listing decision, but they did find some

positive benefits stemming from the delisting decision. They also found no systematic

increases or decreases in market value, based on market to book ratios or on Tobin's q ratio.

They did find a positive reaction of stock prices of these German firms to Rule 12h-6, the rule

that made it possible for firms to delist without having to continue filing reports with the

SEC.

The study of Bessler et al. (2011) confirms some of the conclusions of Chaplinsky and

Ramchand (2008). Where Chaplinsky and Ramchand (2008) seem to defend the

attractiveness of the US capital market with regard to the growing number of delistings,

they do so by pointing out that many companies were not viable candidates for a cross-

listing from the beginning, because of a lack of basic quality in terms of size and profitability.

They note that 60 % of the newly cross-listed firms does not have a single analyst following

them a year after the cross-listing event. Even as most of the German firms are large enough

to be listed in their terms of size which require a market capitalization of more than $230

million, and did have analyst following, there is a similarity in that Bessler et al. (2011) note

that profitability, or rather future expectations as expressed in the "book to market" and

Tobin q ratio for the German firms are below average. Bessler et al. (2011) note that the

average pre-listing market to book ratio for the cross-listed firms is 3.78 compared to an

average 3.14 for the CDAX companies. However, this average is driven by one single firm

23

(SAP) and excluding this firm it drops to 2.82, which is below average. The cross-listing event

did not change this proportion: cross-listed firms' average continued to be below the CDAX

average. These figures are confirmed by a study of the Tobin Q ratios. Their later delisting is

a confirmation for Chaplinsky and Ramchand's statement (2008) that they should not have

cross-listed. Only SAP and Aixtron got an increase in valuation through cross-listing. These

two companies are, together with Deutsche Bank, Fresenius Medical Care and Siemens, the

five German companies that continue to list in the US. It is clear that investors will choose to

invest in companies with a higher book to market ratio as well as a higher Tobin q ratio, as

this is an indication for investors' faith in future growth. Companies with low ratios will

have difficulties in attracting investors' attention, trade in their equity will not reach a

sufficient level of liquidity, the benefits of cross-listing will not materialize, as predicted by

Chaplinsky and Ramchand (2008).

Bessler et al. (2011) continue to search which benefits of cross-listing disappear, and

which remain. To do so, they start by a qualitative analysis of reasons cited by the companies

at the time of their cross-listing decision. These reasons can be financial, or non-financial.

The financial reasons include gaining capital market access, facilitating mergers and

acquisitions, a broadening of the investor base, and improving corporate governance and

transparency. Additionally, some companies also cited non-financial reasons such as building

an international reputation and attracting employees. The most cited reasons for delisting

were reducing compliance and reporting costs, and changes in stock trading patterns. Two

companies also left the US capital markets for reasons of corporate restructuring. Looking

back, the companies were better able to achieve the non-financial goals than the financial

goals. They considered the broadening of the investor base as successful, but Bessler et al.

(2011) note a similar ownership internationalization in non cross-listed German firms, as US

ownership in all DAX firms rose from 5,3 percent to 22,5 percent over the same years. They

attribute this rise in international ownership to the integration of European markets,

changes in European and German corporate governance rules, and other factors. Indeed,

over the last two decades, European capital markets have become less segmented, thereby

reducing the potential benefits of cross-listing. This European integration is due to the

eliminating of the exchange rate risk among Euro countries, and the adoption of the IFRS

standards to harmonize accounting information policies. This, along with further changes in

24

the German corporate governance system and the banking system, worked together to

decrease the benefits associated with the bonding hypothesis.

25

3 RESEARCH

3.1 Selection of the data sample

As we have seen, many companies have voluntarily terminated their cross-listing

over the last decade. Our research will focus on the effects of this decision related to the

traditional elements of Visibility, Liquidity and Market Value. If it is true that a listing event

has a positive influence on these elements, then one might expect the cross-delisting to

have a similar, but inverse effect. For Corporate Governance as the fourth item of

conventional wisdom, it does not seem useful to investigate whether there is an effect on

corporate governances ratings. Licht (2003) already questioned the bonding hypothesis,

indicating that the bonding role had been exaggerated on several items, and pointing out US

regulations had not been able to prevent scandals such as Enron and Worldcom. Neither

had they been able to prevent fraud and embezzlement in a number of Mexican cross-listed

firms. As the regulations in Europe are now very similar to the American, this reason to

cross-list is no longer relevant.

As Cetorelli and Peristiani (2010) have shown that cross-listing only have positive

effects if the target market is more prestigious than the domestic market, we only look at

delistings from the US capital market, which came in their study as the most prestigious

market offering the largest valuation gains. Also, Halling et al. (2006) have concluded that

trading volume only increases for companies from developed markets, while for companies

from emerging markets the domestic trading volume decreases after being cross-listed.

Therefore, we limit our study to companies from mature European markets, terminating

their cross-listing in the US capital market.

We will look only at voluntary delistings. Involuntary delistings often indicate

problems on the corporate level, leading to a take-over or even a bankruptcy, which have

influence on all aspects such as company's value, trading volume, analysts and newspaper

coverage. In the ADR terminology, we will only consider companies who have, or had, a

sponsored ADR program level II or III. Indeed, level I or SEC rule 144 A do not allow for

26

trading on stock exchanges, and unsponsored DR programs do not reflect a managerial

decision.

So, our study focuses on a sample of European firms who were cross-listed on the

NYSE before 2007, and terminated this cross-listing voluntarily in 2007 or later. Our study

considers a time window of three years before, and three years after the delisting event.

Therefore, we only study firms who terminated their cross-listing before 2011, as there is

not sufficient data to study the reactions on the company's value for companies who

terminated later. Our sample includes thus only companies who terminated their cross-

listing after the SEC regulation change in March 2007 which allowed for easier

deregistration, and before 31/12/2010 and which are still in "going concern" conditions

today. As opposed to Bessler et al. (2011) who limited their study to German companies, we

include other European countries, because the German economy comes relatively strong

through the present economic crisis, which may influence stock market prices. However, as

we will check for comparables we limit ourselves to the largest European markets. Also, as

Bessler et al. (2011) point to the further integration of the European capital markets as an

explanation of non-realized cross-listing benefits, we include the United Kingdom in our

study, as this country does not belong to the euro-zone. We plan to compare our delisting

sample to matching companies in their own domestic markets, as in the study by Baker,

Nofsinger and Weaver (1999) thus limiting our possible choices of countries to those whose

capital market indeed offers some comparable companies. So, for the country of origin of

the companies, we limit ourselves to the three largest European markets : Germany, France

and the United Kingdom.

First, we searched for companies from these countries who terminated their cross-

listing between 2007 and 2010. The NYSE website provided information on cross-listed

European firms on a year-by-year basis, allowing us to see which companies terminated their

cross-listing. By searching Bloomberg and other databases, we were able to eliminate the

companies who went through corporate restructuration such as bankruptcy, merger and

acquisition, in the year of delisting or the next year, reducing our data sample to nineteen

companies :

27

Table 3. Delisted sample

Sample: delisted from 2007 to 2010

GERMANY FRANCE UK

Basf (2007) Danone Group (2007) BG GROUP (2007)

Bayer (2007) Lafarge (2007) Wolseley (2007)

E ON (2007) Publicis Groupe (2007) Vodafone Group (2009)

Pfeiffer Vacuum Tech. (2007) Technip (2007)

SGL Carbon (2007) SCOR SE (2007)

Allianz (2009) AIR FRANCE-KLM (2008)

Infineon Technologies (2009) AXA (2010)

Daimler (2010)

Deutsche Telekom (2010)

We compared the data sample, as mentioned above, with a matching sample. We

used a matching sample to take possible extra effects into account. The time period of our

research 2007 – 2010 lies just in the financial crisis. One of the main reasons for the

matching sample is to correct our results for these deviations. To create the matching

sample, we found for each company separately a similar company in their own domestic

exchange. If there were no companies with the same activities, we tried to approach the

original company by looking at the subsectors. We searched for similar companies with as

many assets but for some of the companies it was impossible to find a match, for example

Danone. This allowed us to compose a matching sample of companies who never voluntarily

cross-listed on the US market. It has to be said that most of these companies are traded

"over the counter" on an unsponsored ADR basis, but as this does not reflect a management

decision, we consider this not to be a problem. Likewise, most of our delisted companies

continue to be traded "over the counter", but again, this is not a managerial decision and the

companies have little to say in this.

28

Table 4. Matching sample

Sample: delisted from 2007 to 2010

GERMANY FRANCE UK

K+S Pernod Ricard Tullow oil

Merck KGaA Arkema Travis Perkins

RWE Bouygues Inmarsat

KUKA AG CNP Assurances

Wacker Chemie AG Alstom

Müncher Rück ADP

Continental BNP Paribas

Volkswagen Group

Ecotel communication AG

In the appendix, a business summary for both delisted companies and the matching

companies is included.

29

3.2 Hypothesis development

Conventional wisdom leads to some conclusions regarding the benefits of cross-

listing. In this study, we aim to check whether a later cross-delisting reverses on these

benefits. We will check this for the following hypotheses :

Visibility: visibility is expressed in function of the number of newspaper references

dedicated to the company, and the number of analysts following the company. Our

hypothesis is that these numbers must be negatively influenced by the cross-

delisting.

Liquidity: Liquidity is said to improve through cross-listing. For the purpose of this

study, liquidity will be expressed as the trading volume on the domestic market only.

Our hypothesis is that liquidity has to be reduced through cross-delisting.

Company's value: Conventional wisdom holds that a company's value increases

through cross-listing. As a measure for company's value, we use the tobin's q ratio.

So, our hypothesis here is that the company's value is negatively influenced by the

cross-delisting.

We will check these three hypotheses in the next three sections, providing details

about the specific methodology which is always based on an older and reputed study. At the

same time, we will try to offer some explanation as to why our hypotheses did - or did not –

realize.

Throughout our study, we have investigated the effect of cross-delisting using

statistical analysis. Our method is based on panel data, this allows to control for individual

heterogeneity as the companies are often different in characteristics such as size. The

Breusch-Pagan test and Hausman test indicated a random effects model to be the best

choice for our sample.

To measure the effect of a delisting event, we implemented a dummy "delisting".

This dummy takes on the value of 0 in the period before the delisting, and the value of 1 in

the period after the delisting event. For the matching sample of non cross-listed companies,

the delisting dummy has a value of 0 in both periods.

30

Other variables will be explained in the chapters where they are used. For each

hypothesis, we will first compare the raw data of the dependent variables for the pre-

delisting and post-delisting period. After this, we will do a statistical analysis for our delisted

companies. Finally, we perform also a statistical analyses for the complete sample including

both delisted and their matching companies. In this way, we can exclude other effects, such

as sector specific effects and effects caused by the crisis and see whether a delisting still has

a significant result.

31

4 VISIBILITY

4.1 Methodology and data

For the effect on visibility we follow the same methodology as Baker, Nofsinger and

Weaver in their 1999 study. As visibility cannot be measured directly, they used a proxy by

counting the number of analysts and the number of newspaper references to these

companies, for a period of one year before and one year after the listing event. We will do

likewise, before and after the delisting event.

Using Bloomberg and Lexis Nexis, we found the number of analysts and newspaper

references respectively. The number of newspaper citations is a first proxy for visibility. As

articles relating only to the act of delisting might give a false view, we excluded the period of

three months before to three months after the delisting event, thereby staying in line with

the original study by Baker, Nofsinger and Weaver. In this way, the timeframe for visibility

spans one year before (-15 to -3 months related to the cross-delisting event) and one year

after the date of delisting (+3 to +15 months). To obtain only relevant articles, we search for

the firm’s name in the heading and include only articles related to economics. We selected

three relevant newspapers from the United States (The Wall Street Journal, The New York

Times and USA Today) and three newspapers for each home country. We have chosen the

Wall Street Journal and The New York Times because these are well known newspapers with

a national reputation, and which cover a lot of business and financial news in the United

States. We also included USA Today which is a more general daily newspaper. A list of the

newspapers used in our analysis is included in the appendix.

Another aspect in researching the impact of delisting on the firm's visibility is looking

at the number of analysts following them. The number of analysts has a direct influence on

investor recognition of the companies. Stocks followed by analysts are also more attractive

to institutional investors.

As Baker, Nofsinger and Weaver showed that, by cross-listing, not only the

percentage of analysts increased by 128%, but also the number of references took a steep

32

rise, we were interested whether a delisting has a negative impact on this. It might be

possible for these companies to continue enjoying these benefits because they have been

listed on the NYSE. Analysts and financial newspapers may consider these firms to be worthy

to stay in the focus even after they terminated the cross-listing on the NYSE.

We think visibility depends first on size of the company. Size of the company is

indeed a first element of investor recognition. Asset growth is another important parameter,

as this is an indicator for the future possibilities of companies. Apart from these variables,

and as McNichols and O'Brien (1997, p. 197) have stated that " analysts tend to add firms

they view favorably and drop firms they view unfavorably", we have decided to include

earnings growth per share as an independent variable, because this is a fundamental for a

company and may attract more analyst coverage. As we do not always find sufficient data

for the earnings growth, we have decided to include both "change in earnings" and "change

in earnings per share / book value per share" as possible alternative data for this variable.

We expect these variables to have an influence on the visibility of a company, so we include

them in our statistical analysis.

Like Baker, Nofsinger and Weaver (1999) we measured the firm’s earnings, asset

growth and firm size for the fiscal year before the analysts’ recommendation. “For example,

if a foreign firm lists on the NYSE on date t=0, we measure the number of analysts following

the firm one year before the listing, on t=-1. Next, we calculate the earnings growth in the

year before the analyst recommendation using the earnings per share for the years ending

t=-1 and t=-2. Similarly, we measure the number of analysts following the firm one year after

listing (t=1), and the earnings growth just before the recommendation using earnings from

years ending in t=0 and t=1.” Baker, Nofsinger and Weaver (1999, p.12)

We will do statistical analysis for both the delisted companies, and the mixed sample

of delisted and comparable companies in order to examine whether the delisting effect is

influenced by other factors. These matching companies are not listed on a non-domestic

exchange during our research. We will specifically look at the effect on the variable dummy

delisting to see if there is a difference with the statistical results of the delisted sample.

33

4.2 Results

4.2.1 Number of newspaper references

In Figure 5 we compare the number of citations in news articles one year before and

after date of delisting. Our time window spans the period from fifteen to three months

before delisting, and from three to fifteen months after delisting, the six month period

around the delisting event excluded. The graph covers all nineteen companies from the

sample. We find for eleven firms a decrease in visibility. The differences in the decrease vary

between 0.55% and 55.30%. We also find an increase for Basf, Danone, BG Group, Wolseley,

Air France, Infineon technologies, Deutsche telekom, AXA.

Figure 5. Visibility - Number of newspaper references

If we take a closer look only into the difference in citations in the American

newspapers, we notice a decline with an average of two citations. However these results are

strongly different for each company as you can see in the table below. It is remarkable that

five of the nineteen companies have an increase of citations after the delisting event. The

0

10

20

30

40

50

60

Newspaper references

pre-delisting

post-delisting

34

highest increase with seven citations for Danone can be explained through the melamine

milk scandal in 2008.

Table 5. Visibility - Number of American newspaper references

Before After

Basf 8 5

Bayer 25 13

E.ON 26 9

Pfeiffer Vacuum Tech. 1 1

SGL Carbon 0 0

Danone 3 10

Lafarge 3 2

Publicis Groupe 0 0

SCOR SE 3 1

Technip 0 0

BG Group 1 9

Wolseley 0 2

Air France-KLM 11 12

Allianz 15 3

Infineon Technologies 6 4

Vodafone Group 15 14

Daimler 26 9

Deutsche Telekom 2 2

AXA 7 12

When we look at the statistical analysis, we see the dummy delisting variable has a

negative sign but does not reach a level of statistical significance. All other variables, except

the size variable, are not significant. We have to conclude that our regressions do not offer a

relevant explanation to the changes in newspaper references. These don't seem to be

influenced by the delisting event, as changes go in both ways and none of our variables,

except size, reaches a level of significance. The R² also indicates a low explanatory power for

these regressions.

35

Table 6. Visibility newspaper references - Delisted sample

When we expand our sample with the matching companies, we have to conclude

that there is also a negative but not significant outcome for the delisting dummy. Also the

other variables react in the same way as in the analysis of our delisted sample, however they

are not statistically relevant either. As for the delisted sample, we see that the size is the

only variable with some significance. These results confirms our initial finding that the

number of newspaper references is not dependent on the cross-delisting decision.

Visibility

Newspaper references

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Intercept 138,26***

(6,31)

138,35***

(6,28)

136,23***

(6,60)

139,70***

(6,77)

137,96***

(6,15)

-247,70

(1,58)

-251,46

(-1,54)

-241,58

(-1,55)

-201,88

(1,26)

Dummy

delisting

-11,84

(-1,09)

-11,14

(-0,98)

-8,40

(-0,68)

-8,88

(-0,82)

-11,76

(-1,05)

-14,99

(-1,34)

-14,20

(-1,20)

-11,60

(-0,92)

-11,38

(-1,02)

Change

in

earnings

-1,85

(-1,03)

-1,52

(-0,81)

Earnings

growth

-0,29

(-0,29)

-0,21

(-0,20)

Change

in:

EPS/BVS

-0,00*

(-2,02)

-0,00*

(-1,73)

Asset

growth

3,65

(0,08)

13,40

(0,29)

21,05

(0,44)

25,48

(0,58)

Log size 51,97**

(2,48)

52,33**

(2,40)

50,69**

(2,43)

45,69**

(2,14)

R²

(p-value)

0,01

(0,70)

0,02

(0,72)

0,08

(0,23)

0,15

(0,05)

0,01

(0,83)

0,26

(0,00)

0,26

(0,03)

0,30

(0,02)

0,34

(0,01)

36

Table 7. Visibility newspaper references - Delisted and matching sample

Visibility

Newspaper references

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Intercept 67,05***

(4,77)

66,90***

(4,76)

65,65***

(4,76)

71,76***

(4,95)

67,23***

(4,73)

-113.84

(-1,24)

-129,07

(-1,65)

-107,55

(-1,16)

-198,76**

(-2,06)

Dummy

delisting

-4,08

(-0,37)

-3,14

(-0,27)

1,39

(0,12)

-4,81

(-0,43)

-4,14

(-0,37)

-4,55

(-0,41)

-4,44

(-0,69)

-0,09

(-0,01)

-6,66

(-0,60)

Change

in

earnings

-1,89

(-1,16)

-1,62

(-0,97)

Earnings

growth

-0,09

(-0,10)

1,32

(0,74)

Change

in:

EPS/BVS

-0,00

(1,26)

-0,00**

(-2,38)

Asset

growth

-0,84

(-0,16)

-7,54

(-0,64)

-0,07

(-0,01)

0,74

(0,14)

Log size 24,49*

(1,98)

24,16**

(2,30)

23,48*

(1,88)

37,21***

(2,83)

R²

(p-value)

0,14

(0,00)

0,15

(0,01)

0,19

(0,00)

0,16

(0,01)

0,14

(0,01)

0,22

0,00

0,23

(0,00)

0,25

(0,00)

0,30

(0,00)

4.2.2 Analyst coverage

In the next graph we have shown the number of analysts per company, both pre- and

post-delisting. On average the companies in our sample have around 28 analysts following

them. As for the newspaper references, the results are mixed: seven companies have seen

the number of analysts reduced, but only in a marginal quantity. Twelve other companies

enjoyed an increase in the number of analysts. This result comes as a surprise to us, as we

had expected to find a general decrease. A closer look reveals that the change in number of

analysts can be found on the European markets, giving a first indication that the cross-

delisting is not relevant.

37

Figure 6. Visibility - Analyst coverage

Table 7 shows the analysts only from the U.S. We can see that the delisting event has

little impact on the American analysts. We observe a maximum deviation of one analyst. A

possible explanation for the low or non-existent decrease in the number of analysts can be

found in Chaplinsky and Ramchand (2008) as they indicated that some companies that cross-

listed in the US had so little appeal to American investors that they never attracted even one

single American analyst. Indeed, as stated in table 7, two companies of our sample, Pfeiffer

Vacuum Technology and SGL Carbon, did not have an American analyst while cross-listed in

the US. Even for the largest companies in our sample, such as AXA or Allianz, we found only

seven analysts. These companies however are so large these analysts continued to following

them even after delisting.

0

10

20

30

40

50

60

Analyst coverage

pre-delisting

post-delisting

38

Table 8. Visibility - American analyst coverage

Before After

Basf 4 3

Bayer 4 3

E.ON 4 4

Pfeiffer Vacuum Tech. 0 0

SGL Carbon 0 1

Danone 6 6

Lafarge 5 4

Publicis Groupe 4 3

SCOR SE 5 4

Technip 4 4

BG Group 6 6

Wolseley 1 2

Air France-KLM 3 3

Allianz 7 7

Infineon Technologies 6 6

Vodafone Group 5 6

Daimler 7 6

Deutsche Telekom 5 6

AXA 7 7

Statistical analysis shows a positive, significant and strong relation with the delisting

dummy variable. However, this variable loses much of its value in these regressions where

we include the size variable, as size becomes the most important variable. Earnings growth

per share has a negative relationship, but this is not significant. Surprisingly, asset growth

per share also has a negative and significant relation to the number of analysts. However,

when we look at the raw data, we see that this is caused by the large number of firms where

we find either a decrease in the number of analyst at the same time as an increase in asset

growth, or vice versa. We find seven cases where the number of analysts goes up while

asset growth goes down, and five cases in the inverse situation. Therefore, we have to

conclude that this is not relevant. Besides this, we also see that the explanation power of our

model is relatively low going from an R² of 0,01 when doing a regression analysis with only

the dummy delisting, and reaching an R² of 0,56 to 0,60 when we included the size,

indicating size to be the most explanatory variable.

39

Table 9. Visibility analyst coverage - Delisted sample

Visibility

Analyst coverage

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Intercept 27,00***

(10,21)

27,02***

(10,36)

26,82***

(10,02)

27,03***

(10,10)

27,49***

(10,53)

-

46,75***

(-3,18)

-

44,06***

(-3,04)

-

44,20***

(-3.01)

-

46,08***

(-3,11)

Dummy

delisting

2,16**

(2,49)

2,33**

(2,57)

2,46***

(2,81)

2,23**

(2,53)

2,02**

(2,41)

1,56*

(1,91)

1,48

(1,66)

1,47*

(1,73)

1,36

(1,64)

Change

in

earnings

-0,16

(-1,26)

-0,01

(-0,05)

Earnings

growth

-0,07

(-0,87)

Change

in: EPS /

BVS

-3,26

(-0,56)

Asset

growth

-5,80*

(-1,73)

-4,77

(1,39)

-4,74

(-1,44)

-5,16

(-1,57)

Log size 9,93***

(5,06)

9,62***

(4,97)

9,64***

(4,91)

9,89***

(5,00)

R²

(p-value)

0,01

(0,57)

0,06

(0,35)

0,02

(0,78)

0,01

(0,78)

0,07

(0,29)

0,56

(0,00)

0,59

(0,00)

0,59

(0,00)

0,60

(0,00)

We also analyzed the expanded sample, including the comparable companies. At a

first glance, there seems to be a positively significant effect for the delisting event. Size

becomes never significant in this model, however the high p-value for the R² indicates that

this model does not have any explanatory power. For this sample, there is no relationship

between the number of analysts and the variables used in our sample.

40

Table 10. Visibility analyst coverage - Delisted and matching sample

Visibility

Analyst coverage

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Intercept 25,70***

(12,62)

25,73***

(12,77)

25,62***

(12,70)

25,72***

(12,52)

25,67***

(12,43)

19,93***

(3,36)

19,93***

(3,37)

19,45***

(3,28)

20,36***

(3,34)

Dummy

delisting

1,94**

(2,24)

2,14**

(2,39)

2,31**

(2,66)

1,96**

(2,26)

1,96**

(2,21)

2,04

(2,32)

2,30**

(2,45)

2,61***

(2,87)

2,14**

(2,36)

Change in

earnings

-0,18*

(-1,73)

-0,21*

(-1,92)

Earnings

growth

-0,08

(-1,04)

-0,08

(-1,03)

Change in:

EPS / BVS

-2,54

(-0,69)

-3,07

(-0,72)

Asset

growth

0,20

(0,14)

0,52

(0,34)

1,30

(0,86)

0,83

(0,50)

Log size 0,97

(1,03

0,96

(1,03)

1,00

(1,07)

0,88

(0,92)

R²

(p-value)

0,01

(0,53)

0,04

(0,32)

0,03

(0,40)

0,01

(0,76)

0,01

(0,82)

0,04

(0,35)

0,08

(0,36)

0,08

(0,36)

0,039

(0,72)

To do a more profound analysis, a significance level for the difference in the change

in analyst coverage and number of newspaper references of our delisted sample is

calculated using a Wilcoxon rank-sum test. We see that the average of analysts has

increased from 27 to 29 when delisted, but with a p-value of 0,47 we can not reject the

hypothesis that the pre-delisting and post-delisting values are the same. So there is no

statistical result for a change in analyst following due to the delisting event. For the number

of newspaper references, we see a decrease after the delisting event to an average of

126.42, but this test also accepts the hypothesis of the same average in number of

newspaper references before and after delisting. This test helps us to conclude that there is

also no significant effect for a delisting event on the number of newspaper references. As a

general conclusion for the element of visibility, we can say we have found no conclusive data

allowing us to point to the delisting fact as a factor of influence on either the number of

analysts, or the number of newspaper references. These results are in contrast to the

41

research of Baker, Nofsinger and Weaver (1999) who have found for both proxies

significantly positive results in their research on cross-listings. Also Lang, Lins and Miller

(2003) state that cross-listed firms have greater analyst coverage than firms that are not

cross-listed. However, Chaplinsky and Ramchand (2008) have concluded that most

companies who voluntarily delist in the US indeed did struggle to gain US investor

recognition, a conclusion which is very similar to ours as we see very little effect of the cross-

delisting on visibility, thus indicating that our sample never had much recognition in the US

to start with.

Table 11. Wilcoxon rank-sum test - Number of newspaper references - Delisted sample

Mean of number of newspaper references:

Firms in Sample Pre-delisting Post-delisting

Difference

(paired t-test)

NYSE delisting firms

Z-statistic

p-value

19 138,26 126,42 -11.84

(0,18)

(0,86)

Table 12. Wilcoxon rank-sum test - Analyst coverage - Delisted sample

Mean of analyst coverage:

Firms in Sample Pre-delisting Post-delisting

Difference

(paired t-test)

NYSE delisting firms

Z-statistic

p-value

19 27 29,16 2,16

(-0,72)

(0,47)

42

5 LIQUIDITY

5.1 Methodology and data

Halling et al. (2006) have shown that, for companies in developed markets, the

domestic trading volume increases at the announcement of cross-listing and remains at a

higher level thereafter. It seems thus logical to expect an inverse effect in the case of cross-

delisting. We expect the domestic trading volume to reduce by the cross-delisting and to

remain at this lower level. We will check whether this result indeed materializes.

We follow the methodology of Halling et al. (2006) to examine the effect of cross-

delisting on the liquidity of the shares of the company. In this domain we take a closer look

in the changes of trading activity. We examine this by taking the average total trading

volume on the domestic market for a period of two years before the delisting, and compare

this to the total trading volume on the domestic market for a period of two years after the

delisting event. The year of delisting itself is not included. We have gathered this information

using Datastream, by searching the monthly trading volumes and calculated the yearly

average. We considered it not relevant to look at the trading volume on the cross-listed

market, as all companies cited low trading volume on this market as one of the reasons for

delisting. This trading volume indeed has to be less than five percent of the global trading

volume to allow for deregistration on the US market (Dobbs and Goedhart, 2008).

We believe that more information has a positive effect on the trading volume.

Research published by analysts leads to more information for private investors which results

in an increase in trading volume. This information will be measured through the following

explanatory variables: number of analysts, number of newspaper references, company's size

and asset growth. For the number of analysts and newspaper references, the timeframe

spans two years before and after the date of delisting, excluding the six months period

around the delisting event. These numbers are then summarized in an average over each

43

period. We used Bloomberg to gather the analyst coverage and LexisNexis to find the

number of newspaper references.

We use firm size and asset growth to control for company specific variables. Larger

firms are well known by the people. This leads to more investor recognition which should be

positively reflected in trading volume. We assume that this is also valid for companies with

greater asset growth. For these two variables, we took the average value of the two fiscal

years before the delisting event and compared this to the average value of the two fiscal

years after the fiscal year in which the delisting event occurred. The delisting year itself is

not taken into account. This information was obtained by using Datastream.

44

5.2 Results

The graph shows us the difference between the mean of the logarithm of the trading

volume two years before and after delisting. The graph covers the nineteen companies from

Germany, the UK and France. For thirteen of our nineteen companies we see a decrease in

trading volume. The new domestic trading volumes range between 87,28 and 99,76 percent

of their former level. According to our expectations, this is a normal evolution. However, we

notice an increase for six companies: SGl Carbon, Danone, Lafarge, Scor SE, Infineon

Technologies and BG group.

Figure 7. Liquidity - Trading volume

When looking at the correlation matrix, which is included in our appendix, we can see

that there are no high correlations between most of the variables. However, we notice that

analyst coverage and size are highly correlated with 75%. Also, size and number of

newspaper references show a correlation of 51%. These correlations confirm the proposition

of larger companies leading to higher trading volumes.

A first regression with only the delisting variable included, shows a negative impact

on liquidity, this effect is statistically significant. On average, trading volume two years after

delisting decreases. If we add the size variable to our regression, we see an expected

0

1

2

3

4

5

6

7

8

9

Trading volume

pre-delisting

post-delisting

45

positive influence for size, but this effect is not statistically significant. A separate regression

with the delisting variable and asset growth shows a similar result. However, if we put the

dummy delisting and the variables size and asset growth into one model, we notice that all

three variables are statistically significant. This may be due to the omitted variable bias,

which indicates that multiple regression is better for our analysis.

In the fifth regression, we have limited our model to include the delisting variable

and the number of analysts. This results in an extremely low and negative effect, which is

not statistically significant. Trading the analyst coverage for the number of newspaper

references' variable shows no effect at all. A model including the delisting variable, analyst

coverage and number of newspaper references confirms that the visibility indicators are not

relevant for liquidity.

Finally a last model including all variables shows that company's size is the only really

important variable. The other variables lose their significance due to the degrees of freedom

in our model.

Table 13. Liquidity - Delisted sample

Liquidity - Trading volume

(1) (2) (3) (4) (5) (6) (7) (8)

Intercept 4,49***

(11,20)

0,85

(0,37)

4,47***

(11,08)

-1,21

(-0,49)

4,77***

(8,91)

4,46***

(10,12)

4,75***

(8,71)

-1,35

(-0,55)

Dummy

delisting

-0,12**

(2,49)

-0,15***

(-2,96)

-0,11**

(2,26)

-0,15***

(-3,10)

-0,08

(-1,05)

-0,11**

(-2,05)

-0,05

(-0,61)

-0,06

(-0,71)

Size(log) 0,49

(1,62)

0,76**

(2,35)

0,84**

(2,49)

Asset growth 0,19

(0,85)

0,42*

(1,89)

0,36

(1,48)

Analyst

coverage

-0,01

(-0,81)

-0,01

(-0,91)

-0,02

(-1,28)

Newspaper

references

0,00

(0,17)

0,00

(0,47)

0,00

(0,59)

R²

(p-value)

0,01

(0,83)

0,07

(0,27)

0,01

(0,80)

0,10

(0,31)

0,01

(0,79)

0,01

(0,95)

0,01

(0,92)

0,34

(0,02)

46

Next, we perform these same regressions on a sample consisting of the delisted and

matching companies, to check whether these results can be attributed to the effect of cross-

delisting. In this way, we can adjust for environmental factors which apply to all firms. We

excluded the companies Kuka AG, Wacker Chemie, ADP and Ecotel Communication AG due

to the fact we had not sufficient data for every variable used in our statistical analysis. For

consistency reasons we also excluded the delisted firms to which these were a match. This

gives us a data sample of 30 firms.

There is a negative effect for the delisting variable. However, this effect is not

statistically significant. No other variables are significant, except analyst coverage, which

shows a small positive result. This confirms our expectations of an increase in trading

volume due to more analyst coverage.

Table 14. Liquidity - Delisted and matching sample

Liquidity - Trading volume

(1) (2) (3) (4) (5) (6) (7) (8)

Intercept 4,36***

(16.90)

4.90***

(2.74)

4,34**

(16,61)

4,26**

(2.217)

3,84***

(13,91)

4,20***

(14,26)

3,74***

(12,97)

7,24**

(2,69)

Dummy

delisting

-0,11

(-1.29)

-0.10

(-1,21)

-0,10

(-1,23)

-0,10

(-1,21)

-0,20**

(-2,26)

-0,07

(-0,73)

-0,15

(-1,51)

-0,17

(-1.69)

Size(log) -0,07

(-0,31)

0,01

(0,04)

-0,49

(-1,32)

Asset

growth

0,21

(1,01)

0,22

(0.95)

0,10

(0,44)

Analyst

coverage

0,02*

(1,93)

0,02

(1,67)

0,03**

(2,26)

Newspaper

references

0,00

(1,18)

0,00

(1,05)

0,00

(0,71)

R²

(p-value)

0,03

(0,22)

0,03

(0,46)

0,03

(0,45)

0,03

(0.66)

0,07

(0,13)

0,03

(0,40)

0,07

(0,24)

0,12

(0,22)

47

By performing a statistical analysis for the complete sample, delisted and

matching companies together, we conclude that there is no significant negative effect for

the delisting event. Where Halling et al. (2006) have shown that cross-listing leads to an

increase in the trading volume of a company, we found no proof for the inverse effect.

Considering this result, we have to take into account that there are also other effects,

besides the variables included in our analysis, that influence the trading volume. The

delisting event seems to have little or no consequences on liquidity.

48

6 COMPANY’S VALUE

6.1 Methodology and data

Our method is based on the studies of Cetorelli and Perisitiani (2010) and Bessler et

al. (2011). However the research of Bessler et al. (2011) examines only the effects on the

German firms, we will apply this method to the three countries in our sample: Germany,

France and the United Kingdom.

We will investigate whether a delisting has significant changes on the value of a

company. As dependent variable we use the Tobin q ratio. This ratio is seen as a proxy for

investor's belief in future growth opportunities and the fact that the company will be able to

generate returns in excess of the required rates of return for investment projects (Bessler et

al., 2011, Cetorelli and Perisitiani (2010)). The Tobin q ratio is calculated as follows:

To control for country-specific elements we have included the gross domestic

product per capita as a proxy for a country’s economic growth (GDP growth). Besides the

importance of the GDP-growth, financial literature describes that the underlying institutional

governance structure in a country is a critical factor in supporting the economic growth on

long-term. To take this element into account, we include the Composite World Freedom

Index into the regression. This index determines for each country a score which describes

the overall economic freedom. The four key aspects on which the freedom score is based

are: Rule of law, Government size, Regulatory efficiency and Market openness. We took the

mean of three years before and after the delisting event to add the GDP growth and

Freedom Index to our regression model, this in order to stay in line with the other variables

of our model.

49

Apart from these external factors, we control for several firm characteristics adding

the size of the firm, the sales growth and the return on assets into the regression model. A

larger company, with more asset value, is likely to have a lower Tobin q ratio, as the divisor

for this ratio is indeed the asset value. A more important sales growth or return on assets

indicates better grow perspectives and will lead investors to fork out more money to obtain

shares of this potential growing company, raising the Tobin q ratio. We have gathered the

information of these variables on a yearly basis at the fiscal year of each company.

At last, also variables indicating the visibility of the company are used to research the

influence on the Tobin q ratio. As Lang, Lins and Miller (2003) already have shown, cross-

listing leads to an improvement in analyst coverage and accuracy, which has a positive effect

on company's value. We believe that more analysts following the company and a higher

number of newspaper references lead to an increase in the value of the company, and are

variables that should be taken into account.

To do a profound analysis of the impact of a delisting on the company's valuation, we

used Datastream, LexisNexis and Bloomberg to provide us with our data. The data consists

of the average value of each variable three years before and after, the year of delisting itself

not included. The following firms have not been included in the research of this hypothesis

because we don't have data for the three year post delisting period: Daimler, Deutsche

Telekom and AXA. This is due to the fact that they delisted in 2010, and yearly reports for

2013 are not yet available as this is written.

50

6.2 Results

First we do a describing analysis of the delisted companies to investigate whether a

delisting has significant changes on the value of a company based on Tobin q ratios of each

firm. The graph shows Tobin q ratios for our sample before and after cross-delisting. Our

method is based on the study of Bessler et al. (2011). The ratios while the company was still

cross-listed are calculated as the average of the three years prior to the company's cross-

delisting date. The ratios after cross-delisting are calculated in the same way.

For thirteen out of sixteen companies we see a decrease in value. Wolseley suffered

from the biggest decrease in company’s value with a percentage of 36,59. A possible reason

can be found in the British housing market crisis as this is their primary market. Bayer,

Infineon Technologies and Vodafone Group show an increase of their value. We see the

highest increase for Infineon technologies with 27,01%.

Figure 8. Company's value - Tobin q ratio

0

0,5

1

1,5

2

2,5

3

3,5

Tobin q ratio

pre-delisting

post-delisting

51

In the correlation matrix we can find no special correlations. As expected, size has a

negative correlation (-0,68) with the Tobin q ratio. On the other hand, there is a positive

correlation between size and analyst coverage (0,69), this may explain the negative

correlation between analyst coverage and the Tobin q ratio, which might otherwise be

surprising. We see this same positive correlation between newspaper references and size,

resulting in a similar negative correlation between newspaper references and the Tobin q

ratio. Furthermore, we also see a positive correlation between return on assets and the

Tobin q ratio, which is normal as the Tobin q ratio is a measure for expected future returns.

All other correlations are as expected, we note for example a positive influence of GDP

growth on the Tobin q ratio, the size of the company, return on assets and sales growth.

In the statistical analysis, we first want to check for the effect of delisting on the

company’s value by including only the dummy delisting in our regression. We notice that

delisting has a negative and significant effect on the company’s value. Next, we performed

another regression adding the firm specific variables namely size, ROA and sales growth,

which shows us significant effects for all of them. We see a negative significant impact of size

on the value of the company as expressed by the Tobin q ratio and a positive impact of the

ROA. This is as could be expected, however at a first glance one might be surprised by the

negative coefficient of sales growth. This can be explained by a closer look at the raw data,

which shows that the sales growth was lower in the period of three years after the delisting

event than it was in the period of three years before the delisting event. The economic crisis

had led to a decrease in the growth rate and only four companies of our sample were able to

maintain a positive result. These are Danone, Allianz, Infineon Technologies and Vodafone

Group. It has to be said that three of these companies only delisted in 2009. The timing of

their delisting may have had a positive influence on the post-delisting sales growth as

compared to the pre-delisting figure.

To control for country specific indicators we have included GDP growth per capita

and freedom score. This leads to a small positive but significant result for GDP growth but

the Freedom score seems to have no influence. In a next regression, we have checked for

the influence of the visibility indicators, which are analyst coverage and newspaper

references, on the company's value. The results show a very small influence.

52

In the fifth regression, we have included both the company and country specific

variables. The results are similar as for the regression for company variables only. We see

that GDP growth loses its significance as this is absorbed by the effect on the company

variables.

Finally, if we execute the model with all the variables included, as Cetorelli and

Peristiani (2010) did, we observe still significant results for the delisting variable and firm

specific control variables but we can not find significance for the country specific control

variables. Analyst coverage becomes statistically significant, but remains at a very low level.

In this model, the dummy delisting has the strongest coefficient and the highest statistical

significance of all regressions. At the same time this model has the highest explanatory

power with an R² of 0,82.

Table 15. Company's value - Delisted sample

Company's Value - Tobin q ratio

(1) (2) (3) (4) (5) (6)

Intercept 1.45***

(10.92)

3,16***

(6,25)

0,42

(0,36)

1,94***

(7,05)

2,38***

(3,41)

3,26***

4,04

Dummy delisting -0.13**

(-2.10)

-0,24**

(-2,32)

-0,05

(-0,85)

-0,15

(-1,57)

-0,27**

(-2,29)

-0,34***

(-2,81)

Size log -0,25***

(-3,75)

-0,27***

(-3,89)

-0,42***

(-3,92)

ROA 0,06***

(5,64)

0,06***

(5,29)

0,05***

(4,76)

Sales growth -1,50**

(-2,44)

-1,35**

(-2,22)

-0,74

(-1,10)

Analyst coverage -0,01

(-0,62)

0,02**

(2,10)

Newspaper references -0,01*

(-2,00)

0,00

(0,10)

GDP growth per capita 0,03***

(2,86)

-0,01

(-0,39)

-0,02

(-0,89)

Freedom score 0,01

(0,80)

0,01

(1,54)

0,01

(1,28)

R²

(p-value)

0,02

(0,50)

0,76

(0,00)

0,05

(0,71)

0,19

(0,10)

0,79

(0,00)

0,82

(0,00)

53

Lastly, we analyze also the results of our matching sample together with our delisted

sample. As described above, we had to exclude those firms that delisted in 2010 for lack of

sufficient data. These are Daimler, Deutsche Telekom and Axa. The matching firms to these

three have also been excluded. Similarly, there are several firms in our matching sample we

had to exclude because all data were not available. These are Wacker Chemie, Arkema, ADP

and Munich Re. For consistency reasons, we also excluded the delisted firms matching to

these four , which are SGL Carbon, Lafarge, Air France-KLM, and Allianz. So, we have a mixed

sample of 24 companies.

The delisting variable is now only significant in relation to other company specific

variables. In the last regression, taking all variables into account he loses his significance as

the number of degrees of freedom decreases, due to the standard fault on all variables. The

model has explanatory power when the company specific variables are included but this

does not increase much over the different models. This seems logical but indicates that

company specific elements are determining the Tobin q ratio, whether or not the company

has delisted.

54

Table 16. Company's value - Delisted and matching sample

Company's Value - Tobin q ratio

(1) (2) (3) (4) (5) (6)

Intercept 1,55***

(12,24)

-4,61*

(-2,05)

0,58

(0,20)

1,59***

(4,42)

-3,14

(-1,03)

-6,07*

(1,96)

Dummy delisting -0,14

(-1,15)

-0,34**

(-2,77)

-0,12

(-0,79)

-0,04

(-0,24)

-0,30**

(-2,16)

-0,23

(-1,61)

Size log 0,85**

(2,71)

0,88**

(2,65)

1,01***

(3,18)

ROA 0,04***

(2,91)

0,04**

(2,83)

0,04***

(3,19)

Sales growth -0,75

(-1,64)

-1,06*

-1,79

-1,07*

(-1,95)

Analyst coverage -0,01

(-0,77)

-0,01

(-0,58)

Newspaper references 0,01

(1,26)

0,01**

(2,41)

GDP growth per capita 0,01

(0,74)

0,01

(0,55)

0,01

(0,57)

Freedom score 0,01

(0,32)

-0,02

(-0,67)

0,00

(0,03)

R²

(p-value)

0,01

(0,53)

0,50

(0,00)

0,15

(0,07)

0,13

(0,09)

0,55

(0,00)

0,57

(0,00)

To conclude, we can say that company's value, as expressed by the Tobin q ratio, is

dependent primarily on company specific variables such as size, return on assets, and sales

growth. In the regressions limited to the delisted sample, the delisting element is significant,

but this loses its significance in the model with a mixed sample if we include all variables. If

we limit the number of variables to only the company and country specific variables, the

delisting factor does become significant, indicating that he lost his significance due to the

decrease in number of degrees of freedom. As a final conclusion, we believe the delisting

variable to be significant and to have an influence on the value of the company as expressed

by the Tobin q ratio.

55

Our findings seem to confirm the conventional wisdom on cross-listing, such as

Doidge, Karolyi and Stulz (2002) who stated that the value of companies cross-listed in the

U.S. had Tobin q ratios that are 16,5 % higher than those of firms from the same origin who

did not cross-list. Roosenboom and Van Dijk (2009) came to a similar conclusion, however

the gain in q ratio was limited to 1,3 % in their study. Cetorelli and Peristiani (2010) also

came to the conclusion that firms cross-listing enjoy significant valuation gains. In our study

we found a decline in Tobin q ratio by 8,74 % after the delisting. However we find a similar

decline in Tobin q ratio with 4,90 % for our matching sample of companies who have never

cross-listed. We can conclude that the decline in Tobin q ratio can be attributed for the first

4,90 % to global economical circumstances and the additional 3,84 % can be explained by

the delisting event.

56

7 CONCLUSION

There is an abundant academic literature about the benefits of cross-listing.

However, do these benefits still exist when a company delists its shares on non-domestic

exchanges? This paper examines the effect of the delisting of a company on three aspects:

visibility, liquidity and company’s value. To make our study more relevant, it was vital to

exclude the effects of the financial crisis on our data. We use a matching sample of

companies from the same country and sector to control for these external factors.

The visibility of a company is measured by the number of newspaper references and

the number of analysts following the company. We expected a decrease after the delisting

event, however the results showed a more diverse picture. For the newspaper references,

eight companies enjoyed an increase and twelve companies noted an increase in the

number of analysts. After doing a Wilcoxon rank-sum test, we conclude that there is no

relationship between delisting and visibility.

We also investigate the liquidity of a company by measuring the trading volume on

the domestic market. For the sample of delisted companies, we can find that there is a

decrease of trading volume for 68% of the companies. After the delisting event, the new

domestic trading volumes range between 87,28 and 99,76 percent of their pre-delisting

trading volumes. Although these results seem to be significant at a first glance, after

performing further statistical analysis including the comparable companies, no effect of the

cross-delisting can be proven.

Finally, we examined the company’s value, as expressed by the Tobin q ratio, of the

delisted companies. The results of the describing analyses shows us clearly that the

companies have a decrease in value. We document that 81% of our sample has a decrease.

The largest decrease in our sample has a decrease percentage of 36,59, but this seems an

abnormally high figure and may be caused by external factors. Four other companies

enjoyed an increase in the Tobin q ratio. In the statistical analysis, we noticed that delisting

has a negative significant effect on the company’s value. After controlling for the matching

sample, which also suffered a decline in Tobin q ratio of 4,90 %, we were able to attribute a

loss of 3,84 % in the Tobin q ratio of the delisted sample to the delisting event.

57

For visibility and liquidity, the results were mixed and did not allow to point to the

delisting event as a decisive factor. For company's value, the results lead us to conclude that

there are negative effects of a cross-delisting. However, they also show evidence of the

importance of other factors than only the delisting event.

Our findings suggest a larger scale study to confirm these findings. This may include

companies from other countries which we had to omit because their home market did not

offer comparable companies. The matching sample can then consist of the national stock

markets of these countries with delisted companies. Another area to be researched can be

the difference in the effect of a cross-delisting for companies from emerging and developed

countries as we limited our study only to developed markets. For this research question we

find backing in the study by Marosi and Massoud (2006) who concluded that firms from

countries with weaker corporate governance rules were even more likely to deregister.

58

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62

9 APPENDIX

Business summary retrieved from Bloomberg (May, 2014):

Table 17. Business summary delisted and matching companies

DELISTED COMPANIES MATCHING COMPANIES

BASF SE

BASF SE is a chemical company. The Company

operates in six segments: Chemicals, Plastics,

Performance Products, Functional Solutions,

Agricultural Solutions and Oil & Gas. BASF offers

products for the chemical, automotive,

construction, agriculture, oil, plastics, electrical /

electronics, furniture and paper industries, and

provides a range of system solutions and services.

Exchange: Xetra

Sector: Materials

Industry: Chemicals

K+S AG

K+S AG manufactures and markets within the

fertilizer division standard and speciality fertilizers

to the agricultural and industrial industries

worldwide. In its salt business, the company

produces de-icing salt, food grade salt, industrial

salt and salt for chemical use.

Exchange: Xetra

Sector: Materials

Industry: Chemicals

Bayer AG

Bayer AG produces and markets healthcare and

agricultural products, and polymers. The Company

manufactures products that include aspirin,

antibiotics, anti-infectives, and cardiovascular,

oncology, and central nervous system drugs, over-

the-counter medications, diagnostics, animal

health products, crop protection products,

plastics, and polyurethanes.

Exchange: Xetra

Sector: Health Care

Industry: Biotech & Pharma

Merck KGaA

Merck KGaA is a global pharmaceutical and

chemicals company. The Company researches

drugs in the areas oncology and

neurodegenerative as well as autoimmune and

inflammatory diseases. Also, it markets

cardiovascular, fertility, endocrinology, and over-

the-counter products as well as products for flat

screens and the pharmaceutical, food, cosmetics,

packaging, and coatings.

Exchange: Xetra

Sector: Health Care

Industry: Biotech & Pharma

63

E.ON SE

E.ON SE operates in power generation and gas

production businesses. The Company's operations

include electric generation at conventional,

nuclear, and renewable-source facilities, electric

transmission via high-voltage wires network,

regional distribution of electricity, gas, and heat,

power trading and electricity, gas, and heat sales.

Exchange: Xetra

Sector: Utilities

Industry: Utilities

RWE AG

RWE AG generates, distributes, and trades

electricity to municipal, industrial, commercial,

and residential customers. The Company produces

natural gas and oil, mines coal, delivers and

distributes gas, and supplies drinking water. RWE

AG operates mainly in Europe.

Exchange: Xetra

Sector: Utilities

Industry: Utilities

Pfeiffer Vacuum Technology AG

Pfeiffer Vacuum Technology AG designs,

manufactures and services pumps. The Company

produces turbomolecular vacuum pumps, rotary

vane pumps, root pumps, customized vacuum

systems, helium leak detection systems, gas

analyzers, and mass spectrometers. The Company

markets and services its products in Europe, the

United States, and Asia.

Exchange: Xetra

Sector: Industrials

Industry: Machinery

KUKA AG

KUKA AG manufactures production machinery and

equipment, and offers production engineering

services. The Company designs and builds

automobile factories, and produces welding and

assembly systems, industrial robots, turning

machines, packaging machinery, and measuring

and control instruments for water and gas

suppliers. KUKA operates in Europe, the Americas,

and Asia.

Exchange: Xetra

Sector: Industrials

Industry: Machinery

SGL Carbon SE

SGL Carbon SE produces carbon and graphite

materials, specialty graphite, corrosion protection

products, and fibers and composites. The

Company manufactures graphite and carbon

electrodes, cathodes, mold stock, mechanical

carbons, corrosion resistant products and systems,

composites, and carbon-ceramic brake discs. SGL

markets its products worldwide.

Exchange: Xetra

Sector: Materials

Wacker Chemie AG

Wacker Chemie AG is a globally active chemical

company with a wide range of specialty chemical

products. The Company's products include

hyperpure polysilicon for the electronics and solar

industries, semiconductor wafers, a broad range of

silicones, vinyl acetate based polymers, and

biotech products.

Exchange: Xetra

Sector: Materials

64

Industry: Chemicals

Industry: Chemicals

Danone

Danone is a food processing company. The

Company produces dairy products, beverages,

baby food and clinical/medical nutrition products.

Exchange: EN Paris

Sector: Consumer Staples

Industry: Consumer Products

Pernod Ricard SA

Pernod Ricard SA produces wines and spirits. The

Company produces wines, bitters, whiskies, anis-

based spirits, liqueurs, cognacs and brandies, and

white spirits and rums. Pernod Ricard markets its

products worldwide.

Exchange: EN Paris

Sector: Consumer Staples

Industry: Consumer Products

Lafarge SA

Lafarge SA supplies a wide range of building

materials to contractors, wholesalers, and

manufacturers. The Company produces cement,

aggregates and concrete and gypsum products.

Lafarge markets its products in Europe, Africa,

Asia, North America, and Latin America.

Exchange: EN Paris

Sector: Materials

Industry: Construction Materials

Arkema SA

Arkema SA manufactures and markets a wide

range of chemicals. The Company manufactures

both industrial chemicals and performance

products including acrylics, polymethyl

methacrylate (PMMA), hydrogen peroxide,

technical polymers, specialty chemicals, and

functional additives.

Exchange: EN Paris

Sector: Materials

Industry: Chemicals

Publicis Groupe

Publicis Groupe offers advertising services. The

Company develops advertising campaigns and sells

advertising on billboards and urban furniture, in

newspapers and magazines, on radio, and in

movie theaters. Publicis offers direct marketing,

customer relationship marketing, sales promotion,

public relations and human resources services,

and operates retail drugstores.

Exchange: EN Paris

Sector: Communications

Industry: Media Content

Bouygues SA

Bouygues SA offers construction services,

develops real estate, offers cellular

communications services, produces television

programming and movies, and manages utilities.

The Company offers building, civil engineering,

and oil and gas contracting services, develops

residential, commercial, and office projects,

produces and distributes water and electricity, and

collects waste.

Exchange: EN Paris

Sector: Industrials

Industry: Engineering & Const Srvcs

65

SCOR SE

SCOR SE offers life, accident, property/casualty,

health, and special needs reinsurance. The

Company offers services through subsidiaries in

Europe, the Americas, Asia, and Africa. SCOR also

holds real estate investments.

Exchange: EN Paris

Sector: Financials

Industry: Insurance

CNP Assurances

CNP Assurances offers group and individual life,

health, accident, disability and credit insurance,

and pensions. The Company markets its products

for individuals through banks. CNP operates in

parts of Europe and South America.

Exchange: EN Paris

Sector: Financials

Industry: Insurance

Technip SA

Technip SA designs and constructs industrial

facilities. The Company designs and builds

factories which produce and process petroleum

products, natural gas, and chemicals, and generate

electricity. Technip builds offshore facilities for the

petroleum industry. The Company operates

worldwide.

Exchange: EN Paris

Sector: Energy

Industry: Oil, Gas & Coal

Alstom SA

Alstom SA serves the power generation market

and the rail transport market. The Group offers a

broad range of solutions for the rail industry, from

tramways to high speed trains. Alstom also

provides integrated power plants and associated

services and equipment for a wide variety of

energy sources, and offers technology solutions to

eliminate pollutants and reduce emissions.

Exchange: EN Paris

Sector: Industrials

Industry: Electrical Equipment

BG Group plc

BG Group plc operates as an integrated natural gas

company. The Company explores develops,

produces, liquefies, and markets hydrocarbons

with a focus on natural gas. BG Group extends its

services throughout global networks.

Exchange: London

Sector: Energy

Industry: Oil, Gas & Coal

Tullow Oil plc

Tullow Oil plc explores for and produces oil and

gas. The Group's assets are in Africa, Europe,

South America and Asia.

Exchange: London

Sector: Energy

Industry: Oil, Gas & Coal

Wolseley plc

Wolseley plc distributes bathroom materials,

heating and plumbing supplies, and industrial

pipes, valves, and fittings, to customers in the

Travis Perkins plc

Travis Perkins plc markets and distributes products

to the UK construction and building trade

industries, including timber, building, and

66

United Kingdom, Europe, and the United States.

Wolseley also distributes building materials and

lumber products, as well as operates tool hire

centers.

Exchange: London

Sector: Consumer Discretionary

Industry: Retail Discretionary

plumbing and heating materials.

Exchange: London

Sector: Consumer Discretionary

Industry: Retail Discretionary

Air France-KLM

Air France-KLM offers air transportation services.

The Company operates airlines and offers travel

booking, catering, freight transportation, aircraft

maintenance, and pilot training services.

Exchange: EN Paris

Sector: Consumer Discretionary

Industry: Travel, Lodging & Dining

Aeroports de Paris

Aeroports de Paris (ADP) manages all the civil

airports in the Paris area. The Company also

develops and operates light aircraft aerodromes.

ADP offers air transport related services, and

business services such as office rental.

Exchange: EN Paris

Sector: Industrials

Industry: Transportation & Logistics

Allianz SE

Allianz SE, through subsidiaries, offers insurance

and financial services. The Company offers

property and casualty, life and health, credit,

motor vehicle and travel insurance, and fund

management services.

Exchange: Xetra

Sector: Financials

Industry: Insurance

MunichRe

Muenchener Rueckversicherungs-Gesellschaft AG

(MunichRe) provides financial services. The

Company offers reinsurance, insurance, and asset

management services. MunichRe has subsidiaries

in most major financial centers throughout the

world.

Exchange: Xetra

Sector: Financials

Industry: Insurance

Infineon Technologies AG

Infineon Technologies AG designs, manufactures,

and markets semiconductors and related

products. The Company's products include

microprocessors, memory components,

microcontrollers, integrated circuits, digital and

analog sensors, and fiber optics. Infineon markets

its products to the communications, automotive,

Continental AG

Continental AG manufactures tires, automotive

parts and industrial products. The Company

produces passenger car, truck, commercial vehicle,

and bicycle tires, braking systems, shock

absorbers, hoses, drive belts, conveyor belting,

transmission products, and sealing systems.

Continental markets its products under such

67

industrial, and consumer electronics sectors.

Exchange: Xetra

Sector: Technology

Industry: Semiconductors

brands as Continental, Uniroyal, Gislaved, Viking,

and Barum.

Exchange: Xetra

Sector: Consumer Discretionary

Industry: Automotive

Vodafone Group PLC

Vodafone Group PLC is a mobile

telecommunications company providing a range of

services, including voice and data

communications. The Company operates in

Continental Europe, the United Kingdom, the

United States, Asia Pacific, Africa and the Middle

East through its subsidiaries, associates, and

investments.

Exchange: London

Sector: Communications

Industry: Telecom

Inmarsat PLC

Inmarsat PLC operates a global communications

satellite system. The Company's satellites provide

voice and high-speed data services on a worldwide

basis. Inmarsat's customers include major

corporations from the maritime, media, oil and

gas, construction and aeronautical industries, as

well as governments and aid agencies.

Exchange: London

Sector: Communications

Industry: Telecom

Daimler AG

Daimler AG develops, manufactures, distributes,

and sells a wide range of automotive products,

mainly passenger cars, trucks, vans and buses. The

Company also provides financial and other

services relating to its automotive businesses.

Exchange: Xetra

Sector: Consumer Discretionary

Industry: Automotive

Volkswagen AG

Volkswagen AG manufactures economy and luxury

automobiles, sports cars, trucks, and commercial

vehicles for sale worldwide. The Company

produces the Passat, Golf, Cabrio, Jetta, GTI,

Beetle and other models. Volkswagen also owns

Audi, Seat and Skoda, which manufacture and sell

cars in Spain and in southern and eastern Europe,

and Lamborghini, which makes sports cars in Italy.

Exchange: Xetra

Sector: Consumer Discretionary

Industry: Automotive

Deutsche Telekom AG

Deutsche Telekom AG offers telecommunications

services. The Company offers a full range of fixed-

line telephone services, mobile communications

Ecotel communications ag

Ecotel communication ag provides

telecommunications services to residential and

commercial customers. The Company provides

68

services, Internet access, and combined

information technology and telecommunications

services for businesses.

Exchange: Xetra

Sector: Communications

Industry: Telecom

fixed-line phone service, Internet access, DSL and

related services.

Exchange: Xetra

Sector: Communications

Industry: Telecom

AXA SA

AXA SA is an insurance company which also

provides related financial services. The Company

offers life and non-life insurance, savings and

pension products, and asset management services.

AXA operates in both domestic and international

markets.

Exchange: EN Paris

Sector: Financials

Industry: Insurance

BNP Paribas SA

BNP Paribas SA attracts deposits and offers

commercial, retail, investment, private and

corporate banking services. The Bank also provides

asset management and investment advisory

services to institutions and individuals in Europe,

the United States, Asia and the Emerging Markets.

Exchange: EN Paris

Sector: Financials

Industry: Banking

69

List of Newspapers for each country:

Table 18. Newspapers used in the analysis

Germany Die Welt Handelszeitung Börsen-Zeitung

France les Echos Le Monde

La Tribune

UK The Financial Times The Times The Daily Telegraph

US Wall Street Journal USA Today The New York Times

70

Correlation Matrices of the variables used into the regression models of the delisted companies:

Table 19. Correlation matrix visibility

Size log

Change

in:

EPS/BVS

Earnings

growth

Change

in

earnings

Asset

growth

Dummy

delisting

Newspaper

references

Analyst

coverage

Analyst

coverage 0,74 -0,06 0,24 -0,05 -0,25 0,10 0,33 1,00

Newspaper

references 0,51 -0,39 0,11 -0,28 -0,07 -0,06 1,00

Dummy

delisting 0,04 0,09 0,16 0,25 -0,07 1,00

Asset

growth -0,12 0,12 0,01 0,20 1,00

Change in

earnings -0,17 0,24 0,54 1,00

Earnings

growth 0,22 0,63 1,00

Change in:

EPS/BVS -0,22 1,00

Size (log)

1,00

Table 20. Correlation matrix liquidity

Newspaper

references

Analyst

coverage Size (log) Asset growth

Dummy

delisting

Trading

volume (log)

Trading

volume (log) -0,03 -0,12 0,27 0,11 -0,04 1,00

Dummy

delisting -0,12 0,19 0,04 -0,17 1,00

Asset growth -0,14 -0,33 -0,18 1,00

Size (log) 0,51 0,75 1,00

Analyst

coverage 0,38 1,00

Newspaper

references 1,00

71

Table 21. Correlation matrix company's value

Freedom

score

GDP

growth

per

capita

Newspaper

References

Analyst

Coverage

Sales

growth ROA

Size

(log)

Dummy

delisting

Tobin

q ratio

Tobin q

ratio 0,16 0,05 -0,40 -0,29 0,15 070 -0,68 -0,12 1,00

Dummy

delisting 0,11 -0,49 -0,13 0,28 -0,20

0,10

0,06 1,00

Size (log) 0,11 0,05 0,63 0.69 -0,03 -0,38 1,00

ROA 0,09 0,15 -0,33 -0,19 0,54 1,00

Sales

growth -0,04 0,31 -0,19 -0,26 1,00

Analyst

Coverage 0,20 0,00 0,50 1,00

Newspaper

References -0,02 0,17 1,00

GDP

growth per

capita

-0,21 1,00

Freedom

score 1,00