the share price drivers of dual-listed companies on the...

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The share price drivers of dual-listed companies on the JSE Securities Exchange

A Dissertation

presented to

In partial fulfilment

of the requirements for the degree of

by

Master of Business Administration

Thakhani Ndivhudzannyi Isaac Ligudu

December 2009

Supervisor: Professor Ashley G. Frank

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ACKNOWLEDGEMENTS

I express my deep appreciation to my supervisor, Professor Ashley G. Frank, for his assistance and

giving lots of suggestions, guidance, comments and supervision at all stages of this research. I

would also like to express my deepest gratitude to my beloved wife, Khathutshelo, my adorable

son, Thendo, and my beautiful daughter, Halatedzi, for their undying love and support throughout

my MBA education. I would also like to thank my mother, Mrs. Thinandavha Ligudu and my

siblings for their constant words of encouragement and support.

Without the help and assistance of my extended family, colleagues and friends especially Mr.

Mutshutshu Tsanwani, Mr. Eddie Tshitimbi, Mr. Tendani Matshisevhe, Mr. Khathutshelo

Ramalamula, Mr. Tshifhiwa Bologo, Mr. Haymish Paulse, Mr. Gordon Smith, Mr. Nthumeni

Ligudu, and Mr. Kgele Mathiba, this research would not have been a successful one. I will remain

eternally grateful for their sustained support. Mr. Tom Artz and Mr. Burnie Gunter, my present and

former managers respectively, also deserve a special mention for helping me to achieve balance

between work and study commitments.

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Declaration: Dissertation

1. I know that plagiarism is wrong. Plagiarism is to use another’s work and pretend that it is one’s own.

2. I have not allowed and will not allow anyone to copy this thesis with the intention of passing it off as his or her own work.

3. I certify that except as noted above the thesis is my own work and all references used are

accurately reported in the list of references.

4. This thesis is not confidential. It may be used freely by the Graduate School of Business.

Signed:

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The share price drivers of dual-listed companies on the JSE Securities Exchange

ABSTRACT

In theory, assuming integrated markets, the share prices of a dual-listed company, measured in a

common currency should be identical since they represent identical claims on their future cash

flows. Dual-listed shares have reportedly experienced significant deviation from theoretical price

parity, such divergences of up to 40%. This study evaluates whether changes in the dual-listed

companies share prices are caused by changes in the market of the country where it gains most of

its profits. It is envisaged that the findings of this study will be helpful to investors, market analysts,

asset managers, and management of dual-listed companies. An understanding of the price

dynamics of share returns of dual-listed companies across markets is vital for asset allocation,

hedging strategies, and monetary policies relating to international capital flows. A composite

indicator representing companies which obtain their primary profits in South Africa and are listed

on both the JSE Securities Exchange and London Stock Exchange is created. The dissertation looks

first at the degree of correlation between price changes in the local bourse and the local indicator as

well as the foreign bourse and the foreign indicator. It then examines whether market sentiment

plays a role in determining share price changes. A Box-Jenkins ARIMA model is used to test for

causality between market changes and changes of the composite indicator. Contrary to the theory

the study finds a greater degree of correlation between the foreign bourse and its indicator than

from the local bourse. Interestingly, it concludes that market sentiment in the foreign bourse has a

causal impact on share price changes of dual-listed shares in South Africa. This evidence supports

the view that South African equities get cues from international stock markets.

KEYWORDS: ARIMA, Box-Jenkins models, autocorrelation, dual-listed

companies, correlogram, JSE, LSE

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CONTENTS

ACKNOWLEDGEMENTS .................................................................................. II

ABSTRACT ......................................................................................................... IV

LIST OF TABLES .............................................................................................. VII

LIST OF APPENDICES ................................................................................... VIII

1 INTRODUCTION ...................................................................................... 1

1.1 RESEARCH AREA AND PROBLEM .................................................................................... 1

1.2 RESEARCH QUESTIONS AND SCOPE ................................................................................. 5

1.3 RESEARCH ASSUMPTIONS AND ETHICS ........................................................................... 6

2 LITERATURE REVIEW ........................................................................... 8

2.1 DISCUSSION ................................................................................................................ 8

2.2 CONCLUSION ............................................................................................................. 12

3 RESEARCH METHODOLOGY ............................................................. 13

3.1 RESEARCH APPROACH AND STRATEGY ......................................................................... 13

3.2 RESEARCH DESIGN, DATA COLLECTION METHODS AND RESEARCH INSTRUMENTS ................ 14

3.3 SAMPLING................................................................................................................. 15

3.4 DATA ANALYSIS METHODS .......................................................................................... 17

4 RESEARCH FINDINGS, ANALYSIS AND DISCUSSION .................... 21

4.1 RESEARCH FINDINGS .................................................................................................. 21

4.2 RESEARCH ANALYSIS AND DISCUSSION ........................................................................ 25

4.3 RESEARCH LIMITATIONS ............................................................................................. 28

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5 RESEARCH CONCLUSIONS ................................................................. 28

6 FUTURE RESEARCH DIRECTIONS .................................................... 31

7 REFERENCES ......................................................................................... 32

8 APPENDICES .......................................................................................... 35

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LIST OF TABLES

Table 1: Correlation matrix

Table 2: Autocorrelation functions of the original and differenced time series

Table 3: Cross-correlograms for Residual series

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LIST OF APPENDICES

Appendix 1 – Dual-Listed Companies in the final sample

Appendix 2 – Line Plot of Local Composite Indicator

Appendix 3 – Line Plot of FTSE/JSE All Share Index

Appendix 4 – Line Plot of Foreign Composite Indicator

Appendix 5 – Line Plot of FTSE All Share Index

Appendix 6 – Original and differenced Autocorrelation Function (ACF) – Local Composite

Indicator

Appendix 7 – Original and differenced Autocorrelation Function (ACF) – FTSE/JSE All Share

Index

Appendix 8 – Original and differenced Autocorrelation Function (ACF) - Foreign Composite

Indicator

Appendix 9 – Original and differenced Autocorrelation Function (ACF) – FTSE All Share Index

Appendix 10 – Aquarium Platinum: Pie chart showing Sources of Profits for 2006/2007



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1 INTRODUCTION

A little more than a decade ago, South African companies were chained to their national base due to

sanctions, political isolation and legislative constraints. After the release of Nelson Mandela in

1990, both business and diplomatic relations with the rest of the world began slowly to expand.

Following the democratic elections of 1994, the floodgates opened, and South African corporations

moved into the rest of Africa and beyond. Mining houses led the way, followed by manufacturers

and financial institutions (South Africa.Info, 2008).

As foreign exchange controls were relaxed, companies began investing offshore and listing on

foreign bourses. The companies argued that offshore listings offered certain advantages they would

otherwise be denied if they maintained a primary listing on the JSE Securities Exchange. These

advantages typically include the following: easier access to capital resources at lower cost;

opportunities to raise efficiencies by competing head-on with global competitors; the opportunity to

escape from the volatility of financing costs in an emerging market economy; the opportunity to

promote foreign investment in South Africa; the opportunity to expand their core business into

other countries and regions (Walters & Prinsloo, 2002).

1.1 Research Area and problem

Dual-listed structures usually consist of two companies residing in two different countries or

jurisdictions. For example, South African Breweries is listed on the JSE Securities Exchange in

South Africa and has a foreign entity twin, SABMiller, listed on the London Stock Exchange in the

United Kingdom.

“A dual listed company (DLC) arrangement is a contractual arrangement between two companies

under which the activities of two companies are brought together, managed and operated on a

unified basis as if they were a single economic enterprise while retaining their separate legal

identities, tax residencies and stock exchange listings.” (Australian Accounting Standards Board,

2005, p. 1). Another perspective is that a dual listing of a company is a way for a company to have

two equal listings (i.e., neither being a secondary listing) in different markets. The two listed

companies enter into profit-sharing agreements with each other, and an ‘equalization-ratio’ exists to

keep the economic performance of the two stocks pegged to each other.

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The main features of a dual-listed structure according to the Australian Accounting Standards Board are that: (a) each company continues to retain its separate legal identity and own its own assets. For

example, in the Australian DLCs the shareholders of both companies vote as a single electorate on

substantial issues affecting their combined interests (“joint decisions”), such as the appointment of

directors, approval of financial statements, but vote separately on significant issues which might

affect them differently, such as changes to the equalisation ratio (“separate decisions”);

(b) each company maintains a separate listing and the shareholders of each company do not change

as a result but there is a sharing of rights between shareholder groups, that is, shareholders of each

company retain their existing shares but with an economic interest in the combined assets of both

companies;

(c) the compositions of the separate boards of directors are identical or virtually identical;

(d) arrangements are put in place to satisfy regulatory and legal requirements in the home

jurisdiction of each company and their adoption by the new reporting entity;

(e) there are not normally transfers of assets between the entities at the time of formation;

(f) arrangements are made to ensure the equalisation of dividends and other distributions so that

shareholders of each company have equivalent dividend, capital and voting rights on a per share

basis;

(g) management of the activities is undertaken on a unified basis, i.e., the two companies are

managed having regard to the interests of the shareholders of both companies; and

(i) cross guarantees are made by each company in favour of the other in respect of certain

contractual obligations.

Walters & Prinsloo (2002) reported that by the end of year 2000, five companies had received

permission from the South African Reserve Bank to move their primary listings from the JSE

Securities Exchange (JSE) to the London Stock Exchange (LSE).

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Those companies are: Billiton, South African Breweries, the Anglo American Corporation, Old

Mutual Life Assurance Company (SA) Limited, and Dimension Data. At the time of their listing,

all five of these former South African companies had sufficient critical mass for inclusion in the

FTSE 100 share price index of companies listed on the London stock market. At the time, ranking

in the FTSE 100 of former South African companies was between 21 and 78. There are currently 75

dual-listed companies which maintain either a primary or secondary on the JSE and other listings

on many securities exchanges around the globe (JSE Securities Exchange, 2009).

In theory, assuming integrated markets, the share prices of dual-listed companies, measured in a

common currency should be identical since they represent identical claims on the future cash flows

of the group. However, a difference between the prices (price divergence) of these shares has been

reported (Froot & Dabora, 1999). Peng et al. (2007) in their study of price differentials between

dual-listed A and H-shares on the Mainland China and Hong Kong stock exchanges, found that A-

shares enjoyed a premium over their H-shares counterparts ranging from 10% to 260%. This price

divergence could be a cause for concern for shareholders of dual-listed companies as paper maker

Mondi found out. Shareholders of Mondi expressed concerns about the dual-listing structure as the

London-listed Mondi Plc had reportedly been trading at a substantial discount to the JSE-listed

Mondi Ltd. Some of Mondi’s shareholders have been calling for the unification of the two listings

as a result of this price divergence (Monteiro, 2009). The fact that dual-listed shares do not always

produce the same outcomes for each shareholder might be regarded as an argument against them

(Bartholomeusz, 2005).

Peng et al. (2007) argued that such large price differentials for shares that enjoy equal voting rights

and dividend payments showed that the two capital markets within China were segmented. They

further argued that this price differential raises questions about the efficiency of price discovery and

resource allocation in these markets. They also reported that shareholders of dual-listed companies

on the Mainland China and Hong Kong stock markets called for the integration of these two

financial markets. Bartholomeusz (2005) found that the differential pricing of the dual stocks

related more to the general home market economic and equity market conditions than the

eccentricity of the dual listed company.

Bedi et al. (2003) claim that apart from the work of a few researchers, there appear to be little work

looking specifically at the phenomenon of substantial price differences between dual-listed

companies.

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The purpose of this research was to investigate whether the market of a country where dual-listed

companies gain most of their profits (business location) has a causal impact on the share prices of

those dual-listed companies with listings on both its home and a foreign stock exchange. The

research was therefore deductive in nature as it sought to determine and establish a causal

relationship between quantitative variables.

It is anticipated that the findings of this research present the foundation for future theoretical

research into the factors that impact the share prices of dual-listed companies. According to Billy &

Asta (2005), the globalisation of financial markets has been a quick and ongoing process over the

precedent two decades. The trading of coupled securities in domestic and foreign markets is a

widespread observable fact. There has been a striking increase in the trading of foreign shares as

investors recognise the need for international diversification and as foreign companies seek to

broaden their shareholder base to raise capital. Xu & Fung (2002) found that understanding the

price dynamics of share returns of dual-listed companies across markets is vital for asset allocation,

hedging strategies, and monetary policies relating to international capital flows. Moreover, dual-

listed equities are valuable instruments to test international asset pricing theories (Serra, 1999).

Xiang et al. (2009) advocate that the research on the trends of dual-listed shares will assist a large

numbers of investors in securities to carry out portfolio analysis, and predict prices to spread risk

and lift up revenue. Hauser et al. (1998) argue that the phenomenon of dual-listed shares presents a

distinctive opportunity to study the transmission of pricing information across stock markets. They

further argue that the information gained from observing the same share priced in multiple markets

differs from what may be gained from observing relations of aggregate price indices across stock

markets. They offer that this type of research will assist listed companies specify financing

strategies and achieve capital internationalisation. The current study attempted to offer a more

thorough investigation of the degree of causal impact of business location on share prices of dual-

listed companies.

Hence, it likely also makes a methodological contribution by showing how a causal relationship

between share prices and business location is analysed. The findings of this study are useful for

investors and analysts of dual-listed companies because it sheds light on the causal relationship

between share price and business location in the South African context. It is envisaged that the

findings of this study are helpful to investors, market analysts and asset managers, and management

of dual-listed companies.

http://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=42203�

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1.2 Research questions and scope

The research question is for companies with primary (secondary) listing on the JSE Securities

Exchange and secondary (primary) listing on the London Stock Exchange. The researcher made use

of techniques and statistical tools associated with financial market time series data to answer the

following question:

Are changes in the dual-listed company share price caused by the changes in the market of

the country where it gains most of its profits?

In many ways, most of the previous research on dual-listed share prices tended to focus on:

· Investigating correlatory price-divergence of dual-listed stocks (Froot & Dabora, 1999); and

· Investigating the impact of dual-listing on parameters such as stock returns, liquidity

differences and volatility spillover across markets.

As a consequence, there is currently no evidence of research focusing on the causal impact of

business location on share prices of dual-listed companies. From the literature, the null hypothesis

holds that since international financial markets are integrated, the share prices of dual-listed

companies should only depend on the future cash flows and discount rate for a particular financial

market. Moreover, since the dual-listed companies have a right to combined cash flows based on

the equalisation ratio, their share prices should therefore match the agreed equalisation ratio. For

example, if the two companies have a 50:50 equalisation ratio, it is expected that the dual-listed

shares will have identical share prices. The null hypothesis implies that dual-listed shares should

only track cash flows and the market risk premium. In other words, neither trading location nor

business location should have any influence on dual-listed share prices.

The following hypotheses were constructed and tested in order to provide empirical evidence with

respect to the differences in the impact of business and trading location on dual-listed stocks:

HO: Ceteris paribus, changes in the share price of a dual-listed company are caused by

changes in the market of the country where it gains most of its profits.

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HA: Ceteris paribus, changes in the share price of a dual-listed company are not caused

by changes in the market of the country where it gains most of its profits.

The above hypotheses were based on the fact that the financial markets are segmented and that

dual-listed shares are influenced by other factors such as business location. In other words, business

location influences share prices of dual-listed companies by varying degrees. Chan et al. (2003)

found that companies, which are listed in multiple markets, are usually traded more on the home

market where the core business is located and this was likely to affect their share prices. These

alternative hypotheses are supported by the findings of Bedi et al. (2003) and De Jong et al. (2008)

who observed significant share price divergence from theoretical parity, in violation of the efficient

market theory.

It was not the intention of this study to determine how these factors impact the share prices of dual-

listed stocks. Moreover, there are various factors that affect the price of shares in general such as

investor sentiment, liquidity in the local markets, encouraging and supportive remarks from

government officials, huge increase in national revenue etc. (Abumustafa, 2007). While this

research only focused on the causal impact of business location on the changes in the share price, it

does not, of course, assume that this is the only variable which affects share prices.

The research findings are valid for dual-listed companies, trading with ordinary stocks, listed on the

JSE Securities Exchange and on the London Stock Exchange, which limits broader generalisation

to other dual-listed companies in other stock exchanges. Future research should include dual-listed

companies in other stock exchanges around the world.

1.3 Research Assumptions and Ethics

· It was assumed that statistical tools with time series ARIMA analysis capability will be

accessible. The research supervisor provided the researcher with access to RATS (Regression

Analysis of Time Series) econometric software tools. It was found that RATS software has

strong ARIMA capabilities during data analysis of this study.

· It was assumed that access to external financial and market databases, not already accessible

from the UCT Graduate School of Business (GSB) library, will be possible if required. All the

data required for the research were available from the databases available at the GSB library.

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· JSE and LSE all operate on different time zones. It was assumed that market data will be easy

to adjust for time-zone and foreign-exchange differences, if necessary. Only data for LSE and

JSE was used and there is a 2-hour time difference between London and Johannesburg. The

time-zone difference is not significant to the study because only daily closing prices are used.

· It was assumed that in all these securities markets (JSE and LSE); companies are required to

notify their respective stock exchanges of any events that will affect their share prices.

Examples of notifiable events include substantial acquisitions, major transactions, and related

party transactions. Moreover, it was assumed that companies in these markets are also required

to make public disclosure of any other events that may affect their share prices.

Furthermore, companies are required to publish information, such as semi-annual and annual

results, in a timely manner (Chan et al., 2003). The financial regulatory environments in United

Kingdom and South Africa are comparable. The London and Johannesburg stock exchanges

require financial statements to be prepared according to the International Financial Reporting

Standard (IFRS). Moreover, it was established that listed companies on these stock exchanges

are required to notify the stock exchanges about events that may affect their share prices. Both

the JSE and LSE listing requirements oblige listed companies to publish annual financial

statements within a prescribed period.

This research dealt only with historical financial and stock exchange information which was sought

from online financial and market databases. Therefore, there were no ethical implications to

research participants. However, since the data was extracted from databases access controlled by

UCT, all steps were taken to ensure that the data was only used for the purpose of this study. It was

established that this data was not subjected to data protection legislation. The research was

conducted in compliance with the UCT/GSB research guidelines. To this end, the Research Ethics

form was completed and submitted by 31st August 2009 as required.

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2 LITERATURE REVIEW

2.1 Discussion

Wang & Jiang (2004) observed that during quite a few past decades, many companies have raised

capital outside of their home countries by listing their shares on a number of international securities

exchanges. Lok & Kalev (2006) also observed a similar phenomenon of increased globalisation of

capital markets and the consequent significant increase in international dual-listings of companies.

They reported 379 international (i.e. non-US) stocks listed on the New York Stock Exchange

(NYSE) in 1998; but in 2003, this number had increased to 470. They claim that this has,

consecutively, ignited an increase of academic curiosity and research into this matter.

Unfortunately however, most of the research conducted on dual-listed shares focuses on either US

shares dual-listed on an overseas exchange or foreign firms trading on a US exchange as American

Depositary Receipts (Lok & Kalev, 2006). Billy & Asta (2005) found that most of practical studies

in the finance literature are conducted to study the effect of dual-listings. They also found that these

studies have a tendency to examine the impact of risk and returns as a result of dual-listing and the

relation of shares returns and volatility across markets.

Lok & Kalev (2006) found that there were three core branches within the literature on the practice

of dual-listing. Most researchers have tended to focus first on the costs and benefits associated with

international dual-listing. The second branch in the literature examines the issue of capital

market/segmentation for internationally dual-listed equities. The third branch in the existing

literature relates to the price discovery process. In other words, how the share’s price adjusts to the

availability of new information.

Levy & Yagil (2005) claim that according to the law of one price, two assets that represent identical

claims to the combined cash flow should have the same share price. In other words, the economic

value of the dual-listed shares should be equal because both shares have an equal claim to future

cash flows. They investigated the price disparity between Royal Dutch and Shell equities

throughout the 1980s and found that it took about nine years to correct the mispricing problem, a

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fact that seems incoherent with market efficiency, where information should be reflected

immediately in stocks prices. This view is supported by Froot & Dabora (1999) who reported that

the classical finance paradigm predicts that an asset’s price is unaltered by its location of trade.

“If international markets are entirely integrated, then a given set of risky cash flows has the same

value and risk characteristics when its trade is redistributed across markets and investors” Froot &

Dabora (1999, p. 190).

This conclusion is supported by Wang & Jiang (2004) who also found that based on the assumption

of international capital markets perfect integration, then dual-listed shares, which are apparently

driven by the similar long-term fundamental values, should have the same return and risk

characteristics, and their prices should not be affected by their trading location. However, Bedi et

al. (2003) observed substantial divergence in the pricing of about fourteen dual-listed shares which

they studied. De Jong et al. (2008) also observed large price deviations from theoretical price parity

which ranged from 4% to 12% for sample mean and 15% to 40% for single dual-listed companies

in the sample. “However, in reality, restrictions on foreign ownership, information asymmetry

between domestic and foreign investors, language and cultural differences, and other direct or

indirect barriers lead to segmented markets.” (Wang & Jiang, 2004, p. 1)

A recent study by De Gooijer & Sivarajasingham (2008) found that while there is a wealth of

literature on stock market interdependence and integration, however, depending on the data,

methodology, and theoretical models used there seems to be no clear resolution of the issue of stock

market integration yet. They found that some previous work by researchers indicate that

international stock markets are integrated; while other researchers have found stock markets not to

be integrated.

Several attempts to understand the divergence of share prices of dual-listed companies have been

made since the phenomenon of dual-listing started. According to Bartholomeusz (2005), the

Reserve Bank of Australia (RBA) studied a few dual-listed companies to try to work out why the

Australian and British dual-listed share prices so frequently diverged in defiance of efficient

markets theory, given that each of the companies in the dual-listed structure has an identical claim

on future cash flows. De Jong et al. (2008) found that the relative prices of dual-listed shares

exhibit statistically significant and economically large deviations from theoretical parity.

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The RBA was unable to indisputably explain price divergences. Liquidity differences and currency

movements in the two markets were cited as possible explanations. Bartholomeusz (2005) also

found that another possible explanation to the price divergence is the difference in local market

conditions and the different significance of the dual-listing structure to the markets.

Bartholomeusz (2005) reports that recent research from ABN-Amro shed a little more light on why

trading in the three Australian and British dual-listed companies – BHP Billiton, Rio Tinto and

Brambles appeared to disregard the law of one price. The ABN-Amro study attempted to build on

the study conducted by the RBA a few years earlier. ABN Amro research investigated the price

divergences of dual-listed BHP Billiton, Rio Tinto and Brambles which traded at discounts of

7.8%, 12% and 10.5 % respectively. ABN Amro found that differential pricing of the dual shares

seems to be more a function of the general home economic and equity market conditions than they

are of the eccentricity of the dual listed company (Bartholomeusz, 2005).

In another study, Froot & Dabora (1999) examined many pairs of large and well traded dual-listed

shares. This study found that share prices in that environment were affected by the location of trade.

This shows that dual-listed shares, which have nearly identical cashflows, move more like the

markets where they are traded most intensively. This co-movement between price differential and

market index was found to be present at both long and short horizons. The authors concluded that

the location of where shares are listed appears to be a matter determining pricing of dual-listed

shares. Furthermore, the study listed three possible sources of this price divergence: tax

implications, market-wide noise and institutional inefficiencies.

Xu & Fung (2002) argued that because ordinary shares that are listed in two countries represent the

same company, firm-specific information such as earnings, dividends, and financing

announcements were likely to be dominated by home factors. They further argued that such

information flows should lead from the home market to the offshore market. The majority of

literature surveyed examined the correlation between share price of dual-listed shares and location

of trade. There appeared to be no research done on the causal relationship between share price and

location of its major source of profits.

For example, in the study of the Jardine Group, Chan et al. (2003) were unable to perform a causal

analysis between trade location and the share price due to a small sample size (5 shares and about 7

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years of time series data). In another line of research, and a focus of this study, the influence on a

single-listed share price if the trading location is different from business location was investigated.

Chan et al. (2003) cited the case of the Jardine group which delisted from the Hong Kong Stock

Exchange and listed on the Singapore Stock market but retained operations in Hong Kong and

Mainland China.

According to Chan et al. (2003), operations in Hong Kong and Mainland China accounted for the

largest share of the profits during the period of study but the group derived no profits from

Southeast Asia including Singapore where it later listed. The study found that the share price of the

Jardine group in Singapore correlated more with the Singapore Stock market index and that the

location of business operations seemed irrelevant. Their research established that changes in source

of profits could not explain why the co-movement of Jardine shares with the Singapore market

became higher in the post delisting period. The conclusion of the study is consistent with Froot &

Dabora’s (1999) earlier work, which found that the prices of dual-listed shares are dependent on the

listing or trading location. However, none of the studies reviewed, besides the Jardine group,

looked at the impact of business location on share prices. Chan et al. (2003) found that obvious

issues such as time-varying beta, currency and tax implications could not explain the behaviour of

share prices. They proposed that the geographical closeness of trading and mainstay business

locations affects investor interests and trading conduct.

Overall, these conclusions made obvious the need for additional research in this area. It is clear

from the above discussions that many researchers have only addressed the correlational price

movements of dual-listed shares with listing location and there had not been a single study trying to

analyse the causality relationship between share prices and business location until now.

The literature review found that in the majority of cases, market data and historical financial were

extracted directly from the stock exchanges databases as well as from other databases such as

Datastream and Bloomberg. This study adopted a similar data collection methodology; data was

extracted from credible databases, I-Net Bridge and Datastream, which get their data from the stock

exchanges and other reliable sources.

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2.2 Conclusion

It was shown that there has been an escalating level of interest in the functioning of dual-listed

companies. Most researchers have tended to focus on the benefits related to dual-listing, stock

market integration for dual-listed shares and the price discovery process for these shares. Some

researchers observed price divergence of dual-listed shares, which is reportedly a violation of the

price of one law. Several studies conducted to investigate this phenomenon found that dual-listed

shares were mainly influenced by home market conditions and that they tended to correlate more

with the domestic stock market index. No evidence of research on the impact of business location

on share prices of dual-listed companies was found in the literature. However, numerous studies

have been conducted to investigate the presence of a causal relationship between listing location

and share prices. Those studies employed similar research design and data collection methods as

proposed for this research.

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3 RESEARCH METHODOLOGY

3.1 Research Approach and Strategy

This research was concerned with the analysis of existing quantitative time series financial data to

test the hypotheses about whether there is a causal link between share price performance of dual-

listed companies and business location.

Measurement of share price

In this study, the share price of dual-listed companies was measured by the daily closing price. This

study was interested in the market price and used the adjusted daily closing price because

adjustments for all corporate actions such as stock splits, dividends/distributions and rights

offerings were deemed to be important.

Measurement of business location

Bryman & Bell (2007) define an indicator as something that is devised or already exists and that is

utilised as though it were a measure of a concept. The concept of business location or home market

was represented by the location, which was the major source of profits. The dual-listed companies’

annual statements were surveyed to determine the location of major source of profits. A local

composite indicator representing the companies which obtain their primary profits in South Africa

and were listed on both the JSE Securities Exchange and London Stock Exchange was created.

Measurement of local and foreign market

The concept of trading or listing location was measured by the representative domestic market

index. The use of standard market indices potentially creates a bias when one of the companies is

included in a market index. The daily closing values for FTSE All Share and FTSE/JSE All Share

Index were used as proxies for the home markets. The FTSE/JSE All Share Index was chosen to

represent the South African market because it represents 99% of the full market capitalisation i.e.

before the application of any investibility weightings, of all ordinary securities listed on the main

board of the JSE (FTSE/JSE, 2004). Similarly, the FTSE All Share Index was chosen to represent

the United Kingdom market because it was considered to be representative of the overall London

equity market and it represents approximately 98% of UK’s market capitalisation (FTSE Index

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Company, 2009). It must be noted that FTSE/JSE All Share Index and JSE All Share Index were

used interchangeably.

Desktop research was conducted by analysing quantitative time series financial and market data,

which was gathered from existing databases of financial market information, I-Net Bridge and

DataStream, which were accessed at the University of Cape Town. Therefore, a deductive approach

was proposed for this research because this study sought to deduce from available time series data

whether changes in home market conditions cause changes in share prices of dual-listed companies.

This deductive research approach subscribes to testing a theory/hypothesis-testing after quantitative

data has been collected (Bryman & Bell, 2007). Therefore, the quantitative research strategy and

deductive approach were more appropriate for this type of research. RATS (Regression Analysis of

Time Series) econometric analysis software was used to analyse the financial data therefore bias

from interpretations or data observation was limited.

3.2 Research design, data collection methods and research instruments

Quantitative variables such as share prices, market indices and net profits were collected and

analysed to determine causal relationships between these variables. Historical financial and market

time series data was used in the study.

A longitudinal survey is relevant when research is done on a sample on more than one occasion

(Bryman & Bell, 2007). Longitudinal design seems best suited to large scale data gathering,

especially where factually based time series data is required, as would be the case when

investigating the content of the impact of business or trading location on share prices of dual-listed

companies (Flynn et al., 1997).

Bryman & Bell (2007) argue that a longitudinal research design can allow some insight into the

time order of variables and for that reason may be more able to allow causal inferences to be made.

It therefore seems that a longitudinal methodology is the most appropriate for this type of research.

Time series historical financial and share data were extracted from I-Net Bridge and Datastream,

which are credible sources of reliable market data databases. These databases are suppliers of high

quality online content and applications to the Southern African and other international financial,

corporate and government communities. The use of secondary data presents benefits such as cost

and time savings (Bryman & Bell, 2007).

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Furthermore, downloaded data was checked for any adjustments for corporate events such as

special dividends, placements or share splits.

In fact, adjusted daily closing prices of shares and market indices were downloaded. Since market-

capitalisation weighted indexes weight companies according to market capitalisation, none of the

dual-listed shares in the final sample had large weightings on the FTSE/JSE All Share Index and

FTSE All Share Index. The FTSE/JSE All Share Index and FTSE All Share Index time series data

were extracted from I-Net Igraph and Datastream respectively. It was established that the JSE and

LSE require listed companies to publish audited financial information within prescribed period.

Therefore, it was expected that financial information concerning dual-listed companies would be

fairly reliable.

Time series data for international listed equities was extracted from Datastream. Historical

company financials such as balance sheet, cash flow, and income statements were extracted from I-

Net FAS. Corporate annual reports were accessed through McGregor’s-Library database. All the

databases listed above were accessed from the University of Cape Town Graduate School of

Business (GSB) library. I-Net and Datastream were found to hold all historical time series

information which covered the period of the dual-listing for the companies in the sample.

Datastream, I-Net Graph and I-Net-FAS databases were found to have the capability to export the

data into formats compatible with RATS econometric software.

3.3 Sampling

The JSE Securities Exchange website listed 39 dual-listed companies with both a primary and

secondary listing on the JSE and LSE or NYSE (JSE Securities Exchange, 2009).

The population of interest was all dual-listed companies which had a listing at JSE and another

listing at LSE or NYSE; the sampling frame included only those companies that satisfy the

following conditions:

· South Africa was their major source of profits;

· They had adequate historical time series data.

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The preliminary sample period range for this study was between January 2001 and October 2009.

This sample period was chosen as such because foreign listing started in 2001 when the Reserve

Bank of South Africa relaxed foreign exchange regulations (Walters & Prinsloo, 2002). Practically,

the sample period for all dual-listed shares starts at the date of merger. As mentioned above, during

the sample period, almost 39 companies were dual-listed on the JSE and LSE or NYSE. Of those

39 companies, only three satisfied the sampling frame conditions. None of the companies listed at

NYSE met the sampling frame conditions and were excluded from the final sample. Therefore, the

final sample only consisted of companies with listings at JSE and LSE. These companies were, as

shown in appendix 1, Aquarius Platinum, IPSA group and Lonmin Platinum. All of these

companies but Aquarius Platinum derived 100% of their profits from South Africa during the

period of study. Appendices 10-12 show that Aquarius Platinum gained between 77% and 86% of

its profits from South Africa during the period of study. All these three companies in the sampling

frame were surveyed but due to the small sample size, small scope bias may have been introduced

into the study. A local and foreign composite indicator representing the three above-mentioned

companies was created for both the JSE and LSE.

The study only used dual-listed companies which have the complete set of time series data that

covered the concurrent dual-listing of the three companies mentioned above (7th December 2006 to

31st October 2009). This sample period was deemed lengthy enough to reduce any effects of short-

term economic fluctuations. The sample consisted of adjusted daily closing prices (5 days) of three

shares of South African-based, which were dually listed on the JSE and LSE. Additional data

include daily closing indices of the market capitalisation weighted FTSE/JSE All Share Index and

the FTSE All Share Index, denominated in South African Rands and United Kingdom pounds

respectively. Stevenson (2007) reports that there has been some debate as to the minimum number

of observations required to produce a satisfactory ARIMA model but the majority view is that at

least 50 observations are required.

For the purposes of this study due to the use of daily closing prices from 7th December 2006 to 31st

October 2009, thereby providing 757 observations. The number of time series observations far

exceeded the minimum number of 50 observations necessary for successful application of ARIMA

time series analysis. Share prices in both the JSE and LSE were based on South African Rands and

United Kingdom Pounds respectively.

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3.4 Data analysis methods

This study was interested in explaining the movement of share price by using time series variables,

trading and business location. Therefore this study was a multivariate analysis. As mentioned

previously, the proxies for business location, listing location and market performance were

domestic overall stock market index and closing share price respectively, which are all quantitative

variables. Therefore, a causal multivariate analysis method is employed to determine the

relationship between these variables.

The use of Multivariate analysis presents several challenges, which include (Bryman & Bell,

2007):

1. The relationships between variables could be spurious.

2. There could be a third variable which is moderating the relationship.

It must be borne in mind that the problem of spurious correlations can persist in stationary models

because of omitted variables (Chow & Lawler, 2003). There are a number of tools available that

test for causality between multivariate time series. Janes (2001) claims that in order for a researcher

to arrive at a causality conclusion, three conditions have to be satisfied with appropriate evidence

presented. The first condition is that there has to be a correlation between variables involved. The

second condition is that time series has to be time-lagged. Finally, there should be no evidence of a

spurious relationship among the variables. Fernando & Ramos (2003) suggest that all time series

models are best suited for stable environments (e.g. wide-sense stationary processes) where

sufficient numbers of observations are available.

Lok & Kalev (2006) found that prior studies that have examined the price behaviour of dual-listed

shares have always found that each price series contains a unit root. Hauser et al. (1998) also

argued that causality testing raises the issue of cointegration. They argued that the possibility of

cointegration is suggested by a scenario of two parallel price time series for dual-listed shares,

reflecting the same economic value in two markets. They further argued that when the two time

series are linearly combined they may become nonstationery. Peng et al. (2007) however, could not

find any existence of a unit root when they tested dual-listed A and H-share price differentials. They

argued that the non-existence of a unit root suggests that the price differentials are stationery. A unit

root test to decide whether the vector time series is stationery or not was done. If the data was found

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to be non-stationary, it was transformed into a series of stationery time series values (Bowerman, et

al., 2005). The above conditions were checked for by using a test of time series independence and

cointegration.

If the time series are cointegrated across markets, the empirical model would require an error-

correction mechanism otherwise cointegration would cause a misspecification of tests based on the

ARIMA model (Hauser et al., 1998). It is important to bear in mind that time series data more often

than not has a broad range of characteristics such as stationarity, non-stationarity and seasonality,

cyclic (De Silva et al., 2008). Financial time series are fundamentally noisy and non-stationary. The

noise attribute arises due to the lack of complete information from past behaviour of financial

markets in modeling the dependency between future and past prices.

The information that is missing from the forecasting model is considered as noise while the non-

stationary attribute implies that the distribution of financial time series is changing over time.

Hence, financial time series forecasting is regarded as one of the most challenging tasks of time

series forecasting (Chi-Jie et al., 2009). These characteristics have to be considered when choosing

a suitable causality testing tool. Plenty of research during the 1990s found that financial time series

show signs of non-linear dependence (Panayotis & Costas, 1999). For example, Hsieh (1991)

established verification of noteworthy non-linear dependence in share returns. “Univariate and

multivariate non-linear models represent the proper way to model a real world that is almost

certainly non- linear” (Panayotis & Costas, 1999, p. 22).

Available tools for testing causality include Granger, Autoregression and Moving Average

Integration (ARIMA). Desktop research has found that the Granger causality model has been

widely used in many time series research involving share prices and stock markets. In particular, it

has also been used in studies of dual-listed share prices.

This research used multivariate ARIMA causality tests to study the dynamic relationship between

share prices of dual-listed companies and business and listing location in the South African and

United Kingdom equity markets. A special ARIMA process, the random walk process, is widely

used to describe the behaviour of share prices. Lu & AbouRizk (2009) suggest that in most cases, a

fitted ARIMA model can represent a linear or non-linear relationship within the current situation

and among past situations.

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Hauser et al. (1998) suggest that the ARIMA model is suitable for forecasting where a well-

developed theory is absent. They further argue this model offers the ability to capture information

that is hard to identify in structural models. While Sewell (2001) criticises the ARIMA/cross-

correlation approach to causality testing because it suffers from a few drawbacks: the technique is

susceptible to the choice of lag length for the cross-correlations; and the test is unable to establish

the directionality of causality, it can only detect the presence or absence of causality. The limitation

of causality direction was irrelevant to this study because it is theoretically inconsistent to have a

bidirectional causal relationship between share price and business/trading location. Given that the

JSE All Share and the FTSE All Share market indices represent at least 98% of value-weighted

market capitalisation, it is highly improbable for price changes in few shares to cause significant

changes in those indices.

A literature review conducted by the researcher found that the ARIMA causality model has been

used in different sectors including stock markets, property market and crime intelligence. In

particular, this tool has been used in similar studies of dual-listed shares. For example, Hauser et al.

(1998) used both the ARIMA and the vector Autoregression (VAR) causality models to investigate

the transmission of pricing information for shares of companies based in Israel that are listed on

both the Tel Aviv Stock Exchange (TASE) and the U.S. National Association of Securities Dealers

Automated Quotations (NASDAQ) stock exchange. They used a sample consisting of more than

6000 daily closing prices of five dual-listed shares listed on the TASE and NASDAQ stock

exchanges. These data included all trading days between 1988 and 1993. They also used the daily

figures, for the same period, of the TASE value-weighted general index and the equally weighted

NASDAQ index. The length of period of the above study is similar to the time period for this

research. They found that a comparison between the ARIMA and the VAR models revealed that

results based on the VAR model were qualitatively similar but quantitatively poorer than those

based on the ARIMA model.

The following procedures were employed to test the degree of causal significance business location

has on dual-listed share prices:

Univariate ARIMA models

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Univariate ARIMA models only use the information contained in the time series itself. ARIMA

modeling involves the steps outline below.

Identification and development of univariate and bivariate model

· Analyse the time series behavior of the individual share prices and market indices.

Specifically, non-stationarity is often indicated by an autocorrelation plot with a very slow

decay. If necessary, transform, through differencing and/or natural logarithm, each time

series into a set new set of series that is independent and normally distributed with a mean

of zero and a constant variance (i.e. white noise process).

· Determine whether the form of the process is autoregressive (AR), moving average (MA) or

both (ARMA), and its order(s). Identify the initial ARIMA model based on estimated

Autocorrelation (ACF) and Partial Autocorrelation (PACF).

· Subject the final model to a number of diagnostic checks. If this model is found to be

insufficient, the process is repeated until statistically satisfactory transfer function has been

developed.

· Calculate and inspect the cross-correlation function (CCF), a measure of association

between two pre-whitened time series. If the CCF indicates a statistically significant

relationship exists, then a preliminary ARIMA bivariate transfer is specified and estimated.

In this study, cross-correlation was assessed between the local composite indicator and the

FTSE/JSE All Share Index; the foreign composite indicator and FTSE All Share Index; and

finally between the local composite indicator and FTSE All Share Index. In all cases, both

the FTSE/JSE All Share Index and FTSE All Share Index are explanatory variables.

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4 RESEARCH FINDINGS, ANALYSIS AND DISCUSSION 4.1 Research Findings

Simple Correlations

To get a preliminary picture of the degree of relationship between variables at the JSE, the simple

correlation coefficients between the local bourse as represented by the FTSE/JSE All Share Index

and the local composite indicator, representing companies which obtain their primary profits in

South Africa, were computed. The degree of correlation between the foreign indicator and the

foreign bourse, FTSE All Share Index was also observed. Estimation results show that the local

composite indicator exhibits reasonable strong positive correlation with the local market index,

FTSE JSE All Share Index at 0.84. However, the correlation between the foreign composite

indicator and the foreign bourse, FTSE All Share Index is higher at 0.96. Interestingly, also another

feature of the data is the unexpected higher correlation between the local composite and the foreign

bourse at 0.94. The local composite and the foreign composite indicators are highly correlated at

0.99, an expected result.

Local Composite JSE All Share Foreign Composite FTSE All ShareLocal Composite 1.00JSE All Share 0.84 1.00

Foreign Composite 0.99 0.83 1.00FTSE All Share 0.94 0.85 0.96 1.00

Table 1: Correlation matrix

Table 1 shows that there is a strong relationship between the various variables but the significance

of these relationships has to be assessed as well. The assessment of the significance determines

whether the observed relationship was due to random effects or not. The t-statistic method was used

to test the significance of these relationships. First, to test the significance in the relationship

between the Local Composite Indicator and the JSE All Share Index, the following formula was

used:

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Where n= number of observations

r = correlation coefficient

t = test statistic

The degree of freedom (df) for entering the t-distribution is N-2

For N=757, df = 757-2 = 755

From Table 1, the correlation coefficient between the local composite indicator and the JSE All

Share is 0.84. Working to 95% level of significance and using a one-tailed t-test:

= 0.84 = 42.3

For df (755) and one-tailed test, critical value is t=1.96. The calculated t-statistic of 42.3 lies above

the critical value t=1.96. Therefore, the null hypothesis that there is no correlational relationship

between the Local Composite Indicator and the JSE All Share is rejected. The above steps were

repeated to establish the significance of the relationship between the Foreign Composite Indicator

and the FTSE All Share. From Table 1, the correlation coefficient between these two variables is

0.96. Again working to 95% level of significance and using a one-tailed t-test:

= 0.84 = 94.2

The t-statistic from above also lies above the critical value t=1.96. Therefore, the null hypothesis

that there is no correlational relationship between the Foreign Composite Indicator and the FTSE

All Share is rejected.

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Finally, the t-statistic between the Local Composite Indicator and the FTSE All Share was

computed using a one-tailed t-test and working to 95% level of significance.

= 0.94 = 75.7

The t-statistic from above also lies above the critical value t=1.96. Therefore, the null hypothesis

that there is no correlational relationship between the Local Composite Indicator and the FTSE All

Share is rejected. Overall, it was established that the correlation coefficients between these variable

were statistically significant.

ARIMA Univariate models

The ARIMA model only works with stationary data. The four time series which are the local

composite, FTSE/JSE All Share, foreign composite and FTSE All Share were checked for mean

and variance stationarity. The test method will not be discussed in detail here but only a brief

outline will be given. The time series was tested for the presence of underlying trends or variations

other those due expected sampling variations. First the line plots of the four time series, shown in

appendices 2-5, were observed to check visually for mean and seasonal stationarity. The estimated

autocorrelation functions (ACF) of the time series was used to determine stationarity of the data.

Table 2 and appendices 6-9 depict the correlograms of the original and differenced time series.

Correlogram analyses show that the ACF of all original four time series decreases slowly,

indicating that there is means non-stationarity.

To remove the mean non-stationarity the various time series were transformed by one degree of

differencing to derive series suitable for ARIMA modelling. All transformed time series are shown

in Table 2 and their associated graphs in appendices 6-9; they show no significant serial correlation

after first-differencing the original figures as depicted by the autocorrelation which drops to “0”

fairly quickly. This indicates that stationarity in the mean has been achieved. Visual inspection of

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the ACF showed that all four time series exhibited constant variance at all lags. This means that all

series were found to have seasonal stationary, as well.

The first part of the empirical analysis is the fitting of univariate time-series models to the four sets

of observations.

Using the Box-Jenkins maximum likelihood method, all four time series were found to fit the

general model ARIMA (0,1,0). The values of zero indicate that the Autoregressive (AR) and the

Moving Average (MA) variables are zero. The value of 1 indicates that the model contains a first-

order differencing.

Lag Local Composite

Differenced Local Composite

JSE All Share

Differenced JSE All Share

Foreign Composite

Differenced Foreign Composite

FTSE All Share

Differenced FTSE All Share

1 1.00 0.03 0.99 0.03 1.00 0.01 1.00 -0.07 2 0.99 -0.01 0.99 -0.02 0.99 -0.01 0.99 -0.05 3 0.99 -0.08 0.98 -0.08 0.99 -0.07 0.99 -0.10 4 0.99 0.01 0.97 -0.02 0.99 -0.01 0.99 0.12 5 0.98 0.02 0.97 -0.02 0.98 0.03 0.98 -0.05 6 0.98 -0.01 0.96 -0.05 0.98 0.02 0.98 -0.05 7 0.98 0.05 0.96 0.06 0.98 0.06 0.98 0.03 8 0.97 0.02 0.95 0.02 0.97 0.00 0.98 0.05 9 0.97 0.02 0.95 0.03 0.97 -0.02 0.97 0.00

10 0.97 -0.02 0.94 -0.01 0.97 0.02 0.97 0.02 11 0.96 0.04 0.94 -0.06 0.96 -0.02 0.97 -0.01 12 0.96 0.03 0.93 -0.04 0.96 0.07 0.96 0.00 13 0.96 0.04 0.93 0.01 0.95 -0.01 0.96 -0.02 14 0.95 -0.03 0.92 0.02 0.95 0.02 0.96 -0.01 15 0.95 -0.04 0.92 0.02 0.95 -0.05 0.95 -0.06

Table 2: Autocorrelation functions of the original and differenced time series

Causality Tests

After the different original time series were adjusted to obtain the white noise time series, the

direction of causality, if any, can be investigated. To test whether, in fact, there is empirical

evidence of a causal relationship between the local composite indicator and the local bourse,

ARIMA residuals are cross-correlated at various lags. The cross-correlogram computations are

based on pre-filtered ARIMA series. Table 3 reports the results of the cross-correlograms of the

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appropriate pairs of residual series are computed for lags between 0 and 5 since no correlogram was

significant for higher lags.

Cross CorrelationsSeries (157 observations) Lags 1 2 3 4 5(JSE All Share:t) on (Local Compos ite:t) 0 - 5 0.05 0.02 -0.07 0.02 -0.01(FTSE All Share:t) on (Foreign Compos ite:t) 0 - 5 0.01 0.03 -0.05 0.06 -0.01(FTSE All Share:t) on (Local Compos ite:t) 0 - 5 0.08 0.04 -0.02 0.03 0.05 Table 3: Cross-correlograms for Residual series

4.2 Research Analysis and Discussion

Correlation tests

According to Janes (2001), one of the conditions for causality to exist between variables is that

there has to be a correlation between variables involved. It was found that all the correlation

coefficients were statistically significant. This means that the observed correlations were not due to

random events. Overall, Table 1 shows that the local and the foreign composite indicators exhibit a

strong comovement with their respective domestic market indices. The economic importance of the

comovement is substantial and the correlation coefficient between the various variable ranges from

0.84 to 0.96. Table 1 show that correlation between the local composite and the JSE All Share, at

0.84, is significantly lower than the correlation between the foreign composite and the FTSE All

Share at 0.96. Moreover, the local composite indicator correlates more with the foreign bourse than

with the local bourse. This is an unexpected result in view of the hypothesis which suggests that

local composite and JSE All Share will be highly correlated since the primary profits are derived

from South Africa.

This evidence supports the hypothesis that the price dynamics of shares in both markets are

influenced more strongly by the FTSE All Share index than by the JSE All Share index.

Unexpectedly, the local composite and the foreign composite indicators were found to be very

highly correlated at 0.99. Such an insignificant price gaps between the composite indicators

highlight the integration of the London and Johannesburg capital markets as well as efficiency of

price discovery in these markets. This finding is further supported by the highly significant positive

correlation between the London and Johannesburg markets. Moreover, such as high correlation

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suggests that arbitrage opportunities between the London and Johannesburg Stock exchanges is

very low.

This finding is consistent with the results of the Froot & Dabora (1999) study which found that the

relative price of a dual-listed share is highly correlated with the relative stock market of the

countries where the shares are traded most actively. Shares on the London Stock Exchange are

more significantly traded than those on the JSE.

Interestingly, another feature of the data is the unexpected higher correlation between the local

composite and the foreign bourse at 0.94. This evidence supports the phenomenon of integration

between the South African and the United Kingdom capital markets.

ARIMA Univariate models

Hauser et al. (1998) and others have pointed out that correlations on original time series data are

likely to be spurious. Therefore, ARIMA model specification should be performed on the white-

noise series only. The autocorrelation functions of the various time series revealed that all the time

series were non-stationary around the mean. This finding is consistent with previous studies of time

series of closing prices, which contain non-stationarity properties around the mean (Hauser et al.,

1998).

Causality Tests

The first significant conclusion from the ARIMA model building is that the autoregressive

operators of the two pairs of variables are the same at ARIMA (0,1,0). According to Hanssens

(1980), this means that it is possible that a two-way causal structure between these two pairs of

series exist, i.e. local composite indicator and JSE All Share Index or foreign composite indicator

and FTSE All Share Index can be both endogenous variables. However, the companies in the final

sample have an insignificant market capitalization weighting on the FTSE/JSE All Share and FTSE

All Share Indices. Therefore, it is logical to have the local and foreign composite indicators and not

the FTSE/JSE All Share and FTSE All Share Index as endogenous variables.

Table 3 indicates the cross correlogram function calculated for various pairs of time series. It is

observed that for all time series insignificant positive and negative coefficients at all lags but lag 1

for FTSE All Share on Local Composite. The null hypothesis is that cross-correlation rk coefficient

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is not significantly different from zero. In this study, rk is considered significantly different from

zero if it falls outside the interval [-2 , 2 ]. Here N=757, which is the number of closing prices

and indices values for the study period between 7th December 2006 and 30th October 2009.

The formula for calculating the interval is as follows:

For N=757, [ -2 , 2 ] = [-0.073, 0.073]

Now if the JSE All Share and Local Composite Indicator are dependent, the estimated cross

correlation coefficients are expected to be significantly different from zero for all lags. The figures

in Table 2 and the interval calculated above reveal that this is not clearly the case.

Table 3 shows that none of lagged values are outside of interval [-2 , 2 ]. These results

indicate that the null hypothesis that changes in the share price of a dual-listed company are caused

by the changes in the market of the country where it gains most of its profits can be rejected. This

finding is consistent with the studies of Chan et al. (2003) who in their study of the Jardine Group

found that Jardine stocks correlated less with the Hong Kong market where the group gained most

of its profits. Correlation between two variables is a necessary pre-condition for causality to be

determined (Janes, 2001).

Interestingly, the rk between FTSE All Share and local indicator is found to be outside of the

interval [-2 , 2 ] for lag 1 and not for the rest of the lags. This evidence imply that stock prices

in South Africa react to price changes in United Kingdom that occurred earlier on the previous day.

The significant causality of FTSE All Share on the local composite indicator is consistent with the

strong economic ties between South Africa and United Kingdom, which are dominated by the

larger United Kingdom economy. The findings of the study are a necessary piece of information for

all investors whether in London or Johannesburg dealing with dual-listed shares. Dual-listed

companies could also benefit from the findings by seeing the true intra/inter-market price dynamics.

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4.3 Research Limitations

The study is limited to dual-listed companies which are listed at the JSE and LSE. Extrapolation to

other dual-listed companies on other international stock exchanges demands caution. South African

companies were only allowed to list offshore by the Reserve Bank of South Africa from 2001. This

sets a time limit of about seven years for the period of investigation. The end of October 2009 is set

as the latest date that could be achieved to allow this research report to be finalised in time for the

deadline set by Graduate School of Business for research projects. The final sample for the study

was small and it consisted of only three companies. This introduces the problem of small scale bias.

Literature review conducted did not find any evidence to suggest that a study of this nature has been

done in the past. As a result, literature on this subject is limited. Methodologically, literature review

did not find any evidence of previous use of causality testing between share prices, business and

trading location. Longitudinal research design suffers from limitations such as that the data

collection method may change over time, it needs qualitative research to explain variations and that

this type of research design assumes that present trends will continue unaffected. No evidence was

found to suggest that DataStream and I-Net have changed their data collection methods during the

period of study.

Masih & Masih (1998) argue that while causality models may be helpful for forecasting, they

require a well-defined theoretical structure in order to perform causal interpretation. It was

established during this study that ARIMA modelling requires good judgement from the researcher.

5 RESEARCH CONCLUSIONS

An innovation of the study has been to examine the causal relationship between the local bourse

and the local composite indicator of dual-listed companies which gain most their profits from South

Africa. The data are from three dual-listed companies which are listed on the JSE and LSE. The

causal relationship is investigated using the ARIMA modelling. It was expected that changes in

share price of companies which gain most of their profits from South Africa will be caused by

changes in the local bourse. Contrary to this prediction, evidence indicates that while there is a

positive correlation between the local composite indicator and the local bourse, there is no causal

relationship between the local composite indicator and the local bourse.

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Interestingly, it was found that changes in the foreign bourse causes changes in the local composite

indicator at lag one. This means that the speed of information transmission was one day. It implies

that the information flow from the London market to the Johannesburg market is quite efficient.

This finding is supported by a higher correlation between the local composite indicator and the

foreign bourse compared to correlation between the local composite indicator and the local bourse.

This evidence suggests that South African share prices take their cues from international financial

markets. According to Meera et al. (2000), the United Kingdom market has historically affected

markets in its former colonies because it was the British influence during the colonial times that

started these stock markets. In fact, South Africa is a former British colony and the Johannesburg

Stock Exchange was started by a London businessman, Benjamin Minors Woollan. The integration

view is supported by Hauser et al. (1998) who in their study of international transfer of pricing

information between dual-listed shares found that changes in the United States (U.S.) NASDAQ

caused stock prices in Israel. They found considerable causality of the NASDAQ on the Israel

Stock market, which was consistent with the strong economic ties between the two countries, which

are dominated by the larger U.S. economy. In another study of co-movement of stock markets in

East Asia, Huyghebaert & Wang (2009) found that the U.S. stock market returns aided to explain

returns in East Asia stock markets, except in Mainland China. Furthermore, they found that this was

as a result of an enhanced integration of capital markets in East Asia with the USA, which they

thought was in line with the stronger macro-economic linkages among countries worldwide.

Fernandez-Izquierdo & Lafuente (2004) report that the international correlation of stock prices may

be due to a substantial change in one national stock index being quickly transmitted to other

national stock exchanges, accordingly escalating volatility.

Additionally, this finding suggests that the London and Johannesburg stock markets are integrated

and that international investors do affect the price formation on the JSE. Overall, these results are

consistent with the study by Chan et al. (2003) who in their study of the Jardine Group found that

Jardine stocks correlated less with the Hong Kong market where the group gained most of its

profits. Furthermore, this finding is consistent with the results of a study by Froot & Dabora (1999)

who found that the relative price of a dual-listed share is highly correlated with the relative stock

market of the countries where the shares are traded most actively. Finally, these study results have

implications for the investment strategy of shareholders of South African shares in that changes in

the London stock market have an impact on the prices of JSE dual-listed shares. While the

Copyright UCT


investment strategy should not solely be guided by the impact of the LSE on the JSE, such

influences should not be ignored.

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6 FUTURE RESEARCH DIRECTIONS

The scope of this study was dual-listed companies with at least one listing at the JSE and LSE.

Moreover, it was found that very few dual-listed companies in the sample gained their primary

profits in South Africa. As a consequence, the final sample consisted of only three companies

which may have likely introduced small scope bias. It is recommended that future research should

identify stock exchanges which could provide opportunities for larger sample sizes.

An interesting question would be to investigate why changes in the local bourse did not cause

changes in the share prices of the local composite indicator. The study also found that FTSE All

Share Index cause prices changes of dual-listed stocks on the JSE but it was not within the scope of

this study to determine what factors make this to be the case, and would be taken up by future

research.

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7 REFERENCES

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Fernandez-Izquierdo, A., & Lafuente, A. J. (2004). International transmission of stock exchange volatility: Empirical evidence from the Asian crisis. Global Finance Journal, 15(2), 125– 137 Fernando, F., & Ramos, R. (2003). Forecasts of market shares from VAR and BVAR models: a comparison of their accuracy. International Journal of Forecasting, 19(1), 95–110 Flynn, B. B., Schroeder, R. G., Flynn, E. J., Sakakibara, S., & Bates, K. A. (1997). World-class manufacturing project: overview and selected results. International Journal of Operations & Production Management, 17(7), 671-685

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FTSE Index Company. (2009). FTSE All-Share Index. Retrieved November 20, 2009, from http://www.ftse.com/Indices/UK_Indices/Downloads/FTSE_All-Share_Index_Factsheet.pdf FTSE/JSE. (2004). Ground Rules for the Management of the FTSE/JSE Africa Index Series. Retrieved June 6, 2009, from http://ftse.jse.co.za/docs/about/jse-ground-rules.pdf Hanssens, D.M. (1980). Bivariate time-series analysis of the relationship between advertising and Sales. Retrieved October, 3, 2009, from http://www.anderson.ucla.edu/faculty/dominique.hanssens/content/bivariate_timeseries.pdf

Hauser, S., Tanchuma, Y., & Yaari, U. (1998). International Transfer of Pricing Information between Dually Listed Stocks. Journal of Financial Research, 21(2), 139-157

Hsieh, A. D. (1991). Chaos and Nonlinear Dynamics: Application to Financial Markets. Journal of Finance, 46(5), 1839-1877 Huyghebaert, N., & Wang, L. (2009). The co-movement of stock markets in East Asia. China Economic Review, doi:10.1016/j.chieco.2009.11.001, 1-15

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JSE Securities Exchange. (2009). Dual-listed Company Info. Retrieved June 1, 2009, from http://www.jse.co.za/dual_listed.jsp

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Meera, A. K., Tripathy, N., & Redfearn, M. R. (2000). Wealth and liquidity effects of stock delistings: empirical evidence from the stock exchanges of Singapore and Malaysia. Applied Financial Economics, 10(2), 199-206

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http://web.ebscohost.com/ehost/viewarticle?data=dGJyMPPp44rp2%2fdV0%2bnjisfk5Ie46bZPtae1UbGk5Yvg4ud6rNvmh%2fHqvkmvpbFIraiuSLeWr0m4qrdSsaqeWcuc8orf5PF54ernh%2bzf4lWrqrRLt66xSbGc6nns3bt97JziervY84Ck6t9%2fu7fMPt%2fku0uwq7BKr6akfu3o63nys%2bSN6uLyffbq&hid=102�

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8 APPENDICES

Appendix 1 – Dual-Listed Companies in the final sample

Issuer Code Company Name Name Exchange Name

Listing Type

AQP AQUARIUS PLATINUM LIMITED AQUARIUS

JSE Limited Secondary Australian Stock Exchange Primary London Stock Exchange Secondary

IPSA

IPSA GROUP PLC

IPSA

London Stock Exchange Primary JSE Limited Secondary

LOLMI LONMIN PLC LONMIN

JSE Limited Secondary London Stock Exchange Primary

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Appendix 2 – Line Plot of Local Composite Indicator

Appendix 3 – Line Plot of FTSE/JSE All Share Index

Line Plot of Local CompositeFinal Research Data.sta 14v*757c

132

6394

125156

187218

249280

311342

373404

435466

497528

559590

621652

683714

7450

20

40

60

80

100

120

140

160

180

200

220

240

Loca

l Com

posi

te

Line Plot of JSE All ShareFinal Research Data.sta 14v*757c

132

6394

125156

187218

249280

311342

373404

435466

497528

559590

621652

683714

74516000

18000

20000

22000

24000

26000

28000

30000

32000

34000

JSE

All

Sha

re

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Appendix 4 – Line Plot of Foreign Composite Indicator

Appendix 5 – Line Plot of FTSE All Share Index

Line Plot of Foreign CompositeFinal Research Data.sta 14v*757c

132

6394

125156

187218

249280

311342

373404

435466

497528

559590

621652

683714

7450

2

4

6

8

10

12

14

16

18

Fore

ign

Com

posi

te

Line Plot of FTSE All ShareFinal Research Data.sta 14v*757c

132

6394

125156

187218

249280

311342

373404

435466

497528

559590

621652

683714

7451600

1800

2000

2200

2400

2600

2800

3000

3200

3400

3600

FTS

E A

ll S

hare

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Appendix 6 – Original and differenced Autocorrelation Function (ACF) – Local

Composite Indicator

Autocorrelation FunctionLocal Composite: D(-1)

(Standard errors are white-noise estimates)

Conf. Limit-1.0 -0.5 0.0 0.5 1.00

15 -.042 .0360

14 -.028 .0360

13 +.036 .0360

12 +.031 .0360

11 +.045 .0361

10 -.020 .0361

9 +.024 .0361

8 +.017 .0361

7 +.049 .0362

6 -.006 .0362

5 +.018 .0362

4 +.007 .0362

3 -.079 .0362

2 -.014 .0363

1 +.029 .0363

Lag Corr. S.E.

0

13.86 .5361

12.51 .5651

11.91 .5350

10.91 .5363

10.19 .5132

8.66 .5650

8.34 .5007

7.91 .4426

7.69 .3607

5.86 .4393

5.83 .3232

5.59 .2318

5.56 .1352

.80 .6702

.65 .4209

Q p

Autocorrelation FunctionLocal Composite


Conf. Limit-1.0 -0.5 0.0 0.5 1.00

15 +.950 .0359

14 +.954 .0360

13 +.958 .0360

12 +.961 .0360

11 +.965 .0360

10 +.968 .0361

9 +.972 .0361

8 +.975 .0361

7 +.978 .0361

6 +.981 .0362

5 +.984 .0362

4 +.987 .0362

3 +.990 .0362

2 +.993 .0362

1 +.997 .0363

Lag Corr. S.E.

0

109E2 0.000

102E2 0.000

9518. 0.000

8810. 0.000

8097. 0.000

7380. 0.000

6659. 0.000

5934. 0.000

5205. 0.000

4472. 0.000

3736. 0.000

2996. 0.000

2252. 0.000

1506. 0.000

754.9 0.000

Q p

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Appendix 7 – Original and differenced Autocorrelation Function (ACF) – FTSE/JSE

All Share Index

Autocorrelation FunctionJSE All Share: D(-1)


Conf. Limit-1.0 -0.5 0.0 0.5 1.00

15 +.015 .0360

14 +.015 .0360

13 +.011 .0360

12 -.044 .0360

11 -.063 .0361

10 -.009 .0361

9 +.033 .0361

8 +.024 .0361

7 +.062 .0362

6 -.051 .0362

5 -.019 .0362

4 -.020 .0362

3 -.081 .0362

2 -.024 .0363

1 +.034 .0363

Lag Corr. S.E.

0

18.11 .2569

17.93 .2102

17.75 .1674

17.66 .1264

16.19 .1344

13.17 .2145

13.11 .1579

12.28 .1392

11.83 .1064

8.93 .1777

6.92 .2270

6.64 .1563

6.32 .0970

1.32 .5172

.90 .3433

Q p

Autocorrelation FunctionJSE All Share


Conf. Limit-1.0 -0.5 0.0 0.5 1.00

15 +.920 .0359

14 +.925 .0360

13 +.929 .0360

12 +.933 .0360

11 +.938 .0360

10 +.944 .0361

9 +.949 .0361

8 +.955 .0361

7 +.960 .0361

6 +.964 .0362

5 +.969 .0362

4 +.975 .0362

3 +.980 .0362

2 +.987 .0362

1 +.994 .0363

Lag Corr. S.E.

0

105E2 0.000

9837. 0.000

9176. 0.000

8510. 0.000

7838. 0.000

7160. 0.000

6475. 0.000

5783. 0.000

5083. 0.000

4378. 0.000

3666. 0.000

2948. 0.000

2224. 0.000

1491. 0.000

750.3 0.000

Q p

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Appendix 8 – Original and differenced Autocorrelation Function (ACF) - Foreign

Composite Indicators

Autocorrelation FunctionForeign Composite: D(-1)


Conf. Limit-1.0 -0.5 0.0 0.5 1.00

15 -.053 .0360

14 +.016 .0360

13 -.009 .0360

12 +.073 .0360

11 -.020 .0361

10 +.021 .0361

9 -.025 .0361

8 -.005 .0361

7 +.055 .0362

6 +.021 .0362

5 +.029 .0362

4 -.010 .0362

3 -.066 .0362

2 -.007 .0363

1 +.005 .0363

Lag Corr. S.E.

0

14.46 .4911

12.28 .5835

12.09 .5199

12.03 .4432

7.96 .7169

7.64 .6643

7.31 .6048

6.84 .5540

6.82 .4475

4.48 .6119

4.13 .5310

3.48 .4815

3.40 .3337

.06 .9700

.02 .8815

Q p

Autocorrelation FunctionForeign Composite


Conf. Limit-1.0 -0.5 0.0 0.5 1.00

15 +.947 .0359

14 +.951 .0360

13 +.955 .0360

12 +.959 .0360

11 +.962 .0360

10 +.966 .0361

9 +.969 .0361

8 +.972 .0361

7 +.976 .0361

6 +.979 .0362

5 +.983 .0362

4 +.986 .0362

3 +.989 .0362

2 +.993 .0362

1 +.996 .0363

Lag Corr. S.E.

0

109E2 0.000

102E2 0.000

9482. 0.000

8778. 0.000

8069. 0.000

7356. 0.000

6639. 0.000

5918. 0.000

5193. 0.000

4463. 0.000

3729. 0.000

2991. 0.000

2249. 0.000

1504. 0.000

754.3 0.000

Q p

Copyright UCT


Appendix 9 – Original and differenced Autocorrelation Function (ACF) – FTSE All

Share Index

Autocorrelation FunctionFTSE All Share: D(-1)


Conf. Limit-1.0 -0.5 0.0 0.5 1.00

15 -.064 .0360

14 -.006 .0360

13 -.015 .0360

12 -.005 .0360

11 -.011 .0361

10 +.020 .0361

9 +.002 .0361

8 +.051 .0361

7 +.029 .0362

6 -.048 .0362

5 -.052 .0362

4 +.125 .0362

3 -.101 .0362

2 -.046 .0363

1 -.072 .0363

Lag Corr. S.E.

0

35.45 .0021

32.24 .0037

32.21 .0022

32.03 .0014

32.02 .0008

31.92 .0004

31.59 .0002

31.59 .0001

29.61 .0001

28.95 .0001

27.20 .0001

25.17 .0000

13.27 .0041

5.54 .0625

3.90 .0482

Q p

Autocorrelation FunctionFTSE All Share


Conf. Limit-1.0 -0.5 0.0 0.5 1.00

15 +.953 .0359

14 +.956 .0360

13 +.959 .0360

12 +.963 .0360

11 +.966 .0360

10 +.969 .0361

9 +.972 .0361

8 +.975 .0361

7 +.978 .0361

6 +.981 .0362

5 +.984 .0362

4 +.987 .0362

3 +.989 .0362

2 +.993 .0362

1 +.996 .0363

Lag Corr. S.E.

0

109E2 0.000

102E2 0.000

9524. 0.000

8813. 0.000

8099. 0.000

7380. 0.000

6658. 0.000

5932. 0.000

5202. 0.000

4469. 0.000

3733. 0.000

2993. 0.000

2250. 0.000

1504. 0.000

754.0 0.000

Q p

Copyright UCT


Appendix 10 – Aquarium Platinum: Pie chart showing Sources of Profits for

2006/2007


2007/2008

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2008/2009

the share price drivers of dual-listed companies on the...

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