RELATIONSHIP BETWEEN EXCHANGE RATES AND STOCK PRICES

Category: Publications Published: Friday, 03 February 2017

RELATIONSHIP BETWEEN EXCHANGE RATES AND STOCK PRICES:

EVIDENCE FROM TWO SADC MEMBER STATES


 

 

 

 

 

 

 

 

 

 

 

 

 

 

By

John Kwiyolecha

 

 

 

 

A Dissertation Submitted in Partial Fulfilment of requirements for the Degree of Master of Science in Accounting and Finance (Msc. A&F) of Mzumbe University

2016

CERTIFICATION

We, the undersigned, certify that we have read and hereby recommend for acceptance by the Mzumbe University, a dissertation entitled relationship between exchange rate and stock price: Evidence from two SADC member states, in partial fulfilment of the requirements for award of the degree of Masters of Accounting and Finance (MSc. A&F) of Mzumbe University.

 

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(Dr. Cosmas Mbogela) Major Supervisor

 

____________________

Internal Examiner

 

____________________

External Examiner

 

 

Accepted for the Board of ………………………

 

 

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CHAIRPERSON, FACUTY/DIRECTRORATE BOARD


 

DECLARATION

I, John Kwiyolecha, declare that this dissertation is my own original work and that it has not been presented and will not be presented to any other university for a similar or any other degree award.

 

 

Signature ________________________________

Date ____________________________________


 

COPYRIGHT

©

This dissertation is a copyright material protected under the Berne convention, the Copyright Act 1999 and other international and national enactments, in that behalf, on intellectual property. It may not be reproduced by any means in full or in part, except for short extracts in fair dealings, for research or private study, critical scholarly review or discourse with an acknowledgement, without the written permission of Mzumbe University, on behalf of the author.


 

ACKNOWLEDGEMENT

I thank the Almighty God, for the gift of life, health, strength, protection that I received throughout my studied and for his many blessings and inspiration during this course.

My heartfelt gratitude goes out to my supervisor Dr. Cosmas Mbogela for his patience, encouragement, professional advice, understanding and enlightenment throughout this research.

Furthermore, my thanks also goes to all those who have contributed to this research in one way or another, including my family members, The Principal of DIT Prof. John Kondoro, my immediate boss Dr. Albert Mmari and my valued friends, Eng. Dr. Mashauri Kusekwa, Dr. Amosi Nsanganzelu, Mr. Salum Omary and Mr. William Lohay for their encouragement, advice, financial and other support which enabled the successful completion of this study.


 

DEDICATION

I dedicate this dissertation to my lovely wife, Shanister Triphon and my children Yustina and Cyprian for their remarkable support, encouragement and love.


 

ABBREVIATIONS AND ACRONYMS

ADF

Augmented Dickey Fuller

ARIMA

Autoregressive Integrated Moving Average

BESA

Bond Exchange of South Africa

BOT

Bank of Tanzania

CMSA

Capital Market and Security Authority

DSE

Dar es Salaam Stock Exchange

DSEI

Dar es Salaam Stock Exchange Index

ERP

Economic Recovery program

EXR

Exchange Rate

FSB

Financial Services Board

FTSE

Financial Times Stock Exchange

JSE

Johannesburg Stock Exchange

JSEI

Johannesburg Stock Exchange Index

OLS

Ordinary Least Squares

PP

Philips - Perron

SADC

South Africa Development Community

SAFEX

South African Futures Exchange

SARB

South African Reserve Bank

TSH

Tanzania Shilling

USA

United States of America

VAR

Vector Autoregressive

ZAR

South Africa Rand


 

ABSTRACT

This research examines the relationship between exchange rates and stock prices in Tanzania and South Africa with regards to the period from January 1st 2014 to December 31st 2015. The reason to the selection of this time period is to obtain useful or meaningful relationship between the variables thus I need to study the most recent data that reflects the current situation in the market. Also data for the study were easily accessed in the selected period for Tanzania and South Africa when compared to other SADC member states and even the whole African nations.

The findings have implications for investors, investment managers, policy makers, listed companies, financial institutions and other market players since the variables play significant roles in persuading the development of a country’s economy. Because company’s stocks is normally used as a signal of the overall strength and health of a company, Investors and company’s management need to factor the effects of exchange rate movement on the performance of the stock exchange. The same applies to policy makers because their policies may affect the performance despite their good intention to correct the deteriorating situations in the economy. In the recent years, there is increase of international diversification, cross-market return correlations, continuing elimination of capital inflow barriers and foreign exchange limitations or the implementation of more flexible exchange rate agreements in developing countries like Tanzania hence understanding the linkage between these variables is of primary importance. In Tanzania and South Africa, Dar es Salaam Stock Exchange (DSE) and Johannesburg Stock Exchange (JSE) respectively are the only formal trading places for securities.

Therefore the study used data from DSE and JSE to employ VAR and ARIMA models in establishing relationship amoung variables; the results indicated that there was strong positive and negative bidirectional relationship between exchange rates and stock prices which was in agreement with both the Flow and Stock oriented models arguments. The analysis results were presented in the form of statistical tables and graphs.

TABLE OF CONTENTS

CERTIFICATION.. ii

DECLARATION.. iii

COPYRIGHT.. iv

ACKNOWLEDGEMENT.. v

DEDICATION.. vi

ABBREVIATIONS AND ACRONYMS. vii

ABSTRACT.. viii

TABLE OF CONTENTS. ix

LIST OF TABLES. xiii

LIST OF FIGURES. xv

CHAPTER ONE.. 1

PROBLEM SETTING.. 1

1.1             Introduction. 1

1.2             Background to the Study. 5

1.2.1                  Historical review of the Tanzanian foreign exchange rate policies: 1970 to current....... 7

1.2.2                  Historical review of the South African foreign exchange rate policies: 1960 to current 9

1.2.3                  Development of Stock Exchange Markets in Tanzania (DSE) 12

1.2.4                  Development of Stock Exchange Markets in South Africa (JSE) 13

1.3             Statement of the Problem.. 15

1.4             Objectives of the Study. 17

1.4.1                  Main objective. 17

1.4.2                  Specific Objectives. 17

1.5             Research Questions. 17

1.6             Significance of the Study. 17

1.7             Scope of the Study. 18

1.8             Organization of the paper 18

CHAPTER TWO.. 19

LITERATURE REVIEW... 19

2.1             Introduction. 19

2.2             Definition of terms. 19

2.2.1                  Linear regression. 19

2.2.2                  Multicollinearity. 20

2.2.3                  Heteroskedasticity. 20

2.3             Theoretical Literature Review.. 20

2.3.1                  Exchange Rate Fluctuations. 20

2.3.2                  Stock Prices. 21

2.3.3                  Theories. 21

2.3.4                  Exchange Rate Movement and Stock Market Returns. 23

2.4             Empirical Literature Review.. 25

2.5             Summary of Literature Review.. 27

CHAPTER THREE.. 28

RESEARCH METHODOLOGY.. 28

3.1             Introduction. 28

3.2             Research approach. 28

3.2.1                  Reasons for perfoming statistical tests. 30

3.3             Population and Sample size. 32

3.4             Sampling technique. 32

3.5             Data collection. 33

CHAPTER FOUR.. 34

DATA ANALYSIS, PRESENTATION AND INTERPRETATION OF FINDINGS  34

4.1             Introduction. 34

4.2             Statistical tests. 34

4.2.1                  Testing for stationarity / Unit root 34

4.2.2                  Testing for Co-integration. 44

4.2.3                  Granger causality test 46

4.3             Data Analysis. 47

4.3.1                  Explanation for Regression analysis results in Tables 4.21 – 4.22. 48

CHAPTER FIVE.. 49

DISCUSSION OF FINDINGS. 49

5.1             Introduction. 49

5.2             Exchange rate and stock price relationship in Tanzania and South Africa. 50

5.2.1                  Exchange rate and stock market price movements. 50

5.2.2                  Exchange rate and stock market price relationship extent. 52

5.2.3                  Influence of the foreign exchange market operations on stock markets in Tanzania and South Africa. 53

5.3             Overall relationship between variables. 54

CHAPTER SIX.. 56

CONCLUSIONS AND RECOMMENDATIONS. 56

6.1             Introduction. 56

6.2             Conclusion. 57

6.3             Recommendations. 57

6.4             Limitations of the Study. 58

6.5             Suggestions for further Studies. 59

REFERENCES. 60

APPENDICES. 66

 


 

LIST OF TABLES

Table.1.1         Foreign companies listed at DSE. 12

Table.1.2         Domestic companies listed at DSE. 13

Table.3.1         The summary for research approach. 31

Table.4.1         Augmented Dickey - Fuller test on THS/USD at level 34

Table.4.2         Augmented Dickey - Fuller on DSEI at level 35

Table.4.3         Augmented Dickey - Fuller test on ZAR/USD at level 35

Table.4.4         Augmented Dickey-Fuller on JSEI at level 35

Table.4.5         Phillips-Perron on TSH/USD at level 35

Table.4.6         Phillips-Perron test on DSEI at level 36

Table.4.7         Phillips-Perron on ZAR/USD at level 36

Table.4.8         Phillips-Perron test on JSEI at level 36

Table.4.9         Augmented Dickey - Fuller test on THS/USD at first difference. 39

Table.4.10      Augmented Dickey-Fuller test on DSEI data at first difference. 40

Table.4.11      Augmented Dickey - Fuller test on ZAR/USD at first difference. 40

Table.4.12      Augmented Dickey-Fuller test on JSEI data at first difference. 40

Table.4.13      Phillips-Perron on TSH/USD at first difference. 40

Table.4.14      Phillips-Perron test on DSEI data at first difference. 41

Table.4.15      Phillips-Perron on ZAR/USD at first difference. 41

Table.4.16      Phillips-Perron test on JSEI data at first difference. 41

Table.4.17      Johansen tests for co-integration on DSE data. 45

Table.4.18      Johansen tests for co-integration on JSE data. 45

Table.4.19      Granger causality Wald tests for DSE.. 46

Table.4.20      Granger causality Wald tests for JSE.. 47

Table.4.21      Regression analysis for exchange rate and stock price using DSE data. 48

Table.4.22      Regression analysis for exchange rate and stock price using JSE data. 48

 


 

LIST OF FIGURES

Figure 4.1       Graphical representation of unit root/ Non stationarity for DSE data. 38

Figure 4.2       Graphical representation of unit root/Non stationarity for JSE data. 39

Figure 4.3       Graphical presentation of stationarity in DSE data. 43

Figure 4.4       Graphical presentation of stationarity in JSE data. 44

 


CHAPTER ONE

PROBLEM SETTING

1.1              Introduction

Many reasons, such as gross domestic product, exchange rates, enterprise performance, dividends, interest rates, current account, money supply, employment and stock prices of other countries, have influence on daily stock price Kurihara (2006). Exceptionally, the persistent rising of the global trade and movements in capital have resulted in the exchange rates being one of the chief determinants of business returns and stock prices Kim (2003).

In accounting there are three methods for stock valuations with different treatments

The first method is discounted cash flow valuation which is accounting method for valuation of stock for companies which pay regular dividends or are in a stable developing period, this method compute stock price as the sum of the present value of expected cash flows discounted at a rate that reflects the riskiness of these cash flows. The models under this method are based on the assumption known as going concern in accounting literature. Second method is Liquidation and accounting valuation which is a valuation method that does not rest on the assumption of going concern but values stock as the sum of book value of the assets and net liabilities. The third method is the relative valuation that values a stock by what market pays for a similar asset of the company.

All the three methods are influenced by exchange rate movements when determining stock prices for an International investor. In international trade, one of the key risks in valuation of company and company returns is exchange rate fluctuations. Investors should understand the influence of exchange rate on stock price and company returns so that they can make informed decisions about their investments. Moreover, company’s Management need proper strategies for management of its assets portfolio and reduction of risks the company face for sustainable development.

The relationship between stock prices and exchange rates has gripped the minds of financial experts for different reasons ranging from theory development to empirical evidences as these variables play significant roles in persuading the development of a country’s economy. Now days the continuing elimination of capital inflow barriers, the increasing of international diversification, foreign exchange limitations or the implementation of more flexible exchange rate agreements in developing countries and cross-market return correlations of the foreign exchange and stock markets have made the two markets to depend on each other Odoyo (2014).

These transformations in developing countries have increased the variety of investment opportunities as well as the unpredictability of exchange rates and risk of investment choices as well as portfolio diversification. Therefore, knowing this relationship will help local and international investors for diversification of their portfolio and hedging against exchange rate movements.

While, economic theories recommend that foreign exchange movements can have an imperative influence on the share price by influencing cash flow, investment and earnings of companies, there is no compromise about these relationships and the studies of the empirical relationships are uncertain Joseph (2002). On the other hand, the association between these financial variables can be determined by the instruments of demand for money, wealth, interest rates and many more Mishra (2004).

Traditional method expresses that, exchange rates affect stock prices. While, portfolio balance method expresses, exchange rates are established by market mechanism. That is to say, changes in share prices can affect exchange rate fluctuations. This method expresses that share price is assumed to affect exchange rate with a negative correlation because a reduction in stock prices decreases domestic assets, which in turn lower interest rates and domestic money demand because the decrease in domestic share prices cause International investors to have low demand for domestic assets and money. These movements in demand and supply of local money lead to capital outflows and the raise of exchange rate for local money against foreign money. While, when stock prices go up, foreign investors become keen to invest in a country’s shares. As a result, they will get benefit from international diversification. This situation will cause capital inflows and make local money strong Granger et al. (2000), Stav'arek (2005), Pan et al. (2007).

Exchange rate movements have an effect on the competitiveness of companies through their influence on input and output price Joseph (2002). Whenever the exchange rate appreciates, exporters lose their competitiveness in foreign market, the turnover and returns of exporters get smaller and the share prices will deteriorate.

Conversely, importers will boost their competitiveness in local markets. Therefore, their returns and share prices will rise. The increase of exchange rate will cause unfavourable effects for exporters and importers. Local exporters will be favoured against other countries’ exporters and their turnover and their share prices will be elevated Yau and Nieh (2006).Also currency appreciation has both favourable and unfavourable  effect on the local stock market for an export-dominant and an import-dominated country, respectively. Jorion (1990) indicated that, exchange rate was four times unstable compared to interest rate and ten times unstable compared to inflation rate.

For the investor, movement in exchange rate cause a foreign exchange risk. High movement in exchange rates cause big losses in an investor’s investment portfolio due to uncertainty of earnings from investments. Because of the fact that the shifts in foreign exchange rates have affect on prices of goods on the foreign markets and so affects the returns of exporting and importing firms.

Exchange rate affects share prices for both local and multinational import and export oriented companies. For a multinational company, exchange rate movements will lead to an immediate change in worthiness of its foreign operations and a persistent change in the returns of its foreign operations as can be observed in the succeeding income statements. Therefore, movement in economic worthiness of company’s foreign operations can affect stock prices.

Local companies can also be affected by exchange rate movements as they import some of materials and export their products. For instance, a devaluation of its local money leads to imported materials becoming more expensive and exported products cheaper for a company. Therefore, devaluation will pose positive effect for export companies Aggarwal (1981) and improvement in the cash flows of these companies hence; improve the average level of share prices Wu (2000).

Basing on real interest rate disturbance, when the real interest rate get higher, inflows of capital improves and the exchange rate drops. However, as higher real interest rate reduces the present value of future earnings, share prices will fall. An inflationary disturbance explains negative relationship between exchange rate and share price. That is to say, when inflation rises, the exchange rate also rises and since inflation expectations is elevated, investors will require high risk premium and high cost of capital (rate of return), and as a result share prices will decline Wu (2000).

In contrast, the asset market method to exchange rate estimation reveals a weak or absence of linkage between exchange rates and share prices and exchange rate is treated to be the price of an asset (price of one unit of foreign currency over local currency for case of direct quotation). Thus, according to asset market method to exchange rate estimation exchange rates like all commodities are determined by market mechanism (forces of demand and supply) expected future demand and supply of currencies determine the exchange rates and factors affecting exchange rates and stock price are be different Muhammad and Rasheed (2002).

This research attempted to examine how movements in exchange rate (Represented by the Tanzania shilling and South Africa rand prices to one U.S. dollar) and stock price (Provided by the Dar es Salaam Stock exchange stock index and Johannesburg stock exchange stock index) are related to each other for the case of two South Africa Development Community (SADC) Member states of Tanzania and South Africa over the period of 2014 to 2015.

Policy makers, scholars, economists, business owners, government and the general public in Tanzania need to understand the relationship of stock prices and exchange rates so that they can make informed decisions about investments, foreign exchange transactions and regulations setting that lead to the growth of the foreign exchange and stock markets.

In the past few years a lot development in finance has been occurred for emerging and developing economies. Amoung the remarkable developments in finance are the formation and improvement of stock market in emerging and developing economies and the shift from fixed exchange rate regime to floating exchange rate regime.

1.2              Background to the Study

Economic theory points out that money supply, price level, interest rates, inflation, etc are important variables for estimation of stock prices and exchange rate fluctuations Ross (1976).

 Typical economic models argue that change in exchange rate affects balance sheet items of a company since the company’s competitiveness in international market is affected by exchange rate movements and finally Impact Company’s value and returns. Branson and Masson (1977), Ghartey (1988), Meese and Rogoff (1983), and Wolff (1988) found some relationship between exchange rates and macroeconomic variables in their empirical studies.

 Normally, companies operating outside national boundaries face three types of risks, namely; operating risk, transaction risk, and translation risk.

Transaction risk arises when international debts or credits denominated in a foreign currency are settled without hedging and results in gain and loss. Translation risk (also called accounting risk) results from translating foreign currency denominated financial transaction into consolidated financial statements often expressed in the parent country’s currency. Economic risk or operating risk is a financial risk arising from movements in exchange rates.

The relationship between stock price and exchange rate is explained by two main theories. The goods market theory (also called either the flow-oriented model or the traditional approach) and the portfolio balance theory (also called the stock-oriented model).

The goods market theory explains that exporters will be affected by the appreciation of a local currency since their product will be priced higher in the international market and therefore, the stocks of such companies would become less desirable by investors and affect the stock market in an export-orientated country. This assumes that causality streams to the stock market from the exchange rate market in a negative correlation. Conversely, the portfolio balance theory assumes that causality streams to the exchange rate from the stock market. As per this theory, portfolio adjustments [movements in the foreign capital - inflows and outflows of foreign capital] occur whenever there is a movement in the stock prices. Thus if stock prices are on the higher side, they will invite more foreign capital, while a fall in the stock prices will result in reduced corporate wealth that lead to a reduction in the country’s wealth.

However, existing empirical studies report mixed and contrasting findings on the relationship between exchange rates and stock prices. Frank and Young (1972) were the earliest, who conducted the empirical study and tried to establish the relationship between exchange rates and stock prices. According to their analysis of six different currency exchange rates, they come up with the conclusion that was against the portfolio and traditional approaches because their study rejected or declined that there might be any relationship between stock prices and exchange rate.

Aggarwal and Schrim (1992) conducted the study for the relationship between stock market indices and dollar exchange rates for the sample period 1974-1978, and their results concluded that there is positive correlation between dollar exchange rates and stock market indices by using a simple regression model, they further explained that this relationship is stronger in short-run than it is in long-run.

Ratner (1996) examined the direction and strength of the relationship between U.S stock price indices and U.S dollar exchange rate by applying the co-integration technique. The null hypothesis of co-integration was not rejected in his study and therefore concluded that there is no relationship between these two markets, so the assets based approach was supported his results. Ajayi and Moungoue (1996) examined the relationship between the foreign exchange market and the stock market six industrialized economies, including Germany, Netherlands, Japan, Italy, Canada and France and supported the portfolio and traditional approaches since their results concluded that there is a bi-directional relationship between two financial markets in short as well as over the long-run. Their analysis used the error correction model. Nieh and Lee (2002) used the Engle- Granger and Johansen’s co-integration techniques and according to their results they supported the arguments in the asset based approach, since they concluded that there is no significant relationship between exchange rates and stock market for the long-run period but they concluded significant relationship in G-7 economies for the short run period only.

The adoption of floating exchange rate regime and liberalization of foreign capital controls in Tanzania and South Africa in the years 1994 and 2000 respectively have increased the scope of studying the relationship between stock prices and exchange rates. The possibility of international investment has been opened by liberalization of foreign capital controls and the adoption of free floating exchange rate regime has widened the fluctuations in the currency exchange market. As these two markets (Stock Market and currency exchange market) are theoretically connected but very few empirical studies such as Haji and Jianguo (2014) for Tanzania and Ocran (2010) for South Africa have been made so far to investigate the relationship between stock market and currency exchange market in Tanzania and South Africa as well. This encouraged me to make this study in order to determine the interaction between these variables so as to inform both the financial regulators and potential investors on the dynamic effect between the stock and foreign exchange markets.

1.2.1        Historical review of the Tanzanian foreign exchange rate policies: 1970 to current.

From the year 1970 to 1985, Tanzania had a fixed exchange rate system, which made it illegal for individuals to hold foreign currency. All economic activities at that time were driven by the government only. The government set an exchange rate ceiling that grossly overvalued the Tanzanian shilling. The exchange rate ceiling led to a shortage of foreign currency and it became necessary for the Bank of Tanzania (BoT) to use non-market factors to ration it. For supporting its development priorities the government of Tanzanian used trade restrictions and exchange controls. Producers of traditionally major source of export earnings (export cash crops), sold their products to marketing parastatals which bought at low prices that did not motivate them to produce more.

Exporters of other (non-traditional) products had to surrender their foreign exchange sales and were subjected to a cumbersome and opaque system of export permits that required them to acquire a license for each consignment and effectively gave individual ministries the right to regulate a wide range of exports on an ad hoc basis. Likewise, all imports were regulated through administrative allocations of foreign exchange and an import licensing system, both of which became more restrictive towards the end of the 1970s as foreign exchange reserves fall due to the overvalued exchange rate for Tanzania shilling against US dollar.

In 1986 Tanzania designed a comprehensive Economic Recovery Program (ERP) to accelerate structural reforms and restore economic stability. Two distinct stages of T economic reforms evolved.

First stage, 1986 to mid-1993, Government gradually removed market embargoes in the external sector and adjusted its policy by eliminating import restrictions, progressively reducing the foreign exchange surrender requirements, reforming export marketing by opening the domestic economy to international competition and adjusting the exchange rate regularly, these reforms allowed the communication of appropriate price signals.

The exchange system reform increased speed when the government allowed foreign exchange bureaus to operate, and allowed them to buy and sell currencies at freely negotiated rates. Tanzania’s foreign exchange legislation was passed by the parliament to liberalize citizens holding foreign currency deposits at domestic banks. Later on the parallel market rate premium was removed in August 1993. Tanzania had unified the exchange rate and allowed the floating exchange system for current account transactions as the end of the first stage in 1993.

In 1994, the second stage started the government gradually rationalized and lowered rates of tariff, allowed direct foreign investments, and allowed the flexible exchange rate (floating exchange rate system).Therefore Tanzania adopted formally a floating exchange rate regime in 1994. Since then the exchange rates have been influenced by the demand and supply forces of the market Mwase, Nkunde, and Ndulu (2008).

1.2.2        Historical review of the South African foreign exchange rate policies: 1960 to current

In the 1960 to 1970, South Africa followed a fixed exchange regime, but  adjusted the exchange rates parities to either British pound sterling or the USA dollar at distinct times. However the exchange rate regime temporally changed to a scrambled peg of more currencies in June 1974, but it moved back to fixed peg to the USA dollar in 1975. At this time, stability in exchange rate was part of  the goal since South Africa signed the Breton Woods Agreement for managing fixed exchange rates as other nations did Van der Merwe, (1996) . In addition to the exchange rate regime the government imposed strict exchange control regulations, which assisted to maintain the fixed rates. Capital account transactions of both residents and non-residents were restricted.

However exchange rates pegging to fixed failed due to political instability and  intensified campaign against apartheid by the global community. Campaign against Apartheid became stronger in 1976 by several riots mainly after killing of hundreds  people by police mostly students in Soweto who protested poor education for black people. The international community supported this campaign against Apartheid through imposition of economic sanctions against South African government. The mixture of the confussions on political situation at home and the sanctions of international community pressurised the country economy and prompted high outflow of capital, mostly being nonresident capital.

In1979 the government established a double exchange rate system as a measure against capital outflows. The double exchange rate regime composed of the financial rand rate and commercial rand rate. The commercial rand was the primary rate which was applicable to all residents transactions. It was designed as a floating rate, but with official intervention  of the South African Reserve Bank. On the other hand the financial rand, was a secondary rate which was only applicable to capital transactions of nonresidents. The idea was to improve the country economy thereby minimizing nonresidents outflow of capital under the confused political conditions and economic sanctions by international community.

In February 1983, guided by recommendations of the De Kock Commission, (1985) to introduce flexibility in the domestic foreign exchange rate market, the exchange rate regime reunited to a managed float and the financial rand was discontinued. Exchange control over transactions of both residents and non-residents was also abolished. The authorities furthermore introduced a forward market, to enlarge the scope of the domestic foreign exchange market.

The debt crisis that happened in May 1985 compelled the government to restore the dual exchange rate regime of the financial and commercial rand rates in September 1985. This debt crisis was resulted by the joinning of United States of America in international campaign against Apartheid by putting economic sanctions against South Africa which included freezing of existing credit lines and cessation of new lending. This caused problem to the government of  South Africa because USA banks, especially Chase Manhattan Bank, owed a big amount of debt and the amount was given under short term. When the United States of America refused to extend fresh loans and  renewal of  expiring debt liabilities to South Africa, the country defaulted in repayment of  its foreign debt obligations and declared suspension of its repayments due to economic crisis Ayogu & Dezhbakhsh (2008). As a result, a foreign debt crisis evolved. The crisis blocked external sources for financing the current account deficits.

Again, the commercial rand applied to transactions of residents while those of non-residents were valued at the financial rand rate. The commercial rand was maintained as a managed float, supported by interventions policy, while the financial rand freely floated. However, exchange control over both resident and non-resident transactions was reinstated. While often one can cite the debt crisis as the prime reason for reintroducing the dual rate regime at this stage, the evidence above suggests that political developments were the major contributor.

The exchange rate regime again changed to a single managed float system in march 1995,  following the withdrawal of financial rand. This was a result of successful political reconciliation in 1994 after the transition from Apartheid to all inclusive democracy which marked the end of economic isolation. The exchange rate regime changes was part of a  broader process of gradually liberalization of the financial markets, to restore the nation into the world economy. However official interventions continued for the purpose of stabilizing the rand.

The financial liberalization plan also required a gradual process of eliminating exchange control regulations. Immediately exchange control on transactions of non-residents was eliminated by removing the financial rand. On the other hand those on transactions of residents, were initially unchanged, but they have been changed gradually. The key changes that happened so far include allowing resident institutional investors (i.e. insurers, pension funds, unit trusts as well as other institutional investors) to invest in other counties.

Local companies, similarly, have been allowed since 1997 to acquire foreign direct investments abroad and foreign funding against their local balance sheets. moreover South Africans above 18 years were allowed to invest in other countries.

Inflation targeting was adopted as the operating framework for monetary policy in February 2000.Monetary policy under this framework focused on announced inflation rate benchmarks to be met over a specified time frame, explicit inflation forecast as the intermediate variable, and interest rate as the policy instrument.

The closure of the Reserve Bank’s negative net open position in May 2003 and cessation of its forward book in the foreign exchange market in February 2004 helped to meet the benchmarks. With the inflation-targeting framework, therefore, the exchange rate regime has become consistent with a free float.

1.2.3        Development of Stock Exchange Markets in Tanzania (DSE)

The Dar es Salaam Stock Exchange (DSE) is the only formal trading place for securities in Tanzania. The DSE was established in 1996 under the Companies Ordinance (Cap. 212). It is a private company limited by guarantee with no stock capital. It is a non-profit making body established to facilitate the Government implementation of the economic reforms and stock ownership of all privatized companies in Tanzania. The exchange is registered by Capital Market and Security Authority (CMSA). DSE operates in close association with the Nairobi Securities Exchange in Kenya and the Uganda Securities Exchange in Uganda (Dar es Salaam Stock Exchange website 2015).

DSE started trading on 15th April, 1998 with only one equity product: the Tanzania Oxygen Limited (TOL) and later in the same year, Tanzania Breweries Limited (TBL) became the second listed company. Currently, the DSE is trading equity products, corporate bonds and Government bonds. Out of twenty one listed companies, seven Companies stocks are not only cross listed at the DSE but also at the Uganda Stock Exchange and Nairobi Stock Exchange .Tables 1.1 – 1.2show lists of foreign and domestic companies listed with the Dar es Salaam Stock Exchange.

Table.1.1                   Foreign companies listed at DSE.

SN

NAME OF COMPANY

TRADING SYMBOL

YEAR OF LISTING

1

Kenya Airways Ltd

KA

2004

2

East African Breweries Ltd

EABL

2005

3

Jubilee Holdings Ltd

JHL

2006

4

Kenya Commercial Bank Ltd

KCB

2008

5

National Media Group Plc

NMG

2011

6

African Barrick Gold Plc

ABG

2011

7

Uchumi super Market Ltd

Uchumi

2014

Source: Dar es Salaam Stock Exchange website.

 

 

 

Table.1.2                   Domestic companies listed at DSE.

SN

NAME OF COMPANY

TRADING SYMBOL

YEAR OF LISTING

1

Tanzania Oxygen Limited

TOL

1998

2

Tanzania Breweries Limited (TBL)

TBL

1998

3

TATEPA Ltd

TATEPA

1999

4

Tanzania Cigarette Co. Ltd

TCC

2000

5

Tanga Cement Co. Ltd

SIMBA

2002

6

Swiss port Tanzania Ltd

SWISSPORT

2006

7

Tanzania Portland Cement Co. Ltd

TWIGA

2006

8

DCB Commercial Bank Plc

DCB

2008

9

National Microfinance Bank Plc

NMB

2008

10

CRDB Bank Plc

CRDB

2009

11

Precision Air Services Plc

PAL

2011

12

Maendeleo Bank Plc

Maendeleo

2013

13

Swala gas and Oil

Swala

2014

14

Mkombozi commercial bank

Mkombozi

2014

Source: Dar es Salaam Stock Exchange website.

1.2.4        Development of Stock Exchange Markets in South Africa (JSE)

The Johannesburg's Stock Exchange (JSE) is established in Johannesburg to assist the sudden increase of trade ignited by the discovery of gold in the Witwatersrand. The discovery of gold in 1886 resulted in the establisment of mining and financial companies with investors who required a central facility to access primary capital. Originally trading took place in a miner's tent and shifted to the permanent at the corner of what is now the Saur and Commissioner Streets. Benjamin Minors Wollan suggested to a meeting of the Exchange and Chambers Company board and members that the Johannesburg Stock Exchange should be created. On 8th November 1887 Woollan established the JSE by providing a facility to conduct trading. The establishment of the JSE at this time made it the oldest stock exchange facility in the subcontinent Johannesburg Stock Exchange website (2016)

Development in the mining industry was revealed in the economic growth of the 1890s that the JSE experienced. Between 1887 and 1934 approximately 200 million pounds was invested in the gold industry with more than half from foreign investments. In 1933 a concurrent exchange known as the Union Exchange was formed in Johannesburg. It prolonged to trade until 1958 when it was closed by the Treasury Companies and the companies listed under it were moved to the JSE.  In 1947 the Stock Exchanges Control Act was passed to regulate the operation of stock exchange by stating capital requirements for members and the conduct for brokers. In 1963 the JSE joined the World Federation of Exchanges an international association of the world's leading regulated markets. The physical location of the JSE altered several times throughout its existence as it grew. On 7 June 1996 the open outcry trading floor (where traders shout across the floor or signal to sell or buy shares) was closed and substituted by an order driven, centralised, automated trading system known as the Johannesburg Equities Trading (JET) system Johannesburg Stock Exchange website (2016)

The Johannesburg's Stock Exchange is an equity market for over a century of its existence, designed for both large and small companies, the JSE acquired the South African Futures Exchange (SAFEX) in 2001 and the Bond Exchange of South Africa (BESA) in 2009. Now the exchange gives trading opportunities in equities, bonds as well as currency, equity, commodity and interest rate derivatives Johannesburg Stock Exchange website (2016).

1.2.4.1              Equity market

Whilst a number of heavyweights like British American Tobacco (BAT), SABMiller GlencoreXstrata and BHP Billiton account for a large share of the market, the exchange has a different variety of offerings, which include warrants, exchange traded funds and other specialised products. There are almost 400 companies listed on the exchange across the Main Board. The JSE has approximately 60 Equity Market member firms Johannesburg Stock Exchange website (2016)

The JSE index series is called the FTSE/JSE Africa Index Series, and is a partnership between JSE and the FTSE Group. The two benchmark indices are the FTSE/JSE All Share Index, covering 99% of market capitalisation, and the FTSE/JSE Top 40 index which trace the top listings in a representative spread of sectors. JSE equity market data is sold in more than 40 countries Johannesburg Stock Exchange website (2016).

1.2.4.2              Regulation

The JSE is the main regulator for the exchange, setting and enforcing listing and membership requirements and trading rules. The Financial Services Board (FSB) oversees the JSE in the performance of its regulatory duties. The regulatory model is set to change considerably in the future, as South Africa seems to implement a double top model of oversight. Under the new system, prudential supervision will be transferred to the South African Reserve Bank (SARB) and market conduct regulation will be led by a strengthened FSB. South Africa is recently ranked 1st out of 144 countries in terms of regulation of securities exchanges in the World Economic Forum’s Global Competitiveness Survey for 2014-2015. This is the fifth consecutive year in which the JSE has retained its top position in the survey. This is praise for both the JSE and its regulators. The Report also ranked South Africa third in the ability to raise finance through the local equity market, third in terms of the effectiveness of corporate boards and second in protecting the rights of minority shareholders Johannesburg Stock Exchange website (2016).

1.3              Statement of the Problem

After the growth of international trade, exchange rate fluctuation is a key source of company’s risks, especially for those involved in international investments. When compared to other macroeconomic factors, such as interest rate and inflation rate, exchange rate shifts on average four times and ten times compared to interest rates and inflation respectively Jorion (1990). This phenomenon has compelled financial managers and researchers to find out the effect of exchange rate fluctuation on the value of a firm. Exchange rate movement influences the company’s value of foreign assets and foreign liabilities denominated in foreign currency. Exchange rate changes influence stock price movements of a company Chen et al. (2004). Exchange rate fluctuations affect both International and national companies competitiveness, their raw material and final product prices and supply and demand chains. According to the efficient market theory, significant effect of exchange rate movements should be priced into stock price instantly Chen et al. (2004) Therefore, it is important to understand the effect of exchange rate movements on company’s value and the implications it brings to investors, stakeholders and company managers. It is important for researchers to conduct empirical research so as to understand the causal relationship between exchange rate movement and stock price if it exists. In practice, it is very important for managers to have this knowledge for the purpose of controlling the related risk.

A good number of empirical studies have attempted to explain the relationship between stock price and foreign exchange rate in developed countries with a few focussing on developing countries. However, there is no agreed conclusion with regard to the relationship between these variables. Disagreement exists among financial analysts, academicians and policy makers about whether stock price influence exchange rate or vice versa. Many empirical studies indicated that there is a significant and moderate positive relationship between the variables, such as, Aggarwal (1981), Giovannini and Jorion (1987) and Roll (1992).  Haji and Jianguo (2014) showed moderate positive relationship between the variables using data from Dar es Salaam Stock Exchange (DSE).

Although some studies counter this argument and revealed a significant negative relationship between the variables, such as, Soenen and Hennigar (1988), others concluded that there is no significant relationship between the variables, such as, Frank and Young (1972), Solnik (1987) and Chow et al. (1997), Bahmani and Sohrabian (1992) and Nieh and Lee (2002) who found no long-run relationship between the variables.

So there is no empirical harmony among the researchers regarding the interactions between stock prices and exchange rates which justify the need of more research in this area to contribute to the literature using different methodology such as Autoregressive Integrated Moving Average model (ARIMA) and increasing the sample size since sample size has an important implication in a research study.

1.4              Objectives of the Study

1.4.1        Main objective

To determine the relationship between exchange rates and stock prices in Tanzania and South Africa.

1.4.2        Specific Objectives

a)                  To examine the relationship between exchange rate movements and stock market prices in two SADC member states of Tanzania and South Africa.

b)                 To measure and analyze the extent of the relationship between exchange rate movements and stock market prices in Tanzania and South Africa.

c)                  To investigate how foreign exchange market operations affect stock market operations both in Tanzania and South Africa.

1.5              Research Questions

This research study is required to answer the following research questions.

a)                  What is the relation between exchange rate movements and stock market prices?

b)                 What is the direction and degree of causality between foreign currency exchange rates and stock market prices?

c)                  How foreign exchange market operations affect stock market operations both in Tanzania and South?

1.6              Significance of the Study

Understanding the relationship between currency exchange rates and stock market prices will help the national as well as multinational corporations to manage their foreign exchange exposure. Portfolio investors might use this information in order to hedge or speculate their returns on foreign investments. Regulatory authorities can ensure that pre-cautionary measures are in place in order to save their markets from financial crises. This study will also add to academic literature and serve as secondary data for those who wish to make further studies on relationship between exchange rates and stock prices.

1.7              Scope of the Study

The study focused on exploring the relationship between exchange rates and stock prices hence the researcher only concentrated with short run and long-run relationship between foreign currency exchange rates and stock market prices, the direction of causality between them a reflection from (DSE and JSE) as DSE is the only formal trading place for securities in the United Republic of Tanzania and JSE a major securities exchange market in Africa. This study used secondary data provided by DSE and JSE for a period of two years, from January 2014 to December 2015 and the prevailing exchange rates for the same period from the Bank of Tanzania (BOT) and South African Reserve Bank (SARB) data source.

1.8              Organization of the paper

This study is organised in six chapters, the first chapter is introduction, the second chapter: Literatures review which provide an overview of existing literatures about the relationship between stock prices and exchange rates also review empirical study that have been conducted, the third chapter Research Methodology; sets out research approach, determine the population, sample size and sampling techniques, the fourth chapter Data Analysis presentation and interpretation of findings; this chapter deals with analysis of data results presentations and its interpretation, the fifth chapter Discussion of findings; provides a comprehensive discussion of the findings of the study and the sixth chapter Conclusion and related recommendation; Draw inferences from the data analysis and make conclusion and recommendation.


CHAPTER TWO

LITERATURE REVIEW

2.1              Introduction

There have been many studies in the area of the relationship between exchange rates and stock prices around the globe, most of which have concentrated on the Western economies because it is this area where many researches of exchange rates and stock prices relationship have been conducted. This chapter consists of definition of terms, theoretical and empirical literature review. It concludes with an overview of the literature reviewed.

2.2              Definition of terms

This section explains the meaning of linear regression, multicollinearity and heteroskedasticity which are useful statistical terms.

2.2.1        Linear regression

Regression is the most basic and commonly used predictive analysis. Regression estimates are used to describe data and to explain the relationship between one dependent variable and one or more independent variables. There are 3 major uses for regression analysis which are causal analysis, forecasting an effect and trend forecasting.

Regression analysis assumes a dependence or causal relationship between one or more independent and one dependent variable. However there is a fundamental difference in cross sectional and time series regression models, one most critical being the importance of sequence in time series. The sequence in which the data is analyzed is of significant importance in time series. Also, the dependence of relationships between variables is dependent on the lead or the lag values of the variables, which is unlike cross sectional regression analysis. Therefore before running a regression for time series data it is very important to perform statistical tests for Unit root, Co-integration and other tests.

2.2.2        Multicollinearity

Is a phenomenon in which two or more independent variables in a multiple regression model are highly correlated such that one variable can be linearly explained at high degree of accuracy with the other variables. In this situation the coefficient approximation of the multiple regressions may shift erratically in response to small shifts in the model or the data. This phenomenon does not reduce the explanatory power or reliability of the model as a whole but it only affects calculations regarding individual independent variables.

2.2.3        Heteroskedasticity

Heteroskedasticity in statistics, is when the standard deviations of a variable, monitored over a specific amount of time, are non-constant. The existence of heteroscedasticity is a major concern in the application of regression analysis, including the analysis of variance, as it can invalidate statistical tests of significance that assume that the modelling errors are uncorrelated and uniform hence that their variances do not vary with the effects being modelled. For instance, while the ordinary least squares estimator is still unbiased in the presence of heteroscedasticity, it is inefficient because the true variance and covariance are underestimated.

2.3              Theoretical Literature Review

This theoretical review section explains whether or not existing theories suggest that currency risk exposures should be considered in arriving at the stock prices of individual companies quoted at the Securities’ exchange or not. The section’s main purpose is to establish a concrete foundation for the empirical studies, clarifying the underlying problems of the analysis.

2.3.1        Exchange Rate Fluctuations

Exchange rate fluctuations are a result of the floating exchange rate system that has been adopted by economies. Generally, the rate at which one currency is exchanged for another is what is referred to as the exchange rate.

2.3.2        Stock Prices

For any investor the key issue when making an investment decision is what the expected returns are and if the investment is worthwhile. A share price is the value of a single share of a number of saleable stocks of a company, derivative or other financial asset. It represents a unit of ownership in a company, derivative or financial asset. A stock price is the present value of its future cash flows. The higher the cash flows the higher the stock price.

2.3.3        Theories

Two main theories explain the relationship between stock price and exchange rate; these are the goods market theory (also called either the flow-oriented model or the traditional approach) and the portfolio balance theory (also called the stock-oriented model).

The goods market theory predicts that the appreciation of a local currency should hurt its exporters; and therefore, the stocks of such companies would become less desirable and affect the stock market in an export-orientated country. This presumes that causality streams from the exchange rate to the stock market in a negative correlation Dornbusch and Fischer (1980).

On other hand, the portfolio balance theory asserts that causality streams from the stock market to the exchange rate. As per this theory, portfolio adjustments occur whenever there is a movement in the stock prices. Thus if stock prices are on the higher side, they will invite more foreign capital while a fall in the stock prices will result in reduced corporate wealth and so lead to a reduction in the country’s economic wealth Branson and Masson (1977).

2.3.3.1              Flow Oriented Model

The flow oriented model approach was described in Dornbusch and Fischer (1980). It advocates that causality streams from foreign exchange market to stock market. It further claims that currency movements directly affect international competitiveness. In turn, currency has an effect on the balance of trade within the country. This affects the future cash flows or the stock prices of companies. Stock prices, commonly interpreted as the present values of future cash flows of companies, react to exchange rate movements and form the relationship among future income, interest rate innovations, current investment and consumption decisions Dornbusch and Fischer (1980).

The goods market hypothesis proposes that movements in exchange rates influence the competitiveness of multinational companies and therefore their earnings and stock prices.

A devaluation of the local currency makes exporting goods cheaper and can lead to a rise in foreign demand and sales. Subsequently, the value of an exporting company would benefit from a devaluation of its local currency.

On another side, because of the reduction in foreign demand of an exporting company's products when the local currency appreciates, the company's earnings will deteriorate and so does its stock price. In comparison, for importing companies the sensitivity of company value to exchange rate movements is just the opposite. An appreciation (depreciation) of the local currency leads to a rise (fall) in the company value of importing companies.

Increased, fluctuations in exchange rates affect a company's transaction exposure. That is to say, exchange rate movements affect a company's future payables (or receivables) denominated in foreign currency. For an exporter, an improvement of the local currency reduces earnings, while weakening of the local currency increases earnings. Moreover, exchange rate movements can affect stock prices because such movements will induce equity flows.

2.3.3.2              Portfolio Balance Approach

Portfolio balance approach or stock oriented models was developed by Branson and Masson (1977). In this approach, exchange rates, like all commodities, are determined by market mechanism (forces of demand and supply). A flourishing stock market will attract capital flows, foreign investors and therefore causes a rise in the demand of a country's currency and vice versa. For this reason, rising stock prices are linked to an appreciation in exchange rates. Furthermore, foreign investment in a country's equity securities could eventually increase due to the benefits of international diversification that foreign investors would gain. In addition to returns, capital flows can be caused by less risky investment condition of a country.

An enhancement in a country's investment condition (e.g., a stable political system, a fair legal system, financial openness and liberalization, etc.) will bring about capital inflows and a currency appreciation. Moreover, changes in stock prices can have an effect on exchange rate since investors' wealth and money demand can depend on the performances of the stock market. For instance, during the time of a crisis (e.g., the 1997 Asian financial crisis), an abrupt dislocation of asset can occur because of the hedging behaviour of investors or the loss of confidence in economic and political stability. This dislocation normally results in the change of portfolio preference from domestic assets to assets denominated in other currencies, meaning a fall in the demand of money. This will cause a decrease in the domestic interest rate and consecutively lead to capital outflows. Subsequently, the currency will depreciate Pan et al. (2007).

The stock-oriented model of exchange rates views exchange rates as equating the supply and demand for assets such as stocks. This approach determines exchange rate dynamics by giving the capital account an important role. Since the values of financial assets are determined by the present values of their future cash flows, expectations of relative currency values play a considerable role in their price movements. Therefore, stock price innovations may affect, or be affected by, exchange rate dynamics Zhao (2010).

2.3.4        Exchange Rate Movement and Stock Market Returns

Exchange rates, as any other commodity, are depends on supply and demand for particular forms of currency. Country's fiscal and monetary policies are the ones that make local currency supply changes. A number of factors can influence demand for currency, including views on impending government regulation, interest rates, and inflation. There are number of industry related factors and macroeconomic factors that affect the stock price and companies’ value. The persistent rising in the global trade and movements in capital resulted to exchange rates being one of the chief determinants of business returns and stock prices Kim (2003). Exchange rate movements have an effect on the competitiveness of companies through their influence on input and output price Joseph (2002).

Basically, foreign exchange rate fluctuations affect the value of the company as the future cash flows of the company change with the movements in the foreign exchange rates. Whenever the exchange rate appreciates, exporters lose their competitiveness in foreign market, the turnover and returns of exporters get smaller and the share prices will deteriorate.

Conversely, importers will boost their competitiveness in local markets. Therefore, their returns and share prices will rise. The increase of exchange rate will cause unfavourable effects for exporters and importers. Local exporters will be favoured against other countries’ exporters and their turnover and their share prices will be elevated Yau and Nieh (2006). Also currency appreciation has both favourable and unfavourable effect on the local stock market for an export-dominant and an import-dominated country, respectively. Jorion (1990) indicated that, exchange rate was four times unstable compared to interest rate and ten times unstable compared to inflation rate.

For the investor, movement in exchange rate cause a foreign exchange risk. High movement in exchange rates cause big losses in an investor’s investment portfolio due to uncertainty of earnings from investments. Because of the fact that the shifts in foreign exchange rates have affect on prices of goods on the foreign markets and so affects the returns of exporting and importing firms.

Exchange rate affects share prices for both local and multinational import and export oriented companies. For a multinational company, exchange rate movements will lead to an immediate change in worthiness of its foreign operations and a persistent change in the returns of its foreign operations as can be observed in the succeeding income statements Phylaktics and Ravazzolo (2005). Therefore, movement in economic worthiness of company’s foreign operations can affect stock prices.

Local companies can also be affected by exchange rate movements as they import some of materials and export their products. For instance, a devaluation of its local money leads to imported materials becoming more expensive and exported products cheaper for a company. Therefore, devaluation will pose positive effect for export companies Aggarwal (1981) and improvement in the cash flows of these companies hence; improve the average level of share prices Wu (2000). Thus, understanding this relationship will help local as well as international investors for hedging and diversifying their portfolio.

Stock market normally reflects the financial performance and economic conditions of a country. Stock market fluctuations signify the degree of price movement of the share price in a specific period of time Aggarwal (1981). Commonly a certain degree of market fluctuations is unavoidable or even desirable, since the stock price movements indicates changing values across economic activities and it facilitates better resource allocation. But frequent and high stock market fluctuations cause doubt about the value of an asset and affect the confidence of the investor Ma and Kao (1990). The risk averse and the risk neutral investors may withdraw from a market at sharp price movements. Extreme fluctuation disturbs the smooth functioning of the stock market.

2.4              Empirical Literature Review

Muhammad and Rasheed (2002) studied four South Asian states; these are Pakistan, India, Bangladesh and Sri-Lanka, the period covered in their study range from January 1994 to December 2000. The results of their study showed no short-run relationship between the variables for all four countries and there was no long-run relationship between stock prices and exchange rates for Pakistan and India as well. Though, for Bangladesh and Sri-Lanka there appeared to be a bi-directional causality between these two financial variables Muhammad and Rasheed (2002).

Dimitrova (2005) studied the short run relationship between stock and currency markets in the U.S. and U.K. over the period January 1990 through August 2004. The results supported his hypotheses about the expected signs of the two-way relationship. He made the assertions that, in the short run, an increasing trend in the stock market may lead to currency depreciation, but depreciating currency may cause deterioration in the stock market. For testing these assertions, he used a multivariate model that allows for simultaneous equilibrium in the goods, money, foreign exchange and stock markets in two countries. His results were found to support for the hypothesis that a depreciation of the currency may weaken the stock market. The stock market will respond with a less than one percent decline for a one percent depreciation of the exchange rate. This also means that an appreciating exchange rate improves the stock market Dimitrova (2005).

In another study, Aydemir and Demirhan (2009) investigated the causal relationship between stock prices and exchange rates, using data from 23 February 2001 to 11 January 2008 in Turkey. In this study, national 100, services, financials, industrials, and technology indices was taken as stock price indices. The results of empirical study indicate that there is a bi-directional causal relationship between exchange rate and all stock market indices. While the negative causality exists from national 100, services, financials and industrials indices to exchange rate, there is a positive causal relationship from technology indices to exchange rate. On the other hand, negative causal relationship from exchange rate to all stock market indices is determined Aydemir and Demirhan (2009).

Kutty (2010) investigated the link between stock prices and exchange rates in Mexico. The stock index data for the study was obtained from Dow Jones News and Retrieval provided by Dow Jones and it was made of weekly closing of Bolsa, Mexico's stock index, a market capitalization weighted index of the leading 35-40 stocks. Exchange rate for Mexican Peso per US dollar starting from the first week of January 1989 to the last week of December 2006 was obtained from the International Monetary Market. After removing some of the unsuitable data, a total of 849 data points were produced. The results from Granger causality test showed that stock prices influence exchange rates in the short run, but there is no long-run relationship between these two variables. These findings were in agreement with the findings of Bahmani and Sohrabian (1992), but disagree with the findings of other studies that reported a long-term link between exchange rates and stock prices Kutty (2010).

2.5              Summary of Literature Review

From the studies done it is clear that the direction for the effect of exchange rates to stock prices differs and is dependent on the country a study is being carried out on. Nevertheless, there is lack of consensus on the relationship between exchange rates and stock prices. Although theories suggest causal relations between stock prices and exchange rates, existing empirical evidence on a micro level provides mixed results. It is therefore prudent that more work need to be done in a country of interest to know the kind of relationship that exists between the two variables and whether the direction of causation is unidirectional or bi-directional.

Consequently, this research study aimed at extending the inquiry into the stock market and exchange rate relations in the two SADC member states of Tanzania and South Africa where little has been done on the same so far. This helps bringing insights into the issue also filling the gap in literature for these countries which are in the South Africa Development community.

CHAPTER THREE

RESEARCH METHODOLOGY

3.1              Introduction

Research methodology is an approach to systematically resolving of the research problem Kothari (1990). It is better understood as a science of studying how research is scientifically done. The study consists of the various steps which are the basic step to be adopted by a researcher in establishing a logical relationship on his research problem. It is Important for the researcher to understand both the research methods/techniques and the methodology. Researchers need to know how to perform statistical tests, how to calculate the mean, the mode, the median or the standard deviation or chi-square, how to apply particular research techniques and which of these methods or techniques, are relevant and which are not, and more importantly what would they mean or indicate and why. Researchers also are required to know the assumptions underlying various techniques and the criteria by which they can decide certain techniques and procedures to certain problems. All this emphasizes that it is important for the researcher to design his methodology appropriate for his problem since the same may differ from problem to problem.

This chapter sets out various steps that were followed in conducting this study, in these steps; most decisions are about how research was going to be executed and how the research was to be completed.

The following subsections are included; research Approach, population & sample size, and sampling techniques.

3.2              Research approach

In this study, quantitative research design was applied. Secondary sources such as official records, reports and legal websites were used. The data for research based on the Dar es Salaam Stock Exchange (DSE) and Johannesburg Stock Exchange all stock indices value for the period between January 2014 and December 2015. These indices track the performance of the stocks of companies as provided by the management of DSE and JSE from time to time. The prevailing exchange rates provided to DSE and JSE by the Bank of Tanzania (BOT) and South African reserve Bank respectively for the same period were used. The time series data set consisting of daily stock indices and TSH/USD and ZAR/USD exchange rates from January 2014 to December 2015 excluding weekends and official holidays on which the DSE and JSE do not trade since are not official days.

Analysis of quantitative data is generally classified into descriptive statistics and inferential statistics. For this study, inferential statistics method was applied to draw conclusions or inferences about a population from a sample. It is a bivariate statistics involved to test the relation between two variables which are exchange rate and stock price. Statistics is used to explain the relationship between the two variables and to determine the strength and significance of the relationship.

Regression analysis was applied to analyze statistical data using Autoregressive Integrated Moving Average model to take care of multicollinearlty, autocorrelation of error term and heteroscedasticity problem of time series data to obtain more reliable estimates for the effect of the explanatory variable on the dependent variable.  The tool used for analysis of the data was the data analysis and statistical software package called STATA. Statistical tests on time series data was performed using a bivariate VAR model which contains two variables namely exchange rates (EXR) which are the local currencies exchange rates relative to the US dollar, and stock prices measured using the daily all Stock Index, DSEI for the Dar es Salaam Stock Exchange and JSEI for Johannesburg Stock Exchange. Tests like Stationarity tests for the two variables were done through Augmented Dickey Fuller (ADF) and Phillips Perron (PP) tests to examine unit roots in the variables, the two tests were chosen since they have great ability to test for larger and more complicated set of time series and does not require to select the level of serial correlations especially for the Phillips Perron (PP) test, Co-integration tests were done using Johansen’s co-integration test to test for co-integration amoung the variables and finally  Granger causality test was used to determine the direction and degree of causality between  exchange rate and stock price, then Johansen’s co-integration test which is considered to be the best test for long-run relationship was applied to test the existence of long-run relationship amoung variables.

3.2.1        Reasons for perfoming statistical tests

It is very important to perform Statistical tests when dealing with time series data because using time series face two major problems that a researcher using cross-sectional data will not encounter these are, one time series variable can influence another with a time lag and If the variables are nonstationary, a problem known as spurious regression may arise.

If you have nonstationary time series variables then you should not include them in a regression model. The appropriate route is to transform the variables to stationary by the most common method of differencing before running a regression. An alternative to that is variable co-integration When the variables are co-integrated you don’t need to transform them into stationarity because with co-integration the unit root in variables cancel each other out and the resulting error is stationary.

3.2.1.1              Unit root test

In regression analysis, a researcher is typically interested in measuring the effect of an explanatory variable or variables on a dependent variable. This goal is complicated when the researcher uses time series data since an explanatory variable may influence a dependent variable with a time lag, also the dependent variable may be correlated with lags of itself. According to Koop (2000) Variables with time lags influence are non stationary or have unit roots. Non-stationarity is a commonly observed problem with time series. It stems from the fact that the time series is not independent of time. When a variable is not stationary, its mean and variance are not constant over time, and an observation is correlated with its more recent lags. The method of estimation of the standard regression model, Ordinary Least Square (OLS) method is based on the assumption that the means and variances of these variables being tested are constant over time. Variables whose means and variances change over time are known as non-stationary or unit root variables. Therefore, incorporating non-stationary or unit root variables in estimating the regression equations using OLS method give misleading inferences.

3.2.1.2              Co-integration test

Cointegration is a statistical property possessed by some time series data that is defined by the concepts of stationarity and the order of integration of the series. A stationary series is one with a mean value which will not vary with the sampling period.

In econometrics, cointegration analysis is used to estimate and test stationary linear relations, or cointegration relations, between non-stationary time series variables such as consumption and income, interest rates at different maturities, and stock prices Granger and Newbold (1974). The vector autoregressive (VAR) model framework has been widely applied to model cointegration system. In the modeling of cointegrated systems, the determination of the number of cointegrating relations, or the cointegration rank, is the most important decision. Cointegration is said to exist between two or more non-stationary time series if they possess the same order of integration and a linear combination (weighted average) of these series is stationary. Thus, if xt and yt are non-stationary and are of the same order, there may exist a number b such that, the residual series, gt, (yt - bxt) is stationary. In this case xt and yt are said to be cointegrated with a cointegrating factor of b.

 

Table.3.1                   The summary for research approach

Variables

Unit of measurement

Type of data

Model for Statistical tests.

Analysis Method

1.Foreign exchange rate

2. Stock price

1.TSH/USD,ZAR/USD

2. All stock index (ASI) in TSH for DSE and ZAR for JSE.

 

Time series

Vector Autoregressive  (VAR) model

Regression analysis

ARIMA model

 

3.3              Population and Sample size

Target population is the population of interest to which the findings of the study are going to be generalized and from which the study subjects are obtained. For this study the target population was the stock exchange markets in the South Africa Development Community.

A sample is a selection of respondents chosen in such a way that they represent the total population as good as possible.

In statistics, a population for inferential quantitative study is an entire group about which some information is required to be ascertained. In selecting a population for study, the research question or purpose of the study will suggest a suitable definition of the population to be studied. According to the purpose of this study the target population for the study was the stock exchange markets in the South Africa Development Community. Two sample markets was drawn from the target population which are Dar es Salaam Stock Exchange (DSE) and Johannesburg Stock Exchange (JSE) with 21 companies listed companies at the DSE and 400 listed companies at JSE as at December 2015. Since all stock (Share) price index is the average stock price of all companies in a respective market which can show the general price movements. 493 and 500 stock price indices and prevailing exchange rates from January 2014 to December 2015 were examined for DSE and JSE respectively. The time frame chosen was justified by the ability of the most recent data to provide useful information.

3.4               Sampling technique

The non-probability sampling was used in this study in which daily all stock indices and the prevailing exchange rates from January 2014 to December 2015 were selected to form part of the sample data. The reason to the selection of this time period is to obtain useful or meaningful relationship between the variables thus I need to study the most recent data that reflects the current situation in the market. Data for the study were freely accessed for the two markets in the selected time period as opposed to other SADC member states.

3.5              Data collection

The study used secondary data collected from the Dar es Salaam Stock Exchange and Johannesburg Stock Exchange. The use of secondary data was justified on the basis that some of these sources have information that is very meaningful to this study and has been vetted and accepted. The data collection method was through visiting official websites which keep reliable data.


CHAPTER FOUR

DATA ANALYSIS, PRESENTATION AND INTERPRETATION OF FINDINGS

4.1              Introduction

This chapter presents analysis, findings and interpretation of findings of the study as set out in the research objectives and methodology. The study findings are presented on the relationship between exchange rates and stock prices at the Dar es Salaam Stock Exchange and Johannesburg Stock Exchange.

4.2              Statistical tests

The research performed three statistical tests; Unit root test using Augmented Dickey – Fuller (ADF) and Phillips and Perron tests, Co-integration test using Johansen’s co-integration test and causality test using Granger causality test

4.2.1         Testing for stationarity / Unit root

In testing for unit root the researcher used Augmented Dickey – Fuller (ADF) and Phillips and Perron tests because they have great ability to test for larger and more complicated set of time series and does not require to select the level of serial correlations specifically for the Phillips Perron (PP) test, also the researcher used first difference as a method to remove unit root in the research data. The results are illustrated in Tables 4.1 – 4.16 below.

4.2.1.1              Augmented Dickey – Fuller (ADF) and Phillips and Perron tests using data at level

4.2.1.1.1              Augmented Dickey – Fuller (ADF) tests - DSE data.
Table.4.1                   Augmented Dickey - Fuller test on THS/USD at level

 

Test

Statistic

1%

Critical

Value

5%

 Critical

Value

10%

Critical

Value

Z(t)

0.047

-4.017

-3.441

-3.141

 

Table.4.2                   Augmented Dickey - Fuller on DSEI at level

 

 

Test

Statistic

1%

 Critical

Value

5%

Critical

Value

10%

 Critical

Value

Z(t)

0.669

-4.017

-3.441

3.141

 

 

 

 

4.2.1.1.2              Augmented Dickey - Fuller (ADF) tests - JSE data
Table.4.3                   Augmented Dickey - Fuller test on ZAR/USD at level

 

Test

Statistic

1%

Critical

Value

5%

Critical

Value

10%

Critical

Value

Z(t)

1.330

-3.449

-2.875

-2.570

 

 

 

 

Table.4.4                   Augmented Dickey-Fuller on JSEI at level

 

Test

Statistic

1%

Critical

Value

5%

Critical

Value

10%

Critical

Value

Z(t)

-2.598

-3.449

-2.875

-2.570

 

4.2.1.1.3              Phillips-Perron test on DSE data.
Table.4.5                   Phillips-Perron on TSH/USD at level

 

Test

Statistic

1%

Critical

Value

5%

Critical

Value

10%

Critical

Value

Z(rho)

-3.986

-28.654

-21.402

-18.051

Z(t)

-1.628

-3.985

-3.425

-3.130


 

Table.4.6                   Phillips-Perron test on DSEI at level

 

Test

Statistic

1%

Critical

Value

5%

Critical

Value

10%

Critical

Value

Z(rho)

-1.163

-28.654

-18.051

-21.402

Z(t)

-0.557

-3.985

-3.425

-3.130

 

4.2.1.1.4              Phillips-Perron test on JSE data.
Table.4.7                   Phillips-Perron on ZAR/USD at level

 

 

Test

Statistic

1%

Critical

Value

5%

Critical

Value

10%

Critical

Value

Z(rho)

2.150

-20.409

-14.000

-11.200

Z(t)

1.349

-3.449

-2.875

-2.570

 

 

 

 

Table.4.8                   Phillips-Perron test on JSEI at level

 

 

Test

Statistic

1%

Critical

Value

5%

Critical

Value

10%

Critical

Value

Z(rho)

-10.199

-20.409

-14.000

-11.200

Z(t)

-2.539

-3.449

-2.875

-2.570

 

4.2.1.1.5              Results for unit root tests on DSE data at level

T - statistic results for exchange rate (EXR) at level were 0.047 for Augmented Dickey – Fuller (ADF) test (Figure 4.1) and 1.628 for Phillips and Perron test (Figure 4.5) while corresponding Critical value results were 3.441 for Augmented Dickey – Fuller (ADF) test (Figure 4.1)  and 3.425 for Phillips and Perron test (Figure 4.5).

T - statistic results for stock price at level were 0.669 for Augmented Dickey – Fuller (ADF) test (Figure 4.2) and 0.557 for Phillips and Perron test (Figure 4.6) while corresponding Critical value results were 3.441 for Augmented Dickey – Fuller (ADF) test (Figure 4.2) and 3.425 for Phillips and Perron test (Figure 4.6).

4.2.1.1.6              Results for unit root tests on JSE data at level

T - statistic results for exchange rate (EXR) at level were 1.330 for Augmented Dickey – Fuller (ADF) test (Figure 4.3) and 1.349 for Phillips and Perron test (Figure 4.7) while corresponding Critical value results were 2.875 for Augmented Dickey – Fuller (ADF) test (Figure 4.3) and 2.875 for Phillips and Perron test (Figure 4.3).

T - statistic results for stock price at level were 2.598 for Augmented Dickey – Fuller (ADF) test (Figure 4.4) and 2.539 for Phillips and Perron test (Figure 4.8) while corresponding Critical value results were 2.875 for Augmented Dickey – Fuller (ADF) test (Figure 4.4) and 2.875 for Phillips and Perron test (Figure 4.8).

4.2.1.1.7              Results Implication

The unit root test for data at level using both Augmented Dickey – Fuller (ADF) and Phillips and Perron tests on exchange rate and stock price variables for both DSE and JSE show the existence of unit root/Non stationarity (Figures 4.1 – 4.8) since T - statistic results in all tests have lower absolute value compared to critical value results (5% critical value which base on 95% confidence level).This means that all data from both DSE and JSE at level have unit roots.


 

Figure 4.1                Graphical representation of unit root/ Non stationarity for DSE data

 

1500

2000

2500

3000

01Jan 2014

01June 2014

 

01Dec 2014

01Jan 2015

01June 2015

01Dec 2015

DATE

 

EXR

DSEI


 

Figure 4.2                Graphical representation of unit root/Non stationarity for JSE data

 

10

20

30

40

50

60

01Jan 2014

 

01June 2014

 

01Dec 2014

 

01Jan 2015

 

01June 2015

 

DATE

EXR

JSEI

4.2.1.1.8              Description of the graphs in figures 4.1 and 4.2

The graphs above show that there are no normal distributions in the time series without upwards and downwards movement around the mean, which can be explained that the means or variances of time series data for DSE and JSE increases overtime, a property known as nonstationality.

4.2.1.2              Augmented Dickey – Fuller (ADF) and Phillips and Perron tests using data at first difference

4.2.1.2.1              Augmented Dickey – Fuller (ADF) tests - DSE data.
Table.4.9                   Augmented Dickey - Fuller test on THS/USD at first difference

 

Test

Statistic

1%

Critical

Value

5%

Critical

Value

10%

 Critical

Value

Z(t)

-12.163

-4.017

-3.441

-3.141

Table.4.10               Augmented Dickey-Fuller test on DSEI data at first difference

 

 

Test

Statistic

1%

 Critical

Value

5%

 Critical

Value

10%

 Critical

Value

Z(t)

-7.662

-4.017

-3.441

-3.141

 

4.2.1.2.2              Augmented Dickey - Fuller (ADF) tests - JSE data.
Table.4.11               Augmented Dickey - Fuller test on ZAR/USD at first difference

 

 

 

Test

Statistic

 

1%

Critical

Value

5%

Critical

Value

10%

Critical

Value

Z(t)

-20.982

-3.449

-2.875

-2.570

 

 

 

 

Table.4.12               Augmented Dickey-Fuller test on JSEI data at first difference

 

 

 

Test

Statistic

 

1%

Critical

Value

5%

Critical

Value

10%

Critical

Value

Z(t)

-22.182

-3.449

-2.875

-2.570

 

4.2.1.2.3              Phillips-Perron test on DSE data.
Table.4.13               Phillips-Perron on TSH/USD at first difference

 

 

Test

Statistic

1%

Critical

Value

5%

Critical

Value

10%

Critical

Value

Z(rho)

-253.329

-28.652

-21.401

-18.050

Z(t)

-15.098

-3.985

-3.425

-3.130


 

Table.4.14               Phillips-Perron test on DSEI data at first difference

 

Test

Statistic

1%

Critical

Value

5%

 Critical

Value

10%

 Critical

Value

Z(rho)

-400.590

-28.652

-21.401

-18.050

Z(t)

-20.001

-3.985

-3.425

-3.130

 

4.2.1.2.4              Phillips-Perron test for on JSE data.
Table.4.15               Phillips-Perron on ZAR/USD at first difference

 

 

Test

Statistic

1%

Critical

Value

5%

Critical

Value

10%

Critical

Value

Z(rho)

-411.893

-20.408

-14.000

-11.200

Z(t)

-20.939

-3.449

-2.875

-2.570

 

 

 

 

Table.4.16               Phillips-Perron test on JSEI data at first difference

 

 

Test

Statistic

 

1%

Critical

Value

5%

Critical

Value

10%

Critical

Value

Z(rho)

-444.332

-20.408

-14.000

-11.200

Z(t)

-22.129

-3.449

-2.875

-2.570

 

4.2.1.2.5              Results for unit root tests on DSE data at first difference.

T - statistic results for exchange rate at First difference were 12.163 for Augmented Dickey – Fuller (ADF) test (Figure 4.9) and 15.098 for Phillips and Perron test (Figure 4.13) while Critical value results were 3.441 for Augmented Dickey – Fuller (ADF) test (Figure 4.9) and 3.425 for Phillips and Perron test (Figure 4.13).

T - statistic results for stock price at difference were 7.662 for Augmented Dickey – Fuller (ADF) test (Figure 4.10) and 20.001 for Phillips and Perron test (Figure 4.14) while Critical value results were 3.441 for Augmented Dickey – Fuller (ADF) test (Figure 4.10) and 3.425 for Phillips and Perron test (Figure 4.14).

4.2.1.2.6              Results for unit root tests on JSE data at first difference.

T - statistic results for exchange rate at First difference were 20.982 for Augmented Dickey – Fuller (ADF) test (Figure 4.11) and 20.939 for Phillips and Perron test (Figure 4.15) while Critical value results were 2.875 for Augmented Dickey – Fuller (ADF) test (Figure 4.11) and 2.875 for Phillips and Perron test (Figure 4.15).

T - statistic results for stock price at difference were 22.182 for Augmented Dickey –.Fuller (ADF) test (Figure 4.12) and 22.129 for Phillips and Perron test (Figure 4.16) while Critical value results were 2.875 for Augmented Dickey – Fuller (ADF) test (Figure 4.12) and 2.875 for Phillips and Perron test (Figure 4.16).

4.2.1.2.7              Results Implication

When first difference data were used, the unit root tests for both Augmented Dickey – Fuller (ADF) and Phillips and Perron tests on Exchange rate and stock price variables showed non existence of unit root/Stationarity (Figures 4.9 – 4.16) because T - statistic results in all tests had higher absolute value compared to critical value results (5% critical value which base on 95% confidence level).This can be interpreted that all sample data from both DSE and JSE have no unit roots after being differenced.

 

 

 

 

Figure 4.3                Graphical presentation of stationarity in DSE data

 

-100

-50

0

50

100

01Jan 2014

 

 

01June 2014

 

 

01Dec 2014

01Jan 2015

01June 2015

01Dec 2015

DATE

 

DEXR

DDSEI

Figure 4.4                Graphical presentation of stationarity in JSE data

 

-2

-1

0

1

2

01Jan 2014

 

01June 2014

 

 

01Dec 2014

 

01Jan 2015

 

01June 2015

 

DATE

DEXR

DJSEI

4.2.1.2.8              Description of the graphs in figures 4.3 and 4.4

The graphs above show that there are normal distributions in the time series with upwards and downwards movement around the mean, which can be explained that the means or variances of time series data for DSE and JSE are constant, a property of known as stationality.

4.2.2        Testing for Co-integration

Estimation of long-run relationship between non stationary variables should be based on co-integration method because with co-integration the unit root in variables cancel each other out and the resulting error is stationary.

Co-integration tests are used to ascertain the presence of potential long-run equilibrium relationship. Granger and Newbold (1974) noted that the regression results with non-stationary variables will be spurious (nonsensical). To avoid this, run the regression with the stationary variables (e.g. first differenced variables). Nevertheless, if the variables are non-stationary but are co-integrated, running a regression with first differenced variables may disturb the long-run information as the first differenced regression results is for short run relationship.

The researcher employed Johansen’s co-integration test to test for co-integration and the results are illustrated in the Tables 4.17 - 18 below.

Table.4.17               Johansen tests for co-integration on DSE data.

 

Maximum

 rank

 

 

Parms

 

 

LL

 

 

eigenvalue

 

Trace

statistic

5%

critical

value

0

38

-3870.559

0

13.7607*

15.41

1

41

-3864.9906

0.02279

2.6239

3.76

2

42

-3863.6787

0.00542

 

 

 

 

 

 

Table.4.18               Johansen tests for co-integration on JSE data

 

Maximum

rank

 

 

Parms

 

 

LL

 

 

eigenvalue

 

Trace

statistic

5%

Critical

value

0

30

-3281.2165

0

12.7060*

15.41

1

33

-3277.1816

0.01627

4.6364

3.76

2

34

-3274.8634

0.00938

 

 

 

4.2.2.1              Explanation for Johansen’s co-integration tests results in Tables 4.17 – 4.18

The null hypothesis found in 0 Maximum rank row; states that there is no co-integration amoung variable (Exchange rate and Stock price).

Decision guideline is that when trace statistic result is greater than critical value result you reject the null hypothesis but when the trace statistic result is smaller than critical value result you accept the null hypothesis.

According to the results of Johansen’s co-integration tests the trace statistic result for both tests, DSE and JSE (13.7607, 12.7060 respectively) are smaller than critical value result of 15.41 (Tables 4.17 and 4.18) then the null hypothesis is accepted. Therefore there are no co-integration amoung variables.

4.2.3        Granger causality test

Granger causality is a statistical concept of causality that is based on prediction. According to Granger causality, if event X happens before event Y, then it is possible that X is causing Y. However, it is not possible that Y is causing X.

X “Granger causes” Y if past values of X can help to explain Y. If Granger causality holds this does not guarantee that X causes Y. But, it suggests that X might be causing Y. Granger causality is only relevant with time series variables. Granger causality can be expressed in stationary variables using Autoregressive Distributed Lag (ADL) model and for Co-integrated variables with Error Correction Model because with co-integration the unit root in some variables cancel each other out and the resulting error is stationary Wickens and Breusch (1988).

By applying Granger causality the researcher is able to determine the direction and degree of causality between variables. The results are illustrated in the Tables 4.19 - 20 below.

VAR granger

Table.4.19               Granger causality Wald tests for DSE

Equation

Excluded

F

df

Prob > F

exr

dsei

1.4455

60

0.0246

exr

ALL

1.4455

60

0.0246

dsei

exr

1.395

60

0.0380

dsei

ALL

1.395

60

0.0380


 

Table.4.20               Granger causality Wald tests for JSE

Equation

Excluded

F

df

Prob > F

exr

dsei

1.5386

60

0.0104

exr

ALL

1.5386

60

0.0104

jsei

exr

1.808

60

0.0007

jsei

ALL

1.808

60

0.0007

 

4.2.3.1              Explanation for Granger causality tests results in Tables 4.19 and 4.20

The results from DSE and JSE above shows that exchange rate granger causes stock price because the F probability values for exchange rate are less than allowable error of 5% for the 95% confidence level, these values are 0.0380 for DSE and 0.0007 for JSE. On other hand stock price can also granger cause exchange rate because the F probability values for stock price are smaller than allowable error of 5% for the 95% confidence level the values are 0.0246 for DSE and 0.0104 for JSE. Therefore there is bidirectional significant relationship between Exchange rate and stock.

4.3              Data Analysis

After performing three statistical tests for Unit root , Co-integration and causality the researcher establishes the relationship between nominal exchange rate and stock price from regression analysis using data from Dar es Salaam Stock Exchange  and Johannesburg Stock Exchange. The regression analysis was done using Autoregressive Integrated Moving Average (ARIMA) model in order to take care of  multicollinearlty, autocorrelation of error term and heteroscedasticity problem of time series data to obtain more reliable estimates for the effect of the explanatory variable on the dependent variable.

The results are illustrated in the Tables 4.21 - 22 below

Table.4.21               Regression analysis for exchange rate and stock price using DSE data

ARMA

dsei

 

Coef.

 

Std. Err.

 

z

 

P>|z|

 

[95% Conf. Interval]

exr

.5544422

.0736707

7.53

0.000

.4100502

.6988342

_cons

1226.308

293.7532

4.17

0.000

650.5625

1802.054

 

 

 

 

Table.4.22               Regression analysis for exchange rate and stock price using JSE data

ARMA

jsei

 

Coef.

 

Std. Err.

 

z

 

P>|z|

 

[95% Conf. Interval]

exr

-1069

179.790

-5.95

0.000

-1421.382

-716.6182

_cons

62913.78

2927.724

21.49

0.000

57175.54

68652.01

 

4.3.1        Explanation for Regression analysis results in Tables 4.21 – 4.22

The regression analysis result shows both significant positive and negative relationships between Exchange rate (Independent variable) and stock price (Dependent variable) with P value 0.000 and standard error of 0.0736707 for DSE sample data also P value of 0.000 and standard error of 179.790 for JSE sample data. From the above results it can be interpreted that for case of Tanzania a one TSH positive change in exchange rate causes a positive change in Dar es Salaam Stock Exchange all share index by TSH 0.55 approximately or simply a depreciation of TSH causes an appreciation of stock price and vice versa. While for South Africa a one ZAR positive change in exchange rate causes a negative change in Johannesburg Stock Exchange all share index by ZAR 1069 or simply a depreciation of ZAR Causes a depreciation of stock price and vice versa.


 

CHAPTER FIVE

DISCUSSION OF FINDINGS

5.1              Introduction

This chapter provides the discussion of research results arising from data analysis and presentation of the research on the relationship between exchange rates and stock prices for the Dar es Salaam Stock Exchange and Johannesburg Stock Exchange. Data used for the research were daily records from DSE and JSE covering the period of January 2014 to December 2015. Exchange rates are represented by Tanzania shilling and South Africa Rand to United States of America dollar, the stock price by DSE and JSE indices. Analysis used software package named STATA and the models employed was Vector autoregressive Model (VAR) and Autoregressive Integrated Moving Average (ARIMA) model.

Two commonly known theoretical models for explaining the relationship between exchange rates and stock prices are flow oriented model and stock oriented model (portfolio balance approach).

Flow oriented model claims that causality flows from exchange market to stock market. It further claims that currency movements directly affect international competitiveness. In turn, currency has an effect on the balance of trade within the country. This affects the future cash flows or the stock prices of companies.

Portfolio balance approach on its side argues exchange rates, like all commodities, are determined by market mechanism (forces of demand and supply). A blooming stock market would attract capital flows, foreign investors and hence causes an increase in the demand of a country's currency and vice versa. As a result, rising stock prices are related to an appreciation in exchange rates.

The discussion of the results from analysis of relationship between exchange rate (Represented by Tanzania shilling and South Africa Randto USA Dollar) and stock price (Represented by DSE and JSE indices) have tried to explore the conformity to any of the two models above.

5.2              Exchange rate and stock price relationship in Tanzania and South Africa

The result generally explains that there is significant positive relationship between exchange rates and stock prices for DSE sample data, thus When the exchange rate between Tanzania shilling and US Dollar increase (or in other word when Tanzania shilling depreciate) the stock price also increases. On other hand there existed a significant negative relationship between exchange rates and stock prices for JSE sample data which means that when the exchange rate between South Africa Rand and US Dollar increase (or in other word when South Africa Rand depreciate) the stock price decreases.  This signifies that investors will be more likely to invest in Tanzania when Shilling depreciates against USA dollar and will also be likely to invest in South Africa when Rand appreciates against USA dollar.

5.2.1        Exchange rate and stock market price movements.

From the results of statistical tests and analysis there exist a significant positive and negative relationships between exchange rate movements and stock market prices for DSE and JSE respectively.

For both exchange markets (DSE and JSE) there exist a bidirectional relationship between the variables because at 95% confidence level, exchange rates and stock prices are significant to explain the relationship since their P values are less than 5% allowable error as you can read from the Granger causality Wald tests in chapter four above. However the type of the relationship that exists between the variables are different  in these two markets.From regression analysis in chapter four above DSE shows a postive relationship while JSEshows a negative relationship.

This seems to agree with both the flow oriented model approach that was described in Dornbusch and Fischer (1980) which claims that causality flows from exchange market to stock market and the stock-oriented model which opposes by claiming that causality flows from stock market to exchange market.

The flow oriented model approach  further claims that currency movements directly affect international competitiveness. In turn, currency has an effect on the balance of trade within the country. This affects the future cash flows or the stock prices of companies. Stock prices, normally interpreted as the present values of future cash flows of companies, react to exchange rate movements and form the relationship among future income, interest rate innovations, current investment and consumption decisions Dornbusch and Fischer   (1980).

The findings from empirical investigation by different researchers also support the results of the assertion under flow oriented model approach. Aggarwal and Schrim (1992) who investigated the relationship between Dollar exchange rates and stock market indices for the sample period 1974-1978, and results showed that there is positive correlation between stock market indices and Dollar exchange rates by applying simple regression model, they recorded that this relationship is stronger in short run compared to that in the long-run.

Khalid and Kawai (2003), claim that there exists a link between stock and currency markets. Furthermore, this link is argued to have propagated the Asian financial crisis in 1997. It is believed that the depreciation of the Thai currency led to the collapse of the stock market. Haji and Jianguo (2014) showed moderate positive relationship between the variables using data from Dar es Salaam Stock Exchange (DSE).

On other side stock-oriented model approach further explains that portfolio adjustments [movements in the foreign capital - inflows and outflows of foreign capital] occur whenever there is a movement in the stock prices. If stock prices are on the increase, they will invite more foreign capital and strengthen the home currency. On the other hand, a fall in the stock prices will result in reduced corporate wealth that lead to a reduction in the country’s wealth and a decline in exchange rate for home currency. Also empirical investigation by different researchers, such as Granger et al. (2000), Stav'arek (2005), Pan et al. (2007) support the assertion that causality flows from stock price to exchange rate by expressing that stock price is assumed to lead exchange rate with a negative correlation because a reduction in stock prices decreases domestic wealth, which cause to lower domestic money demand and interest rates because the decrease in domestic stock prices cause foreign investors to lower demand for domestic assets and domestic currency.

These movements in demand and supply of currencies lead capital outflows and the depreciation of domestic currency conversely, when stock prices go up, foreign investors become keen to invest in a country’s stocks. As a result, they will get benefit from international diversification. This situation will cause capital inflows and currency appreciation.

5.2.2        Exchange rate and stock market price relationship extent.

Grander causality test and regression analysis were used to determine the direction and degree of causality between variables. The results showed a bidirectional significant relationship between Exchange rate and stock, however DSE reflected a positive relationship which can be interpreted that when Tanzania shilling depreciates more investors will be willing to invest in Tanzania as demand for investments increase the stock prices will also increase to reflect the law of demand and supply. But JSE reflected a negative relationship which can be interpreted that appreciation of South Africa Rand makes investors more willing to invest in South Africa this may prompted by many reasons including the stability of Rand as movements in Rand is very small compared to Shilling also JSE being a giant security market in Africa it has attracted trust from investors especially foreign investors thus even in the appreciation of Rand they will still be willing to invest in South Africa.

The results also seem to agree with The flow oriented model approach that was described in Dornbusch and Fischer (1980) which claims that causality flows from exchange market to stock market and empirical research by Abdallah and Murinde (1997) who found a unidirectional causality from exchange rates to stock returns in all the sampled countries, except for the Philippines. The study focused on emerging financial markets of India, Korea, Pakistan and the Philippines. Soenen and Hennigar (1988) revealed a significant negative relationship between the variables using monthly data of the USA. dollar effective exchange rate and USA stock market index in the period 1980-1986.

Pan et al. (2007) used daily market data to study the causal relationship between stock prices and exchange rates and found that the exchange rates Granger-cause stock prices with less significant causal relations from stock prices to exchange rate.

Again the results were also in agreement with the stock-oriented model approach which explains that portfolio adjustments [movements in the foreign capital - inflows and outflows of foreign capital] occur whenever there is a movement in the stock prices. If stock prices are on the increase, they will invite more foreign capital and strengthen the home currency. On the other hand, a fall in the stock prices will result in reduced corporate wealth that lead to a reduction in the country’s wealth and a decline in exchange rate for home currency and empirical investigation by different researchers, such as Granger et al. (2000), Stav'arek (2005), Pan et al. (2007).

5.2.3         Influence of the foreign exchange market operations on stock markets in Tanzania and South Africa.

From the statistical tests and regression analysis in chapter four of this study there exist a strong relationship between foreign exchange market and stock market in Tanzania and South Africa as well.

The finding indicated that foreign exchange market and stock market have strong bidirectional relationships. That means exchange rate has a great impact on the participation of foreign investors in the local stock market because the ultimate return to them may be eaten away by the exchange rates fluctuations. A stable currency ensures that the market vibrancy is maintained as the investors continue with their trading. High stock market returns volatility suggests high risks as investors can not estimate precisely how their investment is likely to be at any given point in time.

From the findings, a high exchange rate movement was accompanied by relative high stock market price volatility for DSE. This indicates that at times when the exchange rate movement is high, the foreign investors are reluctant to participate in the local market hence the result of high stock market return volatility reflecting the greater risks they stand to assume at this time. In addition, in times of low exchange rate movement is accompanied by a low volatility in the stock market returns. On the other hand it also indicates that movement in stock price has effect on exchange rate since movement in stock price may attract or discourage investors, for example when stock price is high more investors will be attracted to make investment provided that exchange rate will increase (Tanzania Shilling depreciates) and vice versa. The finding from JSE indicates that exchange rate movement was accompanied by a relative stock market price volatility however the direction was in opposite to that of DSE.

The results of this study agrees with both Flow oriented model approach and Stock oriented model approach which has been described in part 5.2.1-Exchange rate and stock price movements and part 5.2.2-The extent of exchange rate and stock market price relationship and empirical evidences in the same parties since the foreign exchange market trades by exchange rate and the stock market trades by stock price.

5.3              Overall relationship between variables

In this study I have explored the association between two important component of an economy called stock price and exchange rate. First of all, I applied unit root test to find the stationarity of data series. The results showed that all the data series of the variables were non stationary and after differencing they become stationary. Then I applied Johansen procedure to test for the possibility of a co-integrating relationship. Result showed that there was no co-integrating relationship between stock prices and exchange rates. That means there were no long-term co-movement between the variables and none of the variables is predictable on the basis of past values of other variable. In the absence of any co-integrating relationship between the variables I moved to standard Granger causality test to find out any causal relationship between stock prices and exchange rates. Results showed that exchange rates Granger cause stock prices also stock prices do Granger cause exchange rates, so there is bidirectional relationship between exchange rates and stock prices. These results seem to agree with the flow oriented model which claims that causality flows from exchange market to stock market and the stock oriented model which states the opposite also empirical evidences from different researchers as pointed out in parties 5.2.1and 5.2.2. Finally I run the regression to determine the direction of the relationship; the results showed that that exchange rates (or Foreign exchange market) have positive and negative influence on stock prices (or Stock market) for DSE and JSE respectively.


 

CHAPTER SIX

CONCLUSIONS AND RECOMMENDATIONS

6.1              Introduction

This chapter presents the summary of the research results as indicated and explained in chapter five. It gives conclusion and recommendations with regard the link between stock market and exchange rates in the financial market risk management.

Price of a company's stocks is normally used as a signal of the overall strength and health of a company. Commonly, if a company's stock price has sustainable growth over time, the company and its management are well thought-out to be doing a good job. If everybody is glad and the company is doing well, as indicated by its stock price, then management probably will witness a raise and there is minimum risk that they will be fired. The management of a company is at a high risk of being sacked from the company if they are incapable of generating good returns for investors.

This is done by the Boards of Directors who are selected by the stockholders, and are responsible for the hiring and firing of the executives. If the stock price persistently drops, management probably will be fired.

Reward is central reason for the management of a company to maintain the stock price as high as possible. The majority of executives in a company get part of their rewards through stock options, which offers the manager the right to acquire stocks in the company at a price set below market price. Most of the time, the price of these options are set below market price based on the most recent price of the company's stocks when the option is granted to the manager. Sooner or later, for this option to increase in value and improve the benefit for the manager, the price of the stock has to rise. That is why the provision of stock options to managers is well thought-out a superior way to align the interests of the executives and the stockholders, as both will need to see stock prices raise persistently.

Other reasons as to why a company is concerned with its price involve the prevention of a takeover by other strong companies. When a company observes that its stock price drops the possibility of a takeover rises since the company is relatively cheaper. If a takeover transpires, the management of the company is normally kicked out, something which again goes back to management desire of protecting its own interests as no one would like to be fired.

Since exchange rate affect stock price then determining relationship between stock price and exchange rate is important in order to hedge portfolios for investors or make risk management decisions for corporate managers.

6.2              Conclusion

In this study, I have explored the association between two important component of an economy named as stock price and exchange rate. First of all, we applied unit root test to find the stationarity of data series. The results showed that all the data series of the variables were non stationary or had unit root and after differencing they became stationary. I then applied Johansen procedure to test for the possibility of a cointegrating relationship. Result showed that there was no cointegrating relationship between stock price and exchange rate. That means there was no long-term co-movement between the variables and none of the variables is predictable on the basis of past values of other variable. In the absence of any co-integrating relationship between the variables I move to standard Granger causality test to find out any causal relationship between stock prices and exchange rates. Results showed a bidirectional relationship between the variables.

There is a common belief among the investors that there is an association between exchange rate and stock price, this belief is cemented by different researcher’s results including this study and conforms to the flow and stock oriented model approach approaches.

6.3              Recommendations

From the Introduction and conclusions above, this study recommends the following. Firstly the policy makers need to factor the effects of exchange rate movement on the performance of the stock exchange. This is because their policies may affect the performance despite their good intention to correct the deteriorating situations in the economy. The Bank of Tanzania and South African Reserve Bank need to maintain a stable foreign currency exchange if the activities at the Stock exchange are to be promoted. This is because huge exchange rate movements distort the trends of performance at the stock market leaving investors guessing the next cause of action because they may not be able to estimate with certainty the future state of the economy.

There is also a great need of the efficiency of information gathering and the dissemination of the same to the financial market. This will greatly reduce information asymmetry thus the financial market regulators are able to assess the nature of the activities in the market at any given time.

The study further recommends that DSE and JSE have to develop a foreign currency denominated stocks so as to reduce the effects of exchange rate movement on the returns of the foreign investors. This would motivate foreign investors to invest more hence boost operations of the market.

6.4              Limitations of the Study

This research is limited in terms of scope by high cost involved in accessing online official records in many SADC member state’s stock markets since financial resource was not adequate and thus prompted the researcher to conduct a research on two countries only (Tanzania and South Africa) as their official records were cheaply accessible. Therefore further future research about the relationship between exchange rates and stock prices in other African countries need to be done but also more research need to be done in Tanzania since currently there exist only twenty one listed companies in the Dar es Salaam stock exchange. There is need for more companies to be listed on the stock exchange in order to increase the sample size and get a sample that is real representative of any stock market in Africa.

Despite of this limitation, however it is expected that the research finding will be adequate enough to explain the relationship between exchange rates and stock prices.

6.5              Suggestions for further Studies

From the findings presented in this study, two areas are identified for further future research.

First, Research to determine the effects of variations in the exchange rate on the different listed companies in the Dar es Salaam stock exchange. These companies include the importing, exporting and domestic companies. This would bring proper understanding on the detailed evidence if these different companies were distinguishable. The same applies to South Africa researches for the exchange rates influence on stock prices rate the different category of listed companies such as Mining companies, Importing, exporting companies and the 40 top companies at JSE.

Second, Research to determine the relationship between exchange rates and stock prices for listed foreign companies only in DSE and JSE, so that investigator can employ foreign involvement measure to get sample companies that are more likely to experience  currency risk.


 

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APPENDICES

       I.            Appendix One: Secondary data used for the study.

DSE

At Level

At first Difference

JSE

At Level

 

               At first Difference

 

DATE

EXR

DSEI

DEXR

DDSEI

DATE

EXR

JSEI

DEXR

DSEI

02/01/2014

1578.57

1870.18

   

02/01/2014

10.4935

46589.70

   

03/01/2014

1575.39

1876.07

-3.18

5.89

03/01/2014

10.6497

46206.09

0.1562

-383.61

06/01/2014

1579.18

1895.04

3.79

18.97

06/01/2014

10.6821

45897.44

0.0324

-308.65

07/01/2014

1584.83

1903.12

5.65

8.08

07/01/2014

10.6088

45867.52

-0.0733

-29.92

08/01/2014

1594.93

1913.39

10.1

10.27

08/01/2014

10.6378

45696.97

0.029

-170.55

09/01/2014

1603.5

1919.73

8.57

6.34

09/01/2014

10.7962

45444.59

0.1584

-252.38

10/01/2014

1604.91

1916.67

1.41

-3.06

10/01/2014

10.7975

45548.15

0.0013

103.56

15/01/2014

1608.06

1904.2

3.15

-12.47

13/01/2014

10.6444

46030.25

-0.1531

482.10

16/01/2014

1607.27

1898.5

-0.79

-5.7

14/01/2014

10.5053

45982.07

-0.1391

-48.18

17/01/2014

1607.27

1903.34

0

4.84

15/01/2014

10.877

46442.45

0.3717

460.38

20/01/2014

1604.35

1909.33

-2.92

5.99

16/01/2014

10.9059

46728.46

0.0289

286.01

21/01/2014

1604.95

1911.19

0.6

1.86

17/01/2014

10.8818

46675.88

-0.0241

-52.58

22/01/2014

1605.61

1912.9

0.66

1.71

20/01/2014

10.865

46934.59

-0.0168

258.71

23/01/2014

1605.99

1910.91

0.38

-1.99

21/01/2014

10.859

46889.64

-0.006

-44.95

24/01/2014

1607.25

1949.14

1.26

38.23

22/01/2014

10.8502

47000.95

-0.0088

111.31

27/01/2014

1607.61

1940.84

0.36

-8.3

23/01/2014

10.992

47045.44

0.1418

44.49

28/01/2014

1615.79

1934.13

8.18

-6.71

24/01/2014

11.0886

46462.14

0.0966

-583.30

29/01/2014

1615.02

1928.25

-0.77

-5.88

27/01/2014

11.1304

45750.70

0.0418

-711.44

30/01/2014

1613.86

1932.34

-1.16

4.09

28/01/2014

11.059

45724.70

-0.0714

-26.00

31/01/2014

1614.43

1923.57

0.57

-8.77

29/01/2014

10.9279

45564.62

-0.1311

-160.08

03/02/2014

1616.75

1870.09

2.32

-53.48

30/01/2014

11.2877

45178.25

0.3598

-386.37

04/02/2014

1616.34

1885.17

-0.41

15.08

31/01/2014

11.206

45132.10

-0.0817

-46.15

05/02/2014

1616.87

1889.47

0.53

4.3

03/02/2014

11.1878

44956.91

-0.0182

-175.19

06/02/2014

1616.09

1890.41

-0.78

0.94

04/02/2014

11.2603

44451.57

0.0725

-505.34

07/02/2014

1616.33

1881.24

0.24

-9.17

05/02/2014

11.1528

44477.18

-0.1075

25.61

10/02/2014

1616.33

1879.25

0

-1.99

06/02/2014

11.1141

44985.34

-0.0387

508.16

11/02/2014

1615.73

1894.82

-0.6

15.57

07/02/2014

11.0391

45340.76

-0.075

355.42

12/02/2014

1616.9

1901.28

1.17

6.46

10/02/2014

11.0422

45920.87

0.0031

580.11

13/02/2014

1617.01

1906.29

0.11

5.01

11/02/2014

11.11

45866.02

0.0678

-54.85

14/02/2014

1617.63

1938.39

0.62

32.1

12/02/2014

10.9483

46425.11

-0.1617

559.09

17/02/2014

1616.98

1930.85

-0.65

-7.54

13/02/2014

11.0595

46251.77

0.1112

-173.34

18/02/2014

1617.09

1906.78

0.11

-24.07

14/02/2014

11.0141

46628.74

-0.0454

376.97

19/02/2014

1618.39

1882.84

1.3

-23.94

17/02/2014

10.8632

47027.54

-0.1509

398.80

20/02/2014

1619.22

1891.13

0.83

8.29

18/02/2014

10.8572

47112.43

-0.006

84.89

21/02/2014

1619.62

1901.44

0.4

10.31

19/02/2014

10.8745

47438.31

0.0173

325.88

24/02/2014

1619.24

1931.7

-0.38

30.26

20/02/2014

11.0713

47149.35

0.1968

-288.96

25/02/2014

1618.88

1967.26

-0.36

35.56

21/02/2014

10.9979

47452.24

-0.0734

302.89

26/02/2014

1619.33

1978.7

0.45

11.44

24/02/2014

10.953

47394.00

-0.0449

-58.24

27/02/2014

1618.36

1970.95

-0.97

-7.75

25/02/2014

10.8428

46956.75

-0.1102

-437.25

28/02/2014

1619.5

1995.32

1.14

24.37

26/02/2014

10.7201

47017.19

-0.1227

60.44

03/03/2014

1620.4

1972.99

0.9

-22.33

27/02/2014

10.8383

47049.79

0.1182

32.60

04/03/2014

1620.62

1991.71

0.22

18.72

28/02/2014

10.7217

47328.92

-0.1166

279.13

05/03/2014

1621.72

1976.45

1.1

-15.26

02/03/2014

10.5786

48405.90

-0.1431

1076.98

06/03/2014

1621.8

1993.57

0.08

17.12

03/03/2014

10.8069

47138.32

0.2283

-1267.58

07/03/2014

1621.49

2018.97

-0.31

25.4

04/03/2014

10.8501

47602.69

0.0432

464.37

10/03/2014

1623.85

1999.76

2.36

-19.21

05/03/2014

10.7709

47514.80

-0.0792

-87.89

11/03/2014

1623.7

2010.98

-0.15

11.22

06/03/2014

10.7066

47819.53

-0.0643

304.73

12/03/2014

1623.25

1943.88

-0.45

-67.1

07/03/2014

10.621

47786.77

-0.0856

-32.76

13/03/2014

1623.75

1950.04

0.5

6.16

10/03/2014

10.7654

47322.46

0.1444

-464.31

14/03/2014

1623.87

1949.19

0.12

-0.85

11/03/2014

10.7568

47612.81

-0.0086

290.35

17/03/2014

1623.91

1947.33

0.04

-1.86

12/03/2014

10.8714

47188.84

0.1146

-423.97

18/03/2014

1625.49

1965.1

1.58

17.77

13/03/2014

10.7895

46825.52

-0.0819

-363.32

19/03/2014

1628.42

1958.19

2.93

-6.91

14/03/2014

10.8224

46412.40

0.0329

-413.12

20/03/2014

1629.24

1964.74

0.82

6.55

17/03/2014

10.6878

46816.65

-0.1346

404.25

21/03/2014

1630.16

1962.54

0.92

-2.2

18/03/2014

10.7706

47059.19

0.0828

242.54

24/03/2014

1629.7

1959.62

-0.46

-2.92

19/03/2014

10.7441

46666.54

-0.0265

-392.65

26/03/2014

1630.17

1963.62

0.47

4

20/03/2014

10.8631

46508.27

0.119

-158.27

27/03/2014

1630.01

1962.42

-0.16

-1.2

24/03/2014

10.8919

46875.37

0.0288

367.10

28/03/2014

1631.57

1952.83

1.56

-9.59

25/03/2014

10.8334

47387.56

-0.0585

512.19

31/03/2014

1630.7

1958.09

-0.87

5.26

26/03/2014

10.7546

47652.89

-0.0788

265.33

01/04/2014

1629.6

1980.86

-1.1

22.77

27/03/2014

10.6913

47380.98

-0.0633

-271.91

02/04/2014

1629.27

1976.22

-0.33

-4.64

28/03/2014

10.6013

47930.03

-0.09

549.05