Contents CHAPTER 1 5 1

Contents
CHAPTER 1 5
1.1 Introduction 5
1.2 Background 6
1.3 Statement of the Problem 7
1.4 Research Questions 8
1.5 Research Aims and Objectives 9
1.5.1 General Objective 9
1.5.2 Specific Objectives 9
1.5 Significance of the study 9
1.6 Scope of the study 9
1.7 The outline of the chapters 9
CHAPTER 2 11
2 Introduction 11
2.1 Financial Markets 11
2.1.1 Money Markets 12
2.1.2 Capital Markets 13
2.1.3 Primary Markets 13
2.1.4 Secondary Market 14
2.2 Exchange Rates 14
2.2.1 Fixed exchange rate 15
2.2.2 Floating rate 15
2.2.3 Managed exchange rate 15
2.3 Share Index 16
2.4 The Role of Stock Markets 17
2.5 Review of Similar Studies 19
2.6 World Stock Exchanges Performance and Review 21
2.7 Africa Stock Exchanges Performance and Review 22
2.8 Lusaka Stock Exchange 23
2.9 Lessons learnt 24
2.10 Summary 27
CHAPTER 3 28
3 Introduction 28
3.1 Theoretical Framework 28
3.1.1 Goods Market Approach 28
3.1.2 Portfolio Balance Approach 32
3.2 Conceptual Framework 33
3.3 Interpretation of the Conceptual Framework and Hypotheses Development 34
3.4 Operationalization of the Concepts 34
3.4.1 Share Index 34
3.4.2 Exchange Rates 35
3.4.3 Foreign Direct Investment 35
3.4.4 Government Bonds 36
3.5 Summary 36
CHAPTER 4 37
4 Introduction 37
4.1 Research Design 37
4.2 Model Specification 37
4.2.1 The Use and Application of Autoregressive Distributive Lay Model (ARDL) 38
4.3 Sampling Design 39
4.4 Data Collection and Analysis 40
4.5 Validity, Reliability and Practicality 40
4.6 Study Variables 41
4.7 Chapter Summary 41
CHAPTER 5 42
5.1 Introduction 42
5.2 Performance of the Lusaka Stock Exchange 42
5.3 Exchange Rate Performance 43
5.4 Trending of the Variables 43
5.4.1 Exchange Rates 43
5.4.2 All Share Index 43
5.4.3 Secondary Market GRZ Bonds growth rate 44
5.4.4 Foreign Participation on LuSE growth rate 45
5.5 Unit Root Test 45
5.5.1 Unit Root Test on All share index (ASI) 46
5.5.2 Unit Root Test on Secondary market GRZ bond growth is stationary at level (GRZB). 47
5.5.3 Unit Root Test on Foreign Participation on the LUSE 47
5.6 Correlation Matrix 48
5.7 Model 1: Foreign Exchange Growth Rate Verses All Share Index Growth Rate 48
5.7.2 Estimation of Regression Square 49
5.7.3 Q-Statistics Probabilities for All Share Index on the LuSE 50
5.7.4 Stability tests 50
5.7.5 Model Selection Summary; Criteria Graph 51
5.7.6 Bound Tests 52
5.7.7 Error Correction Model (ECM) Estimation Results 54
5.7.8 Diagnostic tests 54
5.7.9 Residual Diagnostics 55
5.7.10 Heteroskedasticity Test 55
5.7.11 Test of Independence of Residuals 55
5.8 MODEL2: Foreign Exchange Growth Rate Verses Secondary Market GRZ Bond Growth Rate 56
5.8.2 Estimation of Regression Square 57
5.8.3 Q-Statistics Probabilities for Secondary Market GRZ Bond 58
5.8.4 Stability tests 58
5.8.5 Model Selection Summary; Criteria Graph 59
5.8.6 Bound Tests 59
5.8.7 Error Correction Model (ECM) Estimation Results 61
5.8.8 Diagnostic tests 62
5.8.9 Residual Diagnostics 62
5.8.10 Heteroskedasticity Test 63
5.8.11 Test of Independence of Residuals 63
5.9 MODEL3: Foreign Exchange Growth Rate verses Foreign Participation on the LuSE Growth Rate 63
5.9.2 Estimation of Regression Square 64
5.9.3 Q-Statistics Probabilities for All Share Index on the LuSE 65
5.9.4 Stability tests 66
5.9.5 Model Selection Summary; Criteria Graph 66
5.9.6 Bound Tests 67
5.9.7 Error Correction Model (ECM) Estimation Results 69
5.9.8 Diagnostic tests 70
5.9.9 Residual Diagnostics 70
5.9.10 Heteroskedasticity Test: 70
5.8.11 Test of Independence of Residuals 71
5.10 Discussion 73
CHAPTER 6 76
6.0 Introduction 76
6.1 Summary of Research Findings 76
6.2 Recommendations 78
6.2.1 Wealth effect 78
6.2.2 Effect on pensions 78
6.2.3 Confidence 78
6.2.4 Investment 79
6.2.5 Exit opportunities to entrepreneurs 79
6.3 Macroeconomic Stability 79
6.4 Banking Sector Development 79
6.5 Institutional Quality 79
6.6 Suggestions for future research 80
6.7 Summary 80

CHAPTER 1

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BACKGROUND OF THE STUDY
1.1 Introduction
Before economic reforms of the 1990s, the financial system was repressed in Zambia. Exchange rates, credit allocation and interest rates were strictly regulated. Under this paradigm of administrative controls, the financial system failed to flourish. The financial system remained under-developed, while lending patterns were inefficient. With inflation increasing faster than nominal interest rates, real interest rates drastically declined leading to low savings and investment and capital flight. Macroeconomic performance also deteriorated as large negative real interest rates induced lower allocative efficiency and growth rates.
The extensive economic reforms of 1991 saw the Zambian government privatize many state owned industries, and maintain positive real exchange rates. Exchange controls were also eliminated and free market principles endorsed. In 1993, with the preparatory technical assistance and support from the International Finance Corporation (IFC) and the World Bank, the Lusaka Stock Exchange (LuSE) was established, (International Finance Corporation Annual Report, 1993). The Exchange opened on 21 February 1994. The formation of the LuSE was part of the government’s economic reform program aimed at developing the financial and capital market in order to support and enhance private sector initiative. It was also expected to attract foreign portfolio investment through recognition of Zambia and the region as an emerging capital market with potentially high investment returns.
Despite the economic reforms, since 1990’s the Zambian currency (Kwacha) has varied considerably against major currencies such the US Dollar and the British Sterling. This variation in the exchange rates introduces uncertainty which may impact negatively on the economy. In addition, these fluctuations in the exchange rates tend to have an effect on prices of commodities which consequently affect demand and consumption.
According to Golaka and Samanta (2003), the Asian crisis of 1997-98 has laid a strong basis to establish linkage between stock prices and exchange rates. During the crisis period, the world noticed that the emerging markets collapsed due to substantial depreciation of exchange rates (in terms of US$) as well as dramatic fall in the stock prices. This is still important because of large cross border movement of funds due to portfolio investment and not due to actual trade flows, though trade flows may still have some impact on stock prices of the companies whose main sources of revenue comes from foreign exchange. Literature suggest that variations in exchange rates affect the competitive position of a firm as fluctuations in exchange rate affects the value of the earnings and its cost of capital as many companies borrow in foreign currencies to fund their operations and hence its stock price.
Depreciation of the local currency makes exporting goods attractive and leads to an increase in foreign demand and hence revenue for the firm and its value would appreciate and hence the stock prices. On the other hand, an appreciation of the local currency decreases profits for an exporting firm because it leads to a decrease in foreign demand of its products. However, the sensitivity of the value of an importing firm to exchange rate changes is just the opposite to that of an exporting firm. In addition, fluctuations in exchange rates affect a firm’s transaction exposure. That is, exchange rate movements also affect the value of a firm’s future payables (or receivables) denominated in foreign currency. This is an indication that exchange rate volatility have real economic costs that affect price stability, firm profitability and a country’s stability.
Zambia is an example of a small open economy which engages in international trade with several countries and hence it’s susceptibility to foreign exchange rate volatility. The openness of a country’s economy is considered as a cause of volatility of its markets. Thus this study looks at the relationship that exist between foreign exchange movements and the Lusaka Stock Exchange all share index.
1.2 Background
Financial markets play a very important role in the foundation of a stable and efficient financial system of an economy. The performance of the stock market can reflect the overall performance of the country’s economy. However, numerous domestic and international factors directly or indirectly affect the performance of the stock market. Such factors can be political, economic or company specific. Zambia’s economic position, that is, being an open economy requires that we
understand how some of these factors mentioned above relate to each other and how they influence the country’s economy.
Source: Lusaka Stock Exchange statistics 2016
From the table above it is observed that the Lusaka Stock Exchange has had low trade volumes from 2000 through to 2016 with the exception for 2001. This mainly arises when participants on the stock markets purchase stock to hold. When investors purchase to hold, it gives rise to liquidity challenges which in turn affects the share price and ultimately the market capitalization. Further, market capitalisation is US dollar denominated which introduces exchange rates in order for it to be denominated in Kwacha terms. Therefore, specific to this research, we study whether the relationship between the Lusaka Stock Exchange all share index and the exchange rates exits and the nature of that relationship.
1.3 Statement of the Problem
The economic reform of the 1990’s created opportunities for companies to grow through their participation on the stock exchange. On the other hand, the stock exchange was and is still perceived to play an important role in accelerating economic growth and development. However, from inception of the Lusaka Stock Exchange in 1994 only twenty three (23) companies were listed and eleven (11) quoted at the time of this study. The Kwacha has also continued to fluctuate against major currencies and (Dornbusch R. and Fischer S., 1980) argues that changes in exchange rates affect the competitiveness of multinational firms and consequently their earnings and stock prices. When happens, local investors have to choose between investing on stock exchange and investing in fixed income securities because of the inverse nature of the relationship that exits between exchange and interest rates. Their choice is influenced by the return they get from investing in either.
On the other hand, the continued depreciation of the Kwacha ranging from K3.11 to K10.3 against the US dollar, i.e. from 2000 through to 2016 has had an effect on the level of foreign direct investment on the Lusaka Stock Exchange. Golaka and Samanta (2003) explain that movements in stock prices may influence exchange rates and money demand because investors’ wealth and liquidity demand could depend on the performance of the stock market and that like all commodities, exchange rates are determined by market mechanism, i.e., the demand and supply condition. A blooming stock market would attract capital flows from foreign investors, which may cause an increase in the demand for a country’s currency. The reverse would happen in case of falling stock prices where the investors would try to sell their stocks to avoid further losses and would convert their money into foreign currency to move out of the country. There would be demand for foreign currency in exchange of local currency and it would lead depreciation of local currency.
Since LuSE is perceived to play a critical role in Zambia’s economic growth and development, it is important to investigate the factors that are affecting the LuSE’s ability to contribute positively to Zambia’s economy, owing to the fact that it is the major capital market player in Zambia.
1.4 Research Questions
a) What are the factors affecting Lusaka Stock Exchange performance?
b) Is there a relationship between the exchange rates and the Lusaka Stock Exchange all share index?
c) What kind of relationship exists between exchange rates and the Lusaka Stock Exchange all share index?
d) How does this relationship impact investors’ decisions?

1.5 Research Aims and Objectives
1.5.1 General Objective
The objective of this research is to establish the relationship that exits between the Lusaka Stock Exchange (LuSE) all share index (ASI) and the changes in exchange rates
1.5.2 Specific Objectives
a) To establish the relationship between all share index and exchange rates
b) To establish whether the volatility of exchange rates affects the all share index.
c) To establish whether there is a positive, negative or no relationship between variables.
d) To give practical implications
1.5 Significance of the study
The study highlights the role that the government plays in order to establish efficiency capital markets. It will help in in improving the knowledge on the relationship of exchange rates and the all share index of the Lusaka Stock Exchange as there is very little empirical research done on the topic. The study will also aim to provide suggestions on improving capital market efficiency as this is critical for economic development and growth.
1.6 Scope of the study
The study focuses on the performance of the Lusaka Stock Exchange all and share index in relation to exchange rate movements in order to establish the subsisting relationship using information from Lusaka stock exchange, Bank of Zambia, Central Statistical Office and Zambia Development Agency. The study will cover a period from 2000 to 2016.
1.7 The outline of the chapters
The study comprises of six chapters as follows:
Chapter one: This chapter covers the introduction of the study and the background. It outlines the research questions and objectives, profile of Lusaka stock Exchange and statement of the problem. It also includes the scope, significance of the study and chapter synthesis of the whole report.
Chapter two: Provides evidence of what other researchers have done on the topic through a review of literature.
Chapter three: provides the theoretical and conceptual framework and explains the relationship between exchange rates and the Lusaka Stock Exchange all share index. It shows the relationship between the variables, hypothesis, and operationalization of concepts.
Chapter four: The chapter covers the method used to collect data for the research as well as the research design, time frame for the study and data collection instruments.
Chapter five: this chapter presents data analysis and discusses the research finding.
Chapter six: provides conclusion and recommendations of the research.

CHAPTER 2

LITERATURE REVIEW
2 Introduction
Chapter one provided the background of the study, the statement of the problem, the research questions and objectives, scope of the research, and the significance of the study, key concepts and also outlined the chapters.
This chapter reviews the literature on stock markets in relation to exchange rates around the World, Africa and Zambia and further presents the lessons learnt from the review process.
2.1 Financial Markets
Kimberly A. (2016) defines financial markets as “where traders buy and sell stocks, bonds, derivatives, foreign exchange and commodities. These markets are where businesses go to raise cash to grow, companies reduce risks, and investors make money,” Saunders A. and Cornett M. (2012) refer to them as structures through which funds flow. Financial markets bring buyers and sellers together to trade in financial assets such as stocks, bonds, commodities, derivatives and currencies. The purpose of a financial market is to set prices for global trade, raise capital and transfer liquidity and risk.
The prices are determined by pure supply and demand principles. Markets work by placing the two counterparts, buyers and sellers, at one place so they can find each other easily, thus facilitating the deal between them. Financial markets may be viewed as channels through which flow loanable funds directed from a supplier who has an excess of assets toward a demander who experiences a deficit of funds Binary Tribune (2017).
From the above literature suggests that financial markets must to able to facilitate movement of loanable funds from suppliers to demanders in order to stimulate economic activity as well as transferring liquidity risk. This is achieved through financial systems which ensure that the rights and interests of the parties involved are safe guarded.
Although there are many components to a financial market, they can be distinguished along two major dimensions namely: (i) money markets and (ii) capital markets.
2.1.1 Money Markets
Saunders A. and Cornett M. (2012), define money markets are markets that trade in debt securities or instruments with maturities of one year or less. In money markets economic agents with short-term excess supplies of funds can lend funds to economic agents with short-term needs or shortages of funds. The short-term nature of these instruments means that fluctuations in their prices in the secondary markets in which they trade is usually small. Thus most money markets are said to be over the counter.
According to Kristina Z. (2016), the money market is often accessed alongside the capital markets. While investors are willing to take on more risk and have patience to invest in capital markets, money markets are a good place to “park” funds that are needed in a shorter time period, usually one year or less. Institutions operating in money markets are central banks, commercial banks and acceptance houses, among others.
Randall D. (2012), re-emphasises that these markets are described as “money markets” because the assets that are bought and sold are short term-with maturities ranging from a day to a year—and normally are easily convertible into cash. Money markets include markets for such instruments as bank accounts, including term certificates of deposit; interbank loans (loans between banks); money market mutual funds; commercial paper; Treasury bills; and securities lending and repurchase agreements (repos).
Money markets provide a variety of functions for either individual, corporate or government entities. Liquidity is often the main purpose for accessing money markets. When short-term debt is issued, it is often for the purpose of covering operating expenses or working capital for a company or government and not for capital improvements or large scale projects. Companies may want to invest funds overnight and look to the money market to accomplish this, or they may need to cover payroll and look to the money market to help. The money market plays a key role in ensuring companies and governments maintain the appropriate level of liquidity on a daily basis, without falling short and needing a more expensive loan or without holding excess funds and missing the opportunity of gaining interest on funds.

2.1.2 Capital Markets
The other component of the financial market is the capital market. A capital market is a financial market in which long-term debt or equity-backed securities are bought and sold. Capital markets are defined as markets in which money is provided for periods longer than a year. Capital markets channel the wealth of savers to those who can put it to long-term productive use, such as companies or governments making long-term investments Principles of Macroeconomics.
According to the NASDAQ website (http://www.nasdaq.com/investing/glossary/c/capital-market) capital markets are referred to as the market for trading long-term debt instruments (those that mature in more than one year). Further, NASDAQ re-emphasises that this where capital is raised and that more recently, the capital markets is being used in a more general context to refer to the market for stocks, bonds, derivatives and other investments.
On the other Frank F. and Pamela D. (2009), define capital markets as the sector of the financial market where long term financial instruments issued by corporations and government trade. Further, they state that long term debt refers to a financial instrument with an original maturity greater than one year and perpetual securities.
Capital markets are further classified as (i) Primary Markets and (ii) Secondary Market.
2.1.3 Primary Markets
In the primary market, investors buy securities directly from the company issuing them. When a company publicly sells new stocks and bonds for the first time, it does so on the primary capital market. When investors purchase securities on the primary capital market, the company offering the securities has already hired an underwriting firm to review the offering and created a prospectus outlining the price and other details of the securities to be issued.
Companies issuing securities via the primary capital market hire investment bankers to obtain commitments from large institutional investors to purchase the securities when first offered. Small investors are not often able to purchase securities at this point, because the company and its investment bankers seek to sell all of the available securities in a short period of time to meet the required volume and must focus on marketing the sale to large investors who can buy more securities at once (http://www.investopedia.com)

2.1.4 Secondary Market
Frank F. and Pamela D. (2009), defines the secondary market as one in which financial instruments are resold among investors. No new capital is raised and the issuer of the security does not benefit directly from the sale. They add that trading takes place among investors. Investors who buy and sell securities on the secondary markets may obtain services of the stock brokers, individuals who buy or sell shares for their clients.

The capital market plays an important role in mobilising resources, and diverting them into productive channels. In this way, it facilitates and promotes the process of economic growth in the country. Various functions and significance of capital market include promoting stability in security prices, link between savers and investors, encouraging saving, encouraging investment as well as promoting growth.
2.2 Exchange Rates
The exchange rate plays an important role in a country’s trade performance. Whether determined by exogenous shocks or by policy, the relative valuations of currencies and their volatility often have important repercussions on international trade, the balance of payments and overall economic performance according to Alessandro N. (2013).
The Investopedia website, (http://www.investopedia.com/terms/e/exchangerate.asp) defines an exchange rate as the price of a nation’s currency in terms of another currency. An exchange rate thus has two components, the domestic currency and a foreign currency, and can be quoted either directly or indirectly. Saunders A. and Cornett M. (2012), on the other hand defines an exchange rate as a price at which one currency (e.g., the Zambia Kwacha) can be exchanged for another currency (e.g., the U.S. dollar). An exchange rate that does not have the domestic currency as one of the two currency components is known as a cross currency, or cross rate and exchange rates are determined in the foreign exchange market, Marc L. (2005).
Exchange rates are determined by demand and supply. But governments can influence those exchange rates in various ways. The extent and nature of government involvement in currency markets define alternative systems of exchange rates. According to Libby R. and Timothy T. (2009), there are three main types of exchange rates, namely (i) Fixed exchange rate, (ii) Floating rate and (iii) Managed exchange rate.
2.2.1 Fixed exchange rate
In a fixed exchange rate system, the exchange rate between two currencies is set by government policy. There are several mechanisms through which fixed exchange rates may be maintained. Whatever the system for maintaining these rates, however, all fixed exchange rate systems share some important features. In this system, governments may seek to fix the values of their currencies, either through participation in the market or through regulatory policy.
2.2.2 Floating rate
In a floating exchange rate system, governments and central banks do not participate in the market for foreign exchange. The relationship between governments and central banks on the one hand and currency markets on the other is much the same as the typical relationship between these institutions and stock markets. Governments may regulate stock markets to prevent fraud, but stock values themselves are left to float in the market. In this system, exchange rates are set purely by private market forces with no government involvement. Values change constantly as the demand for and supply of currencies fluctuate.
2.2.3 Managed exchange rate
Governments and central banks often seek to increase or decrease their exchange rates by buying or selling their own currencies. Exchange rates are still free to float, but governments try to influence their values. Government or central bank participation in a floating exchange rate system is called a managed float.
Countries that have a floating exchange rate system intervene from time to time in the currency market in an effort to raise or lower the price of their own currency. Typically, the purpose of such intervention is to prevent sudden large swings in the value of a nation’s currency.
Dornbusch and Fischer (1980), explains that changes in the exchange rate affect the international competitiveness of an open economy and therefore the profitability of its firms as reflected in share prices. Hence, the direction of causality runs from exchange rates to share prices. However, the effect of exchange rate fluctuations on the share market will depend on whether the constituent firms are preponderantly net exporters or net importers, whether they own foreign subsidiaries and whether they hedge against exchange rate fluctuations. Depending on these and other factors, an appreciation of the home currency may cause a net increase or net decrease in the share market index.
According to theory, foreign exchange market developments have had cost implications for the households, firms and the state. Benita and Lauterbach (2004) showed that exchange rate volatility have real economic costs that affect price stability, firm profitability and a country’s stability. Further, exchange rate volatility has implications for the financial system of a country especially the stock market.
Zambia has maintained a liberal flexible exchange rate system since 1994. Prior to that, the exchange rate was fixed from the time of independence in 1964. A fixed exchange rate regime existed from 1964 to 1982 and 1987 to 1991 while a crawling peg was adopted between 1983 and 1985. An initial float of the kwacha took place between 1985 and 1987. A more flexible exchange rate regime was adopted in the early 1990s as part of the economic reforms. The decision to choose each of these exchange rate regimes was largely influenced by conventional economic and political arguments. A comprehensive review of the exchange rate policy in Zambia is provided by Chipili (2010)
It is worth noting that exchange rates are very key in determining a country’s economy performance and growth that is why to a great extent many government participate in these markets in or order to prevent sudden large swings which may negatively impact the economy.

2.3 Share Index
A stock index or stock market index is a measurement of the value of a section of the stock market. It is computed from the prices of selected stocks (typically a weighted average). It is a tool used by investors and financial managers to describe the market, and to compare the return on specific investments. According to Jazairi N. (2010), defines stock indexes as the measure of movement in the prices of shares and financial assets and they are calculated and published by the private sector, while the New York Stock Exchange defines them as the measure of movement over time in the prices of stocks (or other financial assets such as bonds) traded in particular stock markets (www.nyse.com). On the other hand, NASDAQ (http://www.nasdaq.com/investing/glossary) defines a share index as a statistical composite that measures changes in the economy or in financial markets, often expressed in percentage changes from a base year or from the previous month. Indexes measure the ups and downs of stock, bond, and some commodities markets, in terms of market prices and weighting of companies in the index.
Jazairi N. (2010) further states that the indexes provide sensitive short-term indicators of the changing economic and political conditions affecting the market and reflected in changes in the prices of industrial, technology, transport and utility stocks, among many others. Stock indexes are designated as “portfolio indexes” which are indicators of the performance of real portfolios or the actual holdings of securities by an individual or institution such as a pension fund. They are also widely used by investment fund managers and academics to calculate long-run rates of return, to test finance models such as the capital asset pricing model and in the studies of business fluctuations.
2.4 The Role of Stock Markets
Economic theory and research is very concise about the important role that stock markets play in economic growth. According to International Finance Corporation Journal (2017), capital markets have several beneficial features for different participants in the economy. For a company or entity in need of funding, domestic capital markets provide an alternative source of funding that can complement bank financing. Capital markets can offer better pricing and longer maturities, as well as access to a wider investor base. They can also offer funding for riskier activities that would traditionally not be served by the banking sector, and by doing so contribute significantly to innovation in an economy. While some governments can access international capital markets, the development of local capital markets can increase access to local currency financing and thereby help manage foreign exchange risk and inflation better. For governments, this is a valuable benefit since it can allow them to finance fiscal deficits by borrowing from local markets without exchange rate risk. For investors and savers, capital markets can offer more attractive investing opportunities—with better returns—than bank deposits, depending on risk profile, liquidity needs, and other factors. Further, with a wider range of securities and instruments offered, capital markets can help investors diversify their portfolios and manage risk.
Cherian S. (1996) suggests that in a market economy, the stock market performs functions such as a source for financing investment, a signalling mechanism to managers regarding investment decisions and a catalyst for corporate governance.
Theoretically as observed by Mark S. and Seiichi S. (2009), in smaller economies stock markets provide important benefits. These include more effective monetary and ?scal policies, higher risk transfer, increased corporate ?nancing, and greater integration into the world economy. But the analytical foundations for what it takes to develop ?nancial markets in smaller economies is limited because cross-country research so far has focused on ?nancial market development in advanced and emerging market countries.
While the benefits are clear, there are challenges facing stock markets especially in developing markets. The potential for and timing of capital market development are to a large extent dependent on the level of economic and structural development of a country. That is, a country’s starting point heavily dictates the recipe for timing, sequencing, and even the feasibility of what can be done in terms of developing local capital markets.
There is a high correlation between fundamentals—in particular the size of the economy in terms of aggregate gross domestic product and per capita income—and the level of development of the local capital markets. This explains in large part why, in general, capital markets are at an embryonic stage in smaller and low-income countries. For larger and middle-income countries, significant differences across countries are explained more by institutional development, the size of the institutional investor base, the level of contractual savings such as pension funds, and macroeconomic stability, International Finance Corporation Journal, (2017).
From the above it is important stock markets are all inclusive in they allow participation from a house holds, corporations, institutional investors etc. all at the same time according to their needs.

2.5 Review of Similar Studies
Existing literature relating to the association between stock prices and exchange rates shows diverse outlook. An early attempt to examine the exchange rate and stock price dynamics was by Franck and Young (1972) who showed that there is no significant interaction between the variables. Later, Aggarwal (1981) made a study to find the relationship between exchange rates of US dollar and changes in the indices of US stock prices and found a positive correlation. Giovannini and Jorion (1987) also considered the exchange rates and stock prices of USA and supported Aggarwal (1981). Soenen and Hennigar (1988) studied the same market but considered a different time period and contrast with prior studies by showing a significant negative relationship between stock prices and exchange rates.
Deepti G. and Monika K. (2012), argued that the Interactions between stock and foreign exchange market came to the forefront because these two markets are the most sensitive segments of the financial system and are considered as the barometers of the economic growth through which the country’s exposure towards the outer world is most readily felt. Further, Desislava D. (2005), studied that if there was a link between the stock market and exchange rates that might explain fluctuations in either market. He stated that in the short run, an upward trend in the stock market may cause currency depreciation, whereas weak currency may cause decline in the stock market. To test these assertions, a multivariate, open-economy, short-run model that allows for simultaneous equilibrium in the goods, money, foreign exchange and stock markets in two-countries was used.
In addition Desislava D. (2005), revealed that the relationship between exchange rates and stock prices is important because:
I) It may affect decisions about monetary and fiscal policy and Gavin M. (1989) showed that a booming stock market has a positive effect on aggregate demand. If this is large enough, expansionary monetary or contractionary fiscal policies that target the interest rate and the real exchange rate will be neutralized. Sometimes policy-makers advocate less expensive currency in order to boost the export sector. They should be aware whether such a policy might depress the stock market.
II) The link between the two markets may be used to predict the path of the exchange rate. This will benefit multinational corporations in managing their exposure to foreign contracts and exchange rate risk stabilizing their earnings.
III) Currency is more often being included as an asset in investment funds’ portfolios. Knowledge about the link between currency rates and other assets in a portfolio is vital for the performance of the fund. The Mean-Variance approach to portfolio analysis suggests that the expected return is implied by the variance of the portfolio. Therefore, an accurate estimate of the variability of a given portfolio is needed.
IV) The understanding of the stock price-exchange rate relationship may prove helpful to foresee a crisis. Khalid and Kawai (2003) as well as Ito and Yuko (2004).
In a study conducted by Aydemir and Demirhan (2009), to investigate the causal relationship between stock prices and exchange rates, the results of empirical study indicate that there was bidirectional causal relationship between exchange rate and all stock market indices. While the negative causality exists from 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.
Pan et al. (2007) took the data of seven East Asian countries over the period 1988 to 1998 to examine dynamic linkages between exchange rates and stock prices. The result of study reveals that there is a bidirectional causal relation for Hong Kong before the 1997 Asian crises. Also, there is a unidirectional causal relation from exchange rates and stock prices for Japan, Malaysia, and Thailand and from stock prices to exchange rate for Korea and Singapore. During the Asian crises, there is only a causal relation from exchange rates to stock prices for all countries except Malaysia
Kurihara (2006) studied the period March 2001-September 2005 to investigate the relationship between macroeconomic variables and daily stock prices in Japan. He took Japanese stock prices, U.S. stock prices, exchange rate (yen/U.S. dollar), the Japanese interest rate etc. The empirical results showed that domestic interest rate does not influence Japanese stock prices. However, the exchange rate and U.S. stock prices affect Japanese stock prices. Consequently, the quantitative easing policy implemented in 2001 has influenced Japanese stock prices.
Despite the theoretical literature suggestions that causal relations between stock prices and exchange rates exist, empirical evidence is rather weak. The literature about the relationship between stock markets and exchange rate has found mixed evidence of the impact of exchange rate on stock returns. Solnik (1987) made a slightly different study and tried to detect the impact of several economic variables including the exchange rates on stock prices. He concluded that changes in exchange rates do not have any significant impact over stock prices. Jorion (1990) did a similar study to show the relationship between stock returns of US multinational companies and the effective exchange rate of US dollar and found a moderate relationship between the variables
Rahman and Uddin (2009), in their study on dynamic relationship between stock prices and exchange rate in South Asian countries found no relationship between stock and exchange rates. Bhattacharya and Mukherjee (2003) study causal relationship between exchange rate and stock index in India and their findings reveals absence of causal relationship between stock market index and exchange rate. Other studies that found absence cointegration between stock prices and exchange rates include Okpara and Odionye (2012) and Zia and Rahman (2011).
2.6 World Stock Exchanges Performance and Review
According to Kim I. (2016), the total of the world’s stock markets stood worth more than double it was thirteen years ago and a lot of the growth came from Asia’s so called emerging markets. The value of the world’s stock markets had risen 133 percent since 2003. The value of stock prices multiplied by the total shares – that is, market capitalisation – for the whole world stood at US$65.6 trillion compared to US$28.1 trillion about thirteen years ago. The United States of America had in absolute terms accounted for much of the growth.
The United States of America (USA) stock markets are the primary drivers in the global stock markets and houses the best prestigious and top two best performers in the world by market capitalization; the New York Stock Exchange (NYSE) and the National Association of Securities Dealers Automated Quotation (NASDAQ). NYSE is auction-based which means specialists are physically present on the exchange’s floor, while NASDAQ is an electronic exchange. NYSE and NASDAQ have strict initial listing and maintenance requirements. The Caproasia website (www.coproasia.com) reaffirms that NYSE is the world largest stock exchange. The companies listed on the exchange have a total of US$19.6 trillion, followed by NASDAQ with the market capitalisation of USS$7.8 trillion. The table below ranks the top five exchange markets according to (www.coproasia.com) at close of 2016.

Amongst the fastest growing stock exchange by market capitalisation in 2016, Moscow Stock Exchange grow the fastest in 2016 at the total market capitalization increased to US$0.635 trillion, a phenomenal growth of 61.7% followed by the Brazilian Stock Exchange BOVESPA which notched a huge increase in market capitalisation as the total market value increased by 57.8% to US$0.8 trillion. Johannesburg Stock Exchange, Toronto Stock Exchange and Stock Exchange of Thailand round the top five with between 25% to more than 30% in value.
While most global stock markets rallied in 2016, according to (www.money.cnn.com) a handful closed the year with deep losses: Italy and China were the biggest loser by far. Italy’s main index, the FTSE MIB dropped 10% as investors worried about the banking sector. Shares in all major banks dropped sharply and some corporations were forced to see a bailout after their shares fell by as far as 88%. China stock markets also closed the year with a double digit losses following a gut wrenching fall in early 2016 when the key indexes lost between 25% to 30% of their value in a matter of weeks. The benchmark Shanghai Composite closed the year with a 12% loss.
2.7 Africa Stock Exchanges Performance and Review
Overall, Africa’s stock markets did not perform well in 2016. There were several reasons for the poor performance according to Jamelle C. (2017) on the Capital Markets for Africa website (http://www.relentlessir.com). Painful currency shortages were widespread throughout Africa and major devaluations impacted U.S. dollar and local returns. Devaluations were particularly impactful in Egypt and Nigeria. Currency weakness had been a major headwind for African investments. This is not just true for 2016, but for the past three years. The direct impact from an appreciating U.S. dollar against African currencies is lower U.S. dollar-adjusted returns. Indirectly, share prices have been negatively impacted by lower margins due to higher raw material costs.
When looking at local market returns – meaning returns of locally traded stocks – Egypt, Morocco, and Zimbabwe were the top performers. However, Egypt’s local market performance is misleading. Egypt’s local market return looks good, until we consider the devaluation of the EGP. Zimbabwe performed strongly, though much of its return can be attributed to the last quarter of the year. Below is the table showing stock indices during the year in local market terms as well as USA dollar adjusted.
Country Local Market % USD_Adjusted %
Egypt 76.20 -23.90
Morocco 30.50 27.70
Zimbabwe 25.80 25.80
Namibia 23.50 39.00
South Africa 3.90 17.10
Mauritius -0.10 -0.60
Rwanda -2.60 -12.50
BRVM -3.90 -6.20
Tanzania -5.80 -6.70
Nigeria -6.20 -38.70
Kenya -8.50 -8.60
Malawi -8.50 -17.10
Botswana -11.40 -8.00
Ghana -15.30 -25.00
Uganda -16.30 -21.80
Zambia -27.50 -19.60
Data source: http://www.relentlessir.com
2.8 Lusaka Stock Exchange
The Lusaka Stock Exchange (LuSE) was established with preparatory technical assistance from the International Finance Corporation and the World Bank in 1993. The Exchange opened on 21st February, 1994. The establishment of the Lusaka Stock Exchange was part of government’s broader economic growth reforms aimed at stimulating a dynamic private sector to be the primary engine for economic growth in Zambia. It was also motivated by the need to deepen financial and capital markets in support of the emerging private sector.
The private sector reform program was anchored on the privatisation Act No.21 of 1992. The Act recognised the importance of citizens’ participation in the growing economy. Through the Zambian Privatisation Trust Fund (ZPTF), the Act sought to hold shares from the privatised state owned companies in trust on behalf of Zambians for divesture. The aim was to achieve a wider distribution of shares amongst citizens. The other objectives of a capital market development were:
I) To enable local businesses to raise longer term capital
II) To empower citizens through ownership of shares
III) To attract foreign direct investment
IV) To enable companies achieve improved corporate governance through wider share ownership.
This is according to (http://www.luse.co.zm)
2.9 Lessons learnt
The review of stock markets around the world and Africa presents a number of lessons. These lessons have shown that globalization, inter-linkages of the capital markets, gradual removal of capital inflow barriers and the implementation of more flexible exchange rate mechanism in developed as well as transition economies has created a systematic interdependency between and within the stock and foreign exchange markets. This link is evident by how fluctuations in one region can affect another as can be seen from the global financial crisis that happened about a decade ago. The crisis spread like a wild fire to Europe and then to the rest of the world. Globally, banks had reported about $600 billion of credit – related losses.

One of the key lessons is that the price of the share has an effect on the wealth of individuals, households and later the entire economy. When there is a fall in share price, the first impact is that people with shares will see a fall in their wealth. If the fall is significant it will affect their financial outlook. If they are losing money on shares they will be more hesitant to spend money; this can contribute to a fall in consumer spending. The wealth effect is more prominent in the housing market. In Dec 2016, the value of the Zambian stock market was ZMW59.2 trillion so it has a big impact on wealth. Pension funds invest a significant part of their funds on the stock market. Therefore, if there is a serious fall in share prices, it reduces the value of pension funds. This means that future pension pay-outs will be lower. If share prices fall too much, pension funds can struggle to meet their obligation. The important thing is the long term movements in the share prices. If share prices fall for a long time then it will definitely affect pension funds and future pay-outs.

Stock markets do not exist in a vacuum, macroeconomic variables and other fundamentals such as money supply, interest rates, and inflation do play a very pivotal role in predicting movements in stock prices as well as exchange rates. The other lesson is that investments in capital markets does not only give return in terms of dividend but may result in capital appreciation which is eroded away in situations of high exchange rates therefore, exchange rate stability and continuous growth in capital market are required for financial system stability and monetary policy effectiveness. In addition often share price movements are reflections of what is happening in the economy. E.g. a fear of a recession and global slowdown could cause share prices to fall. The stock market itself can affect consumer confidence. Bad headlines of falling share prices are another factor which discourage people from spending. On its own it may not have much effect, but combined with falling house prices, share prices can be a discouraging factor. However, there are times when the stock market can appear out of step with the rest of the economy.

Further, a fall in the stock market makes other investments more attractive. People may move out of shares and into government bonds, money markets or gold. These investments offer a better return in times of uncertainty. E.g. in Zambia during the year 2016, because of poor performing stock prices due to high inflation rates, investors had to switch from capital on to money markets so as to make sure they were getting a high return.

Another lesson is that, there is link that exits between exchange rates and stock prices is manifested when currency depreciation results in higher exports and therefore corporate profits resulting into higher stock prices. This is usually in the short run. Further this is seen from the competitiveness of the firm’s exports, resulting in changes in the value of the firm’s assets and liabilities culminating into higher profits and reflecting in its stock prices. Currency appreciation on the other hand is bad news for domestic corporations, because it will reduce its competitive ability to export. It must be emphasised both situations are best suited for economies that are pro manufacturing and not trading in nature.
The other lesson is that falling share prices can hamper firms’ ability to raise finance on the stock market. Firms who are expanding and wish to borrow often do so by issuing more shares – it provides a low cost way of borrowing more money. However, with falling share prices it becomes much more difficult.

When it comes to exchange rates, their volatility brings about uncertainty which in turn generates negative economic welfare effects. Further, fluctuations in the exchange rate affect consumer goods prices which in turn affect demand and consequently consumption. Monetary policy is also affected by currency fluctuations especially where domestic growth is underpinned by exports as authorities attempt to support the external sector through exchange rate stabilisation at the expense of inflation stabilisation. Exchange rate uncertainty can also create incentives for trade protectionist tendencies and sharp currency reversals which in turn impose further costs on the economy. There must be various exchange rate regimes to achieve a sound financial system. The exchange rate policy applied at any time should depend on the prevailing conditions in the economy.

Liberalization of foreign capital controls has opened the possibility of international investment. Volatility in exchange rates however can restrict the flow of international capital by reducing direct and portfolio investments. . Speculative capital flows may also be induced by exchange rate volatility under the flexible regime that could in turn contribute to instability in economic conditions. Further, greater exchange rate volatility increases uncertainty over the return of a given investment. Potential investors are attracted to invest in a foreign location as long as the expected returns are high enough to compensate for the currency risk. In view of this, foreign direct investment tends to be lower under higher exchange rate volatility. Central banks globally therefore endeavour to stabilize exchange rates in order to moderate the adjustment and uncertainty costs that a volatile exchange rate imposes on the economy.

The general lesson learnt from the review is that there must be continuous growth in stock markets for them to have a meaningful positive impact on the economy coupled with sound, consistent and adequate regulation for exchange rate controls.

2.10 Summary
The chapter looked at financial markets in general, with its components of money and capital markets, share index, exchange rate subdivided into fixed, floating and managed rates, reviewed the performance of some stock markets around the world, and drew lessons thereof. The chapter also reviewed possible factors that may have affected the performance of Lusaka Stock Exchange Market. It has been discovered from the review process that government through regulatory the framework in capital markets plays a key role in the development of efficient stock exchange markets as well as having adequate exchange rate regulations.

CHAPTER 3

THEORETICAL AND CONCEPTUAL FRAMEWORK
3 Introduction
The previous chapter focused on literature review, looking at stock markets around the world and lessons learnt from the review process. This chapter discusses the theoretical and conceptual framework for the study, explaining in detail theories that have been advanced in literature and choosing one that best relates to the study. In addition, the chapter will discuss the hypotheses development and operationalization of the concepts and the interpretation of the conceptual frame work.
3.1 Theoretical Framework
There are two main theories in literature that discuss the relationship which exists between exchange rates and share prices. These are the goods market approach and the portfolio balance approach. We review these theories by starting with the goods market approach.

3.1.1 Goods Market Approach
This theory according to (Dornbusch R. and Fischer S., 1980) argues that changes in exchange rates affect the competitiveness of multinational firms and consequently their earnings and stock prices. Depreciation of the local currency makes exporting goods cheaper and may lead to an increase in foreign demand and sales. Conversely, when the local currency appreciates, foreign demand of an exporting firm’s products shrinks so the firm’s profit will decrease and so does its stock price. The opposite case holds for importers. In addition, exchange rate movements affect the values of a firm’s outstanding payables and receivables denominated in foreign currencies. The impact of exchange rate fluctuations on stock prices depends on both the weight of a country’s international trade and the degree of the trade imbalance. According to this argument, we expect a causal effect from exchange rates to stock prices.
Depreciation of the local currency makes exporting goods attractive and leads to an increase in foreign demand and hence revenue for the firm and its value would appreciate and hence the stock prices. On the other hand, an appreciation of the local currency decreases profits for an exporting firm because it leads to a decrease in foreign demand of its products. However, the sensitivity of the value of an importing firm to exchange rate changes is just the opposite to that of an exporting firm. In addition, fluctuations in exchange rates affect a firm’s transaction exposure. That is, exchange rate movements also affect the value of a firm’s future payables (or receivables) denominated in foreign currency. This is an indication that exchange rate volatility have real economic costs that affect price stability, firm profitability and a country’s stability.
Further Solnick B., (1987) using the goods market approach argues that currency depreciation will result in higher exports and therefore corporate profits resulting in higher stock prices in the short run. The transmission mechanism according to this approach is the competitiveness of the firm’s exports, resulting in changes in the value of the firm’s assets and liabilities culminating in higher profits and reflecting its stock prices.
Foreign exchange rate movement influences the stock market since the future net cash flows of the firm change with the fluctuations in the foreign exchange rates. When there is appreciation in the local currency, exporters do not only lose their competitiveness in world market, but also results into a reduction in sales revenue as well as profits thus leading to the decline in the stock prices. Contrary, in times of local currency appreciation importers increase their competitiveness in domestic markets leading to an increase in their profit and stock prices. This implies that depreciation of a local currency will have adverse effects on importers and favorable effects on exporters. Philomena K. (2016)
Charles A. Simon K. and Daniel A, (2008) stated that the openness of a country’s economy is recognized as a cause of volatility of its market and with advert of globalization, developing economies are becoming more integrated into developed economies with the results of increasing flow of imports and exports. Consumer price index has a strong relationship with stock market volatility. This means that an increase in consumer price will lead to a rise in stock market volatility and an economy’s financial position is susceptible to its foreign exchange volatility making foreign exchange market developments to have cost implications for the households, firms and the state.
Zambia in itself is an example of an open economy and is not an exception to these fundamentals and exchange rates have eroded the wealth of both the nation and household. A careful examination of foreign exchange rate history in Zambia shows some considerable level of volatility since independence and exchange rate fluctuation is considered to be a major factor in determining the quantum and direction of foreign trade and commerce.
The pivot of the goods market theory is the exchange rate and it is influenced by many other factors such as interest rates, inflation, oil prices and copper prices.
3.1.1.1 Interest Rates
Home and host countries’ interest rates play a significant role in exchange rate determination. The interest rates are adjusted quarterly by the central bank as part economic management. If inflationary pressure prevails in the country, the central bank will increase base lending rate to curtail the money supply among the people and companies to make borrowings expensive. Assuming the host country does not adjust the interest rate, this increase in one country creates inequilibrium in demand and supply for money and in turn it causes the exchange rate to move to equilibrium. If not arbitrage profits are possible in borrowing and investing between countries. If both home and host countries simultaneously increase or decrease the interest rates matching, then there will be no effect on exchange rate due to interest rate. The relative interest rate is an important factor which influences exchange rates.
3.1.1.2 Inflation
This is the general increase in the price level of goods and services in an economy, measured by the Consumer Price Index. In other words price raise is inflation and the same is depreciation of home currency in international parlance. When the home inflation rate is high the home currency will lose value and vice versa. Inflation and exchange rate are negatively correlated. A country with lower inflation exhibits a rising currency value and vice versa. Exchange rate hike indicates the loss of home currency value.This is according to Ravindran R. and Soroush K. (2015).
3.1.1.3 Oil Prices
Oil prices affect the exchange rate in oil exporting countries but still their currencies do not show that much instability as compared to small economies that are mostly oil importing, because they have effective legal and organized system that manage and control the exchange rate. The world economy is greatly affected by the Oil as it is one of the most crucial and important physical commodity. Countries make interventions in oil markets so that they can evade the adverse effects of volatility in oil prices. Due to its influence on other macroeconomic variables like exchange rate, countries must predict its movements and react to the changes in oil prices through effective policy making. Countries that are oil rich and are oil exporters have benefited from the high international prices of oil and their real exchange rate has appreciated significantly due to which other sectors that are not based on resources become less competitive. Raja S. (2014).
3.1.1.4 Copper Prices
Copper is the predominant export product of Zambia and the copper mining industry contributes significant proportions of the gross domestic product and government revenues. Zambian copper is sold in world markets at prices aced on the London Metal Exchange (LME) copper price quotation which is notorious for its short-term volatility. The consequence of the variability of the copper price is that export earnings and government tax revenues from the industry fluctuate considerably. This is according to Obidegwu F. (1980).
Caleb F. (2009) stated that the global financial meltdown caused a contagion effect to the foreign exchange market and led to volatility in the exchange rate of the Kwacha against major currencies. This unfavorable development was partly a consequence of reduced earnings from copper exports arising from the fall in copper prices as was reflected in the lower supply of foreign exchange on the market by mining companies. For instance, the supply of foreign exchange to the market by mining companies has declined by 35.7% to a monthly average of US $62.4 million in the first half of this year from an average of US $97.0 million in the last half of 2008. Further, the weakness of the local currency was as a consequence of increased risk aversion to emerging and developing economy financial assets, as stated earlier, attributed to the deepening global financial crisis. In this regard, the supply of foreign exchange by foreign portfolio investors for the purchases of Kwacha financial assets, such as Government securities and domestic company equities, significantly declined, with most nonresidents preferring to liquidate their investments and externalizing the foreign exchange. The result of this was the volatility in the exchange rate of the Kwacha against major foreign currencies. It should also be noted that the depreciation of the Kwacha against major currencies contributed to higher inflation, particularly as Zambia remains dependent on imports for a wide variety of consumer goods as well as inputs for domestic production. Further, this unfavorable development has a strong role in shaping inflation expectations.

3.1.2 Portfolio Balance Approach
The alternative explanation for the relation between exchange rates and stock prices can be provided through ‘portfolio balance approaches’ that stress the role of capital account transaction. Like all commodities, exchange rates are determined by market mechanism, i.e., the demand and supply condition. A blooming stock market would attract capital flows from foreign investors, which may cause an increase in the demand for a country’s currency. The reverse would happen in case of falling stock prices where the investors would try to sell their stocks to avoid further losses and would convert their money into foreign currency to move out of the country. There would be demand for foreign currency in exchange of local currency and it would lead depreciation of local currency. As a result, rising (declining) stock prices would lead to an appreciation (depreciation) in exchange rates. Moreover, foreign investment in domestic equities could increase over time due to benefits of international diversification that foreign investors would gain. Furthermore, movements in stock prices may influence exchange rates and money demand because investors’ wealth and liquidity demand could depend on the performance of the stock market Golaka and Samanta (2003).
David C. (2007) emphasizes the central assumption of portfolio balance model, being that assets in different countries are not perfect substitutes. The exchange rate enters through valuation effects in the supply and demand for assets, and a risk premium appears in the interest parity condition. Purchasing power parity is not assumed, and so the model allows for fluctuations in the real exchange rate, in contrast to the monetary model. Empirical results for the portfolio balance model have been mixed.
The portfolio balance approach suggests that besides monetary factors, holding of financial assets also can influences the exchange rate. Financial assets include local and foreign bonds. This approach is based upon two financial assets, money and bonds (local and foreign). This approach assumes that the relative supply and demand of money and bonds determine the equilibrium exchange rate between two countries. According to this approach, exchange rate establishes an equilibrium in the investor portfolio (including the money, local and foreign bonds) in such a way that if there is a change in any one of these three assets, investor reestablishes the desired balance in his portfolio. This rebalancing process needs adjustment which influences the demand for the asset and in turn exchange rate. This is according to Aima and Zaheer (2015).
Zambia is an open economic, because of this numerous domestic and international factors directly or indirectly affect the performance of the stock market. The nature of the country’s economic position suggest that there is foreign flow of capital which is on the exchange market affected by exchange rates which is later on driven by the factors of demand and supply. The portfolio balance approach best explains this situation and therefore it is adopted for this study.
3.2 Conceptual Framework
Conceptual Framework
Independent Variables Dependent Variable

H1

H2

H3

3.3 Interpretation of the Conceptual Framework and Hypotheses Development
The conceptual framework in the figure above shows the Lusaka stock exchange performance characterised by the all share index growth rate as an independent variable. The dependent variable is the exchange rate growth rate. The following is the hypotheses for this study:
H01; There is a no significant relationship between exchange rate growth rate and the Government securities (GRZ) bonds growth rate on the Lusaka Stock Exchange.
Ha1; There is a significant relationship significant relationship between exchange rate growth rate and the Government securities (GRZ) bonds growth rate on the Lusaka Stock Exchange.
H02; There is a no significant relationship between exchange rate growth rate and the Foreign Direct Investment (FDI) growth rate on the Lusaka Stock Exchange
Ha2; There is a significant relationship between exchange rate growth rate and the Foreign Direct Investment (FDI) growth rate on the Lusaka Stock Exchange
H03; There is a no significant relationship between exchange rate growth rate and All Share Index on the Lusaka Stock Exchange
Ha3; There is a significant relationship between exchange rate growth rate and All Share Index on the Lusaka Stock Exchange.
3.4 Operationalization of the Concepts
In the study, Lusaka stock exchange performance is characterised by the all share index, the value of Government Securities (GRZ) bonds and Foreign Direct Investment.
3.4.1 Share Index
A stock index or stock market index is a measurement of the value of a section of the stock market. It is computed from the prices of selected stocks (typically a weighted average). It is a tool used by investors and financial managers to describe the market, and to compare the return on specific investments. According to Jazairi N. (2010), defines stock indexes as the measure of movement in the prices of shares and financial assets and they are calculated and published by the private sector. The indexes provide sensitive short-term indicators of the changing economic and political conditions affecting the market and reflected in changes in the prices of industrial, technology, transport and utility stocks, among many others. Stock indexes are designated as “portfolio indexes” which are indicators of the performance of real portfolios or the actual holdings of securities by an individual or institution such as a pension fund. They are also widely used by investment fund managers and academics to calculate long-run rates of return, to test finance models such as the capital asset pricing model and in the studies of business fluctuations.
3.4.2 Exchange Rates
The Investopedia website, (http://www.investopedia.com/terms/e/exchangerate.asp) defines an exchange rate as the price of a nation’s currency in terms of another currency. An exchange rate thus has two components, the domestic currency and a foreign currency, and can be quoted either directly or indirectly. Saunders A. and Cornett M. (2012), on the other hand defines an exchange rate as a price at which one currency (e.g., the Zambia Kwacha) can be exchanged for another currency (e.g., the U.S. dollar). An exchange rate that does not have the domestic currency as one of the two currency components is known as a cross currency, or cross rate and exchange rates are determined in the foreign exchange market, Marc L. (2005). Exchange rates are determined by demand and supply. But governments can influence those exchange rates in various ways. The extent and nature of government involvement in currency markets define alternative systems of exchange rates. This study uses exchange rate growth rates as one the performance indicators for Lusaka stock exchange.
3.4.3 Foreign Direct Investment

According to the Organisation for Economic Co-operation and Development (2008), foreign direct investment (FDI) is a category of investment that reflects the objective of establishing a lasting interest by a resident enterprise in one economy (direct investor) in an enterprise (direct investment enterprise) that is resident in an economy other than that of the direct investor. Further Kenneth F. (1993), states that FDI is the cross-border of expenditures to acquire or expand corporate control of productive assets. FDI implies that the investor exerts a significant degree of influence on the management of the enterprise resident in the other economy. Such investment involves both the initial transaction between the two entities and all subsequent transactions between them and among foreign affiliates, both incorporated and unincorporated. While, Silvio C. and Ariel W. (2009), emphasises that foreign direct investment is an investor’s acquisition of “long-term influence” in the management of a firm in another country. This study uses foreign direct investment growth rates as one the performance indicators for Lusaka stock exchange.

3.4.4 Government Bonds
Lusaka stock exchange operates a bond market where government bonds are issued and traded. Used primarily to finance the public debt of countries, government bonds represent a substantial amount of the bond markets. Government bond are mainly issued by the State Treasury agency of the country although the Central bank may in certain countries be also able to issue government bonds. Because government act as a guarantor to the bond debt, government bonds are considered to be relatively safe investment. Government bonds are sold via treasury auctions. A government bond is a bond issued by a national government dominated in the country’s own currency. The bond’s market value represents the amount at which a bond is trading on the market (Fabozzi and Markowitz 2002, Gitman 2008. This study uses government bonds market value growth rates as one the performance indicators for Lusaka stock exchange.
3.5 Summary
This chapter looked at some theories that discuss the relationship between exchange rates and the all share index, that is, the goods market approach and the portfolio balance approach as well as some empirical studies. The hypothesis was developed and conceptualised.

CHAPTER 4

RESEARCH METHODOLOGY
4 Introduction
The previous chapter discussed the theoretical and conceptual framework for the study, explaining in detail theories that have been postulated by literature and choosing one that best relates to the study. This chapter lays out the methodology used in conducting research in order to establish the subsisting relationship between the Lusaka Stock Exchange all share index and exchange rates in Zambia. In order to ascertain the necessary information, the researcher used quantitative measures to access data for the study. For the purpose of this study, secondary data was used. Data was obtained from the published annual reports and journals for the period 2000 to 2016 from the LuSE and Bank of Zambia on an annual average basis. The chapter includes research design, sampling design, validity and reliability and the method of data collection and analysis. It further outlines the explanatory variables for the study.
4.1 Research Design
Research design is the framework that has been created to find answers to research questions. Research design refers to the logical structure of the inquiry. It articulates what data is required, from whom, and how it is going to answer the research question. Fundamentally research design affects the extent to which causal claims can be made about the impact of the intervention. Research design thus `deals with a logical problem and not a logistical problem. (Yin K 2009). This is a quantitative study using secondary data. Data obtained was for the Lusaka Stock Exchange all share index, exchange rates, secondary government bonds and foreign direct investment on the LuSE. The aim was to establish the relationship between the Lusaka Stock Exchange all share index and the exchange rates and an Autoregressive Distributed Lag model (ARDL) was used to establish the subsisting relationship thereof.
4.2 Model Specification
This study adopted an Autoregressive Distributed Lag model (ARDL) to establish the relationship between the dependent variable and independent variable (s).The analysis covered a period from 2000 to 2016. The functional model used is given as:

ERgt+i = ?0 + ?gt (FDI) + ?gt (GRZBOND) + ?gt (ASI) + ?gt, where:
ERgt + i = Exchange rate growth rate of the market g at time t + i (where i = 0 and1)
?0 = coefficient of variables
?gt (FDI) = Foreign Direct Investment growth rate on the Market g for the annual period ending at time t;
?gt (GRZBOND) = Secondary Government Bonds growth rate on the Market g for the annual period ending at time t;
?gt (ASI)= All Share Index growth rate on the Market g for the annual period ending at time t;
?gt = error term
4.2.1 The Use and Application of Autoregressive Distributive Lay Model (ARDL)
Econometric analysis of long-run relations has been the focus of much theoretical and empirical research in economics. In the case where the variables in the long-run relation of interest are trend stationary, the general practice has been to de-trend the series and to model the de-trended series as stationary distributed lag or autoregressive distributed lag (ARDL) models (Pesaran ; Shin, 1997). Estimation and inference concerning the long-run properties of the model are then carried out using standard asymptotic normal theory. The Autoregressive-Distributed Lag (ARDL) is an infinite lag model that is both flexible and parsimonious.
Assumptions of ARDL Approach Model
i) Lags must be appropriate
ii) Error must be serially independent
iii) Model must be dynamically stable
iv) If variables are stationary at level, we can apply ARDL
v) If variables are stationary at first difference we can also run ARDL
vi) If variables are stationary at level and first difference we can also run ARDL (mixture stationary) and the assumptions where fulfilled, hence, the ARDL model was used for the computation as the application of time series prediction

The ARDL model is as follows:
yt = µ + ?0xt + ?1xt-1 + ?1yt-1 + et ………………………………………………(Eq.4.2)

In this model, we include the explanatory variable xt, and one or more of its lags, with one or more lagged values of the dependent variable (Y)
The model in (Eq. 4.2) is denoted as ARDL (1) as it contains one lagged value of x and one lagged value of y. A model containing p lags of x and q lags of y is denoted ARDL (p, q). If the usual error assumptions on the error term e hold, then the parameters of Equation (Eq.4.2) can be estimated by least squares. Despite its simple appearance, the ARDL (1) model represents an infinite lag. To see this, we repeatedly substitute for the lagged dependent variable on the right-hand side of Equation (Eq.4.2). The lagged value yt-1 is given by
yt-1= µ + ?0xt-1 + ?1xt-2 + ?1yt-2 + et-1………………………………………..……………………… (Eq.4.3)
Substitute Equation (Eq.4.2) into Equation (Eq.4.3) and rearrange,
yt= µ + ?0xt-1 + y1 µ + ?0xt-1 +?1xt-2 + ?1yt-2 + et-1 + et= µ (1 +y1) + ?0xt + (?1 + y1 ?0) xt-1 + y1 ?1xt-2 + xt-2 + y21 y t-2 + (y1 et-1 + et………………………………………………………………………………………………………………………………..Eq. 4.4)
Substitute the lagged value yt-2 = µ + ?0xt-2 + ?1xt-3 + ?1yt-3 + et-2 into Equation (Eq.4.4) to obtain:
yt= µ (1 +y1+ y21) + ?0xt + (?1 + y1 ?0) xt-1 + y1(?1+ y1?1)xt-2 + y21 ?1 xt-2+ y13 yt-3+ (y21 et-2 + y21 et-1 + et)…………………………..…………………………(Eq.4.5)

4.3 Sampling Design
A sample design is a definite plan for obtaining a sample from a given population. It refers to the technique or the procedure the researcher would adopt in selecting items for the sample. Sample design may as well lay down the number of items to be included in the sample i.e., the size of the sample. Sample design is determined before data are collected. There are many sample designs from which a researcher can choose. Some designs are relatively more precise and easier to apply than others (Kothari .C 2004). The Lusaka Stock Exchange was picked to meet the objectives of the study.
4.4 Data Collection and Analysis
This study used Secondary data only. Secondary data is data that has already been collected, analysed, and made available from other sources. Although government agencies are considered the principal source of secondary data, data may come from nongovernment organizations or individuals as well. (White 2010).
Data analysis is a process of bringing order, structure and interpretation of mass collected data (Korir 2015). Data collected was systematically organized in a proper manner to facilitate analysis. Data analysis involved preparation of the collected data, coding, editing and cleaning of data in readiness for processing using Autoregressive Distributive Lag Model (ARDL). The use of ARDL was as suggested by Persaran et al (2001) for integration investigation (time series data) and error correction, i.e., both long run and short run relationships.
Data was collected from Lusaka Stock Exchange and the Bank of Zambia. Other information was obtained from books, journals, and magazines. Eviews software was used to analyze the data because it’s mainly used for time series oriented econometric analysis.

4.5 Validity, Reliability and Practicality
Validity encompasses the entire experimental concept and establishes whether the results obtained meet all of the requirements of the scientific research method while the idea behind reliability is that any significant results must be more than a one-off finding and be inherently repeatable.
Validity determines whether the research truly measures that which it was intended to measure or how truthful the research results are. Researchers generally determine validity by asking a series of questions, and will often look for the answers in the research of others. The extent to which results are consistent over time and an accurate representation of the total population under study is referred to as reliability and if the results of a study can be reproduced under a similar methodology, then the research instrument is considered to be reliable. The data used in the study is from the institutions responsible for generating the same information. These institutions inform the public about the market conditions for stocks and exchange rates. (Joppa M. 2000).
4.6 Study Variables
The explanatory variable, foreign direct investment growth rate, growth in the market value of government bonds and Lusaka Stock Exchange all share index growth rate were regressed against the dependent variable, exchange rate growth rate to determine the relationship between the exchange rates and the Lusaka Stock Exchange all share index.
4.7 Chapter Summary
The chapter focused on the methodology used in conducting this research. Data used is secondary data from annual reports obtained from the Lusaka Stock Exchange and Bank of Zambia. The next chapter will present data and also analyse and discuss the results. The next chapter discusses the data presentations and analyses main findings.

CHAPTER 5
DATA PRESENTATION, ANALYSIS & DISCUSSION
5.1 Introduction
This chapter presents the research data, analysis and findings. To achieve the objectives of the study, the empirical analysis started from the description of data followed by the unit root test for stationarity of the variables to determine appropriate model for the analysis. After these, the respective regression models are estimated for the variables of the study to establish the subsisting relationship between Exchange Rates and the Lusaka Stock Exchange all Share Index.
5.2 Performance of the Lusaka Stock Exchange
The Lusaka Stock Exchange had 25 listed companies as at the date of the study. Despite the small number of listed companies the exchange has shown steady growth by market capitalization as can be seen in table 5.1. Market capitalization increased from US$768m in 2003 to US$4,650m in 2004 there after showing a steady increase up to the year 2014. In the subsequent years there was a reduction in capitalization. Further, growth is indicative by the number of trades between the year 2000 and 2016.
Table 5.1 Overview Performance of the LuSE 2000-2016

Source: Lusaka Stock Exchange
The table above shows the overview performance of the LuSE by number of trades, Volume of shares, turnover and market capitalization. The overall performance of the LuSE over the period of the study appeared to be positive with an average PE ration of 10 despite the adverse increase in exchange rates against all major foreign currencies.
5.3 Exchange Rate Performance
The exchange rates play an important role in a country’s trade performance. Whether determined by exogenous shocks or by policy, the relative valuations of currencies and their volatility often have important repercussions on international trade, the balance of payments and overall economic performance according to Alessandro N. (2013). As can be seen from table 5.1 exchange rate of the Kwacha against the USA dollar had been increasing there by affecting the prices of other goods and commodities. The vitality in exchange rates does also affect other economic fundamentals such as interest rates, there by confirming Alessandro N’s submission on the effects of exchange on the performance of an economy.
5.4 Trending of the Variables
5.4.1 Exchange Rates
The figure below shows an upward trend of exchange rates between the periods 2000-2016.
Figure 5.1: The growth rate in Exchange rates (2000-2016)
Source: Generated by the Author 2017
5.4.2 All Share Index
All Share Index has an upward trend from 2000 to 2007 with a sharp decline in 2008. There after shows an upward trend to the year 2013 but declines from 2014 to 2016.
Figure 5.2: The growth rate in All Share Index (2000-2016)

Source: Generated by the Author
5.4.3 Secondary Market GRZ Bonds growth rate
Figure below shows a stable growth rate in GRZ Bonds between the periods 2000-2007. The year 2008 shows a significant reduction. Thereafter, the stable growth rate continues up the last period.
Figure 5.3: The growth rate in Secondary Market GRZ Bonds (2000-2016)

Source: Generated by the Author
5.4.4 Foreign Participation on LuSE growth rate
Figure below shows a stable growth rate in the foreign participation on the Lusaka Stock Exchange between the periods 2000-2016 except for the year 2002 which shows a significant reduction.
Figure 5.4: The growth rate in Exchange rates (2000-2016)

Source: Generated by the Author
5.5 Unit Root Test
For the analysis of long run relationship between the variables, the economic time series must be stationary at same level; the test applied was Augmented Dickey Fuller (ADF), by checking for stationarity at the level of the time series; the test shows that the series exhibit non- stationarity without taking or considering the trend and constant except the series of the ASI, which failed to reject the null hypothesis of non-stationarity, while by taking the first difference, all the series became stationary at the same level given in statistics presented below. The results indicated that, the results are integrated at first difference or of order one, and they may exhibit some long run linear combination, but GRZ Bonds and Foreign participation on the LuSE are integrated at first difference level and justified the use of ARDL as the series are integrated at different levels.

5.5.1 Unit Root Test on All share index (ASI)
We would not reject the hypothesis of a unit root at 5% significance level (i.e., P>.05).
Table 5.2 All share index is not stationary at level.

The results show that, the Augumented Dickey Fuller Test Statisic had a p-value greater than .05. The Unit root test for All share Index at first difference (D(ASI): In this case, we would reject the null hypothesis of a unit root at the 5% significance level (i.e., p .05).
Table 5.4 Secondary market GRZ bond growth is stationary at level (GRZB)

Unit root test for GRZB at first difference: In this case we do not reject the null hypothesis of a unit root at the 5% significance level (i.e., p >.05). The Augmented Dickey-Fuller Tests statistic result indicated that the p-value was greater than (.05). At first difference (D (GRZB), again the Augmented Dickey-Fuller Tests statistic result indicated that the p-value was greater than (.05). Thus, the null hypothesis was accepted. Since both tests for the original data and the D (GRZB) were all not stationary, the Log (GRZB) was used.
5.5.3 Unit Root Test on Foreign Participation on the LUSE
We do not reject the hypothesis of a unit root at 5% significance level (i.e., P>.05).
Table 5.5 Foreign Participation on the LUSE growth rate is stationary at level.

Unit root test for Foreign Participation on the LUSE at first difference: In this case we do not reject the null hypothesis of a unit root at the 5% significance level (i.e., p >.05).
5.6 Correlation Matrix
The correlation matrix below indicates low correlations among the independent variables (less than 0.80). Thus, multicollinearity is not an issue. However, due to the small sample size (16) a multiple regression could not be specified (time series analysis works well with at-least 50 cases). Thus, the study used a series of single regressions to determine independent variables that are cointegrated with Exchange Rate Growth.
Table 5.6 Correlation Matrix of Variables

5.7 Model 1: Foreign Exchange Growth Rate Verses All Share Index Growth Rate
This model tests the following hypothesis:
H1: There is a significant relationship between exchange rate growth rate and the all share index growth rate on the Lusaka Stock Exchange.
Figure 5.5: Foreign Exchange growth rate against All Share Index growth rate (2000-2016)

5.7.2 Estimation of Regression Square
The estimation of a basic ARDL model explains the Foreign Exchange Growth Rate in terms of the All Share Index Growth Rate.
The estimated result below shows that R-square is less than the DW statistics which is the fundamental criteria for not having spurious regression. Since there is no spurious regression we can proceed further in the analysis.
Table 5.7: ADR Model FEGR in terms of ASI on the LuSE

It’s important that the errors of this model are serially independent – if not, the parameter estimates won’t be consistent (because of the lagged values of the dependent variable that appear as regressors in the model). To that end, we shall run the Diagnostics; Correlogram – Q-Statistics, and this gives the results as shown in the table below:
5.7.3 Q-Statistics Probabilities for All Share Index on the LuSE
The p-values are only approximate, but they strongly suggest that there is no evidence of autocorrelation in the model’s residuals. Note however, that the other residual diagnostics are shown at the end of model 1 analysis.

The stability test for the residuals provides the following output. Since the CUSUM of the residual is within the 5% limits we can conclude that the residuals are stable.
Table 5.8: Q-Statistics Probabilities for ASI on the LuSE

5.7.4 Stability tests
The stability of the model and the coefficients are checked through the CUSUM and CUSUM-Q, while the graphical presentation of the recursive coefficients is used to judge the stability of the coefficients. As it is clear from figures 5.9, 5.10 and 5.11, the plots of both the CUSUM and CUSUM square within the boundaries and hence these statistics confirm the stability of the long run coefficients of regressors.
According to Bahmani and Oskooee (2004), the null hypothesis (i.e., that the regression equation is correctly specified) cannot be rejected if the plot of the statistics remains within the critical bounds of the 5% significance level. The stability test for the residuals with dependent variable ASI provides the following output. Since the CUSUM of the residual is within the 5% limits we can conclude that the residuals are stable.
Figure 5.6: Foreign Exchange growth rate on All Share Index (2000-2016) CUSUM Curve

5.7.5 Model Selection Summary; Criteria Graph
In total, 20 ARDL model specifications were considered. Although an ARDL (1, 1) was finally selected, we can also see how well some other specifications performed in terms of minimizing Akaike information criteria (AIC). The ARDL(1, 1) gives the smallest value.
Figure 5.7: Akaike information criteria
5.7.6 Bound Tests
The results of the regression run for the purpose of bound testing, the Wald and the F-statistic for the ARDL are given below. Through the results, it is clear that both statistics are significant and are indicative of the long run relationship in the model. ARDL F-statistics and Wald-statistics push to accept the hypothesis of Co-integration in the model.

The “Bounds Test”, allows us to see if long-run relationships are present when we have a group of time-series, some of which may be stationary, while others are not. One of the main purposes of estimating an ARDL model is to use it as the basis for applying the “Bounds Test”. The “Bounds Test”, allows us to see if long-run relationships are present when we have a group of time-series, some of which may be stationary, while others are not.
The null hypothesis is that there is no long-run relationship between the variables – in this case, Foreign Exchange Growth Rate and the All Share Index Growth Rate.
In the estimation results, below are the results we will get:

Table 5.9: Bond Test for ARDL FEGR as the Dependent Variable

We see that the F-statistic for the Bounds Test is 6.157, and this clearly exceeds 1% critical value for the upper bound. Accordingly, we strongly reject the hypothesis of “No Long-Run Relationship”. That is there is evidence of long-run relationship between FOREIGN EXCHANGE GROWTH RATE and ALL SHARE INDEX GROWTH RATE.
The above analysis indicates that hypothesis H1 has been supported. There is a significant long-run relationship between exchange rate growth rate and the all share index growth rate on the Lusaka Stock Exchange. The long-run coefficients from the cointegrating equation are also reported, with their standard errors, t-statistics, and p-values. The short-run equilibrium coefficient is not significant (check: D (ALL SHARE INDEX GROWTH RATE).
5.7.7 Error Correction Model (ECM) Estimation Results
The estimation was started by generating the error correction term of each model, and then used the ECM approach (which involved lags of each independent variable).
Table 5.10: Regression results FEGR and ASI

The error-correction coefficient (CointEq(-1)) is negative (-1.124), as required, and is very significant (this is confirmation of long run relationship between the two variables). There is a quick adjustment to long run equilibrium (at a speed of 112%) in the Exchange Growth Rate when the All Share Index Growth Rate changes.
5.7.8 Diagnostic tests
The model that has been used for testing the long-run relationship and coefficients is further tested with the diagnostic tests of Serial Autocorrelation, Hetroskedasticity and any model misspecifications.
5.7.9 Residual Diagnostics
The hypotheses for testing for normality test for the residual are:
Ho: the data is normally distributed
Ha: the data is not normally distributed
There is no evidence of serious deviation from normality that exists. Since the Jarque-Bera statistic is 0.0679 with a p = 0.0667 we reject the Ho and accept Ha and conclude that the data is normally distributed (p ; 0.05). There is no evidence of serious deviation from normality.
Figure 5.8: Residual Diagnostics

5.7.10 Heteroskedasticity Test
The hypotheses for the heteroskedasticity test are as follows:
Ho: The is no heteroskedasticity
Ha: There is heteroskedasticity
Since the p value is 0.9667 is more than 0.05 we do not reject the Ho and conclude that there is insufficient evidence to conclude that there is heteroskedasticity.
5.7.11 Test of Independence of Residuals
The hypotheses test for testing the independence of residuals is as follows:
Ho: The is no serial correlation
Ha: There is serial correlation
Table 5.11: Independence of Residuals FEGR and ASI

Since the p value is 0.992 is more than 0.05 we do not reject the Ho and conclude that there is insufficient evidence to conclude that there is serial correlation.
5.8 MODEL2: Foreign Exchange Growth Rate Verses Secondary Market GRZ Bond Growth Rate
This Model tests the following hypothesis:
H2: There is a significant relationship between exchange rate growth rate and the Government securities (GRZ) bonds growth rate on the Lusaka Stock Exchange.
Figure 5.9: Foreign Exchange growth rate against Secondary Market GRZ Bonds growth rate (2000-2016)

5.8.2 Estimation of Regression Square
The estimation of a basic ARDL model that explains the Foreign Exchange Growth Rate in terms Of Foreign Exchange Growth Rate in terms of Secondary Market GRZ Bond Growth Rate. The estimated result below shows that R-square is less than the DW statistics which is the fundamental criteria for not having spurious regression. Since there is no spurious regression we can proceed further in the analysis.
Table 5.12: ADR Model FEGR in terms of Secondary Market GRZ Bond

It’s important that the errors of this model are serially independent – if not, the parameter estimates won’t be consistent (because of the lagged values of the dependent variable that appear as regressors in the model. To that end, we shall run the Diagnostics; Correlogram – Q-Statistics, and this gives the following results:
5.8.3 Q-Statistics Probabilities for Secondary Market GRZ Bond
The p-values are only approximate, but they strongly suggest that there is no evidence of autocorrelation in the model’s residuals (all the p-values are greater than 0.05). This is good news! (Note: other residual diagnostics are shown at the end of model 2 analysis).
Table 5.13: Q-Statistics Probabilities for Secondary Market GRZ Bond
5.8.4 Stability tests
The stability test for the residuals provides the following output. Since the CUSUM of the residual is NOT within the 5% limits we can conclude that the residuals are NOT stable. However, we shall proceed to test for cointegration.
Figure 5.10: Foreign Exchange growth rate on Secondary Market GRZ Bonds (2000-2016)
5.8.5 Model Selection Summary; Criteria Graph
In total, 20 ARDL model specifications were considered. Although an ARDL(4, 4) was finally selected, we can also see how well some other specifications performed in terms of minimizing Akaike information criteria (AIC). From the “Top Twenty” results: the ARDL(4, 4) gives the smallest value.
Figure 5.11: Akaike information criteria

5.8.6 Bound Tests
The “Bounds Test”, allows us to see if long-run relationships are present when we have a group of time-series, some of which may be stationary, while others are not. One of the main purposes of estimating an ARDL model is to use it as the basis for applying the “Bounds Test”. The “Bounds Test”, allows us to see if long-run relationships are present when we have a group of time-series, some of which may be stationary, while others are not.
The null hypothesis is that there is no long-run relationship between the variables – in this case, Foreign Exchange Growth Rate and Secondary Market Grz Bond Growth Rate.

In the estimation results, below are the results we will get:
Table 5.14: Bond Test for ARDL FEGR as the Dependent Variable

We see that the F-statistic for the Bounds Test is 1.288, and this clearly does not exceed the 10% critical value for the upper bound. Accordingly, we do not reject the hypothesis of “No Long-Run Relationship”. The long-run coefficients from the cointegrating equation are also reported, with their standard errors, t-statistics, and p-values (check levels equation above). So, what do we conclude from all of this?

There’s a no long-run equilibrium relationship between Foreign Exchange Growth Rate and Secondary Market GRZ Bond Growth Rate. Thus, the hypothesis H2 has not been supported. There is no significant long-run relationship between exchange rate growth rate and the Government securities (GRZ) bonds growth rate on the Lusaka Stock Exchange. All the short-run equilibrium coefficients are not significant (check: D (SECONDARY MARKET GRZ BOND GROWTH RATE).
5.8.7 Error Correction Model (ECM) Estimation Results
The estimation was started by generating the error correction term of each model, and then used the ECM approach (which involved lags of each independent variable).
Table 5.15: Regression results FEGR and GRZ BOND GROWTH RATE
The error-correction coefficient (CointEq(-1)) is not significant. However, the coefficient is negative as required. Thus, we cannot conclude that there is cointegration between Foreign Exchange Growth Rate and Secondary Market GRZ Bond Growth Rate.
5.8.8 Diagnostic tests
The model that has been used for testing the long-run relationship and coefficients is further tested with the diagnostic tests of Serial Autocorrelation, Hetroskedasticity and any model misspecifications.
5.8.9 Residual Diagnostics
The hypotheses for testing for normality test for the residual are:

Ho: the data is normally distributed,
Ha: the data is not normally distributed.
Since the Jarque-Bera statistic is 0.1958 with a p = 0.9067 we reject the Ho and accept Ha and conclude that the data is normally distributed (p ; 0.05). There is no evidence of serious deviation from normality.
Figure 5.12: Residual Diagnostics

5.8.10 Heteroskedasticity Test
The hypotheses for the heteroskedasticity test are as follows:
Ho: The is no heteroskedasticity,
Ha: There is heteroskedasticity.
Table 5.16: Heteroskedasticity Test

Since the p value is 0.3601 is more than 0.05 we do not reject the Ho and conclude that there is insufficient evidence to conclude that there is heteroskedasticity.
5.8.11 Test of Independence of Residuals
The hypotheses test for testing the independence of residuals is as follows:
Ho: The is no serial correlation
Ha: There is serial correlation
Table 5.17: Independence of Residuals FEGR and Secondary Market GRZ Bond
Since the p value is 0.7364 is more than 0.05 we do not reject the Ho and conclude that there is
insufficient evidence to conclude that there is serial correlation.
5.9 MODEL3: Foreign Exchange Growth Rate verses Foreign Participation on the LuSE Growth Rate
This Model tests the following hypothesis:
H3: There is a significant relationship between exchange rate growth rate and the Foreign Participation growth rate (FPGR) on the Lusaka Stock Exchange.
Figure 5.13: Foreign Exchange growth rate against Foreign Participation growth rate on the LuSE (2000-2016)

5.9.2 Estimation of Regression Square
The estimation of a basic ARDL model that explains the Foreign Exchange Growth Rate in terms Of Foreign Exchange Growth Rate of Foreign Participation on the LuSE Growth Rate.
The estimated result below shows that R-square is less than the DW statistics which is the fundamental criteria for not having spurious regression. Since there is no spurious regression we can proceed further in the analysis.

Table 5.18: ADR Model FEGR in terms of Foreign Participation on the LuSE Growth Rate

It’s important that the errors of this model are serially independent – if not, the parameter estimates won’t be consistent (because of the lagged values of the dependent variable that appear as regressors in the model. To that end, we can use the VIEW tab to choose, RESIDUAL DIAGNOSTICS; CORRELOGRAM – Q-STATISTICS, and this gives us the following results:

5.9.3 Q-Statistics Probabilities for All Share Index on the LuSE
The p-values are only approximate, but they strongly suggest that there is no evidence of autocorrelation in the model’s residuals (all the p-values are greater than 0.05). This is good news! (Note: other residual diagnostics are shown at the end of model 3 analysis)

Table 5.19: Q-Statistics Probabilities for FPGR growth rate on the LuSE (2000-2016)

5.9.4 Stability tests
The stability test for the residuals provides the following output. Since the CUSUM of the residual is within the 5% limits we can conclude that the residuals are stable.
Figure 5.14: Foreign Exchange growth rate on Foreign Participation on LuSE growth rate (2000-2016) CUSUM Curve

5.9.5 Model Selection Summary; Criteria Graph
In total, 20 ARDL model specifications were considered. Although an ARDL(1, 4) was finally selected, we can also see how well some other specifications performed in terms of minimizing Akaike information criteria (AIC). From the “Top Twenty” results: the ARDL(1, 4) gives the smallest value.
Figure 5.15: Akaike information criteria

5.9.6 Bound Tests
The “Bounds Tests”, allows us to see if long-run relationships are present when we have a group of time-series, some of which may be stationary, while others are not. One of the main purposes of estimating an ARDL model is to use it as the basis for applying the “Bounds Test”. The “Bounds Test”, allows us to see if long-run relationships are present when we have a group of time-series, some of which may be stationary, while others are not.
The null hypothesis is that there is no long-run relationship between the variables – in this case, Foreign Exchange Growth Rate and Foreign Participation on the LuSE Growth Rate.

In the estimation results, below are the results we will get:

Table 5.20: Bond Test for ARDL FEGR as the Dependent Variable

We see that the F-statistic for the Bounds Test is 4.723, and this clearly exceeds 5% critical value for the upper bound. Accordingly, we strongly reject the hypothesis of “No Long-Run Relationship”. That is there is evidence of long-run relationship between Foreign Exchange Growth Rate and Foreign Participation on the LuSE Growth Rate. The above analysis indicates that hypothesis H3 has been supported. There is a significant long-run relationship between exchange rate growth rate and foreign participation on the LUSE growth rate on the Lusaka Stock Exchange. The long-run coefficients from the cointegrating equation are also reported, with their standard errors, t-statistics, and p-values. All the short-run equilibrium coefficients are not significant (check: D(FOREIGN PARTICIPATION ON THE LUSE GROWTH RATE).
5.9.7 Error Correction Model (ECM) Estimation Results
The estimation was started by generating the error correction term of each model, and then used the ECM approach (which involved lags of each independent variable).
Table 5.21: Regression results FEGR and FP on the LuSE Growth Rate
The error-correction coefficient (CointEq(-1)) is negative (-1.208), as required, and is very significant (this is confirmation of long run relationship between the two variables). There is a quick adjustment to long run equilibrium (at a speed of 121%) in The Exchange Growth Rate when the Foreign Participation on the LuSE Growth Rate changes.
5.9.8 Diagnostic tests
The model that has been used for testing the long-run relationship and coefficients is further tested with the diagnostic tests of Serial Autocorrelation, Hetroskedasticity and any model misspecifications.
5.9.9 Residual Diagnostics
The hypotheses for testing for normality test for the residual are:
Ho: the data is normally distributed
Ha: the data is not normally distributed
Since the Jarque-Bera statistic is 0.9111 with a p = 0.6341 we reject the Ho and accept Ha and conclude that the data is normally distributed (p > 0.05). No evidence of serious deviation from normality
Figure 5.16: Residual Diagnostics

5.9.10 Heteroskedasticity Test:
The hypotheses for the heteroskedasticity test are as follows
Ho: There is no heteroskedasticity
Ha: There is heteroskedasticity
Table 5.22: Heteroskedasticity Test

Since the p value is 0.0275 is less than 0.05 we reject the Ho and accept Ha and conclude that there is sufficient evidence to conclude that there is heteroskedasticity. The other statistics the p-values are more than 0.05. This test is inconclusive.
5.8.11 Test of Independence of Residuals
The hypotheses test for testing the independence of residuals is as follows:
Ho: The is no serial correlation
Ha: There is serial correlation
Table 5.23: Independence of Residuals FEGR and Secondary Market GRZ Bond

Since the p value 0.5763 is more than 0.05 we do not reject the Ho and conclude that there is insufficient evidence to conclude that there is serial correlation.
Table 5.24: Results on Hypotheses Testing

Hypotheses Independent Variables
T-Value
P-Value
Remarks
H1 There is a significant relationship between exchange rate growth rate and the all share index growth rate on the Lusaka Stock Exchange All Share Index
3.0183
0.0117 There is evidence of long-run relationship between Foreign Exchange growth rate and All Share Index growth rate.

H2 There is a significant relationship between exchange rate growth rate and the Government securities (GRZ) bonds growth rate on the Lusaka Stock Exchange. Secondary Market
GRZ Bond
1.6520
0.2403 There’s a no long-run equilibrium relationship between Foreign Exchange Growth Rate and Secondary Market GRZ Bond Growth Rate
H3 There is a significant relationship between exchange rate growth rate and the Foreign Participation growth rate (FPGR) on the Lusaka Stock Exchange. Foreign Participation the LuSE
0.5965
0.5768 There is a significant long-run relationship between exchange rate growth rate and foreign participation on the LUSE growth rate on the Lusaka Stock Exchange.

Table 5.25: Hypotheses Testing of Independent Variable on the Dependent Variables using the Bound Test

5.10 Discussion
The study was conducted with the aim of determining the relationship between exchange rate growth rate and all share index growth rate on the Lusaka Stock Exchange. The major analysis to answer this object was regression analysis.
The correlation matrix in 5.6 indicated low correlations among the independent variables (less than 0.80). Thus, multicollinearity was not an issue. However, due to the small sample size (16) a multiple regression could not be specified because the time series analysis works well with at-least 50 cases. Therefore, the study used a series of single regressions to determine independent variables that are cointegrated with Exchange Rate Growth. The significance of the association and relationships was tested at 5% confidence level. Bound test was the main test statistics conducted to test the significance of the relationships.
In Model one when considering when considering whether the subsisting relationship between Exchange Rate growth rate and All Share Index growth rate, it was discovered that the F-statistic for the Bounds Test was 6.157 as shown in table 5.9, clearly exceeded the 1% critical value for the upper bound. Accordingly, we rejected the hypothesis of “No Long-Run Relationship”. That is there is evidence of long-run relationship between FOREIGN EXCHANGE GROWTH RATE and ALL SHARE INDEX GROWTH RATE.
The analysis indicated that hypothesis H1 had been supported. There is a significant long-run relationship between exchange rate growth rate and the all share index growth rate on the Lusaka Stock Exchange. The long-run coefficients from the cointegrating equation were also reported, with their standard errors, t-statistics (3.0183), and p-values (0.0117). However, the short-run equilibrium coefficient was significant.
On the error-correction coefficient (CointEq(-1)) was negative (-1.124), as required, and was very significant (this was confirmation of long run relationship between the two variables). Further there was a quick adjustment to long run equilibrium (at a speed of 112%) in the Exchange Growth Rate when the All Share Index Growth Rate changes. The results from the study show that, there is evidence of long-run relationship between Foreign Exchange growth rate and All Share Index growth rate.
Since the “Bounds Test”, allows us to see if long-run relationships are present when we have a group of time-series, some of which may be stationary, while others are not. One of the main purposes of estimating an ARDL model was to use it as the basis for applying the “Bounds Test”. The “Bound Test”, allows us to see if long-run relationships are present when we have a group of time-series, some of which may be stationary, while others are not.
In Model two, looking at the relationship between Foreign Exchange Growth Rate and Secondary Market GRZ Bond Growth Rate, the null hypothesis was that there is no long-run relationship between the variables. The F-statistic for the Bounds Test was 1.288 as shown in table 5.14, which clearly did not exceed the 10% critical value for the upper bound. Accordingly, we did not reject the hypothesis of “No Long-Run Relationship”. The long-run coefficients from the cointegrating equation were also reported, with their standard errors, t-statistics (1.6520), and p-values (0.2403). From these results were able to conclude the following, that there’s a no long-run equilibrium relationship between Foreign Exchange Growth Rate and Secondary Market GRZ Bond Growth Rate. Thus, the hypothesis H2 has not been supported. There is no significant long-run relationship between exchange rate growth rate and the Government securities (GRZ) bonds growth rate on the Lusaka Stock Exchange. All the short-run equilibrium coefficients are not significant.

The error-correction coefficient (CointEq (-1)) was not significant. However, the coefficient was negative as required. Thus, we could not conclude that there was cointegration between Foreign Exchange Growth Rate and Secondary Market GRZ Bond Growth Rate.

In Model three the null hypothesis was that there is no long-run trem relationship between the variables – in this case, Foreign Exchange Growth Rate and Foreign Participation on the LuSE Growth Rate.
After data analysis the F-statistic for the Bounds Test was 4.723, see table 5.20. This clearly exceeded 5% critical value for the upper bound. Accordingly, we rejected the hypothesis of “No Long-Run Relationship”. That is there is evidence of long-run relationship between Foreign Exchange Growth Rate and Foreign Participation on the LuSE Growth Rate. The above analysis indicates that hypothesis H3 had been supported. There is a significant long-run relationship between exchange rate growth rate and foreign participation on the LUSE growth rate on the Lusaka Stock Exchange. The long-run coefficients from the cointegrating equation were also reported, with their standard errors, t-statistics (0.5965), and p-values (0.5768). All the short-run equilibrium coefficients were not significant

In the estimation results the error-correction coefficient (CointEq(-1)) was negative (-1.208), as required, and very significant (this is confirmation of long run relationship between the two variables). Further, there was a quick adjustment to long run equilibrium (at a speed of 121%) in the Exchange Growth Rate when the Foreign Participation on the LuSE Growth Rate changes.
5.10 Summary of the Findings
This study determined the relationship between exchange rate growth rate and all share index growth rate on the Lusaka Stock Exchange, using different statistical analysis of ARDL and Error Correction Model. The results confirmed that, there exist a long-run relationship between Foreign Exchange growth rate and All Share Index growth rate; the results further confirmed the following for the other independent variables. There is a significant long-run relationship between exchange rate growth rate and foreign participation on the LUSE growth rate on the Lusaka Stock Exchange. However there’s a no long-run equilibrium relationship between Foreign Exchange Growth Rate and Secondary Market GRZ Bond Growth Rate on the Lusaka Stock Exchange.

CHAPTER 6
CONCLUSION AND RECOMMENDATIONS
6.0 Introduction
Chapter five presented the research data, analysis, findings and discussion. This chapter concludes the research study. The chapter presents the summary of research conclusion and recommendations based on the research findings.
6.1 Summary of Research Findings
The study analysed the relationship between Lusaka stock exchange All Share Index growth rate performance and Foreign exchange growth rate from the year 2000 to 2017. All Share Index growth rate, Secondary Market GRZ Bonds growth rate and Foreign Participation on the Lusaka Stock Exchange growth rate were used as independent variables
The main objective of this research was to establish the relationship that exits between the Lusaka Stock Exchange (LuSE) All Share Index (ASI) growth rate and the changes in Exchange Rate growth rate. We discovered that there was significant evidence of long-run relationship between Foreign Exchange growth rate and All Share Index growth rate; this was observed from t-statistics of 3.0183and p-values 0.0117. The F-statistic for the Bounds Test was 6.157 which clearly exceeded the 1% critical value for the upper bound. The results were also similar in the relationship between exchange rate growth rate and foreign participation on the LUSE growth rate with the t-value of 0.5965, p-values 0.5768 while the F-statistic was 4.723 which exceeded 5% critical value for the upper bound. However, the findings in the relationship between Foreign Exchange Growth Rate and Secondary Market GRZ Bond Growth Rate revealed that there was no long-run equilibrium relationship between the variables. The F-statistic for the Bounds Test was 1.288, which clearly did not exceed the 10% critical value for the upper bound while the t-statistics was 1.6520, and p-values 0.2403.

The theories which were revealed in the study were the good market theory and the portfolio balance approach. The goods market theory argues that changes in exchange rates affect the competitiveness of multinational firms and consequently their earnings and stock prices. Depreciation of the local currency makes exporting goods cheaper and may lead to an increase in foreign demand and sales. Conversely, when the local currency appreciates, foreign demand of an exporting firm’s products shrinks so the firm’s profit will decrease and so does its stock price. The opposite case holds for importers. In addition, exchange rate movements affect the values of a firm’s outstanding payables and receivables denominated in foreign currencies. The impact of exchange rate fluctuations on stock prices depends on both the weight of a country’s international trade and the degree of the trade imbalance. According to this argument, we expect a causal effect from exchange rates to stock prices.

The alternative explanation for the relation between exchange rates and stock prices can be provided through ‘portfolio balance approaches’ that stress the role of capital account transaction. Like all commodities, exchange rates are determined by market mechanism, i.e., the demand and supply condition. A blooming stock market would attract capital flows from foreign investors, which may cause an increase in the demand for a country’s currency. The reverse would happen in case of falling stock prices where the investors would try to sell their stocks to avoid further losses and would convert their money into foreign currency to move out of the country. There would be demand for foreign currency in exchange of local currency and it would lead depreciation of local currency. As a result, rising (declining) stock prices would lead to an appreciation (depreciation) in exchange rates. Moreover, foreign investment in domestic equities could increase over time due to benefits of international diversification that foreign investors would gain. Furthermore, movements in stock prices may influence exchange rates and money demand because investors’ wealth and liquidity demand could depend on the performance of the stock market
During data testing, a multiple regression could not be specified due to the small sample size of 16 (time series analysis works well with at-least 50 cases). Thus, the study used a series of single regressions to determine independent variables that are cointegrated with Exchange Rate Growth.

6.2 Recommendations
According to economic theory, movements in the stock market can have a profound economic impact on the economy and everyday people. A collapse in share prices has the potential to cause widespread economic disruption. Yet, daily movements in the stock market can also have less impact on the economy than we might imagine. Plummeting share prices can make headline news. But, how much should we worry when share prices fall? How does it impact on the average consumer? And how does it affect the economy? The following are some of the economic effects of the stock market:
6.2.1 Wealth effect
The first impact is that people with shares will see a fall in their wealth. If the fall is significant it will affect their financial outlook. If they are losing money on shares they will be more hesitant to spend money; this can contribute to a fall in consumer spending. Often people who buy shares are prepared to lose money; their spending patterns are usually independent of share prices, especially for short term losses.
6.2.2 Effect on pensions
Anybody with a private pension or investment trust will be affected by the stock market, at least indirectly. Pension funds invest a significant part of their funds on the stock market. Therefore, if there is a serious fall in share prices, it reduces the value of pension funds. This means that future pension pay-outs will be lower. If share prices fall too much, pension funds can struggle to meet their promises. The important thing is the long term movements in the share prices. If share prices fall for a long time then it will definitely affect pension funds and future payouts.
6.2.3 Confidence
Often share price movements are reflections of what is happening in the economy. E.g. a fear of a recession and global slowdown could cause share prices to fall. The stock market itself can affect consumer confidence. Bad headlines of falling share prices are another factor which discourages people from spending. On its own it may not have much effect, but combined with falling house prices, share prices can be a discouraging factor. However, there are times when the stock market can appear out of step with the rest of the economy. In the depth of a recession, share prices may rise as investors look forward to a recovery two years in the future.
6.2.4 Investment
Falling share prices can hamper firms’ ability to raise finance on the stock market. Firms who are expanding and wish to borrow often do so by issuing more shares – it provides a low cost way of borrowing more money. However, with falling share prices it becomes much more difficult.
6.2.5 Exit opportunities to entrepreneurs
In order to promote entrepreneurship (which I believe is the primary wealth creator in the economy) successful entrepreneurs need to be given an exit from their company. The stock exchanges provide an exit opportunity – there are many examples of this, the most famous of which is Bill Gates – whose personal family office has been selling Microsoft stock and diversifying his holding into several companies.
The findings in this study have both policy and economic implications, and this is supported by economic theory as outlined above therefore the following is recommended:
6.3 Macroeconomic Stability
A stable macroeconomic environment with low and predictable rates of inflation, consistent and sound monetary, fiscal and exchange rate policies is more likely to contribute to stock market development because both domestic and foreign investors will be unwilling to invest in the stock market where there is high macroeconomic volatility.
6.4 Banking Sector Development
This is important to stock market development because at the early stage of its establishment, the stock market is a complement rather than a substitute for the banking sector. Hence support services from the banking system contribute significantly to the development of the Stock market.
6.5 Institutional Quality
Institutional quality is important for stock market development because efficient and accountable institutions tend to broaden appeal and confidence in equity investment. Equity investment thus becomes more attractive as political risk is resolved overtime.
6.6 Suggestions for future research
Future researchers should consider investigating the contribution of Lusaka Stock Exchange foreign portfolio investment to the growth of the economy in terms of gross domestic product. Further research can also on how interest rates impact Zambia’s capital market. The research should include more variables than specified in this study as well increasing the sample size.
6.7 Summary
This chapter set out the conclusion and recommendations of the study and further suggested areas where future researchers can build interest. In order to enhance performance LuSE should encourage more listings on the market by reviewing some areas which may be hindering companies to participate on the market. Further, a stable macroeconomic environment with low and predictable rates of inflation, consistent and sound monetary, fiscal and exchange rate policies is critical to the development of stock market.

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Appendix 1
Foreign Participation on the Lusaka Stock Exchange

Appendix 2
Secondary Market GRZ Bonds

Appendix 3 Appendix 4
Foreign Exchange against the US $ All Share Index on the Lusaka Stock Exchange

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