The African continent is endowed with a variety of natural resources ranging from mineral deposits, oil and vast tracks of arable land . The… [600252]

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CHAPTER 1:
1.1. INTRODUCTION
The African continent is endowed with a variety of natural resources ranging from mineral
deposits, oil and vast tracks of arable land . The economies of man y African countries consist of
mainly agriculture, trade, not so well developed industry and human resources. Many of the
continent‟s 107 billion people still leave in poverty (World Bank report, 2012) . However
according to Rodrik (20013) the last decade has been extraordinary for most developing
countries as they expanded at unprecedented rates bringing with it large reduction in extreme
poverty and significant expansion of the middle class. According to The Ec onomist (2012)
recent growth has been due to growth in sales in commodities, services and manufacturing .
Besides the existence of inequality, a major deterrent in wealth distribution, the GDP of sub-
Saharan Africa in particular is expected to reach $ 29 trillion by 2050. Farid (2013), noted that
despite experiencing rapid growth in number and size of financial markets , the existing evidence
still suggests that African stock markets remain highly fragmented, small, illiquid and
technologically weak, severely affecting their informational efficiency.
1.2 BACKGROUND TO STUDY
There is a commonly held belief in the investing world that if the econom y booms the stock
market will too. This belief is predicated on the notion that if the economy is growing, then
companies do benefiting from it and consumers start spending resulting in corpor ate earnings
rising . In principle, the stock market is expected to accelerate economic growth by providing a
boost to domestic savings and increasing the quantity and the quality of investment. (Singh,

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1997) cited by Adjasi (2007). By encouraging savings stock markets provide individuals with an
additional financial instrument that may better meet their risk preferences and liquidity needs.
.According to Levine and Zervos, (1998) b etter savings mobilization can increase the savings
rate. Growing companies also use the stock markets as an avenue to raise capi tal at lower cost.
Companies in countries with developed stock markets such as in the United States of America
and some European countries are less dependent on bank financing, which can reduce the risk of
a credit crunch. In so doing s tock markets therefore are capable of positively influencing
economic growth through encouraging savings amongst individuals and providing avenues for
firm financing . However according to Paine (2004), n umerous studies have been carried out and
these exhibit the poor relationship between GDP grow th and stock price performance. Paine
noted a study by a New York -based asset management firm Gerstein Fisher eight developed and
developing countries over the period January 1993 to December 2010 indica ted that not only
were the correlations between these economies and stock markets low, but in some cases the
divergence between compounded stock returns to compounded GDP growth was significant.
They gave an example of China‟s economy which expanded by an annualized 15.75% during the
18-year period, yet stocks declined by 2.25% a year. The evolution of financial markets was
explained by Capasso (1996), cited by Butsa (2008) through the identification of optimal
investment strategies. In simple economies suc h as those with a large share of agricultural sector,
the numbers of investment opportunities are limited and as such banking systems are sufficient
for optimal resource allocation. However with the growth of the economy, the complexity of
resource allocat ion increases and the identification of the best possible investment strategies
becomes more difficult as it requires gathering much larger amount of information for optimal
decision –making. In such conditions, when continuous monitoring is necessary for building

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efficient investment strategies stock markets become the optimal solution of the problem. Butsa
(2008) .
The Zimbabwean economy has been on the downward trend since year 2000 after the
government embarked on the land distribution programme. The p eriod was also marked with
disput ed elections of 2000 , 2002, 2005 and 2008. All these events have had a negative effect on
investor confidence and have increased the risk of investing in the country thereby marking the
beginning of an economic crisis. At the epicenter of the economic crisis, was an unprecedented
level of hyper -inflation, sustained period of negative Gross Domestic Product (GDP) growth
rates, massive devaluation of the currency, low productive capacity, and loss of jobs, food
shortages, po verty, massive de -industrialization and general despondency. In 2009 the
Zimbabwean authorities adopted a multicurrency system to counter the ravaging impact of
hyperinflation which had picked at about 231 million percent at the height of its economic cris is
in 2008.Studies by Jecheche (2009 ) before the multicurrency system on the relationship between
economic growth and its determinants with special focus on the stock market development in
Zimbabwe using data for the period from 1991 to 2007 showed positive relationship between
efficient stock market and economic growth both in short run and long run. The study also
showed that financial instability and inflation have negative effect, and human capital and
foreign direct investment have positive effect on growth. His results were consistent with the
theoretical predictions. This paper explore s the effect of economic growth on the stock exchange
market, a case of the Zimbabwe Stock exchange with data from 2009 to 2014, a period where t he
country is using the multicurrency system comprising mainly of United States dollars and S outh
African Rand. This period under study has also been characterized by periods of disinflation ,

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deflation, liquidity problems in the economy as well as volatility of the South African Rand
which is the country‟s biggest trading partner.
1.3 History of the Zimbabwe Stock Market
The Zimbabwe Stock Exchange is a small but active stock exchange in Africa. It was established
in 1896 and has been open to foreign investment since 1993. It has over 70 listed securities.
There are two indices, the Zimbabwe Mining Index (comprising mining companies) and the
Industrial Index (comprising all companies other than mining companies). The Zimbabwe Stock
Exchange operates according to the Stock Exchange Act (Chapter 198). During the late 1990s
the stock exchanges was regarded a s one of the star emerging market performers. This was after
its market capitalization had surged to about 165%, from Z$19. 9 billion to Z$52.8 billion
(roughly US$ 4. 87 billion) in 1996. This surge was due in some part to the listing of Ashanti
Goldfield s following its takeover of Cluff Resources. With more than 40% of company
performances linked to farm output, the industrial index soared from 3972 to 8786 between
January and December in 1996. A total of 722,667,658 shares were bought and sold, valued at
Z$2.6 billion. However in 1998, Zimbabwe's stock market, once regarded as one of the most
promising emerging markets in the region, saw a decline in turnover to 60% of the previous
year's volumes and 88% of its value of shares sold. 1998's fall was attrib uted to high interest
rates which attracted investors to the higher yielding money market and to a loss of confidence
caused by a number of factors such as social unrest (including food protests and mass stay –
always) and the government's stated intention t o acquire comm ercial farms for resettlement. In
2004, the Securities Act was passed for the securities investment purpose. The securities
Commission of Zimbabwe is the capital market regulator and is operational since 2008. Of the
first quarter of 2015 the Zimbabwe stock exchange has a market capitalization of about USD5.5

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billion; the ZSE has an average daily turnover of + USD1.5 million and an average of 75 trades /
day. Market performance since dollarization in February 2009 is about 300% and a Year to
Date (YTD) return of 38%. (ZSE website).
1.4 STATEMENT OF THE PROBLEM
According to Farid (2013), stock markets in Africa can be divided into four categories namely
South Africa, which dominates other African stock markets in terms of both size and
sophistication. The second group is of medium sized markets, many of which have been
established for a long time (e.g., Egypt, Nigeria, Zimbabwe).This is then followed by a group of
small new markets that have shown rapid growth (e.g., Botswana, Mauritius, Ghana) and finally
a small group of new markets that now have recently taken off (e.g., Swaziland, Zambia). The
resea rcher has noted that in some of the countries above ,for example, Zimbabwe , Zambia,
South Africa stock markets are doing very well compa red to economic growth and on the other
hand countries s uch as Botswana, Ghana , and Mauritiu s have been reporting high economic
growth rates at the backdrop of weak stock markets activity . This has influenced the researcher
to investigate if there is any link between economic growth and stock market performance by
taking a closer look at the economy growth outlook of Zimbabwe since the dollarization of the
economy.
1.5 RESEARCH OBJECTIVE S
The main purpose of the study is to investigate the effect of economic growth on the Zimbabwe
Stock Exchange (ZSE)
In order to achieve the main objective stated above the following specific objectives will be
carried:

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 To identify macroeconomic variables or indicators that have any effect on the
performance of the Zimbabwe stock exchange
 To assess the impact of the changes in various macroeconomic variables or indicators on
the performance of the Zimbabwe stock exchange market during the period between 2009
and 2014.
 To r ecommend appropriate macroeconomic policies and provide guidance to policy
makers for sustainable economic growth in Zimbabwe.
1.6 RESEARCH QUESTIONS
The following questions will assist in investigating the effect of economic growth on stock
market returns during the period 2009 up to 2014:
1. What are the major macroeconomic variables that have any effect on the stock market?
2. What are the effects of the changes in var ious macroeconomic indicators on the
performance of the Zimbabwe Stock Exchange?
3. What macroeconomic policies can be dra wn from the relationship between economic
growth and stock?
1.7 HYP OTHESIS
Hypothesis testing is the process of making judgements about a larger group (the population)
on the basis of a smaller group (the sample) that is actually observed. (Daniel et al 1995). The
concept and tools used in hypothesis testing provide an objective means to gauge whether the
available evidence supports the hypothesis. It is only a fter a statistical test of a hypothesis
that a clear idea of the probability that the hypothesis is true or not is obtained. In order to
investigate the effect of economic growth on the Zimbabwe stock market t he following
hypotheses are formulated:

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H0: Economic growth has no effect on the Zimbabwe stock exchange market.
H1: Economic growth affects the Zimbabwe stock exchange market
1.8. SIGNIFICAN CE OF THE STUDY
This study is motivated largely by the need to contribute to the limited existing literature on the
performance of Zimbabwe stock market especially in the post dollarization period. A number of
studies on the interaction between economic growth and stock market in Zimbabwe were carried
out prior to dollarization in 2009. This study therefore will focus on the relationship between
stock market and growth in the post dollarization era. In addition, the re sults of the study can
enable investors and policy makers to come up with effective decisions in evaluating whether it
is prudential invest in the stock exchange. The outcome of the study can be used as the basis of
further investigation by the financial a uthorities in Zimbabwe to come up with broad
macroeconomic policies that can lead to economic growth. The results if verified can assist the
policy makers such as the Ministry of finance, Zimbabwe Stock exchange and the monetary
authorities to formulate po licies that protect the investing public so as to equally distribute
economic resources in the country.
1.9. LIMITATION OF THE STUDY
The pe riod under study (2009 -2014) is a transit ion period where the country changed from using
domestic currency to multi -currency . As a result this presented a challenge to data collection.
The researcher had to overcome some of these challenges by using many sources of data and
comparing it so as to come up with the correct information. This research was limited to a period
of six months. As a part time student, time to do various research processes including data
collection and processing was a challenge. However the researcher took leave days from his full

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time j ob to dedicate to the research. Although this study could have made more sense if it had
also included comparison data from other sub -Saharan economies , it is difficult to generalize
the outcome of the study to similar jurisdictions, because stock market regulations and
economic fundamentals vary from one country to the other and there are significant differences
in content and structure of stock markets all over the world ,however conceptually my study only
focused on the effects of economic growth on the Zimbabwe Stock Exchange
1.10. SCOPE OF THE STUDY
The financial crisis of 2007 –08, also known as the global financial crisis is considered by many
economists as the worst financial crisis since the great depression of the 1930s. (Reuters , 2009) .
It threatened the collapse o f large financial institutions. Most of these institutions survived
because of bailouts which wer e provided by national governments. However stock markets still
dropped worldwide. In many areas, the housing market also suffered, resulting in evictions
foreclosures and prolonged unemployment. The crisis played a significant role in the failure of
key b usinesses, declines in consumer wealth estimated in trillions of U.S. dollars, and a
downturn in economic activity leading to the 2008 –2012 global recession and contributing to the
European sovereign -debt crisis . (Baily and Elliot , 2009 ) This study looks at how the global stock
market is performing relative to performance of the economie s of their respective economies . It
then narrows to stock markets and economies of developing countries such as those in sub –
Saharan Africa. Finally the research looks at the Zimbabwe stock exchange (ZSE) as its case of
study. In general stock markets in developed countries such as the New York Stock Exchanges
(NYSE), London Stock Exchan ge (LSE) etc. are well developed and very active as compared to
stock markets of emerging markets and developing countries such as those mostly found in sub
Saharan Africa.

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DEFINITION OF TERMS
Econo my: According to investopedia.com an economy is the large set of inter -related
production and consumption activities that aid in determining how scares resources are allocated
Economic Growth . This is the sustained expansion of production possibilities measu red as the
increase in real GDP over a given time. It is calculated as the annual percentage change in real
gross domestic product (GDP) or the annual change in real per capita GDP .
Market. Market is the means through which buyers and sellers are brought together to aid in the
transfer of goods and /or services. It does not have to be a physical location nor does it
necessarily have to own goods and services involved
Stock exchange . The busi ness dictionary ( http://www.businessdictionary.com ) defines a stock
exchange as “Organized and regulated financial market where securities (bonds, notes, shares)
are bought and sold at prices governed by th e forces of demand and supply. Stock exchanges
basically serve
CHAPTER SUMMARY
The chapter provided an introduction to the study. It stated with an overview of the topic under
study followed by objectives of the study and the research questions. It went further to formulate
a hypothesis based on what the research will be investigating. The chapter also outlined the
scope of the study . The chapter ended by giving definitions to key terms in the study

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1.11. DISSERTATION STRUCTURE
The dissertation is divided into five chapters.
Chapter 1. This chapter begins with an introduction to the study where an overview of global
economies with particular mentioning of the African continent. The next section is the
background to the study whe re the researcher briefly describes the past and present studies on the
subject. The section also mentions the gaps within previous studies on the subject topic which
motivated him to want to do further investigations. The chapter gives an in -depth backgro und of
the Zimbabwe stock market and goes to formulate the research problem. This is followed by the
main objective of the study the various specific objects to be done in order to achieve the main
objectives. The chapter concludes by looking at limitation s and the general scope of the study.
Chapter 2 . Literature review .This chapter is divided into two sections. The first looks at
theoretical literature of the subject understudy . In this section various theories of economic
growth and stock market as well as principles behind these theories. The second part looks at
different empirical research findings by various academics and researchers in the field on
economics and finance related to the topic understudy
Chapter 3 . Methodolog y: The chapter outlines th e basis for choosing the methodology of the
research. The research philosophy, the research design, sample size, sampling method, collection
approaches, data analysis methods and presentation methods, processes, are described and
justified. An in depth explanation of the process of analysis is used to test the assertions is
presented. The benefits and drawbacks experienced during the data coll ection process are
highligh ted at each stage of discussion .

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Chapter 4. Data presentation, analysis and interpretation: This chapter presents a summary of
the results from data collected for the purpose of investigating as well as interpretations and the
discussion of these results.
Chapter 5 .Conclusions and recommendations .This chapter is made up of a conclusion based on
the findi ngs as well as recommendation.

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CHAPTER 2
LITERATURE REVIEW
2.1.INTRODUCTION
A literature review is an evaluative report of studies found in the literature related to an area of
study . The review describes, summarizes, evaluates and clarifies this literature. It does so by
reviewing the theoretical basis for the research and helps the researcher to determine the nature
of his or her own research. When conducting literature review a selected number of works that
are central to the area are chosen. It goes beyond the search for information and includes the
identification and articulation of relationships between the literature and your field of research.
While the form of the literature review may vary with different types of studies, the ba sic
purposes remain constant . In this study the researcher will look at two basic forms of literature
review namely theoretical and empirical literature. The purpose of theoretical literature
according to guidelines set by the University of California ( http://libguides.usc.edu ) is to
examine the theory that has accumulated in regard to an issue , concept, theory, phenomena. It
assists in establish ing theories that already exist, the relationships between them, to what degree
these existing theories have been investigated, and to develop new hypotheses to be tested. The
other form of literature review in this study is empirical literature review . This involves critically
looking at previously related research done by o ther academics and scholars It critically look s at
how these researcher draw up a wide variety of knowledge ranging from the conceptual level to
practical documents for use in fieldwork in the areas quantitative and qualitative integration,
sampling, inte rviewing, data collection, and data analysis.

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This chapter reviews the existing literature related to effect of economic growth and stock
markets . The chapter is divided into the following sections: Theoretical literature review where
we have the following subsections (1) theories and definitions economic growth (2) the business
cycle (3) Macroeconomic variables/ indicators of economic growth. (4) Methods of measuring
economic growth (5) theories and definitions of the stock exchange market (6) Methods used in
the evaluation of stock markets. The second section looks at a critical review of e mpirical studies
done by other researchers and academics on the effects and relationship between economi c
growt h and stock markets
2.2.THEORETICAL LITERATURE REVIEW
2.2.1 ECONOMIC GROWTH
Economic growth is defined as the process whereby the real per cap ita income of a country
increases over a long period of time subject to stipulations that the number of people below a n
“absolute poverty line” does not increase and that the distribution of income does not become
more unequal. Meier (1989). It is calculated as an annual percentage change in real GDP or
annual change in real per capita GDP. Growth in real GDP measures how rapidly the total
economy is expanding whereas per capita, defi ned as real GDP divided by population,
determines the standard living in each country and the ability of the average pe rson to buy goods
and services. (Case, et al, 2005 ) cited in (CFAI, 2015) . Economic growth is important because
rapid growth in per capita real GDP can transform a poor nation into a wealth one. Even small
difference in growth rates of per capita GDP if sustained over time have a large impact on an
economy„s standard of living. However there is a limit to how fast an economy can grow. Faster
growth is not always better for any economy because there are costs associated with excess
growth such as higher inflation, potential environmental damage and the lower consumption and

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high savings needed to finance the growth. (Funke, 2004) cited in (CFAI , 2015 ). This raises the
issue of sustainable growth, which requires the concept of potential GDP. In this case potential
GDP measures the production capacity of the economy that is the real GDP that an economy
could produce if capital and labour are fully employed. In order to fully understand economic
growth one has to examine sources of economic growth namely labour supply, human capital,
physical capital stock, technology and natural resources. Labour supply can be affected by
population growth, net immigration and the labour force participation rate. Growth of the labour
force is an important source of economic growth. Human capital is a source of growth becaus e
workers who are skilled and well –educated are more productive and better able to take
advantage of advances in technology. Investment in human capital therefore leads to greater
economic growth. A larger capital stock increases labour productivity and p otential GDP . An
increased rate of investment in physical capital can therefore increase economic growth.
Technology increases productivity and potential GDP. More rapid improvements in technology
lead to greater rates of economic growth. Natural resources which are a source of raw materials
inputs such as land and oil are necessary to produce economic output. Countries with well
managed large amounts of productive natural resources can achieve greater rates of economic
growth. For global investors estimati ng the sustainable rate of economic growth for an economy
is important for asset allocation and security selection decisions. In this regard investors need to
understand how the rate of economic growth differs among countries and whether these growth
rates are sustainable .
2.2.2. ECONOMIC GROWTH MODELS
Several theories have been formulated to explain economic growth. These include the Classical
growth model , the Schumpeterian model, the Neoclassical or Solow growth model; the General

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Keynes model and the H arrod –Domar growth model. The English classical growth model
analysis of the process of economic growth was a central feature of the work of different schools
of thought. One notably one is the work of English Classical economists as represented chiefly
by Adam Smith, David Ricardo and Thomas Malthus cited by Harris ( 1978 ). They undertook
their studies against the background of the emergence of what is regarded as a new economic
system known as the system of industrial capitalism. The interest of the cl assical economists in
economic growth derived also from a philosophical concern with the possibilities of progress.
Progress was seen as an essential condition for the development of the material basis of society.
The three economists aimed in carrying ou t the analysis to identify the forces in society that
promoted or hindered development and hence progress and consequently they aimed at providing
a basis for a policy and action plan to influence these forces. Ricardo (1882) „s campaign against
the corn l aws , Malthus „s concern with problem of population growth (1798) and Smith (1776)
‟s attacks against the monopoly privileges associated with mercantilism are some of initiatives
by these economists cited in Harris ( 1978 ). Smith (1776) who contributed gr eatly to the
classical growth theory stated that the sources of growth are: growth in the labour and stock of
capital, improvements in the efficiency with which capital is applied to labour through greater
division of labour and technological process and f inally foreign trade that widens the market and
reinforces the other two sources of growth. Smith argued that the growth process becomes self –
reinforcing and as long as the growth in wealth favours profits, these become savings and
additional capital accu mulation and hence promote further growth. Smith‟s work was similarly
supported by Dornbush and Fischer (1990) who said that sources of growth follow the
exploitation of labour, capital a nd technical knowledge. The Schumpeterian theory , (Schumpeter
1947) According to the Schumpeterian theory which is more developed in evolutionary

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economics, the business cycle and economic growth are intimately related , with their common
cause being burst of technological change driven by the invention and innovation of major
technologies. These tend to come not smoothly over time but in bursts associated with the
innovation of major new technologies of the kind that eventually spread their influence over
much, or even all of the economy. The development of such major ne w technologies is
associated with booms in economic activity, often financed by major expansions of bank credit
extended to innovating firms and those speculating on their success. This will cause investors to
become optimistic, often resulting in stock ma rkets upsurges. Because of the increased
exploitation of these new technological applications, a falloff in new investment may occur
resulting in downward revision of expectations which leads to a stock market decline or crash
and a calling in on bank loan s Louca and Freeman (2001) cited in Lipsey and Chrystal, (2011).
Kuznets (1971) characterized the stage theory of long term economic changes as having the
following features: distinct time segments, characterized by different sources and patterns of
econo mic changes, specific succession of these segments, so that b cannot occur before a or c
before b and having common matrix in that the succession segments are stages in one broad
process, usually of development and growth rather th an of devolution and shrinkage. The
Neoclassical or Solow growth model was developed in the 1950s and 1960s as a result of
research in the field of growth economics. The main contributors to this theory were Robert
Solow and Meade . The theory looks at capital accumulation and its related decision of savings as
an important determinant of economic growth. It considers two factor production functions with
capital and labour as determinants of output. It also adds technology as an exogenous
determinant factor to the production fun ction. Y = AK (k, L) ………………….. (i ) and Y = F
(K, AL)……………… . (ii)Where Y is Gross Domestic Product (GDP), K is the stock of capital,

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L is the amount of unskilled labour and A is exogenously determined the level of technology.In
equation (i) the technology f actor is incorporated into the production function and in this it is
assumed that technological progress augments all factors both capital and labour factors. When
the production function is empirically estimated this way, the contribution of technology (A ) to
the total output is called Solow residual meaning that total factor productivity really measures the
increase in output which is not accounted for by changes in factors, capital and labour. In the
second equation technology parameter in incorporated i n the production function where it is
assumed to argument labour. The neoclassical makes the following assumptions: It assumes that
planned investments and savings are always equal because of immediate adjustments in prices
including interest. It also cons iders unlimited possibilities of substitution between capital and
labour in the production process. By making these assumptions the neoclassical growth theory
focuses its attention on the supply side factors such as capital and technology for determining
rate of economic growth of a country.
The Solow approach remains the first economic growth model tha t students learn usually
presented with a focus on the rise in capital per person as the prime force in raising living
standards over time . The general theory by Keynes, Keynes‟s theory was concerned with the
determination of income and employment in the s hort run. Keynes pointed out that since in the
short –run situation of developed capitalist economies aggregate demand was deficient in
relation to the aggregate supply of output, the equilibrium will be established at less than full
employment level. Keyn es overlooked the effect of investment in a given period on the increase
in productive capacity therefore his model failed to look long-run growth of the economy.
Harrod (1939) and Domar (1946) extended the Keynesian analysis of income and employment to
long run setting and therefore considered both the income and capacity effects of investment.

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The Harrod -Domar growth model uses the average propensity to save and the capital -output ratio
to determine the rate of growth. They used the average propensity to save because it gives the
rate of capital accumulation as well as yielding the percentage of total income that is invested. It
this model they assumed that sa vings are equal to investments. The Harrod –Domar model
measures the growth rate as the average p ropensity to save divided by the capital –output ratio.
The problematic assumption in the Harrod –Domar model is that planned savings equal planned
investment. It can be argued that when investment increases, firms bid up the interest rate in a
competition to get financing and this higher interest rate will induce higher savings. Further
arguments with this model are that savings are mainly a function of income, not of the interest
rate thus this assumption of the equality of planned savings and investment seems to make the
model unrealistic. Other academics have also pointed out that the Harrod -Domar model can still
have problems even if the above assumption is dropped. They argued that if more money is
invested, there is a possibility of depression and col lapse. This happens because more money is
taken out (saved) of the flow of expenditure than is put back in (invested) .The result of this is
level of demand both consumption and investments will not be high enough to absorb the total
supply of output level s. Similarly they argued that if planned investment exceeds savings,
demand will exceed the total supply of output at prevailing prices thereby stimulating price
inflation. In conclusion if the assumption that planned savings, equals planned investment is
dropped, the economy could easily be lead into inflation or depression rather than growth.
Combining both the Keynesian theory of savings which hold that the level of income is the main
determinant of savings and the Harrod -Domar model which states that sa vings is very important
in determining growth rates, that is the higher the level of savings, the higher the growth imply
that poor, low income countries will have low levels of savings that is a low average propensity

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to save which means that these count ries will have low growth rates translating to the saying that
poor countries will remain poor while rich countries with high levels of savings will get richer.
2.2.3 BUSINESS CYCLE S
According to Burns and Mitchell (1994 ) business cycle s are a type of fluctuation found in the
aggregate economic activity of nations that organizes their work mainly in business enterprises .
Business cycles have been recognized since early days of economic theory and considered effort
has been put in identifying different c ycles and explaining them. The cycle consist of expansions
occurring at about the same time in many economic activities and are followed by similarly
general recessions, contractions and revivals which merge into the expansion phase in the next
cycle. The sequence of events is recurrent but not periodic. This sequence of events is made up
of all the phases of economic activity namely recession, recovery and expansion. The end of an
expansion and start of a contraction is the cycle peak whilst the end of a contraction and start of
expansion i s the cycle trough. Boyde et al (2014), explained that when an economy is passing
through different stages of the business cycle the relative perfo rmance of different industry
groups might vary. A good example happens at a trough, just before the economy begins to
recover from recession, cyclical industries such as producers of durable goods, would tend to
outperform other industries. Sales of these goods are particularly sensitive to macroeconomic
conditions and as such the purchasing of these goods can be differed during recession. In general
stocks of cyclical firms are known to show the best results when economic news are positive but
the worse re sults when the news are bad (Boyde et al,2014) .On the contrary defensive industries
such as those in the food, pharmaceutical industries, have little sensitivity to the business cycle.
They tend to have low betas and performance that is relatively unaffect ed by overall market
conditions. Many economic variables and sectors of the economy have distinct cyclical patterns.

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Knowing these patterns can offer an insight into likely cyclical directions overall or can be
particularly applicable to an investment stra tegy that requires more than general cyclical insights
for investment success In general investors prefers cyclical industries when they are relatively
more optimistic about the economy and defensive firms when they are relatively more
pessimistic Howeve r there has been debates amongst different schools of thought on the
interpretations of the business cycle.
2.2.4 . THE ROLE OF GOVERNMENTS IN ECONOMIC GROWTH
Monetary and fiscal policies are measures enacted by various agencies of national governments
to influence the aggregate economies of those countries. Economic conditions that result from
these measures influence all industries and companies within the economies. Fiscal policies such
as taxes credit or tax cuts can encourage spending whereas addition al taxes on income can
discourage consumption. Governments also increase or decre ase spending on highways, defense
etc. These fiscal policies influence the business environment for firms that rely directly on such
government expenditure. Monetary policy al so produces similar economic changes, for example
a restrictive monetary policy that reduces the growth rate of money supply reduces the supply of
funds for working capital and expansion for all business. A restrictive monetary policy that
targets interest rates would raise markets rates and therefore firm‟s costs and make it more
expensive for individuals to finance mortgages and to purchase other durable goods. On the other
end inflation causes differences between real and nominal interest rates and chang es the
spending ,saving and investment behavior of consumers and corporations .Unexpected inflation
make it difficult for firms to plan ,which inhibits growth and innovation. Differential inflation
and interest rates also influence the trade balance betwee n countries and the exchange rate of
currencies. In addition to monetary and fiscal policy ,such events as political upheavals ,war or

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devaluations in international currencies produces changes in the business environment that add to
the uncertainty of sal es and earnings expectations and risk premium req uired by investors.
Adelman, (1999), explained that the first phase, lasting from 1940 to 1979, governments were
assigned a primary, entrepreneurial role. He traced the intellectual roots of these views in the
writings of the pre -Marshallian classical economists and in their immediate post World War II
followers, W. Arthur Lewis, Rosenstein Rodan, Nurkse, Singer, Prebish, Hirschman and
Leibenstein. They viewed economic development as a growth process that requires the
systematic reallocation of factors of production from a low -productivity, traditional tech nology,
decreasing returns, mostly primary sector to a high -productivity, modern, increasing returns,
mostly industrial sector. Hence there is need for government action to propel the economy from
the uncoordinated, low -income, no -long-run-growth static eq uilibrium to the coordinated, high –
income, dynamic equilibrium, golden -growth path. Rodan (1943) cited by Adelman (1999)
recommended the need for a government -financed series of interdependent investments, to take
advantage of external economies and econo mies of scale and propel developing countries from a
low level equilibrium trap, with no growth in per capita income, to a high -level equilibrium path,
character ized by self -sustained growth. In order to remedy both the structural and coordination
failures , government would therefore have to engage in an active role to subsidize investment,
coordinate investment activities, and undertake direct investment itself from the government
budget, despite the, hopefully, mild inflationary pressures these actions wo uld induce. Some
development economists contended that a "big push" of simultaneously undertaken investments
would maximize the external economies generated by investment and generate self -sustained,
growth faster. Others contended that "balanced growth" w ould reduce the bottlenecks and import
needs of the investment programs and thereby raise the marginal efficiency of investment.

22
2.2.5. THE STOCK EXCHANGE MARKET
Economists define a market as a group of buyers and sellers that are aware of each other and a re
able to agree on a price for the exchange of goods and services (von Stackelberg et al 1952)
Stock exchange markets or equity markets are markets in which shares of publicly held
companies are issued and traded either through exchanges or over -the-count er market. The
history of stock markets can be traced back to the middle ages where participants in those
markets were predominantly professional typically large institutional investors such as mutual
funds, banks, insurance companies and pension schemes. Over the years the stock markets have
undergone modernization, replacing antiquated trading floors and practices with ultra -fast
computerized electronic trading. The use of computers through internet has made real time
access coupled with improved security and efficiency. Online trading websites have been
providing a wealth of tools, markets and company information to aid in investment decision
making. In principal, stock markets lie at the heart of financial systems. El -Wassal (2013). The
primary function of stock markets is to serve as a mechanism for transforming savings into
financing for the real sector. From the theoretical perspective si de, stock markets can accelerate
economic growth by mobilizing and boosting domestic savings and improving the quantity and
quality of investment. Better savings mobilization may increase the rate of saving and if stock
markets allocate savings to investme nt projects yielding higher returns, the increasing rate of
return to savers will make savings more attractive. Consequently, more savings will be
channeled into the corporate sector. Efficient stock markets make corporations compete on an
equal basis for funds and help make investment more efficient El -Wassal (2013)

23
2.2.6 STOCK MARKET THEORIES
Successful investing depends on correct predictions about the movements of the market, both as
a whole and in its component parts. Accordingly t here is no fool proof way to successfully
predict market behaviour, which is why there is still no consensus on market theories. However,
according to Zacks economic research group ( http://www.zacks.com ) understanding of the
following t heories of the stock market may offer the best possibility of making an informed
investment.
Industry and Company Fundamentals,, according to this theory the best way to make a n
investment in the stock market an investor needs to do a thorough research on the company
itself. Industrial analysis is important for company analysis because it provides a framework for
understanding the firm. By u nderstanding a firm‟s business environment an investor is provided
with an insight about the firm‟s potential grow th, competition and risks. According to this theory
a company that is successful in its operations will be worth investing. The second theory is the
environment or external influences on industry growth, profitability and risk theory. According
to th is theory a lot of time is spend on analyzing the fundamental factors such as
macroeconomic, technological , demographic, governmental and social influences.
Macroeconomic factors such as GDP can be cyclical or structural trends . Others such as interest
rates affect financing costs for firms and individuals, as well as financial institution profitability,
others such as c redit availability affects consumer and b usiness expenditure and funding whilst
technological change can change industry dramatically through the introduction of new or
improved products .Demographic factors include age distribution and population size, as well as
other change in the composition of the population and s ocial influences relate to how people
work, pla y, spend their money and conduct their lives . When investors pay attention to the se

24
environment al factors they want to know about how their prospects might be for investing in
such economies .
Supply and demand is a theory based on the classical idea of supply and demand in relation to
the stock market. According to this theory the price of any stock is not affected as much by the
company's performance or the general political climate so much as by the interaction of suppl y
and demand. On any giv en day, there might be more people who want to invest than there are
stocks avail able, or vice versa. Given such a scenario , the interaction between the offering of
stocks and investment by the public determines whether the value goes down, in the case of
excessive supply, or up, in the case of excessive demand. (http://www.zacks.com )
The last theory is the investor p sychology theory. This theory challenges the traditional idea that
investors are well informed, or moreov er that they behave rationally . However, this theory does
not discount any possibility for predicting market behavior by investors by on the contrary, it
simply highlights that market doesn't necessarily behave in a way that makes logical sense. This
is the type of viewpoint that observes investors behaving with herd mentality. Generally s tock
prices can skyrocket or plummet with little prompting other than the perception that other s are
buying and selling.
2.2.7 THE EVALUATION OF STOCK MARKET
Bachelier (1900) in his remarkable doctoral thesis “La Théorie de La Spéculation” proposed the
random walk as the fundamental model for financial asset prices many decades before the idea
became widely accepted by other academics. Samuelson (1965) initiated the modern literature by
proving that asset prices in efficient markets fluctuate randomly, and only in response to new
information. Successful investing depends on correct predictions about the movements of the

25
market, both as a whole and in its component parts. There is no fool proof way to successfully
predict market behaviour. Most assets –pricing models assume that markets are rational and that
the intrinsic value of a security reflects this rationality. But market efficiency and asset -pricing
models do not require that individual is rational, rather they say that the market is rational. This
leaves a lot of room for individual behavior to deviate from rationality. Although terms such as
overvaluation and undervaluation are frequently used in relation to stock markets, their
definitions are far from straight forward. Academics fall back on the Efficient Markets
Hypothesis (EMH), which contents that markets must always be fairly valued. Wright et al
(2011).To counter the debate about value among academic economists, stockbrokers made
claims of their own definition of valuing stock markets. Wright et al (2011) pointed out that the
claims of „stockbroker economics‟ have been driven by a search for commission rather than for
the truth, and they have served to muddy the water, rather than advance the discussion and these
claims have been marked by the misuse of data (data mining) and an absence of any theoretical
foundation
2.2.8 . EFFICIENT MARKETS HYPOTHESIS (EMH)
The efficient market hypothesis assumes that all available information fully reflected in stock
prices at any point of time is the best estimate of the real value of the stocks (Malkiel and Fama
1970) cited by Cuong and Zhou (2014). This implies that it is not possible to consistently
outperform the market which reflects the composite judgment of millions of participants in an
environ ment characterized by many competing investors, each with similar objectives and equal
access to the same information. Hagin, (1979). One method of measuring a market‟s efficiency is
to determine the time it takes for trading to cause information to be refl ected in security prices.
Markets are generally neither perfectly efficient no completely inefficient .The degree of

26
informational efficient varies across countries, time and market types. Market efficiency
increases with larger numbers of market participants, more information avai lable to investors,
fewer impediments such as restrictions to shot selling and finally lower transaction costs. Malkiel
and Fama (1970), however, argue that infringement of those conditions does not necessarily
imply the insufficiency of the market due to competitive environment.
In the context of this hypothesis, the word “efficient” means that the market is capable of quickly
digesting new information on the economy, an industry, or the value of an enterprise and
accurately impounding it into securities p rices. In such markets participants can expect to earn no
more, nor less, than a fair return for the risks undertaken. Fama (1970) in a classic paper
contributed a great deal to operationalize the concept of market efficiency. Fama defines three
types of e fficiency, each of which is based on a different notion of what type of information set
is understood to be relevant in the phrase: “a market in which prices „fully reflect‟ all available
information is called „efficient‟. The t hree forms of the efficient market hypothesis hold that
acting on publicly available information cannot improve one‟s performance beyond the market‟s
assessment of a fair rate of return. The weak form hypothesis asserts that stock prices already
reflects all the information that can be derived by market trading data such as historical price
data , trading volumes or short interest. These are efficiently digested and, therefore, are useless
for predicting subsequent stock price changes. This is distinguished from a semi strong form
under which all publicly available information which include in addition to past prices,
fundamental data on the firm‟s product line, quality of management, balance sheet composition
etc. is assumed to be fully discounted in current securities prices. Finally, the strong form
describes a market in which not even those with privileged information can obtain superi or
investment results. The efficient market hypothesis (EMH) has been under academic and

27
professional consideration for many years. According to Bodie et al (2014), the EMH implies
that a great deal of the activity of portfolio managers „s job of searching for undervalued
securities is at best wasted effort and quiet probably harmful to clients because it cost money and
leads to imperfectly diversified portfolios. On the contrary Grossman and Stieglitz (1980 ) argued
that investors will have an incentive to spend time and resources to analyses and uncover new
information only if such activity is likely to gener ate higher investment returns. It is therefore
important for corporate executives to have a c omprehensive understanding of market efficiency
because th eir decisions and actions determine perceived value of companies. It can also be used
to model the development of the stock market being important for stock market operators and
supervisors.
2.2.9 . THE CAPITAL ASSERT PRICING MODEL (CAPM)
The capital asse t pricing model (CAPM) was introduced independently by Willium Sharpe, John
Lintner, Jack Treynor and Jan Mossin. It builds on the Markowitz (1952, 1959 )‟s earlier work on
diversification and modern portfolio theory. The model provides a linear expected return –beta
relationship that precisely determines the expected return given the beta of an asset. In doing so
it makes the transition from total risk to systematic risk, which is a primary determinant of
expected retu rn. Sharpe –Lintner (1965, 1996) added two assumptions to the Markowitz model to
identify a portfolio that must be efficient if the market is to clear. They converted the mean –
variance model into a market -clearing asset -pricing model. The first assumption w as that all
investors agree on the distributions of returns and may borrow or lend without limit at a risk -free
rate. The risk -free rate clears the market for borrowing and lending. Combining the risk -free
asset and risky assets results in a linear mean –variance -efficient frontier that is tangent to the
efficient frontier/risky asset frontier. All who hold risky assets hold this tangent portfolio, the

28
value -weighted portfolio of all risky assets. The CAPM implies that the market portfolio is
efficient. ( Fama and French 2004) .There has been a sequence of papers that challenge the CAPM
prediction that the market portfolio is efficient. Basu‟s (1977) found evidence that when common
stocks are sorted on earnings -price ratios, future returns on high E/P stocks ar e higher than
predicted by the CAPM, and the returns on low E/P stocks are lower than predicted. Banz (1981)
documents a size effect; when stocks are sorted on market capitalization (price times shares
outstanding), average returns on small stocks are high er than predicted by the CAPM. Bhandari
(1988) finds that high debt -equity ratios (book value of debt over the market value of equity, a
measure of leverage) are associated with returns that are too high relative to their market betas.
2.2.9. MONETARY P OLICY AND STOCK RETURNS
Several studies by Jensen, Johnson, and Mercer (1996, 1997, 1998, 2000 ) cited by Reilly and
Brown (2009, 2012 ), showed that the results of several earlier studies that examined the
relationship between some economic or company variables and stock returns can be significantly
affected by the prevailing monetary environment. Specifically Fama and French (1989 ) showed
that the business conditions proxies suggested by the term spread, dividend yield, and the default
spread have diff erent effects on stock returns depending on the prevailing monetary policy.
Jensen, Johnson, and Mercer (1996, 1997, 1998, 2000) cited in Reilly and Brown (2009, 2012)
contend that monetary policy is best identified by changes in the discount rate by the F ederal
Reserve of the United State of America. Declining discount rates implies an easy monetary
policy, whilst rising discount rates imply a restrictive policy. Their studies also showed that the
relationship between stock price returns and both size and the price -to-book value ratio that was
found in studies by Fama and French (1995) and Fairfield (1994) only holds during periods of
easy monetary policy. Subsequent studies by Thorbecke (1997) cited by Reilly and Brown

29
(2009, 2012) that examined how stock returns respond to monetary policy shocks indicated that
expansionary monetary policy increases ex -post stock returns. Patelis (1997) examined whether
shifts in monetary policy affect the predictability of excess stock returns and found that monetary
polic y variables were significant predictors of future stock returns along with dividend yield.
Reilly and Brown (2009, 2012).
The best -known monetary variable is the money supply. Friedman and Schwartz (1963)
thoroughly documented that declines in the rate of growth of the money supply have preceded
business contractions, while increases in the growth rate of the money supply have consistently
preceded economic expansions. Friedman (1969) suggested a transmission mechanism through
which changes in the growth r ate of the money supply affect the aggregate economy. He
hypothesized that in order to implement planned changes in monetary policy; one way of doing
so was through open market operations such as buying or selling of treasury bonds by the Central
bank. The impact of changes in money supply growth on stock prices is really part of the
transmission process whereby money supply affects the aggregate economy. This liquidity
transmission scenario implies that the effect of a change in monetary policy initially a ppears in
financial markets (bonds and stocks) and only later in the aggregate economy. This transmission
discussion is consistent with the earlier observation that the stock market leads the economy. The
initial studies by Sprinkel (1971), Keran (1971), a nd Homa and Jaffe (1971) generally indicated
a strong leading relationship between money supply changes and stock prices. Subsequent
studies by Cooper (1974) and Rozeff (1974) questioned these findings. Specifically, they found a
relationship between the m oney supply and stock prices, but they found that changes in the
growth rate of the money supply consistently lagged stock returns by about one to three months.
Davidson and Froyer (1982) and Hafer (1985) examined the relationship of stock returns to

30
antic ipated and unanticipated money supply growth and found that stock prices adjust very
quickly to unexpected changes in money supply growth. This implies that to take advantage of
this relationship it is necessary to forecast unanticipated changes in money s upply growth. The
impact of inflation especially unanticipated inflation, on stock returns has also received a lot of
scrutiny among several researchers. Augments have risen as to why common stocks might be
helped or hurt by unanticipated inflation. Kessel (1956) cited by Fama and Schwerte (1977)
argued that unanticipated inflation benefit the common stock of firms that have much more long
term commitments to pay fixed nominal amounts that to receive them. In general high rates of
inflation often associated with economies where the demand for goods and services outstrips
productive capacity lead to upward pressure on pric es Bodie, Kane and Marcus (2014
2.3.EMPIRICAL LITERATURE REVIEW
Many academic and professional researchers have investigated the impact of macr oeconomic
variables on stock Returns over the last few decades Levine et al, (1993), Levine and Zervos,
(1996), Ritter, (2004) Paramati et al (2011), Seetanah at el (2012), Hailemariam, (2014 Fama,
1981; Friedman, 1988; Ke ran, 1971, Nelson, 1976, cited by Al-Zararee and Ananzeh (2014).
The evidence by Moore and Cullity (1988 ) and Siegel (1991) not only has indicated a strong
relationship between stock prices and the economy but also has shown that stock prices
consistently turn before the economy does.
Chen, Roll and Ross (1986) in their studies of economic forces and stock market explored the
following set of economic state variables: the spread between long term and short interest rates,
expected and unexpected inflation, industrial production and the spread between high and low
grade bonds as systematic influences on stock market returns as well as examining their

31
influence on asset pricing. These factors were chosen on the basis of empirical studies by Roll
and Ross (1980), Brown and Weinstein (1983). To do this they employed time series of the five
factors and each was regressed on the state variables. They identified and estimated a vector
auto-regressive (VAR) model in an attempt to use its residuals as the unanticipated innovations
factors. They co ncluded that when stock returns are exposed to systematic economic news, they
became priced in accordance with their exposure and that the news can be measured as
innovations in state variables whose identification can be accomplished.
El-Nader, Alraimony (2012) investigated the impact of macroeconomic factors on Amman Stock
Market (ASE) Returns employing monthly data between (1991and 2010). They used six
macroeconomic factors: Real money supply, real gross domestic product, consumer price index,
real exch ange rate, weighted average interest rates on loans and advances, and a dummy
variable. They applied normality test and unit root tests to the data. The results showed that Real
money supply, consumer price index, real exchange rate, weighted average inter est rates on loans
and advances and the Dummy Variable have a negative role on the ASE returns. In contrast, the
real gross domestic product had a positive impact.
Humpe and Macmillan (2007) examined whether a number of macroeconomic variables
influence st ock prices in the United States of America (USA) and Japan. They applied
cointergration analysis in order to model the long term relationship between industrial
production, the consumer price index, money supply, long term interest rates and stock prices i n
the USA and Japan. For the USA they found that the data was consistent with a single
cointergration vector, where stock prices were positively related to industrial production and
negatively related to both the consumer price index and a long term intere st rate. They also found
an insignificant although positive relationship between USA stock prices and the money supply.

32
However, for the Japanese data they found two cointergration vectors. In one vector they found
out that stock prices were influenced pos itively by industrial production and negatively by the
money supply. As for the second cointergration vector they found that industrial production was
negatively influenced by the consumer price index and a long term interest rate. They concluded
that the contrasting results could have been due to the slump that characterized the Japanese
economy during the 1990s and consequent liquidity trap.
Maysami , Howe , and Hamzah (2004) investigated the long-term equilibrium relationships
between selected macroeconom ic variables and the Singapore stock market index (STI), as well
as with various Singapore Exchange Sector indices namely the finance index, the property index,
and the hotel index. The study concluded that the Singapore‟s stock market and the property
index form had cointegrating relationship with changes in the short and long -term interest rates,
industrial production, price levels, exchange ra te and money supply
Filis (2009) examined the relationship of the cyclical components of Consumer Price Index
(CPI), Industrial Production, Stock Market in Greece and the influence of oil prices on these
variables between January 1996 and June 2008 using VAR. They discovered that the Greek CPI
had a significant negative influence on the Greek stock market.
Menike (2006) investigated the effects of macroeconomic variables on stock prices in emerging
Sri Lankan stock market using monthly data for the period from September 1991 to December
2002. The multivariate regression was run using eight macroeconomic vari ables for each
individual stock. His null hypothesis was that money supply, exchange rate, inflation rate and
interest rate variables collectively did not accord any impact on equity prices is rejected at 0.05
level of significance in all stocks. The resul ts indicated a higher explanatory power of
macroeconomic variables in explaining stock prices. Consistent with similar results of the

33
developed as well as emerging market studies, inflation rate and exchange rate reacted mainly
negatively to stock prices i n the Colombo Stock Exchange (CSE). The negative effect of
Treasury bill rate implied that whenever the interest rate on Treasury securities rise, investors
tended to switch out of stocks causing stock prices to fall. However, lagged money supply
variables did not appear to have a strong prediction of movements of stock prices while stocks
did not provide effective hedge against inflation especially in Manufacturing, Trading and
Diversified sectors in the CSE.
Hayworth and Abdullah (1993) used the Granger causality to investigate the relationship
between the United States of America stock prices and the foll owing macroeconomic variables
budget deficit, money growth, industrial product growth, inflation rate and short –term and long
term interest rate. The results showed that stock prices are positively related to money growth
and inflation and negatively related to budget deficits, trade deficits and both short and long term
interest rates.
Flannery and Protopapadakis (2002) identified macroeconomic risk fa ctor candidates by
examining simultaneously the impact of macroeconomic announcements on level and
conditional volatility of daily equity return. They looked at 17 macro series announcements over
the 1980 -1996 periods . From their findings six out of 17 mac ro variables were strong risk factor
candidates. Of these the two inflationary measures.that is , the CPI and PPI affected only the level
of the market portfolio‟s returns. Three real factor candidates namely Balance of Trade,
Employment/unemployment and ho using affected only the returns, conditional volatility.
Monetary Aggregate (M1) affected both returns and conditional volatility. However they also
found out those two popular measures of aggregate economic activity namely Real GNP and
Industrial product ion did not appear among the risk factors. Instead they are associated with

34
lower than higher return volatility. They concluded that by identifying macro variables that
influence aggregate equity returns has two direct benefits. Firstly, it may indicate he dging
opportunities for investors and secondly if investors as a group are averse to fluctuations in these
variables, the variables might in turn constitute priced factors. However Flannery and
Protopapadakis (2002) studies did not investigate whether inve stors earn excess returns for
bearing risk associated with any of these factor candidates.
Al-Sharkas (2004) used the vector error correction model (VECM) by Johan (1991) to analyses
the long term equilibrium relationship between macroeconomic factor and t he Jordanian Stock
market. He used the following macroeconomic variables: industrial production index, consumer
price index, and money supply and Treasury bill rate. He concluded that these macroeconomic
variables are cointegrated . Fisher (1930) cited by B arnor (2014) hypothesized that stock market
returns is independent of inflation expectations, but the two variables, namely inflation and stock
market returns are positively related. Fisher's conclusions and hypothesis gave credence to the
assertion that i f inflation and stock market returns are positively related, then, equities serve as a
hedge against inflation. Rapach (2001) cited by Ozcan (2012) using macroeconomic data from
sixteen developed countries concluded that there was a weak relationship between inflation and
stock prices Apergis and Eleftherion (2002) concluded that amongst the index of Athens stock
exchange inflation had the greatest impact on the performance on the Athens stock exchange
index compared to interest rates. Nishat and Shahe en (2004) investigated the relationship
between a set of macroeconomic variables and the index of Karachi stock exchange. Their
results showed that industrial production has a positive impact on the performance of the index
while inflation has a negative r elationship with stock exchange index. Liu and Shrestha (2008)
employed heteroskedasticity cointergration to examine if there is a relationship between a set of

35
macroeconomic variables and the index of Chinese stock market. They found that there exist a
significant relationship between the index of the Chinese stock market and macroeconomic
variables. Their conclusion was that inflation, exchange rate and interest rate have a negative
relationship with the index of Chinese stock market.
Aydemir and Demirhan (2009) used Toda -Yamamoto causality test to investigate the
relationship between exchange rate and the index of Istanbul stock exchange. They found that
there exist two way causation between stock exchange index and exchange rate . Ahmed (2011)
cited by Ba rnor (2014) examined the long run relationship between money supply and selected
macroeconomic factors in Sudan and established causality between money supply and
macroeconomic variables. The study used a Granger causality test to establish the causality. The
study concluded that money supply variability is one of four other macroeconomic factors that
showed significant influence on expected stock market returns.
Ozcan (2012) studied the relationship between the macroeconomic variables and the Istanbul
Stock Exchange (ISE) industrial index using monthly data from 2003 to 2010. The study showed
that among the macroeconomic variables ,gold price , exchange rate ,oil price , interest rate
,money supply ,current account deficit and export volume exhibit a lo ng run relationship with the
ISE industrial index
Kimani and Mutuku (2013) obtained data from the central bank of Kenya and used quarterly data
for the period between December 1998 and June 2010. They measured inflation by the arithmetic
mean on consumer b asket and computed an index based on the geometric mean of stock prices
for some selected top performing listed firms on the Kenya market. They then used a unit root
test based on the formal ADF test procedures and the Johansen -Juselius VAR based
cointergr ation test procedure. The cointergration model showed an inverse relationship between

36
inflation and stock market performance in Kenya. Talla (2013) investigated the impact of
changes in selected macroeconomic variables on stock prices of the Stockholm Stoc k Exchange
using monthly data from 1993 to 2012 They employed unit root test, Multivariate Regression
Model computed on Standard Ordinary Linear Square (OLS) method and Granger causality test
in their study. Based on estimated regression coefficients and t -statistics, they found that
inflation and currency depreciation had a significant negative influence on stock prices. In
addition, interest rate was negatively related to stock price change, but was not significant in the
model. However, money supply was positively associated with stock prices although not
significant.
Ouma and Muriu (2014) investigated the impact of the macroeconomic variables on stock
returns on the Nairobi Stock Exchange (NSE) during the period 2003 – 2013, using the Arbitrage
Pricing Theory (APT) and Capital Asset Pricing Model (CAPM) framework for monthly data.
Using the Ordinary Least Square (OLS) technique to test the validity of the model and the
relative importance of different variables which may have an impact on the stock retur ns
concluded that with the exception of interest rates, there existed a significant relation between
stock market returns and macroeconomic variables. Money supply and inflation are found to be
significant determinants of the returns at NSE. However excha nge rates were found to have a
negative impact on stock returns, while interest rates is not important in determining long rung
run returns in the NSE.
CHAPTER SUMMARY
The chapter looks at theoretical literature review related to the study. It highlights different
models of economic growth based on extensive studies by different authors and economists.
These models all tries to explain in different ways what constitute w hat is described as economic

37
growth. The chapter also looks at how different authors and academics employed different
methods to establish the relationships between various macroeconomic variables and stock
markets of different countries. They seem to agre e that each macroeconomic variable has a
certain relationship with stock market returns.. A point to note from these studies is that different
stock markets respond differently to each macroeconomic variable and these too have been
highlighted in the chapt er.

38
CHAPTER 3
METHODOLOGY
3.1. INTRODUCTION
Ross et al (2005) describes methodology as the systematic and theoretical analysis of the
methods applied to a field of study. Methodology is not the same with method because it
comprises the theoretical a nalysis to the body of methods and principles associated with the
branch of knowledge .It typically encompasses concepts such as paradigm, theoretical model,
phases and quantitative or qualitative techniques Investigations by Abdelbaki (2013) cited by
Barnor, (2014 ) observed that several models have been developed to investigate the extent to
which the financial markets are affected by economic variables and growth. Thes e study
methodologies span from firm, industry, and country levels. Mostly, data use d to investigate
stock market movements included GDP, investment rate, money supply (M2), GDP deflators,
national accounts, interest rates, inflation rate, exchange rates, and some instances data from the
Bretton Woods institutions. Abdelbaki (2013) went o n to observe that real interest rates and
inflation rates are important indicators to use d when measuring macroeconomic stability,
because these macroeconomic indicators can help financial economists determine the impact of
market capitalization and stock market liquidity . The chapter is structured as follows: its starts by
defining and outlining the research philosophy and is followed by the research design. It goes on
to describe the sampling methods, collection methods and lastly described the statistical me thods
used to analyse the data.

39
3.2. RESEARCH PHILOSOPHY
The term research philosophy relates to the development of knowledge and the nature of that
knowledge, Saunders et al (2009). Remenyi et al (1998) cited by S aunders et al (2009), stressed
the necessity to study the details of the situation to understand the reality or perhaps a reality
working behind them. This is associated with social constructionism. Social constructionism
views reality as being socially constructed. For exampl e social actors such as the investors on the
stock market may plan different interpretations on the situations in which they find themselves.
The individual investors will perceive different situations in varying ways as a consequence of
their own view of the world. These different interpretations are likely to affect their actions and
nature of their social interactions with others. In this sense investors do not only interact with the
environment but also seek to make sense of it through their inte rpretat ions of events and
meanings they draw from these events.
In turn their own actions may be seen by others as being meaningful in the context of these
socially constructed interpretations and meanings . Behavioral finance examines investor
behavior, its effects on financial markets, how cognitive biases may result in abnormalities and
whether investors are rational. Behaviorists argue that investors while risk averse, have risk
preferences that are asymme tric. ( Kaplan, 2015) Loss aversion refers to the tendency for
investors to be more risk averse when a face with potential loses and less risk averse when faced
with potential gains. In other words investors dislike loses more than they dislike gains of equ al
amount and this dislike of loses may explain investor overreaction. Other behavioral biases that
have been identified include, representativeness where investors assume good companies or good
markets are good investments , gambler‟s fallacy where invest ors assume recent results affect
investors estimates of future probabilities , mental accounting where investors classify

40
investments into separate mental accounts instead of viewing them as total portfolios
,conservatism where investors react slowly to ch anges ,disposition effect where investors are
willing to realize gains but unwilling to realize losses and finally narrow framing where investors
view events in isolation. In conclusion behavioral finance can thus account for how securities
prices can devi ate from their rational levels and be biased estimates of intrinsic value. (Kaplan,
2015) Other anomalies in time series data collected for such research include anomalies such as
the January effect or turn of the year effect where stock returns especially for small firms are
significantly higher than the rest of the year. In efficient markets traders would exploit this
opportunity in January and in so doing they eliminate it. The second anormality is the
overreaction and momentum anormalities. The overreac tion effect refers to the finding that the
firms with poor stock returns over the previous three or five years have better subsequent return
than firms that had high stock returns over the prior period .This pattern has been attributed to
investor overreac ting to both unexpected good news and unexpected bad news.
3.3. RESEARCH DESIGN
Research design refers to the method upon which an investigation is based. According to
Creswell (2013) a research design is a master plan that specifies the methods and pro cedures for
collecting and analysing needed information. Creswell, (2013) views it as an arrangement of
conditions for both collection and analysis of data in a manner that aims to combine both
relevance to research purpose and economy in procedure. It ens ures that data collected meets the
research objectives and more importantly the international needs for decision makers. In theory
the linkage between economic growth and stock market movement can be obtained from the
present value model or the dividend di scount model (DDM ) and the arbitrage pricing theory
(APT). The present value model looks at the long –run relationship whereas the APT focuses on

41
the short run relationship between the macroeconomic fundamentals and the stock market
movements. According to these two models any new information about the fundamental
macroeconomic factors such as inflation , money supply , interest rates and so on may influence
the stock market return/price through the impact of expected dividends, the discount rate or
both.(Chen et al, 1986; Rahman et al 2009 ) all cited by Naik and Padhi (2012) . To investigate
the effect of economic growth on stock exchange market, th e research will focus on the
Zimbabwe Stock Exchange and investigate the performance of the industrial index using
monthly data from January 2009 to December 2014. The period of the study was selected
between January 2009 to December 2014 because the Zimba bwe economy had adopted the use
of the multi currency and mix of macroeconomic events have been taking place and these may
factor into the outcome of the studies, thus addressing issues on internal validity. The description
of variables used for statistical analysis for all the five variables under study namely, the
Zimbabwe stock exchange Industrial index (ZSEI), Inflation (INF), Money supply (MS), Interest
rates (IR), Current account deficit (CAD) and debt to GDP (GDP_D) are presented in Tables 1
Table 1. Description of variables
Acronyms Construction of variables Data source
ZSE1 Natural logarithm on month -end industrial index ZSE
INF Natural logarithm on month -end Consumer price index Zimstat
MS Natural logarithm on month -end broad money supply RBZ
IR monthly real interest rates RBZ
CAD Natural logarithm on monthly current account deficit RBZ
GDP_D Natural logarithm on monthly debt to GDP ratio RBZ

42
The stock market returns was selected as the dependent variable as measured by the ZSE
Industrial sector Index and the exogenous variables were all the selected macroeconomic
variables: interest rates (IR), inflation rate (CPI), money supply (MS) , current account deficit
(CAD) and debt to GDP (GDP_D) ratio. The multivariate regression used will begin with the
variables under study being transformed into the logarithmic form.
Log (ZSE ind. Index)t = α1 + β1Log (CPI) t + β2Log (MS) t + β3Log (IR) t + β4Log (CAD) t +
β1Log (GDP/D) t …………………………………….equation 3
Where: ZSE ind. Index is the Zimbabwe industrial stock exchange index , CPI is Consumer Price
index , MS is Money Supply , IR is Interest rates , CAD is Current Account Deficit , debt to
GDP_D ratio, α is constant and , βis coefficient of variables . From the equation above, both the
dependent variable and all the independent variables are log -transformed. This is associated with
the price elasticity meaning that the percentage change in Y is caused by on e percentage change
in X
3.4 JUSTIFICATION OF VARIABLES
Theoretically, stock market should be closely related with the macroeconomic variables of the
country. This is because stock prices are the discounted present value of expected future cash
flows. Bas ed on a simple discount model, which estimate the intrinsic value of a security as the
present value of the future dividends expected to be received from the security. These future
dividends must eventually reflect the real economy activity. Similarly, the volatility of stock
prices should also depend on the volatility of expected future cash flows and future discount
rates. Since the value of corporate equity at the aggregate level depend on the state of economic
activity, it is likely that any changes in the level of uncertainty of future macroeconomic

43
conditions would cause a change in stock return volatility. In other words, stock markets may be
volatile simply because real economic activities fluctuate. Zakaria and Shamsuddin (2012), Al –
Zararee and Anan zeh (2014). Many researchers believe that the stock return is determined by a
number of fundamental macroeconomic variables such as interest rate, industrial production,
money supply, inflation rate, and a good number of these studies have captured the eff ects of
macroeconomic variables on stock returns for different countries. Existing theories offer
different models that make available framework for examining the relationship between stock
return and macroeconomic variables (Quadir, 2012).
3.4.1 The industrial index
Stock market indices are used to represent the performance of an asset class security market or
segment of the market. Their uses in tracking stock market performance have received wide
acceptance in the field of finance. Barnor, (2014 ).Information gathered on the average indices is
useful to all categories of investors; individual households, institutional or even foreign investors
to determine the barometric direction and future performance of the exchange. For example, the
Dow Jones, NYSE, and Nikkei are globally accepted indices that gives direction on stock
performance and returns because these indices show and track changes in the market values of
the various exchanges and thus show the performance of the stock market. They are usu ally
created as portfolios of individual securities, which are referred to as the constituent securities of
the index .It has a numerical value that is calculated from the market prices of its constituent
securities at a point in time. The index return is a percentage change in the index‟s value over a
period. Weighting schemes for stock index include price weighting, equal weighting, market
weighting, capitalization weighting and fundamental weighting. The ZSE industrial index is a
price weighted index whi ch is a simple arithmetic average of the prices of the seventy securities

44
included in the index. the divisor of the weighted is adjusted for stock splits and changes in the
composition of the index when securities are added or deleted such that the value i s unaffected
by such changes
3.4.2 Interest rates
The money market rate here is considered as a proxy for interest rates. From the borrower‟s point
of view interest rate is the cost of borrowing money while from the lender‟s view, interest rate is
the gain from lending money. Different interest rates exists parallel for the same or comparable
time period and they depend on the default probability of the borrower ,the residual term and
other determinants of a loan or credit. Interest -rate targets are an important tool of monetary
policy and are usually taken into account when dealing with variables like investmen t, inflation,
and unemployment. ( Saeed et al, 2004 ). The Central B anks of countries generally tend to reduce
interest rates when they wis h to increase investment and consumption in the country's economy.
However, a low interest rate as a macro -economic policy can be risky and may lead to the
creation of an economic bubble, in which large amounts of investments are poured into the real –
estate market and stock market. In developed economies, interest -rate adjustments are thus made
to keep inflation within a target range for the health of economic activities or cap the interest rate
concurrently with economic growth to safeguard economic moment um. (Saeed et al, 2004) An
increase in the interest rate will result in a decrease in stock prices due to the fact that high
interest rate will increase the opportunity cost of holding money causing substitution of stocks
for interest bearing securities. The interest rate is expected to be negatively associated to stock
market returns .

45
3.4.3 Inflation
The Consumer Price Index is used as a proxy for inflation. The relationship between inflation
and stock returns can either be positive or negative depending on whether the economy is facing
expected inflation or unexpected inflation. Expected inflation is when demand exceeds supply,
causing an increase in prices to stimulate more supply. The positive expectations from firms
cause general price increa ses. Resultantly, earnings would increase leading to higher dividends
payouts which can also result in an increase stocks as well. On the other hand, when inflation is
not expected, an increase in prices will lead to the increase in the cost of living lead ing to a shift
of resources from investment to consumption. As inflation increases nominal interest rate will
also increase. The discount rate used to calculate the intrinsic values of stock will increase and
thus a reduction of the present value of net in come leading to lower stock prices. The inverse
relationship between unexpected inflation and stock prices is hypothesized by Fama (1981) as a
function of the relationship between unexpected inflation and real activity in the economy.
Inflation is expected to be negatively associated to stock prices.
3.4.4 Money Supply (MS)
Money supply (MS) is one real macroeconomic force that ultimately does matter for stock
markets. (Paine 2014). Money supply measures the quantity of money and the magnitude of
spendi ng that is taking place in an economy over the longer term. Because Short periods are
dominated by noise, it is important to understand what drives stock prices up or down over time.
Supported by studies by Paine (2014), consumers feeling positive about t he economy are only
able to act on this sentiment if they have the funds at hand. As money supply grows, so more
money chases fewer goods and services, which triggers rising prices. According to Fama (1981),

46
the expansion of the money supply in the economy is positively related to inflation and this has
an effect of increasing the nominal risk free rate. The increase in the nominal risk free rate will
lead to rise in the discount rate leading to fall in rate of return. An expanding money supply has
the same effect on all asset prices, be it stocks, bonds, or houses. More money also boosts
corporate profits because revenues rise at a faster rate than costs – improving profit margins in
the short to medium term. Again, this has been clearly demonstrated in the aftermath of the 2008
financial crisis. The huge amounts of money released by governments have been responsible for
impressive company profits, notwithstanding subdued GDP growth and consumer confidence .
3.4.5 Current account deficit
The current account largely reflects trade in goods and services. It can be decomposed into
merchandise trade, services such as tourism business services as well as income receipts. The
current account balance is important because it measures the size and direction on international
borrowing. A current account deficit occurs when spending in the economy is relatively high. In
such times, demand for credit is also high which increases interest rates .These interest rates lead
to net capital inflows and resul t in an appreciating currency. However with persistent current
account deficits in the long run , an increase in net borrowing from foreigners results in
significant risk being associated with a country‟s debt leading to currency depreciation.
3.4.6 Debt to GDP Ratio
This is the country‟s national debt in relation to its gross domestic product (GDP). It is used as
an indication of the country‟s ability to pay back its national debt .An economically stable
country is one which can continue to pay interests on debts without refinancing or harming
economic growth. A high debt to GDP ratio makes it difficult for a country to pay its external

47
debts and this may lead to creditors to seek high interest rates when lending. On the other hand a
country that defaults on its debt obligation can cause panic in the domest ic and international
markets. The level of real economic activity as measured by Gross Domestic Product (GDP) is
regarded as the crucial determinant of stock market returns. A low ratio may indicate an i ncrease
GDP compared to debt. The rise in industrial production may signal economic growth and is
marked by an increase in corporate earnings, enhancing the present value of the firm hence
leading to an increase in the investment in stock market thereby in creases in stock prices.
However, interest in investing in emerging markets has grown considerably over the past decade.
Harvey (1995a) cited by Al -Zararee and Ananzeh (2014) shows that returns and risks in
emerging stock markets have been found to be high er, relative to developed markets
3.5. TARGETED POPULATION AND SAMPLING PROCEDURE
A population is sum of all members, elements or cases about which the researcher wishes to
draw conclusions whilst sampling is the process of selecting a sufficient number from a
population, studying the sampled elements the researcher forms an understanding of the
properties or characteristics of the sampled elements in order to generalize the properties of the
sampled to the population of interest. In this study the resea rcher targeted all seventy four listed
firms on the Zimbabwe stock exchange therefore the sampled size is the same as the population.
3.6. DATA COLLECTION PROCEDURE
The data col lected for the study consists of secondary data from Zimbabwe National Statistical
Agency (Zimstat), Zimbabwe Stock Exchange and the Reserve Bank of Zimbabwe Economics
and Research department .The type of data, their sources and the instruments used in gathering
them are as follows . The advantage in us ing this source of data is that secondary data generally

48
provide a source of data that is both permanent and available in a form that maybe checked
relatively easily by others and the findings from the use of this data are more open to public
scrutiny. , D enscombe (2007) cited by Sounders ( 2007.) In this research the data was compiled
and available in RBZ documents
3.7. LIMITATIONS
The study may be limited by the quality of data. This is because some of the data include data
before the demonetization of the Zimbabwean dollar . The follo wing biases or anom alies are
expected :Data mining bias This occurs when a researcher repeatedly use the same database to
search for patterns until one that works is disclosed .To avoid data mining bias the researcher
will investigate first if there is an economic basis for the relationship they find between certain
variables and stock returns and then test the discovered relationship with a larger sample of the
data to determine if the relationships are persistent and are present in various sub periods; sample
selection bias, this o ccurs when some sample data is systematically excluded from the analysis
usually because of the lack availability. This practice render s the observed sample to be non –
random an d any conclusion drawn from this sample cannot be applied to the population because
the observed sample are a portion of the population that was not observed are different. In this
research the researcher used monthly data used by the Reserve Bank of Zimbabwe for economic
focusing ; time period bias , this bias can result if the time period over which the data is gathered
is either too short or too long. If the period is too short research results may reflect phenomena
specific to that period. If the time period is too long the fundamental economic relationships that
underlie the results may have changed. The researcher believes the chosen period is the ideal one
by virtue of a new economic dimension where the economy has just experience 5 years of f ull
dollarization.

49
3.8. RELIABILITY
Reliability refers to the extent to which your data collection techniques or analysis procedures
will yield consistent findings, Sounders (2009). The reliability and quality of the data used in this
study is guaranteed because of the sources from which the data is obtained. The assumption is
that the same data is being used by international agencies such as World Bank and International
Monetary fund
3.9. VALIDITY
Validity is concerned with whether the findings are really about what appear to be about. In this
case is the relationship between the two variables (economic growth and stock market
performance) a causal relationship. The lack of validity of the findings is minimized because of
the variables chosen and how their importance is as described in the research design
3.10. ASSUMPTIONS
The researcher assumes that the Zimbabwe stock exchange is an informationally efficient market
in which the current prices of a security fully, quickly and rationally reflect all available
information about that security.
3.11 STATISTICAL METHODS FOR DATA ANALYSIS
Quantitative data in a raw form convey very little meaning to most people. It is therefore
important to process the data to make it more useful. Several quantitative analysis techniques
such as bar charts, graphs and statistical packages allow researchers to explore, present, describe
and examine relationships and trends within our data. It is important to note that fin ancial data
often exhibit characteristic that violet the underlying assumptions necessary for linear regression

50
together with the associated hypothesis test to be meaningful. The major violations include:
serial correlation, conditional heteroskedasticity and multicollinearity. It is therefore very
important for a researcher to test for each of these conditions and correct the estimates and
hypothesis test to account for the effects of these violations. The present studies employs the time
series data analysis technique to study the effect of selected macroeconomic variables on the
stock exchange industrial index. Amo ngst the s tatistical methods used are the; autocorrelation
test,, heteroskedasticity tests , residuals and Jarque –Bera test The Dickey -Fuller Augmentative
test (ADF), the,the Ordinary least squared test (OLS), and the Granger Causality test
Autocorrelatio n tests are performed to corre ct errors in regression models. This study employed
the Breusch –Godfrey serial correlation LM test. The Breusch –Godfrey serial correlation LM test
is a test for autocorrelation in the errors in a regression model. I t makes use of the residuals from
the model being considered in a regression analysis , and a test statistic is derived from these. The
null hypothesis is that there is no serial correlation of any order up to p. (Baltagi 2005) .
Heteroskedasticity occurs when the error variance has non -constant variance. In that case the
disturbance for each observation will have been drawn from a different distribution with a
different variance. Stated equivalently, the variance of the observed value of the dependent
variable around the regression line will be a non -consta nt The Augmented Dickey and Fuller
(1979) developed a procedure to determine whether each microeconomic time series is stationary
or not. Because time series data analysis is subject to the problem of spurious regression if the
data is non -stationary which can result in unreliable results of the models used. In this regard in
order to avoid spu rious regression, the unit root test (ADF) is used .
The OLS model is a method for estimating the unknown parameters in a linear regression model
with the goal of min imizing the difference between observed responses and some arbitrary

51
dataset and the response predicted by their linear approximation of the data. It is used to test the
relationship between the macroeconomic variables and the Zimbab we Stock market industr ial
index. According to Granger (1969), t he Granger Causality test is a statistical concept of
causality that is based on prediction. According to this test, if a signal X 1 “Granger causes ” a
signal X 2 then the past values of X 1 should contain information that helps predict X 2 above and
beyond the information contained in the past values of X 2 alone.
3.12. ETHICAL CONSIDERATIONS
The Reserve Bank of Zimbabwe as well as the Zimbabwe Stock Exchange has strict policy on
confidential ity and one can pay the ultimate price for the breach of confidentiality. Divulging of
information by employees to a third party can expose the institution to potential legal wrangle
and therefore being mindful of confidentiality . The researcher used offic ial means to obtain all
the information used in the study.
3.13 CHAPERT SUMMARY
This chapter set out the empirical framework used in this study‟s investigation of the effect of
economic growth on the st ock exchange market . It outlines the data analysis and methods of data
collection to be used in this study. The different methods are discussed and the most appropriate
methods for the study identified, and their uses justified. Finally it describes the data used in the
study and how the analysis of result s is conducted. The chapter focused on giving the research
methodology used in collecting and analysing data in this study. The next Chapter four focuses
on Data Analysis, Presentation and Discussion, which gives the results of the research and their
interpretation, meaning and significance

52
CHAPTER 4
4.0 DATA PRESENTATION AND DISCUSSION
4.1 Introduction
Data can be presented in various forms depending on the type of the data collected The purpose
of analysing data is to : obtain usable and useful information irrespective of whether the data is
qualitative or quantitative ; identify the relationships between variables , and to compare
variables and identify the difference between variables and to try predicts outcomes. This
chapter involves the pre sentation of data that was collected for this research. The data was
collected from Reserve Bank of Zimbabwe monthly econo mic reviews and well as Zimstat . It is
concerned with the presentation and analysis of the research finding s from secondary data
collected from January 2009 to December 2014 .The raw data was collected and statistically
analysed using ST ATA statistics software and is hereby presented in tables in a summarized
computer generated format .
4.2 EMPERICAL RESULTS
4.2.1. The unit root test
The null and alternative hypotheses are as follows:
H0 : ῤ = 1 Unit root [Variable is not stationary]
H1: ῤ < 1 No unit root [Variable is stationary]
If the coefficient is significantly different from one (less than one) then the hypothesis that y
contains a unit root is rejected. Rejection of the null hypothesis denotes stationarity in the series.
If we don´t reject the null hypothesis, we conclude we have a unit root

53
Table 1: Results of the Augmented Dickey – Fuller unit root test for stationary
Variables ADF test
H0: Variable is has
unit root Probability
INF -3.133016 0.1068
(D)INF -6.063567*** 0.0000
CAD -5.82334*** 0.0000
IR -2.266269 0.4463
(D) IR -6.679627*** 0.0000
MS -6.751073*** 0.0000
GDP_D -9.54943*** 0.0000
ZSE1 3.698369*** 0.01411
Source: Author’s Computation.
Key: *** implies significant at 1 %, 5%, 10 % level respectively . Absent represent first difference
The results in table show all the series are stationary at 1, 5, 10 % level. Values obtained for all
the variables are less than 0.05 we therefore reject the null hypothesis that each variable has unit
root and conclude that the variables are stationary. The application of the ADF test, the first
difference of the five variables and the second difference of interest rates (IR) and inflation (INF)
was done to obtain stationary variables before the Granger Causality test. See Appendix for more
detailed results.
4.2.2 Serial Correlation LM Test
The presence of serial correlation was examined by Breusch -Godfrey Serial Correlation LM
Test. Residuals for OLS output is tested for serial correlation, using the following hypothesis:
H0 : No autocorrelation
H1: Autocorrelation

54
Table 3. Breu sch-Godfrey serial correlation test :

F-statistic 1.953 Prob. F(2,58) 0.151
Obs*R -squared 4.354 Prob. Chi -Square(2) 0.113
Source: Author’s Computation .
The results in table 3 show that there is no serial correlation since the probability is greater than
0.05. This means that the error term of the different periods is linearly unrelated and thus there is
no autocorrelation. In other words data collected and used does not violet underlying
assumptions our linear regression model and its associa ted hypothesis tests. See Appendix for
more detailed results.
4.2.3 . Heteroskedasticity test
One of the diagnostic test carried out is the heteroscedasticity test. The classical Linear
Regr ession Assumption assumes homosc edasticity of the disturbance term appearing in the
population regression function. When the assumption is violated, Heterosc edasticity occurs. For
the Heterosc edasticity test, the null hypothesis is that the disturbance terms are equal against the
alterna tive that they are different.
H0: The variance of the error term is constant
H1: The variance of the error term is non -constant.
Table 4 below presents the results of the Heteroscedasticity tests that were carried out in this
study.

55
Table 4: Heteroske dasticity test results

F-statistic 2.394 Prob. F(1,66) 0.1266
Obs*R -squared 2.381 Prob. Chi -Square(1) 0.1228
Source: Author’s Computation .
The results in table 4 above show no Heterosc edasticity since the p value is greater than 0.05.
The absence of heteroskedasticity clears our model of any violations of assumptions and
hypothesis associated with the test. See Appendix for more detailed results
4.2.4. The normality test
The normality test is important as it is used to find out whether the error term follows normal
distribution and the hypotheses are stated as follows:
H0: Residuals are normally distributed
H1: Residuals are not normally distributed
Table 5.Histogram of residuals and Jarque –Bera test

0123456789
-0.10 -0.05 0.00 0.05 0.10 0.15Series: Residuals
Sample 2009M04 2014M12
Observations 69
Mean 1.34e-16
Median -0.003698
Maximum 0.153363
Minimum -0.094541
Std. Dev. 0.051891
Skewness 0.625590
Kurtosis 3.635743
Jarque-Bera 5.662658
Probability 0.058934

56
From the results obtained and shown in table 2 it was observed that the residuals of the
underlying variables had a P> 0.058935 . This show that the residuals are normally distributed
since it is greater than the critical value at the 5% interval. The significant coefficient of Jarque –
Bera statistics also indicates that the frequency distribution is normal. The values of kurtosis an d
skewness in table 2 indicate slight deviation from symmetry of the distribution. In general for the
observations to be normally distributed the values of skewness and kurtosis should be 0 and 3
respectively. In addition, if skewness coefficient is in exc ess of unit it is considered fairly
extreme. The high kurtosis value indicates extreme leptokurtic. The value of the standard
deviations shows that there is relatively low volatility amongst the variables.
4.2.5 Estimation Results
A one step Engle Granger technique was applied in order to determine the effects of economic
growth on the Zimbabwe Stock Exchange. The OLS tests results are presented in Table 6 below.
Table 6: Results of the ordinary least squares test
Dependent Variable: ZSE1
Variable Coefficient Std. Error t-Statistic Prob.
ZSE1( -1) -0.133 0.050 -2.669 0.001
INF( -1) 1.168 0.498 2.347 0.022
MS(-1) 0.099 0.040 -2.482 0.016
C -3.176 1.705 -1.863 0.067
D(INF( -1)) -5.972 1.531 -3.900 0.000
D(CAD) -0.004 0.001 -3.027 0.004
D(GDP_D( -2)) -0.001 0.001 -1.495 0.140
D(IR( -1)) -0.008 0.007 -1.178 0.243
DUM_1308 -0.238 0.060 -3.976 0.000

57
R-squared 0.657 Durbin –
Watson stat 1.533
Adjusted R –
squared 0.611
F-statistic 14.369
Prob(F -statistic) 0.000
Source: Author’s Computation
Therefore the model is specified as:
D(ZSE1) = -0.133*ZSE1( -1) + 1.168*INF( -1) – 0.099*MS( -1) – 3.176 – 5.972*D(INF( -1)) –
0.0042047*D(CAD) – 0.001*D(GDP_D( -2)) – 0.008*D(IR( -1)) – 0.238*DUM_1308 (See Appendix for
more detailed resul ts)
The h igh adjusted R2 of 0.66 shows that the model explains 66% of the variation. In an OLS
model, an R2 of 66% is a good fit. The model has a large F statistic which is fairly significant
since its p -value is less than 5%. It therefore reconfirms the earlier observation that the model‟s
overall goodness -of-fit is high. It therefore implies all the coefficients of the variables are j ointly
not equal to zero. The Durbin Watson diagnostic statistic used for checking if the errors are auto
correlated rather than independently distributed. The value of the Durbin -Watson statistic ranges
from 0 to 4. The Durbin Watson in the equation above has a value of 1.53 which generally
implies that the residuals are not correlated as it falls in the acceptable range of between 1.50 and
2.50. In order to test for the stability of the equation recursive tests were carried out and the
results of the test s are shown in the Figure below.

58
Table 7 Stability Tests: Recursive Estimates – Cusum of Squares Tests

The cumulative sum of squares is within the 5% significance lines suggesting that the residual
variance is stable. The results in Table 6 above show the industrial Index (ZSE1) is positively
related to inflation and money supply in the long run. In the short run ZSE1 is negatively related
to inflation, current account deficit (CAD), debt to GDP (GDP_D) and interest rat es are
insignificant. The insignificance of interest rates in the long run can be explained by the fact that
there are limited instruments in Zimbabwe which highlights that the capital markets are highly
under developed
4.2.6 The Granger causality test
In order to explore the direction of causality between the variables, namely industrial index,
money supply, interest rate, inflation, domestic debt to GDP and current account deficit. The null
hypothesis H 0 is that of no causality. The stationary of our d ifferent variables has been checked
-0.40.00.40.81.21.6
III IV I II III IV
2013 2014
CUSUM of Squares 5% Significance

59
using various unit root tests. The results of the Granger causality tests are presented in the Table
below. They should be taken with caution and for illustrative purposes only as they do not
establish causality with cer tainty given that an unobserved third variable, such as new banking
regulations can affect the two endogenous variables and consequently may drive the results.
TABLE 8 : Granger causality Test
Null Hypothesis: F-Statistic Probability
MS does not Granger Cause ZSE1 0.352 0.704
ZSE1 does not Granger Cause MS 1.031 0.362
IR does not Granger Cause ZSE1 0.50628 0.6051
ZSE1 does not Granger Cause IR 1.74327 0.1830
INF does not Granger Cause ZSE1 3.28966 0.0436
ZSE1 does not Granger Cause INF 4.20807 0.0191
GDP_D does not Granger Cause ZSE1 1.96313 0.1487
ZSE1 does not Granger Cause GDP_D 0.57440 0.5659
CAD does not Granger Cause ZSE1 1.51857 0.2267
ZSE1 does not Granger Cause CAD 0.98129 0.3803
The results shown in table 7 indicate that inflation (CPI) granger causes the Zimbabwe stock
exchange industrial index. The result also depicts bidirectional causality between inflation and
the industrial index .However the results also show that industrial index ZSE1 does not Granger
cause money supply and also that money supply does not Granger cause industrial index.
Granger Causality results also shows that there is no bi -directional causality between domestic
debt and ZSE index; current account and ZSE1; inte rest rates and industr ial index

60
4.3. DISCUSSION OF THE RESULTS FROM THE STATISTICAL ANALYSIS.
One of the important roles of the stock market in the economy is to raise capital and to provide
several channels that ensure that the funds raised are utilized in the most profitable opportunity.
This empirical research provides the necessary analysis to answer if changes in the selected
fundamental macroeconomic vari ables affect the Zimbabwe Industrial stock index return. The
study employed regression analysis and Granger causality test to examine these relationships.
This empirical study shows that the industrial Index (ZSE1) is positively related to inflation in
the long run whilst negatively related to inflation in the short run. The ZSE1 is also negatively
related to money supply in the long r un. In the short run the ZSE1 is also negatively related to
the current account defici t (CAD), debt to GDP (GDP_D) whilst interest rates are insignificant.
The regression test results concur with the Granger causality test which indicates that inflation
(CPI) granger causes the Zimbabwe stock exchange industrial index. The result further depicts
bidirectional causality between inflation and the industrial index. The Granger causality test
further concurs with the regression analysis results of the other variables, that is industrial index
(ZSE1)does not Granger cause money supply and also that money supply does not Granger cause
industrial index and that there is no bi -directional causality between domestic debt and ZSE
index; current account and ZSE1; interest rates and industrial index .
4.4 ANALYSIS OF THE EFFECT OF INDI VIDUAL MACROECONOMIC VARIABLE .
The Zimbabwean economy has been facing major challenges during the period under study.
These challenges included severe liquidity challenges, unstable political and economic
environment characterized by deflation; huge curren t account deficit and large debt to GDP ratio.
However f rom the results of the study it is difficult to tell which of the five macroeconomic

61
variables a major or minor driver of stock prices is . The following sections explain how three of
the variables cou ld have driven the stock prices
4.4.1 I nflation
Expected inflation can either positively or negatively impact stocks, depending on the ability to
hedge and the government‟s monetary policy. The results in Table 6 above shows a positive
relationship between inflation and stock returns, with an elasticity of 8.78 which implies that a
1% change in inflation (INF) will result in a 8.78% increase in the industrial index (ZSE1)
ceteris paribus . According to the Katina Zucchini CFA.‟s article on investopedia
(www.investopedia.com) , observed that unexpected inflation has positive correlation to stock
returns especially during economic contractions, demonstrating that the timing of the economic
cycle is particularly important for investors to gauge the impact on stock r eturns. This correlation
is thought to stem from the fact that unexpected inflation contains new information about future
rates. By studying the period under study (2009 -2014) the Zimbabwean economy was coming
out of a period of hyperinflation into more stable dollarized economy where investors had mixed
feelings as to whether the government was going be able to control inflation or not. It is with
this background that investors either expected or did not expect inflation to rise or fall from
previous rates. Whichever result had positive impact on the stock market. This positive
relationship between inflation and stock prices was obtained by Hayworth and Abdullah (1993)
using the Granger causality when they investigated the relationship between the United States of
America stock prices. Other studies cited in the literature however show a negative relationship
between inflation and stock market returns. The bidirectional causality between inflation and the
industrial index explains that when the economy is doing well, the standard of li ving improves
and people have disposable incomes which result in increased demand for goods and services.

62
When demand for goods and services increase , firm‟s increases prices in the long run to justify
increase in the cost of production and therefore if mo netary authorities do not intervene, inflation
will rise.
4.2.2 Interest rates
The insignificance of interest rates can be explained by the fact that there are l imited instruments
in Zimbabwe due to an under developed capital markets structure. In developed economies
characterized by a well -diversified developed capital and money, market interest plays a very
significant role in the pricing of capital and money market instruments. A very good example is
the long standing impact of the Federal Reserve of the United States interest rate decision which
has created so much unsettlement amongst the investing public.
4.2.3. Money supply
There is a positive relationship between the industrial index (ZSE1) and money supply. The
elasticity is 0.08 which implies that a 10% increase in money supply will result in a 0.8%
marginal increase in the industrial index ceteris paribus . The results obt ained in this study are in
line with theory which suggests that an increase in money supply increases stock prices and vice
versa. Generally despite the small size of the stock market and challenges mentioned above , the
Zimbabwe stock market record ed solid performances during the period under study . Like its
peers in the region such as the Johannesburg and Lusaka stock exchanges markets these
markets are offering dramatic returns for investors, making them relatively immune to the global
jitters hitting share values worldwide. Generally this is a distinct characteristic of African equity
markets and as such they offer positive benefits in terms of risk diversification. According to
Paine (2014), globalization also plays a part in the disconnection be tween economies and stock

63
market performance. She pointed out that often the structure and composition of an economy
differs greatly from the drivers of stock performance of its index and corporate earnings may be
realized in a different geography, especia lly when it comes to the large multinationals that
dominate many indices. In such instance, domestic growth and consumer confidence may be
inhibited by circumstances in those other countries.

64
CHAPTER 5
5.0 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
This chapter concludes the study by summarizing the findings, highlighting their implications as
well as making policy recommendations based on the empirical results from the model
estimations. In addition, the researcher indicates the limitations of the study as well make
suggestions for future study.
5.1 SUMMARY
The main purpose of the study is to investigate the effect of economic growth on the Zimbabwe
Stock Exchange (ZSE). T he specific objectives were; to identify macroeconomic variables or
indicators that have an effect on the performance of the Zimbabwe stock exchange; to assess the
impact of the changes in various macroeconomic variables or indicators on the performance of
the Zimbabwe stock exchange market during the period between 2009 and 2014; and to
recommend appropriate macroeconomic policies and provide guidance to policy makers for
sustainable economic growth in Zimbabwe. One of the justifications of the study is that it is
important to investigate the relationships between returns in the Zimbabwe Stock Exchange
(ZSE) and macroeconomic variables of Zimbabwean economy in the post dollarization era. In
addition, the results of the study can enable investors and policy makers to come up with
effective decisions in evaluating whether it is prudential i nvest in the stock exchange.
5.2…CONCLUSION
The study was carried out using ordinary least squares regression with the industrial index being
used as a proxy for market returns as the dependent variable. The independent variables were

65
money supply, infl ation, interest rates, current account deficit and domestic debt to GDP. The
results from the study show that the industrial Index is positively related to inflation and money
supply in the long run. In the short run the industrial Index is negatively relat ed to inflation,
current account deficit, and debt to GDP, interest rates are, however, insignificant. The
insignificance of interest rates in the long run can be explained by the fact that there are limited
instruments in Zimbabwe which highlights that th e capital markets are highly under developed.
These findings may have important implications for government and the Reserve Bank of
Zimbabwe as they may need to review their interest rate policy. The Central Bank must also
introduce more instruments so tha t investors in the money and capital markets have more options
on where to put their investments.
5.3 RECOMMENDATIONS
The findings from this study have highlighted some important policy implications.
1. The positive relationship between inflation and stock market performance in the long run
should be for major concern to the policy makers in Zimbabwe. The recent performance
of the stock exchange which is characterised by loses in major counters can be attr ibuted
to the effects of deflation in which the economy is currently experiencing.
2. These findings may have important implications for government and the Reserve Bank of
Zimbabwe as they may need to review their interest rate policy. The Central Bank must
also introduce more instruments so that investors in the money and capital markets have
more options on where to put their investments.
3. The study also found that inflationary expectations play a key role in determining the
performance of the Zimbabwe Stock Exchange, therefore government need to ensure that

66
it does not introduce price controls and/or interest rate caps on commodities as this will
result i n inflation increasing as people horde commodities and sell them on black
markets.
4. On future research , a possible extension of this study should be carried out to consider the
impact of other macroeconomic variables such as real effective exchange rates a nd the
government spending. In addition, future studies may examine the effect of the
Rand/USS exchange rate on the performance of the Zimbabwe Stock Exchange.

67

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78

APPENDIX
UNIT ROOT TESTS FOR EACH MACROECONOMIC VARIABLE.

Null Hypothesis: CAD has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 1 (Automatic – based on SIC, maxlag=11)

t-Statistic Prob.*

Augmented Dickey -Fuller test statistic -5.823354 0.0000
Test critical values: 1% level -4.094550
5% level -3.475305
10% level -3.165046

*MacKinnon (1996) one -sided p -values.

Augmented Dickey -Fuller Test Equation
Dependent Variable: D(CAD)
Method: Least Squares
Sample (adjusted): 2009M03 2014M12
Included observations: 70 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

CAD( -1) -1.016207 0.174505 -5.823354 0.0000
D(CAD( -1)) -0.009717 0.121978 -0.079666 0.9367
C -4.439661 1.269883 -3.496119 0.0008
@TREND("2009M01") 0.029541 0.025390 1.163501 0.2488

R-squared 0.514737 Mean dependent var -0.007961
Adjusted R -squared 0.492680 S.D. dependent var 5.948956
S.E. of regression 4.237228 Akaike info criterion 5.781141
Sum squared resid 1184.971 Schwarz criterion 5.909626
Log likelihood -198.3399 Hannan -Quinn criter. 5.832177
F-statistic 23.33626 Durbin -Watson stat 2.005028
Prob(F -statistic) 0.000000

79

Null Hypothesis: GDP_D has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic – based on SIC, maxlag=11)

t-Statistic Prob.*

Augmented Dickey -Fuller test statistic -9.545943 0.0000
Test critical values: 1% level -4.092547
5% level -3.474363
10% level -3.164499

*MacKinnon (1996) one -sided p -values.

Augmented Dickey -Fuller Test Equation
Dependent Variable: D(GDP_D)
Method: Least Squares
Sample (adjusted): 2009M02 2014M12
Included observations: 71 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

GDP_D( -1) -1.155785 0.121076 -9.545943 0.0000
C 1.841257 1.182749 1.556762 0.1242
@TREND("2009M01") -0.064938 0.029056 -2.234918 0.0287

R-squared 0.572774 Mean dependent var 0.041280
Adjusted R -squared 0.560208 S.D. dependent var 7.316481
S.E. of regression 4.852055 Akaike info criterion 6.038016
Sum squared resid 1600.885 Schwarz criterion 6.133623
Log likelihood -211.3496 Hannan -Quinn criter. 6.076036
F-statistic 45.58316 Durbin -Watson stat 1.947826
Prob(F -statistic) 0.000000

80

Null Hypothesis: INF has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic – based on SIC, maxlag=11)

t-Statistic Prob.*

Augmented Dickey -Fuller test statistic -3.133016 0.1068
Test critical values: 1% level -4.092547
5% level -3.474363
10% level -3.164499

*MacKinnon (1996) one -sided p -values.

Augmented Dickey -Fuller Test Equation
Dependent Variable: D(INF)
Method: Least Squares
Sample (adjusted): 2009M02 2014M12
Included observations: 71 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

INF(-1) -0.149174 0.047614 -3.133016 0.0026
C 0.669530 0.213814 3.131368 0.0026
@TREND("2009M01") 0.000337 0.000106 3.182330 0.0022

R-squared 0.131969 Mean dependent var 0.000661
Adjusted R -squared 0.106439 S.D. dependent var 0.006989
S.E. of regression 0.006606 Akaike info criterion -7.160265
Sum squared resid 0.002968 Schwarz criterion -7.064659
Log likelihood 257.1894 Hannan -Quinn criter. -7.122245
F-statistic 5.169125 Durbin -Watson stat 0.592575
Prob(F -statistic) 0.008132

81

Null Hypothesis: D(INF) has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic – based on SIC, maxlag=11)

t-Statistic Prob.*

Augmented Dickey -Fuller test statistic -6.063567 0.0000
Test critical values: 1% level -4.094550
5% level -3.475305
10% level -3.165046

*MacKinnon (1996) one -sided p -values.

Augmented Dickey -Fuller Test Equation
Dependent Variable: D(INF,2)
Method: Least Squares
Sample (adjusted): 2009M03 2014M12
Included observations: 70 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

D(INF( -1)) -0.470056 0.077521 -6.063567 0.0000
C 0.001804 0.001114 1.618628 0.1102
@TREND("2009M01") -2.83E -05 2.68E -05 -1.057454 0.2941

R-squared 0.369942 Mean dependent var 0.000448
Adjusted R -squared 0.351135 S.D. dependent var 0.005603
S.E. of regression 0.004513 Akaike info criterion -7.921749
Sum squared resid 0.001365 Schwarz criterion -7.825385
Log likelihood 280.2612 Hannan -Quinn criter. -7.883472
F-statistic 19.66975 Durbin -Watson stat 2.588494
Prob(F -statistic) 0.000000

82

Null Hypothesis: IR has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic – based on SIC, maxlag=11)

t-Statistic Prob.*

Augmented Dickey -Fuller test statistic -2.266269 0.4463
Test critical values: 1% level -4.092547
5% level -3.474363
10% level -3.164499

*MacKinnon (1996) one -sided p -values.

Augmented Dickey -Fuller Test Equation
Dependent Variable: D(IR)
Method: Least Squares
Sample (adjusted): 2009M02 2014M12
Included observations: 71 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

IR(-1) -0.114040 0.050321 -2.266269 0.0266
C 1.676264 0.700180 2.394046 0.0194
@TREND("2009M01") -0.009949 0.005809 -1.712667 0.0913

R-squared 0.079838 Mean dependent var 0.002707
Adjusted R -squared 0.052774 S.D. dependent var 0.943418
S.E. of regression 0.918186 Akaike info criterion 2.708502
Sum squared resid 57.32849 Schwarz criterion 2.804108
Log likelihood -93.15182 Hannan -Quinn criter. 2.746522
F-statistic 2.950017 Durbin -Watson stat 1.537863
Prob(F -statistic) 0.059072

83

Null Hypothesis: D(IR) has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic – based on SIC, maxlag=11)

t-Statistic Prob.*

Augmented Dickey -Fuller test statistic -6.679627 0.0000
Test critical values: 1% level -4.094550
5% level -3.475305
10% level -3.165046

*MacKinnon (1996) one-sided p -values.

Augmented Dickey -Fuller Test Equation
Dependent Variable: D(IR,2)
Method: Least Squares
Sample (adjusted): 2009M03 2014M12
Included observations: 70 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

D(IR( -1)) -0.799570 0.119703 -6.679627 0.0000
C 0.140155 0.232831 0.601961 0.5492
@TREND("2009M01") -0.003839 0.005585 -0.687306 0.4943

R-squared 0.399746 Mean dependent var -0.005468
Adjusted R -squared 0.381828 S.D. dependent var 1.195317
S.E. of regression 0.939805 Akaike info criterion 2.755623
Sum squared resid 59.17665 Schwarz criterion 2.851987
Log likelihood -93.44681 Hannan -Quinn criter. 2.793900
F-statistic 22.30973 Durbin -Watson stat 2.042415
Prob(F -statistic) 0.000000

84

Null Hypothesis: MS has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 1 (Automatic – based on SIC, maxlag=11)

t-Statistic Prob.*

Augmented Dickey -Fuller test statistic -6.751073 0.0000
Test critical values: 1% level -4.094550
5% level -3.475305
10% level -3.165046

*MacKinnon (1996) one -sided p -values.

Augmented Dickey -Fuller Test Equation
Dependent Variable: D(MS)
Method: Least Squares
Sample (adjusted): 2009M03 2014M12
Included observations: 70 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

MS(-1) -0.108513 0.016073 -6.751073 0.0000
D(MS( -1)) -0.397129 0.099915 -3.974654 0.0002
C 1.629126 0.226523 7.191884 0.0000
@TREND("2009M01") 0.000537 0.000432 1.244599 0.2177

R-squared 0.623649 Mean dependent var 0.034879
Adjusted R -squared 0.606542 S.D. dependent var 0.055802
S.E. of regression 0.035003 Akaike info criterion -3.811346
Sum squared resid 0.080862 Schwarz criterion -3.682860
Log likelihood 137.3971 Hannan -Quinn criter. -3.760310
F-statistic 36.45605 Durbin -Watson stat 1.866876
Prob(F -statistic) 0.000000

85

Null Hypothesis: ZSE1 has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic – based on SIC, maxlag=11)

t-Statistic Prob.*

Augmented Dickey -Fuller test statistic -3.968369 0.0141
Test critical values: 1% level -4.092547
5% level -3.474363
10% level -3.164499

*MacKinnon (1996) one -sided p -values.

Augmented Dickey -Fuller Test Equation
Dependent Variable: D(ZSE1)
Method: Least Squares
Sample (adjusted): 2009M02 2014M12
Included observations: 71 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

ZSE1( -1) -0.202524 0.051035 -3.968369 0.0002
C 1.013230 0.242558 4.177271 0.0001
@TREND("2009M01") 0.000558 0.000615 0.906762 0.3677

R-squared 0.240035 Mean dependent var 0.014400
Adjusted R -squared 0.217683 S.D. dependent var 0.088759
S.E. of regression 0.078506 Akaike info criterion -2.209945
Sum squared resid 0.419098 Schwarz criterion -2.114339
Log likelihood 81.45306 Hannan -Quinn criter. -2.171926
F-statistic 10.73889 Durbin -Watson stat 1.537923
Prob(F -statistic) 0.000089

86
The ordinary least Square test results
Dependent Variable: D(ZSE1)
Method: Least Squares
Sample (adjusted): 2009M04 2014M12
Included observations: 69 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

ZSE1( -1) -0.133119 0.049876 -2.669021 0.0098
INF(-1) 1.168085 0.497783 2.346576 0.0223
MS(-1) -0.098944 0.039860 -2.482314 0.0159
C -3.176467 1.704555 -1.863516 0.0673
D(INF( -1)) -5.971723 1.531090 -3.900307 0.0002
D(CAD) -0.003651 0.001206 -3.027566 0.0036
D(GDP_D( -2)) -0.001393 0.000931 -1.495228 0.1401
D(IR( -1)) -0.008152 0.006918 -1.178329 0.2433
DUM_1308 -0.238242 0.059920 -3.975984 0.0002

R-squared 0.657042 Mean dependent var 0.012709
Adjusted R -squared 0.611314 S.D. dependent var 0.088607
S.E. of regression 0.055242 Akaike info criterion -2.833082
Sum squared resid 0.183100 Schwarz criterion -2.541677
Log likelihood 106.7413 Hannan -Quinn criter. -2.717472
F-statistic 14.36856 Durbin -Watson stat 1.533819
Prob(F -statistic) 0.000000

87
Serial Correlation Test results

Breusch -Godfrey Serial Correlation LM Test:

F-statistic 1.953119 Prob. F(2,58) 0.1510
Obs*R -squared 4.353849 Prob. Chi -Square(2) 0.1134

Test Equation:
Dependent Variable: RESID
Method: Least Squares
Date: 12/14/15 Time: 12:17
Sample: 2009M04 2014M12
Included observations: 69
Presample missing value lagged residuals set to zero.

Variable Coefficient Std. Error t-Statistic Prob.

ZSE1( -1) -0.045529 0.054718 -0.832052 0.4088
INF(-1) 0.050259 0.493857 0.101768 0.9193
MS(-1) 0.004023 0.039704 0.101317 0.9196
C -0.059422 1.691027 -0.035140 0.9721
D(INF( -1)) 0.336259 1.531085 0.219622 0.8269
D(CAD) -6.71E -05 0.001196 -0.056131 0.9554
D(GDP_D( -2)) -2.02E -05 0.000931 -0.021725 0.9827
D(IR( -1)) -0.000898 0.006828 -0.131570 0.8958
DUM_1308 -0.002078 0.059376 -0.035002 0.9722
RESID( -1) 0.251069 0.144632 1.735913 0.0879
RESID( -2) 0.104156 0.144392 0.721344 0.4736

R-squared 0.063099 Mean dependent var 1.34E -16
Adjusted R -squared -0.098435 S.D. dependent var 0.051891
S.E. of regression 0.054385 Akaike info criterion -2.840289
Sum squared resid 0.171547 Schwarz criterion -2.484127
Log likelihood 108.9900 Hannan -Quinn criter. -2.698988
F-statistic 0.390624 Durbin -Watson stat 1.987157
Prob(F -statistic) 0.945736

88
Heteroskedasticity Test result

Heteroskedasticity Test: ARCH

F-statistic 2.394422 Prob. F(1,66) 0.1266
Obs*R -squared 2.380614 Prob. Chi -Square(1) 0.1228

Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Date: 12/14/15 Time: 12:19
Sample (adjusted): 2009M05 2014M12
Included observations: 68 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 0.003167 0.000611 5.181760 0.0000
RESID^2( -1) -0.189596 0.122526 -1.547392 0.1266

R-squared 0.035009 Mean dependent var 0.002680
Adjusted R -squared 0.020388 S.D. dependent var 0.004366
S.E. of regression 0.004322 Akaike info criterion -8.021420
Sum squared resid 0.001233 Schwarz criterion -7.956141
Log likelihood 274.7283 Hannan -Quinn criter. -7.995555
F-statistic 2.394422 Durbin -Watson stat 1.820293
Prob(F -statistic) 0.126551

89
Granger Causality test results
Pairwise Granger Causality Tests
Sample: 2009M01 2014M12
Lags: 2

Null Hypothesis: Obs F-Statistic Prob.

MS does not Granger Cause ZSE1 70 0.35233 0.7044
ZSE1 does not Granger Cause MS 1.03090 0.3624

IR does not Granger Cause ZSE1 70 0.50628 0.6051
ZSE1 does not Granger Cause IR 1.74327 0.1830

INF does not Granger Cause ZSE1 70 3.28966 0.0436
ZSE1 does not Granger Cause INF 4.20807 0.0191

GDP_D does not Granger Cause ZSE1 70 1.96313 0.1487
ZSE1 does not Granger Cause GDP_D 0.57440 0.5659

CAD does not Granger Cause ZSE1 70 1.51857 0.2267
ZSE1 does not Granger Cause CAD 0.98129 0.3803