THE EFFECT OF INFLATION ON THE STOCK MARKET RETURNS OF THE
NAIROBI SECURITIES EXCHANGE
BY
HAKIM VENA
D63/79688/2012
A RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILMENT OF THE
REQUIREMENT FOR THE AWARD OF THE DEGREE OF MASTER OF SCIENCE
IN FINANCE, SCHOOL OF BUSINESS, UNIVERSITY OF NAIROBI.
OCTOBER 2014
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DECLARATION
I hereby declare that this research project is my original work and has not been presented for
an award in any other university.
Hakim Vena Date: …………………………………
D63/79688/2012 Sign: …………………………………
This research project has been submitted for examination with my approval as the University
of Nairobi supervisor.
Sign: ……………….............. Date: ……………………………..
Mr. Herick Ondigo
Lecturer
Department of Finance and Accounting
School of Business
University of Nairobi
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ACKNOWLEDGEMENTS
I would like to thank my parents, Mr. and Mrs. Hakim, for their constant motivation and their
unrivaled support during this period. I truly appreciate the commitment you showed me
during this period in time and for inspiring me to be the best.
I especially thank my sisters Greta Hakim, Annette Hakim and Angela Joy Hakim for their
presence and help during this period of study. Heartfelt thanks also go to Mr. and Mrs.
Kirumba. I could not have done it without your support and understanding.
I would also like to thank my supervisor Mr. Herick Ondigo for providing guidance and
much needed knowledge and advice up until the completion of this project. This study could
not have been successful without your assistance.
Special thanks to the University of Nairobi, my colleagues at the school and the staff. The
resources and assistance you provided were vital in making this whole journey seamless.
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DEDICATION
This study is dedicated to my friends and family who have supported me through the whole
period. Thank you all and may God bless you!
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TABLE OF CONTENTS
DECLARATION..................................................................................................................... II
ACKNOWLEDGEMENT .................................................................................................... III
DEDICATION........................................................................................................................IV
LIST OF TABLES ............................................................................................................... VII
LIST OF FIGURES ........................................................................................................... VIII
LIST OF ABBREVIATIONS ...............................................................................................IX
ABSTRACT ............................................................................................................................. X
CHAPTER ONE: INTRODUCTION .................................................................................... 1
1.1 Background of the Study .................................................................................................. 1
1.1.1 Inflation ................................................................................................................... 2
1.1.2 Stock Market Returns ............................................................................................. 4
1.1.3 Effects of Inflation on Stock Market Returns ......................................................... 5
1.1.4 Nairobi Securities Exchange ................................................................................... 6
1.2 Research Problem ........................................................................................................... 8
1.3 Objective of the Study .................................................................................................... 9
1.4 Value of the Study ......................................................................................................... 9
CHAPTER TWO: LITERATURE REVIEW ..................................................................... 11
2.1 Introduction .................................................................................................................. 11
2.2 Theoretical View .......................................................................................................... 11
2.2.1 Fisher Theory ..................................................................................................... 11
2.2.2 Fama’s Proxy Hypothesis ................................................................................... 12
2.2.3 Inflation and Money Illusion Theory ................................................................. 13
2.2.4 Efficient Market Hypothesis............................................................................... 14
2.3 Determinants of Stock Market Returns ........................................................................ 15
2.4 Review of Empirical Studies ........................................................................................ 16
2.5 Summary of Literature Review .................................................................................... 22
CHAPTER THREE: RESEARCH METHODOLOGY .................................................... 24
3.1 Introduction .................................................................................................................. 24
3.2 Research Design ........................................................................................................... 24
3.3 Target Population ......................................................................................................... 24
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3.4 Data Collection ............................................................................................................. 25
3.5 Data Analysis ............................................................................................................... 25
3.6 Analytical Models ........................................................................................................ 25
3.6.1 Model for Stationary Transformation ................................................................. 25
3.6.2 Analytical Model: Linear regression .................................................................. 26
3.6.3 Test of Significance ............................................................................................ 27
3.6.4 GARCH Model .................................................................................................. 27
3.6.5 EGARCH Model ................................................................................................ 28
CHAPTER FOUR: DATA ANALYSIS, RESULTS AND DISCUSSION ....................... 30
4.1 Introduction .................................................................................................................. 30
4.2 Descriptive Statistics and Test for Normality of Variables .......................................... 30
4.2.1 Normality Test results ........................................................................................ 32
4.3 Non Linearity Test Results ........................................................................................... 32
4.4 Correlation Test Results ............................................................................................... 33
4.5 Stationarity/ Unit Root Test Results............................................................................. 34
4.6 Regression Model ......................................................................................................... 37
4.7 Test of significance ...................................................................................................... 40
4.7.1 Presence of ARCH Effects Test ......................................................................... 40
4.7.2 GARCH Model Test Results .............................................................................. 41
4.7.3 EGARCH Test Results ....................................................................................... 42
4.7.4 Impact of Inflation on Conditional Stock Market Volatility .............................. 45
4.8 Interpretations of the Findings ..................................................................................... 47
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS ......... 49
5.1 Introduction .................................................................................................................. 49
5.2 Summary ...................................................................................................................... 49
5.3 Conclusion .................................................................................................................... 51
5.4 Policy Recommendations ............................................................................................. 52
5.5 Limitations of the Study ............................................................................................... 53
5.6 Suggestions for Further Research ................................................................................ 54
REFERENCES ....................................................................................................................... 55
APPENDICES ........................................................................................................................ 61
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LIST OF TABLES
Table 4.1 Summary statistics for nominal stock returns and inflation .................................... 23
Table 4.2 Correlation matrix ................................................................................................... 25
Table 4.3 Results for ADF stationarity test of NSE returns at level ........................................ 26
Table 4.4 Results of ADF stationarity test of inflation ............................................................ 26
Table 4.5 Regression model results ......................................................................................... 27
Table 4.6 Results of serial correlation LM test ....................................................................... 29
Table 4.7 Results of the GARCH model for stock market return series .................................. 30
Table 4.8 EGARCH (1, 1) volatility coefficients for stock market return series ..................... 31
Table 4.9 Results for EGARCH (1, 1) model on the effect of inflation on stock market return
volatility ................................................................................................................................... 32
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LIST OF FIGURES
Figure 4.1: CPI Trend from January 1998 to December 2013 ............................................... 47
Figure 4.2: Estimated inflation level from January 1998 to December 2013......................... 47
Figure 4.3: NSE All Share Index graph from January 1998 to December 2013 .................... 48
Figure 4.4: NSE Market Returns for period under investigation ............................................ 48
Figure 4.5: NSE market returns and inflation......................................................................... 49
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LIST OF ABBREVIATIONS
ADF – Augmented Dickey-Fuller test
AIM – Alternative Investment Markets Segment
ARCH – Autoregressive Conditional Heteroskedasticity
CDS – Central Depository System
CPI – Consumer Price Index
EGARCH – Exponential Generalized Autoregressive Conditional Heteroskedasticity
EMH – Efficient Market Hypothesis
GARCH – Generalized Autoregressive Conditional Heteroskedasticity
Iid – Independent and identically distributed
KNBS – Kenya National Bureau of Statistics
LM – Lagrange Multiplier
MIMS – Main Investments Market Segment
NASI – NSE All Share Index
NSE – Nairobi Securities Exchange
VAR – Vector Autoregressive
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ABSTRACT
The effect of inflation on the stock exchange and ultimately stock prices has been under
scrutiny over the past few decades. The point of argument being if inflation has an impact on
the volatility of stock prices and eventually the stock market. This study examined the effect
of inflation on stock prices at the Nairobi Securities Exchange. Prior studies on this particular
topic yielded negative correlation between the key stock exchange performance indicators
and the rate of inflation. The objective of this study was to examine the effect of the inflation
rate on the performance of the Kenyan Stock Market. Particular attention was paid to the
effects of inflation on various stock market performance indicators, in terms of market
activity and liquidity. An empirical investigation was conducted using monthly data on
selected key market indicators from the NSE from the period 1998-2013 and the correlational
design method of estimation applied using a regression model to test the effects of inflation
on stock market returns. It was revealed that the stock market returns were positively
correlated at 7.9% to the rate of inflation. This seemingly high level of influence of inflation
revealed that investments can thrive well in the stock market regardless of the rate of
inflation. The results seemed to agree with Fisher Hypothesis which states that an increase in
the rate of inflation leads to a change in stock market returns and thus act as a good hedge
against inflation. The R squared statistic measuring the ability of regression to predict the
dependent variable values within the sample indicated that only 0.6% of the stock market
activity can be explained by the inflation variable. This study applied GARCH to examine the
effect of inflation on stock market returns. Additionally, the impact of asymmetry shocks
were examined using the EGARCH model and it was established that the stock market
returns at the NSE are asymmetric and thus the EGARCH model was preferred over the
GARCH model. The EGARCH model captured the asymmetric effects of shocks on stock
market volatility by assessing the impact of positive and negative correlations on stock
market returns. The market return series was found to show evidence of asymmetric effects.
From the EGARCH model, there was evidence of weak but significant support that bad news
had a more adverse effect on stock market volatility as opposed to good news. Given that the
Nairobi Stock Exchange has gone public, it should aim to engage the public in enlightenment
on stock investment procedures and overall stock purchases to enable more investment
opportunities. The government should also come up with measures and policies that will help
control and stabilize the rate of inflation fluctuation so as to boost investor confidence in the
securities market. This will have a significant impact on the performance of the Nairobi
Securities Exchange and will ultimately uphold economic growth.
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CHAPTER ONE
INTRODUCTION
1.1 Background of the Study
The stock market is a market that deals with the exchange of shares of publicly quoted
companies, government, corporate and municipal bonds among other instruments for money.
The NSE was formed in 1954 as a voluntary organization of stock brokers and is now one of
the most active markets in Africa. As a capital market institution, the stock exchange plays an
important role on the economic development. It helps mobilize domestic savings thereby
bringing about re-allocation of financial resources from dormant to active agents. Long term
investments are made liquid, as the transfer of securities among participating public is
facilitated. The exchange has also enabled companies to engage local participation in their
shares ownership, thereby giving Kenyans a chance to own shares of reputable firms.
Companies can also raise extra finance essential for expansion and development. The stock
market enhances the inflow of international capital and facilitates the government’s
privatization programmes.
Increasing inflation is one of the biggest fears of investors because it reduces the real return
on their investments as per Schofman and Schweitzer (2000). Inflation has an adverse effect
on an economy with its effect ranging from positive to negative. The negative effects are
however more pronounced and comprise a decrease in the real value of money as well as
other economic variables over time. Previous studies have concluded that inflation and stock
markets are closely correlated with the rate of inflation influencing the stock market risk and
volatility. Stock markets promote savings and investments by providing an avenue for
portfolio diversification to both individuals and corporate investors. These effects of inflation
on the stock market performance greatly influences the prices of financial assets which are
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essentially determined by the net earnings of a corporation and are hence directly
proportional to the performance of a company.
A highly inflationary environment therefore adversely affects the price of stocks and eventual
returns. High inflation leads to an increase in interest rates leading to decrease in investment
activity and ultimately the stock market growth. Although numerous papers have analyzed
the factors influencing the stock market development in various countries, majority of them
have entirely focused on the developed countries and other emerging markets. Accordingly,
there is scarce evidence on the nature of interaction between equity markets and various
economic fundamentals.
The stock market is a major component in a country’s economy and contributes immensely to
the financial performance of the country. The link between stock market performance and
macroeconomic variables has attracted a great deal of research in the past with growing
literature revealing strong influence of macroeconomic variables on stock market indices. In
a past survey, Cohn and Lessard (1980) established that stock prices in many industrialized
countries to be negatively related to inflation. Contrary to previous studies, Poitra (2004)
argued that he found no significant evidence on the impact of announcements in
macroeconomic fundamentals on the stock prices.
1.1.1 Inflation
The rate of inflation measures the annual percentage increase in prices; the most usual
measure is that of retail prices. The government publishes an index of consumer prices each
month, and the rate of inflation is the percentage increase in that index over the previous 12
months. Johnson (1972) simply defines inflation as the sustained rise in general price level.
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With increase in inflation, every sector of the economy is affected including interest rates,
unemployment, exchange rates, and stock markets and there is an aftermath of inflation in
each sector. The most common measures of inflation are the CPI and the GDP deflator with
the latter measuring inflation within the whole domestic economy and the former measuring
consumer prices.
Keynesian theory on inflation proposed that changes in money supply do not directly affect
prices and that visible inflation is the result of economic pressures in the economy expressing
themselves in prices. Keynesians argue that the government needs to actively intervene to
stabilize the economy. Otherwise, the uncertainty caused by unpredictable fluctuations will
be very damaging to investment and hence long term economic growth. If demand fluctuates,
in the way Keynesians claim, and if the policy of having money supply or inflation rule is
adhered to, interest rates must fluctuate. Targeting inflation alone may make it a poor
indicator of an economy’s state because the money supply will adapt to changes in the
inflationary expectations. This is combated by Taylors rule which takes into account inflation
and either the rate of economic growth or unemployment to get the optimum stability level.
Monetarists believe that the rate of inflation is greatly influenced by how fast the supply of
money grows or shrinks. They consider fiscal policy an ineffective way of controlling
inflation. Inflation tends to cause uncertainty in the business community, especially when the
rate of inflation fluctuates. Generally, the higher the rate of inflation, the more it fluctuates.
Difficulties for firms to predict their costs and revenues may discourage investments and
hence lead to a decrease in economic output and ultimately a company’s share price. Inflation
is categorized as either expected or unexpected. Economists are able to plan annually with the
rates of expected inflation. When the general level of prices rise, people are less likely to hold
money. Unpredictable inflation is harmful to the economy and makes the economy
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inefficient. Barro and Grilli (1994) were convinced that unexpected inflation led to wealth
redistribution between trading partners.
Historically, from 2005 until 2012, Kenya Inflation Rate averaged 12.62 Percent reaching an
all time high of 31.50 Percent in May of 2008 and a record low of 3.18 in October of 2010.
Inflation in Kenya has been relatively high compared to developed countries. In 2009,
inflation eased from 2008 16.2% to 9.2%. In 2010, Inflation was contained within the
Government’s target of 5.0 per cent. The average annual inflation was 4.1 percent in 2010
down from a high of 10.5 percent recorded in 2009. During this period, the stock market
experienced recovery until in 2011 when the inflation rate sharply increased to unstable 18%.
The inflation rate in Kenya was recorded at 6.60 percent in September of 2014. Inflation Rate
in Kenya averaged 11.17 Percent from 2005 until 2014, reaching an all time high of 31.50
Percent in May of 2008 and a record low of 3.18 Percent in October of 2010.There is
therefore need to determine the effect of inflation rate fluctuations on stock return and
volatility.
1.1.2 Stock Market Returns
Returns that investors generate from buying and selling of stocks in an efficient market are
referred to as stock market returns. Depending on the market, they can either be profit or
dividends in nature. Returns are usually floating and subject to market risks. To make the
maximum returns, investors should buy low and sell high. Rational investors act on informed
decisions and conduct either technical or fundamental analysis to determine the future trend
of stocks. Technical analysis mainly focuses on scrutinizing the historical price movements
of a particular stock to predict the future trend of the stock. Fundamental analysis tends to
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focus more on the cash flows, profit growth of companies and any other announcements that
could potentially lead to an increase in the share price of a particular stock.
The stock market is a volatile environment with dramatic moves that can either give investors
a positive or negative stock market return. There is a strong relationship between volatility
and market performance. Volatility declines as the stock market rises and increases as the
stock market falls. Increase in volatility increases the risk involved and reduces the overall
returns on stock prices, (Easterling, 2011). Generally, unexpected volatility has a more
significant effect on stock returns than expected volatility (Chiang , 2001).
Various economic factors contribute to the directional change of the market and hence
volatility. Changes in inflation trends usually influence the long term stock market trends and
volatility. Price earnings ratios that are on the rise tend to reflect economic periods when
inflation is low. Price earnings ratios that are on the decline reflect higher inflationary periods
when prices are more unstable. While policy maker’s main interests lie in discovering the
main determinants of volatility and examining its effects on real economic activities,
financial analysts are more interested in the effects of time varying volatility. Volatility
therefore is an important aspect to consider especially when much reliance on the financial
stability of a country is placed on the capital markets.
1.1.3 Effect of Inflation on Stock Market Returns
Prices of stocks are determined by the net earnings of a company. It depends on how much
profit, the company is likely to make in the long run. Share prices of a company usually
escalate if there is speculation that the company is going to do well in the future. If there is a
6
downward trend speculation of a company’s future stock price movements, then its stock
price will subside.
Stock prices are directly proportional to the performance of a company. In the event of an
increase in inflation, the company’s earnings will also subside and this will adversely affect
the stock prices and eventually the returns from company stocks. The nominal interest rate
consists of a real rate plus expected inflation rate. The expected real rate of an economy is
determined by the real factors such as productivity of capital and time preference of savers. It
is independent of the expected inflation rate. (Fisher, 1930).
Modigliani and Cohn (1979) investigating into failure of equities to act as a hedge against
inflation concluded that a major part of undervaluation of shares was due to cognitive errors
on the part of the investors. They felt that in an inflationary period, the interest expense was
not really an expense but rather a repayment of real principle. A concept they thought
investors were unaware of. The stock market should perform well when there is strong
economic growth and under periods of low inflation. Studies show that inflation indeed
impacts the stock returns negatively. It is this statement that I aim to test if it holds.
1.1.4 Nairobi Securities Exchange
The stock exchange acts as a primary or secondary market where public limited companies
can raise finance by issuing new shares, whether to new shareholders or existing ones. As a
secondary market, the stock exchange operates as a market where investors can sell existing
shares to one another. Nairobi Securities Exchange was constituted as Nairobi Stock
Exchange in 1954 as a voluntary association of stock brokers in the European community
registered under the societies act. It is the fourth largest stock exchange in Africa in terms of
traded volumes. In 2008, NSE All Share Index was introduced as an alternative index. Its
7
measure is an overall indicator of the market performance. The index incorporates all the
traded shares of the day. The share index mainly focuses on overall market capitalization
rather than the price movements of select counters.
NSE 20 Share Index is a price weight index and a major stock market index that tracks the
performance of 20 of the best performing companies listed on the NSE. The companies are
selected based on a weighted market performance for a 12 month period based on market
capitalization, number of traded shares, number of deals and turnover. A well organized and
managed stock market will facilitate an economy’s increase in economic growth by
increasing the liquidity of financial assets, diversification of global and domestic risk,
promotion of wiser investment decisions and influencing better corporate governance.
(Vector, 2005).
The Nairobi Securities Exchange comprises of 62 listed companies with a daily trading
volume of over USD 5 million and a total market capitalization of approximately USD15
billion.NSE has three market segments namely; the Main Investments Market (MIMS), the
Alternative Investment Markets Segment (AIMS) and the Fixed Income Securities Market
Segment (FISMS). The MIMS is the main quotation market, the AIMS provide an alternative
method of raising capital to small, medium sized and young companies that find it difficult to
meet the strict listing requirements of the MIMS while the FISMS provides an independent
market for fixed income securities such as treasury bonds, corporate bonds, preference shares
and debenture stocks, as well as short term financial instruments such as treasury bills and
commercial papers. Automated bond trading started in November 2009 with the KES 25
billion KenGen bond. NSE Trading hours was revised to start from 09:00 to 15:00. Delivery
and settlement is done scrip less via an electronic Central Depository System (CDS) which
8
was installed in 2005. Settlement is currently T+4, but moving to T+3, on a delivery payment
basis. The NSE in 2006 introduced an Automated Trading System (ATS) which ensures that
orders are matched automatically and are executed on a first come/first serve basis. The ATS
has now been linked to the Central Bank of Kenya and the CDS thereby allowing electronic
trading of Government bonds.
NASI has been steadily gaining for the past 12 years from an All Share Index of 2,964 in
2004 to a steady 5,267 in 2014. The index edged down slightly in 2007 due to shocks from
the post election violence experienced from the political turmoil that year but picked up in
2010 closing at an at an index of 4,433.The going public of the Kenyan bourse is arguably
going to be the game changer of the securities exchange. Expectations are for NSE to raise
the Kenyan financial sector to international standards through proper corporate governance
and increased trading volumes.
1.2 Research Problem
Different researchers around the world have come to a consensus that inflation has an
influence on the stock market. In the long run analysis of the stock market variables, it has
been observed that inflation is a major problem that cannot be ignored. In periods of inflation,
an increase in the consumer price index due to increased interest rates leads to dwindling
share price. High inflation creates a high level of stock market volatility which could
potentially destabilize the economy and make it inefficient. Kenyan policy makers should
ultimately strive to make policies that reduce market volatility to make the stock exchange
market more efficient.
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Chinzara (2011) Found that inflation definitely plays a role in affecting the stock market
volatility. In his study on macroeconomic uncertainty and stock market volatility, he found
that the volatility of stock price movements is greatly influenced by macroeconomic
uncertainty. Olweny and Omondi (2011) concluded that macroeconomic factors such as
foreign exchange rates, interest rate and inflation rate affected the stock volatility returns at
the Nairobi Stock Exchange. There is need to identify factors that have a significant effect on
the stock market return as the NSE is a key player in driving up economic growth of Kenya.
Fama and Schwert (1977) found a negative relationship between the performance of the stock
market and inflation. Some significant studies from Pearce and Roley (1985) and Hardouvelis
(1988) showed no significant correlation between the stock returns and inflation and this
proves that there is need for further exploration into the topic. To seek clarity on the
relationship between inflation and stock price movements, further research must be done to
investigate the behavior of the two variables. Since the Nairobi Stock Exchange has been
gaining steadily over the past few years, more is expected in terms of research to further
uncover how inflation as a macroeconomic variable affects stock market returns. This study
intends to address the question: what is the effect of inflation on stock market returns in the
NSE?
1.3 Objective of the Study
To examine the effect of inflation on stock market returns in the Nairobi Securities Exchange.
1.4 Value of the Study
The stock exchange plays a major role in the economy of any given country and its
development. It plays a pivotal role in the growth of the industry and the commercial sector
10
of the country. Ultimately, this leads to a great effect on the economy. The stock exchange is
viewed as a very significant component of the financial sector. Furthermore, it plays a vital
role in the mobilization of capital in many emerging economies. The Nairobi Stock Exchange
plays a huge role in collecting money and encouraging investments, this study was designed
to explore the influences of some key economic factors like inflation on stock market prices.
This study will be useful for the investors who might be able to identify various economic
variables that they should focus on while investing in the stock market and this will give them
an advantage to make sound investment decisions. Research analysts, individual investors,
portfolio managers, foreign and institutional investors will also benefit from this study as it
will assist them in understanding the overall effect inflation has on the stock exchange.
With more and more companies wanting to go public and trading their shares on the stock
exchange, this study aims to be a databank and to shed light on some of the factors that affect
the stock market.
This study therefore attempts to illuminate how inflation as an economic fundamental
impacts on the process of securities market in Kenya, the largest economy in East Africa and
a significant economic powerhouse in the whole of east Africa region. This paper focuses on
the Kenya as a key economy within the East African region.
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CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
The purpose of this section is to examine what other researchers have already written about
the stock exchange and the key macroeconomic indicator being studied.
2.2 Theoretical Review
This section is aimed at looking at different theories proposed by various scholars in their
quest for determining the effect of inflation on stock market returns.
2.2.1 Fisher Theory
Fisher (1930) hypothesized that the ex-ante nominal interest rate should fully anticipate
movements in expected inflation, in order to yield the equilibrium real interest rate. The
expected real interest rate is determined by real factors such as the productivity of capital and
time preference of consumers, and is independent of the expected inflation rate. In principle,
the Fisher hypothesis could be extended to any asset, such as real estate, common stock, and
other risky securities.
The empirical relationship between inflation and common stocks was first investigated by
Jaffe and Mandelker (1976), Bodie (1976) and Nelson (1976). Although employing different
empirical approaches, these authors all concluded for a significant negative relationship
between the proxies of inflation and stock returns. Following these pioneering studies, Fama
and Schwert (1977) investigate the inflation effect on asset returns in a number of assets.
They concluded that, similar to previous studies, common stocks seem to perform poorly as
12
hedge against both expected and unexpected inflation. Since these earlier studies, the
empirical literature on the Fisher hypothesis has been prolific, and the findings have been
largely similar (e.g. Gertler and Grinols (1982), Buono (1989), Park (1997)).
The early studies on the Fisher hypothesis mentioned above were mainly concerned with
documenting and describing the nature of the relationship between stock returns and
inflation, and not with any explanation of the results. Several alternative explanations have
emerged. The Tax-Effect Hypothesis proposed by Feldstein (1980) argues that inflation
generates artificial capital gains due to the valuation of depreciation and inventories (usually
nominally fixed) subject to taxation. This increase corporate tax liabilities and thus reduces
real after-tax earnings. Rational investors would take into account this effect of inflation by
reducing common stock valuation. In this sense, inflation “causes” movement in stock prices.
2.2.2 Fama’s Proxy Hypothesis
The theory revealed that the anomalous relationship observed between real stock returns and
inflation was a consequence of a spurious relationship: the negative relationship between
stock returns and inflation are induced by the positive correlation between stock returns and
real activity and the negative correlation between inflation and real activity.
The argument hinges on the money demand behavior of rational agents who perceive a fall in
economic activity and therefore a decrease in money demand. This causes an excess money
stock and therefore inflation. In this sense, measures of real activity should dominate
measures of inflation when both are used as explanatory variables for real stock returns in
testing the Fisher Hypothesis.
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Benderly and Zwick (1985), Wei and Wong (1992) and Lee(1992) supported the proxy
hypothesis while Ram and Spencer (1983) failed to support the theory as they felt that his
explanation calls into question the conventional theories of the Phillips curve, in which a
positive rather than a negative relationship between inflation and real activity is suggested.
They find consistent evidence of a positive relationship between real activity and inflation
and a negative relationship between real activity and stock returns.
2.2.3 Inflation and Money Illusion Theory
Modigliani and Cohn (1970), theory states that the real effect of inflation is caused by money
illusion. Inflation illusion suggests that when there is a rise in expected inflation, bond yields
rise, but because equity investors incorrectly discount real cash flows using nominal rates, the
increase in nominal yields leads to under pricing of equities. Bekaert and Engstrom (2007).
Stock market investors fail to understand the effect of inflation on nominal dividend growth
rates and extrapolate historical nominal growth rates even in periods of changing inflation.
Thus when inflation increases, bond market participants increase nominal interest rates which
are used by stock market participants to discount unchanged expectations of future nominal
dividends. The dividend-price ratio moves with the nominal bond yield because stock market
investors irrationally fail to adjust the nominal growth rate to match the nominal discount
rate. This implies that stock prices are undervalued when inflation is high and may become
overvalued when inflation falls. The dividend yield that emerges from the interaction of
rational and irrational investors is positively correlated with inflation and the long term
nominal interest rate.
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2.2.4 Efficient Markets Hypothesis
Efficient markets theory dictates that the price of an asset reflects all relevant information that
is available on the intrinsic value of that asset. One of the arguments made in favor of the
stock market is that it acts as an arena within which share values can be accurately or
efficiently prices. If new information comes to the market with regard to a company’s share
and its performance, it will be quickly and rationally transferred into the company’s stock
price.
Fama (1991) noted that market efficiency varies from weak form, strong form to semi strong
form efficiencies. If stock markets were fully efficient, the expected returns from every stock
would be the same and thus only unanticipated random information that can cause share
prices to deviate from the expected average yields. Stock prices normally follow a randomly
distributed pattern. Capital markets with higher information efficiency are more likely to
retain higher operational and allocational efficiencies as observed by Aras and Kurtulus
(2004).
Sanford and Joseph (1980) recognized that an extremely high level of market efficiency is
internally inconsistent. It would exclude the profitable opportunities necessary to motivate the
security analysis required to produce information. Their main point is that market frictions,
including the costs of security analysis and trading limit market efficiency. Therefore, we
should expect to see a different level of efficiency across different markets with respect to
trading levels.
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2.3 Determinants of Stock Market Returns
It is by now widely recognized that a well functioning stock market is crucial to economic
growth. As part of the financial economic system, the stock markets play important roles in
economic growth. Then, the question of what determines stock market returns becomes
important.
Exchange rate can be defined as the price at which a country’s currency can be exchanged for
another country’s currency. Exchange rate volatility has implications on a country’s financial
sector, the stock market to be precise. Benita and Lauterbach (2004) found that exchange rate
volatility have real economic costs that affect price stability, firm profitability and a country’s
stability. Establishing the relationship between stock prices and exchange rates is important
for a few reasons. First, it may affect decisions about monetary and fiscal policy. Gavin
(1989) shows that a booming stock market has a positive effect on aggregate demand.
Exchange rate movement affects output levels of firms and also the trade balance of an
economy. Share price movements on the stock market also affect aggregate demand through
wealth, liquidity effects and indirectly the exchange rate. Specifically a reduction in stock
prices reduces wealth of local investors and further reduces liquidity in the economy. The
reduction in liquidity also reduces interest rates which in turn induce capital outflows and in
turn causes currency depreciation (Adjasi et al., 2008). Hsing (2011) found a positive
relationship between exchange rate and the stock market in Johannesburg Stock Exchange.
Cheng’ et al., (2011) conducted study on Taiwan stock market and the results indicated a
positive relationship between exchange rate and stock return. Bailey and Chung (1995)
conducted a study on Exchange Rate Fluctuations, Political Risk, and Stock Returns at the
16
Mexican stock market and the results proved there is a positive relationship between
exchange rate fluctuation and stock market return.
The interest rate can be defined as the annual price charged by a lender to a borrower in order
for the borrower to obtain a loan. This is usually expressed as a percentage of the total
amount loaned. Traditional theories define interest rate as the price of savings determined by
demand and supply of loanable funds. Ngugi and Kabubo (1998) states that the primary role
of interest rate is to help mobilize financial resources and ensure the efficient utilization of
resources in the promotion of economic growth and development. Chen et al. (1986)
indicated that interest rate had positive impact on stock return. Wongbangpo et al. (2002)
observed interest rate had a negative impact on Southeast Asian countries. In the industrial
analysis, Nguyen (2007) found interest rate spreads had a significant effect on the riskiness of
capital-intensive industries.
Money supply effects can either be positive or negative. Since the rate of inflation is
positively related to the growth rate of money Fama (1981), a rise in money supply could
lead to an increase in the discount rate and thus lower the stock prices. However, this
negative effect may be countered by money growth, which would possibly increase cash
flows and stock prices Mukherjee and Naka (1995).
2.4 Review of Empirical Studies
Fisher (1930) asserted that the nominal interest rate consists of a real rate plus the expected
inflation rate. Fisher Hypothesis stated that expected real rate of the economy is determined
by the real factors such as productivity of capital and time preference of savers and is
independent of the expected inflation rate. If Fisher effect holds, there is no change in
17
inflation and nominal stock returns since stock returns are allowed to hedge for inflation.
Some opposed to Fisher Hypothesis, and claimed that the real rates of common stock return
and expected inflation rates are independent and that nominal stock returns vary in one-to-
one correspondence with expected inflation (Pong and Tong, 2010).
Bakshi and Chen (1996) argue that a negative correlation between inflation and stock prices
has become one of the most commonly accepted empirical facts. However, Caporale and
Jung (1997) test for a causal relationship between both expected and unexpected inflation and
real stock prices, and find that a positive relationship does exist. As they conclude, the
negative effects of inflation on stock prices do not disappear after controlling for output
shocks. This is contrary to Fama’s view.
Ioannides, Katrakilidis et al. (2002) investigated the relationship between stock market
returns and inflation rate for Greece over the period 1985 to 2000. There were arguments that
stock market can hedge inflation in line with to Fisher’s hypothesis. Another argument was
that the real stock market was immune to inflation pressures. This study attempted to
investigate the three types of relationship whether firstly the stock market had been a safe
place for investors in Greece. Empirical evidence classified the relationships into three types.
First, there is positive relationship between the stock market returns and inflation. They used
ARDL co integration technique in conjunction with Granger Causality to test the long-run
and short-run effects between the involved variables as well as the direction of these effects.
There was a long run negative relationship from inflation to stock market returns over the
first sub-period. The findings were consistent with Fama (1981).
18
Madsen (2004) used Fisher’s hypothesis to estimates the relationship between share returns
and inflation. Numerous papers were found that share returns are not hedged against expected
inflation and have interpreted this as evidence against the Fisher hypothesis. Fisher
hypothesis were tested for the process governing inflation, measurement of inflation
expectations, and the time aggregation of the data. The paper demonstrated theoretically and
empirically standard tests of the Fisher hypothesis can be directly misleading and often do not
reveal much about the validity of the Fisher hypothesis that would be explained by
differences in model specification, time aggregation of the data, inflation persistence in the
data sample and whether instruments have been used for expected inflation. The interaction
between model specification and inflation persistence was found to be particularly influential.
The more persistent was inflation the more favorable were estimates which used nominal
share returns as the dependent variable to the Fisher hypothesis. The opposite result applies
used real ex post share returns as the dependent variable, except in the case where inflation
expectations are measured by the actual rate of inflation. Furthermore, tests were more
favorable to the Fisher hypothesis when low frequency data and instruments for expected
inflation were used under the circumstances where nominal share returns were used as the
dependent variable.
Laopodis (2005) examines the dynamic interaction among the equity market, economic
activity, inflation, and monetary policy. Researcher looks into the first issue concerning the
role of monetary policy. Advance econometrics using co integration, causality and error-
methods using bivariate and multivariate Vector Autoregressive (VAR) or multivariate
Vector Error-Correction (VEC) models. With bivariate results, they found that the real stock
returns-inflation pair weakly support negative correlation between stock market and inflation,
meanwhile stock market can hedge against inflation. On the other hand, bivariate results
19
claims a negative and unidirectional relationship from stock returns to FED funds rate in the
1990s but a very weak one in 1970s. With multivariate, they found strong support of short-
term linkages in the 1970s along with the same unidirectional linkage between the two in the
1990s. This showed that stock returns do not respond positively to monetary easing, which
took place during the 1990s, or negatively to monetary tightening. There were no consistent
dynamic relationship between monetary policy and stock prices. This conclusion seems to
contradict Fama’s (1981) proxy hypothesis, which said that inflation and real activity were
negatively related but real activity and real stock returns were positively related.
Bidirectional long run causality resulted in second sub-period. There was a causal effect
running from stock market returns to inflation. Evidence was also found that a causal effect
running from inflation to stock market returns in second sub-period. The second sub-period
showed mixed relationship was also consistent with Spyrou (2001).
Kim and Ravi (2006) were explained the cross-sectional variation in the relation between
international security returns and expected inflation based on their sensitivities to world stock
and bond factors. The paper shows inflation sensitivities of returns on country indexes and
international mutual funds on their sensitivities to world stock and bond indexes. The result
from OLS regression coefficient for return sensitivity of stock to the stock market factor was
negative and significant at the five percent level. The coefficient for return sensitivity to the
bond market factor was positive and significant at the one percent level. Thus, the results
support the hypothesis that the inflation sensitivity of a security was negatively related to its
stock market return sensitivity and positively related to its bond return sensitivity. Concluded
that the inflation sensitivity of a security is positively (negatively) related to its sensitivity to
the world bond index (world stock index).
20
The proxy of risk premium raises more in response to unexpected inflation in recessions as
compared to expansions, contributing to the asymmetric inflation beta across the business
cycle. Merika and Anna (2006) re-examine Fama’s proxy hypothesis which states that
inflation was negatively related to real economic activity and the negative relationship
between stock returns and inflation reflects the positive impact of real variables on stock
returns. The paper tests the hypothesis that stock prices respond negatively to positive real
economic activity.
Wei (2007) investigates the relation between unexpected inflation and stock returns. The
study showed correlations between unexpected inflation and nominal equity return of Fama-
French book-to-market and size portfolios across the business cycle. The study found four
main finding. Firstly, there was strong evidence that equity returns respond more negatively
to unexpected inflation during economic contractions than expansions.
Lee (2009) reevaluate whether the stock return and the inflation relation indeed due to
inflation illusion by reexamining the hypothesis using longer sample period of the US and
international data. The inflation illusion hypothesis explained the post-war relation well; it
was not compatible with some features of the pre-war relation. A major problem is that while
this hypothesis anticipates under pricing of stock prices with high inflation. Thus, the study
observed the overpricing with high inflation in the pre-war period. This implies that although
the mispricing component plays an important role in the stock market and inflation relation in
both subsample periods. The result found the two types of stock return and inflation relations
without imposing a particular permanent and temporary restriction on the two types of
shocks. The two regime hypothesis show positive and negative inflation shocks can be easily
21
compatible with both pre- and post-war relations in the US. There were indeed two distinct
forces in the economy in each period, and they drive the relation in opposite directions. The
observed relations in the pre-war and post-war periods are consistent with the relative
importance of these shocks. The vicariate VAR identification found that there are two types
of stock return and inflation relations in each developed countries. Researcher considered and
the observed negative relations in these countries were again consistent with the relative
importance of the two types of inflation shocks.
Olweny and Omondi (2011) analyzed the effects of macroeconomic factors on stock return
volatility in the NSE. Their findings showed that macroeconomic factors; foreign exchange
rates, interest rates and inflation rates affected the volatility of stock market returns at the
NSE. They found that equities returns are symmetric but leptokurtic and thus not normally
distributed. The results showed that foreign exchange rate, interest rate and inflation rate
affected stock return volatility.
Kemboi and Taurus (2012) examined the stock market macroeconomic determinants for the
period 2000-2009, using quarterly secondary data. The hypothesis on the existence of a
cointergrated relationship between stock market development and macroeconomic
determinants was tested using Johansen- Julius cointergration technique. The results
indicated that macroeconomic factors like levels of income, development of the banking
sector and stock market liquidity are important in the development of the Nairobi Securities
Market. These results indicated that macroeconomic stability is not a significant predictor of
development of the securities market.
22
Aroni (2012) investigated factors influencing stock prices for firms listed in the NSE
covering the period January 2008 to December 2010 using the macroeconomic variables
inflation, exchange rates, interest rates and money supply. They applied the multiple
regression formula to estimate the effect of the selected factors on stock prices. Their findings
showed that inflation exchange and interest rates were significant. Although money supply
had a positive correlation to stock price movements, its relationship was not as significant as
the rest. Their findings were that inflation and money supply had a positive correlation as
opposed to the negative correlation that exchange and interest rates had on stock market
returns. The strong economic activity causes inflation and induces policy makers
implemented a counter cyclical macroeconomic policy. Negative stock price responded to
news of an improving economy was justified if the expected effect of a contractionary policy
was greater than the expected output gain the news suggest. By VAR model test, employment
appears to be significant while it exerts a strong negative effect on stock returns. The reason
for increase in employment forecasts inflation which was expected to erode firms’ profits
while expressed through falling stock returns.
2.5 Summary of Literature Review
Many researchers have been drawn to the study of effect of inflation on the stock market.
Inflation and interest rates are related through influences by the monetary policy. In instances
of close inflationary shocks, real interest rates and therefore real returns on stocks will be
affected. Most studies reveals inflation had negative impact on stock return.
The foundation of the discourse is the Fisher (1930) equity stocks proclamation. According to
the generalized Fisher (1930) hypothesis, equity stocks represent claims against real assets of
a business; and as such, may serve as a hedge against inflation. If this holds, then investors
23
could sell their financial assets in exchange for real assets when expected inflation is
pronounced. In such a situation, stock prices in nominal terms should fully reflect expected
inflation and the relationship between these two variables should be positively correlated.
This argument of stock market serving as a hedge against inflation may also imply that
investors are fully compensated for the rise in the general price level through corresponding
increases in nominal stock market returns and thus, the real returns remain unaltered.
Various theories state that the relationship between these two variables are negative while
others feel like the stock market is not at all affected by changes in the rate of inflation. It is
unclear whether there exists a negative or positive relationship given the varying conclusions
from the literature review.
24
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Introduction
This chapter describes the research design, population, sample design, data collection, and
construction of variables, data analysis and presentation correlation test, test for presence of
ARCH effects, linear regression model and GARCH model estimation process.
3.2 Research Design
The study took on a correlational approach in seeking to find if indeed inflation is one of the
factors that affect the stock market returns and hence the stock exchange. A correlational
study aimed at examining the covariance between two or more variables was used. The
reason for this choice was because of the ability of this approach to determine if variables
show a negative or positive relationship and the magnitude of the relationship given by the
correlation coefficient between the variables being studied. By getting the correlation
coefficient, one can also test the hypothesis to find out if the relationship observed is
statistically significant.
3.3 Target Population
The target population consisted of 61 company stocks listed on the NSE as at September
2013. This population gave a clear picture of the market situation and thus was the
appropriate one. (Appendix A)
25
3.4 Data Collection
Secondary time series data was used in the study. The data from which analysis was
conducted and inferences drawn was collected from the NSE and KNBS. Data collected was
essential and of high quality. The study used time series data from the NSE All Share Index
that covered a total of 15 years from January 1998 to December 2013. The main aim for this
was to achieve a more comprehensive coverage and a better chance of getting more accurate
results. The NSE share index was selected as representative of the overall stock prices and
was sampled to represent the different sectors and the general change in price. This was in
line with Dubravka and Petra (2010) who observed that the stock market index had the
largest statistical significance in explaining stock returns. The NSE all share index shall be
selected as it focuses mainly on price changes within companies in all sectors of the
economy.
3.5 Data Analysis
To get accurate results, financial econometrics models were used to analyze the data
collected. Of particular importance in this data were GARCH models which captured the
effects of inflation on stock market returns. Statistical software SPSS and Excel were used to
carry out data analysis and testing. Data was then presented in graphs and tables.
3.6 Analytical Models
3.6.1 Model for Stationary Transformation
The series was transformed by first taking the differences of the natural logarithms of the
values in each series. This was aimed at attaining stationarity. The two variables were
presented as follows:
26
Rt= ln NSEt – ln NSEt-1
Πt = ln CPIt- CPIt-1
Where:
NSEt represents the NSE 20 share index.
CPIt is the consumer price index.
Rt is the returns of stock is the dependent variable.
Πt is the measure of inflation and is the independent variable.
The equation of the model is given as:
Ri,t = βi,t + βiCPIi,t +εt
Where:
Ri,t is the return for stock i
βi,t is the constant term
βiCPIi,t is the measure of sensitivity of stock returns to the monthly change in the rate of
inflation
εt is the error term
3.6.2 Analytical model: Linear Regression
The linear regression model examines the effects of inflation on stock returns. The rate of
inflation was included as an independent variable and monthly stock market returns as the
dependent variable to the constant linear regression model.
rt = c + ρπt + εt
Where:
rt is the index return in month t
c is the constant term
πt is the logarithmic difference of CPI for month t
27
εt is the error term
The regression model was then estimated using statistical packages and its hypothesis tested.
3.6.3 Test of Significance
Before fitting GARCH (1, 1) model to the series, the presence of ARCH effects in the
residual was tested. This is to ensure that the model was necessary by observing that a
significant effect in the ARCH effect.
A stochastic process yt = c + εt is said to be AR (p) if:
Vart-1 (εt) =𝜎𝑡2
where εt= zt σt and 𝜎𝑡2 = 𝜔 + ∑ 𝜃𝑖𝑒𝑡−𝑖
𝑞𝑖=1
Testing the hypothesis of no significant ARCH effects was based on the Langragian
multiplier approach, where the test statistic is given by:
LM = nR2
Where n is the sample size and R2 is the coefficient of determination for the regression in the
ARCH model using the residuals.
3.6.4 GARCH Model
Since the distribution of series is stated as non linear, the research employed a step-wise
approach, where the standard linear GARCH (1, 1) was applied to first capture the stock
returns volatility.
Descriptive statistics for nominal stock returns and inflation for the entire sample was then
calculated. The coefficients for GARCH (1, 1) volatility model for return series and their
28
standard errors estimated. Diagnostic test statistics, ARCH, LM test and Ljung-Box test were
done to check if the standardized squared residual are serially uncorrelated and
homoskedastic. The basic GARCH model is as follows:
rt = c + ρπt + εt
σt2 = 𝜔 + αε
2t-1 + βσ
2t-1
The model captures time varying volatility of stock market returns. The standard GARCH (1,
1) model therefore doesn’t capture the asymmetric effect of shocks on stock market volatility
and hence the choice of EGARCH. Tests were done to check the presence of asymmetry
effects for NSE returns using an EGARCH model confirmed that the returns are asymmetric
thus EGARCH (1,1) was used as opposed to the GARCH (1,1) model.
3.6.5 EGARCH Model
EGARCH (1, 1) was used in determining the effects of inflation on stock market returns in
the NSE. It is more preferred as a model to the GARCH (1, 1) when studying financial
markets as the GARCH (1, 1) is relatively weaker.
𝑙𝑜𝑔𝜎𝑡2 = 𝜔 + β 𝑙𝑜𝑔𝜎𝑡−1
2 + α [|𝜀𝑡−1
𝜎𝑡−1|- √
2
𝜋] + 𝛾
𝜀𝑡−1
𝜎𝑡−1
The model that was estimated for nominal return series with inflation to integrate the effect of
inflation on the conditional stock exchange volatility is as follows:
rt = c + ρπt + εt
29
𝑙𝑜𝑔𝜎𝑡2 = 𝜔 + β 𝑙𝑜𝑔(𝜎𝑡−1
2) + α [|𝜀𝑡−1
𝜎𝑡−1|-√
2
𝜋]+ 𝛾
𝜀𝑡−1
𝜎𝑡−1 + λ πt-1
Where πt-1 is the previous period’s inflation level and λ, β, α, 𝜇 𝑎𝑛𝑑 𝜔 are the parameters
estimated. The presence of 𝛾 makes it possible to have different impacts on the previous time
period for both positive and negative shocks
30
CHAPTER FOUR
DATA ANALYSIS, RESULTS AND DISCUSSION
4.1 Introduction
This chapter is mainly on presentation of the data analysis conducted and their
interpretations. The analysis includes results from descriptive statistics, Jarque-Bera test, non
linearity test, test of stationarity, correlational test, linear regression test, presence of ARCH,
GARCH and EGARCH effects.
4.2 Descriptive Statistics and Test for Normality of Variables.
Jarque-Bera (JB) test statistic is a goodness of fit test whether sample data has the skewness
and kurtosis that matches a normal distribution. JB was used to test whether inflation and
returns of stocks individually follow the normal probability distribution. The test statistic is as
below:
JB = 𝑛 [ 𝑆2
6+
(𝐾−3)2
24]
Where n is the sample size, S the coefficient of skewness and K is the coefficient of kurtosis.
Normally distributed variables have S=0 and K=3
31
Table 4.1: Summary Statistics for Nominal Stock Returns and Inflation.
LOGCPI LOGNSE
N Valid 192 192
Missing 1 1
Mean .0054 .0021
Median .0134 .0030
Std. Deviation .05199 .06129
Variance .003 .004
Skewness -1.683 -.311
Std. Error of Skewness .175 .175
Kurtosis 5.363 1.956
Std. Error of Kurtosis .349 .349
Range .36 .42
Minimum -.25 -.26
Maximum .11 .16
Sum 1.04 .40
Source: Research Findings
Table 4.1 displays the monthly mean returns, standard deviation, kurtosis, skewness for the
entire sample period. The kurtosis is positive while the skewness results are negative at -
1.683 and -.311 for consumer price index and stock market returns respectively. It is clear
from the results displayed in the table that the average monthly nominal stock returns are
positive. This translates to average monthly returns of 0.21%.
32
4.2.1 Normality Test Results
The market show evidence of fat tails, since the Kurtosis exceeds 3, which is the normal
value, and evidence of negative skewness for both stock market returns and inflation. These
imply that stock market returns and inflation are assymetrically distributed, respectively. The
Jarque-Bera normality tests refute the null hypothesis of normality of returns series and
inflation. The hypothesis that stock returns and Inflation are normally distributed was
rejected.
4.3 Non Linearity Test Results
Linear structural models are unable to explain a number of important features common to
much financial data, including: (i) Leptokurtosis - the tendency for financial asset returns to
have distributions that exhibit fat tails and excess peakedness at the mean. (ii) Volatility
clustering or volatility pooling - the tendency for volatility in financial markets to appear in
bunches. Thus large returns (of either sign) are expected to follow large returns, and small
returns (of either sign) to follow small returns. A plausible explanation for this phenomenon
is that the information arrivals which drive price changes occur in bunches rather than being
evenly spaced over time. (iii) Leverage effects - the tendency for volatility to rise more
following a large price fall than following a price rise of the same magnitude.
Campbell, Lo and MacKinlay (1997) broadly defined a non-linear data generating process as
one where the current value of the series is related non-linearly to current and previous values
of the error term. Campbell, Lo and MacKinlay (1997) usefully characterize models with
non-linear g (•) as being non-linear in mean, while those with non-linear σ (•)2 are
characterized as being non-linear in variance. Models can be linear in mean and variance (e.g.
the CLRM, ARMA models) or linear in mean, but non-linear in variance (e.g. GARCH
33
models). If the variance of the errors is not constant, this would be known as
heteroskedasticity. Models could also be classified as non-linear in mean but linear in
variance (e.g. bicorrelations models). Finally, models can be non-linear in both mean and
variance (e.g. the hybrid threshold model with GARCH errors employed by Brooks, 2001)
The BDS test was applied to the series of estimated residuals to check whether the residuals
are independent and identically distributed (iid). The results for the BDS test statistic
concluded that there is non-linear dependence in stock market returns series, but that the
dependence is best characterized by a GARCH-type process.
4.4 Correlation Test Results
Pearson’s Correlation test was conducted between market returns and inflation. Correlation
test can be seen as the first indication of existence of any interdependency between the time
series. Table 4.2 shows the correlation coefficients between market returns and inflation.
From the derived statistics, it was observed that the coefficient of correlation to be 0.079,
which is indicative of mild positive correlation between the two series. The test shows that
when inflation rate increase by 1%, stock market return increases by 7.9%. Rise in stock
prices therefore acts as a hedge against inflation. Thus, we may state that the two series are
weakly correlated as the coefficient of correlation depicts some interdependency between the
two variables. The third hypothesis that correlation exists between the two variables was
accepted.
34
Table 4.2 Correlation Matrix
LOGNSE LOGCPI
LOGNSE Pearson Correlation 1 .079
N 192 192
LOGCPI Pearson Correlation .079 1
N 192 192
Source: Research Findings
4.5 Stationarity / Unit Root Test Results
Having recognized the non-normal distribution of the two variables, the question of
stationarity of the two time series need to be evaluated. The simplest check for stationarity is
to plot time series graph and observe the trends in mean, variance and autocorrelation. A time
series is said to be stationary (do not contain a unit root) if its mean and variance are constant
over time. Time series data are often assumed to be non-stationary and thus it is necessary to
perform a pretest to ensure there is a stationary relationship between inflation and stock
return volatility in order to avoid the problem of spurious regression (Riman and Eyo (2008)).
Spurious regression is cited in Patterson (2000), to exist where the test statistics show a
significant relationship between variables in the regression model even though no such
relationship exists between them. Therefore, in order to address the issue of non-stationarity
and avoid the problem of spurious regression, quantitative analysis was employed. For the
testing of unit roots, the Augmented Dickey-Fuller test (ADF) was used. If the null
hypothesis (H0: α=0) is rejected, this means that the time series data is stationary. The
35
decision criteria involved comparing the computed test statistic with the MacKinnon critical
values for the rejection of a hypothesis for a unit root. If the computed ADF statistic is less
negative (i.e. lies to the right of the MacKinnon critical values) relative to the critical values,
we do not reject the null hypothesis of non-stationarity in time series variables. The results
are shown in Table 4.3 and Table 4.4.
Table 4.3: Results of ADF Stationarity Test of NSE Returns
Null Hypothesis: R_LOGNSE has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=13)
t-Statistic Prob.*
Augmented Dickey-Fuller
test statistic
-10.26370 0.0000
Test critical values: 1% level -3.476143
5% level -2.881541
10% level -2.577514
*MacKinnon (1996) one-sided p-values.
Source: Research Findings
The obtained ADF statistics for the variable LOGNSE with the critical values for rejection of
hypothesis of existence of unit root, it becomes evident that the obtained statistics for NSE
returns -10.26 respectively fall behind the critical values at 1%, 5% and 10% significance
level of -3.432777, -2.88 and -2.578 (i.e. critical value is greater than ADF statistic).
36
Table 4.4: Results of AD Fuller Stationarity Test of Inflation
Null Hypothesis: LOGCPI has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=13)
t-Statistic Prob.*
Augmented Dickey-Fuller
test statistic
-7.871278 0.0000
Test critical values: 1% level -3.476143
5% level -2.881541
10% level -2.577514
*MacKinnon (1996) one-sided p-values.
Source: Research Findings
The obtained ADF statistics for the variable NSE with the critical values for rejection of
hypothesis of existence of unit root, it becomes evident that the obtained statistics for NSE
returns -10.26 respectively fall behind the critical values at 1%, 5% and 10% significance
level of -3.432777, -2.88 and -2.578 (i.e. critical value is greater than ADF statistic). Thus,
giving probability values 0.00; thereby, leading to the rejection of the hypothesis of unit root
for both the series. Hence, it can be concluded on the basis of ADF test statistics that market
returns as well as inflation series are, both, found to be stationary at level form.
37
4.6 Regression Model
The regression model was estimated to assess whether inflation is a significant explanatory
variable for the stock market return in NSE.
Table 4.5 Regression Model Results
ANOVAa
Model
Sum of
Squares df
Mean
Square F Sig.
1 Regression .004 1 .004 1.192 .276b
Residual .713 190 .004
Total .717 191
a. Dependent Variable: LOGNSE
b. Predictors: (Constant), LOGCPI
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
T Sig.
95.0%
Confidence
Interval for B
B Std. Error Beta
Lower
Bound
Upper
Bound
1 (Constant) .002 .004 .356 .722 -.007 .010
LOGCPI .093 .085 .079 1.092 .276 -.075 .261
a. Dependent Variable: LOGNSE
38
Residuals Statisticsa
Minimum Maximum Mean
Std.
Deviation N
Predicted Value -.0215 .0118 .0021 .00484 192
Residual -.25425 .15322 .00000 .06110 192
Std. Predicted Value -4.866 2.008 .000 1.000 192
Std. Residual -4.150 2.501 .000 .997 192
a. Dependent Variable: LOGNSE
Model Summaryb
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
Durbin-
Watson
1 .079a .006 .001 .06126 1.796
a. Predictors: (Constant), LOGCPI
b. Dependent Variable: LOGNSE
Source: Research Findings
From the results in Table 4.5, the coefficient for inflation is high. This implies that inflation is
good at explaining the stock returns. The result does support the hypothesis that Inflation is a
significant explanatory variable for the stock returns thus the third hypothesis is accepted.
The relationship between stock returns and inflation is positive. The OLS model estimation
findings agree with the Fisher Hypothesis that the two variables are positively correlated in
the sense that an increase in inflation leads to a proportional change in nominal market
39
returns consequently hedging against inflation. As per table 4.5, the R-squared statistic
measuring the success of the regression in predicting the values of the dependent variable
within the sample indicate that only 0.6% of what is happening in the stock market return can
be explained by inflation variable.
A common finding in time series regressions is that the residuals are correlated with their
own lagged values. This serial correlation violates the standard assumption of regression
theory that disturbances are not correlated with other disturbances. The primary problems
associated with serial correlation are; OLS is no longer efficient among linear estimators
since prior residuals help to predict current residuals, Standard errors computed using the
OLS formula are not correct, and are generally understated and finally if there are lagged
dependent variables on the right-hand side, OLS estimates are biased and inconsistent. For
better estimation of the time series data, the GARCH model is desirable.
40
4.7 Test of Significance
This section reviews test results from analyzing the effects of serial correlation from ARCH
effects, tests for GARCH and EGARCH effects.
4.7.1 Presence of ARCH Effects Tests
The null hypothesis of the test is that there is no serial correlation in the residuals up to the
specified order.
Table 4.6: Results of Serial Correlation LM Test
Breusch-Godfrey Serial Correlation LM Test:
F-statistic 3.398090 Prob. F(1,192) 0.0674
Obs*R-squared 3.388787 Prob. Chi-Square(1) 0.0656
Test Equation:
Dependent Variable: RESID
Method: Least Squares
Sample: 1998M01 2013M12
Included observations: 192
Pre sample missing value lagged residuals set to zero.
Variable Coefficient Std. Error t-Statistic Prob.
C 0.000171 0.006257 0.027383 0.9782
LOGCPI -0.026255 0.491152 -0.053456 0.9574
RESID(-1) 0.152939 0.082676 1.849870 0.0664
Source: Research Findings
41
The low probability values resulting from Breusch-Godfrey LM test as shown in Table 4.6
specify that the null hypothesis is rejected. This is indicative of the presence of serial
correlation (ARCH effect) in the residuals of the estimated equation. The GARCH model can
for that reason be employed.
4.7.2 GARCH Model Test Results
In attempt to find the appropriate model for stock return volatility, GARCH and EGARCH
Models are estimated and compared. The basic GARCH (1, 1) estimation results are given in
Table 4.7, with nominal market return as the dependent variable. The coefficient of the last
periods forecast variance, (the GARCH term, β) is significant since the probability is zero.
This implies that stock return volatility this month is explained by approximately 75.6% of
the previous month’s return volatility. Moreover, coefficient of news about volatility from the
previous period, measured as the lag of the squared residual from the mean equation, (the
ARCH term, α) is low at 13% and insignificant. This indicates that new information arrival
into the market has insignificant impact on predicting next month’s stock market volatility.
42
Table 4.7: Results of the GARCH model for Stock Market Return Series
Dependent Variable: R_LOGNSE
Method: ML - ARCH (Marquardt) - Student's t distribution
Sample (adjusted): 1998M01 2013M012
Included observations: 192 after adjustments
Convergence achieved after 19 iterations
Presample variance: backcast (parameter = 0.7)
GARCH = 𝜔+ α*RESID(-1)^2 + β *GARCH(-1)
Variable Coefficient Std. Error z-Statistic Prob.
Mean Equation
C
LOGCPI
0.005977
-0.269442
0.006334
0.464613
0.943565
-0.579929
0.3454
0.5620
Variance Equation
𝜔
α
β
0.000506
0.129824
0.756297
0.000473
0.101970
0.177994
1.070654
1.273165
4.249000
0.2843
0.2030
0.0000
Source: Research Findings
The persistence parameter α +β = 1.046, which is > 1 show a very explosive volatility. The
GARCH coefficient demonstrates the capability of past volatility to explain current volatility
(Engle and Bollerslev, 1986) and because it is very high, the rate at which it diminishes is
rather very slowly. The statistically significant GARCH coefficient implies that past
variances exert significantly positive effect on stock market return volatility. On the basis of
these results, it is evident that there is significant time varying volatility in stock market
returns during the sample periods.
43
4.7.3 EGARCH Test Results
The GARCH (1, 1) results imply that the model is a good fit for explaining volatility but
there is one point that should be emphasized. Although most of the previous studies used
such GARCH (1, 1) model in explaining volatility, this model is not suitable if shocks to
stock return volatility are not symmetric. Asymmetry mean that downward movements in the
stock market are followed by higher volatilities than upwards movements of the same
magnitude. The standard GARCH (1, 1) model therefore does not capture the asymmetric
effect of shocks on stock market volatility and hence the choice of EGARCH. This allows
assessment of the impact of positive and negative innovations on stock returns volatility.
Market returns series was tested for asymmetry. The estimation results for the EGARCH (1,
1) model are as shown in Table 4.8.
44
Table 4.8: EGARCH (1,1) Volatility Coefficients for Stock Market Return Series
Dependent Variable: R_LOGNSE
Method: ML - ARCH (Marquardt) - Student's t distribution
Sample (adjusted): 1998M01 2013M12
Included observations: 192 after adjustments
Convergence achieved after 26 iterations
Presample variance: backcast (parameter = 0.7)
LOG(GARCH)= 𝜔 + α *ABS(RESID(-1)/@SQRT(GARCH(-1)))+β*LOG(GARCH(-1))
+ γ *RESID(-1)/@SQRT(GARCH(-1))
Coefficient Std. Error z-Statistic Prob.
Mean Equation
C
LOGCPI
0.007853
0.229508
0.005943
0.437627
1.321353
-0.524438
0.1864
0.6000
Variance
Equation
𝜔
α
γ
β
-7.949629
0.288393
-0.302194
-0.402674
1.529838
0.175070
0.125914
0.273611
-5.196386
1.647298
-2.400010
-1.471702
0.0000
0.0995
0.0164
0.1411
Source: Research Findings
Since γ is different than zero, it is concluded that there is asymmetry and EGARCH (1, 1)
model should be used instead of a GARCH (1, 1) model. It is discovered that negative returns
increase future volatility by larger amount than positive returns of the same magnitude. As
can be seen from results in Table 4.8, and in line with our expectation, bad news has larger
impact on stock volatility than good news. This is a very important finding in the sense that it
45
conforms with a number of empirical findings in the area. Saryal (2007), for instance, made
similar discovery for Canada where the stock market index (TSE 300) records larger
volatility in response to bad news than good news. Contrary to the GARCH results, Volatility
persistence (α) is higher at 29% and significant while the volatility magnitude (β) is high at
negative 40% and significant.
4.7.4 Impact of Inflation on Conditional Stock Market Volatility
The impact of inflation on stock market returns volatility is investigated through the
estimation of equation (4). The coefficient of inflation λ in EGARCH (1, 1) measures the
predictive power of previous inflation rate on stock market volatility.
46
Table 4.9: Results of the EGARCH(1,1) model on the effect of Inflation on Stock
Market Return Volatility
Dependent Variable: R_LOGNSE
Method: ML - ARCH (Marquardt) - Student's t distribution
Sample (adjusted): 1998M01 2013M12
Included observations: 192 after adjustments
Convergence achieved after 72 iterations
Presample variance: backcast (parameter = 0.7)
LOG(GARCH)= 𝜔 + α *ABS(RESID(-1)/@SQRT(GARCH(-1)))+β *LOG(GARCH(-1))
+ γ *RESID(-1)/@SQRT(GARCH(-1)) + λ*LOGCPI(-1)
Coefficient Std. Error z-Statistic Prob.
Mean Equation
C
LOGCPI
0.008210
-0.231573
0.005713
0.412960
1.436992
-0.560765
0.1507
0.5750
Variance
Equation
𝜔
α
γ
β
λ
-8.390524
0.200278
-0.314462
-0.471756
14.71794
1.458186
0.162703
0.129514
0.259031
13.91880
-5.754083
1.230940
-2.428011
-1.821231
1.057415
0.0000
0.2183
0.0152
0.0686
0.2903
Source: Research Findings
As can be seen from Table 4.9, the coefficient is positive and insignificant implying that an
increase in inflation rate in the previous period increases conditional market volatility this
month. The inflation coefficient is high suggesting that the inflation rate itself has strong
47
predictive power on conditional stock market volatility. From the Table 4.9, volatility
magnitude is high and significant as represented by β. This may be attributable to the fact that
inflation has relatively big positive impact on investment at the stock market. Volatility
persistence as measured by α is low and insignificant which leads to the conclusion that
information slightly impacts on the conditional stock market volatility.
4.8 Interpretations of the Findings
Preliminary investigation into the nature of the data revealed that the market return data is
characterized by average monthly return (in natural log) of 0.21% and a comparatively high
standard deviation of monthly returns of 6.13%, one would expect high conditional stock
market returns volatility. The Jarque Bera statistics confirmed that the distribution of inflation
and stock market returns is non-normal. This posed questions on stationarity of the two
series. The ADF test results showed stationarity at level forms for both the series. The
coefficient of correlation between the two variables was found to be slightly positive while
the Breusch-Godfrey Lagrange multiplier test for general, high-order, ARMA errors found
presence of serial correlation (ARCH effect) in the residuals of the estimated equation.
Fama’s ‘proxy hypothesis’ explains the apparent anomaly of the negative relationship
between inflation and stock market returns as against economic theory suggestion that
equities are a good hedge against inflation. The objective of the project was to investigate
effect of inflation on stock market return and volatility in the NSE. The findings of the study
seem to suggest that stock market returns provide an effective hedge against inflation. This is
explained by the weak positive relationship between inflation and stock market returns. This
is against the Fisher (1930) hypothesis. The study just like reviewed empirical studies in the
area done by Hamilton and Lin (1996), Engle (2004), Engle and Rangel (2005), Rizwan and
48
Khan (2007), etc., established a strong predictive power of inflation on stock market volatility
and returns.
49
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Introduction
This chapter’s main focus is on the summary, conclusions, recommendations and suggestions
for further study.
5.2 Summary
Data for the estimation of models was obtained from NSE and KNBS for stock market index
and inflation rate respectively. First, raw values of data were converted to log normal forms
and descriptive statistics were obtained. From the Jarque Bera statistics it was affirmed that
the distribution is non-normal in case of both the variables. This posed questions on
stationarity of the two series. Hence, the next step was to check stationarity of the two series
with ADF test and the results showed stationarity at level forms for both the series. Then, the
coefficient of correlation between the two variables was computed and it was found to be
slightly positive. The Breusch-Godfrey Lagrange multiplier test for general, high-order,
ARMA errors found presence of serial correlation (ARCH effect) in the residuals of the
estimated equation. This qualitative idea of the dynamics between the variables indicated that
a quantitative model can be developed in order to capture the dynamics between the volatility
of the two variables. Thus, GARCH (1, 1) framework was used to first extract conditional
variances thus capturing stock returns volatility. The EGARCH model captured the
asymmetric effect of shocks on stock market volatility by allowing assessment of the impact
of positive and negative correlation on stock returns volatility. The market return series was
found to show evidence of asymmetric effect. Preliminary investigation into the nature of the
data revealed that the market return data is characterized by a non normal distribution and an
50
average monthly return (in natural log) of 0.21%. With comparatively high standard deviation
of monthly returns of 6.13%, one would expect high conditional stock market returns
volatility.
The results show evidence of time varying volatility in stock market returns and from the
asymmetric model; results indicate that bad news has larger impact on stock volatility than
good news in the NSE. Understanding the effect of inflation on the variability of stock
exchange volatility can help the investors in the stock market and other market operators to
make good portfolio decisions based on their knowledge of past of the economy and
expectations about future as well as stemming the adverse effect of inflation on stock market
volatility. The results of this study show that inflation is one of the underlying determinants
of stock market volatility. This study established that inflation is positively linked with the
stock market return. The inherent reason behind this might be that the increasing inflation
rates in Kenya increases market risk and hence companies adjust for the inflationary
pressures by raising their prices. Loose monetary policy boosts both the stock market and
inflation (Thorbecke, 1997; Bordo and Wheelock, 2004). The asymmetric effects in the long
run, further suggest that an anti-inflationary intervention causes a smaller impact on the stock
market returns than on inflation.
The impact of inflation lag measured by inflation coefficient is high positive and insignificant
implying that an increase in inflation rate in the previous period increases conditional market
volatility this month. However, the impact of change in inflation lag on stock exchange
volatility is negative but insignificant as indicated by very low value of the coefficient. This
means that fluctuations in inflation have minimal predictive power for the stock volatility.
Thus it can be concluded that during times of high inflation, stock returns remain low and
investment is channeled from the stock exchange into businesses ventures which are less
51
affected by inflation. In long run, higher inflation rates increases stock market volatility
which in turn may lead to uncertainty in the minds of investors leading to dried investment in
the stock market causing difficulties for businesses and firms to attract investment.
Accordingly, inflation rates should be stable in order to restore the confidence of the
investors in stock market. The research finally found out that the monetary and real sectors of
the economy may not be independent of each other, as money may also matter in explaining
the behavior of inflationary process in Kenya. Thus policies geared at controlling inflation
should take into account the role of monetary and real variables especially as these will go a
long way in further deepening of the stock market.
5.3 Conclusion
The issue of whether inflation has effect on stock market return and volatility is still a
debatable subject. What is clear is that the relationship may be significant or insignificant
depending on the country, stock market, monetary policy of the country, the methodology
used and the period of study among other factors. The findings from this study are consistent
with other studies as discussed earlier and although stock return volatility is an important
aspect in the expectations and decisions of investors in the stock market, the role played by
the Nairobi Securities Exchange cannot be overlooked. This therefore shows the vast
potential that the Nairobi Securities Exchange may have towards fostering the country’s
economy should the Kenyan government promote a saving culture and consequently improve
investments income of the general public through appropriate policies. The Capital Markets
Authority as a regulator should strive to ensure that impediment to stock market growth such
as legal and other regulatory barriers are addressed.
52
The findings from this study emphasize on the role of the stock exchange market in directing
economic growth i.e. the Nairobi stock exchange has been found to be a leading indicator for
economic growth. Therefore there is need to identify factors that have significant effect on
stock market return. This will enable investors make rational decisions in order to maximize
returns. The regulator will also ensure that measures are put in place to ensure fair play in the
market. The findings as illustrated by figures in the Appendix shows evidence of volatility
clustering over time.
5.4 Policy Recommendations
Based on the findings of the study, the study presents recommendations significant to the
policy makers, investors, financial market regulators and future researchers. The study
recommends that the government through its policy makers should come up with measures
and policies that will help control and stabilize inflation rate fluctuation thus creating investor
confidence in the securities market. This will consequently lower the stock market volatility
thus restoring the confidence of the investors in stock market and increasing market
investment activity. This will then have a significant impact on the performance of the
Nairobi Securities Exchange hence uphold economic growth.
Inflation should be maintained at low levels. A rise in the general level of prices reduces the
expected cash inflow from an investment, as result investors who own some assets are
exposed to potential reduction of the real value of the asset they hold due to inflation. Stock
market returns may be adversely affected by inflation because of inflationary pressures that
threaten future corporate profits and lead to an increase in nominal discount rates which
ultimately reduce the current value of future profits and thus stock market returns. To
53
encourage investment and growth of the financial market, inflation should be kept at the
minimum.
NSE along with the government should take steps to increase the number of mutual fund to
stabilize the market in the long run, which can be done by enforcing a level playing
regulatory measure for public and private mutual funds. Government can also take pro-active
role in building a stable market through tapping the growing interest of general people in the
market by increasing supply of shares. The regulator should ensure that all the market players
comply with the policies and regulations in an effort to ensure efficiency and effectiveness of
the stock market. The study recommends survey to be carried out from time to time on
macro-economic factors affecting stock return. This can be facilitated by availing data for
free to students and other researcher with interest in studying the stock market, factors
affecting the market returns and market efficiency.
5.5 Limitations of the Study
Correlational methods commonly suggest that variables are linearly related to one another.
Since the data is non linear as informed by nonlinearity test, the correlational method reduce
the strength of the relationship. The outliers, observations that are quite a bit different from
the remaining observations also reduce the strength of the relationship. Correlations are
bivariate in nature meaning that two variables from different data sets are compared at a time.
However, this is not realistic because there are almost always multiple relationships and
effects on something.
The extent to which the findings can be generalized beyond the sample period studied is
unclear. The number of observations is too limited for broad generalization. Further empirical
54
evaluations, however, are needed to replicate the findings in larger sample including daily
returns since the findings from the sample may not reflect the behavior of the entire
population. Although correlational study employed suggested that there is a relationship
between inflation and stock market return, the findings cannot prove that inflation causes a
change in stock market return. Thus casual conclusions cannot be made because alternative
explanations for correlational findings cannot be ruled out. In other words, correlation does
not equal causation. Other variables might play a role, including interest rate, exchange rate
and money supply among others.
5.6 Suggestions for Further Research
The main aim of the study was to investigate the effect of inflation on stock market returns in
the NSE. For any country’s economy to experience growth, the stock market has to be
efficient and this makes the securities exchange a very important institution in any economy.
Volatility of returns in the financial markets can be key in attracting investments in
developing economies. Since financial markets are also influenced by other macroeconomic
variables such as foreign exchange rate, money supply, interest rate, monetary policy, fiscal
policy and industrial production; further research should be conducted on these variables and
a possibility of an interrelationship between these macroeconomic variables examined and
their effects on the stock exchange. Further studies on persistence of news on stock returns
will be useful to investors in making rational investment decisions and also assist the
regulator in formulating relevant policies.
55
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61
APPENDICES
Appendix I: Companies Listed on the NSE as at 30th
September 2014.
AGRICULTURAL
1. Eaagads Ltd Ord 1.25
2. Kapchorua Tea Co. Ltd Ord Ord 5.00
3. Kakuzi Ord.5.00
4. Limuru Tea Co. Ltd Ord 20.00
5. Rea Vipingo Plantations Ltd Ord 5.00
6. Sasini Ltd Ord 1.00
7. Williamson Tea Kenya Ltd Ord 5.00
AUTOMOBILES AND ACCESSORIES
8. Car and General (K) Ltd Ord 5.00
9. CMC Holdings Ltd Ord 0.50
10. Sameer Africa Ltd Ord 5.00
11. Marshalls (E.A.) Ltd Ord 5.00
BANKING
12. Barclays Bank Ltd Ord 0.50
13. CFC Stanbic Holdings Ltd ord.5.00
14. I&M Holdings Ltd Ord 1.00
15. Diamond Trust Bank Kenya Ltd Ord 4.00
16. Housing Finance Co Ltd Ord 5.00
17. Kenya Commercial Bank Ltd Ord 1.00
18. National Bank of Kenya Ltd Ord 5.00
19. NIC Bank Ltd 0rd 5.00
20. Standard Chartered Bank Ltd Ord 5.00
21. Equity Bank Ltd Ord 0.50
22. The Co-operative Bank of Kenya Ltd Ord 1.00
COMMERCIAL AND SERVICES
23. Express Ltd Ord 5.00
24. Kenya Airways Ltd Ord 5.00
25. Nation Media Group Ord. 2.50
26. Standard Group Ltd Ord 5.00
27. TPS Eastern Africa (Serena) Ltd Ord 1.00
28. Scangroup Ltd Ord 1.00
29. Uchumi Supermarket Ltd Ord 5.00
30. Hutchings Biemer Ltd Ord 5.00
31. Longhorn Kenya Ltd
CONSTRUCTION AND ALLIED
32. Athi River Mining Ord 5.00
33. Bamburi Cement Ltd Ord 5.00
34. Crown Berger Ltd 0rd 5.00
35. E.A.Cables Ltd Ord 0.50
36. E.A.Portland Cement Ltd Ord 5.00
ENERGY AND PETROLEUM
37. KenolKobil Ltd Ord 0.05
38. Total Kenya Ltd Ord 5.00
39. KenGen Ltd Ord. 2.50
62
40. Kenya Power & Lighting Co Ltd
41. Umeme Ltd Ord 0.50
INSURANCE
42. Jubilee Holdings Ltd Ord 5.00
43. Pan Africa Insurance Holdings Ltd 0rd 5.00
44. Kenya Re-Insurance Corporation Ltd Ord 2.50
45. Liberty Kenya Holdings Ltd
46. British-American Investments Company ( Kenya) Ltd Ord 0.10
47. CIC Insurance Group Ltd Ord 1.00
INVESTMENT
48. Olympia Capital Holdings ltd Ord 5.00
49. Centum Investment Co Ltd Ord 0.50
50. Trans-Century Ltd
INVESTMENT SERVICES
51. Nairobi Securities Exchange Ltd Ord 4.00
MANUFACTURING AND ALLIED
52. B.O.C Kenya Ltd Ord 5.00
53. British American Tobacco Kenya Ltd Ord 10.00
54. Carbacid Investments Ltd Ord 5.00
55. East African Breweries Ltd Ord 2.00
56. Mumias Sugar Co. Ltd Ord 2.00
57. Unga Group Ltd Ord 5.00
58. Eveready East Africa Ltd Ord.1.00
59. Kenya Orchards Ltd Ord 5.00
60. A.Baumann CO Ltd Ord 5.00
TELECOMMUNICATION AND TECHNOLOGY
61. Safaricom Ltd Ord 0.05
GROWTH ENTERPRISE MARKET SEGMENT
62. Home Afrika Ltd Ord 1.00
Source: www.nse.co.ke
Appendix II: Companies Consituting the NSE 20 Share Index
Mumias Sugar
Express Kenya
Rea Vipingo
Sasini Tea
EA cables
Athi River Mining
Kengen
CMC Holdings
Kenya Airways
Safaricom
Nation Media Group
Barclays Bank of Kenya
Kenya Power
East African Breweries
Equity Bank
Kenya Commercial Bank
Standard Chartered Bank
Bamburi Cement
British American Tobacco
Centum Investment
Company
Source: www.nse.co.ke
63
Appendix III: Plotted Graphs
Figure 4.1: CPI Trend in Kenya from January 1998 to December 2013
Figure 4.2: Estimated Inflation Level from January 1998 to December 2013
0
50
100
150
200
250
Oct-95 Jul-98 Apr-01 Jan-04 Oct-06 Jul-09 Apr-12 Dec-14
CP
I
Year
Kenya CPI Trend
Kenya CPI Trend
2013, 5.7
0
2
4
6
8
10
12
14
16
1995 2000 2005 2010 2015
An
nu
al In
flat
ion
Rat
e
Year
Estimated Inflation Level
Estimated Inflation Level
64
Figure 4.3: NSE All Share Index graph from January 1998 to December 2013
Figure 4.4: NSE Market returns for the period under investigation.
Dec-13, 4710
0
1000
2000
3000
4000
5000
6000
7000
Oct-95 Jul-98 Apr-01 Jan-04 Oct-06 Jul-09 Apr-12 Dec-14
NSE
All
Shar
e In
de
x
Year
NSE All Share Index
NSE All Share Index
-0.2
-0.1
0
0.1
0.2
0.3
Oct-95 Jul-98 Apr-01 Jan-04 Oct-06 Jul-09 Apr-12 Dec-14
log
NSE
Year
NSE Market Returns
NSE Market Returns
65
Figure 4.5: NSE Market Returns and Inflation
Appendix IV: CPI and NSE All Share Index Raw Data
Consumer Price Index from Year 1998 to Year 2005
1998 1999 2000 2001 2002 2003 2004 2005
Jan 51 43.31 59.42 61.92 50.07 66.3 71.92 86.71
Feb 50.5 41.91 61.68 62.54 51.26 69.19 72.96 88.96
Mar 49.34 43.84 62.12 60.4 55.88 65.64 76.52 97.29
Apr 49.72 46.76 58.28 61.2 57.56 60.6 77.04 96.61
May 50.15 47.55 62.42 63.39 58.14 61.34 81.03 93.6
Jun 47.96 47.31 64.53 62.4 57.61 62.91 79.08 99.94
Jul 47.32 49.68 62.99 59.54 59.81 63.25 81.43 102.9
Aug 45.99 52.17 63.89 59.79 60.38 64.86 84.59 109.09
Sep 46.96 55.1 67.17 57.74 62.76 62.95 83.67 109.3
Oct 45.89 55.16 66.17 52 62.49 66.33 89.29 106.77
Nov 44.49 57.11 66.99 49.96 59.48 67.26 84.59 102.86
Dec 42.24 58.22 61 49.39 62.59 69.08 81.68 105.97
Consumer Price Index from Year 2006 to Year 2013
2006 2007 2008 2009 2010 2011 2012 2013
Jan 113.1 112.97 162.42 102.39 146.12 182.13 188.4 187.55
Feb 111.74 118.47 171.14 98.16 142.43 190.04 195.87 190.65
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Jan
-98
Oct
-98
Jul-
99
Ap
r-0
0
Jan
-01
Oct
-01
Jul-
02
Ap
r-0
3
Jan
-04
Oct
-04
Jul-
05
Ap
r-0
6
Jan
-07
Oct
-07
Jul-
08
Ap
r-0
9
Jan
-10
Oct
-10
Jul-
11
Ap
r-1
2
Jan
-13
Oct
-13
NSE
Sto
ck R
etu
rns
agai
nst
In
flat
ion
Graph of Market Returns and Inflation
Log CPI
Rt log NSE
66
Mar 113.21 122.37 181.44 100.11 148.9 199.61 201.81 183.73
Apr 123.14 128.91 189.51 104.14 158.01 210.09 197.53 179
May 127.45 129.83 204.07 114.89 146.61 199.53 185.14 179.4
Jun 125.81 132.85 215.69 128.22 143.49 196 169.95 179.17
Jul 130.92 138.05 219.74 123.51 144.05 198.95 177.9 183.55
Aug 130.35 132.92 195.66 132.91 148.45 190.51 185.46 185.66
Sep 119.19 140.6 176.31 127.63 150.28 188.72 186.9 185.06
Oct 116.03 147.67 139.22 134.88 159.55 182.87 183.11 182.36
Nov 117.29 157.78 114.97 140.93 164.9 186.38 180.59 179.65
Dec 120.91 156.8 98.3 140.91 174.9 184.04 182.48 184.26
NSE All Share Index from Year 1998 to Year 2005
1998 1999 2000 2001 2002 2003 2004 2005
Jan 3348 2983 2308 1897 1343 1511 3158 3094
Feb 3362 2989 2277 1933 1314 1558 3175 3213
Mar 3213 2815 2233 1831 1183 1608 2771 3209
Apr 3015 2768 2162 1768 1129 1847 2708 3228
May 3016 2769 2053 1636 1071 2076 2689 3505
Jun 2908 2756 2003 1657 1087 1935 2370 3972
Jul 2853 2745 1967 1621 1098 2005 2708 3982
Aug 2863 2494 1958 1506 1043 2107 2709 3938
Sep 2810 2428 2001 1401 1027 2380 2671 3833
Oct 2784 2309 2043 1473 1116 2457 2830 3939
Nov 2584 2294 1927 1420 1162 2737 2918 3974
Dec 2962 2303 1913 1355 1363 2738 2946 3973
NSE All Share Index from Year 2006 to Year 2013
2006 2007 2008 2009 2010 2011 2012 2013
Jan 4172 5774 4713 3199 3565 4465 3224 4580
Feb 4057 5387 5072 2475 3629 4240 3304 4620
Mar 4102 5134 4843 2805 4073 3887 3367 4950
Apr 4025 5199 5336 2800 4233 4029 3547 4790
May 4350 5002 5176 2853 4242 4078 3651 5001
Jun 4260 5147 5186 3295 4339 3968 3704 4600
Jul 4259 5340 4868 3273 4439 3738 3832 4795
Aug 4486 5372 4649 3103 4455 3464 3866 4700
Sep 4880 5146 4180 3006 4630 3284 3972 4799
Oct 5314 4971 3387 3084 4660 3507 4147 4801
Nov 5615 5215 3341 3190 4395 3155 4083 5100
Dec 5646 5445 3521 3247 4433 3205 4133 4710