the effect of inflation on the stock market returns

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

ii

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.

3

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

4

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.

9

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.

13

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.

14

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.

15

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:


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:


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


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.


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.


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


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


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


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


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


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







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


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







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


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

https://www.nse.co.ke/listed-companies/list.html?view=company&id=25&tmpl=component







































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
























http://www.nse.co.ke/

http://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


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


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

the effect of inflation on the stock market returns

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