Download an investigation of the relationship between inflation rates in kenya

EFFECT OF INFLATION ON PERFORMANCE OF NAIROBI
SECURITIES EXCHANGE 20 SHARE INDEX
BY
BEDAN NDEGWA WAMBUI D61/61643/2010
A
RESEARCH
PROJECT
SUBMITTED
IN
PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF
THE DEGREE OF MASTER OF BUSINESS ADMINSTRATION,
UNIVERSITY OF NAIROBI
NOVEMBER 2013
DECLARATION
I declare that, this Research Project is my original work and has not been presented for
any academic award in any university.
Signed……………………………………………….Date…………………………………
Wambui, B.N
Reg.No.D61/61643/2010
This Research Project has been submitted for examination with my approval as the
University Supervisor.
Signed……………………………………………….Date…………………………………
Mr.Mirie Mwangi
Lecturer, School of Business
Department of Finance and Accounting,
University of Nairobi
ii
ACKNOWLEDGEMENT
I give thanks to God almighty for giving me the strength, knowledge and courage to
complete MBA (Finance) degree.
I appreciate my research project supervisor Mr. Mirie for his guidance and advise in the
course of carrying out my project. I thank the project moderator Mrs. Nyamute for the
positive contribution to the improvement of my project. To the entire University of
Nairobi, School of Business fraternity I have learnt and become more knowledgeable
through your interactions.
I thank my mother Nazarena Wambui for her prayers and making every effort to give me
good education. Special thanks to my dear wife Elizabeth Ndegwa for your prayers,
encouragement and support. To my lovely daughter Baby Nazarene you have inspired me
to work hard to give you a bright future. To my brother Waichungo thanks for your
support. To my nephews Ndegwa and Nderitu thanks for being inspirational.
To all who contributed to the success of this project and my entire MBA program, you
have left an indelible mark in my life. God bless you all.
iii
DEDICATION
I dedicate this project to my lovely daughter Nazarene Wambui Ndegwa and our future
generations. May God almighty make you grow to be great achievers.
iv
ABSTRACT
Inflation in Kenya has been unstable especially during the period of study. Increased
inflation is associated with high cost of living thereby reducing amount available for
investment. The government aims to maintain a constant rate of inflation characterized by
a single digit. The aim has been to make inflation almost predictable and not sporadic as
it has been in the Kenyan economy. Consumer Price Index (CPI) is used in measurement
of inflation in Kenya. It is of concern to establish the effect inflation risk has on the
shares market. The study had the objective of establishing the effect of inflation risk on
the performance of the NSE 20 share index. The study used the NSE 20 share index to
represent the performance of share market in Kenya. The index is the oldest and widely
used share market performance indicator in Kenya. The study considered theories dealing
with macroeconomic indicators including inflation. The study considered various theories
on share prices including Portfolio Selection Theory, Capital Assets Pricing Model
(CAPM) and Arbitrage Pricing Theory (APT) and evaluated empirical evidence from
studies conducted in Kenya and around the world. The study also reviewed behavioural
theories on investment including prospect theory and heuristic driven biases. The study
used secondary data which was analysed through statistical tests. Monthly inflation data
was obtained from Kenya National Bureau of Statistics (KNBS) while the monthly value
of NSE 20 share index was obtained from Nairobi Securities Exchange (NSE). The aim
was to establish a predictor model having the NSE 20 share index as the dependent
variable and inflation as the independent variable. Analysis of the variables used the
SPSS program to conduct tests on the data. Scatter diagram, descriptive statistics and
simple regression models were used in the analysis. The best fit line on the scatter
diagram indicated there is no relationship between the 2 variables. Descriptive statistics
indicated that both variables are widely distributed with Inflation having a mean of 8.86
and standard deviation of 5.26 while NSE 20 share index has a mean of 3,534.04 and
standard deviation of 1,204.9.The value of R squared is 0 meaning there is no
relationship between the variables. The regression model developed is weak in predicting
the NSE 20 share index value. The study recommends controlling of inflation rate to
make it more predictable and create a favourable investment environment. The study
further recommends investigation of the effect of inflation risk on other areas of the
economy not included in the Nairobi Securities Exchange.
v
TABLE OF CONTENTS
DECLARATION……………………………………………………………….…….…ii
ACKNOWLEDGEMENTS…………………………………………….…………........iii
DEDICATIONS………………………………………………………………………...iv
ABSTRACT………………………………………………………………………..……v
LIST OF TABLES……………………………………………………………….……..viii
LIST OF FIGURES…………………………………………………………………..…ix
ABBREVIATIONS……………………………………………………………………..x
1.0 INTRODUCTION......................................................................................................1
1.1 BACKGROUND .....................................................................................................1
1.2 RESEARCH PROBLEM.........................................................................................4
1.3 RESEARCH OBJECTIVES.....................................................................................6
1.4 VALUE OF THE STUDY…............………...............................………………....6
2.0 LITERATURE REVIEW..........................................................................................7
2.1 THEORIES ON MACROECONOMICS................................................................7
2.2 THEORIES ON SHARE PRICES……………………………………………...…10
2.3 EMPIRICAL EVIDENCES……………………………………………………….15
2.4 CONCLUSION……………………………………………………………………19
3.0 RESEARCH METHODOLOGY............................................................................21
3.1 RESEARCH DESIGN.............................................................................................21
3.2 POPULATION ........................................................................................................21
3.3 SAMPLE DESIGN.............……………………………………………………….21
3.4 DATA COLLECTION……………………………………………………………22
3.5 DATA ANALYSIS..………………………………………………………………22
4.0 DATA ANALYSIS, RESULTS AND DISCUSSIONS......................…….…..….24
4.1 INTRODUCTION………………………………………………………………..24
vi
4.2 DATA ANALYSIS AND RESULTS……………………………………………24
4.3 DISCUSSIONS…………………………………………………………………..33
5.0 SUMMARY,CONCLUSIONS AND RECOMMENDATIONS….………….….35
5.1 SUMMARY……………………………………………………………………….35
5.2 CONCLUSIONS………………………………………………………………….36
5.3 RECOMMENDATIONS………………………………………………………….36
5.4 LIMITATIONS OF THE STUDY………………………………………………..37
5.5 SUGGESTIONS FOR FURTHER RESEARH………………………………….38
REFERENCES…………………………………………………………..………….….39
APPENDICES…………………………………………..……………….……………...44
1. NSE LISTED COMPANIES
2. NSE 20 SHARE INDEX CONSTITUENT COMPANIES
3. NSE 20 SHARE INDEX VALUES
4. INFLATION RATE VALUES
vii
LIST OF TABLES
TABLE 1: Descriptive Statistics…………………………………………………….…..25
TABLE 2: Correlation of Inflation and NSE 20 share index…………………….……...29
TABLE 3: Regression model summary……………………………………….…………30
TABLE 4: Analysis of variables…………………………………………………………31
TABLE 5: Coefficients of NSE 20 share index and Inflation……….……………..……32
viii
LIST OF FIGURES
FIGURE 1: Inflation rate trend analysis graph………………………………………26
FIGURE 2: NSE 20 share index trend analysis graph……………………………….27
FIGURE 3: NSE 20 share index vs. Inflation scatter diagram………………..…......28
ix
ABBREVIATIONS
APT
-
Arbitrage Pricing Theory
CAPM
-
Capital Assets Pricing Model
CPI
-
Consumer Price Index
GDP
-
Gross Domestic Product
KNBS
-
Kenya National Bureau of Statistics
NSE
-
Nairobi Securities Exchange
SPSS
-
Statistical Package for Social Sciences
x
CHAPTER ONE
INTRODUCTION
1.1 Background
1.1.1 Concept of Inflation
Tucker (2007) refers inflation as an increase in the general price level of goods and
services in the economy. Inflation is an increase in the overall average level of prices and
not an increase in any specific product. The most widely reported measure of inflation is
the consumer price index (CPI) which measures the changes of the average prices of
consumer goods and services.
Sloman and Kevin (2007) explain that inflation may be either demand pull inflation or
cost push inflation. Demand pull inflation is caused by persistent rises in aggregate
demand thus the firms responding by raising prices and partly by increasing output. Cost
push inflation is associated by persistent increase in the costs experienced by firms. Firms
respond by raising prices and passing the costs on to the consumer and partly cutting
back on production. Hendry (2006) agrees that inflation is the resultant of many excess
demands and supplies in the economy.
Tucker (2007) observed that there are many measures of inflation, because there are
many different price indices relating to different sectors of the economy. Two widely
known indices for which inflation rates are reported in many countries are the CPI, which
measures prices that affect typical consumers, and the GDP deflator, which measures
prices of locally-produced goods and services.
1
1.1.2 Share Prices
Kidwel (2008) defines common stock as the basic ownership claim in a corporation. The
most distinguishing feature of a common stock is that it has a residual claim against
firm’s cash flows or assets. Preferred stock represents ownership interest in the
corporation but receives preferential treatment over common stock with respect to
dividend payment and claim against firm’s assets in the event of bankruptcy or
liquidation. He further explains that share prices refer to the cost at which shares are
offered in the market. This is also referred to as the market price of a share. In general the
share price refers to the average price at which a share is traded at a given time.
1.1.3 Relationship between Inflation and Share Prices
Tucker (2007) explains that inflation is known to affect the economy of a country in
general. Inflation tends to reduce the standard of living due to decline in purchasing
power of money. The greater the rate of inflation the greater the decline in the quantity of
goods that can be purchased with a given nominal income. He noted that inflation is seen
to have effect on wealth. Wealth is the value of the stock of assets owned at some point in
time and it includes real estate, stocks, bonds, bank accounts, life insurance policies, cash
and automobiles. Inflation can benefit holders of wealth because the value of assets tends
to increase as prices rise.
Fisher (1930) sought to explain the relationship between asset returns and inflation. He
explains that the essence of investment is to attain a reasonable return while preserving its
purchasing power. To preserve the purchasing power of an investment while earning a
reasonable return calls for such an investment to attain returns which are above the
inflation rate lest the value of the investment is eroded over time, compromising its
2
purchasing power. In support to Fisher’s hypothesis Hasan and Javed (2009) finds
bidirectional relationship on asset returns and inflation. Feldstein (1980) on the other
hand observed that stock prices boost when inflation rate is at a high constant rate and on
the contrary stock prices fall when expected inflation rate rises. Gallager and Taylor
(2002) observed stock prices to be significantly and negatively correlated with inflation
via supply shocks. Fama and Schwert (1977) in the Proxy hypothesis observed negative
relationship between stock returns and inflation both expected and unexpected. Wei
(2010) on the contrary found that technology shock moves both inflation and stock
returns in the same direction resulting in a positive link between the two variables. Jareno
and Navarro (2010) notes that the more company’s ability to transfer inflation panic to
the selling price, the higher stock will be. Siegel (2008) observes that in the long run
stock is a good way to hedge against inflation thereby investors reducing the inflation risk
by stock investment. In agreement Shahbaz (2007) also notes the positive relation in the
long run. Anari and Kolari (2001) document an inverse relation between stock prices and
inflation but found a positive long run relation. Modigliani and Cohn (1979) claim that
stock market investors are subject to inflation illusion. 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. They observed that
a rational investor implies that stock prices are undervalued when inflation is high and
may become overvalued when inflation falls. On the contrary, Rapach (2002) found little
evidence of inflation illusion in stock market prices in a study of 16 industrialized
countries.
1.2 Research Problem
3
Feldstein (1980) in the paper on Inflation and the Stock Market observed that stock prices
boost when inflation is at high constant rate while they fall when expected inflation rate
rises. However, Fama and Schwert (1977) gave an idea of negative relationship between
stock returns and Inflation (both expected and unexpected). On the other hand Modigliani
and Cohn (1979) in the Inflation Illusion Hypothesis claim that 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. This implies that
stock prices are undervalued when inflation is high and may become overvalued when
inflation falls. Estrada (2007) observed that the Fed model implies that the yield on
stocks (as measured by the ratio of dividends or earning to stock prices) is highly
positively correlated with inflation.
Studies conducted globally on the relation between inflation and share prices have given
divergent results on the issue. Riona, Mariette, Laban and Rangan (2010) in their study
on the long run relationship between inflation and real stock prices using empirical
evidence from South Africa found a positive response to a permanent inflation shock in
the long run, indicating that deviations in the short run real stock prices will be corrected
towards the long run value. This finding differs with Kullapom and Lalita (2010) who
conducted a study on the relationship between inflation and stock prices in Thailand.
They found that movement of stock prices is irrelevant to inflation. Sharpe (2000)
observed that expected inflation has little effect on the long run equity premium.
However, Muhammad and Faridul (2011) in their paper Stocks as a hedge against
inflation found that stocks acts as a good hedge against inflation in Pakistan both in the
long and short run. This finding is in agreement with Fisher (1930) who explained the
4
relationship between asset returns and inflation. He explains that the essence of
investment is to attain a reasonable return while preserving its purchasing power. Geyser
and Lowies (2001) on the contrary found that neither South African nor Namibian
companies can offer a perfect hedge against inflation. Chiou -Wei (nd) found that there is
a positive relationship between stock price and inflation in Singapore and Hong Kong but
negative relationship in South Korea and Taiwan.
In Kenya Ngugi (2008) observed that the performance of stock market is influenced by a
number of factors the main ones being government policies, general performance of the
economy and inflation. Siele (2009) in the Study on the relationship between stock
market and selected macroeconomic indicators observed that market share index is
positively related to inflation rate, treasury bill rate and gross domestic product. Kaimba
(2010) also observed significant relationship between the NSE 20 share index and
selected macro economic variables. Kiptoo (2010) observed significant relationship
between the NSE 20 share index and both exchange rate and inflation.
The dilemma created by the various theories advanced to explain the relationship
between inflation and share prices and the divergent results obtained from the empirical
data on global studies creates the need to have a specific study for Kenya on the effect of
inflation on NSE 20 share index. The study will use monthly data unlike in previous
study by Siele (2009) where quarterly data was used. The study is to establish effect of
inflation risk on the performance of NSE 20 share index for the period January 2002 to
December 2012.
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1.3 Research Objective
The objective of this study is to analyse the effect of inflation on the performance of the
Nairobi Securities Exchange 20 share index.
1.4 Value of the Study
The study has the following significance
The scholars and researchers will use this study as a basis for discussions on the effect of
inflation risk on share prices. This will offer a basis on further improvement of the
various theories advanced to explain the relationship of inflation on share prices and
establish their relevance to an economy like Kenya.
The findings of the study will be important to understanding inflation risk and its impact
on the performance share prices in Kenya. The study will be important in the formulation
of policies by the government with regard to control of inflation and promotion of
investment in the stock market.
6
CHAPTER TWO
LITERATURE REVIEW
2.1 Theories on Macroeconomics
2.1.1The General Theory of Employment, Interest and Money
Keynes (1936) was attempting to explain the devastating Great depression of the 1930s
through the general theory of employment, interest and money. During the period GDP of
the United states declined by more than 25% by 1933 and did not return to its potential
level until the United States entered world war II in 1941.The unemployment rate soared
to 25% by 1933 and did not return to its 1929 level until 1942.Keynes developed a
version of the aggregate demand and the aggregate supply to explain these facts. The
widespread acceptance during 1930s and 1940s of Keynes’s model became known as the
Keynesian revolution.
Keynes (1936) suggested that active government policy could be effective in managing
the economy. Rather than seeing unbalanced government budgets as wrong, Keynes
advocated what has been called countercyclical fiscal policies, that is, policies that acted
against the tide of the business cycle: deficit spending when a nation's economy suffers
from recession or when recovery is long-delayed and unemployment is persistently
high—and the suppression of inflation in boom times by either increasing taxes or cutting
back on government outlays. He argued that governments should solve problems in the
short run rather than waiting for market forces to do it in the long run.
Hubbard and Obrien (2009) notes that there are economists who dispute whether the
aggregate demand and the aggregate supply models are the best way to analyse macro
7
economic issues. The alternative schools of thoughts use models that differ significantly
from the standard aggregate demand and aggregate supply model. The schools of thought
include: The Monetarist Model, The New Classical Model and the Real Business Cycle
Model.
2.1.2 The Monetarist Model
Hubbard and Obrien (2009) explain that The Monetarist model –also known as the NeoQuantity Theory of Money Model-was developed beginning in the 1940s by Milton
Friedman. Friedman argued that Keynesian approach overstates the amount of
macroeconomic instability in the economy. He argued that the economy will ordinarily
be at potential real GDP. He argued that most fluctuations in real output were caused by
fluctuations in the money supply rather than fluctuation in consumption spending or
investment spending. He argued that the severity of the great depression in the United
States was caused by the Federal Reserve’s allowing the quantity of money in the
economy to fall by more than 25% between 1929 and 1933.
Hubbard and Obrien (2009) reports Friedman argument that the Federal reserves should
change its practices and adopt a monetary growth rule which is a plan for increasing the
quantity of money at a fixed rate. Friedman believed that adopting monetary growth rule
would reduce fluctuations in real GDP, employment and inflation.
2.1.3 The New Classical Model
Hubbard and Obrien (2009) reports that The new classical model was developed in the
mid-1970s by a group of economists including Nobel Laureate Robert Lucas of the
University of Chicago, Thomas Sargent of New York University, and Robert Barro of
8
Harvard University. Like the classical economists the Neo classical macro economists
believe that the economy normally will be at a potential real GDP. They believe that
wages and prices adjust quickly to change in demand and supply.
Hubbard and Obrien (2009) reports Lucas argument that workers and firms have rational
expectations, meaning they form their expectations of the future values of economic
variables, such as inflation rate, by making use of all available information on variablessuch as changes in the quantity of money –that might affect the aggregate demand. If the
actual inflation rate is lower than the expected inflation rate, the actual real wage will be
higher than expected real wage. These higher real wages will lead to a recession because
they will cause firms to higher fewer workers and cut back on production. As workers
and firms adjust their expectations to the lower inflation rate, the real wage will decline,
and employment and production will expand, bringing the economy out of recession.
Supporters of new classical model agree with supporters of the monetarist model that the
Federal Reserve should adopt a monetary growth rule which will make it easier for
workers and firms to accurately focus the price level, thereby reducing fluctuations in real
GDP.
2.1.4 The Real Business Cycle Model
Hubbard and Obrien (2009) notes that beginning in the 1980s, some economists,
including Nobel Laureates Finn Kydland of Carnegie Mellon University and Edward
Prescott of Arizona State university, argued that Lucas was correct in assuming that
workers and firms formed their expectations rationally and that wages and prices adjust
quickly to supply and demand but wrong on the source of fluctuations in real GDP. They
argued that fluctuations in real GDP are caused by temporary shocks to productivity.
9
These shocks can be negative, such as a decline in availability of oil or other raw
materials, or positive, such as technological change that makes it possible to produce
more output with the same quantity of inputs. The model focuses on real factorsproductivity shocks-rather than changes in the quantity of money to explain fluctuations
in real GDP, thus it is known as real business cycle model.
2.2 Theories on Share Prices
This section reviews the various theories that have been advanced on share prices. This
includes the models that are used in valuation of shares, comparison of risk and return on
shares and the various factors that influence share prices.
2.2.1 Portfolio Selection Theory
Markowitz (1952) developed the theory of Portfolio Selection Theory. He was concerned
only with portfolio rather than individual assets. He seeks to explain how to arrive at
portfolio selection within a set of relevant beliefs about future performances, leading to
the observation that portfolio selection is an investment process rather than a speculative
behavior. Markowitz (1952) makes one of the basic assumptions that an investor expects
maximum discounted expected returns, and views variance of returns as an undesirable
thing. Variance is a measure of dispersion about the expected. Diversification is seen as a
superior to non diversified portfolios in terms of maximizing expected discounted returns.
To identify the best level of diversification, Markowitz (1952) suggested the efficient
frontier, which suggests that for each level of return, there is a portfolio that offers the
lowest risk and for each level of risk, there a portfolio that offers the highest return. By
plotting all these combinations on a graph, the resulting line is the efficient frontier.
10
Portfolios that are positioned on the upper part of the curve are efficient because they
provide the maximum expected return at a given level of risk. And these are the
portfolios that rational investors should choose.
2.2.2 Capital Assets Pricing Model
The Capital Asset Pricing Model was first developed by Sharpe (1964) and Lintner
(1965). Sharpe and Lintner version of CAPM was based on the one period mean variance
portfolio theory of Markowitz. CAPM is based on some assumptions. It is assumed that
investors are risk adverse as in Markowitz Model and evaluate their investment only in
terms of excepted return and variance of return measure over the same single holding
period. It is also assumed that capital markets are perfect meaning that all assets are
indefinitely divisible, that no transactions cost, short selling restrictions or taxes occurs,
that all investors can lend and borrow at the risk free rate and that all information is
costless and available for everyone. All investors are assumed to have the same
investment opportunities. CAPM assumes that investors estimate the same individual
asset return, correlation amongst assets and standard deviation of return.
Based on these assumptions Sharpe and Lintner developed the following formula which
states that expected return on assets I, E (Ri), is the risk-free rate, Rf, plus a premium per
unit of beta risk, which is calculated by subtracting the risk from the expected return of
the market, E (RM) and multiplying the result with the risk premium in terms of the
asset’s market beta, βiM. The latter one is calculated by the covariance of individual asset
return, Ri, with the Market return, RM, divided by the variance of the market return.
11
CAPM uses a measure of systematic risk that can be compared with other assets in the
market. Using this measure of risk can theoretically allow investors to improve their
portfolios and managers to find their required rate of return.
CAPM mathematical model
E (Ri) =Rf + βi (E (Rm)-Rf)
CAPM model is based on the preposition that beta measures asset’s sensitivity to a
broader portfolio.
2.2.3 Arbitrage Pricing Theory
The APT was developed primarily by Ross (1976). APT is founded on the notion that
investors are rewarded for assuming non-diversifiable (systematic) risk; diversifiable
(unsystematic) risk is compensated. Beta is considered as the most important single
factor in CAPM that captures the systematic risk of an asset. In APT, there are a number
of industry-specific and macro-economic factors that affect the security returns. Thus a
number of factors may measure the systematic (non-diversifiable) risk of an asset under
APT. The fundamental logic of APT is that investors always indulge in arbitrage
whenever they find difference in the returns of assets with similar risk characteristics.
Ross (1976) developed an equilibrium asset pricing theory, which requires fewer
assumptions than the CAPM. He assumes that the expected security returns are generated
by multiple k factors instead of one pervasive market risk premium factor identified in
the CAPM.
12
In APT, the return of an asset is assumed to have two components: Predictable (expected)
and unpredictable (uncertain) returns. Thus, return on asset j will be:
E (Rj) = Rf+UR………………………………………………………………………….(1)
Where Rf is the predictable returns (risk-free return on zero-beta asset) and UR is the
unanticipated part of the returns.
The predictable or expected return depends on the information available to shareholders
that has a bearing on the shares prices. The unpredictable or uncertain return arises from
the future information. This information may be the firm specific and the market-related.
We can rewrite the equation 1 above as follows:
E (Rj) =Rf+URs+URm…………………………………………………………………(2)
URs is the unexpected component of return arising from the specific factors related to the
firm. URm is that component of the unexpected return that arises from the economywide, market-related factors.
It is important to notice that the economy- wide
information may be further divided into the expected part and the unexpected or surprise
part. The risk arising from the firm-specific factors is diversifiable. It is unsystematic
risk.
The risk arising from the market-related factors cannot be diversified.
This
represents systematic risk. APT assumes that market risk can be caused by economic
factors such as changes in gross domestic product, inflation, and the structure of interest
rates and these factors could affects firms differently. Therefore, under APT, multiple
factors may be responsible for the expected return on the share of a firm. Therefore,
under APT the sensitivity of the assets return to each factor is estimated. For each firm,
13
there will be as many betas as the number of factors. Equation (2) can be expressed as
follows:
E (Rj) = Rf + (β1F1+β2F2+β3F3+……… +βnFn) + URs ……………………………….(3)
Where β1 is firm j’s factor one beta, β2 is factor two betas and so on. F represents a
surprise in factors.
2.2.4 Prospect Theory
The prospect theory proposed by Kahneman and Tversky (1979) describes how people
frame and value a decision involving uncertainty. According to the prospect theory,
people look at choices in terms of potential gains or losses in relation to a specific
reference point, which is often the purchase price. Thus, if a person were given two equal
choices, one expressed in terms of possible gains and the other in possible losses, people
would choose the former - even when they achieve the same economic end result.
Prospect theory also explains the occurrence of the disposition effect, which is the
tendency for investors to hold on to losing stocks for too long and sell winning stocks too
soon. The most logical course of action would be to hold on to winning stocks in order to
further gains and to sell losing stocks in order to prevent escalating losses.
2.2.5 Heuristic Driven Biases
Kahneman and Tversky (1974) identified important heuristic-driven biases and cognitive
errors that impair judgement. They noted that investors have the tendency to form
judgments based on stereotypes. This tendency is known as representativeness. Investors
14
may believe that a healthy growth of earnings in the past may be representative of high
growth rate in future. They also observed that people tend to be overconfident and hence
overestimate the accuracy of their forecasts. They argue that when forming estimates,
people often start with some initial, possibly arbitrary value, and then adjust away from
it. After forming an opinion, people are often unwilling to change it, even though they
receive new information that is relevant. They also noted that people are fearful of
ambiguous situations where they feel that they have little information about the possible
outcomes.
2.4 Empirical Evidence
Riona et al (2010) in a study on the long run relationship between inflation and real stock
prices using empirical evidence from South Africa. The study presented some time series
evidence on using South African data, by applying the structural bivariate vector
autoregressive (VAR) methodology proposed by King and Watson (1997). The study
used quarterly data on nominal stock price index and consumer price index from 1980 to
2010.The empirical results provided considerable support of the view that in the long run
real stock prices are invariant to permanent changes in the rate of inflation. The impulse
responses reveal a positive real stock price response to a permanent inflation shock in the
long run, indicating that any deviations in the short run real stock prices will be corrected
towards the long run value.
Kullapom and Lalita (2010) conducted a study on the relationship between inflation and
stock prices in Thailand. The study was carried out in the period January 2000 to March
2010. The statistical method vector auto regression (VAR) was used to find and analyse
the association. Interview was also conducted to gather opinions of investors in stock
15
exchange of Thailand on how inflation affects equity value. The findings demonstrate
that movement to stock prices is irrelevant to inflation.
Shehu (2011) conducted a study on whether inflation has an impact on stock returns and
volatility. The study that was based on evidence from Nigeria and Ghana used
Generalized Autoregressive Conditional Heteroskedacity (GARCH) model to assess the
impact of inflation on stock market returns and volatility using monthly time series data.
Results for Nigeria show weak support for the hypothesis which states that bad news
exert more adverse effect on stock market volatility than good news of the same
magnitude; while a strong opposite case hold for Ghana. Furthermore, inflation rate and
its three month average were found to have significant effect on stock market volatility in
the two countries; therefore, would certainly reduce stock market returns and boost
investor confidence.
Muhammad and Faridul (2011) in their paper Stocks as hedge against inflation used
ARDL bounds testing approach to cointegration to explore whether or not stocks are a
good hedge against inflation in the case of a transition economy such as Pakistan using
annual data for the period 1971-2008.The results suggest that stocks act as good hedge
against inflation in Pakistan both in the long and short run.
In Kenya studies have been conducted to determine the relationship between inflation and
other economic indicators. Opati (2009) in a study on the causal relationship between
inflation and exchange rates in Kenya from the period January 1998 and December 2008
did not focus on effects of inflation on other economic indicators such as movement in
stock exchange indices. However, the study found that there is a causal relationship
16
between inflation and exchange rates in Kenya for only the US Dollar and the Great
Britain pound only for the short run. However, all currencies depicted a causal
relationship with inflation rates in the long run except Uganda Shilling.
Kaimba (2010) conducted a study on relationship between Nairobi Stock Exchange 20
Share Index and Selected macro economic variables. The study was for the period 1990
to 2009.Data analysis was done using descriptive and inferential statistics using
Statistical Package for Social Sciences (SPSS) and Ms Excel spreadsheets. The study
found significant relationship between the NSE 20 Share index with selected Macro
economic variables except for foreign portfolios flows where the relationship was found
to be insignificant.
Nyambok (2010) in her study on the relationship between inflation rates and liquidity of
companies quoted at the Nairobi Stock Exchange observed that the overall inflation rates
influence the stock market liquidity at varying degree depending on segment. Regression
models were developed using monthly inflation rates as independent variable and both
segment wise and market wide trading volume as dependent variable. The study was for
3 years from January 2007 to December 2009. There study however found a positive
relationship between overall inflation rates and market wide liquidity at the NSE which is
an indication that as inflation rates go up, the overall market liquidity in terms of trading
volumes also go up and vice versa. The study made focus on liquidity and not the overall
price level of shares.
Kiptoo (2010) conducted a study on an empirical investigation on the relationship
between selected Macro Economic Variables and stock prices based on evidence from the
17
Nairobi Stock Exchange. The study used NSE 20 Share index to represent all listed
companies and covered the period 1978-2008. Data was analysed using unit root test,
multicollinearity and regression. The study agrees with that of Kaimba (2010) in that
there is significant relationship between the NSE 20 Share Index and both exchange rate
and Inflation. She however found insignificant relationship on interest rates, money
supply and gross domestic product.
Siele (2009) in the Study on the relationship between stock market and some selected
macroeconomic variables in Kenya used NSE 20 share index to represent Kenya Stock
Market and real GDP growth rate, inflation, interest and treasury bill rates as
macroeconomic variables. Quarterly time series data for the period 1999-2008 was
analysed using summary statistics, correlation and regression analysis to ascertain the
relationships. Findings of the study reveal that macro economic variables explain about
70% of the variation of the market share index. The regression coefficients show that the
market share index is positively related to inflation rate, Treasury bill rate and gross
domestic product while it is negatively related to interest rate. This study results with
similar views to those of Kaimba (2010) and Kiptoo (2010).
Ngugi (2008) conducted a study on performance of NSE before and after the last four
general elections in Kenya namely 1992, 1997, 2002 and 2007.The NSE month end
indices for the period between 31st January 1991 and 30th September 2008 obtained from
the NSE were analysed using line graphs, percentages, mean , variance and other
statistical measures. The study results indicate the NSE performance was influenced by
the political activities and expectations around the election period in the short term. The
18
study also reveals that the first two years after election performed better than the last 2
years before the next general election.
2.4 Conclusion
The relation between inflation and share prices is of importance. The theories on macro
economics indicate that inflation is one of the important macroeconomic indicators. The
theories advocate for intervention in government through fiscal and monetary policies to
control inflation.
Various theories have been advanced to explain investment and the various factors that
influence it. The theories on risk and return indicate that investors evaluate the risk and
return with an aim of maximizing the expected return. The theories on behavioural
finance indicate that contrary to those on risk and returns in that investors do not always
make decision based on rational analysis but are influences by other psychological
factors. The theories point at inflation as one of investment risk factors.
Studies conducted globally on relationship between inflation and investment indicates a
significant relationship on the two variables. These studies have been done over years in
diversified economies. This study is aimed at establishing the relationship between the
two variables in Kenya.
19
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Research Design
The study was on the effect of inflation on performance of NSE 20 share index. The
research was for the period from January 2002 to December 2012.
Data was collected on a monthly basis. Monthly inflation rate was from the secondary
data analysed by KNBS while the monthly value of NSE 20 Share Index was
collected from NSE. The index was used since it covers the period of study unlike
other indices which were nonexistent in part of the study period.
3.2 Population
The population of the study was all the 60 firms listed in the Nairobi Securities
Exchange (Appendix 1) and monthly inflation rates during the period of the study.
The population has been taken due to the nature of companies listed in the NSE, in
that they have made their financial information public and represents all sectors of the
economy.
3.3 Sample Design
The sample of the study was all the firms that form the NSE 20 share index
(Appendix 2). The index was from selected companies in the NSE from all sectors
namely Agricultural, Automobiles and Accessories, Banking, Commercial and
Services, Construction and Allied, Energy and Petroleum, Insurance, Investment,
Manufacturing and allied and Telecommunication and Technology. The monthly
NSE 20 share index was analysed against corresponding inflation rates in the period
20
of study. The NSE 20 share index is oldest market index in the market hence provided
the necessary data for the period of study.
3.4 Data Collection
The study required data on monthly inflation rate and NSE 20 share index for the
period from January 2002 to December 2012. The Study will used secondary data
from official sources. The monthly inflation rate data was obtained from KNBS while
the monthly NSE 20 share index was obtained from NSE. The data was recorded in
an SPSS work sheet for data analysis.
3.5 Data Analysis
The data was analysed using regression analysis to establish the relation between the
two variables namely inflation and the value of NSE 20 share index. The study used
two time series variables thus the use of regression analysis as used in other studies
involving analysis of variables to establish their relationship. Siele (2009) used the
time series regression analysis technique to determine whether selected macro
economic variables are related to the NSE 20 share index.
Descriptive and correlation analysis using Statistical Package for Social Sciences
(SPSS) was conducted on the data to establish the relation on the variables. This
method was used by Kaimba (2010) in the study to establish the causal relationship
between stock market performance and economic variables.
The study applied the following regression model:
y = a + bx + e
21
Where x =Monthly rate of inflation.
y = NSE 20 share index at the end of the month.
a = Constant
b = Measure of relationship between variable x and y.
e = Error
The study had a significance level of 5%. The study tested the null hypothesis that there
is no relationship between the dependent variable and the independent variable.
22
CHAPTER FOUR
DATA ANALYSIS, RESULTS AND DISCUSSIONS
4.1 Introduction
The data has been analysed using the SPSS package with an aim of establishing the effect
of inflation risk on the performance of the NSE 20 share index. The data for the 2
variables run from January 2002 to December 2012.Various statistical techniques
discussed below have been used with the aim of meeting the objective of the study.
4.2 Data Analysis and Results
The table below gives descriptive statistics on the two variables during the period of
study. The analysis is of two sets of variable namely Inflation rate and the NSE 20 share
index. Each variable has 132 items representing the monthly value for the 11 years of
Study being from January 2002 to December 2012.The descriptive statistics measured are
mean, standard error mean, median, mode, standard deviation, variance, range, maximum
value and minimum.
23
TABLE 1: Descriptive Statistics
NSE 20 SHARE
INFLATION
N
Valid
INDEX
132
132
0
0
Mean
8.8616
3534.0401
Std. Error of Mean
.45820
104.87335
Median
7.9750
3597.3450
Missing
Mode
5.32
Std. Deviation
a
1043.38
a
5.26437
1.20490E3
27.714
1.452E6
19.26
4730.89
Minimum
.46
1043.38
Maximum
19.72
5774.27
Variance
Range
a.
Multiple modes exist. The smallest value is shown
Source: Researcher 2013
The mean inflation is 8.86 with the minimum being 0.46 and the maximum 19.72.The
standard deviation is 5.26 with the standard error of mean being 0.4582.This indicates a
24
wide distribution of the inflation data over the period of study. The inflation median is
7.975 which is close to mean of 8.86 thus suggesting a distribution that is almost normal.
The mean NSE 20 share index value is 3534.01 with the minimum value being 1043.38
and the maximum value being 5774.27.The standard deviation 1204.9 and standard error
mean of 104.87.The median of 3597.34 is close to the mean of 3534.04 indicating a close
to normal distribution.
FIGURE 1: Inflation rate Trend Analysis
Source: Researcher 2013
The graph above indicates the volatile nature of inflation in Kenya during the period of
study. The lowest point of inflation being at 0.46 and the highest at 19.72.The trend has
25
been characterized by general increase in level of inflation over several months followed
by a decline in following months. A general constant inflation rate was not observed
during the period of study.
FIGURE 2: NSE 20 Share Index Trend Analysis
Source: Researcher 2013
Observation from the graph indicate a general upward trend in the value of the NSE 20
share index from September 2002 to January 2007.The period between January 2007 and
January 2008 is generally constant forming the impression of a plateau on the graph. This
period is followed by a general decline in the value from January 2008 to January
26
2009.The period is followed by a recovery of the value of the index up to Jan 2011 then a
slight decrease. The period of study ends when the index is on an upward trend
FIGURE 3: Scatter Diagram NSE 20 share index Vs Inflation
Source: Researcher 2013
The scatter diagram on inflation and the NSE 20 share index gives various points of
interaction of the variables. The fit line is horizontal to the X axis. This is an indicator
that there is no linear association between the two variables as the value of R squared is
close to zero. A perfect correlation needs a value of 1.0.
27
TABLE 2: Correlation of Inflation and NSE 20 Share Index
Correlations
INFLATION
INFLATION
Pearson Correlation
INDEX
1
Sig. (2-tailed)
.965
N
INDEX
-.004
Pearson Correlation
132
132
-.004
1
Sig. (2-tailed)
.965
N
132
132
Source: Researcher 2013
Correlation of Inflation and the 20 share index is -0.004 with a two tailed significance
level. The Pearson correlation will range from +1 to -1.The further away from 0, the
stronger the relationship. This indicates that there is a weak relationship between the 2
variables during the period of our study.
28
TABLE 3: Regression Model Summary
b
Model Summary
Change Statistics
Model
1
R
.004
R
Adjusted R
Std. Error of the
R Square
Square
Square
Estimate
Change
a
.000
-.008
1209.51924
.000
F
Change df1 df2
.002
1 130
Sig. F
Durbin-
Change
Watson
.965
.034
a. Predictors: (Constant),
INFLATION
b. Dependent Variable: INDEX
Source: Researcher 2013
R square is the measure used to quantify the extent to which straight line fits the data. It
is the correlation squared. When the value is zero there is no linear association while
when it is 1 there is a perfect linear prediction. In the study the value of R is 0.004 while
R squared is close to zero thus confirming that there is no linear relationship between the
variables. Similar conclusions have been made using scatter diagram and Pearson
correlation.
29
TABLE 4: Analysis of Variables (ANOVA)
b
ANOVA
Model
1
Sum of Squares
Regression
df
Mean Square
2878.661
1
2878.661
Residual
1.902E8
130
1462936.789
Total
1.902E8
131
F
Sig.
.002
.965
a. Predictors: (Constant), INFLATION
b. Dependent Variable: INDEX
Source: Researcher 2013
ANOVA test has a significance of 0.965.This indicates the probability that the 2 variables
are non linear. This is a high level of probability that indicates non linear relationship.
This confirms the null hypothesis that there is no relationship between the dependent
variable and the independent variable.
30
a
TABLE 5: Coefficients of NSE 20 share index and Inflation
Coefficients
a
Standar
dized
Unstandardized
Coeffici
95% Confidence
Coefficients
ents
Interval for B
Model
B
Std. Error
Beta
t
Sig.
Collinearity
Correlations
Lower
Upper
Zero-
Bound
Bound
order Partial
Statistics
Tolera
Part
nce
VIF
1 (Const
3541.931
206.704
-.890
20.074
17.135
.000 3132.992 3950.870
ant)
INFLA
-.004
-.044
.965
-40.604
38.823
-.004
-.004
-.004 1.000 1.000
TION
a. Dependent
Variable: INDEX
.
Source: Researcher 2013
The coefficients table indicates a constant of 3,541.931.This is the value of the NSE 20
share index (Dependent variable) when the value of Inflation (Independent variable) is 0
at the Y intercept. The constant is equal to the mean in our study. It may be assumed
there is no linear relationship since there is insignificant change of -0.89 in the NSE 20
share index for every change in a unit of inflation rate.
The equation y = a + bx + e can be expressed as follows using the above coefficients:
Y= 3541 - 0.004x
Where x =Monthly rate of inflation.
31
y = NSE 20 share index at the end of the month.
a = Constant
b = Measure of relationship between variable x and y.
e = Error
The equation is weak in predicting the future value of NSE 20 share index.
4.3 Discussions
The objective of the study was to establish the effect of inflation on the performance of
the NSE 20 share index. Descriptive statistics indicated a wide distribution of inflation
data over the period of study. It also indicated close to normal distribution in the NSE 20
share index data. The graphical expression of the Inflation trend and NSE 20 share index
trend showed little relationship between the two variables. This was characterized by
both positive and negative relation as observed by the direction of slope on the graphs.
The scatter diagram through the best fit line also indicated there is no relationship
between the two variables. These initial tests indicated that there is no effect of inflation
risk on the performance of the NSE 20 share index.
Pearson correlation test indicated a weak relationship between the two variables.
Furthermore the regression model summary confirmed there is no linear relationship
between the variables. A further analysis of variables confirmed the null hypothesis that
there is no relationship between the independent and dependent variable at a significant
level of 5%.The model developed through the coefficients is weak in predicting future
values of NSE 20 share index.
32
The tests discussed above indicated there is insignificant relationship between the NSE
20 share index and inflation rates. Therefore, inflation had insignificant effect on the NSE
20 share index during the period of study.
33
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Summary
The study had the objective of establishing the effect of inflation on the performance of
the NSE 20 share index. Inflation in Kenya has been unstable over the period of study.
The highest being 19.72 while the lowest was 0.46. The mean was 8.82.The government
aims to maintain a constant rate of inflation characterized by a single digit. On the other
hand the NSE 20 share index is the oldest and widely used share index. It is used to
measure the general performance of the shares market in Kenya. The Index has been an
upward trend during most of the period of study.
The study considered theories dealing with inflation and share prices and evaluated
empirical evidence from studies conducted in Kenya and around the world. The study
used secondary data which was analysed through a simple linear regression model. The
aim was to establish a predictor model having the NSE 20 share index as the dependent
variable and inflation as the independent variable. Analysis of the variables used the
SPSS program to conduct tests on the data. Scatter diagram, descriptive statistics and
simple regression models were used in the analysis. The best fit line on the scatter
diagram indicated there is no relationship between the 2 variables. The value of R
squared is 0 meaning there is no relationship between the variables. The regression model
developed is weak in predicting the NSE 20 share index value.
34
5.2 Conclusion
The observation from the analysis of data in previous chapter indicates that there is a very
insignificant relationship between inflation and the NSE 20 share index. This indicates
that inflation risk has insignificant effect on the index. The Beta of -0.004 is an indicator
of the relationship. The scatter diagram indicates a zero relation through the best fit line.
Graphical presentation of the inflation and the NSE 20 share index in the period of study
indicates a general inverse relationship between the two variables in the short run in the
period from January 2005 to January 2007.
The results of the study indicate that inflation risk represented by b in the regression
model Y = a + bX has insignificant effect on the NSE 20 share index represented in the
model by Y. The NSE 20 share index is the widely used measure of the performance of
stock market in Kenya.
5.3 Recommendations
The study mainly focused on the effect of inflation on the performance of the NSE 20
share index. The unstable inflation rate during the period of study seems not to have
effect on the performance of the NSE 20 share index. The inflation is insignificant on the
performance of NSE 20 share index. The unstable inflation rates could have made
potential investors to shun investment in the NSE. Investors in the NSE could have
considered other factors rather inflation in making investment decisions.
The government should aim at controlling inflation rate in Kenya by making it generally
constant at a low level. This may attract more investors in the NSE as it will make the
investment environment more predictable. The study recommends investigation of the
effect of inflation on investment on other areas of the economy that is not included in the
35
Nairobi Securities Exchange. This includes real estate, Saccos and small businesses. This
will assist in economic policy formulation.
5.4 Limitations of the Study
The study had the limitation of using the NSE 20 share index which is comprised of 20
listed companies from various sectors of NSE. This index as opposed to the NSE All
share index does not include all the companies listed in the NSE. However, NSE (2012)
indicates that NSE 20 share index was the only stock market index operational during the
whole period of the study. The results are only for the NSE share index. The study
focused on the effect of inflation on the NSE 20 share index thereby other factors that
may have effect on the performance of the index were not considered in the study. The
results are therefore correct exclusively for the factor considered. The study is limited to
a period of 11 years from January 2002 to December 2012.Results of the study may not
apply to other periods not covered on the study.
The study relied on quantitative data during the period of study. The data was collected
from secondary sources. The study did not consider the perception of investors on the
effect of inflation risk on the performance of NSE 20 share index. The study used simple
linear regression model to analyse the data and make conclusion on the effect of inflation
risk on the performance of NSE 20 share index. The use of other models may reveal other
relations.
36
5.5 Suggestions for Further Research
A study on the effect on inflation on other stock market indicators such as the NSE all
share index may further improve on the results of this study. Since the study focused on
the effect of inflation on the performance of the NSE 20 share index a study on effect on
inflation on the performance of bonds, real estate and other investment avenues may
assist in further improvement in formulating policy on inflation. The study may further be
extended to other countries in the world. This may reveal effect of inflation risk on the
performance of market indicators in the country. This will further improve the results of
the study.
A study on the topic but covering different time frames may improve on the study. This
may reveal both the long term and short term effects of inflation risk on the NSE 20 share
index. A qualitative study on the perception of investors on inflation risk and its effect on
investment may further improve the study. This will reveal whether investors consider
inflation risk as a factor while investing. A similar study may be conducted using another
analysis model than the one used in the study. This may reveal other characteristics of the
data that was not captured in the study.
37
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42
APPENDIX 1
NAIROBI SECURITIES EXCHANGE
LISTED COMPANIES
AGRICULTURAL
Eaagads Ltd Ord 1.25
Kapchorua Tea Co. Ltd Ord Ord 5.00
Kakuzi Ord.5.00
Limuru Tea Co. Ltd Ord 20.00
Rea Vipingo Plantations Ltd Ord 5.00
Sasini Ltd Ord 1.00
Williamson Tea Kenya Ltd Ord 5.00
COMMERCIAL AND SERVICES
Express Ltd Ord 5.00
Kenya Airways Ltd Ord 5.00
Nation Media Group Ord. 2.50
Standard Group Ltd Ord 5.00
TPS Eastern Africa (Serena) Ltd Ord 1.00
Scangroup Ltd Ord 1.00
Uchumi Supermarket Ltd Ord 5.00
Hutchings Biemer Ltd Ord 5.00
Longhorn Kenya Ltd
TELECOMMUNICATION AND TECHNOLOGY
AccessKenya Group Ltd Ord. 1.00
Safaricom Ltd Ord 0.05
AUTOMOBILES AND ACCESSORIES
Car and General (K) Ltd Ord 5.00
43
CMC Holdings Ltd Ord 0.50
Sameer Africa Ltd Ord 5.00
Marshalls (E.A.) Ltd Ord 5.00
BANKING
Barclays Bank Ltd Ord 0.50
CFC Stanbic Holdings Ltd ord.5.00
I&M Holdings Ltd Ord 1.00
Diamond Trust Bank Kenya Ltd Ord 4.00
Housing Finance Co Ltd Ord 5.00
Kenya Commercial Bank Ltd Ord 1.00
National Bank of Kenya Ltd Ord 5.00
NIC Bank Ltd 0rd 5.00
Standard Chartered Bank Ltd Ord 5.00
Equity Bank Ltd Ord 0.50
The Co-operative Bank of Kenya Ltd Ord 1.00
INSURANCE
Jubilee Holdings Ltd Ord 5.00
Pan Africa Insurance Holdings Ltd 0rd 5.00
Kenya Re-Insurance Corporation Ltd Ord 2.50
CFC Insurance Holdings
British-American Investments Company ( Kenya) Ltd Ord 0.10
CIC Insurance Group Ltd Ord 1.00
INVESTMENT
Olympia Capital Holdings ltd Ord 5.00
Centum Investment Co Ltd Ord 0.50
Trans-Century Ltd
44
MANUFACTURING AND ALLIED
B.O.C Kenya Ltd Ord 5.00
British American Tobacco Kenya Ltd Ord 10.00
Carbacid Investments Ltd Ord 5.00
East African Breweries Ltd Ord 2.00
Mumias Sugar Co. Ltd Ord 2.00
Unga Group Ltd Ord 5.00
Eveready East Africa Ltd Ord.1.00
Kenya Orchards Ltd Ord 5.00
A.Baumann CO Ltd Ord 5.00
CONSTRUCTION AND ALLIED
Athi River Mining Ord 5.00
Bamburi Cement Ltd Ord 5.00
Crown Berger Ltd 0rd 5.00
E.A.Cables Ltd Ord 0.50
E.A.Portland Cement Ltd Ord 5.00
ENERGY AND PETROLEUM
KenolKobil Ltd Ord 0.05
Total Kenya Ltd Ord 5.00
KenGen Ltd Ord. 2.50
Kenya Power & Lighting Co Ltd
Source:Nairobi Securities Exchange
45
APPENDIX 2
NSE 20 Share Index Constituent Companies.
Kakuzi
SASINI
BARCLAYS
EQUITY
KCB
STANCHART
CO-OP BANK
KENYA AIRWAYS
NATION
SCANGROUP
Uchumi
ATHI-RIVER
BAMBURI
KENGEN
KENOL
KPLC
BAT(K)
E.A.B.L
MUMIAS
SAFARICOM
Source:Nairobi Securities Exchange
46
APPENDI 3
NSE 20 SHARE INDEX
Month/Year
JANUARY
2000
2001
2,301.07
2002
2,277.89
2,233.18
APRIL
2,162.20
MAY
2,052.90
JUNE
2,003.10
JULY
1,966.52
AUGUST
1,958.96
SEPTEMBER
2,001.32
OCTOBER
2,043.47
NOVEMBER
1,929.67
2006
2007
2008
1,510.63
3,157.88
3,094.30
4,171.80
5,774.27
4,712.71
1,557.74
3,175.36
3,212.81
4,056.63
5,387.28
5,072.41
1,608.34
2,770.60
3,208.66
4,101.64
5,133.67
1,846.63
2,707.60
3,227.59
4,025.21
5,148.07
1,071.07
1,636.47
2,074.67
2,689.14
3,505.39
4,349.75
1,934.96
2,639.75
3,972.15
4,260.49
1,620.70
2,005.08
2,708.03
3,982.00
4,258.54
2,107.43
2,708.86
3,938.70
1,043.40
1,400.87
2,379.91
2,670.69
2,457.21
2,829.65
3,939.45
2,736.98
2,918.34
3,974.12
2,737.59
2,945.58
3,973.04
1,161.63
1,420.45
1,913.35
1,362.85
1,355.05
3224.18
4240.18
2,805.03
3887.07
3,303.75
2,800.10
4,233.24
4029.23
5,336.03
5,175.83
2,852.57
4,241.81
5,185.56
3,294.56
4,339.28
4,868.27
3,273.10
4,648.78
4,843.17
3,366.89
3,546.66
4,078.10
3,650.85
3,968.12
3,738.46
3,703.94
4,438.58
3,102.68
4,454.59
3,465.02
3,832.42
4,486.07
4,879.86
5,371.72
5,146.46
4,180.40
3,005.41
4,629.80
3284
3,865.76
5,314.36
4,971.04
3,386.65
3,083.63
4,659.56
3507.34
5,615.20
5,234.54
3,341.47
3189.55
4,395.17
3,832.69
1,116.36
1,472.91
4464.92
5,340.08
1,043.38
1,505.50
2012
3,629.41
4,072.93
5,146.73
1,097.73
2011
3,565.28
5,001.77
1,086.62
1,657.14
2010
2,474.75
1,129.33
1,767.89
2009
3,198.90
1,183.10
1,830.53
DECEMBER
2005
1,313.57
1,932.85
MARCH
2004
1,343.41
1,897.46
FEBRUARY
2003
47
4083
3,155
5,645.65
SOURCE:NAIROBI SECURITIES
EXCHANGE
3,972.03
4147.28
3,521.18
5,444.83
3247.44
4432.60
4133
3,205
APPENDIX 4
MONTHLY CPI JANUARY 2002-DECEMBER 2012
Base period February 2009=100
INFLATION
Year
Month
Overall CPI
RATE
2002 Jan
52.85
0.46
Feb
52.03
1.20
Mar
51.98
2.03
Apr
52.14
0.86
May
53.28
1.71
Jun
54.47
2.85
Jul
54.51
2.12
Aug
54.74
1.81
Sep
54.55
1.78
Oct
54.50
1.89
Nov
54.77
2.57
Dec
55.63
4.25
2003 Jan
56.21
6.37
Feb
55.91
7.44
Mar
57.24
10.12
Apr
58.21
11.64
May
61.23
14.92
Jun
61.96
13.74
Jul
60.46
10.91
Aug
59.27
8.27
Sep
58.85
7.89
Oct
59.44
9.08
Nov
59.69
8.97
Dec
60.28
8.35
2004 Jan
61.35
9.14
Feb
61.41
9.85
Mar
62.00
8.32
Apr
62.61
7.57
May
64.08
4.65
Jun
65.64
5.94
Jul
65.62
8.54
Aug
68.63
15.80
Sep
70.02
18.96
48
2005
2006
2007
2008
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
70.32
70.07
70.57
70.48
69.97
70.78
72.64
73.54
73.46
73.34
73.35
73.00
72.93
73.33
74.04
76.22
76.19
76.62
76.23
76.48
76.44
76.30
76.87
77.23
77.54
77.82
79.46
79.75
78.57
78.40
77.76
78.08
79.53
80.41
80.86
81.43
81.66
82.47
83.91
86.07
87.25
18.29
17.40
17.08
14.87
13.94
14.15
16.02
14.78
11.92
11.76
6.87
4.27
3.72
4.64
4.91
8.15
8.89
8.26
4.94
3.99
4.06
4.04
4.81
5.79
6.31
6.13
7.32
4.63
3.12
2.31
2.00
2.09
4.05
5.39
5.19
5.45
5.32
5.98
5.60
7.93
11.04
49
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2009 Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2010 Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
2011 Jan
Feb
Mar
Apr
May
Jun
Jul
88.22
90.85
92.68
92.89
92.75
93.79
94.72
95.29
96.95
96.89
97.55
100.00
100.96
101.84
101.84
102.05
102.33
102.94
103.42
103.68
103.87
104.66
104.89
105.18
104.97
105.56
105.79
105.61
105.98
106.25
106.74
106.97
107.86
109.38
110.57
112.05
114.62
118.29
119.48
120.91
122.44
12.53
16.83
18.70
16.79
15.33
15.98
16.32
16.70
17.56
15.48
13.33
14.62
14.44
12.10
9.88
9.86
10.33
9.76
9.19
8.80
7.14
8.02
7.52
5.18
3.97
3.66
3.88
3.49
3.57
3.22
3.21
3.18
3.84
4.51
5.42
6.54
9.19
12.05
12.95
14.48
15.53
50
Aug
Sep
Oct
Nov
Dec
2012 Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
123.97
125.23
127.20
129.13
130.09
130.82
130.76
132.51
133.74
134.09
133.06
131.92
131.51
131.89
132.46
133.33
134.25
16.67
17.32
18.91
19.72
18.93
18.31
16.70
15.61
13.06
12.22
10.05
7.74
6.09
5.32
4.14
3.25
3.20
SOURCE:KNBS
51