EMPIRICAL TEST FOR WEAK-FORM EFFICIENT MARKET HYPOTHESIS OF THE NIGERIAN STOCK EXCHANGE BEING A DISSERTATION PRESENTED TO THE DEPARTMENT OF BANKING AND FINANCE, FACULTY OF BUSINESS ADMINISTRATION, UNIVERSITY OF NIGERIA, ENUGU CAMPUS BY EMENIKE KALU ONWUKWE PG/MSC/06/45745 IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTERS OF SCIENCE (M.Sc.) DEGREE IN BANKING AND FINANCE SUPERVISOR: PROFESSOR C.U. UCHE 2009
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EMPIRICAL TEST FOR WEAK-FORM EFFICIENT MARKET
HYPOTHESIS OF THE NIGERIAN STOCK EXCHANGE
BEING A DISSERTATION PRESENTED TO THE DEPARTMENT OF BANKING AND FINANCE, FACULTY OF BUSINESS ADMINISTRATION,
UNIVERSITY OF NIGERIA, ENUGU CAMPUS
BY EMENIKE KALU ONWUKWE
PG/MSC/06/45745
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF
MASTERS OF SCIENCE (M.Sc.) DEGREE IN BANKING AND FINANCE
SUPERVISOR: PROFESSOR C.U. UCHE
2009
CERTIFICATION
The work embodied in this dissertation is original, except for references
specifically indicated in the text and such help as I have acknowledged, and has
not been submitted to any other tertiary institution for Degree purposes.
APPROVAL This dissertation by Emenike Kalu Onwukwe (PG/MSc/06/47547), presented to
the Department of Banking and Finance in the Faculty of Business
Administration, University of Nigeria, Enugu Campus, for the award of Masters of
Science in Banking and Finance, has been approved by:
------------------------------------------ ----------------------------- Professor Chibuike Ugochukwu Uche Date (Supervisor) -------------------------------------------- ----------------------------- Mrs. N.J. Modebe Date (Ag. Head of Department) -------------------------------------------- ------------------------------- Professor Uche Modum Date (Dean of faculty)
Dedication To God Almighty, for endless grace upon my life and actualization of my dreams
Acknowledgement In collecting materials for this research work and in actual writing of this report, I
incurred many debts of gratitude, which deserve to be specially acknowledged.
First, I wish to record my gratitude to my supervisor, Prof. C.U.Uche, who
introduced me to the world of international journals. When I first came to ask him
the area of his research interest so as to carry out a research there, he said: “go
and read at least 20 international journals with methodology and then you come
and discuss the topic you want to study with me”. That was the beginning of what
we have today as a completed research report. I am proud to complete this work
under his supervision.
I highly appreciate the valuable input from Mr. Arua Nnachi, Lecturer Department
of Banking and Finance, Ebonyi State University Abakaliki, for his
encouragement and assistance, especially in computation of the NSE stock
returns.
I am also greatly indebted to my great lecturer Prof. F.O.Okafor for the solid
foundation he gave me in Finance and Dr. J.U.J Onwumere for teaching us the
basic tenets of research. My thanks also go to a number of friends and
colleagues for their individual contributions to the successful completion of this
work: Chikezie Ogwo, Ochu Michael Chima, Solomon Nwankwoegu, Abali
Chimezie and HRH Eze Stephen Ogbu Iheke.
This acknowledgement will not be complete without giving due recognition to
Onwukwe Kalu and Ochulor Michael for their financial assistance during the
course of this study. University of Nigeria, Enugu Campus library deserve special
mention for providing JSTOR online journal depository which made it possible for
me to have access to the journals cited in this work.
Finally, the contributions of Eugene Fama and other intellectual authorities that
we cited in this study are duly acknowledged.
Abstract This research empirically tested the weak-form efficient market hypothesis of the
Nigerian Stock Exchange (NSE) by hypothesizing normality of the return
distribution series, random walk assumption and efficiency across time. Monthly
all share indices of the NSE were examined for normal distribution and random
walk from January 1993 to December 2007, as well as two sub-periods of
January 1993 to December 1999 and January 2000 to December 2007. Our
normality tests were made using skewness, kurtosis, Jarque-Bera and
studentized range tests; whereas weak-form efficiency was tested using the non-
parametric Runs test for both total and sub-sample periods.
The monthly return series, in aspect of skewness and kurtosis, were found non-
normal, which can be categorized as negative skewness for all periods and
playtykurtic distribution for total sample and sub sample2, while sub-sample1
showed leptokurtic distribution. Same thing resulted from J-B test and
studentized range. As a result, null hypothesis of normality in market returns was
rejected and the alternative hypothesis remained in effect. The results of the
Runs test for the observed returns show that the actual number of runs were
fewer than the expected number of runs for all periods examined, thus indicating
evidence of positive serial correlation in NSE monthly returns. The research
further provided evidence to show that improvements in market microstructure of
the NSE have positive effects on the weak-form efficiency of the NSE. Overall
results from the empirical tests suggest that the NSE is not weak-form efficient.
Relaxing institutional restrictions on trading securities in the market and
strengthening the regulatory capacities of NSE and Nigerian Securities and
Exchange Commission to enforce market discipline were recommended.
TABLE OF CONTENT Title page - - - - - - - - - i Certification - - - - - - - - - ii Approval Page - - - - - - - - iii Dedication - - - - - - - - - iv Acknowledgement - - - - - - - - v Abstract - - - - - - - - - vi CHAPTER ONE: INTRODUCTION 1.1 Background to the study - - - - - - 1
1.2 Statement of the problem - - - - - - 3
1.3 Objectives of the study - - - - - - 4
1.4 Research questions - - - - - - 5
1.5 Research hypotheses - - - - - - 5
1.6 Scope of the research - - - - - - 5
1.7 Significant of the research - - - - - - 6
1.8 Limitation of the study - - - - - - 6
1.9 Definition of terms - - - - - - - 7
References - - - - - - - - - 10
CHAPTER TWO: REVIEW OF LITERATURE 2.1 Theoretical Review - - - - - - - 14
Appendix II: Statistics and Runs Test for Total Sample - - 102
Appendix III: Computation of Monthly Returns for Sub-sample1 - 103
Appendix IV: Statistics and Runs Test for Sub-sample1 - - 105
Appendix V: Computation of Monthly Returns for Sub-sample2 - 106
Appendix VI: Statistic and Runs Test for Sub-sample2 - - - 108
CHAPTER ONE INTRODUCTION
1.1 BACKGROUND OF THE STUDY Efficient Market Hypothesis (EMH) asserts that in an efficient market, prices at all
times fully reflect all available information that is relevant to their valuation
(Fama, 1970). Thus, security prices at any point in time are unbiased reflection of
all available information on the security’s expected future cash flow and the risk
involved in owning such a security (Reilly and Brown, 2003:57). This implies that
investors can expect to earn merely risk-adjusted return from all investment as
prices move instantaneously and randomly to any new information (Kendal,
1953).
Market prices can at times deviate from the securities’ true value; these
deviations are completely random and uncorrelated. Price changes are only
expected to result from the arrival of new information. Given that there is no
reason to expect new information to be non-random, period-to-period price
changes are expected to be random and independent. In other words, they must
be unforcastable if they fully incorporate the expectations and information
available to market participants (Lo, 1997: xii).
Efficiency is categorized into three different levels according to the information
item reflected in the prices. The three levels of EMH are expressed as follows:
weak-form, semi–strong, and strong-form efficiency. The weak-form version of
EMH asserts that prices of financial assets already reflect all information
contained in the history of past prices, trading volume or short interest. Semi-
strong version postulates that stock prices already reflect all the publicly available
information regarding the prospects of a firm. Lastly, the strong-form posits that
the prices of financial assets reflect, in addition to information on past prices and
publicly available information, information available only to company’s insiders
(Fama, 1970; 1991).
Early studies on testing weak-form efficiency started on the developed markets,
generally agree with that stock markets are weak-form efficient based on low
degree of serial correlation and transaction costs (see for example, Kendal,
1953; Cootner, 1962; Fama, 1965). All of these studies support the proposition
that price changes are random and past prices were not useful in predicting
future price changes particularly after transaction costs were taken into
consideration.
However, there are some studies, which found the predictability of share price
changes (anomalies) in developed markets but did not reach a conclusion about
profitable trading rules (see, Fama and French, 1988; Lo and Mackinlay, 1988).
On the other hand, evidence of weak-form efficiency on the emerging markets
has been diverse. The first group found weak-form efficiency in emerging
markets (see, Olowe, 1999; Dickinson and Muragu, 1994; Chan et al., 1992).
The other group provide evidence showing that emerging markets are not weak-
form efficient (see, Appiah-Kusi and Menya, 2003; Cheung and Coutts, 2001;
Claessens et al, 1995; poshakwale, 1996; Ntim et al, 2007)
The empirical literature on the weak-form efficiency of the Nigerian Stock
Exchange (NSE) has, however, been very scanty despite the increase in size
and public participation in the market in recent times. The few exceptions to our
knowledge include Samuel and Yacout (1981), Ayadi (1984), Akpan (1995),
Olowe (1999), and Appiah–Kusi and Menya (2003). This dearth of research,
providing empirical evidence to support or dispute efficiency according to Simons
and Laryea (2004), may explain why many African countries have not attracted
much portfolio or equity investment as the Asian and Latin American countries.
This shortcoming has adversely affected the country’s rapid economic
transformations.
Hence, the need to provide further evidence on the weak-form efficiency of NSE
is of paramount interest to investors (individual and institutional), regulators,
academics, and the economy in general.
1.2 STATEMENT OF THE PROBLEM Nigeria seeks to become one of the twenty largest economies in the world in the
year 2020. The efficiency of stock market in this regard cannot be over-
emphasized, for long-term fund is a critical factor in the economic transformation
process. More so, stock markets afford investors the opportunity to diversify their
portfolios across a variety of assets. Given these importance of efficient stock
market, it is imperative to test the efficiency of the Nigerian Stock Exchange
(NSE), since the extent to which the NSE is efficient affects not only vision 2020
but all those who invest on the bourse; be they individual or institutional
investors.
Surprisingly, the NSE, which has been in operation since 5th July 1961, has had
a few prior empirical studies analyzing it and their conclusion as to the
predictability of future stock returns based on the past returns and volume traded
have been diverse. For instance, Samuel and Yacout (1981) and Olowe (1999)
found evidence of weak-form efficiency, whereas Akpan (1995) and Appiah-Kusi
and Menya (2003) found the market weak-form inefficient. The dearth of
empirical literature is not healthy for the country’s aspiration to become one of
the twenty largest economies in the world since polices that seek to attract
foreign portfolio investment should be informed by some empirical evidence on
the stock market efficiency.
Furthermore, market microstructure existing evidence suggests that improvement
in trading system, market capitalization, membership; value and volume traded
lead to improvements in liquidity and market efficiency (Amihud et al, 1997; &
Suzuki and Yasuda, 2006). The NSE has shown considerable improvements in
trading system. for instance, it established Central Securities Clearing System
(CSCS) in 1997 for clearing and settlement of securities transactions, changed
from call-over system to automated trading system in 1998 (Bellow, 2002).
Membership of the exchange increased from 194 in 1981, 260 in 2000 to 310 by
2007. Market capitalization also increased from N5 billion in 1981, N472.3 billion
in 2000 to N7, 764 billion by April 2007 (www.databank.sec.gov.ng ; NSE, 2005;
Bellow, 2002). Accordingly, it can be conjectured that there should be
commensurate improvements in market efficiency of the NSE.
Testing the absolute efficiency of a market does not seem to be the most
informative method of gauging the efficiency of a given market (Campbell, et al.,
1997:24). Relative efficiency – the efficiency of one market or one index,
measured against the other, appears to be a more useful concept than the view
taken by traditional literature. Even more useful will be the concept of measuring
a market’s efficiency across time to find if the level of efficiency has changed.
This is in accord with Rahman and Hossain (2006) conclusion that market
efficiency changes over time and that stock market is subject to be tested
continuously. This study will, therefore, examine the weak-form efficiency of the
NSE both in absolute and relative terms.
1.3 OBJECTIVES OF THE STUDY The major objective of this study is to examine whether the Nigerian Stock
Exchange is Weak-form efficient. The specific objectives are as follows:
1) To determine whether the stock returns in the NSE Follow normal
distribution.
2) To examine whether the stock returns in the NSE follow a random walk
over the time period of this study.
3) To compare weak-form efficiency evidence across time for the NSE.
1.4 RESEARCH QUESTIONS The answer to the following questions will guide us in collecting materials for this
research:
1) What is the distributional pattern of the NSE stock returns?
2) Are the stock returns in the NSE random over the time period of this
study?
3) What is the nature of the NSE weak-form efficiency across time?
1.5 RESEARCH HYPOTHESES
As a follow up to the research questions and objectives of this study, the
following hypotheses are tested:
Ho1 The stock returns in the NSE follow the normal distribution.
Ho2 The stock returns in NSE are random over the time period of this study.
Ho3 The NSE is weak-form efficient across time.
Though hypotheses of normality and randomness are complementary, we use
them simultaneously in order to establish the robustness of the analysis.
1.6 SCOPE OF THE RESEARCH This study will focus on empirical investigation of the weak-form efficiency
evidence on the NSE within the framework of the efficient market hypothesis. It
will cover period of fifteen years – from January 1993 to December 2007.This
period covers the aspect dealing with our statistical analysis. It is also the period
in which security pricing is deregulated in the Nigeria capital market.
1.7 SIGNIFICANCE OF THE RESEARCH This research empirically examines weak-form efficiency evidence on the
Nigerian Stock Exchange (NSE). It will be of significance to investors, regulators
and academics in the following ways.
To the investors: This study is timely especially now that share ownership is
gaining increasing popularity by the day in Nigeria. From its findings, investors
will formulate investment strategy for trading in the NSE. If, for instance,
evidence of weak-form efficiency does not hold for NSE, Investors can earn
abnormal profit by adopting active investment strategy since future share returns
can be forecasted from past returns, otherwise passive investment strategy my
be the best option (Bodie et al., 1999;337).
To The Regulators: This study will provide evidence that will assist Securities
and Exchange Commission (SEC) and NSE in formulating polices towards
improved performance, efficiency and development of the market.
To The Academics: This study will contribute to knowledge and the extant
literature to be referred to by researchers. It will also throw more light on the
empirical evidence on weak-form efficiency of the NSE and extend the existing
evidence by using recently available data. In addition, it will possibly spur other
research work aimed either sustaining or debunking its evidence.
1.8 Limitation of the Study This research focuses on empirical examination of the weak-form efficiency
evidence on the NSE within the framework of the efficient market hypothesis. As
with other studies of emerging stock markets, especially African stock markets,
we have to contend with data availability problems. Most of the available data are
on restricted basis as subscription to the relevant exchange is required to access
them. To represent the whole market, this study makes use of monthly market
indices rather than prices of individual securities. We also use longer sample
periods and therefore more data to combat thin and infrequent trading, which are
major sources of bias in such studies.
Aside data constraints, we also experienced financial difficulties which restricted
our subscription for access to data. However, these limitations could not affect
the outcome of reliable evidence on weak-form efficiency of NSE based on
current data and sound econometric model and analytical tools.
1.9 DEFINITION OF TERMS
Active Investment Strategy: Active investment strategy is an attempt to achieve
portfolio returns more than commensurate with risk either by forecasting broad
market trends or by identifying mispriced securities in the market (Bodie et al,
1999)
Anomalies: Anomalies are evidence that seem inconsistent with the efficient
market hypothesis.
Automated Trading System (ATS): ATS is a method of trading on quoted
securities using network of computers linked to each other (Bello, 2002).
Bull Market: A bull market is a market that is on the rise. It is typified by a
sustained increase in market share prices. In such times, investors have faith
that the uptrend will continue in the long term.
Call–Over System: Call over system of trading in securities is a system
whereby stockbrokers gather at the floor of the exchange at a particular time in
the morning, the listed securities are read out aloud while brokers indicate their
interest by shouting offer (for sale) or bid (to buy). The call–over clerk confirms
each deal (Bello, 2002).
Correction: A reverse movement, usually negative, of at least 15% in a stock,
bond, commodity or index. Corrections are generally temporary price decline,
interrupting an uptrend in the market or asset.
Filter Rule: Filter rule or technique is a rule for buying or selling a stock
depending on past movement of the stock.
Intrinsic Value: Intrinsic value is the value derived by evaluating and analyzing
performance indicators of a share. It denotes the best valuation of a share and
that the expected return is commensurate with associated risk of the share.
Market Microstructure: Market microstructure is concerned with the functional
set-up of a financial market. It deals with trading on financial assets such as
shares and bonds. It deals, also, on the manner in which financial assets are
traded and how that process affect the prices of assets traded, volume traded
and the behaviour of traders. It is also concerned with the efficiency and liquidity
of the markets.
New information: New information is any news, good or bad, that is yet to be
disseminated to the market participants.
Passive Investment Strategy: Passive investment strategy is buying a well-
diversified portfolio to represent a broad based market index without attempting
to search out mispriced securities (Bodie et al., 1999).
Random: Random here means that period-to-period price changes should be
statistically independent and unforcastable. Price movements result from
responses to information and since new information arrives unpredictably, price
changes should be unpredictable.
Risk-adjusted returns: Risk-adjusted return is the profit from stock trading
commensurate with the risk of the stock.
Serial Correlation: Serial correlation is the tendency for stock returns to be related to past returns. Stock Market Bubble: Stock market bubble occurs when a wave of public enthusiasm, evolving into herd behaviour, causes an exaggerated bull market.
REFERENCES
Akpan, O.E. (1995),” Thin and Thick Capital Market”, Nigerian Journal of Social
and Economic Research, 1, (37), 2-14.
Appiah-Kusi, J. and Menya, K. (2003), Return Predictability in African Stock
Markets”, Review of Financial Economics, 12, 247-270.
Ayadi, O. (1984), “Random Walk Hypothesis and the Behaviour of Stock Price in
Nigeria”, Nigeria Journal of Economics and Social Studies, 26 (1), 57-
71.
Amihud, Y.; Mendelson, H. and Lauterbach, B. (1997),” Market Microstructure
and Securities Values: Evidence From Tel Aviv Stock Exchange,” Journal
of Financial Economics, 45, 356-390.
Bello, I.B. (2002), “An Overview of the Nigerian Capital Market “A paper
Presented at the public enlightenment workshop on opportunities of the Nigerian Capital Market at Lokoja Kogi State.
Bodie, Z.; Kane, A. and Marcus, A.J. (2001), Investments (4th Ed.), Singapore:
McGraw Hill Inc.
Campbell, J.Y.; Lo, A. W. and Mackinlay, A.C. (1997), The Econometrics of
Financial Markets, New Jersey: Princeton University Press Princeton.
Chan. K.C.; Gup, B.E. and Pan, M. (1992), An Empirical Analysis of Stock Prices
In Major Asian Markets and United States,” The Financial Review, 27, (2)
May, 289-307.
Cheung, K.C. and Coutts, J.H. (2001), A Note on Weak Form Market Efficiency
in Security Prices: Evidence From the Hong Kong Stock exchange,”
Applied Economics Letters, 8. 407-410.
Claessens, S.; Dasgupta, S. and Glen, J. (1995),” Returns Behaviour in
Emerging Stock Market”, World Bank Economic Review, 9,131-152.
Cootner, P.H. (1962),” Stock Prices: Random Vs. Systematic Changes”,
Industrial Management Review, 3 (Spring), 24-45.
Dickinson, J.P. and Muragu, K. (1994), “Market Efficiency in Developing
Countries: A Case Study of the Nairobi Stock Exchange”, Journal of Business Finance and Accounting, 21 (1), 133- 150.
Fama, E. (1965), “The Behaviour of Stock Market prices”, Journal of Business, 38, 34-105.
Fama, E. (1970), “Efficient Capital Market: A Review of Theory and Empirical
Tests,” Journal of Finance, 25, 3852, 417.
Fama, E. (1991), “Efficient Capital Market: ll,” The Journal of Finance, 45, (5),
1575 -1617.
Fama, E. and French, K.R. (1988), “permanent and Temporary components of
Stock Prices”, Journal of Political Economy, 96 (Apirl), 24-73.
Hammed, A. and Asharf, H. (2006), “Stock Market Volatility and Weak Form
Efficiency Evidence from an Empirical Market”,
http://.pide.org.pk/psde/pdf/abs/%20hameedapdf.
Kendal. M. (1953), “The Analysis of Economic Time Series, part 1: prices”,
Journal of the Royal Statistical Society, Series A, 96, 11-25.
Lo, A.W. (1997), Market Efficiency Stock Behaviour in Theory and in
Practice, Cheltenhan: Edward Elgar Publishing Ltd.
Lo, A.W. and mackinlay, A.C. (1988) Stock Markets Do Not follow Random walk:
Evidence from A simple specification Test”, Review of Financial Studies,
Jensen (1978) believes that there is no other proposition in economics that has
more solid empirical evidence supporting it than the EMH. Nevertheless a survey
of the research carried out to date shows that although the majority of the
researchers could not reject the EMH, empirical findings range from acceptance
to complete rejection of the hypothesis. In essence there are varying degrees of
partial and sometimes cautioned acceptance and rejection (lo, 1997).
Given that failure to prove weak-form efficiency implies the failure to prove both
semi-strong and strong-form efficiency (Wong and Kwong, 1984). Most of the
researches carried out in emerging stock Markets have been confined to this
basic notion of efficiency. The weak-form basically asserts that price and volume
movements follow a random walk such that price changes are independent of
prior movements. Thus, the test for weak-form efficiency is often conducted by
testing for identifiable patterns in share price movements.
This section reviews various empirical studies on the weak-form efficiency of the
EMH. In so doing, we limit ourselves to a brief discussion of the different
approaches employed, the period and the general conclusions that have evolved.
A review of the research done on NSE is presented first, thereafter emerging
markets and developed markets follow respectively.
2.2.1 Weak-Form Efficiency of the NSE
The first published empirical research on the weak-form efficiency of the NSE is
apparently the study by Samuel and Yacout (1981), which used serial correlation
test to examine weekly price series of 21 listed Nigerian firms from July 1977 to
July 1979. The results show that the stock price changes are not serially
correlated but follow a random walk, thus accepting the notion of weak-form
market efficiency. In 1984, Ayadi tested the price behaviour of 30 securities
quoted on the NSE between 1977 and 1980, using Monday closing prices of
these shares after adjusting for cash dividends and script issues. The results
show that the share price movements on the NSE follow a random walk.
Anyanwu (1998) investigates the efficiency of the NSE from the perspective of
the market’s relationship to economic growth of the nation. He used indices of
stock market development – liquidity, capitalization, market size, among others –
to construct an aggregate index of stock market development and related it to the
long-run economic growth index, emphasizing the GDP growth rate. The results
show a positive association between the two indices and he therefore concludes
the NSE is efficient to the extent that it affects the economic development of the
Nation. Olowe (1999) examined evidence of weak-form efficiency of the NSE
using correlation analysis on monthly returns data of 59 individual stocks listed
on the NSE over the period January 1981 to December 1992. The results provide
support for the work of Samuels and Yacout (1981) and Ayadi (1984), that is, the
NSE is efficiency in the weak-form.
In contrast to the already cited works of Samuel and Yacourt, Ayadi and Olowe,
Akpan (1995) studied the informational efficiency of the NSE including the risk
implications of investing in the market, using time series data of stock market
price indices covering the period 1989 to 1992. His results show evidence to
reject the hypothesis of weak-form efficiency of the NSE.
In 2003, Appiah-Kusi and Menya apply the GARCH – M (Generalized
Autoregressive Conditional Heteroscedasticity) model to examine the weak-form
efficiency in weekly price series of eleven African stock markets indices. Their
results provide evidence showing that the stock markets in Egypt, Kenya,
Morocco, Mauritius and Zimbabwe are weak-form efficient, while those of
Botswana, Ghana, Ivory Coast, Nigeria, South Africa, and Swaziland are not
consistent with weak-form efficiency. Jeffris and Smith (2005) investigate the
changing efficiency of seven stock market indices from South Africa, Egypt
Morocco, Nigeria, Zimbabwe, Mauritius and Kenya. Using a GARCH approach
with time-Varying parameters, a test of evolving efficiency (TEE) is conducted for
period starting from February 1990 and ending in June 2001. This Tee test
detects changes in weak-form efficiency through time and it finds that the
Johannesburg stock market is weak-form efficient throughout the period, and
three stock markets become weak-form efficient towards the end of the period:
Egypt and Morocco from 1999 and Nigeria from early 2001. These contrast with
Kenya, Zimbabwe and Mauritius which show no tendency towards weak-form
efficiency.
Some practitioners and writers have also expressed their views that the NSE is
inefficient. They include Alice and Anao (1986), Akingbounde (1990) Odife
(1990), Osaze (1991) and Apampa (2008). These assertions are based on
personal Opinion, for they are not supported by any empirical study.
2.2.2 Weak-Form Efficiency of the Emerging Markets The research findings of weak-form efficiency on the emerging markets are
controversial. Most of the stock markets in emerging and developing economies
have been demonstrated to be inefficient even in the weak sense, while others
were found to be efficient. This diverse evidence have been found in African,
Asian and Latin American stock markets, often arising from size of the markets,
thinness of trading and quality of information disclosure (Mlambo et al., 2003).
Though it is generally believed that the emerging markets are less efficient, the
empirical evidence does not always support the thought.
2.2.2.1 Weak-Form Efficiency of African Markets In Kenya, Parkinson (1984) used serial correlation test to examine Monthly price
series of 30 listed firms in Nairobi Stock Exchange from 1974 to 1978. His results
show that stock price changes are serially correlated, thus rejecting the notion of
weak-form efficient market. Dickinson and Muragu (1994) apply Runs and serial
correlation tests, as well as spectral analysis to investigate whether weekly stock
price behaviour of 30 listed companies on Nairobi Stock Market are weak-form
efficient from 1979 to 1988. In contrast to the evidence of Parkinson (1994), their
results demonstrate that successive price changes are independent of each
other for the majority of the companies investigated.
In Ghanaian Stock Market, Dewotor and Gborglah (2004) Sought to establish
whether investors in Ghana can form profitable trading strategies based on the
information content of historical stock prices. They employed serial and cross-
sectional correlation test to ascertain the relationship between daily, monthly,
quarterly and yearly stock returns. The results show that stock returns are not
normally distributed in Ghana and that the daily and monthly stock returns are
positively serially and cross-sectionally correlated in a significant way. Quarterly
returns were insignificantly positively correlated and the yearly returns were
negatively correlated. In agreement with the evidence of Appiah-Kusi and Menya
(2003), their test results suggest that Ghana stock market is weak-form
inefficient. Ntim et al, (2007) empirically re-examine the weak-form efficient
market hypothesis of the Ghana stock market using both parametric and non-
parametric variance-ratio tests. Their main finding is that stock returns are
conclusively not efficient in the weak-form.
In Egypt, Mecagni and Sourial (1999) looked at the Egyptian Stock Exchange
using GARCH estimating techniques and found that the four best known daily
indices exhibited significant departure from efficient market hypothesis.
Simons and Laryea (2004) investigated the efficiency of four stock indices from
Egypt, Ghana, Mauritius, and South Africa from 1990 to 2003, applying serial
correlation, Runs, and the multiple Variance ratios tests. In agreement with
Mecagni and Sourial (1999) they found Egyptian stock Market weak-form
inefficient. They found also that apart from South Africa, the index price
behaviour of the Ghana and Mauritius Stock Markets were weak-form inefficient.
Mlambo, Biekpe and Smit (2003) also investigated the random walk behaviour of
stock returns on four African stock markets Egypt, Kenya, Morocco and
Zimbabwe. On all four markets, the hypothesis that stock returns are normally
distributed was rejected. Almost half of the stocks on each of the four markets
showed significant positive serial correlation and there was therefore not enough
evidence to accept the hypothesis of a random walk.
In South Africa, Jammine and Hawkins (1974) applied serial correlation test to
examine the random walk properties on the Johannesburg Securities Exchange
over the period 1966 to 1973 using weekly changes in price indices. They
concluded that technical analysis could be used to profit since price changes did
not follow a random walk. In 1977, Gillberston and Roux investigated if there are
any trading rules that can be demonstrated to perform better than a simple buy-
and-hold strategy. The found that a buy-and-hold strategy consistently
outperformed the four trading rules that they tested on 24 shares. They
concluded that the dependencies in price changes were too small to be profitably
exploited; therefore there was not enough evidence to reject the weak-form
efficiency.
2.2.2.2 Weak-Form Efficiency of the Asian Markets Empirical studies on weak-form efficiency in Asian market have been extensively
conducted in recent years. Chan et al (1992), use unit not and cointegration tests
to examine the relationships among the stock markets in Hong King, South
Korea, Singapore, Taiwan, Japan, and the United States. The findings suggest
that the stock prices in major Asian markets and the United States are weak-form
efficient.
In Chinese Markets, Mookerjee and Yu (1999), investigate the weak-form
efficiency of daily stock price indices of shanghai and Shenzhen stock exchange
for the period from December 19, 1990 to December 17, 1993 and from April 3
1991 to December 17, 1993 respectively. The autocorrelation, Runs and unit root
tests results reject the weak-form efficiency of both stock exchanges. Similarly,
Groenewold et al. (2003) document that these market (Shanghai and Shenzhen
Stock Exchanges) are not weak-form efficient using autocorrelation and unit root
tests on daily returns for seven indices of the exchanges for the 1992 – 2001
period. In addition, Lima and Tabak (2004) using Variance ratio test on daily
stock price index of shanghai and Shenzhen (China), Hong Kong, and Singapore
Stock Exchanges over the period from June 1992 to December 2000. The results
support weak-form efficiency for Hong Kong and A Shares for both Shanghai and
Shenzhen Stock Exchanges. But reject it for Singapore Stock Exchange and B
shares of the China Stock Exchange. However, seddighi and Nian (2004)
document that the Shanghai Stock Exchange is weak-form efficient for the period
from June 4, 2000 to December 31, 2000.
In Taiwan, Fawson et al. (1996) find the Taiwan Stock Exchange (TSE) weak-
form efficient using autocorrelation and Taylor’s Binomial Distribution test on
monthly stock market returns for the index of TSE during the period between
January 1967 and December 1993.
In a like manner, Alam et al. (1999) using variance ratio test on monthly return
data for market index of Hong Kong, Malaysia, Taiwan, Sri Lanka and
Bangladesh covering from November 1986 to December 1995, find all the
markets weak-form efficient, except Sri Lanka.
For Hong Kong Stock Market, Cheung and courts (2001) apply variance ratio test
to investigate whether daily stock market indices for Hong Kong Stock Exchange
are weak form efficient from January 1, 1985 to June 30, 1997. Their results
provide evidence supporting weak form efficiency.
Hammed and Ashraf (2007) apply GARCH model to test whether daily closing
prices for the Pakistani Stock Market are efficient in the weak-form from
December 1998 to March 2006. Their results reject the notion of weak-form
efficiency of the Pakistani Stock Market.
In 2006, Rahman and Hossain seek evidence whether Dhaka Stock Exchange
(DSE) is efficient in the weak-form or not by hypothesizing normality of the
distribution series and random walk assumption, Runs, Lilliefors and
autocorrelation tests as well as ARIMA were used on all share price indices and
the general price indices for 12 years ranging from 1994 to 2005. The overall
results suggest that the DSE of Bangladesh is not efficient in weak-form.
Barnes (1986) apply serial correlation and Runs tests to examine if the monthly
stock price series of 30 individual stocks and 6 sector indices on Kuala Lumpur
Stock Exchange are weak-form efficient for the 6 years ended June 30, 1980. He
documents evidence that the market is efficient in the weak-form (only a few
individual stocks do not follow the random walk process).
Sharman and Kennedy (1977), utilize Runs test and spectral analysis to
determine whether monthly stock price index for Bombay follow a random walk
for the period from 1963 to 1973. The study find that the stock price changes on
the Bombay Stock Exchange follow a random walk; hence it is weak-form
efficient. However, Poshakwale (1996) investigates the weak-form efficiency and
the day of the week effect in Bombay Stock Exchange over a period of 1987 –
1994. His results provide evidence of the weekend effect as the returns on
Fridays are significantly higher compared to the rest of the week, and that the
stock market is not weak-form efficient.
Abraham et al (2002) show evidence to reject the hypothesis of weak-form
efficiency for stock markets in Sri-Lanka, Kuwait, Saudi Arabia and Bahrain
during the period between October 1992 and December 1998 by applying
variance ratio and Runs tests to weekly market index of exchange. Their results
reject weak-form efficiency for the Gulf stock markets when the observed indices
are used, but cannot be rejected when infrequent trading of these markets is
corrected.
2.2.2.3 Weak-Form Efficiency of Emerging European Markets
Regarding the emerging markets of Europe, Wheeler et al. (2002) apply
Autocorrelation and Runs tests to investigate whether daily returns series of 16
individual stocks listed on Warsaw Stock Exchange (Poland) are weak-form
efficient from 1991 to 1996. The empirical evidence fails to support the null
hypothesis of weak-form efficiency for the market.
In 1997, Dockery and Vergari use variance ratio test to determine whether the
weekly stock market index of Budapest Stock Exchange are weak-form efficient
from Jun. 1991 to May 1995. Their results suggest that the exchange is efficient
in the weak-form.
Buguk and Brorsen (2003) show empirical evidence to support the null
hypothesis of weak-form efficiency for the stock market in Turkey, by using unit
root and Variance ratio tests to examine the weekly market index of the Istanbul
Stock Exchange’s composite industrial and financial index for the period 1992 to
1999.
2.2.2.3 Weak-Form Efficiency of Latin American Countries In Latin America, Urrutia (1995) apply variance ratio and Runs tests to
investigate whether the monthly and daily indexes for the markets in Argentina,
Brazil, Chile, and Mexico follow a random walk from December 1975 to March
1991. He provides mixed evidence on the weak-form efficiency for these stock
markets. Specifically, results for variance ratio test reject the random weak
hypothesis for all markets while findings from the runs tests indicate that these
markets are weak-form efficient. Consistent with the results reported by Urrutia
(1995), Grieb and Reyes (1999) show empirical findings, which are obtained from
variance ratio tests from December 30 1988 to June 30 1995, to reject the
hypothesis of random walk for all stock market indexes and individual stocks in
Brazil and Mexico. In a related development, Karemera et al. (1999) find that
stock return series in Brazil, Chile, and Mexico do not follow the random walk,
based on the result of single variance ratio tests, but Argentina does. However,
when the multiple variance ratio test is applied, the market index returns in Brazil
is observed to follow the random walk process (the others are not changed).
In Romania, Bogdan et al. (2007) apply Chow’s Breakpoint test and cointegration
analysis to examine whether financial sector index of the Bucharest Stock
Exchange (BET-FI) is informational efficient from October. 31, 2000 to October
12, 2007. Their results confirm informational efficiency, in its weak-form of the
BET-FI. But the results of the all share index show weak-form inefficiency.
2.2.3 Weak Form Efficiency of Developed Markets
The first statement and test of the random walk model, according to Fama
(1970), was that of Bachelier in 1900. His fundamental principle for the behaviour
of prices was that speculation should be a fair game, i.e., the expected profits to
the speculator should be Zero. After Bachelier, research on the behaviour of
security prices lagged until the advent of computer. In 1953, Kendal examined
the behaviour of weekly changes in nineteen indices of British industrial share
prices and in spot price for cotton in New York and wheat in Chicago from 1928
to 1938. After extensive analysis of serial correlation, he suggests, in quite
graphic terms:
“The series look like a wandering one, almost as if once a week the Demon of chance drew a random number from a symmetrical population of fixed dispersion and added it to the current price to determine the next week’s price”.
Thus, suggesting that common stock price changes are not serially correlated but
follow random walk. Cootner (1962) investigated the random walk model using a
sample of 45 stocks all drawn from the New York Stock Exchange (NYSE). Five
of the series covered a ten-year period; 40 were weekly observations for 5 years
(1956 to 1960) period. His tests of the autocorrelation of weekly stock price
changes show deviations from random behaviour, but the deviations are
uniformly small. He concluded that trading strategy based on history of prices
cannot outperform buy and hold strategy when transaction cost is considered.
Similarly Fama (1965) tests empirically the random walk model of stock price
behaviour using daily prices for each of the thirty stocks of the Dow Jones
industrial Average (DJIA). The time period range from stock to stock but covered
from the end of 1957 to September 26, 1962. The results of runs and
autocorrelation tests show that there is no evidence of substantial Linear
dependence between lagged price changes or return. In absolute terms, the
measured serial correlations are always close to zero. This implies that past
returns are not useful in forecasting future price changes particularly after
transaction costs are considered.
Alongside random walk tests other researchers investigated whether trading
strategies and rules designed to exploit identifiable patterns are effective.
Alexander (1961) tests a variety of systems, but the most thoroughly examined
can be described as follows:
if the price of a security moves up at least 5%, buy and hold the security until its
price moves down at least 5% from a subsequent high, at which time
simultaneously sell and go short. The short position is maintained until the price
rise at least 5% above a subsequent low, at which time one covers the short
position and buys. Moves less than 5% in either direction are ignored. Such a
system is called a 5% filter. After extensive tests using daily data on price indices
from 1897 to 1959 and filters from one to fifty percent, Alexander concludes: “Infact, at this point I should advise any reader who is interested only in practical results, and who is not a floor trader and so must pay commissions, to turn to other sources on how to beat buy and hold”
The conclusion that the filter rule cannot beat buy and hold is support for the
EMH. Further support is provided by Fama and Blume (1966) who compare the
profitability of various filters to buy-and-hold for the individual stocks of the Dow-
Jones Industrial Average. They demonstrate that even though prices do not
literally follow a random walk, the degree of non randomness is insufficient for
investors to trade profitably after transaction costs. Dryden (1970) found similar
results in United Kingdom. Jensen sand Bemington (1970) who tested other
trading strategies developed to exploit price trends also concluded that no trading
strategy could be demonstrated to outperform a simple buy-and-hold strategy.
2.2.4 Predicable Patterns in Developed Stock Markets There are, however, some studies which found predictability of share price
changes in developed markets but did not reach a conclusion about profitable
trading rules. An example of such study is poterba and summers (1988) who
applied variance ratio tests to market returns for the United States over the 1871-
1986 period and for seventeen other countries over the 1957-1985 period as well
as to returns on individual firms over the 1926 -1985 period. Their results show
consistent evidence that stock returns are positively serially correlated over the
short horizons, and negatively autocorrelated over long horizons. Also, Lo and
Mackinlay (1988) find that weekly returns on portfolios of NYSE stocks grouped
according to size (stock price multiplied by outstanding shares) show reliable
positive autocorrelation. The autocorrelation is stronger for portfolio of small
stocks. In the same vein, Fama and French (1988) examine the mean-reverting
components of stock prices from 1926 to 1985. They find that the autocorrelation
is weak for the daily and weekly holding periods but stronger for long-horizon
returns. They concluded that large negative autocorrelation for long-horizons
beyond a year suggest that predictable price variation due to mean reversion
account for large transactions of 3-5 years return variances. Conrad and Kaul
(1988) examine weekly returns of NYSE stocks. Their results provide support for
the work of Lo and Mackinlay (1988) and Poterba and Summers (1988), that is,
positive serial correlation over short horizons. Thus, while these studies
demonstration weak price trends over short periods, the evidence does not
clearly suggest the existence of trading opportunities (Bodie et al., 1999: 345).
2.2.4.1 Fads Hypothesis Although studies of short-horizon returns have detected positive serial correlation
in stock market prices, tests of long-horizon returns (i.e., returns over multi year
periods) have found suggestions of pronounced negative long term serial
correlation (Poterba and summers, 1988; Fama and French, 1988). The latter
result has given rise to a FADS hypothesis, which asserts that stock prices might
overreact to relevant news. Such overreaction leads to positive serial correlation
over short time horizons. Subsequent correction of the overreaction leads to poor
performance following good performance and vice versa. The correction mean
that, a run of positive returns eventually will tend to followed by negative returns,
leading to negative serial correlation over longer horizons (Bodie et al., 1999:
345).
2.2.5 Return Anomalies In financial theory and in practice criticism is often made through highlighting
various anomalies in actual behaviour of securities prices. Various researchers
have empirically tested these predictable patterns in share prices. Their results
reveal a number of the so called anomalies, that is, evidence that seems
inconsistent with the efficient market hypothesis. These anomalies in stock
returns include: the small-firm- effect, neglected-firm effect, reversal effect, book-
to-market effect and day-of-the-week effect.
2.2.5.1 The Small – Firm - In January Effect
One of the most important anomalies with respect to the efficient market
hypothesis is the so called size or small firm effect, originally documented by
Banz (1981). Banz divided all NYSE stock into five quintiles according to firm
size and He found that the average annual return of firms in the smallest size
quintile was 19.8% greater than the average return of firms in the largest size
quintile. He concluded that both total and risk adjusted rates of return tend to fall
with increases in the relative size of the firm as measured by the market value of
the firms outstanding equity.
Later study by Keim (1983) shows that the small firm effect occurs virtually
entirely in January, in the first two weeks of January. To illustrate the January
effect, keim ranked firms in order of increasing size as measured by market value
of equity and then divided them into 10 portfolio grouped by size of each firm. In
each month of the year he calculated the difference in the average excess return
of firms in smallest firm portfolio and largest firm portfolio over the years 1963 to
1979. He found that January clearly stands out as an exceptional month for small
firms with an average small firm premium of .714% per day and 8.16% for the
first five trading days.
2.2.5.2 The Neglected Firm Effect As evidence for the neglected firm effect, Arbel (1985) measured the information
deficiency of firms using the coefficient of variation of analysts’ forecasts of
earnings. The correlation coefficient between the coefficient of variation and total
return was .676. In a related test Arbel divided firms into highly research,
moderately researched and neglected groups based on the number of institutions
holding the stock. His results show that the January effect was the largest for the
neglected firms.
2.2.5.3 Reversal Effect
Debondt and Thaler (1985) and Chopra et al. (1992) find Strong tendencies for
poorly performing stocks in one period to experience sizeable reversals over the
subsequent period, while the best performing stocks in a given period tend to
follow with poor performance in the following period. Debondt and Thaler rank
ordered the performance of stocks over a five year period and then group stocks
into portfolio based on investment performance, the base period loser portfolio
(35 stock with the worst investment performance) outperformed the winner
portfolio (the top 35 stock) by an average of 25% cumulative return in the
following three year period. This reversal effect suggests that the stock market
over reacts to relevant news. After the overreaction is recognized extreme
investment performance is reversed. This phenomenon would imply that a
contrarian investment strategy - investing in recent losers and avoiding recent
winners - should be profitable.
A later study by Ball et al. (1995), however, suggests that the reversal effect may
be an illusion. They showed that if portfolio is formed by grouping based on past
performance periods ending in mid-year rather than in December, the reversal
effect is substantially diminished.
2.2.5.4 Book-to-Market Effect Fama and French (1992) and Reinganum (1988) show that a powerful predictor
of returns across securities is the ratio of the book value of the firm’s equity to the
market value of equity. Fama and French stratified firms in 10 groups according
to book-to-market ratios and examined the average monthly rate of return of
each of the 10 groups during the period July 1963 through December 1990. Their
results show that the deciles with the highest book to market ratio has an
average monthly return of 1.65% while the lowest ratio deciles averaged only
.72% per month. They concluded, after controlling for size and book to market
effects, that beta seemed to have no power to explain average security returns.
However, a study by Kothari et al (1995) finds that when betas are estimated
using annual rather than monthly returns, securities with high beta values do in
fact have higher average returns. They concluded that the empirical case for the
importance of the book to market ratio may be somewhat weaker than the Fama
and French study would suggest.
2.2.5.5 The Day of the Week Effect The day of the week effect is another anomaly which has been found in most
developed market and even in some emerging markets. French (1980) apply the
OLS method with dummy variables for each day of week on daily returns of the
S&P 500 for the period between 1953 and 1977. His results show significant
negative Monday effect and positive Wednesday, Thursday and Friday effect
similarly, Jaffe and Westerfield (1985) use the OLS method with dummy
variables for each day of the week on daily returns for stock market index of
Japan, Canada, Australia, the U.K. and U.S. (S&P 500) during the period of 1970
– 1983, 1976 – 1983, 1973-1983, 1950-1984, and 1962-1983 respectively. Their
results show significant negative Monday effect in the U.S., Canada and the
U.K., negative Tuesday effect in Japan and Australia and the U.K., and positive
Friday effect in all the markets, except the U.K.
On the emerging markets, Wong et al (1992) examine the daily data for stock
indexes of Singapore, Malaysia, Hong Kong, Taiwan and Thailand over the 1975
to 1988 period using non-parametric tests for the difference in mean returns
across days of the week. They find negative Monday effect in Singapore,
Malaysia and Hong Kong negative Tuesday effect in Thailand, and positive
Friday effect in the four markets. Poshakwale (1996) finds the weekend effect
evident as the returns on Fridays are significantly higher compared to the rest of
the week on the Bombay stock exchange. In 2000, choudlry found significant
negative Monday mean return in Indonesia, Malaysia and Thailand, negative
Tuesday mean return in South Korea, Taiwan and positive Friday mean return in
India, Malaysia, the Philippines and Thailand.
There is a consensus among researcher on negative Monday effect and positive
Friday effect in both developed and emerging market.
2.2.5.6 Why Anomalies Lakonishok et al., (1995) asserts that anomalies are evidence of inefficient
market, more specifically, of systematic errors in the forecasts of stock analysts.
They believe that analysts extrapolate past performance too far into the future,
and therefore overprice firms with recent good performance and under price firms
with recent poor performance. Ultimately, when market participants recognize
their errors, prices reverse. This explanation is consistent with the reversal effect
and also, to a degree, consistent with the small firm and book to market effects
because firms with sharp price drops may tend to be small or have high book to
market ratio.
A study by La Porta (1996) is consistent with this pattern. He finds that equity of
firms for which analysts predict low growth rates of earnings actually perform
better than those with high expected earnings growth. Nevertheless, a
compelling explanation of these effects is yet to be offered.
2.3 THE SYNTHESIS OF RELATED LITERATURE It is evident from this review of literature that there are two schools of thought
about weak-form efficient market Hypothesis. On the one hand, one of them
argues that markets are efficient and returns are unpredictable. The works of
Kendal (1953), Fama (1965), Dickinson and Muragu (1994), Ojah and Karemera
(1999), are some of the works which largely conclude that the stock market is
efficient. On the other hand, the works of Banz (1981), Arbel (1985), Poterba and
Summers (1988), Lo and Mackinlay (1988), etc, document empirical evidence of
anomalies that appeared to contradict the theory of efficient markets. Among
other findings, stocks returns are found to be negative from close of trading on
Friday to the close of trading on Monday (Day of the week effect). The average
annual returns of small firms are greater than the average returns of large firms
(small firm effect) and the small firm effect occurs virtually entirely in January
(January effect). Moreover, poorly performing stocks in one period experience
sizeable reversal over the subsequent period, while the best performing stocks
in a given period tend to follow with poor performance in the following period
(Reversal effect) and a powerful predictor of returns across securities is the ratio
of the book value of firm’s equity to the market value of equity (Book to market
effect).
From this review, also, it is clear that the empirical literatures on the weak-form
efficiency of the NSE are few and these few existing literature produced mixed
evidence. While some researchers provide evidence showing that the NSE is
weak-form efficient, others debunked such evidence. The researchers who found
evidence of weak-form efficiency on the NSE are Samuel and Yacout (1981),
Ayadi (1984), Anyanwu (1998) and Olowe (1999); Whereas Akpan (1995) and
Appiah-Kusi and Menya (2003) found the NSE weak-form inefficient. While these
studies on the NSE offered evidence by analyzing the price series of samples of
individual stocks, none of them used a long-time period which, according to
Rahman and hossain (2006), reduces the problem of infrequent trading bias.
Also, these studies use parametric tests without hypothesizing the distribution of
market returns for normality, which is one of the conditions for using parametric
methods. Furthermore, none of these studies compared weak-form efficiency
across time for the NSE.
This study seeks to fill these lacunas by empirically examining the weak-form
efficiency evidence on the NSE. In doing so, it will make significant contribution
to the extant literature. Firstly, it will provide empirical evidence using the monthly
All Share Index of the NSE and will also use a longer–time period. These will
overcome the weakness associated with infrequent trading of some of the
individual stocks. Secondly, it will examine the distribution of the excess stock
returns for normality and a non–parametric test will be used to analyze data so
that non–normal distribution will not bias findings. Thirdly, unlike prior studies
which test absolute efficiency of the NSE, it will compare efficiency across time
for the NSE. Finally, this study will extend the existing evidence by using recently
available data. Thus, this study will not only overcome the weaknesses of the
earlier studies, but will also establish the true position of the NSE with respect to
the level of its weak-form efficiency.
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