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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.
----------------------------- Emenike Kalu Onwukwe (PG/MSc/06/47547)
--------------------------------- Date
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
2.1.1 Efficient Market Hypothesis (EMH) - - - - 14
2.1.1.1 Assumptions of EMH - - - - - 15
2.1.2 Versions of EMH - - - - - - - 15
2.1.2.1 The Weak-form Hypothesis - - - - 15
2.1.2.2 The Semi-Strong Form Hypothesis - - - 16
2.1.2.3 The Strong Form Hypothesis - - - - 16
2.1.3 The Models of EMH - - - - - - 17
2.1.3.1 The Expected Return or Fair Game Model - - 17
2.1.3.2 The Sub martingale Model - - - - 18
2.1.3.3 The Random Walk Model - - - - - 19
2.1.4 Technical Analysis - - - - - - - 20
2.1.5 Fundamental Analysis - - - - - -- 21
2.1.6 The Nigeria Stock Exchange (NSE) - - - - 23
2.1.6.1 History of the NSE - - - - - - 23
2.1.6.2 Functions of the NSE - - - - - 25
2.1.6.3 Trading on the NSE - - - - - 25
2.1.6.4 Transaction Costs on the NSE - - - - 26
2.1.6.5 Clearing and Settlement on NSE - - - - 27
2.1.6.6 Trade Guarantee Fund (TGF) - - - - 28
2.1.6.7 Electronic Bonus - - - -- 28
2.1.6.8 Trade Alert - - - - - - - 28
2.1.6.9 All-Share Index of the NSE - - - - 29
2.1.6.10 Growth of the NSE - - - - - - 29
2.1.6.11 Size of the NSE - - - - - - 30
2.1.6.12 Liquidity of the NSE - - - - - 31
2.1.6.13 Information Dissemination on the NSE - - - 32
2.1.6.14 Regulation of the NSE - - - - - 33
2.1.6.15 Internationalization of the NSE - - - - 35
2.2 Empirical Review - - - - - - 36
2.2.1 Introduction - - - - - - - 36
2.2.2 Weak-form Efficiency of the NSE - - - - - - 36
2.2.3 Weak-form Efficiency of Emerging Markets - - - 38
2.2.3.1 Weak-form Efficiency of African Markets - - 39
2.2.3.2 Weal-form Efficiency of Asian Markets - - - 40
2.2.3.3 Weak-form Efficiency of Emerging European Markets - 43
2.2.3.4 Weak-form Efficiency of Latin American Markets - 43
2.2.4 Weak-form Efficiency of Developed Markets - - - 44
2.2.4.1 Predictable Pattern in Developed Stock Markets - 46
2.2.4.2 The Fads Hypothesis - - - - - 47
2.2.5 Return Anomalies - - - - - - 47
2.2.5.1 Small Firm in January Effects - - - - 48
2.2.5.2 The Neglected Firm Effect - - - - - 48
2.2.5.3 The Reversal Effect - - - - - 49
2.2.5.4 The Book to Market Effect - - - - - 49
2.2.5.5 The Days of the Week Effect - - - - 50
2.2.5.6 Reasons for Anomalies - - - - - 51
2.3 The Synthesis of Related Literature - - - - 51
References - - -- - - - - 54
CHAPTER THREE: RESEARCH METHODOLOGY 3.1 Introduction - - - - - - - - 64
3.2 Research Design - - - - - - - 64
3.3 Model Specification - - - - - - - 65
3.4 Nature and Sources of Data - - - - - 66
3.5 Description of Research Variables - - - - 66
3.6 Techniques of Data Analysis - - - - - 67
References - - - - - - - - - 71
CHAPTER FOUR: DATA PRESENTATION, ANALYSIS AND
INTERPRETATION 4.1 Introduction - - - - - - - - 73
4.2 Test for Normality for Nigerian Stock Exchange - - 74
4.3 Runs Test for NSE Monthly Returns - - - - 76
4.4 Relative Weak-form Efficiency for the NSE - - - 77
4.5 Effects of the Market Microstructure on the NSE - - 79
Reference - - - - - - - - - 80
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATION 82
5.1 Introduction - - - - - - - - 82
5.2 Summary of the Research - - - - - - 82
5.3 Conclusion - - - - - - - - 83
5.4 Recommendation for Policy Thrust - - - - 84
5.5 Recommendation for Further Studies - - - - 85
Reference - - - - -- - - - - 86
BIBLIOGRAPHY - - - - - - - - 87
TABLES Table 2.1 NSE Growth Trend 1961-2007 - - - - - 29
Table 2.2 NSE Fifteen Years Performance Summary - - - 30
Table 2.3 Size of the NSE - - - - - - - 31
Table 2.4 Liquidity of the NSE - - - - - - 32
Table 4.1 Monthly All Share Index of the NSE - - - - 73
Table 4.2 Results of Normality Tests for the NSE Monthly Stock Returns 75
Table 4.3 Result of Runs Test for Weak-form Efficiency on NSE - 77
Table 4.4 Results of Runs Tests for Relative of the NSE - - 78
APPENDICES Appendix I: Computation of NSE Monthly Stock Returns - - 98
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.
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CHAPTER TWO REVIEW OF LITERATURE
2.1 THEORETICAL REVIEW 2.1.1 EFFICIENT MARKET HYPOTHESIS (EMH)
The Efficient Market Hypothesis (EMH) states that in an efficient market, stock
prices adjust so quickly to new information that:
(a) Security prices fully reflect all available information.
(b) Successive changes in security prices are in dependent
(Okafor,1983:186; Fama, 1970).
Defining efficient market in this way has the following important implications:
(1) An investor cannot use available information to earn non-zero
abnormal returns.
(2) In an efficient market, when new information is added to the
information set, its revaluation implications are unbiased and
instantaneously impounded into the current market prices (Rahman
and Hossain, 2006).
(3) The real financial position of a company will in the long run always be
reflected in company’s share price. If management makes a positive
investment decision, and is made known to the public security price will
always reflect the manager’s action (Ibenta, 2005: 247).
(4) In a situation where the market is efficient, investor’s choice is passive
portfolio strategy, but in a not so efficient market, the key element to
investors’ choice is active portfolio management to enable them detect
and exploit perceived departures from efficiency (Bodie et al.,
1999:337).
2.1.1.1 ASSUMPTIONS OF EMH Fama (1970) postulates that the sufficient but not necessary conditions for
market efficiency are:
(i) There are no transactions costs in trading securities,
(ii) All available information is costlessly available to all market
participants, and
(iii) All agree on the implications of current information for the current price
and distributions of future prices of each security.
2.1.2 VERSIONS OF EMH
It is common to distinguish among three versions of the EMH: the weak-form,
semi-strong form, and strong-form of the hypothesis. These versions differ by
their notion of what is meant by the term “all available information.”
2.1.2.1 The Weak Form Hypothesis The weak-form hypothesis posits that stock prices already reflect all information
that can be derived by examining market trading data such as the history of past
prices, trading volume or short interest (Bodie et al., 1999: 331). Weak-form
efficiency also means that unanticipated return is not correlated with previous
unanticipated returns. In other words, the market has no memory; knowing the
past does not help to earn future returns. This version of EMH implies that trend
analysis is fruitless. Past stock price data are publicly available and virtually
costless to obtain. This version holds that if such data ever conveyed reliable
signals about future performance, all investors would have learned already to
exploit the signals. Ultimately, the signals lose their value as they become widely
known because a buy signal, for instance, would result in an immediate price
increase. In a weak-form efficient market, past prices and volume data are
already impounded in security prices and no amount of chart reading or any
other trading device is likely to consistently outperform the buy and hold strategy.
2.1.2.2 The Semi – Strong Form Hypothesis This version, according to Okafor (1983: 186), States that stock prices already
reflect not only historical information but also all published information about the
company whose securities are under consideration. Such information includes
fundamental data on the firm’s product line, quality of management, balance
sheet composition, patents held, earning forecasts, and accounting practices.
Again, efforts to acquire and analyze such information from publicly available
sources would confer no advantage.
In a semi-strong efficient market, investors would have no publicly available
source of information that could lead them to consistently beat the market. Of
course, they could expect to make profit in the market, but their profit would be
commensurate with the riskiness of their investment. However, such activities as
analyzing financial statements forecasting earnings, and following advice of a
popular investment newsletter would not contribute to increased investment
returns and might even lower returns by increasing costs while not adding to
profit.
2.1.2.1 The Strong Form Hypothesis The strong-form version of EMH posits that stock prices reflect all information
relevant to the firm, even including information available only to company
insiders’. This implies that those who have access to privilege information about
companies, or those who have access to relevant information, such as security
analysts, portfolio managers and floor specialists, cannot use such information to
earn abnormal profits (Okafor, 1983: 186). The corporate insiders, their relatives
and any associate who trades on information supplied by insiders are considered
in violation of the law (Bodie et al., 1999: 322).
2.1.3 The Models of EMH The definitional statement of EMH is that prices ‘fully reflect’ all the available
information. To verify this, the process of price formation has to be specified in
model form, in order to define more precisely the empirical implication of fully
reflect. Fama (1970), Suggested three models for testing weak-form efficiency:
the Expected Return or Fair Game model, the Submartingale model, and the
Random walk model.
2.1.3.1 The Expected Return or Fair Game Model In general, the Fair Game Model States that a stochastic process Xt with the
condition on information set t, is a fair game if it has the following property:
E (Xt +1 / t) = 0 …………………………………............ (2.1)
In the case of stock market, Fama (1970) introduced a model of EMH that is
derived from the fair game property for expected returns and expressed it in the
following equations:
Xj,t+1 = Pj,t+1 – E(Pj,t+1 / t) …………………………………………. (2.2)
With E (Xj,t+1 / t) = E{Pj,t+ 1 – (Pj,t+ 1/ t)} = O …………………..( 2.3)
Where: Xj,t+ 1 is the excess market value of security J at time t + 1, Pj, t + 1 is the
observed (actual) price of security J at time t + 1, and E (Pj, t+1 / t ) is the
expected price of security J that was projected at time t, conditional on the
information set t or equivalently:
Zj, t + 1 =rj, t+1 – E (rj,t+1 / t) ………………………………….(2.4)
With E (Zj,t+1 / t) = E(rj,t+1 – (rj,t+1 / t)} = 0…………………(2.5)
Where: Zj,t+1 / t is the unexpected (excess) return for security J at time t+1, rjt+1 is
the observed or actual return for security J at time t+1, and E(rj,t+1 / t) is the
equilibrium expected return at time t+1 on the basis of the information set t.
This model implies that the excess market value of security J at time t+1 (Xj,t+1) is
the difference between actual price and expected price on the basis of the
information set t. similarly, the excess return for security J at time t + 1 (Zj,t+1) is
measured by the difference between the actual and expected return in that
period conditioned on the set of available information at time t. according to the
fair game model, the excess market value and the excess return are Zero. In
other words, equation 2.3 and 2.5 indicate that the excess market value
sequence (Xj, t+1) and (Zj,t+1) respectively are fair game with respect to the
information sequence t.
2.1.3.2 The Submartingale Model The Submartingale Model is the fair game with small adjustment in expected
return. In this model, the expected return is considered to be positive instead of
Zero as in the fair game model. The adjustment implies that prices of securities
are expected to increase overtime. In other word, the returns on investments are
projected to be positive due to the risk inherent in capital investment. The
Submartingale model can be estimated as:
E (Pj, t+1 / t) Pjt or equivalently E (rj, t+1 / t) 0 …………………….. (2.6)
This model states that the expected return sequence (rj,t+1) follows a
Submartingale with respect to the information set t, which is to say that the
expected return for the next period, as projected on the basis of the information
set t, is equal to or greater than Zero (Fama, 1970). The important empirical
implication of the Submartingale model is that no trading rule based on the
information set t can have greater expected returns than a strategy of always
buying and holding the security during the future period in question.
2.1.3.3 The Random Walk Model The financial asset’s price series is said to follow a random walk if the successive
price changes is independent and identically distributed (Fama, 1970). However,
in practice, according to Ntim et al. (2007), a stock price is said to follow a
random walk if successive residual increments are independent and identically
distributed (IID). This implies that future price changes cannot be predicted from
historical price changes. Campbell et al., (1997:33), Demonstrate that a financial
assets price series is said to follow a random walk if:
Pt = + Pt-1 + t, t IIDN (0, σ 2) ……………… (2.7)
Where:
Pt = Securities price under consideration
= Drift parameter (i.e. the expected price change)
t = Random error term (residual)
IIDN (0, σ2) = Independent and identically distributed as a normal distribution with
zero mean and homoscedastic variance.
The main essence of the random walk model is that the price changes during
period t are independent of the sequence of price changes during previous
period. Fama (1970) argue that the random walk model is an extension of the fair
game model. Specifically, the fair game model just indicate that the conditions of
market equilibrium can be stated in terms of expected returns while the random
walk gives details of the stochastic process generating returns. Therefore, he
concluded that empirical tests of the random walk model are more powerful in
support of EMH than test of the fair game model.
2.1.4 Technical Analysis
The basic assumption of all the technical theories is that history tends to repeat
itself, that is, past patterns of price behaviour in individual securities will tend to
recur in the future. Thus, the way to predict stock prices (and, of course, increase
one’s potential gains) is to develop a familiarity with past patterns of price
behaviour in order to recognize situation of likely reoccurrence (Fama, 1965).
Technical analysis, according to Ibenta (2005:225) is the science of recording,
usually in graphic form, the actual history, that is, price changes and volume of
transactions in a particular stock or in the averages and then, deducing the
probable future trend. Technical analysts believe that prices are determined by
demand and supply. In the view of Bodie et al. (1999: 331), technical analysis is
essentially the search for recurrent and predictable patterns in stock price.
Okafor (1983: 167) identifies the following major tenets of the technical approach:
(1) The price of securities is determined by forces of demand and
supply.
(2) Demand and supply forces are influenced by both rational and
irrational factors.
(3) Movements in stock prices tend to follow identifiable,
systematic, self sustaining and recurring trends.
(4) Market trends constitute solid foundations on which profitable
trading rules can be erected.
Based on these tenets, technical analysts study the market conditions for
predicting demand and supply as well as price movements believing that
successive price changes in individual securities are dependent. Essentially they
attempt to use knowledge of the past behaviour of a price series to predict the
probable future behaviour of the series.
Technical analysts are sometimes called chartists because they study records or
charts of past stock prices hoping to find patterns they can exploit to make a
profit. The various chartist theories assume that the sequence of price changes
prior to any given day is important in predicating the price change for that day.
The techniques of the chartists have always been surrounded by a degree of
mysticism, they may draw lines connecting the high and low prices for the day to
examine trends in the prices, draw more complex patterns such as breakaway,
head or shoulders or resistance and support levels, which are believed to convey
clear buy or sell signals.
Although the technicians recognize the value of information regarding future
economic prospects of a firm, they believe that such information is not necessary
for a successful trading strategy. This is because whatever the fundamental
reason for a change in stock price, if the price responds slowly enough; the
analyst will be able to identify a trend that can be exploited during the adjustment
period (Bodie et al., 1999: 332).
2.1.5 Fundamental analysis
Fundament analysis involves an estimate of the intrinsic value of a security by
evaluating the basic financial and economic facts about the company that issues
the security (Ibenta, 2005: 221). It uses earnings and dividend prospects of the
firm, expectations of future interest rates, and risk evaluations of the firm to
determine proper stock prices or intrinsic value. Fundamental analysis represents
an attempt to present discounted value of the payments a shareholder will
receive from each share of stock (Bodie et al., 1999: 336).
Okafor (1983: 121) identifies the following assumptions of the fundamental
approach:
(1) Every security has an intrinsic value.
(2) The intrinsic value of every security is reflected by its market price
(3) Basic economic facts about a firm determine the intrinsic value of
securities issued by it.
Given these assumptions, the fundamental analysts usually start with a study of
past earnings, an examination of company financial statements and the quality of
the firm’s management. They supplement this analysis with further detailed
economic analysis, ordinarily including an evaluation of macroeconomic indices,
the firm’s standing within its industry, and the prospects for the industry as a
whole. The hope is to attain insight into future performance of the firm. Through a
careful study of these fundamental factors, the analyst arrives at an estimate of
the intrinsic value of the company’s stock.
Once the intrinsic value is determined, it is then compared with the current
market price. If the current market price is below the intrinsic value, a buy
recommendation will be issued as it is underpriced. On the contrary, when the
market price is higher than its intrinsic value, the share is perceived to be over
priced. The investor is advised to sell such share (Ibenta, 2005: 221).
Thus, attempting to determine the intrinsic value of security is equivalent to
making a prediction of its future price, and this is the essence of the prediction
procedure implicit in fundamental analysis. The amount of discrepancy and
speed with which the market value approaches an intrinsic value may be
regarded as indications of the degree of efficiency in the market. There will be no
discrepancies between intrinsic and market value of securities, if the market is
efficient. Hence there will be little or no opportunity for an investor to consistently
beat the market and make abnormal profit.
2.1.6 The Nigerian Stock Exchange (NSE) Nigeria has two exchanges, namely the NSE and the Abuja Security and
Commodity Exchange (ASCE). The ASCE is a market for trading in derivatives
and commodities but the derivative segment of the market is not functioning yet.
The NSE is the only stock exchange operating in Nigeria with 10 branches
nationwide. There is a statutory provision for the operation of more than one
stock exchange, but only the NSE is in operation. Since its formation, the NSE
has gone through vast changes, including numerous changes in branches,
trading system, membership among others while some of the changes are
presented in this review, the review is by no means exhaustive. Attention is given
to changes that are believed to have impacted on the efficiency of trading,
reduced costs of trading and increased the exchange’s efficiency in information
dissemination. Changes that affect weak-form efficiency as postulated in EMH
take centre stage. It must again be noted that the main focus is not on the
chronology but on the purpose of these changes, or rather, their purported effect
on market efficiency.
2.1.6.1 History of the NSE The history of the NSE dates back to the late 50s when the federal ministry of
industries set up the Barback committee to advise it on the ways and means of
setting up a stock market. The committee recommended the creation of facilities
for transacting in shares, establishment of rules regulating transfer, reduction or
elimination of stamp duties on transfer and elimination of tax deduction at source,
as well as measures to encourage savings, and issuing of securities by the
government and other organizations (Nwankwo, 1980).
Following the Barback report, the Lagos stock exchange was incorporated under
the company’s ordinance as an association limited by guarantee in September
1960 by a group comprising four frontline businessmen and three financial
institutions under the inspiration of the federal government and the Central Bank
of Nigeria. With the enactment of the Lagos Stock Exchange Act in 1961, it
started business on 5th June 1961 (Okafor, 1983).
The Lagos stock exchange was re-organized and renamed the Nigerian stock
exchange in 1977 following the Okigbo financial system Review committee report
of 1976. This committee recommended the establishment of two independent
stock exchanges in addition to the Lagos stock exchange. The Government
rather approved the establishment of the Nigerian Stock Exchange (NSE) but
with trading floors at Lagos (head office), Port Harcourt, and Kaduna (Okafor,
1983). However, the number of functional trading floors has increase to ten and
includes Kano, Onitsha, Benin, Uyo, Yola, Ibadan, and Abuja (Okereke-Onyiuke,
2000; NSE, May 07).
The NSE operated as the only stock exchange in Nigeria until 1998 when the
Government approved the establishment of the Abuja stock Exchange (ASE).
The ASE was initially designed as a stock exchange but had its scope expanded
to trade in commodities and securities (derivatives) (Ndanusa, 2003). With the
expansion in its scope, ASE was converted to Abuja security and commodity
exchange (ASCE).
The NSE introduced the second – Tier securities market (SSM) on 30th April,
1985 primarily to attend to the needs of small and medium-size enterprises which
cannot meet the strict listing requirements of the first-tier market. In effect, it
provides an avenue for smaller companies to access public issues for expansion.
2.1.6.2 Functions of the NSE Nwankwo (1980) summarizes the functions of the NSE as follows:
(i) To provide a central meeting place for members to buy and sell
existing stocks and shares and for granting quotation to new one.
(ii) To provide opportunities for raising new or fresh capital
(iii) To provide machinery for mobilizing private and public savings and
making these available for productive investment through stocks and
shares.
(iv) By facilitating the purchase and sale of securities, to help reduce the
risk of illiquidity. Ideally, this function should encourage more
investments in stocks and shares since investors are sure that in case
of need, they could realize their investments easily and with minimum
loss and in many cases at a profit in the exchange.
(v) Through its rules, regulations and operational codes and practices, to
protect the public from shady dealings and practices in quoted
securities with the objective of ensuring fair dealings
(vi) To act as channel for implementing the indigenization Decree by
providing facilities for foreign businesses to offer their shares to the
Nigerian public for subscription.
(vii) To the extent that it is a condition for specified existing or new
companies, to have a percentage of shares owned by Nigerians and to
be extent that the firms prefer to do this through public issues, provide
opportunities for continuous operation and attraction of foreign capital
for Nigeria development.
2.1.6.3 Trading on the NSE
During the mid-90s, there was need to bring about transformation on the NSE as
competitive pressure from revolutionary technological developments in the
financial market across the globe was being felt. The pressure to reduce costs
and develop more efficient and transparent trading method was increasing with
the globalization of financial markets and the improvements in communication
systems. In line with global developments, in 1998, the call-over system of
trading in securities was closed to give way to an Automated Trading System
(ATS) (Bello, 2002). Thus, the NSE is a fully automated exchange with on-line
floor trading of securities (Ndanusa, 2003). The floor trading is being replaced
with a remote trading system, which would see stockbrokers execute trades from
their offices. Remote trading requires a good level of information technology,
which many operators have attained.
NSE open for trading from 9.30 am to 3.30 pm every week day.
2.1.6.4 Transaction Cost on the NSE
Transaction cost is the cost an investor will incur in buying or selling securities. It
is usually charged in percentage and paid as commission. Different rates of
commission are fixed for securities listed on the NSE. According to Okafor
(1983:94), commission vary from 1/32 to 3/8 per cent of the total consideration
for government securities and 1 ¼ per cent of the total consideration or market
capitalization (whichever is higher) for industrial securities. Before April 2007,
transaction cost on equities in the primary market was 6.92 per cent, while that of
bonds was 7.03 per cent. But on April 24, 2007, they were reduced to 4.32 and
4.79 per cent respectively. Similarly, transaction costs on equities in the
secondary market were reduced, as cost on the buy side has been reduced from
4.07 to 2.36, while commission on sales fell from 4.12 per cent to 2.65 per cent
(Chuks, 2007; Nwaora, 2007; Thani, 2007).
Dealers are prohibited from accepting less than the prescribed commission.
Contravention attracts a fine, suspension or expulsion, as may be determined by
the council of the exchange.
2.1.6.5 Clearing and settlement on NSE Clearing and settlement is done electronically through Central Securities
Clearing System (CSCS). The CSCS, provided by NSE, is the Central Securities
Depository (CSD) for the Nigerian capital market. CSCS was incorporated on
July 29, 1992, declared open on April 8, 1997 and commenced operation on April
14, 1997 (NSE, July 2007). The CSCS started with T + 5 settlement cycle in
1997, but operates a T + 3 cycle from March 1, 2000. This has had a
considerable effect on liquidity.
CSCS also performs the following functions and activities in NSE: online stock
transaction detail confirmation / enquires, dematerialization of share certificates,
use of shares in CSCS system as collateral for loan, ensuring that financial
aspect of settlement is handled by central Bank of Nigeria through its agent
called Nigeria inter-Bank settlement system (NIBSS), and provision of electronic
bonus (NSE July 2005). By the end of 2006, there are 1, 136, 998 shareholders
in the CSCS system. From 1997 to 2006, CSCS has cleared and settled 120.3
billion units of shares worth over N1.249 trillion. In the same period, CSCS
dematerialized 5.4 million share certificates. These 5.4 million share certificates
represent 134.7 billion units of shares. 5143 shareholders have used their
shareholding as collateral for loan since inception of CSCS to December 2006
(NSE, Jan. 2007).
The CSCS has ushered in a new era of clearing and settlement, which did not
only boost the NSE’s competitiveness in the international financial markets, but
also improved Nigeria’s standing in terms of settlement and operational risk in
the region. CSCS was admitted and registered as a member of Africa and Middle
East Depository Association (AMEDA) in 2006 at Marrakech, Morocco (NSE,
Jan. 2007). The trading and settlement system in NSE has clearly improved with
the introduction of ATS and CSCS. They are innovations in the stock market
which facilitates all the operational procedure in the transfer of shares between a
seller and buyer on a timely basis (Englama et al., 2004).
2.1.6.6 Trade Guarantee fund (TGF)
Trade Guarantee fund was established by the dealing member firms of the NSE
to further ensure all financial settlement of stock transactions. Under the system,
CSCS Ltd was mandated to open nominee account in each of the stock market
settlement banks. Currently each dealing member firm makes a one-time
contribution of N 100,000 (one hundred thousand naira) to the fund. The fund
grows by way of interest and penalty charges. The fund ensure that there will be
no cancellation of trades (NSE, Sept. 2007)
The TGF as at year ended 2006, stands at N29.47 million.
2.1.6.7 Electronic Bonus E-bonus refers to electronic form of bonus shares. It means that when a quoted
company declares script or bonus issues, rather than issues physical bonus
share certificates to investors, they are converted to electronic form and credited
to the investors’ stock account in the CSCS depository under the investors stock
broking firm account with CSCS and a credit advice sent to the investor through
the company registrar. Securities and Exchange Commission (SEC) in
consultation with NSE, formally announced the approval and commencement of
e-bonus in the NSE effective July 1, 2005 (NSE, Feb., 2007)
2.1.6.8 Trade Alert
Another massive leap in NSE with the sole aim of protecting investors interest is
the trade alert. Trade alert is an electronic-driven devise that monitors daily share
transaction on all the trading floors of the NSE and thereafter notify buyers and
sellers of the details of such transactions on their GSM phones. In essence,
inbound, outbound, authorized, unauthorized transactions are forwarded to the
GSM phones of subscribers for their information and necessary action.
2.1.6.9 NSE All-Share Index
The Exchange maintains an All-Share Index formulated in January 1984
(January 3, 1984 = 100). Only ordinary shares are included in the computation of
the index. The index is a value-relative and is computed daily. By December
1994, the index has grown to 5092.2, 24088.8 by 2004 and 57990.2 by 2006
(http://www.databank.sec.gov.ng).
2.1.6.10 Growth of the NSE The Nigeria Stock Exchange (NSE) has witnessed unprecedented growth in
market capitalization, value and volume traded, and number of listed securities
since inception. Trading commenced with 2 Federal Government Development
stocks, 1 preference share and 3 domestic equities in 1961. By 1971, it grew to
60 securities and 310 in 2007. The value of securities traded was N 2.3m in
1961, N 332.1 million in 1971 and N 2068542.63 million in 2007. Table 2.1 below
provides a snapshot of the 10 year interval up to 2007 and table 2.2 illustrates
the performance in the last fifteen years.
Table 2.1 NSE’s Growth Trend 1961-2007
Years No of listed
securities
Market
capitalization
(N million)
Value of securities
traded (N million)
1961 13 Na 2.3
1971 60 Na 18.1
1981 163 5.0 332.1
1991 239 23.1 136.2
2001 261 662.5 57,637.2
2007 310 13295 2068542.6
Source: http://www.databank.sec.gov.ng
Table 2.2 NSE’s fifteen years performance summary
Years No of listed
Securities
Market
capitalization
(N Billion)
Value of securities
traded ((N million)
1993 272 47.5 402.3
1994 276 66.3 569.7
1995 276 180.4 103.8
1996 276 288.8 7067.7
1997 264 281.9 11072.0
1998 264 262.6 13572.4
1999 268 300.0 14027.4
2000 260 470.3 28154.6
2001 261 662.5 57637.2
2002 258 764.9 60088.7
2003 265 1359.3 102703.0
2004 272 2112.5 225820.5
2005 288 2900 262929.64
2006 288 5121.0 470253.80
2007 310 13295.0 2068542.63
Source: http://www.databank.sec.gov.ng
2.1.6.11 Size of the NSE A major performance indicator is the size of the stock market. Size, in the
literature, is more appropriately measured by looking at market capitalization
relative to GDP, that is, the value of listed share divided by GDP (Masha, et al.,
2004). The statistic about the size of the market is summarized in table 2.3
below.
Table 2.3 Size of NSE
Years Size of NSE (market capitalization /
GDP) %
1993 6.9
1994 7.4
1995 9.3
1996 10.6
1997 10.1
1998 9.7
1999 9.4
2000 10.4
2001 9.6
2002 9.8
2003 13.1
2004 18.5
2005 19.8
Source: http://www.databank.sec.gov.ng
2.1.6.12 Liquidity of NSE
Liquidity generally refers to the ability to easily buy and sell securities. Savers are
very often unwilling to place their saving in financial instruments for long period of
time. Yet many profitable capital rejects require a long term commitment of funds.
Liquid stock markets therefore, allow companies on the one hand to have a
permanent access to capital through equity issues and, on the other, allow
savers to switch out of equity if they need access to their fund or if they want to
change the composition of their portfolio. Masha et al. (2004), proposed two
different measures to compare stock market Liquidity across countries: trade
value divided by GDP (%) and the turnover ratio (%). Total value traded ratio
measures the market capitalization as a percentage of the GDP while turnover
ratio equals value to market trading as a percentage of the market capitalization.
Liquidity of the NSE is summarized in table 2.4 below:
Table 2.4 Liquidity of NSE
Years (Turnover ratio %)
1993 0.7
1994 0.7
1995 1.0
1996 2.5
1997 3.9
1998 5.1
1999 4.8
2000 6.0
2001 8.9
2002 7.9
2003 8.6
2004 11.6
2005 10.1
Source: http://www.databank.sec.gov.ng
2.1.6.13 Information dissemination on the NSE The need to keep the market adequately informed cannot be overemphasized.
Investors use information on companies, in particular, and the economy in
general to make their investment decisions. This information is disseminated into
the market through company announcements, announcement or release of
fiscal and monetary policies, NSE publications such as monthly stock market
review. In addition, the market/quote prices, along with the All-Share Index, are
published daily in The Stock Exchange Daily Official list, The Nigerian Stock
Exchange CAPNET (an intranet facility), The Nigerian Stock Exchange website
(www.nigerianstockexchange.com), Newspapers and on the stock market page
of the Reuter Electronic Contributor System. The on-line code in the Reuters
Network is NSXA-B (http://en.wikipedia.org/wiki/NigerianStockExchange).
Information dissemination in NSE, according to Dada (2003; 35), is through
computer networks: CAPNET and Reuters Electronic contributor system and the
mass media (TV, Radio and Newspaper).
2.1.6.14 Regulation of the NSE For a stock exchange to operate successfully, investors must have confidence
that they can deal at genuine and fair prices, and that the market is not
manipulated to their disadvantage. A proper regulatory framework that is adhered
to by all market participants, and is enforced by the appropriate regulatory
authorities, brings about this confidence and integrity. The regulatory bodies of
the NSE consist of the Securities and Exchange Commission (SEC), Nigerian
Stock Exchange (NSE), Central Bank of Nigeria (CBN) and Federal Ministry of
Finance (FMF). The following are the statutes that have provisions for guiding the
operations of the NSE:
(i) The Lagos Stock Exchange Act, 1961;
(ii) Trustee Investment Act, 1962
(iii) Companies and Allied Matters Act, 1990
(iv) Banks and other Financial Institutions Act, 1991
(v) Nigerian Investment Promotion Act, 1995
(vi) Foreign exchange (Miscellaneous Provisions) Act, 1995
(vii) Securities and Exchange Commission Act, 1999
(viii) Investment and Securities Act (ISA), 1999
Transactions on the Exchange are regulated by the NSE, as a self regulatory
organization (SRO), and the Securities and Exchange Commission (SEC), which
administers the Investment & Securities Act, 1999. Prior to the enactment of this
Act, the market was regulated by the Securities and Exchange previous statute
of 1979. The Objectives of the ISA are to protect investors and uphold the
integrity of the market by ensuring, orderliness, fairness, efficiency and
transparency in securities trading (Ndanusa, 2003). The ISA established the SEC
as the apex regulatory body for the Nigeria capital market. The SEC is the lead
regulator of the market and the administrator of the securities law. The Act,
amongst other things, empowers the commission to:
(i) Regulate investments and securities business in Nigeria.
(ii) Register all securities to be offered to the public for sale or
subscription.
(iii) Register stock exchanges, commodity exchanges and capital trade
point.
(iv) Register clearing and settlement companies’, custodian and
depositories.
(v) Register all market operators such as stock brokers, registrars, issuing
houses, investment advisers, portfolio managers and capital market
consultants such as solicitors, accountants estate valuers etc.
(vi) Register all securities to be traded on the exchanges.
(vii) Regulate mergers, acquisition and all forms of business combinations.
(viii) Regulate collective investment schemes including pension funds,
venture capital and Esusu (Ndanusa, 2003).
The commission interprets the law, makes rules and regulations and ensures
compliance with a view to achieving its objectives.
As a means complementing the efforts of the SEC, the NSE performs self
regulation. It functions through a number of committees; the most important are
quotation, administrative proceeding, disciplinary and surveillance committees.
The quotation committee is saddled with the responsibility of analyzing and
scrutinizing the application from companies seeking quotation of the exchange. It
ensures that such companies comply with the disclosure requirements and
adheres to accounting standard in the preparation of their financial statements.
The surveillance committee performs oversight function as it monitors the
operation of the market with a view to ensuring that operating guidelines are
adhered to.
In the interest of self- regulation the ISA requires the NSE to draft its own rule
book which must be approved by SEC. The NSE has the authority and discretion
to alter the trading period, close, suspend or halt trading, or take any such steps
necessary to maintain an orderly market. The rules also detail the security
procedure, reporting procedure and resources required by members to ensure
the efficiency and integrity of the market.
2.1.6.15 Internationalization of the NSE Following the deregulation of the capital market in 1993, the Federal Government
in 1995 internationalized the capital market, with the abrogation of laws that
constrained foreign participation in the Nigerian capital market.
Consequent upon the abrogation of the Exchange Control Act of 1962 and the
Nigerian Enterprises promotion Decree of 1989, foreigners can now participate in
market both as operators and investors. Also, there are no limits any more to the
percentage of foreign holding in any company registered in the country.
Ahead of this development, the Exchange had since June 2, 1987, linked up with
Reuter Electronic Contributor System for online global dissemination of stock
market information – trading statistics, All-Share Index, company investment
ratios, and company news (financial statement and corporate actions).
In November, 1996 the Exchange launched its internet system (CAPNET) as one
of the infrastructural support for meeting the challenges of internationalization
and achieving service delivery
(http://en.wikipedia.org/wiki/NigerianStockExchange).
2.1 EMPIRICAL REVIEW
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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CHAPTER THREE RESEARCH METHODOLOGY
3.1 INTRODUCTION Researchers have looked at the Concept of Market Efficiency in various ways
and analyzed it using different models. The hypothesis has been tested using
different statistical techniques and in different markets over different time periods.
The volume of research in this area has led to numerous advances in both
theoretical modeling and statistical analysis surrounding the EMH. However,
despite all these advances it still appears that the EMH is not yet empirically well
defined.
The statement that prices fully reflect all the available information is theoretically
robust, but to make it empirically operational one has to specify the process of
prices formation in a model. The empirically testable model of EMH includes too
many assumptions such that the ultimate test for EMH have becomes a test for
several ancillary hypothesis as well (Lo, 1997: xvii).
This chapter is therefore concerned with the methods and procedure to be
employed in testing the weak-form EMH on NSE.
3.2 RESEARCH DESIGN Research design according to Onwumere (2005:115) is a kind of blueprint that
guides the researcher in his or her investigation and analysis. To kerlinger
(1983), it aims at providing answers to research questions and controlling
variances. Asika (1991:27) however, posits that no type of research design ever
gives secondary data. Consequently this study will adopt empirical research
design, since it will use secondary data.
Empirical research design according to moody (2002) is a class of research in
which observations or data are collected in order to answer particular research
questions. Wikipedia sees empirical research as any research that bases its
findings on direct or indirect observation as its test of reality
(http://en.wikipedia.org/wiki/empirical-research). These definitions tell us that
empirical research is research which is based on observed phenomena and it is
research that derives knowledge from actual experience rather than from theory
or belief.
This research, therefore, uses time series data derived from actual observation of
the NSE monthly all share Index to investigate the weak-form efficiency of the
market.
3.3 MODEL SPECIFICATI0N The random walk hypothesis posits that in an efficient market, successive price
changes follow a random walk. In practice, however, a stock price series is said
to follow a random walk if successive residual increments are independent and
identically distribution (IID), (Ntim et al; 2007). This means that future price
changes cannot be predicted from historical price changes. The random walk
model is given as:
Pt = + Pt-1 + t, t IIDN (0, σ 2) ……………… (3.1)
Where:
Pt = Securities price under consideration
= Drift parameter (i.e. the expected price change)
t = Random error term (residual)
IIDN (0, σ2) = Independent and identically distributed as a normal distribution
With zero mean and homoscedastic variance
This model indicates that the price of a share at time (month) t is equal to the
price of the share at time (month) t-I plus the residual value that depends on new
(unpredictable) information arriving between time t-1 and t.
3.4 NATURE AND SOURCES OF DATA
Secondary data is used in this research. The data primarily consist of monthly all
share index (ASI) of the NSE. The ASI is a value weighted index made up of all
listed equity on the NSE. The use of monthly ASI for this study is in line with
some of the previous empirical studies on weak from efficiency on emerging
stock markets around the world (See, Urrutia, 1995; Olowe, 1999; Simon and
Laryea, 2004; Branes, 1986; Claessens et al., 1995; Rahman and Hossain,
2006). The period under consideration for the ASI begins from January 1993,
and ends on December 2007. This yields a total of 180 time series observations,
lagged one month. The data is downloaded from Securities and Exchange
Commission (SEC) databank in its website: http://www.databank.sec.gov.ng
Other data for this study are extracted from NSE Annual reports and other
publications, CBN annual reports and other publications, SEC annual reports and
other publications, FOS monthly economic indicator and annual abstract of
statistics.
3.5 Description of research variables This study uses monthly returns as individual time series variables. The monthly
returns are computed from the NSE monthly indices without adjustment of
dividend, bonus and right. Many researchers confirm that their conclusions
remain unchanged whether they adjust their data for dividend or not (see for
example, Lakonishok and Smidt, 1988; Fishe et al., 1993). Monthly market
returns are calculated from the monthly all share price indices as follows:
Rmt = Ln(Pt) – Ln(Pt-1) …………………………………………………… (3.2)
Where:
Rmt = Monthly returns for ASI for period t
Pt = Monthly AS1 for period t
Pt = monthly AS1 for period t – 1.
Ln= Natural Logarithm
A key assumption underpinning our use of logarithm is that stock returns are not
only log-normal, but also are traded on a continuous basis (Ntim et al, 2007;
Bodie et al, 1999:170).
3.6 Techniques of data analysis The techniques of data analysis involve descriptive analysis, normality tests and
non-parametric runs test. Our descriptive analysis will be made using
percentages, mean, median, mode, minimum and maximum returns. Normality Tests: Random Walk hypothesis posits that in an efficient market,
successive residual increments follow the normal distribution (Ntim et al., 2007).
It is therefore necessary to investigate the extent to which the return series on
NSE approximates a normal distribution. Normality tests will be performed using
skewness, the kurtosis and the Jarque-Bera (JB) test.
Skewness is a measure of asymmetry of the distribution of the series around its
means. Skewness is computed as:
S = E(∆Yi - ∆Ȳ)3 / σ3 ……………………………………. (3.3)
Where, S is the skewness, ∆Yi is the first differenced NSE return series, ∆Ȳ is
the mean of the observations and σ3 is the cube of the Standard deviation.
The Skewness of a symmetric distribution, such as the normal distribution, is
zero (0). Positive skewness means that the distribution has a long right tail and
negative skewness implies that the distribution has a long left tail.
Kurtosis measures the peakedness or flatness of the distribution of the return
series, it is computed as:
K = E(∆Yi - ∆Ȳ)4 / σ4 …………………………………….. (3.4)
Where, K is the kurtosis and other variables are as described above. The
Kurtosis of a normal is 3. If the kurtosis exceeds 3, the distribution is peaked
(Leptokurtic) relative to the normal; if the Kurtosis is less than 3, the distribution is
flat (Platykurtic) relative to normal.
JB test is a statistics for testing whether or not a series is normally distributed. It
measures the difference of the skewness and kurtosis of a series with those from
a normal distribution.
JB test is estimated as:
JB = n [S2 /6 + (K-3)2 /24]
Where: n = sample size
S = skewness coefficient, and
K = kurtosis coefficient.
For a normally distributed series, S=0 and K=3. Therefore, the JB test of
normality is a test joint hypothesis that S and K are 0 and 3 respectively. Under
the null hypothesis of normality in distribution, the JB is equal to 0. Positive or
negative JB value indicates evidence against normality in series.
Our non-parametric analysis will be made using runs test. The runs test is a non
parametric test designed to examine whether or not an observed sequence is
random. It has, extensively, been used by former researchers of weak-form
efficiency in emerging markets. (See, Branes, 1986; Claessens et al., 1995;
Dickinson and Muragu, 1994, Simon and Laryea, 2004; Rahman and Hossain,
2006. It is based on the premise that if a series of data is random, the observed
number of runs in the series should be close to expected number of runs. If there
are two many runs, it would mean that the residuals change sign frequently, thus
indicating negative serial correlation. Similarly, if there are too few runs, they may
suggest positive autocorrelation (Gujarati, 2003:465). Implicitly, too many runs
and or few runs indicate evidence against the hypothesis of random walk
(Spiegel and Stephens, 1999: 405).
Under the null hypothesis of independence in share returns, the expected
number of runs can be estimated as:
M = 2N1N2 + 1 ………………………………. (3.5)
N
Where:
N = Total number of observation (N1+N2)
N1 = Number of + symbols (i.e. + residuals)
N2 = Number of – symbols (i.e. - residuals)
M = Expected number of runs
For a large number of observations (N > 30), the sampling distribution of M is
approximately normal and the variance is given by:
σ2 m = 2N1N2 (2N1N2 - N) …………………………… (3.6)
(N)2 (N -1)
The standard normal Z statistics which will be used to test whether the actual
number of runs is consistent with the hypothesis of independence is given by:
Z = R-M …………………………………………… (3.7)
m
Where: R = the actual number of runs. We will accept the null hypothesis of
randomness at 5% significance level if – 1.96 ≤ Z ≤ 1.96, and reject it otherwise.
For the last hypothesis, the runs test will be computed for two Sub-periods of
January 1993 – December 1999 and January 2000 – December 2006. We will
accept the null hypothesis that the NSE is weak-form efficient across time if the
number of observed runs is close to the expected number of runs for both sub-
samples and reject it otherwise.
REFERENCES Asika, N. (1991), Research Methodology in Behavioural Science, Lagos:
Longman.
Branes, P. (1986), “Thin Trading and stock Market Efficiency: A Case of the
Kuala Lumpur Stock Exchange”, Journal of Business Finance and
Accounting, (4), 609-617.
Claessens, S.; Dasgupta, S. and Glen, J. (1995),” Returns Behaviour in
Emerging Stock Market,” The World Bank Economic Review, 9,131-
152.
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.
Fishe, R.; Gosnell, T. and Lasser, D. (1993), “Good News, Bad News, Volume
and the Monday Effect”, Journal of Business Finance and Accounting, 20, 881-892.
Gujarati, D.N. (2003), Basic Econometrics (4th Ed), Delhi: McGraw Hill Inc.
Lackonishok, J. and Smidt, S. (1988), “Are Seasonal Anomalies Real? A Ninety
Year Perspective” Review of Financial Studies, 1, 435-455.
Lo, A.W. (1997), Market Efficiency Stock And Behaviour in Theory and in
Practice, Cheltenhan: Edward Elgar Publishing Ltd.
Moody, D. (2002), “Empirical Research Methods”,
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Ntim, C.G. Opong, K.K. and Danbolt, J. (2007), “An Empirical Re-Examination of
the Weak Form Efficient Market Hypothesis of the Ghana Stock Market
Using Variance Ratio Test,” African Finance Journal, 9 (2).
Olowe, R.A. (1999), “Weak Form Efficiency of the Nigerian Stock Exchange
Market: Further Evidence”, African Development Review, 11 (1) 54-68.
Onwumere, J.U.J. (2005), Business and Economic Research Methods, Lagos: Don-Vinton Ltd.
Rahman, S. and Hossain M.F. (2006), “Weak Form Efficiency: Testimony of
Dhaka Stock Exchange”, Journal of Business Research, 8, 1-12.
Simons, D. and Laryea, S.A. (2004), “Testing the Efficiency of selected African
Stock Markets”, A Working Paper.
http://paper.ssrn.com/so13/paper.cfm?abstract_id=874808.
Spiegel, M.R. and Stephens, L.J. (1999), Schaum’s Outline of Theory and Problems of Statistics (3rd Ed.), USA: McGraw Hill Inc.
Urrutia, J.L. (1995), “Test of Random Walk and Market Efficiency” Journal of
Business Research, 18 (3), 299-309.
CHAPTER FOUR
DATA PRESENTATION, ANALYSIS AND INTERPRETATION
4.1 INTRODUCTION This research is an empirical test for weak-form efficient market hypothesis of the
Nigerian Stock Exchange (NSE). The major research problem is to examine
whether the monthly share returns from NSE are weak-form efficient during the
time period of this study. The major data for this research is the monthly All
Share Indices (ASI) of the NSE and covers the period from January 1993 to
December 2007 as presented in table 4.1.
Table 4.1 Monthly All Share Index of the NSE Year Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
1993 1113.4 1119.9 1130.5 1147.3 1186.9 1187.5 1180.8 1195.5 1217.3 1310.9 1414.5 1543.8
1994 1666.3 1715.3 1792.8 1845.6 1875.5 1919.1 1926.3 1914.1 1956 2023.4 2119.3 2205
1995 2285.3 2379.8 2551.1 2785.5 3100.8 3586.5 4314.3 4664.6 4858.1 5068 5095.2 5092.2
1996 5135.1 5180.4 5266.2 5412.4 5704.1 5798.7 5919.4 6141 6501.9 6634.8 6775.6 6992.1
1997 7268.3 7699.3 8561.4 8729.8 8592.3 8459.3 8148.8 7682 7130.8 6554.8 6395.8 6440.5
1998 6435.6 6426.2 6298.5 6113.9 6033.9 5892.1 5817 5795.7 5697.7 5671 5688.2 5672.7
1999 5494.9 5376.5 5456.2 5315.7 5315.7 5977.9 4964.4 4964.2 4890.8 5032.5 5133.2 5266.4
2000 5752.9 5955.7 5966.2 5892.8 6095.4 6466.7 6900.7 7394.1 7298.9 7415.3 7164.4 8111
2001 8794.2 9180.5 9159.8 9591.6 10153.8 10937.3 10576.4 10329 10274.2 11091.4 11169.6 10963.1
2002 10650 10581.9 11214.4 11399 11486.7 12440.7 12458.2 12327.9 11811.6 11451.5 11622.7 12137.7
2003 13298.8 13668.8 13531.1 13488 14086.3 14565.5 13962 15426 16500.5 18743.5 19319.3 20128.9
2004 22172.8 24797.4 22896.4 25793 27730.8 28887.4 27062.1 23774.3 22739.7 23354.8 23270.5 23844.5
2005 23078.3 21953.5 20682.4 21961.7 21482.1 21564.8 21911 22935.4 24635.9 25873.8 24355.9 24085.8
2006 23679.4 23843 23336.6 23301.2 24745.7 26316.1 27880.5 33096.4 32554.6 32643.7 32632.5 33189.3
2007 36784.5 40730.7 43456.1 47124 49930.2 51330.5 53021.7 50291.1 50229 50201.8 54189.9 57990.2
Source: www.databank.sec.gov.ng
This chapter presents, analyzes and discusses the results of our statistical tests.
To streamline the analyses, the order of the hypotheses formulated in chapter
one is followed. The normality tests are presented first, followed by the runs tests
for the total sample period and the sub-sample periods.
4.2 Test of Normality for the Nigerian Stock Exchange Our null hypothesis one posits that stock returns on the NSE follow a normal
distribution. Normality of distribution is one of the basic assumptions underlying
the weak-form efficiency (Simons and laryea, 2006). Thus, if NSE monthly
returns follow normal distribution, we shall conclude that the NSE is weak-form
efficient.
Skewness, kurtosis, Jarque-Bera and Studentized range have been used to test
the hypothesis of normality. Monthly market returns data from NSE all share
indices were used in these tests. The descriptive statistics of the log of the
market returns were calculated and presented in Table 4.2. The results show that
the returns are not normally distributed. In a symmetrical distribution the value of
the mean, median and the mode are alike (Spiegel and Stephens, 2008:65). As
the value of mean is lesser than the mode, the market return series do not follow
symmetric distribution. Generally, values for skewness (zero) and kurtosis (3)
represents that the observed distribution is perfectly normally distributed. The
kurtosis coefficient (-1.710) and skewness value (-0.039) for the total sample
period, exhibited extreme playtykurtic distribution with a negative long tail. Sub-
sample1 displayed negative skewness (-.1.36) and peaked distribution (4.23),
whereas sub-sample2 was negatively skewed with a flat distribution. Thus,
negative skewness and playtykurtic distribution of monthly stock return series on
the NSE reject our null hypothesis of normality as well as contradict the random
walk model.
We further justify the hypothesis with the Jarque-Bera (JB) test. Under the null
hypothesis of normality in distribution, JB is zero. The positive JB value of
165.396 for the full sample period, 132.71 for sub-sample1 and 304.13 for sub-
sample2, indicate evidence against normality in distribution in all the periods
studied.
Fama (1965) suggests that the Studentized range is another test of the degree to
which data deviates from normality. If the Studentized range is greater than 6,
then the null hypothesis of normal distribution is rejected. The values in Table 4.2
are larger than 6 for full sample period and sub-sample1 further suggesting that
the stock returns on NSE are not normally distributed. Sub-sample2 was less
than 6 but we cannot accept normality in return distribution based solely on this
result.
Table 4.2 Results of Normality Tests for the NSE Monthly Stock Returns
Variable Description Full Sample Sub-sample 1 Sub-sample 2
(Rmt) Observation 179 83 96
Mean 0.022 0.018 .0250
Median 0.017 0.016 0.0255
Mode 0.18 0.18 .17
Variance 0.003 0.002 0.003
Std. Dev 0.051 0.049 0.05237
MinimumRt -0.19 -0.19 -0.13
MaximumRt 0.18 0.18 .17
Range 0.37 0.37 0.30
Skewness -0.039 -0.136 0.013
Kurtosis -1.710 4.232 0.096
Jarque-Bera 165.396 132.71 304.126
Studentized
Range*
7.255 7.476 5.728
*Studentized range is ( Max(Rt) – Min(Rt) ) —————
σRt
The total scenario represents a deviation from the assumption of normality in
distribution and random walk. These results are in line with Mlambo et al, (2003)
conclusion that emerging market returns are not normally distributed. Even in
developed markets, stock returns have been found to be either leptokurtic or
playtykurtic (see for example, Kendal, 1953; Fama, 1965). Mlambo et al, suggest
that when there is a strong deviation from normality, correlation analysis should
be done using non-parametric testing methods, such as the Runs test, since they
do not assume a specific distribution. The following section will lead the
discussion in depth of random walk by testing randomness of the series.
4.3 Runs test for the NSE Monthly Stock Returns To detect the Weak-form efficiency of the Nigerian stock market, the non-
parametric Runs test was used. This test is regarded as powerful test to prove
random walk as it disregards the properties of distribution. The null hypothesis of
this test is that the observed series is a random series. The result of the Runs
test for the total sample period is presented in Table 4.3 below. Specifically, the
results of the Runs test for monthly observed returns indicate that the actual
number of runs for all series was significantly smaller than their corresponding
expected runs at 5% level, about 60 per cent of the expected runs. Also, the Z
statistics of the monthly market returns of -5.416 was lesser than -1.96 and
negative, which confirms that the observed number of runs was fewer than the
expected number of runs under the null hypothesis of independence. The fewer
number of runs indicates evidence of positive serial correlation in NSE monthly
returns. Positive autocorrelation implies that positive returns tend to be followed
by positive returns or negative returns tend to be followed by negative returns
(Gujarati, 2003:70; Bodie et al, 1999: 345; Fischer and Jordan, 2005:546).
In addition to above evidence, the positive mean value of .0221 contradicted the
random walk model which postulates zero mean. In a weak-form efficient stock
market, the positive returns cancel out the negative returns so that their average
effect on investment is zero. The positive mean value indicates evidence against
the null hypothesis of independence in NSE return series.
More so, the asymptotic significance (2-tailed), which is the p-value
corresponding to the Z value, show a probability of (0.000) for the Z for the NSE
monthly returns data. Under the null hypothesis of random walk in return series,
asymptotic significance corresponding to the Z value should be greater than or
equal to significance level, in this case 5%. Hence, we can accept the alternative
hypothesis that the return series do not follow random walk. In brief, the results of
the Runs test on the Nigerian Stock Exchange suggest that the monthly stock
returns are not random as the Z statistic did not fall in between ±1.96. These
results are the same with findings of Akpan (1995) and Appiah-kusi and Menya
(2003) but disagree with the findings of Samuels and yacout (1981), Ayadi (1984)
and Olowe (1999) which largely conclude that the NSE is weak-form efficient.
Table 4.3 Result of Runs Test for Weak Form Efficiency on NSE
Period Observations Actual Runs
Expected Run
Test Value
Z-Statistics
Asymp.sig (2-tailed)
Jan1993-Dec2007
179 54 89 .0221 -5.416 .000
4.4 Relative Weak Form Efficiency of the Nigerian Stock Exchange Table 4.4 presents two sets of results. The first set includes the result of Runs
test for sub-sample one which starts from January 1993 to December 1999. The
second set of result is the result of the Runs test for sub-sample two which starts
from January 2000 and ends on December 2007. The null hypothesis is that the
NSE is weak-form efficient across time period of this study.
Table 4.4 Results of Runs Tests for Relative of the NSE Periods Observations Actual
Runs Expected Run
Test Value
Z-Statistics
Asymp.sig (2-tailed)
Sub1 (Jan1993-dec1999)
83 16 42 .0187 -5.842 .000
Sub2 (Jan 2000-Dec
96 33 49 .0250 -3.283 .001
2007) The results show that the Z statistics of sub-sample1 (-5.842) and (-3.283) for
sub-sample2 were lesser than ±1.96 and negative, which show that the actual
number of runs fall short of the expected number of runs at 5% significance level.
From Table 4.4, we see that the actual runs are 33% of the expected runs in sub-
sample1 and 67% of expectation in sub-sample2. Negative Z value and few
observed runs indicate positive serial correlation in returns, with momentum
property being higher in sub-sample1 and lower in sub-sample2.
Also, the positive mean values indicate evidence against random walk in monthly
return series of both sub-sample periods. The positive mean values imply that
future returns can be predicted from historical returns and volumes.
The asymptotic significance (2-tailed) corresponding to the Z values, show a
probability of (0.000) for sub-sample1 and (0.001) for sub-sample2. Thus, we can
accept the alternative hypothesis that the NSE is not weak-form efficient across
time, since sub-sample1 and 2 do not follow random walk
In brief, the results of Runs tests for the total sample period, sub-sample1 and 2
on the Nigerian Stock Exchange indicate that the monthly stock returns of the
NSE are not random as the Z statistics do not fall in between ±1.96 in any of the
periods examined. The returns in all the periods appeared to fit a momentum
process which may suggest a potential bubble.
4.5 Effect of Market Microstructure on the NSE Market microstructure is concerned with the functional set-up of a financial
market. It deals with trading in financial assets such as stocks and bonds. It
deals, also, with the manner in which the assets are sold and how that process
affects the prices of assets traded, value and volume traded and the behaviour of
traders in the market. The existing evidence on market microstructure suggests
that improvements in trading systems, market capitalization, membership, value
and volume traded lead to improvements in market efficiency (Amihud et al,
1997; Suzuki and Yasuda, 2006). From the results of the Runs tests for sub-
sample1 (Jan 1993 - Dec 1999) and sub-sample 2 (Jan 2000 – Dec 2007) in
Table 4.4, improvements in market microstructure of the NSE have positive effect
on market efficiency. This was evidenced from the higher percentage of expected
runs in sub-sample2 (67%) than in sub-sample1 (33%), which shows that the
former has greater tendency towards weak-form efficiency than the latter.
Similarly, the Z value of sub-sample2 (-3.283) show significant improvement over
the Z statistic for sub-sample1 (-5.842) at 5% critical value, which is -1.96. Also
the asymptotic significance of sub- sample2 (0.001) indicated increase in market
efficiency over that of sub-sample1 (0.000).
Generally, the effect of improvements in market microstructure is positive on the
weak-form efficiency of the Nigerian Stock Exchange. Whether the positive
impact is statistically significant or not is left for further studies.
4.6 REFERENCES Akpan, O.E. (1995),” Thin and Thick capital Markets”, Nigerian Journal of
Social and Economic Research, 1 (37), 2-4.
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.
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.
Bodie, Z.; Kane, A. and Marcus, A.J. (2001), Investments (4th Ed.), Singapore:
McGraw Hill Inc.
Fama, E. (1965), “The Behaviour of Stock Market prices”, Journal of Business, 38, 34-105.
Fischer, D.E. and Jordan, R.J. (2005), Security Analysis and Portfolio
Management (6th Ed), Prentice-Hall of India: New Delhi Gujarati, D.N. (2003), Basic Econometrics (4th Ed), Delhi: McGraw Hill Inc.
Kendal. M. (1953), “The Analysis of Economic Time Series, part 1: prices”
Journal of the Royal Statistical Society, Series A, 96, 11-25.
Mlambo, C.; Biekpe, N. and Smit E.M. (2003), “Testing the Random Walk
Hypothesis on Thinly Traded Markets: The Case of four African Stock
Markets”, the African Journal of Finance, 5 (1), 16-35.
Simons, D. and Laryea, S.A. (2004), “Testing the Efficiency of selected African
Stock Markets”, A Working Paper.
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Samuels, J.M. and Yacout, M. 91981), “Stock Exchange in Developing
Countries”, Savings and Development, 5, (4), 309-328.
Spiegel, M.R. and Stephens, L.J. (2008), Schaum’s Outline of Theory and Problems of Statistics (4rd Ed.), USA: McGraw Hill Inc.
Suzuki, K. and Yasuda, Y. (2006), “Market Microstructure and Stock Prices:
Firms and their Selection of Trading Mechanism in the JASDAQ Market”,
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CHAPTER FIVE SUMMARY, CONCLUSION AND RECOMMENDATION 5.1 INTRODUCTION In this chapter, we give a summary of our study, the conclusions that were drawn
from the findings and the recommendations that follow the conclusions.
5.2 Summary of the Research This research is an empirical test for weak-form efficient market hypothesis of the
Nigerian stock exchange. The need for such examination rested on the back of
four overriding themes; that (1) the empirical literature on the weak-form
efficiency of the NSE are few, (2) there is need to extend the few existing
evidence on the weak-form efficiency of the NSE, (3) there is need to test if the
NSE has been weak-form efficient across time since efficiency changes
overtime, and (4) the rising interest in investment opportunities in NSE raises
questions about its efficiency. Three hypotheses were tested and they are: that
(I) the stock returns on the NSE follow the normal distribution, (II) the stock
returns on the NSE are random over the time period of this study, and (III) the
NSE is weak-form efficient across time. These hypotheses were tested using
econometric approaches such the skewness, kurtosis, Jarque-Bera and
studentized range for normality; the Runs test of randomness for random walk in
stock returns; and the Runs test for two sub-sample periods for efficiency across
time for the NSE.
The research has come out with the following findings:
a) The stock returns on the NSE do not follow the normal distribution but are
characterized by playtykurtic distribution with negative long left tail.
b) The stock returns on the NSE do not follow a random walk over the time
period of this study but exhibit momentum property in returns.
c) The NSE is not weak-form efficient across time but show evidence of
weak-form inefficiency in the two sub-periods examined.
d) Improvements in market microstructure have positive effect on the
efficiency of the NSE.
5.3 Conclusions
This research mainly hunted for the evidence of weak-form efficiency by
hypothesizing normality of the return distribution series and random walk
assumption. In the aspect of skewness and kurtosis, the monthly return series
were found non-normal, but could be categorized as negatively skewed and
playtykurtic distribution. Same thing resulted from Jarque-Bera and studentized
range, which were positive and above 6 respectively. As a result, null hypothesis
of normality in return series has been rejected and alternative hypothesis
remained in effect. Runs test result rejected the randomness of the return series
of the NSE and the alternative hypothesis of non-randomness in series was
accepted. Also results of the runs tests for the two sub-sample periods rejected
the null hypothesis that NSE is weak-form efficient across time and accepted the
alternative that the NSE is weak-form inefficient across time. The study, also,
provided evidence showing that improvements in market microstructure have
positive effects on weak-form efficiency of the NSE.
Overall result from the empirical analysis suggests that the Nigerian stock
exchange is not efficient in the weak-form. The rejection of weak-form efficiency
is not only consistent with some previous studies (see, Akpan, 1995; Appaih-kusi
and Menya, 2003), but also theoretically not surprising. Illiquidity and paucity of
instruments traded dominate the NSE, for instance Apampa (2008) observes that
of the 200 odd listed securities, only about 40 are liquid. Because there so few
liquid instruments, supply and demand of those instruments control prices and
investment decisions more than the actual performance of the company in
question. Also, associated high average cost of transaction results in limited
market activity. Nevertheless, these are only persuasive rather than empirical
arguments. These theoretical arguments, although not sufficient, explain the
rejection of the weak-form efficiency of the NSE.
A major economic implication of this evidence for investors of the NSE is that
stock returns are predictable from historical returns and volume traded, but
whether exploitation will be profitable after transaction costs is unknown.
5.4 Recommendations for policy Thrusts The policy implications of this inquiry are that the NSE, as an emerging market,
must be closely monitored to achieve an optimal maturity level. Investors must be
aware that, in inefficient stock markets, heavy gains are just as likely as heavy
losses. Furthermore, in anticipation of the dreaded bursting of the bubble, the
Securities and Exchange Commission should take a leading role in regulating
abnormal financial activity. In the meantime, an inefficient Bull market could
suffer over inflated stock prices, speculation, and insider trading, all potentially
intensified by herding behaviour. Several measures can be taken to improve the
efficiency of the NSE, including (and not limited to):
1) Reduction of transaction cost so as to improve market activities and,
in turn, liquidity.
2) Establishing a stock exchange news service, which will be responsible
for early, equal and wide dissemination of price sensitive news such as
financial results and other information that are material to investors’
decision. This will ensure that participants have access to high quality
and reliable information.
3) Minimize institutional restrictions on trading of securities in the bourse.
4) The NSE and SEC also need to strengthen their regulatory capacities
to enhance investor confidence. This will involve training personnel to
enforce financial regulations, perform market surveillance, analytical
and investigative assignments
5.5 Recommendation for Further Studies Most of the research in market efficiency, including this one, has concentrated on
using statistical tests to investigate dependence in returns. The results are often
used to make conclusions on the various technical techniques. However,
technical analysts claim that the techniques they used are so varied that they
cannot be proved or disproved by simple statistical analysis. We therefore
recommend empirical tests on the relevance of the various technical theories and
techniques on the NSE, and even compare their performance against the naïve
buy-and-hold strategy.
It could also be worthwhile to test if predicting future share returns form the past
returns on the NSE would be profitable after transaction costs are considered.
This research finds that improvements in market microstructure have positive
effects on the weak-form efficiency of the NSE but did not know whether the
positive effect is statistically significant. It is therefore recommended that the
impact of market microstructure on the securities value be empirically tested on
the NSE to know whether the positive effect is statistically significance or not.
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APPENDICES Appendix I THE COMPUTATION OF MONTHLY STOCK RETURNS USING NSE ASI (JAN.1993-DEC.2007) Date Indices Level, Pt LnPt LnPt - LnPt-1 31/12/2007 57990.2 10.96803 0.067779487 Where: 30/11/2007 54189.9 10.90025 0.076443662 Pt = NSE Monthly All Share Index 31/10/2007 50201.8 10.82381 -0.000541667 LnPt = Natural Log of Monthly ASI 30/9/2007 50229 10.82435 -0.001235574 LnPt – LnPt-1 = NSE Monthly Return 31/8/2007 50291.1 10.82558 -0.052873141 31/7/2007 53021.7 10.87846 0.032416146 30/6/2007 51330.5 10.84604 0.027659087 31/5/2007 49930.2 10.81838 0.057843605 30/4/2007 47124 10.76054 0.081031192 31/3/2007 43456.1 10.67951 0.064769125 29/2/2007 40730.7 10.61474 0.101905547 31/1/2007 36784.5 10.51283 0.102849026 31/12/2006 33189.3 10.40998 0.016918811 28/11/2006 32632.5 10.39306 -0.000343157 31/10/2006 32643.7 10.39341 0.002733202 30/9/2006 32554.6 10.39067 -0.016505835 31/8/2006 33096.4 10.40718 0.171496995 31/7/2006 27880.5 10.23568 0.057745841 30/6/2006 26316.12 10.17794 0.061529943 31/5/2006 24745.7 10.11641 0.060146875 30/4/2006 23301.2 10.05626 -0.001518082 31/3/2006 23336.6 10.05778 -0.02146773 29/2/2006 23843 10.07925 0.006885201 31/1/2006 23679.4 10.07236 -0.017016983 31/12/2005 24085.8 10.08938 -0.011151665 30/11/2005 24355.9 10.10053 -0.060456754 31/10/2005 25873.8 10.16099 0.049026145 30/9/2005 24635.9 10.11196 0.07152316 31/8/2005 22935.4 10.04044 0.045692775 31/7/2005 21911 9.994744 0.015926438 30/6/2005 21564.8 9.978818 0.003842326 31/5/2005 21482.1 9.974975 -0.022079997 30/4/2005 21961.7 9.997055 0.06001693 31/3/2005 20682.4 9.937038 -0.059643483 28/2/2005 21953.5 9.996682 -0.049966202 31/1/2005 23078.3 10.04665 -0.032660801 31/12/2004 23844.5 10.07931 0.024367119 30/11/2004 23270.5 10.05494 -0.003616066 31/10/2004 23354.8 10.05856 0.026690235 30/9/2004 22739.7 10.03187 -0.04449287 31/8/2004 23774.3 10.07636 -0.12952906 31/7/2004 27062.1 10.20589 -0.065271289 30/6/2004 28887.4 10.27116 0.040861805 31/5/2004 27730.8 10.2303 0.072440572 30/4/2004 25793 10.15786 0.119123444
31/3/2004 22896.4 10.03873 -0.079760326 29/2/2004 24797.43 10.1185 0.111870098 31/1/2004 22172.88 10.00663 0.09671134 31/12/2003 20128.94 9.909914 0.041053984 30/11/2003 19319.3 9.86886 0.030257571 31/10/2003 18743.5 9.838602 0.127456342 31/9/2003 16500.5 9.711146 0.067336286 31/8/2003 15426 9.64381 0.099715044 31/7/2003 13962 9.544095 -0.042316365 30/6/2003 14565.5 9.586411 0.033453025 31/5/2003 14086.3 9.552958 0.043402293 30/4/2003 13488 9.509556 -0.003190338 31/3/2003 13531.1 9.512746 -0.010125124 28/2/2003 13668.8 9.522871 0.027442058 31/1/2003 13298.8 9.495429 0.091357494 31/12/2002 12137.7 9.404072 0.043356229 30/11/2002 11622.7 9.360715 0.014839357 31/10/2002 11451.5 9.345876 -0.030961374 30/9/2002 11811.6 9.376837 -0.042782887 31/8/2002 12327.9 9.41962 -0.010514054 31/7/2002 12458.2 9.430134 0.001405685 30/6/2002 12440.7 9.428729 0.079783511 31/5/2002 11486.7 9.348945 0.007664212 30/4/2002 11399 9.341281 0.016326965 31/3/2002 11214.4 9.324954 0.058053672 29/2/2002 10581.9 9.2669 -0.006414898 31/1/2002 10650 9.273315 -0.028975196 31/12/2001 10963.1 9.30229 -0.018660714 30/11/2001 11169.6 9.320951 0.007025769 31/10/2001 11091.4 9.313925 0.076534135 31/9/2001 10274.2 9.237391 -0.005319575 31/8/2001 10329 9.242711 -0.023669631 31/7/2001 10576.4 9.26638 -0.033553862 30/6/2001 10937.3 9.299934 0.074330946 31/5/2001 10153.8 9.225603 0.056960304 30/4/2001 9591.6 9.168643 0.046063371 31/3/2001 9159.8 9.12258 -0.002257325 28/2/2001 9180.5 9.124837 0.042989256 31/1/2001 8794.2 9.081848 0.080871248 31/12/2000 8111 9.000976 0.124096848 30/11/2000 7164.4 8.87688 -0.034421116 31/10/2000 7415.3 8.911301 0.015821781 30/9/2000 7298.9 8.895479 -0.012958733 31/8/2000 7394.1 8.908438 0.069059529 31/7/2000 6900.7 8.839378 0.064956924 30/6/2000 6466.7 8.774421 0.059131544 31/5/2000 6095.4 8.71529 0.033803122 30/4/2000 5892.8 8.681487 -0.012378942 31/3/2000 5966.2 8.693865 0.001761465 29/2/2000 5955.7 8.692104 0.034644669
31/1/2000 5752.9 8.657459 0.088357058 31/12/1999 5266.4 8.569102 0.025617771 30/11/1999 5133.2 8.543485 0.019812368 31/10/1999 5032.5 8.523672 0.028560989 30/9/1999 4890.8 8.495111 -0.014896267 31/8/1999 4964.2 8.510007 -4.02877E-05 31/7/1999 4964.4 8.510048 -0.185776891 30/6/1999 5977.9 8.695825 0.11740463 31/5/1999 5315.7 8.57842 0 30/4/1999 5315.7 8.57842 -0.026087871 31/3/1999 5456.2 8.604508 0.014714972 29/2/1999 5376.5 8.589793 -0.021782784 31/1/1999 5494.9 8.611576 -0.031844806 31/12/1998 5672.7 8.64342 -0.002728659 30/11/1998 5688.2 8.646149 0.003028385 31/10/1998 5671 8.643121 -0.004697116 30/9/1998 5697.7 8.647818 -0.017053679 31/8/1998 5795.7 8.664872 -0.003668402 31/7/1998 5817 8.66854 -0.012827806 30/6/1998 5892.1 8.681368 -0.023781097 31/5/1998 6033.9 8.705149 -0.0131713 30/4/1998 6113.9 8.71832 -0.029746642 31/3/1998 6298.5 8.748067 -0.020071874 28/2/1998 6426.2 8.768139 -0.001461693 31/1/1998 6435.6 8.7696 -0.0007611 31/12/1997 6440.5 8.770361 0.006964652 30/11/1997 6395.8 8.763397 -0.024556081 31/10/1997 6554.8 8.787953 -0.084225824 30/9/1997 7130.8 8.872179 -0.0744565 31/8/1997 7682 8.946635 -0.058990747 31/7/1997 8148.8 9.005626 -0.037395751 30/6/1997 8459.3 9.043022 -0.015600025 31/5/1997 8592.3 9.058622 -0.015876007 30/4/1997 8729.8 9.074498 0.019478732 31/3/1997 8561.4 9.055019 0.106134313 29/2/1997 7699.3 8.948885 0.057606989 31/1/1997 7268.3 8.891278 0.038741486 31/12/1996 6992.1 8.852536 0.031453017 30/11/1996 6775.6 8.821083 0.0209994 31/10/1996 6634.8 8.800084 0.020234082 30/9/1996 6501.9 8.77985 0.057106847 31/8/1996 6141 8.722743 0.036752503 31/7/1996 5919.4 8.68599 0.020601338 30/6/1996 5798.7 8.665389 0.01644854 31/5/1996 5704.1 8.64894 0.052492597 30/4/1966 5412.4 8.596448 0.027383578 31/3/1996 5266.2 8.569064 0.016426766 28/2/1996 5180.4 8.552638 0.008782956 31/1/1996 5135.1 8.543855 0.00838936 31/12/1995 5092.2 8.535465 -0.000588963
30/11/1995 5095.2 8.536054 0.005352658 31/10/1995 5068 8.530702 0.042298847 30/9/1995 4858.1 8.488403 0.040645329 31/8/1995 4664.6 8.447757 0.078066999 31/7/1995 4314.3 8.36969 0.18475829 30/6/1995 3586.5 8.184932 0.145516654 31/5/1995 3100.8 8.039415 0.107232752 30/4/1995 2785.5 7.932183 0.087902752 31/3/1995 2551.1 7.84428 0.069508188 28/2/1995 2379.8 7.774772 0.040519144 31/1/1995 2285.3 7.734253 0.035769798 31/12/1994 2205 7.698483 0.039641663 30/11/1994 2119.3 7.658841 0.046306581 31/10/1994 2023.4 7.612535 0.033877693 30/9/1994 1956 7.578657 0.021654033 31/8/1994 1914.1 7.557003 -0.006353526 31/7/1994 1926.3 7.563356 0.003744738 30/6/1994 1919.1 7.559612 0.022981036 31/5/1994 1875.5 7.536631 0.016070863 30/4/1994 1845.6 7.52056 0.029025784 31/3/1994 1792.8 7.491534 0.044190651 28/2/1994 1715.3 7.447343 0.028982393 31/1/1994 1666.3 7.418361 0.07635869 31/12/1993 1543.8 7.342002 0.087470798 30/11/1993 1414.5 7.254531 0.076062188 31/10/1993 1310.9 7.178469 0.074078633 30/9/1993 1217.3 7.104391 0.018070784 31/8/1993 1195.5 7.08632 0.012372333 31/7/1993 1180.8 7.073947 -0.005658082 30/6/1993 1187.5 7.079606 0.000505391 31/5/1993 1186.9 7.0791 0.03393351 30/4/1993 1147.3 7.045167 0.014751343 31/3/1993 1130.5 7.030415 0.009420617 29/2/1993 1119.9 7.020995 0.005820999 31/1/1993 1113.4 7.015174
APPENDIX II Descriptive Statistics for Total Sample Period
Statistics
VAR00001179
0.0221
.00381.0169
-.19a
.05101
.00260-.039.182
1.710.361.37
-.19.18
3.95
ValidMissing
N
MeanStd. Error of MeanMedianModeStd. DeviationVarianceSkewnessStd. Error of SkewnessKurtosisStd. Error of KurtosisRangeMinimumMaximumSum
Multiple modes exist. The smallest value is showna.
Non-Parametric Test for Total Sample Period
Runs Test
.02219782
17954
-5.416.000
Test Valuea
Cases < Test ValueCases >= Test ValueTotal CasesNumber of RunsZAsymp. Sig. (2-tailed)
VAR00001
Meana.
Appendix III COMPUTATION OF MONTHLY RETURNS FOR SUB-SAMPLE1 (JAN.1993-DEC.1999)
Date Indices Level, Pt Ln Pt LnPt-LnPt-1
31/12/1999 5266.4 8.569102 0.025618 30/11/1999 5133.2 8.543485 0.019812 31/10/1999 5032.5 8.523672 0.028561 30/9/1999 4890.8 8.495111 -0.0149 31/8/1999 4964.2 8.510007 -4E-05 31/7/1999 4964.4 8.510048 -0.18578 30/6/1999 5977.9 8.695825 0.117405 31/5/1999 5315.7 8.57842 0 30/4/1999 5315.7 8.57842 -0.02609 31/3/1999 5456.2 8.604508 0.014715 29/2/1999 5376.5 8.589793 -0.02178 31/1/1999 5494.9 8.611576 -0.03184 31/12/1998 5672.7 8.64342 -0.00273 30/11/1998 5688.2 8.646149 0.003028 31/10/1998 5671 8.643121 -0.0047 30/9/1998 5697.7 8.647818 -0.01705 31/8/1998 5795.7 8.664872 -0.00367 31/7/1998 5817 8.66854 -0.01283 30/6/1998 5892.1 8.681368 -0.02378 31/5/1998 6033.9 8.705149 -0.01317 30/4/1998 6113.9 8.71832 -0.02975 31/3/1998 6298.5 8.748067 -0.02007 28/2/1998 6426.2 8.768139 -0.00146 31/1/1998 6435.6 8.7696 -0.00076 31/12/1997 6440.5 8.770361 0.006965 30/11/1997 6395.8 8.763397 -0.02456 31/10/1997 6554.8 8.787953 -0.08423 30/9/1997 7130.8 8.872179 -0.07446 31/8/1997 7682 8.946635 -0.05899 31/7/1997 8148.8 9.005626 -0.0374 30/6/1997 8459.3 9.043022 -0.0156 31/5/1997 8592.3 9.058622 -0.01588 30/4/1997 8729.8 9.074498 0.019479 31/3/1997 8561.4 9.055019 0.106134 29/2/1997 7699.3 8.948885 0.057607 31/1/1997 7268.3 8.891278 0.038741 31/12/1996 6992.1 8.852536 0.031453 30/11/1996 6775.6 8.821083 0.020999 31/10/1996 6634.8 8.800084 0.020234 30/9/1996 6501.9 8.77985 0.057107 31/8/1996 6141 8.722743 0.036753 31/7/1996 5919.4 8.68599 0.020601 30/6/1996 5798.7 8.665389 0.016449 31/5/1996 5704.1 8.64894 0.052493 30/4/1966 5412.4 8.596448 0.027384
31/3/1996 5266.2 8.569064 0.016427 28/2/1996 5180.4 8.552638 0.008783 31/1/1996 5135.1 8.543855 0.008389 31/12/1995 5092.2 8.535465 -0.00059 30/11/1995 5095.2 8.536054 0.005353 31/10/1995 5068 8.530702 0.042299 30/9/1995 4858.1 8.488403 0.040645 31/8/1995 4664.6 8.447757 0.078067 31/7/1995 4314.3 8.36969 0.184758 30/6/1995 3586.5 8.184932 0.145517 31/5/1995 3100.8 8.039415 0.107233 30/4/1995 2785.5 7.932183 0.087903 31/3/1995 2551.1 7.84428 0.069508 28/2/1995 2379.8 7.774772 0.040519 31/1/1995 2285.3 7.734253 0.03577 31/12/1994 2205 7.698483 0.039642 30/11/1994 2119.3 7.658841 0.046307 31/10/1994 2023.4 7.612535 0.033878 30/9/1994 1956 7.578657 0.021654 31/8/1994 1914.1 7.557003 -0.00635 31/7/1994 1926.3 7.563356 0.003745 30/6/1994 1919.1 7.559612 0.022981 31/5/1994 1875.5 7.536631 0.016071 30/4/1994 1845.6 7.52056 0.029026 31/3/1994 1792.8 7.491534 0.044191 28/2/1994 1715.3 7.447343 0.028982 31/1/1994 1666.3 7.418361 0.076359 31/12/1993 1543.8 7.342002 0.087471 30/11/1993 1414.5 7.254531 0.076062 31/10/1993 1310.9 7.178469 0.074079 30/9/1993 1217.3 7.104391 0.018071 31/8/1993 1195.5 7.08632 0.012372 31/7/1993 1180.8 7.073947 -0.00566 30/6/1993 1187.5 7.079606 0.000505 31/5/1993 1186.9 7.0791 0.033934 30/4/1993 1147.3 7.045167 0.014751 31/3/1993 1130.5 7.030415 0.009421 29/2/1993 1119.9 7.020995 0.005821 31/1/1993 1113.4 7.015174
APPENDIX IV Descriptive Statistics for Sub-sample I
Statistics
VAR00001830
.0187.00543.0164
-.19a
.04949
.00245-.136.264
4.232.523.37
-.19.18
1.55
ValidMissing
N
MeanStd. Error of MeanMedianModeStd. DeviationVarianceSkewnessStd. Error of SkewnessKurtosisStd. Error of KurtosisRangeMinimumMaximumSum
Multiple modes exist. The smallest value is showna.
Non-Parametric Tests for Sub-sample I
Runs Test 2
.018744398316
-5.842.000
Test Valuea
Cases < Test ValueCases >= Test ValueTotal CasesNumber of RunsZAsymp. Sig. (2-tailed)
VAR00001
Meana.
Appendix V COMPUTATION OF MONTHLY STOCK RETURNS FOR SUB-SAMPLE 2 (JAN. 2000-DEC.2007) Date Indices Level, Pt LnPt LnPt-LnPt-1 31/12/2007 57990.2 10.96803 0.067779 30/11/2007 54189.9 10.90025 0.076444 31/10/2007 50201.8 10.82381 -0.00054 30/9/2007 50229 10.82435 -0.00124 31/8/2007 50291.1 10.82558 -0.05287 31/7/2007 53021.7 10.87846 0.032416 30/6/2007 51330.5 10.84604 0.027659 31/5/2007 49930.2 10.81838 0.057844 30/4/2007 47124 10.76054 0.081031 31/3/2007 43456.1 10.67951 0.064769 29/2/2007 40730.7 10.61474 0.101906 31/1/2007 36784.5 10.51283 0.102849 31/12/2006 33189.3 10.40998 0.016919 28/11/2006 32632.5 10.39306 -0.00034 31/10/2006 32643.7 10.39341 0.002733 30/9/2006 32554.6 10.39067 -0.01651 31/8/2006 33096.4 10.40718 0.171497 31/7/2006 27880.5 10.23568 0.057746 30/6/2006 26316.12 10.17794 0.06153 31/5/2006 24745.7 10.11641 0.060147 30/4/2006 23301.2 10.05626 -0.00152 31/3/2006 23336.6 10.05778 -0.02147 29/2/2006 23843 10.07925 0.006885 31/1/2006 23679.4 10.07236 -0.01702 31/12/2005 24085.8 10.08938 -0.01115 30/11/2005 24355.9 10.10053 -0.06046 31/10/2005 25873.8 10.16099 0.049026 30/9/2005 24635.9 10.11196 0.071523 31/8/2005 22935.4 10.04044 0.045693 31/7/2005 21911 9.994744 0.015926 30/6/2005 21564.8 9.978818 0.003842 31/5/2005 21482.1 9.974975 -0.02208 30/4/2005 21961.7 9.997055 0.060017 31/3/2005 20682.4 9.937038 -0.05964 28/2/2005 21953.5 9.996682 -0.04997 31/1/2005 23078.3 10.04665 -0.03266 31/12/2004 23844.5 10.07931 0.024367 30/11/2004 23270.5 10.05494 -0.00362 31/10/2004 23354.8 10.05856 0.02669 30/9/2004 22739.7 10.03187 -0.04449 31/8/2004 23774.3 10.07636 -0.12953 31/7/2004 27062.1 10.20589 -0.06527 30/6/2004 28887.4 10.27116 0.040862 31/5/2004 27730.8 10.2303 0.072441 30/4/2004 25793 10.15786 0.119123 31/3/2004 22896.4 10.03873 -0.07976
29/2/2004 24797.43 10.1185 0.11187 31/1/2004 22172.88 10.00663 0.096711 31/12/2003 20128.94 9.909914 0.041054 30/11/2003 19319.3 9.86886 0.030258 31/10/2003 18743.5 9.838602 0.127456 31/9/2003 16500.5 9.711146 0.067336 31/8/2003 15426 9.64381 0.099715 31/7/2003 13962 9.544095 -0.04232 30/6/2003 14565.5 9.586411 0.033453 31/5/2003 14086.3 9.552958 0.043402 30/4/2003 13488 9.509556 -0.00319 31/3/2003 13531.1 9.512746 -0.01013 28/2/2003 13668.8 9.522871 0.027442 31/1/2003 13298.8 9.495429 0.091357 31/12/2002 12137.7 9.404072 0.043356 30/11/2002 11622.7 9.360715 0.014839 31/10/2002 11451.5 9.345876 -0.03096 30/9/2002 11811.6 9.376837 -0.04278 31/8/2002 12327.9 9.41962 -0.01051 31/7/2002 12458.2 9.430134 0.001406 30/6/2002 12440.7 9.428729 0.079784 31/5/2002 11486.7 9.348945 0.007664 30/4/2002 11399 9.341281 0.016327 31/3/2002 11214.4 9.324954 0.058054 29/2/2002 10581.9 9.2669 -0.00641 31/1/2002 10650 9.273315 -0.02898 31/12/2001 10963.1 9.30229 -0.01866 30/11/2001 11169.6 9.320951 0.007026 31/10/2001 11091.4 9.313925 0.076534 31/9/2001 10274.2 9.237391 -0.00532 31/8/2001 10329 9.242711 -0.02367 31/7/2001 10576.4 9.26638 -0.03355 30/6/2001 10937.3 9.299934 0.074331 31/5/2001 10153.8 9.225603 0.05696 30/4/2001 9591.6 9.168643 0.046063 31/3/2001 9159.8 9.12258 -0.00226 28/2/2001 9180.5 9.124837 0.042989 31/1/2001 8794.2 9.081848 0.080871 31/12/2000 8111 9.000976 0.124097 30/11/2000 7164.4 8.87688 -0.03442 31/10/2000 7415.3 8.911301 0.015822 30/9/2000 7298.9 8.895479 -0.01296 31/8/2000 7394.1 8.908438 0.06906 31/7/2000 6900.7 8.839378 0.064957 30/6/2000 6466.7 8.774421 0.059132 31/5/2000 6095.4 8.71529 0.033803 30/4/2000 5892.8 8.681487 -0.01238 31/3/2000 5966.2 8.693865 0.001761 29/2/2000 5955.7 8.692104 0.034645 31/1/2000 5752.9 8.657459 8.657459
APPENDIX VI Descriptive Statistics for Sub-sample2
Statistics
VAR00001960
.0250.00535.0255
-.13a
.05237
.00274.013.246.096.488.30
-.13.17
2.40
ValidMissing
N
MeanStd. Error of MeanMedianModeStd. DeviationVarianceSkewnessStd. Error of SkewnessKurtosisStd. Error of KurtosisRangeMinimumMaximumSum
Multiple modes exist. The smallest value is showna.
Non-Parametric Test for Sub-sammple2
Runs Test
.025048489633
-3.283.001
Test Valuea
Cases < Test ValueCases >= Test ValueTotal CasesNumber of RunsZAsymp. Sig. (2-tailed)
VAR00001
Meana.
