stock market efficiency: an empirical study...
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STOCK MARKET EFFICIENCY: AN EMPIRICAL STUDY OF
SELECT SECTORS IN NSE
1.1 Introduction
The efficient market hypothesis is one of the most important paradigms in modern
finance and was largely accepted to hold by the early 1970s. In 1970, Michale Jensen
declared his belief that ―there is no other proposition in economics which has more solid
empirical evidence hopeful it.‖ Market efficiency since then has expanded the basis of
many financial models and forms the foundation of the investment strategies of many
individuals and corporation. Because of the efficient market hypothesis, technical
analysts have become the objective of passive management and widespread criticism has
seen a boom in recent years.
Despite these effective credentials, several cracks have been developed in the efficient
market edifice over recent years. additional strong statistical techniques, the coming on of
reasonable computing and the cheap data storage lead to an blast in the total of data
available to researchers have handed the academic community more complicated tools
for empirical studies. Although new financial research was mighty to discount many prior
information of market inefficiency on basis of new statistical insights, a number of
anomalies were irrepealably and today form part of a growing body of literature at odd
with efficient market hypothesis.
1.1.1 Efficient Market Hypothesis
In 1953 Maurice Kendall published a study in which he found that stock price
movements followed no discernible pattern. That is, they represented no serial
correlation. Prices were as likely to go down as they were to go up on any given day,
irrespective of their changes in the past. These results lead to the question of what,
exactly, affected stock prices. Past performance clearly did not. In fact, had this been the
case, investors could have made money simply. Easily building a model to analyze the
probable next price change would have enabled market participants to achieve large
profits without (or with decreased) risk. Therefore, if everybody could have done so,
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stocks would have immediately increased because large numbers of investors required
buying them, whereas those holding the stock would not performance previously
available stock data.
In an efficient market where everyone has equal access to information, the price of a
share can impound information quickly and accurately. With a steady current of new
information in an efficient market characterized by immediate price adjustment,
successive price changes are random. The Efficient Market Hypothesis (EMH) states that
stock prices reflect all available information so that prices are close to their intrinsic
value. Market efficiency has an effect on the strategy of the investment related to an
investor. One of the most essential functions of the capital market is to canalize resources
for creative use. It can perform this function effectively only if it is able to build up
investors’ confidence by ensuring that the expected return from an investment
opportunity is matching with the risk related with it both in the primary and the
secondary markets. Now, if the market is efficient, trying to select on the winner stocks
will be consumption of time for the investors. Particular their risk in an efficient market,
there will be no undervalued stocks offering higher than predictable returns. On the other
hand, if markets are inefficient, excess returns can be made by properly selection the
winner stocks.
In the early writings, dating back to the beginning of the last century, empirical reason
was wanted for the first form of the EMH theory, namely the Random Walk Hypothesis
(RWH) (Bachelier, 1900). The random walk containing that successive price changes are
independent to each other, occupied a significant proportion of research till the late 1960s
(Cowles and Jones, 1937; Kendall, 1953; Osborne, 1959; Granger and Morgenstern,
1963; Cootner, 1962 and 1964; Moore, 1964). There were many empirical evidences but
what the issue was the absence of a suitable theory. This was filled up by a more general
model based on the concept of efficiency of the markets in which shares are traded–the
EMH (Fama, 1965). In most cases, a hypothesis is postulated whose validity is later
empirically tested. If the results confirm the hypothesis, then it graduates to the theory.
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1.1.2 Market Efficiency Definition
In concept of efficiency that is adopted for this thesis is one which is regarding the
incorporation of information into security prices. Generalizing from the results of the
above paragraph leads to the proposition that available information which could influence
a company’s stock performance should already be reflected in said company’s stock
price. In a performance should already be reflected in efficient market, therefore, security
price should equal the security’s investment value, which investment value is the
discounted value of the security’s future cash flows since anticipated by capable and
discriminating analysts sharp and Alexander (1990).
Under this definition, one thing which can yet affect the stock prices is new information.
When new information of a company becomes accessible, the above processes create
stock prices move directly to reflect the new circumstance. Naturally, this new
information needs to be unpredictable; otherwise the prediction about the new
information (which is itself a piece of information) would already have caused share
prices to change. These considerations suffice to formulate the efficient market
hypothesis. In its original syllogism, it stated that ―an EMH is one in which stock prices
fully reflect available information‖. Later texts have weakened this clarification in
arrange to let for investors to become informed. A good description of market efficiency
and the underlying mechanics is the one by Cootner (1964):
―If any substantial group of buyer through prices were too low, their purchases would
force the price up. The reverse would be right for sellers. Except for approval due to
earnings retention, the conditional vision of tomorrow’s price, given today’s price, is
today’s price.‖
In such a condition, the instantly price changes that would happen are those that
consequence from new information. Since there is not any reason to anticipate that
information to be non-random in appearance, the time to time price changes of a stock
should be random changes, statistically independent of one another.
In an ideal market, these evaluation criteria are clearly fulfilled. In such a market,
information and transactions are costless, convening that market participants have full
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information and can react to news without incurring costs. Nevertheless, while ideal
markets are a sufficient assumption for market efficiency, they are not an essential
condition.
1.1.3 Forms of Market Efficiency
Fama (1970) defined three forms of market efficiency, namely, weak, semi–strong and
strong forms. Each one is relevant with the adjustment of stock prices to one related
information subset clarified as follow.
1. Weak form of the hypothesis states that prices efficiently reflect all information
enclosed in the past series of stock prices. In this case it is not possible to earn
abnormal profit by using past stock price data. The lower is the market efficiency;
the greater is the predictability of stock price changes.
2. Semi strong of the hypothesis affirms that if by enlarging the information set to
include all publicly available information (i.e., information on interest rates,
exchange rate, money supply, announcement of dividends, annual/quarterly
earnings, stock splits, and etc.), it is not possible for a market participant to make
abnormal earnings, then the market is said to be semi strong form of efficient.
3. Strong form of the hypothesis states that if by rising the information situate
extra to include insiders/private’ information in addition, it is impossible for a
market participant to make abnormal profits, then the market is assumed to be
strong–form of efficient. Direct test of the effect of private information is not
possible and so indirectly the existence and influence of such type of information
are indirect. Not much work has been conducted to test this form of EMH;
therefore, there is an acute dearth of literature. However, a precondition for the
strong version is that information and trading costs are always zero. While
operating in the stock market where information has a cost, it is difficult for
markets to be informational efficient (Grossman and Stiglitz, 1980). The extreme
version of the market efficiency hypothesis is very unlikely to hold since there are
positive trading and information costs.
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Despite earlier evidences on the randomness of stock price changes (Kendall, 1953;
Mandelbrot, 1961; Moore, 1964; Fama, 1965; Fama, Fisher, Jensen, and Roll, 1965;
MacDonald and Fisher, 1972), there are pieces of evidence of anomalous price behaviour
where certain series appeared to follow predictable paths (Ball and Brown, 1968,
Ibbotson and Jaffe, 1975, Solnik, 1973). Due to these anomalies there is necessity to
carefully review both the acceptance of the efficient market theory and the
methodological procedures of it. Subsequently, Fama (1991) changed the categories and
coverage of informational efficiency.
1.2 Research Problems
A preliminary examination of the literature gives us a vivid picture of the present
efficient market’s development, practices, their applicability and the critics. Market
efficiency is used to explain the relationship between quantum of information and its
impact in the prices of securities in the stocks market literature. An efficient stock market
is normally consideration of as market in which security prices fully reflect all relevant
information that is available about the true value of the securities. In an efficient market,
security prices reacts instantaneously unbiased manner to impound new information in
such a way that leaves no opportunity to market participants to every time earn abnormal
return. With the meaning of check investment performance of equity shares this study
will be made to test the Indian stock efficient market NSE for the period from 1st April
2009 to 31st March 2014.
The effective and efficient operation of financial markets, mainly capital markets,
constitutes the foundation of the development of the modern economy. The stock markets
play a key role in capital allocation and its transformation from savings to financing new
investment initiatives, consequently creating more wealth. The financial investments on
capital markets refer to the flow of all streams of funds managed by Banks and financial
institutions, particularly the stock exchange and institutions investing in it, i.e.,
investment funds, pension funds and insurance companies. The main objective of stock
markets is to provide capital inflow for entities issuing stocks, thereby allowing them to
grow and to create wealth for investors, who invest their free capital in stocks, which they
perceive as attractive investments. Furthermore, the capital market is a place, where the
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current market value of a company is determined by the supply and demand of its shares.
Reliability of the stock valuation process is substantially correlated with results obtained
in the proof of the hypothesis of the stock market efficiency. The subject of market
efficiency is very often brought into question by practitioners and theoreticians from the
financial sectors, which build and verify investment strategies. They try to find an answer
to the question: Is it possible to develop a long–term investment strategy, which will
enable investors to achieve abnormal rates of return?
The presence of kinds form efficiency on the market implies that it is impossible to
achieve above–average profits when having access to a full set of information. As a
result, access to basic information about the stock price as well as knowledge of available
information does not guarantee the progress of a long term, profit making investment
strategy. One can talk about efficiency market when all information, is soon reflected in
the stock market prices. The overall approval of this form of efficiency specifies that
investors with contact to general information, as well as those having access to
information, are not able to ―beat the market‖ and get abnormal rates of return. Also
access to available information is necessary condition for developing stock
recommendations.
1.3 Significance of the Study
The necessary criteria of market efficiency shown as follow:
Easy and economical availability of information to all buyers and sellers,
Presenting a large number of buyers and sellers with their easy access to the
market,
Providing awareness of all investors regarding the effect of available information
on the current price,
Capability of the market to quickly incorporate the information by adjusting the
stock prices up and down,
Investors could have made money easily, and
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Basically making a model to determine the possible next price movement would
have enabled market participants to gain large profits without (or with reduced)
risk.
Therefore if everybody could have done so, stocks that were about to increase would
have risen immediately, because large numbers of investors would have required buying
them, while that investment the stock would not have necessary selling. This method
suggests that the market ―prices in‖ the performance data that is previously available
about a stock.
1.4 Objectives of the Study
The main objective of the study is to examine the behavior of the stock prices in the
Indian stock market after the introduction of the various financial sector reforms using
different methodologies. The researcher wants to study whether the Indian stock market
is efficient in its weak form over the overall period of this study. Other objective is also
to test the efficiency of the Indian stock market in its semi–strong form on the basis of the
publicly available information regarding the publication of quarterly corporate reports by
firms. The study simultaneously tests whether the semi–strong form efficiency are sector
specific or not. The objectives of this study are as follows:
To examine the stock market efficiency in select sectors in NSE.
To assess the impact of historic price of the stock on the current stock price of
select sectors in NSE.
To examine the impact of historic price of the stock on the current stock price in
NSE.
To measure the impact of corporate event announcements on the stock price of
select sectors in NSE.
To investigate the impact of corporate event announcements on the stock price in
NSE.
To suggest investment strategies for the investors and analysts of select sectors in
NSE.
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To suggest investment strategies for the investors and analysts in NSE.
1.5 Hypotheses of the Study
There is only one broad–based single line hypothesis for undertaking this research study,
i.e. under the strategy of the introduction of new economic policy measures in the Indian
financial market, the stock returns obtained in the pre–announcement period is identical
to the stock returns obtained is the post–announcement period and historical stock price
data cannot be used for the purpose of future prediction. From this general hypothesis, the
following main testable null and alternative hypotheses with reference to the objectives
are formulated in three parts:
a) Weak-Form Efficiency Hypothesis
This hypothesis states that stock prices fully reflect in the historical price.
H01: Current stock price does not fully reflect information contained in the historic
realizations of the price of Indian firms as well as Sectoral Indices of NSE.
Ha1: Current stock price fully reflect information contained in the historic realizations
of Indian firms as well as Sectoral Indices of NSE.
b) Semi–Strong Form Efficiency Hypotheses
This hypothesis states that the security prices reveal all publicly accessible information
within the scope of the efficient market hypothesis. In this case, the market reflects even
those forms of information which may be relating to with the announcement of a firm’s
most recent earnings announcement returns predict and adjustments which will have be
accomplished in the security prices.
H02: Stock price of select NSE sectors does not fully reflect all earning
announcements returns available information.
Ha2: Stock price of select NSE sectors fully reflects all earning announcements
returns available information.
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H03: Stock price of Indian firms of NSE does not fully reflect all earning
announcements returns available information.
Ha3: Stock price of Indian firms of NSE fully reflects all earning announcements
returns available information.
c) Value Optimization Hypotheses
Events announcement lead to positive expected, total realized gains and there is attracted
to investors make investment from these announcement.
H04: There is no value optimization to investor on quarter earnings announcement
through different sectors of NSE.
Ha4: There is value optimization to investor on quarter earnings announcement
through different sectors of NSE.
H05: There is no value optimization to investor on quarter earnings announcement
through individual stocks of NSE.
Ha5: There is value optimization to investor on quarter earnings announcement
through individual stocks of NSE.
1.6 Research Methodology
In order to achieve the study stated objectives, the researcher has explained separately
both weak and semi–strong form methodologies as follow:
1.6.1 Methodology of Weak Form Market Efficiency
The parallel between weak form market efficient condition and random walk made it
interesting for researchers to test weak form market efficiency indirectly, by testing if
stock returns are following a random walk. Random walk model (RWM) or random walk
hypothesis (RWH) has been employed to examine the EMH in this empirical research.
Consequently, in this study in order to test weak form efficiency is a chosen random walk
model, techniques of models include: (1) Non–Parametric test consist: Runs test,
Kolmogrov Smirnov, and (2) Parametric tests consist: Unit Root Test, Auto–Correlation
Test, Q– Statistic, and Variance ratio.
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1.6.2 Methodology of Semi–Strong Form Market Efficiency
In this study to test and semi–strong form efficiency is selected event studies by provide
an ideal tool for examining the information content of the disclosures. To test the semi–
strong form of efficiency examined on the basis of publicly available information on
quarterly earnings announcements.
The event study methodology seeks to determine whether there is an abnormal stock
price effect associated with an event. From this, the researcher can infer the significance
of the event. The key assumption of the event study methodology is that the effect of
quarterly earnings announcements on the market then must be efficient. As a result, the
market model has become the most common choice normal performance model alongside
the constant mean return model.
To test semi–strong form of efficiency on the basis of publicly available information on
quarterly earnings announcements, the researcher select 21 constituent companies of the
CNX Nifty index listed continuously throughout the study period of five years (2009–
2014). Then, the researcher collected three sets of data, namely, the dates on which the
Board of Directors meet and approve the quarterly financial results of the firm; weekly
closing prices and actual quarterly earnings per share of listed stocks continuously and
also the ordinary S&P CNX Nifty index prices. These data were collected from Prowess
database of the Centre for Monitoring Indian Economy (CMIE) website, the websites of
the respective companies, and Capitaline package. Two–stage approach has been used to
test the stock price responses to quarterly earnings announcements. In the first stage, two
untested expectation models, namely, a martingale (which forecasts the expected EPS for
a quarter as equal to the actual EPS of the same quarter from the previous year), and a
martingale with non–constant drift (which forecasts the change in the quarter’s earnings
from the same quarter in the previous year as the average change in the prior three quarter
earnings from the corresponding quarters of the previous year) in order to compute the
expected EPS. Also the researcher calculates the estimated (EPS) as well as parameter
estimates using a market model. In this study, the method used for the classification of
companies into portfolios depends on whether the reported quarterly EPS, Ej,q is larger
than, equal or less than to the expected quarterly EPS.
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Therefore to examine the information contained in the reported quarterly EPS, the three
portfolios of firms formed are named as favourable, neutral and unfavorable depending
on whether reported quarterly EPS is greater than, equal to or less than expected quarterly
EPS respectively. To examine the information contained in the magnitude of an
unanticipated earnings change, comparison of reported and expected EPS are done using
three schemes, namely, Absolute Residual Scheme (A), 20% Residual Scheme (B) and
40% Residual Scheme (C). In the second stage, the estimated parameters are used to
calculate abnormal returns around the announcement date which are then averaged to find
the Abnormal Performance Index (API), cumulated across securities and overtime to
compute the Cumulative Average Performance Index (CAPI) for each of the twelve out
of the eighteen earnings signal models (neglecting the six neutral signal models, one each
corresponding to each of the three classification schemes and the two expectation
models). The statistical significance of the indices are checked using a t–test and also a
linear trend line is fitted to find if there exist any trend overtime.
The following Tables show some of the particular studies which are relevant to the
present research methodology are summarized as below:
Table 1.6.2.1
Summary of Weak Form Market Efficiency Research Methodology
Research Methodology of Weak Form Market Efficiency
Type of Research Descriptive
Research Approach Deductive
Research Strategy Quantitative
Method of Research Secondary Data Study
Model of Research Walk Random Model
Time Horizon Longitudinal (Time Series)
Period of Study 1st April 2009 to 31
st March 2014
Sampling Technique Convenience and Judgment
Scope of Study 1) 29 constitute of 7 sectors of CNX Nifty
2) 7 sectoral indices of CNX Nifty
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Source of Data Secondary Source )Daily Closing Price(
Techniques of Models
1) Non–Parametric Tests such as:
Runs Test, Kolmogrov Smirnov Test.
2) Parametric Tests such as:
Unit Root Test, Autocorrelation Test and Q–
Statistic, Variance Ratio.
Table 1.6.2.2
Summary of Semi-Strong Form Market Efficiency Research Methodology
Research Methodology of Semi-Strong Form Market Efficiency
Type of Research Descriptive
Method of Research Secondary Data Study
Research Approach Deductive
Research Strategy Quantitative
Model of Research Event Study Model
Time Horizon Longitudinal (Time series)
Period of Study 1st April 2009 to 31
st March 2014
Sampling Technique Convenience and Judgment
Scope of Study 21 firms from 5 Sectors of CNX Nifty
Source of Data Secondary Source (Weekly Closing Price)
Way of Collecting Data 13 weeks before quarterly earnings report and 13
weeks after announcement
Aspects of testing stock price
responses to quarterly
earnings announcements
1) Martingale
2) Martingale with non- constant drift
Aspect of testing expected
weekly stock returns for each
earnings announcement
Market Model
Techniques of Models
1) Average Performance Index,
2) Marginal Price Adjustment Models,
3) Cumulative Average Performance Index ,
4) T-Test, and
5) Linear Trend Line.
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Table 1.6.2.3
Aspects of Testing Stock Price Responses
Aspects of Testing Stock Price Responses to Quarterly Earnings & Expected Weekly
Stock Returns Announcements
Model 1 Martingale
E j,q = Ej,q−4
Model 2
Martingale with Non-Constant Drift
E j,q = Ej,q−4 + 1 3 Ej,q−1 − Ej ,q−5 + Ej,q−2 − Ej,q−6
+ Ej,q−3 − Ej,q−7
Model 3 Market Model
E(rjt) = αj + βj rmt + eit
Examine Information
Contained Magnitude
of an Unanticipated
Earnings Change,
Comparison of
Reported and Expected
EPS
3 Schemes
A) Absolute Residual
Scheme
Favourable: Ej,q > E j,q
Neutral: Ej,q = E j,q
Unfavorable: Ej,q < E j,q
B) 20 % Residual
Scheme
Favourable: Ej,q > 1.2 E j,q
Neutral: 1.2 E j,q ≥ Ej,q ≥ 0.8 E j,q
Unfavorable: Ej,q < 0.8 E j,q
C) 40 %
Residual Scheme
Favourable: Ej,q > 1.4 E j,q
Neutral: 1.4 E j,q ≥ Ej,q ≥ 0.6 E j,q
Unfavorable: Ej,q < 0.6 E j,q
Total: 2 Income expectation models
and 3 residual classifications result
in 6 combinations.
1A, 1B, 1C
2A, 2B, 2C
1.6.3 Database
To test the weak form of efficiency has been used daily closing price data CNX Nifty
were collected for the study period from 1st April 2009 to 31
st March 2014. However, it
has been found that only 29 firms were listed continuously throughout in the study period
of six years and also 7 sectors of CNX Nifty index. These 29 firms have been selected of
7 sectors and also selected from sectoral indices 7 sectors, according to available data. In
this case of 29 firms, 2 firms are from Consumer Goods, 5 firms of IT sector, 5
companies of petroleum/oil and gas sector, 1 firm in the telecom sector, 1 firm in the
Construction material, 10 of Banks and finance sector, and 5 firms of automobile sector.
To test the semi–strong form of efficiency on the basis of publicly available information
on quarterly earning announcements for the CNX Nifty were collected for the study
period from 1st April 2009 to 31
st March 2014. The researcher found a list of 21
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constituent firms of five sectors (consumer goods, automobile, IT, petroleum/oil and gas,
Banks and finance service) of the CNX Nifty index listed continuously throughout the
study period. Then the researcher collected three sets of data. The semi–strong efficiency
data consists of quarterly earning announcements made by the sample firms. The
researcher has used three sets of data, this includes the dates on which the Board of
Directors meets and approves the quarterly financial results of the firm. These were
obtained from the websites of the respective firms and NSE. The second set of data
consists of the weekly closing prices and the actual quarterly earnings per share of the
continuously listed stocks on the Nifty indexes for the period of the study. This data were
collected from Capitaline package, and the Prowess database of Centre for Monitoring
Indian Economy (CMIE) website. The third set consists of the ordinary CNX Nifty index
prices compiled and published by the NSE for the study period of six years.
1.6.4 Scope of Study
The scope of present study was confined only to all stocks listed on the CNX Nifty index
for a period of five years from 1st April 2009 to 31
st March 2014. The empirical analysis
of weak form efficiency was based on available data and sectoral indices from 29 firms
have been selected from 7 selected sectors (as shown in following Table1.6.4.1 and Table
1.6.4.3). And also for testing the semi–strong form of efficiency, 21 constituent firms of
five sectors of the Nifty index listed continuously throughout the study period (as shown
in following Table4 Table 1.6.4.2). Data were collected through the issues of Economic
Times over this period.
Table 1.6.4.1
List of Constituent Firms
Sl.
No. Sectors Firms Total
1 Consumer
Goods ITC Ltd., & INDUNILVR Ltd. 2
2 IT
Tata Consultancy Services Ltd., Infosys Ltd., HCL
Technologies Ltd., Wipro Ltd., & Tech Mahindra
Ltd.
5
3
Petroleum /
Oil and Gas
Sector
(Oil & Natural Gas Corporation Ltd., Reliance
Industries Ltd., GAIL (India) Ltd., Cairn India Ltd.,
& Bharat Petroleum Corporation Ltd.
5
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4 Telecom Bharti Airtel Ltd. 1
5 Construction
Material Larsen & Toubro Ltd. 1
6 Banks and
Finance
HDFC Bank Ltd., State Bank of India, ICICI Bank
Ltd., Housing Development Finance Corporation
Ltd., Axis Bank Ltd., Kotak Mahindra Bank Ltd.,
Bank of Baroda, Punjab National Bank, IndusInd
Bank Ltd., & IDFC Ltd.
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7 Automobile
Tata Motors Ltd., Maruti Suzuki India Ltd.,
Mahindra and Mahindra Ltd., Bajaj Auto Ltd., &
Hero MotoCorp Ltd
5
Total 7 Sectors 29 firms
Table 1.6.4.2
List of Sectoral Indices
Sl. No. 1 2 3 4 5 6 7
Sectoral Indices AUTO FMCG Finance Bank Energy IT PHARMA
Table 1.6.4.3
List of the Constituent firms of NSE Quarterly Earnings Announcements
Sl.
No. Sectors Firms Total
1 Consumer
Goods (ITC Ltd.,) 1
2 IT Tata Consultancy Services Ltd., Infosys Ltd., HCL
Technologies Ltd., Wipro Ltd., & Tech Mahindra Ltd. 5
3
Petroleum
/ Oil & Gas
Sector
Oil & Natural Gas Corporation Ltd., Reliance Industries
Ltd., Cairn India Ltd., & Bharat Petroleum Corporation
Ltd.
4
4 Banks and
Finance
HDFC Bank Ltd., ICICI Bank Ltd., Housing
Development Finance Corporation Ltd., Axis Bank Ltd.,
Kotak Mahindra Bank Ltd., Bank of Baroda, Punjab
National Bank, & IDFC Ltd.
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5 Automobile Maruti Suzuki India Ltd., Mahindra and Mahindra Ltd., &
Hero MotoCorp Ltd. 3
Total 5 Sectors 21 firms
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1.6.5 Source of Data
Sources of data to study of weak and semi–strong forms efficiency have collected from 7
sectors for weak form efficiency and 5 sectors for semi–strong form efficiency out of 22
sectors on CNX Nifty of NSE. This study is dependent on the secondary data gathered
some from online databases. Online databases were extensively used to source
information related the daily traded data bases such as the Capitaline package, Economic
Times Website, EBSCO, SSRN, Google scholar, Wileyonlinelibrary, www.nseindia.com,
www.moneycontrol.com, www.yahoofinance.com, www.googlefinance.com, and etc.
1.7 Limitations
The researcher was aware about the limitations of research and this research was not an
exception. The present research work was undertaken to maximize objectivity and
minimize the errors. However, there were certain limitations of the study which were to
be taken in consideration for the present research work. In carrying out the present
research, the researcher faced some limitations, the important of which are presented
hereunder:
Global financial crisis could have affected the results of the study, but the
researcher had tried to overcome this difficulty by study during after finished the
time period (2007 October to 2008 April), i.e., the time period in which Indian
markets were severely hit by the financial crisis.
The study is confined to only 7 sectors out of CNX Nifty of NSE in event study.
Some of firm data in period of study unavoidable.
This study did not control for the potential contamination of other information
releases on the stock returns at the earnings announcement dates.
The problems were compounded by the use of weekly data, since stock price
increases prior to the announcement and the valuation effects of other
announcements that occurred in the same month were included in the
announcement return.
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A vast majority of the studies have used annual or monthly or weekly data to
check the semi–strong form efficiency of the stock market, however, daily data on
returns severely boosts the accuracy of semi–strong tests. When the
announcement of an event can be dated to a certain day, daily data allow precise
measurements of the speed of the stock price response, the central issue for
market efficiency.
Most of the studies did not include the transaction costs while calculating the
abnormal returns from using a particular strategy. It may not only reduce the
profitability of the strategy making it statistically insignificant but also may
eliminate it totally.
Most of the studies have focused on short–return windows as they assume that
any lag in the response of prices to an event is short–lived. However, if we
assume that stock prices adjust slowly to information, then we must also examine
returns over longer horizons to get a full view of market efficiency or
inefficiency.
Restricting the information that is harder to measure strong form of efficiency
market and perhaps also more expensive to acquire. For example, it is not usually
asserted that a market is efficient with respect to insider information since this
information is not widely accessible and hence cannot be expected to be fully
incorporated in the current price. Strong form efficiency can be tested not directly,
e.g. by considering the performance of fund manager and testing if they manage
to gain profits net of risk premium after accounting for the cost of obtaining
private information.
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1.8 An Overview of the Thesis
For analytical convenience, the present dissertation has been organized into five chapters.
A short overview of each chapter was presented in the following as shown in Figure
1.8.1.
Chapter 1: In this chapter, an introduction to concepts of efficiency market hypothesis,
market efficiency, and three form of market efficiency were provided. It then went on to
present the background of the study, statement of the problem, need of the study, purpose
of the study, significance of the study, objectives, hypotheses, methodology of the
research, scope and limitations of the research, and also the chapter scheme of the thesis.
Chapter 2: In the second chapter, relevant theoretical areas and review of literature were
presented. It also reviewed certain topics related to the research and research variables
used in the present study. It then went on to present the summaries of literature reviews.
Chapter 3: In the third chapter, research design appropriate for achieving the defined
objectives was explored.
Chapter 4: In the fourth chapter, the data were analyzed and interoperated. In analyzing
the data, certain statistical tools and also software packages include Statistical Software
Package (SPSS), Eviews, Event Study Metric and Excel Software of MS Office were
used.
Chapter 5: In the fifth chapter, summary of the findings was presented. Finally,
conclusions and suggestions as well as contributions and implications in addition to
recommendations for future research were brought up.