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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.

10

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.

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Figure 1.8.1: An Overview of the Thesis