Prashanth 0385 Seasonality Stock Market

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A RESEARCH REPORT ON SESONALITY IN INDIAN

STOCK MARKET

A Dissertation Submitted In Partial Fulfillment Of The Requirements For

MBA Degree Of Bangalore UniversitySubmitted By

PRASHANTH P.BReg. No 03XQCM6073

Under The Guidance Of

Dr. T.V. NARASIMHA RAOProfessor,MPBIM

(Internal Guide)

03%,5/$,167,787(2)0$1$*(0(17

$662&,$7(%+$5$7,


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MP Birla Institute Of Management

2


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DECLARATION

I hereby declare that the project work embodied in this dissertation titled A

Research Report on Sesonality in Indian stock market has been carried

out by me under the guidance and supervision ofDr. T.V. Narasimha Rao,

Professor (Internal Guide), M.P.Birla Institute of Management, Bangalore.

I also declare that this dissertation has not been submitted to any other

university/ Institution for the award of any other Degree/Diploma.

Place: Bangalore (Prashanth P.B)

Date: 17th

June, 2005 Reg. No: 03XQCM6073


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Principals Certificate

I hereby certify that this dissertation is a bonafide research work undertaken

and completed by Ms. Prashanth P B under the guidance and supervision of

Dr. T.V. Narasimha Rao (Internal Guide) MPBIM, Bangalore.

Place: Bangalore (Dr. N.S. Malavalli)

Date: Principal MPBIM,

Bangalore


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Guide Certificate

This to certify that this report titled A Research Report on Sesonality in

Indian stock market has been prepared by Ms. Prashanth P.B, Student

Executive,M P Birla Institute of Management in partial fulfillment of the

award of the degree of Master of Business Administration at Bangalore

University, under my guidance and supervision.

This report or a similar report on this topic has not been submitted for any

other examination and does not form a part of any other course undergone

by the candidate.

Place: Bangalore (Dr T.V. Narasimha Rao)

Date:17-06-2005 Professor, MPBIM

Bangalore


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ACKNOWLEDGEMENT

I sincerely thank Dr. Nagesh Malavalli (Principal), M.P.Birla

Institute of Management, Bangalore for granting me the permission to

do this Dissertation Project.

I would like to express my immense gratitude to Dr. T.V. Narasimha

Rao, M.P.Birla Institute of Management, Bangalore for his guidance,

continuous encouragement and valuable suggestions at every stage of

the project.

I extend my deep sense of gratitude to all my family and friends who

have directly or indirectly encouraged and helped me to complete this

project successfully.

I would like to extend my thanks to all the unseen hands that have

made this project possible.

Place: Bangalore (Prashanth P.B)

Date: 17th

June 2005 03XQCM603


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CHAPTERS CONTENTS PAGE NOS

EXECUTIVE SUMMARY

1INTRODUCTION 2-28

1.1 Problem statement 3

1.2 Background of the study 4

1.3 Justification and Significance 4

1.4 Objectives of the study 5

1.5 Hypothesis 6

1.6 Theoretical background the study 8

2 REVIEW OF LITERATURE29-37

3RESEARCH METHODOLOGY

38-41

4.1 Study Design 38

4.2 Types of Data 38

4.3 Sample Frame 38

4.4 Sample Technique 38

4.5 Data Gathering Procedures 38

4.6 Research Techniques 39

4.7 Statistical Method 40

4.8 Scope of the study 41

4.9 Limitation of the study 41

4DATA ANALYSIS AND INTERPRETATION 42-44

5CONCLUSIONS

45

6 ANNEXURES

6.1 Bibliography 46

6.2 Data


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List of Tables

TABLE NO TITLE PAGE NOS

1 Constituents list of S&P CNX NIFTY 16

2 Analysis of Variance from April 2000 to

March 2005

42

3 Analysis of Variance from Oct 2002 to March

2005

42

4 Analysis of Variance from April 2000 to Sep

2002

42

5 Result of F-Test 43

6 Result of Z-Test 43

7 Computation of Mean Returns 44

8 Computation of Standard Deviation 44


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Abstract

This study empirically analyzes the pattern of daily common stock returns and

tests for the presence of the day-of-the-week effect in the Indian Stock

Market. The objective of this paper is to examine the day-of-the-week effect

in the Indian Stock Market. The paper in particular studies the day-of-the-

week-effect with respect to the settlement system followed. The daily closing

price data on the S&P Nifty Index for the period April 2000-March 2005 has

been used in the study. The first step was testing of the null hypothesis that

the mean returns on all trading days of the week are not equal using F- test.The null hypothesis that the means returns are not equal across all trading

days was true at 5% significance level. Then second part involved testing of

the null hypothesis that the mean returns on Monday and Friday are not equal

using Z- test. The null hypothesis that the mean returns on Monday and Friday

are not equal was true at 5% significance level.


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CHAPTER 1

Introduction


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Introduction

The day of the week effect refers to the observation that equity returns are not

independent of the day of the week. This effect was first documented by

Osborne(1962) and has subsequently been examined in numerous studies.

Such empirical observations have been mainly verified in the USA. The last

trading days of the week, particularly Friday, are characterised by positive and

substantially positive returns, while Monday, the first day of the week, and

differs from other days, even producing negative returns (Cross, 1973;

Lakonishok and Levi, 1982; Hirsch, 1986). Such an effect also seems to be

present in the equity markets of other countries such as Canada, Japan and

Australia where the same pattern as in USA is repeated. In other equity

markets such as Japan and Australia negative returns have been observed on

Tuesday also.

The equity markets across many countries seem to exhibit the day-of-the-

week effect. Studies have also been conducted to identify the causes behind

the patterns observed. Institutional features of the national stock markets, such

as settlement procedures and in particular, delays between trading andsettlement in the stocks, pricing misquotes and measurement errors,

specialists behaviour, or dividend patterns have been put forward as the main

reasons for such an effect. However none of these reasons have been

conclusively proved to be the cause of the effect. Explanations of the day-of-

the-week effect based on human nature have also been put forward to explain

the patterns observed (Jacobs and Levy, 1988). The human behaviour of

disclosing good news quickly on the weekdays and waiting for the weekend to

disclose the bad news so as to allow the market the weekend to absorb the

shock, have been explanations provided for the day-of-the-week effect.

The human behaviour of disclosing good news quickly on the weekdays and

waiting for the weekend to disclose the bad news so as to allow the market the


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weekend to absorb the shock, have been explanations provided for the day-of-

the-week effect. This paper presents the findings of a study on various aspects

of the behavior of share prices on both the National Stock Exchanges. It

presents an empirical analysis of day-of-the week effects in the benchmarkindices of the National Stock Exchange.

Problem Statement

The testing for market anomalies in stock returns has become an active field

of research in empirical finance and has been receiving attention from not

only in academic journals but also in the financial press. Among the more

well-known anomalies are the size effect, the January effect and the day-of-

the week effect. The day of the week effect is a phenomenon that constitutes a

form of anomaly of the efficient capital markets theory. According to this

phenomenon, the average daily return of the market is not the same for all

days of the week, as we would expect on the basis of the efficient market

theory.

This research aims an empirical analysis of week effects in the benchmark

indices of the National Stock Exchange

Background of the study

There is an evidence suggests that stock prices can be predicted with a fair

degree of reliability. Two competing explanations have been offered for such

behavior. Proponents of EMH (e.g. Fama and French [1995]) maintain that

such predictability results from time-varying equilibrium expected returns

generated by rational pricing in an efficient market that compensates for the

level of risk undertaken. Critics of EMH (e.g. La Porta, Lakonishok, Shliefer,

and Vishny [1997]) argue that the predictability of stock returns reflects the

psychological factors, social movements, noise trading, and fashions or "fads"


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of irrational investors in a speculative market. The question about whether

predictability of returns represents rational variations in expected returns or

arises due to irrational speculative deviations from theoretical values has

provided the impetus for fervent intellectual inquiries in the recent years.

Justification and Significance

In recent years the testing for market anomalies in stock returns has become

an active field of research in empirical finance and has been receiving

attention from not only in academic journals but also in the financial press.

Among the more well-known anomalies are the size effect, the January effect

and the day-of-the week effect. The day of the week effect is a phenomenon

that constitutes a form of anomaly of the efficient capital markets theory.

According to this phenomenon, the average daily return of the market is not

the same for all days of the week, as we would expect on the basis of the

efficient market theory.

Earlier studies have found the existence of the day of the week effect not only

in the USA and other developed markets but also in the emerging markets like

Malaysia, Hong Kong, Turkey). For most of the western economies, (U.S.A.,

U.K., Canada) empirical results have shown that on Mondays the market has

statistically significant negative returns while on Fridays statistically

significant positive returns. In other markets such as Japan, Australia,

Singapore, Turkey and France the highest negative returns appear on

Tuesdays.


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Purpose and Objective of study

T h e p u r p o s e o f t h e s tudy is the an a lysis o f p a tt e rn s in d a i ly c o m m o n

stock returns a n d da y - o f - the w e e k e f fe c ts in the be n c h m ar k ind ices o f the

N a t i ona l Stock Ex c h a n g e M an y a u t h o r s h a v e c o n c lu d e d f r o m th e

ex iste n c e of the Day of the Week Effect an d o t h e r c a l e n d a r a n o m al ies

tha t the c api ta l m a rke t s a re ineff ic ien t .Th is wou ld be o f spec ia l r e levance ,

becau se desp i te inc reas ing a tten t ion to the Ind ian s tock marke t , l it tl e

em pir ica l ana lys i s has been per fo rm ed on the cap i ta l marke ts in Ind ia .

Hypothesis

Day of the week effect in Indian stock

According to this phenomenon, the average daily return of the market is not

same for all days of the week

H0: Return Monday= Return Tuesday= Return Wednesday= Return Thursday= Return Friday

H1:Return Monday5HWXUQ Tuesday5HWXUQ Wednesday5HWXUQ Thursday5HWXUQ Friday

The hypothesis is two tailed and the hypothesis is tested for 5% level of

significance

There is a belief that there is a selling pressure on Friday due to the weekend

and everybody is under pressure to square their positions. To test this

perception the following hypothesis that is mean returns are equal across

Monday and Friday.

H0: Return Monday= Return Friday

H1: Return Monday5HWXUQ Friday

The hypothesis is two tailed and the hypothesis is tested for 5% level of

significance


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THEORETICAL BACKGROUND TO THE STUDY

For many years, it was believed (especially under academics) that stock prices

follow random walks, i.e. the best prediction of the next periods stock price

are todays price plus a drift term. This would imply that stock returns are not

predictable. There is growing evidence that stock market returns are

predictable to some degree. The literature documents predictability of stock

index returns from lagged returns lagged financial and macroeconomic

variables, and calendar dummies.

The guiding principle that asset markets are efficient and stock prices can be

described by a random walk is simply stated, but its implications are manyand subtle. The Efficient Market Hypothesis (EMH) has its roots in the

pioneering work of Gibson (1889) who writes that when shares become

publicly known in an open market, the value which they acquire may be

regarded as the judgment of the best intelligence concerning them, Gibson

(1889, p.11). It should be stressed that the views regarding the EMH are not

the results from doctrinaire beliefs, but result from a large body of empirical

work. The EMH may be expressed in a number of alternative ways and the

differences between these alternative representations can become rather

entangled. The general idea behind the EMH is that asset prices are

determined by the supply and demand in a competitive market with rational

investors. These rational investors gather relevant information very rapidly

and immediately incorporate this information into stock prices. If this

information is immediately incorporated into prices, only new information,

i.e. news, should cause change in prices. Since news is unpredictable by

definition, price changes (returns) should be unpredictable. Contrary to most

preceding research, Malkiel (1992) offers an explicit definition of the EMH:

A capital market is said to be efficient if it fully and correctly reflects all

relevant information in determining security prices. Formally, the market is


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said to be efficient with respect to some information set if security prices

would be unaccepted by revealing that information to all participants.

Moreover, efficiency with respect to an information set implies that it is

impossible to make economic profits by trading on the basis of that

information set.

To test the efficient market hypothesis, it is necessary to specify a model of

normal expected returns. The classic assumption used to be that expected

stock returns are constant over time, but there has been an increasingly

amount of literature that provides evidence against this assumption. In

particular, dividend yields and interest rates seem to have some significant

predictive power. This phenomenon occurs over business cycle and longer

horizons. Technical systems for predicting daily and weekly stock returns are

still close to useless after transaction costs (see, e.g., Hawanini and Keim,

1995). While most financial economists seem to have accepted these views,

they do not agree about the degree of the predictability. Evidence of return

predictability does not necessarily mean that markets are not (reasonably)

efficient. Because of time-varying expected returns due to changing business

conditions and risks, returns can be partly predictable, even when the EMH

holds. Consequently, testing for efficient markets critically depends on the

assumed model for the returns.

Trading in Indian stock exchanges is limited to listed securities of public

limited companies. They are broadly divided into two categories, namely,

specified securities (forward list) and non-specified securities (cash list).

Equity shares of dividend paying, growth-oriented companies with a paid-up

capital of atleast Rs.50 million and a market capitalization of atleast Rs.100

million and having more than 20,000 shareholders are, normally, put in the

specified group and the balance in non-specified group.


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Two types of transactions can be carried out on the Indian stock exchanges:

a) spot delivery transactions "for delivery and payment within the

time or on the date stipulated when entering into the contract which shall not

be more than 14 days following the date of the contract"b) Forward transactions "delivery and payment can be extended by

further period of 14 days each so that the overall period does not exceed 90

days from the date of the contract". The latter is permitted only in the case of

specified shares. The brokers who carry over the outstanding pay carry over

charges (cantango or backwardation) which are usually determined by the

rates of interest prevailing.

A member broker in an Indian stock exchange can act as an agent, buy and

sell securities for his clients on a commission basis and also can act as a trader

or dealer as a principal, buy and sell securities on his own account and risk, in

contrast with the practice prevailing on New York and London Stock

Exchanges, where a member can act as a jobber or a broker only.

The nature of trading on Indian Stock Exchanges are that of age old

conventional style of face-to-face trading with bids and offers being made by

open outcry. However, there is a great amount of effort to modernize the

Indian stock exchanges in the very recent times.

Bombay Stock Exchange (BSE) and National Stock Exchange of India Ltd.

(NSE) are the two primary exchanges in India. In addition there are 22

Regional Stock Exchanges. However BSE and NSE have established

themselves as the two main exchanges and account for about 80% of the

volume traded in Indian equity markets. Approximately NSE and BSE are

equal in size in terms of daily volume traded. NSE has about 1500 shares

which are traded and has a total market capitalisation of around Rs. 9,21,500


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Crores (Rs. 9215-bln). BSE has over 6000 stocks traded and has a total

market capitalisation of around Rs. 9,68,000 Crores (Rs.9680-bln). Most key

stocks are available on both exchanges and hence the investor could buy them

on either exchange. Both exchanges have a different settlement cycles. Theprimary index of BSE is BSE Sensex, which comprises of 30 Stocks. NSE has

its own S& P NSE 50 Index (Nifty) which comprises of fifty stocks. Both

these indexes are calculated on the basis of market capitalisation and contain

the most highly traded shares from the key sectors. Daily volume on both

exchanges is approximately Rs. 4500 Crores each. (Rs. 45-bln.) The key

regulator governing Stock Exchanges, Brokers, Depositories, Depository

participants, Mutual Funds, FIIs and other participants in Indian secondary

and primary market is Securities and Exchange Board of India (SEBI) Ltd.

National Stock Exchange (NSE)

The National Stock Exchange (NSE), located in Bombay, is India' s first debt

market. It was set up in 1993 to encourage stock exchange reform through

system modernization and competition. It opened for trading in mid-1994. It

was recently accorded recognition as a stock exchange by the Department of

Company Affairs. The instruments traded are, treasury bills, government

security and bonds issued by public sector companies.

The number of members trading on the exchange has been on a steady

increase, helping integrate the national market and providing a modern system

with a complete audit trail of all transactions.

The National Stock Exchange (NSE) is India' s leading stock exchange

covering various cities and towns across the country. NSE was set up by

leading institutions to provide a modern, fully automated screen-based trading

system with national reach. The Exchange has brought about unparalleled

transparency, speed & efficiency, safety and market integrity. It has set up


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facilities that serve as a model for the securities industry in terms of systems,

practices and procedures.

NSE has played a catalytic role in reforming the Indian securities market in

terms of microstructure, market practices and trading volumes. The market

today uses state-of-art information technology to provide an efficient and

transparent trading, clearing and settlement mechanism, and has witnessed

several innovations in products & services viz. demutualisation of stock

exchange governance, screen based trading, compression of settlement cycles,

dematerialisation and electronic transfer of securities, securities lending and

borrowing, professionalisation of trading members, fine-tuned risk

management systems, emergence of clearing corporations to assume

counterparty risks, market of debt and derivative instruments and intensive

use of information technology.

S&P CNX Nifty

The "Nifty-Fifty" was a group of large-cap growth stocks that became the

market' s darlings in the late 1960s and into the early 1970s. They were great

companies that became known as "one-decision" stocks - stocks that you

should buy, no matter how expensive, and hold forever. The Nifty-Fifty term

was coined because there were about fifty of these companies with very high

price-to-earnings (P/E) ratios. Many had a P/E of fifty, or even higher. For

example, at year-end 1972 Xerox traded at forty-nine, Avon at sixty-five, and

Polaroid at ninety-one times earnings. The bear market of 1973-74 saw these

stocks collapse in a manner that was quite similar in speed and size to the

collapse that occurred in technology stocks, and large-cap growth stocks in

general, in early 2000 when the that bubble burst.


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S&P CNX Nifty is a well diversified 53 stock index accounting for 24 sectors

of the economy. It is used for a variety of purposes such as benchmarking

fund portfolios, index based derivatives and index funds.

S&P CNX Nifty is owned and managed by India Index Services and ProductsLtd. (IISL), which is a joint venture between NSE and CRISIL. IISL is India' s

first specialised company focused upon the index as a core product. IISL have

a consulting and licensing agreement with Standard & Poor' s (S&P), who are

world leaders in index services.

The average total traded value for the last six months of all

Nifty stocks is approximately 58% of the traded value of all stocks on the

NSE.

Nifty stocks represent about 60% of the total market

capitalisation as on on March 31, 2005.

Impact cost of the S&P CNX Nifty for a portfolio size of Rs.5

million is 0.07%

S&P CNX Nifty is professionally maintained and is ideal for

derivatives trading


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Constituents list of S&P CNX NIFTY

Asea Brown Boveri Ltd. Electrical Equipment

ssociated Cement Companies Ltd. Cement And Cement Products

Bajaj Auto Ltd.

Automobiles - 2 And 3

Wheelers

Bharat Heavy Electricals Ltd. Electrical Equipment

Bharat Petroleum Corporation Ltd. Refineries

Bharti Tele-Ventures Ltd. Telecommunication - Services

Cipla Ltd. Pharmaceuticals

Colgate-Palmolive (India) Ltd. Personal Care

Dabur India Ltd. Personal Care

Dr. Reddy' s Laboratories Ltd. Pharmaceuticals

Gas Authority of India Ltd. Gas

Glaxosmithkline Pharmaceuticals

India Ltd. Pharmaceuticals

Grasim Industries Ltd. Diversified

Gujarat Ambuja Cements Ltd. Cement And Cement Products

HCL Technologies Ltd. Computer Software

HDFC Bank Ltd. Banks

Hero Honda Motors Ltd.

Automobiles - 2 And 3

Wheelers

Hindalco Industries Ltd. Aluminium

Hindustan Lever Ltd. Diversified

Hindustan Petroleum CorporationLtd. Refineries

Housing Development Finance

Corporation Ltd. Finance - Housing

I T C Ltd. Cigarettes

ICICI Banking Corporation Ltd. Banks

Indian Petrochemicals Corporation

Ltd. Petrochemicals

Infosys Technologies Ltd. Computers - Software

Larsen & Toubro Ltd. Engineering

Mahanagar Telephone Nigam Ltd. Telecommunication - ServicesMahindra & Mahindra Ltd. Automobiles - 4 Wheelers

Maruti Udyog Ltd. Automobiles - 4 Wheelers

ational Aluminium Company Ltd. Aluminium

il & Natural Gas Corporation Ltd. Gas

Oriental Bank of Commerce Banks

Punjab & National Bank Banks


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Ranbaxy Laboratories Ltd. Pharmaceuticals

Reliance Energy Ltd. Power

Reliance Industries Ltd. Petrochemicals

Satyam Computer Services Ltd. Computers - Software

Shipping Corporation of India Shipping

State Bank of India Banks

Steel Authority of India Ltd Steel & Steel Products

Sun Pharmaceutical Industries Ltd. Pharmaceuticals

Tata Chemicals Ltd. Diversified

Tata Consultancy Services Ltd. Computers - Software

Tata Engineering & Locomotive

Co. Ltd. Automobiles - 4 Wheelers

Tata Iron & Steel Co. Ltd. Steel And Steel Products

Tata Power Co. Ltd. Power

Tata Tea Ltd. Tea And CoffeeVidesh Sanchar Nigam Ltd. Telecommunication - Services

Wipro Ltd. Computers - Software

Zee Telefilms Ltd. Media & Entertaintment

India Index Services & Products Ltd. (Iisl)

India Index Services & Products Ltd. (IISL) is a joint venture between the

National Stock Exchange of India Ltd. (NSE) and CRISIL Ltd. (formerly the

Credit Rating Information Services of India Limited). IISL has been formed

with the objective of providing a variety of indices and index related services

and products for the capital markets.

IISL has a consulting and licensing agreement with Standard and Poor' s

(S&P), the world' s leading provider of investible equity indices.

OBJECTIVES OF IISL

IISL pools the index development efforts of CRISIL and NSE into a

coordinated whole - India' s first specialised company focused upon the index

as a core product. IISL has the following objectives:


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To develop, construct and maintain indices on Indian equities and

commodities that serve as useful market performance benchmarks and are the

underlying indices for derivatives trading

To develop related products and services which can be used by

investors for managing their exposures in the equity and commodity markets

To provide data and information on the trading activity in the

Indian stock markets

To provide market participants with value added research on the

Indian equity and Commodity markets

All the erstwhile indices of NSE and CRISIL, such as Nifty, Nifty Junior,

Defty, CRISIL 500, CRISIL Midcap 200 index etc. have been transferred to

IISL which now maintains, develops, compiles and disseminates the indices.

Calendar effects are anomalies in stock returns that relate to the calendar, such

as the day-of-the-week, the month-of-the-year, or holidays. Two leading

examples are the Monday effect and the January effect. Economically small

calendar specific anomalies need not violate no-arbitrage conditions, but the

reason for their existence, if they are indeed real, is intriguing. Much effort

continues to be devoted to research on calendar effects. Yet, the literature

remains open about the significance of these effects for asset markets. One

reason is that the discovery of specific calendar effects could be a result of

data mining. Even if there are no calendar anomalies, an extensive search or

data mining exercise across a large number of possible calendar effects canyield significant results of an anomaly by pure chance.1 Another reason

data mining is a plausible explanation is that theoretical explanations have

been suggested only subsequent to the empirical discovery of the

anomalies.


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The universe of possible calendar effects is not given ex ante from economic

theory. Rather, the number of different calendar effects that potentially could

be analyzed is only bounded by the creativity of interested researchers. Sincean extensive empirical analysis of calendar effects is likely to suffer from data

mining problems, it is therefore surprising that there is little work that aims to

limit the problem.

The test combines and incorporates the information from all calendar

anomalies to achieve good power properties without compromising test size

by exploiting the correlation structure that is specific to this testing problem.

The new test is asymptotically F-distributed. However, we implement a

bootstrap version of the test that diminishes possible small sample problems.

New test of calendar effects can be interpreted as a generalized-F test. It is

related to some recent methods for comparing forecasting models that have

been proposed by White (2000) and Hansen (2001), who builds on results of

Diebold & Mariano (1995) and West (1996). These tests exploit indirectly the

sample information about the dependence across forecasting models, which

are being compared. This is analogous to our generalized-F test because it

depends on the covariance of returns given the calendar effects being studied.

Calendar Effects

Often calendar effect are written short for possible calendar effect. Hence,

calendar effect need not imply that there is an anomaly associated with the

possible calendar effect, onl y the alternative hypothesis that it may exist.

Day-of-the-week

This effect states that expected return, or standardized return, are not the

same for all weekdays. This effect was first documented by Osborne (1962),


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and subsequently analyzed by Cross (1973), French (1980), Gibbons & Hess

(1981), Lakonishok & Levi (1980), Smirlock & Starks (1983), Keim &

Stambaugh (1983), Rogalski (1984) and Jaffe & Westerfield (1985). In our

universe, we include the five day-of-the-week calendar effects: Monday,Tuesday,Wednesday, Thursday, and Friday. The Friday effect considers the

return from the preceding trading days closing price (typically a Thursday) to

Fridays closing price, and similarly for the other days. The returns on

Mondays are found to be negative in many studies, which is commonly

referred to as the weekend-effect.

Month-of-the-year

Rozeff and Kinney (1976) were the first to document evidence of higher mean

returns in January as compared to other months. Using NYSE stocks for the

period 1904-1974, they find that the average return for the month of Januarywas 3.48 percent as compared to only .42 percent for the other months. Later

studies document the effect persists in more recent years: Bhardwaj and

Brooks (1992) for 1977-1986 and Eleswarapu and Reinganum (1993) for

1961-1990. The effect has been found to be present in other countries as well

(Gultekin and Gultekin, 1983). The January effect has also been documented

for bonds by Chang and Pinegar (1986). Maxwell (1998) shows that the bond

market effect is strong for non-investment grade bonds, but not for investment

grade bonds. More recently, Bhabra, Dhillon and Ramirez (1999) document a

November effect , which is observed only after the Tax Reform Act of 1986.

They also find that the January effect is stronger since 1986. Taken together,

their results support a tax-loss selling explanation of the effect. We interact


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day-of-the-week with month-of-the-year, (Mondays in December,

Wednesdays in June, etc.) to add 60 (= 5 12) calendar effects.

Week-of-the-month

Weeks are constructed such that the first trading day of the month defines the

first day of the first week. If the first trading day is a Thursday, the first week

consists of two days (a Thursday and a Friday). The last week-of-the-month is

defined similarly, which means there will often be fewer than five days in a

week. Week-of-the-month effects are discussed in Ariel (1987), Lakonishok

& Smidt (1988), and Wang, Li & Erickson (1997). This adds 65 (= 5 + 5

12) effects to our universe.

Semi-month

Definition of semi-months follows that of Lakonishok & Smidt (1988). The

trading days are partitioned into two sets. The first set consists of trading days

for which the date is 15 or less, and the other set contains dates that are 16 or

higher. By interacting these two semi-month of- the-year effects with month-

of-the-year effects we obtain another 24 semi-months that adds another 26 (=

2 + 2 12) effects to our universe.

Turn-of-the-month

There are eight effects that relate to turn-of-the-month, one for each of the lastfour trading days of the month and one for each of the first four trading days

of the month. This type of calendar effects is discussed in Ariel (1987),

Lakonishok & Smidt (1988), and Hensel & Ziemba (1996).


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End-of-Year

The days are grouped at the end of December into three calendar effect, which

follows Lakonishok & Smidt (1988):

Pre-Christmas from mid-December: the trading days from mid

December up to, but no including, the last trading day before

Christmas, (e.g.,December 15th 23rd).

Between Christmas and New Year: from the first trading day after

Christmas up to, but not including, the last trading day before New

Years Day.

Pre-Christmas and New Year: the last trading day before Christmas,

and the last trading day before New Years Day.

Holiday-effects

It can be classified into pre- and post-holiday effect. Pre-holidays are those

trading days which directly precede a day where the market is closed, but

would normally be open for trading. Post-holidays are those trading days that

follow pre-holidays. This adds two calendar effects to our universe.

The Indian Stock market had been in the clutches of the regulators for too

long and the process of liberalization has begun in the last decade only. The

Indian market operates in different institutional, regulatory, and tax

environments and hence there is a need to test for the existence of the calendar

effects in the Indian market.


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CHAPTER 2

Review of literature


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Review of literature

Seasonality and the Nontrading Effect on Central-European Stock

Markets. Vit Bubak and Filip Zikes September 27, 2004

Abstract

The paper investigates seasonality and the nontrading effect on Central-

European Stock Markets within the framework of periodic autoregressive

model for both mean and volatility of stock returns. We find significant day-

of-week effects in the mean of returns on the Czech index PX-D and Polish

WIG, and significant seasonality in the volatility of the Hungarian BUX

index. Similarly, our empirical results indicate the presence of the non-trading

effect in the mean of Czech and Polish stock returns and in the volatility of

the Hungarian BUX.

Introduction

The analysis of seasonal patterns in the behavior of stock market returns has

been of considerable interest during recent decades. The reason behind this

curiosity remains clear: any predictable pattern in stock returns and variances

may provide investors with returns in excess of the stock market average or

from a specific portfolio benchmark. This paper focuses on one of the most

common seasonal patterns, the so called day-of-week effect. It has been

observed in numerous studies that the distribution of stock returns may be

different across the days of the week.

The stock price behavior can be examined in either of two ways. One

possibility assumes a combination of return and volatility techniques. The

study at hand is illustrative of this option and we will describe it later. The

alternative is to entertain each of the two techniques mentioned separately.


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Thus, there exists a set of studies which focus on the calendar patterns in

mean returns only

Conclusion

In the empirical finance literature, seasonality effects have been studied

extensively in both equity and foreign exchange markets. Still, the analysis of

seasonal patterns on stock markets in Central Europe has found its way to

only a limited number of research papers. The paper at hand extends this

empirical work in several ways. It investigates the seasonality and the

nontrading effect on Czech (PX-D), Polish (WIG) and Hungarian (BUX)

stock indices within the framework of periodic autoregressive models for both

the mean and variance of stock returns suggested in Franses and Paap (2000).

Our results provide evidence of significant day-of-week effects in the mean of

Czech and Polish stock returns. In addition, a significant seasonality has been

found in the volatility of the Hungarian BUX index. In a similar way, we find

significant non-trading effect in the mean of PX-D and WIG stock indices and

in the volatility of BUX It is worth emphasizing that predictability and

seasonality of stock returns found in this paper need not imply market

inefficiency. Although our results can be useful in the real-world investment

process, they do not imply that profitable trading strategies yielding superior

returns when adjusted for transaction cost exist. A further investigation into

the economic (and not only statistical) significance of stock returns

predictability and seasonality on Central-European stock markets is therefore

called for.


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The Day Of The Week Effect On Stock Market Volatility Istanbul Stock

Exchange Hakan Berument, Ali Inamlik

Abstract

This study investigates the day of the week effect on return and volatility for

Istanbul Stock Exchange (ISE) throughout the period 1986 and 2003. Using

generalized autoregressive conditional hetroskedasticity (GARCH) model, we

find statistically significant evidence to report that there is the day of the week

effect. Friday has the highest effect on return with 0,015 while Monday has

the lowest return with-0,003 compared to return on Wednesday. When

volatility of return is concerned, Monday has the highest volatility with 0,933

and Tuesday has the lowest volatility with 0,716 compared to return on

Wednesday

Conclusion

There is a new set of evidence that day of the week effect is present for both

returns and volatility for the developed economies. Our study investigates this

topic for ISE by using a GARCH specification. By using daily observation we

show that highest volatility is observed for Mondays and lowest for Fridays.

Moreover, Friday has the highest return and Monday has the lowest return.


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The Day-End Effect on the Paris Bourse

David Michayluk, Gary C. Sanger

Abstract

The high return observed at the close in many securities markets is one of

many unexplained phenomena in finance. We study this effect on the Paris

Bourse, which is a computerized order-driven market with multiple competing

dealers. In the Paris market certain stocks are assigned a Registered Dealer

with special market making responsibilities similar to those of an exchange

specialist. This simultaneous trading of stocks, some with and some without a

Registered Dealer, provides an excellent opportunity to examine the effect of

market structure on day-end returns. Using all securities continuously traded

on the Paris Bourse during the period January 1995 to December 1995, we

find high day-end transaction returns for all stocks. However, the magnitude

of the effect is nearly four times larger for stocks with a Registered Dealer,

which supports the market structure hypotheses. The day-end price rise is

largely explained by a shift from the bid to the ask price, and it is partly

reversed overnight. Finally, a change in closing procedure to a call auction in

May 1996 for a subset of stocks did not reduce the day-end effect.

Conclusion

We find that the day-end effect first observed in the U.S. is also present on the

Paris Bourse (Euronext Paris). Several features of the day-end effect are

similar in both markets. The effect is present only when the final trade of the

day is within a few minutes of the close of trading. Also, there are no

pronounced weekday or month-end patterns in the average day-end return. Ananalysis of quote midpoints reveals that a large portion of the effect is caused

by an increase in the relative frequency of trades at the ask price.

We do find some differences between the Paris and U.S. results. Unlike Harris

(1989) we do not find a monotonic relation between the final return and stock


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price. This may be due to the variable pricing grid in the Paris market. Also,

while Harris (1989) failed to find an overnight reversal of the day-end return,

we find that approximately 30% of the final return is reversed overnight.


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Investigation of the Weekend Effect in Chinas Stock Market

BY SIMIAO ZHOU

Introduction

Chinas stock market is quickly developing with different characteristics from

other mature markets. Whether it also has a weekend effect is a question to be

answered. Furthermore, one main feature of Chinas stock market is its

locality. Examining such an isolated market helps to put aside one set of the

reasons, and focus on within-market reasons. In summation, this paper aims to

answer two questions:

First, as an emerging market, does Chinas stock mark et have a weekend

effect?

Second, since the microstructures of Chinas stock market are different from

those in developed markets, if it has a weekend effect, is it similar to what has

been observed in other countries?

The Shanghai Stock Exchange and Shenzhen Stock Exchange are highly

correlated and have the same organization features. This paper takes the

Shanghai Stock Exchange, which is the larger of the two, as the market of

interest. Like other literature, this paper uses the index to calculate the stock

returns.

Conclusion

Using the close-to-close daily returns from 1992 to 2002 in Shanghai

Composite Index, this paper examines the evidence of the weekend effect in

the Chinese stock market. To do so, both the OLS and ARCH (1) models are

carried out. ARCH (1) model is better than the OLS model. The results from

the ARCH (1) model show that all returns on five weekdays are not equal.

There exists some kind of weekend effect for the entire sample period. The


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highest return is observed on Friday. The lowest return, however, occurs on

Wednesday, which is different from what bas been observed in other mature

markets. Some possible reasons are examined too. Regression results for the

bull-market and bearmarket sub periods show that significant weekend effectsexist in both sub periods. So the information hypothesis may not well explain

the effect. After partitioning the sample period into two sub periods, this paper

finds that the weekend effect disappears after the 10 percent up and down

trading limits are put into effect on Dec. 26, 1996. This result implies that the

trading patterns and market volatility may explain this effect partially.

The day of the week effect in Indian market was examined by many

researchers (Chaudhury (1991), Poshakwala (1996), Goswami and Anshuman

(2000), Choudhry (2000), Bhattacharya, Sarkar and Mukhopadhyay (2003)).

All studies except Choudhry (2000) and Bhattacharya et al (2003) have been

based on data of mid-1980s and mid-1990s and all these studies have used

conventional methods like serial autocorrelation tests and or fitting an OLS.

Choudhry (2000) examined seasonality of returns and volatility under a

unified framework but the study has a misspecification issue with regard to

conditional mean. The Indian market operates in different institutional,

regulatory, and tax environments and hence there is a need to test for the

existence of the calendar effects in the Indian market.


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CHAPTER 3

Research Methodology


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Research Methodology

Study Design

In this study an attempt has been made to experiment the Seasonality in

Indian Stock Market closing value of S&P Nifty Index as the sample.

Types of Data

Secondary data has been used for the analysis purpose.

Sample Frame

Sampling frame includes the closing value of S&P Nifty Index. Sample

includes historical closing values of S&P Nifty index value for the period of

five years from 3rd

March 2000 to 3rd

March 2005

Sample Technique

The sampling technique used is the convenient sampling. As the name

implies, the sample is selected because they are convenient. S&P CNX Nifty

is a benchmark stock index based on the selected stocks traded at National

Stock Exchange (NSE).

Data Gathering Procedure

The major data relevant for this research is secondary data which has been

collected from Bangalore Stock Exchange ( BGSE ).


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Instrumentation techniques

The usual definition of return as the percentage change in price has a very

serious problem in that it is not symmetric. For example, if the index rises

from 1000 to 2000, the percentage return would be 100%, but if it falls backfrom 2000 to 1000, the percentage return is not -100% but only -50%. As a

result, the percentage return on the negative side cannot be below -100%,

while on the positive side, there is no limit on the return.

The return were calculated using the below equation for the whole five years

by using the formulae

Rt = (Vt/Vt-1)

Vt = The closing value of the index on day t

Vt-1= The closing value of the index on theprevious day

The data was then segregated into respective days such as Monday, Tuesday,

Wednesday, Thursday, and Friday with there respective return. This data was

then spilt for different period from 3rd

March 2000 to 8th

Sep 2002 which

formed the first two and half years and then from 9th

Sep 2002 to 9th

march

2005 which formed the second two and half years .Then for the whole five

years. The day of the week effect was checked whether it varies with time


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ANOVA TEST

Analysis of variance is used to test for the significance of the difference

among more than two sample mean. Using analysis of variance we can make

inference about whether our samples are drawn from population having the

same mean in order to use analysis of variance we must assume that the each

of the samples is drawn from a normal population and that each of these

populations has the same variance .however if the sample size are large

enough, assumption of normality may not be needed.

Firstly we must estimate Between-Column variance.

The second step is to calculating the variance within the samples.

The comparing the two estimate of the population variance by computing

their Ratio

F =

Then the F Tabulated is seen from the Table for given degree of freedom and

at 5% level of significance. If F-calculated is greater than F-tabulated the

Hypothesis is rejected and if F-calculated is lees than F-tabulated the

Hypothesis is accepted.

First estimate of the population variance based on

the variance among the sample means

second estimate of the population variance based on

the variance within the sample


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Z-test(test for difference between means large sample)

When studying two population we use sampling distribution of the difference

between sample means denote the means of the samples drawn

from the first and second population respectively, having means 1 and 2 and

standard deviations 1 and 2 and if the sizes of the samples are n1 and n2,

then it can be proved that the distribution of the difference between the means

is normal with mean (1 - 2 ) and S.D. is given by

Then the Z from the Table is for given the level of significance is seen. If

calculated is greater than tabulated the hypothesis is rejected if less than

tabulated it is accepted.


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CHAPTER4

Data Analysis and

Interpretation


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Results

Table No1

Result of F-TestTable 1(a)

Analysis of Variance from March 2000 to March 2005

One-Way Analysis Of Variance

SourceDegree ofFreedom

Sum ofSquares

MeanSquares Fcal

Model 4 0.000903 0.000226 0.94807

Error 1243 0.295938 0.000238

Corrected Total 1247 0.296841

Table No 1(b)

Analysis of Variance from Oct 2002 to March 2005


SourceDegree ofFreedom

Sum ofSquares

MeanSquares Fcal

Model 4 0.000421 0.000105 0.503573

Error 616 0.128748 0.000209Corrected Total 620 0.129169

Table No 1(c)

Analysis of Variance from March 2000 to Sep 2002


Source

Degree of

Freedom

Sum of

Squares

Mean

Squares Fcal

Model 4 0.00198 0.000495 1.875

Error 621 0.163944 0.000264

Corrected Total 625 0.165924


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H0: Return Monday5HWXUQ Tuesday5HWXUQ Wednesday5HWXUQ Thursday5HWXUQ Friday

H1: Return Monday= Return Tuesday= Return Wednesday= Return Thursday= Return Friday

Period Fcalculated Ftable HypothesisMarch 2000 to Sep 2002 1.875 2.37 Accepted

Oct 2002 to March 2005 0.503572871 2.37 Accepted

March 2000 to March 2005 0.94807 2.37 Accepted

The null hypothesis as seen from Table is accepted in all the three cases

Showing. Important and startling conclusion that Indian stock markets are

indeed efficient as far as the day-of -the week effect is concerned.

Table No 2

Result of Z test

H0: Return Monday5HWXUQ Friday

H1: Return Monday= Return Friday

Period Zcalculated Ztable HypothesisMarch 2000 to Sep 2002 0.037488352 1.96 Accepted

Oct 2002 to March 2005 -1.471239558 -1.96 Accepted

March 2000 to March 2005 -0.88727 -1.96 Accepted

The null hypothesis as seen from the Table 2 is accepted in all the three cases

so the popular perception that the returns are positive an Monday and lower

on Friday is proved false


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Table No 3

Computation of Mean Returns

March 2000 toSep 2002

Oct 2002 toMarch 2005

March 2000 toMarch 2005

Monday -0.002444211 5.99455E-05 -0.001187104

Tuesday -0.001110348 0.001674367 0.000282009

Wednesday 0.002357556 0.000559057 0.00147622

Thursday -0.000713873 0.001250334 0.000256677

Friday -0.002519489 0.002411908 -4.36436E-05

Table No 4

Computation ofStandard Deviation

March 2000 to

Sep 2002

Oct 2002 to

March 2005

March 2000 to

March 2005

Monday 0.01643527 0.017923434 0.01720955

Tuesday 0.015977153 0.013633844 0.01488745

Wednesday 0.01596006 0.011464377 0.013940943

Thursday 0.014713872 0.013205813 0.013996183

Friday 0.018131318 0.015220182 0.01688033


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CHAPTER 5

Conclusion


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Conclusion

The objective of this paper is to examine the day-of-the-week effect in the

Indian Stock Market. The paper in particular studies the day-of-the-week-

effect with respect to the settlement system followed. The daily closing price

data on the S&P Nifty Index for the period March 2000-March 2005 has

been used in the study. The first step was testing of the null hypothesis that

the mean returns on all trading days of the week are not equal using F- test.The null hypothesis that the means returns are not equal across all trading

days was true at 5% significance level. Then second part involved testing of

the null hypothesis that the mean returns on Monday and Friday are not

equal using Z- test. The null hypothesis that the mean returns on Monday

and Friday are not equal was true at 5% significance level.


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Bibliography

BOOKS:

Modern portfolio theory and investment Analysis

Elton & Gruber

Statistics For Management

- Richard I Levin & David S Rubin

Research papers

1. Seasonality and the Nontrading Effect on Central-European Stock

Markets. Vit Bubak and Filip Zikes September 27, 2004

2. The Day Of The Week Effect On Stock Market Volatility Istanbul

Stock Exchange Hakan Berument, Ali Inamlik

3. Investigation of the Weekend Effect in Chinas Stock Market BY

SIMIAO ZHOU

4. An Empirical Analysis of the Day-of-the-Week Effect in Stock

Returns: The Case of the Bombay Stock Exchange by Harishankar. R

and Priya B of IIM Ahmedabad

Websites

www.moneycontrol.com

www.nseindia.com

www.indiainfoline.com

www.domain-b.com

www.investopedia.com

National dailies

Business Lines

Business Standard

Economic Times


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