A STUDY ON DAY OF THE WEEK AFFECTS ON SHARE PRICE VOLATILITY FOR

COMPANIES LISTED IN NSE WITH REFERENCE TO MFSL

M.PREETHA

Reg. No.412512631090

Of

SRI SAIRAM ENGINEERING COLLEGE

A FINAL PROJECT REPORT

Submitted to the

FACULTY OF MANAGEMENT SCIENCES

In partial fulfillment of the requirements for the award of the degree

Of

MASTER OF BUSINESS ADMINISTRATION

ANNA UNIVERSITY

CHENNAI – 600 025

JUNE – 2014

DECLARATION

I hereby declare that this project report titled A STUDY ON DAY OF THE WEEK AFFECTS

ON SHARE PRICE VOLATILITY FOR COMPANIES LISTED IN NSE WITH REFERENCE

TO MFSL submitted by me to the department of Master of Business Administration of Sri

sairam Engineering college is a bonafide work undertaken by me and this project work is

submitted in partial fulfilment of the requirements for the award of the degree of Master of

Business Administration.

Name Signature

Date

ABSTRACT

The name “stock market” which when comes into the mind, everyone has different

opinion. One feels it is risky to invest in stock market, for their Company others may perceive

that it is game of gambling. Many of the investors may feel it’s great opportunity to make profit

in the stock market. The opinion differs from person to person, investor to investors.

But the recent trend in the stock regarding its volatility which leads to the depression and

also losses for many investors. If when the investors ask him about why did the stock market

behaved in this way; the factor may be many. One has to develop a bird’s view over the stock

market and analyze every factor with tools and technique so that he/she may not go wrong in the

investment decision.

A project report on stock market is being prepared in attempts to interpret in-depth study

of volatility in Indian stock market. This report helps us to understand various terminologies in

stock market. This report gave me opportunity to have complete idea about volatility in stock

market.

This paper investigates the nature and characteristics of stock market volatility in India.

The volatility in the Indian stock market exhibits characteristics similar to those found earlier in

many of the major developed and emerging stock markets. Various volatility estimators and

diagnostic tests indicate volatility clustering, i.e., shocks to the volatility process persist and the

response to news arrival is asymmetrical, meaning that the impact of good and bad news is not

the same.

ACKNOWLEDGEMENT i

ABSTRACT ii

TABLE OF CONTENTS iii

LIST OF TABLES iv

LIST OF CHARTS vi

TABLE OF CONTENTS

CHAPTER TITLE PAGE NO.

1 INTRODUCTION1.1 INTRODUCTION 11.2 INDUSTRY PROFILE 41.3 COMPANY PROFILE 81.4 NEED FOR THE STUDY 161.5 OBJECTIVES OF THE STUDY 171.6 SCOPE OF THE STUDY 181.7 REVIEW OF LITERATURE 191.8 RESEARCH METHODOLOGY 241.9 LIMITATIONS OF THE STUDY 27

2 DATA ANALYSIS AND INTERPRETATION 282.1 CORRELATION 362.2STANDARD DEVIATION 452.3BETA 542.4FUND RETURN AND INDEX RETURN 622.5SHARPE RATIO 632.6 TREYNOR RATIO 642.7 RANKING FOR YEAR WISE RETURN

3 FINDINGS,SUGGESTION & CONCLUSION 673.1 FINDINGS 683.2 SUGGESTION 693.3 CONCLUSIONBIBLIOGRAPHY

ANNEXURES

LIST OF TABLES

TABLE NO TITLE PAGE NO

2.1.1 Correlation for kotak gold etf 28

2.1.2 Correlation for quantum gold etf 29

2.1.3 Correlation for reliance gold etf 30

2.1.4 Correlation for sbi gold etf 31

2.1.5 Correlation for UTI gold etf 32

2.1.6 Correlation for RELIGARE gold etf 33

2.1.7 Correlation for GOLD BEES gold etf 34

2.1.8 Correlation between NSE and MCX 35

2.2.1 Standard deviation for kotak gold etf 37

2.2.2 Standard deviation for quantum gold etf 38

2.2.3 Standard deviation for reliance gold etf 39

2.2.4 Standard deviation for sbi gold etf 40

2.2.5 Standard deviation for UTI gold etf 41

2.2.6 Standard deviation for RELIGARE gold etf 42

2.2.7 Standard deviation for GOLD BEES gold etf 43

2.2.8 Standard deviation between NSE and MCX 44

2.3.1 Beta value for kotak gold etf 46

2.3.2 Beta value for quantum gold etf 47

2.3.3 Beta value for reliance gold etf 48

2.3.4 Beta value for sbi gold etf 49

2.3.5 Beta value for UTI gold etf 50

2.3.6 Beta value for RELIGARE gold etf 51

2.3.7 Beta value for GOLD BEES gold etf 52

2.3.8 Beta value between NSE and MCX 53

2.4.1 Fund and index return for kotak gold etf 54

2.4.2 Fund and index return for quantum gold etf 55

2.4.3 Fund and index return for reliance gold etf 56

2.4.4 Fund and index return for sbi gold etf 57

2.4.5 Fund and index return for UTI gold etf 58

2.4.6 Fund and index return for RELIGARE gold etf 59

2.4.7 Fund and index return for GOLD BEES gold etf 60

2.4.8 Fund and index return between NSE and MCX 61

2.5 Sharpe ratio 62

2.6 Treynor ratio 63

2.7.1 Ranking for kotak gold etf 64

2.7.2 Ranking for quantum gold etf 65

2.7.3 Ranking for reliance gold etf 66

LIST OF CHARTS

FIGURE.NO TITLE PAGE.NO

2.1.8 Correlation between NSE and MCX 35

2.2.8 Standard deviation between NSE and MCX 44

2.3.8 Beta value between NSE and MCX 53

2.4.8 Fund and index return between NSE and MCX 61

2.5 Sharpe ratio 62

2.6 Treynor ratio 63

2.7.1 Ranking for kotak gold etf 64

2.7.2 Ranking for quantum gold etf 65

2.7.3 Ranking for reliance gold etf 66

CHAPTER 1INTRODUCTION

1.1 INTRODUCTION

Volatility is the most basic statistical risk measure. It can be used to measure the market

risk of a single instrument or an entire portfolio of instruments. While volatility can be expressed

in different ways, statistically, volatility of a random variable is its standard deviation. In day-to-

day practice, volatility is calculated for all sorts of random financial variables such as stock

returns, interest rates, the market value of a portfolio, etc. Stock return volatility measures the

random variability of the stock returns. Simply put, stock return volatility is the variation of the

stock returns in time. More specifically, it is the standard deviation of daily stock returns around

the mean value and the stock market volatility is the return volatility of the aggregate market

portfolio.

MEANING OF “VOLATILITY”:

Volatility refers to the amount of uncertainty or risk about the size of changes in a

security's value. A higher volatility means that a security's value can potentially be spread out

over a larger range of values. This means that the price of the security can change dramatically

over a short time period in either direction. A lower volatility means that a security's value does

not fluctuate dramatically, but changes in value at a steady pace over a period of time.

Volatility is a measure for variation of price of a financial instrument over time. Historic

volatility is derived from time series of past market prices. An implied volatility is derived from

the market price of a market traded derivative (in particular an option). The symbol σ is used for

volatility, and corresponds to standard deviation which should not be confused with the similarly

named variance, which is instead the square, σ2.

Volatility as described here refers to the actual current volatility of a financial

instrument for a specified period (for example 30 days or 90 days). It is the volatility of a

financial instrument based on historical prices over the specified period with the last observation

the most recent price. This phrase is used particularly when it is wished to distinguish between

the actual current volatility of an instrument and

actual historical volatility which refers to the volatility of a financial instrument over a

specified period but with the last observation on a date in the past

actual future volatility which refers to the volatility of a financial instrument over a

specified period starting at the current time and ending at a future date (normally the expiry

date of an option)

historical implied volatility which refers to the implied volatility observed from historical

prices of the financial instrument (normally options)

current implied volatility which refers to the implied volatility observed from current prices

of the financial instrument

future implied volatility which refers to the implied volatility observed from future prices

of the financial instrument

Investors care about volatility for five reasons:-

1. The wider the swings in an investment's price, the harder emotionally it is to not worry;

2. Price volatility of a trading instrument can define position sizing in a portfolio;

3. When certain cash flows from selling a security are needed at a specific future date,

higher volatility means a greater chance of a shortfall;

4. Higher volatility of returns while saving for retirement results in a wider distribution of

possible final portfolio values;

5. Higher volatility of return when retired gives withdrawals a larger permanent impact on

the portfolio's value;

6. Price volatility presents opportunities to buy assets cheaply and sell when overpriced.

ABOUT THE VOLATILE MARKET

Volatile markets are ones where the price moves vigorously and unpredictably. Some

commodities are more volatile in character than others but volatility is mainly a varying

characteristic that affects all markets at different times. If a market tends to be generally too

volatile for your liking, then you are best advised to avoid that particular market because you

will not feel comfortable having it as part of your trading universe. Otherwise, you need

volatility to some degree in your markets because if prices do not move sufficiently, you will not

be able to make money trading them.

Volatility is closely related to risk. The more volatile the market, the more risky it will be

to trade – but you must take risks if you want to make money. Risk is inextricably tied to

prospective return as well. Even if the return is well balanced with the volatility (risk) you still

must not have more risk than suits your own personal comfort – otherwise you will not stick to

your system. You must also have a way of measuring volatility against the return your system

can deliver and these needs to be embodied in your trading method. 

The standard deviation indicator is the best known measure of volatility and this is

commonly incorporated into other indicators and methods to produce ratios traders employ to

asses market performance.

Stock prices are changed every day by the market. Buyers and sellers cause prices to

change as they decide how valuable each stock is. Basically, share prices change because of

supply and demand. If more people want to buy a stock than sell it - the price moves up.

Conversely, if more people want to sell a stock, there would be more supply (sellers) than

demand (buyers) - the price would start to fall. Volatility in the stock return is an integral part of

stock market with the alternating bull and bear phases. In the bullish market, the share prices soar

high and in the bearish market share prices fall down and these ups and downs determine the

return and volatility of the stock market. Volatility is a symptom of a highly liquid stock market.

Pricing of securities depends on volatility of each asset. An increase in stock market volatility

brings a large stock price change of advances or declines. Investors interpret a raise in stock

market volatility as an increase in the risk of equity investment and consequently they shift their

funds to less risky assets. It has an impact on business investment spending and economic growth

through a number of channels. Changes in local or global economic and political environment

influence the share price movements and show the state of stock market to the general public. 

1.2 INDUSTRY PROFILE

INTRODUCTION OF STOCK MARKET

In most industrialized countries, a substantial part of financial wealth is not managed

directly by savers, but through a financial intermediary, which implies the existence of an agency

contract between the investor (the principal) and a broker or portfolio manager (the agent).

Therefore, delegated brokerage management is arguably one of the most important agency

relationships intervening in the economy, with a possible impact on financial market and

economic developments at a macro level.

As the per-capita-income of the city is on the higher side, so it is quite obvious that they

want to invest their money in profitable ventures. On the other hand, a number of brokerage

houses make sure the hassle free investment in stocks. Asset management firms allow investors

to estimate both the expected risks and returns, as measured statistically.

INDIAN STOCK MARKET

Share or stock is a document issued by a company, which entitles its holder to be one of

the owners of the company. A share is issued by a company or can be purchased from the stock

market.

Share market: Where dealing of securities is done is known as share market.

There are two ways in which investors gets share from market:

Primary market: markets in which new securities are issued are known as primary market. This

is part of the financial market where enterprises issue their new shares and bonds. It is

characterized by being the only moment when the enterprise received money in exchange for

selling its financial assets.

Secondary Market: Market in which existing securities are dealt is known as secondary market.

The market where securities are traded after, they are initially offered in the primary market.

Most trading is done in the secondary market.

The Stock Market is an invisible market that trades in stocks of various companies

belonging to both the public and private sectors. The Indian Stock Market is often referred to as

the Share Market since it deals primarily with shares of various companies.

A Stock Exchange is a place where the stocks are listed and traded. Such exchanges may

be a corporation or mutual organization which specializes in the business of introducing the

sellers with the buyers of stocks and securities.

The Indian Stock Market in India comprises of two stock exchanges:

Bombay Stock Exchange (BSE)

National Stock Exchange (NSE)

BSE

The Bombay Stock Exchange (BSE) was established in 1875.The BSE India Stock

Exchange serves as the most important for companies to raise money. The chief function of the

Stock Market of India is to help raise money as capital for the growth and expansion of various

private and public sector enterprises. Besides, the Stock Market of India provides able assistance

to the individual investors through daily updates on current position of the stocks of the

respective companies that are enlisted in the Stock Index in which the movement of prices in a

section of the market are captured in price indices. The popular acronym for Stock Index is

Sensitive index or sensex. Moreover, the liquidity provided by the exchange enables the

investors to sell securities owned by them easily and quickly. Hence a person, who is subjected

to sudden dearth of funds, can immediately sell his shares for cash in India Stock Market.

The BSE Sensex, also known as “BSE 30” is a widely used market index not only in

India but across Asia. In terms of volume of transactions, it is ranked among the top five stock

exchanges in the world.

NSE

The National Stock Exchange of India Ltd. (NSE), set up in the year 1993, is today the

largest stock exchange in India and a preferred exchange for trading in equity, debt and

derivatives instruments by investors. NSE has set up a sophisticated electronic trading, clearing

and settlement platform and its infrastructure serves as a role model for the securities industry.

The standards set by NSE in terms of market practices; products and technology have become

industry benchmarks and are being replicated by many other market participants.

NSE provides a screen-based automated trading system with a high degree of

transparency and equal access to investors irrespective of geographical location. The high level

of information dissemination through the on-line system has helped in integrating retail investors

across the nation.

The exchange has a network in more than 350 cities and its trading members are

connected to the central servers of the exchange in Mumbai through a sophisticated

telecommunication network comprising of over 2500 VSATs.

NSE has around 850 trading members and provides trading in equity shares and debt

securities. Besides this, NSE provides trading in various derivative products such as index

futures, index options, stock futures, stock options and interest rate futures.

In addition to these organizations there are other organizations highlighting on the share

trading in the Indian Stock Market are:

Securities and Exchange Board of India (SEBI)

NSDL

CDSL

The Nifty and the Sensex are the indicators which are the parameters denoting the prices of the

stocks of the major companies of the NSE and the BSE respectively.

Stock Broking Sector in India

The Indian broking industry is one of the oldest trading industries that has been around

even before the establishment of the BSE in 1875. Despite passing through a number of changes

in the post liberalization period, the industry has found its way towards sustainable growth. In

this section our purpose will be of gaining a deeper understanding about the role of the Indian

stock broking industry in the country’s economy.

Broking houses in India

India is a country having a big list of Broking Houses. The Equity Broking Industry in

India has several unique features like it is more than a century old, dynamic, forward looking,

and good service providers, well conversant, highly innovative and even adaptable. The

regulations and reforms been laid down in the Equity Market has resulted in rapid growth and

development. Basically, the growth in the equity market is largely due to the effective

intermediaries.

The Broking Houses not only act as an intermediate link for the Equity Market but also

for the Commodity Market, Foreign Currency Exchange Market, and many more. The Broking

Houses has also made an impact on the Foreign Investors to invest in India to certain extent.

In the last decade, the Indian brokerage industry has undergone a dramatic

transformation. From being made of close groups, the broking industry today is one of the most

transparent and compliance oriented businesses. Long settlement cycles and large scale bad

deliveries are a thing of the past with the advent of T+2 settlement cycle and dematerialization.

Large and fixed commissions have been replaced by wafer thin margins, with competition

driving down the brokerage fee, in some cases, to a few basis points.

There have also been major changes in the way business is conducted. Technology has

emerged as the key driver of business and investment advice has become research based. At the

same time, adherence to regulation and compliance has vastly increased. The scope of services

have enhanced from being equity products to a wide range of financial services. Investor

protection has assumed significance.

1.3 COMPANY PROFILE

In India, centuries before banks and finance companies came into being, there were the

Marwaris. The Marwaris, from the region of Marwar in Rajasthan, transformed money as a

commodity with buying power, into a financial product and marketed it to individuals and

businessmen, to meet their needs and to accelerate progress. With their impeccable and high

standard of ethics, Marwaris turned financing into a fine art. Their work was their bond. Integrity

their way of life. And prudence, their inborn instinct. The tradition of financial acumen that's

evident even today at the Munoth Group. The Munoth family from Rajasthan has rich experience

in a variety of business including finance down the generations for over a hundred years now.

Faithful to their ancestor’s philosophy, the Munoths’ manage finance in the most prudent

manner, planning and executing financial schemes with an accent on long-term growth and

steady returns, working together as a team. Seamlessly operating for the benefit of the customer

and weathering recessions and low market morale.

Munoth Financial Services Limited (MFSL) was incorporated on 01st November, 1990 under the

Companies Act of 1956 and is a widely held Public Limited Company. The equity shares of the

company are listed on the Bombay Stock Exchange (BSE) & the Madras Stock Exchange

(MSE).

Vision statement:

To become a renowned business enterprise by providing and delivering  superior capital market

solutions with best technology and services

Mission statement:

To achieve our objectives upholding core values of transparency, integrity and accountability in

all facets of operations and adding value to customers, shareholders, vendors,  employees

&.society at large

MFSL is category I Merchant Banker, a member of the National Stock Exchange (NSE), Madras

Stock Exchange (MSE), a dealer on Over the Counter Exchange of India (OTCEI), Share

Transfer Agent (STA), and Depository Participant (DP) with NSDL and provides Portfolio

Management Services (PMS). It is registered with Securities and Exchange Board of India

(SEBI) to provide all these services.

MFSL has a qualified and trained manpower which comprises of Chartered Accountants,

Company Secretaries, Cost Accountants and Management Graduates. It has the right mix of

qualified and experienced personnel to provide effective and efficient service, backed by state of

art infrastructure.

Munoth Financial Services Limited (MFSL) having its registered office at Munoth Centre, Suite

No.46 & 47, 343, Triplicane High Road, Chennai 600005, was incorporated on 1st November

1990 under the Companies Act 1956 as Private Limited Company and subsequently converted as

Public Limited Company by passing a Special Resolution on 16.08.95 U/S.44 of the Companies

Act and obtained a fresh Certificate of incorporation on 17.11.1995.

The Company tapped the capital market with an Initial Public Offering of 2.73 million shares of

Rs.10 each in April 1996 taking the post-paid capital to 5.01 million shares. The shares were

listed on Madras and Bombay Stock Exchanges. It further issued on a preferential allotment

basis 2,25,000 shares of Rs.10 each at a premium of Rs.90 to Priory Investments (Mauritius)

Limited in March 2001 and the new shares were also listed on Madras & Bombay Stock

Exchanges

MFSL provides investment banking services since 1994. . MFSL is a Category I Merchant

Banker (INM000003739) and has permanent registration from SEBI, offering services in areas

of Issue Management, Mergers & Acquisition, Investment Banking, Advisory services, Re-

structuring, ESOPS, etc since 1994. The issue management services include preparation of offer

documents, pre & post issue formalities and compliance.

MFSL has leaded in managing 48 Issues and has managed 62 other issues in various capacities.

MFSL has worked among others with SBI Capital Markets Limited, Canara bank Merchant

Banking Services, Indian Bank Merchant Banking Services Limited, JM Financial Services

Limited and Integrated Advisory Services Limited in the capacity of Merchant Banker.

MFSL’s Merchant banking clients include Orchid Pharmaceuticals & Chemicals Limited,

Sandisk Corporation, USA , Temenos India Pvt Ltd, SPIC Fine chemicals Limited, Sriram City

Union Finance Limited, Dr. Agarwal’s eye Hospital Limited, Madras Fertilizers Limited, BPL

Engineering, Indbank Merchant Bank, Apple Credit Corporation Ltd, NEPC Paper Board Ltd,

Spic Petrochemicals Ltd, Sterling Holiday, etc.

MFSL acts as Investment Managers, appointed by IL &FS Trust Company Limited for Valmark

Infra and Realty Trust, a SEBI registered Alternate Investment Fund – II Fund.

MFSL provides Stock broking services since 1995 and is active in retail & institutional segment

and is empanelled with several public sector banks and insurance companies. The institutional

clients include United India Insurance Company Limited, Oriental Insurance Company Limited,

General Insurance Corporation of India, Canara Bank, Indian Bank, Indian overseas Bank, UCO

Bank, Federal Bank etc.

MFSL provides depository services since 1997 and has over 5000 clients. It has got the

permanent registration certificate for Depository Participant business with NSDL from SEBI and

offers facilities both institutional and retail investors to maintain their investments in securities in

electronic form.

MFSL provides Portfolio management services since 1999 under discretionary and non-

discretionary basis and clients include non-resident Indians & domestic Indians.

MFSL was registered with SEBI as deemed FII during 2000.

Profile of Directors

1. Mr.Lalchand Munoth, Chairman

Mr. Lalchand Munoth aged 74 years has over 46 years of experience in the field of

money management. He has been associated with the company since inception. He has

vast experience in financing films, cheque discounting and other financial instrument. His

firm M/s.Misrimal Navajee had handled large scale imports, and was a prominent player

in trading of chemicals & dyes. He is the Chairman of Tamilnadu Educational & Medical

Trust which manages Misrimal Navajee Munoth Jain Engineering College & Misrimal

Navajee Munoth Jain School of Architecture. He is a trustee of many religious trusts

which manage Jain temples.

2. Mr. Jaswant Munoth, Managing Director

Mr. Jaswant Munoth aged 47 years is a Commerce Graduate with a Masters Degree in

Business Administration. He is the Managing Director of Munoth Financial Services

Limited since 1990. He Heads Merchant Banking, Portfolio Management Division. He is

a trustee of TamilNadu Educational & Medical Trust, Secretary of Rajasthan Foundation-

Chennai Chapter, Past President of Southern India Rajasthan Chamber of Commerce &

Industry, & Past Vice-President of Jain International Trade Organization (JITO) Apex.

He has delivered talks on capital market at TIE Con Southern California Chapter, USA,

Asian Indian Chamber of Commerce, New Jersey, International Rajasthan Conclave,

Jaipur, and Confederation Indian Industry – Connect 2004, among others.

3. Mr. Bharat Munoth, Director.

Mr. Bharat Munoth, aged 42 years is a Commerce Graduate with an extensive knowledge

of stock markets. He takes care of Stock Broking, the Share Transfer Agency and the

Depository divisions of the company. He heads Stock broking services since 1995 and is

managing retail & institutional clients of the Company. He also heads depository

participant division since inception in 1997 and handles over 5000 clients

4. Mr. Vikas Munoth, Whole Time Director

Mr. Vikas Munoth aged 35 years is a Commerce graduate and holds a post-graduate

diploma in Business Management specializing in Finance and Marketing. He is also a

Chartered Financial Analyst and specializes in fundamental and technical analysis. He

heads the equity research team and the investment committee of portfolio Management

Services. He has over 12 years of experience in the field of capital markets.

5. Mr M Jayantilal Jain, Independent Director .

Mr. M Jayantilal Jain, aged about 48 is a Member of Institute of Chartered Accountants

of India, He is a partner of M/s. Krishnan & Giri , Chartered Accountants, Chennai. He is

in the profession for the last 22 years and is in charge of finalization of Statutory and Tax

Audits of Corporate and Non Corporate entities and has made representation before

various tax authorities. He is also associated with many philanthropic Jain Association

and religious trusts both as an auditor and member. He is also having vast experience in

Capital Market segment and derivatives.

6. Mr. Ajit Kumbhat, Independent Director

Mr. Ajit Kumbhat, aged around 60 years is a Practising Chartered Accountant with more

than 30 years experience in finance and taxation. He is the partner of M/s. Kumbhat &

Co, Chartered Accountants, Chennai. He is a director of M/s. Kumbhat Financial

Services Limited, a company specializes in financial services. He is also well known

person in sports field and holds various positions in different governing bodies.

7. Mr. Tansri Rajandram, Independent Director

Mr. Tansri Ranjandram aged 75 years is a Fellow of Australian Society of Certified

Practicing Accountants, a member of the association of Certified Public Accountants

(Malaysia) and an Associate of the Bankers' Institute of Australasia. He was the

Executive Deputy Chairman of Rating Agency Malaysia, an independent Credit Rating

Agency established in 1990 in response to an emerging Malaysian Corporate bond

market.

He was the Chairman of Corporate Debt Restructuring Committee (CDRC), Advisor of

the Central Bank of Malaysia (Bank Negara Malaysia), Secretary of the Capital Issues

Committee (CIC) before his secondment to the Ministry of Finance which has give vast

experience in the areas of corporate debt restructuring,  recapitalizing  financial

institution, strengthening the banking system, capital market and capital issues.

8. Mr. Mah Sau Cheong, Independent Director

Mr. Mah Sau Cheong aged 62 years is the owner of the South Malaysian Industries Group with

interest in Real Estate, Entertainment, Stock Broking and Insurance.

1.4 NEED FOR THE STUDY

The main purpose of study is to select the sectors/industry based on its market

capitalization rate and interpret the level of volatility in stock market. The next need is to predict

the future price movement of shares of selected companies listed in NSE. Gauging the current

risk of either buying or selling has often been a key factor to identify whether trade turns out

profitable or not. By using the tools like mean, standard deviation, skewness and kurtosis, the

level of volatility and both risk and return levels can be interpreted easily. This may allow

investors to adjust their portfolios by taking into account day of the week variations in stock

market. Hence this study will give the better advice to investors to make more profit in their

trade in stock market.

1.5 OBJECTIVES OF THE STUDY

PRIMARY OBJECTIVE

To study the day of the week affects on share price volatility of selected companies listed

in NSE.

SECONDARY OBJECTIVES

To analyze price movement of shares in Indian stock market while taking Nifty as a

source of secondary data which broadly represent Indian stock market.

To analyze the basic descriptive statistics like mean, standard deviation, kurtosis and

skewness for day of the week effect.

To examine the significance of regression coefficient for valuating the return using

multivariate technique.

To provide information regarding the factors which are making Indian stock market

volatile.

To forecast the future trend and provide the suitable suggestions.

.

1.6 SCOPE OF THE STUDY

The scope of study is limited to few selected industries. Those are Pharmaceutical,

Information Technologies, and Automobile industries. In those industries, only some are

selected. They are 3 companies each from pharmaceutical, I.T. and Automobile industry.

This study is analysis of previous 5 years (Jan 2009 to Dec 2013) data relating to prices

of shares in National Stock Exchange only.

This study investigates the day of the week effect of stock market, present in both return

and volatility equations. This report gave me opportunity to have complete idea about

volatility in stock market and useful to find out the future price trend of the scrips that

floats in the NSE.

1.7 REVIEW OF LITERATURE

1. Admati and Pfleiderer (1988) and Foster and Viswanathan (1990) develop models to

explain time-dependent patterns in security trading caused by the arrival of private information.

Both studies demonstrate how information is incorporated into pricing and how various groups

of investors influence prices. Specifically, both Admati and Pfleiderer and Foster and

Viswanathan take into account the roles of liquidity and informed traders in explaining variations

in volume and volatility. Accordingly, traders would try to minimize their trading costs and

therefore trade when the trading costs are lower (or liquidity is higher). The difference between

the Admati and Pfleiderer and Foster and Viswanathan models lies in the assumption about the

trading patterns of informed and liquidity traders. While the Admati and Pfleiderer model

predicts that both informed and liquidity traders trade together, the Foster and Viswanathan

model predicts that private information is short lived and liquidity traders avoid trading with

informed traders. The implications of these two models are as follows: Foster and Viswanathan

suggest that liquidity traders avoid trading with informed traders when private information is

intense. The resulting volume would be low and this would imply low volume comes with high

volatility. Admati and Pfleiderer speculate that trading volume would be high when price

volatility is high.

2. Anup Agrawal and Kishore Tandon, (1994) they examined five seasonal patterns in stock

markets of eighteen countries: the weekend, turn-of-the-month, end-of-December, monthly and

Friday-the-thirteenth effects. They found a daily seasonal in nearly all the countries, but a

weekend effect in only nine countries. Interestingly, the daily seasonal largely disappears in the

1980s. The last trading day of the month has large returns and low variance in most countries.

Many countries have large December pre-holiday and inter-holiday returns. The January returns

are large in most countries and a significant monthly seasonal exists in ten countries."

3. Franses and Paap (2000) they examined that the day-of-the-week effect in the Karachi stock

market in order to get the information whether this anomaly exist or not. Stock exchanges play

an important role in a vibrant economy. Accordingly, the purpose of this paper is to test for the

existence of daily stock market anomalies in the Karachi stock exchange (KSE), in an attempt to

check the level of market efficiency in this emerging capital market. The data includes the daily

market index returns covering the period from January 2000 to January 2011. The findings show

that Monday returns are negative, and lower than the returns on the rest of the trading days which

suggest that the returns in the last trading day are as low as in the first trading day. Furthermore,

the results show that market return volatility is greatly influenced by the bad news than good

news, a result similar to other markets. Accordingly, it might be argued that the day of the week

effect is present in the case of Karachi stock exchange.

4. Hakan Berument and Halo Kiymaz (2001) they tested the presence of the day of the week

effect on stock market volatility by using the S&P 500 market index during the period of January

1973 and October 1997. The findings show that the day of the week effect is present in both

volatility and return equations. While the highest and lowest returns are observed on Wednesday

and Monday, the highest and the lowest volatility are observed on Friday and Wednesday,

respectively. Further investigation of sub-periods reinforces their findings that the volatility

pattern across the days of the week is statistically different.

5. Halil Kiymaza,*, Hakan Berument (June 2003) they investigated the day of the week effect

on the volatility of major stock market indexes for the period of 1988 through 2002. Using a

conditional variance framework, we find that the day of the week effect is present in both return

and volatility equations. The highest volatility occurs on Mondays for Germany and Japan, on

Fridays for Canada and the United States, and on Thursdays for the United Kingdom. For most

of the markets, the days with the highest volatility also coincide with that market’s lowest trading

volume. Thus, this paper supports the argument made by Foster and Viswanathan [Rev. Finance.

Stud. 3 (1990) 593] that high volatility would be accompanied by low trading volume because of

the unwillingness of liquidity traders to trade in periods of high stock market volatility.

6. Ankara, Turkey (June 2003) in his study investigates the day of the week effect on the

volatility of major stock market indexes for the period of 1988 through 2002. Using a conditional

variance framework, we find that the day of the week effect is present in both return and

volatility equations. The highest volatility occurs on Mondays for Germany and Japan, on

Fridays for Canada and the United States, and on Thursdays for the United Kingdom. For most

of the markets, the days with the highest volatility also coincide with that market’s lowest trading

volume. Thus, this paper supports the argument made by Foster and Viswanathan [Rev. Finance.

Stud. 3 (1990) 593] that high volatility would be accompanied by low trading volume because of

the unwillingness of liquidity traders to trade in periods of high stock market volatility.

7. Golaka c nath & Manoj dalvi (December 2004) they examined empirically the day of the

week effect anomaly in the Indian equity market for the period from 1999 to 2003 using both

high frequency and end of day data for the benchmark Indian equity market index S&P CNX

NIFTY. Using robust regression with biweights and dummy variables, the study finds that before

introduction of rolling settlement in January 2002, Monday and Friday were significant days.

However after the introduction of the rolling settlement, Friday has become significant. This also

indicates that Fridays, being the last days of the weeks have become significant after rolling

settlement. Mondays were found to have higher standard deviations followed by Fridays. The

existence of market inefficiency is clear. The market inefficiency still exists and market is yet to

price the risk appropriately.

8. Mahendra Raj and Damini Kumari (2006) investigated the presence of seasonal effects in

the Indian stock market. They tested Week day effects, day-of-the-week, weekend, January and

April effects by applying a variety of statistical techniques. The results are interesting and

contradict some of the findings found elsewhere. The negative Monday effect and the positive

January effects were not found in India. Instead the Monday returns are positive while Tuesday

returns are negative. The seasonal effects in the Indian market have been examined by the two

major indices, the Bombay Stock Exchange Index and the National Stock Exchange Index.

However, it must be remembered that the Indian economy became deregulated from 1991 and

this may have had an impact on the markets.

9. Klesov Andriy (2008) he demonstrated that the day of the week effect in logarithmic changes

in spot CAD/USD foreign currency rates are not robust to a GARCH model with normal, GED

or double exponential error distribution respectively. In addition, the degree of statistical

significance varies inversely with the extent of leap to kurtosis in the error distribution. Most

strikingly, the day of the week effect in conditional variance disappears completely when we

account for auto correlation, heteroscedasticity and non normality. We assert that earlier research

in support of day of the week effect in returns and conditional variance may be the artifact of

using inadequate methodology, ascribing attempts to give an economic explanation to an “effect”

that may not exist.

10. Murat Cinko, Emin Avci, Turkey (2009) they examined that existence of day of the week

effect for Istanbul Stock Exchange (ISE) was analyzed on the basis of ISE-100 index returns, the

returns of all stocks traded in ISE and market capitalization based portfolio returns during 1995-

2008. In line with the previous findings, the results of the study presented that the ISE-100 index,

ISE traded stocks and market capitalization based portfolios had significant negative Monday

and significant positive Thursday and Friday returns.

11. Lukas Mazal (2008-2009) used a dummy variable approach and an extended dummy

variable approach to test for the existence of calendar effects in the rates of return of common

stocks. It applies the extended dummy variable approach based on a factor model to returns of 30

stocks traded at the German Stock Exchange and the dummy variable approach to returns of 28

world indices. Furthermore, it investigates time persistence and evolution of these calendar

effects. Finally, it simulates two portfolio strategies based on the Monday effect and the

September effect. By estimating a rolling dummy variable regression this thesis provides

evidence confirming that the day of the week effect started disappearing in the second half of

1990s. The simulated portfolios are able to outperform the buy and hold strategy in all the eight

indices considered. This means existence of unexploited profit opportunities, which seriously

undermines the efficient market hypothesis

12. Wing-Keung Wong, Weiwei Qiao, Zhuo Qiao (2010) they adopted a non-parametric

stochastic dominance (SD) approach to examine the day-of-the-week effects in Chinese stock

markets. In contrast to the extensive evidence of day-of-the-week effects disclosed by a

parametric mean-variance (MV) approach, our SD tests show that the day-of-the-week effect is

much weaker. We find that there are only Wednesday effects in Chinese A-share and B-share

stock markets.

13. Talat, Ibrahim & Muhsin (2011) they examined well-known fact that the day-of-the-week

effect in stock markets is one of the most prominent puzzling seasonal anomalies in finance and

has been increasingly attracting attention from researchers and practitioners, as well as

academics. This paper scrutinizes the day-of-the-week effect in the emerging equity market of

Saudi Arabia, TADAWUL. By using a non-linear GARCH model and covering the data from

January 2001 to December 2009, the findings of the study reveal that the returns on the five

trading days follow different process. This confirms that mean daily returns are significantly

different from each other and validates the day-of-the-week effect in TADAWUL.

14. Pengurusan (2011)

This paper investigates the existence of day-of-the-week effect for ten FTSE Bursa Malaysia

indices. Standard procedure of determining calendar anomaly with additional GARCH related

models are employed to determine the significance of the day-of-the-week effect. Results

suggest that the day-of-the-week effect only exist for the FTSE Bursa Malaysia MESDAQ Index.

However, the effect might be due to changing volatility since the negative and lowest Monday

return does not appear to be significant in the EGARCH model.

15. C Chipeta (2012) This study is the first in South Africa to test directly for the day of the

week effect on skewness and kurtosis on the nine listed economic stock market sector indices of

the JSE. The empirical results of this study show no evidence of the day of the week effect on

skewness and kurtosis for eight of the nine JSE stock market sectors. However, the Monday

effect was detected for the basic materials sector only. The study also finds the JSE to be weak

form efficient Changing Patterns.

In the day of the week effects in African Stock Markets Shakeel Kalidas, University of the

Witwatersrand, the changing patterns in returns on African stock markets have not been

adequately documented. This paper addresses this gap by testing for the day of the week effect

and the changes in the patterns of returns for several African stock markets. A direct test on

skewness and kurtosis is used to capture higher statistical moments in the search for seasonal

patterns in returns. Daily Index data for South Africa, Zambia, Botswana, Nigeria, and Morocco

are used for the period 2004 to 2012. Day of the week effects are documented for all the

countries with the exception of South Africa. Furthermore, significant changes in patterns over

time are observed for these same countries. Each day in the pre-financial crisis period shows

significantly different patterns to every other day in post-crisis epoch. Further, the patterns

displayed amongst the countries with significant results are largely similar in terms of

highest/lowest mean returns.

1.8 RESEARCH METHODOLOGY

A research process consists of stages or steps that guide the project from its conception

through the final analysis, recommendations and ultimate actions. The research process provides

a systematic, planned approach to the research project and ensures that all aspects of the research

projects are consistent with each other. It aims to understand the research methodology

establishing a frame work of evaluation and revaluation of the primary and secondary research.

RESEARCH DESIGN

A research design in the specification of methods and procedure for acquiring the

information needed to structure or to solve the problem. It is the overall operation pattern of

frame work of the project that stipulates what information to corrected, from which sources, and

by what procedure. If it is a good design, it will ensure that objective and economical procedure.

The research design used in this project is analytical in nature, in which researcher has to

use facts or information already available, which are analyzed and evaluation are made to resolve

the problem.

DATA COLLECTION

The researcher used to collect secondary data. The data of the three industries under

which three companies in each sectors have been collected from the internet.

Secondary data used:

Secondary data can be collected with help of following

Company websites

NSE websites

Magazines and Journals

TOOLS FOR ANALYSIS

Mean,

Standard deviation

Skewness

Kurtosis

Regression analysis

MEAN:

The mean is the average of the numbers i.e., a calculated “central value” of a set of numbers. To

calculate, just add up all the numbers then divide by total number of items.

A sample    is the sum the sampled values divided by the number of items in the sample:

STANDARD DEVIATION:

In statistics and probability theory, the standard deviation (SD) (represented by the Greek letter

sigma, σ) shows how much variation or dispersion from the average exists. A low standard

deviation indicates that the data points tend to be very close to the mean (also called expected

value); a high standard deviation indicates that the data points are spread out over a large range

of values.

The standard deviation of a random variable, statistical population, data set, or probability

distribution is the square root of its variance. 

SKEWNESS:

In probability theory and statistics, skewness is a measure of the asymmetry of the probability

distribution in a set of statistical data. The skewness value can be positive or negative, or even

undefined, depending on whether data points are skewed to the left (negative skew) or to the

right (positive skew) of the data average.

The bars on the right side of the distribution taper differently than the bars on the left side. These

tapering sides are called tails, and they provide a visual means for determining which of the two

kinds of skewness a distribution has:

Negative skew: The left tail is longer; the mass of the distribution is concentrated on the

right of the figure. The distribution is said to be left-skewed, left-tailed, or skewed to the

left.

Positive skew: The right tail is longer; the mass of the distribution is concentrated on the

left of the figure. The distribution is said to be right-skewed, right-tailed, or skewed to the

right.

KURTOSIS:

In probability theory and statistics, kurtosis (from the Greek word kyrtos or kurtos, meaning

curved, arching) is any measure of the "peakedness" of the probability distribution of a real-

valued random variable.

It is a statistical measure used to describe the distribution of observed data around the mean. It is

sometimes referred to as the "volatility of volatility." A high kurtosis portrays a chart with fat

tails and a low, even distribution, whereas a low kurtosis portrays a chart with skinny tails and a

distribution concentrated toward the mean. Distributions with negative or positive excess

kurtosis are called platykurtic distributions or leptokurtic distributions respectively.

REGRESSION ANALYSIS:

In statistics, regression analysis is a statistical process for estimating the relationships among

variables. It includes many techniques for modeling and analyzing several variables, when the

focus is on the relationship between a dependent variable and one or more independent variables.

More specifically, regression analysis helps one understand how the typical value of the

dependent variable (or 'criterion variable') changes when any one of the independent variables is

varied, while the other independent variables are held fixed. Regression analysis is widely used

for prediction and forecasting, where its use has substantial overlap with the field of machine

learning. Regression analysis is also used to understand which among the independent variables

are related to the dependent variable, and to explore the forms of these relationships.

1.9 LIMITATIONS OF THE STUDY

The data collected is secondary in nature.

A detailed study was not possible due to shortage of time.

This study is limited to some selected industries.

(Pharmaceutical, Information Technology & Automobile)

Situations in stock market are always subject to change.

CHAPTER 2DATA ANALYSIS

AND INTERPRETATION

2. DATA ANALYSIS AND INTERPRETATION

2.1 DESCRIPTIVE ANALYSIS

2.1.1 AUTOMOBILE SECTOR

TABLE NO. 2.1.1.1 TABLE SHOWING DESCRIPTIVE ANALYSIS OF BAJAJ AUTO

MONDAY

OPCP

TUESDAY

OPCP

WEDNESDAY

OPCP

THURSDAY

OPCP

FRIDAY

OPCP

MEAN1625.33 1623.5 1630.25 1632 1609.63

1609.72 1625.1

1623.76

1619.8

1622.9

S.D450.487

450.705 450.379

449.873 452.243

450.799 448.042 448.63

458.48

458.36

SKEWNESS -0.4219

-0.3677 -0.3499

-0.3534 -0.4196

-0.3966 -0.4208

-0.3692 -0.423

-0.416

KURTOSIS 1.06705

1.11711 1.21941

1.26015 1.10557 1.0139 1.06442

1.02929

1.0434

1.0606

CHART NO. 2.1.1.1 CHART SHOWING DESCRIPTIVE ANALYSIS OF BAJAJ AUTO

BAJAJ AUTO

Monday Tuesday Wednesday Thursday Friday-200

0

200

400

600

800

1000

1200

1400

1600

1800

MEANS.DSKEWNESSKURTOSIS

Interpretation

Here Bajaj auto has highest mean value which falls on Tuesday and has negative skewness

which falls on Friday.

TABLE NO. 2.1.1.2 TABLE SHOWING DESCRIPTIVE ANALYSIS OF MAHINDRA & MAHINDRA

MONDAY

OPCP

TUESDAYOP CP

WEDNESDAYOP CP

THURSDAY

OPCP

FRIDAYOP CP

MEAN760.271 760.538 763.652 763.686 760.075 759.525 764.627 764.149 759.83 760.03

S.D171.664 170.257 172.701 172.188 174.278 173.455 175.805 175.784 174.22 173.45

SKEWNESS-0.4139 -0.3805 -0.4623 -0.4401 -0.4046 -0.4158 -0.4927 -0.4923 -0.537 -0.517

KURTOSIS0.49652 0.52032 0.68742 0.7105 0.65244 0.65909 0.64732 0.60259 0.773 0.7017

CHART NO. 2.1.1.2 CHART SHOWING DESCRIPTIVE ANALYSIS OF MAHINDRA & MAHINDRA

MAH

INDR

A&M

AHIN

DRA

Monday Tuesday Wednesday

Thursday Friday

-2000

200400600800

1000

MEANS.DSKEWNESSKURTOSIS

Interpretation

Here M&M has highest mean value which falls on Tuesday and has negative skewness which

falls on Friday.

TABLE NO. 2.1.1.3 TABLE SHOWING DESCRIPTIVE ANALYSIS OF TATA MOTORS

MONDAY

OPCP

TUESDAYOP CP

WEDNESDAYOP CP

THURSDAY

OPCP

FRIDAYOP CP

MEAN539.841 540.423 534.83 535.084 546.256 547.803 545.597 545.468 537.54 535.12

S.D362.581 363.454 354.302 353.518 365.394 366.138 546.293 546.174 360.12 355.71

SKEWNESS0.78611 0.78547 0.78639 0.78069 0.76172 0.75956 0.76738 0.76909 360.12 355.71

KURTOSIS-0.8774 -0.8668 -0.8187 -0.8192 -0.8508 -0.8519 -0.8356 -0.8415 -0.812 -0.849

CHART NO. 2.1.1.3 CHART SHOWING DESCRIPTIVE ANALYSIS OF TATA MOTORS

TATA

MO

TORS

Monday Tuesday Wednesday Thursday Friday

-100

0

100

200

300

400

500

600

MEANS.DSKEWNESSKURTOSIS

Interpretation

Here Tata motors have highest mean value which falls on Wednesday and has negative kurtosis

which falls on Monday.

2.1.2 INFORMATION TECHNOLOGY SECTOR

TABLE NO. 2.1.2.1 TABLE SHOWING DESCRIPTIVE ANALYSIS OF HCL

MONDAY

OPCP

TUESDAYOP CP

WEDNESDAYOP CP

THURSDAYOP CP

FRIDAYOP CP

MEAN 78.9 78.25 79.56 78.94 81.47 81.11 80.47 80.04 79.12 78.7

S.D 41.87 41.34 42.64 42.15 42.11 41.92 42.71 42.7 41.81 41.7

SKEWNESS 0.314 0.297 0.315 0.303 0.252 0.244 0.266 0.287 0.34 0.34

KURTOSIS -1.195 -1.226 -1.22 -1.23 -1.23 -1.24 -1.276 -1.25 -1.162 -1.2

CHART NO. 2.1.2.1 CHART SHOWING DESCRIPTIVE ANALYSIS OF HCL

HCLMonday Tuesday Wednesday Thursday Friday-10

0

10

20

30

40

50

60

70

80

90

MEANS.DSKEWNESSKURTOSIS

Interpretation

Here HCL have highest mean value which falls on Wednesday and has negative kurtosis which

falls on Thursday.

TABLE NO. 2.1.2.2 TABLE SHOWING DESCRIPTIVE ANALYSIS OF TCS

MONDAY

OPCP

TUESDAYOP CP

WEDNESDAYOP CP

THURSDAYOP CP

FRIDAYOP CP

MEAN1396 1109 1399 1108 1386 1078 1394 1099 1391 1099

S.D199.1 404.2 203.8 411.1 183.9 399.4 198.6 408.8 193.5 405

SKEWNESS2.686 0.555 2.649 0.551 2.855 0.535 2.73 0.514 2.739 0.54

KURTOSIS6.037 0.21 5.837 0.126 7.069 0.124 6.312 0.015 6.307 0.16

CHART NO. 2.1.2.2 CHART SHOWING DESCRIPTIVE ANALYSIS OF TCS

TCS

Monday Tuesday Wednesday

Thursday Friday

0

200

400

600

800

1000

1200

1400

1600

MEANS.DSKEWNESSKURTOSIS

Interpretation

Here TCS have highest mean value which falls on Tuesday and has least positive skewness

which falls on Monday.

TABLE NO. 2.1.2.3 TABLE SHOWING DESCRIPTIVE ANALYSIS OF WIPRO

MONDAYOP CP

TUESDAYOP CP

WEDNESDAYOP CP

THURSDAYOP CP

FRIDAYOP CP

MEAN437.3 438.4 440.6 441.2 438.5 438.8 440.7 440.5 436 435

S.D108.5 110 111.7 110.8 111.8 112 113.1 112.5 112.1 111

SKEWNESS0.88 0.873 0.833 0.821 0.783 0.752 0.795 0.796 0.8 0.79

KURTOSIS0.851 0.853 0.709 0.684 0.686 0.637 0.617 0.59 0.758 0.73

CHART NO. 2.1.2.3 CHART SHOWING DESCRIPTIVE ANALYSIS OF WIPRO

WIP

RO

Monday Tuesday Wednesday Thursday Friday

050

100150200250300350400450500

MEANS.DSKEWNESSKURTOSIS

Interpretation

Here WIPRO have highest mean value which falls on Tuesday and has least positive kurtosis

which falls on Thursday.

2.1.3 PHARMACEUTICAL SECTOR

TABLE NO. 2.1.3.1 TABLE SHOWING DESCRIPTIVE ANALYSIS OF CIPLA

MONDAY

OPCP

TUESDAY

OPCP

WEDNESDAY

OPCP

THURSDAY

OPCP

FRIDAY

OPCP

MEAN331.2 330.49 330.26 329.8 327.4 327.09 330.59

329.33 329.04 329.12

S.D55.884

55.6227 56.184

56.304 56.23 56.028 56.93 56.61 56.636 56.394

SKEWNESS -0.3823

-0.4278 -0.4158 -0.398 -0.393

-0.3354 -0.408 -0.418

-0.4143

-0.3868

KURTOSIS 0.1235

0.10975 0.1635

0.1226 0.167 0.1027 0.1248

0.1304 0.2232 0.1833

CHART NO. 2.1.3.1 CHART SHOWING DESCRIPTIVE ANALYSIS OF CIPLA

CIPL

A

Monday Tuesday Wednesday

Thursday Friday-50

0

50

100

150

200

250

300

350

400

MEANS.DSKEWNESSKURTOSIS

Interpretation

Here Cipla have highest mean value which falls on Monday and has negative skewness which

falls on Thursday.

TABLE NO. 2.1.3.2 TABLE SHOWING DESCRIPTIVE ANALYSIS OF LUPIN

MONDAYOP CP

TUESDAYOP CP

WEDNESDAYOP CP

THURSDAYOP CP

FRIDAYOP CP

MEAN786.79 787.625 792.56 792.38 790.2 790.47 794.91 794.85 790.44 790.12

S.D141.11 140.958 144.95 145.08 143.3 144.04 144.84 145.57 144.64 143.94

SKEWNESS0.3055 0.35799 0.3767 0.3805 0.44 0.4006 0.3272 0.3343 0.3414 0.3479

KURTOSIS-0.5251 -0.5419 -0.5472 -0.457 -0.453 -0.483 -0.568 -0.602 -0.5472 -0.5642

CHART NO. 2.1.3.2 CHART SHOWING DESCRIPTIVE ANALYSIS OF LUPIN

LUPI

N

Monday Tuesday Wednesday

Thursday Friday

-100

0

100

200

300

400

500

600

700

800

900

MEANS.DSKEWNESSKURTOSIS

Interpretation

Here Lupin has highest mean value which falls on Thursday and has negative kurtosis which

falls on Thursday.

TABLE NO. 2.1.3.3 TABLE SHOWING DESCRIPTIVE ANALYSIS OF DR.REDDY LAB’S

MONDAYOP CP

TUESDAY

OPCP

WEDNESDAYOP CP

THURSDAYOP CP

FRIDAYOP CP

MEAN1526.6 1525.87 1523.5 1523 1494 1495.4 1510.6 1509.4 1515.2 1515.6

S.D492.59 491.106 491.76 491.48 489.4 490.9 499.23 497.73 489.6 490.15

SKEWNESS-0.4041 -0.4004 -0.352 -0.357 -0.396

-0.3768 -0.359 -0.362 -0.4039 -0.3935

KURTOSIS0.066 0.06336 0.0143 0.0077 -0.086

-0.0642 -0.051 -0.066 0.0677 0.0699

CHART NO. 2.1.3.3 CHART SHOWING DESCRIPTIVE ANALYSIS OF DR.REDDY LAB’S

REDD

Y

Monday Tuesday Wednesday Thursday Friday

-200

0

200

400

600

800

1000

1200

1400

1600

1800

MEANS.DSKEWNESSKURTOSIS

Interpretation

Here Lupin has highest mean value which falls on Monday and has negative skewness which

falls on Monday.

2.2 REGRESSION ANALYSIS

TABLE NO. 2.2.1 TABLE SHOWING REGRESSION ANALYSIS OF AUTOMOBILE SECTOR

Model Summary

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 .458a .210 .209 153.523

a. Predictors: (Constant), tm, ba

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) 488.216 17.112 28.531 .000

Ba .178 .010 .461 18.163 .000

Tm -.027 .012 -.057 -2.251 .025

a. Dependent Variable: mm

Interpretation

The independent variables accounts for 21 % (R-square) and Bajaj Auto is dominant (based on t-

value ) in determining the dependent variable M&M in automobile sector.

TABLE NO. 2.2.2 TABLE SHOWING REGRESSION ANALYSIS OF INFORMATION TECHNOLOGY SECTOR

Model Summary

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 .863a .744 .744 205.01927

a. Predictors: (Constant), wipro, hcl

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) 1124.891 23.943 46.983 .000

hcl -9.917 .166 -1.027 -59.702 .000

wipro 1.729 .063 .472 27.433 .000

a. Dependent Variable: tcs

Interpretation

The independent variables accounts for 74.4% (R-square) and Wipro is dominant (based on t-

value) in determining the dependent variable TCS in IT sector.

.

TABLE NO. 2.2.3 TABLE SHOWING REGRESSION ANALYSIS OF PHARMACEUTICAL SECTOR

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .909a .826 .826 23.38795

a. Predictors: (Constant), reddy, lupin

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) 145.310 2.757 52.706 .000

Lupin .029 .002 .220 18.147 .000

Reddy .106 .001 .932 76.739 .000

a. Dependent Variable: cipla

Interpretation

The independent variables accounts for 82.6% (R-square) and Dr.Reddy is dominant (based on

t- value ) in determining the dependent variable Cipla in pharmaceutical sector.

CHAPTER 3

FINDINGS, SUGGESTIONS &

CONCLUSION

3.1FINDINGS

3.1 Following are the main findings from descriptive analysis

3.1.1.AUTOMOBILE SECTOR

• With regard to average, highest and lowest value is found for BAJAJ AUTO (1625.33347

and 1623.4962) which falls on Tuesday and Tata motors (534.82992 and 535.08353 )

which falls on Tuesday respectively.

• Similarly with regard to standard deviation, highest and lowest value is found for M&M

has more deviation ( 175.804596 and 175.78369) which falls on Thursday and Tata

motors (354.301504 and 353.51832 ) which falls on Tuesday respectively.

• With respect to kurtosis Tata motors has highly negative peakedness which falls on

Monday and Bajaj auto has highly positive peakedness which falls on Tuesday.

• Similarly with regard to skewness, Bajaj auto has highly negative skewness which falls

on Friday and Tata Motors has highly positive skewness which falls on Monday.

3.1.2. IT SECTOR

• With regard to average, highest and lowest value is found for TCS (1398.56205 and

1107.5548 ) which falls on Tuesday and HCL (78.9024194 and 78.246169 ) which falls

on Monday respectively.

• Similarly with regard to standard deviation, highest and lowest value is found for TCS

has more deviation ( 203.77961 and 411.13875 ) which falls on Tuesday and HCL

(41.813105 and 41.7098142 ) which falls on Friday respectively.

• With respect to kurtosis TCS has highly positive peakedness which falls on Wednesday

and HCL has highly negative peakedness which falls on Monday.

• Similarly with regard to skewness, HCL has highly negative skewness which falls on

Wednesday and TCS has highly positive skewness which falls on Wednesday.

3.1.3. PHARMACEUTICAL SECTOR

• With regard to average, highest and lowest value is found for Dr.Reddy (1526.6494 and

1525.873 ) which falls on Monday and Cipla (327.432992 and 327.08934 ) which falls on

Wednesday respectively.

• Similarly with regard to standard deviation, highest and lowest value is found for

Dr.Reddy has more deviation (499.229419 and 497.73149 ) which falls on Thursday and

Cipla ( 55.8838986 and 55.622665 ) which falls on Monday respectively.

• With respect to kurtosis Cipla has highly positive peakedness which falls on Friday and

Lupin has highly negative peakedness which falls on Thursday.

• Similarly with regard to skewness, Cipla has highly negative skewness which falls on

Tuesday and Lupin has highly positive skewness which falls on Wednesday.

3.2 Following are the main findings from regression analysis

The independent variables accounts for 21 % (R-square) and Bajaj Auto is dominant

(based on t-value) in determining the dependent variable M&M in automobile

sector.

The independent variables accounts for 74.4% (R-square) and Wipro is dominant

(based on t-value) in determining the dependent variable TCS in IT sector.

The independent variables accounts for 82.6% (R-square) and Dr.Reddy lab’s is

dominant (based on t-value ) in determining the dependent variable Cipla in

pharmaceutical sector.

3.2 SUGGESTIONS

• This study recommended some companies and industries on the basis of variation. It is

clearly known from the study that the highest volatility on stock prices held on

Mondays, Wednesdays and less volatility held on Tuesdays, Thursdays and Fridays

respectively. In case of industries investor better to go for Pharmaceutical Industry

followed by IT Industry and if he wants to diversify the risk then last preference shall

give to automobile industry.

• The study suggests that Long term Investors should not invest into panic market, which

led investors to erode their wealth.

• The study suggests that that Investors should take into consideration various things before

investing into scripts such as:

1. Financial positions of company

2. Liquidity position

3. Dividend policy

3.3 CONCLUSION

Investors seek a safe hedge against rising inflation and diversify their portfolio to offset losses in

the volatile stock market. The study presented a comprehensive analysis of the stock price

behavior, more specifically on the seasonality effect, in the Indian stock market. The seasonality

effect is examined by a detailed analysis of day of the week effect, and the period of study over

five year i.e. from 2009 to 2013. The study found significant day of week effect for specific

trading days. On the whole, the price series in the Indian stock market showed signs of return

seasonality with respect to day of week effect.