IMPACT ON STOCK PRICE WITH CHANGES IN THE CNX NIFTY INDEX

A Proposal

Presented to

Professor Dr. Suveera Gill

University Business School

Panjab University, Chandigrah

On

11 September 2012

In

Partial fulfillment of

Masters of Business Administration (MBA)

By

Sarabjit Singh

S.No. Particulars Page No.

1 Introduction 1

1.1 Changes in the Index 1

1.2 S&P CNX Nifty 1

1.3 Efficient Market hypothesis 4

1.4 Volatility 5

2 Review of Literature 5

3 Need and Significance of the study 7

4 Proposed Objectives of the study 8

5 Research Design 8

5.1 Hypotheses of the study 8

5.2 sample selection 8

5.3 Sources of data 10

5.4 Period of study 10

5.5 Tools for analysis 10

6 References 13

7 Appendices 15

1

1 INTRODUCTION

1.1 Changes in the Index:

Changes in an index are a regular phenomenon and they take place due to the inclusion and

exclusion of stocks from the index. A stock index reflects the mood and direction of the overall

market movements. The stock indices, apart from being an indicator of the market movements,

serve as a bench mark for measuring the performance of stocks under that index. The stock

indices are rarely static and their composition keeps changing so that the objectives behind the

construction of indices are served. The changes might also be effected by other reasons like

mergers and corporate restructuring which might cause some of the stocks to exit from the

market. Although changes in an index like Nifty are a regular phenomenon, these changes have

implications for the markets in general. When a stock is added to (or deleted from) the Nifty, the

index will try to include it in their portfolio and these actions may induce buying/selling pressure

and correspondingly, the price level is increased (decreased) and the volume of both types of

stocks increased.

1.2 S&P CNX Nifty:

National Stock Exchange (NSE) is a stock exchange located at Mumbai, India. It is the

16th largest stock exchange in the world by market capitalization and largest in India by daily

turnover and number of trades, for both equities and derivative trading. Though a number of

other exchanges exist, NSE and the Bombay Stock Exchange are the two most significant stock

exchanges in India and between them are responsible for the vast majority of share transactions.

S&P CNX Nifty, also called the Nifty 50 or simply the Nifty, is a stock market index,

and one of several leading indices for large companies which are listed on National Stock

Exchange of India, index based derivatives and index funds. Nifty is owned and managed by

India Index Services and Products Ltd. (IISL), which is a joint venture between NSE and

CRISIL(Credit Rating and Information Services of India Ltd). (IISL) is India's first specialized

company focused upon the index as a core product. IISL has a marketing and licensing

agreement with Standard & Poor's for co-branding equity indices. 'CNX' in its name stands for

'CRISIL NSE Index'.

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The present composition of the S&P CNX NIFTY is given in the table below.

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4

S&P CNX NIFTY COMPOSITIONS (as on 1-Sept-2012)

Company Name Industry

ACC Ltd. CEMENT AND CEMENT PRODUCTS

Ambuja Cements Ltd. CEMENT AND CEMENT PRODUCTS

Asian Paints Ltd. PAINTS

Axis Bank Ltd. BANKS

Bajaj Auto Ltd. AUTOMOBILES - 2 AND 3 WHEELERS

Bank of Baroda BANKS

Bharat Heavy Electricals Ltd. ELECTRICAL EQUIPMENT

Bharat Petroleum Corporation Ltd. REFINERIES

BhartiAirtel Ltd. TELECOMMUNICATION – SERVICES

Cairn India Ltd. OIL EXPLORATION/PRODUCTION

Cipla Ltd. PHARMACEUTICALS

Coal India Ltd. MINING

DLF Ltd. CONSTRUCTION

Dr. Reddy's Laboratories Ltd. PHARMACEUTICALS

GAIL (India) Ltd. GAS

Grasim Industries Ltd. CEMENT AND CEMENT PRODUCTS

HCL Technologies Ltd. COMPUTERS – SOFTWARE

HDFC Bank Ltd. BANKS

Hero MotoCorp Ltd. AUTOMOBILES - 2 AND 3 WHEELERS

Hindalco Industries Ltd. ALUMINIUM

Hindustan Unilever Ltd. DIVERSIFIED

Housing Development Finance Corporation Ltd.

FINANCE – HOUSING

I T C Ltd. CIGARETTES

ICICI Bank Ltd. BANKS

IDFC Ltd. FINANCIAL INSTITUTION

Infosys Ltd. COMPUTERS – SOFTWARE

Jaiprakash Associates Ltd. CONSTRUCTION

Jindal Steel & Power Ltd. STEEL AND STEEL PRODUCTS

Kotak Mahindra Bank Ltd. BANKS

Larsen & Toubro Ltd. ENGINEERING

Mahindra & Mahindra Ltd. AUTOMOBILES - 4 WHEELERS

Maruti Suzuki India Ltd. AUTOMOBILES - 4 WHEELERS

NTPC Ltd. POWER

Oil & Natural Gas Corporation Ltd. OIL EXPLORATION/PRODUCTIO

(Source: NSE website, http://www.nse-india.com)

1.3 Efficient market hypothesis: Efficient market hypothesis states it is impossible to "beat

the market" because stock market efficiency causes existing share prices to always incorporate

and reflect all relevant information. According to the EMH, stocks always trade at their fair

value on stock exchanges, making it impossible for investors to either purchase undervalued

stocks or sell stocks for inflated prices. As such, it should be impossible to outperform the

overall market through expert stock selection or market timing, and that the only way an investor

can possibly obtain higher returns is by purchasing riskier investments.

The efficient market hypothesis (EHM) predicts that the security prices reflect all

publicly available information. Therefore, one corollary of the EMH is that one can sell (or buy)

large blocks of stocks nearer to the market price as long as one could convince other investors

that one has no private or inside excess demand for a single security will be very elastic, and the

purchase of a large number of shares will have no impact on price. The purpose of this research

study is to analyze the impact of inclusion into or exclusion from the stock index of a certain

stock on the price of the relevant stock.

1.4 Volatility: Volatility refers to the degree of (typically short-term) unpredictable change over

time of a certain variable. Since it is a standard measure of financial vulnerability, it plays a key

role in assessing the risk/return tradeoffs and forms an important input in asset allocation

decisions. Volatility is caused by the random arrival of new information about the future returns

from the stock, arbitrage (Arbitrage is the simultaneous buying and selling of an asset to profit

from price discrepancies), technology, inflation and interest rates volatilities, etc.

Volatility plays a key role in assessing risk/return trade off. Volatility is central to many

investment decisions in new product areas and is the critical variable in options. The volatility of

the underlying asset dictates the extent and likelihood of the option’s payout. Most option pricing

models including Black Scholes require a Volatility input defined as Standard deviation of log

relative prices. It is important to option traders because volatility is a measure of the possible

price changes of the assets in the future.

2. REVIEW OF LITERATURE

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A body of literature examining the effect of stock inclusions (exclusions) to (from) has

the S&P 500 as the focal point. The extant literature provides conflicting evidence for the S&P

500 for various reasons.

Shleifer (1986) investigated the index effect and examined the price impact related to

changes in S&P 500 between 1966 and 1983. The study found that there was an abnormal price

increase of 2.79 percent and the cumulative returns persisted. The returns were positively related

to measures of buying index funds and the results were attributed to the downward sloping

curves for stocks.

Lynch and Mendehall (1997) documented significant post-announcement abnormal

returns that were only partially reserved following the additions to or deletions from the S&P

500 index in their research. Their evidence of permanent trading volume contributing towards

added stocks provides support for both the PPH and the imperfect substitute hypothesis.

Dhillon and Johnson (1991), examined only the additions to the S&P 500 index during

1978-88 and found that price levels persisted for around 60 days after the announcement, which

is inconsistent with PPH.

Harris and Gurel (1986) found 3.13 percent abnormal returns resulting from additions to

the S&P 500. This increase was almost reserved after two weeks and thus, they attributed the

abnormal returns to the increased demand for the index funds. Their evidence is consistent with

the price pressure hypothesis.

Pruitt and Wei (1989) provided direct evidence that institutional investors cause for

demand changes. The price pressure is attributed to institutional portfolio strategies that seek to

match the S&P 500 index returns by purchasing the stocks newly added to the index.

Machnes and Kula (2011) estimated the premium effect of the S&P 500 index after a

newly stock appears on the index list. Most of the changes in the newly added (or removed)

stock took place two months before official announcement of the updated list of companies on

the index, and hence the liquidity of the stocks will increase and their price will rise. Index

oriented institutional investors who choose to be linked to the TA25 index pay most of the

premium. Traders who are familiar with the index effect buy stocks they expect to be on the

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index and short-sell stocks expected to be dropped, and thus make a profit from the index

reputation.

Jain (1987) found that the stocks added to S&P 500 experienced excess returns of

3percent on the announcement day and this excess returns were added to S&P supplementary

indices. This study contested the PPH and downward sloping demand curve (DSDC) hypothesis

and ascribed the excess returns to the information content hypothesis.

In a series of articles Beneish and Whaley (1996, 1997, 2002) documented that excess

returns associated with index revisions has increased from around 2.79% (during 1976-83) to

5.94 % (during 1989-95) to 8% (during 1996-2001) and they attributethis to the growing index

fund industry in the U.S. Hedge and Mc.Dernott (2003) test for liquidity changes and they found

a permanent increase in liquidity measured by decreased effective spreads, increased quote depth

and as well as increase in volume.

Vijaya and Vedpuriswar (2003) have investigated in their article ‘The Dynamics around

Sensex Reconstitutions’ the price effects for the sensex. Though this study reports a weak

permanent effect for deletions, the researches pointed out that the study suffers from the problem

of assumed announced dates as Bombay Stock Exchange (BSE) did not maintain a record of the

exact announcement dates. Therefore, the study has limited research focus on account of

uncertain announcement dates.

In conclusion it is evident that the existing literature is more or less unanimous on the

premise that index revisions are associated with price effects but the debate is whether the price

effects are temporary or permanent and also there is disagreement on the explanations for these

findings. In the broad sense this study is quite relevant to the present scenario, the share market

is facing more volatility, and because of this investors have lost their confidence due to more ups

and downs in the market, and also much of the work is not done in this case. So it is imperative

to study the impact of the NSE on the share prices of various companies which are included or

excluded form the NSE and the volatility

3. NEED FOR THE STUDY

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There is a need to know how the market moves before and after the effect of inclusion or

exclusion of particular company scrip. Besides, there is a need to measure the impact and study

about how the market is good to the investor. This study aims to analyze the excess returns for

the stocks before and after the inclusive and exclusive period for S&P CNX Nifty. The results of

the study will be useful for the National Stock Exchange (NSE), index fund manager and broadly

to the discipline of the market efficiency.

The result of the study will be useful for index funds and self-indexers, who balance their

portfolios in line with the changes in the index. Local investors,foreign investors and portfolio

managers are also gets benefits with this study. This study also contributes to the existing

literature so also beneficial for the academicians.

4. PROPOSED OBJECTIVES OF THE STUDY

The main objectives of this study are as follows:

1. To analyze the effects of changes in both inclusion and exclusion of companies in S&P

CNX Nifty companies during the period from January 2005 to December 2011.

2. To study the volatility of stocks of companies included and excluded in Nifty during the

study period.

5. RESEARCH DESIGN

5.1 Hypotheses of the study

The following hypotheses are tested in this study:

H1: There are no excess returns recorded by Nifty companies in the pre-announcement

window.

H2: There are no excess returns recorded by Nifty companies in the post-effective window.

H3: There is no impact on the volatility of the companies included and excluded in Nifty on

announcement day.

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H4: There is no impact on the volatility of the companies included and excluded in Nifty on

effective day.

5.2 Sample Selection: The purpose of this study is to analyze the price pressure effect on S&P

index. The sample for this study comprised all changes on account of inclusion and exclusion in

the Nifty during the period January 2005 to December 2011. The criteria for selection of the

stocks in the sample were:

Changes in the Index due to corporate restructuring like Mergers and Acquisitions

were not taken as the sample.

Non-availability of announcement or effective dates.

Finally, it resulted in a sample of 20 exclusions and 20 inclusions for the study.

The following table represents the stocks which were included and excluded during the study

period:

S. No Company Name Date of Inclusion Date of Exclusion

1 Asian Paints 27/4/2012 -2 Bank of Baroda 27/4/2012 -3 Grasim 25/3/2012 -4 Sesa Goa 1/10/2010 -5 Dr. Reddy 1/10/2010 -6 Bajaj-Auto 1/10/2010 -7 Kotak Bank 8/4/2010 -8 IDFC 22/10/2009 -9 JP Associate 22/10/2009 -

10 Jindal Steel 17/6/2009 -11 Reliance Caital 12/1/2009 -12 Reliance Power 10/9/2008 -13 DLF 14/3/2008 -14 Cairn 12/12/2007 -15 Idea 12/12/2007 -16 NTPC 24/9/2007 -17 RPL 4/4/2007 -18 STER 4/4/2007 -19 Jet Airways 26/9/2006 -20 Reliance Communication 1/9/2006 -

9

21 Reliance Communication - 27/04/201222 Relaince Power - 27/04/201223 Suzlon - 25/03/201224 Unitech - 1/10/201025 Idea - 1/10/201026 ABB - 1/10/201027 Grasim - 8/4/201028 Nationalum - 22/10/200929 Tata Communivcation - 22/10/200930 RPL - 17/6/200931 Satyam Computer - 12/1/200932 Dr. Reddy - 10/9/200833 Glaxo - 14/3/200834 Hind Pertro - 12/12/200735 MTNL - 12/12/200736 Dabur - 24/9/200737 Orient Bank - 4/4/200738 Jet Airways - 4/4/200739 SCI - 27/6/200640 Colgate-Palmolive - 26/9/2005

(Source: NSE website. http://www.nse-india.com)

5.3 Sources of Data: The study depended on the secondary data and the required data were

collected from websites like http://www.nseindia.com,

http://www.yahoofinance.com,http://www.moneycontrol.com ,and Prowess Corporate database.

5.4 Period of the study: The period for this studystarts from 1 January 2005 and ends with 31

December 2011. The study covers only seven years. The analysis period covers 21 days window

period, that is, 10 days before and after effective date. Announcement day means, NSE may

announce to the press that this company is going to be excluded or included on the particular

date.

5.5 Tools used for Analysis:

In order to analyze the price pressure effects of changes under both inclusion and exclusion in

S&P CNX Nifty, the following tools will be used.

5.5.1 Tools for analyzing the volatility in returns of included and excluded companies:

10

GARCH Model: GARCH model was developed by Bollersler (1986) as a generalized

version of Engle’s (1982) Autoregressive conditional heterscedasticity (ARCH). In the GARCH

model the conditional variance at time ‘t’ depends on the past values of the squared error terms

and the past conditional variances. GARCH forecast variance as a weighted average of three

different variance forecasts. One is the constant variance that corresponds to the long run

average, 2nd is the forecast made in previous years, the 3rd is the new information that was not

available when the previous forecasts was made.

Negative returns seemed to be more important predictions of volatility than positive

returns. Large price decline forecasts greater volatility than similar large price increase.

σ12 = α0+α1ε2

t-1 + --------βPα2t-p (1)

(as used by Selvam, Indhumathi and Lydia for measuring volatility in “Impact on Stock price by

the Inclusion to and Exclusion form CNX Nifty Index”)

where,

ε2t-1 – information available on t-1 day;

α = constant; and

β = coefficient on a time period.

5.5.2 Tools for analyzing the price effect on included and excluded companies:

I. Abnormal Returns: To examine whether the float adjustment causes abnormal returns.

Abnormal returns (AR) are calculated as the excess returns earned by a sample stock over

the bench mark portfolio.

ARjt= Rjt – α – βj Rmt

ARji = the abnormal return of the particular stock j on the day t;

Rjt = the return of the particular stock j on the day t;

α = the average returns of the firm compared to the market average;

β = the market risk of this stock; and

Rmt = the returns on a market index for day t.

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II. Mean Abnormal Returns (MAR): Mean abnormal returns is the average of the excess

returns across the N firms on the day ‘t’.

N

MARt = 1/N ∑ ARj,t

j=1

III. Announcement price reaction of the firms added to the CNX Nifty

i. Cumulative Abnormal Return (CAR): A cumulative abnormal return (CAR) is

defined as the sum of all the excess returns over the window of interest. The

formula for calculating CAR is as follows:

T2

CARj,t = ∑ARj,t

T1

ii. Mean Cumulative Abnormal Returns (MCAR): The average of the CAR across

the observations is a measure of the abnormal performance over the event period.

The formula for calculating MCAR is as follows:

N

MCARt = 1/N∑CARj,t

j=1

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6. REFRENCES

Dhillon, U., and Johnson, H. (1991).Changes in the S&P 500.Journal of Business,64(1) 75-85.

Harris, L., and Gurel, E. (1986). Price and volume effects associated with changes in the S&P

500 list: New evidence for the price pressures. Journal of Finance, 41(4),815-829.

Lynch, A., and Mendenhall, R. (1997). New evidence on stock price associated with changes in

the S&P 500 index. Journal of Business, 70(3).351-383.

Shleifer, A. (1986). Do demand curves foe stocks slope down? Jouanal of Finance, 41(3), 579-

590.

Vijaya, B.M., and Vedpuriswar (2003).The dynamics around sensex reconstitutions.ICFAI

Journal of Applied Finance,9(4), 5-13

Jain, P.C. (1987). The effects on stock price of inclusion or exclusion from S&P 500. Financial

Analysts Journal, 43(1),58-65

Platikanova,P.,(2008). Long-term price effect of S&P 500 additions and earnings

quality.Financial Analysts Journal64, pp. 62-76

Denis, D.K., et.al. S&P 500 index additions and earnings expectations. The Journal of Finance

58,pp.1821-1840

Chen, H., Noronha and V. Singal (2004). The price response to S&P 500 index additions and

deletion: Evidence of asymmetry and a new explanation. The Journal of Finance 59,pp.1901-

1929

Beneish,M.,and Whaley, R. An anatomy of the S&P game: The effect of changing the rules. The

Journal of Finance 51,pp.1909-1930

Barberis, N., A. Shleifer, and J. Wurgler, (2005), “Comovement,” Journal of Financial

Economics75, 283-317.

Becker-Blease, J.R., and D.L. Paul, (2006), “Stock Liquidity and Investment

Opportunities:Evidence from Index Additions,” Financial Management 35(3), 35-51.

13

Blitzer, D.M., (2003), “Standard & Poor’s U.S. Indices: the S&P 500, S&P MidCap 400 and

S&P SmallCap 600,” Standard and Poor’s Corporation.

Bos, R., and M. Ruotolo, (2000), “General Criteria for S&P U.S. Index Membership,” Standard

and Poor’s Corporation.

Elliott, W.B., B.F. Van Ness, M.D. Walker, and R.S. Warr, (2006), “What Drives the S&P 500

Inclusion Effect? An Analytical Survey,” Financial Management 35(4), 31-48.

Elliott, W.B., and R.S. Warr, (2003), “Price Pressure on the NYSE and Nasdaq: Evidence from

S&P 500 Index Changes,” Financial Management 32(3), 85-99.

Hegde, S.P., and J.B. McDermott, (2003), “The Liquidity Effects of Revisions to the S&P 500

Index: An Empirical Analysis,” Journal of Financial Markets 6, 413-459.

Vijh, A.M., (1994), “S&P 500 Trading Strategies and Stock Betas,” The Review of

FinancialStudies 7, 215-251.

Wall Street Journal, (1995), “S&P Plans Changes in Stocks for its Index,” October 19, C21.

Wurgler, J., and E. Zhuravskaya, (2002,) “Does Arbitrage Flatten Demand Curves for

Stocks?”The Journal of Business 75, 583-608.

Brown, S. J. and J. B. Warner, (1980).Measuring security price performance, Journal of

FinancialEconomics 8, 205-258.

Cha, H. and B. Lee, (2001). The market demand curve for common stocks: Evidence from equity

mutual fund flows, Journal of Financial and Quantitative Analysis 36, 195-220.

Chung, R. and L. Kryzanowski, (1997). Analyst following and market behavior around TSE300

index revisions, in SBF – Bourse de Paris, ed.: Organization and Quality of EquityMarkets

(Presses Universitaires de France, Paris).

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7.APPENDIX

Tentative Work Plan

2012 2013

Activity/ Month August September October November

December January Feburary March

Finalizing Research Topic

Review of Literature

Project Synopsis

Data Collection

Data Analysis

Compilation of Results

Final Research Report

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