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A RESEARCH REPORT
ON
IMPACT OF STOCK SPLITS ON STOCKRETURNS VOLATILITY
(AN ANALYTICAL STUDY OF THE EFFECT OF STOCK SPLITS ON LIQUIDITY ANDRETURNS: EVIDENCE FROM INDIAN COMPANIES)
Submitted in partial fulfillment of requirement for the award
of the degree of
MASTER OF BUSINESS ADMINISTRATION
OF
BANGALORE UNIVERSITY
BY:
SANDEEP R. BHAT
REG. NO: 06XQCM6071
UNDER THE GUIDANCE AND SUPERVISION
OF
PROF. PRAVEEN BHAGAWAN
M.P.BIRLA INSTITUTE OF MANAGEMENTASSOCIATE BHARATIYA VIDYA BHAVAN
#43, RACE COURSE ROAD, BANGALORE-560 001
2008
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DECLARATION
I hereby, declare that this dissertation report:
Impact of stock splits on stock returns volatility - An analytical study
of the effect of stock splits on liquidity and returns: Evidence from
Indian Companies, submitted in partial fulfillment for the award ofMaster
of Business Administration of Bangalore University is a record of
independent work carried out by me under the guidance of Prof. Praveen
Bhagawan, faculty member, M P Birla Institute of Management (Associate
Bharatiya Vidya Bhavan), Bangalore.
I also declare that this report is a result of my own effort and has not
been submitted earlier for the award of any degree or diploma of Bangalore
University or any other University.
Place: Bangalore SANDEEP R. BHAT
Date: REG. NO: 06XQCM6071
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PRINCIPALS CERTIFICATE
This is to certify that the dissertation report entitled, Impact of
stock splits on stock returns volatility - An analytical study of the
effect of stock splits on liquidity and returns: Evidence from Indian
Companies, has been prepared by Mr. Sandeep R. Bhat bearing the
registration number06XQCM6071, under the guidance and supervision of
Prof. Praveen Bhagawan, faculty member, M P Birla Institute of
Management (Associate Bharatiya Vidya Bhavan), Bangalore.
To the best of my knowledge this report has not formed the basis for
the award of any other degree.
Place: Bangalore PrincipalDate: (Dr. N. S. Malavalli)
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GUIDES CERTIFICATE
This is to certify that the dissertation report entitled, Impact of
stock splits on stock returns volatility - An analytical study of the
effect of stock splits on liquidity and returns: Evidence from Indian
Companies, prepared by Mr. Sandeep R. Bhat bearing the registration
number06XQCM6071 is a bonafide work done under my guidance during
the academic year 2007-2008 in partial fulfillment of the requirement for the
award of MBA degree by Bangalore University.
To the best of my knowledge this report has not formed the basis for
the award of any other degree.
Place: Bangalore
Date: Prof. Praveen Bhagawan
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ACKNOWLEDGEMENT
It is with great pleasure and gratitude that I acknowledge the
contribution of several individuals towards the successful
completion of the project.
I sincerely thank Dr. Nagesh Malavalli, Principal, M. P. Birla
Institute of Management, Bangalore for granting me permission to
take up the project.
I wish to express my heartfelt gratitude to my guide
Prof. Praveen Bhagawan, faculty member, M P Birla Institute of
Management, Bangalore, for his invaluable suggestions and
guidance throughout the project period which was a source of great
motivation for me.
Finally, I am indebted to all those persons who have spared
their precious time in guiding me in the preparation of my
dissertation report and have been a part of this project work.
Place:
Date: SANDEEP R. BHAT
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1. RESEARCH ABSTRACT
Stock splits are conceptually a very simple corporate event thatconsists in the division of each share into a higher number of shares of
smaller par value. These operations have long been a part of financial
markets. Non-stationarity in the distribution of security returns is a
theoretically acceptable phenomenon that is well confirmed empirically.
Less acceptable from a theoretical viewpoint is the possibility that the non-
stationarity should be associated with irrelevant events such as stock
splits.
Past research on stock splits stands evidence to the fact that stock
splits increases volatility of returns after the effective date of split. Empirical
evidences have been documented regarding the extreme behavior of stock
returns around the ex-days of stock split though going by the actual
definition of stock split there shouldnt be any change in the returns
volatility, post split.
This study investigates into the effects of stock splits on the stock
returns volatility and the liquidity of the stock. The study investigates in the
Indian context, if the simple corporate action of stock split results in
significant deviation in the stock returns and also if it leads to increase in
liquidity of stock as it is intended to be
The study presents empirical evidence on the effects of stock split
and concludes that stock splits results in significant change in returns
volatility post-split when compared to volatility before split in India. It also
concludes that liquidity after stock split increases in terms of average daily
trading volume, but decreases in terms of average daily turnover, thus not
providing enough evidence as to the increase in the liquidity of stock after
stock split.
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2. Introduction
Stock splits remain one of the most popular and least understoodphenomena in equity markets. Stock splits have confounded financial
economists for years because they merely increase the number of
outstanding shares without providing any new funds to the company and
without changing the shareholders claims on the firms assets.
Nevertheless, companies bear real costs to undertake these transactions.
Stock splits are corporate actions by which a company lowers the
face value of its stocks; thereby increasing the number of shares owned byeach shareholder. Such actions increase the number of outstanding shares
without providing any additional cash inflows to the company. Further, there
is no change in the shareholders' claims on the assets of the firm.
All the same, companies take the trouble and costs to carry out
these activities. Therefore, the question arises as to why companies resort
to stock splits? As such, stock splits world over have puzzled researchers
who tried to find out possible explanations for such actions, for years. Over
the years, enough evidence has been gathered to show that this so-called
`cosmetic change' appears to enhance the value of the firm, as positive
abnormal returns are witnessed at the announcement and execution of
stock splits. In recent years, several Indian companies, too, have resorted
to stock splits. As the subject has not received much attention amongst
researchers in India, there is very little understanding on the effects of
stock splits in the Indian context.
Before going deep into the study of effects of stock splits it is very
essential to understand the concept of stock split and other aspects related
to it.
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2.1 Meaning of Stock Split:
A stock split is a decision by the company's board of directors toincrease the number of shares that are outstanding by issuing more shares
to current shareholders. A stock split is a corporate action that increases
the number of the corporation's outstanding shares by dividing each share,
which in turn diminishes its price. The stock's market capitalization,
however, remains the same. For example, in a 2-for-1 stock split,
every shareholder with one stock is given an additional share. So, if a
company had 10 million shares outstanding before the split, it will have 20
million shares outstanding after a 2-for-1 split.
A stock's price is also affected by a stock split. After a split, the stock
price will be reduced since the number of shares outstanding has
increased. In the example of a 2-for-1 split, the share price will be halved.
Thus, although the number of outstanding shares and the stock price
change, the market capitalization remains constant.
A stock split is a procedure that increases or decreases a
corporation's total number of shares outstanding without altering the firm's
market value or the proportionate ownership interest of existing
shareholders. This action, which requires advance approval from the
company's board of directors, usually involves the issuance of additional
shares to existing stockholders.
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2.2 Different Types of Stock Splits
Different types of stock splits can have different effects based on thereasons they are implemented. Several of the concepts to understand
about this technique are literal stock splits, reverse stock splits and
dividend payouts.
2.2.1 Literal stock splits:
Because of the existence of reverse splits, it is necessary to
differentiate between the two. For example, a literal stock split occurs when
a company announces that it will do a 2-for-1 split of their common stock. If
ABC Industries has 1,000 shares of public stock at Rs.50 per share before
a 2:1 split, they will have 2,000 shares of public stock at Rs.25 per share
after.
2.2.2 Reverse splits:
Reverse stock splits are less common and have a somewhat
negative investment strategy attached to them. If the price of a stock dropstoo low, many mutual funds will not purchase them and they even run the
risk of being de-listed, or removed from the market indexes. In addition, the
low stock prices create a psychological stigma as people view them as
worthless. By doing a reverse stock split, companies can raise the stock
price by lowering the number of outstanding shares, eliminating the
problems caused by the low stock prices.
2.2.3 Dividends
Sometimes a company will choose to avoid a stock split and lower
the share prices by paying a stock dividend to shareholders. The effect of
this move is somewhat the same as a split in that it lowers the share price
since the company is worth less after the payout. This can be a good
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investment philosophy for companies that already have a large number of
available shares plus the move is usually well received by stockholders,
since this is basically investing a portion of the profits back into the people
that have already invested in the company
2.3 Reasons for Companies to Issue Stock Splits
A stock split is usually done by companies that have seen their
share price increase to levels that are either too high or are beyond the
price levels of similar companies in their sector. The primary motive is to
make shares seem more affordable to small investors even though the
underlying value of the company has not changed.
So, if the value of the stock doesn't change, what motivates a
company to split its stock? Good question. There are several reasons
companies consider carrying out this corporate action.
The first reason is psychology. As the price of a stock gets higher
and higher, some investors may feel the price is too high for them to buy, or
small investors may feel it is unaffordable. Splitting the stock brings the
share price down to a more "attractive" level. The effect here is purelypsychological. The actual value of the stock doesn't change one bit, but the
lower stock price may affect the way the stock is perceived and therefore
entice new investors. Splitting the stock also gives existing shareholders
the feeling that they suddenly have more shares than they did before, and
of course, if the prices rise, they have more stock to trade.
Another reason, and arguably a more logical one, for splitting a
stock, is to increase a stock's liquidity, which increases with the stock's
number of outstanding shares. You see, when stocks get into the
thousands of rupees per share, very large bid/ask spreads can result by
splitting shares a lower bid/ask spread is often achieved, thereby
increasing liquidity.
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Current literature has studied the reasons for splits, to a large extent,
from the perspectives of management teams (board of directors) and
market makers. This is understandably so as shareholder approval for
stock-split decisions is generally not needed because the split is affected
as a stock dividend and in most cases the number of shares outstanding
after the split is still below the maximum number of shares authorized by
the splitting companys shareholders. Almost always only board of
directors approval is necessary and enough.
Thus, most theories trying to explain the reasons-for-splits are
management centric and most of these theories also look for reasons to
explain how market-makers would benefit from split decisions. Yet, studies
documenting the changes in clientele structures following splits suggest
that one should consider existing (pre-split) and prospective (post-split)
shareholders as meaningful actors of any theory hoping to explain why
companies split their stocks.
2.4 Theories for Stock Splits
In the letter dated June 9, 2006, Terri L. Turner, Corporate Secretary
for Marriot International, Inc. tells the existing shareholders of her company
why the Board of Directors has decided to undertake a 2-for-1 stock split
with the following statement taken from the letter dated June 9, 2006 from
the Management of Marriot International, Inc. to shareholders of Marriot
International, Inc. informing them about the 2-for-1 stock split decision
undertaken by the Board of Directors on April 28, 2006, with an effective
split ex-date of June 12, 2006.
It is my pleasure to inform you that on April 28, 2006, the Board of
Directors of Marriott International, Inc. (Marriott) approved a two-for-one
split of the companys Class A common stock in the form of a stock
dividend. The stock split was declared in recognition of our strong
confidence in our companys strength, competitive position, and growth
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prospects. We also believe that the split will make a share of Marriott
common stock more affordable to a broader range of potential investors
and increase liquidityin the trading of Marriott shares.
Surveys show that managers justify splits on the basis that they
improve liquidity and marketability. Every year, on average there are one
several stock-split events taking place and most board of directors mention
similar reasons as to why they have decided to split their stock. Finance
literature has tested these and other possible explanations. The following
are the major theories that have been offered as possible reasons-for-
splits:
2.4.1 Signaling Theory:
According to the signaling hypothesis, managers declare stock splits
to convey favorable private information about the current value of the firm.
Managers obtain pertinent information about the future because of their
expertise in making operating and investment decisions. Splits credibly
signal such information if it is costly for firms without favorable information
to signal falsely.
Fama et al. (1969) theorize that management decides to undertake
a split if it believes that the future dividends of the company will be higher.
First formalization of the signaling theory, however, has been put forward
by Grinblatt, Masulis, and Titman (1984) (GMT) as a possible explanation
of the excess returns observed around split announcement and split-ex
dates. GMT (1984) hypothesize that a management team with preference
for a specific price range for its stock may choose the timing of a stock split
in order to reveal private managerial information regarding future stock-
returns.
It has been suggested that, given asymmetric information between
managers and investors, the former might use financial decisions, such as
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stock splits and stock dividend distributions, to convey favorable
information to the latter.
2.4.2 Optimal Price / Optimal Trading Range Theory:
It is a widely held belief, particularly among practitioners, that stock
splits are intended to keep the price of shares within some "optimal" range.
Specifically, investors of small means are presumably penalized by high
stock prices that deny them the economies of buying stocks in round lots.
On the other hand, wealthy investors and institutions will save brokerage
costs if securities are priced high because of the fixed per-share
transaction cost component. Therefore, the argument goes, there exists an
optimal price range that equilibrates the preferences of these classes of
investors. Managers interested in a broad and heterogeneous stockholder
base or in "wider marketability" may strive to adjust stock prices to such an
optimum by splitting their stock or distributing stock dividends. It is
interesting to note here that a survey of managers' motives for stock splits
revealed that 98.4 percent of the respondents indicated that splits make it
easier for small investors to purchase round lots, and 93.7 percent believed
that splits keep a firm's stock price in an optimal range and increase thenumber of stockholders.
2.4.3 Self Serving Management and Dispersion of Control Theory /
Enlarged Clientele:
A possible explanation for stock splits claims that a self-serving
management prefers a diffused ownership since small investors can not
exercise much control over the company and a stock split would likelyachieve this. However studies done by experts (Maloney-Mulherin (1992)
and Powell- Baker-1993-1994) show that management dispersion
hypothesis does not hold. On the contrary their results show that stock
splits accompany increases in institutional ownership for firms. It seems
that the shareholder base of a company subsequent to a split expands, yet
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the theory of a self-serving management that aims to reduce overall
institutional ownership does not hold as the empirical results disprove this
theory. Evidence against this theory is too strong. By stock splits
management reaches the goal of a more diversified Clientele, yet this
does not mean that the splitting firms end up with a more diffused
shareholder base.
2.4.4 Increased Liquidity Theory:
Researchers have also proposed the idea that companies split their
stock to achieve greater liquidity. The fundamental question in regards to
this theory is whether splits increase or decrease liquidity, and if so how
one should go about measuring liquidity around a stock split. There is also
a related question: if liquidity decreases subsequent to splits, is the
abnormal return achieved around the split event just a liquidity premium?
Past literature has attempted to measure changes in liquidity around stock
splits by analyzing the changes in three parameters: trading volume,
relative effective bid-ask spread, and number of limit orders.
After analyzing all the competing theories it can be concluded thatthe following hypothesis proposed by one expert best explains the causes
and results of stock splits in agreement with empirical measurements:
1.Signal:Management team of an exceptionally well performing company
with private information regarding the future performance of their firm
decides to split the companys shares.
This signal may be strengthened if it is accompanied with an
announcement that declares futures dividends will be larger, or that
the company, if it was not paying dividends up to that point, will
initiate giving dividends
This signal may also be strengthened if the company went through a
previous stock split event. The market would expect the companys
stock to split to the post-split price level of the previous split event.
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If the split factor is bigger than this expected level future expected
returns are even higher
The signal is costly to the firm. There are two cost components
o Relative bid-ask spread increases due to the split and this
results in higher order processing costs, which in turn reduces
liquidity
o Pre-split shareholders incur higher aggregate costs resulting
from per share based commission costs.
2. Promotion: As per-share based commissions and order processing
costs will increase following the split market-maker has a lot to gain from
increased trading. Market maker heavily promotes the stock to add new
clientele.
3. Enlarged Clientele Base: With more coverage prospective shareholder
buys the stock, based on the management teams signal and the market
makers information dissemination in order to reap the benefits of excess
returns surrounding split announcement and split-ex dates. Prospect
expects that these excess returns will not be negated in the year
subsequent to the split ex-date. Furthermore prospect further diversifies hisportfolio with non-negative long-run excess returns. Prospect can be a
small investor or an institutional investor, but in most likelihood he is a
small investor.
4. Increased Number of Trades: Companys shareholder base is
enlarged; management has a more diversified clientele making their jobs
more secure. Furthermore the management has solidified the gains of the
exceptional returns period with this new clientele base. Shareholders earnexcess returns around the announcement and ex-dates and keep their
gains in the long-run. Market makers increase their revenues. At the same
time as the number of small shareholders increases so does the number of
small trades and aggregate number of trades. Due to the increase in
number of trades stock return volatility also goes up around the stock split
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event. As the split is costly, this signal has value over the year following the
split thus the number of trades in the subsequent year doesnt decrease.
Market makers promotion efforts also help sustain the level of trading
activity. Hence the increase return volatility is not temporary and is
sustained for over a year.
2.5 Advantages of Stock Splits for Investors
There are plenty of arguments over whether a stock split is an
advantage or disadvantage to investors. One side says a stock split is a
good buying indicator, signaling that the company's share price is
increasing and therefore doing very well. This may be true, but on the otherhand, you can't get around the fact that a stock split has no affect on the
fundamental value of the stock and therefore poses no real advantage to
investors. Despite this fact many experts have taken note of the often
positive sentiment surrounding a stock split. Critics would say that this
strategy is by no means a time-tested one and questionably successful at
best.
2.6 Actors Affected by Stock Split Decisions
A stock-split decision concerns four major parties. These are the
following:
1. The Board of Directors (in some cases the management team) of
the splitting firm (Decision-maker, possible beneficiary)
2. Market makers (possible beneficiary and the intermediary between
the management and Prospective shareholders)
3. Existing shareholders of the splitting firm (pre-split)
4. Prospective shareholders of the splitting firm (post-split or post-
announcement)
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2.7 Background of the study:
In an efficient market, the market value of a firms equity should beindependent of the number of shares it has outstanding. Therefore one
should expect to see no change in the distribution of stock returns around
ex-dates of stock splits. The ex-date for a stock split should simply involve
a change in the number of shares outstanding along with a change in the
level of the stock price. There should be no change in the distribution of
stock returns around ex dates of stock splits.
Previous research has documented changes in stock return
distributions. Researches have proved abnormal returns around
announcement days and ex-dates of splits. Several researches have also
proved the occurrence of short-term excess returns following stock splits. In
addition to changes in the distribution of stock returns around ex-dates of
stock splits, some experts through their research have showed that stock
return volatilities jump significantly after stock splits as well and that these
volatility changes hold for more than a year subsequent to the split ex date.
While these facts stand, there is no convincing theory that
affirmatively explains why companies continue to split their stocks, and
furthermore the cause and effect of the increase in stock return volatility
following the split ex-date remains uncertain. In recent years, several Indian
companies, too, have resorted to stock splits. As the subject has not
received much attention amongst researchers in India, there is very little
understanding on the effects of stock splits in the Indian context.
Therefore, this research dissertation has been conducted to analyze
and interpret the effect of stock split on the stock returns volatility and the
liquidity in the Indian context.
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2.8 Problem Statement
In a perfect market, the market value of a firm's equity isindependent of the number of shares outstanding. Therefore the ex-date for
stock split should simply involve a change in the number of shares
outstanding along with a change in the level of the stock price. There
should be no change in the distribution of stock returns around ex-dates of
stock dividends and stock splits. Previous research has documented
changes in stock return distributions around these ex-dates, i.e. post split
dates. This suggests that there is an implied volatility in stock returns after
the stock splits.
2.9 Research Objectives
The present study has been undertaken with the following objectives.
To examine the effects of stock splits on equity prices and returns of
the companies.
To investigate the changes in volatility of returns around the ex-
dates.
To investigate the effect of stock splits on liquidity and trading
volumes post split.
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2.10 Hypothesis
H0: (Null hypothesis), there is no change in return volatility post-split,when compared with pre-split
H0: (Null hypothesis), there is no increase in liquidity post stock split,
when compared to pre stock split liquidity
H1: (Alternative Hypothesis), there is a change in return volatility
post-split compared with pre-split.
H1: (Alternative Hypothesis), there is an increase in liquidity post
stock split, when compared to pre stock split liquidity.
2.11 Scope of the Study
The scope of the research investigation is restricted only to the
following:
1. The study focuses on investigating the effect of stock split on the
returns volatility. It does not provide any evidence as to the
movement of returns upwards or downwards or positive returns or
negative returns earned due to stock split. Thus, it restricts itself to
the investigation of post split volatility of returns.
2. The study does not take into consideration the ratio of the split while
investigating the effects of stock split on distribution of returns
around ex-split date. Thus the effect of split ratio on returns volatility
cannot be judged from this study.
3. The study does not investigate the reasons or factors responsible for
the change in returns volatility if there exists any. It explains only the
existence or non-existence of returns volatility after stock split.
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2.12 Significance of the Study
This study is essential as there has been evidence in the foreign
market about the existence of volatility around the ex-dates. But
there has been no strong empirical evidence in the Indian context
concerning the Indian companies if stock splits really imply changes
in returns distribution around ex-dates.
The study is essential to analyze if there exists a change in volatility
post split, i.e. ex-dates for Indian companies that have resorted to
stock split and has it resulted in change in return distribution around
the ex-dates for the investors. The study is essential to understand the impact of stock split on the
returns volatility and liquidity in the Indian context and help
companies and investors in taking rational decisions pertaining to
stock splits.
2.13 Importance of the Study
The investigation can be used by the company to decide upon the
action to split to control the liquidity and returns volatility around
stock splits.
The investigation can also be used by the investors to decide upon
the timing of their investment while dealing with the stocks going for
the split.
The investigation can also be used by the investors to understand
the impact of stock splits on the returns volatility and liquidity and
plan the investment accordingly.
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2.14 Research Gap
In recent years, several Indian companies, too, have resorted tostock splits. There is no convincing evidence that affirmatively explains the
cause and effect of the increase in stock return volatility following the split
ex-date. As the subject has not received much attention amongst
researchers in India, there is very little understanding on the effects of
stock splits in the Indian context.
Therefore, this research dissertation has been conducted to
analyze and interpret the effect of stock split on the stock returns volatility
and the liquidity in the Indian context.
2.15 Limitations of the Study
The study confines itself to the data of companies which have gone
for stock splits between the years 2000 to 2007. The stock splits
before the period have not been taken into consideration.
Due to time constraint the study has been confined to only 28
companies from four different sectors, thus limiting the scope.
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2.16 Operational Definitions
1. 2-for-1 split: If the number of pre-split outstanding shares is N, aftera 2-for-1 split the number of outstanding shares rises to 2*N.
2. Split Ratio: If split ratio is x% and the number of total outstanding
shares before the split is N, then the number of total outstanding
shares post-split is (1+x%)*N.
3. Announcement date: The day on which the board of directors of
the company decides and announces to split the common stock ofthe company on a certain future date is called the announcement
day. The announcement of a split is an unanticipated event in which
the firm announces the size and date of the split.
4. Ex-date: It is the date on which the stock is split. In other words the
day on which the stock split is brought into effect and the price of the
stock is decreased by the ratio pre-determined is termed as Ex-
dates. On the split date (or split ex-date), the stock begins trading at
the new, split-adjusted price.
5. Reverse Split:A reverse split occurs when a company reduces the
number of its shares outstanding by a pre-determined value. For
example, if a firm has 100 shares outstanding pre-reverse split and
its reverse-split factor is 1-for-1 then the total number of shares
outstanding post-event is 50. (100 / (1+1) = 50)
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3. Review of Literature
This section of the report would deal with the literature available withrespect to stock splits proposed by experts in the field of finance and their
findings with respect to effects of stock splits on returns volatility post split
or split ex-dates.
3.1 Long Term Excess Returns Preceding Split Announcements:
The first empirical study on stock splits was done by Fama, Fisher,
Jensen and Roll in 1969. In this paper Fama et al. (1969) examine 940
stock splits over the period 19271959. Using a market model and monthlyreturns they find on average an abnormal excess return of 34.07% over the
29 months preceding the split date for splitting companies. Fama et al. find
no abnormal returns after the ex-split date.
Lakonishok and Lev (1987) expand on the Fama et al. (1969) paper
and define split events around announcement dates using a sample size of
1015 in the time frame from 1963 to 1982. They also find significant
abnormal returns for splitting firms preceding the splits (on average they
find 53% excess returns in aggregate during the 5 years preceding the split
announcement).
Further validating these studies Asquith et al. (1989) find statistically
significant market adjusted excess returns of 56.8% for splitting firms for
the 240 day time period preceding a split in the 1970-1980 time period.
McNichols and Dravid (1990), and Maloney and Mulherin (1992) also find
similar results and these numbers are also confirmed by later studies such
as Ikenberry, Rankine, and Stice (1996) and Desai and Jain (1997) for
different time-periods. These studies confirm without a doubt that split
events are preceded by very strong performances for the splitting
companies as they compare to non-splitting firms.
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Earlier papers by Ohlson and Penman (1985), Dravid (1987),
Dubofsky (1991), and Koski (1998) have documented a significant increase
in return volatility of US-based equities following stock splits with split
factors larger than 5-for-4. Furthermore Reboredo (2003) has documented
similar results for 2-for-1 and larger stock splits that take place in the
Spanish Stock Market, Bolsa de Madrid for the 1998-1999 time period.
Ohlson and Penman show that the increase in return volatility is
permanent as there is no fading in the volatility value one year following the
split. This increase also is quite large and Ohlson and Penman find a mean
increase of 30% in the standard deviation of returns. Some in the literature
have related this huge increase in return volatility to measurement error.
Blume and Stambaugh (1983), Gottlieb and Kalay (1985), and
Amihud and Mendelson (1987) show that bid-ask spreads and price
discreteness induce an upward bias in the estimated volatility of observed
stock returns. It has been suggested since such measurement biases
increase at lower price levels the increase in return volatility around splits
may be due to measurement error.
In response to this possibility Koski (1998) shows that almost none
of the observed increase in realized volatility is due to bid-ask spreads or
price discreteness. Furthermore, in their recent paper Julio and Deng
(2006) show a clear response to stock split announcements in the options
market and provide additional evidence that changes in volatility around the
split ex-date are real and not due to error in the measurement procedure. In
this section these empirical results will be explained and the measurement
techniques used carefully.
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3.2 Standard Method of Measuring Realized Volatility Increases
Around Split Ex-Dates:
In order to test for changes in volatility subsequent to a split Ohlsonand Penman (1985) used a non-parametric test. In order to test for
changes in volatility subsequent to a split most studies use a non
parametric test proposed by Ohlson and Penman (1985). Dravid (1987),
Dubofsky (1991), Koski (1998), Reboredo (2003) all use the same
methodology
The Methodology:
The binomial proportionality statistic, P, where P=Pr(x2 > x1) isapplied to test the hypothesis:
H0: P = 0.05 (H), no change in return volatility post-split, when
compared with pre-split
H1: P= 0.05 (A), there is change in return volatility post-split
compared with pre-split
Where x1 and x2 denote pre and post split values for the variable of
interest.
x1 and x2 in thiscase denote daily stock return volatility.
Since squared values of expected daily returns (E2[r]) are about
1/1000th of expected squared daily returns (E[r2]), Ohlson and
Penman approximate for daily return volatilities with expected
squared daily returns. Hence x1 and x2 simplify to pre and post-split
values of E[r2].
To control for day of the week effects on the variables of interest,
Ohlson and Penman (1985) compare pre- and post-split squared daily
returns by matching the squared return for the first trading day following the
split declaration date with the squared return for the first same day of the
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week following the split date (for example Monday to Monday). This
process is repeated for the second day, and so on until the day just prior to
the split date for the whole time period between split announcement and
split ex-date. The number of comparisons for each split is equivalent to the
number of trading days between the declaration and split dates.
The value of the binomial z-statistic is used for statistical
significance. With this result one can calculate the change in stock return
volatility subsequent to the split as:
Percent Change in Volatility = (post split-pre-split)
-Pre-split
Where = standard deviation
Conclusion:
The study conducted by Ohlson and Penman documents that, for
stock splits larger than two-for-one (one hundred percent), the volatility of
stock returns after the ex-split date is significantly higher than the pre-split
volatility. The increase in the standard deviation of daily returns is of theorder of twenty-eight to thirty-five percent and persists for as long as a full
year after the ex-date.'
Ohlson and Penman show that the increase in return volatility is
permanent as there is no fading in the volatility value one year following the
split. This increase also is quite large and Ohlson and Penman find a mean
increase of 30% in the standard deviation of returns.
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3.3 Stock Splits, Volatility Increases, and Implied Volatilities
A study on stock splits and its implied volatilities and increase in
volatility around stock split conducted by Mr. Aamir M. Sheikh, published
in the Journal of Finance, Vol. 44, No. 5. (Dec., 1989), presents a test of
the efficiency of the Chicago Board Options Exchange, relative to post-split
increases in the volatility of common stocks. The Black-Scholes and Roll
option pricing formulas are used to examine the behavior of implied
standard deviations (ISDs) around split announcement and ex-dates.
In this paper, he examines the announcement and ex-date behavior
of return volatilities implied by call prices, thereby controlling for stock price
and maturity changes. The Black-Scholes (1973) and Roll (1977) call
option pricing formulas are used to solve for implied standard deviations
(ISDs) of stock returns.
Methodology:
Data:
The study includes all CBOE option able stocks that split between
December 1, 1976 and December 31, 1983, had split factors larger than
0.25, and did not undergo major structural changes, such as mergers, in
the nine months following the split. This leads to a sample of 83 stock
splits, of which 30 had split factors smaller than one. The smallest length of
time between announcement and ex-dates was 34 days, the largest 165
days. The splits were identified from the CRSP Monthly Master file and
checked against a list of stocks with CBOE options.
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Tests:
Because the results may be sensitive to the way ISDs are
aggregated, three different measures are used to test the effect of the split
announcement and ex-dates on anticipated volatility. The first, anticipated
volatility, uses the sample mean of the ISDs of each stock to obtain a single
ISD for that stock both before and after the event; e.g., all the ISDs
obtained from IBM options in the two weeks before the split announcement
are averaged into a single pre-announcement ISD for IBM, and all the ISDs
for IBM in the two weeks after the announcement are averaged into a
single post-announcement ISD for IBM. Percent changes in anticipated
volatility are then obtained for each stock, and the paired-sample sign test
and the Wilcoxon signedrank test are applied to these percent changes
across the sample of stocks. The null hypothesis of no change in ISDs is
tested against the one-sided alternative of an increase in ISDs.
Results:
The post-split standard deviation estimated from daily returns was
found to be larger than the pre-split estimated standard deviation in 55 out
of 83 cases, with an associated z-statistic of 2.85, which is significantlypositive at the 1% level. The median change in estimated standard
deviations was 20.6%. Thus, consistent with the findings of Ohlson and
Penman, the split sample exhibits a significant ex-date increase in return
variances.
3.4 Firm and Split Characteristics and Realized Volatility Changes
around Stock Split Ex-Dates:
Julio and Deng (2006) incorporated firm characteristics into their
split dataset. They report that small splits (factors less than 50%)
experience no significant increase in realized volatility at the ex-date, while
volatility increases for medium-sized splits (with factors between 50% and
200%) are the largest and significant. For very large splits (split factor
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greater than 200%), the change in volatility is still significant but not as
large as for the medium-sized splits.
3.5 Risk Changes Induced by Stock Splits:
Dubofsky (1991) conducted a study that was basically an extension
of a previous study by Ohlson and Penman (1985). In contrast to these
authors, Dubofsky focused on both NYSE stocks and AMEX stocks and
used a large time period from July 2, 1962 to December 31, 1987. The
results obtained for the two exchanges lead the author to conclude there
was a more pronounced increase in variance connected to NYSE stocks.
Desai et al. (1998) conducted a more in-depth study of volatility
changes. These authors reported a significant increase in volatility following
the split. Their conclusions were stronger than those of Dubofski (1991)
since their calculations took into account the effects of price discreteness in
the bid-ask bounce. They reported an increase in the relative bid-ask
spread, which in turn lead to the need to estimate volatility with more
complex procedures.
3.6 Intra-Daily Estimation of Realized Volatility Increases around SplitEx-Dates:
Squared daily returns have quite often been used as a measure of
volatility. However, Anderson and Bollerslev (1998) demonstrate that the
incorporation of high-frequency data vastly improves ex-post volatility
measurements.
Poteshman, another expert shows that almost half of the forecastingbias in the S&P 500 index (SPX) options market is eliminated if one
estimates realized volatility using intraday observations on SPX futures that
are sampled every five minutes rather than using daily close values.
Julio and Deng (2006) use NYSE TAQ database and observe prices
for 16 splitting stocks every 5 minutes in the 1996-2003 time period.
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The Methodology:
They define ph,tbe the natural logarithm of the stock price at time h
on date t, where h=1, ,H and t =1, ,T.
H represents the number of intra-daily observations used per day
and
Tis the number of days in the sampling period.
They calculate a series of intra-day log returns
rh,t = ph,t ph- 1,t and
Then using the squared values of these intra-day log returns they
find an unbiased estimator of the population return variance SD t2 :
St2 , where S2t is as follows:
SD = Standard Deviation of daily returns
The Results:
With this methodology Julio and Deng (2006) calculate 2 average
intra-day return volatilities for each split event: One for the 20 days
preceding the split ex-date and the other for the 20 days subsequent to the
split ex-date. On average for all split events they find a statistically
meaningful 32.83% increase in realized volatility around the ex-date. This
value is similar in magnitude to the change reported by earlier studies.
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4. Research Design and Research Methodology
4.1 Type of Research
The research conducted in this case is of a quantitative type of
research wherein the data collected is basically of quantitative type in the
form of share prices of the stocks before and after the split. The share
prices thus collected have been used to calculate price relatives and daily
returns.
The results will be presented in terms of quantitative data and
interpretation of the data analysis will be done using the quantitative
parameters. Thus, this research is a quantitative research.
4.2 Sources of data
All the data concerning the stock split and share prices for the study
are collected through websites like capital online and NSE-India.
4.3 Sampling Technique
The sampling technique used in this research is random sampling
technique as each company had an equal opportunity of making it to the
final sample. The population size is finite as the companies qualifying for
the research are the companies that have resorted to stock splits during
2000 to 2007. Thus, the sampling technique used is the random samplingtechnique.
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4.4 Sample Size
The sample includes 28 companies from different sectors whichhave resorted to stock splits during the period 2000-2007. Thus, the
sample size has been restricted to 28 for this research.
4.5 Data Collection
Information about the companies that have resorted to stock split
during the period 2000-2007 has been gathered from Capital online plus
database. The dates of stock split could be gathered from the same
database.
The information regarding the share price before and after stock split
has been gathered from the NSE website. The closing price of the shares
of the respective companies has been taken into consideration for the
analysis.
For the purpose of this analysis and hypothesis testing, the share
prices of fifteen days duration before split and after split has been taken
into consideration, assuming that the fifteen days price fluctuations would
give a considerable idea about the returns volatility before and after split.
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4.6 Data processing and analysis plan
Methodology and tools used for testing of hypothesis: The price relative of stock based on daily closing price of the stock has
been calculated using the formula:
(Pt/Pt-1)
Where:
Pt =Price of the stock at the time t
Pt-1= Price of the stock at the time t-1
The daily returns have been calculated there from using the log
naturals, i.e.
Ln(Pt/Pt-1)
The mean and standard deviation and the volatility of the daily returns
have been determined using the Excel Application.
The percentage change in the volatility of daily returns has been
determined using the formula:
Post-split of returns Pre-split of returns
Pre-split of returns
Where = standard deviation
t Test:
t test is used to test for the significance of differences of means of
daily returns and to find out if there is any significant change in volatility of
returns after stock split.
To find whether there is significant difference in mean before and
after the event the t test is conducted at 5% level of significance.
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Formula Used:
The formula used for the calculation of t statistic is as under:
t = [(sample mean 1-sample mean 2)-(mean of population 1-
mean of population 2)]
Square Root [Pooled ^2(1/n1 + 1/n2)]
Pooled SD^2 = [(n1-1) 1^2 + (n2 1) 2^2] / [(n1 - 1) + (n2 1)]
Where:
Pooled SD^2 = Pooled variance of the means of daily returns before
split and after split
n1 = Number of sample before split
n2 = number of sample after split
SD = standard deviation
P-value approach:
The P-value approach has been followed to decide upon the
acceptance and rejection of the hypothesis.
The P-value has been determined using the Excel application.
If P-value < , we reject Ho at level of significance.
If P-value > , we fail to reject Ho at level of significance.
Post Split Liquidity Measurement:
In order to examine the effect of stock splits on liquidity, two
measures of trading activity were used:
(a) Trading volume which is the daily number of shares traded,
(b) Daily turnover
The percentage change in trading volume and turnover has been
determined using the excel application.
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Paired t test:
To test the significance of change in liquidity after stock split Paired
t-test has been used at 5 percent level of significance.
Formula Used:
The formula used for the calculation of t statistic is as under:
D 0t =
diffnWhere:
D = Mean of differences between observations
diff= Standard deviations of differences
n = Number of matched pairs
Rejection region method:
Rejection region method has been followed to decide upon the
acceptance or rejection of the hypothesis in paired t-test.
Regression analysis:
A regression analysis has been conducted to determine the co-efficient
of determination of the volatility in the returns, post split.
The regression analysis has been conducted using the Excel
application.
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Steps followed:
Price relatives were calculated using the fifteen day share prices,
both before and after split
Daily returns were determined using the log naturals
Means and standard deviations of the daily returns before and after
split were then calculated.
Using the means of daily returns before and after split
t-test has been applied to test the significance of difference between
the means before and after split.
t-cal was calculated using the excel application
Based on the P-value the acceptance and rejection of hypothesiswas decided upon.
Based on the regression analysis the cause for the volatility in the
post split returns was determined.
The effect of stock split on liquidity was measured by determining
the percentage change in average daily trading volume and average
daily turnover after split when compared to trading volume and
turnover before split.
The significance of increase in the liquidity was then tested using the
paired t-test and hypothesis was accepted or rejected based on the
tabulated value of students t-distribution.
Tools and software used for data analysis:
All the calculations pertaining to data analysis has been done using
the MS-Excel application software.
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4.7 Chapter Scheme
This report has been presented in seven chapters:
Chapter 1 presents the research extract
Chapter 2 deals with the conceptual framework of stock splits. It
throws some light on the theoretical aspects of stock split. It also
presents the statement of problem, research objectives, significance
of the study, scope of the study, importance, limitations of the study
and some operational definitions.
Chapter 3 deals with the review of literature and presents the
analysis of different researches conducted by different authors andtheir results.
Chapter 4 deals with the research design and the methodology
followed. It highlights the research design implemented in this study
and the tools and methods used for data analysis and interpretation.
Chapter 5 deals with theprofile of the sample industry. It presents in
brief, information about the industry from which the sample
companies have been selected.
Chapter 6 deals with data analysis and interpretation. It presents
the data analysis and interpretations made there from and testing of
significance of hypothesis.
Chapter 7 deals with the research findings, conclusions,
implications and suggestions for future research.
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5. Industry Profile
The sample of 28 companies includes companies that have resortedto stock splits during the period 2000-2007. The companies belong to
different sectors. The sectors that the companies belong to in this research
are:
Pharmaceuticals Industry - Indian - Bulk Drugs & Formulation
Large
Construction Industry Civil Turnkey large, small and medium
Petrochemicals Industry
Information Technology Industry - Hardware and Software.
5.1 Pharmaceutical Industry:
The Indian Pharmaceutical Industry today is in the front rank of
Indias science-based industries with wide ranging capabilities in the
complex field of drug manufacture and technology. A highly organized
sector, the Indian Pharma Industry is estimated to be worth $ 4.5 billion,
growing at about 8 to 9 percent annually. It ranks very high in the third
world, in terms of technology, quality and range of medicines
manufactured. From simple headache pills to sophisticated antibiotics and
complex cardiac compounds, almost every type of medicine is now made
indigenously.
The Indian Pharmaceutical sector is highly fragmented with more
than 20,000 registered units. It has expanded drastically in the last two
decades. The leading 250 pharmaceutical companies control 70% of the
market with market leader holding nearly 7% of the market share. It is an
extremely fragmented market with severe price competition and
government price control.
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The pharmaceutical industry in India meets around 70% of the
country's demand for bulk drugs, drug intermediates, pharmaceutical
formulations, chemicals, tablets, capsules, orals and injectibles. There are
about 250 large units and about 8000 Small Scale Units, which form the
core of the pharmaceutical industry in India (including 5 Central Public
Sector Units). These units produce the complete range of pharmaceutical
formulations, i.e., medicines ready for consumption by patients and about
350 bulk drugs, i.e., chemicals having therapeutic value and used for
production of pharmaceutical formulations.
5.1.1 ADVANTAGE INDIA:
Competent workforce: India has a pool of personnel with high managerial
and technical competence and also skilled workforce. It has an educated
work force and English is commonly used. Professional services are easily
available.
Cost-effective chemical synthesis: Its track record of development,
particularly in the area of improved cost-beneficial chemical synthesis for
various drug molecules is excellent. It provides a wide variety of bulk drugsand exports sophisticated bulk drugs.
Legal & Financial Framework: India has a 53 year old democracy and
hence has a solid legal framework and strong financial markets. There is
already an established international industry and business community.
Information & Technology: It has a good network of world-class
educational institutions and established strengths in InformationTechnology.
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Globalization: The country is committed to a free market economy
and globalization. Above all, it has a 70 million middle class market, which
is continuously growing.
Consolidation: For the first time in many years, the international
pharmaceutical industry is finding great opportunities in India. The process
of consolidation, which has become a generalized phenomenon in the
world pharmaceutical industry, has started taking place in India.
5.1.2 Growth Prospects:
The industry's exports were worth more than $3.75 billion in 2006-07
and they have been growing at a compound annual rate of 22.7 percent
over the last few years, according to the government's draft National
Pharmaceuticals Policy for 2006, published in January 2006. The Policy
estimates that, by the year 2010, the industry has the potential to achieve
$22.40 billion in formulations, with bulk drug production going up from
$1.79 billion to $5.60 billion: India's rich human capital is believed to be the
strongest asset for this knowledge-led industry. Various studies show that
the scientific talent pool of 4 million Indians is the second-largest English-speaking group worldwide, after the USA.
5.1.3 Top Ten Indian Pharmaceutical Companies: The top ten Indian
Pharmaceutical Companies are:
Aurobindo Pharma
Aventis Pharma
Cipla
Dr. Reddys Laboratories
Lupin
Nicholos Piramal
Ranbaxy Laboratoriess
Sun Pharma
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Wockhardt
Zydus Cadila
5.2 Construction Industry:
5.2.1 Industry Definition
The construction industry comprises establishments that are
primarily engaged in theconstruction of buildings or engineering projects
(e.g., highways and utility systems). This mayinclude new work, additions,
alterations, or maintenance and repairs. The construction industry is
divided into three major sectors. The first is the construction of buildings
(both residential andnonresidential). The second involves heavy and civil
engineering construction such as utility systems, land subdivision, and
highways, streets, and bridges. Firms in these first two sectors areprimarily
engaged in contracts that include responsibility for all aspects of individual
projects andare commonly know as general contractors. The third major
sector of the construction industry includes establishments in the specialty
trades, which are primarily engaged in activities to produce a specific
component (e.g. masonry, painting, and electrical work) of a project.
5.2.2 Industry Profile
The construction industry is the second largest industry in India after
agriculture. It accounts for about 11% of Indias GDP. It makes significant
contribution to the national economy and provides employment to large
number of people.
There are mainly three segments in the construction industry like
real estate construction which includes residential and commercial
construction; infrastructure building which includes roads, railways, power
etc; and industrial construction that consists of oil and gas refineries,
pipelines, textiles etc.
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According to a study by ASSOCHAM, the burgeoning Indian
construction industry, currently worth $70 billion, will rise to US$120 billion
by 2010.
5.2.3 Industry Trends
The Indian construction industry recorded a consistent double-digit
year-on-year growth of 12% during 2000-2005, and is expected to grow at
25-30% during 2005-2010. The key drivers of this growth are government
investment in infrastructure creation and real estate demand in the
residential and industrial sectors.
The industry is experiencing increasing polarization between large
and small players. The top 10 players, such as Gammon India, Patel
Engineering Limited (PEL), and Hindustan Construction Company (HCC),
contributed to 75% of the total revenue in 2005.
These players are increasing their market share through large-scale
contracts, joint ventures and foreign operations. Though an increasing
number of small players are also entering the market, most of them do not
have the resources to bid for big contracts.
The Indian construction industry is facing the challenges of outdated
land and property ownership regulations, infrastructural bottlenecks and a
shortage of civil engineers.
5.2.4 Top players
The few heavy weights in the Indian construction Industry are:
Larsen & Toubro Limited
Jaiprakash Associates
Hindustan Construction Company
Gammon India
Royal Indian Raj International Corporation (RIRIC) etc.
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5.3 Petrochemicals Industry
Petrochemicals are useful chemicals obtained from crackingpetroleum feedstock. The three main petrochemicals produced are
ethylene, propylene and benzene.
The Indian petrochemicals industry is small by international
standards, with ethylene capacity accounting for only 2.2% of global. The
Indian polymers industry is also small, accounting for only around 2.5% of
the global production. India was ranked 13th in the world as on January 1,
2005 in terms of ethylene capacity. The capacity had registered increasesduring early 2000 following the commissioning of crackers of Haldia
Petrochemicals Limited (HPL) and Indian Petrochemicals Corporation
Limited (IPCL).
The petrochemical industry is a capital-intensive and high-volumes
industry. The demand elasticity is high in petrochemicals. The raw material
cost is the major expense any petrochemical industry has to endure. The
raw material cost generally accounts for 50 per cent of the selling price.
There are only three major players: Reliance Industries, NOCIL and
IPCL. All the three companies have fully integrated plants. As the domestic
market is closely linked to global events and occurrences, some companies
will witness squeezing margins, owing to depressed global petrochemical
prices.
Polymers constitute over 70 per cent of petrochemicals produced.
The largest end-user of plastics includes packaging industry, PVC pipes
and fittings industry and molded luggage industry. In the last five years,
these industries have grown at the rate of 16-18 per cent per annum. The
domestic plastic processing industry is highly fragmented and scattered.
There are more than 15,000 manufacturing units. Most of them are small-
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scale industries. The medium scale operators produce almost 60 per cent
of the total production. The unorganized sector, with the 40 per cent of the
balance production, is an important player. The total capacity of the
industry to process polymers is estimated at 4.8 million tpa.
Although, by global standards, Indian polymers (the key
petrochemical product) demand is small, it is amongst the fastest growing
countries in the world. In the past, because of higher GDP growth than the
world GDP growth and higher presence of the traditional materials
(therefore, more potential for substitution by polymers), India has shown a
significantly higher growth rate in polymer consumption in the last five
years. The growth rate in Indian polymer consumption is even higher than
China and other key Asian Economies.
5.3.1 Players
The Indian basic petrochemicals manufacturers are integrated from
basic petrochemical to polymers, manmade fibers, fiber intermediates and
downstream petrochemical production. Non integrated players are present
in production of polyvinyl chloride, polystyrene, manmade fibers and
products such as phenol, linear alkyl benzene, phthalic anhydride etc.
The Indian polymers market is dominated by local players, with the
foreign stake holding in Indian polymer plants restricted to Haldia
Petrochemicals Limited (HPL), BASF Styrenics India Private Limited
(BSIL), and LG Polymers Limited. HPL is a joint sector company in which
Chatterjee Petrochem, Mauritius, has a 43% stake along with the West
Bengal Industrial Development Corporation. As for BSIL, it is a 100%
subsidiary of BASF AG, a multinational chemicals company and the worlds
second largest PS producer, while LG Polymers is a wholly-owned
subsidiary of the LG Group, South Korea. The production of polyethylene
and polypropylene in India is accounted for almost in their entirety by
companies with integrated petrochemical complexes. Such companies are
Reliance Industries Limited (RIL), Indian Petrochemicals Corporation
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Limited (IPCL), Haldia Petrochemicals Limited (HPL), and Gas Authority of
India Limited (GAIL).
5.4 Information Technology Industry:
Information Technology (IT) industry in India is one of the fastest
growing industries. Indian IT industry has built up valuable brand equity for
itself in the global markets. IT industry in India comprises of software
industry, hardware and information technology enabled services (ITES),
which also includes business process outsourcing (BPO) industry. India is
considered as a pioneer in software development and a favorite destination
for IT-enabled services.
The IT/ITES industrys contribution to the countrys GDP has been
steadily increasing from a share of 1.2% in FY98 to 5.2% in FY07; it has
contributed to foreign exchange reserves of the country by increasing
exports by almost 36%.
Software and IT enabled services have emerged as a niche sector
for India. This was one of the fastest growing sectors in the last decade
with a compounded annual growth rate exceeding 50 percent.
Software service exports increased from US$ 0.5 millions in 1990 to
$5.9 billion in 2000-01 to $23.6 billion in 2005-06 recording a 34% growth.
The major players in this sector currently are:
Infosys
TCS
Wipro
HCL
Satyam, etc.
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6. Data Analysis, Interpretation and Presentation
This section of the report deals with the data analysis, interpretationand presentation of results of the research. This part has been divided into
two main sections.
The first section presents:
The determination of daily stock returns of the sample companies.
The determination of percentage change in volatility after stock split
and the inferences drawn there upon.
Testing of significance of hypothesis concerning the effect of stock
split on returns volatility and the inferences drawn there upon.
The regression analysis and the inferences drawn there upon.
The second section presents:
The measurement of effect of stock split on liquidity and inferences
drawn there upon.
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6.1 Hypothesis
H0: (Null hypothesis), there is no change in return volatility post-split,
when compared with pre-split
H0: (Null hypothesis), there is no increase in liquidity post stock split,
when compared to pre stock split liquidity
H1: (Alternative Hypothesis), there is a change in return volatility
post-split compared with pre-split.
H1: (Alternative Hypothesis), there is an increase in liquidity post
stock split, when compared to pre stock split liquidity.
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6.2 Determination of daily returns of stock
Alembic Pharma
Table 1.0: Daily Returns before ex-dates of stock split
Dates
Closingstockprice
Pricerelative(Si / Si-1)
Daily returns(ln of Si / Si-1)
1-Sep-06 340.1
4-Sep-06 344.4 1.01264 0.01256
5-Sep-06 353.6 1.02671 0.02636
6-Sep-06 378.1 1.06929 0.06699
7-Sep-06 365.25 0.96601 -0.03458
8-Sep-06 356.95 0.97728 -0.02299
11-Sep-06 354.8 0.99398 -0.00604
12-Sep-06 354.45 0.99901 -0.0009913-Sep-06 354.8 1.00099 0.00099
14-Sep-06 365.65 1.03058 0.03012
15-Sep-06 368.4 1.00752 0.00749
18-Sep-06 372.25 1.01045 0.01040
19-Sep-06 375.15 1.00779 0.00776
20-Sep-06 377.75 1.00693 0.00691
21-Sep-06 380.5 1.00728 0.00725
Table 1.1: Daily Returns after ex-dates of stock split
Dates
Closingstockprice
Pricerelative(Si / Si-1)
Daily returns(ln of Si / Si-1)
26-Sep-06 72.9
27-Sep-06 69.65 0.95542 -0.04561
28-Sep-06 69.45 0.99713 -0.00288
29-Sep-06 69.65 1.00288 0.00288
3-Oct-06 69.65 1.00000 0.00000
4-Oct-06 67.15 0.96411 -0.03655
5-Oct-06 68.75 1.02383 0.02355
6-Oct-06 67.25 0.97818 -0.02206
9-Oct-06 66.65 0.99108 -0.00896
10-Oct-06 66.2 0.99325 -0.00677
11-Oct-06 64.65 0.97659 -0.02369
12-Oct-06 63.85 0.98763 -0.01245
13-Oct-06 64.6 1.01175 0.01168
16-Oct-06 65.95 1.02090 0.02068
17-Oct-06 68.05 1.03184 0.03135
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Aurobindo Pharma
Table 2.0: Daily Returns before ex-dates of stock split
Dates
Closingstockprice
Pricerelative(Si / Si-1)
Daily return(ln of Si / Si-1)
1-Oct-03 665.5
3-Oct-03 655 0.98422 -0.01590
6-Oct-03 654.5 0.99924 -0.00076
7-Oct-03 669.8 1.02338 0.02311
8-Oct-03 691.35 1.03217 0.03167
9-Oct-03 692.6 1.00181 0.00181
10-Oct-03 677 0.97748 -0.02278
13-Oct-03 690.85 1.02046 0.02025
14-Oct-03 660.95 0.95672 -0.04424
15-Oct-03 672.35 1.01725 0.01710
16-Oct-03 664.05 0.98766 -0.01242
17-Oct-03 663.75 0.99955 -0.00045
20-Oct-03 640.05 0.96429 -0.03636
21-Oct-03 596.25 0.93157 -0.07089
22-Oct-03 594.3 0.99673 -0.00328
Table 2.1: Daily Returns after ex-dates of stock split
Dates
Closing
stockprice
Price
relative(Si / Si-1)
Daily return(ln of Si / Si-1)
25-Oct-03 309.2
27-Oct-03 299.1 0.96734 -0.03321
28-Oct-03 298.25 0.99716 -0.00285
29-Oct-03 297.6 0.99782 -0.00218
30-Oct-03 303.8 1.02083 0.02062
31-Oct-03 327.2 1.07702 0.07420
3-Nov-03 340.35 1.04019 0.03940
4-Nov-03 335.7 0.98634 -0.01376
5-Nov-03 325.1 0.96842 -0.03209
6-Nov-03 316 0.97201 -0.02839
7-Nov-03 311.9 0.98703 -0.01306
10-Nov-03 316.9 1.01603 0.01590
11-Nov-03 315.15 0.99448 -0.00554
12-Nov-03 312.05 0.99016 -0.00989
13-Nov-03 304.9 0.97709 -0.02318
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Cipla Ltd.
Table 3.0: Daily Returns before ex-dates of stock split
Dates
Closingstockprice
Price relative(Si / Si-1)
Daily return(ln of Si / Si-1)
17-Apr-04 1,240.85
19-Apr-04 1,249.75 1.00717 0.00715
20-Apr-04 1,250.00 1.00020 0.00020
21-Apr-04 1,311.25 1.04900 0.04784
22-Apr-04 1,335.45 1.01846 0.01829
23-Apr-04 1,301.60 0.97465 -0.02567
27-Apr-04 1,344.10 1.03265 0.03213
28-Apr-04 1,312.15 0.97623 -0.02406
29-Apr-04 1,344.15 1.02439 0.02409
30-Apr-04 1,369.80 1.01908 0.01890
3-May-04 1,398.65 1.02106 0.02084
4-May-04 1,372.95 0.98163 -0.01855
5-May-04 1,375.60 1.00193 0.00193
6-May-04 1,353.60 0.98401 -0.01612
7-May-04 1,329.90 0.98249 -0.01766
Table 3.1: Daily Returns after ex-dates of stock split
Dates
Closing
stockprice
Price relative(Si / Si-1)
Daily return(ln of Si / Si-1)
12-May-04 260.95
13-May-04 262.15 1.00460 0.00459
14-May-04 246.85 0.94164 -0.06014
17-May-04 216.5 0.87705 -0.13119
18-May-04 236.85 1.09400 0.08984
19-May-04 244.15 1.03082 0.03036
20-May-04 246.9 1.01126 0.01120
21-May-04 245.25 0.99332 -0.00671
24-May-04 255.6 1.04220 0.04134
25-May-04 251.4 0.98357 -0.01657
26-May-04 255.05 1.01452 0.01441
27-May-04 246.9 0.96805 -0.03248
28-May-04 240.15 0.97266 -0.02772
31-May-04 234.15 0.97502 -0.02530
1-Jun-04 239.9 1.02456 0.02426
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Dr. Reddys Labs
Table 4.0: Daily Returns before ex-dates of stock split
Table 4.1: Daily Returns after ex-dates of stock split
Dates
Closing
stockprice
Price relative(Si / Si-1)
Daily return(ln of Si / Si-1)
15-Oct-01 927.1
16-Oct-01 933.6 1.00701 0.00699
17-Oct-01 969.95 1.03894 0.03820
18-Oct-01 963.95 0.99381 -0.00621
19-Oct-01 981.2 1.01790 0.01774
22-Oct-01 974.75 0.99343 -0.00660
23-Oct-01 999.5 1.02539 0.02507
24-Oct-01 1078.4 1.07894 0.07598
25-Oct-01 1101.2 1.02114 0.02092
29-Oct-01 1041.65 0.94592 -0.05559
30-Oct-01 1031.1 0.98987 -0.01018
31-Oct-01 1041.7 1.01028 0.01023
1-Nov-01 1,082.20 1.03888 0.03814
2-Nov-01 1,038.10 0.95925 -0.04160
5-Nov-01 993.75 0.95728 -0.04366
Dates
Closingstockprice
Price relative(Si / Si-1)
Daily return(ln of Si / Si-1)
19-Sep-01 1846.55
20-Sep-01 1769.05 0.95803 -0.04288
21-Sep-01 1760 0.99488 -0.00513
24-Sep-01 1726.5 0.98097 -0.01922
25-Sep-01 1799.25 1.04214 0.04127
26-Sep-01 1771.75 0.98472 -0.01540
27-Sep-01 1767.15 0.99740 -0.00260
28-Sep-01 1789.75 1.01279 0.01271
1-Oct-01 1785.6 0.99768 -0.00232
3-Oct-01 1764.6 0.98824 -0.01183
4-Oct-01 1792.85 1.01601 0.01588
5-Oct-01 1796.2 1.00187 0.00187
8-Oct-01 1789.35 0.99619 -0.00382
9-Oct-01 1789.05 0.99983 -0.00017
10-Oct-01 1829.45 1.02258 0.02233
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Glenmark Pharmaceuticals Ltd.
Table 5.0: Daily Returns before ex-dates of stock split
Dates
Closingstockprice
Price relative(Si / Si-1)
Daily return(ln of Si / Si-1)
16-Aug-07 620.95
17-Aug-07 616 0.99203 -0.00800
20-Aug-07 619.95 1.00641 0.00639
21-Aug-07 600.45 0.96855 -0.03196
22-Aug-07 607.85 1.01232 0.01225
23-Aug-07 629.35 1.03537 0.03476
24-Aug-07 649.95 1.03273 0.03221
27-Aug-07 665.85 1.02446 0.02417
28-Aug-07 665.25 0.99910 -0.00090
29-Aug-07 660 0.99211 -0.00792
30-Aug-07 663.5 1.00530 0.00529
31-Aug-07 687.75 1.03655 0.03590
3-Sep-07 696.9 1.01330 0.01322
4-Sep-07 731.6 1.04979 0.04859
5-Sep-07 729.55 0.99720 -0.00281
Table 5.1: Daily Returns after ex-dates of stock split
Dates
Closing
stockprice
Price relative(Si / Si-1)
Daily return(ln of Si / Si-1)
10-Sep-07 367.35
11-Sep-07 376.3 1.02436 0.02407
12-Sep-07 379.15 1.00757 0.00755
13-Sep-07 381.3 1.00567 0.00565
14-Sep-07 374.8 0.98295 -0.01719
17-Sep-07 379 1.01121 0.01114
18-Sep-07 380.55 1.00409 0.00408
19-Sep-07 391.25 1.02812 0.02773
20-Sep-07 388.2 0.99220 -0.00783
21-Sep-07 376.15 0.96896 -0.03153
24-Sep-07 388.95 1.03403 0.03346
25-Sep-07 400.9 1.03072 0.03026
26-Sep-07 395.6 0.98678 -0.01331
27-Sep-07 415 1.04904 0.04787
28-Sep-07 422.45 1.01795 0.01779
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JB Chem Ltd.
Table 6.0: Daily Returns before ex-dates of stock split
Dates
Closingstockprice
Price relative(Si / Si-1)
Daily return(ln of Si / Si-1)
10-Mar-05 433.25
11-Mar-05 434.4 1.00265 0.00265
14-Mar-05 430.65 0.99137 -0.00867
15-Mar-05 428.55 0.99512 -0.00489
16-Mar-05 427.7 0.99802 -0.00199
17-Mar-05 429.65 1.00456 0.00455
18-Mar-05 426.45 0.99255 -0.00748
21-Mar-05 421.1 0.98745 -0.01262
22-Mar-05 413.05 0.98088 -0.01930
23-Mar-05 398.35 0.96441 -0.03624
24-Mar-05 395.55 0.99297 -0.00705
28-Mar-05 413.3 1.04487 0.04390
29-Mar-05 401.2 0.97072 -0.02971
30-Mar-05 402.8 1.00399 0.00398
31-Mar-05 413.15 1.02570 0.02537
Table 6.1: Daily Returns after ex-dates of stock split
Dates
Closing
stockprice
Price relative(Si / Si-1)
Daily return(ln of Si / Si-1)
5-Apr-05 86.5
6-Apr-05 85.85 0.99249 -0.00754
7-Apr-05 84.1 0.97962 -0.02060
8-Apr-05 83 0.98692 -0.01317
11-Apr-05 81.25 0.97892 -0.02131
12-Apr-05 79.5 0.97846 -0.02177
13-Apr-05 79.45 0.99937 -0.00063
15-Apr-05 77.05 0.96979 -0.03067
18-Apr-05 74.65 0.96885 -0.03164
19-Apr-05 75.3 1.00871 0.00867
20-Apr-05 74.8 0.99336 -0.00666
21-Apr-05 75.6 1.01070 0.01064
22-Apr-05 75.15 0.99405 -0.00597
25-Apr-05 76 1.01131 0.01125
26-Apr-05 77.75 1.02303 0.02277
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Morepen Laboratories Ltd.
Table 7.0: Daily Returns before ex-dates of stock split
Dates
Closingstockprice
Price relative(Si / Si-1)
Daily return(ln of Si / Si-1)
12-Oct-00 491.2
13-Oct-00 486.95 0.99135 -0.00869
16-Oct-00 531.55 1.09159 0.08764
17-Oct-00 512.1 0.96341 -0.03728
18-Oct-00 502.6 0.98145 -0.01873
19-Oct-00 516.7 1.02805 0.02767
20-Oct-00 538.15 1.04151 0.04067
23-Oct-00 535.35 0.99480 -0.00522
24-Oct-00 523.45 0.97777 -0.02248
25-Oct-00 564.8 1.07900 0.07603
26-Oct-00 616.65 1.09180 0.08783
27-Oct-00 601.35 0.97519 -0.02512
30-Oct-00 505.15 0.84003 -0.17432
31-Oct-00 512.55 1.01465 0.01454
1-Nov-00 507.8 0.99073 -0.00931
Table 7.1: Daily Returns after ex-dates of stock split
Dates
Closing
stockprice
Price relative(Si / Si-1)
Daily return(ln of Si / Si-1)
6-Nov-00 120.25
7-Nov-00 124.5 1.03534 0.03473
8-Nov-00 123.3 0.99036 -0.00969
9-Nov-00 121.55 0.98581 -0.01429
10-Nov-00 117.8 0.96915 -0.03134
13-Nov-00 113.4 0.96265 -0.03807
14-Nov-00 117.45 1.03571 0.03509
15-Nov-00 119.3 1.01575 0.01563
16-Nov-00 118.85 0.99623 -0.00378
17-Nov-00 119 1.00126 0.00126
20-Nov-00 121 1.01681 0.01667
21-Nov-00 122.7 1.01405 0.01395
22-Nov-00 123.8 1.00896 0.00893
23-Nov-00 123.35 0.99637 -0.00364
24-Nov-00 122.35 0.99189 -0.00814
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Nicholas Piramal Ltd.
Table 8.0: Daily Returns before ex-dates of stock split
Dates
Closingstockprice
Price relative(Si / Si-1)
Daily return(ln of Si / Si-1)
2-De