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Market Reaction to Stock Splits in Largeand Liquid Stocks: Evidence from the

Indian Stock Market

Nehal Joshipura

Abstract

This study investigates market reaction to stock splits

using the standard event study methodology. The

study uses stock splits in large and liquid stocks in the

Indian markets during the years 2001 to 2012.

According to a semi-strong form of efficient market

hypothesis, any information content associated with

corporate announcements must be reflected fully on

announcement day itself resulting in an abnormal

return. However, several studies report abnormal

returns surrounding announcement as well as

effective day of stock split, and many competing

hypotheses are presented to explain such abnormal

returns. Studies from India on market reaction to stock

splits offer mixed results. (Gupta & Gupta, 2007) find

significant abnormal returns surrounding ex-split day

and not surrounding announcement day. (Joshipura,

2009) and (Ray, 2011) find abnormal returns

surrounding announcement day. This study reports

reaction to stock split in large and liquid stocks that are

constituents of NIFTY or NIFTY Junior.

JEL Classification: G14

Market Reaction to Stock Splits in Large and Liquid Stocks:Evidence from the Indian Stock Market

ISSN: 0971-1023 | NMIMS Management ReviewDouble Issue: Volume XXIII October-November 2013 University Day Special Issue January 2014

I. INTRODUCTION

Stock split, as the term suggests, results in reduction in

face value of a stock and thereby corresponding

increase in number of outstanding shares. For

instance, if a company goes for a 1:10 stock split, Rs. 10

face value stock is divided into 10 shares with face

value of Re. 1 each. Nothing changes as far as total

share capital of a company is concerned. Hence, when

a company decides to split its stocks, price of the stock

should come down in the proportion of the split ratio.

If 1:10 stock split is announced, then stock price should thcome down to 1/10 of the price before the split. This

means that stock split is nothing but a mere cosmetic

event and hence, announcement about the stock split

does not contain any information to affect stock price

in a way that leads to abnormal positive returns. Even if

there is any information content associated with

announcement of the stock split, it should be reflected

in the form of abnormal returns on the announcement

day itself. Having said that, several studies on

developed markets report abnormal returns

surrounding announcement and ex-days of stock split.

Not many studies are available in the Indian context as

stock split was not a feasible alternative for Indian

companies earlier. Prior to 1999, SEBI allowed only two

face values - rupees ten and rupees hundred. Some of

the important studies examining the effect of stock

split in the Indian context are by (Mishra, 2007), (Gupta

& Gupta, 2007), (Joshipura, 2009), (Ray, 2011) and

(Chakraborty, 2012). Evidences from these studies are

mixed so far. (Chakraborty, 2012) reports that the

abnormal positive returns effect associated with stock

split is concentrated to small and illiquid stocks.

II. Motivation for the Study

This study stems from the fact that not many studies

are available in the Indian context. Also, the results are

mixed and do not provide any conclusive evidence on

market reaction to stock split. This study intends to

contribute by studying market reaction to stock split in

large and liquid stocks only. This is to eliminate any

possibility of abnormal returns (if any) associated with

stock split announcements being attributed to small

size and illiquidity of stocks as explained by

(Chakraborty, 2012) in her paper. If such abnormal

returns are not associated with stock split, it provides

an important result in the Indian context.

The paper is organized as follows. Section III provides

literature review, section IV describes data and

methodology, the results are discussed in section V

and conclusion is provided in section VI.

III. LITERATURE REVIEW

In theory, stock splits are mere cosmetic corporate

events and therefore, announcement of stock split

should not have any significant impact on market

returns of the firm. However, empirical evidence from

developed markets suggests that announcement of a

stock split leads to significant positive returns

Even if one believes that stock split carries some

information content about the future prospects,

according to semi strong form of efficient market

hypothesis, the entire positive abnormal return should

be reflected on such announcement itself and no

abnormal returns should be present surrounding ex-

day. However, as mentioned earlier, that is not the

case. Many hypotheses are presented to explain

p o s i t i v e a b n o r m a l r e t u r n s s u r r o u n d i n g

announcement and ex-date of stock split. Review of

literature from the developed markets with major

competing hypotheses is presented below, followed

by review of literature on studies on market reaction to

stock split from Indian markets.

130 131

Market Reaction to Stock Splits in Largeand Liquid Stocks: Evidence from the

Indian Stock Market

Nehal Joshipura

Abstract

This study investigates market reaction to stock splits

using the standard event study methodology. The

study uses stock splits in large and liquid stocks in the

Indian markets during the years 2001 to 2012.

According to a semi-strong form of efficient market

hypothesis, any information content associated with

corporate announcements must be reflected fully on

announcement day itself resulting in an abnormal

return. However, several studies report abnormal

returns surrounding announcement as well as

effective day of stock split, and many competing

hypotheses are presented to explain such abnormal

returns. Studies from India on market reaction to stock

splits offer mixed results. (Gupta & Gupta, 2007) find

significant abnormal returns surrounding ex-split day

and not surrounding announcement day. (Joshipura,

2009) and (Ray, 2011) find abnormal returns

surrounding announcement day. This study reports

reaction to stock split in large and liquid stocks that are

constituents of NIFTY or NIFTY Junior.

JEL Classification: G14

I. INTRODUCTION

Stock split, as the term suggests, results in reduction in

face value of a stock and thereby corresponding

increase in number of outstanding shares. For

instance, if a company goes for a 1:10 stock split, Rs. 10

face value stock is divided into 10 shares with face

value of Re. 1 each. Nothing changes as far as total

share capital of a company is concerned. Hence, when

a company decides to split its stocks, price of the stock

should come down in the proportion of the split ratio.

If 1:10 stock split is announced, then stock price should thcome down to 1/10 of the price before the split. This

means that stock split is nothing but a mere cosmetic

event and hence, announcement about the stock split

does not contain any information to affect stock price

in a way that leads to abnormal positive returns. Even if

there is any information content associated with

announcement of the stock split, it should be reflected

in the form of abnormal returns on the announcement

day itself. Having said that, several studies on

developed markets report abnormal returns

Not many studies are available in the Indian context as

stock split was not a feasible alternative for Indian

companies earlier. Prior to 1999, SEBI allowed only two

face values - rupees ten and rupees hundred. Some of

the important studies examining the effect of stock

split in the Indian context are by (Mishra, 2007), (Gupta

& Gupta, 2007), (Joshipura, 2009), (Ray, 2011) and

(Chakraborty, 2012). Evidences from these studies are

mixed so far. (Chakraborty, 2012) reports that the

abnormal positive returns effect associated with stock

split is concentrated to small and illiquid stocks.

II. Motivation for the Study

This study stems from the fact that not many studies

are available in the Indian context. Also, the results are

mixed and do not provide any conclusive evidence on

market reaction to stock split. This study intends to

contribute by studying market reaction to stock split in

large and liquid stocks only. This is to eliminate any

possibility of abnormal returns (if any) associated with

stock split announcements being attributed to small

size and illiquidity of stocks as explained by

(Chakraborty, 2012) in her paper. If such abnormal

returns are not associated with stock split, it provides

an important result in the Indian context.

The paper is organized as follows. Section III provides

literature review, section IV describes data and

methodology, the results are discussed in section V

and conclusion is provided in section VI.

III. LITERATURE REVIEW

In theory, stock splits are mere cosmetic corporate

events and therefore, announcement of stock split

should not have any significant impact on market

returns of the firm. However, empirical evidence from

developed markets suggests that announcement of a

stock split leads to significant positive returns

Even if one believes that stock split carries some

information content about the future prospects,

according to semi strong form of efficient market

hypothesis, the entire positive abnormal return should

be reflected on such announcement itself and no

abnormal returns should be present surrounding ex-

day. However, as mentioned earlier, that is not the

case. Many hypotheses are presented to explain

p o s i t i v e a b n o r m a l r e t u r n s s u r r o u n d i n g

announcement and ex-date of stock split. Review of

literature from the developed markets with major

competing hypotheses is presented below, followed

by review of literature on studies on market reaction to

stock split from Indian markets.

130 131

Signalling hypothesis: Stock split is one of the ways to

give signal about the future growth of the company.

Evidence for positive abnormal return on

announcement day of stock split by several studies -

(Asquith, Healy, & Palepu, 1989), (Ikenberry, Rankine,

& Stice, 1996) provides evidence for signalling

hypothesis. Further, (Fama, Fisher, Jensen, & Roll,

1969), (Lakonishok & Lev, 1987) suggest that the

abnormal returns associated with announcement of

stock split is due to the fact that stock split is one of the

financial signals for higher dividends in future.

However, providing a sound conceptual framework for

explaining validity of this hypothesis has always been a

challenge and therefore, the researcher started

searching for a more convincing explanation to explain

the wealth effect associated with stock split

announcement.

Optimal trading range hypothesis: According to this

hypothesis, each stock market has a popular trading

range for stocks (i.e. it is unlikely to see a stock trading

at Rs. 50,000 per share in the Indian stock market!).

Stock splits are used as a tool by firms who find that

their stock price has moved way above the popular

trading range. Announcement of stock split is done

with an intention to bring the stock price back to the

normal trading range and thereby making it more

affordable for small investors to buy round lots of

shares. Marketability of the stock increases post-split,

which results in improved liquidity and positive

abnormal returns due to buying activity of deprived

wealth-constrained small investors who could not

afford buying the stock at the higher pre-split price.

((Lakonishok & Lev, 1987); (McNichols & Dravid,

1990)). If there is merit in this hypothesis, positive

price returns should be evident immediately on ex-day

and after that. Relevance of this hypothesis has come

down significantly in Indian markets in the last decade

as the lot size for stocks in the Indian market is one

share only.

While (Lakonishok & Lev, 1987) and (Han, 1995)

provided some empirical evidence on the existence of

an optimal trading range in the U.S., studies by

(Copeland, 1979) and (Conroy, Harris, & Benet, 1990)

find results contradicting optimum trading range

hypothesis. Copeland observes decrease in trading

activity after stock split.

Liquidity hypothesis

A related hypothesis that explains positive abnormal

returns post stock split is the liquidity hypothesis.

According to this hypothesis, the motivation of

management to bring the stock price back to the

popular trading range is to improve liquidity.

(Muscarella & Vetsuypens, 1996) show that liquidity

after stock split improves, results in wealth gains for

investors. Their findings support the model of (Amihud

& Mendelson, 1986), which predicts a positive relation

between equity value and liquidity. According to this

model, rational investors discount illiquid securities

heavier than liquid ones due to the higher transaction

costs and greater trading frictions they face. On the

other end, (Conroy, Harris, & Benet, 1990) show an

increased bid-ask spread after stock split

announcements. (Ferris, Hwang, & Sarin, 1995) report

reduction in depth post-split. These results show

reduction in liquidity rather than increase.

Small firm or neglected firms hypothesis: Stock split is

the way of catching attention of the market by a firm

which feels that they are undervalued in the market

due to negligence of market participants, which means

there is little known about a firm and its shares trade at

a discount. Thus, firms use the split to draw attention

of market participants and try to ensure that

information about the company is disseminated over a

larger number of market participants. This is more

relevant for small firms than large firms and that is one

of the reasons why they may go for stock split

according to (Arbel & Strebel, 1983), (Arbel &

Swanson, 1993), (Grinblatt, Masulis, & Titman, 1984),

(Wulff, 2002).

Market reaction to stock split has generated significant

interest among researchers in India and there are

several studies reporting market reaction to stock split

in the past few years. Presented below is a review of

some of the noteworthy studies in the Indian context

that provide mixed evidence of market reaction to

stock split.

(Mishra, 2007) reports negative wealth effect on stock

split announcement using stock split data of 1999 to

2005 from Indian markets and hence, rejects signalling

hypothesis. (Gupta & Gupta, 2007) report no positive

stock split but find evidence of positive wealth effect

on ex-day. It rejects trading range hypothesis as

majority of the stock splits are announced in stocks

which were trading at a low price already.

(Joshipura, 2009) finds positive abnormal returns both

on announcement as well as effective days but

observes that it does not sustain and reverses soon

after. (Ray, 2011) reports significant positive abnormal

return on announcement day of stock split and also

reports significant improvement in liquidity post-split.

(Chakraborty, 2012) also reports significant positive

return on the ex-split date and attributed such positive

reaction to small firm hypothesis and liquidity

hypothesis. However, the same study also reports

reversal of such profits in post-split period.

Thus, evidences from Indian stock markets on reaction

to stock split surrounding announcement and ex-date

are mixed. This study intends to add to the body of

literature by providing empirical evidence on market

reaction to stock split in large and liquid stocks and

thereby minimizing the possibility of any positive

abnormal return surrounding stock sp l i t

announcement and ex-date dominated by small and

illiquid firms as reported by (Chakraborty, 2012). In

that regard, it takes the work forward in the Indian

context and adds to the studies on wealth effect

associated with stock split announcements.

VI. DATA AND METHODOLOGY

The sample of the study comprises of constituents of

NIFTY and NIFTY Junior as of August 2012. These are

large and liquid stocks. Forty six stock splits have been

observed during January 2001 to August 2012 in the

sample stocks. The following criteria have been

applied to include a company in the sample.

1. Announcement and ex-dates are available for

stock splits of the company.

2. Adjusted closing price data for a period of 120 days

prior to stock split announcement date and up to

10 days post ex-split date are available for the

company going for stock split.

3. Stock split does not coincide with any other

corporate event that potentially contaminates the

results.

After applying the above criteria, the number of stock

splits available for analysis was 41. This is not a very

large sample; however, it is a highly relevant sample for

the focused study to capture market reaction to stock

splits in large and liquid stocks.

Standard event study pioneered by (Fama, Fisher,

132 133

Signalling hypothesis: Stock split is one of the ways to

give signal about the future growth of the company.

Evidence for positive abnormal return on

announcement day of stock split by several studies -

(Asquith, Healy, & Palepu, 1989), (Ikenberry, Rankine,

& Stice, 1996) provides evidence for signalling

hypothesis. Further, (Fama, Fisher, Jensen, & Roll,

1969), (Lakonishok & Lev, 1987) suggest that the

stock split is due to the fact that stock split is one of the

financial signals for higher dividends in future.

However, providing a sound conceptual framework for

explaining validity of this hypothesis has always been a

challenge and therefore, the researcher started

searching for a more convincing explanation to explain

the wealth effect associated with stock split

announcement.

Optimal trading range hypothesis: According to this

hypothesis, each stock market has a popular trading

range for stocks (i.e. it is unlikely to see a stock trading

at Rs. 50,000 per share in the Indian stock market!).

Stock splits are used as a tool by firms who find that

their stock price has moved way above the popular

trading range. Announcement of stock split is done

with an intention to bring the stock price back to the

normal trading range and thereby making it more

affordable for small investors to buy round lots of

shares. Marketability of the stock increases post-split,

which results in improved liquidity and positive

abnormal returns due to buying activity of deprived

wealth-constrained small investors who could not

afford buying the stock at the higher pre-split price.

((Lakonishok & Lev, 1987); (McNichols & Dravid,

1990)). If there is merit in this hypothesis, positive

price returns should be evident immediately on ex-day

and after that. Relevance of this hypothesis has come

down significantly in Indian markets in the last decade

as the lot size for stocks in the Indian market is one

share only.

While (Lakonishok & Lev, 1987) and (Han, 1995)

provided some empirical evidence on the existence of

an optimal trading range in the U.S., studies by

(Copeland, 1979) and (Conroy, Harris, & Benet, 1990)

find results contradicting optimum trading range

hypothesis. Copeland observes decrease in trading

activity after stock split.

Liquidity hypothesis

A related hypothesis that explains positive abnormal

returns post stock split is the liquidity hypothesis.

According to this hypothesis, the motivation of

management to bring the stock price back to the

popular trading range is to improve liquidity.

(Muscarella & Vetsuypens, 1996) show that liquidity

after stock split improves, results in wealth gains for

investors. Their findings support the model of (Amihud

& Mendelson, 1986), which predicts a positive relation

between equity value and liquidity. According to this

model, rational investors discount illiquid securities

heavier than liquid ones due to the higher transaction

costs and greater trading frictions they face. On the

other end, (Conroy, Harris, & Benet, 1990) show an

increased bid-ask spread after stock split

announcements. (Ferris, Hwang, & Sarin, 1995) report

reduction in depth post-split. These results show

reduction in liquidity rather than increase.

Small firm or neglected firms hypothesis: Stock split is

the way of catching attention of the market by a firm

which feels that they are undervalued in the market

due to negligence of market participants, which means

there is little known about a firm and its shares trade at

a discount. Thus, firms use the split to draw attention

of market participants and try to ensure that

information about the company is disseminated over a

larger number of market participants. This is more

relevant for small firms than large firms and that is one

of the reasons why they may go for stock split

according to (Arbel & Strebel, 1983), (Arbel &

Swanson, 1993), (Grinblatt, Masulis, & Titman, 1984),

(Wulff, 2002).

Market reaction to stock split has generated significant

interest among researchers in India and there are

several studies reporting market reaction to stock split

in the past few years. Presented below is a review of

some of the noteworthy studies in the Indian context

that provide mixed evidence of market reaction to

stock split.

(Mishra, 2007) reports negative wealth effect on stock

split announcement using stock split data of 1999 to

2005 from Indian markets and hence, rejects signalling

hypothesis. (Gupta & Gupta, 2007) report no positive

stock split but find evidence of positive wealth effect

on ex-day. It rejects trading range hypothesis as

majority of the stock splits are announced in stocks

which were trading at a low price already.

(Joshipura, 2009) finds positive abnormal returns both

on announcement as well as effective days but

observes that it does not sustain and reverses soon

after. (Ray, 2011) reports significant positive abnormal

return on announcement day of stock split and also

reports significant improvement in liquidity post-split.

(Chakraborty, 2012) also reports significant positive

return on the ex-split date and attributed such positive

reaction to small firm hypothesis and liquidity

hypothesis. However, the same study also reports

reversal of such profits in post-split period.

Thus, evidences from Indian stock markets on reaction

to stock split surrounding announcement and ex-date

are mixed. This study intends to add to the body of

literature by providing empirical evidence on market

reaction to stock split in large and liquid stocks and

thereby minimizing the possibility of any positive

abnormal return surrounding stock sp l i t

announcement and ex-date dominated by small and

illiquid firms as reported by (Chakraborty, 2012). In

that regard, it takes the work forward in the Indian

context and adds to the studies on wealth effect

associated with stock split announcements.

VI. DATA AND METHODOLOGY

The sample of the study comprises of constituents of

NIFTY and NIFTY Junior as of August 2012. These are

large and liquid stocks. Forty six stock splits have been

observed during January 2001 to August 2012 in the

sample stocks. The following criteria have been

applied to include a company in the sample.

1. Announcement and ex-dates are available for

stock splits of the company.

2. Adjusted closing price data for a period of 120 days

prior to stock split announcement date and up to

10 days post ex-split date are available for the

company going for stock split.

3. Stock split does not coincide with any other

corporate event that potentially contaminates the

results.

After applying the above criteria, the number of stock

splits available for analysis was 41. This is not a very

large sample; however, it is a highly relevant sample for

the focused study to capture market reaction to stock

splits in large and liquid stocks.

Standard event study pioneered by (Fama, Fisher,

132 133

Jensen, & Roll, 1969) has undergone several

refinements over the years thanks to the contributions

of many researchers.

(Saens & Sandoval, 2005) compares results of the

event study using three methods for return-generating

models (RGM) and three versions of parametric t-tests

as reported below and concluded that the

standardized t-test turns out to be the most reliable

alternative.

The following are the alternatives considered both for

return generating models and parametric t-test before

finalizing the return generating model and the model

for parametric t-test.

Return Generating Models:

1) OLS Market Model as used by this study where

abnormal return is return above the expected

return obtained from OLS values from the

estimation period. This model is comparatively

more sound.

2) Market-adjusted Returns Model where abnormal

return is return above the market index return.

3) Mean-Adjusted Returns Model where abnormal

return is return above the average return for the

estimation period.

Test Statistics alternatives:

1) The standardized t-test

Following Patell (1976) many authors have used

standardized abnormal returns (SAR), where each

abnormal security return is normalized by its

estimation period standard deviation before applying

t-test to check the significance of abnormal returns.

2) The cross sectional t-test

In this model, t-statistic is estimated by dividing the

average event period abnormal return by its

contemporaneous cross-sectional standard deviation.

This procedure, however, has some limitations. If the

variance differs across sample securities or security

abnormal performances are correlated across firms,

the test statistics is likely to be misspecified.

3) The portfolio t-test

For each day t, the cross-sectional average abnormal

return for sample securities is computed. The portfolio

t-test is the ratio of mean abnormal return on any

given day to its standard deviation during the event

window.

(Saens & Sandoval, 2005) conclude that symptoms of

non-normality in security returns and security

abnormal returns persist. and individual securities

and portfolio level, Methods based on the use of

parametric tests for samples of ten or more securities

are well specified to test the significance of abnormal

returns associated with an event such as stock split and

standardized t-test turns out to be the best alternative

amongst all the available alternatives.

Hence, this study uses OLS market model adjusted

abnormal returns and standardized t-test for the

purpose of calculating abnormal returns and test of

significance. The study uses AD-130 to AD-30 days of

estimation window and AD-10 to AD+10 and ED-10

and ED+10 days of effective window.

The first step in this process of determining price or

wealth effect is to calculate abnormal return. The

return on security i on day t is calculated as R = ln it

(P /P ).it i(t-1)

The market model is defined using estimation window

of AD-130 to AD-30 days, daily returns data on stock i

as dependent variable and market as independent

variable as follows.

Where,

R is the return on security i on day t; i,t

R is the return on a market index on day tm,t

Abnormal return for stock i on day t for each day in the

event window AD-10,

In order to draw overall inferences for the event of

interest, the abnormal return observations are

aggregated along two dimensions – through time and

across securities. The following measures of abnormal

performance are used:

1. Cumulative Abnormal Return (CAR): Cumulative

sum of stock i’s prediction error (abnormal

returns) over the window (T , T ).1 2

2. Mean Abnormal Return (MAR): An average of

abnormal returns across the N firms on a day t.

3. Mean Cumulative Abnormal Return (MCAR):

Average of the cumulative abnormal returns

across observations (firms); it is a measure of the

abnormal performance over the event period,

4. Testing for statistical significance

The test statistics are calculated using

standardized t-test ((Patell, 1976), (Brown &

Warner, 1980), (Brown & Warner, 1985)) and non-

parametric sign test. Standardized t-test uses

standardized abnormal return (SAR), where each

security abnormal return is normalized by its

estimation period standard deviation.

SAR = AR / σ(AR ), Where, σ(ARit) is standard it it it

deviation of abnormal returns of security i during

its estimation period.

A nonparametric sign test based on sign of abnormal

return is also employed. The hypothesis is abnormal

returns are independent across securities and that the

expected proportion of positive abnormal returns

under the null hypothesis is 0.5.

The test statistic is computed as

+where N is the sample size and N is the number of

cases where the abnormal return is positive. This test is

conducted to supplement parametric test.

V. Results And Discussion

Table-1 and Table-2 report Cross sectional mean

abnormal return (MAR) and mean cumulative

abnormal returns (MCAR) with corresponding t-

statistics values and z-values for non-parametric

sign test. Table-1 shows results for announcement

window, whereas Table-2 shows results for

effective window.

134 135

Jensen, & Roll, 1969) has undergone several

refinements over the years thanks to the contributions

of many researchers.

(Saens & Sandoval, 2005) compares results of the

event study using three methods for return-generating

models (RGM) and three versions of parametric t-tests

as reported below and concluded that the

standardized t-test turns out to be the most reliable

alternative.

The following are the alternatives considered both for

return generating models and parametric t-test before

finalizing the return generating model and the model

for parametric t-test.

Return Generating Models:

1) OLS Market Model as used by this study where

abnormal return is return above the expected

return obtained from OLS values from the

estimation period. This model is comparatively

more sound.

2) Market-adjusted Returns Model where abnormal

return is return above the market index return.

3) Mean-Adjusted Returns Model where abnormal

return is return above the average return for the

estimation period.

Test Statistics alternatives:

1) The standardized t-test

Following Patell (1976) many authors have used

standardized abnormal returns (SAR), where each

abnormal security return is normalized by its

estimation period standard deviation before applying

t-test to check the significance of abnormal returns.

2) The cross sectional t-test

In this model, t-statistic is estimated by dividing the

average event period abnormal return by its

contemporaneous cross-sectional standard deviation.

This procedure, however, has some limitations. If the

variance differs across sample securities or security

abnormal performances are correlated across firms,

the test statistics is likely to be misspecified.

3) The portfolio t-test

For each day t, the cross-sectional average abnormal

return for sample securities is computed. The portfolio

t-test is the ratio of mean abnormal return on any

given day to its standard deviation during the event

window.

(Saens & Sandoval, 2005) conclude that symptoms of

non-normality in security returns and security

abnormal returns persist. and individual securities

and portfolio level, Methods based on the use of

parametric tests for samples of ten or more securities

are well specified to test the significance of abnormal

returns associated with an event such as stock split and

standardized t-test turns out to be the best alternative

amongst all the available alternatives.

Hence, this study uses OLS market model adjusted

abnormal returns and standardized t-test for the

purpose of calculating abnormal returns and test of

significance. The study uses AD-130 to AD-30 days of

estimation window and AD-10 to AD+10 and ED-10

and ED+10 days of effective window.

The first step in this process of determining price or

wealth effect is to calculate abnormal return. The

return on security i on day t is calculated as R = ln it

(P /P ).it i(t-1)

The market model is defined using estimation window

of AD-130 to AD-30 days, daily returns data on stock i

as dependent variable and market as independent

variable as follows.

Where,

R is the return on security i on day t; i,t

R is the return on a market index on day tm,t

Abnormal return for stock i on day t for each day in the

event window AD-10,

In order to draw overall inferences for the event of

interest, the abnormal return observations are

aggregated along two dimensions – through time and

across securities. The following measures of abnormal

performance are used:

1. Cumulative Abnormal Return (CAR): Cumulative

sum of stock i’s prediction error (abnormal

returns) over the window (T , T ).1 2

2. Mean Abnormal Return (MAR): An average of

abnormal returns across the N firms on a day t.

3. Mean Cumulative Abnormal Return (MCAR):

Average of the cumulative abnormal returns

across observations (firms); it is a measure of the

abnormal performance over the event period,

4. Testing for statistical significance

The test statistics are calculated using

standardized t-test ((Patell, 1976), (Brown &

Warner, 1980), (Brown & Warner, 1985)) and non-

parametric sign test. Standardized t-test uses

standardized abnormal return (SAR), where each

security abnormal return is normalized by its

estimation period standard deviation.

SAR = AR / σ(AR ), Where, σ(ARit) is standard it it it

deviation of abnormal returns of security i during

its estimation period.

A nonparametric sign test based on sign of abnormal

return is also employed. The hypothesis is abnormal

returns are independent across securities and that the

expected proportion of positive abnormal returns

under the null hypothesis is 0.5.

The test statistic is computed as

+where N is the sample size and N is the number of

cases where the abnormal return is positive. This test is

conducted to supplement parametric test.

V. Results And Discussion

Table-1 and Table-2 report Cross sectional mean

abnormal return (MAR) and mean cumulative

abnormal returns (MCAR) with corresponding t-

statistics values and z-values for non-parametric

sign test. Table-1 shows results for announcement

window, whereas Table-2 shows results for

effective window.

134 135

Table 1: MAR, MCAR and test statistics in AD window for entire sample, (N=41)

As we can see from the table-1, MAR for the

announcement day is 0.75% but it is statistically not

significant at 5% level. In addition, only 23 out of 41

stock splits report positive abnormal returns on

announcement day at that is statistically not

significant at 5% level with corresponding z-statistic of

0.78 for generalized sign test. It clearly suggests that

there is no significant market reaction to stock split

announcement associated with announcement of the

stock split in large and liquid stocks. MCAR also leads to

the same interpretation. MCAR on announcement day

(it represents cross sectional cumulative abnormal

returns between AD-10 to AD) is 1.54% with

corresponding t-statistics of 1.35 and that is

statistically not significant at 5% level. Non-parametric

sign test also indicates the same. 24 out of 41 stock

splits having MCAR positive on announcement day

with corresponding z-statistics for the generalized sign

test of 1.03 is also not significant. Moreover, it can also

be observed that MCAR is statistically not significant

during the entire announcement window (AD-10 to

AD+10) and that suggests that there is no market

reaction to stock split announcement and stock splits

do not contain any information content whatsoever.

Table 2: MAR, MCAR and test statistics in ED window for the entire sample, N(41)

136 137

Table 1: MAR, MCAR and test statistics in AD window for entire sample, (N=41)

As we can see from the table-1, MAR for the

announcement day is 0.75% but it is statistically not

significant at 5% level. In addition, only 23 out of 41

stock splits report positive abnormal returns on

announcement day at that is statistically not

significant at 5% level with corresponding z-statistic of

0.78 for generalized sign test. It clearly suggests that

there is no significant market reaction to stock split

announcement associated with announcement of the

stock split in large and liquid stocks. MCAR also leads to

the same interpretation. MCAR on announcement day

(it represents cross sectional cumulative abnormal

returns between AD-10 to AD) is 1.54% with

corresponding t-statistics of 1.35 and that is

statistically not significant at 5% level. Non-parametric

sign test also indicates the same. 24 out of 41 stock

splits having MCAR positive on announcement day

with corresponding z-statistics for the generalized sign

test of 1.03 is also not significant. Moreover, it can also

be observed that MCAR is statistically not significant

during the entire announcement window (AD-10 to

AD+10) and that suggests that there is no market

reaction to stock split announcement and stock splits

do not contain any information content whatsoever.

Table 2: MAR, MCAR and test statistics in ED window for the entire sample, N(41)

136 137

Table-2 shows that MAR on ex-day is 0.77% with

corresponding t-statistics of 2.97 that is significant at

5% level and 26 out of 41 stock splits have seen positive

abnormal returns on effective day. Z-statistics for

generalized sign test is 1.72 that is statistically

significant at 5% for one tailed test. However, value of

MCAR on the ex-split day on effective window

(represents cross sectional abnormal returns from ED-

10 to ED) is 1.51% with corresponding t-statistics of

1.15 and that is statistically not significant. Only 23 out

of 41 stock splits report positive MCAR in effective

window as on ED with corresponding z-statistics of

0.78 which is statistically not significant. What this

means is that while there is some evidence for

significant positive abnormal return on ex-date of

stock split, there is nothing surrounding it. In fact,

MCAR at the end of the event window on ED+10 day is -

2.86% and is statistically significant at 5% level on one

tailed test. Out of 41, 27 splits end the effective

window with negative MCAR and it is statistically

significant with z-statistics of -2.03 for generalized sign

test. The interpretation here is that there is some

evidence of positive abnormal return on ex-date of

stock splits but that does not result in any sustainable

long term positive effect. In fact, it immediately

reverses. A possible explanation to such immediate

reversal is provided in the conclusion.

VI. Conclusion

The results of the study lead to the conclusion that

there is no evidence for any positive impact of stock

splits on their announcement in a sample of large and

liquid stocks. Therefore, there is evidence against

signalling hypothesis as well as neglected firm

hypothesis. There is evidence of positive abnormal

return on ex-split date but it reverses soon after. And

therefore, there is also evidence against liquidity or

trading range hypothesis. Our findings are consistent

with (Gupta & Gupta, 2007) who find no positive effect

of stock split announcement but report positive return

on ex-split date. Our results also lend support to

results of (Chakraborty, 2012) who reports positive

abnormal returns on ex-split date which reverses soon

after. The only difference is that she attributed

presence of such positive abnormal returns to small

and illiquid stocks in the sample; this study concludes

that it is true for stock split announcement in large and

liquid stocks as well. The possible explanation is that

on the ex-date of stock split, many small wealth-

constrained/deprived investors put demand for stocks

that have just undergone split and therefore have

become more affordable in terms of unit price and that

leads to positive abnormal returns only to realize later

that there is no positive impact of stock split on future

prospects of the company and hence, there is no way

to earn superior return by buying stocks that have just

gone ex-split.

• Amihud, Y., & Mendelson, H. (1986). Asset pricing and the bid-ask spread. Journal of Financial Economics, 17,

223-249.

• Arbel, A., & Strebel, P. J. (1983, Winter). Pay Attention to Neglected Firms. Journal of Portfolio Management, 9,

37-42.

• Arbel, A., & Swanson, G. (1993). The role of information in stock split announcement effects. Quarterly Journal

of Business and Economics, 32(2), 14-25.

• Asquith, P. K., Healy, P., & Palepu. (1989). Earnings and stock splits. The Accounting Review, 64, 387-403.

• Brown, S. J., & Warner, B. J. (1980). Measuring Security Price Performance. Journal of Financial Economics,

8(3), 205-258.

• Brown, S. J., & Warner, B. J. (1985). Using Daily Stock Returns: The Case of Event Studies. Journal of Financial

Economics, 14(1), 3-31.

• Chakraborty, M. (2012). The Equity Market around the Ex-Split Date: Evidence from India. Vikalpa, 37(1), 57-

• Conroy, R. M., Harris, R. S., & Benet, B. A. (1990, September). The effect of stock splits on bid-ask spreads.

Journal of Finance, 45(4), 1285-1295.

• Copeland, T. E. (1979). Liquidity changes following stock splits. Journal of Finance, 37, 115-142.

• Fama, E. F., Fisher, L., Jensen, M. C., & Roll, R. (1969). The Adjustment of Stock Prices to New Information.

International Economic Review, Vol. 10, 1-21.

• Ferris, S. P., Hwang, C.-Y., & Sarin, A. (1995). A microstructure Examination of Trading Activity Following Stock

Splits. Review of Quantitative Finance and Accounting, 14, 27-41.

• Grinblatt, M. S., Masulis, R. W., & Titman, S. (1984). The valuation effect of stock splits and stock dividends.

Journal of Financial Economics, 13, 461-490.

• Gupta, A. P., & Gupta, O. P. (2007, January). Market Reaction to Stock Market Splits: Evidence from India. The

ICFAI Journal of Applied Finance, 13(1), 6-12.

• Han, K. (1995). The effect of reverse splits on the liquidity of the stock. Journal of Financial and Quantitative

Analysis, 30(1), 159-169.

• Ikenberry, Rankine, & Stice. (1996). What do stock splits really signal? Journal of Financial and Quantitative

analysis, 31, 357-375.

• Joshipura, M. (2009). Price and Liquidity Effects of Stock Split: Empirical Evidence from Indian Stock Market.

Indian Journal of Finance, 3(10).

• Lakonishok, J., & Lev, B. (1987). Stock splits and stock dividends: Why, who and when. Journal of Finance, 913-

• McNichols, M., & Dravid, A. (1990). Stock Dividends, Stock Splits and Signaling. Journal of Finance, 45, 857-

• Mishra, A. K. (2007). The Market Reaction to Stock Splits-Evidence from India. International Journal of

Theoretical and Applied Finance, 10(2), 251-271.

References

138 139

Table-2 shows that MAR on ex-day is 0.77% with

corresponding t-statistics of 2.97 that is significant at

5% level and 26 out of 41 stock splits have seen positive

abnormal returns on effective day. Z-statistics for

generalized sign test is 1.72 that is statistically

significant at 5% for one tailed test. However, value of

MCAR on the ex-split day on effective window

(represents cross sectional abnormal returns from ED-

10 to ED) is 1.51% with corresponding t-statistics of

1.15 and that is statistically not significant. Only 23 out

of 41 stock splits report positive MCAR in effective

window as on ED with corresponding z-statistics of

0.78 which is statistically not significant. What this

means is that while there is some evidence for

significant positive abnormal return on ex-date of

stock split, there is nothing surrounding it. In fact,

MCAR at the end of the event window on ED+10 day is -

2.86% and is statistically significant at 5% level on one

tailed test. Out of 41, 27 splits end the effective

window with negative MCAR and it is statistically

significant with z-statistics of -2.03 for generalized sign

test. The interpretation here is that there is some

evidence of positive abnormal return on ex-date of

stock splits but that does not result in any sustainable

long term positive effect. In fact, it immediately

reverses. A possible explanation to such immediate

reversal is provided in the conclusion.

VI. Conclusion

The results of the study lead to the conclusion that

there is no evidence for any positive impact of stock

splits on their announcement in a sample of large and

liquid stocks. Therefore, there is evidence against

signalling hypothesis as well as neglected firm

hypothesis. There is evidence of positive abnormal

return on ex-split date but it reverses soon after. And

therefore, there is also evidence against liquidity or

trading range hypothesis. Our findings are consistent

with (Gupta & Gupta, 2007) who find no positive effect

of stock split announcement but report positive return

on ex-split date. Our results also lend support to

results of (Chakraborty, 2012) who reports positive

abnormal returns on ex-split date which reverses soon

after. The only difference is that she attributed

presence of such positive abnormal returns to small

and illiquid stocks in the sample; this study concludes

that it is true for stock split announcement in large and

liquid stocks as well. The possible explanation is that

on the ex-date of stock split, many small wealth-

constrained/deprived investors put demand for stocks

that have just undergone split and therefore have

become more affordable in terms of unit price and that

leads to positive abnormal returns only to realize later

that there is no positive impact of stock split on future

prospects of the company and hence, there is no way

to earn superior return by buying stocks that have just

gone ex-split.

• Amihud, Y., & Mendelson, H. (1986). Asset pricing and the bid-ask spread. Journal of Financial Economics, 17,

223-249.

• Arbel, A., & Strebel, P. J. (1983, Winter). Pay Attention to Neglected Firms. Journal of Portfolio Management, 9,

37-42.

• Arbel, A., & Swanson, G. (1993). The role of information in stock split announcement effects. Quarterly Journal

of Business and Economics, 32(2), 14-25.

• Asquith, P. K., Healy, P., & Palepu. (1989). Earnings and stock splits. The Accounting Review, 64, 387-403.

• Brown, S. J., & Warner, B. J. (1980). Measuring Security Price Performance. Journal of Financial Economics,

8(3), 205-258.

• Brown, S. J., & Warner, B. J. (1985). Using Daily Stock Returns: The Case of Event Studies. Journal of Financial

Economics, 14(1), 3-31.

• Chakraborty, M. (2012). The Equity Market around the Ex-Split Date: Evidence from India. Vikalpa, 37(1), 57-

• Conroy, R. M., Harris, R. S., & Benet, B. A. (1990, September). The effect of stock splits on bid-ask spreads.

Journal of Finance, 45(4), 1285-1295.

• Copeland, T. E. (1979). Liquidity changes following stock splits. Journal of Finance, 37, 115-142.

• Fama, E. F., Fisher, L., Jensen, M. C., & Roll, R. (1969). The Adjustment of Stock Prices to New Information.

International Economic Review, Vol. 10, 1-21.

• Ferris, S. P., Hwang, C.-Y., & Sarin, A. (1995). A microstructure Examination of Trading Activity Following Stock

Splits. Review of Quantitative Finance and Accounting, 14, 27-41.

• Grinblatt, M. S., Masulis, R. W., & Titman, S. (1984). The valuation effect of stock splits and stock dividends.

• Gupta, A. P., & Gupta, O. P. (2007, January). Market Reaction to Stock Market Splits: Evidence from India. The

ICFAI Journal of Applied Finance, 13(1), 6-12.

• Han, K. (1995). The effect of reverse splits on the liquidity of the stock. Journal of Financial and Quantitative

Analysis, 30(1), 159-169.

• Ikenberry, Rankine, & Stice. (1996). What do stock splits really signal? Journal of Financial and Quantitative

analysis, 31, 357-375.

• Joshipura, M. (2009). Price and Liquidity Effects of Stock Split: Empirical Evidence from Indian Stock Market.

Indian Journal of Finance, 3(10).

• Lakonishok, J., & Lev, B. (1987). Stock splits and stock dividends: Why, who and when. Journal of Finance, 913-

• McNichols, M., & Dravid, A. (1990). Stock Dividends, Stock Splits and Signaling. Journal of Finance, 45, 857-

• Mishra, A. K. (2007). The Market Reaction to Stock Splits-Evidence from India. International Journal of

Theoretical and Applied Finance, 10(2), 251-271.

References

138 139

• Patell, J. M. (1976). Corporate Forecast of Earnings per Share and Stock Price Behaviour. Journal of Accounting

Research, 14, 246-276.

• Ray, K. K. (2011, January). Market Reaction to Bonus Issues and Stock Splits in India: An Empirical Study. IUP

Journal of Applied Finance, 17(1), 56-69.

• Saens, R., & Sandoval, E. (2005). Measuring Security Price Performance Using Chilean Daily Stock Returns: The

Event Study Method. Cuadenos De Economia, 42, 307-328.

• Wulff, C. (2002, July). The Market Reaction to Stock Splits - Evidence from Germany. Schmalenbach Business

Review, 54, 270 – 297.

Muscarella, C. J., & Vetsuypens, M. R. (1996). Stock splits: Signaling or liquidity? The case of ADR solo splits.

Nehal Joshipura is an Assistant Professor in Finance at Durgadevi Saraf Institute of Management Studies,

Mumbai. She earned MBA in Finance from Bhavnagar University and MCA from Gujarat University. She is

currently pursuing Ph.D. on “Exploring risk anomaly in the Indian stock market: The test of market

efficiency.” She has nearly a decade long experience in industry and academia. She was with Alliance

University, Bangalore in her last assignment. She teaches courses in the area of portfolio management and

corporate finance. She has published a number of papers in refereed management journals. In addition, she

has presented papers at national and international conferences. Her research interests lie in market

efficiency and portfolio theory. She can be reached at [email protected]

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