Why companies split their stocks;
Evidence on the Dutch Market
Roeland van Ark
0206105
Bachelor thesis
Supervisor: H.T. Wu
Date of Completion: 21-12-2007
University of Amsterdam
Faculty of Economics and Econometric
Finance
1
Table of Contents
1 Introduction
2
2 Reasons for Stock Splits
3
2.1 Signalling Hypothesis
3
2.2 Liquidity Hypothesis
5
3 Event Study
8
4 Analysis of the Results
13
5 Conclusion
16
References
17
2
1 Introduction
Stock splits are, simply speaking, just accounting events, which should have no direct effect
on the cash flows of the stock splitting company. However around stock splits, the stocks are
getting a lot of attention and a lot of effort is done to come to a split. This is why there seems
to be more to the event than just pure accounting. In this thesis I will be looking at what the
effect of stock split is at the announcement date on the Dutch market and what the underlying
reason is for the stock split. First I will be focusing on what seem to be the two most probable
reasons, namely that the management of the firm is trying to signal good news to the public or
that the management is trying to increase its stocks liquidity. Then I will investigate with an
event study whether the announcement of the stock split has any abnormal effect on the value
of the share price on the Dutch market. After this I will analyse the results of the event study
and link the result to the possible reasons for splitting the stock.
3
2 Reasons for stock splits
2.1 Signalling hypothesis
The first most likely reason for stock splitting is the so called signalling hypothesis. This
means that because of asymmetric information, the management of the firm knows more
about the value of the company and the future earnings and profits in combination with the
value of the stock than the public, the management knows that the value of the stocks is lower
than they are worth according to its expectations of future earnings and profits and is trying to
signal this to the public. Of course if the company signals good news to public, which the
public wasn’t aware of so far, the market will react favourably and the stock price will
increase. This good news can be about pre-split earnings and can be about future earnings and
profits, leading to potentially higher dividends. A condition for the hypothesis to be valid is
that there must be some costs of signalling, because otherwise every firm could be able to
increase its share price in the short run by splitting the stock, making in impossible for
investors to see if a company is actually giving good news, so making a stock split a
incredible signal and removing the positive effect on the share price. Overall you can say the
there must be some net benefit of a stock split present for firms that have good news net to
signal and no net benefit for other firms to let the signal be credible. This condition is
described first, then the earnings before and after the split will be discussed.
An important part of the signalling hypothesis is that the signal must be brought
credible to the public. This can only be done if it is costly for the company to give a false
signal to the public. The most obvious costs associated with stock splits are the administrative
costs. A lot of paperwork has to be done and a shareholders meeting has to be convened.
Though this is not a cost of false signalling, one could ask if a stock split would be worth the
trouble if management is trying to give a false signal, since there are easier and less costly
ways to mislead the shareholders. If this is revealed, it will damage management’s reputation
and credibility, which can lead to more difficulties when executing their job.
There are also costs for the shareholders. The first cost is though a small one occurs
when old shareholders who have a round number of lots, get because of the split factor, get in
return an odd number of lots, which are harder to sell. This cost is greater for small investors
than for large investors (Brennan and Copeland 1988). The most important cost for investors
is also described by these authors. They believe that splitting the stock has a long-run effect
on the costs that occur when buying and selling the stocks. If there were no information
4
asymmetries the company would choose the optimal number of shares that would minimize
costs. However there are asymmetries which results in a number of shares chosen by the
company that are higher than the optimal number of shares and above the cost minimizing
level. They find empirical evidence for the assumption of decreasing transaction costs when
the stock price increases, so a split causes the transactions costs to increases. Brennan and
Copeland argue that their research shows the management is able to signal news by changing
the number of shares outstanding, because, since there is a optimum number of shares, for the
companies that have good news the benefits of giving the good news to the public outweigh
the cost, which doesn’t hold for companies that do not have good news.
Research on differences between firms that split their stock and other firms is done by
Lakonishok and Lev (1987). They find some evidence of higher earnings growth and higher
dividends in comparison with control firms, not only in the years before the split but also in
the year after the split earnings growth is higher for splitting firms than for the firms in their
control group. But in the years there after the difference between the firms in earnings growth
is insignificant. Also the dividend growth is higher for splitting firms than for the firms in the
control group in a five year period after the split, though they slowly disappear from the first
to the fifth year. The authors believe that their evidence is consistent with the signalling
hypothesis, though they aren’t sure if the good news is only a guarantee about the past good
performance or also a signal about future profitability.
Asquith, Healy and Palepu (1989) believe that the announcement of a stock split
convinces the investors that the high earnings growth in the years prior to the stock split is not
transitory but will be permanent. They refer earlier research of Brook and Buckmaster (1980),
Beaver et al. (1980) and Freeman et al. (1982), who found evidence that high earnings
increases are most of the times followed by earnings decreases and that investors know this,
so investors do not expect the high earnings to last. Asquith, Healy and Palepu (1989) find
evidence for high earnings growth in the years before the firms split and that these high
earnings growth is not transitory, so the stock split could signal that the high past earnings
growth is permanent.
Huang, Liano and Pan (2006) find little evidence of the hypothesis that the market is
trying to signal good news about future profitability to the public. They examine the earnings
growth in the five years after the split. They find that earnings growth is the highest in the
year of announcement of the split. In the year hereafter the earnings growth declines with fifty
percent. They believe that this might imply that the management is too optimistic about future
5
earnings in the long run or that the management does not intend to signal higher future
profitability. Only for firms that pay dividends and split their stock 3 for 2 seems to have the
split seems to have a positive relation with future profitability, for the rest of the firms the
split is negatively related. The authors believe that for the firms that where there seems to be a
positive effect, the signal is not intended to signal higher future profitability but stabilized
future profitability. Overall they reject the signalling hypothesis.
Since there are costs involved with stock splits and there are costs for false signalling,
the condition for credibly giving a signal of good news to the public through stock splits is
satisfied. Based on the research of Asquith, Healy and Palepu (1989) and that of Huang,
Liano and Pan(2006) I believe that the earnings growth that the splitting firms had before split
does not revert, so it looks like the stock split could be a signal of the management that the
past growth will be permanent. However there is little evidence, looking at the earnings
growth after the stock split, if the management of stock splitting firms is also trying to signal
that the future profitability will be better than expected by the public. Only Lakonishok and
Lev (1987) do find evidence that dividends increases for stock splitting firms in comparison
with their control group, though the differences diminish after one year, so there is a
possibility that the dividend increase could be a lagged response to the high earnings growth
in the years before the split.
2.2 Liquidity hypothesis
The other most likely reason for stock splits is the liquidity hypothesis, also referred to as the
price correction hypothesis. The hypothesis is that when stocks are split the price of the stock
is lower and the liquidity increases, which makes the stock available to a broader range of
investors. For this hypothesis to be a valid reason for splitting the stock, liquidity must
actually increase after a split. Liquidity changes can be measured by changes in volume, so
the number and value of trades and in effective bid-ask spread. I will discuss the volume
measure first and then the spread.
Lakonishok and Lev (1987) do research about the volume of trades. For volume of
trade they use the monthly number of shares traded in comparison with the number of shares
outstanding. They argue that the volume preceding the split is abnormally high and that the
normal volume is about one year before the split, because splitting firms have increased
volume in comparison with a control group starting from one year before the split. They
6
compare this normal volume with the volume after the split and find no significant effect of
the stock split on the volume of trade.
More or less the same number for volume is used by Mohanty and Moon (2007). They
measure the volume of trade as the monthly number of shares traded relative to the numbers
of shares left outstanding at the same date. They compare the average volume of the twelve
month period before the split with the average volume of the twelve month period after the
split and find that the average volume significantly increases after the split. In their opinion
their findings imply an increase in liquidity.
According to Lamoureux and Poon (1987) the number of daily transactions and
number of shareholders increases after a split, but they believe that this is not the right way to
measure liquidity of the stocks. Instead they test whether market adjusted and split adjusted
daily trading volume decline after a stock split. Of the 215 stocks 87 have a significantly
decreasing volume, while twenty-seven have a significantly increasing volume. They also test
for forty-nine reverse splits and find that fifteen have a significantly increasing and two have a
significantly decreasing volume. They conclude from this test that the value of the shares
traded decreases after a split and that the increase in the number of transactions and number of
shareholders is not due an increase in liquidity but in spite of a decrease in liquidity. But they
believe that there is no indication for a price-effect because of the change in liquidity.
The effect of stock splits on bid-ask spread was first investigated by Copeland (1979).
He tests if there are higher bid-ask spreads after splits assuming that volume increases less
than proportionally. Even though he doesn’t has much data, which makes the evidence
limited, he concludes that there is a statistically significant increase in bid-ask spread as a
percentage of the bid price for one, twenty and forty days and that increase stays on for at
least two months (Copeland,1979).
Murray (1985) does research not only on the short-run, but also the long run effect of
stock splits on the bid-ask spread. He measures the spread on the first Friday after the split,
six months and one year after the split. In comparison to Copeland (1979) he believes that
there is no effect on the bid-ask spread in the short run or in the long run. He believes the
difference with Copeland could be, because Copeland’s results were biased because of the
lack of a control group.
Conroy, Harris and Benet (1990) also investigated what happens to the bid-ask quotes
when stocks are split. Instead of using representative bid-ask quotes like Copeland and
Murray they examine the bid-ask spread for NYSE-listed companies using high quality inside
quotes, which capture the lowest and the highest possible quotes an investors can get. They
7
compare these companies to a random sample of non-splitting NYSE-listed companies. They
find evidence of a decrease in absolute bid-ask spread, which is a direct effect of the lower
share price after the split. However the percentage bid ask spread, which is the effective
spread and the measure of liquidity, increases which causes the liquidity of the shares to drop.
Dennis (2003) also investigates the change in liquidity after stock splits measured in
volume and in spread. Only he divides the investors in large and small ones and measures
liquidity from those groups apart and together. He finds that for the whole group turnover is
unchanged and trading volume doesn’t increases. But the interesting part of his findings is
that after the division by trade size is done, the liquidity for small investors is increased,
because volumes are higher after than before the split for this group. He also finds increased
spread which decreases liquidity. These results make him conclude that the lower after split
share price helps small investors, but hurt large investors, who are not constrained by their
wealth and care about the increase in bid-ask spread.
There seems to be some mixed evidence that the number of trades and the amount of
shareholders increases after a stock split; Lakonishok and Lev (1987) find no significant
changes and Mohanty and Moon (2007) find an increase, though it is at least questionable if
the these numbers are a correct measure of changes in liquidity. I agree with Lamoureux and
Poon (1987) that a better measure is the change in value of the traded volume. If liquidity is
measured this way, it doesn’t seem to increase, but it actually decreases.
The evidence on the other measure of liquidity, effect bid-ask spread seems to be a bit
clearer. Conroy, Harris and Benet (1990) find as well as Copeland (1979) that the effective
spread increases in the short run, causing the liquidity to decrease, though Murray find no
evidence of this in the short run or the long run.
Only an interesting conclusion is made by Dennis (2003). He believes that small
investors do have an advantage of the split, concluded by the higher volume of trades done by
small investors, but large investors are worse of after a split, because of the increase in bidask spread.
8
3 Event study
I am doing an event study on stock splits. I will be looking at the effect of the announcement
of the stock split on the value of the stock. I have found 26 Dutch stocks that are split between
January 2001 and October 2007. The actual event window is the day of the announcement,
but I will also be looking at returns 30 days before and 30 days after the announcement, to see
if the return on the announcement is different because of the event or is different for another
reason in which case the return on the announcement day will be different from the estimated
return but not different from the return in the period before or after the announcement.
The abnormal return is the actual return minus the estimated return for that time period. For
modelling the normal return I will be using the market model, which assumes a stable linear
relation between the stock return and the market return. This model is
Rit = αi + β iRmt + εit
E[ε ] = 0
Var[εit ] = σεi 2
where Rit is the return of the stock, αi and β i are the parameters specific for the different
stocks, Rmt is the return of the market and εit is the abnormal return of the stock.
The parameters of this model will be estimated using the returns of the stocks of about
one year before the event window, more specifically 256 trading days. For the market return I
will be using the return of the Amsterdam Stock Exchange, the AEX. The α and the β of the
model will be calculated using ordinary least squares. By looking at the abnormal return on
the announcement day I can now look for every individual stock if the actual return on the
announcement is statistically different from the estimation period using a student’s
distribution.
Then I will look if the cumulative abnormal returns of the stock splitting firms together on
announcement day have a mean that is statistically different from zero. I will also look if the
mean is different from zero in the days before and after the announcement to see if the mean
on announcement day is different than on the other days to make sure the mean on
announcement day is different because of the event. The null hypothesis and the alternative
hypothesis are the following:
H 0 : εit = 0
H 1 : εit > 0
9
Following is table 11, which contains the results of the abnormal returns from each individual
stock, its standard deviation, the result of the t-statistic and the calculated parameters alpha
and beta for every stock. Out of the twenty-six stocks, nineteen have positive abnormal
returns on announcement day and only 7 have negative abnormal returns. These positive
abnormal returns are for two stocks statistically different from zero at a significance level of 5
%. The negative abnormal returns are not statistically different from zero at a 5 % level.
Table 1. The abnormal returns on the announcement day of a stock split
Stock
Imtech
Grontmij
Boskalis
Aalberts
TKH Group
OPG
Sligro
Smit
Usg People
SBM Offshore
Unilever
BAM group
Macintosh
Beter Bed
Ten Cate
DSM
Fugro
Eriks
Accel Group
Heineken
Sligro
Batenburg
Heineken
ING
Vd Moolen
Unit 4
Abnormal Return on
announcement day
3.58%
1.45%
-2.80%
1.67%
0.75%
0.64%
-1.25%
0.39%
0.33%
3.30%
0.25%
1.07%
2.20%
2.71%
-0.13%
0.88%
0.56%
-0.57%
1.48%
0.54%
-0.02%
2.00%
3.18%
-0.01%
-2.04%
0.51%
T-statistic
2.4656
0.9350
-1.3975
0.9602
0.3383
0.4550
-0.7264
0.2142
0.1471
2.3380
0.4013
0.5762
1.3100
1.4009
-0.0719
0.9701
0.4537
-0.5056
1.1319
0.3936
-0.0112
1.5192
1.5527
-0.0062
-0.8256
0.1160
Alpha
0.0014
0.0017
0.0012
0.0018
-0.0003
0.0010
0.0013
0.0005
0.0024
0.0009
-0.0003
0.0019
0.0032
0.0032
0.0015
0.0011
0.0015
0.0021
0.0019
-0.0003
0.0006
0.0005
0.0013
0.0018
0.0042
0.0030
Bèta
1.1110
0.6666
1.0572
1.0606
0.2898
0.4645
0.7117
0.6649
1.3030
0.9918
0.8313
0.7012
-0.0920
0.3887
0.4673
0.5984
0.2522
0.2597
0.1948
0.3780
0.1502
0.0378
0.4809
0.7570
1.1695
1.9174
Table 2 and 3 contain the combined average abnormal returns of the twenty-six stocks in
relation to the day of the event, the announcement day, where every stock has equally weight
in the portfolio. Table 2 contains the thirty days before the announcement and table 3 contains
the announcement day and the following thirty days. The average abnormal return is 0,80 %
on announcement day. In the thirty days prior to the event only one day has a higher average
1
The data I used to calculate the number in all tables and graph I collected from Thomson Datastream.
10
abnormal return and in the thirty days after the event not any day has a higher average
abnormal return. Also is the percentage of stock that have a positive abnormal return the
highest on announcement day, namely 76,92 %, which implies that the average abnormal
result is not caused by a couple outliers, but is wide spread through the group of stocks.
Further more are the t-statistic and the 95 % confidence interval of the mean included,
resulting in seven days where the mean of the average abnormal return statistically differs
from zero at a 5 % significance level. Conspicuously is that the positive average abnormal
returns that are different from zero are on the announcement day and on the fourth and fifth
day after the announcement.
Table 2. Average Abnormal Returns thirty days prior to the announcement day.
Average
% stock with
Abnormal larger return than
Event
Day
Returns
0
-30
-0.21%
42.31%
-29
0.31%
50.00%
-28
0.20%
65.38%
-27
0.17%
50.00%
-26
0.88%
57.69%
-25
-0.07%
42.31%
-24
-0.28%
42.31%
-23
-0.22%
46.15%
-22
0.36%
57.69%
-21
-0.22%
42.31%
-20
0.14%
50.00%
-19
-0.04%
38.46%
-18
-0.05%
42.31%
-17
-0.38%
38.46%
-16
0.26%
50.00%
-15
0.08%
34.62%
-14
0.29%
61.54%
-13
-0.16%
30.77%
-12
-0.62%
30.77%
-11
0.16%
46.15%
-10
0.04%
53.85%
-9
-0.24%
42.31%
-8
0.18%
57.69%
-7
0.10%
42.31%
-6
-0.01%
42.31%
-5
-0.53%
30.77%
-4
-0.01%
50.00%
-3
-0.16%
38.46%
-2
0.67%
50.00%
-1
-0.65%
38.46%
Tstatistic
-0.5622
1.0570
0.5169
0.4296
1.7438
-0.2823
-1.0347
-0.7030
1.3586
-0.7026
0.4860
-0.1836
-0.1833
-1.6458
0.6434
0.1579
0.9183
-0.4649
-2.5711
0.4540
0.1729
-0.7626
0.7860
0.4724
-0.0626
-2.2249
-0.0497
-0.4572
1.0613
-1.5006
95 % confidence
interval
Lower Upper
-0.0097
0.0055
-0.0029
0.0092
-0.0061
0.0101
-0.0063
0.0097
-0.0016
0.0192
-0.0059
0.0045
-0.0085
0.0028
-0.0087
0.0043
-0.0019
0.0090
-0.0085
0.0042
-0.0046
0.0075
-0.0046
0.0039
-0.0056
0.0047
-0.0087
0.0010
-0.0057
0.0110
-0.0090
0.0105
-0.0036
0.0093
-0.0085
0.0054
-0.0112
-0.0012
-0.0057
0.0090
-0.0042
0.0050
-0.0088
0.0041
-0.0028
0.0064
-0.0033
0.0052
-0.0049
0.0046
-0.0103
-0.0004
-0.0054
0.0051
-0.0087
0.0055
-0.0063
0.0198
-0.0155
0.0024
11
Table 3. The average abnormal returns on announcement day and on the thirty
days after the announcement.
Event
Day
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Average
Abnormal
Returns
0.80%
-0.12%
0.22%
0.35%
0.50%
0.77%
-0.05%
0.41%
-0.54%
-0.20%
-0.56%
0.22%
-0.18%
-0.27%
0.25%
-0.90%
0.66%
0.19%
0.55%
0.16%
-0.30%
-1.09%
0.31%
-0.40%
0.03%
0.27%
-0.59%
-0.44%
0.14%
-0.48%
-0.28%
% stock with larger
return than 0
76.92%
57.69%
42.31%
65.38%
73.08%
61.54%
42.31%
50.00%
30.77%
38.46%
50.00%
46.15%
50.00%
30.77%
46.15%
23.08%
46.15%
61.54%
50.00%
38.46%
42.31%
42.31%
46.15%
38.46%
50.00%
50.00%
34.62%
38.46%
38.46%
26.92%
34.62%
Tstatistic
2.6498
-0.2479
0.5657
1.1022
2.0656
2.2646
-0.1437
1.0440
-2.0171
-0.7945
-1.0710
0.5214
-0.4693
-0.5762
0.5139
-1.7017
0.9344
0.3190
1.6649
0.3988
-0.9566
-2.3258
0.7374
-1.0112
0.0845
0.6096
-2.2072
-1.7941
0.3464
-1.5404
-0.6933
95 % confidence interval
0.0018
0.0141
-0.0114
0.0089
-0.0057
0.0100
-0.0030
0.0099
0.0000
0.0099
0.0007
0.0146
-0.0071
0.0062
-0.0040
0.0122
-0.0110
0.0001
-0.0074
0.0033
-0.0163
0.0051
-0.0064
0.0108
-0.0097
0.0061
-0.0123
0.0069
-0.0075
0.0125
-0.0199
0.0019
-0.0080
0.0212
-0.0102
0.0139
-0.0013
0.0123
-0.0067
0.0099
-0.0094
0.0034
-0.0205
-0.0012
-0.0055
0.0117
-0.0120
0.0041
-0.0081
0.0088
-0.0065
0.0119
-0.0115
-0.0004
-0.0095
0.0007
-0.0071
0.0100
-0.0111
0.0016
-0.0110
0.0055
12
Graph 1 reflects the cumulative average abnormal returns around the stock split
announcements. It shows that the thirty day period before the announcement the cumulative
abnormal returns are positive, but these positive cumulative abnormal returns disappear
entirely in the 12 day period before the announcement and amount to almost zero on
announcement day. From the announcement day the cumulative average abnormal returns are
getting larger with a top on the seventh day after the announcement, though fading away
again in the twenty-three trading days following this top.
Graph 1. Cumulative Average Abnormal Returns around Stock Split Announcements
Cumulative Average Abnormal Returns around Stock Split Announcements
0,035
0,03
Cumulative Average Abnormal Returns
0,025
0,02
0,015
Series1
0,01
0,005
0
-30
-20
-10
0
10
-0,005
-0,01
Day Relative to Stock Split Announcement
20
30
13
4 Analysis of the Results
First I will some factors that have possibly influenced the outcome of the study and then I will
link the result of the event study to the possible reason for splitting the stock.
First point is that the selection of stocks is solely based on whether or not the stock
was splitting and not whether or not there was any other news released at the same time as the
announcement. Since the shareholders must agree with the split of a stock, there must be a
shareholders meeting first. Such a shareholders meeting rarely has only one agenda point. The
result of this is that the abnormal returns of the stock on announcement, either positive or
negative, could be influenced by other news, which could be affecting the stocks price, like
dividend increases or decreases or good or bad financial statements.
Another point is that the news of the stock split could already be incorporated in the
stock price, if the split is already known to the public. Since the agenda of the shareholders
meeting is public, the possibility of the stock split is known. The shareholders must get notice
of the agenda at least two week before the meeting, but it is common to announce the agenda
between two months and two weeks before the meeting, so this is quite different between the
stocks. In the thirty day-period before the announcement of the stock split, there seem to be
some positive cumulative abnormal return at first, but this is reverted in the twelve days
before the announcement. This implies that investors are selling the stock in this period before
the announcement. In my opinion this is caused by uncertainty, not in the announcement of
the stock split, but in the other news the meeting will might show. Since investors on average
do not like risks, they sell the stocks to be sure not to lose money.
Further is important to say that in most of the cases the stock splits follow a period of
positive returns of the stock. This is demonstrated by the positive alpha twenty-three of the
twenty-six stocks have. The effect on this is that the estimated returns are higher, lowering the
abnormal returns, since the alpha is subtracted from the actual return. The effect of the alpha
is that it lowers the average abnormal return on announcement day with 15,9 %. It is of course
not right say that the alpha should not be incorporated in the abnormal return, the point here is
that high average returns in the period before the stock split result in negatively biased
abnormal returns, though testing the alpha of every firm results that for no firm the alpha is
statistically different from zero.
Not only the parameter alpha of the model is biased, also the beta is biased. This beta
is partly based on variances in stock and market returns in the estimation window, in which
period the returns of the stock splitting companies are on average pretty high. Having a higher
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return in the estimation window could have an effect on the variance, which results in a small
biased beta, which can go be positively or negatively biased for every individual stock. On
average these biased betas will be more or less ruled out, so it will not be a problem for the
outcome of the event study. More important of the factor variance is that one could argue that
the announcement of a stock split not only increases the return but also the variance of the
return This would affect to result of the t-statistic in the way that if the variance is understated
the result of the t-statistic is overstated and the null hypothesis is rejected to easy, but since
the variance is probably already overstated in the estimation window, in comparison with
companies that do not split their stock, this error will be small.
Another factor that influences the outcome of the combined t-statistic of the twentysix stock is that I have given equal weight to every stock in the portfolio. One could argue that
stocks with higher prices or higher split factors should get a bigger weight in the portfolio or
that the size of the firms should be incorporated in the weight, but given unequal weight to
stock would become quite arbitrary, because giving firms extra weight based on some factors,
requires the these factors to be well researched. Otherwise the outcome of the study would be
biased because of the factors. Since I lack the research of these factors to make a good
decision about weighting the firms in a different manner, I chose to give every firm equal
weight, which is a fair way.
The results of the event study do imply that the announcement of a stock split have a short
term positive effect on its price. Although the null hypothesis can on the individual stocks
level only be rejected for two stocks, nineteen stocks have positive abnormal returns. Further
is the null hypothesis that the cumulative abnormal returns on announcement day for all
twenty-six stocks on average have a mean equal to zero rejected at a 5 % significance level.
The abnormal returns are found on the announcement day of the stock split, so the stocks are
not split yet and the par value and the number of stocks are not changed. This implies that
there can not be a liquidity effect, either positive or negative, yet, so the abnormal returns I
found on the announcement day can solely be attributed to the signalling hypothesis.
Evidence on the liquidity effects of stock splits is ambiguous for the total group of
investors. Although the effects of stock splits on liquidity are not yet of influence on the stock
price on the day of the announcement, an important implication of this ambiguity is that the
liquidity hypothesis can not be a reason for the management to split the stock, since liquidity
is suppose to increase for the liquidity hypothesis to be valid.
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Because there are only net benefits of splitting the stock for companies that have good news
and I found evidence for positive abnormal return on announcement day and in a period of 7
days after the split, a period where there can not be any liquidity effect, I believe that the
reason why companies split their stock is that the management is trying to give good news to
the public, which it wasn’t aware of so far, so I agree with the signalling hypothesis. Only it is
not clear whether the management is trying to signal that the past high earnings growth will
be permanent or that the management is also trying to signal good news about future higher
earning and dividends.
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5 Conclusion
Stock splitting is a widespread phenomenon throughout the economy, though it is not clear
why companies split their stock. The two most likely reasons are explained by the signalling
hypothesis and the liquidity hypothesis. The signalling hypothesis is that management of
companies use stock split to signal good news to the public. The liquidity hypothesis is that
the management is splitting the stock to increase the stock’s liquidity.
I have investigated the effect of the announcement of the stock split to find out which
hypothesis is correct. The results of the event study pointed to a positive effect of the
announcement on the price of the stock. Combining this evidence with the costs and benefits
of the stock split and research about the earnings before and after the stock split and the
liquidity effects, made me conclude that the reason why management is splitting the stock is
that they are trying to signal could news to the public. This good news consists of an
affirmation about past earnings growth and possibly about future earnings and dividends
growth.
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