Download Effect of Investor Sentiment on Market Response to Stock Splits

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Effect of Investor Sentiment on
Market Response to Stock Splits
김근수(경희대)
변진호(이화여대)
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Effect of Investor Sentiment on Market
Response to Stock Splits
Keun-Soo Kim
Kyung Hee University
Graduate School of Pan-Pacific International Studies
E-mail: [email protected]
Jinho Byun
Ewha Womans University
School of Business
E-mail: [email protected]
Feb. 2009
Preliminary Draft
This work was supported by FnGuide as the second research support project of Korea
Securities Association in 2008 and FnGuide provided research data.
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Abstract
This paper examines whether the investor sentiment affects the market response to stock split
announcement and the market response reverses over 12 subsequent months following stock
splits. By using stock splits from 1999 to 2006, the paper reports three major empirical results.
First, the market response to stock split announcement is positively related to the sentiment.
Second, the market response is more pronounced especially for small, young and high
volatile stocks, the valuations of which are highly subjective and difficult to arbitrage. Third,
we find negative long-term performance of splitting stocks, consistent with empirical
evidence of Byun and Jo (2007). Further, the long-term performance is negatively correlated
with the sentiment controlled for business cycle in the month prior to stock split
announcement and positively with firm size, implying that the initial market response
overreacts to stock split announcement, depending on firm size and the sentiment condition.
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I. Introduction
A large number of earlier event studies have assumed that stock price reaction to a
corporate news event is independent of investor sentiment based on the efficient market
hypothesis. This assumption, however, is challenged by recent behavioral finance studies.
They commonly imply that stocks tend to be overpriced (underpriced) during periods of high
(low) sentiment which leads to predictable low (high) subsequent returns (Baker and Wurgler
(2006, 2007), Kumar and Lee (2006), Franzzini and Lamont (2005), Baker and Stein (2004),
Lee, Shleifer, and Thaler (1991), Qui and Welch (2006), and Derrien (2005)).
In addition, Barber and Odean (2008) suggest the hypothesis that individual investors
are net buyers of attention grabbing stocks such as stocks in the news, stocks experiencing
high abnormal trading volume and stocks with extreme one day returns. Attention-driven
buying results from the difficulty that investors have searching the thousands of stocks they
can potentially buy. This paper refers the hypothesis to the attention bias hypothesis.
Combined with theoretic and empirical analysis of the investor sentiment, the attention-bias
hypothesis implies that investors may overreact (underreact) to corporate events in the hot
(cold) sentiment period.
This paper is motivated to test whether the sentiment affects market response to stock
split announcement. We select the stock split event for two reasons. First, stock split is a
relatively uncomplicated event. A stock split increases the number of outstanding equity
shares but has no effect on both the firm’s value and shareholders’ proportional ownership of
shares. As such, information implications probably can be gauged rather easily by investors.
For stock split event, there are only two dominant hypotheses such as trading range
hypothesis (Lakonishok and Lev(1987)) and signaling hypothesis (Grinblatt et al. (1984),
Asquith et al. (1989), and McNichols and Dravid(1990)). Given the stylized fact that the
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market positively responds to a sock split announcement, the announcement is commonly
believed to convey favorable private information for future firm’s value. As such, this paper
can mainly focus on whether the positive market response is mainly due to the true signal of
the future value or the sentiment combined with investor’s other behavioral bias.
Second, earlier studies about stock splits in Korean stock markets document that the
signaling hypothesis does not appear to explain positive market response to stock split
announcement (Nam (2000) and Byun (2003)). Further, Byun and Jo (2007) find that longterm performance of splitting firms’ stocks turns out to be negative, suggesting that the longterm poor performance may result from initial market overreaction to split announcement.
This finding is contrary to overseas empirical evidence. For example, it is obvious that there
is no evidence in U.S. market suggesting that the market initially overreact to stock split
(Ikenberry and Ramnath (2002) and Byun and Rozeff (2003)). As such, we reexamine stock
split phenomena and attempt to explain the empirical results of stock splits in Korean stock
markets from the view points of behavioral finance.
Following Baker and Wurgler (2006)’s principal components analysis, the paper
calculates monthly investor sentiment index of 1999 through 2007 by using 6 individual
sentiment proxies; monthly trading unbalance of individual investors (Kumar and Lee
(2006)), stock fund flows (Franzzini and Lamont(2005)), Customer Expectation Index for
business cycle (Qiu and Welch (2006)), relative equity issuance, and turnover ratio (Baker
and Wurgler (2006, 2007). This study uses the sentiment index without controlling business
cycle and the index controlled business cycle.
By using stock splits from 1999 to 2006, we report three major empirical results. First,
the market response to stock split announcement is positively related to the sentiment. Second,
the market response is more pronounced especially for small, young and high volatile stocks
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the valuations of which are highly subjective and difficult to arbitrage. Third, we find
negative long-term performance of splitting stocks, consistent with empirical evidence of
Byun and Jo (2007). Further, the long-term performance is negatively correlated with the
sentiment controlled for business cycle in the month prior to stock split announcement and
positively with firm size, implying that the initial market response overreacts to stock split
announcement, depending on firm size and the sentiment condition. However, long-term
performance of split stocks is not negatively correlated to the investor sentiment without
controlling business cycle at 10% significance level.
Mian and Sankaraguruswamy (2008) report that market-wide investor sentiment
influences the stock market response to earnings, dividend changes and stock split
announcement in the U.S. market by using Baker and Wurgler’s monthly sentiment index.
They, however, do not analyze either long-term performance of stock splits or the effect of
splitting firm’s characteristics on the market response.
Section II discusses the literature review of investor sentiment and stock splits and
explains our empirical hypotheses. Section III describes data of investor sentiment and stock
split. Section IV presents the main empirical tests and results. Section V concludes.
II. Literature Review and Hypothesis
Recent advances in behavioral financial literature, however, have yielded reliable
proxies of investor sentiment that appear to influence not only cross-sectional variation but
also time series variation in stock returns. They commonly imply that stocks tend to be
overpriced (underpriced) during periods of high (low) sentiment which leads to predictable
low (high) subsequent returns. In this chapter, we review several investor proxies and provide
existing evidence of that investor sentiment influences stock returns.
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Baker and Stein (2004) build a model where high liquidity is a symptom of the fact that
the stock market is dominated by irrational investors and hence overvalued. They find that
increases in liquidity, measured by share turnover, have predictive power for future CRSP
equal-weighted market returns. By using a data base of more than 1.85 million retail investor
transaction over 1991- 1996, Kumar and Lee (2006) show that these trades have common
directional component and systematic trading of retail investors explains comovements in
stock returns, suggesting that investor sentiment based on the direction of these retail trades
influence returns on stocks with high retail concentration. Frazzini and Lamont (2005) show
that high sentiment predicts low future returns at long horizon by using mutual fund flows as
a measure for individual investor sentiment for different stocks.
Baker and Wurgler (2000) document that the share of equity issues in total new equity
and debt issues is a strong predictor of U.S. stock market returns between 1928 and 1997. In
particular, firms issue relatively more equity than debt just before periods of low market
returns, suggesting that the relative issues of new equities including IPOs and SEOs will
forecast market returns. Qui and Welch (2006) find that the although consumers polled for the
University of Michigan Consumer Confidence Index are not asked directly for their views on
securities prices, changes in the consumer confidence index correlate highly with small stock
returns and the returns of firms held disproportionately by retail investors, supporting the
view that sentiment plays a role in financial markets. They provide evidence that consumer
confidence index is a better proxy of investor sentiment than the closed-end fund discount
suggested by Lee, Shleifer, and Thaler (1991).
In addition, the IPO market is often viewed as sensitive to sentiment, with high first day
returns on IPOs cited as a measure of investor enthusiasm (Ritter (1991)). Moreover, Derrien
(2005) shows that investor demand of IPOs tends to increase with investor sentiment and
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influence IPO’s initial returns. Baker and Wurgler (2004) use the dividend premium defined
as the difference of the average market-to-book ratios of dividend paying stocks and nondividend paying stocks as the sentiment proxy. Since dividend paying stocks resemble bonds
in that their predictable income stream represents a salient characteristic of safety, investors
prefer dividend payers to non-payers at the low investor sentiment. Thus, payers are priced at
premium in the low sentiment period, resulting in high dividend premium.
Barber and Odean (2008) provide the hypothesis that individual investors are net buyers
of attention grabbing stocks such as stocks in the news, stocks experiencing high abnormal
trading volume and stocks with extreme one day returns. Attention-driven buying results
from the difficulty that investors have searching the thousands of stocks they can potentially
buy. Consistent with their hypothesis, they find that individual investors are net buyers on
high volume days, following both extremely negative and extremely positive one day returns,
and when stocks are in the news. We refer this hypothesis to the attention-bias hypothesis.
In short, behavioral finance literature provides substantial evidence that sentiment
influences stock returns. Since investors are more likely to buy stocks than to sell them in the
hot sentiment period, they will buy more stocks that attract their attention if the attention-bias
hypothesis holds. Thus, we can expect the investor sentiment may substantially affect returns
on firms’ stocks at the time when they announce financial events such as stock splits,
dividend increase or decrease, earnings surprise, stock repurchase and so on.
To test our expectation, we select the stock split event for two reasons. First, stock split
is a relatively uncomplicated event. A stock split increases the number of outstanding equity
shares but has no effect on both the firm’s value and shareholders’ proportional ownership of
shares. As such, information implications probably can be gauged rather easily by investors.
For the simplicity of stock split event, there are only two dominant hypotheses such as
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trading range hypothesis (McNichols and Dravid (1990), and Lakonishok and Lev(1987)) and
signaling hypothesis (Grinblatt et al. (1984), Asquith et al. (1989), and McNichols and
Dravid(1990)). As pointed out by Ikenberry et al. (1996), these two hypotheses are not
mutually exclusive. If managers believe that there are benefits from share prices trading
within some range, but also perceive that is costly for prices to trade below some lower limit,
the decision to split will be made conditional on managers’ expectation about future
performance. Thus, stock splits can convey a signal of firms’ future performance in the
context of the trading range hypothesis. Ikenberry et al. refers this idea to the self-selection
hypothesis and they show evidence consistent with the hypothesis.
Second, although stock split announcement effect is positive in Korean stock markets
like in U.S. stock markets, the positive market response does not appear to be explained by
the signaling hypothesis or the self-selection hypothesis. Byun (2003) provides the empirical
results that stock split does not convey a favorable signal of splitting firms’ future earnings.
Further, Byun and Jo (2007) find that long-term performance of splitting firms’ stocks turns
out to be negative, suggesting that the long-term poor performance may result from initial
overreaction of the market to split announcement. Although long-term performance of stock
splits is controversial, it is obvious that there is no evidence in U.S. market suggesting that
the market initially overreact to stock split.1 As such, we reexamine stock split phenomena
and attempt to explain the empirical results of stock splits in Korea from the view points of
behavioral finance.
1
Ikenberry et al. (2002) report a drift of 9% in the year following stock split announcement by using stock split
data from 1988 to 1997. On the contrary, Byun and Rozeff (2003) find that there is no significant long-term
abnormal returns by using 12,747 stock splits from 1927 to 1996.
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Three hypotheses are proposed here from the analysis of the above literature review.
Hypothesis 1: The market response to stock splits will be positively related to the investor
sentiment in the month preceding the split announcement.
Hypothesis 1 is supported by two theoretic and empirical analyses as discussed above;
investor sentiment has an effect on stock returns, and such an effect will be stronger at the
time of stock split announcement if the attention bias hypothesis holds. By following Baker
and Wurgler(2006)’s principal components analysis, this paper will create the investor
sentiment index that will be discussed in chapter III.
Hypothesis 2: The market response will be stronger for small, young, high volatile, or low
profitable stocks in the hot sentiment period in the month preceding stock split announcement.
A body of theoretical and empirical research shows the impact of behavioral biases is
not uniform across all stocks and stronger for young, small, unprofitable, extreme growths, or
distressed stocks. Such stocks are more difficult to value and harder to arbitrage because of
high idiosyncratic risk and high transaction costs (Shleifer and Vishny (1997) and Baker and
Wurgler (2006)). Since the market interprets stock split announcement as a good signal, the
positive market response will be stronger for those stocks in the low investor sentiment
period.
Hypothesis 3: The post-split performance will be negatively correlated to the investor
sentiment in the month preceding stock split announcement and the post-split performance
will be poorer for small, young, high volatile, or low profitable stocks if the investor
sentiment is high.
The investor sentiment drives stock split announcement returns and the post-split
performance will be negatively related to the initial investment sentiment. This prediction is
supported by substantial evidence of earlier studies that beginning of period proxies for
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sentiment is negatively correlated with subsequent returns. (Baker and Wurgler (2000, 2006),
Frazzini and Lamont (2005)). According to the hypothesis 2, the split announcement return
will be stronger for small, young, high volatile, or low profitable stocks in the hot sentiment
period. If then, the post-split performance of those stocks will be poorer in the future. The
following two chapter will discuss how to test these hypotheses and show the empirical
results.
III. Data of Investor Sentiment and Stock Split
1. Sentiment Index
Prior work suggests a number of proxies to use as time-series conditioning variables.
Considering the limit of data availability, Baker and Wurgler (2006, 2007) form a composite
index of sentiment based on the common variation in six underlying proxies of sentiment: the
closed-end fund discount, NYSE share turnover, the number and average first-day returns on
IPOs, the equity share in new issues, and the dividend premium. We create sentiment index
by following their method. Only their three proxies such as share turnover, the equity share,
and the dividend premium are monthly available in case of Korea.2 The dividend premium
does not appear to be significant variable in Korea since dividend amounts that most of firms
pay are negligible. Therefore, four other proxies for sentiment are added to the list of
individual sentiment proxies.
First, we consider monthly trading unbalance of individual investors (BSI) that Kumar
and Lee (2006) use. BSI is defined as the net buying volume of retail investors divided by
their total trading volume. 3 They show that retail investors’ trading are systematically
2
IPOs were not issued in a number of months from 1999 to 2006 and closed-end fund were not listed as well.
3
, where Dt is the number of days in month t; VBijt (VSijt) is the currency-denomenated
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correlated and this systematic retail trading explains return comovements particularly for
stocks with high retail concentration such as small-cap, value, lower institutional ownership,
and lower-priced stocks. Their empirical results imply that when BSI becomes high, retail
investors tends to be optimistic. They suggest BSI as retail sentiment. We create monthly BSI
for all firms by aggregating individual investor’s daily trading from FnGuide.
Second, sock fund flows can be a sentiment proxy. Franzzini and Lamont(2005) argue
that fund flows of individual investors can be used as sentiment since they consider economic
prospect when they reallocate their funds between different type of funds4. Thus, we can
conjecture that when investors expect that economic prospect is promising, the stock fund
flows relatively increase. This paper calculates stock fund flow by using monthly Net Asset
Value (NAV) of Funds and their monthly returns from ZEROIN. Fund is defined as NAVt –
NAVt-1 (1+Rt) where Rt is monthly return on fund from t-1 to t. Since stock fund flow shows
positive trend over times, Fund Flow is detrended by the 2 year moving average.
Third, Customer Expectation Index for business cycle, from Korea National Statistic
Office, is added to the list of sentiment proxy. UBS/Gallop Index is made by directly asking
randomly- selected investor households how optimistic they are in stock markets, which may
be the best proxy for investor sentiment. According to Qiu and Welch (2006), the University
of Michigan Consumer Confidence Index is highly correlated with UGS/Gallop Index
although the Confidence Index is not directly made by consumers’ views on securities prices.
Like Michigan Consumer Confidence Index, Korea National Statistic Office monthly
provides Customer Expectation Index by surveying consumers’ expectation about business
buy (sell) volume for stock i on day j of month t.
By using mutual fund flows of individual investors from 1980 to 2003, Franzzini and Lamont show that
individual investors tend to reallocate their money to the funds whose future performance becomes poor. They
claim that individual investors indirectly buy more stocks of a specific company via inflows of mutual fund that
own the high proportion of the company’s stocks, resulting in overvaluation of these stocks. As a result, the
future performance of mutual fund which their money flows into becomes poor in the process of correcting
overvaluation of their stocks.
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conditions, their financial situation and consumption expenditure in 6 month. This paper uses
the natural log of Customer Expectation Index, referred to CEI for sentiment index.
Fourth, customer’s deposit for stock investment, from Korea National Statistic Office, is
used as a sentiment proxy variable. Since the customer’s deposit is regarded as temporary
deposit for buying stocks, practitioners or media frequently consider an increase in
customer’s deposits a signal of investors’ optimism about stock markets. We define CD as
current month’s customer deposits minus the 2 year moving average of customer deposits,
divided by current month’s customer deposits. Positive CD may indicate that the market
becomes more optimistic.
Following Baker and Wurgler (2006, 2007), we define TURN as the natural log of
turnover ratio in Korea Stock Exchange and SR as the amount of gross equity issuance
divided by the amount of gross equity issuance plus gross bond issuance using data from
Korea National Statistic Office.5
As Baker and Wurgler (2006) point out, these six proxies are likely to include sentiment
components as well as idiosyncratic, non-sentiment-related components. We use principle
components analysis to isolate the common component. By following Baker and Wurgler’s
two-stage method, we define SENT as the first principal component of the correlation matrix
of six variables including BSIt-1, FUNDt-1, CEIt-1, CDt-1, TURNt-1, and SRt-1.
Two-stage principal component analysis leads to the following sentiment index.
SENTt = 0.219 BSIt-1 + 0.209 FUNDt-1 + 0.571 CEIt-1 + 0.583 CDt-1
+0.141 TURNt-1 +0.472 SRt-1
(1)
5
Baker and Stein (2004) suggest that turnover, or more generally liquidity, can serve as a sentiment index. In a
market with short-sales constraints, irrational investors participate, and thus add liquidity, only when they are
optimistic. Thus, high liquidity is a symptom of overvaluation. Baker and Wurgler (2000) find that high values
of the equity share predict low market returns.
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where the sentiment index is standardized with mean=0 and standard deviation=0. The first
principal component explains 40 percent of the sample variance.
The coefficient of each proxy variable in equation (1) is consistent with the expected
sign. For example, when the individual investors buy stocks more than sell in the preceding
month, Sentiment Index in the current month increases as well. Likewise, when fund flow,
customer’s deposit, turnover ratio, and equity issuance relatively increase in the preceding
month, Sentiment Index rises. Unlike Baker and Wurgler (2006)’s results, the share of equity
issuance in total equity and bond that is firm’s supply variable is not leaded by the other
proxies indicating investor behavior.6 The monthly sentiment index may be difficult in
capturing the expected timing of individual proxy variables related to the investor sentiment
since it may take several months for the firm to reflect the investor sentiment by increasing
equity supply.
Baker and Wurgler (2006) also consider the possibility that the principal components
analysis may not distinguish between a common sentiment component and a common
business cycle component. To avoid this possibility, they construct a second sentiment index
that explicitly removes business cycle variation from each of the six raw proxies prior to the
principal components analysis. Following their methodology, we construct the sentiment
index controlled for business cycle by regressing each of the six proxies on the six business
cycle related variables: growths of industrial production index, durable sales index, semidurables sales index, and non-durable sales index, service production index, and coincident
composite index for business cycle change (Korea National Statistic Office).
The residuals from these regressions may be cleaner sentiment proxies. We construct the
sentiment index by using these residuals that are orthogonal to business cycle variables. We
6
While investor behavior immediately reflects sentiment in stock markets, firm’s supply may reflect the
sentiment with time lags.
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refer this sentiment index to BSENT. The same procedure as before provides BSENT as
follows.
BSENTt = 0.303 RBSIt-1 + 0.118 RFUNDt-1 + 0.537 CEIt + 0.574 CDt-1
+0.061 TURNt-1 +0.522 SRt-1
(2)
where all independent variables indicate the residuals from regression of the 6 individual
proxies on the six business cycle variables. The first principal component explains about 39
percent of the sample variance of the orthogonalized variables. All residuals of the 6 variables
are positively associated with the sentiment index controlled for business cycle which is
consistent with expected signs.
Figure 1 shows the sentiment indices graphically. These two indices move similarly
although SENT changes smoothly. They were high in 1999 when IT bubble rose to the peak
and low in the late of 2000 when IT bubble burst. The sentiment level, however, is not
necessarily high when the stock price is high. For instance, BSENT reached the lowest level
in November of 2006, when KOSPI index was 1,295. Thereafter, KOSPI index sharply rose
up to higher than 2,000 at the end of October, 2007, suggesting that the sentiment index may
have predictability power of future stock returns. Figure 1 also shows that the sentiment does
not sharply change over short time and tends to persist at least over several months.
In Figure 2, SENT and BSENT are divided into four groups based on their level in
ascending order. We refer these sentiment quartiles to SENT4 and BSENT4. When SENT4=1
(4), the sentiment index is the lowest (highest). When SENT4 and BSEN4 are equal to 1, siz
month buy and hold returns are on average 18.48 percent, and 18.81 percent, respectively. On
the contrary, when SENT4 and BSENT4 are equal to 4, the subsequent six month buy and
hold returns are averaged to -4.69 percent and -3.47 percent, respectively. These results
suggest that the sentiment index may have predictability of future stock returns.
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2. Stock Splits
We manually collected stock split sample including the stock split company’s name,
code, announcement date, and split factor from Korea Exchange. The sample is formed by
identifying all KSE and KOSDAQ stocks from 1999 to 2006. We also require that the firm’s
daily return be reported for at least 10 trading days around the announcement date, resulting
in 608 stock split sample. FnGuide provided all firm’s daily returns, closing prices, and
equity market value (size). Size is calculated by the number of outstanding ordinary stocks
multiplied by the closing price at the end of each month. Financial data such as equity book
value 7 and operating earnings were downloaded from the data base of Korea Listed
Companies Association. The number of sample, however, varies depending on the availability
of data such as the firm’s size (n=551) and equity book value (n=519).
Table 1 displays the number of stock splits based on announcement year, the sentiment
index and split factor. Monthly sentiment quintile SENT4 (BSENT4) is formed on the basis
of SENT (BSENT) in the month prior to split announcement from January of 1999 to
December of 2006. More than 50 percent stock splits occur in the month after the sentiment
is the highest (SENT4=4, BSENT4=4). 8 This result suggests that a company tends to
conduct stock splits at the time of high investor sentiment. It, however, does not necessarily
imply that the company recognizes high sentiment time or uses it as a good opportunity of
stock split. The trading range hypothesis suggests that splits realign per-share prices to a
preferred price range (McNichols and Dravid(1990)). Since the need to realign share prices
7
Negative book value is treated as missing value since such a book to market ratio is not comparable.
8
The number of sample for BSENT4 is 38 less than that for SENT4 since BSENT is not available in January of
1999 for the lack of one business cycle variable
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usually stems from a presplit price runup (Laknoishok an Lev(1987)), the trading range
hypothesis links splits more to past performance than to future performance. Since the
sentiment index is positively correlated with stock price, the high sentiment comes with high
stock price. Concentration of stock splits on high sentiment time is consistent with the trading
range hypothesis as well.
73 percent of stock splits are ten-for-one stock splits in Korea as shown in <Table 1>,
while two-for-one stock splits are dominant in U.S.(Byun and Rozeff(2003)). There are 6
cases with more than 10-for-one stock splits. Two-for-one or five-for-one stock splits are
common in case of less than ten-for-one stock split in Korea.
IV. Market Reaction to Split Announcement and Sentiment
The market reaction to split announcements is examined by computing 11-day market
adjusted returns from five days before to two days after the split announcement. Marketadjusted returns are calculated by subtracting the 11 day holding period return on the valueweighted market portfolio from 11 day holding period return for the splitting firms. The
market-adjusted returns is referred to as MAR(-5,5).
1. Univariate Analysis
Table 2 reports the market reaction to the split announcements for all splitting firms by
the sentiment index quartile in the month preceding the split announcements. The mean
announcement return of 12.22 percent (t=7.90) for all splitting cases (608) indicates that the
market interprets split announcements as good news. The mean announcement return for 588
splitting cases (excluding data in January of 1999) is 12.70 percent (t=7.96).
Partitioning the sample by the sentiment index quintile shows that the market adjusted
returns significantly vary across the sentiment index quintile groups. When the split
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announcement belongs to SENT4=1 or 2, the market adjusted return of 5.66 percent (t=1.29)
and 1.89 percent (t=0.62) respectively is much less than the mean announcement return and
not statistically significant. The split announcement return at SENT4=4 is 18.72 percent
(t=8.09), much higher than the mean announcement return of 12.70 percent. The return
difference between the highest sentiment quintile group and the lowest sentiment group is
13.06 percent (t=2.63).
The market adjusted return by the sentiment quintile controlled for business cycle also
shows the similar result and consistently increases as BSENT4 increases. Thus, the market
response to the split announcement is only statistically significant when the sentiment is high.
In Table 3, the market reaction is reported for the splitting firms by firm size, book to
market ratio, post split quintile and split factors. The firm size and book to market quintiles
are formed in ascending order at the end of the month preceding stock split announcement.
Post split price is calculated by taking the closing price for the month preceding the split
announcement, and dividing the price by split factor within the same size quintile. The post
split price quintile is formed in ascending order as well.
The number of splitting firms with the largest size quintile is only 57, while that of the
other splitting firms is at least over 110. The small number of stock splits with large firm is
contrary to the evidence of Ikenberry et al. (1996) that large firm usually undertakes stock
splits in the U.S. market. The announcement return is 12.37 percent (t=3.24) for the smallest
size quintile, and 1.88 percent for the largest size quintile. The return difference of 10.49
percent (t=2.25) is consistent with evidence of Brennan and Copeland (1988), and Grinblatt
et al. (1984) that split announcement returns are higher for small firms. If split announcement
contains new information about the firm’s value, the announcement is more informative for
small firms since less information about small firms generally is available to investors.
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The negative relation between size and announcement return, however, can be explained
by the attention bias suggested by Barber and Odean (2006). They argue that individual
investors are net buyers of attention grabbing stocks, e.g., stocks in the news, stocks
experiencing high abnormal trading volume, and stocks with extreme one day returns. Such
an attention bias results from the fact that investors have time to weigh the merits of only a
limited number of stocks. We conjecture that the attention bias of the investors can be more
serious for small stocks since the investors usually do not have information even about their
names. As such, although stock splits do not convey any valuable information, individual
investors may buy more small firms’ stocks than large firms’ at split announcement event.
The buying pressure would be higher in the hot sentiment period, resulting in substantial
increase in the stock price.
Table 3 also reports the relation between stock split and book-to-market quintile. About
37 percent of splitting firms fall in low book-to-market quintile, which is consistent with the
finding of earlier studies that splitting firms’ usually experienced the stock price runup prior
to stock split (McNichols and Dravid (1990), Lakonishok and Lev (1987)). Lakonishok, et al.
(1994) and Haugen (1995) suggest book-to-market ratio as a measure of undervaluation, with
high book-to-market firms being more likely to be undervalued. As the signaling hypothesis
suggests, if a stock split is a signal of undervaluation, and if book-to-market is a good proxy
of the degree of undervaluation, the book-to-market quintile is expected to have positive
association with the market response to stock splits. However, the book-to-market quintile
does not appear to be a significant factor in determining the market response to split
announcement, while the empirical results of Ikenberry et al. (1996) reveals the negative
relation between the market response and book-to-market quintile, contrary to the expectation
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of the signaling hypothesis combined with the argument that book-to-market is a good proxy
of the degree of undervaluation.
Brennan and Hughes (1991) argue that splits resulting in low post-split share prices
provide more credible signals of management’s private information, suggesting that the
announcement returns should be negatively related to post-split share prices. Contrary to their
argument, the result of Table 3 reveals that the largest announcement return is observed for
the lowest post-split price firms, which is not consistent with the signaling hypothesis. More
than 50 percent of the sample falls in the lowest post-price quintile because high split factor
such as ten-for-one split is dominant in Korea.
Table 3 also reports the positive relation between the market response and the split factor.
The highest announcement return of 26.19 percent (t=1.08) is observed in the split factor
group that is higher than ten-for-one split. The announcement return for the high split factor
group, however, is not statistically significant and its number is only 6. The return difference
between the low split factor and middle split factor groups is 9.76 percent (t=3.55), indicating
that the market response to ten-for-one split announcement is higher than for two-for-one or
five-for one split announcement. McNichols and Dravid (1990) argue that the firm signals
their private information about future earnings by their choice of split factor. They find that
split factors are increasing in earnings forecast errors, implying that higher split factor signals
more promising private information about future earnings. The positive relation between the
market response and the split factor reported by Table 3 is consistent with this signaling
hypothesis. The positive relation, however, is also consistent with the attention bias
hypothesis since higher split factor can attract more attention of investors.
2. Multivariate Analysis
184
The market response is regressed on those variables mentioned in the univariate analysis
to control their interaction in Table 4. Sentiment index and split factor are positively related
to the market response, and firm size is negatively related to the market response. The
positive relation between investor sentiment (SENT4 or BSENT4) and the market response
(MAR(-5,5)) is consistent with the first hypothesis that the market response to stock splits
will be positively related to the investor sentiment.
All these results are consistent with the univariate evidence. Book-to-market does not
appear to contribute to explaining the split announcement returns, which is also consistent
with the result of the univariate analysis. The positive relation between the post-split price
and the announcement return is opposite to not only the evidence of Ikenberry et al. (1996)
but also the signal related argument suggested by Brennan and Hughes (1991). Since high
split factor such as ten-for-one split is common in Korea, the market may not interpret low
post split as favorable private information. It, however, is not clear why there is positive
correlation between the post split share price and announcement return.
Behavioral finance literature documents that the impact of behavioral biases is not
uniform across all stocks. Small, young, volatile, unprofitable, extreme growth, and nondividend paying stocks are more likely to vulnerable to the sentiment since they are more
difficult to hedge and to arbitrage (See, for example, Shleifer and Vishny (1997) and Baker
and Wurgler (2006)). Thus, we expect the second hypothesis that the market response would
be greater for these stocks when the sentiment is high.
To test the validity of this hypothesis, we include additional variables such as age, stock
return volatility and low profits of the firms in the sample. Age quintile is determined in
ascending order on the basis of the trading days from the firm’s first appearance on Korea
Stock Exchange or KOSDAQ Exchange to the last trading days of the month prior to split
185
announcement. When firm’s age belongs to age quintile 1 or 2, it is classified as the young
firm. Stock return volatility is measured as the standard deviation of monthly returns over the
preceding 12 months. Firms are referred to high volatile firm when their stock return
volatility belongs to the first and second highest volatility group. Profits are measured as
ROE in terms of operating earnings divided by total equity book value. Low profitable firm is
indentified when its ROE belongs to the first or second lowest ROE quintile. Table 5 includes
four additional dummy variables such as Small Size-D, Young-D, High Vol.-D, and Low
Profit-D. These four dummies are equal to one when two conditions are satisfied. First,
SENT4 (BSENT4) should be equal to 4. Second, Young-D, High Vol.-D, and Low Profit-D
should be classified as young firms, high volatile firms, and low profitable firms as discussed
above, respectively. Similarly, Small Size-D is one when firms belong to SZ=1 or 2 with
SENT4 (BSENT4)=1. If both conditions are not satisfied, these dummies are zero.
Table 5 shows that the effect of sentiment on the announcement returns depends on the
firm’s characteristics such as size, age and return volatility. In R1 of Table 5, the coefficient
of Small Size-D is 12.69 percent (t=2.44). The market response to stock split announcement
is stronger for small firms in the hot sentiment period. The coefficient of SZ becomes not
statistically significant when Small Size-D is added to regression equation. This result
implies that the effect of firm size on the market response is statistically significant only in
the hot sentiment period.
Young-D and High Vol.-D have significant effects on the market response, indicating
that the effect of the sentiment on announcement returns is stronger for both young firms and
high volatile firms particularly in the hot sentiment. Further, the investor sentiment does not
significantly affect the market response when Young-D is added to the equation in R2 of
Table 5. This result suggests that the sentiment effect concentrate on young firms. The market
186
response, however, is not significantly higher for low profitable firms in the hot sentiment.
Thus, except low profitable stocks, the results of Table 5 are consistent with our second
hypothesis. All these results are same regardless of whether the sentiment is measured as
SENT4 or BSENT4. The coefficients of the other variables are similar to those of Table 4
Since the signaling hypothesis does not claim that stock split announcement is more
positively informative for small stocks in the hot sentiment period than in the other sentiment
period, the empirical results of Table 5 is hardly reconciled with the signaling hypothesis.
Except Low Profit-D, the significant effects of the other dummies on the announcement
returns are consistent with evidence of Baker and Wurgler (2006) that a wave of investor
sentiment has larger effect on securities whose valuation are highly subjective and difficult to
hedge. Our findings are also consistent with the attention bias hypothesis of Barber and
Odean (2008) that investors tend to buy the attention grabbing stocks that have event
announcement.
As such, we conjecture that the market response may overreact to stock split
announcement particularly in the hot investor sentiment period. Further, stock split
announcement may not contain any favorable signal of firm’s value but investors may
misinterpret the announcement as favorable signal particularly in the hot sentiment period. To
test our conjecture, we undertake long-term performance test for splitting stocks in regard to
the sentiment index.
3. Analysis on Long-term Performance
The long-term performance subsequent to the split announcements is calculated in a manner
similar to the buy-and-hold used by Ikenberry, et al. (1996). We calculate the buy-and-hold return
beginning in the month following the split announcement. If a splitting firm stops trading for some
reason, the investment in the splitting firm is maintained through the month trading ceases. The
187
remaining months are assumed to be invested to the reference portfolios. The reference portfolio is
formed using size and book-to-market benchmarks. Benchmarks are determined by sorting all eligible
KRX firms each month into size quintiles on the basis of market value. Within each size quintile,
firms are further sorted into book-to-market quintiles. Thus, for each month in the sample period, all
eligible KRX firms are sorted into one of 25 possible size and book-to-market portfolios. Each split
firm is matched with one of the 25 reference portfolios formed in the month of split announcement.
The overall reference portfolio return is computed assuming both equal-weighting and valueweighting and monthly rebalancing among the portfolios matched with each of the split firms. Excess
performance (EBHAR or VBHAR) is defined as the difference between the annual return to split
firms and the concurrent mean return to the matched size and book-to-market reference portfolios.
BHAR(1,12) is classified by the sentiment quintiles that are formed in the month preceding split
declaration.
Table 6 reports the long-term abnormal returns for the stock split firms by the sentiment
index quartile in the month preceding the split announcements. Panel A reports the both
equally-weighted buy-and-hold abnormal returns (EBHARs) and value-weighted buy-andhold abnormal returns (VBHARs) for 12 months after the announcement. In order to
minimize the effect of extreme values, we winsorize the data at 1% in Panel B. The mean
long-term abnormal return of -13.92% (t=-2.46) to -20.82% (t=-3.98) for all splitting cases
indicate that the market overreacts to split announcements. These results are consistent with
previous studies of Byun and Jo (2007) which show the negative long-term performance of
stock splits in Korean stock market. The mean buy-and-hold abnormal returns for
winsorizing sample also show the similar results which are -7.79% (t=-2.41) to -18.81% (t=4.66).
Partitioning the sample by the sentiment index quintile shows that the long-term BHARs
significantly vary across the sentiment index quintile groups. When the split announcement
188
belongs to SENT4=1 or 4, the mean of BHARs is much less than the mean BHARs of
SENT4=2 or 3. The long-term performance of stock splits is worse in low or high investor
sentiment than in the middle sentiment. However, the statistical reliability of the long-term
performance in low investor sentiment consistently is lower than that in high investor
sentiment. Particularly in case of the sentiment quartile controlled for business cycle,
EBHAR(1,12) is statistically significant only at 10%, while all BHAR (1,12) are significant
at 1%. Nevertheless, we fail to find strong negative correlation between the sentiment and
long-term performance in univariate analysis since poor long-term performance is observed
in low investor sentiment.
The result of cross sectional regression of long-term performance is presented in Table 7.
We test with both equally-weighted and value-weighted returns as dependent variables. The
regression result shows that size quintile is positively related to the long-term performance of
splits. It means as size increases the long-term BHAR also increases, reversing initial
negative relation between firm size and the market response. The sentiment quintile
controlled for business cycle also shows significant negative relation to the performance.
Thus, the negative relation of the post-split performance with the sentiment controlled for
business cycle is consistent with our third hypothesis discussed in Section II.
However, the
third hypothesis is not valid for investor sentiment without controlling business cycle.
In Table 8, we regress long-term BHAR (EBHARs) to BSENT4, SZ, and four dummy
variables of firm characteristics. Dummy variables are Small Size-D (size quintile=1 and 2,
others=0), Young-D (tradingdays=1 and 2, others=0), High Vol.-D (stand5=1 and 2, others=0),
and Low Profit-D (ROE5=1 and 2, others=0). When we include small size dummy, the
individual effect of sentiment index is disappeared. Interaction of small size and sentiment
becomes the most significant factor in determining long-term performance of split stocks.
189
This result implies that the market tends to overreact to split announcement for small stocks
in the hot sentiment (Table 5) by dramatic increases in return announcement and market
overreaction be reversed over the following 12 months. Unlike Table 5, however, long-term
under-performance of split firms seems to be subject to small firm effect without its
interaction of investor sentiment.
In short, long-term under-performance of split firms over 12 months following stock
split announcement is consistent with the previous empirical results documented by Byun and
Jo (2007). In addition, long-term performance of split firms is negatively related to the
investor sentiment controlled for business cycle and stronger for small firms combined with
investor sentiment, consistent with our third hypothesis. However, investment sentiment
without controlling business cycle, young or volatile firms do not appear to explain long-term
performance of split stocks. According to Byun and Jo (2007) who use stock split data from
1998 to 2002, 12 month buy and hold returns do not appear to be significantly negative while
24 month returns to be significantly negative. As such, one possible explanation about such
weak results except small firms may be due to shorter holding period months.
V. Conclusion
This paper examines whether the investor sentiment affects the market response to stock
split announcement and the market response reverses over 12 subsequent months following
stock splits. By using stock splits from 1999 to 2006, the paper reports three major empirical
results. First, the market response to stock split announcement is positively related to the
sentiment. Second, the market response is more pronounced especially for small, young and
high volatile stocks the valuations of which are highly subjective and difficult to arbitrage.
Third, we find negative long-term performance of splitting stocks, consistent with empirical
190
evidence of Byun and Jo (2007). Further, the long-term performance is negatively correlated
with the sentiment controlled for business cycle in the month prior to stock split
announcement and positively with firm size, implying that the initial market response
overreacts to stock split announcement, depending on firm size and the sentiment condition.
However, long-term performance of split stocks is not negatively correlated to the investor
sentiment without controlling business cycle at 10% significance level and not stronger for
young, high volatile stocks.
Our empirical studies contribute to the recent financial research in the following ways.
Our evidence improves understanding of stock market response to stock split announcement
by showing that investor sentiment tends to influence the market response in Koreas stock
markets. In addition, poor long-term performance related to the sentiment generally suggests
that long-term post-announcement return as well as short-term announcement return may
depend on investor sentiment condition at the time corporate events announcement. For
instance, when corporate events convey favorable signs to the market in hot (low) sentiment
period, the market response may overreact (underreact) to the events, long-term performance
may reverse (continue in the same direction of market response) later.
In conclusion, our evidence shows that investor sentiment plays an important role in
determining stock prices in case of stock split event. It, however, is still questionable whether
the sentiment can affect short-term announcement return and long-term post- announcement
return in case of other events. Further, a large body of behavioral finance literature documents
that retail or individual investors are more vulnerable to the sentiment but we still do not
know how they behave in high or low sentiment period especially at the time of corporate
announcement events. Future research will be needed to better understand how investor
sentiment can change their trading behaviors.
191
Figure 1. Monthly Sentiment Index: 1999 to 2007
SENT is the sentiment index that is the first principal component of levels and changes in six
measures of sentiment: trading unbalance of individual investors (BSI), stock fund flow (FUND),
customer expectation index for business cycle (CEI), customer’s deposit for stock investment (CD),
log of turnover ratio (TURN), and the equity share in new issues (SR). BSENT is another sentiment
index that is the first principal component of levels and changes in the 6 residuals generated by
regression of the above 6 measures of sentiment on growths of industrial production index, durable
sales index, semi-durables sales index, and non-durable sales index, service production index, and
coincident composite index for business cycle change (Korea National Statistic Office).
SENTt = 0.219 BSIt-1 + 0.209 FUNDt-1 + 0.571 CEIt-1 + 0.583 CDt-1 +0.141 TURNt-1 +0.472 SRt-1
BSENTt = 0.303 RBSIt-1 + 0.118 RFUNDt-1 + 0.537 RCEIt + 0.574 RCDt-1 +0.061 RTURNt-1 +0.522 RSRt-1
ZKHUHWLQGLFDWHVPRQWK
192
Figure 2. Predictability of the sentiment index quintile on KOSPI Returns
Monthly sentiment quintile SENT4 (BSENT4) is formed on the basis of monthly sentiment
index in the ascending order from January of 1999 to December of 2006. For instance, the
lowest quartile is SENT4(BSENT4)= 1 and the highest quartile is SENT4(BSENT$)= 4. Likewise, P6
is six month buy and hold return quintile in ascending order on the basis of six month buy and hold
return from t-5 to t=0 where t indicates month. Six month buy and hold returns on KOSPI from t=1 to
t=6 are calculated.
193
Table 1. Number of Stock Splits based on Year, Sentiment Index Quartile,
and Split Factor from 1999-2006
The table indicates the number of stock splits by announcement year, the sentiment index and split
factor. Monthly sentiment quintile SENT4 (BSENT4) is formed on the basis of SENT (BSENT) from
January of 1999 to December of 2006. More than 50 percent stock splits occur in the month after the
sentiment is the highest (SENT4=4, BSENT4=4).9 Spilt factor is divided into three groups. The first
group’s split factor is less than 10-for-1 stock split, and the second (third) group is equal to (higher
than) 10-for-1 stock splits.
Year
1999
2000
2001
2002
2003
2004
2005
2006
Total
SENT4
0
(0)
4
(0)
41
(16)
1
(1)
9
(12)
5
(14)
7
(22)
8
(10)
75
(75)
Sentiment Index Quartile
SENT4
SENT4
0
0
(0)
(8)
2
2
(3)
(5)
28
1
(32)
(19)
0
15
(16)
(22)
37
6
(30)
(6)
34
0
(25)
(0)
10
26
(2)
(19)
2
43
(7)
(30)
113
93
(115)
(109)
SENT4
142
(114)
108
(108)
0
(3)
77
(54)
0
(4)
0
(0)
0
(0)
0
(6)
327
(289)
<10
Split Factor
=10
>10
18
121
3
142
(122)
13
103
1
116
17
53
0
70
28
63
2
93
20
32
0
52
16
23
0
39
20
22
1
43
26
27
0
53
158
444
6
608
(588)
Total
Parenthesis indicates the number of BSENT4 covering Feb. 1999 through Dec. 2006.
The number of sample for BSENT4 is 38 less than that for SENT4 since BSENT is not available in January of
1999 for the lack of one business cycle variable
9
194
Table 2. Announcement Returns for Stock Splits by Sentiment Index Quartile: 1999 to 2006
The market reaction to split announcements is examined by computing 11-day market adjusted returns
from five days before to two days after the split announcement. Market-adjusted returns are calculated
by subtracting the 11 day holding period return on the value-weighted market portfolio from 11 day
holding period return for the splitting firms. The market-adjusted returns is referred to as MAR(-5,5).
MAR (-5,5) is classified by the sentiment quintiles that are formed in the month preceding split
declaration.
Sentiment Index
Quartile
SENT4=1
SENT4=2
Sentiment
SENT4=3
Index
SENT4=4
Total
Sentiment BSENT4=1
BSENT4=2
Index
controlled BSENT4=3
Business BSENT4=4
Cycle
Total
N
MAR(-5,5)
75
113
93
327
608
75
115
109
289
588
5.66
1.89
7.19
18.72
12.22
1.82
5.58
6.79
20.59
12.70
*, **, *** indicate 10%, 5% and 1% statistical significance.
t-Stat.
1.29
0.62
2.78***
8.09***
7.90***
0.47
1.60
3.32***
8.00***
7.96***
195
Table 3. Announcement Returns for Stock Splits by Size, B/M, Post Split Quintile and Split Factor:
1999 to 2006
The market reaction to split announcements is examined by computing 11-day market adjusted returns
from five days before to two days after the split announcement. Market-adjusted returns are calculated
by subtracting the 11 day holding period return on the value-weighted market portfolio from 11 day
holding period return for the splitting firms. The market-adjusted returns is referred to as MAR(-5,5).
MAR(-5,5) of the splitting stocks are classified by firm size, book to market ratio, post split
quintile and split factors. The firm size and book to market quintile are formed in ascending
order at the end of the month preceding stock split announcement. Post split price is
calculated by taking the closing price for the month preceding the split announcement, and
dividing the price by split factor within the same size quintile. Post split price quintile is
formed in an ascending order within the same quintile. Spilt factor is divided into three groups.
The first group’s split factor is less than 10-for-1 stock split, and the second (third) group is equal to
(higher than) 10-for-1 stock splits.
By
Size
Quintile
(551)
Book to
Market
Quintile
(519)
Post Split
Quintile
(551)
Split
Factor
(608)
SubGroup
SZ=1
SZ=2
SZ=3
SZ=4
SZ=5
BM=1
BM=2
BM=3
BM=4
BM=5
PP=1
PP=2
PP=3
PP=4
PP=5
SF=1
SF=2
SF=3
N
MAR(-5,5)
112
121
130
131
57
194
119
91
73
42
300
120
72
40
19
158
444
6
12.37
15.79
12.60
9.27
1.88
11.60
11.89
8.15
11.92
12.82
8.18
14.99
9.00
19.00
30.95
4.88
14.64
26.19
*, **, *** indicate 10%, 5% and 1% statistical significance.
t-Stat.
3.24***
3.90***
3.63***
3.43***
0.71
4.31***
3.18***
3.02***
3.58***
1.93*
4.20***
4.22***
2.30**
2.40**
2.25**
2.55**
7.43***
1.08
196
Table 4.
Cross Sectional Regressions of the Announcement Reaction to Stock Split: 1999 to 2006
This table reveals regression coefficients from regressing the announcement return on sentiment
quartile(SENT4 and BSENT4), size quintile, book-to-market quintile, post-split quintile, and split
factor. The market reaction to split announcements is examined by computing 11-day market adjusted
returns from five days before to two days after the split announcement. Market-adjusted returns are
calculated by subtracting the 11 day holding period return on the value-weighted market portfolio
from 11 day holding period return for the splitting firms. The market-adjusted returns is referred to as
MAR(-5,5). The firm size (SZ) and book to market quintile (BM) are formed in ascending
order at the end of the month preceding stock split announcement. Post split price is
calculated by taking the closing price for the month preceding the split announcement, and
dividing the price by split factor within the same size quintile. Post split price quintile (PP) is
formed in an ascending order within the same quintile. Spilt factor is divided into three groups
(SF). The first group’s split factor is less than 10-for-1 stock split, and the second (third) group is
equal to (higher than) 10-for-1 stock splits.
Mar (-5,5)i = Į + ȕ1 SENT4 (or BSENT4) + ȕ2 SZi + ȕ3 BMi + ȕ4 PPi + ȕ5 SFi + İi
splitting stock.
Į
Intercept
SENT4
R1
-13.14
(-1.29)
4.64
(3.18)***
R2
-18.96
(-2.32)**
5.16
(3.57)***
BSENT4
SZ
ȕj
BM
PP
SF
2
Adj-R
N
-4.10
(-3.15)***
0.35
(0.27)
3.57
(2.50)**
8.13
(2.36)**
4.70%
519
-3.72
(-2.97)***
5.15
(3.57)***
8.83
(2.55)**
5.59%
551
R3
-12.55
(-1.22)
R4
-18.75
(-2.28)**
4.92
(3.28)***
-3.90
(-2.86)***
0.31
(0.23)
3.06
(2.06)**
7.93
(2.22)**
4.63%
499
5.57
(3.74)***
-3.52
(-2.68)***
Parenthesis indicates t-value. *, **, *** indicate 10%, 5% and 1% statistical significance.
where i indicates
4.64
(3.11)***
8.58
(2.40)**
5.60%
531
197
Table 5.
Cross Sectional Regressions of the Announcement Reaction to Stock Split in regard to
small, young, volatile, or low profitable firms in the hot sentiment: 1999 to 2006
This table reveals regression coefficients from regressing the announcement return on sentiment
quartile(SENT4 and BSENT4), size quintile, post-split quintile, and split factor, and four additional
dummy variables. All variables except four dummies are same in Table 4. Four dummies are Small
Size-D, Young-D, High Vol.-D, and Low Profit-D. They are equal to one when two conditions are
satisfied. First, BSENT4 should be equal to 4. Second, splitting firms should be classified as small
firms, young firms, high volatile firms, and low profitable firms defined as below. Small firm is
identified when they belong to the first or second smallest size quintile. Young firms are identified
when their trading days up to the last trading day of the month prior to split announcement belong to
the first and second shortest trading day quintile. High volatile firms are firms whose monthly stock
return standard deviation, over the preceding 12 months, belong to the first or second highest standard
deviation quintile. When the firms’ ROE, defined as operating earnings divided by equity book value,
belong to the first or second lowest ROE quintile, they are called as low profitable firms. If these two
conditions are not satisfied, all these dummies are zero.
Mar (-5,5)i = Į + ȕ1 SENT4 (or BSENT4) + ȕ2 SZi + ȕ3 BMi + ȕ4 PPi + ȕ5 SFi + ȕ6 Dummy + İi
where i indicates splitting stock and Dummy is Small Size-D, Young-D, High Vol.-D or Low Profit-D.
Į
Intercept
BSENT4
SZ
PP
SF
ȕj
Small
Size-D
YoungD
High
Vol.-D
Low
Profit-D
Adj-R2
N
R1
-20.94
(-2.55)**
3.24
(1.90)*
-1.47
(-0.98)
4.86
(3.27)***
8.94
(2.51)**
15.06
(2.76)***
R2
-8.20
(-0.92)
0.72
(0.33)
-3.48
(-2.67)***
4.35
(2.93)***
7.63
(2.14)**
R3
-16.14
(-1.95)*
3.97
(2.41)**
-3/49
(-2.66)***
4.88
(3.27)***
8.40
(2.35)**
R4
-16.81
(-2.02)**
4.57
(2.80)***
-3.51
(-2.67)***
4.72
(3.17)***
8.38
(2.34)**
14.79
(3.0)***
10.12
(2.24)**
6.78%
531
7.00%
531
6.31%
531
Parenthesis indicates t-value. *, **, *** indicate 10%, 5% and 1% statistical significance.
6.84
(1.49)
5.81%
531
198
Table 6.
Long-term Abnormal Returns (12 month) for Stock Splits
by Sentiment Index Quartile: 1999 to 2006
The long-term performance subsequent to the split announcements is calculated in a manner similar to
the buy-and-hold used by Ikenberry, et al. (1996). We calculate the buy-and-hold return beginning in
the month following the split announcement. If a splitting firm stops trading for some reason, the
investment in the splitting firm is maintained through the month trading ceases. The remaining
months are assumed to be invested to the matching portfolios. The reference portfolio is formed using
size and book-to-market benchmarks. Benchmarks are determined by sorting all eligible KRX firms
each month into size quintiles on the basis of market value. Within each size quintile, firms are further
sorted into book-to-market quintiles. Thus, for each month in the sample period, all eligible KRX
firms are sorted into one of 25 possible size and book-to-market portfolios. Each split firm is matched
with one of the 25 reference portfolios formed in the month of split announcement. The overall
reference portfolio return is computed assuming both equal-weighting and value-weighting and
monthly rebalancing among the portfolios matched with each of the split firms. Excess performance
(EBHAR or VBHAR) is defined as the difference between the annual return to split firms and the
concurrent mean return to the matched size and book-to-market reference portfolios. BHAR(1,12) is
classified by the sentiment quintiles that are formed in the month preceding split declaration. In order
to minimize the effect of extreme values, we also winsorize the data at 1%.
Sentiment Index
Quartile
SENT4=1
SENT4=2
Sentiment
Index
SENT4=3
SENT4=4
Total
Sentiment
Index
controlled
Business
Cycle
BSENT4
=1
BSENT4
=2
BSENT4
=3
BSENT4
=4
Total
EBHAR(1,12)
-21.56
(-1.93)*
-14.89
(-2.29)**
-5.61
(-0.85)
-21.88
(-2.30)**
-17.96
(-3.26)***
-20.42
(-1.84)*
-9.80
(-1.54)
15.88
(0.99)
-39.02
(-5.11)***
-20.82
(-3.98)***
VBHAR(1,12)
-21.56
(-1.93)*
-14.89
(-2.29)**
-5.61
(-0.85)
-23.47
(-3.64)***
-18.81
(-4.66)***
-11.04
(-1.24)
-8.24
(-1.36)
8.51
(0.74)
-15.29
(-3.93)***
-8.97
(-2.71)***
winsorize at 1%
EBHAR(1,12) VBHAR(1,12)
-18.44
-18.64
(-1.79)*
(-1.80)*
-13.90
-13.90
(-2.19)**
(-2.19)**
-4.59
-4.59
(-0.72)
(-0.72)
-17.23
-13.92
(-2.91)***
(-1.52)
-14.75
-12.99
(-3.95)***
(-2.46)**
-9.98
-17.00
(1.14)
(-1.65)
-7.88
-9.03
(-1.34)
(-1.47)
9.03
18.84
(0. 83)
(1.11)
-13.52
-31.88
(-3.46)***
(-4.78)***
-7.79
-16.16
(-2.41)**
(-3.26)***
Parenthesis indicates t-value. *, **, *** indicate 10%, 5% and 1% statistical significance.
199
Table 7.
Cross Sectional Regressions of the long-term BHAR to Stock Split: 1999 to 2006
This table reveals regression coefficients from regressing the long-term performance on sentiment
quartile(SENT4 and BSENT4), size quintile, post-split quintile, and split factor. The long-term
performance to split announcements is calculated by 12 month buy-and-hold abnormal returns after
the split announcement month. The reference portfolio is formed using benchmark returns
corresponding to each particular split sample. The overall reference portfolio return is computed
assuming both equal-weighting and value-weighting and monthly rebalancing among the portfolios
matched with each of the split firms. Excess performance (EBHAR or VBHAR) is defined as the
difference between the annual return to split firms and the concurrent mean return to the matched size
and book-to-market reference portfolios. The firm size (SZ) is formed in ascending order at the end of
the month preceding stock split announcement. Post split price is calculated by taking the closing
price of for the month preceding the split announcement, dividing the price within the same size
quintile. Post split price quintile (PP) is formed in an ascending order within the same quintile. Spilt
factor is divided into three groups (SF). The first group’s split factor is less than 10-for-1 stock split,
and the second (third) group is equal to (higher than) 10-for-1 stock splits.
BHAR (1, 12)i = Į + ȕ1 SENT4 (or BSENT4) + ȕ2 SZi + ȕ3 PPi + ȕ4 SFi + İi where i indicates splitting
stock.
ǹ
Intercept
SENT4
EBHAR
-0.426
(-2.31)
-0.030
(-0.83)
VBHAR
-0.366
(-2.12)
-0.013
(-0.38)
BSENT4
ȕj
SZ
PP
SF
Adj-R2
N
0.208
(6.64)***
0.004
(0.10)
-0.048
(-0.56)
7.89%
519
0.165
(5.64)***
-0.005
(-0.15)
-0.027
(-0.34)
5.42%
519
EBHAR
-0.430
(-2.49)
-0.081
(-2.33)**
0.235
(7.75)***
0.006
(0.17)
-0.021
(-0.25)
11.63%
499
Parenthesis indicates t-value. *, **, *** indicate 10%, 5% and 1% statistical significance.
VBHAR
-0.357
(-2.20)
-0.065
(-1.98)**
0.188
(6.55)***
-0.001
(-0.03)
0.004
(0.03)
8.26%
499
200
Table 8.
Cross Sectional Regressions of the long-term BHAR to Stock Split in regard to small,
young, volatile, or low profitable firms: 1999 to 2006
This table reveals regression coefficients from regressing the 12 month long-term buy-and-hold
abnormal return on sentiment quartile(SENT4 and BSENT4), size quintile, post-split quintile, and
split factor, and four additional dummy variables. All variables except four dummies are same in
Table 4. Four dummies are Small Size-D, Young-D, High Vol.-D, and Low Profit-D. They are equal to
one when two conditions are satisfied. First, BSENT4 should be equal to 4. Second, splitting firms
should be classified as small firms, young firms, high volatile firms, and low profitable firms defined
as below. Small firm is identified when they belong to the first or second smallest size quintile. Young
firms are identified when their trading days up to the last trading day of the month prior to split
announcement belong to the first and second shortest trading day quintile. High volatile firms are
firms whose monthly stock return standard deviation, over the preceding 12 months, belong to the
first or second highest standard deviation quintile. When the firms’ ROE, defined as operating
earnings divided by equity book value, belong to the first or second lowest ROE quintile, they are
called as low profitable firms. If these two conditions are not satisfied, all these dummies are zero.
EBHAR (1, 12)i = Į + ȕ1 BSENT4 + ȕ2 SZi + ȕ3 Dummy + İi where i indicates splitting stock and
Dummy is Small Size-D, Young-D, High Vol.-D or Low Profit-D.
ǹ
Intercept
BSENT4
SZ
Small
Size-D
ȕj
YoungD
High
Vol.-D
Low
Profit-D
Adj-R2
N
EBHAR
-0.368
(-4.10)
0.027
(0.71)
0.135
(4.04)***
-0.698
(-5.80)***
EBHAR
-0.444
(-4.83)
-0.102
(-2.69)***
0.239
(8.22)***
EBHAR
-0.443
(-4.82)
-0.095
(-2.65)***
0.236
(8.09)***
EBHAR
-0.462
(-5.02)
-0.075
(-2.03)**
0.238
(8.15)***
0.121
(1.19)
0.133
(1.12)
17.50%
499
12.14%
499
12.12%
499
Parenthesis indicates t-value. *, **, *** indicate 10%, 5% and 1% statistical significance.
-0.046
(-0.44)
11.93%
499
201
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