The Ex-Distribution Trading Effects of Employee Shares
In the Chinese Stock Market
Cherry C. Chen
Faculty of Business Administration
The Chinese University of Hong Kong
Hong Kong SAR, China
I am grateful to Raymond So for helpful comments. All remaining errors, however, are my
Address correspondence to Cherry C. Chen, Faculty of Business Administration, The Chinese
University of Hong Kong, Shatin, N.T., Hong Kong SAR, China, Phone: +(852) 2609-7442,
Fax: +(852) 2603-5473, E-mail: email@example.com.
The Ex-Distribution Trading Effects of Employee Shares
In the Chinese Stock Market
In the Chinese stock market, employee shares are not tradable for a certain period
after initial issuance. When they become tradable, their distributions will have
impacts on the trading activity. In this study, trading volume around ex-distribution
day of employee shares is investigated. It is found that trading volume and turnover
ratio of most stocks increase greatly around the ex-distribution day. This increase is
more pronounced for the less actively traded stocks. However, negative abnormal
volume is also found around the ex-day. The difference in trading volume indicates
that there is not a general ex-day anomaly. Different economic explanations for these
results are discussed.
JEL classification: G14
Key words: Trade volume, employee shares, information and market efficiency,
China stock market
The Ex-Distribution Trading Effects of Employee Shares
In the Chinese Stock Market
The Chinese capital market grew rapidly shortly after the adoption of open
door policy in China. The development of a primary share market began informally in
the early 1980s. Initially, stocks were issued largely to employees, rather than to the
public, sometimes in lieu of bonus payments. Public stock issue began from 1984, but
share trade was not legalized until the formal recognition of the Shanghai and
Shenzhen Stock Exchange in 1990 and 1991. The policies for practicing the
shareholding system were set forth in 1992. Shares are now issued to a cross-section
of investors, and listed and traded on domestic and overseas exchanges. Yet, the issue
and trading of shares in China still has some special characteristics. Pursuant to
China’s socialist market economy policy, shares issued by limited firms are
segregated into different types. The shares are classified according to the identities of
investors. The proportion of shares held by private investors is typically small, and
therefore the extent to which shareholders can be expected to influence governance is
very limited. Using the stock market as a mechanism for merger or takeover threats is
rare, and thus government could retain a high degree of control. The classification by
investor identity also determines the marketability of the share. Some types of shares,
e.g., state shares, can never be publicly traded. Some shares, i.e., ordinary shares are
traded in totally segmented markets due to multiple share categories. The other kind
of shares, i.e., employee shares, is not tradable within certain period after their initial
The distribution of employee shares is another unique phenomenon in the
Chinese stock market. When the employee shares are distributed, they become part of
A-shares and can be transferred from specified employee shareholders to members of
the public. Both the share structure of the firm and number of its shares traded in
market change. This study focuses on the reactions of investors, especially those
employee shareholders, towards the distribution of employee shares. The purpose of
the paper is to test directly for the trading induced by the distribution of employee
shares by observing the trading activities around ex-distribution days.
Based on previous literature on trading volume (Karpoff, 1986; Lakonishok
and Vermaelen, 1986; Stickel, 1991; and Michaely and Vila, 1996), volume is related
to information. Karpoff (1986) set up a model and illustrated that informational events
affect trading volume. Empirically, Lakonishok and Vermaelen (1986), Stickel
(1991), and Michaely and Vila (1996) each examined trading volume around ex-
dividend days. Lakonishok and Vermaelen and Stickel found that trading volume
increases significantly around the ex-dividend day. They also discovered the positive
relationship between normal trading activity and abnormal trading volume around the
ex-day. For more liquid stocks, there is significantly positive abnormal trading
volume around the ex-day. Michealy and Vila documented significantly positive
abnormal trading volume around the ex-dividend day as well.
This study extends previous studies by investigating the reactions of market
participants to a unique event in an emerging market. Empirical results indicate that
there is abnormal trading volume associated with the distribution of the employee
shares. Most stocks have significant abnormal volumes around the ex-day. Besides,
less actively traded stocks are more likely to have a significant positive abnormal
volume on the ex-day.
The remainder of the paper is organized as follows. Section II introduces the
background of the Chinese stock market, as well as different types of shares. Section
III presents the data, methodology, and hypotheses. Empirical results are discussed in
Section IV. The final section concludes the paper.
II. The Chinese Stock Market
After China first launched its economic reform programme, she is seeking a
way to build socialism with Chinese characteristic, which includes building a socialist
market economic structure. Such an economic structure requires the macroeconomic
control of the state. The ownership under such a socialist market economic structure
maintains the ‘public sector’ and allows the ‘private sector’ as the ‘supplement’.
This theory of ‘socialist market economy’ is also applied to the stock market.
The Chinese leadership pointed out that shareholding system and securities market are
neutral instruments for conducting production both in a capitalist society and a
socialist society. As a result, conversion of state enterprises into joint-stock
corporations, and all activities of firms in the primary market are all subject to central
control and national planning for the purpose of serving socialism.
The purposes of applying the shareholding system to an enterprise are to
enhance the operational efficiency of state assets, to facilitate the efficient allocation
of social resources, and to change the enterprise into an independent economic entity
responsible for its own profits and losses (Yao, 1998; Gul, 1999). Pursuant to the
theory of the socialist shareholding system, shares issued by corporations are
segregated into different types of shares to fit an investor-specific shares structure,
e.g., state, legal person, Chinese individual, and foreign person. Each type of share is
subject to a unique set of laws which aim to preserve the dominance of the state
ownership. The government has created a state shareholding ratio for itself. State
shares and state-owned legal person shares are not publicly traded. Individual Chinese
citizens can only hold Chinese individual shares. Chinese individual shares are
subdivided into public shares, i.e., those held by individual members of the public,
and employee shares, i.e., those held by employees of the issuer.
We can also classify the shares of a firm by their marketability. Unmarketable
shares include state owned shares, domestic promoter legal person shares, overseas
legal person shares, social legal person shares, and employee shares. The marketable
shares include A-shares, B-shares, and H-shares, which are traded on different stock
markets. A-shares are only available to Chinese citizens. Quotes and clearances of A-
shares are in RMB. B-shares are quoted in Hong Kong dollars and are available only
to foreign investors. A-shares and B-shares are traded on the Shanghai Stock
Exchange and the Shenzhen Stock Exchange. H-shares are listed on the Hong Kong
Stock Exchange and traded in Hong Kong dollars. H-shares may only be subscribed
by, and traded among non-Chinese nationals.
At the end of 1998, there are 233.14 billion shares issued in the Chinese
capital markets and 73.36 billion shares are traded in the Shanghai and Shenzhen
Stock Exchanges. In other words, the tradable part accounts for about 32% of the total
shares of listed companies.
B. Employee Shares in the Chinese Stock Market
The history of employee shares can be traced as early as the emergence of the
joint-stock corporations. Employees holding shares of their own company is the
primitive form of a shareholding system. The employee shares also indicate the start
of the conversion of state-owned enterprises into stock corporations since 1984.
Under the regulations on employee shares, ‘employee shares’ are defined as shares
issued by a company limited by shares adopting the ‘targeted flotation method’, and
the shares are held by employees ‘in the capacity of an investor.’ ‘Employees’ are
defined to include both current and retired employees, and to exclude members of the
public. ‘Targeted flotation’ is defined as the flotation of shares to the issuer’s
employees only, and not to members of the public at large. When the company issues
stock to both its employees and members of the public at the same time, the total
number of shares held by employees may not exceed 10% of the total number of
shares held by the public, and the average number of shares held by each employee
may not exceed 5,000, regardless of the total number of shares issued and
Employee shares are not tradable within 6 months to three years after initial
issuance of the shares. After obtaining government authorization from China
Securities Regulatory Commission (CSRC), the employee shares can be listed and
traded on the stock exchange. As a result, the employee shares of the firm will change
into A-shares in the market, which can be held by the public.
There is a huge amount of employee shares in the Chinese stock market. From
1994 to 1998, 8.66 billion employee shares have been issued, which accounts for
3.72% of the total shares. Among the employee shares, 3.24 billion shares have been
distributed, which is about 4.42% of the total amount of A-shares (Sha, 1999).
III. Data, Hypotheses, and Methodology
In this study, the distribution of employee shares covers the period from
January 1, 1996, to December 31, 1999 in Shanghai, and from January 1, 1997, to
December 31, 1998 in Shenzhen. The sample and distribution date were collected
from 2 sources: (i) Shanghai Stock Exchange (SSE) Statistics Annual and SSE
Monthly Statistics for the employee shares distributed in the Shanghai market and (ii)
Shenzhen Stock Exchange Fact Book for the employee shares distributed in the
Shenzhen market. Day 0 was defined to be the trading day of ex-distribution of the
employee shares. Ex-distribution day events were included in the sample if:
1. The distribution was for the entire stock of employee shares issued by the
2. the stocks were traded on the ex-day; and
3. there were at least 29 daily volume observations in the estimation period
(days - 45 to -16) that can be used to estimate the “normal” daily trading volume.
Subsequently, the final sample consists of 232 ex-day events, including 138
samples in Shanghai and 94 samples in Shenzhen.
The daily trade volumes for this study were obtained from Taiwan Economics
Journal China Data Bank (TEJ). Table 1 contains descriptive statistics for the sample.
[Insert Table 1 About Here]
The trading behavior of investors around employee shares distribution day is
examined by investigating the pattern of trading volume around the ex-distribution
day. Trading volume around ex-days is expected to be higher than normal for the
First, employees paid a discount from the market price for the employee
shares. There are 433 listing companies which have distributed their employee shares
from 1994 to 1998. The average offering price of the employee shares of these
companies is 4.67 RMB per share. While the average offering price of the public A-
shares is 5.24 RMB per share, 12.26% higher than that of employee shares (Sha,
1999). Even for A-shares, the opening price on trading has been more than seven
times higher than the offering price (Kumar et al., 1997). Thus, the possession of
employee shares creates the possibility to gain returns which are several or even
several dozens times higher than the original investment. There exists a price-
incentive to sell the shares.
Second, the companies issued their employee shares and assigned to
employees as a kind of welfare. The allocation of the shares is egalitarian based on the
position ranking. From this perspective, holding shares is like holding a zero-premium
option. Employee shareholders can exercise the option after distribution of the shares.
Third, the only difference between A-shares and employee shares is the time
lag of the liquidity of employee shares. They have the same voting rights on the
operation of the firm. So employees do not have any advantages over the public on
the decision making process of the firm. If investors invest in the stock market, it
would be no difference between holding their own company’s shares or choosing
other securities. Rational investors will diversify the risks, and thus, there is no need
to put the money on the employee shares any longer if the return is not so high as
Fourth, the return of holding shares and the dividend received are low at
present. Historical data indicate that one-week risk-adjusted returns are about 0.12%
and 0.125% for A-shares in the Shanghai and Shenzhen Stock Exchange respectively,
while twenty-week risk-adjusted returns decline to 0.284% and 0.0% (Kumar et al.,
1997). It is not comparable with other investment. The large initial gains from the gap
between the offering price and trading price possibly fall off when time elapse.
Fifth, under some circumstances, employees were forced by the employer-
issuer to purchase the employee shares under the pain of dismissal. Guo (1992) stated
that every person who joins the enterprise as a regular employee shall purchase at
least one share of the company stock. The holding of the shares is compulsory at the
beginning. When the shares become marketable, the employees are willing to transfer
the shares in hand.
Therefore, it is expected that employee shareholders are the source of increase
trading on and after ex-days. The first set of hypotheses to be tested is:
H10: Abnormal trading volume around the ex-distribution day is equal
H1a: Abnormal trading volume around the ex-distribution day is
significantly bigger than zero.
Another prediction is that abnormal volume around the ex-day is an increasing
function of liquidity. Liquidity is a nebulous concept. Broadly defined, it can refer to
the willingness of stock market participants to engage in trades. A measure of this
concept might be daily volume on the stock exchange (Stumpp and Scott, 1991). In
this sense, I use turnover ratio as a proxy of liquidity of each stock in the study. If the
stock is traded more actively, then its normal turnover ratio is higher, the excess
turnover ratio on day 0 will be lower. The reasoning goes in accordance with the
signalling hypothesis in financial theory. If the securities of a firm are liquid, it is a
signal that the company is doing well; otherwise, investors will be unwilling to
holding its stocks. Thus, high liquidity of the stocks indicates high confidence of the
firm. Employee shareholders will hold the shares instead of transferring them. The
abnormal volume on the ex-distribution day will be low. Based on this reasoning, the
second set of hypotheses for this study is:
H20: The excess trade volume on day 0 has no significant relationship
with the liquidity of the stocks.
H2a: The excess trade volume on day 0 is negatively related to the
liquidity of the stocks.
The research method employed uses a traditional event study approach
developed by Fama, Fisher, Jensen and Roll (1969). The abnormal trade volume is
defined as the volume in excess of the normal volume, and is examined around day 0.
The normal trade volume is computed from mean-adjusted model. If the distribution
of employee shares has impact on trading behavior of the investors, it can be expected
that a significant increase in trading volume will be observed around ex-days.
1. Ex-day Abnormal Trade Volume for Each Stock
For each stock, the abnormal trade volume on the ex-distribution date is
computed. The normal volume for each stock is estimated as the average daily trade
volume of the stock using a 30-day estimation period (-45 to -16).
First, the mean daily trade volume for days - 45 to -16 for security i (ATV i) is
ATVi = ∑ TVit ,
T t = −45
where TVit is the daily trade volume for security i on day t, and T is the number of
days with valid volume observations in the estimation period. Assuming the normal
turnover ratio of the security keeps unchanged across time, the normal trade volume
on day 0 (ATV i’) should be adjusted according to the new-added A-shares:
ATVi ' = ATVi / S i ⋅ ( S i + Di ) , (2)
where Si and Di are the number of shares outstanding before distribution and the
number of employee shares distributed for security i, respectively. Then the trade
volume on the ex-distribution day (TVi0) is compared with the estimated volume
(ATVi’). Abnormal trade volume on day 0 (Ai0) is calculated as the difference
between the two volumes, and the value of z-statistic is calculated for each security:
Ai 0 Ai 0
σ i / T −1 −16
t = −45
it − ATVi ' ) 2 /(T − 1)]
Ai 0 = TVi 0 − ATVi ' . (4)
If the z-statistic for security i is positive and significant at the 5 per cent level,
it means that the observed trade volume for security i on ex-day falls among the 2.5
per cent highest volumes. Thus security i has a significantly increase in trade volume
on the ex-day. On the contrary, if the z-statistic is negative and significant at 5 per
cent level, it indicates security i has a significantly decrease in trade volume on day 0.
2. Ex-day Abnormal Trade Volume for the Sample as a whole
Based on the mean-adjusted model, the t-test is used to assess the statistical
significance of the abnormal trade volume. The null hypothesis (H10) to be tested is
that the mean day 0 abnormal trade volume ( A0 ) is equal to zero. The t-statistic is the
ratio of the day 0 mean abnormal trade volume to its estimated standard deviation,
and is calculated for each group classified by the z-statistics of the securities:
t = A0 / S ( A0 ) , (5)
i0 , (6)
S ( A0 ) = ( ∑ ( At − A ) ) / 29 ,
t = −45
A = (1 / 30 ⋅ ∑A ),
t = −45
in which N is the number of sample securities in the group. If the t-statistic is
significant, it indicates that the abnormal trade volume is significant on the ex-date.
Then H10 is rejected.
Next, abnormal turnover ratio is defined as the daily turnover relative to the
normal turnover. Normal turnover ratio is the average turnover ratio (ATO), and is
calculated by mean in the 30-day estimation period. The mean daily turnover for days
- 45 to -16 is:
ATO = ∑ TOt ,
30 t = −45
where TOt is the average daily turnover (shares traded relative to shares outstanding)
on day t.
Then, the abnormal volume (AV) is defined as the change in the turnover
ratio. The AV for the 31 days centered around ex-distribution day is calculated. For
each day in the event period (day –15 to +15), I calculated the AV as:
AVt = TOt / ATOt − 1 t = -15, ……, +15. (10)
If the AV on the day is significant, then there is a significant abnormal volume
on the day. And H10 is rejected.
3. Ex-day Abnormal Turnover Ratio as a Function of Liquidity
The relation between ex-day abnormal turnover ratio ()TO0) and liquidity is
examined by regression analysis. Parameters of the following equation are estimated
using ordinary least squares:
∆TO0 = β 0 + β 1 LIQUIDITY + ε , (11)
LIQUIDITY = ATO . (12)
The normal daily turnover ratio (ATO) serves as a proxy for LIQUIDITY, and γ is the
random error. The null hypothesis to be tested is that )TO0 has no significant
relationship with LIQUIDITY. If ∃1 is significantly different from zero, then H20 is
rejected. It implies that the abnormal trading volume is associated with liquidity of the
IV. Results and Discussion
Based on the z-statistic of each security for the trade volume on the ex-
distribution day, all cases in the analysis were divided into 3 groups, which are
increase significantly, decreasing significantly, and non-significant change,
respectively. Table 2 illustrates the results of the classification. Using a significance
level of 5% for z-statistic, 64 out of 232 samples (27.6%) had a significantly lower
trade volume on the ex-day than estimation, 30 (12.9%) had a non-significant change,
and 138 (59.5%) had a significantly higher actual trade volume than estimation.
[Insert Table 2 About Here]
T-tests were performed for each group. The t-statistics for the three groups are
190.64, -1.11, and -14.45, respectively. This result shows that nearly 60% of the
securities exhibited a significant increase in trade volume on the trading day of the
distribution of the employee shares. Therefore, H10 is rejected. The result in Table 2
also indicates that the absolute value of t-statistic is the largest for the significantly
increasing group (t-statistic = 190.64), which indicates the actual trade volume is
highly significantly different from estimation, the increasing is substantial. And the
change of increasing is much more highly significant than that of decreasing (t-
statistic = -14.45).
[Insert Table 3 About Here]
Table 3 details the abnormal turnover ratio (AV) for the 31 days centered
around the ex-distribution day for the sample. It can be found in the Table that the AV
for the significantly increase group is the largest on the ex-day. It jumps to 316.3% on
day 0. For the significantly increase group, the AV is positive and significantly
different from zero on several days around day 0. For the period from day –4 to day
+6, all the trading days have an average AV bigger than 1. This means that during the
period, the trade volumes of the stocks are more than double the normal amount.
Thus, H10 is rejected.
The results indicate that trade volume increases significantly in the days
before the ex-distribution day. Therefore, the trading due to the upcoming employee
shares distribution does not occur only on the cum- and ex-days, but starts several
days before the ex-day, and ends several days after. The fact that the trading occurs
before the ex-day can be explained as follows. Most employee shares are distributed
immediately after the short-term prohibition on trade is released. Usually the period is
6 months. Investors can anticipate the distribution of the employee shares and are
afraid of the inburst of the new shares into the market. But they are not sure of the
exact distribution date and will not take the risk to wait until the last minute.
Therefore, they would like to trade several days before the ex-day. When the
distribution day comes, the employee shareholders are able to transfer their stocks in
hand. To them, the stocks are just one kind of welfare. Holding the shares can be
considered as holding a zero-premium option. They are more risk averse than those
stock market investors are, so they prefer to exercise the option, sell the shares, and
cash out. Thus, on the ex-day and the days following, employee shareholders become
the major source of the abnormal trade volume.
[Insert Figure 1 About Here]
Nevertheless, this trading activity around the ex-day is only pronounced for
the significantly increase group. It can be found in Table 3 that the stocks in the other
two groups do not delineate any sharp change in trading volume due to the event.
Results from Figure 1 suggest that there are no distinct patterns of trading volume for
the other two groups around the ex-day. There appears to be no relevant information
surrounding the distribution date for these two groups. One possible reason is that
there may well be information leakage prior to the distribution. The investor traded
their shares prior to the employees’ setting foot into the markets.
[Insert Table 4 About Here]
Table 4 reports the summary statistics for the trading volume on the ex-
distribution day. As the figure shows, for the significantly increase group, the average
excess daily trade volume is 3.91 million shares and significant. The turnover ratio
jumps to 10.18% from an average of 3.11% per day with about 7.07% excess.
It can also be found in Table 4 that both the average trade volume (ATV’) and
turnover ratio (TO) before ex-day are smallest for the increase significantly group.
Both the excess trade volume and excess turnover ratio monotonically increase with
the decrease of ATV’ and TO.
[Insert Table 5 About Here]
A further comparison becomes possible by regressing the abnormal trading
volume on the ex-day against the liquidity of the stock. The result of the regression is
shown in Table 5. The result implies that a 1% decrease in liquidity produces a
turnover ratio increase of 0.5% on the ex-day. The less liquid the stock is, the normal
turnover ratio is lower. People are unwilling to engage in trades. Employees want to
exercise their stocks instead of holding them. As a result, the abnormal trading
volume on the ex-day will be higher. This finding of negative association between the
liquidity of the stock and the turnover on distribution day consists with the signalling
hypothesis and rejects H20.
The study documents the trading effect of the ex-distribution of the employee
shares in the Chinese stock market. The abnormal volumes around the ex-day are
examined. The sample consists of 232 firms that have their employee shares
distributed during the period from 1996 to 1999. It is found in the sample that most
stocks have significantly positive abnormal trading volume around the employee
shares distribution date. A proxy for liquidity of the stock, which is the daily turnover
ratio of the stock, is significantly related to the abnormal trading volume. The
evidence generally consists with the signalling hypothesis.
Although the results of this paper show that trading volume of most stocks
increases significantly before and after ex-distribution days, 27.6% of the stocks had
negative abnormal trade volumes on the ex-distribution day, and 12.9% had the
abnormal volumes which are not significantly different form zero. This contrast poses
an interesting challenge for future research.
Besides, there may be some challenges in the data aggregation. It is possible
that, because this analysis is based on event time, I may not capture time variation
effects. For example, if markets have been suffering a downturn, investors were
unwilling to trade, we might witness a decrease across time in abnormal trade
volumes around the distribution date. There is a need to develop new time-varying
event methodology, but this is beyond the scope of the current paper.
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Descriptive Statistics for the Sample: During the Period from 1996 to 1999
(000s) (% of the total shares) (% of the shares outstanding)
Panel A: Employee shares distributed N = 232
Mean 883.33 4.17 12.94
Maximum 7620.00 38.46 60.00
Median 500.00 2.88 10.00
Minimum 75.00 0.18 1.10
Panel B: Shares outstanding before Event N = 232
Mean 5816.23 26.22 NA
Maximum 32000.00 67.45 NA
Median 4500.00 25.71 NA
Minimum 1000.00 0.88 NA
Panel C: Shares outstanding after Event N = 232
Mean 6699.56 30.00 NA
Maximum 35000.00 73.63 NA
Median 5000.00 29.00 NA
Minimum 1167.86 1.00 NA
NA= Not Available.
Notes: Shares outstanding before event is the number of A-shares of the firm
traded before the distribution of its employee shares. Shares outstanding after
event is the sum of the shares outstanding before event and the employee shares
Change in Trade Volume on the Ex-Distribution day
Number of stocks % of total cases t-statistic
Increase Significantly 138 59.483 190.6429 *
Non-significant Change 30 12.931 - 1.1093
Decrease Significantly 64 27.586 - 14.4514 *
TOTAL 232 100.000 –
* Significance at 1%.
Notes: All of the stocks in the sample are classified into 3 groups – increase
significantly, non-significant change, and decrease significantly – according to their
z-statistics for the excess trade volume on the ex-distribution day. The normal trade
volume is calculated as the average daily trade volume adjusted for the ex-day using
data from day - 45 until day -16. T-value for each group is computed.
Abnormal Volume around the Ex-Distribution Day
Event Day Increase Significantly Non-significant Change Decrease Significantly
-15 -0.233 0.2467 0.1738
-14 -0.275 0.5640 0.3472
-13 -0.367 0.3534 0.3311
-12 -0.321 -0.0989 0.3894
-11 -0.332 0.1427 0.4921
-10 -0.349 0.4083 0.5039
-9 -0.392 0.4351 0.4372
-8 -0.445 0.2035 0.4051
-7 -0.415 -0.0296 0.6186
-6 -0.415 0.0645 0.5671
-5 -0.358 0.0567 0.7327
-4 -0.450 0.0337 1.0487
-3 -0.436 0.0434 1.0918
-2 -0.424 -0.1167 1.4536
-1 -0.479 0.0834 1.6507
0 -0.535 -0.0060 3.1630
1 -0.531 -0.1113 1.8801
2 -0.358 0.0440 1.7427
3 -0.325 0.0118 1.6368
4 -0.446 -0.0554 1.3776
5 -0.480 0.3481 1.3392
6 -0.334 0.1772 1.3104
7 -0.469 0.0660 0.8171
8 -0.523 0.0113 0.8341
9 -0.473 0.1420 0.8334
10 -0.427 0.2611 0.7269
11 -0.359 0.3068 0.8270
12 -0.270 0.0357 0.8441
13 -0.213 -0.0019 1.2105
14 -0.382 0.1025 0.8839
15 -0.170 0.0609 0.9211
Notes: Abnormal volume (AV) is defined as the change in the turnover ratio
compared with the normal volume. The samples are categorized into three
groups by the significance of their z-statistics for the trading volume on the ex-
day. Day 0 is defined as the ex-distribution date.
-15 -10 -5 0 5 10 15
Negative Change Non-Significant Change Postive Change
Figure 1 Plot of Abnormal Volume for Ex-Distribution of Employee
Shares from Event Day –15 to Event Day +15
Notes: The abnormal volume (AV) is defined as change in turnover ratio
from estimation. AV is calculated using the average turnover ratio as the
normal volume. The estimation window is during the pre-distribution period
from day -45 to day -16. The three groups are classified based on the
significance of z-statistics for the trading volume on the ex-distribution day.
Summary Statistics for the Trade Volume on the Ex-Distribution Day
Trade Volume (1000s) Turnover Ratio (%)
Panel A: Increase Significantly
Before Distribution 1567.568 3.10971
Ex-Distribution Day 5477.258 10.18432
Excess Trading Volume 3909.690 7.07461
Panel B: Non-significant Change
Before Distribution 2375.261 3.75485
Ex-Distribution Day 2130.222 4.14229
Excess Trading Volume - 245.039 0.38744
Panel C: Decrease Significantly
Before Distribution 3031.311 5.17946
Ex-Distribution Day 1478.323 2.59608
Excess Trading Volume - 1552.988 - 2.58338
Panel D: Total Sample
Before Distribution 2001.731 3.75485
Ex-Distribution Day 4142.377 7.69162
Excess Trading Volume 2140.646 3.93677
Notes: The three panels (Panel A, B, and C) are classified based on the
significance of z-statistics for the trade volume on the ex-distribution day. Before
distribution statistics are served as the benchmark for the normal trade volume.
The average trade volume (ATV’) is the group mean of normal trade volume
computed by average daily trade volume and adjusted for the ex-day according
to the amount of the employee shares distributed. Average turnover ratio (TO) is
the normal daily turnover (shares traded relative to shares outstanding) averaged
across firms in the group. Excess trade volume is the change of trade volume and
is calculated as the difference between average and observed value on ex-
Regression of Abnormal Trading Volume on the Ex-Distribution Day
Against Liquidity of the Stock
)TO0 = 5.380 – 0.492 LIQUIDITY
Adj. R = 0.044 F = 11.685 Observations = 232
* Significance at 1%.
Notes: )TO0 = average abnormal turnover ratio on the ex-day, measured as a
percentage; LIQUIDITY = normal daily turnover ratio in percentage. (t-
statistics in parentheses)