hk liquidity
TRANSCRIPT
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Free Float and Market Liquidity:
Evidence from Hong Kong Governments Intervention
Kalok Chana,*
Yue-Cheong Chanb
Wai-Ming Fongc
aDepartment of FinanceHong Kong University of Science & Technology
Clear Water Bay, Hong Kong
bDepartment of Business Studies
Hong Kong Polytechnic UniversityHung Hom, Hong Kong
cDepartment of FinanceThe Chinese University of Hong Kong
Shatin, Hong Kong
March 1, 2002
Abstract
In August 1998, the Hong Kong Government intervened in the stock market, buying up to HK$118.1billion in shares of the 33 Hang Seng Index (HSI) component stocks. That represented about 7.3percent of all the shares in the stocks, causing a substantial decrease in the free floats. This paperexamines whether there is any adverse change in market liquidity of the stocks as a result of thedecrease in free floats. We study the market liquidity in terms of the price impact of trades. We findthat, relative to a group of control stocks, the HSI component stocks have experienced an increase in
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1. Introduction
On August 14, 1998, the Hong Kong government made an unprecedented move into the stock
market, buying up to an estimated HK$3 billion in shares of the Hang Seng Index (HSI) component
stocks. The government claimed that the intervention was to drive currency speculators out of the
financial markets. These speculators were accused of deploying a double play strategy, attacking
the Hong Kong currency market and the stock market simultaneously, threatening the currency peg
and causing panic in the financial markets. The Hong Kong government continued to purchase the
blue chip stocks until the end of August. As disclosed by the government in late October of 1998, she
had bought HK$118.1 billion in shares of the 33 HSI constituent stocks. That represented about 7.3
percent of all the shares in the companies in the index.
The intervention by the Hong Kong government offers a natural experiment for examining
how the market liquidity is adversely affected by a substantial decline in free floats being available.
For many listed companies in the Asian markets or emerging markets in general, a large percentage of
shares is controlled by such large shareholders as the government, the parent companies, the affiliated
companies, and the majority owners. As a result, the amount of shares outstanding not held in large
blocks and considered available for trading could be relatively small. When investigating the
liquidity of these markets, it is common to determine the amount of free-floating shares being
available. For example, for some of the popular international stock market indices such as FTSE and
MSCI indices, the weights of constituent stocks are free float adjusted to reflect government holdings
and restricted ownership to ensure a more accurate representation of available stocks in the market.
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decline in the amount of free-floating shares to the market.1 The decline in free floats can be
determined precisely as the government disclosed the amount of shares that she bought for each
company. Furthermore, because the government only bought the component stocks that underlie the
HSI, we could compare the HSI component stocks with the other companies to ascertain how the
market liquidity is adversely affected by the government intervention.
Market liquidity is concerned with the ability of the market to absorb temporary order
imbalance. Our analysis of market liquidity focuses on the price impact of trades, which is a measure
of how much the price will change for a given size of incoming orders. It is also an effective measure
of the variable trading cost paid by the customers when executing the trades. For a relatively liquid
stock, a trade of a given size should have a small impact on prices, so that the effective trading cost is
low. The price impact is modeled explicitly by the measure of Kyle (1985). Kyles model shows
that the parameter is an increasing function of informational asymmetry and a decreasing function
of liquidity trading.2 The Hong Kong stock market does not adopt the market maker mechanism as
stipulated in Kyles model. Nevertheless, the parameter is still an effective measure for Hong Kong
stock traders variable trading cost.
In this paper, we will examine how the price impact of trades for the HSI stocks will be
affected after the decline in free floats resulting from the government intervention. In order to control
for any exogenous effects on the general market liquidity, we compare the change in price impact for
the HSI stocks with that for a group of control stocks. In addition, we will also investigate whether
there is any cross-sectional relationship between the change in price impact and the government
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A number of recent research work also focus on the price impact of trades. Glosten and
Harris (1988), Foster and Viswanathan (1993), and Hasbrouck (1993) propose different econometric
frameworks to estimate the parameter. In another study, Breen, Hodrick, and Korajczyk (2000) find
that the price impact of trades is endogenously determined. They find that it is related to cross-
sectional variation of some firm characteristics, such as the firm size, trading volume, degree of
adverse selection, and the extent of shareholder heterogeneity.
The paper is organized as follows. Section 2 briefly describes the Hong Kong government
intervention. Section 3 discusses the data and provides some summary statistics. Section 4 discusses
the methodology, and Section 5 presents the empirical results. This is followed by a conclusion in
Section 6.
2. The Intervention
We first trace the stock market performance before and after the governments intervention.
Figure 1 displays the movement of the HSI along with the turnover aggregated across all 33 HSI
constituent stocks from May to November 1998. Note that the governments stock purchases began
on August 14 and ended on August 28. It is evident that the stock market fell steadily during the
period from May to mid-August. The HSI started at 10563 on May 1 and dropped to 6660 on August
13, the day before the governments intervention. When the Hong Kong Monetary Authority
(HKMA, Hong Kongs central bank) intervened in the stock and futures market on August 14, the
HSI was pushed to 7224 at the close of that day. The HKMA continued its stock purchases until
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Table 1 presents the sample of the 33 HSI component stocks, along with the percentage of
shares (as a fraction of the total number of shares outstanding) bought by the Hong Kong government
during the intervention.4 The percentage of shares bought up varies across the stocks, ranging from
2.49% for Amoy Properties to 12.28% for Swire Pacific A. We also obtain the amount of free-
floating shares estimated by a brokerage firm before the governments intervention, and calculate the
percentage decrease in free float. The decline varies from 3.9% for Hang Lung Development to
29.9% for China Telecom. We will use these figures later to see whether they are correlated with the
change in the price impact of trades for the HSI stocks after the governments intervention.
3. Data
The Stock Exchange of Hong Kong (SEHK) is operated based on a pure order-driven market.
Security prices are determined by the buy and sell orders submitted by investors in the absence of
designated market makers. Limit orders are placed through brokers and are consolidated into the
electronic limit-order book and executed through an automated trading system, known as the
Automatic Order Matching and Execution System (AMS). The limit orders for a specified price and
quantity are stored in the system and executed using strict price and time priority. The trading system
only accepts limit orders. However, investors could submit market orders to their brokers who will
place them in the form of limit orders that match the best price on the other side of the book.
Investors are allowed to cancel or decrease orders at any time prior to matching, but they cannot
enlarge the order already submitted.
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and ask prices, along with the broker identity (broker code) of those who submit orders at the
respective bid/ask prices being shown, and the number of orders and shares demanded or offered at
each of the five bid and ask queues.
As mentioned earlier, the governments intervention took place in August 1998. For our
analysis, we collect data for two sub-sample periods from January to June 1998 (pre-intervention)
and from January to June 1999 (post-intervention). Our sample stocks include two groups. The first
is the 33 component stocks underlying the HSI. The second is the other 67 component stocks
underlying the HSI 100.5 Yet, since one of the stocks underlying the HSI 100 is not traded actively
enough, we end up with only 66 other stocks, which will form the control group. For all of these 99
stocks (33 HSI component stocks and 66 control stocks), we collect transactions and quotations data
during the two sub-sample periods. We obtain our data from the Trade Record and the Bid and Ask
Record, both provided by the SEHK. The Trade Record includes all transaction price and volume
records with a time stamp recorded to the nearest second. The Bid and Ask Record contains
information on limit-order prices and order quantity. It tracks the number of orders and shares in each
of the five queues at every 30-second interval.
Since market liquidity is concerned with the ability of the market to absorb temporary order
imbalance, we expect that the change in market liquidity will be more easily observed when we
perform our analyses with short time intervals than with long time intervals. Yet, analyses based on
too short time intervals will lead to thin trading problems. To make a compromise, we divide each
trading day into 30-minute intervals. The SEHK is open during 10:00-12:30 and 14:30-16:00 (Hong
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To measure order imbalance during the intervals, we need to classify each trade into buyer- or
seller- initiated. Each trade will be identified as buyer- or seller- initiated by comparing the trade
price to the prevailing bid-ask quotes. To be eligible, the quotes have to be immediately before the
trades. Following Lee and Ready (1991) and Harris (1989), when a trade takes place within the
spread, it will be classified as buyer- (seller-) initiated if the trade price is closer to the prevailing ask
(bid) price.6 If a trade takes place at the midpoint of the quotes, outside the spread, or not
immediately following the quotes, we will resort to the tick test. The tick test classifies a trade as
buyer-initiated if it occurs on an uptick or a zero-uptick, and as seller-initiated if it occurs on a
downtick or a zero-downtick. If a trade occurs before the appearance of the first bid-ask quotes and
before the first intraday price change, it will not be signed. But if a trade occurs before the
appearance of the first bid-ask quotes but after the first intraday price change, a tick test is used to
classify the direction of the trades.
For each time interval, we construct a measure of intraday order imbalance. The measure is
net share volume, which is computed as the difference between buyer-initiated share volume and
seller-initiated share volume for the interval. When the net volume is positive (negative), there are
buy (sell) order imbalance for the time interval. Since some of the trades are not classified, the
measure is a noisy proxy for the intraday order imbalance. However, since there is no prior reason
why buyer-initiated trades or seller-initiated trades would be more likely to be unclassified, our proxy
should still be an unbiased measure of the intraday order imbalance.
Table 2 reports the average volume (in thousand shares) and the average net volume (buy
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1,195 thousand shares in 1998 to 1,343 thousand shares in 1999 for the 66 control stocks, it drops
from 739 thousand shares to 554 thousand shares for the 33 HSI stocks. Our statistical tests indicate
that while 23 out of 33 HSI stocks show significant volume decrease in 1999, 35 out of 66 control
stocks show significant increase. Table 2 also shows that the HSI stocks are under heavy selling in
1998 the average net volume of HSI stocks is -27 thousand shares while the average net volume of
66 control stocks is 20 thousand shares. In 1999, both the HSI stocks and control stocks record net
buying activities as both of their net volumes are positive.
4. Methodology
Our focus is to study the price impact of net volume for the time intervals. First, we calculate
the price change (in dollars or in percent) for each interval, which is based on the difference between
the last prices in two adjacent time intervals. For each interval, we regard the prevailing bid-ask
midpoint at the interval end as the last price. If it is not available, we use the last transaction price.
For the first interval of a day, the price change is calculated as the difference between the last price
before 10:30 and the one before 10:10, so that the overnight price change is not included. Sometimes,
there is neither bid-ask midpoint nor transaction price from the opening to 10:10, so that there will be
missing price for the 10:10 time interval. For the first time interval in the afternoon, the price change
is calculated as the difference between the last price before 15:00 and the one before 12:30.
For each stock, we pool the time intervals in 1998 and 1999, and sort them into six classes of
equal number of intervals according to the signed value of the net volume. Thus, the six classes
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=
=
=
=
=
++++++=5
1w
5
1x
5
1x t,iit,xx,iit,xx,i
1
0p 1t,iiptp,it,ww,i
5
1v t,vv,it,i .99NVfNVeRdMRc99DbDaR
(1)
where is the price change (in dollars or in percent) for stock i in interval t. We include the first
lag of to control for any pattern of price reversal or price continuation. To control for the effect
of other stocks returns on , we include the current value and first lag of , the equal-weighted
average price change for the other 98 stocks in interval t, as a regressor. Note that the dependent
variable does not include the overnight return (from 16:00 of the previous day to 10:10 of a day), as it
cannot be explained by the net volume from 10:00 to 10:10. However, for the first interval of a day,
the first lag of and the first lag of are calculated from 16:00 of the previous day to 10:10 of
the day.
tiR ,
tiR ,
tiR ,
D
tMR
tiR , tMR
itxNV ,
7 Other controlling variables include the five weekday dummies for 1998, , and the five
weekday dummies for 1999, .
The variables of interest are and . equals the net volume in interval t
if the net volume is in the class x, and zero otherwise. We assume that the price impact to be the same
for the classes of small positive volume and small negative volume, so that the time intervals are
grouped into five net volume classes (large negative, medium negative, small negative/positive,
medium positive, and large positive). If a buy (sell) order imbalance results in positive (negative)
price movement, the coefficient estimate of will be positive. By allowing the coefficient
i diff l l d h diff i l i i
tvD ,
tw,99
itxNV ,99
itx,
itxNV ,
NV
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sum of the coefficient estimates of and , and the coefficient estimate for
represents the difference in the price impact of net volume in the class x between 1999 and 1998. If
the governments intervention results in lower market liquidity so that the price impact becomes
higher in 1999, the coefficient estimate of will be positive. Furthermore, for the class of
large negative net volume and large positive net volume, it is likely that the additional price impact
will be bigger as the large order imbalance will magnify the liquidity problem. Thus, the coefficient
estimates of the five s are our focus.
itxNV , itxNV ,99
itx,99
itxNV ,99
NV
itxNV ,99
Note that we include and its first lag as regressors, so cross-equation correlations are
mitigated. We will estimate the above regression equation separately for each stock.
tMR
9 All the
coefficients are obtained based on Generalized Method of Moments (GMM) estimation, with Newey-
West (1987) 10-lag kernel. The coefficient estimates will be the same as those obtained from OLS
estimation, but the t-statistic for each coefficient estimate will be consistent in the presence of
heteroskedastic and autocorrelated residuals.
5. Empirical Results
Table 3 presents the estimation results of equation (1). Panel A is based on dollar price
changes and Panel B is based on percentage price changes. We obtain the coefficient estimates for
each stock and pool them together across the 33 HSI component stocks and the other 66 control stocks
separately. We also report the cross-sectional means of the coefficient estimates, along with the
di i l i i
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positive, and large positive net volume). These figures seem to suggest that the price impact
coefficients tend to decline with the size of net share volume. It should, however, be noted that the
coefficient estimates measure the price impact per share. If we multiply the price impact coefficient
estimates by the average net volume in the respective class, the total price impact could be shown to
be biggest for the large positive net volume class and the large negative net volume class.10
The coefficient estimates of are also positive in all five net volume classes. They
are significant only for large negative, medium negative, and large positive net volume classes,
consistent with our conjecture that the additional price impact will be bigger for larger order
imbalance, which magnifies the liquidity problem. The evidence therefore supports the hypothesis
that there is an additional price impact for the 33 HSI stocks after they suffer a decline in free floats
due to the governments intervention. These results can be contrasted with the control stocks. While
the coefficient estimates of remain positive for the 66 control stocks, the coefficient estimates
of are either close to zero or negative. Therefore, the increase in price impact in 1999 is not
a market-wide phenomenon, but is confined to the HSI stocks that are bought up by the Hong Kong
government.
Results based on percentage price changes (Panel B) are qualitatively similar. For the HSI
component stocks, the coefficient estimates of are 0.146, 0.319, 0.446, 0.281, and 0.154 for the
five net volume classes. The coefficient estimates of are also positive in all five net volume
classes, although none of them is significant. On the other hand, for the 66 control stocks, the
itxNV ,99
itxNV ,
itxNV ,99
itx
NV,
itxNV ,99
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References
Breen, W., L. Hodrick and R. Korajczk, 2000, The determinants of equity illiquidity, Working paper,Northwestern University.
Chan, K. and W. Fong, 2000, Trade size, order imbalance, and the volatility-volume relation, Journalof Financial Economics 57, 247-273.
Glosten, L. and L. Harris, 1988, Estimating the components of the bid/ask spread, Journal of Financial
Economics 21, 123-142.
Glosten, L., 1994, Is the electronic open limit order book inevitable? Journal of Finance 49, 1127-1161.
Foster, D. and S. Viswanathan, 1993, Variations in trading volume, return volatility, and trading costs:Evidence on recent price formation models, Journal of Finance 48, 187-211.
Harris, L., 1989, A day-end transaction price anomaly, Journal of Financial and Quantitative Analysis24, 29-45.
Hasbrouck, J., 1993, Assessing the quality of a security market: A new approach to transaction-costmeasurement, Review of Financial Studies 6, 191-212.
Kyle, A., 1985, Continuous auctions and insider trading, Econometrica 53, 1315-1335.
Lee, C. and M. Ready, 1991, Inferring trade direction from intraday data, Journal of Finance 46, 733-
746.
Odders-White, E., 2000, On the occurrence and consequences of inaccurate trade classification,Journal of Financial Markets 3, 259-286.
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Table 1Percentage of shares bought by the Hong Kong Government during their intervention in August 1998.Percentage decrease in free float is to divide the amount of shares held by the Hong Kong government by theestimated amount of free-floating shares prior to the intervention.
Stock Name Hong Kong GovernmentHolding
Percentage Decreasein Free Float (Estimated)
Amoy Properties 0.0249 0.055
Hang Lung Development 0.0250 0.039
Wheellock & Co. 0.0308 0.062
Henderson Investment 0.0324 0.091
Sino Land 0.0343 0.094
Shangrai-La Asia 0.0344 0.059
Cathy Pacific 0.0352 0.114
China Telecom 0.0406 0.299
Cheung Kong Infrastructure 0.0428 0.282
Great Eagle Holding 0.0451 0.101
Hopewell Holding 0.0478 0.066
Henderson Ltd Development 0.0496 0.137
Hong Kong & Shanghai Hotel 0.0497 0.104
Wharf Holding 0.0530 0.113
CLP Holding Ltd 0.0550 0.103
Hang Seng Bank 0.0571 0.151
Hysan Development 0.0589 0.103
First Pacific 0.0607 0.131
Bank of East Asia 0.0610 0.066Hong Kong Electric 0.0615 0.096
Guangdong Investment 0.0630 0.110
HK.& China Gas 0.0667 0.199
Citic Pacific 0.0690 0.161
HutcHSIon Whampoo 0.0786 0.157
Sun Hung Kai Properties 0.0801 0.157
Hong Kong Telecom 0.0816 0.247Shanghai Industrial Holding 0.0849 0.248
TV. Broadcasts 0.0856 0.168
China Resource Enterprise. 0.0878 0.183
HSBC Holding. 0.0880 0.089
Cheung Kong Holding 0.1034 0.156
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Table 2
Summary statistics of volume and net volume for the 33 HSI component stocks and the 66 control stocks based on the trading data in January-June 1998 andJanuary-June 1999. The average volume (in thousand shares) and the average net volume (buy volume minus sell volume in thousand shares) in 30-minute
intervals (the first interval of a day is from 10:10 to 10:30) of 1998 and 1999 are first calculated for each stock. We report below the cross-sectional mean(standard deviation in parentheses) of the average volume and average net volume for the 33 HSI component stocks and the 66 control stocks.
Volume Net Volume
33 HSI stocks 1998 739 -27(655) (37)
1999 554 27(520) (31)
# of stocks with significantincrease (decrease) in1999
8 (23) 26 (0)
66 control stocks 1998 1,195 20(1,600) (83)
1999 1,343 65(1,744) (114)
# of stocks with significantincrease (decrease) in1999
35 (20) 27 (0)
15
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Table 3
The following regression is run separately for each of the 33 HSI component stocks and the 66 control stocks based on the trading data in January-June 1998 andJanuary-June 1999 (using GMM estimation, with Newey-West (1987) 10-lag kernel):
5 5 515
= ==
=
++++++=1 1 1
,,,,,0
1,,,,1
,,, .9999w x x
tiitxxiitxxip
tiiptpitwwiv
tvviti NVfNVeRdMRcDbDaR =
tiR , is the price change (in dollars or in percent) for stock i in interval t; are the five weekday dummies for 1998, and are the five weekday
dummies for 1999; is the equal-weighted average price change for the other 98 stocks in interval t; equals the net volume (buy volume minus sell
volume in thousand shares) in interval t if the net volume is in the class x (x is large negative, medium negative, small negative or small positive, medium
positive, or large positive) and equals zero if the net volume is not in the class x; NV99 equals zero if interval t is in 1998 and equals NV if interval t is in
1999. Reported below are the average coefficient estimates for the five and the five across the 33 HSI component stocks and the 66 control
stocks. The corresponding cross-sectional t-statistics in italics (with asterisk indicating significance at the 5% level, 2-tailed test) are also shown.
tvD ,
itxNV ,
twD ,99
tMR itxNV ,
itx,
NV
itx,
itx,99
itxNV , : Price impact of net volume in 1998 itxNV ,99 : Additional price impact of net volume in 1999
Large - Medium - Small - / + Medium + Large + Large - Medium - Small - / + Medium + Large +
Panel A: Price change in dollars33 HSI 0.026 0.049 0.075 0.048 0.028 0.007 0.014 0.019 0.014 0.010Stocks
4.89* 5.17* 3.66* 5.16* 5.37* 2.10*
2.14* 1.95 1.69 2.20*
66 control
0.017 0.040 0.064 0.040 0.017 0.000 -0.000 -0.009 -0.011 -0.000Stocks
3.39* 3.39* 2.16* 3.85* 4.00* 0.02
-0.05 -0.25 -2.98* -0.10
Panel B: Price change in percent33 HSI 0.146 0.319 0.446 0.281 0.154 0.006 0.010 0.068 0.071 0.034Stocks
9.20* 5.42* 3.90* 6.32* 8.36* 0.46
0.44 1.45 1.25 1.42
66 control
0.186 0.394 0.542 0.433 0.202 -0.046 -0.036 0.037 -0.144 -0.047Stocks
6.72* 6.46* 3.96* 6.19* 7.04* -2.71*
-0.93 0.21 -2.95* -2.88*
16
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Table 4
The following regression is run separately for each of the 33 HSI component stocks and the 66 control stocks based on thetrading data in January-June 1998 and January-June 1999 (using GMM estimation, with Newey-West (1987) 10-lagkernel):
= = ==
=
++++++=5
1
5
1
5
1,,,,,
1
01,,,,
5
1,,, .9999
w x xtiitxxiitxxi
ptiiptpitwwi
vtvviti NVfNVeRdMRcDbDaR
tiR ,
D99
xNV
is the price change (in dollars or in percent) for stock i in interval t; are the five weekday dummies for 1998, and
are the five weekday dummies for 1999; is the equal-weighted average price change for the other 98 stocks
in interval t; equals the net volume (buy volume minus sell volume in thousand shares) in interval t if the net
volume is in the class x (x is large negative, medium negative, small negative or small positive, medium positive, or large
positive) and equals zero if the net volume is not in the class x; equals zero if interval t is in 1998 and equals
if interval t is in 1999. For each of the five , we report below the percentage of stocks having a
significantly positive coefficient estimate and the percentage of stocks having a significantly negative coefficient estimateat the 5% level, 2-tailed test. We also report theZ-statistic, which is calculated by adding individual coefficient estimate t-statistics across stocks and then dividing the sum by the square root of the number of stocks. An asterisk indicates that theZ-statistic is significant at the 5% level, 2-tailed test.
tvD ,
it
tw,
it,
tMR
NV
itxNV ,
xNV ,99
itx,99
itxNV ,99 : Additional price impact of net volume in 1999
Large - Medium - Small - / + Medium + Large +
Panel A: Price change in dollars
33 HSI % of stocks being + signif. 30.3 18.2 12.1 18.2 39.4
Stocks % of stocks being - signif. 15.2 9.1 6.1 15.2 15.2Z-statistic 3.38* 1.85 1.53 2.44* 5.00*
66 control % of stocks being + signif. 7.6 4.5 4.5 3.0 7.6Stocks % of stocks being - signif. 59.1 47.0 18.2 22.7 56.1
Z-statistic -18.97* -10.66* -6.25* -8.38* -14.95*
Panel B: Price change in percent
33 HSI % of stocks being + signif. 6.1 6.1 3.0 12.1 21.2Stocks % of stocks being - signif. 9.1 6.1 3.0 9.1 12.1
Z-statistic -0.05 0.17 0.69 0.37 2.00*
66 control % of stocks being + signif. 6.1 3.0 3.0 6.1 15.2Stocks % of stocks being - signif. 27.3 12.1 4.5 10.6 27.3
Z statistic 7 53* 2 01* 0 83 2 68* 6 64*
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Table 5
The following regression is run separately for each of the 33 HSI component stocks and the 66 control stocks based on thetrading data in January-June 1998 and January-June 1999 (using GMM estimation, with Newey-West (1987) 10-lagkernel):
= = ==
=
++++++=5
1
5
1
5
1,,,,,
1
01,,,,
5
1,,, .9999
w x xtiitxxiitxxi
ptiiptpitwwi
vtvviti NVfNVeRdMRcDbDaR
tiR ,
D99
xNV
is the price change (in dollars or in percent) for stock i in interval t; are the five weekday dummies for 1998, and
are the five weekday dummies for 1999; is the equal-weighted average price change for the other 98 stocks
in interval t; equals the net volume (buy volume minus sell volume in thousand shares) in interval t if the net
volume is in the class x (x is large negative, medium negative, small negative or small positive, medium positive, or large
positive) and equals zero if the net volume is not in the class x; equals zero if interval t is in 1998 and equals
if interval t is in 1999. For each of the HSI component stocks, we report below the coefficient estimates of the
five and the corresponding t-statistics (in italics). An asterisk indicates that the t-statistic is significant at the 5%
level, 2-tailed test.
tvD ,
it
tw,
it,
NV
tMR
itxNV ,
it
xNV ,99
x,99
itxNV ,99 : Additional price impact of net volume in 1999
Stock Large - Medium - Small - / + Medium + Large +
Cheung Kong 0.036 0.004 0.029 -0.004 0.0033.78* 0.20 0.91 -0.23 0.34
CLP Hldgs 0.019 0.023 0.007 -0.014 -0.0061.92 1.34 0.17 -0.62 -0.56
HK Gas 0.001 -0.005 -0.005 0.000 0.005
0.39 -1.62 -0.77 0.11 2.00*
Wharf 0.011 0.010 0.018 0.001 -0.0012.05* 1.08 1.08 0.09 -0.13
HSBC Hldgs plc 0.062 0.022 0.103 0.201 0.0891.85 0.25 0.64 2.69* 2.26*
HK Electric -0.012 -0.020 -0.014 -0.062 -0.013
-2.47* -1.61 -0.50 -3.63* -2.10*
HK Telecom 0.003 0.006 0.004 0.007 0.0041.71 2.73* 0.98 2.77* 2.02*
Hang Lung Dev -0.001 -0.023 -0.013 0.008 -0.016
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New World Dev -0.006 -0.018 -0.032 -0.026 -0.006
-1.67 -2.12* -2.24* -2.90* -1.36
Swire Pac (A) 0.004 0.016 -0.044 0.002 0.0380.25 0.43 -0.58 0.05 2.88*
Wheelock 0.005 0.009 0.012 0.012 0.0041.29 1.17 0.84 1.29 0.93
Bk of East Asia 0.011 0.012 0.001 0.013 0.0102.18* 0.98 0.03 0.88 2.27*
Great Eagle Hldgs 0.016 0.077 0.119 0.077 0.0541.60 2.40* 1.77 2.39* 4.17*
HK Hotels 0.017 0.004 -0.003 0.030 0.0172.13* 0.23 -0.07 1.59 3.05*
Hopewell Hldgs 0.007 0.008 0.016 0.018 0.0172.48* 4.01* 3.61* 7.72* 4.87*
Shangri-La Asia 0.013 0.036 0.104 0.109 0.0361.09 1.23 1.35 3.01* 3.24*
Sino Land 0.000 -0.002 -0.002 0.000 -0.001-0.09 -1.39 -1.67 -0.42 -0.90
Henderson Inv -0.004 -0.008 -0.001 0.012 0.005
-1.47 -1.12 -0.04 1.42 1.12
Amoy Prop 0.004 0.004 0.023 0.005 0.0022.27* 0.71 2.36* 0.88 0.81
First Pacific 0.004 0.006 0.017 -0.002 0.0043.54* 2.22* 2.18* -0.38 3.86*
CITIC Pacific -0.006 -0.003 -0.020 -0.005 0.009
-1.37 -0.28 -0.79 -0.32 1.22
Guangdong Inv -0.001 -0.001 -0.002 -0.002 -0.001-4.05* -2.95* -1.74 -2.62* -3.68*
China Resources -0.005 -0.003 -0.012 0.000 0.001-2 10* -0 86 -1 56 -0 05 0 75
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7/31/2019 HK Liquidity
21/21
Figure 1Hang Seng Indices Performance and Turnover during the Government Intervention
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
4000000
05-01
-98
05-11
-98
05-19
-98
05-27
-98
06-04
-98
06-12
-98
06-22
-98
06-30
-98
07-09
-98
07-17
-98
07-27
-98
08-04
-98
08-12
-98
08-21
-98
08-31
-98
09-08
-98
09-16
-98
09-24
-98
10-07
-98
10-15
-98
10-23
-98
11-03
-98
11-11
-98
11-19
-98
11-27
-98
Turnov
er(byno.ofthou
sand
sharestraded)
0
2000
4000
6000
8000
10000
12000
Hang
Seng
Indices
Government intervention
20