HK Liquidity

7/31/2019 HK Liquidity

1/21

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


2/21

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.


3/21

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


4/21

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


5/21

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.


6/21

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


7/21

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


8/21

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


9/21

=

=

=

=

=

++++++=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


10/21

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


11/21

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


12/21


13/21


14/21

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.


15/21

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


16/21

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


17/21

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


18/21

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*


19/21

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


20/21

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


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

HK Liquidity

Documents

Transcript of HK Liquidity