A Microstructure Approach to the Domestic and Foreign Shares in the Chinese Stock Markets

by

Yea-Mow Chen

and

Yan He*

San Francisco State University

January 12, 2001

________________________

* Contact author: Yan He, Department of Finance, College of Business, San Francisco State University, 1600 Holloway Avenue, San Francisco, CA 94132, Phone: 415-338-2600, Fax: 415-338-0997, Email: yhe@sfsu.edu.

A Microstructure Approach to the Domestic and Foreign Shares in the Chinese Stock Markets

Abstract

This paper finds that the average bid-ask spread is significantly higher for the

foreign shares (B shares) than the domestic shares (A shares) traded in the Chinese stock

markets. To explain the spread disparity between the A and B shares, we estimate the

informed trading cost for each stock by using daily data. A measure of the informed

trading cost is developed based on the model of George, Kaul, and Nimalendran (1991).

Our test results show that the B-share market in China contains higher informed trading

cost than the A-share market. When the informed trading cost is held constant, the bid-

ask spread disparity between the A and B shares disappears. Therefore, the higher bid-ask

spread in the B-share market can be attributed to the higher informed trading cost faced

by B-share investors.

JEL Classification: G15

Keywords: Ownership Restriction, Chinese Markets, Market Microstructure,

Bid-Ask Spread, Informed Trading Cost

1

A Microstructure Approach to the Domestic and Foreign Shares in the Chinese Stock Markets

1. Introduction

In many emerging stock markets, foreign investors face restrictions on owning

domestic shares. It is widely documented that ownership restrictions result in price

differentials among classes of shares. Bailey and Jagtiani (1994) find that foreign

investors generate significant price premiums over domestic investors, using data from

the Stock Exchange of Thailand. Stulz and Wasserfallen (1995) construct a demand

function to explain why shares available to foreign investors sell at a premium, using data

from Switzerland. Foerster and Karolyi (1999) test foreign stocks listed in the U.S., and

their results support for market segmentation hypothesis and investor recognition

hypothesis. Recently, Henry (2000) and Bekaert and Harvey (2000) report positive

reactions in a country's equity market when the country liberalizes its stock ownership

and capital market, such as reduce foreign ownership restriction and relax currency

control.

The newly developed Chinese stock markets implement foreign stock ownership

restrictions. Class A shares are domestic shares and class B shares are foreign shares. The

percentage of shares owed by foreign investors is constrained for individual firms.

Throughout the first half of the 1990s, B shares were traded at a discount compared with

A shares, and B-share returns were higher than A-share returns. Su (1999) explains the

return premiums on the foreign-owned B shares in the Chinese stock markets by testing a

one-period capital asset price model (CAPM). He argues that foreign investors are more

risk-averse than domestic investors and so CAPM holds. In addition, Chui and Kwok

2

(1998) find that the returns on B shares lead the returns on A shares, which induces an

asymmetric positive cross-autocorrelation between the returns on B and A shares. They

argue that A- and B-share investors have differential access to information, and

information will more often reach the B-share market before it reaches the A-share

market in China.

The ownership restriction in the Chinese stock markets creates two distinct groups

of investors for a single firm: the domestic and the foreign investors. These two groups of

investors have different access to information and bear different risk. Therefore, they may

face different information asymmetry risk in submitting orders, take different strategies in

posting bids and offers, and bear different costs in executing trades. In all, the ownership

restriction may influence the behaviors of A- and B-share investors and cause bid-ask

spread disparity between the A- and B-share markets, though the two markets have

similar quoting and trading mechanisms.

This paper adopts a microstructure approach to investigate the execution costs of

trading A and B shares in the Chinese stock markets. It finds that the average bid-ask

spread is significantly higher for the foreign shares (B shares) than the domestic shares

(A shares). To explain the spread disparity between the A and B shares, we estimate the

informed trading cost for each stock by using daily data. Our results indicate that the B-

share market contains higher informed trading cost than the A-share market and the

higher bid-ask spread of B shares can be attributed to the higher informed trading cost of

B shares in the Chinese stock markets.

The remainder of the paper is organized as follows. Section 2 introduces the

quoting and trading structure of the Chinese stock markets and presents preliminary test

3

results on bid-ask spreads. Section 3 constructs a model for measuring informed trading

cost. Section 4 discusses empirical methodology. Section 5 describes data samples. Section

6 presents and analyzes empirical results. Finally, Section 7 summarizes the findings of this

paper.

2. Trading structure and bid-ask spreads

The Shanghai Stock Exchange and the Shenzhen Stock Exchange are the major

stock exchanges in China. The markets of the two exchanges are continuous and order-

driven. There are no designated dealers or market makers on the exchanges. The

mechanism of these exchanges is similar to that of the Stock Exchange of Hong Kong

(SEHK).1 All of the order flows on the Chinese stock exchanges must be displayed on

computer terminals viewable by investors on and off the exchanges. Limit orders are the

only order type permitted on the exchanges. A buy limit order must give the bid price and

number of shares to be purchased. A sell limit order must give the ask price and number

of shares to be sold. When a trader submits a limit order, the order is entered into a

computer system where orders can be matched and executed automatically. The system

prioritizes orders first by price and then by time. Bid prices are arranged in priority from

highest to lowest, while ask prices are arranged in priority from lowest to highest. The

spread between the highest (or best) bid and the lowest (or best) ask represents the bid-

ask spread.

If a buyer requires an immediate fill, he will submit a limit bid that is high enough

to touch the lowest posted ask. This buy order will then be executed at the best ask. We

1

? Brockman and Chung (2000) describe the mechanism of the SEHK.

4

call this trade a buyer-initiated trade. If a seller requires an immediate fill, he will submit

a limit ask that is low enough to touch the highest posted bid. This sell order will then be

executed at the best bid. We call this trade a seller-initiated trade. For each trade, the

party who initiates the trade bears the execution cost, while the counter party gains from

the bid-ask spread as compensation for his expected loss to informed traders and as

compensation for providing liquidity.

This paper focuses on the investigation of the A- and B-share bid-ask spreads.

Table 1 presents the summary statistics on bid-ask spreads for 82 pairs of A and B shares

traded in the Chinese stock markets during January 1, 1998 to May 1, 2000. The average

half-spread is 0.13% for the A shares and 1.38% for the B shares. Hence, the quoted

percentage bid-ask spread is significantly higher on the B-share market than the spread

on the A-share market, with a t-value of 7.97.

Now the question is why the B-share market has higher execution costs than the

A-share market in China. This paper will explore the characteristics of the domestic (A-

share) and the foreign (B-share) investors, examine the informed trading cost component

of bid-ask spreads, and explain the disparity in bid-ask spreads between the A and B

shares.

3. The model of informed trading cost

Bid-ask spread can be decomposed into an order-processing cost component and

an informed trading cost component. The order-processing cost component is regarded as

the compensation to the trader for providing liquidity services. The informed trading cost

component exists because an uninformed trader may trade with an informed trader who

5

possesses private information. This cost component is regarded as the compensation to

the uninformed trader for his expected loss to the informed trader. In practice, this cost

component is approximated by the revision in the trader's expectation of the value of a

stock resulting from the submission of an order.

Stoll (1989) and George, Kaul and Nimalendran (1991) develop covariance

models to estimate the proportion of bid-ask spread due to informed trading cost. Their

models assume that all the stocks in a market have the same proportion of bid-ask spread

due to informed trading cost. Based on their models, we can only obtain one single

estimate for the informed trading cost in the market. In this paper, we extend the model of

George, Kaul and Nimalendran (1991) to estimate the informed trading cost for each

individual stock.

We now present the model. Let Pt be the last transaction price in day t. The last

transaction can be a buyer- or seller-initiated trade. Let Qt be the indicator for the buyer-

seller classification of Pt. Qt is equal to +1 for a buyer-initiated trade and -1 for a seller-

initiated trade. For each trade, the party who initiates the trade bears the execution cost,

while the counter party gains from the bid-ask spread. For a buyer-initiated trade, the

transaction price would be at the best offer. For a seller-initiated trade, the transaction

price would be at the best bid. Here, the best bid and offer refer to the best quotes

displayed right before the transaction.

After each trade, rational investors will incorporate new information into their

bids and offers. Conditional on a buyer-initiated trade, investors will revise their

expectation of the stock's value upward. Conditional on a seller-initiated trade, investors

will revise their expectation of the stock's value downward. The revision in the investor's

6

expectation of the stock's value serves as a proxy for the informed trading cost. The

difference between the transaction price and the revised value serves as a proxy for the

order processing cost.

Let Vt be a stock's revised value after the last trade in day t. Based on Vt, the bid

and offer prices will be updated immediately. Let HSPRt be the updated half-spread. We

use to denote the order processing cost component of HSPRt. So, 1- denotes the

informed trading cost component of HSPRt. Thus, the order processing cost, *HSPRt,

can be approximated by the difference between Pt and Vt. We have

Pt = Vt + *HSPRt*Qt . (1)

Equation (1) is consistent with the model of George, Kaul, and Nimalendran (1991).

Since Vt is unobservable, we will find an observable variable as a proxy for Vt.

According to Huang and Stoll (1997), quote midpoint is adjusted relative to the

fundamental value of a stock on the basis of accumulated inventory in order to induce

inventory-equilibrating trades. Since there are no dealers or market makers on the

Shanghai and Shenzhen Stock Exchanges, traders have no obligation to make a market or

to keep certain levels of stock and cash inventories. Thus, the inventory control cost can

be treated as zero and so the quote midpoint equals the fundamental value of the stock.

Let Mt be the midpoint of the best bid and offer prices immediately following Pt. We

have Vt = Mt. Thus, equation (1) can be rewritten as

Pt = Mt + *HSPRt*Qt. (2a)

Similarly, in day t-1, we have

Pt-1 = Mt-1 + *HSPRt-1*Qt-1. (2b)

Subtract (2b) from (2a), we have

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Pt - Mt - (Pt-1 - Mt-1) = *(HSPRt*Qt - HSPRt-1*Qt-1), (3)

where Pt, Mt, Pt-1, Mt-1, HSPRt, and HSPRt-1 are observable, but Qt is unobservable. We

take absolute values on both sides of (3) to eliminate the trade indicator variable Qt.2

Thus,

|Pt-Mt-(Pt-1-Mt-1)| = *|HSPRt±HSPRt-1|, (4)

where either |HSPRt-HSPRt-1| or |HSPRt+HSPRt-1| is computed for day t. If the

transactions in day t and day t-1 have the same side of trade initiation, |HSPRt-HSPRt-1| is

calculated. If the transaction in day t has the opposite side of trade initiation as the

transaction in day t-1, |HSPRt+HSPRt-1| is calculated.3 The parameter denotes the order

processing cost component of bid-ask spreads, which is restricted in the range of 0 and 1.

The parameter 1- denotes the informed trading cost component of bid-ask spreads.

4. Empirical methodology

4.1. Estimate informed trading cost

Based on (4), we develop the following model to estimate parameter for each

stock:

|Yt| = a + *|HSPRt ± HSPRt-1| + et, (5)

where Yt and HSPRt can be in dollar or percentage terms. Yt in dollar terms is defined as

the dollar change from day t-1 to day t in the difference between the last transaction price

and the quote midpoint following the last transaction price, and Y t in percentage terms is

2

? If intra-day quote and trade records are available, we can estimate Qt by using the method of Lee and Ready (1991). However, it is difficult to obtain intra-day quote and trade records in the Chinese stock markets. Thus, we simplify equation (3) by taking absolute values on both sides to eliminate variable Qt.3 The judgment for the same or opposite side of initiation in two continuous days is discussed in Section 4.

8

defined as the dollar change from day t-1 to day t divided by the quote midpoint of the

last bid and ask in day t.4 HSPRt in dollar terms is defined as the last half-spread in day t,

and HSPRt in percentage terms is defined as the HSPRt in dollar terms divided by the

quote midpoint of the last bid and ask in day t.5 For each t, either |HSPRt-HSPRt-1| or |

HSPRt+HSPRt-1| is calculated, depending on the same or opposite side of trade initiation

in days t and t-1. The judgment for the same or opposite side of initiation in two

continuous days is based on the restriction 0<<1. If |Pt-Mt-(Pt-1 - Mt-1)| is less than |

HSPRt-HSPRt-1|, we assume the same side of trade initiation in days t-1 and t so that |

HSPRt-HSPRt-1| is calculated for day t. If |Pt-Mt-(Pt-1 - Mt-1)| is equal to or larger than |

HSPRt-HSPRt-1|, we assume the opposite side of trade initiation in days t-1 and t so that |

HSPRt+HSPRt-1| is calculated for day t. In this way, we roughly satisfy the restriction

0<<1 and minimize the standard deviation of the estimate.

In order to estimate parameter , we regress |Yt| against |HSPRt±HSPRt-1|. Two

potential problems may appear in the regression analyses. First, the dependent variable

and/or the independent variable may be serially autocorrelated, which induces serial

correlation in the regression residuals. Second, the regression residuals are likely to be

conditionally heteroskedastic. Therefore, the t-statistics are computed with the Newey-

West (1987) correction for heteroscedasticity and serial correlation in the error terms.

Based on (5), we obtain the estimate for , which represents the proportion of the

quoted spread due to the order processing cost. Then, we calculate 1-, which represents

the proportion of the quoted spread due to the informed trading cost. Define

INFO = 1- , (6)

4

? Yt in dollar terms = Pt-Mt-(Pt-1-Mt-1), and Yt in percentage terms = [Pt-Mt-(Pt-1-Mt-1)]/Mt.5 HSPRt in percentage terms = HSPRt in dollar terms / Mt.

9

where INFO is an estimate for the informed trading cost component of bid-ask spread,

and INFO can be INFO1 or INFO2. INFO1 is obtained from the regression test based on

Yt and HSPRt in dollar terms, while INFO2 is obtained from the regression test based on

Yt and HSPRt in percentage terms.

4.2. Explain spread disparity

We attempt to explain the cross-sectional variation in bid-ask spreads, and to

ascribe the difference in bid-ask spreads between the A and B shares to the difference in

informed trading costs. As equation (7) shows, the bid-ask spreads are regressed against

the information variables.

HSPRi = 0 + 1INFOi - D(0` + 1`INFOi) + ei, (7)

where i represents an individual stock that can be either domestically owned or foreign

owned, HSPRi refers to the average percentage half-spread for stock i, INFO refers to the

average informed trading cost for stock i, and D is a dummy variable that is equal to 1 for

domestically owned shares (A shares) and 0 for foreign owned shares (B shares). Here,

INFO can be INFO1 or INFO2.

Parameters 0` and 1` measure the differences in the intercept and coefficient

between the A and B shares. The dummy intercept 0` will be used to test whether the

bid-ask spread for A shares is lower relative to the spread for B shares when the informed

trading cost is held constant.

In addition, we employ the Generalized Method of Moments (GMM) to estimate

(7) to account for heteroskedasticity in the error term (ei).

10

5. The data

Data are obtained from the Great China Economic Data Base. The sample period

is from January 1, 1998 to May 1, 2000. The data sample contains a total of 82 pairs of

A- and B-share stocks traded on the Shanghai and Shenzhen Stock Exchanges in China.

Daily data are collected from the database. The daily price is the last transaction

price of each trading day. The daily bid and ask are the last posted bid and ask of each

trading day. The last posted quotes are typically subsequent to the last transaction price

on any given day. George, Kaul, and Nimalendran (1991) argue that the only occasions

on which the transaction and quotes are measured simultaneously is when market-at-

close orders are placed, and there is evidence to suggest that such orders are quite

infrequent.

Table 2 provides summary statistics on the characteristics of selected stocks. First,

the A- and B-share samples are significantly different in total shares (SHT) as well as in

publicly owned shares (SHP). In the Chinese stock markets, A shares include public,

institute, and state shares, whereas B shares are all publicly owned shares. A shares are

domestic shares, whereas B shares are foreign shares. For an individual firm, the number

of outstanding A shares on average is much more than the number of outstanding B

shares with a t-value of –6.33. In contrast, the number of publicly owned A shares is

much less than the number of publicly owned B shares with a t-value of 3.71.

Second, the A- and B-share samples are significantly different in trading volume

(VOLUME) and turnover rate (TR). Turnover rate is defined as VOLUME divided by

SHP. The daily trading volume and the turnover rate of a firm's A-share stock are much

higher than those of the firm's B-share stock with t-values of -7.12 and -16.58

11

respectively. Thus, A shares are much more frequently traded than B shares. According

to Easley, Kiefer, O'Hara, and Paperman (1996), frequently traded stocks have low

informed trading costs and so their bid-ask spreads are low. Thus, it is expected that A

shares have lower informed trading costs than B shares and A-share bid-ask spreads are

narrower than B-share spreads.

Third, the A- and B-share samples are similar in daily price returns (RET). The

daily price returns of the A and B shares are insignificantly different at the five-percent

level with a t-value of 0.65.6 Therefore, the return premiums on B shares have

disappeared in recent years. The domestic (A-share) and foreign (B-share) shares of a

firm have begun to generate similar returns.7 This phenomenon is consistent with

fundamental valuation of stocks since the A- and B-share stockholders of a firm are

entitled to the same future cash flows of the firm.

Finally, the A- and B-share samples are different in volatility of daily returns

(VRET). The return volatility of a firm's A-share stock is lower than the return volatility

of the firm's B-share stock with a t-value of 3.24. Thus, A-share traders may face lower

level of price uncertainty than B-share traders, and the lower price uncertainty may be

related to the lower risk of information asymmetry in the A-share market.

Overall, the A- and B-share stocks generate similar daily and monthly returns.

The differences between the A and B shares lie in their quoting and trading

6 We also examine monthly returns and excess returns. It is found that the monthly price returns and the monthly excess price returns of the A and B shares are insignificantly different at the five-percent level with t-values of -1.50 and -1.08 respectively. In specific, the average monthly price return is 0.021 for the A shares and 0.026 for the B shares. The average monthly excess price return is 0.019 for the A shares and 0.022 for the B shares. The monthly excess price return is calculated as the monthly price return minus the monthly riskfree rate. For A shares, the monthly riskfree rate is computed by using 3-month RMB interest rates. For B shares, the monthly riskfree rate is computed by using 3-month T-bill rates.7 Su (1999) finds that B-share returns are significantly higher than A-share returns, using data from the Chinese stock markets during the mid 1990s. In contrast, this study uses data during the late 1990s.

12

characteristics. For example, the A shares are more frequently traded than the B shares,

and the A-share returns are less volatile than the B-share returns. These imply that the A-

and B-share investors may face different risk of information asymmetry and incur

different execution costs.

6. Empirical results

6.1. Estimation of informed trading costs

We estimate the order processing cost component () for each stock by

conducting regression tests with the Newey-West correction for serial correlation and

heteroskedasticity in the error terms. Table 3 presents the summary results of the

regression tests. When Yt and HSPRt in dollar terms are employed, the average estimate

for is 0.689 for the A shares and 0.574 for the B shares. The estimations are statistically

significant at the five-percent level with an average t-value of 13.34 for the A shares and

11.00 for the B shares. The average adjusted R-square is 67% for the A-share regression

and 54% for the B-share regression. When Yt and HSPRt in percentage terms are

employed, the average estimate for is 0.681 for the A shares and 0.585 for the B shares.

The estimations are statistically significant at the five-percent level with an average t-

value of 13.05 for the A shares and 11.75 for the B shares. The average adjusted R-square

is 65% for the A-share regression and 54% for the B-share regression.

Based on the estimates, we compute the informed trading costs for each stock.

Table 4 provides summary statistics on the informed trading costs. INFO1 is calculated

based on the estimate from the dollar term regression, while INFO2 is calculated based

on the estimate from the percentage term regression. The average INFO1 is 0.311 for

13

the A-share sample and 0.426 for the B-share sample, and the difference between the two

samples is significant at the five-percent level with a t-value of 2.23. Similarly, the

average INFO2 is 0.319 for the A-share sample and 0.415 for the B-share sample, and the

difference between the two samples is significant at the ten-percent level with a t-value of

1.91. Therefore, the B-share sample has significantly higher informed trading cost

component than the A-share sample. The result indicates that the B-share investors face

higher risk of information asymmetry than the A-share investors.

The higher information content of B shares may result from the ownership

restriction in the Chinese stock markets. The restrictive ownership regulation segregates

investors into two distinct groups. In the A-share market, public shares are traded mostly

by individual investors who are not equipped with sophisticated investment skills. These

investors tend to select stocks based on rumors or market sentiments rather than private

information. Thus, the buy and sell orders in the A-share market are more likely to come

from uninformed traders. In contrast, B shares are traded by foreign investors who are

equipped with sophisticated portfolio management skills and who have quick access to

information. Thus, the buy and sell orders in the B-share market are more likely to come

from informed traders. So, the B-share market contains higher risk of information

asymmetry than the A-share market, and the B-share investors face higher informed

trading costs than the A-share investors.

6.2. Explanation of spread disparity

Since the A- and B-share samples differ in characteristics related to the

microstructure of quoting and trading, we first examine whether these differences can

14

explain the bid-ask spread disparity between the two samples. Table 5 provides the GMM

test results. The bid-ask spreads are regressed against the stock feature variables SHT,

SHP, VOLUME, TR, RET, and VRET respectively. The dummy intercept 0` measures

the average amount that the spread of B shares exceeds that of the A shares after

accounting for a sample characteristic. The results show that the 0` estimates are all

positive and significant. Hence, the differences in sample characteristics cannot explain

the bid-ask spread disparity between the A- and B-share samples.

Next we examine the effect of informed trading cost on bid-ask spreads. Table 6

reports the GMM test results. As shown, the information variable (INFO1 or INFO2)

explains about 76% to 79% of the cross-sectional variation in the quoted spreads (HSPR).

Consistent with microstructure theory, the informed trading cost of the B shares has a

positive relation with the quoted spreads of the B shares. The higher the informed trading

costs, the larger the bid-ask spreads. In addition, the effects of informed trading costs on

bid-ask spreads are different between the A- and B-share stocks.

The constant term 0` measures the mean difference in bid-ask spreads between

the A- and B-share samples after controlling for the informed trading costs. As shown,

0` is not significantly different from zero at the five-percent level in all tests. When the

informed trading costs are held constant, the t-value for the spread difference is 0.55 for

INFO1, and 1.37 for INFO2. Hence, the spread disparity between the A and B shares can

be explained by the difference in the informed trading costs. The larger bid-ask spread of

B shares can be attributed to the higher informed trading costs faced by foreign investors.

7. Conclusions

15

There have been arguments over the price returns of the domestic and foreign

shares in the Chinese stock market. Previous studies find that the foreign shares (B

shares) have significantly higher returns than the domestic shares (A shares), using data

of the mid 1990s. However, this study finds that the return difference has disappeared,

using data of the late 1990s. Given similar returns of A and B shares, this study adopts a

microstructure approach to explore the difference in quoting and trading behaviors

between the two classes of shares. We find that the average quoted spread is significantly

higher for the foreign shares (B shares) than the domestic shares (A shares). In order to

explain the spread disparity between the A and B shares, we estimate the informed

trading cost for each stock by using daily data. A measure of the informed trading cost is

developed based on the model of George, Kaul, and Nimalendran (1991). After

controlling for the informed trading costs, we find that the spread disparity between the A

and B shares disappears. Our results indicate that the higher bid-ask spreads of B shares

can be attributed to the higher informed trading costs faced by B-share investors.

16

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Brockman, Paul and Dennis Y. Chung, 2000, Informed and uninformed trading in an electronic, order-driven environment, Financial Review 35, 125-146.

Chui, Andy C.W. and Chuck C.Y. Kwok, 1998, Cross-autocorrelation between A Shares and B Shares in the Chinese Stock Market, Journal of Financial Research, 21, 333-354.

Easley, David, Nicholas M. Kiefer, Maureen O'Hara, and Joseph B. Paperman, 1996, Liquidity, information, and infrequently traded stocks, Journal of Finance 51, 1405-1436.

Foerster, Stephen R. and G. Andrew Karolyi, 1999, The effects of market segmentation and investor recognition on asset prices: Evidence from foreign stocks listing in the United States, Journal of Finance 54, 981-1013.

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Henry, Peter B., 2000, Stock market liberalization, economic reform, and emerging market equity prices, Journal of Finance 55, 529-564.

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Table 1Summary Statistics on Bid-Ask Spreads

This table provides summary statistics on bid-ask spreads for 82 pairs of A and B shares in the Chinese stock markets. The sample period is from 1/1/1998 to 5/1/2000. HSPR refers to the average half-spread in percentage terms.

Parameter A-share B-share Mean difference (B-A)t-value

HSPR Mean 0.13 1.38 7.97*

(%) Std. Dev. 0.03 0.51

Min. 0.07 0.45

Median 0.12 1.27

Max. 0.26 2.42

*: a significance level of five percent or better for a two-tailed test.

19

Table 2Summary Statistics on Stock Characteristics

This table provides summary statistics on stock characteristics for 82 pairs of A and B shares in the Chinese stock markets. The sample period is from 1/1/1998 to 5/1/2000. SHT refers to the total shares of a security. SHP refers to the publicly owned shares of a security. For A shares, SHP does not include institute or state shares. For B shares, SHP equals SHT. VOLUME is the daily number of shares transacted. TR is the turnover rate, defined as VOLUME divided by SHP. RET is the daily price returns. VRET is the volatility of daily total price returns.

Parameter A-share B-share Mean difference (B-A)t-value

SHT Mean 311 132 -6.33*(1,000,000s) Std. Dev. 211 90

Min. 72 22

Median 260 108

Max. 1,107 415

SHP Mean 70 132 3.71*(1,000,000s) Std. Dev. 62 90

Min. 8 22

Median 46 108

Max. 298 415

VOLUME Mean 983 335 -7.12*(1000s) Std. Dev. 724 288

Min. 234 34

Median 779 246

Max. 4,554 1,410

TR Mean 1.93% 0.25% -16.58*Std. Dev. 0.85% 0.12%

Min. 0.39% 0.02%

Median 1.78% 0.23%

Max. 4.05% 0.54%

RET Mean 0.11% 0.11% 0.65Std. Dev. 0.09% 0.14%

Min. -0.08% -0.14%

Median 0.12% 0.09%

Max. 0.33% 0.54%

VRET Mean 3.24% 5.16% 3.24*Std. Dev. 0.57% 1.51%

Min. 2.17% 3.54%

Median 3.17% 4.81%

Max. 5.12% 11.60%

*: a significance level of five percent or better for a two-tailed test.

Table 3

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Summary of Regression Tests with the N-W Correction

This table presents the summary of regression tests with Newey-West correction for 82 pairs of A and B shares in the Chinese stock markets. The sample period is from 1/1/1998 to 5/1/2000. For each stock, parameter is estimated by testing on the following model.

|Yt| = a+ *|HSPRt ± HSPRt-1| + et,

where

Yt in dollar terms = Pt - Mt - (Pt-1 - Mt-1),Yt in percentage terms = [Pt - Mt - (Pt-1 - Mt-1)]/Mt.

Pt is the last transaction price in day t. Mt is the midpoint of bid and ask quotes after Pt in day t. HSPRt is the half-spread in day t. HSPRt can be in dollar or percentage terms. When the transaction in day t has the opposite side of initiation as the transaction in day t-1, |HSPRt + HSPRt-1| is calculated. When the transaction in day t has the same side of initiation as the transaction in day t-1, |HSPRt - HSPRt-1| is calculated. Panel A presents the cross-sectional mean of the test results based on Yt and HSPRt in dollar terms. Panel B presents the cross-sectional mean of the test results based on Yt and HSPRt in percentage terms. Parameter represents the order processing cost, and 1- represents the informed trading cost. The parameter and the adj. R2 are estimated from the OLS regression. The t-statistics are computed with the Newey-West correction for serial correlation and heteroskedasticity in the error terms.

Panel A. Yt and HSPRt in dollar termsA-share B-share

mean 0.689 0.574

t-value on mean 13.34* 11.00*

Adj. R2 mean 67% 54%

Panel B. Yt and HSPRt in percentage termsA-share B-share

mean 0.681 0.585

t-value on mean 13.05* 11.75*

Adj. R2 mean 65% 54%

*: A significance level of five percent or better for a two-tailed test.

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Table 4Summary Statistics on Informed Trading Costs

This table provides summary statistics on informed trading costs for 82 pairs of A and B shares in the Chinese stock markets. The sample period is from 1/1/1998 to 5/1/2000. INFO1 is obtained from the regression test based on variables in dollar terms. INFO2 is obtained from the regression test based on variables in percentage terms.

Parameter A-share B-share Difference (B-A)t-value

INFO1 Mean 0.311 0.426 2.23*

Std. Dev. 0.149 0.074

Min. 0.140 0.339

Median 0.302 0.425

Max. 0.481 0.513

INFO2 Mean 0.319 0.415 1.91**

Std. Dev. 0.143 0.064

Min. 0.151 0.345

Median 0.312 0.405

Max. 0.491 0.507

*: A significance level of five percent or better for a two-tailed test.**: A significance level of ten percent for a two-tailed test.

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Table 5GMM Tests of Bid-Ask Spreads on Stock Characteristics

This table provides the GMM test results for 82 pairs of A and B shares in the Chinese stock markets. The sample period is from 1/1/1998 to 5/1/2000. The testing model is

HSPRi = 0 + 1Xi - D(0` + 1`Xi) + ei,

where i =1, 2, …, 154, HSPRi refers to the average percentage half-spread for stock i, Xi represents the stock characteristic for stock i, X can be SHT, SHP, VOLUME, TR, RET, or VRET, and D is a dummy variable that is equal to 1 for A shares and 0 for B shares. SHT refers to the total shares of a security. SHP refers to the publicly owned shares of a security. For A shares, SHP does not include institute or state shares. For B shares, SHP equals SHT. VOLUME is the daily number of shares transacted. TR is the turnover rate, defined as VOLUME divided by SHP. RET is the daily price returns. VRET is the volatility of daily total price returns. 0` measures the mean difference in bid-ask spreads between the A and B shares after controlling for a stock characteristic.

B-share Difference (B-A) Adj. R2

X1

CONST0`

X1`

X=SHT Coeff. -0.003 1.682 -0.003 84%

t (-7.38*) (22.15*) (-7.31*)

X=SHP Coeff. -0.003 1.672 -0.003 84%

t (-7.38*) (22.05*) (-6.75*)

X=VOLUME Coeff. -0.001 1.550 -0.001 82%

t (-6.78*) (23.10*) (-6.70*)

X=TR Coeff. -40.913 1.390 -42.522 75%

t (-0.88) (11.66*) (-0.92)

X=RET Coeff. 244.032 1.011 230.839 86%

t (8.37*) (19.55*) (7.90*)

X=VRET Coeff. 18.038 0.360 16.970 82%

t (6.97*) (2.44*) (6.44*)

*: A significance level of five percent or better for a two-tailed test.

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Table 6GMM Tests of Bid-Ask Spreads on Informed Trading Cost Variables

This table provides the GMM test results for 82 pairs of A and B shares in the Chinese stock markets. The sample period is from 1/1/1998 to 5/1/2000. The testing model is

HSPRi = 0 + 1INFOi - D(0` + 1`INFOi) + ei,

where i = 1, 2, …, 154, HSPRi refers to the average percentage half-spread for stock i, INFOi refers to the informed trading cost for stock i, INFO can be INFO1 or INFO2, and D is a dummy variable that is equal to 1 for A shares and 0 for B shares. INFO1 is obtained from the regression test based on variables in dollar terms. INFO2 is obtained from the regression test based on variables in percentage terms. 0` measures the mean difference in bid-ask spreads between the A and B shares after controlling for an informed trading cost variable.

B-share Difference (B-A) Adj. R2

INFO1

CONST0`

INFO1`

INFO=INFO1 Coeff. 2.678 0.117 2.699 79%

t (5.26*) (0.55) (5.29*)

INFO=INFO2 Coeff. 1.805 0.500 1.825 76%

t (2.11*) (1.37) (2.14*)

*: A significance level of five percent or better for a two-tailed test.

24