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Journal of Multinational Financial Management 10 (2000) 421 – 438

Market segmentation and information diffusion

in China’s stock markets

Boo Sjo¨o¨

a,b

_{, Jianhua Zhang}

_*

a_{Department of Economics}_,_{School of Economics and Commercial Law}_,_{Go¨teborg Uni}₆_ersity_, P.O. Box 640,Vasagatan 1,405 30 Go¨teborg, Sweden

b_{Department of Economic and Political Sciences}_,_Uni₆_{ersity College of Sko¨}₆_de_,

541 28 Sko¨6de, Sweden

Received 15 July 1999; accepted 20 March 2000

Abstract

This study analyses the information diffusion between Chinese A shares (restricted to domestic investors) and B shares (restricted to foreign investors). The results show that there is an important long-run information diffusion between A and B shares. In the Shanghai stock market, information flows from foreign to domestic investors. However, in the smaller and less liquid Shenzhen stock market, the information diffusion goes in the opposite way. The direction of the information diffusion is determined by the choice of stock exchange rather than firm size. © 2000 Elsevier Science B.V. All rights reserved.

JEL classification:G12; G14

Keywords:Information flow; Information diffusion; A and B shares; Premium

www.elsevier.com/locate/econbase

1. Introduction

Firms often issue different types of equity to discriminate between different investors. In China, firms are required to discriminate between domestic and foreign investors to ensure that ownership remains under Chinese control. Domes-tic investors can only buy A shares and foreign investors can only buy B shares. The shares are identical in terms of voting power and dividend claims. Due to the

* Corresponding author. Tel.: +46-31-7732689; fax:+46-31-7731043. E-mail address:jianhua.zhang@economics.gu.se (J. Zhang).

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existing regulations, the amount of outstanding B shares is always smaller, so foreign investors are forced to be minority shareholders. The outcome is that the equity of the same firm is traded at the same time, at the same exchange, but by two different investor groups and at quite different prices. Typically, A shares trade at a premium over B shares. Moreover, the premium is not constant. It changes over time in a way that resembles an integrated stochastic process.

This study tests a number of aspects concerning the observed information diffusion between A and B shares in China’s emerging stock market (ESM). The objectives are to learn more about the role of foreign investors in ESMs and to investigate where price information is produced.

Several factors can cause information diffusion between domestic and foreign investors in emerging markets. First, the foreign investors in China are mainly big financial institutions. Compared with the domestic investors, foreign institutional investors can in general be assumed to be more experienced, have better means of obtaining information, and have access to more advanced technology to analyze data. Thus, the presence of foreign investors can be a buy signal for the relatively uninformed domestic investors. In this situation, the prices of B shares would lead those of A shares reflecting that domestic investors get information from foreign investors.

Second, the domestic investors might have the information advantage. They can be better in acquiring relevant news from local sources. In this case, the prices of A shares would lead the prices of B shares, because of foreign investors learning from domestic investors. Third, it follows from the discussion that the price information can flow in both directions. Different investor groups can have different comparative advantages in acquiring information. Finally, as an extreme case, the markets for A and B shares might be completely segmented, showing no correlation and lead-lag relations what so ever. Foreign investors can face severe political risk in emerging financial markets, and they might form quite different conditional expectations about the future prospects of the Chinese economy in general and of the cash flow of the individual firms in particular.

Earlier studies on Chinese stock markets have focused on either the price premia, or on the lead-lag structure between the returns of A and B shares. Chui and Kwok (1998) investigate the cross-autocorrelation structure of A and B share returns in China. Their conclusion is that the returns on B shares lead the returns on A shares. This result, however, is based on an implicit assumption of a complete long-run segmentation between A and B shares. There is no ground for making such an assumption about the relationship between the prices of A and B shares. In fact, it is natural to assume that the difference between the price levels of A and B shares contains information of coming returns. We test this hypothesis and investi-gate its consequences on the flow of information by modeling the prices as a

multivariate vector error correction process1

1_{Harris et al. (1995) discuss cointegration in stock transactions data. They focus on specification and}

estimation of an error correction mechanism for IBM price on different exchanges in order to investigate the price-discovery theory.

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B.Sjo¨o¨,J.Zhang/J.of Multi.Fin.Manag.10 (2000) 421 – 438 423

Our results support the view in Chui and Kwok (1998), that information flows from foreign investors to domestic investors, but only for the Shanghai market. We obtain this result for both the short and the long run. In the smaller Shenzhen market, the causality is more ambiguous. Here, foreign investors affect returns only in the short run. In the long run, information flows from domestic to foreign investors. Our results suggest that the most important factor for whether informa-tion flows from domestic or foreign investors is the choice of stock exchange rather than firm size.

The study is organized as follows. Section 2 discusses theories and hypotheses related to this study. Section 3 explains the use of cointegration and vector error correction models for analyzing information diffusion. Section 4 presents the empirical results. Finally, Section 5 concludes the study.

2. Theoretical framework and hypotheses

The type of information diffusion that we observe between A and B shares on the Chinese stock markets is related to the price-discovery theory, the small-firm effect and studies on the general behavior of institutional investors. The theory of price discovery attempts to determine ‘the process whereby markets attempt to find

equilibrium’ (Schreiber and Schwartz, 1986)2_{. We address a similar type of problem}

in this study by asking whether new information is produced in A-share markets or in B-share markets.

The information flow between firms with large market capitalization and firms with small capitalization is investigated by Lo and MacKinlay (1990), Chan (1993), among others. The existing evidence shows that the returns of large firms’ stocks lead those of small firms’ stock. Large firms’ stocks are more liquid than small firms’ stocks. Thus, new information reflected in the shares of large firms by the end of the trading day will be reflected in small firms’ stocks in the following day.

Other explanations build on imperfect information and institutional investors. Chan (1993) argues that information from large firms is of better quality than that from small firms. Thus, investors usually focus on large firms. Market makers adjust the prices of small stocks after observing previous price changes of large stocks. Badrinath et al. (1995) argue that the returns on institutionally favored stocks are leading the returns on stocks not favored by institutional investors. Their empirical study on the US stock markets supports this hypothesis. According to the information hypothesis (Bailey and Jagtiani, 1994), foreign investors prefer to invest in larger domestic firms where the financial disclosure and information availability are better.

Based on the discussion in this section, our hypotheses are the following: Chinese B shares are likely to lead A shares, because the prices of B shares are the outcome 2_{See Grunbichler et al. (1994) (Germany), Harris et al. (1995) (US), Kleidon and Werner (1993) (UK),}

Pallmann (1992) (Germany) and Roell (1992) (France and UK). Also, see Bailey (1995), Garbade et al. (1979) and Hausbrouck (1991).

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of more active investment decisions. Furthermore, foreign investors have alternative investment opportunities and are more experienced in evaluating the expected future prospects of firms. Large firms’ B shares with high liquidity could be expected to lead A shares. Finally, these effects might be stronger in the initial period when the stock markets are more ‘emerging’ than later.

3. Cointegration, error correction and information diffusion

This section discusses the formulation of a stochastic representation of A and B share prices. If domestic and foreign investors are identical, and have access to the same information, the prices of A and B shares would be the same, adjusted for some minor transaction costs. Obviously, this is not the case judging from Figs. 1 – 3 showing the share prices and the premium of A shares over B

shares3_.

Assume that the prices of A and B shares form a stochastic vector with the following vector error correction representation,

G_(L)(1₋_L)x

t=Pxt−1+m+ot, (1)

Fig. 1. Average weekly A- and B-share prices in Shanghai. This figure plots the weekly average A-share price and the weekly average B-share price in natural log form in Shanghai (22 firms) from July 1993 to June 1997.

3_{The Dickey – Fuller tests do not reject the hypothesis of a unit root for the price series, or for the}

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B.Sjo¨o¨,J.Zhang/J.of Multi.Fin.Manag.10 (2000) 421 – 438 425

Fig. 2. Average weekly A- and B-share prices in Shenzhen. This figure plots the weekly average A-share price and the weekly average B-share price in natural log form in Shenzhen (19 firms) from July 1993 to June 1997.

Fig. 3. Average A-share price premium. This figure plots the weekly average A-share premium in Shanghai (22 firms) and the weekly average A-share premium in Shenzhen (19 firms) from July 1993 to June 1997.

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where, L is the lag operator, m is a vector of deterministic components (if they

exist), and ot is a residual white noise vector with a normal distribution. The G(L)

and P _{matrices describe the flow of information in the system.}

Transitory shocks going from the return of one market to the other are indicated

by significant off-diagonal elements in G_{(L). Permanent shocks common to the}

prices of A and B shares result in cointegration, and that P₌a_b%, where a _{is a}

matrix of adjustment coefficients, b% _{is the cointegrating vector.}

The off-diagonal parameters in G_{(L), together with the adjustment parameters}

(a_{), reflect how information is spread — or not spread — between the markets.}

One significant off-diagonal element inG_{(L) indicates a one-way short-run causality}

in the return series. The vector of adjustment parameters (a_{) indicates which market}

is driving the price levels in the long run. If both markets have access to the same information at the same time and process news in the same way, both coefficients

in a _{will be significant and of the same magnitude. If one market is driving the}

other, only one of the coefficients is significant. In the case of complete

segmenta-tion in the long run, both coefficients in aare 0. If domestic and foreign investors

have access to different information, the price of the share, which is based on superior information, is expected to lead that of the other shares.

The main methodological difference between our study and Chui and Kwok

(1998) is the error correction term a_b%_x_t _{in Eq. 1. If A and B share prices form a}

long-run steady state relation, reflecting the fact that the prices are formed from expectations on the same firm’s cash flow, inference based only on historical return

series [G_(L)(1₋_L)x_t_{] could be misleading because it leaves out important}

informa-tion in a_b%_x_t_.

The efficient market hypothesis (EMH) says that the prices of two assets cannot be cointegrated because cointegration implies predictability in at least one direction. If share prices can be predicted from historical prices, either market efficiency is violated or the model captures a stationary risk premium. In this study, with A and B shares of the same firm, can the two share prices be cointegrated under the EMH? Given that stock prices are based on the expected future cash flow of the same firm, cointegration is expected. For the types of A and B shares traded on the Chinese markets, finding a cointegrating price vector means that domestic and foreign investors have the same information in the long run. If only one type of share price is predicted by the cointegrating vector, one investor group can assumed to have superior information.

In this context, we argue that the most likely cause for no cointegration would be the presence of a non-stationary political risk premium. Under the EMH, most types of information differences should be temporary. As investors in one group start trading on their superior information, they will transmit their information not only to their own market but to the other market as well. In an ESM, foreign investors might have to be extremely sensitive to changes in the economic and political environment. If information regarding the political risk appears repeatedly and in a stochastic way, the outcome could be a permanent non-stationary stochastic price premium, rather than stationary process. Accordingly, the prices of A and B shares would follow separate stochastic trends in the end. This permanent

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B.Sjo¨o¨,J.Zhang/J.of Multi.Fin.Manag.10 (2000) 421 – 438 427

diffusion might exist only during the ESM period and disappear, if or when the institutions behind the markets become more credible. We find it unlikely that the political risk premium is an integrated stochastic process. It is more likely that the

premium shifts at discrete intervals, if it shifts at all4_.

4. Data and empirical results

4.1. Data

Chinese enterprises began to raise capital by issuing bonds and stocks in the 1980s. Since then, China’s financial markets have evolved quickly. There are two stock exchanges in China, the Shanghai stock exchange and the Shenzhen stock exchange. Both were inaugurated in the early 1990s. The Shenzhen exchange is a relatively smaller and less liquid market. The market for B shares opened in 1992, which was more than 1 year after the A shares were first listed in the Shanghai stock exchange. Table 1 presents basic statistics of the two

exchanges5_.

The sample in this study includes weekly time series of 41 firms issuing both A and B shares from July 1993 to June 1997 in either the Shanghai stock exchange or the Shenzhen stock exchange. Among them, 22 are from the Shanghai stock exchange and 19 from the Shenzhen stock exchange. Using firm-specific data, we construct average price series (in natural log form) for different exchanges, by evenly weighting the share prices of the firms in each exchange.

Table 1

Descriptive statisticsa

Shenzhen stock Shanghai stock

exchange exchange

Number of A shares listed 328 300

44 45

Number of B shares listed

A- and B-share market capitalization (billion RMB) 101.2 76.3 4.26

Average daily trading volume of B to A shares (%) 2.99

a_{This table contains basic statistics of China’s stock markets. The sample period is July 1993–June}

1997.

4_{A possible consequence of changes in the political risk premium is segmented trends in the price}

series. This could bias our tests towards finding I(1) processes (see Perron, 1989). Without any detailed information about when these possible shifts might occur, we have not investigated this idea further.

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4.2. Results for the Shanghai and Shenzhen stock markets

Empirical tests of cointegration are sensitive to the properties of the estimated model, and tabulated critical values are valid only for normally distributed white noise residuals. To construct a VAR representation of our samples, such that the assumption of normally and independently distributed white noise residuals cannot be rejected, takes around 10 – 20 dummy variables. A closer investigation of our

models reveals that cointegration depends critically on two observations, 51 and 526_.

Figs. 1 and 2 reveal a downward trend in the prices of B shares during the first year of the sample, up to observation 52. Imposing a dummy for what is the end of a downward trend, after which the prices quickly adjust back again, seems ad hoc. Removing these critical outliers will make A and B shares look more alike than they

really are7

The approach here is to avoid a huge number of dummy variables. We focus on a sufficient number of lags to ensure that the null of no autocorrelation in the models cannot be rejected. This is achieved by using two and three lags in the models.

For the aggregated Shanghai and Shenzhen price series, Johansen’s trace test and the max test statistics reject cointegration at the 5% risk level, in Table 2, based on Johansen (1995) and Hendry and Doornik (1996). The result changes if we include some dummy variables, but we cannot reject the hypothesis of no cointegration with a margin. Thus, we find the cointegration test inconclusive about whether the series are cointegrating or not.

The fact that we cannot easily establish cointegration is an important result because it tells us that there are substantial differences between A and B share prices. A long-run stationary relation between A and B share prices cannot be taken for granted. As discussed above, we are skeptical to the no-cointegration hypothesis. In this situation, with just two variables, an alternative test is to impose a reduced rank

of the Pmatrix in Eq. (1), and test the significance of the adjustment parameters8_.

After imposing one cointegrated vector in the system, we find one significant error

correction mechanism. The a₁ _{parameter is significant for the Shanghai market,}

showing that foreign B-share prices drive the domestic A-share prices. In the Shenzhen market, the relationship is reversed. In this market, the A-share prices drive the B-share prices.

Tables 3 and 4 show the estimated parameters of the VECMs. In both markets, B shares affect A shares, but there are important differences between the short and the long run. In the short run, we find an uni-directional link from historical returns

6_{These observations are} _c_{51, July 22, 1994 and} _c_{52, July 27, 1994.}

7_{Some empirical studies deal with this problem by deleting observations according to some statistical}

properties, for example, Xu and Wang (1997).

8_{With only one I(1) or I(0) variable, the}_t_{-statistics of the adjustment vector will have an asymptotic}

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Table 2

Cointegration test statistics of the Shanghai and Shenzhen average price seriesa

Adjustment parameters (aˆ) for Eigenvalues (mˆ) Normalized Eigenvectors Cointegration test statistics

(b.) r=1

H0 mˆ maximum mmaximum Trace Trace (95%) B shares

SH –A _{SH –B} A shares leading

B shares (a2)

(95%) leading A

shares (a1)

Panel A:Shanghai stock exchange

14.10 14.85 15.40

0.066 0.011 1.000 −0.348 r=0 12.80 0.120** −0.010

3.80 2.05 3.80 (3.27) (0.46)

2.05 1.000

−0.073 r51

Panel B:Shenzhen stock exchange

r=0 11.16 14.10 13.31 15.40 −0.032 −0.071**

1.000

0.059 0.012 −1.186

r51 2.15 3.80 2.15 3.80 (1.29) (3.27)

1.017 1.000

This table reports the Johansen cointegration test statistics. The variables are SH –A, Shanghai A-share average price; SH–B, Shanghai B-share average price; SZ –A, Shenzhen A-share average price; SZ–B, Shenzhen B-share average price. Thet-values are in parentheses.

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Table 3

Vector error correction model results for the Shanghai stock exchangea

Dependent variable

D_{SH –B (ii)} D_{SH –A (i)}

−0.002

D_{SH –A}_t₋₁ 0.015

(−0.031) (0.339)

0.014 −0.038

D_{SH –A}_t₋₂

(−0.900) (0.191)

0.082 0.470**

D_{SH –B}_t₋₁

(3.507) (1.059)

−0.405**

D_{SH –B}_t₋₂ −0.064

(0.810) (−2.942)

0.216** −0.016

Constant

(2.955) (−0.389)

−0.110** 0.008

b%xt−1

(−2.971) (0.389)

Vector residual tests Vector AR 1-2 F(8, 344)=1.756 [0.085] Vector normalityxi2(4)=91.72 [0.000]

Vector Xi2F(42, 475)=2.384 [0.000]

Vector Xi×XjF(105, 417)=2.505 [0.000]

a_{This table reports the VECM results for Shanghai. The variables are,}_D

SH –A, Shanghai A-share average price, first difference;D_{SH –B, Shanghai B-share average price, first difference. The}t-values are in parentheses. The P-values are in brackets.

** Significant at the 0.01 level or better.

on B shares to A shares. In the long run, B-share prices drive the A-share prices in Shanghai. In Shenzhen, the long-run effect is just the opposite; here A-share prices drive the B-share prices.

These results support the assumption that foreign investors in the Shanghai stock exchange have better information, and that domestic investors adjust towards the prices of B shares. However, in the smaller and less liquid Shenzhen market, the domestic investors have better information about the future long-run prospects of the firms. This could be a type of neglected firm effect, if foreign institutional investors do not find it worthwhile or too costly to examine the firms listed in this exchange.

4.3. Sensiti6_{ity tests}

In the following, we test the sensitivity of these results with respect to various assumptions regarding the information process. First, the sample is split into different

regimes9

. Second, the series from Shanghai and Shenzhen are pooled into one model. Pooling the data will permit us to ask more detailed questions about the information flow.

9_{When viewing the results based on different sub-periods, it is important to remember that the tests}

of cointegration are based on the asymptotic properties of assumed infinite processes. Therefore, when we split the sample into sub-periods, we are not formally testing for structural breaks; we are only demonstrating the consequences, assuming that there are different regimes in the sample period.

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B.Sjo¨o¨,J.Zhang/J.of Multi.Fin.Manag.10 (2000) 421 – 438 431

Figs. 1 and 2 reveal that the prices of A shares fall during the first 52 weeks. It could be that foreign investors play a larger role in the early stages of an ESM. Suppose that this first part of the sample represents a different regime, with different behavior of domestic investors. To analyze this possible regime change, the models are re-estimated with the first 60 observations truncated from the sample. The results are reported in Table 5. The cointegration test statistics are now

significant for both exchanges. More interesting, the significance of the a

parame-ters is changing. As for the whole sample, foreign investors determine domestic A-share prices in Shanghai, and domestic investors determine long-run B-share prices in Shenzhen. The change is that foreign investors also drive A shares in Shenzhen. If we assume a regime shift, the role of foreign investors seem more important over time, in the sense that their influence spreads to the Shenzhen stock

exchange as the markets develop10_.

Table 4

Vector error correction model results for the Shenzhen stock exchangea

Dependent variable

D_{SZ –B (ii)} D_{SZ –A (i)}

D_{SZ –A}_t₋₁ −0.055 0.038

(−0.680) (0.536)

−0.029 0.057

D_{SZ –A}_t₋₂

(−0.366) (0.823)

D_{SZ –A}_t₋₃ −0.071 −0.025

(−0.365) (−0.921)

0.178**

D_{SZ –B}_t₋₁ −0.141

(−1.876) (2.073)

D_{SZ –B}_t₋₂ −0.153 −0.131

(−1.750) (−1.705)

−0.145 −0.102

D_{SZ –B}_t₋₃

(−1.672) (−1.346)

−0.025**

−0.010 Constant

(1.213) (−2.721)

0.072** 0.030

b%xt−1

(3.320) (−0.923)

Vector residual tests Vector AR 1-2 F(8, 344)=0.734 [0.662] Vector normalityxi2(4)=90.63 [0.000]

Vector Xi2F(48, 470)=1.463 [0.027]

Vector Xi×XjF(132, 390)=2.185 [0.000]

a_{This table reports the VECM results for Shenzhen. The variables are,}_D

SZ –A, Shenzhen A-share average price, first difference;D_{SZ –B, Shenzhen B-share average price, first difference. The}t-values are in parentheses. The P-values are in brackets.

** Significant at the 0.01 level or better.

10_{Another observation from Figs. 1 and 2 is that the markets can be characterized as bear markets}

until the end of 1995, and bull markets thereafter. We also estimated these periods separately, but the results did not lead us to change our conclusions from above.

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Table 5

Vector error correction model results for both Shenzhen and Shenzhen stock exchanges — sub-samplea

Shanghai stock exchange Shenzhen stock exchange

3 3

Lag

Yes Yes

Cointegration ECMs

−0.067** (2.28) 0.181** (3.01)

B shares leading A shares (a₁)

A shares leading B shares (a₂) −0.011 (0.31) −0.113** (3.67) AR (1-2)=0.902 [0.516] AR (1-2)=0.839 [0.570]

Vector residual tests

Normality=100.7 [0.000] Normality=62.64 [0.000] Vector Xi2=1.827 [0.036]

Vector Xi2=0.779 [0.816]

Vector Xi×Xj=2.112 [0.000]

Vector Xi×Xj=0.805 [0.876]

a_{This table summarizes the cointegration tests and the VECM results for both Shanghai and}

Shenzhen in the period of October 1994–June 1997, with 130 observations. The t-values are in parentheses. TheP-values are in brackets.

** Significant at the 0.01 level or better.

Our second sensitivity test is to pool the A and B shares of the two exchanges in one model. The cointegration test statistics from this ‘pooled’ model suggest two, or possible one cointegrating vector, depending on the choice of risk level, see Table 6. In the following, we explore the different hypotheses that follow by assuming one or two cointegrating vectors.

Suppose there is only one cointegrating vector in the system. The system would consist of three common stochastic trends and one stationary relation. The latter could be a common risk premium for A shares over B shares in both exchanges. Alternatively, there is a stationary risk premium between the Shanghai and the Shenzhen markets.

To test for these hypotheses, we start by testing for exclusion of exchanges or types of shares from the vector. All these hypotheses are rejected; means all four variables are needed to form the stationary relation. Next, we test if the premium of A shares over B shares in Shanghai together with the premium in Shenzhen form a stationary relation. This joint hypothesis, the vector is made up of two premia, is

rejected by the data. The x2_{(2) statistics is 10.702, with probability value of}

0.004711_.

The alternative is that the two non-stationary A-share series cointegrate with the two non-stationary B-share series. This hypothesis assumes a joint risk premium of A shares over B shares in the two markets. To test this hypothesis, we impose the restrictions of a ratio of A-share series and a ratio of B-share series on the

11_{The exclusion tests are not presented here since they are all insignificant. In the tests,}_x₌

[SH – A, SH –B, SZ–A, SZ–B], thebvector is restricted as [b1= −b2] and [b3= −b4=1]. The test [b1= −

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B . Sjo ¨o ¨ , J . Zhang / J . of Multi . Fin . Manag . 10 (2000) 421 – 438 433 Table 6

Cointegration test statistics of the Shanghai and Shenzhen pooled price seriesa

Cointegration test statistics

Restricted cointegration vectors Adjustment parameters (aˆ) forr=2

H0 mˆmaximum mmaximum Trace Trace (95%) SH_–A (B SH_–B (A

–A SH–B SZ–A SZ–B SZ–A (B SZ–B (A

shares leading

(95%) shares leading shares leading shares leading

B shares)

A shares) A shares) B shares)

27.10 54.04**

0.000 47.20 a₁₁,−0.146**

−1.235

1.000 0.000 r50 26.33 a₂₁, 0.037 a₃₁,−0.061 a₄₁,−0.154**

(1.01) (2.93)

(1.09) (2.38)

21.10 29.70* 29.70 a₁₂, 0.108

−1.446 a₂₂,−0.019

1.000 a₃₂, 0.047

0.000

0.000 r51 15.61 a₄₂, 0.178**

(1.35) (3.76)

(0.60) (1.20)

14.10 14.09 15.40

r52 8.83

r53 5.26 3.80 5.26 3.80

Vector residual tests

Vector Vector

Vector AR 1-2 Vector

normalityxi2 Xi2=1.756

F(32, 602) Xi×Xj=2.00

[0.000] [0.000]

=1.001 (8)=127.8

[0.000] [0.468]

a_{This table reports the Johansen cointegration test statistics. The variables are: SH}

–A, Shanghai A-share average price; SH–B, Shanghai B-share average price; SZ–A, Shenzhen A-share average price;

–B, Shenzhen B-share average price. The optimal lag length in this model is 3. Thet-values are in parentheses. TheP-values are in brackets.

* Significant at the 0.05 level. ** Significant at the 0.01 level or better.

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cointegrating vector. The data does not reject the hypothesis that these two ratios

cointegrate, x2₍₂₎

=1.570, with probability value of 0.4566. Our conclusion from

these tests is that one cointegrating vector is not sufficient to correctly describe the system. There is a stationary risk premium between A and B shares, but this premium is not necessarily of the same magnitude in both markets.

Assume that there are two cointegrating vectors, one vector represents the stationary premium in the Shanghai stock exchange and the other premium in the

Shenzhen stock exchange12_{. The next question is how the two markets interact with}

each other in the long run.

the B share market in Shenzhen, as suggested by the significanta₄₁-parameter. Since

the first vector occurs in two exchanges, but for different types of stocks, the foreign investors seem to use the same information to price B shares in Shenzhen as domestic investors used in Shanghai.

4.4. The flow of information between the markets

Why should the prices of B shares lead the prices of A shares in Shanghai? Chui and Kwok (1998) suggest that foreign investors are better informed and receive news faster than domestic investors because of the information barriers in China. An additional factor is that B-share investors are mostly big financial institutions, while domestic A-share investors are relatively smaller. Thus, the returns of the institutional favored shares could lead those of institutional unfavored shares, as suggested by Badrinath et al. (1995).

If information barriers are crucial, domestic investors have a problem in obtain-ing information, mainly because of the low creditability of domestic media. The cost of obtaining information about the stock market in general and the prospects for individual firms is high for domestic investors. Therefore, a cost-effective way of getting information is to observe the price movements of the foreign B shares. Then, the question is why A-share prices follow B-share prices in Shanghai, but not in Shenzhen. The answer could be that the Shenzhen exchange is relatively smaller in terms of total market capitalization and number of listed firms, or because the Shenzhen stock exchange is dominated by small firms.

In Table 1, we see that the total market capitalization of the Shanghai stock exchange is 101.2 billion RMB, and that of the Shenzhen stock exchange is 76.3 billion. By June 1997, in Shanghai, the number of A-share listing firms is 328, while 12_{This hypothesis is not rejected by the data. The test statistic is}_x2₍₂₎₌_{0.4735, with probability value}

of 0.7892. Here, the two cointegrating vectors are restricted as [b11=1, b13=b14=0] and [b23=1,

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Table 7

Summary of the cointegration test results of individual firms

Stock exchange Cointegrated (%) Not cointegrated (%)

(N=22)

Shanghai stock exchange 45.5 55.5

Shenzhen stock exchange (N=19) 73.7 26.3 (N=41)

Total 58.5 41.5

in Shenzhen, this number is 300. If we calculate the ratio of the average daily trading volume of B shares to A shares in 1997, we find that this ratio is 4.26% for

Shanghai, and 2.99% for Shenzhen13_{. The Shanghai market is bigger and the B}

shares are much more liquid than those in Shenzhen. The result that foreign investors are leading domestic investors in Shanghai could be in line with various small firms and liquidity effects found in other markets. The next section analyses the firm size effect in detail.

4.5. The firm size effect

The lead-lag effect and the information hypotheses suggest that firm size could be an important factor for foreign institutional investors. Therefore, we test if the prices of B shares have a tendency to lead those of A shares for firms with larger market capitalization. Table 7 summarizes the cointegration test results, which show that more than half of the A and B shares are cointegrated. The share of firms with cointegration among the assets is 58.5%.

Table 8

Summary of the vector error correction model results — classified by firm size and stock exchangea

a₂Significant: A a₁anda₂ a₁anda₂ a₁Significant: B

shares leading B significant insignificant shares leading A

shares shares

Panel A:firm size

62.5% – 12.5%

Large 25.0%

36.0% 36.0%

Medium 20.0% 22.0%

Small 87.5% 12.5% – –

Panel B:stock exchange

72.7% 9.1% 13.6% 4.5%

Shanghai stock exchange

52.7%

26.3% 10.5%

Shenzhen stock 10.5%

exchange

a_{This table summarizes the estimated results from individual firms classified by firm size and stock}

exchange, respectively. Large firms are in the top 20%; small firms are in the bottom 20%; and in the middle 60%, they are the medium ones. Significant stands for significant at the 0.05 level or better.

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Table 8 summarizes the VECM results, classified by firm size and exchange,

respectively14_{. Firm size is measured by adding the market capitalization of A and}

B shares (all in local currency, RMB) at the end of June 1997. The sample is then split into three groups, big firms are the top 20%; small firms are the bottom 20%; medium firms 60%. Panel A of Table 8 reveals that B shares lead A shares for big firms (62.5%) as we expected. However, for small firms, B shares lead A shares as well (87.5%), which is inconsistent with our expectation. We check firm size in different exchanges in our sample and find that most of the firms in both the top and the bottom 20% are from the Shanghai stock exchange. We proceed to test if the choice of exchange determines the investment decisions of the foreign investors. Panel B of Table 8 shows that in the Shanghai stock exchange most of the B shares lead A shares (72.7%), while in the Shenzhen stock exchange, on the contrary, most of the firms’ A shares lead B shares (52.7%). The results demonstrate that it is the Shanghai stock exchange that determines that B shares are leading A shares. In Shanghai, a larger number of A shares is driven by B shares compared with the Shenzhen stock exchange. Thus, the choice of stock exchange is the most important factor behind the conclusion of B shares driving A shares.

5. Summary and conclusions

This study tests various aspects of the information diffusion resulting in different prices on domestic investors’ A shares and foreign investors’ B shares in the emerging Chinese stock markets.

If both investor groups have the same information, information will flow in both directions between domestic and foreign investors. We expect A and B shares of the same firm to be correlated, both in levels (prices) and in first differences (returns). If one investor group is leading the other, due to superior information, information will go in one direction only. If the markets are totally segmented, no information will be passed between the markets of A and B shares.

Our main conclusion is that the information diffusion between A and B shares goes from foreign investors to domestic investors in the larger and more liquid Shanghai stock exchange. However, in the smaller Shenzhen stock exchange, the causality is more ambiguous. In Shenzhen, foreign investors affect returns only in the short run. In the long run, the information goes from domestic to foreign investors. These conclusions are quite stable under various assumptions of regime changes, and after taking account of firm size. In the end, the most important factor for determining the information flows is the choice of stock exchange rather than firm size.

We argue that foreign investors drive the prices of A shares in China’s stock markets because domestic investors have problems in acquiring relevant and trustworthy firm information from domestic and foreign media. Domestic investors 14_{The cointegration tests and the VECM results for the individual firms are available on request from}

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B.Sjo¨o¨,J.Zhang/J.of Multi.Fin.Manag.10 (2000) 421 – 438 437

therefore condition their investment decisions on observed B-share prices to find a good long-run valuation of the stock. In a smaller exchange, this foreign informa-tion advantage might not exist, other than in the short run. In this type of market, foreign investors rely on the locals to determine the correct long-run future prospects of the firms. Future research will reveal if the information diffusion between A and B shares in Shanghai and Shenzhen is stable, or changes as the markets evolve over time.

Acknowledgements

The authors thank Richard J. Sweeney and Clas Wihlborg for discussions and suggestions, Roger Huang and Lars Meuller for valuable comments. The remaining errors are the fault of the authors. Part of this study was written when the first author was visiting the Department of Management, McGill University.

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Sjo ¨o ¨ , J . Zhang / J . of Multi . Fin . Manag . 10 (2000) 421 – 438 433 Table 6

Cointegration test statistics of the Shanghai and Shenzhen pooled price seriesa

Cointegration test statistics

Restricted cointegration vectors Adjustment parameters (aˆ) forr=2

H0 mˆmaximum mmaximum Trace Trace (95%) SH_–A (B SH_–B (A SH

–A SH–B SZ–A SZ–B SZ–A (B SZ–B (A

shares leading

(95%) shares leading shares leading shares leading

B shares)

A shares) A shares) B shares)

27.10 54.04**

0.000 47.20 a₁₁,−0.146**

−1.235

1.000 0.000 r50 26.33 a₂₁, 0.037 a₃₁,−0.061 a₄₁,−0.154**

(1.01) (2.93) (1.09)

(2.38) 21.10 29.70* 29.70 a₁₂, 0.108

−1.446 a₂₂,−0.019

1.000 a₃₂, 0.047

0.000

0.000 r51 15.61 a₄₂, 0.178**

(1.35) (3.76) (0.60)

(1.20) 14.10 14.09 15.40

r52 8.83

r53 5.26 3.80 5.26 3.80

Vector residual tests

Vector Vector

Vector AR 1-2 Vector

normalityxi2 Xi2=1.756

F(32, 602) Xi×Xj=2.00

[0.000] [0.000] =1.001 (8)=127.8

[0.000] [0.468]

a_{This table reports the Johansen cointegration test statistics. The variables are: SH}

–A, Shanghai A-share average price; SH–B, Shanghai B-share average price; SZ–A, Shenzhen A-share average price; SZ

–B, Shenzhen B-share average price. The optimal lag length in this model is 3. Thet-values are in parentheses. TheP-values are in brackets. * Significant at the 0.05 level.

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cointegrating vector. The data does not reject the hypothesis that these two ratios cointegrate, x2₍₂₎

=1.570, with probability value of 0.4566. Our conclusion from these tests is that one cointegrating vector is not sufficient to correctly describe the system. There is a stationary risk premium between A and B shares, but this premium is not necessarily of the same magnitude in both markets.

Assume that there are two cointegrating vectors, one vector represents the stationary premium in the Shanghai stock exchange and the other premium in the Shenzhen stock exchange12_{. The next question is how the two markets interact with}

each other in the long run.

The non-significant adjustment parameters of the pooled system suggest that there are two long-run exogenous prices in the system, the B-share prices in Shanghai and the A-share prices in Shenzhen. The significant parameters confirm the findings above that the foreign investors drive the Shanghai market, and that the domestic investors are more important in Shenzhen. The new result from the pooled system is that the price information in the Shanghai exchange spills over to the B share market in Shenzhen, as suggested by the significanta₄₁-parameter. Since the first vector occurs in two exchanges, but for different types of stocks, the foreign investors seem to use the same information to price B shares in Shenzhen as domestic investors used in Shanghai.

4.4. The flow of information between the markets

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Table 7

Summary of the cointegration test results of individual firms

Stock exchange Cointegrated (%) Not cointegrated (%) (N=22)

Shanghai stock exchange 45.5 55.5 Shenzhen stock exchange (N=19) 73.7 26.3

(N=41)

Total 58.5 41.5

in Shenzhen, this number is 300. If we calculate the ratio of the average daily trading volume of B shares to A shares in 1997, we find that this ratio is 4.26% for Shanghai, and 2.99% for Shenzhen13_{. The Shanghai market is bigger and the B}

4.5. The firm size effect

Table 8

Summary of the vector error correction model results — classified by firm size and stock exchangea

a₂Significant: A a₁anda₂ a₁anda₂ a₁Significant: B

shares leading B significant insignificant shares leading A

shares shares

Panel A:firm size

62.5% – 12.5%

Large 25.0%

36.0% 36.0%

Medium 20.0% 22.0%

Small 87.5% 12.5% – –

Panel B:stock exchange

72.7% 9.1% 13.6% 4.5%

Shanghai stock exchange

52.7%

26.3% 10.5%

Shenzhen stock 10.5%

exchange

a_{This table summarizes the estimated results from individual firms classified by firm size and stock} exchange, respectively. Large firms are in the top 20%; small firms are in the bottom 20%; and in the middle 60%, they are the medium ones. Significant stands for significant at the 0.05 level or better.

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Table 8 summarizes the VECM results, classified by firm size and exchange, respectively14_{. Firm size is measured by adding the market capitalization of A and}