segmentation of the a- and b-share chinese equity markets

The Journal of Financial Research. Vol. XXIII, No.2. Pages 179-195. Summer 2000

SEGMENTATION OF THE A- AND B-SHARECHINESE EQUITY MARKETS

Hung-Gay FungUniversity of Missouri-St. Louis

Wai LeeJ.P. Morgan Investment Management Inc.

Wai Kin LeungUniversity of Hong Kong

Abstract

In this study we use the latent variable asset pricing model to examinethe pricing ofA and B shares in the Chinese stock markets. The hypothesis testedis whether markets for the A and the B shares of the same companies aresegmented. We document only one latent variable in both A- and B-share markets.However, the latent risk premiums for the A and B shares are only weaklycorrelated, indicating the two-tier markets are loosely related. The weakcorrelation implies the two markets reflect different fundamental forces.Additional analysis demonstrates that the Shanghai market responds to theShenzhen market rather than the other way around.

JEL classification: C32, F36, G12.

I. Introduction

On the two major exchanges in China (Shanghai Stock Exchange andShenzhen Stock Exchange), the same company can issue A shares and B shares. Ashares (denominated and settled in Yuan or RenminbilRMB) are issued only toChinese citizens; B shares are issued to foreign residents (settled in U.S. dollars onthe Shanghai Exchange or in Hong Kong dollars on the Shenzhen Exchange).Although A and B shares differ in terms of ownership, both types of shares conveyequal rights to the same company.

Leung acknowledges financial support from a research grant provided by the Committee on Researchand Conference Grants at the University of Hong Kong. Part of the data are provided by the Main Libraryat the University of Hong Kong. The authors would like to thank an anonymous reviewer for helpfulcomments, and Shirley Murphy and Patricia Peat for editorial assistance.

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180 The Journal of Financial Research

Many Chinese state-owned enterprises have been authorized to issue bothA and B shares on the Shanghai and Shenzhen stock exchanges in recent years.' Asof May 1997, 354 companies had issued A shares and 47 companies had issued Bshares on the Shanghai Stock Exchange, with a total capitalization value of aboutU.S.$l 02 billion. During the same time, 324 companies had issued A shares and 49had isssued B shares on the Shenzhen Stock Exchange, with a market capitalizationvalue of about U.S.$83 billion. Table 1, Panel A, reports these statistics. The rapidand continuing privatization program of state-owned enterprises in China will addto these numbers in the future.

Although shares similar to A and B shares are traded in other countries(Domowitz, Glen, and Madhavan (1997), Bailey and Jagtiani (1994)), pricingbehavior in the China A and B markets is unique. Although in other countries theprice ofB shares is typically higher than the price of the corresponding A shares, Bshares in China are generally traded at a discount relative to A shares (Wo (1997),Bailey (1994)). Market segmentation and liquidity are possible explanations for theprice differential in these markets (Poon, Firth, and Fung (1998), Wo (1997)). Sofar, only limited research has been done on market segmentation in the A- and Bshare Chinese equity markets.

Market segmentation can be seen in measures such as price volatility andthe effect ofpolitical factors on the stock market. Table 1, Panel B, shows that pricevolatility of A shares on both exchanges is typically higher than price volatility ofB shares. The simple return volatility is more dramatic than that of the continuouscompounding return. Furthermore, the Shanghai A index jumped from 328.85 onJuly 29, 1994, to 700.58 on August 5, 1994, an increase of 113 percent in the priceindex in one week. It went on to move up to 1014.46 on September 16, 1994, anincrease of208 percent in less than two months. A similar scenario happened to theShenzhen A index. The Shanghai and Shenzhen B indexes did not experience thesame kind of change during the period.

The big price change was attributed primarily to the Chinese government'sannouncement that Chinese financial companies would be allowed to team up withforeign companies to set up joint venture mutual funds to invest in A shares,originally closed to foreigners.' The indexes shot up in anticipation of a hugeamount of foreign money entering the A-share markets.

In addition, the Shanghai and Shenzhen A indexes increased 60 percent and58 percent, respectively, between December 27, 1996, and May 9, 1997, because of

'Typically, A shares include state shares (held by central government), legal shares (held by domesticcompanies and nonbanking institutions), employee shares (held by employees), and public individual shares.State and employee shares are not traded in the market, although legal shares for some companies can betraded in the Stock Trading Automated Quotation System (STAQS) or National Exchange and TradingSystem (NETS) in Beijing.

2The teaming up ofSalomon Brothers with Shanghai Industrial Investment (Holdings), a business armof the city government, to form a joint venture asset management company is one example. The newcompany, Saloman-Shanghai Industrial Asset Management, will manage funds invested in Greater China.

Segmentation in Chinese Equity Markets

TABLE 1. Market Size and Returns of A- and B-Share Markets.

Panel A. Market Size, May 1997

181

Exchange Share No. of Companies Listed Market Capitalization

Shanghai A 354 RMB 827 billion (or US$IOObillion)B 47 US$2.66 billion

Shenzhen A 324 RMB 666 billion (or US$80.4 billion)B 49 HK$22.7 billion (or US$2.91 billion)

Panel B. Weekly Return Statistics (1993/5-1997/6)

Variable T Mean Min. Max. Std. Dev. Skewness Kurtosis

Index: continuous compounding returnSHA 210 -0.0002 -0.2319 0.7563 0.0825 4.0508- 35.2925-SHB 210 0.0002 -0.1519 0.2269 0.0448 1.2244- 6.1932-SZA 208 0.001l -0.3435 0.5550 0.0802 1.7657- 13.2527-SZB 208 0.0014 -0.1909 0.2564 0.0509 1.6478- 9.5029-

10 Individual stocks: continuous compounding returnSHA 2100 -0.0023 -0.6456 0.8791 0.0981 2.7715-- 23.1059--SHB 2088 -0.0020 -0.6241 0.6289 0.0737 0.1643" 9.0345"SZA 2089 0.0004 -0.4510 0.7360 0.0937 1.4931-- 9.9098--SZB 2089 0.0000 -0.5560 0.6393 0.0899 1.1139-- 9.6102--

Index: simple returnSHA 210 0.0036 -0.2069 1.1304 0.1023 6.8723" 72.0926--SHB 210 0.0012 -0.1409 0.2547 0.0463 1.6820-- 8.1714"SZA 208 0.0045 -0.2907 0.7419 0.0883 3.3514-- 25.2886--SZB 208 0.0027 -0.1738 0.2923 0.0535 2.2929-- I I. 7494--

10 individual stocks: simple returnSHA 2100 0.0031 -0.4757 1.4088 0.1188 5.7989-- 58.1630--SHB 2088 0.0007 -0.4643 0.8756 0.0755 1.4Ill-- 13.5294"SZA 2089 0.0050 -0.3630 1.0875 0.1035 3.ll44-- 23.3702--SZB 2089 0.0042 -0.4265 0.8952 0.0971 2.5636-- 16.5628--

Source: Chinese Stock Database, Taiwan Economic Journal.

Note: SH denotes Shanghai, SZ denotes Shenzhen, and T denotes the number of observations. Exchangerate US$I is equal to RMB8.279 or HK$7.8.

--Significant at the 5 percent level.-Significant at the 10 percent level.

rumors that the Chinese government planned to provide a facelift to the Shanghaiand Shenzhen stock markets before the 15th National Congress of the ChineseCommunist Party to be held in September 1997. Of course, B shares would besubstantially less affected by these events because their shareholders are alreadynon-Chinese.


We use a latent variable asset pricing approach to examine the pricingbehavior of the A and B shares on the Shanghai and Shenzhen exchanges. Weinvestigate whether the return-generating processes underlying the two markets areclosely related to determine the degree to which these two markets are segmented.We also examine the dynamics of the latent variables in these two exchanges.

The latent variable model is used often to examine the behavior of timevarying expected U.S. stock returns (Zhou (1994), Ferson and Foerster (1994),Ferson, Foerster, and Keim (1993), Ferson (1992), Campbell (1987), among others).Other studies extend such models to study financial assets such as bonds and foreignexchange and stock markets in other countries (Lee, Fung, and Lo (1996), Hansenand Hodrick (1983)). The latent variable approach suggests the time-varyingexpected excess return is a linear function of a few unobservable factor riskpremiums, called latent variables, and it allows expected returns and risk premiumsto vary over time.

We find only one latent variable in both A and B shares for the Shanghaiand Shenzhen exchanges. Correlation oflatent variables between A and B shares islow (0.375 for the Shanghai shares and 0.401 for the Shenzhen shares), indicatingthe two-tier markets are not closely related. Further analysis demonstrates theShanghai markets take more time to respond to information than does the Shenzhenmarket.

II. Research Methodology

In the standard asset pricing frameworks, such as those of Cox, Ingersoll,and Ross (1985), Merton (1973), and Sharpe (1964), expected asset returns can beexpressed as a linear combination of expected risk premiums as follows:

(1)

where \"j =1,2, ..., K, are the unobserved risk premiums (state variables, hedgeportfolios, or factors); bi) are the betas of the asset i for thej risk factor conditionalon the information 2'-1; and AOt is the zero-beta security.

When the expected returns and risks of assets at time t are conditioned onthe known information set, their betas and expected risk premiums are also timevarying. The latent variable asset pricing model relates the time-varying expectedreturns to a small number ofunobservable risk premiums, or latent variables, whileassuming constant ratios of the beta coefficients.' Empirical tests of the latentvariable models are based on the assumption that expected returns are time varyingbut correlated with observable instruments, 2'-1 that are known at time t.

'The constant ratio ofbeta is part ofthe null hypothesis that cannot be independently tested in the latentvariable framework.

Segmentation in Chinese Equity Markets 183

In examining the joint hypothesis (HI) of linear expectations for expectedreturns and K latent variables, Ferson, Foerster, and Keirn (1993) formulate thelatent variable approach for equation (I) as:

I'C= I

(2a)

(2b)

(2c)

where R = (R I' R2) is a T x N matrix ofall asset returns, partitioned by columns withK+1 in RI and N - K -1 in R2 where T is the number of observations.

Because R represents the entire set of asset returns, and there are fewerlatent variables than the number of assets, there must be some restrictions on someasset returns in R. R is thus partitioned into two types of asset returns: R I , whichincludes the reference assets, and R2, which includes the rest of the asset returns.The reference assets are chosen so that their beta matrix and a unit vector arenonsingular. In addition, the reference assets must span the K risk factors, and theycannot have identical betas on any combination of risk factors. R2 can thus bethought of as some linear combination of R I • Z is a TxL matrix of predeterminedinstrumental variables; I is a vector of one; d, and Care L x (K+1) and (K+1) x

(N-K -1) matrices of parameters, respectively.Examination of HI can be accomplished using the generalized method of

moments (GMM), which allows autocorrelation and conditional heteroskedasticityin the disturbance term (Hansen (1982), Hansen and Singleton (1982)). Theparameters are estimated to minimize the quadratic function of Tg' Wg, where g isthe vector of the orthogonality conditions and W is the inverse of the covariancematrix ofthe orthogonality conditions. Under HI' the orthogonality condition testedis E(V' Z) = 0, where V = (VI' V2) . Under the null hypothesis, the minimized valueof the quadratic form is asymptotically distributed as a chi-square statistic withdegrees offreedom equal to the number oforthogonality conditions less the numberof parameters. Following Ferson, Foerster, and Keirn (1993), we label this chisquare statistic the GMM 1 test statistic.

We can also examine the null hypothesis of testing K latent variableswithout imposing the linear expectations restriction on HI. This hypothesis (H2) isthus more general than HI. Testing ofH 2 can be formulated as follows:

I'C= I

(3a)

(3b)

H 2 can also be examined using the GMM test with the orthogonalitycondition asE(V'Z) = 0. The statistics that test Zi, are called GMM2. In fact, GMM2


is the minimized chi-square value of the quadratic form specified in equations (3a)and (3b).

To extract the latent variable or risk premiums (A) from the asset pricingmodel, we express the return (excess return) of equation (1) in matrix form asfollows:

E(rlz) = A(Z)B (4)

where A (Z) are the risk premiums on the K factors; Z is TxL the instrumentalvariables; B is the K x (N -1) conditional betas; and r is T x (N - 1) excess returnsover T periods.

It can be shown that E(r) = ze and A(Z) = ZA, where A is an LxK constantmatrix (see Zhou (1994)). As a result, we have ze = A(Z)B = ZAB, which impliese =AB. The estimates of A and B are not unique, but the estimate of e is. As aresult, we can normalize A and B using any KxK invertible matrix to get an estimateof A and B, which gives rise to an unique estimate of e.4 Once the A matrix isobtained, we can derive the latent risk premiums, A(Z) (i.e., A(Z) = ZA).

To examine the predictability of returns using the predeterminedinstrumental variables, we use five instruments in the analysis. We primarily followFerson and Harvey (1993) in selecting the variables. These instruments include aconstant, the lagged weekly return ofthe market index, the lagged dividend yield ofthe market index, the lagged return of the exchange rate, and a lagged termpremium. In the literature, measures of short- and long-term yield variables as theterm premium are used (e.g., Solnik (1993), Harvey (1991), Campbell (1987)).

We conduct our tests for two sets ofassets. For each reference asset, R I . I andR I , 2' we perform the following regression:

k=I,2andj=A,B (5)

The market returns, Rrn, on the Shanghai and Shenzhen Stock Exchanges for thecorresponding shares (A or B) are used in this study.

Ill. Empirical Results

Weekly individual company and market return data are obtained from theChinese Stock Database of the Taiwan Economic Journal covering May 1993through June 1997 for both the Shanghai and Shenzhen stock exchanges. Ten of thecompanies in our sample are listed on both exchanges, and each of the companies

·We use the same reference asset in our normalization procedure.


issued both A and B shares.' In total, twenty companies are in the sample (thecompanies are listed in the Appendix). Companies with a shorter period of dataavailability are excluded."

Because the dividend data are not available in the database, we compute thedividend yield as follows. First, for each stock, dividends in the previous fourquarters are first calculated; dividend yield is the total dividend for four quartersdivided by the closing price at a certain point. Second, dividend yield is counted aszero if there is no dividend information for that stock. Third, we compute thedividend yield of the stocks in each index, which incorporates the marketcapitalization weight of the stocks within the index.

The weekly data on the exchange rates (Renminbi) are obtained from DR!Money Markets and Fixed Income Database for the same period as the stock returnsdata. The return of exchange rate variables for the B shares on the Shanghai andShenzhen exchanges is the log (ratio of exchange rates at t and t-l).

The term structure variable (TERM) is computed on the basis ofthe ten-yearbond yield less the three-month yield on the interest rate instruments. The interestrate data are provided by the Research Department, Bank of China, Hong KongBranch.'

Asset Returns with Instrumental Variables

In testing HI and H2, we need to select the referenced assets for thefollowing latent variable models. We experimented with different referenced assets;all results are similar (not reported here). We report only the results using the firsttwo stocks listed in the Appendix as the reference assets.

Table 2 reports the results of equation (5), which indicate that most of theinstruments are not statistically significant." These results imply the returns are notclosely linked to fundamental factors that are thought to underlie pricing in theUnited States." The weak results of the term structure variable on the stock returnssuggests monetary policies may not have a significant effect on the returns.

5Bailey (1994) supports the concept of stationarity in Chinese stock returns. In addition, we find thatChinese stock returns are stationary using the conventional augmented Dickey-Fuller test.

6Although more companies are listed on both the Shanghai and the Shenzhen exchanges in recent years,these companies have limited observations and thus are likely to have low statistical power in the GMM tests.As a result, they are not included in our sample. Use ofindividual company data versus portfolios may resultin potential idiosyncratic bias.

'The short-term and long-term rates in the United States and China are likely to differ because of theinherently different market structure.

8We also experimented with other variables such as ownership percentage by government and privateowners. These variables were insignificant. Thus, they are not reported here.

9If the residuals of the regression are not normally distributed, the standard errors of the coefficientestimates may not be efficient.


TABLE 2. Regression Results for the Weekly Reference Asset Returns.

Shares Const. Rmt-I,J DY,_I.j FX,_I TERM'_I R2

Shanghai A sharesFirst stock 0.017 -0.025 2.663 -6.62 -1.282 0.045

(0.40)' ( -0.29) (1.33) (-1.98)** (-0.91)Second stock 0.039 0.Q28 3.417 -7.767 -1.998 0.060

(0.90) (0.27) (1.66)* ( - 2.09)** ( -1.38)

Shanghai B sharesFirst stock 0.007 0.297 1.193 -3.711 -0.665 0.061

(0.23) (2.33)** (1.32) (-2.11)** (-0.70)Second stock 0.029 0.030 1.757 -0.548 -1.296 0.033

(1.39) (0.27) (2.21)** ( -0.29) ( -2.01)**

Shenzhen A sharesFirst stock 0.Q28 0.120 1.289 -6.326 -1.169 0.046

(0.69) (1.20) (2.26)** ( -2.75)** ( -1.06)Second stock 0.003 0.059 1.502 -6.236 -0.557 0.048

(0.08) (0.63) (2.62)** ( -1.77)* ( -0.56)

Shcnzhen B sharesFirst stock 0.011 0.290 -0.171 3.448 -0.248 0.044

(0.26) (1.20) ( -0.36) (1.0 I) ( -0.22)Second stock -0.000 0.142 -0.281 -1.140 0.212 0.008

(-0.01) (0.63) ( -0.52) ( -0.28) (0.20)

Note: The model is as follows:

R'.

k•j = Go + G,Rm.,_I.} + G2DY,_I.} + G3FX,_1+ G4TERM'_1+ u" k = 1,2 and) = A,B

where R,.k .} is the returns of the asset, R; is the market index return, DY is the dividend yield, FX is theforeign exchange rate, and TERM is the term premium (ten-year bond yield less three-month bond yield).

'The z-values are in parentheses.

**Significant at the 5 percent level.-Significant at the 10 percent level.

Results on Number ofLatent Variable Hypothesis Testing

In estimating the models, we use the iterated GMM. An identity matrix isused for W in the first stage, which obtains estimates of the error terms, and a newweighting matrix is then calculated for each iteration until convergence. Ferson andFoerster (1994) find that this iterative approach has superior finite sampleproperties. 10

IOThe iterative procedure is desirable because the coefficient estimates are approximately unbiased forsimple models, and goodness-of-fit statistics seem to conform well to the asymptotic distribution even forcomplex models.

Segmentation in Chinese Equity Markets

TABLE 3. Tests of Asset Pricing Models with K = 1 Latent Variable.

187

Exchange/Assets

Shanghai/A shares with 1st reference assetShanghai/A shares with 2nd reference assetShanghaiIB shares with Ist reference assetShanghaiIB shares with 2nd reference asset

Shenzhen/A shares with 1st reference assetShenzhen/A shares with 2nd reference assetShenzhenIB shares with 1st reference assetShenzhenIB shares with 2nd reference asset

Model:

I'C= I

GMMI GMM2(p-value) (p-value)

0.444 0.6820.749 0.4440.228 0.7970.263 0.930

0.740 0.9120.501 0.9770.927 0.8320.800 0.950

where R = (R j , Rz) is a TxN matrix of all returns, partitioned by columns with K+I in R, and N-K -I in Rzwhere T is the number of observations; I is a vector of one; Z contains the instrumental variables; and d, isL x (K+I) and Cis L x (N -K -I). GMM I is the chi-square value for the above model with E(U' Z) = 0 as theorthogonality condition. The GMM2 statistic is computed for the reformulated model:

I'C = I

Table 3 summarizes the tests ofthe latent variable models using weekly datafor the individual companies for the two exchanges. GMM 1 and GMM2 tests arereported here. Using GMM2 is)mportant because this test statistic does not imposethe linear restriction, as GMMI does, and because Table 2 indicates the relationbetween the reference asset and the instrumental variables is not strong.

At the standard significance level, Table 3 indicates K=1 cannot be rejectedusing GMMI and GMM2. Only one latent variable is found. This finding differsfrom the results of Ferson, Foerster, and Keirn (1993) who find two latent variablesfor the Dow Jones 30 common stocks and three latent variables for the New YorkStock Exchange common stocks. The one latent variable found here is consistentwith the results found for the Hong Kong stock market (Lee, Fung, and Lo (1996)).

Results on Latent Risk Premiums

The relation of risk premiums among markets can shed light on marketsegmentation. In examining market segmentation, we use an approach that useslatent risk premiums instead of raw returns because: (1) the presence of latent riskpremiums is theoretically justifiable, and (2) the raw returns contain much noise.


TABLE 4. Summary Statistics of the Latent Risk Premiums in the Two Exchanges.

Panel A. Latent Risk Premiums

Shares Mean Std. Dev. Skewness Kurtosis

Shanghai A 0.0001 0.0052 1.273-- 8.652--ShanghaiB -0.0016 0.0097 -0.191 10.213--Shenzhen A -0.0015 0.0053 1.491-- 10.022--Shenzhen B 0.0003 0.0067 -0.179 17.465--

Panel B. Sign and Wilcoxon Matched-Pairs Signed-Rank Tests

Markets

Shanghai A and B shareShenzhen A and B shareShanghai A/Shenzhen A shareShanghai B/Shenzhen B shale.3--

Sign Test

-4.92---14.4--

-8.39---43.4--

Wilcoxon Test

-48.9---40.6---39.9--

Panel C. Correlations Between Latent Risk Premiums

Shanghai A Shanghai B Shenzhen A Shenzhen B

Shanghai A 1.00 0.375 0.554 0.236Shanghai B 1.000 0.401 0.858Shenzhen A 1.000 0.429Shcnzhcn B 1.000

Note: The null hypothesis in the tests in Panel B is whether the two markets have the same median.

--Significant at the 5 percent level.

Under a competitive financial market, we expect the latent variables of theA and B shares for the same company to be the same. The correlation for A and Bshares of the same company is expected to be one (i.e., perfect correlation). Inaddition, the latent variables of the A and B shares for the same company shouldcome from the same distribution of the return-generating process (even withimperfect correlation because of market frictions). The argument that the latentvariables ofA and B shares for the same company come from the same distributionis a weaker condition than perfect correlation.

Alternatively, we expect the correlation between the latent variables of theA and B shares of the same company listed on the same exchange to be higher thanthat between the latent variables of different companies across exchanges. Thiscondition is intuitive, and it is an even weaker condition than the perfect correlationrequirement.

Table 4, Panel A, reports the summary statistics ofthe latent risk premiums,which are generated independently from each set of stocks from the A shares andB shares ofthe Shanghai and Shenzhen exchanges. Because A and B shares are fromthe same company, we intuitively expect the latent premiums to be the same,


although they are generated independently. Our analysis does not restrict the nullhypothesis of the latent premiums of the A- and B-share markets to be the same.Casual observations from Panel A suggest the A- and B-share latent risk premiumsare different.

We also conduct two nonparametric tests to examine the null hypothesis ofwhether the A- and B-share risk premiums have the same median. The appeal of thenonparametric tests is that they accommodate the violation ofnormality assumptionin testing. The first test is the sign test and the second test is the Wilcoxon matchedpairs signed-rank test (see Daniel (1978) for details). Table 4, Panel B, reports theresults of the tests. We find that A- and B-share markets in both exchanges havedifferent medians (location parameters), implying they come from a differentpopulation. Both test statistics are significant at the 5 percent level. These resultsconfirm the conjecture that the A- and B-share markets are segmented.

Table 4, Panel C, reports the correlations between the latent risk premiumsof the A and B shares on the two exchanges. The correlation of the latent riskpremiums between the A and B shares on the Shanghai exchange is 0.375, and thecorrelation of the A and B shares on the Shenzhen exchange is 0.429. These lowcorrelations are striking because A and B shares come from the same company; weshould expect a stronger correlation, which is close to one. The weak correlation ofthe latent risk premiums of the A- and B-share markets implies the latent riskpremiums of these markets are different, which further suggests these two marketsare segmented. This interpretation ofmarket segmentation is reasonable because thelatent risk premiums of the A- and B-share markets are conditioned on the sameinformation set (i.e., the same instrumental variables) when they are generated fromthe return data.

We also find that the interexchange correlation between the latent variablesappears to be higher than the intraexchange correlation. The correlation between theShanghai A and Shenzhen A shares is 0.554, whereas the correlation between theShanghai B shares and Shenzhen B shares is 0.858. These correlations are higherthan the correlations of the A and B shares of the same company. That is, pricing ineach corresponding tier of the market seems to reflect more of the fundamentals inthe respective market. These results are consistent with the observation that stockmarkets in China are politically driven. This is especially true for the A-sharemarkets. The higher correlation between the B-share markets of the Shanghai andShenzhen exchanges suggests they reflect similar information. Because foreigninvestors in the B-share markets tend to be more sophisticated, they are expected toprice stocks in these markets based on economic reasoning (such as the firm'searnings or its growth potential). Because political factors permeate the country andexchanges in the A-share markets, and economic values drive foreign investors inthe B-share markets, our results confirm that the A- and B-share markets aresegmented.


Results on the Bivariate Vector Autoregressive Models ofthe Latent Risk Premiums

When examining information flows across markets, the common approachin the literature is to use a bivariate vector regression that analyzes the leads and lagsofthe underlying variables. The bivariate regression model enables us to understandhow information is spread from one market to another and how quickly informationflows across markets.

We formulate a bivariate vector regression model as follows:

4 4

AAt = a + L hi AA,t-i + L ci AB,t-i + EAti=1 i=1

4 4

A Bt = d + L ei AA,t-1 + L J; AB,t-i + EB,ti=1 i=1

(6a)

(6b)

where AA is the latent variable for A shares; and As is the latent variable for B shares;and a, b, c, d, e, andfare constants.

Table 5 reports the empirical results of equations (6a) and (6b) for theShanghai and Shenzhen exchanges. In this analysis we use a lag length m, whichis equal to 4. Because we have weekly data, m = 4 basically represents a month.Results using different lag lengths are similar, and they are not reported here.

Two models (Shanghai and Shenzhen) are presented. For the Shanghaimodel, we find a strong interaction between the A- and B-share markets. That is, therisk premiums of the A-share market Granger cause the B-share market, and viceversa, as shown from the F-tests. The A-share market appears to affect the B-sharemarket more the other way around because the AA,I-l coefficients in both equationsare larger and more significant than the As, l - l coefficients. For the Shenzhen model,we find no Granger causality between the two markets. Information has beenincorporated in the contemporaneous latent premiums, resulting in less effect for thelagged variables. This result is consistent with that of the correlation analysisreported in Table 4, Panel C. The correlation between A and B shares for theShenzhen market is higher than the corresponding correlation for the Shanghaimarket.

In Table 5 the AS,t-l coefficients in both the Shanghai Band Shenzhen Bmodels are strongly negative, suggesting negative autocorrelation or price reversalsin these markets, whereas the AA.t-l coefficients in both Shanghai A and ShenzhenA markets are positive, suggesting a positive autocorrelation or a price-trendingsituation. The difference in the price behavior ofthe A- and B-share markets impliesthey belong to different return-generating processes, which further supports themarket segmentation hypothesis.


TABLE 5. Results of Bivariate Vector Autoregressive Model for the A and B Latent Variables.

Shanghai Model Shenzhen Model

Shanghai A Shanghai B Shenzhen A Shenzhen B

Constant 0.000 -0.002 -0.002 0.001(0.03) (-2.14)-- (-3.13)-- (1.36)

AA,t-1 0.257 0.446 0.038 0.124(3.35)-- (2.32)-- (0.45) (1.17)

AA.I-2 0.171 -0.036 -0.034 -0.012(2.22)-- (-0.19) (-0.41) (-0.11)

AA,t-3 0.223 0.107 -0.008 0.021(2.89)-- (0.55) ( -0.09) (0.20)

AA,1-4 0.178 0.135 0.027 0.063(2.43)-- (0.73) (0.33) (0.62)

A B.1- 1 0.059 -0.132 0.012 -0.234(1.91)- ( -1.69)- (1.78)- (-2.79)--

A B•I- 2 -0.034 -0.032 0.040 -0.035( -1.06) ( -0.41) (0.57) ( -0.40)

A B•, - 3 -0.051 0.020 0.032 0.061(-1.63) (0.26) (0.44) (0.68)

A B,1-4 -0.029 -0.008 0.050 0.026( -0.94) (-0.10) (0.74) (0.31)

F-stat (A) 24.88-- 3.16-- 0.12 0.44F-stat (B) 2.02- 0.76 0.95 2.13-

Note: F-stat (A or B) tests the hypothesis of whether all the lagged coefficients of the latent variables of Aor B are zero.

Model:4 4

AAt ; a + L b, AA,t-i + L Cj AB.t-i + CAtjol jol

4 4

ABt ; d + L etAA.,-1 + L !;A B. t-j + CB.,tol jol

AA(B) denotes the latent risk premiums for A(B) shares on either Shanghai or Shenzhen Exchanges.

--Significant at the 5 percent level.-Significant at the 10 percent level.

Granger Causality a/the Latent Variable Between Exchanges

To analyze the relation between the same type ofshares between exchanges(e.g., A shares on both exchanges), we conduct Granger (1981) causality tests usinga bivariate autoregressive framework as follows:


TABLE 6. Cross-Market Granger Causality Test for the Latent Risk Premiums of the Same Typein Both Exchanges.

Latent Variable(Dependent Variable)

Shanghai A

Shenzhen A

ShanghaiB

Shenzhen B

Model:

F-statistic for Testingthe Effect of Its Own

Lagged Variables'

50.332(0.000)'0.671(0.61)

2.542(0.041)0.399

(0.809)

F-statistic for Testing theEffect of Cross-Market

Variables"

9.521(0.000)0.199

(0.938)

3.135(0.016)0.909

(0.460)

4 4

ASH./ = constant + L b, ASH.t-I + Lei ASZ•t-i + Ut;=1 ;=1

4 4

ASZ•/ = constant + Lei ASH.t-I + L J; ASZ•t-I + v,;01 101

ASH(SZ) denotes the latent risk premiums for Shanghai (SH) or Shenzhen (SZ) in either A or B shares.

'This F-statistic is correct if all its own lagged variables are equal to zero.bThis F-statistic is correct if all cross-market lagged variables are equal to zero.'The p-values are in parentheses.

4 4

ASH,t = constant + L biASH,t-i + L C)"SZ,t-i + »,i=! i=!

4 4

ASZ, t = constant + Lei ASH, t-t + L J; ASZ, t-t + Vti=! i=!

(7a)

(7b)

where ASH denotes the latent risk premiums for the Shanghai Stock Exchange (SH),and Asz denotes the latent risk premiums for the Shenzhen Stock Exchange (SZ).

Table 6 reports the empirical results of equations (7a) and (7b). In Panel A,the F-value, which shows the effect ofShenzhen A shares on Shanghai A shares, is9.521, which is significant at the 5 percent level; however, the Shanghai A-share riskpremiums do not Granger cause the Shenzhen A-share market. The F-value is 0.199,which is not significant. Table 6, Panel B, shows cross-market interactions betweenthe B shares on both exchanges. Shenzhen B shares appear to Granger cause the


Shanghai B-share market. The F-value is 3.135, which is significant at the 5 percentlevel. Similarly, we do not find Granger causality from Shanghai B shares toShenzhen B shares.

The correlation between the Shenzhen and Shanghai B-share markets is high(0.858) as shown in Table 4, Panel C, implying the latent risk premiums in the Bshare market are likely to respond to similar underlying forces. The Grangercausality test result between the two markets suggests the Shenzhen B-share marketincorporates information more quickly than the Shanghai B-share market. TheGranger causality test complements the results of the correlation analysis andenables us to understand how information flows are spread across markets.

IV. Conclusions

In this study we employ the latent variable asset pricing framework toexplore the degree to which two-tier markets of A and B shares in China aresegmented. We use weekly stock return data for the A and B shares traded on boththe Shanghai Stock Exchange and the Shenzhen Stock Exchanges to examinemarket pricing behavior. The study period is May 1993 through June 1997.

We find the correlation of the latent risk premiums between Shanghai Ashares and Shenzhen A shares is 0.554, whereas the correlation between theShanghai B shares and the Shenzhen B shares is 0.858. These correlations are higherthan the correlations oflatent risk premiums for the A and B shares, which are 0.375for Shanghai stocks and 0.429 for Shenzhen stocks. These results suggest the A- andB-share markets on both the Shanghai and Shenzhen exchanges are segmented. Thepricing in each share market seems to reflect more ofthe similar fundamentals in therespective markets. These findings ofmarket segmentation are consistent with thosereported in Poon, Firth, and Fung (1998).

The Granger causality test results for the cross-market relation betweenexchanges indicate the latent risk premiums in Shanghai A or B shares respond toinformation in the corresponding market on the Shenzhen exchange. However, thelatent risk premiums in Shenzhen A or B shares do not reflect information of thelatent risk premiums in the corresponding markets on the Shanghai exchange. Theresults suggest the Shanghai markets respond to information less quickly than theShenzhen markets.

The results in this study confirm the casual observation that the Chinesemarkets are driven by political forces, which are reflected on the exchanges.Although owners of the A and B shares share equal rights in the same companies,they react to different underlying forces. The segmentation of the A and B marketsin China seems to provide profitable opportunities to U.S. companies that haveaccess to both markets as the Chinese government loosens its regulation of thefinancial markets.


We demonstrate that the latent variables of the A- and B-share markets aredifferent, which implies market segmentation between the two markets. However,we do not address how and to what extent these latent risk premiums respond tounderlying forces such as economic policies or administrative orders related togovernmental regulation and intervention. These issues are left for future research.

Appendix

Companies in Sample

Shanghai Stocks:China Textile Mach A and BChlor Alkali A and BDazhong Taxi A and BErfangji A and BFirst Pencil A and BRefrige Compressor A and BRubber Belt A and BTyre & Rubber A and BVacuum Electron A and BWing Sung A and B

Shenzhen Stocks:China Bicycle A and BChiwan Wharf Holding A and BHuafa Electronics A and BKonka Group A and BShenbao Industrial A and BShenzhen Petrochemical A and BShenzhen Properties A and BSouthern Glass A and BVictor Onward Textile A and BZhonghao A and B

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segmentation of the a- and b-share chinese equity markets

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