This article tests for the random walk hypothesis in the four Chinese stock markets of Shanghai "A," Shanghai "B," Shenzhen "A," and Shenzhen "B" and explores the impact of the market liberalization that occurred in 2001 on the efficiency of these markets. Empirical results show that the market liberalization in 2001 has a significant effect on the efficiency of Chinese stock markets.Chinese stock markets; market liberalization; market efficiency; variance ratio tests

Qian Liu

Shigeyuki Hamori

English

Research Papers in Economics

     
 
 
  
  
Volume 30, Issue 3 
  
The efficiency of the Chinese stock market and the role of market 
liberalization 
  
 
 
Qian Liu  
Kobe University 
Shigeyuki Hamori  
Kobe University
Abstract 
This article tests for the random walk hypothesis in the four Chinese stock markets of Shanghai "A," Shanghai "B," 
Shenzhen "A," and Shenzhen "B" and explores the impact of the market liberalization that occurred in 2001 on the 
efficiency of these markets. Empirical results show that the market liberalization in 2001 has a significant effect on the 
efficiency of Chinese stock markets.
We are grateful to Mark Wynne for helpful comments and suggestions. 
Citation: Qian Liu and Shigeyuki Hamori, (2010) ''The efficiency of the Chinese stock market and the role of market liberalization'', 
Economics Bulletin, Vol. 30 no.3 pp. 2524-2532. 
Submitted: Sep 05 2010.   Published: September 28, 2010. 
 
     1 
 
1. Introduction 
 
China’s main stock exchanges, the Shanghai Stock Exchange (SSE) and Shenzhen Stock 
Exchange (SZE), were established on November 26 and December 1, 1990, respectively, and 
started operations on December 29, 1990, and June 3, 1991, respectively. The SSE trades 
mostly shares of large corporations and state-run enterprises, while the SZE trades entities 
belonging to large corporations and export-oriented companies. Both exchanges trade in "A" 
and "B" shares—the former is traded in renminbi exclusively by mainland Chinese investors 
and the latter, in U.S. dollars (in Hong Kong dollars on SZE) exclusively by foreign 
investors. 
Investors in A shares are mostly individuals, while those in B shares are foreign 
institutional investors. Due to the language barrier and accounting conventions, foreign 
investors may encounter difficulty in grasping the financial information of Chinese 
corporations, thus leading to information asymmetries between market participants trading in 
markets for A and B shares, respectively. This situation suggests the possibility of a 
difference in market efficiency between markets for A and B shares. According to the 
efficient market hypothesis (EMH), all information on a specific asset is reflected in the price 
of that asset (Fama, 1970). Based on this theory, all usable information is reflected in the 
share price, and  rationally formed share prices can be accomplished only through new 
information. Since new information is not predictable, share price movements are random 
and predictions are impossible according to the theory. In markets where the EMH is born out, 
share prices therefore follow a random walk. 
The efficiency of China’s stock markets has been analyzed in research using random walk 
test. Laurence et al. (1997) implemented serial correlation test based on daily data from 1993 
until 1996. The results show the market for A shares to be efficient and that for B shares to be 
inefficient. Liu et al. (1997) implemented unit root test using daily data from 1992 until 1995. 
On the basis of the results, it was asserted that both the Shanghai and Shenzhen markets are 
efficient. Lima and Tabak (2004) implemented variance ratio (VR) tests using daily data from 
1992 until 2000. The results, it was asserted, demonstrated that the market for A shares was 
efficient, while that for B shares was not. 
This paper tests the EMH using daily data from the China’s stock markets. This paper has 
two specific features. First, it analyzes the EMH through VR tests using daily data of the 
Shanghai and Shenzhen stock markets for A and B shares from 1992 until 2010. Second, 
occasioned by China’s market liberalization in 2001, foreign investors and investors from 
mainland China have since been able to trade in A and B shares respectively. This paper 
examines the effect of this liberalization on the efficiency of stock markets. 
 
2. Empirical Techniques 
2.1 Variance Ratio Test 
 
Denote by  t P  the stock price at time t and define  t X  as the natural logarithm of  t P  
(l n ( ) tt X P = ). Our maintained hypothesis is given by the recursive relationship as follows: 
 
  1 , tt t XX μ ε − =+ + ,   1, 2, , tT = L   (1) 
 
or 
 
  , tt X μ ε Δ=+   1, 2, , tT = L   (2) 
 2 
 
where  1 tt t X XX − Δ= −  and  [ ] 0 t E ε = . 
  The VR test of Lo and MacKinlay (1988) is based on the property that the variance 
of  () tt q XX − −  is  q  times the variance of  1 () tt XX − − . Therefore, the random walk 
hypothesis (RWH) can be checked by comparing  1 q times the variance of  () tt q XX − −  to 
the variance of  1 () tt XX − − . Then, the VR,  () VR q , is defined as 
 
  2
2
()
()
(1)
q
VR q
σ
σ
=    (3) 
 
where 
2() q σ  is 1 q  times the variance of  () tt q XX − −  and 
2(1) σ   is the variance of 
1 () tt XX − − . The null hypothesis is that  () VR q   is equal to one. 
  Consider the following estimators for the unknown parameters  μ , 
2(1) σ , and 
2() q σ : 
 
 
  1 1
1 ˆ () ,
T
tt t XX
T
μ − = =− ∑   (4) 
 
 
 
 
2
2
1 1
1 ˆˆ (1) ( ) ,
T
tt t XX
T
σ μ − = =− − ∑   (5) 
 
 
 
2
2
1
1 ˆˆ () ( ).
T
tt q t qX X q
qT
σ μ − = =− − ∑   (6) 
 
Thus, the corresponding VR estimator is given by 
 
 
 
2 ^
2
ˆ ()
() .
ˆ (1)
q
VR q
σ
σ
=   (7) 
 
Lo and MacKinlay show that the VR test statistic given by 
 
 
 
^
2
() 1
()
ˆ ()
VR q
Zq
sq
−
=  (8) 
 
is asymptotically  (0,1) N . Note that we have
1 
 
 
 
2
11 1 1 2
1 2
1
ˆˆ () ( ) 2( ) ˆ () .
ˆ ()
T
tj tj t t q tj
T j
tt j tj
XX X X qj
sq
q XX
μ μ
μ
−− − − − =+
=
− =+
−−− − ⎛⎞ −
= ⎜⎟
−− ⎝⎠
∑
∑
∑
  (10) 
 
                                                  
1Under the assumption that  t ε   is iid, we have 
2 2(2 1)( 1) ˆ ()
3
qq
sq
qT
− −
= . 3 
 
2.2 Multiple Variance Ratio Test 
 
Chow and Denning (1993) extend the conventional VR test introduced by Lo and MacKinlay 
(1988) and propose a multiple VR test. Consider a set of VR estimates, 
{ } () | 1 , 2 , , i VR q i L = L , corresponding to a set of predefined number of lags 
{ } |1 , 2 , , i qi L = L . Chow and Denning (1988) propose a multivariate VR (MVR) test for the 
joint null hypothesis  0 :( ) 1 f o r 1 , 2 , , . i HV R q i L == L  against the alternative hypothesis that 
:( ) 1 Ai HV R q ≠  for  some  i q . The test statistic is of the form: 
 
   
*
1 () m a x | ( ) | . i iL Z qZ q
≤≤ =   (11) 
 
The null hypothesis is rejected at α  level of significance if 
*() Z q  is greater than the 
* [1 2]th α −  percentile of the standard normal distribution where 
*1 / 1( 1 )
L αα =− − . In this 
research,  2,4,8,16 q =  are used to calculate VR estimates and test statistics. Thus, 
{| 1 , 2 , 3 , 4 } i qi =  such  that  123 2, 4, 8, qqq ===  and  4 16. q =  
Kim (2006) proposed a wild bootstrap approach to improve the small sample properties of 
VR tests. His approach involves computing the Lo-MacKinlay and Chow-Denning test 
statistics on samples of T observations formed by weighting the original data by mean 0 and 
variance 1 random variables and using the bootstrap distribution of the test statistics. 
 
3. Data 
 
This paper uses daily index data for A and B shares from the SSE and SZE. The data source is 
Thomson Financial DataStream. The sample period of each stock market is as follows: 
 
Shanghai A shares: January 2, 1991–April 27, 2010 
Shanghai B shares: February 21, 1992–April 27, 2010 
Shenzhen A shares: October 5, 1992–April 27, 2010 
Shenzhen B shares: October 5, 1992–April 27, 2010 
 
A shares are traded in renminbi exclusively by mainland Chinese investors and B shares, in 
U.S. dollars—in Hong Kong dollars on the SZE—exclusively by foreign investors. Since 
2001, foreign institutional investors have been able to transact in A shares and investors from 
mainland China, in B shares. To examine the effects of this liberalization, a period split was 
set at the year 2001. For each subsample period, we analyze the efficiency of each market 
using variance ratio tests. 
 
4. Empirical Results 
4.1 Total sample 
 
Table 1 demonstrates the VR estimates and test statistics of the RWH for the entire period 
based on the methodology of the conventional VR test by Lo and MacKinlay (1988). Note 
that we report two p-values; one is based on the asymptotic normality and the other is based 
on the wild bootstrap developed by Kim (2006). Kim (2006) demonstrates that the wild 
bootstrap tests have desirable size properties and exhibit higher power than their alternatives. 
According to Table 1, the null hypothesis that the VR is equal to one is statistically accepted 4 
 
at all number of  q’s at the 1% significance level for the A board, while the null hypothesis is 
rejected at any number of  q’s for the B board. Thus, the RWH is accepted for the A board 
but not for the B board. 
Test statistics based on the methodology of the multiple VR test by Chow and Denning 
(1993) are also reported in Table 1. The null hypothesis that the VR is equal to one is 
statistically accepted for the A board but is rejected for the B board. Thus, the empirical 
results of the individual VR test are reinforced by the empirical results of the multiple VR 
tests. 
 
4.2 Role of Market Liberalization 
 
Domestic investors were excluded from B stock markets before 2001 but were admitted after 
2001. It is likely that market liberalization would have significant effects on the efficiency of 
the market. We go on to examine whether the efficiency was affected when the B market was 
liberalized to domestic investors. 
The results of the conventional VR test after dividing the entire study period into two 
subsample periods are presented in Tables 2 and 3. Table 2 shows the empirical results up to 
the end of 2000, while Table 3 shows the empirical results from the beginning of 2001. 
For the first subsample, the empirical results of Table 2 are consistent with the results of 
Table 1. The null hypothesis that the VR is equal to one is statistically accepted at all number 
of  q’s at the 1% significance level for the A board, while the null hypothesis is rejected at 
any number of  q’s for the B board. Thus, the RWH is accepted for the A board but not for 
the B board for the first subsample. 
For the second subsample, the empirical results of Table 3 are different from the results of 
Table 1. The null hypothesis that the VR is equal to one is statistically accepted at all number 
of  q’s at the 1% significance level not only for the A board but also for the B board. Thus, 
the RWH is accepted for A as well as B boards for the second subsample. 
Test statistics based on the methodology of the multiple VR test by Chow and Denning 
(1993) are also reported in Tables 2 and 3. For the first subsample, the null hypothesis that the 
VR is equal to one is statistically accepted for the A board but is rejected for the B board. For 
the second subsample, the null hypothesis that the VR is equal to one is statistically accepted 
not only for the A board but also for the B board. Thus, the empirical results of the individual 
VR test are reinforced by the empirical results of the multiple VR tests. We find that 
efficiency tended to be favorably affected when markets were liberalized. 
 
5. Some Concluding Remarks 
 
This paper examined the market efficiency of China’s stock markets. The markets were 
analyzed for A and B shares on the SSE and SZE, using MVR test. In particular, the effects of 
the liberalization of investment in B shares by domestic investors since 2001 were examined. 
The main results of the analysis are as follows. 
(1) The results of the analysis for the entire period bear out the EMH, as prices of A shares 
on the Shanghai and Shenzhen stock markets did follow a random walk. By contrast, since 
prices of B shares did not follow a random walk, the EMH was shown not to hold. 
(2) The results of the analysis for the period before the market liberalization of 2001 bear 
out the EMH, as A shares on the SSE and SZE followed a random walk. By contrast, since 
prices of B shares did not follow a random walk, the EMH was shown not to hold. 
(3) The results of the analysis for the period before the market liberalization of 2001 
showed the EMH to hold, as both A and B shares on the SSE and SZE followed a random 5 
 
walk. Hence, it was demonstrated that the liberalization in 2001 that allowed domestic 
investors to trade in B shares had a positive effect on the efficiency of the market for B 
shares. 
 
 
References 
 
Chow, K. V. and K. C. Denning (1993) "A simple multiple variance ratio test" Journal of 
Econometrics 58, 385-401. 
Fama, E. F. (1970) "Efficient capital markets: A review of theory and empirical work" 
Journal of Finance, 25, 382–417. 
Kim, J. H. (2006) "Wild bootstrapping variance ratio test" Economics Letters 92, 38-43. 
Laurence, M., F. Cai and S. Quan (1997) "Weak-form efficiency and causality tests in 
Chinese stock markets" Multinational Finance Journal 1, 291-307. 
Lima, E. J. A. and B. M. Tabak (2004) "Tests of the random walk hypothesis for equity 
markets: Evidence from China, Hong Kong and Singapore" Applied Economics Letters 11, 
255-258. 
Liu, X., H. Song and P. Romilly (1997) "Are Chinese stock markets efficient? A cointegration 
and causality analysis" Applied Economics Letters 4, 511-515. 
Lo, A. W. and A. C. MacKinlay (1988) "Stock market prices do not follow random walks: 
Evidence from a simple specification test" Review of Financial Studies 1, 41-66. 
 
  6 
 
Table 1 Results of Variance Ratio Tests: Total Sample 
 
Shanghai A 
Individual Tests 
Period 
(q) 
Variance 
Ratio 
Test Statistic  p-value
a p-value
b 
2 1.029 1.134  (0.257)  (0.279) 
4 1.101 1.867  (0.062)  (0.070) 
8 1.179 2.024  (0.043)  (0.040) 
16 1.202 1.620  (0.105)  (0.101) 
Joint Test 
Test Statistic  p-value
a p-value
b 
2.024 (0.161)  (0.107) 
      
Shanghai B 
Individual Tests 
Period 
(q) 
Variance 
Ratio 
Test Statistic  p-value
a p-value
b 
2 1.127 5.293  (0.000)  (0.000) 
4 1.215 4.935  (0.000)  (0.000) 
8 1.332 4.970  (0.000)  (0.000) 
16 1.490 5.224  (0.000)  (0.000) 
Joint Test 
Test Statistic  p-value
a p-value
b 
5.293 (0.000)  (0.000) 
      
Shenzhen A 
Individual Tests 
Period 
(q) 
Variance 
Ratio 
Test Statistic  p-value
a p-value
b 
2 1.009 0.386  (0.699)  (0.707) 
4 1.059 1.255  (0.210)  (0.237) 
8 1.142 1.914  (0.056)  (0.059) 
16 1.180 1.743  (0.081)  (0.079) 
Joint Test 
Test Statistic  p-value
a p-value
b 
1.914 (0.205)  (0.136) 
      
Shenzhen B 
Individual Tests 
Period 
(q) 
Variance 
Ratio 
Test Statistic  p-value
a p-value
b 
2 1.107 3.870  (0.000)  (0.000) 
4 1.222 4.443  (0.000)  (0.000) 
8 1.404 5.331  (0.000)  (0.000) 
16 1.612 5.792  (0.000)  (0.000) 
Joint Test 
Test Statistic  p-value
a p-value
b 
5.792 (0.000)  (0.000) 
Note: 
p-value
a: probability value based on the asymptotic normality. 
p-value
b: probability value based on the wild bootstrap. 7 
 
Table 2 Results of Variance Ratio Tests: Before the Market Liberalization 
 
Shanghai A 
Individual Tests 
Period 
(q) 
Variance 
Ratio 
Test Statistic  p-value
a p-value
b 
2 1.038 1.203  (0.229)  (0.237) 
4 1.130 1.946  (0.052)  (0.058) 
8 1.225 2.039  (0.041)  (0.039) 
16 1.244 1.578  (0.115)  (0.116) 
Joint Test 
Test Statistic  p-value
a p-value
b 
2.039 (0.156)  (0.116) 
      
Shanghai B 
Individual Tests 
Period 
(q) 
Variance 
Ratio 
Test Statistic  p-value
a p-value
b 
2 1.177 5.071  (0.000)  (0.000) 
4 1.296 4.714  (0.000)  (0.000) 
8 1.401 4.122  (0.000)  (0.000) 
16 1.527 3.886  (0.000)  (0.000) 
Joint Test 
Test Statistic  p-value
a p-value
b 
5.071 (0.000)  (0.000) 
      
Shenzhen A 
Individual Tests 
Period 
(q) 
Variance 
Ratio 
Test Statistic  p-value
a p-value
b 
2 0.995  -0.165  (0.869)  (0.881) 
4 1.058 0.900  (0.368)  (0.377) 
8 1.158 1.531  (0.126)  (0.124) 
16 1.173 1.218  (0.223)  (0.229) 
Joint Test 
Test Statistic  p-value
a p-value
b 
1.531 (0.416)  (0.265) 
      
Shenzhen B 
Individual Tests 
Period 
(q) 
Variance 
Ratio 
Test Statistic  p-value
a p-value
b 
2 1.144 3.198  (0.001)  (0.000) 
4 1.299 3.726  (0.000)  (0.000) 
8 1.477 3.975  (0.000)  (0.000) 
16 1.637 3.839  (0.000)  (0.001) 
Joint Test 
Test Statistic  p-value
a p-value
b 
3.975 (0.000)  (0.001) 
Note: 
p-value
a: probability value based on the asymptotic normality. 
p-value
b: probability value based on the wild bootstrap. 8 
 
Table 3 Results of Variance Ratio Tests: After the Market Liberalization 
 
Shanghai A 
Individual Tests 
Period 
(q) 
Variance 
Ratio 
Test Statistic  p-value
a p-value
b 
2 0.997  -0.114  (0.909)  (0.906) 
4 0.996  -0.088  (0.930)  (0.933) 
8 1.022 0.283  (0.777)  (0.777) 
16 1.070 0.602  (0.547)  (0.546) 
Joint Test 
Test Statistic  p-value
a p-value
b 
0.602 (0.958)  (0.852) 
      
Shanghai B 
Individual Tests 
Period 
(q) 
Variance 
Ratio 
Test Statistic  p-value
a p-value
b 
2 1.062 1.944  (0.052)  (0.067) 
4 1.075 1.295  (0.195)  (0.183) 
8 1.178 1.984  (0.047)  (0.048) 
16 1.299 2.345  (0.019)  (0.018) 
Joint Test 
Test Statistic  p-value
a p-value
b 
2.345 (0.074)  (0.055) 
      
Shenzhen A 
Individual Tests 
Period 
(q) 
Variance 
Ratio 
Test Statistic  p-value
a p-value
b 
2 1.040 1.495  (0.135)  (0.118) 
4 1.064 1.253  (0.210)  (0.205) 
8 1.120 1.499  (0.134)  (0.123) 
16 1.214 1.841  (0.066)  (0.053) 
Joint Test 
Test Statistic  p-value
a p-value
b 
1.841 (0.238)  (0.139) 
      
Shenzhen B 
Individual Tests 
Period 
(q) 
Variance 
Ratio 
Test Statistic  p-value
a p-value
b 
2 1.053 1.700  (0.089)  (0.099) 
4 1.074 1.294  (0.196)  (0.217) 
8 1.180 2.019  (0.044)  (0.036) 
16 1.209 1.642  (0.101)  (0.085) 
Joint Test 
Test Statistic  p-value
a p-value
b 
2.019 (0.163)  (0.093) 
Note: 
p-value
a: probability value based on the asymptotic normality. 
p-value
b: probability value based on the wild bootstrap. 

Are Chinese stock markets efficient? A cointegration and causality analysis&quot;

Denning

Efficient capital markets: A review of theory and empirical work&quot;

Stock market prices do not follow random walks: Evidence from a simple specification test&quot;

Tabak

Weak-form efficiency and causality tests in Chinese stock markets&quot;

Wild bootstrapping variance ratio test&quot;

The efficiency of the Chinese stock market and the role of market liberalization

http://www.accessecon.com/Pubs/EB/2010/Volume30/EB-10-V30-I3-P232.pdf

The efficiency of the Chinese stock market and the role of market liberalization

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