暨 南 大 学本科生毕业论文

论文题目_ 中国股票市场弱式有效 性的实证 检验 _

学 院 国际学院

学 系 会计学系

专 业 会计学（ CGA ）

姓 名 ___ 李 慧斯 _

学 号____ 2004050176

指导教师_____ 沈洪涛 _____ _ _

2008 年 4 月 20 日

Statement of Originality

I hereby declare that the thesis presented is the result of research performed by me personally,

under guidance from my supervisor. This thesis does not contain any content (other than those

cited with references) that has been previously published or written by others, nor does it

contain any material previously presented to other educational institutions for degree or

certificate purpose to the best of my knowledge. I promise that all facts presented in this thesis

are true and creditable.

Signed: Date：

An Empirical Test on Weak Form Efficiency of China’s Stock Market

Abstract: Building on the theory of Efficient Market Hypothesis (EMH), this thesis performs

the unit root test, serial autocorrelation test and run test on the daily closing prices of

Shanghai Composite Index, Shanghai 30 Index, Shenzhen Composite Index and Shenzhen

Component Index covering the period from the year 1991 to 2007, in attempt to examine the

weak form efficiency of China’s stock market. Comparing with the previous study, it extents

the empirical work in terms of more extensive data and multiple forms of tests. The results

from the three tests are generally consistent with each other. It is found that China’s stock

market is inefficient and rejects the hypothesis of weak form efficiency during the initial

years. But as the market grows and learns it has become weak form efficient since the year

1997 and tends to be more efficient over time. Furthermore, it is concluded that China’s stock

market is filled with characteristics of emerging market and has the synchronization effect.

Combining the empirical test results and the unique characteristics of China’s emerging

market, it further puts forward five main policy implications for the efficiency improvement.

Key Words: Efficient Market Hypothesis (EMH), weak form efficiency, stock market, index,

unit root test, serial autocorrelation test, Q statistic, run test

Contents

1. INTRODUCTION.................................................................................................................1

1.1 BACKGROUND AND IMPLICATION OF THE RESEARCH.....................................................11.2 METHODOLOGIES AND ORIGINALITY OF THE RESEARCH...............................................11.3 STRUCTURE OF THE THESIS............................................................................................2

2. THEORETICAL BACKGROUND AND LITERATURE REVIEW...............................3

2.1 THEORETICAL BACKGROUND OF EMH..........................................................................32.1.1 Definition of EMH...............................................................................................32.1.2 Empirical implications of EMH..........................................................................42.1.3 Methodologies of EMH test.................................................................................5

2.2 LITERATURE REVIEW OF EMH TEST FOR CHINA’S STOCK MARKET..............................52.2.1 Literatures supporting inefficiency.....................................................................62.2.2 Literatures supporting weak form efficiency.......................................................62.2.3 Literatures for other conclusions........................................................................7

3. EMPIRICAL TEST ON WEAK FORM EFFICIENCY OF CHINA’S STOCK MARKET...................................................................................................................................7

3.1 METHODOLOGY..............................................................................................................73.1.1 Unit root test........................................................................................................73.1.2 Serial autocorrelation test...................................................................................83.1.3 Run test................................................................................................................9

3.2 DATA..............................................................................................................................93.3 EMPIRICAL TEST RESULTS............................................................................................10

3.3.1 Unit root test......................................................................................................103.3.2 Serial auto-correlation test................................................................................113.3.3 Run test..............................................................................................................14

4. SUMMARY OF THE EMPIRICAL TEST.......................................................................15

4.1 CONCLUSION AND ANALYSIS OF EMPIRICAL RESULTS..................................................154.2 LIMITATIONS OF THE RESEARCH...................................................................................16

5. IMPLICATIONS FOR CHINA’S STOCK MARKET....................................................17

5.1 IMPROVEMENT IN THE ADEQUACY AND QUALITY OF INFORMATION FLOW IN THE STOCK MARKET......................................................................................................................17

5.2 IMPROVEMENT IN THE AUTOMATION AND REGULATION OF THE STOCK MARKET........175.3 IMPROVEMENT IN THE KNOWLEDGE AND AWARENESS OF INVESTORS.........................175.4 IMPROVEMENT IN THE QUALITY OF INTERMEDIARIES..................................................175.5 IMPROVEMENT IN THE OWNERSHIP STRUCTURE...........................................................18

6. CONCLUSION....................................................................................................................18

ACKNOWLEDGEMENT......................................................................................................19

REFERENCES........................................................................................................................20

1. Introduction

1.1 Background and implication of the research

Stock market efficiency is one of the most long-standing and contentious issues in the

economics literature around the world due to its importance in financial market and

difficulties in measurement. As a basis of modern investment theory (such as CAPM, Black-

Scholes model, APT, etc.), Efficient Market Hypothesis (EMH) is playing a crucial role in

pricing and allocation of capital. The implications of whether the market prices reflect all

relevant information are enormous for both policy markers and investors, who make decisions

based on current market values and expected risk-return trade-offs. Since Fama (1970) first

formalized the EMH theory and provided overwhelming evidence to support an efficient

market hypothesis for U.S. stock markets, a variety of empirical researches have been

undertaken and different results have been at the center stage of debate in financial literatures

for several decades.

In spite of the short history of China’s stock market (the Shanghai Stock Exchange from

December 1990 and the Shenzhen Stock Exchange from July 1991), the high economic

growth in the last two decades has speeded up the development of the stock market, attracting

increased academic attention on the market efficiency. Numerous studies have addressed the

issues in this area in the recent past based on different empirical tests. However, China’s stock

market is unique in many ways and worth the effort of empirical work for its own sake, for it

is in the range of emerging markets and has a lot of peculiar features with the rapid

development. For example, the market volatility in the initial years (1991-1993) due to the

speculation activities and lack of regulatory system, the thin trading problem, the ad hoc

intervention by government, which suggests substantial inefficiency in the infant stage of

China’s stock market. As the market grows and learns, it is becoming more and more mature

in terms of information utilization and regulation enforcement. In other words, efficiency is

evolving in this emerging market compared with other developed market in the world.

Consequently, the studies in this area are found to be important and deserved more empirical

work by means of more advanced test tools.

1.2 Methodologies and originality of the research

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While a considerable amount of research has been conducted on testing the efficiency of

China’s stock market, there is no clear conclusion on this issue. The empirical test results are

conflicting and ambiguous because they are based on different methodologies over different

time periods. This thesis tends to examine the random walk hypothesis in the emerging

China’s stock market in attempt to test the weak form efficiency. By employing several

popular empirical tools, including serial autocorrelation test, run test and unit root test, it

extends the previous studies of Random Walk Hypothesis (RWH) in several ways as follows:

Firstly, this thesis is based on a much more extensive database from around the inception of

stock exchange to the end of the year 2007. The covering period is much longer than previous

studies and such a full sample can potentially increase the validity of the test results.

Secondly, this thesis examines the data for the entire period as well as several sub-periods by

unit root test in order to clarify some ambiguity of abnormal volatility. After presenting a big

picture, it performs serial autocorrelation test and run test on the four selected indexes for

every single year, in attempt to capture the evolution process of market efficiency in China’s

stock market, and contribute to a clearer outlook to all the investors and policy makers.

Thirdly, the empirical tests conducted in this thesis make it possible to compare the two stock

markets and provide convincing evidence to prove their synchronization and dependency.

Meanwhile, the results from Shanghai 30 Index and Shenzhen Component Index further

reinforce the results from the two composite indexes.

Finally, the multiple empirical tests conducted here not only revisit the empirical work of

previous studies but also shed additional light on their controversial results. It provides

evidence from three different statistical methodologies and evaluates the consistency among

the results, bringing analysis on the influence from limitations of the tests themselves.

1.3 Structure of the thesis

This thesis is divided into five sections. The second section introduces the theoretical

background of the EMH and provides a brief literature review for China’s stock market. The

core process of empirical tests of EMH is discussed in the third section, including the

methodologies, data and the specific empirical results. The fourth section draws a conclusion

of the tests and gives some analysis of the corresponding results, followed by some policy

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implications for China’s stock market in the final section.

2. Theoretical Background and literature review

2.1 Theoretical background of EMH

The efficient market hypothesis emerged as a prominent theoretic position in the mid-1960s.

Paul Samuelson had begun to circulate Louis Bachelier's work among economists. In 1964,

Bachelier's dissertation “The Theory of Speculation” along with the empirical studies

mentioned above was published in an anthology edited by Paul Coonter. In 1965, Eugene

Fama published his dissertation arguing for the random walk hypothesis and Samuelson

published a proof for a version of the efficient market hypothesis. In 1970 Fama published a

review of both the theory and the evidence for the hypothesis. The paper extended and refined

the theory, included the definitions for three forms of market efficiency.

2.1.1 Definition of EMH

Efficient market hypothesis (EMH) assumes that stock prices adjust rapidly to the infusion of

any new information, and thus current prices fully absorb and reflect all available information.

Fama (1970) first formalized the EMH theory in terms of a fair game model and clarified the

EMH into three sub-concepts in terms of their information sets: (1) the weak form EMH

contends that current stock prices fully reflect all market information, including historical

prices, trading volumes, and any market oriented information, such as block trades, odd-lot

transactions, etc.; (2) the semi-strong form EMH assumes that prices fully reflect all public

information, including non-market information, such as earnings and dividend

announcements, and economic and political news; (3) the strong form EMH asserts that all

information from public and other sources will be fully contained in the stock price changes.

Whether a market is efficient or not has to do with the speed with which information is

impounded into security prices. An efficient market is characterized by a large number of

profit-driven individuals who act independently. In addition, new information regarding

securities arrives in the market in a random manner. Given this setting, investors adjust to new

information immediately and buy and sell the security until they feel the market price

correctly reflects the new information. Under the efficient market hypothesis, information is

reflected in security prices with such speed that there are no opportunities for investors to

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profit from publicly available information. Investors competing for profits ensure that security

prices appropriately reflect the expected earnings and risks involved and thus the true value of

the firm.

There are mainly four assumptions of EMH as follows: First, investors are rational and

therefore value investments rationally – that is, by calculating the net present values of future

cash flows, appropriately discounted for risk. Investors maximize their profit by evaluating

the investments independently. Second, important current information is almost freely

available to all participants who response immediately, so the stock prices fluctuate randomly.

Third, the number of market players and the volume of transactions are large enough to

guarantee the speed of price adjustment, which is an important factor of market efficiency.

Fourth, there is almost no friction and no transaction cost in the market so that information

cost can be ignored.

2.1.2 Empirical implications of EMH

To test for strong form efficiency, a market need not exist where investors can consistently

earn deficit returns over a short period of time. Even if some money managers are not

consistently observed to be beaten by the market, no refutation even of strong-form efficiency

follows: with hundreds of thousands of fund managers worldwide, even a normal distribution

of returns (as efficiency predicts) should not be expected to produce a few dozen "star"

performers.

To test for semi-strong form efficiency, the adjustments to previously unknown news must be

of a small size and must be instantaneous. To test for this, consistent downward adjustments

after the initial change must be looked for. If there are any such adjustments it would suggest

that investors had interpreted the information in an unbiased fashion and hence in an efficient

manner. Event study is widely used to test semi-strong form efficiency.

This thesis focuses on the empirical test of weak form efficiency since it is the prerequisite to

the analysis of semi-strong or even strong form efficiency. The weak-form of the EMH asserts

that successive returns of securities are independent, resembling a random walk. In general,

weak-form efficiency has been tested in two ways: (1) by showing that successive changes in

stock prices are independent of each other and therefore cannot contain information for

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predicting future prices; (2) by showing that technical trading rules based on historical prices

do not outperform a buy-and-hold strategy.

Random walk model is the most widely applied model to examine the market efficiency. In

the early literature, discussions of the efficient market model were phrased in terms of random

walk model, though Fama (1970) argued later that early authors were, in fact, concerned with

general versions of the ‘fair game’ model. If the random walk model holds, the weak form of

the efficient market hypothesis must hold though not vice versa (Fama, 1970; Copeland and

Weston, 1983). Thus, evidence supporting the random walk model is the evidence supporting

weak form efficiency.

2.1.3 Methodologies of EMH test

The principal tools for testing the RWH and EMH are serial correlation test, run test, unit root

test, variance ratio test, trading rules, Box-Pierce test, ARIMA model, GARCH model, etc.

For example, early research used serial correlation coefficients and runs tests to investigate

whether price series follow a random walk (Fama, 1965). More explicit tests of random walks

examine whether unit roots exist in price series. Dickey and Fuller (1979, 1981) proposed unit

root tests and their procedure (Augmented Dickey–Fuller, ADF) has the null hypothesis that a

series has a unit root. A complementary test developed by Kwiatkowski, Phillips, Schmidt,

and Shin (KPSS) (Kwiatkowski et al., 1992) uses the null hypothesis that the time series of

prices is stationary. Lo and MacKinlay (1988) refuted the random walk hypothesis for the

U.S. weekly returns and presented later researchers with a powerful variance ratio test for the

investigation of the applicability of the random walk hypothesis as a description of stock price

movements for non-U.S. markets.

This focus of this thesis is to employ some of the most commonly used techniques to

determine the independence of the stock prices in China’s two stock exchanges. Section three

will present the methodologies of autocorrelation test, run test and unit root test in detail.

2.2 Literature review of EMH test for China’s stock market

Qiu (2001) summarized the 25 literatures of empirical studies on EMH for China’s stock

market from 1993 to 2000 and found out that 13 of them agree with the weak form efficiency

in China’s stock market, accounting for 52% while 10 of them insist inefficiency with 40%,

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followed by 2 of them, which cannot draw a clear conclusion on the extent of efficiency.

Simultaneously, the author pointed out some deficiencies of the empirical work (samples are

not representative, test models departure from reality, problems of the data itself, etc.), which

might influence the test results.

A further summarization was conducted by Huang (2006) on the related literatures in the

more recent past. The empirical results from different literatures vary as expected due to the

different series used and different sample periods over which the data were measured.

Concluding their results of weak form efficiency, inefficiency, enhancing efficiency, market

value effect, and price effect and so on, Huang gave a remark that the efficiency of China’s

stock market is enhancing but has not reached weak form efficiency.

Based on the excellent review summary of the previous studies, the literatures can be

reclassified into three areas (from 2.2.1 to 2.2.3).

2.2.1 Literatures supporting inefficiency

The first area of previous studies lends creditability to market inefficiency. Wu (1993, 1994)

firstly investigated the market efficiency by correlation test and showed that the Shenzhen

stock market and the Shanghai stock market were neither efficient. Yu (1994) supported his

conclusion by analyzing the data from the inception to the year 1994. Other earliest studies in

this field were carried by Deng (1995), Sun (1997), Zhao (1998), Chen (1999) and Wei (2000)

respectively and concluded that the China’s stock market in the sample periods can hardly be

regarded as efficient. Regarding the recent past studies in the 21st century, Peng and Pang

(2002) employed the AR-X-GARCH (1.1) model, indicating no evidence of weak form

efficiency. In the same vein, the empirical study based on daily data from Shanghai stock

market by Shi (2003) provided no evidence for market efficiency. Furthermore, Li (2004), Lu

and Xu (2004), Wang and Sun (2004) confirmed the market inefficiency by conducting serial

correlation test, unit root test, auto regression analysis, EGARCH model and other advanced

statistical techniques over different sample periods.

2.2.2 Literatures supporting weak form efficiency

There are also a variety of literatures providing evidence for weak form efficiency of China’s

stock market. For example, one of the earliest findings by Song (1995) was examined by run

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test and serial correlation test with 29 individual stocks covering the year 1993 and 1994.

Chen (1997) applied the random walk model and Dickey-Fuller test to show that Shanghai

stock market has been weakly efficient since 1993. Hu (1998) identified the weak form

efficiency by looking at the daily closing prices of composite index, while the same

conclusion was made by Wen (1999) with normal distribution test and non-parameter test.

More recently, the empirical evidence provided by Li (2000) indicated the weak form

efficiency from sub-period analysis based on correlation test and run test. Deng, Hu (2001)

obtained the same result of weak form efficiency though refuted the strong form efficiency by

event study. Li (2006) and Cheng (2006) both made a concluding remark of weak form

efficiency in China’s stock market after taking multiple popular empirical examinations.

2.2.3 Literatures for other conclusions

Some of the literatures find it hard to draw a definite conclusion about the efficiency form in a

given period but prefer to state that the efficiency in China’s stock market is enhancing or in a

transition period. For example, Shi (2000) analyzed the Shanghai composite index and

Shenzhen component index from the period spanning from July 1991 to December 2000 by

Kalman filter model, pointing out the enhancing efficiency and importance of regulation

enforcement. Huang (2001) found that the market has been closed to weak form efficiency

since 1997 and nearly the same period Fan (2000) claimed that market value effect, book

value effect and price effect apparently exist in China’s stock market.

3. Empirical test on weak form efficiency of China’s stock market

3.1 Methodology

Among the most popular empirical methodologies, the thesis applies the following three to

examine the weak form efficiency of China’s stock market.

3.1.1 Unit root test

Augmented Dickey–Fuller (ADF) Test is one of the unit root tests proposed by Dickey and

Fuller (1979, 1981). The ADF test is used to test the null hypothesis of a unit root. A unit root

is a necessary condition for a random walk. The following regression is estimated for each

series:

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(Model 1) (1)

(Model 2) (2)

Where△represents first differences and Y is the daily closing price index. α0 is a constant and

t in Model 2 represents a linear trend. The length of k is selected with the Akaike Information

Criterion (AIC) and should be large enough to achieve a white noise structure inεt. The ADF

test statistic is the ratio of the estimatedβto its calculated standard error obtained from an OLS

regression. The null hypothesis is thatβequals 0. The null hypothesis of a unit root is rejected

if the pseudo t statistic is larger than the critical value at different significance levels, which

indicates that the time series is non-stationary (the statistical properties of time series vary

over time). And the result would be reserved if t statistic is smaller than critical value, which

demonstrates that the time series is stationary. The test statistic does not have a t distribution

and a table of significance levels has been provided by MacKinnon (1991).

3.1.2 Serial autocorrelation test

The correlation between any values of the series Xt, Xt-1,…, Xt-k, which composes the time

series, is called autocorrelation. This thesis tests the hypothesis of weak-form efficiency by

calculating the sample autocorrelations. The degree of autocorrelation is measured by

autocorrelation coefficient

(3)

where ρk is the autocorrelation of lag k, Xt represents the first difference of the log of price

index (Xt = lnPt – lnPt-1). The value of ρk is varied from -1 to 1. The absolute value of ρk is

more approaching to 1, the degree of autocorrelation is stronger. For a lag period of k periods,

if ρk is significantly different from zero, then the null hypothesis of weak form hypothesis

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should be rejected, otherwise the time series follow a random walk so that the weak form

hypothesis should be accepted.

Simultaneously Q statistic is introduced by G. P. E. Box and G. M. Ljung:

(4)

whereρk is the autocorrelation coefficient of residual of k order and T is the volume of sample,

L is the degree of freedom. If the value of Q of one lag-order is not zero significantly, the

series consist of correlations to some extent and reject our null hypothesis of weak form

efficiency.

3.1.3 Run test

The runs test was the most commonly used nonparametric test of the RWH. The advantage is

that it does not require that return distributions are normally or identically distributed a

condition that most stock-return series cannot satisfy. Moreover, it eliminates the effect of

extreme values often found in returns data.

The runs test is conducted to check for the randomness of stock prices on the two exchanges.

A sequence of consecutive stock price changes in the same direction is defined as a run.

Normally there are two runs: prices go up and prices go down. Under the hypothesis of

randomness of stock price changes, the mean and standard error of the total runs are

(5)

(6)

where n is the total number of stock price observations, nA is the number of upward

observations, nB is the number of downward observations, and R is the number of runs. When

n is reasonably large, this distribution is approximately normal. Since there is evidence of

non-randomness when R is too small or too large, the test is a two-tailed one. The

standardized variable is

(7)

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Since the distribution of Z is N (0, 1), the critical value of Z at 5% significance level is±1.96

while at 1% is±2.58. If Z value is larger than the critical value at different significant levels,

the null hypothesis that the stock prices have no predictability and follow a random walk is

rejected, indicating the stock market has not reached the weak form efficiency.

3.2 Data

The data consist of the daily closing price of four indexes form the Shanghai Stock Exchange

and the Shenzhen Stock Exchange throughout the period around inception to December 2007,

which is from CSMAR database in GTA Research Service Center:

Shanghai Composite Index: 02/01/1991 - 28/12/2007 (4169 observations)

Shanghai 30 Index: 01/07/1996 - 28/12/2007 (2781 observations)

Shenzhen Composite Index: 03/07/1991 - 28/12/2007 (4056 observations)

Shenzhen Component Index: 03/04/1991 - 28/12/2007 (4135 observations)

For ADF test, this thesis divides the data into several sub-periods to capture the general

position of market efficiency based on the main changes of transaction rules in China’s stock

market (On May 21st of 1992, Shanghai Stock Exchange removed the daily price fluctuation

limit; On December 16th of 1996, Shanghai Stock Exchange and Shenzhen Stock Exchange

imposed on the price limit at 10%):

Shanghai Composite Index: 19/12/1990 - 20/05/1992;

21/05/1992 - 15/12/1996; 16/12/1996 - 28/12/2007

Shanghai 30 Index: 01/07/1996 - 15/12/1996; 16/12/1996 - 28/12/2007

Shenzhen Composite Index: 03/07/1991 - 15/12/1996; 16/12/1996 - 28/12/2007

Shenzhen Component Index: 03/04/1991 - 15/12/1996; 16/12/1996 - 28/12/2007

For serial autocorrelation test and run test, this thesis utilizes each daily closing price in each

year for the full period to analyze the evolution process from no efficiency to weak form

efficiency in China’s stock market.

3.3 Empirical test results

3.3.1 Unit root test

Table 3-1 Augmented Dickey Fuller Test for the four selected indexes in China’s stock market

ADF at level form ADF at first difference formWith C With C&T With C With C&T

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Shanghai Composite IndexEntire period 1.382586 0.260272 -10.56898*** -10.70739***

19/12/1990 - 20/05/1992 1.482054 1.766982 1.429521 0.55194721/05/1992 - 15/12/1996 -2.732223** -2.551215 -9.576295*** -9.623506***

16/12/1996 - 28/12/2007 1.517449 0.704608 -8.582632*** -8.813448***

Shanghai 30 IndexEntire period 2.141199 1.576135 -8.849450*** -9.153728***

01/07/1996 - 15/12/1996 -1.560154 -2.145407 -9.750439*** -4.555431***

16/12/1996 - 28/12/2007 2.072776 1.556719 -8.475031*** -8.801841***

Shenzhen Composite IndexEntire period 0.974338 -0.005443 -10.06585*** -10.19902***

03/07/1991 - 15/12/1996 -0.642168 -0.703698 -14.32148*** -14.33753***

16/12/1996 - 28/12/2007 1.400109 0.811545 -8.718646*** -8.958186***

Shenzhen Component IndexEntire period 2.173030 1.168479 -9.858500*** -10.12220***

03/04/1991 - 15/12/1996 -0.094465 -0.348595 -14.20033*** -14.25480***

16/12/1996 - 28/12/2007 2.301406 1.523850 -7.831419*** -8.238971***

Note: C: constant, T: trend. ***Significant at 1% level. **Significant at 5% level. *Significant at 10% level.The ADF tests with the four indexes are reported in Table 3-1, including both with and

without a trend in the “Dickey-Fuller equation”. To extent the examination for possibility of a

second unit, this thesis further applies the test for the first differences of the series. Generally

the results are consistent with the hypothesis of a unit root, where the ADF values are less

than the critical values of the three different significance levels at 1%, 5% and 10% at the

level form, while a second unit root is rejected since the ADF values are very significant at

1% with the first difference level form. The main exceptions to this finding are the first two

periods of Shanghai Composite Index before 1997, where a second unit root exists before the

removal of price limit in 1992 and the null hypothesis without trend is rejected at 5%

significant level in the period from May 21st, 1992 to Dec 15th, 1996, which might be due to

the immaturity in the early stage of the stock market. However, the overall results indicate

that the null hypothesis of unit roots should not be rejected at level form. So it can be

confirmed that the index series is integrated of order one and thus the necessary condition for

a random walk is met. Therefore, both stock markets examined are weak-form efficient for the

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entire period.

3.3.2 Serial auto-correlation test

Table 3-2 Serial autocorrelation test and Ljung-Box Q statistic for the Composite Index of Shanghai Stock Exchange

Year Total Case ρ1 ρ2 ρ3 Q

1991 255 0.736 0.660 0.598 356.001(0.000)1992 255 0.158 0.077 0.015 8.004(0.046)1993 257 -0.045 0.020 0.155 6.944(0.074)1994 252 -0.058 0.109 0.082 5.621(0.132)1995 251 0.119 -0.006 -0.254 20.102(0.000)1996 247 0.051 0.035 0.145 6.306(0.098)1997 243 -0.115 -0.023 -0.029 3.566(0.312)1998 246 0.094 -0.072 -0.010 3.526(0.317)1999 239 -0.026 -0.089 0.217 13.618(0.003)2000 239 0.082 0.056 -0.064 3.403(0.334)2001 240 0.004 -0.160 0.039 6.586(0.086)2002 237 0.018 0.066 -0.102 3.613(0.306)2003 241 -0.015 -0.054 0.061 1.702(0.637)2004 243 0.000 -0.017 0.106 2.866(0.413)2005 242 0.016 -0.025 0.053 0.929(0.819)2006 241 0.057 -0.017 0.050 1.463(0.691)2007 241 -0.037 -0.060 0.047 1.772(0.621)

Note: ρ1,ρ2, ρ3 are the autocorrelation coefficients for the selected time series for lags k=1,2,3.Q represents the Ljung-Box Q statistic value for lag of 3. The numbers in parentheses with the Q are probability values.

Table 3-3 Serial autocorrelation test and Ljung-Box Q statistic for the 30 Index of Shanghai Stock Exchange

Year Total Case ρ1 ρ2 ρ3 Q

1996 128 0.080 0.087 0.015 1.873(0.599)1997 243 -0.121 -0.039 -0.035 4.295(0.231)1998 246 0.126 -0.108 -0.060 7.802(0.050)

1999 239 0.005 -0.090 0.20011.739(0.008

)2000 239 0.105 0.079 -0.083 5.830(0.120)2001 240 -0.047 -0.178 0.066 9.292(0.026)2002 237 0.012 0.118 -0.098 5.748(0.125)

2003 241 -0.347 -0.011 -0.01529.509(0.000

)2004 243 -0.013 -0.004 0.103 2.704(0.439)2005 242 -0.011 -0.023 0.043 0.615(0.893)

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2006 241 0.026 -0.012 0.040 0.604(0.896)2007 241 -0.030 -0.047 0.057 1.548(0.671)

Note: ρ1,ρ2, ρ3 are the autocorrelation coefficients for the selected time series for lags k=1,2,3.Q represents the Ljung-Box Q statistic value for lag of 3. The numbers in parentheses with the Q are probability values.

Table 3-4 Serial autocorrelation test and Ljung-Box Q statistic for the Composite Index of Shenzhen Stock Exchange

Year Total Case ρ1 ρ2 ρ3 Q

1991 152 0.059 0.041 0.073 1.634(0.652)1992 257 0.190 -0.019 0.003 9.495(0.023)1993 251 -0.112 0.140 0.039 8.576(0.035)1994 252 0.001 0.072 0.021 1.423(0.700)

1995 244 0.105 -0.041 -0.27121.397(0.000

)1996 247 0.054 0.130 0.134 9.470(0.024)1997 243 -0.051 -0.031 0.037 1.211(0.750)1998 246 0.097 -0.074 -0.040 4.137(0.247)

1999 239 -0.043 -0.101 0.21614.282(0.003

)2000 239 0.071 0.072 -0.060 3.347(0.341)2001 240 0.025 -0.171 0.026 7.423(0.060)2002 237 0.034 0.083 -0.085 3.675(0.299)2003 241 0.015 -0.063 0.053 1.726(0.631)2004 243 0.036 -0.031 0.145 5.754(0.124)2005 242 0.031 -0.014 0.083 1.967(0.579)2006 241 0.049 -0.040 0.023 1.104(0.776)2007 241 0.045 -0.012 0.064 1.542(0.673)

Note: ρ1,ρ2, ρ3 are the autocorrelation coefficients for the selected time series for lags k=1,2,3.Q represents the Ljung-Box Q statistic value for lag of 3. The numbers in parentheses with the Q are probability values.

Table 3-5 Serial autocorrelation test and Ljung-Box Q statistic for the Component Index of Shenzhen Stock Exchange

Year Total Case ρ1 ρ2 ρ3 Q

1991 229 0.068 0.043 0.077 2.909(0.406)

1992 257 0.214 -0.013 0.01512.044(0.007

)1993 251 -0.100 0.132 0.049 7.583(0.055)1994 252 0.023 0.074 0.037 1.896(0.594)

1995 246 0.068 -0.020 -0.27119.633(0.000

)1996 247 0.039 0.090 0.091 4.527(0.210)

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1997 243 -0.027 -0.005 0.085 1.961(0.580)1998 246 0.073 -0.110 -0.075 5.757(0.124)

1999 239 0.039 -0.067 0.21712.988(0.005

)2000 239 0.139 0.079 -0.086 7.972(0.047)2001 240 0.002 -0.145 0.029 5.334(0.149)2002 237 0.056 0.097 -0.107 5.791(0.122)2003 241 -0.012 -0.017 0.058 0.943(0.815)2004 243 -0.015 -0.029 0.131 4.555(0.207)2005 242 0.022 0.017 0.063 1.183(0.757)2006 241 0.046 -0.035 -0.015 0.876(0.831)2007 241 -0.008 -0.047 0.082 2.197(0.532)

Note: ρ1,ρ2, ρ3 are the autocorrelation coefficients for the selected time series for lags k=1,2,3.Q represents the Ljung-Box Q statistic value for lag of 3. The numbers in parentheses with the Q are probability values.Table 3-2 to 3-5 shows the distribution of autocorrelation coefficients for lags k = 1, 2, 3 for

the four selected indexed of China’s stock market, using the daily closing price data from

beginning to December 28,2007.

Clearly there is significant autocorrelation in the early stages of both Shanghai and Shenzhen

stock markets though the value of ρdeclines gradually, reflecting the predictability is

decreasing with the development of the markets.

From 1991 to 1993, the Q statistics are very large and rejected the null hypothesis significant

at about 5% level except for the Shenzhen market in 1991. The significant autocorrelation in

this period indicates the low efficiency in China’s stock market. The result is convincing

regarding the immaturity at the inception. Generally, Q statistics are relatively large and

significantly different from zero until 1996 though some extreme cases happened in 1994 for

Shenzhen market and in 1995 for both markets. According to Fama (1976), it seems

reasonable to expect some extreme values especially if many autocorrelations are estimated.

The year 1996 seems to be a transition point from inefficiency to weak form efficiency. Q

statistics of Shanghai Composite Index is different from zero significantly at about 10% and

larger than 1% for Shenzhen Composite Index. From the year of 1997, Q statistics are deemed

to be zero in statistics except for the year 1999 (affected by the 5.19 event) and 2001 (affected

by the plunge of stock prices). The results of Shanghai Composite Index and Shenzhen

Composite Index are reinforced by Shanghai 30 Index and Shenzhen Component Index

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respectively, confirming that the efficiency of China’s stock market is improving over these

two decades and has transited from no efficiency to weak form efficiency since 1997.

Finally, the evolution processes of efficiency are similar comparing the results from the two

stock markets. This result is understandable regarding the synchronization of the two markets

in China. Facing nearly the same external environment, they are in the same stage of

development and exist dependently, inevitably sharing the same efficiency to a great extent.

3.3.3 Run test

Table 3-6 Run test for the four selected indexes in China’s stock market

Year

Shanghai Composite Index

Shanghai 30 Index

Shenzhen Composite Index

Shenzhen Component

Index

ZAsymp.

Sig.Z

Asymp. Sig.

ZAsymp.

Sig.Z

Asymp. Sig.

1991 -12.200 0.000 — — -2.400 0.016 -5.870 0.0001992 -4.660 0.000 — — -2.430 0.015 -2.200 0.0281993 1.440 0.150 — — 0.537 0.591 0.151 0.8801994 1.362 0.173 — — 0.576 0.565 0.084 0.9331995 1.449 0.147 — — 1.527 0.127 -0.150 0.8791996 1.206 0.228 1.448 0.148 -1.530 0.127 -0.760 0.4491997 0.040 0.968 0.215 0.830 -0.540 0.593 -0.270 0.7871998 -0.690 0.493 -1.090 0.277 -0.190 0.849 0.827 0.4081999 -0.840 0.400 0.727 0.467 -1.340 0.180 -0.210 0.8372000 -0.490 0.627 -1.160 0.246 -1.530 0.126 1.231 0.2182001 -1.550 0.121 -1.150 0.250 -1.040 0.301 -0.230 0.8162002 0.470 0.639 0.765 0.444 -0.040 0.966 0.276 0.7822003 0.975 0.329 0.975 0.329 0.975 0.329 -0.040 0.9662004 -0.180 0.855 -0.260 0.793 0.215 0.830 0.571 0.5682005 0.942 0.346 1.039 0.299 0.645 0.519 0.903 0.3672006 0.648 0.517 -0.600 0.546 -0.690 0.490 0.374 0.7082007 -0.510 0.610 -0.550 0.584 -0.830 0.404 -0.170 0.868The results of run test of the four selected indexes are reported in Table 3-6. It is very clear

that the Z values of all the indexes in both markets significantly reject the hypothesis of

randomness at least at 5% level from the year 1991 to 1992 while those of the Shanghai

Composite Index are even significant at 1% level in this period. This result indicates a

positive dependence of stock prices and is consistent with that of serial autocorrelation test

above. But from the year 1993 to 1997, the hypothesis of randomness can not be rejected at

any significant levels.

15

4. Summary of the empirical test

4.1 Conclusion and analysis of empirical results

Based on the evidence from the three empirical tests above, the thesis finds that China’s stock

market has experienced a process from inefficiency to weak form efficiency. The test results

are consistent with most of the previous studies. In the initial years (1991–1993), the stock

market was very volatile and substantially inefficient. The lack of weak form efficiency in this

period is connected with several factors such as thin trading, speculation activities, lack of

adequate regulation of securities exchanges, weak disclosure requirements, and insider trading

and fierce competition, which could be proved by the serial autocorrelation test and run test

with a highly significant level in both stock exchanges. This period is the so called “infant”

stage in China’s stock market and the data from which rejects the hypothesis of weak form

efficiency.

However, as China’s stock market grows and learns, it is becoming more and more mature in

terms of information transparency and utilization, law and regulation enforcement. Over time,

as the market becomes more liquid, normalized regulations are strengthened and ad hoc

regulations reduced, the predictability of two stock returns based on historical information

gradually dies out. From the year 1997, all the indexes examined exhibit no statistical

significance except for few special years. So that it can be concluded that by the year of 1997,

China’s stock market has basically reached weak form efficiency. Furthermore, by the tests

conducted on each single year, the thesis manages to capture this evolution process from

immature to relatively mature stages.

From this point of view, it can be concluded that China’s stock market is filled with

characteristics of emerging markets, whose informational efficiency could be brought about

by improving liquidity, ensuring that investors have access to high quality and reliable

information and minimizing the institutional restrictions on trading. It is expected that the

efficiency in China’s stock market will be increasing more and more and gradually transit

from weak form to semi-strong form.

In addition, comparing the empirical data from China’s two stock markets, the thesis also

16

finds the similarity and synchronization effect. This is within the expectation since both

markets are facing nearly the same external environment.

4.2 Limitations of the research

First, the models might not be perfect enough to incorporate certain characteristics of the data.

For example, the ADF tests are based on the assumption of a normal distribution, but this

might not be strictly valid for many time series. From a statistical point of view, failure to

account for this fact could result in misleading inference about the random walk hypothesis or

the EMH. The assumptions in the models sometimes are too restrictive to capture the patterns

of series. Furthermore, the conventional tests of random walk used in this thesis might be

susceptible to some degrees regarding the nonlinearity aspect. With reference to evidence in

favor of efficiency in the later years, this is perhaps the outcome of using linear models to test

efficiency of markets characterized by inherent nonlinearities. So it is suggested that a further

sophisticated techniques be employed to overcome these problems.

Second, .in the range of emerging markets, China’s stock market is surely unique in many

ways and worth the effort of empirical work both for its own sake (it has many peculiar

features) and for the light it can throw on the relationship between efficiency and market

development. The conventional tests might not fit this rapidly growing market very well as

applied in the western developed markets. Consequently, further research is necessary to

incorporate the special market qualities to lend stronger creditability to the conclusions.

5. Implications for China’s stock market

5.1 Improvement in the adequacy and quality of information flow in the stock market

Whether the stock price can reflect all available information is the criterion to judge market

efficiency. In other words, it is the adequacy and quality of information flow that counts.

Aiming at improving the efficiency in China’s stock market, authorities should guarantee the

transparency and the smooth transfer of information. Improvement in the enforcement of

disclosure regulations on listed companies can help to reduce the information asymmetry,

leading to increase market efficiency.

5.2 Improvement in the automation and regulation of the stock market

Learning lessons from market inefficiency during the initial years, investors and authorities

17

realize the balance of automation and regulation is essential for China’s stock market. Along

with the transition from planned economy to market economy, government interventions

should be reduced but adequate regulations should be imposed in the stock market to ensure

the healthy development, because a more automated and adequately regulated market is the

premise of efficiency.

5.3 Improvement in the knowledge and awareness of investors

Efficient Market Hypothesis assumes that investors are rational. With the vigorous

development of capital market, more and more individual investors will participate in the

stock market. So the quality of these investors is important for the market efficiency. In order

to reduce the irrational behaviors in the market, it is recommended to improve the investment

knowledge and legal sense of the individual investors and develop institutional investors.

5.4 Improvement in the quality of intermediaries

Building up a qualified team of intermediate institutions is important to improve the quality of

market information. Assurors and auditors can not lend creditability to financial information

unless they are ethical and competent enough. Undoubtedly improving the quality of the

intermediaries plays a key role in the market efficiency.

5.5 Improvement in the ownership structure

Before the non-tradable share reform in 2005, the ownership structure in China’s stock market

was unreasonable. China’s listed companies were controlled by state owned enterprises and

state owned shares accounted for a majority part. The shortcomings of the non-tradable share

were apparent: the interests of individual investors could not be protested, the supply and

demand were imbalanced and the state owned assets were difficult to preserve and increase

value. The non-tradable share reform in 2005 clearly is the best way to solve the problem and

increase the market efficiency. As a result, accelerating the reform is a vital step to reach the

semi-strong efficiency stage.

6. Conclusion

This thesis analyzes the theory of Efficient Market Hypothesis (EMH) and takes a review of

the previous studies on efficiency test of China’s stock market. Building on the work of

previous studies, it extents the empirical work in terms of more extensive data and multiple

18

forms of tests to examine whether China’s stock market has reached weak form efficiency. By

employing the unit root test, serial autocorrelation test and run test on the daily closing prices

of Shanghai Composite Index, Shanghai 30 Index, Shenzhen Composite Index and Shenzhen

Component Index from 1991 to 2007, it finds that China’s stock market has experienced a

process from inefficiency to weak form efficiency. The stock market was very volatile and

substantially inefficient due to the immaturity in initial years. As China’s stock market grew

and learned, it became more and more mature in terms of information transparency and

regulation enforcement. By the year of 1997, China’s stock market has basically reached

weak form efficiency and the efficiency is increasing gradually nowadays. The tests

conducted on each single year help to capture this evolution process from immature to

relatively mature stages. Combining the empirical test results and the unique characteristics of

China’s emerging market, the thesis further puts forward five implications for market

efficiency. They are improvements in the adequacy and quality of information flow,

automation and regulation, knowledge and awareness of investors, quality of intermediaries

and ownership structure.

Acknowledgement

I would like to express thanks to Ms. Shen Hongtao for providing constructive suggestions

and guidance in the process of writing this thesis. I am also grateful to all my teachers who

influenced me by their profound knowledge and useful recommendations during these four

years in Jinan University.

19

References

Allan Timmermann, Clive W.J. Granger, 2004, Efficient market hypothesis and forecasting,

International Journal of Forecasting, Volume 20, page 15-27.

Cumhur Buguk, B. Wade Brorsen, 2003, Testing weak-form market efficiency: Evidence from

the Istanbul Stock Exchange, International Review of Financial Analysis, Volume 12,

page 579-590.

Denis Kan, B. Andreosso-O’Callaghan, 2007, Examination of the efficient market hypothesis

—the case of post-crisis Asia Pacific countries, Journal of Asian Economics, Volume 18,

page 294-313.

Elena Goldman, 2000, Testing efficient market hypothesis for the dollar-sterling gold standard

exchange rate 1890-1906: MLE with double truncation, Economics Letters, Volume 69,

page 253-259.

Fama, F. E., 1970, Efficient capital markets: a review of theory and empirical work, Journal of

20

Finance, Volume 25, page 383-417.

Fama, F. E., 1991, Efficient capital markets:Ⅱ, Journal of Finance, Volume 46, page 1575-

1617.

Francisco J. DePenya, Luis A. Gil-Alana, 2007, Serial correlation in the Spanish Stock

Market, Global Finance Journal, Volume 18, page 84-103.

Jeffrey A. Busse, T. Clifton Green, 2002, Market efficiency in real time, Journal of Financial

Economics, Volume 65, page 415-437.

Kam C. Chan, Benton E. Gup, Ming-Shiun Pan, An Empirical Analysis of Stock Prices in

Major Asian Markets and the United States, The Financial Review, Volume 27, page

289-307.

Keith Jefferis, Graham Smith, 2005, The changing efficiency of African stock markets, South

African Journal of Economics, Volume 73, page 54-67.

Laura Cavallo, Paolo Mammola, 2000, Empirical tests of efficiency of the Italian index

options market, Journal of Empirical Finance, Volume 7, page 173-193.

Li Xiaoming, 2003, China: Further evidence on the evolution of stock markets in transition

economies, Scottish Journal of Political Economy, Volume 50, page 341-358.

Luo Bo, Sun Linyian, Richard Mweene, 2005, Empirical study of trading rule discovery in

China stock market, Expert Systems with Applications, Volume 28, page 531-535.

Nicolaas Groenewold, Sam Hak Kan Tang, Yanrui Wu, 2003, The efficiency of the Chinses

stock market and the role of the banks, Journal of Asian Economics, Volume 14, page

593-609.

Panayotis Alexakis, Nicholas Apergis, 1996, ARCH effects and cointegration: Is the foreign

exchange market efficient?. Journal of Banking and Finance, Volume 20, page 687-697.

Rajen Mookerjee, Qiao Yu, 1999, An empirical analysis of equity markets in China, Review

of Financial Economics, Volume 8, page 41-60.

Sunil Poshakwale, 2002, The Random Walk Hypothesis in the Emerging Indian Stock Market,

Journal of Business Finance and Accounting, Volume 29, page 1257-1299.

Y. Alex Tung, James R. Marsden, 1998, Test of Market Efficiencies Using Experimental

Electronic Markets, Journal of Business Research, Volume 41, page 145-151.

21

Dai Xiaofeng (戴晓凤)，Yang Jun (杨军)，Zhang Qinghai (张清海)，2005，中国股票市

场的弱式有效性检验：基于单位根方法，系统工程，11 期，23-28 页。

Deng Zilai (邓子来)，Hu Jian (胡健)，2001，市场有效理论及我国股票市场有效性的实

证检验，金融市场，10 月刊，44-49 页。

Geng Junhui (耿军会)，Shi Huijuan (石会娟)，2006，中国股票市场有效性的实证检验，

科技情报开发与经济，2 期，110-111 页。

Geng Xiulin (耿修林)，Zhang Lin (张琳)，2004，计量经济学，科学出版社，407-426 页。

Han Dezong (韩德宗)，Yu Danhong (虞红丹)，2002，中美股票市场弱式有效性的比较

研究，财经论丛，2 期，39-44 页。

Hu Jinyan (胡金焱)，2002，中国股票市场弱有效性的三种统计检验，世界经济论坛，4

期，38-48 页。

Huang Shan (黄山)，2006，中国股票市场有效性研究的文献综述，价值工程， 4 期，

107-109 页。

Lan Qiujun (兰秋军)，Ma Chaoqun (马超群)，Gan Guojun (甘国君)，Wu Jianhong (吴建

宏)，2005，中国股市弱有效吗？——来自数据挖掘的实证研究，中国管理科学，

4 期，17-23 页。

22

Li Xiaojing (李晓静)，2006，中国股票市场有效性实证检验，财经界，10 月刊，30-31

页。

Liu Siwei (柳思维)，Liu Fenggen (刘凤根)，2003，股票市场弱势有效与股票价格的随

机游走，系统工程，6 期，71-74 页。

Qiu Yigan (邱宜干)，2001，我国股市是否达到弱式有效，东南学术，1 期，46-53 页。

Wu Shinong (吴世农)，1996，我国证券市场效率分析，经济研究，4 期。

Yu Qiao (俞乔)，1994，市场有效、周期异常与股价波动，经济研究，3 期。

Zhang Bing (张兵)，Li Xiaoming (李晓明)，2003，中国股票市场的渐进有效性研究，经

济研究，1 期，54-61 页。

Zhang Jianhua (张建华)，Zhang Ling(张玲)，2006，中国股票市场的有效性分析，系统

工程理论方法应用，3 期，266-269 页。

Zhang Yichun (张亦春)，Zhou Yinggang (周颖刚)，2001，中国股市弱式有效研究综述，

当代财经，8 期，30-32 页。

Zhou Sijun (周四军)，2003，中国股市的弱式有效性检验，统计与信息论坛，2 期，42-

44 页。

23

24

Graduation Thesis Evaluation for Undergraduate Students

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