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  ...
Statement of Originality


I hereby declare that the thesis presented is the result of research performed by me personally...
Component Index covering the period from the year 1991 to 2007, in attempt to examine the
weak form efficiency of China’s ...
Contents
1. Introduction

1.1 Background and implication of the research

Stock market efficiency is one of the most long-standing ...
1.2 Methodologies and originality of the research

While a considerable amount of research has been conducted on testing t...
methodologies, data and the specific empirical results. The fourth section draws a conclusion
of the tests and gives some ...
securities arrives in the market in a random manner. Given this setting, investors adjust to new
information immediately a...
This thesis focuses on the empirical test of weak form efficiency since it is the prerequisite to
the analysis of semi-str...
determine the independence of the stock prices in China’s two stock exchanges. Section three
will present the methodologie...
market by Shi (2003) provided no evidence for market efficiency. Furthermore, Li (2004), Lu
and Xu (2004), Wang and Sun (2...
3.1 Methodology

Among the most popular empirical methodologies, the thesis applies the following three to
examine the wea...
series, is called autocorrelation. This thesis tests the hypothesis of weak-form efficiency by
calculating the sample auto...
randomness of stock price changes, the mean and standard error of the total runs are


           n + 2n A n B
E ( R) =   ...
Shenzhen Component Index:        03/04/1991 - 28/12/2007 (4135 observations)

For ADF test, this thesis divides the data i...
Shenzhen Component Index
Entire period             2.173030                  1.168479        -9.858500***     -10.12220***...
1998            246            0.094         -0.072          -0.010        3.526(0.317)
    1999            239           ...
1999             239            -0.043          -0.101            0.216      14.282(0.003)
    2000             239       ...
From 1991 to 1993, the Q statistics are very large and rejected the null hypothesis significant
at about 5% level except f...
2000     -0.490      0.627     -1.160     0.246      -1.530      0.126       1.231      0.218
2001     -1.550      0.121  ...
regulations reduced, the predictability of two stock returns based on historical information
gradually dies out. From the ...
development. The conventional tests might not fit this rapidly growing market very well as
applied in the western develope...
market information. Assurors and auditors can not lend creditability to financial information
unless they are ethical and ...
emerging market, the thesis further puts forward five implications for market efficiency. They
are improvements in the ade...
References


Allan Timmermann, Clive W.J. Granger, 2004, Efficient market hypothesis and forecasting,
     International J...
Finance, Volume 25, page 383-417.

Fama, F. E., 1991, Efficient capital markets:Ⅱ, Journal of Finance, Volume 46, page
   ...
Electronic Markets, Journal of Business Research, Volume 41, page 145-151.


Dai Xiaofeng ( 戴晓凤 ), Yang Jun ( 杨军) ,Zhang Q...
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。


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...
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。




                                                  25
Graduation Thesis Evaluation for Undergraduate Students
Supervisor’s Comments:




Marks:_______/100        Signature:

  ...
Dean’s Comments:




                   Signature:

                   Date: (dd/mm/yyyy)
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  1. 1. 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
  2. 2. 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
  3. 3. 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
  4. 4. Contents
  5. 5. 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
  6. 6. 1.2 Methodologies and originality of the research 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 2
  7. 7. 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 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 3
  8. 8. 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 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. 4
  9. 9. 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 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 5
  10. 10. 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%, 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 6
  11. 11. 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 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 7
  12. 12. 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: k ∆Yt = α 0 + β Yt −1 + ∑ α i ∆Yt −1 + ∈t (Model 1) (1) i =1 k ∆Yt = α 0 + θt + βYt −1 + ∑ α i ∆Yt −1 + ∈t (Model 2) (2) i =1  H 0: : β = 0   H1 : β < 0 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 inε t. 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 8
  13. 13. 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 n−k ∑(X t − X )( X t + k − X ) ρk = t =1 n (3) ∑ (X t − X )2 t =1 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 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: L ρ k2 QLB = T (T + 2)∑ (4) k =1 T − k 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 9
  14. 14. randomness of stock price changes, the mean and standard error of the total runs are n + 2n A n B E ( R) = (5) n 2n A n B ( 2n A n B − n) σR = (6) n 2 (n − 1) 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 − E ( R) Z= (7) σR Since the distribution of Z is N (0, 1), the critical value of Z at 5% significance level i s±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) 10
  15. 15. 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 form With C With C&T With C With C&T Shanghai Composite Index Entire period 1.382586 0.260272 -10.56898*** -10.70739*** 19/12/1990 - 20/05/1992 1.482054 1.766982 1.429521 0.551947 21/05/1992 - 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 Index Entire 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 Index Entire 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*** 11
  16. 16. Shenzhen Component Index Entire 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 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) 12
  17. 17. 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 0.200 11.739(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 -0.015 29.509(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) 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 -0.271 21.397(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) 13
  18. 18. 1999 239 -0.043 -0.101 0.216 14.282(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 0.015 12.044(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 -0.271 19.633(0.000) 1996 247 0.039 0.090 0.091 4.527(0.210) 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 0.217 12.988(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. 14
  19. 19. 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 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 Shanghai 30 Shenzhen Shenzhen Composite Index Index Composite Index Component Index Z Asymp. Z Asymp. Z Asymp. Z Asymp. Sig. Sig. Sig. Sig. 1991 -12.200 0.000 — — -2.400 0.016 -5.870 0.000 1992 -4.660 0.000 — — -2.430 0.015 -2.200 0.028 1993 1.440 0.150 — — 0.537 0.591 0.151 0.880 1994 1.362 0.173 — — 0.576 0.565 0.084 0.933 1995 1.449 0.147 — — 1.527 0.127 -0.150 0.879 1996 1.206 0.228 1.448 0.148 -1.530 0.127 -0.760 0.449 1997 0.040 0.968 0.215 0.830 -0.540 0.593 -0.270 0.787 1998 -0.690 0.493 -1.090 0.277 -0.190 0.849 0.827 0.408 1999 -0.840 0.400 0.727 0.467 -1.340 0.180 -0.210 0.837 15
  20. 20. 2000 -0.490 0.627 -1.160 0.246 -1.530 0.126 1.231 0.218 2001 -1.550 0.121 -1.150 0.250 -1.040 0.301 -0.230 0.816 2002 0.470 0.639 0.765 0.444 -0.040 0.966 0.276 0.782 2003 0.975 0.329 0.975 0.329 0.975 0.329 -0.040 0.966 2004 -0.180 0.855 -0.260 0.793 0.215 0.830 0.571 0.568 2005 0.942 0.346 1.039 0.299 0.645 0.519 0.903 0.367 2006 0.648 0.517 -0.600 0.546 -0.690 0.490 0.374 0.708 2007 -0.510 0.610 -0.550 0.584 -0.830 0.404 -0.170 0.868 The 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. 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 16
  21. 21. 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 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 17
  22. 22. 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 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 18
  23. 23. 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 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 19
  24. 24. 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. 20
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  30. 30. Graduation Thesis Evaluation for Undergraduate Students Supervisor’s Comments: Marks:_______/100 Signature: Date:(dd/mm/yyyy) Evaluator’s Comments: Mark:_______/100 Signature: Date: (dd/mm/yyyy)
  31. 31. Dean’s Comments: Signature: Date: (dd/mm/yyyy)

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