AN EMERGING MARKET: EVIDENCE FROM VIETNAM'S STOCK MARKET
T r a n M a n h T u y e n , N g u y e n T r u n g C h i n h '
Abstract
The majority of efficient market research to date has focused on the major United States and European securities markets. Far fewer have investigated the developing and less developed country markets; and no study on this area has been performed on the Vietnam stock market The study .,eeks evidence supporting the existence of at least weak-form efficiency of the Vietnam stock market. The sample includes the daily price indices of all the listed securities on the Hochiminh Stock market for two periods, one in the year of 2009 (sample J) and the other in 2011 (sample 2). The hypothesis of the study is whether the Vietnam stock market (VSM) is weak form efficient. The remits of Unit-root. Portmanteau, Auto-regression. BDS, ARCH LM, Variance ratio tests show that the Vietnam stock market does not follow random walk and the market is not weak-form efficient. Interestingly, we found the evidence of bull and bear effects on VSM. We also used the GARCH models for forecasting out-of-sample for both samples.
Keywords: Vietnamese stock market, weak form efficiency, efficient market hypothesis, emerging market. stock return, predictability. unit root, bull, bear.
' Tran Manh Tuyen, Hochiminh National Academy of Politics. Nguyen Trung Chinh, Foreign Trade University.
l.Introduction
The Efficient Market Hypothesis (EMH) is the most controversial and well- studied theory in finance. EMH has been tested in many studies by using different statistical techniques over the last four decades. Many worthwhile studies on weak form efficiency of various stock markets from developed economies including Osborne (1959), Fama (1965), Lo & Mackinlay (1988), Huber (1997), Karemera & Ojah (1999), Abrosimova & Linowski (2002), Laopodis (2003), Jarrett & Kyper (2006). Evidence from stock markets in developing countries, however, is controversial.
The first trading of HOSE (Hochiminh Stock Exchange) was only started on 28/07/2000 with only two securifies and the first trading of HNX (Hanoi Stock Exchange) on 14/07/2005. The index of all stocks listed on HOSE and HNX are called Vn-index and HNX-index, respectively. The number of listed companies has been increased quickly. From only 2 listed companies in July 2000, to 422 listed companies in November 2009 and 695 listed companies in December 2011, with the market capitalization around US$30 billion approximately 20% GDP of Vietnam. However, the volume of stock transaction is quite small, around 1000-2000 billion Vietnam dong (around $50-100 million) par days. Given that there is no a study of efficiency on Vietnamese stock market (VSM). By employing Unit-root, Portmanteau, BDS, ARCH LM and Variance ratio tests the main purpose of this paper is to seek evidence supporting the existence of at least weak-form efficiency in a less developed emerging market like VSM. The paper is organizes as follows: Section 2 provides data information. Section 3 presents the methodology. Section 4 shows the main empirical resuhs. Finally, section 5 gives out conclusion and our findings.
2.Data
The data employed in this study comprise 197 daily observations on the Vietnamese stock market (Vn-index) covering the period 02/01/2009 - 16/10/2009 (sample 1) and 348 daily observations on the Vietnamese stock market covering the sample 12/08/2010 - 30/12/2011 (sample 2). Closing prices for stock indices were obtained from website: Phutoan.com. Table 1 gives the descriptive statistics for daily stock market prices and return series of 2 samples. Using the daily close index, a series of daily simple gross return of Vn-index, denoted R,, are calculated by the formula Ri=Pi/Pi-i. After the necessary computational data adjustments, the final sample has 196 and 347 observation of R, for sample 1 and sample 2, respectively. Figure la and 2a are plots of daily close price of Vn-index for the sample 1 and sample 2, respectively.
Similariy, figure lb and 2b are plots of return series of Vn-index for sample 1 and
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sample 2. We assume that at the first day of our data for 2 samples, return of Vn-index equals 1.00.
From table 1 the kurtosis is about 2.4978 and 3.2063 for sample 1 and sample 2, respectively. Thus, the series of daily simple gross return of Vn-index (Rt) have short tail for sample 1 and heavy tail for sample 2, indicating that the distribution puts less mass on the tails of its support than a normal distribution for the sample 1 and puts more mass on the tails of its support than a normal distribution for the sample 2. The skewness is about 0.0125 and 0.0461 for sample 1 and sample 2, respectively. Rt series are significantly skewed to the right at the 5% level for both samples. The return series have positive skewness Implying that the distribution of 2 samples has a short right tails.
Besides, the Augmented Dickey-Fuller (ADF) statistic, used in the test, is about -10.58 for sample 1 and -13.98 for the sample 2. We compare these values with the critical values; see more at table 1. The more negative it is, the stronger the rejection of the hypothesis that there is a unit root at all levels of the confidence 1%, 5% and 10%. We reject the null hypothesis and accept the alternative hypothesis for both samples. This means that the series of daily simple gross return of Vn-index (Rt) is stationary. Thus, the samples have all financial characteristics: volatility clustering or volatility pooling and platykurtic or leptokurtic.
Figure la: Plot of daily close prices of Vn- Figure lb: Plot of returns of Vn-index index (02/01 /2009-16/10/2009) (02/01 /2009-16/10/2009)
Figure 2a: Plot of daily close prices of Vn- index (12/08/2010-30/12/2011)
Figure 2b: Plot of returns of Vn-index (12/08/2010-30/12/2011)
Table 1: Descriptive Statistics & ADF Tests for return series of both samples Sample 1
Mean Median Maximum Minimum Std. Dev.
Skewness Kurtosis Jarque-Bera Probability Observations ADF (Level) A D F ( 1 " diff)
1.003627 1.002637 1.047564 0.954360 0.021365 0.012540 2.497897 2.064018 0.356290 196 -10.58820 -11.51058
Sample 2 Mean
Median Maximum Minimum Std. Dev.
Skewness Kurtosis Jarque-Bera Probability Observations ADF (Level) A D F ( 1 " diff)
0.999387 0.999481 1.036274 0.959686 0.013248 0.046120 3.206373 0.738792 0.691152 347 -13.98818 -14.79468 ADF critical values for Vnl: (1%) -3.4637, (5%) -2.8761, (10%) -2.5746.
ADF critical values for Vn2: (1%) -3.4492, (5%) -2.8697, (10%) -2.5712.
3.Methodology
Fama (1970) suggested that weak form of EMH claims that prices on traded assets (e.g., stocks, bonds, or property) already reflect all publicly available information, therefore suggesting that charts and technical analysis that use past prices alone would not be useful in finding undervalued stocks. For testing the weak form of EMH, we will employ some tests below. For saving space, we do not present details all the content of the tests here. All the content of tests can be found at the references, (i) Unit Root test (or Augmented Dickey-Fuller (ADF) test) was developed by Dickey and Fuller (1979) for testing stationary property of time series data, (ii) Portmanteau test (Box-Pierce and Ljung-Box Q statistic) was modified by Ljung and Box (1978) for testing autocorrelation coefficient, (iii) BDS test was developed by Brock, Dechert and Sheinkman (1996) for testing the hypothesis of independently and identically distributed (I.l.D.) of the data, (iv) ARCH LM test was proposed by Engle (1982) using for testing the autoregressive conditional heteroscedasticity (ARCH) effects of the residuals, (v) Variance Ratio test was presented by Lo and MacKinlay (1988) test for evaluating the random walk properties of stock prices.
4.Empirical results Portmanteau test
From the results reported in table 2, we see that Q*(6) is 22.64 for the sample 1 and 30.12 for the sample 2, higher than its critical value, X^"\ooii=16.81. Moreover, p- values are less than 0.01, meaning that we can reject the null hypothesis and accept the 44
alternative hypothesis at 1% significant level in all two samples. ACF and PACF of Ri series for both series has serial correlation at lags 1, implying that there are some possible models including AR(1), MA(1), ARMA(1,1), for expressing the Rt series.
Test of auto-regression
In both cases, the two auto-regression coefficients at first lag is significant at 1%
level of significance proving that the series are not independent and the market is not weak-form efficient. We also use the Ljung-Box statistic to examine the residuals series of two models. The results show that these models are adequate. See at table 3. In this study, we need to add two variables in our model Including Bull and Bear variables in order to examine whether or not the bull/bear effects exist on the Vietnamese stock market. Bull variable get two values: 1 or 0, Bull =1 when close > close(-l), otherwise Bull=0 and Bear variable also get two values lor 0, Bear=l when close < close(-l), otherwise Bear ^ 0. The resuhs show that two models are statistically significant. See at table 4. If we compare two models for two samples of Vn-lndex including Bull/Bear with the model without Bull/Bear, we will choose the two models with Bull/Bear because these models have AIC and SIC smaller than these models without Bull/Bear.
BDS test
The results reported in the table 5a and 5b show that it Is possible to reject the null hypothesis at 1% level of significance for both samples because BDS statistics in two cases are different from zero significant. In other word, non-linear dependence is not absent from the return series. Hence, in order to detect what type of nonlinearity is present in the data, the Engle's test for ARCH effects will be employed for the residuals series of both models.
ARCH LM test
To test for heteroscedasticity, the ARCH LM test is employed to the residuals of two models. The test is based on the regression of square residuals on lagged squared residuals. To save the space. I do not write here. For sample 1, at lag 14 with t statistics is 2.01 larger than 1.96. Moreover, p-values Is 0.046. smaller than 0.05. The results propose that we can use GARCH model. Similarly, for sample 2 we also found the ARCH effect at the first lag with t statistic is 3.28 and p-value Is 0.001. The results also show that we can use GARCH model.
Estimation of GARCH model
For the first sample, all the coefficients of the GARCH model are significant excepting for the coefficient of c in volatility equation is not statistically significant but this coefficient is quite small so we can omh this coefficient without changing the
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meaning of this study. See at table 6a. Similarly, for sample 2, all the coefficients are also significant. See at table 6b.
The resuhs of Ljung box for the residuals series and squared residuals series show that both models are adequate. See at table 7a and 7b.
Interestingly, we found both bull effect for sample 1 and bear effect for sample 2.
Variance ratio tests
For sample 1, we see that all the variance ratio values are less than one, implies negative serial correlation. All the z-statistics, in absolute, are larger than 1.96 suggesting that we can reject the null hypothesis at the 5% level of significance. The result is similar for sample 2. See at table 8.
Forecasting Last but not least, we also used model AR(1)-GARCH(1,0) and AR(1)-GARCH(1,1) with bull and bear effects to forecast for daily close market price (Vn-index) of two samples. See figure 3a and 3b below.
From the figure 3a and 3b, we see that all the coefficients of the forecast process for sample 1 and sample 2 are statistically suitable. Moreover, the Theil Inequality Coefficients for both samples are quite small, 0.042 and 0.014, respectively, meaning that the forecast processes are acceptable. We forecast out-of-sample for the next transaction of both samples and we also compare the forecast value with the real value.
The difference between the forecasting values with the real values is about 1.2% and 0.06% for sample 1 and sample 2, respectively. The results are acceptable. (See table 9a and 9b).The graph of the standardized residuals for both models are asymptotic white noise. See at Figure 4.
Table 2 results of Portmanteau test
Number of lags (k) 1
2 3 4 5 6
Sample ! Q'(k)
13.787 13.898 14.184 15.238 17.635 22.641
p-value 0.000 0.001 0.003 0.004 0.003 0.001
Sample 2 O'Ck)
26.576 28.039 28.466 28.473 28.474 30.123
p-value 0.000 0.000 0.000 0.000 0.000 0.000 The lag length is chosen as m=ln(n), we have m=ln(197) =5.28 for sample 1, and m=ln(347) =5.85 for sample 2
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Table 3: The result of estimation for two samples
Variable
c AR(1) AIC SIC R-square
Sample 1 Coefficient
1.003 0.263
Std.Error
0.002 0.069
Statistic 498.13 3.797
Prob
0.000 0.000 -4 906
-4.872 0.069
Sample 2 Coefficient
0.999 0.275
Std.Error
0.001 0.051
t-Statistic
1055.29 5.32
Prob
0.000 0.000 -5.879
-5.857 0.076
Table 4: The result of estimation for Vn-
Variable c AR(1) Bull Bear AIC SIC
Sample 1 Coefficient
0.9847 0.1785 0.0338
Std.En-or 0.0015 0.0704 0.0018
t-Statistic 641.79 2.5326 17.881
Prob 0.000 0.012 0.000
-5.869 -5.819
ndexwith Bull/Bear variables
Sample 2 Coefficient
1.009 0.219
Std.Error 0.001 0.052
l-Statistic 1423.12 4 15
Prob 0.000 0.000
-0.019 1 0.001 1 -23.15 1 0.000 -6.8 P
-6.781
Table 5a: BDS Test statistics for residuals from ARMA (1,0) model for Sample 1 m/e
2 3 4 5
0.5o 0.0026(3.08) 0.0018(3.56) 0.0005(2.39) 0.0005(6.65)
o 0.0020(0.82) 0.0011(0.41) -4.84E-05(-0.01) 0.0004(0.23)
1.5 o 0.004(1.39) 0.005(0.95) 0.004(0.70) 0.001(0.27)
2 o 0.004(1.49) 0.003(0.76) 0.002(0.40) 0.001(0.10)
Table Sb: BDS Test statistics for residuals from ARMA (1,0) model for sample 2 m/e
2 3 4 5
[_0.5o 0.0075(7.22) 0.0046(6.83) 0.0022(6.74)
o 0.0183(5.84) 0.0206(5.36) ,0.0189(5.40) 0.0010(6.16) 1 0.0157(5.60)
1.5 a 0.0229 (6.02) 0.0356(5.69) 0.0462(6.00) 0.0505(6.09)
2 a 0.0203(7.17) 0.0367(6.76) 0.0546(7.00) 0.0701(7.14) Note: critical value is 2.58 at 1% significant level and the value in parenthesis is Z- statistic
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Table 6a: The result of estimation of GARCH(0,1) model for sample 1
Mean equation c
Bull AR(1) Variance equation c
GARCH(-l) R-Square AIC
coefficient 0.985 0.033 0.1901 9.13E-06 0.946
Std Error 0.0016 0.0022 0.068 1.09E-05 0.067
Z-statistic 583.06 15.082 2.784 0.8351
13.979
Prob 0.000 0.000 0.005 0.4036 0.000 0.648
-5.866
Table 6b: The result of estimation of GARCH(0,1) model for sample 2
Mean equation c
Bear AR(1) Variance equation c
ARCH(-l) GARCH(-l) R-Square AIC
coefficient 1.0091 -0.0187 0.1294 1.02E-05 0.1254 0.7004
Std Error 0.0006 0.0009 0.0569 4.64E-06 0.0401 0.0981
Z-statistic 1497.89 -19.64 2.27 2.20 3.12 7.13
Prob 0.000 0.000 0.022 0.027 O.OOI 0.000 0.633
-6.893
Table 7a results of Portmanteau test for the series of standardizes residual of GARCH model
Number of lags (k) 1
2 3 4 5 6
Sam Q'(k) 0.001 1.129 1.204 1.223 2.563 2.589
pie I p-value 0.288 0.548 0.747 0.633 0.763
Sample 2 Q'(k)
0.678 0.711 1.267 3.245 3.508 8.512
p-value 0.399 0.531 0.355 0.477 0.130 The lag length is chosen as m=ln(n), we have m=ln(197) =5.28 for sample I, and m=ln(347) =5.85 for sample 2
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Table 7b results of Portmanteau test for the series of squared standardizes residual of GARCH model
Number of lags (k) I
2 3 4 5 6
Sam Q'(k)
1.066 2.478 2.556 2.853 6.070 6.417
p l e l p-value 0.115 0.279 0.415 0.194 0.268
Sample 2 Q'(k)
0.578 1.408 1.495 1.549 1.984 1.991
p-value 0.235 0.473 0.671 0.739 0.850 The lag length is chosen as m=ln(n), we have m=ln(I97) =5.28 for sample 1, and m=ln(347) =5.85 for sample 2
Table 8: Variance ratio test results for return series for both samples
Number of lags(q) 2
3 4 5 6 7 8 9 10 11 12 13 14 15
Sample 1 VR(q)
0.701 0.477 0.321 0.249 0.198 0.193 0.179 0.178 0.169 0.132 0.102 0.091 0.099 0.101
Z(q) -3.882"
-4.605' -4.795' -4.559' -4.327' -3.950' -3.698' -3.449' -3.276' -3.238' -3.182' -3.075' -2.921' -2.800'
Prob 0.0001 0.0000 0.0000 0.0000 0.0000 0.0001 0.0002 0.0006 O.OOI I 0.0012 0.0015 0.0021 0.0035 0.0051
Sample 2 VR(q)
0.662 0.421 0.337 0.299 0.285 0.218 0.195 0.191 0.159 0.156 0.137 0.126 0.112 0.109 0.104
Z(q) -4.070' -4.288' -3.771' -3.377' -3.072' -3.061' -2.914' -2.735' -2.674' -2.539' -2.479' -2.391' -2.330' -2.253"
-2.189' Prob 0.0000 0.0000 0.0002 0.0007 0.0021 0.0022 0.0036 0.0062 0.0075 O.OIII 0.0135 0.0168 0.0198 0.0242 0.0285
• indicates rejection of the random walk hypothesis at the 5% level of significance
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Figure 3a: forecast result for both samples Sample 1 Sample 2
Figure 4: The standardized residuals for both samples
Sample I Sample 2
2 1
0
• 1
2
25 50 75 100 125 150 175 [ — - Standardized Residuals]
50 too ISO 200 250 300
I — Stendafdized Residials |
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Table 9a: the forecast result of daily market close price (Vn-index) for sample 1 Trading sessions
13/10/2009 14/10/2009 15/10/2009 16/10/2009 19/10/2009 (Out of sample)
Real value
589.89 605.65 617.40 609.54 607.11
Forecast value 596.16 606.98 618.00 608.82 599.78
The different (%)between forecast value and real value
1.063 0.219 0.097 0.II8 1.207
Table 9a: thf forecast result of daily market close price (Vn-index) for sample 2 Trading
sessions 27/12/2011 28/12/2011 29/12/2011 30/12/2011 4/1/2012 (out of sample)
Real value
347.80 350.66 350.51 351.55 348.84
Forecast value 349.04 352.22 348.83 352.02 348.63
The different (%) between forecast value and real, value
0.356 0.445 0.479 0.133 0.060
S.Conclusion
In conclusion, the results of all tests show that VSM does not follow random walk and the market is not weak-form efficient. We tried to give out the proposed model for forecasting of both samples. The forecasting values for both daily close market prices are acceptable. Interestingly, we found both bull and bear effects on VSM.
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