UNIVERSITY OF ECONOMICS HO CHI MINH CITY International School of Business Phan Dang Bao Anh HERDING BEHAVIOR IN VIETNAMESE STOCK MARKET: EMPIRICAL EVIDENCE FROM QUANTILE REGRESSION ANALYSIS MASTER OF BUSINESS (Honours) Ho Chi Minh City – Year 2015 UNIVERSITY OF ECONOMICS HO CHI MINH CITY International School of Business Phan Dang Bao Anh HERDING BEHAVIOR IN VIETNAMESE STOCK MARKET: EMPIRICAL EVIDENCE FROM QUANTILE REGRESSION ANALYSIS ID: 22130006 MASTER OF BUSINESS (Honours) SUPERVISOR: A.Pro.Dr VO XUAN VINH Ho Chi Minh City – Year 2015 ACKNOWLEDGEMENT Firstly, I would like to express my gratefulness to my supervisor A.Prof Dr.Vo Xuan Vinh for his professional guidance, intensive support, valuable suggestions, instructions and continuous encouragement during the time of research and writing this thesis I would like to express my deepest appreciation to ISB Research Committee for their valuable time as their insightful comments and meaningful suggestions were contributed significantly for my completion of this research My sincere thanks also go to all of all of my lecturers at International Business School- University of Economics Ho Chi City for their teaching and guidance during my Master course Last but not least, I would like to thanks my family, whom were always supporting me and encouraging me with their best wishes TABLE OF CONTENT CHAPTER 1: INTRODUCTION 1.1 Research background 1.2 Research gap 1.3.Research objectives 1.4 Research methodology and scope 1.5 Research structure CHAPTER 2: LITERATURE REVIEW 2.1 Theoretical literature review 2.2 Empirical literature review 11 2.3 Measuring herding in financial markets 19 2.4 Hypothesis development 25 CHAPTER 3: RESEARCH METHODOLOGY .27 3.1 Data collection and sample description 27 3.2 Regression model for testing the hypotheses 28 3.2.1 Regression model for testing the presence of herding bahavior in Vietnamese stock market: 28 3.2.2 Regression model for estimation the degree of herd in rising and falling market: 29 3.3 Regression methodology 30 3.3.1 Research process 31 3.3.2 Quantile regression analysis 31 CHAPTER 4: EMPIRICAL RESULT .34 4.1 Decriptive statistics 34 4.2 Correlation analysis among variables 35 4.3 Regression result 36 4.3.1 Evidence on herd presence in Vietnamese stock market 36 4.3.2 Herding behavior in up and down markets 38 4.4 Regression result from Quantile regression analysis 39 CHAPTER 5: CONCLUSION AND IMPLICATIONS 45 5.1 Conclusion 45 5.2 Implications of herding behavior in Vietnamese stock market 46 5.3 Limitations and further research direction 48 REFERENCES 50 APPENDICES 55 LIST OF TABLES Table 1.1: A summary of empirical evidence on herding behavior 15 Table 3.1: Summary of data observations used in the study 27 Table 4.1: Descriptive statistics for daily market return and cross-sectional absolute deviation (CSAD) for the Vietnamese stock market from 1/2005 to 4/2015 34 Table 4.2 Correlation among main variables 35 Table 4.3: Regression result of herding behavior in Vietnamese stock market 36 Table 4.4: Regression results of herding behavior in rising and declining market 38 Table 4.5: Analysis of herding behavior in Vietnamese stock market by quantile regression 40 Table 4.6: A summary of research results 43 ABSTRACT This study examines the herding behavior of investors in Vietnamese stock market using data sample of 299 companies listed on Ho Chi Minh City Stock Exchange Using a least square method, the author finds evidence of herding presence in rising and falling market when considering over the period of 2005 – 4/2015 as well as in the periods of pre-crisis and post-crisis By applying quantile regression analysis to estimate the herding equation, the author find supporting evidence of herding during the period studied as well as when splitting the market into two sub-periods; however, the level of this trend is somewhat different conditional on quantile region Key words: herding behavior, Vietnamese stock market, quantile regression, asymmetry CHAPTER 1: INTRODUCTION This chapter presents the introduction of the study It contains the research background, research gap, research objectives, research methodology and scope and research structure 1.1 Research background Traditional financial framework understands financial market by using models which meet four foundation conditions: (i) investors are assumed to be rational, (ii) market is efficient, (iii) investors make a decision on portfolios based on the rules of mean-variance portfolio theory, and (iv) the expected returns are a function of risk (Statman, 2014) Among them, the condition of rational investor is considered a central assumption in which people make decisions reasonably and no biases in their future prediction However, the world economy has been shaken by the global financial crisis in 2008, which originated from US and then expanded globally As soon as the crisis began, many economists and financial forecasters were no longer able to analyze the bankruptcy of a variety of enterprises or banks in an intensive way The Vietnamese stock market is not an exception From its foundation in 2000, the Vietnamese stock market experiences “hot” growth and drastical fluctuation without stability causing virtual stock matter The value of VN-Index in 2000 of 100 points increases to 571 points after just one year and a half which astonishes economic experts; however, this increment does not last long and rush to fall under 140 points in 2003, 150-200 points in 2004 The peak of growing phase is in the period of 20062007 as Vietnamese stock market has the highest growth of 1100 points (approximately 145%) in Asia – Pacific region, even exceeding the Shanghai stock market growth of 135% Particularly, the VN-Index reaches to the record of 1170.67 points on March 12th, 2007 – the highest level in the world This event makes stock experts and market managers difficult to understand, thereby bring out the fear of bubble formation in the stock market After a long time of increasing prices, the Vietnamese stock market has signal to considerably decrease with the lowest record of 236 points on February 24th, 2009 The happening in the market during this period is very complicated to anticipate Once again, economics experts doubt the precision of the efficient market theory A paradox is present that when the stock price is driven further from the fundamental value of 30% investors still trade constantly; whereas, when the stock prices decrease at an attractive level in declining market investors massively sell stocks instead of buying Is it true that the Vietnamese stock market operation does not abide by any rules or there are phenomena dominating the market which cause an unusual fluctuation? Failure of the economists as well as their theories leads to a list of different questions in different context: Are people rational? Or are they influenced by emotion such as fear, greed which caused wrong decision? Then, a new branch of financial research appears beside traditional financial framework which helps economic experts and finance researchers partial explain unusual fluctuation Behavioral finance is a new strand of finance which investigates the behavior of investors in financial market; in other word, it is a combination between psychology and finance It considers psychological factors as essential input to financial analysis Behavioral finance can elucidate several financial reactions that contrast with standard financial theory and can thus make a contribution to avoidance of mistakes as well as advancing investment strategies (Fromlet, 2001) Previous researchers put sustained effort to understand investors’ behaviour in the market as well as its impact on stock price These investment behaviors are influenced by some factors such as investors’ insight, criterion to measure investment efficiency or market instability… In terms of psychology, investors are assumed to be rational and always strive to optimize their actions but the fact that the rationality appears to be inhibited by numerous cognitive biases, such as overconfidence, overoptimism, herding, representativeness … and so on In this research, the author focuses on the investment behavior of market participants regarding to their tendency to follow the actions of others, which engages in herd behavior Herding behavior is defined as the trend of investors to imitate the actions of others (Luu, 2013) This tendency is considered an inherent psychology of investors but it becomes stronger as they have to make decision in a market condition with high uncertainty and low transparency Over last decades, research regarding this topic receives an attention from scientists and empirical researchers A numerous theories are developed and empirical investigations are conducted to examine the presence and reasons of this phenomenon in financial market Researchers in this field believe that the presence of herding behavior has impact on results derived from asset pricing model because it influences stock price fluctuation, thus influencing risk and return of stocks (Tan et al, 2008) Similar to speculation, herding behavior may be rational or irrational If market participants follow market consensus, the fluctuation is more and more serious that can leads to instability in financial system, particularly in the period of global crisis In addition, herding behavior lasting so long can drive the stock prices further fundamental value which causes destabilization If investors are dominated by sentiment such as greedy or fear of loss, they can trade in a “frenzied” way; as a results, economic bubbles are created and may collapse the stock market In sum, herding behavior can lead to bad consequences of reducing the efficiency of market, even result in the market instability and financial collapse Basing on these arguments, doing research about herding behavior can help investors have an objective overview and be prudent when making investment decision Therefore, the author decides to a research of “Herding behavior in Vietnamese stock market: an empirical evidence from Quantile regression analysis” The study applies research model proposed by Chang, Cheng and Khorona (2000) and modified by Chiang et al (2010) to investigate the presence of herd in Vietnamese stock market 1.2 Research gap Several empirical studies have examined and detected the herding behavior in many region throughout the world, form developed to emerging countries For 58 Dependent Variable: CSAD T Method: Least Squares Date: 11/05/15 Time: 11:28 Sample: 749 Included observations: 749 Variable Coefficient Std Error t-Statistic Prob C TRU_D*RM D01*RM TRU_D*RM2 D01*RM2 0.939460 0.593464 -0.987413 -0.063148 -0.201384 0.044725 0.063486 0.085127 0.015280 0.023048 21.00534 9.347882 -11.59930 -4.132617 -8.737610 0.0000 0.0000 0.0000 0.0000 0.0000 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.254873 0.250867 0.698969 363.4864 -792.0226 63.62199 0.000000 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter Durbin-Watson stat 1.492718 0.807566 2.128231 2.159064 2.140112 0.962411 The Wald test Wald Test: Equation: Untitled Test Statistic t-statistic F-statistic Chi-square Value df Probability -11.26159 126.8234 126.8234 744 (1, 744) 0.0000 0.0000 0.0000 Value Std Err -0.924264 0.082072 Null Hypothesis: C(3)-C(4)=0 Null Hypothesis Summary: Normalized Restriction (= 0) C(3) - C(4) Restrictions are linear in coefficients  Period of 2008 – 4/2015 Dependent Variable: CSAD T Method: Least Squares Date: 11/05/15 Time: 11:31 Sample: 1819 Included observations: 1819 Variable Coefficient Std Error t-Statistic Prob 59 C TRU_D*RM D01*RM TRU_D*RM2 D01*RM2 1.716533 0.378105 -0.345985 -0.122695 -0.095775 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) 0.021273 0.035210 0.033094 0.009821 0.008529 0.118982 0.117039 0.448863 365.4805 -1121.457 61.24541 0.000000 80.69076 10.73869 -10.45448 -12.49372 -11.22918 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter Durbin-Watson stat 0.0000 0.0000 0.0000 0.0000 0.0000 1.870446 0.477686 1.238545 1.253680 1.244129 0.800091 The Wald test Wald Test: Equation: Untitled Test Statistic t-statistic F-statistic Chi-square Value df Probability -7.277658 52.96430 52.96430 1814 (1, 1814) 0.0000 0.0000 0.0000 Value Std Err -0.223290 0.030682 Null Hypothesis: C(3)-C(4)=0 Null Hypothesis Summary: Normalized Restriction (= 0) C(3) - C(4) Restrictions are linear in coefficients Appendix 4: Quantile regression analysis to test for the presence of herding behavior  Period of 2005 – 4/2015 Dependent Variable: CSAD T Method: Quantile Regression (tau = 0.1) Date: 11/05/15 Time: 11:40 Sample: 2568 Included observations: 2568 Huber Sandwich Standard Errors & Covariance Sparsity method: Kernel (Epanechnikov) using residuals Bandwidth method: Hall-Sheather, bw=0.025266 Estimation successfully identifies unique optimal solution 60 Variable Coefficient Std Error t-Statistic Prob C TRU_D*RM D01*RM TRU_D*RM2 D01*RM2 0.434347 0.991820 -0.867615 -0.216511 -0.176847 0.027960 0.044010 0.061686 0.010362 0.013204 15.53436 22.53644 -14.06508 -20.89488 -13.39332 0.0000 0.0000 0.0000 0.0000 0.0000 Pseudo R-squared Adjusted R-squared S.E of regression Quantile dependent var Sparsity Prob(Quasi-LR stat) 0.141964 0.140625 0.972313 0.873103 2.583978 0.000000 Mean dependent var S.D dependent var Objective Restr objective Quasi-LR statistic 1.760276 0.617355 255.7348 298.0466 363.8822 Wald Test: Equation: Untitled Test Statistic t-statistic F-statistic Chi-square Value df Probability -10.94419 119.7753 119.7753 2563 (1, 2563) 0.0000 0.0000 0.0000 Value Std Err -0.651104 0.059493 Null Hypothesis: C(3)-C(4)=0 Null Hypothesis Summary: Normalized Restriction (= 0) C(3) - C(4) Restrictions are linear in coefficients Dependent Variable: CSAD T Method: Quantile Regression (tau = 0.25) Date: 11/05/15 Time: 11:43 Sample: 2568 Included observations: 2568 Huber Sandwich Standard Errors & Covariance Sparsity method: Kernel (Epanechnikov) using residuals Bandwidth method: Hall-Sheather, bw=0.049137 Estimation successfully identifies unique optimal solution Variable Coefficient Std Error t-Statistic Prob C TRU_D*RM D01*RM TRU_D*RM2 D01*RM2 1.046455 0.776987 -0.754474 -0.194648 -0.177619 0.052128 0.054572 0.058406 0.010951 0.011571 20.07473 14.23787 -12.91770 -17.77435 -15.35033 0.0000 0.0000 0.0000 0.0000 0.0000 Pseudo R-squared 0.103571 Mean dependent var 1.760276 61 Adjusted R-squared S.E of regression Quantile dependent var Sparsity Prob(Quasi-LR stat) 0.102172 0.676439 1.461366 1.671628 0.000000 S.D dependent var Objective Restr objective Quasi-LR statistic 0.617355 454.0348 506.4926 334.7335 Wald Test: Equation: Untitled Test Statistic t-statistic F-statistic Chi-square Value df Probability -10.93351 119.5416 119.5416 2563 (1, 2563) 0.0000 0.0000 0.0000 Value Std Err -0.559826 0.051203 Null Hypothesis: C(3)-C(4)=0 Null Hypothesis Summary: Normalized Restriction (= 0) C(3) - C(4) Restrictions are linear in coefficients Dependent Variable: CSAD T Method: Quantile Regression (Median) Date: 11/05/15 Time: 11:47 Sample: 2568 Included observations: 2568 Huber Sandwich Standard Errors & Covariance Sparsity method: Kernel (Epanechnikov) using residuals Bandwidth method: Hall-Sheather, bw=0.070948 Estimation successfully identifies unique optimal solution Variable Coefficient Std Error t-Statistic Prob C TRU_D*RM D01*RM TRU_D*RM2 D01*RM2 1.477117 0.575438 -0.602372 -0.154175 -0.151820 0.022724 0.043788 0.036133 0.014345 0.010275 65.00365 13.14154 -16.67100 -10.74749 -14.77555 0.0000 0.0000 0.0000 0.0000 0.0000 Pseudo R-squared Adjusted R-squared S.E of regression Quantile dependent var Sparsity Prob(Quasi-LR stat) Wald Test: Equation: Untitled 0.085607 0.084180 0.601376 1.794159 0.992950 0.000000 Mean dependent var S.D dependent var Objective Restr objective Quasi-LR statistic 1.760276 0.617355 534.2016 584.2147 402.9448 62 Test Statistic t-statistic F-statistic Chi-square Value df Probability -13.92476 193.8989 193.8989 2563 (1, 2563) 0.0000 0.0000 0.0000 Value Std Err -0.448196 0.032187 Null Hypothesis: C(3)-C(4)=0 Null Hypothesis Summary: Normalized Restriction (= 0) C(3) - C(4) Restrictions are linear in coefficients Dependent Variable: CSAD T Method: Quantile Regression (tau = 0.75) Date: 11/05/15 Time: 11:48 Sample: 2568 Included observations: 2568 Huber Sandwich Standard Errors & Covariance Sparsity method: Kernel (Epanechnikov) using residuals Bandwidth method: Hall-Sheather, bw=0.049137 Estimation successfully identifies unique optimal solution Variable Coefficient Std Error t-Statistic Prob C TRU_D*RM D01*RM TRU_D*RM2 D01*RM2 1.787334 0.455551 -0.518298 -0.106855 -0.127885 0.028105 0.065074 0.052372 0.024822 0.016677 63.59565 7.000529 -9.896518 -4.304771 -7.668512 0.0000 0.0000 0.0000 0.0000 0.0000 Pseudo R-squared Adjusted R-squared S.E of regression Quantile dependent var Sparsity Prob(Quasi-LR stat) 0.051418 0.049938 0.660173 2.095829 1.425604 0.000000 Mean dependent var S.D dependent var Objective Restr objective Quasi-LR statistic Wald Test: Equation: Untitled Test Statistic t-statistic F-statistic Chi-square Value df Probability -8.745658 76.48654 76.48654 2563 (1, 2563) 0.0000 0.0000 0.0000 Value Std Err Null Hypothesis: C(3)-C(4)=0 Null Hypothesis Summary: Normalized Restriction (= 0) 1.760276 0.617355 444.5464 468.6433 180.2981 63 C(3) - C(4) -0.411443 0.047045 Restrictions are linear in coefficients Dependent Variable: CSAD T Method: Quantile Regression (tau = 0.9) Date: 11/05/15 Time: 11:50 Sample: 2568 Included observations: 2568 Huber Sandwich Standard Errors & Covariance Sparsity method: Kernel (Epanechnikov) using residuals Bandwidth method: Hall-Sheather, bw=0.025266 Estimation successfully identifies unique optimal solution Variable Coefficient Std Error t-Statistic Prob C TRU_D*RM D01*RM TRU_D*RM2 D01*RM2 2.207301 0.169536 -0.375276 0.001163 -0.077309 0.054455 0.115812 0.114200 0.037119 0.039652 40.53411 1.463886 -3.286124 0.031327 -1.949662 0.0000 0.1433 0.0010 0.9750 0.0513 Pseudo R-squared Adjusted R-squared S.E of regression Quantile dependent var Sparsity Prob(Quasi-LR stat) 0.033567 0.032058 0.898029 2.433319 4.003594 0.000000 Mean dependent var S.D dependent var Objective Restr objective Quasi-LR statistic Wald Test: Equation: Untitled Test Statistic t-statistic F-statistic Chi-square Value df Probability -3.654565 13.35584 13.35584 2563 (1, 2563) 0.0003 0.0003 0.0003 Value Std Err -0.376439 0.103005 Null Hypothesis: C(3)-C(4)=0 Null Hypothesis Summary: Normalized Restriction (= 0) C(3) - C(4) Restrictions are linear in coefficients  Period of 2005 – 2007 1.760276 0.617355 274.3341 283.8624 52.88753 64 Dependent Variable: CSAD T Method: Quantile Regression (tau = 0.1) Date: 11/05/15 Time: 11:56 Sample: 749 Included observations: 749 Huber Sandwich Standard Errors & Covariance Sparsity method: Kernel (Epanechnikov) using residuals Bandwidth method: Hall-Sheather, bw=0.038099 Estimation successfully identifies unique optimal solution Variable Coefficient Std Error t-Statistic Prob C TRU_D*RM D01*RM TRU_D*RM2 D01*RM2 0.271696 0.960971 -0.916017 -0.199567 -0.180967 0.026013 0.065505 0.054277 0.017676 0.012001 10.44473 14.67008 -16.87686 -11.28999 -15.07892 0.0000 0.0000 0.0000 0.0000 0.0000 Pseudo R-squared Adjusted R-squared S.E of regression Quantile dependent var Sparsity Prob(Quasi-LR stat) 0.300338 0.296576 1.000545 0.555308 1.425830 0.000000 Mean dependent var S.D dependent var Objective Restr objective Quasi-LR statistic Wald Test: Equation: Untitled Test Statistic t-statistic F-statistic Chi-square Value df Probability -14.11402 199.2057 199.2057 744 (1, 744) 0.0000 0.0000 0.0000 Value Std Err -0.716450 0.050762 Null Hypothesis: C(3)-C(4)=0 Null Hypothesis Summary: Normalized Restriction (= 0) C(3) - C(4) Restrictions are linear in coefficients Dependent Variable: CSAD T Method: Quantile Regression (tau = 0.25) Date: 11/05/15 Time: 11:57 Sample: 749 Included observations: 749 Huber Sandwich Standard Errors & Covariance Sparsity method: Kernel (Epanechnikov) using residuals Bandwidth method: Hall-Sheather, bw=0.074094 Estimation successfully identifies unique optimal solution 1.492718 0.807566 56.63094 80.94040 378.8741 65 Variable Coefficient Std Error t-Statistic Prob C TRU_D*RM D01*RM TRU_D*RM2 D01*RM2 0.376316 1.037830 -1.017762 -0.210179 -0.201663 0.032397 0.081857 0.066547 0.023459 0.015235 11.61563 12.67859 -15.29395 -8.959387 -13.23696 0.0000 0.0000 0.0000 0.0000 0.0000 Pseudo R-squared Adjusted R-squared S.E of regression Quantile dependent var Sparsity Prob(Quasi-LR stat) 0.281481 0.277618 0.898598 0.866259 1.154455 0.000000 Mean dependent var S.D dependent var Objective Restr objective Quasi-LR statistic 1.492718 0.807566 120.5742 167.8093 436.4316 Wald Test: Equation: Untitled Test Statistic t-statistic F-statistic Chi-square Value df Probability -13.15809 173.1354 173.1354 744 (1, 744) 0.0000 0.0000 0.0000 Value Std Err -0.807583 0.061375 Null Hypothesis: C(3)-C(4)=0 Null Hypothesis Summary: Normalized Restriction (= 0) C(3) - C(4) Restrictions are linear in coefficients Dependent Variable: CSAD T Method: Quantile Regression (Median) Date: 11/05/15 Time: 11:58 Sample: 749 Included observations: 749 Huber Sandwich Standard Errors & Covariance Sparsity method: Kernel (Epanechnikov) using residuals Bandwidth method: Hall-Sheather, bw=0.10698 Estimation successfully identifies unique optimal solution Variable Coefficient Std Error t-Statistic Prob C TRU_D*RM D01*RM TRU_D*RM2 D01*RM2 0.609556 1.094023 -1.119272 -0.211234 -0.218877 0.047779 0.133225 0.098342 0.044499 0.025989 12.75769 8.211876 -11.38140 -4.746984 -8.421889 0.0000 0.0000 0.0000 0.0000 0.0000 Pseudo R-squared Adjusted R-squared 0.217135 0.212926 Mean dependent var S.D dependent var 1.492718 0.807566 66 S.E of regression Quantile dependent var Sparsity Prob(Quasi-LR stat) 0.769742 1.366989 1.367792 0.000000 Objective Restr objective Quasi-LR statistic 186.2682 237.9314 302.1702 Wald Test: Equation: Untitled Test Statistic t-statistic F-statistic Chi-square Value df Probability -10.03523 100.7059 100.7059 744 (1, 744) 0.0000 0.0000 0.0000 Value Std Err -0.908037 0.090485 Null Hypothesis: C(3)-C(4)=0 Null Hypothesis Summary: Normalized Restriction (= 0) C(3) - C(4) Restrictions are linear in coefficients Dependent Variable: CSAD T Method: Quantile Regression (tau = 0.75) Date: 11/05/15 Time: 11:59 Sample: 749 Included observations: 749 Huber Sandwich Standard Errors & Covariance Sparsity method: Kernel (Epanechnikov) using residuals Bandwidth method: Hall-Sheather, bw=0.074094 Estimation successfully identifies unique optimal solution Variable Coefficient Std Error t-Statistic Prob C TRU_D*RM D01*RM TRU_D*RM2 D01*RM2 1.175330 0.839159 -1.152727 -0.115215 -0.253403 0.111808 0.238357 0.164406 0.074829 0.036752 10.51206 3.520591 -7.011484 -1.539705 -6.894953 0.0000 0.0005 0.0000 0.1241 0.0000 Pseudo R-squared Adjusted R-squared S.E of regression Quantile dependent var Sparsity Prob(Quasi-LR stat) Wald Test: Equation: Untitled 0.130885 0.126213 0.785466 1.945318 2.793505 0.000000 Mean dependent var S.D dependent var Objective Restr objective Quasi-LR statistic 1.492718 0.807566 179.4033 206.4208 103.1629 67 Test Statistic t-statistic F-statistic Chi-square Value df Probability -7.389879 54.61031 54.61031 744 (1, 744) 0.0000 0.0000 0.0000 Value Std Err -1.037511 0.140396 Null Hypothesis: C(3)-C(4)=0 Null Hypothesis Summary: Normalized Restriction (= 0) C(3) - C(4) Restrictions are linear in coefficients Dependent Variable: CSAD T Method: Quantile Regression (tau = 0.9) Date: 11/05/15 Time: 12:00 Sample: 749 Included observations: 749 Huber Sandwich Standard Errors & Covariance Sparsity method: Kernel (Epanechnikov) using residuals Bandwidth method: Hall-Sheather, bw=0.038099 Estimation successfully identifies unique optimal solution Variable Coefficient Std Error t-Statistic Prob C TRU_D*RM D01*RM TRU_D*RM2 D01*RM2 1.902265 0.342670 -1.128594 0.026434 -0.232765 0.183386 0.147998 1.001092 0.016752 0.537223 10.37301 2.315377 -1.127363 1.577929 -0.433274 0.0000 0.0209 0.2600 0.1150 0.6649 Pseudo R-squared Adjusted R-squared S.E of regression Quantile dependent var Sparsity Prob(Quasi-LR stat) 0.096434 0.091576 1.218471 2.617441 5.739929 0.000000 Mean dependent var S.D dependent var Objective Restr objective Quasi-LR statistic Wald Test: Equation: Untitled Test Statistic t-statistic F-statistic Chi-square Value df Probability -1.167432 1.362897 1.362897 744 (1, 744) 0.2434 0.2434 0.2430 Null Hypothesis: C(3)-C(4)=0 Null Hypothesis Summary: 1.492718 0.807566 110.9516 122.7930 45.84421 68 Normalized Restriction (= 0) C(3) - C(4) Value Std Err -1.155028 0.989375 Restrictions are linear in coefficients  Period of 2008 – 4/2015 Dependent Variable: CSAD T Method: Quantile Regression (tau = 0.1) Date: 11/05/15 Time: 12:01 Sample: 1819 Included observations: 1819 Huber Sandwich Standard Errors & Covariance Sparsity method: Kernel (Epanechnikov) using residuals Bandwidth method: Hall-Sheather, bw=0.028344 Estimation successfully identifies unique optimal solution Variable Coefficient Std Error t-Statistic Prob C TRU_D*RM D01*RM TRU_D*RM2 D01*RM2 1.380765 0.284422 -0.208675 -0.103835 -0.075821 0.025035 0.057098 0.076041 0.011533 0.015855 55.15396 4.981310 -2.744244 -9.003446 -4.782013 0.0000 0.0000 0.0061 0.0000 0.0000 Pseudo R-squared Adjusted R-squared S.E of regression Quantile dependent var Sparsity Prob(Quasi-LR stat) 0.145327 0.143442 0.620296 1.397002 1.727691 0.000000 Mean dependent var S.D dependent var Objective Restr objective Quasi-LR statistic Wald Test: Equation: Untitled Test Statistic t-statistic F-statistic Chi-square Value df Probability -1.483557 2.200941 2.200941 1814 (1, 1814) 0.1381 0.1381 0.1379 Value Std Err -0.104840 0.070668 Null Hypothesis: C(3)-C(4)=0 Null Hypothesis Summary: Normalized Restriction (= 0) C(3) - C(4) Restrictions are linear in coefficients 1.870446 0.477686 136.3845 159.5750 298.2854 69 Dependent Variable: CSAD T Method: Quantile Regression (tau = 0.25) Date: 11/05/15 Time: 12:02 Sample: 1819 Included observations: 1819 Huber Sandwich Standard Errors & Covariance Sparsity method: Kernel (Epanechnikov) using residuals Bandwidth method: Hall-Sheather, bw=0.055123 Estimation successfully identifies unique optimal solution Variable Coefficient Std Error t-Statistic Prob C TRU_D*RM D01*RM TRU_D*RM2 D01*RM2 1.440740 0.455537 -0.455989 -0.142075 -0.127934 0.017582 0.027583 0.035027 0.006002 0.008005 81.94500 16.51527 -13.01806 -23.67010 -15.98173 0.0000 0.0000 0.0000 0.0000 0.0000 Pseudo R-squared Adjusted R-squared S.E of regression Quantile dependent var Sparsity Prob(Quasi-LR stat) 0.131758 0.129844 0.506878 1.616798 0.861683 0.000000 Mean dependent var S.D dependent var Objective Restr objective Quasi-LR statistic Wald Test: Equation: Untitled Test Statistic t-statistic F-statistic Chi-square Value df Probability -9.589841 91.96505 91.96505 1814 (1, 1814) 0.0000 0.0000 0.0000 Value Std Err -0.313914 0.032734 Null Hypothesis: C(3)-C(4)=0 Null Hypothesis Summary: Normalized Restriction (= 0) C(3) - C(4) Restrictions are linear in coefficients Dependent Variable: CSAD T Method: Quantile Regression (Median) Date: 11/05/15 Time: 12:03 Sample: 1819 Included observations: 1819 Huber Sandwich Standard Errors & Covariance Sparsity method: Kernel (Epanechnikov) using residuals Bandwidth method: Hall-Sheather, bw=0.07959 Estimation successfully identifies unique optimal solution 1.870446 0.477686 221.1246 254.6809 415.3896 70 Variable Coefficient Std Error t-Statistic Prob C TRU_D*RM D01*RM TRU_D*RM2 D01*RM2 1.610446 0.502064 -0.514584 -0.155817 -0.142199 0.017704 0.027269 0.030259 0.006596 0.008268 90.96537 18.41180 -17.00625 -23.62119 -17.19805 0.0000 0.0000 0.0000 0.0000 0.0000 Pseudo R-squared Adjusted R-squared S.E of regression Quantile dependent var Sparsity Prob(Quasi-LR stat) 0.104237 0.102262 0.454335 1.860376 0.776175 0.000000 Mean dependent var S.D dependent var Objective Restr objective Quasi-LR statistic 1.870446 0.477686 279.8111 312.3717 335.6005 Wald Test: Equation: Untitled Test Statistic t-statistic F-statistic Chi-square Value df Probability -12.62066 159.2810 159.2810 1814 (1, 1814) 0.0000 0.0000 0.0000 Value Std Err -0.358768 0.028427 Null Hypothesis: C(3)-C(4)=0 Null Hypothesis Summary: Normalized Restriction (= 0) C(3) - C(4) Restrictions are linear in coefficients Dependent Variable: CSAD T Method: Quantile Regression (tau = 0.75) Date: 11/05/15 Time: 12:04 Sample: 1819 Included observations: 1819 Huber Sandwich Standard Errors & Covariance Sparsity method: Kernel (Epanechnikov) using residuals Bandwidth method: Hall-Sheather, bw=0.055123 Estimation successfully identifies unique optimal solution Variable Coefficient Std Error t-Statistic Prob C TRU_D*RM D01*RM TRU_D*RM2 D01*RM2 1.850534 0.459811 -0.443785 -0.137079 -0.111889 0.024866 0.043582 0.044073 0.012669 0.013867 74.42045 10.55054 -10.06925 -10.82045 -8.068438 0.0000 0.0000 0.0000 0.0000 0.0000 Pseudo R-squared Adjusted R-squared S.E of regression Quantile dependent var 0.058128 0.056051 0.493676 2.118398 Mean dependent var S.D dependent var Objective Restr objective 1.870446 0.477686 244.6209 259.7178 71 Sparsity Prob(Quasi-LR stat) 1.175340 0.000000 Quasi-LR statistic 137.0106 Wald Test: Equation: Untitled Test Statistic t-statistic F-statistic Chi-square Value df Probability -7.533914 56.75986 56.75986 1814 (1, 1814) 0.0000 0.0000 0.0000 Value Std Err -0.306706 0.040710 Null Hypothesis: C(3)-C(4)=0 Null Hypothesis Summary: Normalized Restriction (= 0) C(3) - C(4) Restrictions are linear in coefficients Dependent Variable: CSAD T Method: Quantile Regression (tau = 0.9) Date: 11/05/15 Time: 12:05 Sample: 1819 Included observations: 1819 Huber Sandwich Standard Errors & Covariance Sparsity method: Kernel (Epanechnikov) using residuals Bandwidth method: Hall-Sheather, bw=0.028344 Estimation successfully identifies unique optimal solution Variable Coefficient Std Error t-Statistic Prob C TRU_D*RM D01*RM TRU_D*RM2 D01*RM2 2.230599 0.273430 -0.279396 -0.080383 -0.054256 0.047262 0.131146 0.054016 0.046309 0.009021 47.19599 2.084928 -5.172449 -1.735771 -6.014653 0.0000 0.0372 0.0000 0.0828 0.0000 Pseudo R-squared Adjusted R-squared S.E of regression Quantile dependent var Sparsity Prob(Quasi-LR stat) 0.024946 0.022796 0.694855 2.396891 2.915309 0.000004 Mean dependent var S.D dependent var Objective Restr objective Quasi-LR statistic Wald Test: Equation: Untitled Test Statistic Value df Probability 1.870446 0.477686 154.9891 158.9543 30.22524 72 t-statistic F-statistic Chi-square -4.053626 16.43188 16.43188 1814 (1, 1814) 0.0001 0.0001 0.0001 Value Std Err -0.199013 0.049095 Null Hypothesis: C(3)-C(4)=0 Null Hypothesis Summary: Normalized Restriction (= 0) C(3) - C(4) Restrictions are linear in coefficients ... market 36 Table 4.4: Regression results of herding behavior in rising and declining market 38 Table 4.5: Analysis of herding behavior in Vietnamese stock market by quantile regression ... exhibits Finally, he finds evidence supporting the existence of herding in Vietnamese stock market There are other studies doing research regarding herding behavior in Vietnamese stock market such... ECONOMICS HO CHI MINH CITY International School of Business Phan Dang Bao Anh HERDING BEHAVIOR IN VIETNAMESE STOCK MARKET: EMPIRICAL EVIDENCE FROM QUANTILE REGRESSION ANALYSIS ID: 22130006