(Luận văn thạc sĩ) after market returns of initial public offering the case of viet nam

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIET NAM THE NETHERLANDS VIET NAM – NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS - AFTERMARKET RETURNS OF INITIAL PUBLIC OFFERING THE CASE OF VIETNAM By NGUYEN LE NGOC KHOA MASTER OF ARTS IN DEVELOPMENT ECONOMICS Ho Chi Minh City, July 2014 UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIET NAM THE NETHERLANDS VIET NAM – NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS AFTERMARKET RETURNS OF INITIAL PUBLIC OFFERING THE CASE OF VIETNAM A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By NGUYEN LE NGOC KHOA Academic supervisor Dr TRUONG DANG THUY Ho Chi Minh City, July 2014 ACKNOWLEDGEMENTS This paper has could not be started and completed without the help of several individuals who supported me directly and indirectly First of all, I appreciate my supervisor Dr Nguyen Dang Thuy so much for his enthusiastic assistance He has not only given me intellectual guidance in academy but also encouraged me a lot through the analysis process It is so hard for me to complete this research without his profound advices I am also thankful to Dr Nguyen Trong Hoai and Dr Pham Khanh Nam for sharing his knowledge and practice experiences in researching which are very useful for this study I also thank my colleague, Ms Ngo Thi Kim Thanh for sharing her suggestion on the ideas to this thesis as well as econometric techniques Abstract Initial public offerings (or IPO) are usually hot topic in financial world This thesis is to examine return behavior after IPOs in short-run and long-run on Vietnam stock market by using abnormal returns to measure the stocks return Market Efficiency hypothesis is applied to test the long-run performance Furthermore, given a regression model, the thesis also aims to determine which factors most impact on the stock’s performance aftermarket The thesis uses IPO price and trading price data from listed companies on Ho Chi Minh City Stock Exchange (HSX) and Hanoi Stock Exchange (HNX) in the period 2001 – 2013 The results showed that most of these companies are undervalued average of 63.5% in the first trading day after IPO events Then, stock returns are negative due to investors’ taking profit In the longterm, average rate of return of stocks are higher than the Vietnam’s benchmarks (VN – Index and HNX Index) within one year, two years and three years after the IPO In addition, the study shows that in short-term, the abnormal return of IPO events in the first trading day affected by firm size, listing exchange and industry But in the long-term, one year, two years, and three years after the IPO, there are no variables have statistical significance It implies that the accumulated abnormal returns were not affected by the model’ factors Keywords: Stock market, underpricing Contents CHAPTER I: INTRODUCTION 1.1 Problem statement 1.2 Research objectives 1.3 Scope of study CHAPTER II: LITERATURE REVIEW 2.1 Theoretical studies 2.2 Empirical studies 2.3 Conceptual framework 13 CHAPTER III: RESAERCH METHODOLOGY AND DATA 14 3.1 Research methodology 14 3.2 Research data 15 3.3 Variables description 19 3.4 Testing methods 22 CHAPTER IV: RESEARCH RESULT 25 4.1 Variables descriptive statistics 25 4.2 Multicollinearity testing 26 4.3 Multivariate regression model 27 CHAPTER V: CONCLUSION AND POLICY RECOMMENDATION 32 5.1 Conclusion 32 5.2 Policy recommendation 33 REFERENCE 35 APPENDICES 38 Table of charts and figures Figure 1: VN Index 16 Figure 2: HNX Index 16 Figure 3: No of IPO events during period 2001 - 2010 17 Table 1: IPO events by stock exchange 17 Table 2: IPO events by industry 18 Table 3: IPO events by underwriters .18 Table 4: Descriptive statistics all variables 25 Table 5: Correlation matrix .26 Table 6: Variance inflation factor test .27 Table 7: Mean cumulative abnormal returns 27 Table 8:Short-term regressions 28 Table 9: Underpricing regression model 28 Table 10: White test result 30 Table 11: Skewness/kurtosis tests for Normality 30 Table 12: Underpricing regression model after data trimming 30 Table 13: Regression models in long-term 31 Table 14: Simple t-test to dependent variables 38 Table 15: Descriptive statistics detail to all variables .40 Table 16: Underpricing regression model 46 Table 17: CAR4D regression model 46 Table 18: CAR5D regression model 47 Table 19: CAR6M regression model 47 Table 20: CAR6MN regression model .48 Table 21: CAR1Y regression model 48 Table 22: CAR2Y regression model 49 Table 23: CAR3Y regression model 49 CHAPTER I: INTRODUCTION 1.1 Problem statement Initial public offering (IPO) plays an important role in development of issuers and investors Firstly, IPO helps to attract more investment capital for the issuers’ development in long-terms and improve its image in investor’s eyes (Ritter and Welch, 2002) Secondly, IPO of large and potential companies has positive effect in catching the investors’ attention and then increase the market’s liquidity In addition, success of IPO depends on participation of investors who always want to look for large profit from the stock market It means that investors should be known that they would buy a good bargain in the IPO events and then would receive the profits as selling stocks in the market Therefore, this obviously motivates so-called “underpircing” in IPO to make it to be more interesting for investment (Rock, 1986) However, a drawback to profit-seekers from IPO events is Efficient Market Hypothesis (EMH) (Fama, 1970 and 1997) Under the hypothesis, investors seem hard to seek more profit aftermarket, especially in long-term The daily trading price will fully reflect available information and thus there will be no more abnormal return Nevertheless, EMH, itself, could not explain convincingly many extraordinary phenomena in the financial history For instance, the Black Monday on October 19th in 1987 indicated a great decline of 30% within a day in the U.S stock market and the other international stock markets in the world They stabilized and recovered quickly not long after that and generate a huge profit or abnormal returns to investors In Vietnam, the stock market has experienced ups and downs since its start in 2001 When Vietnam had been going to join WTO, along with outbreak of VN-Index, IPO issuance also broke out in 2006–2007 period Most of investors wanted to jump in the IPO events to seek abnormal returns A large cash flow was poured into the IPO events due to lacking of investment opportunities at that time and they hoped to earn profits as selling Since end 2008, because of influence of economic recession, Vietnamese corporation’ IPOs have not usually succeeded Thanks to positive signs for the economy, Vietnam is urging to speed up equitization process However, such one of the youngest stock markets over the world as Vietnam, whether investors can earn profit from IPOs, at least in short-term, or not and how about in long-run? Many theories and studies have explained underpricing in various ways using different data from different countries and in different time periods in the world However, there are very few empirical studies on this issue in Vietnam Several studies have tried to explain underpricing but just in terms of descriptive statistics My goal is to use the updated data available with a longer event study to examine the IPO underpricing in Vietnam If there are more evidences indicate existing profitable of investment in IPOs or investors can score a success for their investment decisions, it can be more interesting for investors That is the reason for this research “Aftermarket return of Initial Public Offering – The case of Vietnam” 1.2 Research objectives Firstly, the research aims to investigate aftermarket stocks’ performance of initial public offerings in short-run and long-run Data from hundreds companies went public and listing on HSX and HNX after IPO events are used to demonstrate the existing of abnormal returns The hypothesis here is returns in short-run is different from zero Obviously, the returns are expected as large as possible In the long-run, under the EMH hypothesis, it is expected there is no existence of abnormal returns Secondly, by using a regression model the thesis also is to determine which factors can impact to IPOs’ performance aftermarket There are many models are applied to different period 1.3 Scope of study The research has studied on the IPO and listed events on Vietnam's stock market in the period 2001 – 2013 For the IPO events in a certain year, (ex 2001), the next three years (ex: 2002, 2003, and 2004) will be considered as the first, second and third year since the first listed year Data will end in 2010 as the years 2011, 2012, and 2013 are used for long-term analysis CHAPTER II: LITERATURE REVIEW 2.1 Theoretical studies Efficient Market Hypothesis (EMH) was developed by Eugene E Fama (1970), in which he gave three forms of the theory, including weak form, semi-strong form and strong form Two first forms have been accepted commonly than the last one The first form claims that the current price of securities fully reflects its past information and investors cannot win the market given the past information The second form claims that securities’ current price “fully reflect all obviously publicly available information” (Fama, 1998) By efficient, most proponents of the theory mean that investors cannot earn above-average returns on the stock aftermarket It means that difference between stock returns and market returns equals zero The theory then has been challenged by behavior finance economists However, in the latest research on this theory in 1998, Fama reiterated his most important conclusion related to abnormal return in long-run He reckons that “apparent anomalies can be due to methodology, most long-term return anomalies tend to disappear with reasonable changes in technique” 2.2 Empirical studies Most research on the issue recognized the existence of underpricing as returns on the first listing day is usually positive (Miller and Really, 1987; Allen and Faulhaber, 1989; Rock, 1986; and Tinic, 1998) However, based on EMH, there are unending discussion between its challengers and proponents about stocks performance in aftermarket in long-term The challengers said that IPO stocks often underperformed the market returns at least in the period three-to-five years The proponents imply that there are no evidences for underperformance of IPO stocks in long-term based on the EMH 2.2.1 IPO stock returns versus the market returns in short-term REFERENCE Aggarwal.R (2003), Allocation of initial public offerings and flipping activity, Journal of Financial Economics 68, 111-135 Allen, F and Faulhaber, G (1989), Signaling by Underpricing in the IPO Market, Journal of Financial Economics Brav A, Geczy.C and Gompers P.A (2000), Is the abnormal return following equity issuances anormalous?, Journal of Financial Economics 56, 209-249 Ellis, K (2006), Who trades IPOs? A close look at the first days of trading, Journal of Financial Economics 79, 339–363 Fama, E.F (1998), Market efficiency, long- term returns, and behavioral finance, Journal of Financial Economics 49, 283–306 Fernando, S.B.U, Mohammad, T.S (2007), Return Behavior of Initial Public Offerings and Market Efficiency, School of Economic and Management, Lund university Gavriel, A A (2011), Underpricing and Long-term Performance of Auctioned IPOs: the Case of Viet Nam, International Research Journal of Finance and Economics Geczy, C., Musto, D., Reed, A (2002), Stocks are special too: an analysis of the equity lending market, Journal of Financial Economics 66, 241–269 Gompers, P A., and Lerner, J (2003), The really long-run performance of initial public offerings: The Pre-Nasdaq evidence, Journal of Finance 58, 1355–1392 Grinblatt, M and Hwang, C Y (1989), Signalling and the pricing of new issues, Journal of Finance 44, 393-420 35 Loughran, T., Ritter, J., Rydqvist, K (1994), Initial public offerings: International insights, Pacific-Basic Journal of Finance 2, 165–199 Ly T.T.H, Kha Duong (2013), Bằng ch ứng về hiện t ượng đị nh d ưới giá của các IPO tại Việt Nam , Tạp chí phát triển kinh tế – Trường Đại học kinh tế Tp HCM, số 270 Mayshar, J (1983), On divergence of opinion and imperfections in capital markets, American Economic Review 73, 114–128 Miller, E.M (1977), Risk, uncertainty and divergence of opinion, Journal of Finance 32, 1151–1168 Miller, R.E and Reilly F.K (1987), An Examination of Mispricing, Returns and Uncertainty for Initial Public Offerings, Financial Management Ogden, J.P., Jen, F.C and O’Connor (2003), Advanced Corporate Finance: Policies and Strategies, Prentice Hall Purnanandam, A.K and Bhaskaran S (2004), Are IPOs really underpriced?, Review of Financial Studies 17, 811–848 Ritter, J.R (1991), The long run performance of initial public offerings, Journal of Financial 46, 3-27 Rock, K (1986), Why new issues are underpriced?, Journal of Financial Economics 15, 187–212 Zheng, S.X (2006), Are IPOs really overpriced?, Journal of Empirical Finance, 14(3), 287-309 Houge, T., Loughran, T., Suchanek, G., & Yan, X (2001) Divergence of opinion, uncertainty, and the quality of initial public offerings Financial Management, 30(4), 5–23 36 Welch, I (1989), Seasoned Offerings, Imitation costs, and the Underpricing of Initial Public Offerings, Journal of Finance 44, 421-449 Zheng, S.X (2007), Market under-reaction to free cash flows from IPOs, The Financial Review 42, 75-97 37 APPENDICES Table 14: Simple t-test to dependent variables ttest underpricing ==0 One-sample t test Variable Obs Mean underp~g 577 6353576 Std Err Std Dev [95% Conf Interval] 0384528 9236677 5598328 mean = mean(underpricing) Ho: mean = Ha: mean < Pr(T < t) = 1.0000 t = degrees of freedom = Ha: mean != Pr(|T| > |t|) = 0.0000 7108824 16.5231 576 Ha: mean > Pr(T > t) = 0.0000 ttest underpricing ==0 One-sample t test Variable Obs Mean underp~g 577 6353576 Std Err Std Dev [95% Conf Interval] 0384528 9236677 5598328 mean = mean(underpricing) Ho: mean = Ha: mean < Pr(T < t) = 1.0000 t = degrees of freedom = Ha: mean != Pr(|T| > |t|) = 0.0000 7108824 16.5231 576 Ha: mean > Pr(T > t) = 0.0000 ttest car4d ==0 One-sample t test Variable Obs Mean car4d 577 -.0109783 Std Err Std Dev .0054209 1302144 mean = mean(car4d) Ho: mean = Ha: mean < Pr(T < t) = 0.0217 [95% Conf Interval] -.0216254 t = degrees of freedom = Ha: mean != Pr(|T| > |t|) = 0.0433 -.0003311 -2.0252 576 Ha: mean > Pr(T > t) = 0.9783 ttest car5d ==0 One-sample t test Variable Obs Mean car5d 577 -.0128202 mean = mean(car5d) Ho: mean = Ha: mean < Pr(T < t) = 0.0196 Std Err Std Dev .0062045 1490383 [95% Conf Interval] -.0250064 t = degrees of freedom = Ha: mean != Pr(|T| > |t|) = 0.0393 -.0006339 -2.0663 576 Ha: mean > Pr(T > t) = 0.9804 38 ttest car6m ==0 One-sample t test Variable Obs Mean car6m 577 -.0106421 Std Err Std Dev .0173085 4157653 mean = mean(car6m) Ho: mean = [95% Conf Interval] -.0446376 t = degrees of freedom = Ha: mean < Pr(T < t) = 0.2694 Ha: mean != Pr(|T| > |t|) = 0.5389 0233535 -0.6148 576 Ha: mean > Pr(T > t) = 0.7306 ttest car6mn ==0 One-sample t test Variable Obs Mean car6mn 577 0658163 Std Err Std Dev [95% Conf Interval] 0124769 2997051 0413105 mean = mean(car6mn) Ho: mean = Ha: mean < Pr(T < t) = 1.0000 t = degrees of freedom = Ha: mean != Pr(|T| > |t|) = 0.0000 090322 5.2751 576 Ha: mean > Pr(T > t) = 0.0000 ttest car1y ==0 One-sample t test Variable Obs Mean car1y 577 1056617 Std Err Std Dev [95% Conf Interval] 0248689 5973725 0568169 mean = mean(car1y) Ho: mean = Ha: mean < Pr(T < t) = 1.0000 t = degrees of freedom = Ha: mean != Pr(|T| > |t|) = 0.0000 1545066 4.2487 576 Ha: mean > Pr(T > t) = 0.0000 ttest car2y ==0 One-sample t test Variable Obs Mean car2y 577 1633753 mean = mean(car2y) Ho: mean = Ha: mean < Pr(T < t) = 1.0000 Std Err Std Dev [95% Conf Interval] 0301043 7231301 1042477 t = degrees of freedom = Ha: mean != Pr(|T| > |t|) = 0.0000 2225029 5.4270 576 Ha: mean > Pr(T > t) = 0.0000 39 ttest car3y ==0 One-sample t test Variable Obs Mean car3y 577 2596255 Std Err Std Dev [95% Conf Interval] 0374593 8998032 1860521 mean = mean(car3y) Ho: mean = Ha: mean < Pr(T < t) = 1.0000 t = degrees of freedom = Ha: mean != Pr(|T| > |t|) = 0.0000 333199 6.9309 576 Ha: mean > Pr(T > t) = 0.0000 Table 15: Descriptive statistics detail to all variables Underpricing 1% 5% 10% 25% 50% 75% 90% 95% 99% Percentiles Smallest -0.31638 -0.66905 -0.2 -0.56833 -0.12298 -0.54918 Obs 0.017241 -0.44223 Sum of Wgt 0.25 0.97614 1.767285 2.424599 4.079365 Mean Std Dev Largest 4.341405 5.574567 Variance 5.989247 Skewness 6.791458 Kurtosis 577 577 0.635358 0.923668 0.853162 2.247368 10.59535 CAR4D 1% 5% 10% 25% 50% 75% 90% 95% 99% Percentiles Smallest -0.30697 -0.40968 -0.2302 -0.36587 -0.17357 -0.34718 Obs -0.09499 -0.34199 Sum of Wgt -0.00616 0.060474 0.15732 0.200033 0.33158 Mean Std Dev Largest 0.390295 0.417058 Variance 0.428221 Skewness 0.436341 Kurtosis 577 577 -0.01098 0.130214 0.016956 0.166082 3.53359 40 CAR5D 1% 5% 10% 25% Percentiles Smallest -0.35206 -0.42227 -0.25612 -0.41152 -0.20376 -0.39348 Obs -0.10451 -0.39278 Sum of Wgt 50% -0.01193 75% 90% 95% 99% 0.063137 0.186839 0.237734 0.400097 Mean Std Dev Largest 0.424478 0.507867 Variance 0.521988 Skewness 0.548964 Kurtosis 577 577 -0.01282 0.149038 0.022212 0.30574 3.789345 CAR6M 1% 5% 10% 25% 50% 75% 90% 95% 99% Percentiles Smallest -0.93768 -1.41373 -0.6275 -1.16002 -0.48218 -0.98943 Obs -0.25857 -0.97696 Sum of Wgt -0.03313 0.197 0.502304 0.622336 1.409753 Mean Std Dev Largest 1.513104 1.525453 Variance 1.642111 Skewness 2.160695 Kurtosis 577 577 -0.01064 0.415765 0.172861 0.707686 5.391155 CAR6MN 1% 5% 10% 25% 50% Percentiles Smallest -0.58208 -0.75518 -0.38946 -0.66065 -0.2976 -0.61224 Obs -0.12348 -0.60494 Sum of Wgt 0.040087 Mean Std Dev Largest 75% 0.239326 577 577 0.065816 0.299705 1.0439 41 90% 95% 99% 0.414679 0.557358 0.971172 1.224109 Variance 1.237031 Skewness 1.287239 Kurtosis 0.089823 0.55343 4.146726 CAR1Y 1% 5% 10% 25% 50% 75% 90% 95% 99% Percentiles Smallest -1.07895 -1.5481 -0.64895 -1.44445 -0.5373 -1.36171 Obs -0.29551 -1.22936 Sum of Wgt 0.044486 0.364732 0.755023 1.13612 2.402156 Largest 2.676075 2.759723 3.294496 3.416511 577 577 Mean Std Dev 0.105662 0.597373 Variance Skewness Kurtosis 0.356854 1.35995 7.589898 CAR2Y 1% 5% 10% 25% 50% 75% 90% 95% 99% Percentiles Smallest -1.19303 -1.48414 -0.84161 -1.47599 -0.6483 -1.26558 Obs -0.31893 -1.26041 Sum of Wgt 0.094294 0.565535 1.023519 1.363511 2.709902 Mean Std Dev Largest 3.171163 3.346584 Variance 3.402626 Skewness 4.019183 Kurtosis 577 577 0.163375 0.72313 0.522917 1.141476 6.203909 CAR3Y 1% 5% 10% 25% Percentiles Smallest -1.74589 -2.5667 -1.0365 -2.5271 -0.75382 -2.34493 Obs -0.35887 -1.98038 Sum of Wgt 577 577 42 50% 75% 90% 95% 99% 0.201069 0.740374 1.339683 1.74092 3.159156 Mean Std Dev Largest 3.407122 3.412995 Variance 3.757902 Skewness 4.537312 Kurtosis 0.259626 0.899803 0.809646 0.620486 4.920336 Exchange Percentiles Smallest 1% 5% 10% 25% 0 0 0 Obs Sum of Wgt 50% Mean Std Dev 0.422877 0.494445 1 Variance Skewness Kurtosis 0.244476 0.312229 1.097487 Largest 75% 90% 95% 99% 1 1 577 577 Underwrite Percentiles Smallest 1% 5% 10% 25% 0 0 0 Obs Sum of Wgt 50% Mean Std Dev 0.573657 0.494974 1 Variance Skewness Kurtosis 0.244999 -0.29788 1.088731 Largest 75% 90% 95% 99% 1 1 577 577 industry Percentiles Smallest 43 1% 5% 10% 25% 0 0 0 Obs Sum of Wgt 50% Mean Std Dev 0.672444 0.469729 75% 90% 95% 99% 1 1 1 Variance Skewness Kurtosis 0.220646 -0.73486 1.540023 Largest 577 577 Size 1% 5% 10% 25% 50% 75% 90% 95% 99% Percentiles Smallest 4.014865 3.78689 4.206411 3.837273 4.384355 3.856971 Obs 4.69897 3.900476 Sum of Wgt 5.193342 5.774517 6.224566 6.609559 7.180355 Mean Std Dev Largest 7.217005 7.234274 Variance 7.343636 Skewness 7.579783 Kurtosis 577 577 5.275119 0.732751 0.536924 0.436326 2.729569 freefloat 1% 5% 10% 25% 50% Percentiles Smallest 0.063521 0.03 0.182194 0.039521 0.240133 0.042524 Obs 0.391452 0.044763 Sum of Wgt 0.526016 Largest 75% 90% 95% 99% 0.794301 0.951319 0.991046 577 577 Mean Std Dev 0.577304 0.295283 1 Variance Skewness 4.05 Kurtosis 0.087192 2.862139 34.41164 44 volumeFD 1% 5% 10% 25% 50% 75% 90% 95% 99% Percentiles Smallest 1.00E-05 1.00E-07 8.24E-05 2.88E-07 0.000234 5.03E-07 Obs 0.001962 1.11E-06 Sum of Wgt 0.007419 0.02294 0.050498 0.078699 0.191626 Mean Std Dev Largest 0.215279 0.234544 Variance 0.288042 Skewness 0.298208 Kurtosis 577 577 0.019854 0.035047 0.001228 3.99415 23.74868 Marketperform 1% 5% 10% 25% 50% 75% 90% 95% 99% Percentiles Smallest -0.29251 -0.34471 -0.21261 -0.33017 -0.15473 -0.30544 Obs -0.07981 -0.30311 Sum of Wgt -0.00365 0.120174 0.305167 0.34467 0.391237 Mean Std Dev Largest 0.434888 0.434888 Variance 0.434888 Skewness 0.434888 Kurtosis 577 577 0.030847 0.166506 0.027724 0.511066 2.598982 45 Table 16: Underpricing regression model Source SS df MS Model Residual 34.7330939 456.688187 569 4.96187056 802615443 Total 491.421281 576 853161946 underpricing Coef exchange underwrite industry size freefloat volumefd marketperform _cons -.3240889 1239653 -.0483915 -.1478495 -.0404479 2196697 2014639 1.526533 Std Err Number of obs F( 7, 569) Prob > F R-squared Adj R-squared Root MSE t 0948473 0788634 0820116 0649935 1288044 1.106028 2356582 3596543 -3.42 1.57 -0.59 -2.27 -0.31 0.20 0.85 4.24 P>|t| 0.001 0.117 0.555 0.023 0.754 0.843 0.393 0.000 = = = = = = 577 6.18 0.0000 0.0707 0.0592 89589 [95% Conf Interval] -.5103824 -.0309336 -.2094739 -.2755059 -.293438 -1.952727 -.2614022 8201209 -.1377954 2788642 112691 -.0201931 2125422 2.392067 66433 2.232945 Table 17: CAR4D regression model Source SS df MS Model Residual 124408053 9.64212923 568 015551007 01697558 Total 9.76653728 576 016955794 car4d Coef underpricing exchange underwrite industry size freefloat volumefd marketperform _cons 0010184 0107471 -.0153646 -.0021509 -.0045424 -.0296152 175275 -.0336279 0327063 Std Err .0060968 0139346 0114941 0119307 009495 0187338 160857 0342941 0531267 Number of obs F( 8, 568) Prob > F R-squared Adj R-squared Root MSE t 0.17 0.77 -1.34 -0.18 -0.48 -1.58 1.09 -0.98 0.62 P>|t| 0.867 0.441 0.182 0.857 0.633 0.114 0.276 0.327 0.538 = 577 = 0.92 = 0.5025 = 0.0127 = -0.0012 = 13029 [95% Conf Interval] -.0109566 -.0166225 -.0379407 -.0255846 -.0231919 -.0664113 -.1406721 -.1009867 -.0716424 0129935 0381167 0072115 0212829 0141072 0071809 4912222 0337309 137055 46 Table 18: CAR5D regression model Source SS df MS Model Residual 170244973 12.6240978 568 021280622 022225524 Total 12.7943428 576 022212401 car5d Coef underpricing exchange underwrite industry size freefloat volumefd marketperform _cons 003331 0143399 -.0164255 -.0018809 -.0047339 -.0332528 2310864 -.0413886 0305444 Std Err Number of obs F( 8, 568) Prob > F R-squared Adj R-squared Root MSE t 0069762 0159444 0131519 0136515 0108645 0214358 1840575 0392404 0607892 0.48 0.90 -1.25 -0.14 -0.44 -1.55 1.26 -1.05 0.50 P>|t| 0.633 0.369 0.212 0.890 0.663 0.121 0.210 0.292 0.616 = 577 = 0.96 = 0.4685 = 0.0133 = -0.0006 = 14908 [95% Conf Interval] -.0103712 -.0169772 -.0422578 -.0286945 -.0260733 -.075356 -.1304301 -.1184626 -.0888546 0170332 0456571 0094068 0249326 0166055 0088504 5926029 0356855 1499433 Table 19: CAR6M regression model Source SS df MS Model Residual 1.95763482 97.6101823 568 244704353 171848913 Total 99.5678172 576 172860794 car6m Coef underpricing exchange underwrite industry size freefloat volumefd marketperform _cons -.0143801 0201555 -.0017859 0607777 0431019 -.0403512 4584353 -.1582942 -.2581655 Std Err .0193983 0443359 0365709 0379601 0302103 0596057 5118009 1091141 1690338 Number of obs F( 8, 568) Prob > F R-squared Adj R-squared Root MSE t -0.74 0.45 -0.05 1.60 1.43 -0.68 0.90 -1.45 -1.53 P>|t| 0.459 0.650 0.961 0.110 0.154 0.499 0.371 0.147 0.127 = = = = = = 577 1.42 0.1833 0.0197 0.0059 41455 [95% Conf Interval] -.0524812 -.0669268 -.0736166 -.0137817 -.0162357 -.1574257 -.5468179 -.3726106 -.5901731 0237211 1072378 0700449 135337 1024395 0767233 1.463689 0560222 0738421 47 Table 20: CAR6MN regression model Source SS df MS Model Residual 911751767 50.8263756 568 113968971 089483056 Total 51.7381273 576 089823138 car6mn Coef underpricing exchange underwrite industry size freefloat volumefd marketperform _cons 0059108 028521 0200025 0325774 0009717 0405111 -.6546244 0679616 -.0009939 Std Err Number of obs F( 8, 568) Prob > F R-squared Adj R-squared Root MSE t 0139978 0319928 0263896 0273921 0217998 0430115 3693159 0787368 1219749 0.42 0.89 0.76 1.19 0.04 0.94 -1.77 0.86 -0.01 P>|t| 0.673 0.373 0.449 0.235 0.964 0.347 0.077 0.388 0.994 = = = = = = 577 1.27 0.2545 0.0176 0.0038 29914 [95% Conf Interval] -.021583 -.0343176 -.0318307 -.0212247 -.0418463 -.04397 -1.380016 -.0866893 -.2405709 0334046 0913597 0718356 0863795 0437898 1249921 0707671 2226124 238583 Table 21: CAR1Y regression model Source SS df MS Model Residual 3.85716751 201.690707 568 482145939 355089273 Total 205.547875 576 356853949 car1y Coef underpricing exchange underwrite industry size freefloat volumefd marketperform _cons -.0250202 -.0053292 0257031 11109 0784249 073866 -.5065132 -.0390571 -.4107172 Std Err .0278842 063731 0525692 0545661 0434261 0856808 7356926 156847 2429791 Number of obs F( 8, 568) Prob > F R-squared Adj R-squared Root MSE t -0.90 -0.08 0.49 2.04 1.81 0.86 -0.69 -0.25 -1.69 P>|t| 0.370 0.933 0.625 0.042 0.071 0.389 0.491 0.803 0.092 = = = = = = 577 1.36 0.2123 0.0188 0.0049 59589 [95% Conf Interval] -.079789 -.1305063 -.0775507 003914 -.0068704 -.0944238 -1.951523 -.3471281 -.8879644 0297487 119848 1289569 2182661 1637202 2421558 9384969 2690138 06653 48 Table 22: CAR2Y regression model Source SS df MS Model Residual 4.96739393 296.232917 568 620924241 521536825 Total 301.200311 576 522917206 car2y Coef underpricing exchange underwrite industry size freefloat volumefd marketperform _cons -.0446089 -.006867 0288714 1503665 049789 1139951 1059776 -.0539988 -.2519445 Std Err Number of obs F( 8, 568) Prob > F R-squared Adj R-squared Root MSE t 0337935 0772368 0637097 0661298 0526289 1038382 8916001 1900859 2944711 -1.32 -0.09 0.45 2.27 0.95 1.10 0.12 -0.28 -0.86 P>|t| 0.187 0.929 0.651 0.023 0.345 0.273 0.905 0.776 0.393 = = = = = = 577 1.19 0.3022 0.0165 0.0026 72218 [95% Conf Interval] -.1109843 -.1585716 -.0962639 0204778 -.0535821 -.0899586 -1.645258 -.427356 -.8303297 0217665 1448376 1540067 2802552 1531601 3179487 1.857213 3193583 3264406 Table 23: CAR3Y regression model Source SS df MS Model Residual 9.88594216 456.470086 568 1.23574277 803644518 Total 466.356028 576 809645882 car3y Coef underpricing exchange underwrite industry size freefloat volumefd marketperform _cons -.0420027 -.094994 1056874 1831482 0759797 20885 7209385 0927293 -.3958477 Std Err .041949 0958769 0790851 0820893 0653302 1288981 1.106776 2359606 3655377 Number of obs F( 8, 568) Prob > F R-squared Adj R-squared Root MSE t -1.00 -0.99 1.34 2.23 1.16 1.62 0.65 0.39 -1.08 P>|t| 0.317 0.322 0.182 0.026 0.245 0.106 0.515 0.694 0.279 = = = = = = 577 1.54 0.1411 0.0212 0.0074 89646 [95% Conf Interval] -.1243969 -.2833105 -.0496476 0219126 -.0523386 -.0443251 -1.452934 -.3707326 -1.113818 0403915 0933224 2610223 3443837 2042979 4620251 2.894811 5561911 3221228 49 ... AFTERMARKET RETURNS OF INITIAL PUBLIC OFFERING THE CASE OF VIETNAM A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS... success for their investment decisions, it can be more interesting for investors That is the reason for this research “Aftermarket return of Initial Public Offering – The case of Vietnam” 1.2 Research...UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIET NAM THE NETHERLANDS VIET NAM – NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS AFTERMARKET