Whether momentum or contrarian phenomenon exist in vietnam stock market

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HO CHI MINH CITY -o0o - TA THU TIN WHETHER MOMENTUM OR CONTRARIAN PHENOMENON EXIST IN VIETNAM STOCK MARKET MAJOR: FINANCE – BANKING MAJOR CODE: 60.31.12 MASTER THESIS ADVISOR: Ph.D TRAN PHUONG NGOC THAO HO CHI MINH CITY, 2011 ACKNOWLEDGEMENT At first, I would like to express my deep gratitude to my instructor, Dr Tran Phuong Ngoc Thao for her intensive guidance and valuable suggestions during time of my study I would like to thank Dr Vo Xuan Vinh for valuable comments and suggestions he share with me In addition, I also give my appreciation to all of my lecturers at Faculty of Banking and Finance, University of Economics Hochiminh City for their teaching and knowledge during my master course My sincere thank goes to Nguyen Hiep Phat, my colleague at Au Viet Securities, he spent a lot of time to help me make a software program to process raw data in this thesis Finally, I am thankful to my family for giving me facilitation to complete my thesis i ABSTRACT This thesis investigates whether momentum or contrarian phenomenon exist on Vietnamese Stock Market over the period from January 2005 to June 2011 We employ the famous methodology by Jegadeesh and Titman (1993) to calculate the profit of momentum and contrarian strategies which were built base on the historical return of 424 stocks listed on Ho Chi Minh Stock Exchange and Ha Noi Stock Exchange We found that all 16 trading contrarian strategies always make abnormal profit with statistical significance at the level of 10% The most profitable contrarian strategy with portfolio based on month formation and month holding has a average monthly return of 2,829% (equivalent to annually return of 33,95%) with significance level of 2% Our research demonstrates that the abnormal profit on trading contrarian strategy can not be accounted for by beta-risk as well as market size But we found a evidence of P/B ratio explaining contrarian phenomenon on Vietnamese Stock Market Key words: Momentum; Contrarian strategies ii TABLE OF CONTENTS ACKNOWLEDGEMENT……………………………………………………………… i ABSTRACT…………………………………………………………………………… ii TABLE OF CONTENTS……………………………………………………………… iii LIST OF FIGURES…………………………………………………………………… vi LIST OF TABLES…………………………………………………………………… vii ABBREVIATIONS…………………………………………………………………… viii Introduction………………………………………………………………………… 1.1 Overview of Momentum and Contrarian strategies…………………………… 1.2 Research Objective……………………………………………………………… 1.3 Research Methodology and Scope……………………………………………… 1.4 Thesis Structure………………………………………………………………… 1.5 Vietnamese Stock Market……………………………………………………… Literature Review………………………………………………………………… 2.1 Efficient Market Hypothesis…………………………………………………… 2.2 Momentum Strategy…………………………………………………………… 2.3 Contrarian Strategy…………………………………………………………… 15 Data Collection and Research Method………………………………………… 18 3.1 Data Collection………………………………………………………………… 18 3.1.1 Stock Prices…………………………………….……………………… 18 3.1.2 Adjusted Stock Prices………………………………………………… 19 3.1.3 P/B ratio………………………………………………………………… 21 3.1.4 Market Capitalization…………………………………………………… 22 3.2 Research Method……………………………………………………………… 22 Empirical Result…………………………………………………………………… 24 4.1 Raw Data Processing………………………………………………………… 24 4.2 Empirical Result……………………………………………………………… 28 4.3 Why does the contrarian phenomenon exist in Vietnam stock market? 32 4.4 Some factors may account for the contrarian phenomenon in Vietnam Stock Market………………………………………………………………………… 35 4.4.1 Market Risk…………………………………………………………… 36 iii 4.4.2 Firm Size……………………………………………………………… 39 4.4.3 Price to Book…………………………………………………………… 40 Conclusion………………………………………………………………………… 42 5.1 Main findings………………………………………………………………… 42 5.2 Implications of Research……………………………………………………… 43 5.3 Limitations of Research……………………………………………………… 44 REFERCENCES……………………………………………………………………… 45 APPENDIX…………………………………………………………………………… 48 Table A1: List of 424 investigated stocks……………………………………………………48 Table B1-Table B16: The average monthly return of Winner and Loser Portfolios in 16 strategies…………………………………………………………………… 59 Table C1-Table C16: The average monthly return of Loser portfolio compare to the one of Winner portfolio in 16 strategies………………………………… 75 Table D11: Estimation of Beta of Winner portfolio in J=3/K=3 strategy……………… 83 Table D12: Estimation of Beta of Loser portfolio in J=3/K=3 strategy……………… 84 Table D21: Estimation of Beta of Winner portfolio in J=6/K=3 strategy……………… 85 Table D22: Estimation of Beta of Loser portfolio in J=6/K=3 strategy……….……… 86 Table D31: Estimation of Beta of Winner portfolio in J=9/K=3 strategy……………….87 Table D32: Estimation of Beta of Loser portfolio in J=9/K=3 strategy…………………88 Table D41: Estimation of Beta of Winner portfolio in J=12/K=3 strategy…………… 89 Table D42: Estimation of Beta of Loser portfolio in J=12/K=3 strategy……………….90 Table E1: Average Market Capitalisation of Loser portfolio compare to Market Capitalisation of Winner portfolio in J=3/K=3 strategy………………………………… 91 Table E2: Average Market Capitalisation of Loser portfolio compare to Market Capitalisation of Winner portfolio in J=6/K=3 strategy………………………………… 91 iv Table E3: Average Market Capitalisation of Loser portfolio compare to Market Capitalisation of Winner portfolio in J=9/K=3 strategy………………………………… 92 Table E4: Average Market Capitalisation of Loser portfolio compare to Market Capitalisation of Winner portfolio in J=12/K=3 strategy……………………………… 92 Table F1: The average P/B ratio of Loser portfolio compare to the average P/B ratio of Winner portfolio in J=3/K=3 strategy……………………… 93 Table F2: The average P/B ratio of Loser portfolio compare to the average P/B ratio of Winner portfolio in J=6/K=3 strategy……………………… 93 Table F3: The average P/B ratio of Loser portfolio compare to the average P/B ratio of Winner portfolio in J=9/K=3 strategy……………………….94 Table F4: The average P/B ratio of Loser portfolio compare to the average P/B ratio of Winner portfolio in J=12/K=3 strategy…………………… 94 v LIST OF FIGURES Figure 1.1 VN-Index Chart over the period from July 2000 to September 2011………….6 Figure 3.1 Formation and Holding periods in two strategies…………………………… 23 Figure 4.1 The screen of the Analyzing Stock Price Data program after importing data from Excel file…………………………………………………………….25 Figure 4.2 The screen of the Analyzing Stock Price Data after stocks are ranked in descending order on the basis of their average monthly returns…………… 26 Figure 4.3: The screen of Stock Grouping program shows the Winner and Loser portfolios, and their average returns…………………………………………… 27 vi LIST OF TABLES Table 3.1 Adjusting price of KDC share………………………………………………….… 20 Table 3.2 Adjusting price of OPC share………………………………………………….… 21 Table 4.1 The average return of Winner and Loser Portfolios and their difference in J=3/K=3 Strategy (Formation Period: months; Holding Period: months)…… ……28 Table 4.2 Summary of the average monthly return of loser and winner portfolio; and their differences (profitability of contrarian strategies) for 16 strategies over the period from 01/2005 to 06/2011……………………………………………….… 30 Table 4.3 Monthly and annually profitability of 16 contrarian strategies are ranking in descending and their significances……………………………………… 31 Table 4.4 Beta coefficient after perform regression……………………………………… 38 Table 4.5 The comparison in average market capitalization between loser and winner portfolios and their differences……………………………………… .39 Table 4.6 The comparison in average P/B ratio between loser and winner portfolios…41 vii ABBREVIATIONS CAPM Capital Asset Pricing Model EMH Efficient Market Hypothesis HOSE Ho Chi Minh City Stock Exchange HNX Ha Noi Stock Exchange VND Vietnam Dong viii CHAPTER 1: INTRODUCTION 1.1 Overview of Momentum and Contrarian Strategies In 1970, Efficient Market Hypothesis (EMH) developed by Professor Eugene Fama proclaimed that in the efficient market no one could consistently beat the market and stock prices follow a random walk Thus, future prices of stocks could not be predicted from their past prices, it means that the abnormal return from trading should be zero However, a lot of investors and researchers have doubts about the efficient market hypothesis both empirically and theoretically They always try to find some abnormal returns to prove the inefficiency of the markets Consequently, forecasting the price movements in stock markets has become a major challenge for investors, brokers and speculators Studying the movement of stock prices become one of the most attractive fields of research due to its commercial applications and benefits it offers Recently, there are many researchers and traders have studied stock price predictions such as Fundamental Analysis, Technical Analysis, CANSLIM, etc… And one of the most attractive trading strategies is momentum (and contrarian) strategy The momentum strategy appeared firstly in the 1960s However, it became widely known only in the early 1990s after Narasimham Narasimhan Jegadeesh and Sheridan Titman published their study Momentum and contrarian strategies are two opposite investment strategies which use historical price/return data in order to forecast the future development of stock performance to make excess returns Momentum investing strategy, also sometimes known as “Trend following”, believes that stocks which have good performance in the past will keep doing so in the future, it buys (go long) stocks that have outperformed in the recent past, and short sell (go short) those that have underperformed over the same period In contrast, a contrarian strategy believes that stocks which have good historical performance will be bad in the future, so it suggests short selling past winning stocks and buying past losing stock The contrarian strategy was introduced first Table C11: The average monthly return of Loser portfolio compare to the one of Winner portfolio in (J=9/K=9) strategy Paired Samples Statistics Mean Pair N Std Deviation Std Error Mean ReturnL 001168 60 0577317 0074531 ReturnW -.013718 60 0651826 0084150 Paired Samples Correlations N Pair ReturnL & ReturnW Correlation 60 Sig .898 000 Paired Samples Test Pair ReturnL ReturnW Mean Std Deviation 0148867 0287110 Paired Differences 95% Confidence Interval of the Difference Std Error Mean Lower 0037066 0074698 Upper 0223035 t df 4.016 Sig (2-tailed) 59 000 Table C12: The average monthly return of Loser portfolio compare to the one of Winner portfolio in (J=9/K=12) strategy Paired Samples Statistics Mean Pair N Std Deviation Std Error Mean ReturnL 004667 57 0526694 0069762 ReturnW -.014268 57 0602593 0079815 Paired Samples Correlations N Pair ReturnL & ReturnW Correlation 57 Sig .891 000 Paired Samples Test Pair ReturnL ReturnW Mean Std Deviation 0189351 0273923 Paired Differences 95% Confidence Interval Std of the Difference Error Mean Lower 0036282 0116669 80 Upper 0262033 t 5.219 df Sig (2-tailed) 56 000 Table C13: The average monthly return of Loser portfolio compare to the one of Winner portfolio in (J=12/K=3) strategy Paired Samples Statistics Mean Pair N Std Deviation Std Error Mean ReturnL -.003749 63 1018228 0128285 ReturnW -.019383 63 1203549 0151633 Paired Samples Correlations N Pair ReturnL & ReturnW Correlation 63 Sig .852 000 Paired Samples Test Pair ReturnL ReturnW Mean Std Deviation 0156333 0630585 Paired Differences 95% Confidence Interval of the Difference Std Error Mean Lower Upper 0079446 -.0002478 0315144 t df 1.968 Sig (2-tailed) 62 054 Table C14: The average monthly return of Loser portfolio compare to the one of Winner portfolio in (J=12/K=6) strategy Paired Samples Statistics Mean Pair N Std Deviation Std Error Mean ReturnL -.001422 60 0717755 0092662 ReturnW -.019410 60 0829858 0107134 Paired Samples Correlations N Pair ReturnL & ReturnW Correlation 60 Sig .899 000 Paired Samples Test Pair ReturnL ReturnW Mean Std Deviation 0179883 0364293 Paired Differences 95% Confidence Interval of the Difference Std Error Mean Lower 0047030 81 0085777 Upper 0273990 t 3.825 df 59 Sig (2-tailed) 000 Table C15: The average monthly return of Loser portfolio compare to the one of Winner portfolio in (J=12/K=9) strategy Paired Samples Statistics Mean Pair N Std Deviation Std Error Mean ReturnL 001579 57 0573630 0075979 ReturnW -.019600 57 0641303 0084943 Paired Samples Correlations N Pair ReturnL & ReturnW Correlation 57 Sig .889 000 Paired Samples Test Pair ReturnL ReturnW Mean Std Deviation 0211789 0293178 Paired Differences 95% Confidence Interval of the Difference Std Error Mean Lower 0038832 0133999 Upper 0289580 t 5.454 df Sig (2-tailed) 56 000 Table C16: The average monthly return of Loser portfolio compare to the one of Winner portfolio in (J=12/K=12) strategy Paired Samples Statistics Mean Pair N Std Deviation Std Error Mean ReturnL 003915 54 0512852 0069790 ReturnW -.017980 54 0604985 0082328 Paired Samples Correlations N Pair ReturnL & ReturnW Correlation 54 Sig .897 000 Paired Samples Test Pair ReturnL ReturnW Mean Std Deviation 0218944 0268918 Paired Differences 95% Confidence Interval of the Difference Std Error Mean Lower 0036595 82 0145544 Upper 0292345 t 5.983 df Sig (2-tailed) 53 000 Table D11: Estimation of Beta of Winner portfolio in J=3/K=3 strategy Model Summary Model R Adjusted R Std Error of the Square Estimate R Square 751 a 564 558 1332701 a Predictors: (Constant), ReturnVNI ANOVAb Model Sum of Squares df Mean Square Regression 1.608 1.608 Residual 1.243 70 018 Total 2.852 71 F 90.562 Sig .000a a Predictors: (Constant), ReturnVNI b Dependent Variable: ReturnW Coefficientsa Model Standardized Unstandardized Coefficients B Std Error (Constant) 130 016 ReturnVNI 1.202 126 Coefficients Beta t 751 a Dependent Variable: ReturnW 83 Sig 8.256 000 9.516 000 Table D12: Estimation of Beta of Loser portfolio in J=3/K=3 strategy Model Summary Model R Adjusted R Std Error of the Square Estimate R Square 801 a 642 637 0836642 a Predictors: (Constant), ReturnVNI ANOVAb Model Sum of Squares df Mean Square Regression 877 877 Residual 490 70 007 1.367 71 Total F 125.324 Sig .000a a Predictors: (Constant), ReturnVNI b Dependent Variable: ReturnL Coefficientsa Model Standardized Unstandardized Coefficients B Std Error (Constant) -.105 010 ReturnVNI 888 079 Coefficients Beta t 801 a Dependent Variable: ReturnL 84 Sig -10.615 000 11.195 000 Table D21: Estimation of Beta of Winner portfolio in J=6/K=3 strategy Model Summary Model R Adjusted R Std Error of the Square Estimate R Square 774 a 599 593 1243803 a Predictors: (Constant), ReturnVNI ANOVAb Model Sum of Squares df Mean Square Regression 1.549 1.549 Residual 1.037 67 015 Total 2.585 68 F 100.098 Sig .000a a Predictors: (Constant), ReturnVNI b Dependent Variable: ReturnW Coefficientsa Model Standardized Unstandardized Coefficients B Std Error (Constant) 089 015 ReturnVNI 1.180 118 Coefficients Beta t 774 a Dependent Variable: ReturnW 85 Sig 5.900 000 10.005 000 Table D22: Estimation of Beta of Loser portfolio in J=6/K=3 strategy Model Summary Model R Adjusted R Std Error of the Square Estimate R Square 825 a 680 675 0807934 a Predictors: (Constant), ReturnVNI ANOVAb Model Sum of Squares df Mean Square Regression 929 929 Residual 437 67 007 1.366 68 Total F 142.312 Sig .000a a Predictors: (Constant), ReturnVNI b Dependent Variable: ReturnL Coefficientsa Model Standardized Unstandardized Coefficients B Std Error (Constant) -.069 010 ReturnVNI 914 077 Coefficients Beta t 825 a Dependent Variable: ReturnL 86 Sig -7.108 000 11.929 000 Table D31: Estimation of Beta of Winner portfolio in J=9/K=3 strategy Model Summary Model R Adjusted R Std Error of the Square Estimate R Square 758 a 575 568 1303350 a Predictors: (Constant), ReturnVNI ANOVAb Model Sum of Squares df Mean Square Regression 1.468 1.468 Residual 1.087 64 017 Total 2.555 65 F 86.419 Sig .000a a Predictors: (Constant), ReturnVNI b Dependent Variable: ReturnW Coefficientsa Model Standardized Unstandardized Coefficients B Std Error (Constant) 084 016 ReturnVNI 1.157 124 Coefficients Beta t 758 a Dependent Variable: ReturnW 87 Sig 5.218 000 9.296 000 Table D32: Estimation of Beta of Loser portfolio in J=9/K=3 strategy Model Summary Model R Adjusted R Std Error of the Square Estimate R Square 840 a 705 701 0809276 a Predictors: (Constant), ReturnVNI ANOVAb Model Sum of Squares Regression Mean Square 1.003 1.003 419 64 007 1.422 65 Residual Total df F 153.127 Sig .000a a Predictors: (Constant), ReturnVNI b Dependent Variable: ReturnL Coefficientsa Model Standardized Unstandardized Coefficients B Std Error (Constant) -.064 010 ReturnVNI 956 077 Coefficients Beta t 840 a Dependent Variable: ReturnL 88 Sig -6.454 000 12.374 000 Table D41: Estimation of Beta of Winner portfolio in J=12/K=3 strategy Model Summary Model R Adjusted R Std Error of the Square Estimate R Square 851 a 725 720 0971881 a Predictors: (Constant), ReturnVNI ANOVAb Model Sum of Squares Regression Mean Square 1.518 1.518 576 61 009 2.094 62 Residual Total df F 160.676 Sig .000a a Predictors: (Constant), ReturnVNI b Dependent Variable: ReturnW Coefficientsa Model Standardized Unstandardized Coefficients B Std Error (Constant) 076 012 ReturnVNI 1.178 093 Coefficients Beta t 851 a Dependent Variable: ReturnW 89 Sig 6.191 000 12.676 000 Table D42: Estimation of Beta of Loser portfolio in J=12/K=3 strategy Model Summary Model R Adjusted R Std Error of the Square Estimate R Square 801 a 641 635 0917973 a Predictors: (Constant), ReturnVNI ANOVAb Model Sum of Squares df Mean Square Regression 918 918 Residual 514 61 008 1.432 62 Total F 108.953 Sig .000a a Predictors: (Constant), ReturnVNI b Dependent Variable: ReturnL Coefficientsa Model Standardized Unstandardized Coefficients B Std Error (Constant) -.056 012 ReturnVNI 916 088 Coefficients Beta t 801 a Dependent Variable: ReturnL 90 Sig -4.836 000 10.438 000 Table E1: Average Market Capitalisation of Loser portfolio compare to Market Capitalisation of Winner portfolio in J=3/K=3 strategy Paired Samples Statistics Mean Pair N Std Deviation Std Error Mean MCofL 1289.4449 69 1596.1638 192.1555 MCofW 1430.3666 69 1435.8786 172.8594 Paired Samples Correlations N Pair MCofL & MCofW Correlation 69 Sig -.190 118 Paired Samples Test Pair MCofL MCofW Paired Differences 95% Confidence Interval of the Difference Std Error Mean Std Deviation -140.9217 2341.0956 Mean Lower 281.8347 -703.3143 t Upper 421.4708 df -.500 Sig (2-tailed) 68 619 Table E2: Average Market Capitalisation of Loser portfolio compare to Market Capitalisation of Winner portfolio in J=6/K=3 strategy Paired Samples Statistics Mean Pair N Std Deviation Std Error Mean MCofL 1400.7539 66 1405.2451 172.9737 MCofW 1402.6749 66 1282.2037 157.8283 Paired Samples Correlations N Pair MCofL & MCofW Correlation 66 Sig -.217 080 Paired Samples Test Pair MCofL MCofW Mean Std Deviation -1.9209 2097.9251 Paired Differences 95% Confidence Interval of the Difference Std Error Mean Lower 258.2367 -517.6553 91 Upper t 513.8133 -.007 df 65 Sig (2-tailed) 994 Table E3: Average Market Capitalisation of Loser portfolio compare to Market Capitalisation of Winner portfolio in J=9/K=3 strategy Paired Samples Statistics Mean Pair N Std Deviation Std Error Mean MCofL 1465.7924 63 1370.1273 172.6198 MCofW 1587.7079 63 1313.2070 165.4485 Paired Samples Correlations N Pair MCofL & MCofW Correlation 63 Sig -.268 033 Paired Samples Test Pair MCofL MCofW Paired Differences 95% Confidence Interval of the Difference Std Error Mean Std Deviation -121.9156 2137.1734 Mean Lower 269.2585 -660.1557 Upper 416.3245 t df -.453 62 Sig (2-tailed) 652 Table E4: Average Market Capitalisation of Loser portfolio compare to Market Capitalisation of Winner portfolio in J=12/K=3 strategy Paired Samples Statistics Mean Pair N Std Deviation Std Error Mean MCofL 1468.5251 60 1377.3767 177.8185 MCofW 1653.2063 60 1406.3949 181.5648 Paired Samples Correlations N Pair MCofL & MCofW Correlation 60 Sig -.262 043 Paired Samples Test Paired Differences Pair MCofL MCofW Mean Std Deviation -184.6811 2211.6097 Std Error Mean 285.5175 92 95% Confidence Interval of the Difference Lower -756.0005 Upper 386.6382 t -.647 df 59 Sig (2-tailed) 520 Table F1: The average P/B ratio of Loser portfolio compare to the average P/B ratio of Winner portfolio in J=3/K=3 strategy Paired Samples Statistics Mean Pair N Std Deviation Std Error Mean PBofL 1.998330 57 1.3657651 1858571 PBofW 2.920030 57 1.6470804 2241393 Paired Samples Correlations N Pair PBofL & PBofW Correlation 57 Sig .740 000 Paired Samples Test Pair PBofL PBofW Paired Differences 95% Confidence Interval of the Difference Std Error Mean Std Deviation -.9217000 1.1172115 Mean Lower Upper 1520332 -1.2266400 -.6167600 t -6.062 df Sig (2-tailed) 56 000 Table F2: The average P/B ratio of Loser portfolio compare to the average P/B ratio of Winner portfolio in J=6/K=3 strategy Paired Samples Statistics Mean Pair N Std Deviation Std Error Mean PBofL 1.903778 54 1.0553875 1436200 PBofW 3.215028 54 1.8753974 2552093 Paired Samples Correlations N Pair PBofL & PBofW Correlation 54 Sig .791 000 Paired Samples Test Pair PBofL PBofW Mean Std Deviation -1.3112500 1.2239253 Paired Differences 95% Confidence Interval of the Difference Std Error Mean Lower 1665551 -1.6453173 93 Upper -.9771827 t -7.873 df 53 Sig (2-tailed) 000 Table F3: The average P/B ratio of Loser portfolio compare to the average P/B ratio of Winner portfolio in J=9/K=3 strategy Paired Samples Statistics Mean Pair N Std Deviation Std Error Mean PBofL 1.849206 51 9986417 1398378 PBofW 3.340682 51 2.1535133 3015522 Paired Samples Correlations N Pair PBofL & PBofW Correlation 51 Sig .842 000 Paired Samples Test Pair PBofL PBofW Mean Std Deviation -1.4914765 1.4188900 Paired Differences 95% Confidence Interval of the Difference Std Error Mean Lower Upper t 1986843 -1.8905457 -1.0924072 -7.507 df Sig (2-tailed) 50 000 Table F4: The average P/B ratio of Loser portfolio compare to the average P/B ratio of Winner portfolio in J=12/K=3 strategy Paired Samples Statistics Mean Pair N Std Deviation Std Error Mean PBofL 1.912927 48 1.1018710 1590414 PBofW 3.483648 48 2.2243238 3210535 Paired Samples Correlations N Pair PBofL & PBofW Correlation 48 Sig .881 000 Paired Samples Test Pair PBofL PBofW Mean Std Deviation -1.5707208 1.3572179 Paired Differences 95% Confidence Interval of the Difference Std Error Mean Lower Upper t 1958975 -1.9648158 -1.1766258 -8.018 94 df 47 Sig (2-tailed) 000 ... three main following objectives: Investigating whether the momentum or contrarian phenomenon exist on the Vietnamese Stock Market? Determining factors account for the momentum or contrarian phenomenon. .. therefore if we find out that the momentum or contrarian momentum exist on Vietnam Stock Market, we have an evidence prove that Vietnamese Stock Market is not a weak form of Efficient Market. .. following research questions: Based on historical stock price data in Vietnamese stock market, would momentum (or contrarian) strategy make abnormal profit? Why does Vietnamese stock market have momentum/ contrarian