Benenish m score model in market returns measurement empirical evidence in vietnam

MINISTRY OF EDUCATION AND TRAINING THE STATE BANK OF VIETNAM BANKING UNIVERSITY OF HO CHI MINH CITY ********************* GRADUATION THESIS DUONG MINH KHANH BENEISH M-SCORE MODEL IN MARKET RETURNS MEASUREMENT: EMPIRICAL EVIDENCE IN VIETNAM GRADUATE THESIS MAJOR: BANKING - FINANCE CODE: 7340201 Ho Chi Minh City, November 2022 vi MINISTRY OF EDUCATION AND TRAINING THE STATE BANK OF VIETNAM BANKING UNIVERSITY OF HO CHI MINH CITY ********************** Student: DUONG MINH KHANH Code: 050606180159 Class: HQ6-GE10 BENEISH M-SCORE MODEL IN MARKET RETURNS MEASUREMENT: EMPIRICAL EVIDENCE IN VIETNAM GRADUATE THESIS MAJOR: BANKING - FINANCE CODE: 7340201 Supervisor: PhD NGUYEN DUY LINH Ho Chi Minh City, November 2022 vii COMMENTS OF THE GRADUATE THESIS ADVISOR Ho Chi Minh City, …………, … …… 2022 Advisor PhD Nguyen Duy Linh COMMENTS OF REVIEW BOARD Ho Chi Minh City, …………, … …… 2022 Chairman of the Review Board i ABSTRACT The thesis aims to apply the Beneish M-score model to investigated a potential link between the probability of manipulation in financial statements of 4,336 observations corresponding from 2011-to 2021 From there, classifying potentially manipulation companies according to M-score, the study also shows a significant difference in stock returns between these two groups of companies Companies with a high risk of manipulation (high M-score) will have low stock returns, and vice versa At the same time, the regression model finding the relationship between the return to scale adjusted for the M-score index and other predictors also shows a negative correlation with the M-score, a positive correlation with the M-score, Accruals, Momentum, MVE and BTM Thereby, the M-score is proven to have a significant impact on the profitability when impacting other forecast plates Keywords: Beneish M-score, financial statement fraud, stock return ii DECLARATION I hereby declare that this is my research work, and the research results presented in the thesis are honest and objective The sources of information cited in this thesis are clearly indicated I am responsible for my thesis Signature Khanh Duong Minh Khanh iii ACKNOWLEDGEMENT First of all, I am grateful and would like to send my sincerest and special thanks to my supervisor, PhD Nguyen Duy Linh for helping me to finish this challenging thesis Despite my defects throughout the process of performing the thesis, he always showed his caring and enthusiasm to support me to overcome Without PhD Nguyen Duy Linh con0scientious guidance and support, this thesis cannot be successfully accomplished Next, I would like to express my deepest gratitude to the teachers who are teaching at the Banking University of Ho Chi Minh City, who have spread the fire, passion and knowledge about economics from the most basic subjects, helping me to have a background in finance - banking Finally, I would like to thank to my family, friends, and colleagues who have always encouraged and helped me in the process of studying and researching the topic Although I have tried a lot, the thesis is not free of shortcomings I hope to receive the sympathy, guidance, help, and support of my colleagues iv TABLE OF CONTENTS ABSTRACT .i DECLARATION ii ACKNOWLEDGEMENT iii LIST OF TABLES AND FIGURES vi CHAPTER I: INTRODUCTION 1.1 Rationales 1 Research objectives and research questions 1.2.1 Research objectives 1.2.2 Research questions 3 Subject and scope of the study subject 1.3.1 Subject 1.3.2 Research scope 1.4 Structure of the study CHAPTER 2: THEORETICAL BASIS AND LITERATURE REVIEW .5 2.1 Theoretical overview 2.1.1 Definition of financial statement 2.1.2 Definition of financial reporting fraud 2.1.3 Theory explaining the motives for fraudulent financial statements 2.1.4 Acts of performing fraudulent financial statements 2.2 Literature review 2.2.1 Identify distortions in financial statements 2.2.2 Beneish M-score model 2.2.3 Research on developing the Beneish M-score model 11 2.2.4 Research on the correlation between financial statement distortion and stock market profitability 12 CONCLUSION CHAPTER 15 CHAPTER 3: DATA AND RESEARCH METHODS .16 3.1 Research data 16 3.2 Research methods 17 3.2.1 M – Score model 18 v 3.2.2 M – score and forecast future expected return 19 3.2.2.1 Calculate adjusted rate of return on a security (BHSAR) 19 3.2.2.2 Comparison of BHSAR (+1) of two groups of companies classified by M-score 20 3.2.2.3 Consider the relationship between M – Score and profitability 20 3.2.2.4 Consider the relationship between profitability and other factors 20 3.2.2.5 Empirical model about the relationship between M-score and return on stock market 21 3.2.2.6 Regression fit method – GMM 22 CONCLUSION CHAPTER 23 CHAPTER 4: RESEARCH RESULTS AND DISCUSSION 24 4.1 Financial distortion in Vietnam 24 4.1.1 Financial distortion from the perspective of the exchange 24 4.1.2 Fraud risk according to the capitalization classification 28 4.2 Impact of financial fraud on stock returns 29 4.3 M – Score and the influence of other factors on profitability 30 4.3.1 Correlation matrix between M - score and other influencing factors 30 4.3.2 Combined effect of M-score and other factors on stock return 31 4.3.2.1 Find the defects of the model 31 4.3.2.2 Regression by GMM method 34 4.3.2.3 Interpretation of regression results 35 CONCLUSION CHAPTER 36 CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS 37 5.1 Concluding remarks 37 5.2 Limitations and suggestions for further studies 38 5.3 Proposing solutions to limit fraud in financial statements 39 CONCLUSION CHAPTER 41 ENGLISH REFERENCES 42 VIETNAMESE REFERENCES 43 APPENDIX 44 vi LIST OF TABLES AND FIGURES Figure 4.1 Statistics on the number of companies at risk of fraud on stock exchanges Figure 4.2 Statistics on the number of companies at risk of fraud over the years Figure 4.3 Statistics by percentage of companies at risk of fraud over the years Table 3.1 Statistics of research observations on HOSE and HNX 2011 - 2021 Table 4.1 Average M-score of fraudulent and non-fraudulent firms by exchange Table 4.2 Fraud risk according to the capitalization classification Table 4.3 Compare the average BHSAR return (t+1) of the two groups of companies Table 4.4 Correlation matrix between M - score and other influencing factors Table 4.5 Regression results Table 4.6 Table of test results for VIF coefficient Table 4.7 Modified Wald Test Results Table 4.8 Wooldridge Test Result Table Table 4.9 Table of regression results according to GMM method 44 APPENDIX Appendix 1: Correlation matrix between dependent variable and independent variable Appendix 2: Regression results according to Pooled OLS method Appendix 3: Correlation matrix between variables 45 Appendix 4: VIF Test 46 Appendix 5: Modified Waled Test Appendix 6: Wooldridge Test 47 Appendix 7: Regression results according to GMM method 48 Appendix 8: Data DSR Ticker 2011 AAA 0.51 1.27 4.03 1.34 0.96 0.93 0.01 1.14 2012 AAA 0.77 1.18 0.40 1.11 0.74 1.05 -0.06 2013 AAA 1.37 1.20 1.07 1.15 1.31 0.90 2014 AAA 0.90 1.32 1.22 1.35 0.83 2015 AAA 1.96 1.00 2.36 1.03 2016 AAA 0.94 0.82 0.73 1.33 I GMI AQI SGI DEPI SGAI TAT Year LVGI M- Moment SITUATION Code -1.67 GIAN LẬN 0.012 -64.2% 111,870 5.32 0.73 -3.61 KHÔNG (0.061) 47.2% 275,220 3.04 -0.02 1.25 -3.13 KHÔNG (0.024) 45.3% 350,460 2.19 0.92 -0.04 0.86 -2.89 KHÔNG (0.036) 7.8% 550,440 1.88 0.77 0.92 0.06 1.30 -2.47 KHÔNG 0.058 10.6% 608,850 1.77 1.90 0.57 0.02 1.19 -3.01 KHÔNG 0.020 105.6% A SCORE Accruals um MVE BTM 1,214,46 0.96 2,767,16 2017 AAA 1.11 1.06 0.32 1.90 0.89 0.98 0.08 0.93 -2.35 KHÔNG 0.075 49.3% 2018 AAA 0.85 1.61 6.08 1.97 0.68 0.75 0.02 0.94 0.04 GIAN LẬN 0.023 -39.7% 0.68 2,516,64 1.31 2,174,24 2019 AAA 1.12 0.75 0.66 1.16 0.91 1.19 0.00 0.98 -3.46 KHÔNG - -11.1% 2020 AAA 1.00 1.08 1.74 0.80 1.01 1.49 -0.04 0.90 -3.38 KHÔNG (0.038) 24.0% 1.60 3,193,34 1.30 6,593,97 2021 AAA 0.61 1.06 1.25 1.77 0.84 1.63 -0.01 0.86 -2.65 KHÔNG (0.012) 58.8% 0.83 2013 AAM 1.18 0.93 3.23 1.10 0.91 1.11 -0.04 1.36 -2.67 KHÔNG (0.036) -32.6% 141,087 2.83 49 2014 AAM 0.89 1.19 1.42 0.82 0.95 0.86 -0.03 0.93 -3.31 KHÔNG (0.031) 8.2% 143,074 2.61 2015 AAM 1.66 1.15 0.54 0.80 1.03 0.90 0.02 1.67 -3.64 KHÔNG 0.016 -17.5% 103,331 3.07 2016 AAM 1.38 1.11 1.22 0.79 1.00 0.84 0.01 0.22 -2.98 KHÔNG 0.014 -8.5% 94,588 3.29 2017 AAM 0.99 0.92 1.88 0.82 1.37 1.07 -0.22 0.84 -4.10 KHÔNG (0.223) 14.0% 102,835 2.84 2018 AAM 0.58 0.78 0.59 0.97 0.89 0.83 -0.03 1.89 -4.07 KHÔNG (0.031) 40.5% 107,678 2.19 2019 AAM 0.62 1.08 0.98 0.99 0.91 0.97 0.15 0.89 -2.59 KHÔNG 0.154 32.6% 133,775 1.65 80.4 2020 AAM 1.61 1.04 0.56 1.21 1.56 0.06 1.12 38.49 GIAN LẬN 0.059 -8.6% 117,053 1.66 2021 AAM 0.41 0.02 1.14 1.11 0.98 0.69 -0.22 0.42 -4.51 KHÔNG (0.224) 12.5% 131,685 1.48 2019 ABR 0.96 0.29 1.18 1.27 0.00 4.05 0.33 2.43 -2.94 KHÔNG 0.334 185.4% 35,100 6.03 2020 ABR 0.25 0.84 0.80 3.63 1.69 3.76 0.01 0.90 -1.51 GIAN LẬN 0.006 113.7% 500,000 0.47 2021 ABR 2.16 1.10 0.73 0.93 1.27 0.72 0.02 1.11 -3.18 KHÔNG 0.019 0.0% 500,000 0.51 2013 ABT 1.60 0.92 0.70 0.84 1.22 1.25 0.02 1.56 -3.67 KHÔNG 0.022 6.8% 466,042 1.73 2014 ABT 2.50 0.83 1.12 0.85 0.76 0.99 0.07 0.92 -3.02 KHÔNG 0.068 46.7% 603,603 1.18 2015 ABT 0.66 0.93 1.00 1.05 0.98 0.88 -0.10 0.81 -3.72 KHÔNG (0.105) 3.1% 557,617 1.13 2016 ABT 0.64 1.41 0.77 0.89 0.93 0.82 -0.07 1.21 -3.69 KHÔNG (0.071) -3.0% 505,879 1.18 2017 ABT 1.32 1.41 7.49 0.91 1.10 1.20 0.02 1.01 -0.45 GIAN LẬN 0.020 -26.8% 344,918 1.57 2018 ABT 0.76 0.55 1.20 1.03 1.16 0.96 0.01 0.60 -3.26 KHÔNG 0.014 48.4% 462,190 1.10 50 2019 ABT 1.02 1.52 1.03 0.91 0.99 0.92 0.13 1.08 -2.51 KHÔNG 0.126 -1.1% 413,901 1.09 2020 ABT 0.95 1.29 0.97 0.88 1.02 1.22 0.01 1.05 -3.28 KHÔNG 0.009 -8.7% 366,762 1.19 2021 ABT 1.20 0.62 0.98 1.07 0.89 1.77 0.06 1.21 -3.37 KHÔNG 0.060 15.5% 410,452 1.05 2013 ACC 1.37 1.43 0.62 0.96 1.16 0.90 -0.09 0.80 -3.56 KHÔNG (0.089) 17.0% 265,000 1.35 2014 ACC 1.52 0.83 1.76 0.95 1.18 1.75 -0.10 1.66 -3.88 KHÔNG (0.099) 32.6% 319,000 1.15 2015 ACC 0.76 0.73 0.91 1.02 0.82 1.74 0.00 0.98 -3.63 KHÔNG (0.005) -16.8% 243,000 1.42 2016 ACC 1.14 1.01 0.96 1.15 0.95 0.84 0.16 0.96 -2.39 KHÔNG 0.162 39.2% 310,000 0.98 2017 ACC 0.92 1.13 1.11 0.69 0.86 0.69 -0.04 1.05 -3.65 KHÔNG (0.037) -30.2% 202,000 1.42 2018 ACC 0.57 1.10 1.22 1.38 1.16 1.05 -0.08 1.12 -3.27 KHÔNG (0.083) 18.0% 218,000 1.25 2019 ACC 1.64 1.06 0.79 1.24 2.15 0.77 0.04 1.36 -2.85 KHÔNG 0.045 -2.5% 194,000 1.37 2020 ACC 1.23 0.69 0.82 1.02 0.99 1.14 0.37 0.89 -1.76 GIAN LẬN 0.369 14.6% 444,000 1.18 1,024,50 2021 ACC 1.15 1.33 4.27 0.73 0.53 0.80 0.42 1.34 -0.21 GIAN LẬN 0.422 138.3% 0.46 2013 ACL 1.06 0.79 1.39 0.96 0.84 1.69 -0.14 0.94 -4.06 KHÔNG (0.144) -21.6% 174,797 1.99 2014 ACL 1.15 1.09 0.85 0.87 0.85 0.68 0.08 1.06 -3.03 KHÔNG 0.080 20.0% 209,756 1.73 2015 ACL 0.95 0.95 2.85 1.33 1.00 0.88 0.07 1.13 -1.99 KHÔNG 0.065 -11.7% 176,637 2.09 2016 ACL 0.71 1.02 0.46 1.13 0.94 1.00 -0.03 0.93 -3.55 KHÔNG (0.033) -5.5% 193,797 2.10 2017 ACL 1.32 0.98 1.83 0.92 0.93 1.05 -0.12 0.94 -3.58 KHÔNG (0.117) -4.7% 2.34 51 184,677 2018 ACL 0.90 0.59 0.36 1.42 1.02 0.73 0.05 0.89 -3.10 KHÔNG 0.054 291.5% 683,990 0.89 2019 ACL 0.86 1.14 1.06 0.84 0.95 1.06 0.09 0.93 -2.94 KHÔNG 0.093 -18.2% 528,952 1.30 2020 ACL 1.36 1.41 0.95 0.67 0.94 0.97 0.15 1.07 -2.70 KHÔNG 0.148 38.9% 734,830 0.98 2015 VGC 1.07 0.91 1.21 0.98 0.00 1.12 -0.04 0.94 -3.59 KHÔNG (0.043) 46.7% 16,920 248.95 2016 VGC 0.89 0.90 1.09 1.04 1.00 1.00 -0.03 0.93 -3.42 KHÔNG (0.032) 33.9% 19,980 245.89 2017 VGC 0.92 1.04 1.00 1.13 1.11 0.89 -0.05 0.88 -3.34 KHÔNG (0.047) 19.6% 21,240 316.37 2018 VGC 0.89 1.00 1.16 0.96 0.98 1.18 -0.05 1.01 -3.56 KHÔNG (0.053) -22.9% 16,380 419.53 2019 VGC 0.90 0.97 1.04 1.15 0.95 1.03 -0.15 1.10 -3.93 KHÔNG (0.153) -1.1% 16,200 435.50 2020 VGC 0.95 0.97 1.20 0.93 0.94 1.08 -0.10 1.04 -3.81 KHÔNG (0.099) -44.4% 9,000 780.46 2021 VGC 0.74 0.96 0.79 1.19 0.43 0.75 -0.16 0.92 -4.01 KHÔNG (0.160) 112.0% 19,080 437.99 2013 VHC 0.77 1.09 1.23 1.21 0.93 0.94 0.04 0.72 -2.72 KHÔNG 0.045 23.6% 205,219 11.65 2014 VHC 1.30 0.91 1.41 1.24 1.29 0.80 0.09 1.49 -2.62 KHÔNG 0.089 33.7% 266,425 10.56 2015 VHC 1.72 1.06 2.25 1.03 0.79 1.00 0.04 0.90 -2.48 KHÔNG 0.040 -25.0% 190,818 15.59 2016 VHC 0.86 0.85 0.84 1.12 1.19 0.94 -0.09 0.89 -3.71 KHÔNG (0.088) 117.1% 398,557 7.88 2017 VHC 1.04 1.02 1.10 1.12 0.89 0.91 0.03 0.90 -2.97 KHÔNG 0.032 -1.2% 345,917 9.80 2018 VHC 1.32 0.65 1.29 1.14 1.12 0.69 0.12 0.87 -2.55 KHÔNG 0.121 -1.1% 342,157 13.52 52 2019 VHC 0.89 1.13 0.62 0.85 1.02 1.41 -0.05 0.72 -3.75 KHÔNG (0.047) -15.1% 290,570 19.34 2020 VHC 1.25 1.35 1.22 0.89 0.98 0.76 0.05 1.07 -2.86 KHÔNG 0.045 103.1% 539,028 10.10 1,840,27 2021 VHC 0.98 0.74 1.41 1.29 0.83 1.56 0.09 1.16 -2.77 KHÔNG 0.089 259.3% 3.20 45.5 2013 VHM 0.01 -2.33 7.17 0.88 0.14 0.19 0.95 38.08 GIAN LẬN 0.191 176.3% 238,500 25.65 2014 VHM 1.08 1.01 2.43 0.93 0.63 1.50 -0.03 1.06 -3.07 KHÔNG (0.032) 81.5% 488,000 10.00 2015 VHM 6.39 0.77 0.98 0.76 1.14 1.97 -0.32 0.87 -4.76 KHÔNG (0.317) 34.4% 614,400 19.94 2016 VHM 0.52 0.84 1.12 2.28 0.76 1.37 -0.07 1.04 -2.68 KHÔNG (0.068) 56.0% 918,400 17.12 1,400,00 2017 VHM 3.94 1.18 0.23 1.36 0.38 0.90 -0.02 1.08 -3.11 KHÔNG (0.024) 59.0% 10.67 2018 VHM 0.69 1.30 2.20 2.53 0.35 0.34 0.14 0.74 -0.55 GIAN LẬN 0.136 -45.2% 725,000 92.68 2019 VHM 0.82 0.49 1.22 1.34 1.99 1.13 -0.14 1.12 -3.80 KHÔNG (0.140) 16.6% 775,000 106.83 2020 VHM 0.53 1.47 1.72 1.39 4.02 0.93 0.02 0.87 -1.97 KHÔNG 0.015 -4.3% 675,000 153.68 2021 VHM 0.92 0.64 1.12 1.19 0.66 0.72 0.09 0.73 -2.77 KHÔNG 0.095 8.2% 675,000 209.33 2013 VIC 0.43 1.26 1.10 2.32 1.61 0.95 -0.03 0.94 -2.05 KHÔNG (0.028) 176.3% 238,500 148.46 2014 VIC 0.88 1.02 1.52 1.51 0.38 1.09 -0.07 0.92 -3.03 KHÔNG (0.068) 81.5% 488,000 103.23 2015 VIC 2.24 1.09 0.82 1.23 1.25 3.26 -0.18 1.07 -4.24 KHÔNG (0.181) 34.4% 614,400 105.11 2016 VIC 0.78 1.14 0.87 1.69 0.92 1.33 0.00 0.99 -2.75 KHÔNG - 56.0% 918,400 86.59 53 1,400,00 2017 VIC 0.97 1.02 1.05 1.55 1.09 0.79 -0.05 1.02 -2.98 KHÔNG (0.052) 59.0% 55.41 2018 VIC 1.34 1.25 0.98 1.36 1.07 0.91 0.06 0.87 -2.48 KHÔNG 0.056 -45.2% 725,000 190.91 2019 VIC 1.20 0.82 0.68 1.07 1.32 1.32 -0.02 1.07 -3.59 KHÔNG (0.020) 16.6% 775,000 199.07 2020 VIC 0.97 1.84 1.27 0.85 0.96 0.60 -0.03 0.97 -2.96 KHÔNG (0.027) -4.3% 675,000 234.24 2021 VIC 1.21 0.58 1.03 1.14 0.72 0.84 0.02 0.92 -3.26 KHÔNG 0.016 8.2% 675,000 254.20 2013 VID 1.54 0.74 1.86 0.39 0.12 1.68 0.10 0.58 -3.21 KHÔNG 0.104 176.3% 238,500 2.21 2014 VID 1.19 5.09 1.23 0.39 4.66 0.77 -0.01 0.72 -1.08 GIAN LẬN (0.008) 81.5% 488,000 1.07 2015 VID 1.05 -0.18 1.04 1.32 0.91 0.75 0.11 1.21 -3.15 KHÔNG 0.105 34.4% 614,400 0.82 2016 VID 2.38 -2.34 0.82 0.62 5.56 1.13 0.03 0.83 -4.66 KHÔNG 0.033 56.0% 918,400 0.65 1,400,00 2017 VID 0.17 1.20 0.77 7.78 0.45 0.19 0.34 1.13 4.30 GIAN LẬN 0.339 59.0% 0.48 2018 VID 0.85 0.62 0.68 1.44 0.80 2.01 0.22 1.34 -2.54 KHÔNG 0.220 -45.2% 725,000 0.93 2019 VID 0.95 1.08 1.34 1.22 0.78 2.02 0.05 1.12 -2.94 KHÔNG 0.049 16.6% 775,000 0.79 2020 VID 0.78 0.96 1.22 1.03 0.96 0.99 -0.06 0.96 -3.50 KHÔNG (0.064) -4.3% 675,000 0.89 2021 VID 1.13 0.70 0.77 0.98 0.94 1.35 0.07 1.18 -3.36 KHÔNG 0.075 8.2% 675,000 0.87 2013 VIP 1.06 0.75 1.22 0.78 0.87 1.16 -0.07 0.80 -3.85 KHÔNG (0.073) 45.5% 109,225 9.18 2014 VIP 1.09 1.05 1.79 0.88 0.84 0.77 0.03 0.96 -2.89 KHÔNG 0.034 28.1% 139,944 7.43 54 2015 VIP 1.15 0.87 1.77 0.83 0.91 1.28 -0.07 0.73 -3.50 KHÔNG (0.066) -22.0% 109,227 9.60 2016 VIP 1.43 1.24 0.63 1.09 1.17 1.10 -0.05 1.13 -3.49 KHÔNG (0.053) -53.1% 51,200 21.71 2017 VIP 0.64 0.79 1.07 1.16 0.76 0.74 -0.10 0.87 -3.69 KHÔNG (0.104) 126.7% 116,053 9.53 2018 VIP 1.36 0.98 0.95 1.07 0.86 0.64 -0.16 0.90 -3.91 KHÔNG (0.160) -61.8% 44,373 25.27 2019 VIP 0.92 1.27 1.09 0.80 0.90 1.05 -0.11 0.90 -3.82 KHÔNG (0.106) -23.1% 34,133 31.79 1.0000 2017 VNF 1.04 1.02 1.73 1.14 1.11 1.17 -0.08 00019 -3.33 KHÔNG (0.084) 8.9% 307,148 0.92 2018 VNF 1.07 1.13 1.21 0.89 0.91 1.14 0.02 0.84 -3.13 KHÔNG 0.019 -52.9% 192,665 1.89 2019 VNF 0.83 1.13 1.13 0.91 0.84 0.92 0.00 0.92 -3.26 KHÔNG 0.002 49.1% 273,082 1.33 2020 VNF 0.97 1.59 0.78 1.63 0.97 0.43 -0.04 1.22 -2.69 KHÔNG (0.036) 8.6% 291,511 1.22 2021 VNF 0.97 0.49 0.68 1.98 1.60 2.74 0.19 0.91 -2.15 KHÔNG 0.186 6.9% 551,482 1.10 2018 VSM 0.85 1.02 8.56 1.13 1.08 0.51 -0.05 0.99 -0.28 GIAN LẬN (0.047) -3.4% 36,600 1.66 2019 VSM 0.97 1.21 2.26 1.15 1.07 0.47 -0.02 1.14 -2.60 KHÔNG (0.021) -7.1% 30,500 1.96 2020 VSM 1.27 0.93 1.03 1.11 0.76 0.96 -0.08 1.07 -3.63 KHÔNG (0.080) 58.3% 43,920 1.34 2021 VSM 0.90 0.92 1.12 1.42 1.08 0.90 -0.04 0.96 -3.08 KHÔNG (0.044) 101.1% 81,435 0.78 2013 VTC 0.59 0.94 0.95 1.17 1.24 0.91 -0.10 0.99 -3.67 KHÔNG (0.096) 3.7% 12,682 7.81 2014 VTC 3.39 0.91 0.72 1.22 0.80 1.00 0.10 2.00 -2.93 KHÔNG 0.103 78.6% 22,646 4.72 2015 VTC 0.82 0.81 1.16 1.00 1.08 1.19 0.04 0.80 -3.13 KHÔNG 0.039 12.0% 25,363 4.69 55 2016 VTC 0.56 2.28 0.53 2.73 0.86 0.35 0.14 1.46 -0.72 GIAN LẬN 0.137 96.4% 49,821 2.62 2017 VTC 2.24 1.15 0.27 1.44 1.05 0.74 0.17 1.40 -2.30 KHÔNG 0.170 -10.7% 40,762 3.06 2018 VTC 0.92 0.90 1.20 1.15 0.96 1.23 0.10 1.01 -2.73 KHÔNG 0.101 -6.7% 38,045 3.39 2019 VTC 0.91 1.34 0.94 1.33 1.02 0.93 -0.04 1.02 -3.05 KHÔNG (0.043) -27.9% 24,910 4.84 2020 VTC 1.02 0.70 0.86 0.66 0.99 1.58 -0.05 0.91 -4.13 KHÔNG (0.052) 91.3% 43,027 2.58 2021 VTC 1.39 0.73 1.31 0.33 0.52 1.76 -0.17 0.88 -4.83 KHÔNG (0.174) 71.3% 67,937 1.36 2015 VTH 0.83 0.87 2.27 0.92 1.14 0.00 -0.12 2.28 -3.74 KHÔNG (0.123) 17.6% 83,000 1.32 2017 VTH 0.28 1.84 4.70 1.93 1.04 1.26 0.04 1.23 -0.53 GIAN LẬN 0.041 40.2% 72,500 1.22 2018 VTH 1.86 5.47 0.20 0.78 0.87 1.02 -0.11 1.27 -2.01 KHÔNG (0.109) -28.3% 50,000 1.50 2019 VTH 2.89 0.08 3.04 0.61 1.01 3.79 0.04 1.23 -3.51 KHÔNG 0.042 5.0% 52,500 1.50 2020 VTH 1.48 1.14 0.93 1.06 1.07 2.64 0.12 1.38 -3.00 KHÔNG 0.124 -10.5% 47,000 1.68 2015 VTJ 1.87 1.31 0.81 0.34 1.54 0.04 5.27 5.29 GIAN LẬN 0.044 -21.6% 91,200 1.70 2016 VTJ 0.37 1.91 0.70 2.90 0.88 0.33 -0.26 1.38 -2.55 KHÔNG (0.258) 70.8% 147,060 1.06 2017 VTJ 0.58 1.25 0.29 0.46 3.25 0.81 -0.31 0.34 -4.93 KHÔNG (0.311) -28.2% 95,760 1.39 2018 VTJ 1.68 1.78 0.03 0.73 19.78 0.18 0.50 -3.05 KHÔNG 0.181 -46.8% 46,740 2.25 2019 VTJ 1.02 2.31 0.28 0.74 1.14 -0.13 0.44 -0.85 GIAN LẬN (0.126) 0.0% 46,740 2.49 2020 VTJ 0.25 0.12 0.91 1.26 0.43 0.00 -0.24 3.70 -5.55 KHÔNG (0.241) -4.9% 44,460 2.09 12.0 30.4 34.0 56 2021 VTL 1.50 -0.46 0.93 1.51 0.00 0.93 0.01 1.01 -3.67 KHÔNG 0.012 -13.3% 65,780 0.36 2013 VTV 1.33 1.12 1.31 0.96 0.65 0.98 -0.02 0.96 -3.24 KHÔNG (0.015) 51.1% 166,920 4.31 2014 VTV 1.07 1.02 1.03 1.02 1.96 0.90 0.01 1.12 -3.13 KHÔNG 0.013 135.1% 358,800 1.90 2015 VTV 1.28 0.97 21.47 0.92 0.24 1.05 0.07 1.01 5.13 GIAN LẬN 0.070 31.8% 446,158 1.49 2016 VTV 1.11 1.03 1.11 1.03 7.18 0.79 0.12 1.09 -1.93 KHÔNG 0.122 45.7% 614,637 1.06 2017 VTV 0.79 1.26 1.28 1.19 3.30 0.89 0.08 0.96 -2.24 KHÔNG 0.081 -11.9% 499,197 1.35 2018 VTV 1.20 0.81 0.55 0.88 0.18 1.02 -0.09 0.98 -4.19 KHÔNG (0.093) -23.4% 327,598 1.60 2019 VTV 0.97 0.88 1.27 0.63 0.76 1.25 -0.24 0.91 -4.76 KHÔNG (0.243) 2.4% 296,398 1.48 2020 VTV 1.43 1.11 1.17 0.69 0.88 0.85 -0.22 0.90 -4.40 KHÔNG (0.225) -42.2% 162,239 2.48 2021 VTV 0.68 1.02 0.93 1.34 0.92 1.09 -0.21 0.98 -4.05 KHÔNG (0.212) 82.7% 296,398 1.40 2013 VXB 1.51 0.67 1.06 1.00 1.30 1.70 -0.06 0.97 -3.77 KHÔNG (0.061) 77.3% 48,993 2.30 2014 VXB 1.57 0.76 1.54 1.01 1.09 1.01 0.06 1.09 -2.90 KHÔNG 0.059 65.3% 72,477 1.42 2015 VXB 1.20 1.02 1.01 0.88 0.88 0.99 0.03 1.02 -3.26 KHÔNG 0.026 -34.9% 42,110 2.13 2016 VXB 1.06 1.02 1.13 1.03 1.03 1.15 0.01 1.05 -3.20 KHÔNG 0.006 -1.8% 36,846 2.18 2017 VXB 0.85 0.93 1.24 0.98 0.98 0.88 0.06 1.00 -2.98 KHÔNG 0.055 31.6% 42,110 1.61 2018 VXB 1.03 1.11 1.04 1.09 1.13 0.79 0.03 1.06 -2.98 KHÔNG 0.031 23.8% 48,588 1.26 2019 VXB 0.79 2.19 1.38 0.58 0.98 1.66 -0.08 1.01 -3.41 KHÔNG (0.080) -32.5% 32,797 1.44 57 2020 VXB 1.50 1.24 1.16 0.74 0.92 0.85 -0.19 1.12 -4.21 KHÔNG (0.192) -11.1% 29,153 1.01 2019 X20 0.22 0.93 0.56 0.87 1.07 0.95 -0.11 0.76 -4.13 KHÔNG (0.112) 3.3% 162,150 1.54 2020 X20 0.57 0.73 0.61 0.85 0.79 1.36 -0.19 0.97 -4.75 KHÔNG (0.185) -4.3% 155,250 1.67 2021 X20 1.15 1.09 0.65 1.18 0.75 1.43 -0.16 1.09 -4.11 KHÔNG (0.164) 46.0% 215,625 1.18 58 ... BENEISH M- SCORE MODEL IN MARKET RETURNS MEASUREMENT: EMPIRICAL EVIDENCE IN VIETNAM GRADUATE THESIS MAJOR: BANKING - FINANCE CODE: 7340201 Supervisor: PhD NGUYEN DUY LINH Ho Chi Minh City, November... distorting financial statements by the M- Score model  Finding a suitable model to evaluate the impact of M- Score model on stock market return  Testing the impact of credit growth on M- Score model. .. analyzing and identifying the company''s actual situation In Vietnam, the research work "Using the Beneish M- score model to assess the quality of financial statements in Vietnam" by Vo Minh Duong