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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 CHAPTER I: INTRODUCTION 1.1 Rationales Nowadays, financial statements play a central and vital role in making investment decisions in the stock market Financial statements provide information about the assets, sources of capital owned, results of operations, cash flows, and interpretation of material transactions during the business period enterprise Thereby helping users of financial statements check and monitor the current status of business activities in the past period, how enterprises use capital, and assess the ability to raise capital and generate revenue potential, future profits However, many businesses have committed fraud and manipulated financial statements, and this behavior is increasing day by day For various purposes, a company's management may seek to influence and alter the figures in the reports to make financial results better, but not to reflect the actual situation Typically, the manipulation of the global financial statements was Enron in 2002 – the seventhlargest company in the US The Enron scandal caused a loss of more than $80 billion in capitalization for investors investing in this company Also, it led to the collapse of the fifth auditing firm in the world at that time - Arthur Andersen After Eron was the collapse of Lehman Brothers when fraudulently recording short-term debt financing operations into sales worth up to $50 billion This scandal cost Lehman Brothers' auditing company at the time, EY, up to USD 109 billion to settle this case and compensate Lehman's investors In Vietnam, the financial scandal of Cuu Long Pharmaceutical Company in 2014 or the manipulation of Viet Nhat JVC Medical Joint Stock Company in 2015 up to VND 900 billion are typical examples and many the case of other listed companies on the stock exchange These behaviors have highly negative impacts on the capital market and the economy in general It leads to financial statements' quality that does not guarantee stakeholder decision-making Therefore, assessing the reliability of financial statements is always an urgent need when not everyone is capable of 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 (2016) has demonstrated the feasibility of using the Beineish M-score model to examine the quality of financial statements by detecting signs of data distortion of companies in Vietnam However, no research explicitly studies how this distortion affects the return of the company's stock during the Covid-19 Pandemic Therefore, the range of studies assesses financial statement fraud through the M-score model and extends to understanding the correlation between financial statement fraud and the profitability of stocks in the stock market before and during the Covid-19 epidemic The study results can apply as a reference measure for investors and credit providers to evaluate the company's transparency and add a supporting tool to Invest in the stock market Research objectives and research questions 1.2.1 Research objectives Overall objectives The object of the study revolves around detecting fraudulent financial statements of listed companies, thereby affecting the profitability of these companies on the stock market in Vietnam Detail objectives Measure the possibility of 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 on stock market return, making appropriate assessments Proposing and giving appropriate solutions to limited fraud in financial statements in Vietnam 1.2.2 Research questions In this study, the author aims to answer three main research questions as follows: Firstly, the M-Score model measure the possibility of distorting financial statements? Second, what is a suitable model to evaluate the impact of M-Score model on stock market return? Third, correlation of M-score model with the profitability of listed companies on Vietnam stock market Fourth, what is solution to limited fraud in financial statements in Vietnam? Subject and scope of the study subject 1.3.1 Subject The subject of the study detects fraudulent financial statements of listed companies, thereby affecting the profitability of these companies on the stock market in Vietnam 1.3.2 Research scope Scope of space: The companies listed on Ho Chi Minh Stock Exchange and Hanoi Stock Exchange Scope of time: The data are collected from 2011 to 2021 Research content: In this study, the author focuses on studying the impact of financial statement distortion on stock profitability Thereby, we clarify the influence of the Beneish M-score model to detect fraudulent financial statements and find out the relationship between it and the profitability of companies on the stock market in Vietnam 1.4 Structure of the study The content of the study is divided into five specific chapters: Chapter 1: Introduction to the topic Chapter 2: Theoretical basis and literature review Chapter 3: Data and research methods Chapter 4: Research result and discussion Chapter 5: Conclusion and recommendations CHAPTER 2: THEORETICAL BASIS AND LITERATURE REVIEW 2.1 Theoretical overview 2.1.1 Definition of financial statement Financial statements are consolidated reports that show the current financial position of an enterprise and how that business uses capital from shareholders and credit providers The financial statement includes four main reports: (1) the balance sheet shows a company's financial position at a point in time, (2) the income statement shows how a company's profits have formed over a period, and (3) cash flow statements show the cash inflows and outflows of a company over some time and the last is the statement of changes in equity 2.1.2 Definition of financial reporting fraud Financial statements fraud is the company's use of techniques to change information on financial statements, making the numbers no longer truthful and objective to achieve specific purposes These could be reducing taxes, matching reporting with analysts' forecasts, securing debt contracts, and managers achieving short-term compensation However, fraud in financial statements causes information distortion, affects the market and decisions of investors and credit providers, and reduces the quality of the company's financial statements 2.1.3 Theory explaining the motives for fraudulent financial statements Fraud Triangle by Donald R Cressey (1919 - 1987) Cressey focuses on analyzing fraud from the perspective of embezzlement and embezzlement by surveying about 200 cases of economic crimes to find out the causes of the above violations He came up with a model: The Fraud Triangle consisting of Motivation - Attitude - Opportunity Managers gain short-term compensation Healy (1985) argues that short-term bonuses, dividends, or incentives motivate managers to distort financial statements His subsequent study et al (1999) further extended that senior managers have a high proportion of stock or stock options in compensation, while lower-level managers have cash account for a high percentage As a result, low-level managers tend to focus on maximizing short-term bonuses Haulthausen, Lacker, and Sloan (1995) and Guidry, Leone, and Rock (1999) give similar results Cheng and Warfield (2005) argue that managers who hold many common stocks will sell their shares in the future, so they have an incentive to distort financial statements to give an excellent signal to the market Research results also show that such managers will report profits that meet or exceed analyst expectations Similar to analysts' forecasts Graham, Harvey, and Rajpogal (2005) interviewed more than 400 CFOs and found that 73.5% of respondents agreed or strongly agreed that the analyst consensus is for earnings per share The current quarter is the key criterion when reporting quarterly earnings, and they tend to guide financial statements by these analyses Most managers want to avoid projects with a positive NPV but cause earnings to fall in the current quarter They want to trade economic value for a stable return because it reduces uncertainty about returns Achieve high prices on initial public offerings (IPOs) or additional issues Companies want to manipulate financial statements to increase the amount of money from IPOs or additional issues of shares Ducharme, Malatesta, and Sefcik (2002) suggest that pre-IPO extraordinary accruals have a positive relationship with the initial value of the firm However, this will reduce investors' returns in subsequent years, an average of years, according to the research of Teoh, Welch, and Wong (1998) Negotiate better contract terms and avoid violations a clause in the loan contract According to Bowen, Ducharme, and Shores (1995), a company can get better contract conditions from suppliers and related parties if it can report stable profits Debt contracts often include clauses about the company's earnings Thus, managers may adopt a policy of increasing reported earnings or other financial statement items in order to avoid a breach or proximity to a breach of such provisions In addition, beautifying financial statements can increase the willingness of lenders or suppliers to obtain short-term credit 2.1.4 Acts of performing fraudulent financial statements Distortion of financial statements often has the following mechanism (Schweser, 2015) Aggressive revenue recognition includes the activity of accelerating the supply of goods to distribution channels beyond their ability to sell (channel stuffing), recognizing revenue when the goods not yet reach consumers (bill and hold sales), or pretending to sell and then re-enter (outright fake sales); The company uses a finance lease for outsourced assets to exclude rent from expenses to increase profits; Record revenue/non-operating income into operating revenue/income and recognize operating expenses as operating expenses; Record gains in net income and losses in OCI (Other comprehensive income); Inappropriate selection of depreciation estimation models; Recognition of current items as long-term items; Recognition of provisions that are higher or lower than they are needed; Under recognition of tangible assets and high recognition of intangible assets for M&A related purpose; Performing transactions affecting cash flow from business activities; Record cash flow from operating activities into investment cash flow 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 ... 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. .. Linh 2016, Using the M- score Model in Detecting Earnings Management: Evidence from Non-Financial Vietnamese Listed Companies, Journal of Economic Development Finance in Vietnam, Master thesis... stock market Step 4: Modeling factors affecting return on the stock market 18 3.2.1 M – Score model This paper uses a newer version adapted from the Beneish (1999) model used in the “Earning Manipulation
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