Measures the competitiveness and stability of Vietnamese commercial banks in the context of joining the CPTPP agreement

tive research methods. 3.2.1. Qualitative research methods The method and order of the study conducted by the author were as follows: - To evaluate the current state of commercial banks, the evaluation indicators are based on the CAMELS analysis framework - the system of monitoring and rating banks in the US and the set of financial health indicators (Financial Soundness Indicators - FSIs) according to IMF standards. - To compare the positions of Vietnamese commercial banks with the rest of the CPTPP member countries, the evaluation criteria are used macro-specific factors such as financial capacity, profitability assessment criteria. , the level of development and the contribution of the banking industry to the economy and network operations. - To assess strengths, weaknesses, opportunities and challenges for Vietnamese commercial banks when integrating into CPTPP, the thesis applies the SWOT model to build an analytical framework, arguments and assessments based on theory. mentioned in chapter 2; Conclusions and discussion of results are based on the hypotheses set forth in the thesis. - Policy implications are built on the basis of theory and research results of the topic. 12 3.2.2. Quantitative research method The thesis uses three linear regression models with table data to measure the impact of factors on competitiveness, bank stability, and the impact of competition on stability of 31 banks. Vietnamese trade. 3.2.2.1. Experimental research model for Vietnamese commercial banks  Competitiveness research model of Vietnamese commercial banks: The proposed model is as follows: Lernerit = Ф0 + Ф1Lerner it-1 + Ф2ETAit + Ф3B_SIZEit + Ф4LTAit + Ф5LLPit + Ф6HDVit + Ф7HHIit + Ф8GroTAit + Ф9FS1it + Ф10FS2 + Ф11GDPt + Ф12INFt + Ф13Originalt + eit (MH1)  Research model of stability of Vietnamese commercial banks: The proposed model is as follows: ZscoreMH2it = Ф0 + Ф1Zscoreit-1 + Ф2ETAit + Ф3B_SIZEit + Ф4LTAit + Ф5LLPit + Ф6HDVit + Ф7HHIit + Ф8GROTAit + Ф9FS1it + Ф10FS2 + Ф11GDPt + Ф12INFt + Ф13Originalt + eit (MH2)  Research model of the impact of competition on the stability of Vietnamese commercial banks: The proposed model is as follows: ZscoreMH3it = Ф0 + Ф1Zscoreit-1 + Ф2Lerner it + Ф3ETAit + Ф4B_SIZEit + Ф5LTAit + Ф6LLPit + Ф7HDVit + Ф8HHIit + Ф9GROTAit + Ф10FS1it + Ф11FS2 + Ф12GDPt + Ф13INFt + Ф14Originalt + eit (MH3) The significance of factors affecting the stability and competitiveness of Vietnamese commercial banks Independent variables: Group of characteristics of banks: EAT: Equity size B_SIZE: Size of the bank's capital LTA: Credit scale 13 HDV: Ability to mobilize capital LLP: Proportion of credit risk provision expense HHI: The ability to diversify income GroTA: Growth rate of total assets Group of competitive environment factors FS1: the number of SBV on the total number of banks in Vietnam FS2: The proportion of SBV's assets compared to the total assets of the CI system Group of macroeconomic factors GDP: GDP growth rate INF: Inflation rate Dummy variables: Original: 1 - State owned, 0 - not State owned 3.2.2.2. Procedures for implementing regression estimation of experimental model The author estimates the empirical model regression by calculating the variables in the model, descriptive statistics, running the model, testing the model, estimating competitiveness and bank stability. on Stata 14.0 software include the following steps: Step 1: Compute the variables in the experimental model Step 2: Descriptive statistics of the variables of the experimental model Step 3: Select regression method for the research model Step 4: Test the estimated coefficients and the suitability of the model Step 5: Check the defects of the model  Check the multi-collinearity phenomenon  Check the variance change phenomenon  Check for self-correlation  Check for endogenous phenomena Step 6: Estimating the competitiveness and stability of Vietnamese commercial banks with appropriate regression method 3.3. Research hypothesis Based on the theoretical basis, the empirical research results presented above, the thesis analyzed and selected to build a suitable research model to measure the 14 competitiveness and banking stability of Vietnamese commercial banks in the period 2010 - 2018, in the context of joining the CPTPP. On that basis, in order to achieve the research objectives and answer the research questions, the thesis sets out the following research hypotheses: H1. Equity size is positively correlated with competitiveness and banking stability. H2. The size of bank assets is positively correlated with bank competitiveness and stability. H3. The ratio of lending to total assets is positively correlated with competitiveness and opposite to banking stability. H4. The ratio of mobilization to total assets is inversely correlated with competitiveness and in the same direction with banking stability. H5. The rate of provision for credit risks is inversely correlated with competitiveness and banking risks. H6. The ratio of capital mobilization to total assets is positively correlated with bank stability. H7. The ability to diversify income is positively correlated with the competitive competence and bank stability. H8. The growth rate of total assets is positively correlated with the competitive competence and banking stability H9. The group of variables measuring the presence of foreign banks in Vietnam (FS1 and FS2) is expected to be positively correlated with bank competitiveness and stability. H10. GDP growth has a positive impact on the competitive competence and banking stability H11. The inflation rate is inversely correlated with the competitive competence and banking stability H12. Ownership form is inversely correlated with the competitive competence and banking stability H13. Competitiveness is positively correlated with bank stability. 15 CHAPTER 4 RESEARCH RESULTS AND DISCUSSION 4.1. The current status of the operation of a Vietnamese commercial bank in the period 2010 - 2018 The status of operating results of Vietnamese commercial banks is assessed through the following criteria: Capital and assets, Capital Adequacy Ratio (CAR), Capital mobilization and lending, Liquidity safety, Debt Bad and Some other factors such as: Product system, service, Technology level, Human resources, Brand. 4.2. Some results of banking activities in the CPTPP sector in the period 2010 - 2018 Table 4.1: Competitive ranking of global capacity of CPTPP countries Nation 2010 (/139) 2011 (/142) 2012 (/144) 2013 (/148) 2014 (/144) 2015 (/140) 2016 (/138) 2017 (/137) 2018 (/140) 1 Australia 16 20 20 16 22 21 19 18 14 2 Brunei 28 28 28 26 - - 58 46 62 3 Canada 10 12 14 14 15 13 15 14 12 4 Chile 30 31 33 34 33 35 33 33 33 5 Japan 6 9 10 9 6 6 8 9 5 6 Malaysia 26 21 25 24 20 25 18 23 25 7 Mexico 66 58 53 55 61 57 51 51 46 8 Peru 73 67 61 61 65 69 67 72 63 9 New Zeland 23 25 23 18 17 16 13 13 18 10 Singapore 3 2 2 2 2 2 2 3 2 11 Vietnam 59 65 75 70 68 56 60 65 77 Source: Authors' synthesis from WEF data 16 4.2.1. Financial capacity Table 4.2: Ranking of development indicators of financial systems of CPTPP countries (compared to 140 ranked countries in 2018) Nation 1 2 3 4 5 6 7 8 9 Rank Australia 15 18 35 16 18 4 10 110 112 13 Brunei 85 71 82 121 112 69 69 134 18 107 Canada 4 21 31 11 19 2 4 117 99 11 Chile 25 41 36 21 33 5 31 49 107 20 Japan 7 14 17 12 7 20 18 122 79 10 Malaysia 19 5 5 9 32 38 23 126 83 15 Mexico 97 94 54 51 65 39 37 102 81 61 New Zeland 8 10 15 46 41 9 3 22 110 26 Peru 84 79 70 50 73 42 65 81 101 63 Singapore 17 4 6 3 17 3 14 127 72 5 Vietnam 24 85 51 60 91 113 39 101 111 59 Source: Authors' synthesis from WEF data Bảng 4.1: Tỷ lệ tín dụng tiêu dùng so với GDP giữa các quốc gia CPTPP Đơn vị: % STT Quốc gia 2010 2011 2012 2013 2014 2015 2016 2017 2018 1 Canada - - - - - - - - - 2 Australia 125,50 122,33 121,28 124,98 128,73 136,59 142,52 140,90 139,59 3 Singapore 123,28 126,18 128,91 125,34 129,50 124,00 127,43 128,21 121,90 4 Malaysia 129,64 133,89 136,80 119,79 120,53 125,02 123,77 118,73 121,79 5 Nhật Bản 217,69 228,08 231,43 104,05 103,57 101,95 103,61 106,67 107,89 6 Việt Nam 114,85 99,80 106,46 96,80 100,30 111,93 123,82 130,72 133,31 7 New Zeland 92,66 - - 140,53 141,19 144,22 144,34 142,35 146,55 8 Mexico 30,63 30,70 31,33 22,20 21,94 23,89 25,93 27,02 26,78 17 9 Chile 66,76 74,97 77,64 76,19 78,60 78,65 79,80 78,62 81,27 10 Peru 38,76 38,16 40,86 37,71 40,84 43,84 42,87 42,33 43,97 11 Brunei 67,27 59,38 58,65 30,92 32,91 41,14 43,57 38,81 34,40 Source: Authors' synthesis from WB data (2018) Chart 4.1: The ratio of NPLs to total outstanding loans over the years of CPTPP Unit: % Source: Authors' synthesis from WEF data (2018) Table 4.4: Capital-to-asset ratio of the banking system in the CPTPP group Unit: % Nation 2010 2011 2012 2013 2014 2015 2016 2017 2018 Australia 5,43 5,29 5,13 5,13 5,22 5,97 6,55 6,88 6,86 Brunei Darussalam 10,25 8,9 9,1 11,58 11,76 13,23 13,05 10,86 12,32 Canada 4,66 4,89 4,90 4,96 4,94 5,07 5,16 5,22 5,20 Chile 8,28 7,77 8,01 8,13 7,97 7,57 8,41 8,44 8,42 2010 2011 2012 2013 2014 2015 2016 2017 2018 Australia 2.15 1.97 1.75 1.40 1.05 0.92 0.98 0.89 0.93 Brunei Darussalam 6.87 6.03 5.38 4.53 3.85 4.00 4.75 3.66 3.92 Canada 1.19 0.84 0.65 0.57 0.52 0.52 0.60 0.45 0.00 Chile 2.69 2.35 2.16 2.11 2.06 1.87 1.83 1.92 1.87 Japan 1.40 1.20 1.10 Malaysia 3.35 2.68 2.02 1.85 1.65 1.60 1.61 1.55 1.46 Mexico 2.04 2.12 2.44 3.24 3.04 2.52 2.09 2.09 2.05 New Zealand 0.00 0.00 0.00 3.23 2.98 2.71 2.54 2.31 1.94 Peru 3.03 2.89 3.23 3.50 3.95 3.93 4.29 4.70 3.27 Singapore 1.41 1.06 1.04 0.87 0.76 0.92 1.22 1.40 1.31 Vietnam 2.09 2.79 3.44 3.11 2.94 2.34 2.28 1.82 2.02 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 18 Japan 5,48 5,52 5,82 5,47 5,38 5,51 Malaysia 9,38 8,89 9,39 9,59 9,95 10,46 11,00 11,24 11,24 Mexico 10,40 9,92 10,59 10,36 10,84 10,45 9,94 10,4 10,7 Peru 9,98 10,56 10,42 10,15 10,67 10,09 11,42 12,08 12,46 Singapore 8,97 8,32 8,92 8,22 8,41 9,00 9,23 9,18 9,03 Vietnam 8.87 9.30 9.93 9.54 8.77 8.26 7.77 7.36 8,40 Source: Authors' synthesis from IMF data (2018) Table 4.5: Banking system size and CAR ratio of CPTPP group in 2018 STT Nation The size of the banking system (billion USD) Credit / GDP ratio Capital Adequacy Ratio CAR 1 Canada 7.741 214,2% 14,81% 2 Australia 3.084 141% 14,55% 3 Singapore 925 128,2% 17,08% 4 Malaysia 609 50,8% 17,08% 5 Japan 574 160,8% 16,66% 6 Vietnam 436 130,7% 12,23% 7 New Zeland 348 172,9% 14,40% 8 Mexico 326 35,5% 15,57% 9 Chile 319 112,5% 13,76% 10 Peru 201 35,0% 15,22% 11 Brunei 16 39,5% 18,11% Source: Authors' synthesis from IMF data (2018) 19 4.2.2. The criteria for evaluating profitability Figure 4.2: ROA rates of CPTPP countries in the period 2010 - 2018 Unit: % Source: Authors' synthesis from IMF data (2018) 2010 2011 2012 2013 2014 2015 2016 2017 2018 Australia 0.0 1.2 1.2 1.4 1.2 1.4 0.8 1.2 1.3 Brunei Darussalam 1.7 1.2 0.7 1.4 1.7 1.6 1.4 1.5 1.7 Canada 1.1 1.1 1.1 1.1 1.1 1.0 1.0 1.1 1.2 Chile 1.7 1.6 1.4 1.5 1.5 1.3 1.2 1.3 1.3 Japan 0.4 0.3 0.3 0.3 0.3 Malaysia 1.5 1.5 1.6 1.5 1.5 1.2 1.3 1.4 1.4 Mexico 1.8 1.5 1.8 2.1 1.7 1.6 1.7 2.0 2.2 Peru 2.3 2.3 2.2 2.0 1.9 2.1 2.0 2.1 2.2 Singapore 1.4 1.1 1.4 1.2 1.1 1.2 1.1 1.3 1.2 Vietnam 1.6 1.5 0.8 0.6 0.3 0.5 0.5 0.7 0.9 0.0 0.5 1.0 1.5 2.0 2.5 20 Figure 4.3: ROE rates of CPTPP countries in the period 2010 - 2018 Unit: % Source: Authors' synthesis from IMF data (2018) 4.2.3. The level of development and the contribution of the banking industry to the economy Table 4.6: The ratio of broad money to GDP among countries in the CPTPP Unit: % Nation 2010 2011 2012 2013 2014 2015 2016 2017 2018 Australia 100,70 99,87 101,28 105,69 108,73 113,42 118,20 116,84 113,60 Singapore 123,28 126,18 128,91 130,17 129,81 124,44 131,35 129,69 122,65 Malaysia 129,65 133,89 136,80 140,09 137,10 134,93 130,35 124,25 126,64 Japan 217,69 228,08 231,43 235,34 237,35 236,07 242,38 247,87 252,10 Vietnam 114,85 99,80 106,47 117,03 127,55 137,65 151,10 155,28 158,27 2010 2011 2012 2013 2014 2015 2016 2017 2018 Australia 0.5 22.6 23.0 27.0 22.9 23.8 12.1 16.7 19.4 Brunei Darussalam 15.8 11.8 7.3 13.7 14.1 12.4 10.8 13.1 15.0 Canada 23.0 23.6 22.7 22.3 22.5 20.7 19.9 21.4 22.1 Chile 20.7 20.8 17.3 18.3 19.3 17.7 13.8 15.4 15.5 Japan 8.6 7.6 7.6 6.6 6.4 Malaysia 16.3 16.8 17.3 15.8 15.0 12.3 12.3 13.1 12.6 Mexico 16.8 15.5 17.5 19.3 15.9 15.4 16.3 19.6 20.9 Peru 22.9 23.5 21.5 19.9 18.2 21.1 19.2 17.7 17.8 Singapore 15.5 13.8 16.4 15.3 13.2 13.6 11.3 14.0 13.0 Vietnam 17.7 16.4 8.2 6.0 3.2 5.5 6.6 8.3 11.7 0.0 5.0 10.0 15.0 20.0 25.0 30.0 21 New Zeland 92,66 92,88 95,18 99,44 100,29 101,00 105,09 Mexico 30,63 30,70 31,33 32,97 34,46 36,45 37,82 38,80 37,86 Chile 66,76 74,97 77,64 62,58 82,49 83,84 82,93 78,52 76,89 Peru 38,76 38,16 40,86 11,13 11,20 11,03 10,77 10,56 47,25 Brunei 67,27 59,38 58,65 62,58 67,45 80,80 92,60 86,69 81,58 Source: Authors' synthesis from WB data (2018) Chart 4.4: Broad money growth rate of among countries in the CPTPP Unit: % Source: Authors' synthesis from WB data (2018) Table 4.7: The ratio of consumer credit to GDP among CPTPP countries Unit: % Nation 2010 2011 2012 2013 2014 2015 2016 2017 2018 Australia 125,50 122,33 121,28 124,98 128,73 136,59 142,52 140,90 139,59 2010 2011 2012 2013 2014 2015 2016 2017 2018 Australia 10.13 7.97 7.35 6.75 7.03 5.98 6.67 4.52 2.37 Brunei Darussalam 4.81 10.05 0.90 1.47 3.22 -1.76 1.51 -0.44 2.84 Chile 7.71 22.88 10.30 11.74 8.74 9.15 4.90 0.56 4.18 Japan 1.78 2.90 2.20 3.45 2.94 3.02 3.91 3.47 2.43 Malaysia 7.35 14.63 8.85 7.40 6.30 3.04 2.80 4.64 7.69 Mexico 12.76 9.98 10.08 8.28 12.19 12.19 12.33 11.22 5.46 New Zealand 8.36 6.71 9.85 7.66 7.30 6.42 Peru 21.05 11.74 15.02 16.48 6.27 13.39 2.76 9.33 5.43 Singapore 8.59 9.99 7.23 4.32 3.33 1.52 8.04 3.20 3.86 Vietnam 29.71 11.94 24.54 21.40 19.74 14.91 17.88 14.26 12.70 -5.00 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 T ố c đ ộ t ăn g t ư ở n g 22 Singapore 123,28 126,18 128,91 125,34 129,50 124,00 127,43 128,21 121,90 Malaysia 129,64 133,89 136,80 119,79 120,53 125,02 123,77 118,73 121,79 Japan 217,69 228,08 231,43 104,05 103,57 101,95 103,61 106,67 107,89 Vietnam 114,85 99,80 106,46 96,80 100,30 111,93 123,82 130,72 133,31 New Zeland 92,66 140,53 141,19 144,22 144,34 142,35 146,55 Mexico 30,63 30,70 31,33 22,20 21,94 23,89 25,93 27,02 26,78 Chile 66,76 74,97 77,64 76,19 78,60 78,65 79,80 78,62 81,27 Peru 38,76 38,16 40,86 37,71 40,84 43,84 42,87 42,33 43,97 Brunei 67,27 59,38 58,65 30,92 32,91 41,14 43,57 38,81 34,40 Source: Authors' synthesis from WB, IMF data (2018) Table 4.8: Commercial bank branch density distribution in the CPTPP (calculated on average per 100,000 people) STT Nation 2010 2011 2012 2013 2014 2015 2016 2017 2018 1 Australia 30,75 30,41 30,93 30,12 29,14 28,73 27,72 29,61 28,19 2 Brunei Darussalam 23,28 23,16 22,71 22,93 20,26 20,84 19,59 18,36 17,18 3 Canada 24,05 24,18 24,3 23,32 23,14 22,84 22,27 20,79 20,05 4 Chile 17,45 17,35 17,27 17,1 16,87 16,14 15,77 14,85 14,03 5 Japan 33,82 33,9 33,95 33,9 33,89 34,14 34,1 34,03 34,07 6 Malaysia 10,92 11,24 11,14 10,95 10,79 10,67 10,42 10,22 10,25 7 Mexico 14,59 14,62 15,5 15,44 15,34 14,57 14,64 14,62 14,43 8 New Zealand 34,49 33,91 33,31 30,82 29,55 28,96 29,71 27,3 26,78 9 Peru 6,87 7,12 7,75 7,92 8,24 8,37 8,18 7,72 7,36 10 Singapore 10,22 9,96 9,77 9,44 9,32 9,26 8,98 8,49 8,36 11 Vietnam 3,21 3,54 3,11 3,65 3,83 3,76 3,83 3,45 3,91 Source: Authors' synthesis from WB data (2018) Table 4.9: Density of ATMs distributed in CPTPP countries (calculated on average per 100,000 people) Quốc gia 2010 2011 2012 2013 2014 2015 2016 2017 2018 Australia 168,66 170,47 164,64 161,09 165,13 163,79 167,59 160,22 146,03 Brunei Darussalam 82,01 80,71 92,5 82,56 81,35 79,25 75,86 69,17 74,13 23 Canada 218,16 207,57 203,32 220,94 221,81 221,46 224,08 228,87 220,6 Chile 61,98 64,98 67,88 64,16 56,85 56,07 53,4 51,79 49,91 Japan 130,93 128,58 127,9 128,3 127,49 127,65 127,8 127,77 127,59 Malaysia 53,52 53,56 53,21 54,58 52,3 51,18 48,89 47,53 46,63 Mexico 45,36 45 48,85 48,73 50,39 52,78 54,42 55,41 58,58 New Zealand 72,22 76,37 74,81 72,37 70,7 69,28 66,04 65,88 64,66 Peru 26,86 31,43 38,8 41,76 56,57 122,78 111,54 109,02 114,66 Singapore 61,51 62 61,39 60,27 59,5 60,02 57,75 65,16 66,46 Vietnam 17,03 19,6 20,69 21,88 22,71 23,76 24,26 24,58 25,28 Source: Authors' synthesis from WB data (2018)) Table 4.10: Credit information depth index of countries in the CPTPP (0=low to 8=high) STT Quốc gia 2013 2014 2015 2016 2017 2018 1 Australia 7 7 7 7 7 7 2 Brunei Darussalam 5 5 6 7 7 8 3 Canada 8 8 8 8 8 8 4 Chile 7 7 7 7 7 7 5 Japan 6 6 6 6 6 6 6 Malaysia 7 7 7 8 8 8 7 Mexico 8 8 8 8 8 8 8 New Zealand 7 8 8 8 8 8 9 Peru 8 8 8 8 8 8 10 Singapore 7 7 7 7 7 7 11 Vietnam 6 6 7 7 7 7 Source: Authors' synthesis from WB data (2018) 24 4.2.4. Network works Chart 4.5: Average number of commercial bank branches in CPTPP (calculated on average per 100,000 people) Source: Authors' synthesis from WB data (2018) 2010 2011 2012 2013 2014 2015 2016 2017 2018 Australia 30.75 30.41 30.93 30.12 29.14 28.73 27.72 29.61 28.19 Brunei Darussalam 23.28 23.16 22.71 22.93 20.26 20.84 19.59 18.36 17.18 Canada 24.05 24.18 24.3 23.32 23.14 22.84 22.27 20.79 20.05 Chile 17.45 17.35 17.27 17.1 16.87 16.14 15.77 14.85 14.03 Japan 33.82 33.9 33.95 33.9 33.89 34.14 34.1 34.03 34.07 Malaysia 10.92 11.24 11.14 10.95 10.79 10.67 10.42 10.22 10.25 Mexico 14.59 14.62 15.5 15.44 15.34 14.57 14.64 14.62 14.43 New Zealand 34.49 33.91 33.31 30.82 29.55 28.96 29.71 27.3 26.78 Peru 6.87 7.12 7.75 7.92 8.24 8.37 8.18 7.72 7.36 Singapore 10.22 9.96 9.77 9.44 9.32 9.26 8.98 8.49 8.36 Vietnam 3.21 3.54 3.11 3.65 3.83 3.76 3.83 3.45 3.91 0 5 10 15 20 25 30 35 40 25 Figure 4.6: Average number of ATMs in CPTPP countries (calculated on average per 100,000 people) Source: Authors' synthesis from WB data (2018) 4.3. Results of experimental research 4.3.1. The results of the estimation of the competitiveness regression model of Vietnamese commercial banks 4.3.1.1. Descriptive statistics of research variables Table 4.11: Descriptive statistics table of the variables in the Lerner model Variable Observation Mean Standard deviation Min Max Dependent variable 2010 2011 2012 2013 2014 2015 2016 2017 2018 Australia 168.66 170.47 164.64 161.09 165.13 163.79 167.59 160.22 146.03 Brunei Darussalam 82.01 80.71 92.5 82.56 81.35 79.25 75.86 69.17 74.13 Canada 218.16 207.57 203.32 220.94 221.81 221.46 224.08 228.87 220.6 Chile 61.98 64.98 67.88 64.16 56.85 56.07 53.4 51.79 49.91 Japan 130.93 128.58 127.9 128.3 127.49 127.65 127.8 127.77 127.59 Malaysia 53.52 53.56 53.21 54.58 52.3 51.18 48.89 47.53 46.63 Mexico 45.36 45 48.85 48.73 50.39 52.78 54.42 55.41 58.58 New Zealand 72.22 76.37 74.81 72.37 70.7 69.28 66.04 65.88 64.66 Peru 26.86 31.43 38.8 41.76 56.57 122.78 111.54 109.02 114.66 Singapore 61.51 62 61.39 60.27 59.5 60.02 57.75 65.16 66.46 17.03 19.6 20.69 21.88 22.71 23.76 24.26 24.58 25.28 0 50 100 150 200 250 26 Lerner 271 0.1794 0.0888 -0.4789 0.4366 Independent variables Lerner1 270 0.1790 0.0853 -0.5039 0.4354 ETA 271 18.3747 1.1685 15.9227 20.9956 B_SIZE 271 0.0949 0.0429 0.0326 0.2554 LTA 271 0.5437 0.1319 0.1473 0.8075 HDV 271 0.6362 0.1348 0.2508 0.8937 LLP 271 -0.0254 0.0724 -0.6766 0.0397 HHI 271 0.2945 0.2651 -2.7370 0.4995 GroTA 271 1.8372 23.8438 -0.9282 392.8397 FS1 271 0.2000 0.0216 0.1837 0.2391 FS2 271 0.1027 0.0057 0.0954 0.1130 GDP 271 0.0624 0.0058 0.0525 0.0708 INF 271 0.0661 0.0635 -0.0019 0.2126 Source: Author's calculations from STATA software 4.3.1.2. Analyze correlation among research variables Table 4.12: Correlation among variables in the competitive estimation model Source: Author's calculations from STATA software Lerner Lerner1 B_SIZE ETA LTA HDV LLP HHI GroTA FS1 FS2 GDP INF Lerner 1 Lerner1 0.6489 1 B_SIZE -0.0248 -0.0603 1 ETA 0.3903 0.3612 -0.725 1 LTA 0.1571 0.0695 -0.0957 0.3225 1 HDV 0.2612 0.2152 0.1413 -0.0265 0.02 1 LLP -0.2472 -0.0736 -0.3121 0.4045 0.6247 -0.196 1 HII 0.4526 0.0307 -0.1007 0.2545 0.1626 -0.0273 0.2233 1.0000 GroTA 0.0826 0.1061 0.0034 0.0259 -0.0517 0.0312 -0.104 0.0035 1 FS1 0.0613 -0.0892 -0.235 0.2243 0.2775 -0.0214 0.1936 -0.168 -0.0411 1 FS2 0.0602 0.0549 0.1804 -0.1802 -0.2706 0.0455 -0.1943 0.0258 -0.0318 -0.52 1 GDP 0.0791 -0.0402 -0.265 0.1896 0.2418 -0.0414 0.1289 -0.145 -0.1124 0.6356 -0.4601 1 INF 0.1665 0.296 0.2556 -0.2278 -0.2967 0.3075 -0.594 0.1964 0.0463 -0.2787 0.147 -0.254 1 27 4.3.1.3. The measurement results impact factors on the competitiveness of Vietnamese commercial banks Figure 4.7: Average Lerner index of Vietnamese commercial banks for the period 2010 - 2018 Source: Author's calculations from financial statements of commercial banks Chart 4.8: Average Lerner index of Vietnamese commercial banks by ownership Source: Author's calculations from financial statements of commercial banks Table 4.13: Summary of regression results for the competitiveness measurement model 24% 19% 17% 16% 16% 14% 15% 17% 19% 2010 2011 2012 2013 2014 2015 2016 2017 2018 LERNER bình quân 23% 18% 15% 14% 15% 12% 13% 16% 18% 29% 24% 23% 24% 24% 23% 24% 23% 23% 2010 2011 2012 2013 2014 2015 2016 2017 2018 NHTM cổ phần NHTM Nhà nước 28 Variable OLS FEM REM GLS GMM_Lerner Lerner1 0.4893*** 0.2303*** 0.4893*** 0.4893*** 0.2291*** ETA 0.5882*** 0.7083*** 0.5882*** 0.5882*** 0.5825*** BANKSIZE 0.0296*** 0.0564*** 0.0296*** 0.0296*** 0.0386*** LTA 0.0456 0.0579 0.0456 0.0456 0.2469** LLP -0.1136** -0.0694 -0.1136** -0.1136** -0.1456*** HDV -0.0253 -0.1477*** -0.0253 -0.0253 -0.1469** HHI 0.1242*** 0.1419*** 0.1242*** 0.1242*** 0.1290*** GroTA 0.0001 0.0001 0.0001 0.0001 0.0001*** FS1 0.1529 -0.0274 0.1529 0.1529 0.1923* FS2 1.8311*** 2.0381*** 1.8311*** 1.8311*** 1.9301*** GDP 1.4103* 1.3059* 1.4103* 1.4103* -1.2536* INF 0.1536** 0.1959** 0.1536** 0.1536** 0.1383** Original 0.0264** 0.0464** 0.0264** 0.0264** 0.0490** _cons -0.7380*** -1.0991*** -0.7380*** -0.7380*** -0.7264*** Number of observations 271 Number of groups 31 Variable tools 26 Mean VIF 1.94 F-test F test that all u_i=0: F(30, 228) = 3.02 Prob > F = 0.0000 Hausman test Test: Ho: difference in coefficients not systematic chi2(12) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 878.20 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) 29 Breusch Pagar test Test: Var(u) = 0 chibar2(01) = 0.00 Prob > chibar2 = 1.0000 Wooldridge test Wooldridge test for autocorrelation in panel data H0: no first order autocorrelation F( 1, 30) = 41.221 Prob > F = 0.0000 AR(2) Pr > z = 0.314 Sargan test Prob > chi2 = 0.523 Hansen test Prob > chi2 = 0.524 Note: The symbols (***), (**), (*) represent the statistical significance of 1%, 5%, 10%, respectively. Source: Author's calculations from STATA software 4.3.2. The results of the regression of the stability model of Vietnamese commercial banks 4.3.2.1. Descriptive statistics of the variables Table 4.14: Statistical table describing the variables in the stability measurement model Variable Observation Mean Standard deviation Min Max Dependent variable ZscoreMH2 271 12.6665 5.6891 0.9030 33.9246 Independent variable Zscore1 271 12.9026 5.6303 0.9030 33.9246 ETA 271 0.0949 0.0429 0.0326 0.2554 B_SIZE 271 18.3747 1.1685 15.9227 20.9956 LTA 271 0.5437 0.1319 0.1473 0.8075 HDV 271 0.6362 0.1348 0.2508 0.8937 LLP1 271 0.0338 0.0688 0.0025 0.6766 HHI 271 0.2945 0.2651 -2.7370 0.4995 GroTA 271 1.8372 23.8438 -0.9282 392.8397 30 FS1 271 0.2000 0.0216 0.1837 0.2391 FS2 271 0.1027 0.0057 0.0954 0.1130 GDP 271 0.0624 0.0058 0.0525 0.0708 INF 271 0.0661 0.0635 -0.0019 0.2126 Original 271 0.1661 0.3728 0.0000 1.0000 Source: Author's calculations from STATA software 4.3.2.2. Correlation between variables in the research model Table 4.15: Correlation among variables in the Zscore estimation model ZscoreMH2 Zscore1 ETA B_SIZE LTA LLP1 HDV HHI GroTA FS1 FS2 GDP INF Original ZscoreMH2 1 Zscore1 0.7801 1 ETA 0.986 0.8008 1 B_SIZE -0.6803 -0.6707 -0.725 1 LTA -