The impact of credit growth on profitability and credit risk the case of vietnamese commercial banks

MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM BANKING UNIVERSITY OF HO CHI MINH CITY NGUYEN TRAN THANH TRUC THE IMPACT OF CREDIT GROWTH ON PROFITABILITY AND CREDIT RISK: THE CASE OF VIETNAMESE COMMERCIAL BANKS BACHELOR THESIS PROPOSAL MAJOR: FINANCE – BANKING NUMBER: 7340201 HCMC, NOVEMBER 2021 MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM BANKING UNIVERSITY OF HO CHI MINH CITY NGUYEN TRAN THANH TRUC THE IMPACT OF CREDIT GROWTH ON PROFITABILITY AND CREDIT RISK: THE CASE OF VIETNAMESE COMMERCIAL BANKS BACHELOR THESIS PROPOSAL MAJOR: FINANCE – BANKING NUMBER: 7340201 INSTRUCTOR: DR NGUYEN THI HONG VINH HCMC, NOVEMBER 2021 ABSTRACT The objectives of this study are to: (i) Study the impact of bad debt on profitability and credit risk of Vietnamese commercial banks (ii) Determination of effective credit growth threshold Research using GMM dynamic panel data estimation to evaluate the impact of credit growth on the performance of Vietnamese commercial banks in the period 2012-2020 And Hansen's PTR threshold model (1999) to estimate credit growth threshold The research results show that credit growth with a lag of to years has a positive impact on ROA and has a positive effect on NPL This positive relationship shows that lending is still a driving force for profitability of Vietnamese commercial banks and an excessive increase in outstanding loans will lead to a narrower amount of loanable credit and an increase in bad debt The study also finds the optimal credit growth threshold at which the initial credit growth is also positive with ROA but beyond that point will be opposite In addition, the thesis has made important contributions to Vietnam's policy makers in stabilizing the banking system as well as to bank administrators in controlling credit growth well to increase profits and minimize risks Keywords: Credit growth, credit growth threshold, Vietnamese commercial banks i PROMISE The author declares that this thesis has never been submitted for a doctorate degree at any university This thesis is the author's own research work, the research results are honest, in which there are no previously published contents or contents made by others except the cited sources fully in the thesis The author assumes full responsibility for his honour statement Ho Chi Minh City, November 29, 2021 Author Nguyễn Trần Thanh Trúc ii ACKNOWLEDGEMENTS First of all, I would like to express my deep gratitude to Dr Nguyen Thi Hong Vinh for her dedicated guidance and wholehearted support in assisting me in performing estimation techniques, providing research data, correcting errors even though she is busy with work but always help me immediately when I need it And importantly, her research is a great source of inspiration and encouragement because of the thoughtfulness, seriousness, and insight I recognize in each of her research papers I would also like to express my gratitude from the bottom of my heart to my mother who sacrificed her time to take care of me, who was always busy from early morning to late evening so that I could have precious time to this thesis and not have to worry about anything other than focusing on the thesis I would also like to thank every lecturer at Banking University of Ho Chi Minh City, whether I have studied face-to-face or only read through books The valuable knowledge the teachers teach will follow me for the rest of my life I would like to thank my friends who studied at Banking University, especially Phong, I learned a lot of virtues from you, enthusiasm, patience, curiosity, both competition and support over a four-year period Finally, I thank the people who did not appear in my life directly, but have provided me with immense spiritual support: Zen master Thich Nhat Hanh, Jared Diamond author of Guns, Germs and Steel, Elon Musk CEO of SpaceX and Tesla, Megan Rapinoe American professional soccer player And most of all, I am extremely happy in every moment of my life, including the difficult period when doing the thesis, because I have learned the teachings of the Buddha iii LIST OF ACRONYMS GDP Gross domestic product GMM Generalized method of moments IMF International Monetary Fund PTR Panel Threshold Regression FEM Fixed Effect Model REM Random Effect Model SOCB State-Owned Commercial Bank JSCB Joint Stock Commercial Bank SBV State Bank of Vietnam iv LIST OF TABLES Table 2.1 Previous research summary 22 Table 2.2 Summarizing the formula of variables and the sign expectation 24 Table 4.1 Number of Vietnamese banks over time 40 Table 4.2 Statistical table describing the variables 58 Table 4.3 Correlation analysis matrix 59 Table 4.4 Multicollinearity check 60 Table 4.5 Estimated results of the dependent variable ROA by GMM method 61 Table 4.6 Results of estimating the dependent variable NPL by GMM method 66 Table 4.7 Estimate threshold CGR dependent variable ROA 71 Table 4.8 Regression results of the dependent variable ROA 71 Table 4.9 Estimate threshold CGR dependent variable NPL 72 Table 4.10 Regression results of the dependent variable NPL 72 v LIST OF FIGURES Figure 2.1 Supply curve shift 16 Figure 2.2 Demand curve shift 17 Figure 2.3 Productivity shift 18 Figure 4.1 Asset size of Vietnamese joint stock commercial banks 41 Figure 4.2 Deposit amount and deposit growth rate 44 Figure 4.3 Classify customer deposits by term 45 Figure 4.4 Bank profits over the years 45 Figure 4.5 Outstanding loans and credit growth of the bank over the years 48 Figure 4.6 ROA of joint stock commercial banks over the years 49 Figure 4.7 ROE of joint stock commercial banks over the years 51 Figure 4.8 NIM of joint stock commercial banks over the years 52 Figure 4.9 Non-performing loan of Vietnam's banking industry over the years 54 Figure 4.10 Structure of credit balance by industry 55 Figure 4.11 Provision ratio of Vietnam's banking industry over the years 57 Figure 4.11 Non-linear model testing 70 vi LIST OF APPENDIXS Appendix1: Estimation results of GMM dependent variable ROA model A Appendix 2: Result of GMM estimation of dependent variable ROA model B Appendix 3: Result of GMM estimation of dependent variable NPL model C Appendix 4: Estimation results of GMM dependent variable NPL model D Appendix 5: Threshold model checks for non-linearity between ROA and CGR^2 E Appendix 6: Threshold model checks for nonlinearity between NPL and CGR^2 F Appendix 7: Credit growth threshold with dependent variable ROA G Appendix 8: Credit growth threshold with dependent variable NPL H vii TABLE OF CONTENT ABSTRACT i PROMISE ii ACKNOWLEDGEMENTS iii LIST OF ACRONYMS iv LIST OF TABLES v LIST OF FIGURES vi CHAPTER 1.1 The importance of research topic 1.2 Relevant research situation and research problem 1.3 Research Objectives 1.4 Research Scope and Subject 1.5 Research Methods 1.6 New contributions of the study 1.7 Research process and structure of the thesis CHAPTER 2.1 Concept of credit growth, profitability and credit risk 2.1.1 Concept of credit growth 2.1.2 Concept of profitability and credit risk 10 2.2 Theoretical basis of credit growth 13 2.2.1 Theory of macroeconomic factors 13 2.2.2 Theory of bank-specific factors 15 2.3 Review of previous studies 19 CHAPTER3 23 3.1 Research models 23 3.1.1 Model of the impact of credit growth on bank profitability 26 3.1.2 Model of the impact of credit growth on credit risk 31 3.1.3 Credit growth threshold model 34 3.2 Research Methods 34 3.3 Research data 35 3.4 Data collection source 35 viii but due to the limitation of Research time, research data and research methods cannot be avoided from the following restrictions: Firstly, the research has not approached the annual credit quota assigned by the State Bank to each commercial bank, so it is impossible to know in that fiscal year whether the commercial banks have used up their credit limit yet thereby giving more accurate assessment of credit growth or abnormal credit growth of commercial banks Secondly, the thesis has not approached the dynamic table threshold model to accurately evaluate the threshold factor of credit growth with lag to synchronize with the variable GMM method with lag The next research direction when there are conditions and research data will be carried out, which is: (i) Expanding the research scope, not only Vietnamese commercial banks but also comparable with commercial banks in other countries in the region area (ii) Credit growth analysis based on abnormal credit growth (iii) Applying dynamic panel threshold model to find the threshold of credit growth with different lagged values 77 REFERENCES Fahlenbrach, R., Prilmeier, R., & Stulz, R M (2018) Why does fast loan growth predict poor performance for banks? 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(2017) Global Innovation Index 2017 Cornell University APPENDICES Appendix 1: Result of GMM estimation of dependent variable ROA model Appendix1: Estimation results of GMM dependent variable ROA model Dynamic panel-data estimation, two-step system GMM Group variable: bank1 Time variable : year Number of instruments = 19 Wald chi2(9) = 1640.15 Prob > chi2 = 0.000 Number of obs Number of groups Obs per group: avg max Std Err z P>|z| = = = = = 120 20 6.00 roa Coef [95% Conf Interval] roa L1 .4755244 1143992 4.16 0.000 251306 6997428 cgr L1 L2 L3 -.0158286 0183665 0038947 0067373 0065697 0017704 -2.35 2.80 2.20 0.019 0.005 0.028 -.0290334 0054901 0004248 -.0026238 0312429 0073647 ta eta ldr gdp rel _cons -.0099913 0748548 0092989 -.1428727 -.0400778 5630434 0988532 0241551 0029702 0632984 0190724 2.115974 -0.10 3.10 3.13 -2.26 -2.10 0.27 0.919 0.002 0.002 0.024 0.036 0.790 -.2037401 0275116 0034774 -.2669353 -.077459 -3.584189 1837574 1221979 0151204 -.01881 -.0026967 4.710276 Warning: Uncorrected two-step standard errors are unreliable Instruments for first differences equation Standard D.(L3.roa L3.cgr L3.ta L3.eta ldr L.gdp L.rel) GMM-type (missing=0, separate instruments for each period unless collapsed) L(5/8).(L3.ta L3.eta ldr L4.gdp L3.rel) collapsed Instruments for levels equation Standard L3.roa L3.cgr L3.ta L3.eta ldr L.gdp L.rel _cons GMM-type (missing=0, separate instruments for each period unless collapsed) DL4.(L3.ta L3.eta ldr L4.gdp L3.rel) collapsed Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but -1.75 -0.57 overid restrictions: chi2(9) = 2.50 but not weakened by many instruments.) overid restrictions: chi2(9) = 5.91 weakened by many instruments.) Pr > z = Pr > z = 0.080 0.567 Prob > chi2 = 0.981 Prob > chi2 = 0.749 Difference-in-Hansen tests of exogeneity of instrument subsets: GMM instruments for levels iv(L3.roa L3.cgr L3.ta L3.eta ldr L.gdp L.rel) Appendix 2: Result of GMM estimation of dependent variable ROA model Appendix 2: Result of GMM estimation of dependent variable ROA model xtabond2 roa l.roa l.cgr l2.cgr l3.cgr ta eta ldr, gmm ( l4.ta l3.eta ldr, la > dr ) twostep Favoring speed over space To switch, type or click on mata: mata set matafavor Warning: Two-step estimated covariance matrix of moments is singular Using a generalized inverse to calculate optimal weighting matrix for two-step Difference-in-Sargan/Hansen statistics may be negative Dynamic panel-data estimation, two-step system GMM Group variable: bank1 Time variable : year Number of instruments = 20 Wald chi2(7) = 16830.72 Prob > chi2 = 0.000 Number of obs Number of groups Obs per group: avg max roa Coef Std Err roa L1 .7196861 0646274 cgr L1 L2 L3 .0044851 0048038 0074458 ta eta ldr _cons 0988837 0489054 0054446 -2.838192 z = = = = = 120 20 6.00 P>|z| [95% Conf Interval] 11.14 0.000 5930188 8463534 0022288 0010358 0016322 2.01 4.64 4.56 0.044 0.000 0.000 0001167 0027736 0042468 0088536 006834 0106449 0631863 0189034 0022008 1.330409 1.56 2.59 2.47 -2.13 0.118 0.010 0.013 0.033 -.0249591 0118555 0011312 -5.445746 2227266 0859553 009758 -.230638 Warning: Uncorrected two-step standard errors are unreliable Instruments for first differences equation Standard D.(L3.roa L2.cgr L3.ta L3.eta ldr) GMM-type (missing=0, separate instruments for each period unless collapsed) L(3/8).(L4.ta L3.eta ldr) collapsed Instruments for levels equation Standard L3.roa L2.cgr L3.ta L3.eta ldr _cons GMM-type (missing=0, separate instruments for each period unless collapsed) DL2.(L4.ta L3.eta ldr) collapsed Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but -2.29 -1.28 overid restrictions: chi2(12) = 30.23 but not weakened by many instruments.) overid restrictions: chi2(12) = 12.22 weakened by many instruments.) Pr > z = Pr > z = 0.022 0.201 Prob > chi2 = 0.003 Prob > chi2 = 0.428 Difference-in-Hansen tests of exogeneity of instrument subsets: GMM instruments for levels Hansen test excluding group: chi2(9) = 9.91 Prob > Difference (null H = exogenous): chi2(3) = 2.31 Prob > iv(L3.roa L2.cgr L3.ta L3.eta ldr) Hansen test excluding group: chi2(7) = 5.38 Prob > Difference (null H = exogenous): chi2(5) = 6.84 Prob > chi2 = chi2 = 0.358 0.511 chi2 = chi2 = 0.613 0.233 Appendix 3: Result of GMM estimation of dependent variable NPL model Appendix 3: Result of GMM estimation of dependent variable NPL model Dynamic panel-data estimation, two-step system GMM Group variable: bank1 Time variable : year Number of instruments = 20 Wald chi2(9) = 1420.39 Prob > chi2 = 0.000 npl Coef npl L1 Number of obs Number of groups Obs per group: avg max = = = = = 120 20 6.00 Std Err z P>|z| [95% Conf Interval] 422313 136282 3.10 0.002 1552052 6894208 cgr L1 L2 L3 .0496904 014951 027885 0135152 0032043 0077217 3.68 4.67 3.61 0.000 0.000 0.000 0232011 0086707 0127508 0761797 0212312 0430192 ta eta ldr gdp rel _cons 7461705 2541935 -.0147473 199923 069796 -18.4068 195632 0977359 0114804 0882517 0303581 4.729247 3.81 2.60 -1.28 2.27 2.30 -3.89 0.000 0.009 0.199 0.023 0.022 0.000 3627388 0626347 -.0372485 0269528 0102951 -27.67595 1.129602 4457524 0077538 3728931 1292968 -9.137643 Warning: Uncorrected two-step standard errors are unreliable Instruments for first differences equation Standard D.(L2.npl L2.cgr L2.ta L2.eta L.ldr L.gdp L.rel) GMM-type (missing=0, separate instruments for each period unless collapsed) L(4/8).(L3.ta L2.eta L4.ldr L3.gdp L4.rel) collapsed Instruments for levels equation Standard L2.npl L2.cgr L2.ta L2.eta L.ldr L.gdp L.rel _cons GMM-type (missing=0, separate instruments for each period unless collapsed) DL3.(L3.ta L2.eta L4.ldr L3.gdp L4.rel) collapsed Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Pr > z = Pr > z = 0.025 0.175 Prob > chi2 = 0.689 Prob > chi2 = 0.670 Difference-in-Hansen tests of exogeneity of instrument subsets: GMM instruments for levels Hansen test excluding group: chi2(6) = 1.96 Prob > chi2 = Difference (null H = exogenous): chi2(4) = 5.62 Prob > chi2 = iv(L2.npl L2.cgr L2.ta L2.eta L.ldr L.gdp L.rel) Hansen test excluding group: chi2(3) = 0.92 Prob > chi2 = 0.923 0.230 Sargan test of (Not robust, Hansen test of (Robust, but -2.24 1.36 overid restrictions: chi2(10) = 7.38 but not weakened by many instruments.) overid restrictions: chi2(10) = 7.57 weakened by many instruments.) 0.820 Appendix 4: Estimation results of GMM dependent variable NPL model Appendix 4: Estimation results of GMM dependent variable NPL model xtabond2 npl l.npl l.cgr l2.cgr l3.cgr ta eta ldr, gmm ( l3.ta l2.eta l3.ldr, > l.ldr) twostep Favoring space over speed To switch, type or click on mata: mata set matafavor Warning: Two-step estimated covariance matrix of moments is singular Using a generalized inverse to calculate optimal weighting matrix for two-ste Difference-in-Sargan/Hansen statistics may be negative Dynamic panel-data estimation, two-step system GMM Group variable: bank1 Time variable : year Number of instruments = 16 Wald chi2(7) = 1833.21 Prob > chi2 = 0.000 Number of obs Number of groups Obs per group: avg max npl Coef Std Err npl L1 .3722045 1423245 cgr L1 L2 L3 .0267103 010883 0266866 ta eta ldr _cons 3173461 1331558 -.004631 -7.206453 z = = = = = 120 20 6.00 P>|z| [95% Conf Interval] 2.62 0.009 0932537 6511554 006885 0034573 0106488 3.88 3.15 2.51 0.000 0.002 0.012 013216 0041067 0058153 0402047 0176593 0475579 1175997 0653649 0093706 2.129419 2.70 2.04 -0.49 -3.38 0.007 0.042 0.621 0.001 0868549 005043 -.0229971 -11.38004 5478373 2612685 013735 -3.032868 Warning: Uncorrected two-step standard errors are unreliable Instruments for first differences equation Standard D.(L2.npl L2.cgr L2.ta L2.eta L.ldr) GMM-type (missing=0, separate instruments for each period unless collapsed) L(4/8).(L3.ta L2.eta L3.ldr) collapsed Instruments for levels equation Standard L2.npl L2.cgr L2.ta L2.eta L.ldr _cons GMM-type (missing=0, separate instruments for each period unless collapsed) DL3.(L3.ta L2.eta L3.ldr) collapsed Arellano-Bond test for AR(1) in first differences: z = Arellano-Bond test for AR(2) in first differences: z = Sargan test of (Not robust, Hansen test of (Robust, but overid restrictions: chi2(8) = 12.60 but not weakened by many instruments.) overid restrictions: chi2(8) = 10.46 weakened by many instruments.) -1.50 0.46 Pr > z = Pr > z = 0.135 0.642 Prob > chi2 = 0.126 Prob > chi2 = 0.234 Difference-in-Hansen tests of exogeneity of instrument subsets: GMM instruments for levels iv(L2.npl L2.cgr L2.ta L2.eta L.ldr) Appendix 5: Threshold model checks for non-linearity between ROA and ���� Appendix 5: Threshold model checks for non-linearity between ROA and CGR^2 regress roa cgr cgr2,beta Source SS df MS Model Residual 3.19554643 59.2103378 177 1.59777321 334521683 Total 62.4058843 179 348636225 roa Coef cgr cgr2 _cons 0111042 -.0001417 6968131 Std Err .003788 0000482 0705535 t 2.93 -2.94 9.88 Number of obs F(2, 177) Prob > F R-squared Adj R-squared Root MSE 180 4.78 0.0095 0.0512 0.0405 57838 P>|t| Beta 0.004 0.004 0.000 3622756 -.3635743 utest cgr cgr2 Specification: f(x)=x^2 Extreme point: 39.17643 Test: H1: Inverse U shape vs H0: Monotone or U shape Interval Slope t-value P>|t| = = = = = = Lower bound Upper bound -52.10545 025873 3.088202 0011692 108.2034 -.019565 -2.538547 0059966 Overall test of presence of a Inverse U shape: t-value = 2.54 P>|t| = 006 Appendix 6: Threshold model checks for nonlinearity between NPL and CGR^2 Appendix 6: Threshold model checks for nonlinearity between NPL and CGR^2 regress npl cgr cgr2,beta Source SS df MS Model Residual 15.6031366 290.519021 177 7.80156831 1.6413504 Total 306.122158 179 1.71017965 npl Coef cgr cgr2 _cons -.0163414 0003185 2.071378 Std Err .0083907 0001067 1562814 t -1.95 2.99 13.25 Number of obs F(2, 177) Prob > F R-squared Adj R-squared Root MSE Beta 0.053 0.003 0.000 -.2407165 3689513 Specification: f(x)=x^2 Extreme point: 25.65169 Test: H1: U shape vs H0: Monotone or Inverse U shape Interval Slope t-value P>|t| -52.10545 -.0495351 -2.669214 004155 Overall test of presence of a U shape: t-value = 2.67 P>|t| = 00416 180 4.75 0.0098 0.0510 0.0402 1.2812 P>|t| utest cgr cgr2 Lower bound = = = = = = Upper bound 108.2034 0525895 3.080452 0011985 Appendix 7: Credit growth threshold with dependent variable ROA Appendix 7: Credit growth threshold with dependent variable ROA xthreg roa, rx(lcgr) qx(lcgr) thnum(1) trim(0.01) grid(400) bs(300) Estimating the threshold parameters: 1st Done Boostrap for single threshold + 50 + 100 + 150 + 200 + 250 + 300 Threshold estimator (level = 95): model Threshold Lower Upper Th-1 3.5454 3.5127 3.5587 Threshold effect test (bootstrap = 300): Threshold RSS MSE Fstat Prob Crit10 Crit5 Crit1 Single 28.6948 0.1678 22.99 0.0433 20.8479 22.2842 31.2085 Fixed-effects (within) regression Group variable: bank1 Number of obs Number of groups = = 180 20 R-sq: Obs per group: = avg = max = 9.0 within = 0.1329 between = 0.0069 overall = 0.0705 corr(u_i, Xb) F(2,158) Prob > F = -0.0370 Std Err t P>|t| = = 12.11 0.0000 roa Coef [95% Conf Interval] _cat#c.lcgr 0322705 -.081318 0122844 0251955 2.63 -3.23 0.009 0.002 0080076 -.1310814 0565334 -.0315547 _cons 8257005 0433525 19.05 0.000 7400753 9113256 sigma_u sigma_e rho 414311 42616008 48590467 (fraction of variance due to u_i) F test that all u_i=0: F(19, 158) = 8.10 Prob > F = 0.0000 Appendix 8: Credit growth threshold with dependent variable NPL Appendix 8: Credit growth threshold with dependent variable NPL xthreg npl, rx(lcgr) qx(lcgr) thnum(1) trim(0.01) grid(400) bs(400) Estimating the threshold parameters: 1st Done Boostrap for single threshold + 50 + 100 + 150 + 200 + 250 + 300 + 350 + 400 Threshold estimator (level = 95): model Threshold Lower Upper Th-1 4.2334 Threshold effect test (bootstrap = 400): Threshold RSS MSE Fstat Prob Crit10 Crit5 Crit1 Single 188.6854 1.1034 22.78 0.0400 15.3612 20.5826 28.7079 Fixed-effects (within) regression Group variable: bank1 Number of obs Number of groups = = 180 20 R-sq: Obs per group: = avg = max = 9.0 within = 0.0978 between = 0.0084 overall = 0.0708 corr(u_i, Xb) F(2,158) Prob > F = -0.0108 Std Err t P>|t| = = 8.57 0.0003 npl Coef [95% Conf Interval] _cat#c.lcgr -.0430795 4283154 0313995 1188969 -1.37 3.60 0.172 0.000 -.1050965 1934831 0189375 6631477 _cons 2.022311 1091182 18.53 0.000 1.806792 2.237829 sigma_u sigma_e rho 72711513 1.1082338 3009294 (fraction of variance due to u_i) F test that all u_i=0: F(19, 158) = 3.82 Prob > F = 0.0000 ... first and second research objective is to analyze the impact of credit growth on the profitability and credit risk of banks, based on the fundamental theory of credit supply and demand of Kenton... FRAMEWORK AND PREVIOUS RESEARCH OVERVIEW ON CREDIT GROWTH RATE OF COMMERCIAL BANK 2.1 Concept of credit growth, profitability and credit risk 2.1.1 Concept of credit growth The term credit growth. .. representative of the group of commercial banks in Vietnam At the same time, the thesis focuses on the main target groups: (i) Credit growth and its impact on profitability of Vietnamese commercial banks