Factor affecting bank profitability of commercial banks in vietnam from 2008 to 2020

MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM BANKING UNIVERSITY OF HO CHI MINH CITY TIEU QUOC PHONG FACTORS AFFECTING BANK PROFITABILITY OF COMMERCIAL BANKS IN VIETNAM FROM 2008 TO 2020 BACHERLOR THESIS MAJOR: FINANCE – BANKING NUMBER: 7340201 HCMC, SEPTEMBER 2021 MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM BANKING UNIVERSITY OF HO CHI MINH CITY TIEU QUOC PHONG FACTORS AFFECTING BANK PROFITABILITY OF COMMERCIAL BANKS IN VIETNAM FROM 2008 TO 2020 BACHERLOR THESIS MAJOR: FINANCE – BANKING NUMBER: 7340201 INSTRUCTOR: PH.D NGUYEN THI NHU QUYNH HCMC, SEPTEMBER 2021 ACKNOWLEDGEMENT It is my radiant sentiment to place on record my best regards, deepest sense of gratitude to my supervisor at Banking University in Ho Chi Minh City - Ms Nguyen Thi Nhu Quynh for her patience, motivation, enthusiasm, and immense knowledge She has wholeheartedly helped and guided me in the process of writing this thesis I choose this moment to acknowledge her contribution as she has been supportive of my study and has actively provided me with enormous information to achieve the goal Also I would like to thank all of my friends, who always support and help me through the study together with the inspiration they gave me Author Tieu Quoc Phong TABLE OF CONTENTS LIST OF ABBREVIATIONS i LIST OF TABLES ii CHAPTER INTRODUCTION .1 1.1 THE NECESSITY OF THE RESEARCH 1.2 RESEARCH OBJECTIVES 1.2.1 General objective 1.2.2 Specific objectives 1.3 RESEARCH QUESTIONS 1.4 SUBJECTS AND THE SCOPE OF THE RESEARCH .2 1.4.1 Research subject 1.4.2 Research scope 1.5 DATA AND RESEARCH METHODOLOGY 1.5.1 Data 1.5.2 Methodology 1.6 THE THESIS CONTRIBUTIONS .4 1.6.1 Practical implications 1.6.2 Academy implications 1.7 STRUCTURE OF THE THESIS SUMMARY OF CHAPTER CHAPTER THEORETICAL BASIC AND LITERATURE REVIEW 2.1 THEORETICAL FRAMEWORK 2.1.1 Overview of commercial bank 2.1.2 Functions of commercial banks 2.1.3 Measure bank profitability 2.2 EMPIRICAL STUDIES REVIEW .9 2.2.1 Foreign research 2.2.2 Domestic research 11 2.3 RESEARCH GAP .12 SUMMARY OF CHAPTER 12 CHAPTER DATA AND METHODOLOGY 14 3.1 RESEARCH METHODOLOGY 14 3.2 RESEARCH MODELS .15 3.2.1 Research model .16 3.2.2 Dependent variables 16 3.2.3 Independent variable .19 3.3 RESEARCH DATA 25 SUMMARY OF CHAPTER 25 CHAPTER EMPIRICAL RESULTS 27 4.1 DATA DESCRIPTION .27 4.2 CORRELATION MATRIX 28 4.3 REGRESSION MODELS 30 4.3.1 Model – Dependent variable: ROE 30 4.3.2 Model – Dependent variable: ROA 34 4.3.3 Model – Dependent variable: NIM 38 4.4 DISCUSSION 41 4.4.1 Bank size (SIZE) 41 4.4.2 Capital ratio (CAP) .42 4.4.3 Liquidity (LOAN) 42 4.4.4 Deposit ratio (DEP) .43 4.4.5 Gross domestic product (GDP) .44 4.4.6 Inflation rate (INF) 44 4.4.7 Credit growth rate (CGR) .45 SUMMARY OF CHAPTER 45 CHAPTER CONCLUSIONS AND RECOMMENDATIONS 47 5.1 CONCLUSIONS .47 5.2 RECOMMENDATIONS 48 5.3 RESEARCH LIMITATIONS AND THE NEXT SUGGEST STUDY 49 REFERENCES iii APPENDIX iv i LIST OF ABBREVIATIONS Abberviation Meaning FEM Fixed effect model REM Random effect model GDP Gross domestic product ROE Return on equity ROA Retuen on asset NIM Net interest margin Pooled OLS Pooled Ordinary Least Squares VIF Variance Inflation Factor INF Inflation FGLS Feasible Generalized Least Squares ii LIST OF TABLES Table 3.1 Summarizing the formula of variables and the sign expectation .23 Table 4.1 Statistical table describing the variables 27 Table 4.2 Correlation analysis matrix 29 Table 4.3 Fixed effect model of ROE 30 Table 4.4 Random effect model (REM) of ROE .31 Table 4.5 Chi-test valued of FEM and REM of ROE 32 Table 4.6 Autocorrelation diagnostics of ROE 32 Table 4.7 Multicollinearity diagnostics of ROE 33 Table 4.8 Heteroskedasticity diagnostics of ROE 33 Table 4.9 Model fix of ROE .34 Table 4.10 Fixed effect model (FEM) of ROA 34 Table 4.11 Random effect model (REM) of ROA 35 Table 4.12 Model choice beetwen FEM and REM of ROA 35 Table 4.13 Autocorrelation diagnostics of ROA 36 Table 4.14 Multicollinearity diagnostics of ROA 36 Table 4.15 Heteroskedasticity diagnostics of ROA 37 Table 4.16 Model fix of ROA 37 Table 4.17 Fixed effect model (FEM) of NIM 38 Table 4.18 Random effect model (REM) of NIM 38 Table 4.19 Model choice beetwen FEM and REM of NIM .39 Table 4.20 Autocorrelation diagnostics of NIM 39 Table 4.21 Multicollinearity diagnostics of NIM 40 Table 4.22 Heteroskedasticity diagnostics of NIM 41 Table 4.23 Model fix of NIM 41 CHAPTER INTRODUCTION 1.1 THE NECESSITY OF THE RESEARCH One of the characteristics of banks as financial intermediaries in the economy they collected money from the surplus unit and distribute to deficit units, the bank makes a profit from the difference in interest rates between the two financial products And the goal of a bank like all other companies is to maximize profits and minimize costs and possible risks But since Vietnam joined the World Trade Organization (WTO) in 2007, the competition of foreign banks in the market has increased, affecting the profitability of domestic banks In addition, financial institutions, fintech companies that have been growing rapidly in recent years contribute to the amount of credit available in the financial system Furthermore, Vietnamese authorities have also taken steps to liberalize the banking system by lifting interest rate ceilings, phasing out direct lending, and slowly opening capital accounts As the banking restructuring process is still ongoing, it is difficult to conclude how it might affect the performance of the Vietnamese banking system On the other hand, healthy and sustainable profitability is one of the main predictors of financial distress and banking crisis (Demirgỹỗ-Kunt & Detragiache 2000) From the perspective of emerging markets, this article is expected to find out the determinants of bank profitability in Vietnam In addition, examining the factors driving the profitability of banks is an important tool for banking regulators as it aids in prudential analysis From those reasons, the author chooses the topic of analyzing the factors affecting bank profitability to better understand the basic operations of the bank 1.2 RESEARCH OBJECTIVES 1.2.1 General objective The general objective of the study is to focus on analyzing factors affecting the profitability of commercial banks in Vietnam From the research results, the thesis suggests some recommendation to improve the profitability of commercial banks in Vietnam 1.2.2 Specific objectives To achieve the general objective, the thesis conducts these specific objectives following: Find out the factors that can affect the profitability of commercial banks The research assesses the factors affect the profitability of commercial bank in Vietnam The research suggests some recommendations to improve banking profitability 1.3 RESEARCH QUESTIONS Suggests the following research questions: What are the factors affecting the profitability of commercial banks in Vietnam? How is the impact level and direction of the factors on the profitability of commercial bank? What recommendations are there to improve the profitability of commercial banks in Vietnam? 1.4 SUBJECTS AND THE SCOPE OF THE RESEARCH 1.4.1 Research subject The thesis analyzes the factors affecting profitability of commercial banks in Vietnam 1.4.2 Research scope There are a total of 31 commercial banks in Vietnam, but some of them not publish enough data so the thesis choose 25 banks with enough data and according to statistics of the State Bank, as of December 31, 2020, the total assets of commercial banks in Vietnam account for more than 80% of the banking system, so these 25 banks ensure the representativeness of the banking system The time scope: The thesis focuses on analyzing the banking performance’s efficiency commercial banks from 2008 to 2020 Because this is the period that witnessed the competition for profits was very fierce among commercial banks in 51 Barnor, Charles, and Theodora Akweley Odonkor "Capital adequacy and the performance of Ghanaian banks." 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Central Bank Review 20.2 (2020): 65-73 56 APPENDIX 01 List of commercial banks selected for the research ABB An Binh Commercial Joint Stock Bank ACB Asia Commercial Joint Stock Bank BID Joint Stock Commercial Bank for Investment and Development of Vietnam CTG Vietnam Joint Stock Commercial Bank for Industry and Trade EIB Vietnam Commercial Joint Stock Export Import Bank BVB Viet Capital Commercial Joint Stock Bank HDB Ho Chi Minh City Development Joint Stock Commercial Bank KLB Kien Long Commercial Joint Stock Bank 10 LPB Lien Viet Post Joint Stock Commercial Bank 11 MBB Military Commercial Joint Stock Bank 12 MSB Vietnam Maritime Commercial Join Stock Bank 13 NAB Nam A Commercial Joint Stock Bank 14 NVB National Citizen Commercial Joint Stock Bank 15 OCB Orient Commercial Joint Stock Bank 16 PGB Petrolimex Group Commercial Joint Stock Bank 18 SSB Southeast Asia Commercial Joint Stock Bank 19 SGB Saigon Bank For Industry And Trade 20 SHB Saigon Hanoi Commercial Joint Stock Bank 21 STB Sai Gon Thuong Tin Commercial Joint Stock Bank 22 TCB Vietnam Technological and Commercial Joint Stock Bank 23 TPB Tien Phong Commercial Joint Stock Bank 24 VAB Vietnam - Asia Commercial Joint Stock Bank 25 VCB Bank for Foreign Trade of Vietnam 26 VIB Vietnam International Commercial Joint Stock Bank 27 VPB Vietnam Prosperity Joint Stock Commercial Bank 57 APPENDIX 03 – Research resuts Picture Descriptive statistic Variable Obs Mean ROA ROE NIM SIZE CAP 325 325 325 325 325 0092942 1002911 0311145 18.26195 0856415 LOAN DEP GDP INF CGR 325 325 325 325 325 5491628 6257929 0593077 0721538 2900471 Std Dev Min Max 0079371 0822761 0110026 1.287053 0576206 -.0599 -.5633 -.0089 14.6987 026 0557 315 0709 21.1398 4428 1360206 1272956 0105511 0639153 6342109 1139 1851 029 006 -.6095 8517 8937 071 231 10.5886 Picture Correclation analysis ROA ROE NIM SIZE CAP LOAN DEP ROA 1.0000 ROE 0.8149 0.0000 1.0000 NIM 0.6018 0.0000 0.4062 0.0000 1.0000 SIZE 0.0234 0.6745 0.4093 0.0000 -0.0928 0.0948 1.0000 CAP 0.0744 0.1811 -0.3153 0.0000 0.1603 0.0038 -0.7549 0.0000 1.0000 LOAN 0.0773 0.1646 0.1559 0.0048 0.1350 0.0149 0.2646 0.0000 -0.1931 0.0005 1.0000 DEP -0.1431 0.0098 0.0107 0.8473 -0.0764 0.1696 0.3916 0.0000 -0.3606 0.0000 0.6000 0.0000 1.0000 GDP -0.0979 0.0779 -0.0537 0.3348 -0.0351 0.5282 0.0158 0.7768 -0.0536 0.3350 0.0007 0.9895 -0.0021 0.9700 INF 0.1558 0.0049 0.0441 0.4277 0.1469 0.0080 -0.3952 0.0000 0.3981 0.0000 -0.3165 0.0000 -0.4723 0.0000 CGR 0.1711 0.0020 GDP 0.0809 0.1459 INF 0.0459 0.4095 CGR -0.1448 0.0089 0.0451 0.4177 -0.1502 0.0067 -0.1601 0.0038 GDP 1.0000 INF -0.0787 0.1570 1.0000 CGR -0.0261 0.6390 -0.0323 0.5622 1.0000 58 Picture REM output (ROE) Source SS df MS Model Residual 631929928 1.56134202 317 090275704 004925369 Total 2.19327195 324 006769358 ROE Coef SIZE CAP LOAN DEP GDP INF CGR _cons 0310016 -.1254584 1459075 -.136861 -.3335051 3205965 0213132 -.4591299 Std Err .004807 1057794 0361122 0425838 3719643 0734477 0063758 1000707 t 6.45 -1.19 4.04 -3.21 -0.90 4.36 3.34 -4.59 Number of obs F(7, 317) Prob > F R-squared Adj R-squared Root MSE P>|t| 0.000 0.236 0.000 0.001 0.371 0.000 0.001 0.000 = = = = = = 325 18.33 0.0000 0.2881 0.2724 07018 [95% Conf Interval] 0215439 -.3335768 0748576 -.2206436 -1.065336 17609 0087689 -.6560165 0404594 0826599 2169574 -.0530783 3983256 4651031 0338575 -.2622432 Picture FEM output (ROE) Fixed-effects (within) regression Group variable: BANK Number of obs Number of groups = = 325 25 R-sq: within = 0.2065 between = 0.3876 overall = 0.2637 Obs per group: = avg = max = 13 13.0 13 corr(u_i, Xb) F(7,293) Prob > F = -0.0535 Std Err t ROE Coef SIZE CAP LOAN DEP GDP INF CGR _cons 0259521 -.1625919 2154085 -.2585065 -.3868463 2248669 0195122 -.3151853 0093057 1126663 0452698 046143 3383491 0870874 0063596 1840024 sigma_u sigma_e rho 0371487 06361185 25431268 (fraction of variance due to u_i) 2.79 -1.44 4.76 -5.60 -1.14 2.58 3.07 -1.71 F test that all u_i=0: F(24, 293) = 3.87 P>|t| = = 0.006 0.150 0.000 0.000 0.254 0.010 0.002 0.088 10.89 0.0000 [95% Conf Interval] 0076376 -.3843298 1263132 -.3493202 -1.052749 0534708 0069959 -.6773192 0442667 059146 3045037 -.1676927 2790563 3962631 0320285 0469486 Prob > F = 0.0000 59 Picture Model choice between OLS and REM (ROE) Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of ROE chi2(1) Prob > chi2 = = 1.15 0.2832 Picture Model choice between FEM and REM (ROE) Coefficients (b) (B) fem rem SIZE CAP LOAN DEP GDP INF CGR 0259521 -.1625919 2154085 -.2585065 -.3868463 2248669 0195122 0303493 -.1373529 1870345 -.2137377 -.3609264 2745254 0207386 (b-B) Difference sqrt(diag(V_b-V_B)) S.E -.0043971 -.025239 028374 -.0447688 -.0259199 -.0496585 -.0012264 0068371 0403508 020571 0149775 0450644 0018254 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(7) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 9.57 Prob>chi2 = 0.2144 (V_b-V_B is not positive definite) Picture Autcorrelation diagnostics (ROE) Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 24) = 5.314 Prob > F = 0.0301 Picture Multicollinearity diagnostics (ROE) Variable VIF 1/VIF SIZE CAP DEP LOAN INF CGR GDP 2.52 2.44 1.93 1.59 1.45 1.08 1.01 0.397142 0.409200 0.517341 0.630052 0.689808 0.929721 0.986960 Mean VIF 1.72 60 Picture Heteroskedasticity diagnostics (ROE) Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (25) = Prob>chi2 = 1591.56 0.0000 Picture 10 FGLS output (ROE) Cross-sectional time-series FGLS regression Coefficients: Panels: Correlation: generalized least squares heteroskedastic no autocorrelation Estimated covariances = Estimated autocorrelations = Estimated coefficients = ROE Coef SIZE CAP LOAN DEP GDP INF CGR _cons 0277758 -.1792932 1633088 -.1875255 -.3368676 3161654 0476268 -.3882183 25 Std Err .0033824 0697838 0258959 030367 2582159 0547084 0087799 0699943 Number of obs Number of groups Time periods Wald chi2(7) Prob > chi2 z 8.21 -2.57 6.31 -6.18 -1.30 5.78 5.42 -5.55 P>|z| 0.000 0.010 0.000 0.000 0.192 0.000 0.000 0.000 = = = = = 325 25 13 236.07 0.0000 [95% Conf Interval] 0211464 -.3160668 1125537 -.2470438 -.8429614 2089389 0304186 -.5254046 0344053 -.0425195 2140639 -.1280073 1692262 4233919 064835 -.2510319 61 Picture 11 FEM output (ROA) Fixed-effects (within) regression Group variable: BANK Number of obs Number of groups = = 325 25 R-sq: within = 0.2084 between = 0.0011 overall = 0.1255 Obs per group: = avg = max = 13 13.0 13 corr(u_i, Xb) F(7,293) Prob > F = -0.2387 = = ROA Coef SIZE CAP LOAN DEP GDP INF CGR _cons 0014456 0168425 0200341 -.0285028 -.0625815 0115604 0027691 -.0096394 0009819 0118884 0047768 004869 0357022 0091894 0006711 0194157 sigma_u sigma_e rho 00397522 00671226 25966516 (fraction of variance due to u_i) Std Err t 1.47 1.42 4.19 -5.85 -1.75 1.26 4.13 -0.50 P>|t| 0.142 0.158 0.000 0.000 0.081 0.209 0.000 0.620 11.02 0.0000 [95% Conf Interval] -.0004869 -.006555 0106329 -.0380854 -.1328469 -.0065251 0014484 -.0478514 F test that all u_i=0: F(24, 293) = 4.00 0033782 0402401 0294354 -.0189203 0076839 0296459 0040898 0285726 Prob > F = 0.0000 Picture 12 REM ouput Random-effects GLS regression Group variable: BANK Number of obs Number of groups = = 325 25 R-sq: within = 0.2061 between = 0.0028 overall = 0.1351 Obs per group: = avg = max = 13 13.0 13 corr(u_i, X) Wald chi2(7) Prob > chi2 = (assumed) ROA Coef Std Err z SIZE CAP LOAN DEP GDP INF CGR _cons 0016485 0192092 0182896 -.0231126 -.0599259 0162481 0027474 -.016452 0006547 0111165 0042393 0046094 0360333 0078486 000645 0132513 sigma_u sigma_e rho 00283095 00671226 15101671 (fraction of variance due to u_i) 2.52 1.73 4.31 -5.01 -1.66 2.07 4.26 -1.24 P>|z| 0.012 0.084 0.000 0.000 0.096 0.038 0.000 0.214 = = 69.16 0.0000 [95% Conf Interval] 0003654 -.0025787 0099807 -.0321469 -.1305499 0008652 0014833 -.0424241 0029317 0409971 0265985 -.0140783 010698 0316311 0040116 0095201 62 Picture 13 Model choice between FEM and REM (ROA) Coefficients (b) (B) fem rem SIZE CAP LOAN DEP GDP INF CGR 0014456 0168425 0200341 -.0285028 -.0625815 0115604 0027691 0016485 0192092 0182896 -.0231126 -.0599259 0162481 0027474 (b-B) Difference sqrt(diag(V_b-V_B)) S.E -.0002029 -.0023667 0017446 -.0053902 -.0026556 -.0046877 0000217 0007318 0042141 0022014 0015685 0047795 0001853 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(7) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 12.48 Prob>chi2 = 0.0858 (V_b-V_B is not positive definite) Picture 14 Autocorrelation diagnostics (ROA) Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 24) = 10.996 Prob > F = 0.0029 Picture 15 Heteroskedasticity diagnostics (ROA) Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (25) = Prob>chi2 = 3260.44 0.0000 63 Picture 16 FGLS ouput (ROA) Cross-sectional time-series FGLS regression Coefficients: Panels: Correlation: generalized least squares heteroskedastic no autocorrelation Estimated covariances = Estimated autocorrelations = Estimated coefficients = ROA Coef SIZE CAP LOAN DEP GDP INF CGR _cons 0015788 0072151 0126162 -.0167076 -.0606518 0250256 0059752 -.0173016 25 Number of obs Number of groups Time periods Wald chi2(7) Prob > chi2 Std Err .0003431 0086088 0026385 0031367 0267762 0052393 0009548 0071252 z P>|z| 4.60 0.84 4.78 -5.33 -2.27 4.78 6.26 -2.43 0.000 0.402 0.000 0.000 0.024 0.000 0.000 0.015 = = = = = 325 25 13 130.41 0.0000 [95% Conf Interval] 0009063 -.0096578 0074448 -.0228554 -.1131322 0147568 0041037 -.0312668 0022513 024088 0177876 -.0105598 -.0081715 0352943 0078466 -.0033363 Picture 17 FEM output (NIM) Fixed-effects (within) regression Group variable: BANK Number of obs Number of groups = = 325 25 R-sq: within = 0.1172 between = 0.0158 overall = 0.0471 Obs per group: = avg = max = 13 13.0 13 corr(u_i, Xb) F(7,293) Prob > F = -0.1832 Std Err t NIM Coef SIZE CAP LOAN DEP GDP INF CGR _cons 0015348 0203431 0165152 -.0299318 -.0258413 0133162 0014895 0111453 0013255 0160487 0064484 0065728 048196 0124051 0009059 0262101 sigma_u sigma_e rho 00664655 00906116 34982766 (fraction of variance due to u_i) 1.16 1.27 2.56 -4.55 -0.54 1.07 1.64 0.43 F test that all u_i=0: F(24, 293) = 5.85 P>|t| = = 0.248 0.206 0.011 0.000 0.592 0.284 0.101 0.671 5.56 0.0000 [95% Conf Interval] -.001074 -.0112422 0038241 -.0428677 -.1206955 -.0110982 -.0002934 -.0404387 0041436 0519285 0292064 -.0169958 0690129 0377307 0032724 0627293 Prob > F = 0.0000 64 Picture 18 REM output Random-effects GLS regression Group variable: BANK Number of obs Number of groups = = 325 25 R-sq: within = 0.1130 between = 0.0035 overall = 0.0683 Obs per group: = avg = max = 13 13.0 13 corr(u_i, X) Wald chi2(7) Prob > chi2 = (assumed) NIM Coef SIZE CAP LOAN DEP GDP INF CGR _cons 000985 0212743 0197104 -.0256492 -.0239667 0147658 0013456 0164973 0009709 0152958 005945 0063517 0487431 0109595 0008813 0195389 sigma_u sigma_e rho 00495741 00906116 23036937 (fraction of variance due to u_i) Std Err z 1.01 1.39 3.32 -4.04 -0.49 1.35 1.53 0.84 P>|z| 0.310 0.164 0.001 0.000 0.623 0.178 0.127 0.398 = = 35.32 0.0000 [95% Conf Interval] -.0009179 -.0087048 0080584 -.0380984 -.1195014 -.0067144 -.0003817 -.0217982 0028879 0512534 0313623 -.0132 0715681 036246 0030729 0547928 Picture 19 Model choice between FEM and REM (NIM) Coefficients (b) (B) fem rem SIZE CAP LOAN DEP GDP INF CGR 0015348 0203431 0165152 -.0299318 -.0258413 0133162 0014895 000985 0212743 0197104 -.0256492 -.0239667 0147658 0013456 (b-B) Difference sqrt(diag(V_b-V_B)) S.E .0005498 -.0009312 -.0031951 -.0042826 -.0018746 -.0014496 0001439 0009025 004858 0024979 0016904 0058118 0002097 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(7) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 19.56 Prob>chi2 = 0.0066 (V_b-V_B is not positive definite) 65 Picture 21 Autocorrelation diagnostics (NIM) Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 24) = 117.024 Prob > F = 0.0000 Picture 20 Heteroskedasticity diagnostics (NIM) Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (25) = Prob>chi2 = 1243.97 0.0000 Picture 22 FGLS output (NIM) Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (25) = Prob>chi2 = 1243.97 0.0000 xtserial NIM SIZE CAP LOAN DEP GDP INF CGR Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 24) = 117.024 Prob > F = 0.0000 xtgls NIM SIZE CAP LOAN DEP GDP INF CGR, panel (hetero) Cross-sectional time-series FGLS regression Coefficients: Panels: Correlation: generalized least squares heteroskedastic no autocorrelation Estimated covariances = Estimated autocorrelations = Estimated coefficients = NIM Coef SIZE CAP LOAN DEP GDP INF CGR _cons 0004895 0226087 0268994 -.0123669 -.0175986 0344217 0019831 0101503 25 Std Err .0005097 0143554 0042175 0049615 039179 0073885 001162 0107375 Number of obs Number of groups Time periods Wald chi2(7) Prob > chi2 z 0.96 1.57 6.38 -2.49 -0.45 4.66 1.71 0.95 P>|z| 0.337 0.115 0.000 0.013 0.653 0.000 0.088 0.344 = = = = = 325 25 13 69.93 0.0000 [95% Conf Interval] -.0005095 -.0055274 0186332 -.0220912 -.094388 0199405 -.0002944 -.0108948 0014884 0507449 0351656 -.0026425 0591909 048903 0042605 0311953 ...MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM BANKING UNIVERSITY OF HO CHI MINH CITY TIEU QUOC PHONG FACTORS AFFECTING BANK PROFITABILITY OF COMMERCIAL BANKS IN VIETNAM FROM 2008 TO. .. From the perspective of emerging markets, this article is expected to find out the determinants of bank profitability in Vietnam In addition, examining the factors driving the profitability of. .. What are the factors affecting the profitability of commercial banks in Vietnam? The author will consider perspectives to determine the bank' s profitability including net interest margin (NIM),