Based on the aforementioned limitations, the author proposes the following directions for further research to deepen the investigation on this topic.
Firstly, to enhance the objectivity of the evaluation results, the author suggests supplementing the missing data. Regarding data collection, it is proposed to extend
52
the study period. Eleven years may not be sufficient to draw conclusions; hence, extending it to 20 years could provide more reliable results. Simultaneously, expanding the geographical scope of the research is recommended. Besides the 20 selected companies studied by the author, other companies from various sectors should also be chosen to validate the proposed hypotheses. Additionally, conducting detailed studies by specific regions such as North-Central-South or by economic key areas would contribute to a more accurate research topic.
Secondly, in the data processing stage, the author proposes using the GMM model for analyzing panel data with the goal of addressing the shortcomings encountered when estimating FEM and REM models.
Thirdly, considering additional variables such as operating costs with ROA or ROE to gain an overall view of cash flows, thereby evaluating the profitability of companies.
53
CHAPTER 5 CONCLUSION
According to the experimental research presented in Chapter 5, the study made numerous recommendations to improve the operational efficiency of commercial banks in Vietnam. These ideas include measures to resolve non-performing loans, increase scale and financing sources, plan for risk management, and diversify business operations. The report noted that enhancing bank profitability necessitates efforts from management and employees and ongoing support from the government and the State Bank of Vietnam. Recognizing limitations such as the short study period (2013-2023) and a sample size of 20 banks, the author proposed extending the study period to 20 years, including more banks for better data accuracy, and employing the GMM model for panel data analysis to improve on the limitations of FEM and REM models. These steps are aimed at assisting Vietnamese commmercial bank managers in formulating income diversification policies to enhance operational efficiency and achieve their goals effectively.
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REFERENCES
Ahmad, F., Tahir, S. H., Aziz, B. J. T. s. R. J. o. S. S., & Management. (2014).
Impact of loan loss provision on bank profitability in Pakistan. 3(12), 34-41.
Alhadab, M., Alsahawneh, S. J. I. J. o. B., & Management. (2016). Loan loss provision and the profitability of commercial banks: Evidence from Jordan. 11(12), 242-248
Al-Homaidi, E. A., Tabash, M. I., Farhan, N. H., & Almaqtari, F. A.
(2018). Bank-specific and macro-economic determinants of profitability of Indian commercial banks: A panel data approach.Cogent Economics & Finance,6(1), 1548072
Anh, & Nam, N. Nâng cao năng lực tài chính của ngân hàng thương mại:
Nghiên cứu điển hình tại Ngân hàng.
Bala, S. A. R., Auwal, B. M., Salisu, M. Y. J. I. J. o. A., Finance, &
Management, R. (2023). The Impact of Credit Risk on the Value of Shareholders of Listed Banks in Nigeria. 7(1), 27-33
Bhatt, T. K., Ahmed, Naveed Iqbal, Muhammad Babar Ullah, & Mehfooz.
(2023). Examining the determinants of credit risk management and their relationship with the performance of commercial banks in Nepal. 16(4), 235.
Bhattarai, Y. R. J. P. I. J. o. M., & Research. (2016). Effect of non-performing loan on the profitability of commercial banks in Nepal. 10(2), 1-9.
Bordeleau, É., & Graham, C. (2010). The impact of liquidity on bank profitability.
Butola, Punyata Dube, Praveen Jain, & Kumar, V. (2023). A study on impact of credit risk management on the profitability of Indian banks. 11(3), 103-114.
Dietrich, A., & Wanzenried, G. (2009, April). What determines the profitability of commercial banks? New evidence from Switzerland. In12th conference of the Swiss society for financial market researches, Geneva(pp. 2-39).
Gelos, M. G. (2006). Banking Spreads in Latin America (EPub): International Monetary Fund.
55
Geller, Stacie E Koch, Abigail R Garland, Caitlin E MacDonald, E Jane Storey,
& Francesca Lawton. (2018). A global view of severe maternal morbidity: moving beyond maternal mortality. 15, 31-43.
Hambolu, Victor Olufunsho Omuemu, Stanley Osasere Majeed, & Abdul, A.
T. (2022). The impact of credit risk on the profitability of commercial banks in Nigeria. 3(2).
Huỳnh Thanh Tuấn (2020), Các yếu tốtác động đến khảnăng sinh lời của ngân hàng thương mại tại Việt Nam, Luận văn Thạc Sĩ, Trường Đại Học Ngân Hàng TP.
Hồ Chí Minh
Karim, Bakri Abdul Kadri, Norlina Min, & Yong, K. L. (2018). Credit Risk and Banks’ Profitability in Malaysia. 2(1).
Kithinji, A. M. (2010). Credit risk management and profitability of commercial banks in Kenya.
Koch, Timothy W MacDonald, Steven Scott He, & Ziyun. (2000). Bank management: Dryden Press Orlando.
Laeven, L. & Levine, R. (2009), ‘Bank governance, regulation and risk taking’, Journal of financial economics, 93(2), 259-275
Lý Hoàng Khang, Lê Vũ Kỳ Nam, & Chi, N. K. (2021). Các yếu tố tác động đến khả năng sinh lời của các Ngân hàng Thương Mại Việt Nam (Bachelor), Trường Đại học Công nghiệp Thành phố Hồ Chí Minh, Hội nghị Khoa học trẻ lần 3 năm 2021.
Nguyễn Thị Hồng Ánh, & Trung, L. T. (2020). Tác động của rủi ro tín dụng đến sự ổn định tài chính của các ngân hàng thương mại Việt Nam. Tạp chí Công Thương.
Nguyễn Tuyết Anh. (2021). Rủi ro tín dụng là gì? Cách phân loại rủi ro tín dụng. Luanvan1080.
Norlina Kadri, Bakri Abdul Karim, & Ming, K. L. Y. (2018). Credit Risk and Banks’ Profitability in Malaysia. Universiti Malaysia Sarawak (UNIMAS)
56
Pan, Q., & Pan, M. J. O. J. o. S. S. (2014). The impact of macro factors on the profitability of china’s commercial banks in the decade after WTO Accession. 2(9), 64-69.
Poudel, R. P. S. J. I. J. o. a., & commerce. (2012). The impact of credit risk management on financial performance of commercial banks in Nepal. 1(5), 9-15
Saleh, D. S., Winarso, E. J. I. J. o. M., & Understanding, M. (2021). Analysis of non-performing loans (NPL) and loan to deposit ratio (LDR) towards profitability.
8(1), 423-436.
Sari, L., Septiano, R. J. J. o. A., & Management, F. (2020). Effects of intervening loan to deposit ratio on profitability. 1(5), 228-241.
Shetty, Chetan Yadav, & S, A. (2019). Impact of financial risks on the profitability of commercial banks in India. 7, 550.
Supervision, & Banking. (2011). Basel committee on banking supervision.
Tan, Y. (2016). The impacts of risk and competition on bank profitability in China.Journal of International Financial Markets, Institutions and Money,40, 85-110
Trung, L. Đ. D. (2020, 12/2020). Các nhân tố tác động tới khả năng sinh lời của ngân hàng thương mại tại Việt Nam: Tiếp cận theo mô hình thực nghiệm động.
Tạp chí Công Thương. Retrieved from https://tapchinganhang.gov.vn/cac-nhan-to- tac-dong-toi-kha-nang-sinh-loi-cua-ngan-hang-thuong-mai-tai-viet-nam-tiep-can- theo-mo-hi.htm
Wooldridge, J. M. J. C., ma. (2002). Econometric analysis of cross section and panel data MIT press. 108(2), 245-254.
Yao, H., Haris, M., & Tariq, G. (2018). Profitability determinants of financial institutions: evidence from banks in Pakistan .International Journal of Financial Studies,6(2), 53.
57
Zulfikar, A. (2018). Disclosure of isr and gri index on sharia banking in indonesia. Nisbah: Jurnal Perbankan Syariah,4(1), 64-71
APPPENDICES APPENDIX 1
LIST OF COMMERCIAL BANK IN RESEARCH
No. Brand Name Registered English Name Time 1 ACB Asia Commercial Joint Stock Bank 2013 - 2023
2 ABBank An Binh Commercial Joint Stock Bank 2013 - 2023
3 BIDV Bank for Investment and Development of
Vietnam 2013 - 2023
4 Vietinbank Vietnam Bank for Industry and Trade 2013 - 2023
5 Eximbank Vietnam Export-Import Commercial Joint
Stock Bank 2013 - 2023
6 HDBank Ho Chi Minh City Development
Commercial Joint Stock Bank 2013 - 2023 7 Kienlongbank Kien Long Commercial Joint Stock Bank 2013 - 2023 8 MB Military Commercial Joint Stock Bank 2013 - 2023
9 MSB Vietnam Maritime Commercial Join Stock
Bank 2013 - 2023
10 Nam A Bank Nam A Commercial Joint Stock Bank 2013 - 2023 11 NVB National Citizen Commercial Joint Stock
Bank 2013 - 2023
12 OCB Orient Commercial Joint Stock Bank 2013 - 2023
13 SeABank Southeast Asia Commercial Joint Stock
Bank 2013 - 2023
14 SAIGONBANK Saigon Bank for Industry and Trade 2013 - 2023
15 SHB Saigon-Hanoi Commercial Joint Stock
Bank 2013 - 2023
16 Sacombank Sai Gon Thuong Tin Commercial Joint-
stock Bank 2013 - 2023
17 Techcombank Viet Nam Technological and Commercial
Joint Stock Bank 2013 - 2023 18 Vietcombank Joint Stock Commercial Bank for Foreign
Trade of Vietnam 2013 - 2023 19 VIB Vietnam International Commercial Joint
Stock Bank 2013 - 2023
20 VPBank Vietnam Prosperity Joint Stock
Commercial Bank 2013 - 2023
APPENDIX 2 RESEARCH DATA
Bank Year ROA LTD NPL LEV SIZE LIQ GDP
ABB 2013 0,0024 0,6363 0,0431 0,1107 31,6850 0,3114 5,5535 ABB 2014 0,0017 0,5758 0,0481 0,0926 31,8426 0,3069 6,4222 ABB 2015 0,0014 0,6504 0,0212 0,0988 31,7957 0,2160 6,9872 ABB 2016 0,0033 0,7724 0,0213 0,0855 31,9374 0,1961 6,6900 ABB 2017 0,0058 0,8274 0,0277 0,0781 32,0678 0,1851 6,9402 ABB 2018 0,0079 0,8382 0,0189 0,0826 32,1308 0,1352 7,4650 ABB 2019 0,0098 0,8164 0,0231 0,0828 32,2614 0,2478 7,3593 ABB 2020 0,0096 0,8729 0,0209 0,0829 32,3878 0,2581 2,8654 ABB 2021 0,0129 1,0169 0,0234 0,1074 32,4263 0,2174 2,5616 ABB 2022 0,0104 0,9749 0,0288 0,1115 32,4997 0,1820 8,0198 ABB 2023 0,0028 0,9675 0,0291 0,0907 32,7192 0,1103 7,6580 ACB 2013 0,0050 0,7761 0,0303 0,0811 32,7466 0,0740 5,5535 ACB 2014 0,0053 0,7524 0,0216 0,0741 32,8218 0,0580 6,4222 ACB 2015 0,0051 0,7738 0,0131 0,0678 32,9366 0,0871 6,9872 ACB 2016 0,0057 0,7892 0,0043 0,0640 33,0850 0,0719 6,6900 ACB 2017 0,0074 0,8224 0,0105 0,0598 33,2811 0,0778 6,9402 ACB 2018 0,0156 0,8538 0,0001 0,0682 33,4281 0,1080 7,4650 ACB 2019 0,0157 0,8720 0,0004 0,0780 33,5804 0,1231 7,3593 ACB 2020 0,0173 0,8819 0,0010 0,0867 33,7280 0,1243 2,8654 ACB 2021 0,0182 0,9526 0,0077 0,0930 33,8997 0,1699 2,5616 ACB 2022 0,0225 0,9994 0,0074 0,1064 34,0410 0,1778 8,0198 ACB 2023 0,0223 0,9990 0,0121 0,1095 34,2086 0,1952 8,0198 BID 2013 0,0074 1,1538 0,0039 0,0621 33,9380 0,1174 5,5535 BID 2014 0,0077 1,0119 0,0049 0,0539 34,1085 0,1208 6,4222 BID 2015 0,0075 1,0598 0,0082 0,0524 34,3769 0,1122 6,9872 BID 2016 0,0062 0,9968 0,0052 0,0459 34,5452 0,1050 6,6900 BID 2017 0,0058 1,0080 0,0057 0,0423 34,7230 0,1297 6,9402
BID 2018 0,0057 0,9991 0,0050 0,0473 34,8111 0,1255 7,4650 BID 2019 0,0057 1,0025 0,0060 0,0550 34,9375 0,1367 7,3593 BID 2020 0,0048 0,9899 0,0074 0,0554 34,9553 0,0970 2,8654 BID 2021 0,0062 0,9813 0,0082 0,0515 35,1051 0,1234 2,5616 BID 2022 0,0087 1,0330 0,0116 0,0517 35,2905 0,1633 8,0198 BID 2023 0,0096 1,0191 0,0126 0,0564 35,3721 0,1193 9,6366 CTG 2013 0,0101 1,0324 0,0056 0,1036 33,9878 0,1493 5,5535 CTG 2014 0,0087 1,0370 0,0112 0,0912 34,1251 0,1360 6,4222 CTG 2015 0,0073 1,0915 0,0092 0,0776 34,2897 0,1065 6,9872 CTG 2016 0,0071 1,0106 0,0105 0,0679 34,4860 0,1193 6,6900 CTG 2017 0,0068 1,0501 0,0114 0,0618 34,6296 0,1226 6,9402 CTG 2018 0,0045 1,0474 0,0001 0,0614 34,6909 0,1380 7,4650 CTG 2019 0,0076 1,0476 0,0002 0,0665 34,7545 0,1310 7,3593 CTG 2020 0,0103 1,0252 0,0010 0,0680 34,8326 0,1268 2,8654 CTG 2021 0,0093 0,9732 0,0126 0,0651 34,9651 0,1202 2,5616 CTG 2022 0,0093 1,0205 0,0124 0,0636 35,1312 0,1566 8,0198 CTG 2023 0,0099 1,0246 0,0113 1,5343 35,2481 0,1625 13,4780 EIB 2013 0,0039 1,0488 0,0049 0,0946 32,7659 0,3628 5,5535 EIB 2014 0,0003 0,8597 0,0246 0,0957 32,7130 0,2752 6,4222 EIB 2015 0,0003 0,8611 0,0019 0,1177 32,4581 0,1008 6,9872 EIB 2016 0,0024 0,8490 0,0271 0,1166 32,4893 0,1092 6,6900 EIB 2017 0,0055 0,8620 0,0227 0,1055 32,6374 0,1393 6,9402 EIB 2018 0,0043 0,8766 0,0185 0,1080 32,6592 0,1822 7,4650 EIB 2019 0,0052 0,8132 0,0171 0,1038 32,7522 0,2043 7,3593 EIB 2020 0,0067 0,7525 0,0252 0,1171 32,7089 0,2391 2,8654 EIB 2021 0,0058 0,8348 0,0196 0,1201 32,7420 0,1841 2,5616 EIB 2022 0,0159 0,8781 0,0180 0,1244 32,8517 0,1824 8,0198 EIB 2023 0,0107 0,8886 0,0265 0,0118 32,9364 0,2450 9,0198 HDB 2013 0,0025 0,7058 0,0068 0,1106 32,0880 0,1574 5,5535 HDB 2014 0,0048 0,6420 0,0063 0,0982 32,2314 0,2016 6,4222 HDB 2015 0,0059 0,7587 0,0032 0,1018 32,2990 0,1522 6,9872
HDB 2016 0,0061 0,7960 0,0025 0,0708 32,6436 0,1504 6,6900 HDB 2017 0,0103 0,8669 0,0020 0,0845 32,8745 0,1336 6,9402 HDB 2018 0,0148 0,9615 0,0014 0,0845 33,0066 0,1847 7,4650 HDB 2019 0,0175 1,1611 0,0008 0,0975 33,0668 0,1358 7,3593 HDB 2020 0,0146 1,0212 0,0016 0,0839 33,3966 0,1719 2,8654 HDB 2021 0,0172 1,1087 0,0165 0,0896 33,5569 0,1875 2,5616 HDB 2022 0,0197 1,2227 0,0167 0,1034 33,6624 0,1522 8,0198 HDB 2023 0,0172 0,9152 0,0179 0,2593 34,0318 0,2338 13,4780 KLB 2013 0,0147 0,9117 0,1293 0,0063 30,6931 0,1902 5,5535 KLB 2014 0,0076 0,8163 0,1184 0,0040 30,7710 0,1812 6,4222 KLB 2015 0,0065 0,8076 0,0113 0,0143 30,8627 0,1067 6,9872 KLB 2016 0,0040 0,8636 0,0105 0,0173 31,0471 0,1409 6,6900 KLB 2017 0,0054 0,9449 0,0195 0,0390 31,2507 0,1835 6,9402 KLB 2018 0,0055 1,0091 0,0094 0,0177 31,3760 0,1837 7,4650 KLB 2019 0,0013 1,0170 0,0122 0,0161 31,5648 0,2656 7,3593 KLB 2020 0,0022 0,8262 0,0542 0,1428 31,6790 0,2823 2,8654 KLB 2021 0,0092 0,7469 0,0189 0,0081 32,0597 0,4256 2,5616 KLB 2022 0,0063 0,8564 0,0189 0,0082 32,0826 0,3016 8,0198 KLB 2023 0,0066 0,8992 0,0193 0,0104 32,0966 0,2894 13,4780 MBB 2013 0,0127 0,6447 0,0016 0,0090 32,8261 0,1743 5,5535 MBB 2014 0,0125 0,6000 0,0273 0,0445 32,9318 0,1432 6,4222 MBB 2015 0,0114 0,6683 0,0161 0,0342 33,0294 0,1723 6,9872 MBB 2016 0,0113 0,7738 0,0132 0,1158 33,1772 0,1501 6,6900 MBB 2017 0,0111 0,8365 0,0120 0,1041 33,3800 0,1976 6,9402 MBB 2018 0,0171 0,8947 0,0133 0,1041 33,5236 0,1583 7,4650 MBB 2019 0,0196 0,9179 0,0116 0,1073 33,6508 0,1370 7,3593 MBB 2020 0,0174 0,9593 0,0109 0,1126 33,8355 0,1380 2,8654 MBB 2021 0,0218 0,9451 0,0090 0,1147 34,0398 0,1277 2,5616 MBB 2022 0,0249 1,0383 0,0109 0,1227 34,2221 0,1048 8,0198 MBB 2023 0,0223 1,0565 0,0160 0,1140 34,4822 0,1231 9,0198 MSB 2013 0,0031 0,2038 0,0012 0,0963 32,3049 0,2453 5,5535
MSB 2014 0,0014 0,1599 0,0019 0,0995 32,2790 0,1910 6,4222 MSB 2015 0,0011 0,3584 0,0043 0,1501 32,2784 0,1457 6,9872 MSB 2016 0,0015 0,2741 0,0236 0,1721 32,1594 0,1185 6,6900 MSB 2017 0,0011 0,2459 0,0223 0,1393 32,3517 0,1252 6,9402 MSB 2018 0,0063 2,0514 0,0036 0,1115 32,5566 0,1991 7,4650 MSB 2019 0,0066 0,1392 0,0027 0,1046 32,6871 0,1729 7,3593 MSB 2020 0,0114 0,1529 0,0022 0,1056 32,8055 0,1190 2,8654 MSB 2021 0,0198 0,2207 0,0174 0,1213 32,9475 0,1867 2,5616 MSB 2022 0,0217 0,0845 0,0171 0,1432 32,9913 0,2083 8,0198 MSB 2023 0,0174 1,1090 0,0287 0,1328 33,2183 0,2497 9,0198 NAB 2013 0,0047 0,0255 0,1701 0,1277 30,9908 0,2750 5,5535 NAB 2014 0,0050 0,8184 0,1311 0,0981 31,2498 0,4147 6,4222 NAB 2015 0,0055 0,8563 0,0739 0,1065 31,1997 0,2240 6,9872 NAB 2016 0,0008 0,7054 0,0430 0,0871 31,3888 0,1017 6,6900 NAB 2017 0,0044 0,9118 0,0000 0,0722 31,6281 0,1162 6,9402 NAB 2018 0,0079 0,9378 0,0001 0,0597 31,9493 0,1755 7,4650 NAB 2019 0,0077 0,9548 0,0197 0,0553 32,1816 0,1668 7,3593 NAB 2020 0,0060 0,9076 0,0083 0,0517 32,5312 0,1289 2,8654 NAB 2021 0,0094 0,8902 0,0157 0,0553 32,6630 0,1560 2,5616 NAB 2022 0,0102 0,9564 0,0163 0,0767 32,8104 0,1517 8,0198 NAB 2023 0,0125 0,9619 0,0211 0,0783 32,9776 0,1905 9,0198 NVB 2013 0,0006 0,7333 0,0115 0,1238 31,0009 0,2138 5,5535 NVB 2014 0,0002 0,6809 0,0093 0,0955 31,2375 0,2095 6,4222 NVB 2015 0,0001 0,6004 0,0122 0,0715 31,5070 0,1767 6,9872 NVB 2016 0,0002 0,6066 0,0459 0,0491 31,8653 0,1946 6,6900 NVB 2017 0,0003 0,7023 0,0129 0,0469 31,9055 0,1680 6,9402 NVB 2018 0,0005 0,7566 0,0101 0,0467 31,9135 0,1182 7,4650 NVB 2019 0,0005 0,6415 0,0114 0,0566 32,0180 0,1990 7,3593 NVB 2020 0,0000 0,5592 0,0171 0,0500 32,1264 0,1558 2,8654 NVB 2021 0,0000 0,6450 0,0300 0,0613 31,9321 0,0733 2,5616 NVB 2022 0,0000 0,6688 0,1793 0,0686 32,1291 0,1758 8,0198
NVB 2023 -0,0070 0,7061 0,2976 0,0559 32,1980 0,1142 13,4780 OCB 2013 0,0074 1,0556 0,0653 0,1375 31,1213 0,1314 5,5535 OCB 2014 0,0056 0,8981 0,0126 0,1145 31,2970 0,1008 6,4222 OCB 2015 0,0042 0,9386 0,0015 0,0934 31,5319 0,1509 6,9872 OCB 2016 0,0061 0,8942 0,0175 0,0798 31,7870 0,0922 6,6900 OCB 2017 0,0097 0,9056 0,0179 0,0785 32,0654 0,1677 6,9402 OCB 2018 0,0176 0,9330 0,0229 0,1126 32,2358 0,1541 7,4650 OCB 2019 0,0219 1,0282 0,0184 0,1262 32,4031 0,1716 7,3593 OCB 2020 0,0232 1,0237 0,0169 0,1291 32,6584 0,1403 2,8654 OCB 2021 0,0239 1,0329 0,0132 0,1340 32,8486 0,1472 2,5616 OCB 2022 0,0181 1,1722 0,0223 0,1498 32,8988 0,1271 8,0198 OCB 2023 0,0138 1,1489 0,0265 0,1349 33,1121 0,1734 13,4780 SSB 2013 0,0019 0,5784 0,1785 0,0772 32,0114 0,4148 5,5535 SSB 2014 0,0011 0,7121 0,0018 0,0763 32,0153 0,3748 6,4222 SSB 2015 0,0011 0,7507 0,0024 0,0730 32,0708 0,2188 6,9872 SSB 2016 0,0011 0,8178 0,0014 0,0603 32,2693 0,1769 6,6900 SSB 2017 0,0024 0,8811 0,0030 0,0520 32,4594 0,1710 6,9402 SSB 2018 0,0035 0,9948 0,0046 0,0628 32,5761 0,1556 7,4650 SSB 2019 0,0070 1,0302 0,0045 0,0746 32,6898 0,1901 7,3593 SSB 2020 0,0075 0,9611 0,0043 0,0821 32,8251 0,1672 2,8654 SSB 2021 0,0123 1,1622 0,0165 0,0967 32,9860 0,2290 2,5616 SSB 2022 0,0175 1,3324 0,0225 0,1278 33,0753 0,2417 8,0198 SSB 2023 0,0010 1,2205 0,0194 0,1285 33,2150 0,0309 9,0198 SGB 2013 0,0118 0,9877 0,0006 0,3130 30,3178 0,0738 5,5535 SGB 2014 0,0114 0,9484 0,0035 0,2825 30,3925 0,0558 6,4222 SGB 2015 0,0024 0,8836 0,0903 0,2362 30,5073 0,1149 6,9872 SGB 2016 0,0073 0,8846 0,0966 0,2263 30,5780 0,1343 6,6900 SGB 2017 0,0257 0,2765 0,4369 0,1909 30,6906 0,1924 6,9402 SGB 2018 0,0020 0,9314 0,1346 0,2028 30,6453 0,1657 7,4650 SGB 2019 0,0063 0,9291 0,1763 0,1850 30,7583 0,2598 7,3593 SGB 2020 0,0041 0,8477 0,1239 0,1782 30,8067 0,2703 2,8654
SGB 2021 0,0050 0,9114 0,0197 0,1775 30,8341 0,2580 2,5616 SGB 2022 0,0069 0,9129 0,0212 0,1638 30,9524 0,2234 8,0198 SGB 2023 0,2325 0,8400 0,0203 0,1483 31,0810 3,7994 13,4780 SHB 2013 0,0059 0,8430 0,0673 0,0777 32,5982 0,2283 5,5535 SHB 2014 0,0047 0,8447 0,0409 0,0661 32,7611 0,1990 6,4222 SHB 2015 0,0039 0,8831 0,0441 0,0582 32,9526 0,1762 6,9872 SHB 2016 0,0039 0,9748 0,0460 0,0599 33,0861 0,1460 6,6900 SHB 2017 0,0054 1,0174 0,0377 0,0541 33,2870 0,1353 6,9402 SHB 2018 0,0052 0,9634 0,0319 0,0532 33,4095 0,1099 7,4650 SHB 2019 0,0066 1,0229 0,0234 0,0534 33,5316 0,1249 7,3593 SHB 2020 0,0063 1,0068 0,0183 0,0618 33,6537 0,1155 2,8654 SHB 2021 0,0099 1,1076 0,0169 0,0754 33,8588 0,1586 2,5616 SHB 2022 0,0140 1,0662 0,0281 0,0845 33,9426 0,1453 8,0198 SHB 2023 0,0058 0,9595 0,0302 0,0863 34,0775 0,0830 9,0198 STB 2013 0,0138 0,8399 0,0090 0,1182 32,7148 0,0929 5,5535 STB 2014 0,0116 0,7851 0,0075 0,1052 32,8770 0,0671 6,4222 STB 2015 0,0022 0,7123 0,0083 0,0818 33,3079 0,0558 6,9872 STB 2016 0,0003 0,6818 0,0100 0,0716 33,4362 0,0521 6,6900 STB 2017 0,0032 0,6970 0,0467 0,0673 33,5404 0,0450 6,9402 STB 2018 0,0044 0,7345 0,0213 0,0646 33,6375 0,0513 7,4650 STB 2019 0,0054 0,7385 0,0004 0,0627 33,7482 0,0739 7,3593 STB 2020 0,0054 0,7951 0,0004 0,0625 33,8305 0,0734 2,8654 STB 2021 0,0065 0,9077 0,0150 0,0704 33,8870 0,0552 2,5616 STB 2022 0,0085 0,9646 0,0098 0,0698 34,0144 0,0768 8,0198 STB 2023 0,0114 0,9303 0,0228 0,0727 34,1448 0,1049 9,0198 TCB 2013 0,0041 0,5857 0,0075 0,0960 32,6993 0,1293 5,5535 TCB 2014 0,0062 0,6098 0,0238 0,0931 32,8009 0,1297 6,4222 TCB 2015 0,0080 0,7887 0,0166 0,0938 32,8885 0,1052 6,9872 TCB 2016 0,0134 0,8222 0,0158 0,0908 33,0922 0,1151 6,6900 TCB 2017 0,0239 0,9408 0,0161 0,1111 33,2272 0,1365 6,9402 TCB 2018 0,0264 0,7941 0,0175 0,1924 33,4024 0,1518 7,4650
TCB 2019 0,0267 0,9979 0,0001 0,1930 33,5809 0,1460 7,3593 TCB 2020 0,0286 1,0002 0,0007 0,2044 33,7169 0,0976 2,8654 TCB 2021 0,0324 1,1035 0,0066 0,1956 33,9744 0,1389 2,5616 TCB 2022 0,0292 1,1733 0,0072 0,1937 34,1807 0,1410 8,0198 TCB 2023 0,0214 1,1272 0,0116 0,1833 34,3756 0,1587 9,0198 VCB 2013 0,0093 0,8256 0,0273 0,0994 33,7816 0,2615 5,5535 VCB 2014 0,0080 0,7658 0,0231 0,0813 33,9888 0,2906 6,4222 VCB 2015 0,0079 0,7736 0,0184 0,0718 34,1448 0,2369 6,9872 VCB 2016 0,0087 0,7804 0,0151 0,0650 34,3004 0,2271 6,6900 VCB 2017 0,0088 0,7670 0,0114 0,0535 34,5735 0,3252 6,9402 VCB 2018 0,0136 0,7879 0,0016 0,0615 34,6102 0,2550 7,4650 VCB 2019 0,0152 0,7913 0,0020 0,0709 34,7399 0,2436 7,3593 VCB 2020 0,0139 0,8137 0,0024 0,0764 34,8211 0,2384 2,8654 VCB 2021 0,0156 0,8462 0,0064 0,0836 34,8859 0,1882 2,5616 VCB 2022 0,0165 0,9209 0,0068 0,0808 35,1342 0,2341 8,0198 VCB 2023 0,0180 0,8896 0,0098 0,0985 35,1483 0,2224 13,4780 VIB 2013 0,0007 0,8150 0,0011 0,1159 31,9732 0,1249 5,5535 VIB 2014 0,0065 0,7783 0,0030 0,1178 32,0213 0,1209 6,4222 VIB 2015 0,0062 0,8963 0,0080 0,1138 32,0655 0,1009 6,9872 VIB 2016 0,0054 1,0155 0,0258 0,0913 32,2804 0,1342 6,6900 VIB 2017 0,0091 1,1680 0,0249 0,0768 32,4445 0,1208 6,9402 VIB 2018 0,0158 1,1329 0,0251 0,0830 32,5667 0,0861 7,4650 VIB 2019 0,0177 1,0559 0,0256 0,0785 32,8488 0,1312 7,3593 VIB 2020 0,0190 1,1275 0,0208 0,0793 33,1310 0,1242 2,8654 VIB 2021 0,0207 1,1610 0,0232 0,0852 33,3660 0,1757 2,5616 VIB 2022 0,0247 1,1590 0,0245 0,1053 33,4682 0,1855 8,0198 VIB 2023 0,0209 1,1078 0,0314 0,1020 33,6469 0,1905 9,0198 VPB 2013 0,0084 0,6259 0,0281 0,0681 32,4290 0,1248 5,5535 VPB 2014 0,0077 0,7234 0,0254 0,0582 32,7263 0,1163 6,4222 VPB 2015 0,0124 0,8966 0,0269 0,0742 32,8982 0,0954 6,9872 VPB 2016 0,0172 1,1687 0,0291 0,0812 33,0637 0,0616 6,6900
VPB 2017 0,0232 1,3678 0,0339 0,1197 33,2578 0,0956 6,9402 VPB 2018 0,0228 1,2992 0,0350 0,1204 33,4096 0,0905 7,4650 VPB 2019 0,0219 1,2021 0,0342 0,1260 33,5638 0,0690 7,3593 VPB 2020 0,0249 1,2458 0,0341 0,1442 33,6690 0,0683 2,8654 VPB 2021 0,0210 1,4691 0,0457 0,1871 33,9362 0,1284 2,5616 VPB 2022 0,0268 1,4459 0,0573 0,1962 34,0783 0,0960 8,0198 VPB 2023 0,0104 1,2466 0,0502 0,2063 34,3374 0,1282 9,0198
APPENDIX 3
Descriptive statistics of variables
Correlation matrix between variables
GDP 220 .7804228 .1781013 .4085052 1.129626 SIZE 220 32.92371 1.146205 30.31783 35.37206 LEV 220 16.66694 24.90462 .6517747 252.1449 NPL 220 .0259123 .045323 .000028 .436897 LTD 220 .8831692 .2409341 .0254705 2.05136 LLP 220 .0146144 .0359554 .0000105 .4558144 ROA 220 .0106584 .0166979 -.0069565 .2325274 Variable Obs Mean Std. dev. Min Max . sum ROA LLP LTD NPL LEV SIZE GDP
GDP 0.0754 0.1260 0.0269 0.0904 0.0253 -0.0271 1.0000 SIZE 0.0670 -0.1932 0.3195 -0.3625 -0.1890 1.0000
LEV -0.0662 0.0845 -0.0462 0.0963 1.0000 NPL -0.0349 0.1597 -0.1873 1.0000
LTD 0.1522 -0.0462 1.0000 LLP 0.7575 1.0000
ROA 1.0000
ROA LLP LTD NPL LEV SIZE GDP (obs=220)
. corr ROA LLP LTD NPL LEV SIZE GDP
Pooled Ordinary Least Squares Model (Pooled OLS)
_cons -.0645425 .0219886 -2.94 0.004 -.1078857 -.0211994 GDP -.0015938 .0038351 -0.42 0.678 -.0091535 .0059659 SIZE .0019735 .0006729 2.93 0.004 .0006472 .0032998 LEV -.0000638 .0000277 -2.31 0.022 -.0001183 -9.33e-06 NPL -.0298593 .0161687 -1.85 0.066 -.0617306 .0020119 LTD .0088237 .0029704 2.97 0.003 .0029687 .0146788 LLP .3774202 .0193802 19.47 0.000 .3392187 .4156217 ROA Coefficient Std. err. t P>|t| [95% conf. interval]
Total .061061344 219 .000278819 Root MSE = .00999 Adj R-squared = 0.6421 Residual .021254431 213 .000099786 R-squared = 0.6519 Model .039806913 6 .006634485 Prob > F = 0.0000 F(6, 213) = 66.49 Source SS df MS Number of obs = 220 . reg ROA LLP LTD NPL LEV SIZE GDP
Fixed-effects model (FEM)
. est sto fem
F test that all u_i=0: F(19, 194) = 8.01 Prob > F = 0.0000 rho .56971145 (fraction of variance due to u_i)
sigma_e .00783482 sigma_u .00901522
_cons -.2201706 .0406793 -5.41 0.000 -.3004011 -.1399401 GDP -.0008921 .0030315 -0.29 0.769 -.0068709 .0050868 SIZE .0067702 .0012635 5.36 0.000 .0042783 .0092621 LEV .0000626 .0000315 1.99 0.048 4.38e-07 .0001248 NPL -.0530647 .0139736 -3.80 0.000 -.0806244 -.025505 LTD .0037678 .0031956 1.18 0.240 -.0025348 .0100705 LLP .3851452 .0160373 24.02 0.000 .3535154 .416775 ROA Coefficient Std. err. t P>|t| [95% conf. interval]
corr(u_i, Xb) = -0.2937 Prob > F = 0.0000 F(6,194) = 109.06 Overall = 0.5508 max = 11 Between = 0.0029 avg = 11.0 Within = 0.7713 min = 11 R-squared: Obs per group:
Group variable: BANK Number of groups = 20 Fixed-effects (within) regression Number of obs = 220 . xtreg ROA LLP LTD NPL LEV SIZE GDP, fe
Random Effects Mode (FEM)
. est sto rem
rho .36711365 (fraction of variance due to u_i)
sigma_e .00783482 sigma_u .00596714
_cons -.1429899 .0315582 -4.53 0.000 -.2048428 -.081137 GDP -.0014328 .0030988 -0.46 0.644 -.0075064 .0046409 SIZE .0043786 .0009759 4.49 0.000 .0024659 .0062913 LEV .0000308 .0000301 1.02 0.307 -.0000282 .0000898 NPL -.0446822 .0140095 -3.19 0.001 -.0721403 -.0172241 LTD .0063355 .0030208 2.10 0.036 .0004148 .0122562 LLP .3870833 .0162365 23.84 0.000 .3552605 .4189062 ROA Coefficient Std. err. z P>|z| [95% conf. interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 Wald chi2(6) = 612.60 Overall = 0.6155 max = 11 Between = 0.0348 avg = 11.0 Within = 0.7661 min = 11 R-squared: Obs per group:
Group variable: BANK Number of groups = 20 Random-effects GLS regression Number of obs = 220 . xtreg ROA LLP LTD NPL LEV SIZE GDP, re
Hausman test results
Wooldridge test results
LM-Breusch and Pagan Multiplier test results
(V_b-V_B is not positive definite) Prob > chi2 = 0.0030
= 19.83
chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B)
Test of H0: Difference in coefficients not systematic
B = Inconsistent under Ha, efficient under H0; obtained from xtreg.
b = Consistent under H0 and Ha; obtained from xtreg.
GDP -.0008921 -.0014328 .0005407 .
SIZE .0067702 .0043786 .0023916 .0008025 LEV .0000626 .0000308 .0000318 9.34e-06 NPL -.0530647 -.0446822 -.0083825 . LTD .0037678 .0063355 -.0025676 .0010425 LLP .3851452 .3870833 -.0019381 .
fem rem Difference Std. err.
(b) (B) (b-B) sqrt(diag(V_b-V_B)) Coefficients
. hausman fem rem
Prob > F = 0.1653 F( 1, 19) = 2.083 H0: no first-order autocorrelation
Wooldridge test for autocorrelation in panel data . xtserial ROA LLP LTD NPL LEV SIZE GDP
Prob>chi2 = 0.0000 chi2 (20) = 1125.20
H0: sigma(i)^2 = sigma^2 for all i in fixed effect regression model
Modified Wald test for groupwise heteroskedasticity . xttest3