Factors affecting the liquidity risk of joint stock commercial banks on stock exchanges in viet nam

MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM BANKING UNIVERSITY OF HO CHI MINH CITY FACTORS AFFECTING THE LIQUIDITY RISK OF JOINT STOCK COMMERCIAL BANKS ON STOCK EXCHANGES IN VIETNAM GRADUATION DISSERTATION SPECIALIZED: BANKING AND FINANCE CODE: 52340201 HO CHI MINH CITY, JANUARY 2020 MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM BANKING UNIVERSITY OF HO CHI MINH CITY FACTORS AFFECTING THE LIQUIDITY RISK OF JOINT STOCK COMMERCIAL BANKS ON STOCK EXCHANGES IN VIETNAM GRADUATION DISSERTATION SPECIALIZED: BANKING AND FINANCE CODE: 52340201 INSTRUCTOR: HO CHI MINH CITY, JANUARY 2020 i ABSTRACT Liquidity problems are re-emphasised as Vietnamese commercial banks are making an effort in deploying Basel II for hoping a greater stability and decrease the likelihood of repeating the financial crisis events in 2007 Therefore, the aim of this research is to identify factors that affect liquidity risk of 17 Joint-stock commercial banks listed on stock exchanges in Vietnam and the data covers the period from 2010 to 2018 Multivariate regression models (Pooled-OLS, FEM, REM) were used to test the effects and levels of determinants; and after being selected by F-test and Hausman test, REM was the most appropriate However, REM had heteroscedasticity in variance of error and plus, autocorrelation in the dataset Therefore, FGLS regression model is used to fix autocorrelation and unconstant variance of error to ensure a consistent and effective estimation The result reveals that from 2010 to 2018, size of the bank and the ratio of equity to total assets have positive effects on liquidity risk and this can be explained by the famous “too big to fail” theory that big banks are seemed to secure against liquidity risk exposure not by holding high liquidity, but by assistance from interbank market or Lender of Last Resort (Vodova, 2013); plus, equity is considered as one of the last defense, a shield that against many kinds of risk If banks see themselves as “big banks”, their motivation to hold liquidity is limited Besides, the relation between liquidity risk and return on equity, non-performing loan ratio and provision credit losses ratio is ambiguous From the result obtained, the study proposes conclusions and a number of recommendations to banks themselves to increase the efficiency and improve the liquidity of Vietnamese commercial banks, as well as to the Governement on the management of the banking system in the coming period ii SUMMARY In recent year, along with the emergence of globalization and free trade, economic individuals have created an environment of growth and competition Financial markets are no exception, particularly the commercial banks – intermediaries that connect individuals, companies and other institutions together, keep the economy going In addition to competition from domestic financial institutions, banks also face foreign financial ones which enter Vietnam gradually Banking industry is obviously one of the most sentimental activities not just in Vietnam but worldwide and plays an extremely important role in economic development Banks not only influence but also promote the integration of economic activities such as resource mobilization, development activities, allocation of public finance and even social welfare distribution The administration of banking is therefore always a matter of particular concern for the government to carry out its management and supervisory activities Banks need to adapt, thrive and evolve effectively to survive in harsh environments, if they not, they will be eliminated With a default of one bank, it could lead to the collapse of the entire financial and economic system due to its interconnectability Global financial crisis that happened in 2007 could be a typical example of the banks’ strong influence on the economy that led to a series of bankruptcy, pushing the economic stagnation to its peak Besides, the stock market in Vietnam is still quite young, the financial system is not really healthy and open, creating difficulties and barriers for banking activities Thus, as liquidity problems are re-emphasised as Vietnamese commercial banks are making an effort in deploying Basel II for hoping a greater stability and decrease the likelihood of repeating the financial crisis events in 2007 Moreover, after joining the ASEAN Economic Community in 2015, Vietnam has committed itself to alleviating restrictions in the banking sector, giving this sector many oppoturnities; but also many challenges such as competitive pressure from regional iii banks and international banks, in particular with regard to the limited financial potential of Vietnam compared to other banks in other countries Therefore, the aim of this research is to identify factors that affect liquidity risk of Joint-stock commercial banks listed on stock exchanges in Vietnam If the banks have strong liquidity, this not only helps to stabilize the financial market but also helps to grow the economy in Vietnam Thus, to determine and evaluate the level of impact of these determinants and give conclusions and recommendation from the obtained results This research systematized the theoretical framework including theory definitions and liquidity risk impacts to the customers, the bank itself and the economy; and then evaluated the factors affecting liquidity risk in Vietnamese commercial banks and give empirical evidence based on previous studies There are two basic types of determinants of liquidity risk which are objective factors and subjective factors However, due to limited time, the author only focused on subjective factors without considering the affect of factors on “market” level and government policies on bank liquidity Model of this research is based on Vodova (2011) and Trương Quang Thông (2013) panel data regression models as follows: In which, LR is liquidity risk as a dependent variable; ETA, NPL, ROE, LnSIZE, PCL is ratio of equity to assets, non-performing loan ratio, return on equity, size of the bank, provision for credit losses respectively as independent variables; is error term; is the 17 joint-stock commercial banks according to the list on the Government’s website; is the year from 2010 to 2018 The data was collected from financial statements of 17 Join-stock commercial banks that listed on stock exchanges in Vietnam The estimated effects have also been presented with a positive correlation between LR and ROE, LnSIZE, PCL and a negative correlation between LR and ETA, NPL iv Stata software was then used to describe statistically the dataset and test the correlation matrix between variables and the result was that ETA has a negative correlation with LR, whereas ROE and LnSIZE has a positive correlation with LR Multivariate regression models (Pooled-OLS, FEM, REM) were used to test the effects and levels of determinants; and after being selected by F-test and Hausman test, REM was the most appropriate Although REM did not have multi-collinearity phenomenon, it still had heteroscedasticity in variance of error and plus, autocorrelation in the dataset Therefore, FGLS was used to fix autocorrelation and unconstant variance of error to ensure a consistent and effective estimation The result is as follows: Due to the characteristics of FGLS, the R2 value does not count as meaningful when it comes to measure the suitability of the model, however, it can be used to calculate statistical values as above Whereby, both ETA and LnSIZE has positive effects on LR Firstly, the higher bank’s size, the higher liquidity risk exposure which is consistent with hypothesis H4 This result can be explained by the “Too big to fail” theory as big banks are seemed to secure against liquidity risk exposure not by holding high liquidity, but by assistance from interbank market or Lender of Last Resort (Vodova, 2013) Secondly, there is a strong positive effect of the ratio of equity-to-assets to liquidity risk meaning when the ratio of equity-to-assets decreases, liquidity risk will decrease as well This result is inconsistent with hypothesis H1, but suprisingly consistent with the result on the influence of the bank’s size on liquidity risk Equity is considered as one of the last defense, a shield that against many kinds of risk If banks see themselves as “big banks”, their motivation to hold liquidity is limited This result is in line with the result of Trương Quang Thông (2013) However, the relation between liquidity risk and return on equity, non-performing loan ratio and provision credit losses ratio is ambiguous v From the result obtained, the study proposes a number of conclusions and recommendations to increase the efficiency and improve the liquidity of Vietnamese commercial banks in the coming period Particularly, due to banks’ reliance too much on the Gorvernment, the Government has enacted the Law Amendments to some articles of the Law on Credit Institutions (Law No 17/2017/QH14), is effective from January 15, 2018 that banks can be able to go bankrupt if they are poorly operating and are put under special control by the Government, which has changed entire situation Therefore, banks need to rely more on themselves than on passive strategies as they used to, which is why the author then gave some recommendations to banks themselves to improve their liquidity and operational management, as well as some recommendations to the Governement on the management of the banking system In particular, banks need to strengthen internal control system, ensure capital mobilization, prepare specific plans for upcoming risk cases from the best to the worst The Government needs to their leadership role for banks, inspect and control banking activities effectively, improve the organizational structure and apply effectively the Basel’s principles on managing liquidity However, there still exists some limits of this research such as: this research is only conducted on join-stock commercial banks, not the whole banking system in Vietnam; the author only used one measurement to measure liquidity of the bank; the result of FGLS model can not be given out R-squared value to measure the suitability of the model; this study only conducted internal determinants Therefore, the author hopes to study further to provide a more general measurement of liquidity risk, plus to build a better model to make it a more useful reference for students’ extensive researches vi ASSURANCE LETTER I assure that the “factors affecting liquidity risk of joint-stock commercial banks on stock exchanges in Vietnam” dissertation is my own report The figures and sources of information in this research are derived clearly and honestly from the banks' consolidated financial statements In addition, the tests were conducted publicly and transparently with no intervention to correct the results of regression models, in which there are no previously published content or content made by others except for full citations in the report Author Nguy n Thu Ng n vii THANK YOU LETTER I would like to thank the teachers and friends in the Banking University in Ho Chi Minh city; and with the deepest gratitude, I would like to send to the personnel in the Department of Finance and Department of Banking the most sincere thanks for the knowledge and dedication, who has devoted to us during our school time Especially in the program of implementing the graduation dissertation with the guidance of ssociation Professor and Doctor of Philosophy ng V n D n, I have been helped a lot in choosing the topic, writing the research, as well as in-depth guidance in how to work properly Finally, I would like to thank my family, friends and relatives who have always been there to support and encourage me to complete my graduation dissertation I sincerely thank! viii INDEX ABSTRACT i SUMMARY ii ASSURANCE LETTER .vi THANK YOU LETTER vii INDEX viii LIST OF ACRONYMS xii LIST OF TABLES xiii LIST OF GRAPHS .xiv CHAPTER INTRODUCTION 1.1 Introduction 1.2 Previous studies 1.3 Research objectives .4 1.4 Research questions 1.5 Research subjects and scope 1.5.1 Research subjects .5 1.5.2 Research scope 1.6 Methodology 1.7 Contribution of the study 1.8 Dissertation structure CHAPTER THEORETICAL FRAMEWORK 2.1 Theory of liquidity risk of join-stock commercial banks .8 2.1.1 Joint stock Commercial banks 2.1.2 Bank liquidity risk ii Trương Quang Thông 2013, “C c y u t t c ng n r i ro kho n c a h th ng ng n h ng thương m i Vi t Nam”, h t tri n inh t , vol 276, p 50-62 V Th ng 201 , “C c y u t thương m i Vi t Nam”, nh hư ng n kho n c a c c ng n h ng p h ph t tri n v h i nh p, vol 23, p 32-49 English documents and websites Athanasolou, P P., Delis, M.D., Staikouras, C.K 2006, “Determinants of bank profitability in the South Eastern European Region”, Bank of Greece working paper, no 47 Aspachs, O., Nier, E., Tiesset, M 2005, Liquidity, Banking Regulation and the Macroeconomy roof of sh res, b n liquidity from p nel the b n ’s UK- resident, Bank of England Working Papers Basel Committee on Banking Supervision, “Bael III: International framework for liquidity risk measurement, standards and monitoring”, Bank for International Settlements 2009 Basel Committee on Banking Supervision, “Basel III: The liquidity coverage ratio and liquidity risk monitoring tools”, Bank for International Settlements 2013 Bonfim, D., Kim, M 2012, “Liquidity risk: is there herding?”, Working Papers w201218, Banco de Portugal, Economic and Research Department Chung-Hua Shen et al 2009, Bank Liquidity Risk and Performance, Working paper Joseph E Stiglitz 2009, Too Big to Live, Available from , [20 December 2019] Lee, S.W., 200 , “Ownership structure, regulation, and bank risk-taking: Evidence from Korean banking industry”, Investment Management and Financial Innovations, vol 5, Issue 4, pp 70-74 iii Luchetta, M 2007, “What data say about monetary policy, Bank liquidity and Bank Risk Taking?”, Economic Notes Banca Monte dei Paschi di Siena SpA, vol 36, no 2, p 189-203 Nikolaou, N 2009, “Liquidity (Risk) Concepts: Definitions and Interactions”, European Central Bank working paper series, no 1008 Praet, P & erzberg, V 200 , “Market liquidity and banking liquidity: linkages, vulnerabilities and the role of disclosure”, Banque de France Financial Stability Review, no 11, p 95-109 Rychtarik, S 200 , “Liquidity Scenario nalysis in the Luxembourg Banking Sector”, Banque Centrale Du Luxembourg Working Paper, no 41 Valla, N., Ses-Escorbiac, B 200 , “Bank liquidity and financial stability”, Banque de France Financial Stability Review, no 9, p 89 – 104 Vodov , P 2011, “Liquidity of Czech Commericla Banks and its determinants”, International Journal of Mathematical Models and Methods in Applied Sciences, vol 5, Issue p 1060-1067 Vodov , P 2013, “Determinants of Commerical banks’ liquidity in The financial internet quaterly “e-Fin nse”, vol 9, no 3, p 64-71 ungary”, iv APPENDIX A # Joint-stock Commercial Banks list Code ACB BAB Vietnamese name NHTMCP Ch u BID triển Vi t Nam CTG EIB Trade Ph t triển TP.H Chí Minh KLB LPB NHTMCP Kiên Long NHTMCP Ch u Bank HNX B c Bank UPCoM BIDV HOSE Vietinbank HOSE Eximbank HOSE HDBank HOSE Vietnam Vi t Nam NHTMCP HDB Development of for Industry and Nam Investment and Công Thương Vi t exchange JSCB for Vietnam JSCB Xuất Nhập Stock Bank NHTMCP NHTMCP Commercial Commercial JSB u tư v Ph t Short name Asia NHTMCP B c Bac A NHTMCP English name Vietnam Commercial Joint Stock Export Import Bank Ho Chi Minh Development JSCB Kien Long JSCB Kiên Long Bank UPCoM LienViet Post LienVietPostBank UPCoM v Bưu i n Liên JSCB Vi t MBB 10 NVB 11 SHB NHTMCP Qu n i NHTMCP National Citizen Qu c D n JSCB NHTMCP S i Saigon Hanoi Gòn - JSCB N i NHTMCP S i 12 STB Gịn Thương Tín 13 14 15 16 TCB TPB VCB VIB VPB Sai Gon Thuong Tin JSCB NHTMCP Kỹ Vietnam Thương Vi t Technological Nam and JSCB NHTMCP Tien Phong Tiên Phong JSCB NHTMCP Bank for Foreign Ngo i Thương Trade of Vi t Nam Vietnam NHTMCP Vietnam Qu c t Vi t International Nam JSCB NHTMCP 17 Military JSCB Vi t Nam Th nh Vượng Vietnam Prosperity JSCB MBBank HOSE NCB HNX SHB HNX Sacombank HOSE Techcombank HOSE TPBank HOSE Vietcombank HOSE VIB VPBank UPCoM HOSE Source: sbv.gov.vn vi B Calculated dataset # Year Bank LR ETA NPL ROE LnSIZE PCL 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 2010 2011 2012 2013 2014 2015 2016 2017 2018 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 ACB ACB ACB ACB ACB ACB ACB ACB ACB BAB BAB BAB BAB BAB BAB BAB BAB BID BID BID BID BID BID BID BID BID CTG CTG CTG CTG CTG CTG CTG CTG CTG EIB EIB EIB EIB 42.51% 36.58% 58.32% 64.34% 64.76% 66.53% 69.92% 69.82% 70.00% 65.52% 66.17% 58.67% 63.72% 65.80% 63.34% 60.46% 65.94% 69.40% 72.44% 70.12% 71.31% 68.53% 70.35% 71.91% 72.10% 75.30% 63.69% 63.71% 66.20% 65.29% 66.52% 69.03% 69.79% 72.20% 74.28% 47.55% 40.67% 44.03% 49.08% 5.55% 4.26% 7.16% 7.51% 6.90% 6.35% 6.02% 5.64% 6.38% 12.61% 9.33% 6.58% 7.21% 7.90% 7.65% 6.95% 7.30% 6.61% 6.01% 5.47% 5.84% 5.12% 4.98% 4.38% 4.06% 4.15% 4.94% 6.19% 6.68% 9.38% 8.32% 7.20% 6.36% 5.82% 5.79% 10.30% 8.88% 9.29% 8.64% 0.34% 0.28% 0.89% 2.40% 2.79% 1.32% 0.87% 0.70% 0.73% 0.64% 5.66% 2.32% 2.15% 0.70% 0.81% 0.63% 0.76% 2.53% 2.76% 2.70% 2.26% 2.03% 1.68% 1.99% 1.62% 1.90% 0.66% 0.75% 1.47% 1.00% 1.12% 0.92% 1.02% 1.14% 1.58% 1.42% 1.61% 1.32% 1.98% 21.74% 27.49% 6.38% 6.58% 7.64% 8.17% 9.87% 14.08% 27.73% 9.38% 1.08% 5.95% 7.37% 8.33% 9.26% 9.89% 10.06% 17.95% 13.20% 10.10% 13.77% 15.15% 16.66% 14.12% 14.60% 14.23% 22.15% 26.76% 19.81% 13.21% 10.47% 10.25% 11.59% 11.98% 8.25% 13.51% 20.39% 13.32% 4.32% 19.14 19.45 18.99 18.93 19.01 19.12 19.27 19.47 19.61 17.06 17.33 17.73 17.86 17.97 18.15 18.33 18.39 19.72 19.82 20.00 20.12 20.29 20.56 20.73 20.91 21.00 19.72 19.95 20.04 20.17 20.31 20.47 20.67 20.81 20.88 18.69 19.03 18.95 18.95 0.82% 0.96% 1.46% -1.87% -1.66% -1.50% -1.36% -0.87% -0.50% 0.87% 1.20% -1.40% -1.29% -1.27% -0.89% -1.02% -0.88% 2.08% 1.99% 1.74% -1.52% -1.33% -1.18% -1.63% -1.63% -1.64% 1.18% 1.03% 1.10% -0.57% -0.76% -0.80% -1.02% -1.11% -2.40% 1.01% 0.83% 0.81% -1.08% vii # Year Bank LR ETA NPL ROE LnSIZE PCL 5 5 6 6 6 6 7 7 7 7 8 8 8 8 9 9 9 9 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018 EIB EIB EIB EIB EIB HDB HDB HDB HDB HDB HDB HDB HDB HDB KLB KLB KLB KLB KLB KLB KLB KLB KLB LPB LPB LPB LPB LPB LPB LPB LPB LPB MBB MBB MBB MBB MBB MBB MBB MBB MBB 54.10% 67.89% 67.46% 67.83% 68.16% 34.10% 30.76% 40.07% 51.06% 42.19% 53.11% 54.71% 55.19% 56.99% 55.50% 47.08% 52.11% 56.75% 58.55% 64.05% 64.91% 66.13% 69.66% 28.11% 22.73% 34.62% 37.12% 40.96% 52.20% 56.16% 61.57% 68.07% 44.51% 42.53% 42.41% 48.64% 50.16% 54.90% 58.82% 58.68% 59.25% 8.73% 10.53% 10.44% 9.54% 9.75% 6.86% 7.88% 10.22% 9.97% 8.92% 9.24% 6.62% 7.80% 7.79% 25.54% 19.36% 18.54% 16.26% 14.56% 13.32% 11.05% 9.51% 8.86% 11.74% 11.75% 11.13% 9.14% 7.33% 7.06% 5.87% 5.74% 5.83% 8.10% 6.95% 7.33% 8.40% 8.26% 10.49% 10.38% 9.43% 9.43% 2.46% 1.86% 2.95% 2.27% 1.85% 0.83% 2.11% 2.35% 3.53% 2.04% 1.59% 1.46% 1.52% 1.53% 1.11% 2.77% 2.93% 2.47% 1.95% 1.13% 1.06% 0.83% 0.86% 0.42% 2.14% 2.71% 2.48% 1.23% 0.88% 1.11% 1.07% 1.41% 1.26% 1.59% 1.84% 2.45% 2.73% 1.61% 1.32% 1.20% 1.33% 0.39% 0.29% 2.32% 5.94% 4.53% 12.97% 14.44% 7.30% 3.11% 5.46% 5.48% 7.46% 14.14% 18.00% 9.00% 11.81% 10.17% 9.06% 5.14% 4.90% 3.59% 5.83% 6.35% 17.21% 18.26% 12.42% 7.72% 6.36% 4.67% 13.34% 15.45% 9.80% 21.71% 22.96% 20.49% 16.25% 15.62% 12.56% 11.47% 12.32% 19.17% 18.90 18.64 18.67 18.82 18.84 17.35 17.62 17.78 18.27 18.42 18.48 18.83 19.06 19.19 16.35 16.70 16.74 16.88 16.96 17.05 17.23 17.44 17.56 17.37 17.84 18.01 18.19 18.43 18.49 18.77 18.91 18.98 18.51 18.75 18.98 19.01 19.12 19.21 19.36 19.56 19.71 -18.24% -21.75% -3.45% -1.28% -1.62% 0.72% 1.02% 0.92% -3.21% -1.01% -1.12% -1.01% -0.59% -0.42% 0.88% 1.13% 1.47% -0.40% -0.78% -0.83% -1.40% -1.09% -1.10% 0.80% 0.92% 1.75% -1.05% -1.01% -1.99% -0.91% -0.90% -1.54% 1.51% 1.85% 1.76% -0.77% -0.98% -0.79% -0.71% -0.61% -0.52% viii # Year Bank LR ETA NPL ROE LnSIZE PCL 10 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11 12 12 12 12 12 12 12 12 12 13 13 13 13 13 13 13 13 13 14 14 14 14 14 2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 NVB NVB NVB NVB NVB NVB NVB NVB NVB SHB SHB SHB SHB SHB SHB SHB SHB SHB STB STB STB STB STB STB STB STB STB TCB TCB TCB TCB TCB TCB TCB TCB TCB TPB TPB TPB TPB TPB 53.79% 57.41% 59.70% 46.35% 45.17% 42.36% 36.74% 44.70% 49.26% 47.76% 41.08% 48.86% 53.27% 61.58% 64.20% 69.41% 69.33% 67.12% 54.13% 56.93% 63.33% 68.51% 67.45% 63.66% 59.89% 60.51% 63.20% 35.22% 35.15% 37.94% 44.23% 45.65% 58.14% 60.59% 59.71% 49.83% 25.01% 14.73% 40.23% 37.17% 38.54% 10.10% 14.30% 14.76% 11.02% 8.72% 6.67% 4.68% 4.48% 4.46% 8.20% 8.21% 8.16% 7.21% 6.20% 5.50% 5.66% 5.14% 5.05% 9.20% 10.28% 9.01% 10.57% 9.52% 7.56% 6.68% 6.31% 6.07% 6.25% 6.93% 7.39% 8.76% 8.52% 8.57% 8.32% 10.00% 16.13% 15.31% 6.72% 21.95% 11.53% 8.23% 2.24% 2.92% 5.64% 6.07% 2.52% 2.15% 1.54% 1.53% 1.67% 1.40% 2.23% 8.81% 4.06% 2.02% 1.72% 1.93% 2.33% 2.40% 0.54% 0.58% 2.05% 1.46% 1.23% 5.80% 5.35% 4.16% 2.13% 2.29% 2.83% 2.70% 3.65% 2.38% 1.67% 1.58% 1.61% 1.75% 0.02% 0.67% 3.66% 1.97% 1.01% 9.84% 6.35% 0.07% 0.58% 0.25% 0.20% 0.34% 0.68% 1.12% 14.98% 15.04% 0.34% 8.56% 7.59% 7.32% 7.46% 11.02% 10.78% 15.24% 14.47% 7.10% 14.49% 12.56% 3.23% 0.40% 4.40% 7.48% 24.80% 28.79% 5.93% 4.84% 7.49% 9.73% 17.47% 27.71% 21.56% 6.69% -56.33% 4.66% 10.87% 13.50% 16.81 16.93 16.89 17.19 17.42 17.69 18.05 18.09 18.10 17.75 18.08 18.57 18.78 18.95 19.14 19.27 19.47 19.59 18.84 18.77 18.84 18.90 19.06 19.49 19.62 19.72 19.82 18.83 19.01 19.01 18.88 18.99 19.07 19.28 19.41 19.59 16.85 17.03 16.53 17.28 17.76 1.19% 1.23% 1.70% -11.33% -24.02% -32.27% -26.79% -16.36% -10.83% 1.12% 1.22% 2.20% -1.40% -1.32% -1.79% -1.97% -1.85% -1.80% 0.99% 1.01% 1.50% -0.61% -0.62% -3.48% -27.44% -2.33% -1.97% 1.15% 1.40% 1.65% 1.69% -0.89% -0.76% -0.47% -0.29% -0.28% 1.32% 1.65% 1.52% 0.98% -0.37% ix # Year Bank LR ETA NPL ROE LnSIZE PCL 14 14 14 14 15 15 15 15 15 15 15 15 15 16 16 16 16 16 16 16 16 16 17 17 17 17 17 17 17 17 17 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018 TPB TPB TPB TPB VCB VCB VCB VCB VCB VCB VCB VCB VCB VIB VIB VIB VIB VIB VIB VIB VIB VIB VPB VPB VPB VPB VPB VPB VPB VPB VPB 37.05% 44.09% 51.10% 56.68% 57.50% 57.11% 58.18% 58.49% 56.04% 57.41% 58.49% 52.49% 58.83% 44.48% 44.87% 52.12% 45.84% 47.33% 56.67% 57.58% 64.85% 69.08% 42.34% 35.24% 35.98% 43.27% 48.01% 60.25% 63.24% 65.77% 68.66% 6.30% 5.37% 5.38% 7.80% 6.74% 7.81% 10.02% 9.04% 7.51% 6.70% 6.10% 5.08% 5.79% 7.03% 8.42% 12.87% 10.38% 10.54% 10.21% 8.36% 7.14% 7.67% 8.70% 7.24% 6.47% 6.37% 5.50% 6.91% 7.51% 10.69% 10.75% 0.66% 0.71% 1.09% 1.12% 2.91% 2.03% 2.40% 2.73% 2.31% 1.84% 1.48% 1.14% 0.98% 1.59% 2.70% 2.62% 2.82% 2.51% 2.07% 2.58% 2.49% 2.52% 1.20% 1.82% 2.72% 2.81% 2.54% 2.69% 2.91% 3.39% 3.50% 12.44% 10.79% 15.59% 20.87% 22.55% 17.02% 12.53% 10.38% 10.65% 12.01% 14.65% 18.06% 25.46% 16.58% 8.66% 6.33% 0.61% 6.34% 6.09% 6.47% 12.83% 22.55% 12.98% 14.28% 10.19% 14.17% 15.01% 21.42% 25.75% 27.48% 22.83% 18.15 18.48 18.64 18.73 19.54 19.72 19.84 19.97 20.17 20.33 20.48 20.76 20.79 18.36 18.39 17.99 18.16 18.21 18.25 18.46 18.63 18.75 17.91 18.23 18.45 18.61 18.91 19.08 19.25 19.44 19.59 -0.47% -0.72% -0.70% -0.49% 3.15% 2.54% 2.19% -1.47% -1.55% -1.61% -1.19% -0.89% -0.70% 1.13% 1.58% 1.69% -18.42% -1.70% -1.44% -1.81% -0.84% -0.40% 0.91% 1.08% 1.03% -0.59% -0.90% -0.73% -0.53% -0.49% -0.48% x C Regression results with Stata 13 C.1 Panel data description xtset bank_n Year, yearly panel variable: bank_n (unbalanced) time variable: Year, 2010 to 2018 delta: C.2 year Variables statistics sum Variable | Obs Mean Std Dev Min Max -+ LR | 152 5508276 1221898 1473 753 ETA | 152 084348 0325799 0406 2554 NPL | 152 0199199 0120789 0001832 0880662 ROE | 152 1115408 0879786 -.5633 2879 -+ - C.3 LnSIZE | 152 18.77798 1.044619 16.35141 20.99561 PCL | 152 -.0158546 055379 -.3227 0315 Variables correlation pwcorr LR ETA NPL ROE LnSIZE PCL, sig | LR CAP NPL ROE LnSIZE PCL -+ -LR | 1.0000 | | ETA | -0.1777 | 0.0285 1.0000 | NPL | 0.0078 0.1509 1.0000 | 0.9244 0.0636 ROE | 0.1510 -0.1179 -0.1978 | 0.0633 0.1481 0.0146 LnSIZE | 0.4929 -0.6053 -0.0905 0.3894 | 0.0000 0.0000 0.2677 0.0000 PCL | 0.0142 0.1304 -0.1359 0.3291 0.0584 | 0.8622 0.1094 0.0950 0.0000 0.4746 | 1.0000 | 1.0000 | 1.0000 xi C.4 Pooled-OLS regression reg LR ETA NPL ROE LnSIZE PCL Source | SS df MS -+ Number of obs = 152 F(5, 146) = 10.90 Model | 612980389 122596078 Prob > F = 0.0000 Residual | 1.64150111 146 011243158 R-squared = 0.2719 Adj R-squared = 0.2470 Root MSE = 10603 -+ -Total | 2.2544815 151 014930341 -LR | Coef Std Err t P>|t| [95% Conf Interval] -+ -ETA | 7746363 3469517 2.23 0.027 0889399 1.460333 NPL | 1902182 7418828 0.26 0.798 -1.275999 1.656435 ROE | -.0882946 1149841 -0.77 0.444 -.3155429 1389537 LnSIZE | 0755623 0113881 6.64 0.000 0530555 0980691 PCL | -.0595918 1688418 -0.35 0.725 -.3932816 2740979 _cons | -.9283039 2280532 -4.07 0.000 -1.379016 -.477592 - est store pooled C.5 FEM regression xtreg LR ETA NPL ROE LnSIZE PCL, fe Fixed-effects (within) regression Number of obs = 152 Group variable: bank_n Number of groups = 17 R-sq: Obs per group: = 0.3547 = between = 0.2717 avg = 8.9 overall = 0.2600 max = within corr(u_i, Xb) = -0.6628 F(5,130) = 14.29 Prob > F = 0.0000 -LR | Coef Std Err t P>|t| [95% Conf Interval] -+ -ETA | 9965115 3104977 3.21 0.002 3822289 1.610794 NPL | 3914505 6255708 0.63 0.533 -.8461665 1.629067 ROE | -.0251111 0897472 -0.28 0.780 -.2026652 152443 LnSIZE | 1412136 0172636 8.18 0.000 1070597 1753675 xii PCL | 2264421 1506219 1.50 0.135 -.0715454 5244295 _cons | -2.186338 3347695 -6.53 0.000 -2.84864 -1.524037 -+ -sigma_u | 10773889 sigma_e | 07633008 rho | 66580828 (fraction of variance due to u_i) -F test that all u_i=0: F(16, 130) = 9.48 Prob > F = 0.0000 est store fem C.6 REM regression xtreg LR ETA NPL ROE LnSIZE PCL, re Random-effects GLS regression Number of obs = 152 Group variable: bank_n Number of groups = 17 R-sq: Obs per group: within = 0.3528 = between = 0.2746 avg = 8.9 overall = 0.2635 max = Wald chi2(5) = 65.03 Prob > chi2 = 0.0000 corr(u_i, X) = (assumed) -LR | Coef Std Err z P>|z| [95% Conf Interval] -+ -ETA | 8877727 3065612 2.90 0.004 2869238 1.488622 NPL | 3201227 6242817 0.51 0.608 -.903447 1.543692 ROE | -.0251456 0904312 -0.28 0.781 -.2023875 1520963 LnSIZE | 1138689 0147467 7.72 0.000 0849658 142772 PCL | 121211 1469859 0.82 0.410 -.166876 409298 _cons | -1.662797 2882409 -5.77 0.000 -2.227739 -1.097856 -+ -sigma_u | 08175963 sigma_e | 07633008 rho | 5343043 (fraction of variance due to u_i) - est store rem C.7 Pooled-OLS, FEM, REM regression esttab pooled fem rem xiii -(Pooled-OLS) (FEM) LR (REM) LR LR -ETA 0.775* NPL ROE LnSIZE 0.997** (2.23) (3.21) (2.90) 0.190 0.391 0.320 (0.26) (0.63) (0.51) -0.0883 -0.0251 -0.0251 (-0.77) (-0.28) (-0.28) 0.0756*** PCL _cons 0.888** 0.141*** 0.114*** (6.64) (8.18) (7.72) -0.0596 0.226 0.121 (-0.35) (1.50) (0.82) -2.186*** -1.663*** -0.928*** (-4.07) (-6.53) (-5.77) -N 152 152 152 -t statistics in parentheses * p