Tóm tắt tiếng anh: Ứng dụng mô hình CAMELS trong kiểm định các yếu tố tác động đến tăng trưởng cho vay của các NHTM Việt Nam.

Tóm tắt tiếng anh Ứng dụng mô hình CAMELS trong kiểm định các yếu tố tác động đến tăng trưởng cho vay của các NHTM Việt Nam.Tóm tắt tiếng anh Ứng dụng mô hình CAMELS trong kiểm định các yếu tố tác động đến tăng trưởng cho vay của các NHTM Việt Nam.Tóm tắt tiếng anh Ứng dụng mô hình CAMELS trong kiểm định các yếu tố tác động đến tăng trưởng cho vay của các NHTM Việt Nam.Tóm tắt tiếng anh Ứng dụng mô hình CAMELS trong kiểm định các yếu tố tác động đến tăng trưởng cho vay của các NHTM Việt Nam.Tóm tắt tiếng anh Ứng dụng mô hình CAMELS trong kiểm định các yếu tố tác động đến tăng trưởng cho vay của các NHTM Việt Nam.Tóm tắt tiếng anh Ứng dụng mô hình CAMELS trong kiểm định các yếu tố tác động đến tăng trưởng cho vay của các NHTM Việt Nam.

MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM BANKING UNIVERSITY OF HO CHI MINH CITY NGUYEN HOANG DIEU HIEN AN APPLICATION OF THE CAMELS FRAMEWORK IN TESTING THE DETERMINANTS OF LOAN GROWTH OF VIETNAMESE COMMERCIAL BANKS SUMMARY OF DOCTORAL DISSERTATION Specialization: Finance – Banking Code: 34 02 01 Academic supervisor: Associate Professor Dang Van Dan HO CHI MINH CITY – 2022 CHAPTER INTRODUCTION 1.1 Research motivations Loan growth at an appropriate level from the perspective of the country will greatly support economic growth; from a bank's perspective, it will help them get a good profit from their core business Thus, in the context that lending is always an important channel of operation for banks and the economy, it is necessary to understand the factors affecting the loan growth of banks, in line with the practice of state management and business development of the society Researchers worldwide have studied theoretically and experimentally the factors affecting the growth of bank loans The studies combine internal and macroeconomic banking factors to find the most comprehensive answer for the markets surveyed However, for Vietnam, those findings cannot clarify the issue when there are almost no comprehensive studies directly exploiting this market Moreover, studies have not generally approached the issue in a systematic way, instead of assembling each of the factors that are believed to be important and affect loan growth Since 2019, the SBV has agreed on a banking evaluation framework for the whole industry based on ideas from the CAMELS principles, with components: capital adequacy, asset quality, management efficiency, earnings, liquidity, and market sensitivity This approach is a comprehensive set of international standards, meaningful to banks themselves in assessing their performance and useful to regulators to perform their inspection and supervision functions From the perspective of the SBV, CAMELS is a set of tools to evaluate and rank banks However, for the banks themselves, these are standards for them to aim and focus on improving in order to improve efficiency and development 1.2 Research objectives The main objective of the study is to investigate the impact of internal bank factors on lending activities of Vietnamese commercial banks, using a framework of CAMELS factors Specifically, the study will endeavor to achieve the following specific objectives:  Identify appropriate representative variables for each factor in the CAMELS framework and thereby assess the financial situation, operational efficiency and soundness of the Vietnamese banking system based on established criteria  Find out the impact of internal factors according to CAMELS on the loan growth of banks Accordingly, internal factors according to CAMELS include capital adequacy, asset quality, management efficiency, earnings, liquidity, and market sensitivity  Provide appropriate explanations for the findings in the context of the market situation and related management policies during the survey period 1.3 Research object and scope The research object of the thesis is the internal factors of the bank and the bank's loan growth The bank's internal factors based on the CAMELS framework include capital adequacy, asset quality, management efficiency, earnings, liquidity, and market sensitivity The scope of the thesis is based on financial information of Vietnamese commercial banks in the period from 2007 to 2019 1.4 Research methodology and data The study uses data taken from two sources, including bank-level data on banks' annual financial statements and Vietnam's macro data from the World Development Indicators (WDI) for the research period from 2007 to 2019 The sample includes 31 banks, including both listed and unlisted banks, creating an unbalanced panel dataset To test the research models, the thesis uses the generalized method of moments (GMM) for the main estimator and the ordinary least squares (OLS) and the generalized least squares (GLS) methods to test the sensitivity of the estimate 1.5 Contributions of the study For academic respect, this study adds to the existing literature on assessing the impact of internal factors on the lending growth of commercial banks in Vietnam For the first time, the study applies the CAMELS framework to assess loan growth at banks Existing studies lack a uniform standard framework for selecting influencing factors to include in the model Thereby, the CAMELS approach is a new point of research when it has the ability to improve the approach of the existing literature, providing a comprehensive and systematic understanding of the research problem Furthermore, each of the factors that affect loan growth according to CAMELS is supported by the existing theoretical basis so that empirical testing of these effects is appropriate and yields useful and meaningful results In particular, factors such as management efficiency and market sensitivity were also included in this empirical study for the first time to investigate the lending growth behavior of banks Filling in this research gap is expected to expand existing knowledge on the determinants of bank lending behavior For practical respect, research results may bring policy implications to help bank administrators and state management agencies solve the problems that are needed to be clarified in the Vietnamese banking market Regarding the application of CAMELS, besides its application to determine the bank's capacity, it is also an effective tool to assess the bank's loan growth Regarding credit growth, the findings can provide tools and suggestions for the SBV in determining credit growth limits for each bank based on the bank's internal potential 1.6 Research layout Chapter Introduction Chapter Theoretical basis and overview of the literature Chapter Research methodology and data Chapter Empirical results and discussions Chapter Conclusions and implications CHAPTER THEORETICAL BASIS AND OVERVIEW OF THE LITERATURE 2.1 Bank loan growth 2.1.1 Bank loans 2.1.2 Loan growth 2.1.3 Meaning of loan growth 2.2 CAMELS framework 2.2.1 Introduction The CAMELS framework was developed in 1979, recommended by the US Federal Reserve This framework has been used by financial institutions in the US and later worldwide CAMELS currently includes six factors: capital adequacy, asset quality, management efficiency, earnings, liquidity, and market sensitivity The CAMELS framework emphasizes the parameters of the banking system by using the income statement to evaluate the performance and the balance sheet to assess the financial position of the banks In which, details of the components evaluated according to this framework are as follows: + Capital The supervisory authority assesses the capital adequacy of banks through an analysis of capital trends In order to obtain a high capital adequacy rating, banks must also adhere to rules and practices regarding interest rates and dividends Other factors related to the rating and assessment of an organization's capital adequacy are its growth plan, business environment, ability to control risk, lending, and investment concentration + Asset quality Asset quality includes the quality of a bank's loan Assess asset quality in relation to the investment risk factors the bank may face and compare them with its capital income The watchdog also examines how banks are affected by the market value of their investments when doubled to book value Ultimately, asset quality is reflected by the effectiveness of a bank's investment policies and practices + Management efficiency The management assessment determines whether a bank can properly respond to financial stress This component rating is reflected by management's ability to measure and control risk in the bank's day-to-day operations It includes management capabilities to ensure the safe operation of banks as they comply with internal and external regulations + Earnings A bank's ability to generate consistent profits in order to be able to expand, remain competitive and raise capital is an important factor in assessing a bank's continued viability The watchdog determines this by assessing a company's growth, stability, net profit margin, and existing asset quality + Liquidity To assess a bank's liquidity, the supervisory authority considers interest rate risk sensitivity, availability of assets that can be easily converted into cash, depending on shortterm financing sources, the structure of liabilities, and assets + Sensitivity to market risk The supervisory authority assesses a bank's sensitivity to market risk by monitoring its credit portfolio management In this way, they can see how lending to specific industries affects a bank 2.2.2 CAMELS factors 2.2.2.1 Capital adequacy (C) 2.2.2.2 Asset quality (A) 2.2.2.3 Management efficiency (M) 2.2.2.4 Earnings (E) 2.2.2.5 Liquidity (L) 2.2.2.6 Market risk sensitivity (S) 2.3 Theoretical and empirical basis on the impact of bank-specific factors on loan growth 2.3.1 Impact of bank capital on loan growth 2.3.1.1 Theoretical research 2.3.1.2 Empirical research 2.3.2 Impact of asset quality on loan growth 2.3.2.1 Theoretical research 2.3.2.2 Empirical research 2.3.3 Impact of management efficiency on loan growth 2.3.3.1 Theoretical research 2.3.3.2 Empirical research 2.3.4 Impact of earnings on loan growth 2.3.4.1 Theoretical research 2.3.4.2 Empirical research 2.3.5 Impact of liquidity on loan growth 2.3.5.1 Theoretical research 2.3.5.2 Empirical research 2.3.6 Impact of market risk sensitivity on loan growth 2.3.6.1 Theoretical research 2.3.6.2 Empirical research 2.4 An assessment of the research situation and exploitable gaps In summary, when looking at the research done on the topic, there are still many research gaps that need to be exploited: (i) There are not many studies focusing on empirically assessing the factors affecting loan growth in Vietnamese banks Most of the existing studies are not comprehensive and have many limitations in terms of data, methods, and approaches (ii) Existing studies lack a unified standard framework for selecting influencing factors to include in the model Thereby, the CAMELS approach is a new point of research when it has the ability to improve the problem approach of the existing literature, providing a comprehensive understanding of the research problem and systematic (iii) Bank-specific factors such as management efficiency and market risk have not been empirically investigated much in relation to bank loan growth CHƯƠNG RESEARCH METHODOLOGY AND DATA 3.1 Research variables 3.1.1 Loan growth variable The thesis analyzes the impact of bank-specific factors on the loan growth of banks, so the dependent variable will be determined by the bank's annual loan growth rate 3.1.2 Bank internal variables according to CAMELS 3.1.2.1 Capital Regarding the impact of bank capital on loan growth, the available literature suggests that this effect can go both ways For example, for financial stability, holding sufficient capital will help the bank absorb possible losses and thereby maintain better lending capacity (Distinguin et al 2013) Accordingly, banks with high capital levels can extend loans faster than banks with small capital levels Furthermore, in times of financial stress, banks with high capital levels are better able to cope with crises and support lending than the rest of the banks (Košak et al 2015) However, some views indicate that bank capital is considered a determining factor in management motivation Then banks with high capital ratios will become more cautious and extend lending to a smaller extent than banks with weak capital ratios (Goodhart 2013) Recent empirical analyses also show that bank capital's impact on loan growth is not clear across different markets Therefore, the impact of bank capital can be either positive or negative on loan growth Accordingly, the regression coefficient of the capital variable (C) is predicted to have a positive or negative sign 3.1.2.2 Asset quality Regarding the impact of asset quality on loan growth, the available literature mainly supports the following view A bank with a high amount of NPLs will tend to focus on strengthening risk management and improving asset quality at the same time rather than on credit expansion (Bernanke and Blinder 1988; Altunbas et al 2010) Thus, improved asset quality will allow strong banks to expand their lending business Furthermore, high credit risk constrains the bank's resources, reducing the bank's profitability and leading to higher funding costs The result is that banks are forced to reduce the supply of credit Numerous empirical studies conducted in different markets have also confirmed this direction of impact Thus, the impact of asset quality could be positive on loan growth Based on the variables used, the regression coefficients of the variables loan loss provisions (A) and nonperforming loans (A) are expected to have negative signs (these variables are the inverses of asset quality) 3.1.2.3 Management efficiency Regarding the impact of management efficiency on loan growth, the literature currently points to two opposing directions of impact With poorly managed banks, poor monitoring and evaluation practices will lead to a rapid increase in the number of loans granted after a while, the loan portfolio becomes much larger, and thereby, low efficiency is expected to lead to faster loan growth (Berger and DeYoung 1997) Furthermore, as suggested by the “moral hazard” hypothesis, bank managers have an incentive to take more risk, especially when the bank is less efficient (Jeitschko and Jeung 2005) However, an opposing mechanism is also observed With short-term cost-effectiveness due to lower operating costs for the same amount of loans to customers, the quality of loans remains unaffected in the short term, thereby expanding the loan portfolio during this period (Berger and DeYoung 1997) Therefore, the impact of management efficiency can be either positive or negative on loan growth Based on the variables used, the regression coefficients of Non-interest expenses/Revenue (M) and Operating expenses/Assets (M) are predicted to have a negative or positive sign 3.1.2.4 Earnings Regarding the impact of bank profitability on loan growth, the research literature points to different directions Banks with high profitability can better reduce funding costs and accumulate bank equity faster, as well as make better use of capital raising (deposits, equity) thanks to their reputation and a higher credit rating, thereby creating favorable conditions for faster loan expansion (Holmstrom and Tirole 1997) However, higher profitability may reduce the incentive for banks to seek profit, thereby making lending behavior safer with a lower growth rate (Rajan 2006) The pooled empirical studies also have evidence to support both directions of action Therefore, the impact of bank profitability can be either positive or negative on loan growth Based on the variables used, the regression coefficients of the variables ROA (E), ROE (E), and NIM (E) are predicted to be either negative or positive 3.1.2.5 Liquidity Regarding the impact of bank liquidity on loan growth, the motive for storing liquidity for provisioning purposes is most relevant to explaining the impact mechanism According to Gennaioli et al (2014), banks can optimally choose to hold highly liquid assets as a way to store liquidity to finance future investments Moreover, to ensure optimal cash flow and profit, banks can temporarily invest a part of the capital received in short-term assets with high liquidity immediately after raising money from the market Therefore, the impact of bank liquidity could be positive on loan growth Based on the variables used, the regression coefficient of the Liquid Assets variable (L) is expected to have a positive sign, and the Loan/Deposits variable (L) is expected to have a negative sign (this variable is inverse of bank liquidity) 3.1.2.6 Market risk sensitivity Regarding the impact of interest rate risk on loan growth, the theory suggests that if market interest rates move in an unfavorable direction, the resulting losses can deplete a bank's economic capital This makes it possible for banks to actively reduce lending to maintain compliance with capital requirements imposed by regulators or market participants when the bank itself is too sensitive to risk from the market (Myers 2001) Therefore, the impact of interest rate risk could be negative on loan growth Accordingly, the regression coefficient of the variable Asset-Liability Gap (S) is expected to have a negative sign 3.1.3 Macro control variables Table 3.1 Definition and expected impact of bank capital, asset quality and management efficiency variables Variables Definition Loan growth (Loans to customers this year – Loans to customers last year)/Loans to customers last year (%) Capital (C) Loan loss provisions (A) Nonperforming loans (A) Non-interest expenses/Re venue (M) Operating expenses/As sets (M) Equity/Total assets (%) Expected impact Related document Dependent variable +/– Provision for customer loans/Loans to customers (%) – Total bad debt (debt group 3-45)/Loans to customers (%) – Total non-interest expenses/Total revenue (%) +/– Operating expenses/Average total assets (%) +/– (+): Blum (1999), Gambacorta and Mistrulli (2004), Repullo (2004), Coval and Thakor (2005), Thakor (2005), Bayoumi and Malander (2008), Distinguin et al (2013), Košak et al (2015) (–): Holmstrom and Tirole (1997), VanHoose (2007), Goodhart (2013), Anginer et al (2018) Bernanke and Blinder (1988), O’Brien (1992), Altunbas et al (2010), Heid and Kruger (2011), Balgova et al (2016), Roulet (2018) (+): Berger and DeYoung (1997), Jeitschko and Jeung (2005) (–): Berger and DeYoung (1997), Naceur et al (2018), Vo et al (2020) Table 3.2 Definition and expected impact of earnings, liquidity and market risk sensitivity variables Variables ROA (E) ROE (E) Definition Profit after tax/Average total assets (%) Profit after tax/Average equity (%) Expected impact +/– +/– NIM (E) Net interest income/Average earning assets (%) Liquid assets (L) Liquid assets/Total assets (%) + Loans/Deposits (L) Loans to customers/Deposits from customers (%) – Asset – liability gap (S) (Interest-sensitive assets – Interestsensitive liabilities)/Total assets (%) – Economic growth Annual GDP growth rate + Inflation Annual inflation rate 3.2 +/– +/– Related document (+): Johnson and Lee (1994), Nier and Zicchino (2006), Kupiec et al (2017) (–): Laidroo (2010), Adesina (2019), Caglayan and Xu (2016) Fama (2013), Alper et al (2012), Gennaioli et al (2014) Fama (2013), Alper et al (2012), Gennaioli et al (2014) Beutler et al (2020), Gomez et al (2020) Bertay et al 2015, Davydov et al 2018, Zins and Weill 2018 (–): Adesina (2019) (+): Bakker and Gulde (2010), Guo and Stepanyan (2011), Louhichi and Boujelbene (2017) Model To investigate the determinants of bank lending for Vietnamese commercial banks, our estimation equation is specified as follows: Loan growthi,t = α0 + α1×Loan growthi,t–1 + α2×CAMELSi,t–1 + α3×Macrot + ui,t (3.13) where the dependent variable Loan growthi,t is the annual percentage change of gross customer loans by bank i in year t We not consider loans to other entities such as the government or financial institutions due to the heterogeneity in lending regimes and incentives CAMELS is a vector of bank-specific factors defined using the CAMELS components Macro is a vector of macroeconomic variables to control the external environment, including the business cycle of the economy (the growth rate of GDP) and the inflation (the annual rate of inflation) ui,t is the error term The lagged dependent variable is inserted on the right side of the equation to capture the dynamic feature of bank lending 3.3 Estimation method 3.3.1 GMM method for dynamic model (main regressions) 3.3.2 OLS/GLS method for static model (robustness tests) 3.4 Data We manually collect the research data from Vietnamese commercial banks’ financial reports as our analysis requires almost all the information on balance sheets We obtain macroeconomic data from the World Development Indicators database For the sample of commercial banks, we exclude those that are subject to special control or acquired entirely by the central bank as these banks exhibit different operation strategies and face strict constraints on their activities Banks that not meet our information criteria required for the calculation of specific variables will also be deleted As a result, our final unbalanced panel dataset consists of 31 commercial banks from 2007 to 2019, covering approximately the entire banking system in Vietnam Depending on individual variables constructed, the number of observations varies between 340 and 384 bank-year observations To mitigate the impacts of extreme outliers, we winsorize all bank-specific variables at the 2.5th to 97.5th percentiles CHAPTER EMPIRICAL RESULTS AND DISCUSSIONS 4.1 Evaluation of the Vietnamese banking system through the CAMELS framework 4.1.1 Context of the Vietnamese banking system 4.1.2 Evaluation of the Vietnamese banking system with descriptive statistics of research variables The survey period showed many strong fluctuations in Vietnamese banks' asset structure, capital potential, and business performance However, equity has decreased because its accumulation level has not kept up with the scale of expanding assets However, the data also shows that the capital adequacy ratio exceeds the minimum requirement of 8.0% of the Basel Committee and even exceeds the minimum requirement of 9.0% of the SBV Asset quality was not guaranteed because the bad debt had a very high growth period However, through the efforts of the SBV and the banks in the system, the number of bad debts has decreased significantly in the time since it peaked in 2012 Income and profit, including return on assets and return on equity, both showed a downward trend in the years 2012–2015 as a result of bad debts and inefficient banking operations in cost savings The average ratio of liquid assets to total assets decreased continuously from 2007–2016 and then remained relatively stable from 2016 to 2019, showing the SBV's policy efforts to support liquidity After a period of hot credit growth, bad debts arose, and many flaws in the banking legal framework, the Government issued Project 254 in early 2012, approving the plan to restructure credit institutions Encouraging progress has been made in relation to the stated goals, most notably addressing systemic illiquidity, strengthening bank supervision, consolidating weak banks, and especially bad debt settlement At the same time, stricter regulations on capital adequacy, prudent loan classification, and provisioning more in line with international standards have been issued Many weak banks have been effectively controlled through mergers or direct control of the SBV, thereby effectively controlling systemic risks 4.2 Correlation analysis among research variables 4.3 Estimation results and discussion 4.3.1 Impact of bank capital on loan growth High capital ratios at banks will play a pivotal role in providing credit, helping to promote production and business activities and economic growth There are several possible mechanisms to explain this finding:  Theoretically, a higher capital ratio is considered an excellent buffer to improve the bank's ability to absorb risks, reduce losses that threaten the bank's ability to sustain operations, enable the bank to withstand temporary financial difficulties, and continue to expand (Repullo 2004) Furthermore, banks with much equity tend to be more confident and willing to take on risks than banks with small capital buffers; as a result, these banks may be willing to increase their loans disbursed to the economy at a faster rate (Thakor 2005)  With a banking market in the developing stage like Vietnam, the government has issued stringent credit growth control measures for banks Capital adequacy is an essential criterion for the regulator to consider and assign a loan extension limit for each bank As for the banks themselves, when they have met the capital requirements, they have been equipped with a good cushion and thereby are licensed to expand their business more In the context that the Vietnamese banking system is managing to implement Basel II's capital adequacy standards, this result has many important practical implications, highlighting the importance of appropriate bank capital buffer to address business strategy and growth Table 4.3 Estimation results for each factor of bank capital, asset quality and management efficiency with GMM (1) (2) (3) (4) (5) Lagged dependent variable 0,228*** 0,201*** 0,274*** 0,250*** 0,242*** (0,016) (0,009) (0,023) (0,011) (0,011) Capital (C) 1,338*** (0,143) Loan loss provisions (A) −6,431*** (1,419) Non-performing loans (A) −1,469*** (0,390) Operating expenses/Assets (M) 4,997*** (1,097) Non-interest expenses/Revenues 0,164 (M) (0,122) Economic growth Inflation Observations −5,831*** −9,305*** −10,648*** −8,018*** (0,827) (1,091) (0,683) (0,870) −11,572*** (1,014) −1,224*** −1,209*** −1,231*** −1,109*** −1,172*** (0,095) (0,101) (0,081) (0,104) (0,105) 353 353 313 353 353 AR(1) test 0,000 0,000 0,000 0,000 0,000 AR(2) test 0,358 0,217 0,535 0,271 0,285 Hansen test 0,178 0,153 0,189 0,165 0,183 Notes: The dependent variable is the rate of loan growth Standard errors are reported in parentheses *, **, and *** indicate the significance levels 10%, 5%, and 1%, respectively The validity tests of the GMM estimator are shown with p-values 4.3.2 Impact of asset quality on loan growth The negative impact of credit risk indicated in this study confirms a previous finding also examined in the Vietnamese commercial banking system from 2005 to 2015 The impact mechanism can be explained through the existing theoretical foundations and the actual situation of Vietnam's banking industry over time as follows:  With a strict credit risk management process and a cautious tendency in investment strategies in a high-risk industry, banks are forced to focus on strengthening risk management, finding ways to recover from losses, and improving asset quality when there is a decrease in asset quality Faced with risks and the risk of capital loss, then the option of increasing credit is considered a secondary and no longer a priority strategy of the bank (Bernanke and Blinder 1988; Altunbas et al 2010) An increase in a bank's credit risk will put pressure on the bank's capital or net asset value and thereby reduce the bank's desire to lend Banks therefore expect that once credit risk is controlled and asset quality is improved, they will have full power to expand their lending business  Deteriorating asset quality, risk provisions, and bad debts make revenue low, leading to a decrease in the bank's profit or loss Even excluding the effect of reducing revenue, expenses also increased, or other revenue decreased significantly, including increased bad debt management costs and reduced revenue from other related services to the loan The increase in expenses makes the remaining profit lower than the original estimate Due to the failure to recover loans, bad debt narrows the bank's capital size or net asset value, thereby slowing down the process of transferring capital to borrowers of the bank Moreover, high credit risk also negatively affects the bank's reputation, causing a disadvantage in competition with other banks, especially in mobilizing capital from the economy 4.3.3 The impact of management efficiency on loan growth This result is different from Vo et al (2020) found for banks in the US market during the period 1990–2017, where well-managed banks could generate large volumes of loans Thus, the thesis findings have contributed to expanding the existing documents investigating the meaning of management efficiency with bank lending behavior The significance of the finding becomes even more believable when supported by the following arguments:  The finding can be appropriately explained by the “poor management” hypothesis proposed by Berger and DeYoung (1997) Accordingly, the efficiency of bank management can be expressed in terms of cost-effectiveness High administrative costs imply that banks are not managed effectively For these banks, Berger and DeYoung (1997) argue that they will not have as much experience or skill in assessing loans, customers, and collateral, and hence they tend to be easier in loan decision making Even these loans are quite risky with not enough added value As a result, with this mechanism, the number of loans granted quickly increases after a while, and the loan portfolio becomes much larger, especially when compared to competitors equipped with a more effective management system, which is very experienced in loan approval  Besides, the "moral hazard" hypothesis can also be used to explain the finding of a negative correlation between management efficiency and loan growth 4.3.4 Impact of bank earnings on loan growth The finding of a positive correlation between bank profitability and loan growth in the Vietnam study is consistent with what has been shown in several other studies worldwide (Nier and Zicchino 2006) For the indicated significant effects, the research can also use arguments to explain the following:  Based on theoretical models that demonstrate that asymmetric information tends to decrease as bank profitability increases (Holmstrom and Tirole 1997), banks will have many advantages to develop their operations and, in particular, the lending segment When profits increase, it will contribute to the bank's cash flow, thereby creating more opportunities for investment in the future In addition, a bank's profits contribute to enhancing the value of the bank to shareholders and managers Banks with high profitability can therefore have a good competitive advantage over rival banks They can thereby reduce the cost of raising capital better, access to many different funding sources more easily This can lead to faster loan expansion at high-margin banks than at low-margin banks Moreover, higher profits can make banks more confident and thereby loosen lending conditions (due to better comparative advantage from information asymmetry), willing to lower interest rates for bank loans As a result, a bank's loan portfolio could easily have grown to a greater extent (Dell'Ariccia and Marquez 2006)  Higher profit has great significance for banks' retained earnings, thereby providing the basis for making capital raising decisions In connection with the context of Vietnamese banks in recent years, when the requirement to increase capital is always considered an urgent requirement, the meaning of increasing profits and retained earnings is even clearer The increased capital base has enabled Vietnamese banks to meet regulations set forth by the regulator, improving the assigned credit growth limit Table 4.4 Estimation results for each factor of earnings, liquidity and sensitivity to market risk with GMM (1) (2) Lagged dependent variable 0,214*** 0,249*** (0,017) (0,010) ROA (E) 7,163*** (3) (4) 0,255*** 0,219*** (0,010) (0,014) (5) (6) 0,241*** 0,256*** (0,008) (0,010) (0,756) ROE (E) 0,116* (0,070) NIM (E) 0,093 (0,331) Liquid assets (L) 0,913*** (0,077) Loans/Deposits (L) −0,167*** (0,037) Asset – liability gap (S) −0,056 (0,112) Economic growth −8,160*** Inflation −1,435*** (1,069) −8,378*** −8,232*** −5,164*** −8,515*** −8,601*** (0,889) (0,961) (1,047) (0,865) (0,871) −1,220*** −1,175*** −1,716*** −1,003*** −1,205*** (0,136) (0,105) (0,097) (0,136) (0,098) 353 353 353 353 353 353 AR(1) test 0,000 0,000 0,000 0,000 0,000 0,000 AR(2) test 0,343 0,289 0,281 0,289 0,275 0,288 Hansen test 0,143 0,175 0,177 0,157 0,200 0,148 Observations (0,107) Notes: The dependent variable is the rate of loan growth Standard errors are reported in parentheses *, **, and *** indicate the significance levels 10%, 5%, and 1%, respectively The validity tests of the GMM estimator are shown with p-values 4.3.5 Impact of bank liquidity on loan growth The positive effect of bank liquidity on loan growth can be reasonably explained by the following arguments:  The fact that banks maintain high liquidity positions may be related to an important motive of liquidity provision (Gennaioli et al 2014) Theory suggests that banks may choose to hold highly liquid assets as an optimal way of storing liquidity to finance future disbursements This is especially relevant when banks, after mobilizing money from the economy, cannot immediately disburse this source of money Therefore, to ensure optimal cash flow and profits, they can quickly invest part of their temporary capital in short-term, highly liquid assets This liquidity reserve mechanism helps banks have enough resources to expand lending later 4.3.6 Impact of market risk sensitivity on loan growth Regression results on the model with only variables of interest and control variables for the macro-environment indicate a negative impact of interest rate risk on loan growth However, this effect is not statistically significant at an acceptable level It is possible that using a model that is too simple reduces its explanatory power, further reducing the regression significance of the survey variables 4.3.7 Combined results on complete regression models of CAMELS variables 4.4 Robustness checks of the research results In summary, by re-examining the robustness of the findings, the study has a solid basis for believing the conclusions Bank capital and liquidity are factors that strongly influence bank lending behavior Meanwhile, bank profitability and asset quality also significantly impact loan growth Finally, management efficiency and interest rate sensitivity have a relatively weaker impact on banks' lending behavior than other factors CHAPTER CONCLUSIONS AND IMPLICATIONS 5.1 Conclusion on research results 5.2 Implications First, banks need to have a roadmap to increase equity, which will help the bank operate more proactively in business expansion The fact that banks need to invest in capital to maintain lending and improve credit quality is very necessary and needs to be done in a harmonized manner to be effective, especially in the context that Vietnamese banks are trying to apply Basel II at the request of the regulator Second, high asset quality will strengthen the bank's ability to expand lending Therefore, banks need to take measures to limit the impact of credit risks and thereby ensure resources for loan growth The higher the credit risk, the lower the bank's business expansion ability Banks need to strengthen solutions to limit credit risk when not only helping them improve asset quality but also solving business growth problems As a manager, the SBV needs to closely monitor the asset quality control situation of banks Third, banks with lower-cost management efficiency extend lending more and vice versa The core issue here that can help Vietnamese banks operate in a healthy way, especially with safe lending growth, is moral hazard, and the banks' governance capacity needs to be controlled Therefore, Vietnamese banks need to improve the management capacity and efficiency of their apparatus in order to limit the negatives and, at the same time, focus on fostering ethical qualities for the management team and employees State management agencies need to pay special attention to credit growth that is beyond the control of banks, in which banks with poor management efficiency should be paid more attention Fourth, high profits more positively support banks' lending growth Therefore, in order to maintain market share in the present and in the future, banks need to strengthen their competitive advantages in the market by focusing on increasing profits, thereby increasing prestige in the market, pulling along with many other benefits such as access to abundant capital at low cost On the part of the regulator, they can research to make detailed decisions about the credit growth limit of the bank in proportion to their profit Fifth, bank liquidity actively supports lending activities Therefore, bank managers and policymakers need to accurately assess the importance of bank liquidity related to growth strategies or state management of credit limits in administrative monetary policy management and banking supervision Banks and regulators themselves also need to revisit the previous view that too many liquidity reserves will be wasteful and narrow the loan supply – the main driver of economic growth Sixth, in order to limit the adverse impact of the market on the bank's business, banks need to focus on the information system so that banking activities are in sync with developments in the market, thereby promoting push banking activities in a safe direction The supervisory information system needs to be smooth to ensure stricter enforcement to timely capture market developments, reduce information asymmetry, and thereby help banks make quick decisions On the part of regulators, the SBV needs to take appropriate measures to create a stable macro environment for the bank, avoiding negative impacts of the financial market on the economy In the coming time, the SBV needs to take measures to stabilize interest rates within a reasonable range in order to create favorable conditions for banks' business activities 5.3 Limitations of the study and future works ... framework of CAMELS factors Specifically, the study will endeavor to achieve the following specific objectives:  Identify appropriate representative variables for each factor in the CAMELS framework... criteria  Find out the impact of internal factors according to CAMELS on the loan growth of banks Accordingly, internal factors according to CAMELS include capital adequacy, asset quality, management... applies the CAMELS framework to assess loan growth at banks Existing studies lack a uniform standard framework for selecting influencing factors to include in the model Thereby, the CAMELS approach