1 MINISTRY OF EDUCATION & TRAINING VIETNAM NATIONAL UNIVERSITY HO CHI MINH CITY UNIVERSITY OF ECONOMICS AND LAW FINANCE AND BANKING ~~~~~~ * ~~~~~~ COMMERCIAL BANK The factors affecting LDR of commerc[.]
1 MINISTRY OF EDUCATION & TRAINING VIETNAM NATIONAL UNIVERSITY HO CHI MINH CITY UNIVERSITY OF ECONOMICS AND LAW FINANCE AND BANKING ~~~~~~ * ~~~~~~ COMMERCIAL BANK The factors affecting LDR of commercial Vietnam banks during the 11 - year period from 2010 to 2021 when strongly influenced by the domestic economy and international economy Supervisor: Ths Nguyễn Thị Diễm Hiền Group of students done: Hoàng Thị Bảo Ngọc - K204040231 Trương Tuệ Minh - K204041235 Trần Thị Ngọc Thy - K204041250 Phạm Thị Thanh Na - K204041236 Ho Chi Minh City - 2023 COMMENTS OF THE SUPERVISOR TABLE OF CONTENT STATISTICS OF VARIABLES IN RESEARCH MODEL INTRODUCTION 1.1 The reason of choosing the research topic 1.2 Research scope 1.2.1 Research content 1.2.2 Space 1.3 Research objectives 1.3.1 Overall objectives 1.3.2 Detail objectives LITERATURE REVIEW 2.1 Theoretical review 9 2.1.1 Profitability of Commercial Bank 2.1.2 Liquidity 2.2 Extant literature review 11 2.2.1 Studies in the world 11 2.2.2 Domestic studies 12 THE RESEARCH METHODOLOGY 13 3.1 Check correlation matrix 13 3.2 Model selection 14 3.3 Accreditation disability models 14 3.3.1 Accreditation Heteroskedasticity 14 3.3.2 Accreditation Autocorrelation 15 3.3.3 Accreditation Multicollinearity 15 3.4 Fix model 16 RESEARCH RESULTS 17 CONCLUSION & RECOMMENDATIONS 22 5.1 Conclusion 22 5.2 Recommendations 22 APPENDICE 24 REFERENCE 24 LIST OF GRAPH, DIAGRAMS AND TABLE Figure Correspondence matrix 13 Figure Accreditation Hausman 14 Figure Accreditation Heteroskedasticity 14 Figure Accreditation Autocorrelation 15 Figure Accreditation Multicollinearity 15 Figure Fix the model using the xtgls function 16 Figure Model fix when removing ROE variable using xtgls function 16 Figure Changes in LDR of commercial banks in the period 2010 – 2020 18 Figure Changes in NPL of commercial banks in the period 2010 – 2020 19 Figure 10 Changes LOANS of commercial banks in the period 2010 – 2020 20 Figure 11 Changes in INF of commercial banks in the period 2010 – 2020 21 Table Descriptive statistics of the variables used in the model 17 STATISTICS OF VARIABLES IN RESEARCH MODEL Variables Notation Dependent variable Loan to Deposit LDR Independent variable Return on Equity ROE Non Performing Loan NPL Size SIZE Loan ratio LOANS Inflation rate INF Capital Adequacy ratio CAR Net interest margin NIM The factors affecting LDR of commercial Vietnam banks during the 11 - year period from 2010 to 2021 when strongly influenced by the domestic economy and the international economy Summary: Because of the decline in credit quality, liquidity unstable system accounts, the risk of system crash, the Bank restructuring project (2016 - 2020) was approved by the Government in Decision No 1058/QD-TTg The main reason is that commercial banks have not met the requirements on ensuring liquidity safety set by the State Bank That is the reason why this study was conducted The purpose of this study is to ascertain how the liquidity of commercial banks is impacted by the economy's micro-macro dynamics 14 Vietnamese commercial banks contributed data that was gathered between 2010 and 2021 for the study The study's analysis of panel data and fixed effects model (FEM) produced the following results: Non Performing Loan (NPL), loan ratio (LOANS), and inflation rate (INF) are positively related to liquidity risk (LDR) Besides, two factors capital adequacy (CAR) and Net interest margin (NIM) are not correlated with Loan to Deposit (LDR) Factors such as bank size (SIZE) and return on equity (ROE) were not statistically significant The study also offered suggestions for policy on liquidity management for both state banks and commercial banks based on the findings Keywords: LDR, ROE, CAR, INF, NPL, LOANS, SIZE, NIM INTRODUCTION 1.1 The reason of choosing the research topic Since 2010, due to the influence of the world market as well as the consequences of the previous rapid expansion, the banking system has revealed a number of inadequacies Credit quality declines, liquidity of the system is unstable, the risk of system breakdown Therefore, at the beginning of 2012, the commercial banking system started the restructuring process under the Project on restructuring the credit institution system for the period 2011-2015 However, the system still has concerns about cross-ownership, high level of bad debt as well as poor financial capacity of commercial banks Therefore, the Bank Restructuring Project phase (2016 - 2020) was approved by the Government in Decision No 1058/QD-TTg Currently, the commercial banking system is in the process of continuing to restructure to ensure operational safety and towards management and administration in accordance with international practices In particular, specific issues are detailed in separate legal documents In Particular, liquidity management of commercial banks is concerned and regulations are constantly updated The cause of the liquidity risk situation of commercial banks during this time is due to many factors, from objective conditions to subjective factors of commercial banks Objective conditions can be mentioned are the effects of the world economic crisis and domestic macroeconomic conditions But the main cause is still the subjective factors of the system when commercial banks not meet the requirements on ensuring liquidity safety set forth by the State Bank as well as the problem of handling information crises related to the banking system prestige and influence of the Bank's Board of Directors Besides, In the context of the first months of 2022, the negative credit and deposit gap plus the capital need to meet business activities and payment at the end of the year caused the interest rate increase to show no sign of stopping The race in deposit interest rates in the past time has also shown the level of tension when banks had to increase capital to compensate for the loan out portion in the first months of the year According to the State Bank of Vietnam (SBV), by the end of October, credit growth reached 11.5%, capital mobilization growth reached 4.8% compared to the beginning of the year The difference between deposit and credit has been negative since July, the credit growth rate is higher than the deposit growth rate, causing the credit to deposit ratio (LDR) at banks to be negative goods increased While the net LDR ratio has exceeded 99%, the LDR ratio under Circular 22 (including interbank deposits) remains at less than 85%, SSI Research's November industry report said Net LDR ratio at many banks has exceeded 100% such as MSB, Techcombank, VIB or VPBank Therefore, it can be seen that the ratio of outstanding loans to deposits plays an extremely important role This ratio is used to assess the creditworthiness or safety of banks If the LDR is high, it will reflect that the profitability of banks is high At the same time, the quick level of capital mobilization means that the bank has money to pay at any time when customers withdraw money or lend to businesses immediately without having to wait for a long time In addition, when the LDR increases, it also reflects that the bank's trust level is large Thereby reducing the risk of customers withdrawing suddenly, limiting the phenomenon of collapse The study was conducted with the aim of proving that the ratio of outstanding loans to deposits has an influence on the safety and efficiency of the banking system 1.2 Research scope 1.2.1 Research content The topic focuses on researching, evaluating the factors affecting Loan to Deposit (LDR) of 14 commercial banks in Vietnam 1.2.2 Space Research on the correlation between Loan to Deposit (LDR) and independent variables (Inflation, Capital Adequacy, Size, Loans, Net interest margin, Return on equity, Nonperforming Loan) of 14 commercial banks in Vietnam The data is taken from a variety of state-owned commercial banks to domestic private joint stock commercial banks Time: The period from 2010 to 2021 1.3 Research objectives 1.3.1 Overall objectives The study studied the factors affecting LDR of commercial banks during the 11-year period from 2010 to 2021 when strongly influenced by the domestic economy and the international economy 1.3.2 Detail objectives Determining the current status of Loan to Deposit of Vietnamese commercial banks; Determine the impact of liquidity and other variables (Inflation, Capital Adequacy, Size, Loans, Net interest margin, Return on equity, Nonperforming Loan) of the Vietnam commercial banks; Make recommendations and propose solutions to appropriate Loan to Deposit (LDR) for Vietnamese commercial banks 9 LITERATURE REVIEW 2.1 Theoretical review 2.1.1 Profitability of Commercial Bank The concept of profitability of commercial banks According to the dictionary Nguyen Van Ngoc (2012), in Economics "Profitability is a number that evaluates the ability of an enterprise to generate profits in the long run, assuming all current operating conditions are generally unchanged.” Nguyen Van Ngoc (2006) According to Definition.USLegal.com “Profitability is the ability of an enterprise to earn a return on its invested capital after paying its owners and employees, fulfilling its obligations, and fully recognizing its expenses business while following good accounting practices.” Thus, profitability is an important factor to evaluate the Bank's operations, showing the rate of return from the owner's investment amount through to individuals or businesses or opening a book savings and many other activities Understanding the concept of profitability will help us better visualize the impact of capital adequacy on profitability of commercial banks later in this essay 2.1.2 Liquidity The concept of Liquidity Liquidity is a term that describes the degree of flexibility of any asset in that trading in the market does not change the market value of that asset More simply, liquidity represents the ability of an asset or product to be converted into cash Liquidity is an important criterion for credit institutions to assess a business's ability to pay its debts The Basel Committee on Banking Supervision said that: "Liquidity is a specialized term that refers to the ability to meet the needs of using available capital for business activities at all times, such as payment of money deposit, loan, payment, capital transaction, " At different times, Basel has different concepts and emphasis on liquidity, but in general, liquidity is defined as the ability to increase asset funds and meet due obligations with the cost of acceptance In 2010, in the textbook of Commercial Administration, Truong Quang Thong (2010) said: "Liquidity is the ability to convert an asset into cash quickly, with the lowest possible cost Rather, based on both asset and capital approaches, liquidity is the ability to access assets and capital at a reasonable cost to serve the various operational needs of a bank” Measurement methods This is a method of measuring the liquidity of banks based on numbers only calculated from the balance sheet, Including the following indicators: 𝐿1 = 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝑎𝑠𝑠𝑒𝑡𝑠 𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠 ∗ 100% This ratio shows how much of a commercial bank's total assets are liquid assets Usually, the higher this ratio, the better the bank's liquidity, but if this ratio is too high, the bank 10 needs to re-evaluate its asset holdings because highly liquid assets are often not very profitable 𝐿2 = 𝐿𝑖𝑞𝑢𝑖𝑑𝑖𝑡𝑦 𝑎𝑠𝑠𝑒𝑡𝑠 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑠 + 𝑆ℎ𝑜𝑟𝑡 − 𝑡𝑒𝑟𝑚 𝑏𝑜𝑟𝑟𝑜𝑤𝑖𝑛𝑔 ∗ 100% Liquidity ratio 𝐿2 shows how liquid assets are compared to deposits and mobilized capital, if this ratio is greater than or equal to 100%, the bank can meet the withdrawal needs of customers individuals, households, businesses or any organization at the same time when an extraordinary event occurs This ratio is similar to the 𝐿1 ratio, that is, the higher it is, the better the bank's liquidity 𝐿3 = 𝐿𝑜𝑎𝑛𝑠 𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠 ∗ 100% This ratio shows how much of a bank's total assets are outstanding loans This ratio is high means the liquidity of the bank is low and vice versa 𝐿4 = 𝐿𝑜𝑎𝑛𝑠 𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑠 + 𝑆ℎ𝑜𝑟𝑡 − 𝑡𝑒𝑟𝑚 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑛𝑔 ∗ 100% Similar to 𝐿3 , the higher this ratio, the lower the bank's liquidity After thorough research and understanding, this study will be based on the calculation of 𝐿4 to measure liquidity 𝐿4 here can also be understood as the loan-to-deposit ratio (DLR) The concept of Loan to Deposit (LDR) The loan-to-deposit ratio (LDR), which compares a bank's total loans to its total deposits for the same time period, is used to determine how liquid a bank is A percentage is used to represent the LDR The bank may not have adequate liquidity to meet any unforeseen funding needs if the ratio is too high If the ratio is too low, on the other hand, the bank could not be making as much money as it could According to Circular 22/2019/TT-NHNN, General formula: LDR = 𝐿 𝐷 * 100% Inside: ● LDR: The ratio of outstanding loans to total deposits ● L: Total loans (summarizing loans, financial leases, guarantees and cash flows in other banking operations such as factoring, discounting valuable papers, etc.) ● D: Total deposits (Mobilized capital = Customer deposits – specialized capital deposits – Margin deposits + Valuable papers) One of the notable regulations in Circular 22/2019/TT-NHNN stipulating the limits and ratios of safety assurance in the operations of banks and foreign bank branches, that is from January In January 2020, the maximum loan-to-deposit ratio (LDR) is 85% According to the old regulations in Circular 36/2014/TT-NHNN, the maximum LDR of commercial banks is 90%; Joint-stock commercial banks, joint-venture banks, banks with 100% foreign capital are 80% 11 Reasonable LDR rate Theoretically, LDR will be in the range of - 100%, but this index can increase to more than 100% because the amount of loan to customers will sometimes exceed the level of capital mobilization If the LDR is high, it shows that the amount of money that the bank lends to individuals, organizations and businesses is much higher than the level of capital mobilization, leading to many risks This is not a good sign for commercial banks Small LDR means that the amount of money the bank mobilizes from other sources is quite large but the amount lent back This shows low liquidity and poor credit service quality According to economic experts, the safety level of LDR stops at 80% or 90% However, this number is not really accurate, it depends on each bank 2.2 Extant literature review 2.2.1 Studies in the world Anamika Singh, Anil Kumar Sharma (2016) The paper analyzes empirically on specific macroeconomic and banking factors affecting the liquidity of Indian banks It was performed based on OLS, 59 banks' data from 2000 to 2013 were used for the study The bank's Liquidity research model is measured by independent variables including: Size of bank, profitability, financing costs, capital adequacy and deposits GDP, inflation and unemployment are the macroeconomic factors to be considered The results show that bank ownership affects bank liquidity Additionally, it was discovered that GDP and bank size had a detrimental effect on bank liquidity On the other hand, bank liquidity is positively impacted by deposits, profitability, adequate capital, and inflation Unemployment rates and funding costs have little impact on bank liquidity Faruque Ahamed (2021) The study looks at both internal and external factors that have an impact on liquidity risk in Bangladeshi commercial banks 23 banks' data from 2005 to 2018 were used for the study, and panel data were used for the regression analysis The bank's Liquidity research model is measured by independent variables including: Size of bank, CAR (capital adequacy ratio), LAR (Loan to Asset rate), GDP growth and dependent variable: liquidity risks The result, Asset size is one of the bank-specific characteristics that has a bad association with liquidity risk The liquidity position and liquidity risk are better and lower the bigger the bank is The capital adequacy ratio and return on equity have a slight but favorable link with liquidity risks In terms of macroeconomic parameters, GDP and domestic credit have a favorable impact on the liquidity risks whereas inflation has a negative impact Syed Quaid Ali Shah , Imran Khan , Syed Sadaqat Ali Shah and Muhammad Tahira (2018) The study aimed to determine factors affecting liquidity of banks operating in Pakistan Through the financial statement and annual report, 23 commercial banks were employed in the studies from 2007 to 2016 The bank's Liquidity research model is measured by independent variables including: capital adequacy ratio (CAR), cost of funds and bank size are statistically significant The study finds that external or macro factors, 12 such as GDP is statistically significant but affect liquidity of the banks differently Unemployment, another external factor, also impacts liquidity of banks very differently but it is statistically significant in the first measure of liquidity and statistically insignificant in the second measure of banks’ liquidity Further, the results revealed that profitability is insignificantly related to liquidity while the relationship between deposits and bank liquidity is negative and statistically significant 2.2.2 Domestic studies Le Quoc Cuong (2019) The study aimed to determine the factors affecting the liquidity of the Commercial banks in Vietnam and propose solutions to improve liquidity for Vietnamese commercial banks Through the financial statement and annual report, 25 commercial banks were employed in the studies These banks collectively account for a significant amount of the loan market share, mobilization, and related service provision in Vietnam from 2008 to 2017.The bank's Liquidity research model is measured by independent variables including: Size of banks, NPL (Non Performing Loan), GDP (economic growth) and dependent variable: LCR (Liquidity Coverage Ratio) The resulting loan growth and the increase of medium- and long-term loans relative to the total loan have an adverse connection with liquidity NPL ratio has a negative relationship with liquidity, while other factors including asset size, equity and economic development affect liquidity positively These impacts are not statistically significant, though Dang Quang Vang (2018) The study aimed to identify the factors affecting the liquidity of Vietnamese commercial banks, thereby providing solutions and recommendations for the most effective liquidity management of the bank Through the financial statement and annual report, 31 commercial banks were employed in the studies from 2005 to 2015 The bank’s liquidity research model is measured by independent variables including: external fundings (EFD), equity to total assets (ETA), listing variable (LISTED), provision for credit on total outstanding balance (LLPTL), rate of return on equity (ROE), size of banks (SIZE), GDP, INF, money supply M2, and financial crisis (CRISIS) The study has combined GMM and percentile regression methods to analyze the model according to each group of bank sizes The results show that with the GMM regression model, in both cases of bank size, the variables ETA, LLPTL, and ROE have the same direction as liquidity 13 THE RESEARCH METHODOLOGY 3.1 Check correlation matrix Figure Correspondence matrix Source: Stata The team considers the correlation between the LDR dependent variables and the independent variables ORE, CAR, NPL, Size, INF, LOANS and NIM to make an assessment of the data of these variables based on the collected data source As a result, the correlation coefficients with ROE, NPL, SIZE, LOANS, INF are less than 0.05, while the other two variables, CAR AND NIM, have relative coefficients greater than 5% Therefore, variables NPL, SIZE, LOANS, INF, LDR are correlated with dependent variables From there we have the function: 𝐿𝐷𝑅 = 𝛼 + 𝛽1 𝑅𝑂𝐸 + 𝛽2 𝑁𝑃𝐿 + 𝛽3 𝑆𝐼𝑍𝐸 + 𝛽4 𝐿𝑂𝐴𝑁𝑆 + 𝛽5 𝐼𝑁𝐹 + 𝑒𝑖𝑡 ⇒ So the function can perform regression analysis 14 3.2 Model selection Figure Accreditation Hausman Source: Stata The group uses the Hausman test to select a suitable model, namely between FEM, REM and OLS models Performing Hausman test (𝜒 (6) = 26.82; 𝑃 − 𝑣𝑎𝑙𝑢𝑒 = 0.0001 < 0.05) shows that the FEM model is more suitable than REM in studying the factors affecting the liquidity of commercial banks trade in Vietnam So the FEM model is suitable for the function: 𝐿𝐷𝑅 = 𝛼 + 𝛽1 𝑅𝑂𝐸 + 𝛽2 𝑁𝑃𝐿 + 𝛽3 𝑆𝐼𝑍𝐸 + 𝛽4 𝐿𝑂𝐴𝑁𝑆 + 𝛽5 𝐼𝑁𝐹 + 𝑒𝑖𝑡 3.3 Accreditation disability models 3.3.1 Accreditation Heteroskedasticity Figure Accreditation Heteroskedasticity Source: Stata Using xttest3 gives p-value = 0.00001 < 0.05, so reject 𝐻0 , violating model defect because of variable variance 15 3.3.2 Accreditation Autocorrelation Figure Accreditation Autocorrelation Source: Stata Using xtserial function gives p-value = 0.00001 < 0.05, so reject 𝐻0 , violating model defect because of autocorrelation 3.3.3 Accreditation Multicollinearity Figure Accreditation Multicollinearity Source: Stata Test for multicollinearity using the variance magnification factor (VIF) The team initially regressed the variables with the regress command, then ran the VIF command The risk of multicollinearity is high only when the VIF exaggerated factor index is greater than 10, however, here, the VIF exaggeration factor of the variables in the model is mostly less than 10, the average VIF value The average is 1.48, which means that the group's model does not have multicollinearity Multicollinearity can lead to wider confidence intervals that produce less reliable probabilities of the influence of independent variables in a model 16 3.4 Fix model To overcome two defects, variable variance and autocorrelation, the group used the function xtgls [dependent variable] [independent variable] Figure Fix the model using the xtgls function Source: Stata The results show that ROE, SIZE variables are not significant with ROE (p-value > 0.05), so the group continues to remove ROE, SIZE variables Figure Model fix when removing ROE variable using xtgls function Source: Stata Now all variables are significant for LDR because p-value < 0.05 Therefore, the model 𝐿𝐷𝑅 = 𝛼 + 𝛽1 𝑁𝑃𝐿 + 𝛽2 𝐿𝑂𝐴𝑁𝑆 + 𝛽3 𝐼𝑁𝐹 + 𝑒𝑖𝑡 has no multicollinearity defect Conclusion: LDR= -0.07233 + 0.13165NPL + 1.24561LOANS + 0.32296INF+ eit 17 RESEARCH RESULTS Table shows descriptive statistics of the variables used in the model, including: mean, standard deviation, minimum and maximum values, and observational data Data from 14 Vietnamese commercial banks for the period 2010 – 2021 with 168 observations for each variable Table Descriptive statistics of the variables used in the model VARIABLE LDR ROE CAR NPL NIM SIZE LOANS MEAN 0.693 4756 0.105 1214 0.131 6756 0.021 4405 0.034 3214 18.86 06 0.59289 17 0.054 9083 STANDARD DEVIATION 0.134 5987 0.065 4316 0.043 5656 0.012 5353 0.014 3554 1.252 236 0.11000 94 0.047 3164 MAX 1.047 0.263 0.361 0.088 0.093 21.28 95 0.8006 0.185 MIN 0.358 0.000 0.080 0.005 0.005 16.31 58 0.3076 0.006 168 168 168 168 168 168 168 168 OBSERVATION INF Source: Data from 14 Vietnamese commercial banks for the period 2010 – 2021 For the LDR variable, the average value of 0.6892847 is lower than the maximum regulated level of 85% according to Circular 22/2019/TT-NHNN, which shows that the liquidity ratios of banks are relatively good Median value is 0.689128, standard deviation is 0.1365338, minimum value is 0.3586344 of HDBank in 201, maximum value is 1.047746 of BIDV in 2011 - exceeding the level regulated by the state The ROE variable has an average value of 0.1051214, the smallest ROE value of 0.0003 is 2020 of NCB and the maximum value of 0.2639 is 2021 of VIB The standard deviation of ROE is low at only 0.0654316, indicating that the value is very close to the mean and the Roe volatility of 14 Banks over 11 years is small The CAR of commercial banks with an average value of 0.1316756 is much higher than the minimum requirement of the State Bank of Vietnam currently as stipulated in Circular 41/2018/TT-NHNN of %, in addition, the standard deviation of this coefficient is also low at 0.0435656 indicating that the values are less volatile with each other Along with that, the minimum value is 0.0802 in 2010 of ICB Bank and the maximum value is 0.3616 of KLB Bank in 2010 For the NPL variable, the average value is 0.0214405, which is lower than the value of 3% representing the bad debt ratio of banks over the average of 10 years in accordance with the provisions of Document No 6561/NHNN-TTGSNH Besides, the standard deviation is 0.0125353, the minimum value is 0.0052124 of STB Bank in 2010, the maximum value is 0.0880662 of SHB in 2012 18 For the SIZE variable, the mean value is 18,8606, the standard deviation is 1.155158, the minimum value is 1631.58 for Kien Long Bank in 2010 and the maximum value is 21.2895 for BIDV Bank For the variable LOANS, the mean value is 0.5894149, the standard deviation is 0.1105361, the deviation is 0.1172193, the minimum value is 0.3075548 of HDBANK in 2011 and the maximum value is 0.8006246 of BIDV 2020 For the INF variable with a mean value of 0.0549083, the standard deviation is 0.0473164, showing that the inflation rate in Vietnam from 2010 to 2020 is less volatile The minimum value is 0.0063 in 2015, the lowest since 2001 and later This is much lower than the operating target of 5% The reason the inflation rate has reached such a low level is because the prices of goods in the country and the world have decreased, most notably crude oil The maximum value is 0.1858 in 2011 - the highest since 2008 to present 18.58% is also the highest level when compared to other countries in the ASEAN region Similar to the cause of the decrease in inflation, the increase in inflation is due to the high price of export materials for production such as electricity, gasoline, and oil, in addition, the USD/VND exchange rate increased to +9.3% For the NIM variable, the mean value is 0.0343214, the standard deviation is 0.0143554, the minimum value is 0.0058371 of HDBank in 2013 and the maximum value is 0.093506 of VPBank 2019 Figure Changes in LDR of commercial banks in the period 2010 – 2020 Source: Compiled from financial statements of commercial banks During the 11-year period (2010 - 2021), BIDV always held the highest loan-to-deposit ratio among the 14 surveyed banks (ranging from 81.76% to 104.77%) In the period from 2015 to 2021, this ratio does not seem to have too much difference between banks Banks VCB, KLB, NAB, MB, VIB, NCB, STB, SHB, VPB, PGB, EIB all kept their LDR ratio stable 19 LDR = -0.07233+0.13165NPL+ 1.24561LOANS + 0.32296INF+ eit The results from the model show that the bad debt ratio (NPL) has a positive effect on Loan to Deposit (LDR) with a beta of 0.13165, which means that the higher the bad debt ratio, the higher the liquidity risk of the bank The bigger the bank, the more negative impact on the bank's liquidity That can be explained simply because the failure to timely recover the granted credits will greatly affect the problem of deposit payment for savers, slowing down the circulation and circulation process capital source of the bank, in the worst case, the bank is forced to merge or go bankrupt More specifically, at banks today, the main income still comes from credit activities Through 11-year statistics of 14 banks, most banks tend to reduce bad debts and reach below the legal threshold of 3%, especially the two big banks Vietcombank and BIDV both tend to reduce bad debt to less than 1% Research by Le Quoc Cuong (2019) when using data from 2008 to 2017 of 25 banks also concluded that NPL has a negative impact on liquidity, along with that, the research team's data obtained the majority banks all have LDR and NPL that change in the same direction as BIDV, EIB, HDB, Banks should actively consider recovering bad debts quickly, especially in the post-pandemic economic development Currently, when the demand for loans is very large From the beginning of 2022 to the beginning of the third quarter of 2022, the SBV raised interest rates many times because of the economic recovery, there were many liquidity strains In addition, the high non-performing loan ratio, which occurs continuously and shows no sign of recovery, will partly affect the reputation and trust of customers in the Bank, making the ability to raise capital less likely adversely affected Therefore, an increase in the bad debt ratio will negatively affect liquidity Banks have also been implementing policies to reduce bad debts: specifically at VCB (2.83% in 2010, 1.84% in 2015 and 0.62% in 2020); of NAB (in 2010 it was 2.18%, in 2015 it was 0.91%, in 2020 it was 0.83%), Based on the figure…, SHB, NCB, PGBANK, STB, KLB had the highest bad debt ratio And the majority of banks BIDV, VCB, ICB always account for a low percentage Figure Changes in NPL of commercial banks in the period 2010 – 2020 Source: Compiled from financial statements of commercial banks 20 Loans are in the same direction as Loan to Deposit, showing that the more banks expand their loans, the effect of increasing LDR, along with decreasing liquidity With a positive coefficient and a Beta index of 1.24561, it tells us that when banks expand credit activities, forgetting the ability to mobilize capital as well as real capital will cause liquidity risks Most commercial banks in Vietnam tend to expand their lending activities over the years, such as Bank for Investment and Development of Vietnam (Loan was 69.4 percent in 2010, 70.35 percent in 2015 and 70.35 percent in 2015 2021 76.89%), or Vietcombank (Loan in 2010 is 57.5%, in 2015 is 57.41% and 67.91% in 2021) It is very difficult to balance between expanding lending activities and reducing credit risks, so banks need to have a clear strategy, as well as carefully consider customers to lend In general, the LOANS of 14 banks in the period from 2010 to 2021 are all over 30% and similar to the LDR, this ratio of 14 banks does not differ too much over 11 years Figure 10 Changes LOANS of commercial banks in the period 2010 – 2020 Source: Compiled from financial statements of commercial banks Macro factor - inflation rate (INF) has a positive impact with LDR, with a Beta coefficient of 0.32296, showing that the more inflation the economy, the lower the liquidity of the bank Notably, in 2022, because of the conflict between Russia and Ukraine, the world's inflation rate increased rapidly, including the US and European countries In Vietnam, the Fed's continued interest rate hike in the coming period will put pressure on Vietnam to raise interest rates in the interbank market, which means that the SBV must continue to sell USD to intervene in the market In fact, the deposit interest rate at large commercial banks has increased from 4.5% to about 5.6%, while smaller banks, with less pressure, increased by about 0.5% It is the suppression of interest rates by the State-owned commercial banking system that will lead to a higher disparity in deposit and credit growth In addition, experts said that from 2013, total deposits in the banking system was lower than total credit in the economy, which will put pressure on the liquidity system in the medium and long term Along with that, in the period from 2010 to 2020 see the chart … the inflation growth rate of 2011 reached the highest level (over 18%) and 2015 reached the lowest level (under ... margin NIM The factors affecting LDR of commercial Vietnam banks during the 11 - year period from 2010 to 2021 when strongly influenced by the domestic economy and the international economy Summary:... objectives The study studied the factors affecting LDR of commercial banks during the 11- year period from 2010 to 2021 when strongly influenced by the domestic economy and the international economy. .. Loan) of 14 commercial banks in Vietnam The data is taken from a variety of state-owned commercial banks to domestic private joint stock commercial banks Time: The period from 2010 to 2021 1.3