MINISTRY OF EDUCATION AND TRAINING THE STATE BANK OF VIETNAM HO CHI MINH CITY UNIVERSITY OF BANKING HOCHIMINH UNIVERSITYOF BANKING BUI NHU Y FACTORS AFFECTING VIETNAMESE COMMERCIAL BANKS’ DEPOSIT GROW[.]
INTRODUCTION
Rationale
Deposits may be crucial for both developed and developing nations because they allow depositors to earn interest on money they don't need right away Additionally, it gives banks a platform to direct those funds to people and organizations that need them immediately (Boadi, Li and Lartey, 2015) According to Jaber and Manasrah, (2017) one of the most crucial foundational components of banks, whether Islamic or commercial, is bank deposits A deposit is a contract between a client and a bank whereby the client deposits money with the bank for investment or conservation, and the bank agrees to return the money to the client upon request at a predetermined date and under predetermined terms (Jaber and Manasrah, 2017).
In developing countries, where the banking system contributes 40% to 50% of gross domestic product (GDP), deposits are key to economic growth because deposits are the basis of capital formation (Duc, N.C., & Nguyen,T.T & Lam, P.T, 2021) The banking system creates idle financial resources by mobilizing capital from deposits of all economic sectors, then providing this capital for production, investment and economic development which brings profit for the bank so deposit mobilization is the core activity of all commercial banks Therefore, banks can earn high profits due to deposits(Gunasekara, H.U and Kumari, P, 2018) However, Commercial banks cannot manage deposits without knowing and controlling the factors affecting it Stemming from that practical need, the author chose the topic "Factors affecting deposits of Vietnamese commercial banks from 2012 to 2020" as a research topic.
Research objectives
The objective of the study is to determine factors affecting deposit growth of Vietnamese commercial banks during the period 2012 – 2020 After that, the author makes implications and suggestions improve Vietnamese commercial banks’ deposit growth.
Research questions
In line with the objectives highlighted above, the two specific research questions were formulated as follows:
Research question 1: What factors affect deposit growth of Vietnamese commercial banks?
Research question 2: To what extent does factors affect deposit growth of
Subject and scope of the study
The subject of this study is factors affecting deposit growth in bank business.
Space scope: 22 commercial banks in Vietnam.
Research method
The thesis combines both qualitative research methods and quantitative methods. Qualitative research methods: The thesis is based on the collected data and information, conducts comparison, from which to make comments, evaluate research content At the same time, the thesis uses the deductive method to argue and explain the characteristics of each detail in the data analysis process.
Quantitative research method: Regression analysis of panel data to determine factors affecting deposit growth of Vietnamese commercial banks, combined with analysis including: ordinary least squares method (Pooled OLS), random effects model (REM), fixed effects model (FEM) The thesis proceeds to build research model, present independent variables and Dependent variables in the model, data sources are taken from financial statements and annual reports of banks and macro variables are taken from the General Statistics Office In addition, the thesis also uses methods such as synthesis, comparison, analysis, inference, description, etc in order to compare with reality, consider and evaluate factors affecting deposit growth of Vietnamese commercial banks.
Contribution of the thesis
In practical terms, the research results of the study will provide more empirical evidence for regulators as well as commercial bank managers to understand more about the relationship between macro factors and deposit growth in Vietnamese commercial banks and assist regulators in making regulatory decisions as well as suggesting to managers important policy implications in the development of mobilized capital.
From the previous researches, it is observed that bank deposit growth was determined by macro-economic and bank specific variables But from their findings, it is observed there was no generally accepted relationship between commercial bank deposit growth and its determinants To the knowledge of the researcher, there were a few studies conducted in Vietnam and this study aims to fill a gap in the research on the determinants of bank deposits in the banking industry in Vietnam, such as macroeconomic indicators related to bank deposit creation such as money supply rate M2.
Structure of the thesis
The research thesis will be presented in five chapters, including:
An overview of the research problem, the reason for choosing the topic, the research problem, the research objective, the object, the research scope and the meaning of the research.
LITERATURE REVIEW
Theoretical framework
Commercial banks are credit institutions that perform all banking activities and other related business activities for profit purposes (Consolidated documents No. 20/2018/VBHN-NHNN, the State Bank of Vietnam).
A commercial bank is a financial institution with two business functions: one is to federally insure deposits and pay interest to depositors, and the other is to use its charter to make loans, check cash balances, provide clearing services, and underwrite securities (Getter, 2016) Commercial banks play a prominent role in the modern economy by circulating financial resources (Tariq et al., 2014) Another definition of a commercial bank is a financial intermediary that effectively channels idle funds in the market to those who need credit to invest in valuable production, business opportunities, and personal purposes (Tuyishime et al., 2015) Commercial banking is further divided into public sector banking (the bank with the majority of its stake held by the government), private sector banking (the bank with the majority of its stake held by private individuals), and foreign banking (the bank with its head office located in other countries outside of a nation) (Kalpana & Rao, 2017) Commercial banks establish maladjustment and impediments and contribute to the development of the economy (Mongid et al., 2012). The operation of commercial banks is aligned with the monetary policies of a nation, and they play the main role of controlling cash flow given the expected rate of returns and emissions (Erina & Lace, 2013).
In summary, the term of commercial bank used in the study is defined as a financial institution which operates in the way of holding deposit, paying interests to the depositors, opening credit to different economic sectors and facilitates economic development of a country.
Commercial banks' largest liabilities are deposits with commercial banks.According to (Kelvin, 2001), deposits make up about 75% of the liabilities of commercial banks Commercial banks use this liability to lend money and earn interest on deposits, which helps them run their businesses Therefore, if banks are mobilizing more deposits, they will perform better Deposits were also mentioned by Mahendra (2005) as a foundation upon which banks thrive and expand, as well as special items on a bank's balance sheet that set them apart from other types of business organizations According to E.A Shaw (1995), the cost of intermediation for mobilizing deposits is a very important component of the overall intermediation cost of the banking system Despite the challenges, deposits are crucial to the banking industry as well as the overall economy.
Commercial bank deposits are reliant on customer deposits According to Keynes' theory of money demand, there are three main motives for people to hold money: trading, hedging, and investing To cater to these motives, commercial banks offer different types of deposits: deposits and savings deposits The type of savings deposit meets the needs of people who want to save and earn more money if the deposit is used to carry out daily transactions Depositors in savings accounts also have a hedging motive (Haron, S., Azmi, W N W., & Shafie, S., 2006) Deposits can be viewed from the perspectives of consumers who need an income to cover their living expenses and from the perspectives of entrepreneurs who need money and want to keep it in reserve to carry on their businesses.
The majority of the money supply used by the public is made up of bank deposits, and changes in the money supply have a strong positive correlation with changes in the cost of goods and services in the economy Deposit variability is frequently included as one of the important determinants of portfolio strategy; the more volatile a bank deposit is, the more liquid its mix of assets will be Deposit variability affects bank holdings of cash and excess reserves, the distribution of total member bank reserves within the banking system, and thereby the path and speed of monetary policy action This is why deposits are very important for banks and, as a result, for the economy of a country Banks need to inflow money from the people so that they can be able to give loans or financing to promote productivity and economic growth and at the same time gain profit for themselves through interest or margin applied (Ostadi, H.,
There are three different types of deposits: demand or current deposits, fixed or time deposits or term deposits, and savings deposits Depositors keep their money in banks so they can use it for future endeavors There are motivations to save money, some of which are listed by Bhatt (1970): To cover unexpected expenses, own a home, To finance the marriage and education of children, to plan ahead for old age.
2.1.3 Factors affecting for Deposit growth
The size of the bank is one of the elements that is frequently mentioned as influencing deposit variability According to available data, the size of the bank affects both the quantity and diversity of ownership of individual deposit accounts as well as the distribution of deposits by type A bank's total assets are used as a representation of a bank's size Previous studies have had mixed conclusions about the impact of bank size on bank deposits In general, larger banks can take advantage of economies of scale, thereby increasing their deposits The findings of ĩnvan, Y A., & Yakubu, I N (2020) and Legass, H A., Shikur, A A., & Ahmed, O M (2021) indicated that larger banks with economies of scale and a larger branch network are more efficient at attracting deposits than smaller banks However, Islam, S N., Ali, M J., & Wafik, A (2019) argued that smaller banks, while generating fewer deposits in absolute terms, had relatively higher deposit growth rates than other Large banks, therefore, can be beneficial to small banks in achieving higher deposit growth.
Bank Profitability is an important indicator of bank performance; it represents the rate of return a bank has Profit can be measured as a return on asset (ROA) or return on equity (ROE) The study uses ROA to measure the bank's profitability It is defined as the ratio of profit to assets Bank deposit performance is best measured by ROA in that ROA is not distorted by high equity multipliers and ROA represents a better measure of the ability of a firm to generate returns on its portfolio of assets Abebe, M (2019).
According to the studies of Cekrezi,A V (2022), Thao, V T P (2021),
Haddaweea, A H., & Flayyihb, H H (2020), ROA variable has a positive impact on deposit growth For banks with high a ROA in the previous quarter, deposit growth is faster Higher bank profits tend to signal bank stability, which can make it easier for these banks to attract more deposits However, ĩnvan, Y A., & Yakubu, I N (2020) discovered a negative and statistically significant relationship between ROA and deposits.
One of the measures of capital adequacy in banks is the ratio of equity to total assets If this ratio is high, the bank has enough capital in reserve to manage a possible amount of loss in the future In this case, clients will be more confident in giving their money to the bank The findings of Cekrezi,A V (2022) reached an identical conclusion that the impact of capital adequacy ratios on deposits is negative and significant This suggests that banks with a greater capitalization ratio rely less on deposits for operations Banks make no attempts to raise deposits The findings of ĩnvan, Y A., & Yakubu, I N (2020), who discovered a negative but insignificant correlation.
The loan-to-deposit ratio is used to assess a bank's liquidity by comparing a bank's total loans to its total deposits for the same period A loan-to-deposit ratio shows a bank's ability to cover loan losses and withdrawals by its customers.The LDR helps investors to assess the health of a bank's balance sheet Loans given to its customers are mostly not considered liquid meaning that they are investments over a longer period of time Although a bank will keep a certain level of mandatory reserves, they may also choose to keep a percentage of their non-lending investing in short term securities to ensure that any monies deeded can be accessed in the short term loan-deposit ratio (Teshome, F., 2017).
If the ratio is less than 1, the bank did not borrow from outside sources in order to fund loans to its customers Instead of relying solely on its own deposits, the bank borrowed money if the ratio was greater than 1, which it then reloaded at a higher rate If the ratio is too low, banks might not be making the best return possible The banks may not have enough liquidity to deal with any unanticipated funding needs or economic crises if the ratio is too high However, the high loan amounts disbursed to customers may cause the banks' total deposits to rise This indicates that a large sum of money is in the hands of the populace.
According to Teshome, F (2017) and Vong et al (2009) study findings, there is a positive relationship between loan-to-deposit ratio and deposit And another study of Islam, S N., Ali, M J., & Wafik, A (2019) demonstrate that Loan- to-deposit ratio had positive impact but insignificant correlation on the banks’s deposit growth rate of the private commercial banks in Bangladesh This implies that the more loans we have, the more money is in the hands of the customer, which may lead to an increase in deposit.
Bad loan (NPL) is represented by the ratio of bad debts / total outstanding loans Since a bank's loan portfolio carries the majority of its risk, it is crucial to evaluate the strength and quality of the bank's assets by keeping track on the borrower's financial situation and using the NPL ratio as a proxy for credit quality (Baral, 2005) The credit quality declines as the NPL ratio rises.
Research on deposits in Vietnam, Thao, V T P (2021) demonstrated a negative but insignificant relationship between deposit growth and bad debt ratios in banks.The amount of money deposited at the bank is decreased as a result of the high NPL ratio, which makes depositors feel insecure when making deposits into those banks According to Ikuko & Konishi (2007), one of the elements that has a detrimental impact on deposit growth is the bad loan ratio Deposits will decline for banks that don't adhere to the standards for sound financial management and security.
In many previous empirical studies, the effect of inflation was not clearly defined; it has different effects on the deposits of commercial banks in different research samples.
RESEARCH METHOD
Research model
Based on previous studies by the authors Cekrezi,A V (2022); Thao, V T P. (2021); Yakubu, I N., & Abokor, A H (2020); Islam, S N., Ali, M J., & Wafik, A.
(2019) on factors affecting bank deposit growth The authors extracted to develop a standard model and thereby can examine the factors affecting deposit growth at Vietnamese commercial banks In order to determine the factors affecting the deposit growth of commercial banks in Vietnam, the Bank Deposit Growth Rate (DG) was used as a dependent variable The independent variables applied in this study include two main groups: internal variables related to banks and macroeconomic variables. The research model is built as follows:
DG it =P o +P i SIZE it +P 2 ROA it +P 3 LDR it +P 4 NPL it +P 5 CAP it +P 6 GDP it +P 7 lNF it + p 8 M2GROWTH it + £ it , where:
DG it : measure the Deposit growth rate which is a proxy of total deposit at year t minus total deposit at year t-1 divided by total deposit at year t-1 of each commercial bank.
SIZE it : variable measure variable Bank size of the “i” commercial bank in year t.
ROA it : variable measure the profitability of commercial bank “i” in year t.
LDR it : variable measure Loan-to-Deposit Ratio of the “i” commercial bank in year t.
NPL it : variable measure the non-performing loans of the “i” commercial bank in year t.
CAP it : variable measure of the Equity ratio of the “i” commercial bank in year t.
GDP it : variable measure Economic growth rate in year t.
INF it : variable measure Inflation rate in year t.
M2GROWTH it : variable measure Money supply growth rate in year t e i,t : model error term.
The factors affecting deposit growth are included in the model: (1) Bank size,
(2) Equity ratio, (3) The profitability, (4) Loan-to-Deposit Ratio,(5) Non- performing loans, (6) Inflation rate, (7) Gross Domestic Product growth rate, (8) Money supply growth.
Bank size (SIZE) is an independent variable that is calculated as the logarithm of total assets Total assets data is taken from the bank's balance sheet.
Calculation formula: SIZE = Log (total assets)
Equity ratio (CAP): is measured by the ratio of equity to total assets of the bank Bank equity and total assets value data is collected from the bank's balance sheet.
Calculation formula: CAP = Total equity/Total assets
The profitability (ROA):is expressed in the ROA ratio The profitability of banks can be measured by different ratios, the most common being the ratio of profit after tax to average assets (ROA)
Calculation formula: ROA= Profit after tax/Total assets
Loan-to-Deposit Ratio (LDR): is used to assess a bank's liquidity by comparing its total loans with its total deposits for the same period Loans are listed as assets while deposits are listed as liabilities.
Calculation formula: LDR=Total loans/Total deposits
Non-performing loan (NPL): Ratio of bad debts to outstanding loans This variable reflects the quality of the bank's outstanding loans The data is calculated based on the bank's balance sheet.
The inflation rate (INF) raises the price level of the economy It shows the level of inflation of the economy The inflation rate is based on the consumer price index collected from the report of the General Statistics Office.
Calculation formula: INF = (CPI t - CPI t-1 )/( CPI t-1 ) * 100%
Economic growth (GDP) is an independent variable collected from the report of the General Statistics Office.
Calculation formula: GDP = (GDP t -GDP t-1 )/( GDP t-1 ) * 100%
Money supply growth (M2 Growth): Money supply is a measure of the total amount of money in an economy Money supply (M2) is the summation of currency in circulation, demand deposit, time deposit and saving deposit The data is collected from the report of the General Statistics Office.
Research hypothesis
A hypothesis is a provisional answer to a wide research question that is expressed as a clearly defined relationship between an independent (cause) and dependent (effect) variable The following was the study's hypothesis:
• H1: Bank size has a positive impact on banks’ deposit growth rate.
• H2: Profitability has a positive impact on banks’ deposit growth rate.
• H3: Equity ratio has a positive impact on banks’ deposit growth rate.
• H4: Loan-to-deposit ratio has a positive impact on banks’ deposit growth rate.
• H5: Non-performing loan has a negative impact on banks’ deposit growth rate.
• H6: GDP growth rate has a possitive impact on banks’ deposit growth rate.
• H7: Inflation rate has a negative impact on banks’ deposit growth rate.
• H8: Money supply growth rate has a negative impact on banks’ deposit growth rate of commercial banks in Viet Nam.
Research data
The research data is extracted from the consolidated financial statements of commercial banks and the World Bank to form a data table for the period from 2012 to 2020.
Financial and accounting data representing factors inside the bank, including the items in the balance sheet and income statement, are collected from the bank's annual audited financial statements GDP, inflation, and M2 data are collected from the WB's database The research period was selected based on the fact that this is a period of many fluctuations for banks such as mergers and acquisitions deals due to the impact of the economic crisis, leading to a great change in the Vietnamese commercial banking system The study was carried out with the type of joint stock commercial bank, however, one bank did not publish the financial statements or explain the attached financial statements, so the remaining research results were 22 banks with data for the period from 2012 to 2020, forming panel data with 198 observations Panel data are suitable for research because by combining time series of cross-observations, panel data gives us data with more useful information, more bias, less multicollinearity between variables more, more degrees of freedom and more efficient.
Selection of regression model and tests
With the aim of studying the factors affecting deposit growth of Vietnamese commercial banks, the study was carried out with the following process: Using descriptive statistics, correlation analysis, and regression analysis methods of panel data with the help of Stata 14 software to determine study results.
Pooled OLS –Pooled Ordinary Least Squares
Pooled OLS –Pooled Ordinary Least Squares is a regression model in which all coefficients are constant over time and on individuals This is the simplest approach and the simplest model as it does not consider the space and time of the combined data but only estimates by conventional OLS regression Therefore, this model may give incomplete results and distort reality about the relationship between independent and dependent variables.
Pooled OLS model basic table data has the form:
Y it = α + β 1 X 1it + β 2 X 2it + + βnX nit + u nit , where:
Yit: The dependent variable of the observation i in the period t α: Intercept coefficient β1, β2, , βn: Individual regression coefficients
X 1it , X 2it , , X nit : Independent variables of observation I in the period t
With the assumption that each unit has distinct characteristics that can affect the explanatory variables, FEM analyzes this correlation between the residuals of each unit and the explanatory variables, thereby controlling and separating the influence of these separate characteristics (time-constant) from the explanatory variables so that we can estimate the net effects of the regressor on the dependent variable.
The simple FEM model has the form:
Y it = α i + β 1 X 1it + β 2 X 2it + + β n X nit + u nit
The above model has added the index “i” for the intercept coefficient “α” to distinguish the interception coefficient of each different bank that may be different, this difference may be due to the different characteristics of each bank or the difference between management policies and operations of banks.
This model assumes that variation between units is assumed to be random and uncorrelated with the explanatory variables The simple REM model takes the form:
Y it = α + β 1 X 1it + β 2 X 2it + + βnXnit + ε i + u nit
With ε i : Error of composition of different objects (different characteristics of each enterprise)
Unit: Error of other combined components of both individual characteristics by object and over time.
After running the model, perform a model fit test to select a suitable model.
Based on the model's adjusted coefficient of determination R 2 , this coefficient shows how much of the independent variables in the model explain the variation of the dependent variable From there, conclude the appropriateness of the model.
Check the fit of the model
H0: R 2 =0 (All independent variables have no effect on the dependent variable)
H 1 : R 2 ≠0 (There is at least one independent variable that affects the dependent variable)
Based on the regression results, if:
Prob value of F statistic < 0.05: Reject the hypothesis H0
Prob value of the F statistic > 0.05: Accept the hypothesis H 0
The restricted F test is used to select the appropriate model between the two Pooled OLS and FEM models A traditional OLS specification may serve as the
“restricted” model (R) because it imposes the common intercept across cases The
“unrestricted” models (UR) may be LSDV or another FEM.
With: к: total number of additional variables added in UR model л: total number of observations l: total number of variables in the UR model
When the P_value < 0.05, we reject H0, so the FEM model is more suitable and vice versa.
To consider the more suitable FEM or REM model, we use Hausman test. The essence of this test is to see if there is an autocorrelation between ε i and the independent variables.
H0: εi and the independent variable are not correlated
H 1 : ε i and independent variable are correlated
When the value of P_value < 0.05, we reject H 0 , then εi and the independent variable are correlated with each other and the FEM model is more suitable In contrast, the REM model is more suitable.
Choose between REM model and Pooled OLS model, with the following hypothesis:
If the P-value > α allows the conclusion to accept the hypothesis H 0 , then thePooled OLS model will be selected, otherwise the REM model will be suitable for the study if H 0 is rejected.
RESEARCH RESULTS AND DISCUSSION
Descriptive statistics
Table 4.1 summarizes the basic characteristics of the research data sample from 2012-2020 From the table below, we observe that:
Table 4-1: Descriptive statistics of variables in the regression model
Variable Obs Mean Std Dev Min Max
The average value of the Deposit growth variable is 0.2, with the largest value being 0.827 and the lowest value being -0.131 The logarithmic variable of an asset varies from 16.502 to 21.140 and has an average value of 18.728 The variable equity over total assets fluctuates from a value of 0.238 to 0.041 and has an average value of 0.092 The variable of bad debt ratio has an average value of 0.023, the maximum value is 0.126 and the lowest value is 0.
In terms of the Loans to Deposits ratio, the mean value are 0.908, Min and max values are 0.544 and 1.387, respectively The variable Gross Domestic Product fluctuates from 0.029 to 0.071 and has an average value of 0.059 The consumer price index variable fluctuates from 0.006 to 0.068 and has an average value of 0.039 Variable Money supply growth (M2) ranging from 0.113 to 0.250 and mean at 0.175.
4.1.1 Check the correlation between variables
To determine the relationship between the variables in the model, the study uses correlation coefficient analysis to measure the degree of correlation between the independent and dependent variables The positive correlation coefficient reflects the positive correlation relationship between the dependent variable and the independent variable, whereas the negative correlation coefficient reflects the negative correlation relationship between the dependent variable and the independent variable.
Table 4-2: Correlation matrix between variables
DG ROA CAP SIZE NPL LDR INF GDP M2
Note: *Significant at 0.10 level, ** Significant at 05 level, *** Significant at 01 level
With regards to the correlations between the independent variables, low correlation coefficients as shown in table 4.2 indicates that relationship between the variable GDP and M2GROWTH, BANK SIZE and ROA are highly correlated The other variables have a low correlation.
Table 4-3: Test results for multicollinearity
Through Table 4.3, we can see that there is a relationship between the variables and there is a phenomenon of multicollinearity To avoid misinterpreting the regression results from the reality that needs to be evaluated, multicollinearity should be measured From the above table, we can see that the value of the variance inflation factor -VIF (Variance inflation factor) in the range 1.12 to 2.59 is all less than 10, so it can be concluded that there is no strong correlation between the independent variables, multicollinearity in the model is not significant.
Analysis of factors affecting Deposit Growth
The study tests the relationship between the independent variables on the dependent variable by the Pooled OLS, FEM and REM regression models, which are presented in Table 4.4 The results of the regression models are as follows:
Table 4-4: Summarize the results of regression analysis with DG
(Source: Retrieved from Stata 14) (Note: ***, **, * are equivalent to significance level of 1%,5% and 10%)
The regression results according to Pooled OLS shown in Table 4.4 show that the R 2 coefficient is 0.1269, implying that the independent variables included in the model explain 12.69% of the change of the dependent variable DG In which, the independent variables NPL, LDR, GDP, M2GROWTH have no statistical significance in the model Independent variables CAP and SIZE are accepted to explain the dependent variable DG at 1% significance level and ROA, INF variables at 5% significance level.
With the FEM method, the results shown in Table 4.4 show that the coefficient R 2 is 0.1768, implying that the independent variables included in the model explain 17.68% of the variation of the dependent variable DG In which, the independent variables NPL, LDR, INF, GDP and M2GROWTH have no statistical significance in the model Independent variable SIZE is accepted to explain the dependent variable DG at 1% significance level. Independent variables ROA and CAP are accepted to explain the dependent variable DG at 10% significance level.
The regression results according to the REM model shown in Table 4.4 show that the
R 2 coefficient is 0.1216, meaning that the independent variables included in the model explain 12.16% of the change of the dependent variable DG In which, the independent variables NPL, LDR, GDP, M2GROWTH have no statistical significance in the model. Independent variable SIZE is accepted to explain the dependent variable DG at 1% significance level, CAP at 5% significance level and INF, ROA variables at 10% significance level.
Table 4-5: F-test, Breusch-Pagan and Hausman model selection for variable DG
Breusch and Pagan Lagrangian test chibar2 12.40 Prob > chibar2 = 0.0002
Hausman test chi2 43.83 Prob > chi2 = 0.000
Table 4.5 shows the results of the F-test to support whether to choose the OLS model or the FEM model, the result is that with Prob = 0,0010 < a = 5%, H0 should be rejected (H0: Pooled model is suitable) Thus, the FEM model is more suitable than the OLS model.
Table 4.5 shows the results Prob.Chibar2 = 0,0002 < α = 5%, so we conclude to reject the H0 hypothesis (H0: Pooled OLS model should be chosen).Thus, the REM estimation method will be more suitable than Pooled OLS.
Table 4.5 shows the results of Hausman test on whether to choose FEM model or REM model The results show that Prob.Chi-Square = 0,000 < α = 5%, so we reject hypothesis H 0 (H 0 : should choose REM model) Thus, the FEM estimation method will be more suitable than the REM estimation method.
In summary, the FEM model is the most suitable model to analyze the results In addition, the study also conducts testing of hypothesis violations such as multicollinearity, variable variance as well as autocorrelation The results of testing the hypothesis violations are analyzed in the next sections.
4.2.3 Testing the hypothesis violations of the FEM
Table 4-6: The results of Heteroskedasticity diagnostics with DG chi2 2264.49 Prob>chi2 0.0000
The results in table 4.6 show that Pro > chi2 = 0.0000 is less than α = 5%, showing that the model has the phenomenon of variance of error Autocorrelation diagnostics.
Table 4-7: The results of Autocorrelation diagnostics with DG
In Table 4.7, Pro > F = 0.0003 is less than α = 5%, showing that the model has autocorrelation phenomenon.
4.2.3.3 Fix the defects of the model
In this thesis, using the FGLS method to overcome the autocorrelation defects and variable variance of the FEM model, giving a regression model, giving the following results:
Table 4-8: DG regression model results after fixing
DG= 1.2855 + 4.5506ROA - 1.4881CAP - 0.0418SIZE + 0.8643NPL - 0.0427LDR +0.6833INF - 2.0693GDP - 0.5946M2GROWTH.
CONCLUSIONS AND RECOMMENDATIONS
Conclusions
This study examined the determining factors of bank deposit growth in the case of Commercial Bank of Vietnam in the period 2012-2020 In order to quantify the deposit growth of each bank included in the study, the researcher used a change in the annual deposit While, the respective explanatory variables such as loan to deposit ratio, the bad debt, bank size, equity ratio, the profitability, GDP growth rate, broad money supply and inflation has been used basing acceptable measurement using prior empirical works Based on the result of descriptive and empirical analysis, the study had concluded the following:
Research results show that , variables that have a negative impact on on deposit growth of commercial banks in Vietnam include: Bank size, Equity ratio, GDP growth rate, Money supply (M2) From the regression output the profitability has a positive significant effect on the deposit growth of commercial banks in Vietnam The amount of deposits is observed to be significantly impacted by any changes in the bank's profit rate This study thus indicates that Vietnamese bank customers are driven by financial gain This represents how customers typically behave So, put up with the effects of substitution in the established system Customers of commercial banks are impacted by their profit margins.
The result of this study showed that, among the bank-specific variables, the loan-to- deposit ratio (LDR) and the bad debt (NPL) have an insignificant impact on the deposit growth of commercial banks in Vietnam Also, the study found that inflation (INF) has a positive but insignificant effect on Vietnamese commercial banks' deposits Thus, it is concluded that as inflation increases in the economy, it does not have an effect on deposits in Vietnamese commercial banks Since 2011, the Vietnamese economy has faced many difficulties as economic growth tends to slow and many risks in the banking system that accumulated many years ago begin to have a negative impact on macroeconomic stability The period 2016–2019 is phase 2 of the project to restructure the financial and banking systems Depositors find that the bank mergers have caused some disruption, but their interests have not been affected They do not pay much attention to the risk factors of the bank when depositing Mergers are just grouping banks together, and deposits are just moving from one bank to another.
Recommendations
Based on the research findings, the following are recommendations for commercial banks in Vietnam as a way to increase their deposits.
• Since profitability has a positive and significant effect on deposit growth, commercial banks should expand profits in order to increase their deposits Accumulated profit is the best and most sustainable source of capital, not only for the banking sector but for all businesses In order to accumulate profits, banks must improve service quality, diversify products, increase service revenue, control costs, use capital effectively by increasing lending to subjects with high interest margins, and at the same time manage credit risk, limit bad debt, etc.
• Deposits fell when the capital adequacy ratio was raised The likelihood of insolvency is low because a bank with a high capital adequacy ratio is solvent Because they have enough money to cover withdrawals or investments, banks in this situation do not use politics to entice depositors Commercial banks must maintain a suitable profit margin to remain competitive and increase deposits A commercial bank is an extremely public institution Thus, it must be lucrative to attract deposits.
Firstly, commercial banks need to ensure the minimum capital adequacy ratio according to international standards, the regulations of the State Bank of Vietnam, and BaselIII standards Accordingly, commercial banks need to determine an appropriate percentage of net profit to be retained annually to increase charter capital, or they may merge and acquire small banks to form a bank with greater financial potential, or they may call for contributions from shareholders to improve financial potential.
Secondly, banks need to identify levers to reduce capital waste without changing business models and optimize scarce capital resources to achieve efficiency in the use of equity.
• As for commercial banks, when the economy grows, they should focus more on gaining market share and promoting more services In contrast, when the economy declines, it is necessary to consider solutions to narrow the level of promotion of services to the market.
In short, the government agencies need to carry out synchronous and effective solutions to economic stability, create an environment and a driving force for economic development, improve productivity, efficiency, and competitiveness as well as the quality of human resources, strengthen scientific and technological potentials to contribute to stability, and promote economic growth.
• Seeing as the money supply has a negative and significant effect on deposit growth, the government should decrease the money supply in the economy By selling bonds to the public, the government can absorb the excess cash in circulation The State Bank can continuously control the money supply by looking at the reserve requirements of banks and applying expansionary and contractionary policies in order to control the level of money supply.
Limitation of the study
The thesis studies the effects of factors on the deposit growth of Vietnamese commercial banks, and the research model focuses on eight key factors affecting deposit growth However, there are many other factors with a stronger impact that the topic has not yet analyzed, because deposit growth of banks is not only influenced by bank characteristics but also by macro-factors such as the unemployment rate, fluctuations in exchange rates, reserve requirements, government bond yields, stock indices, etc.
Data sources of Vietnamese commercial banks are still limited: many small banks do not publish data, and full information is difficult to access, so the thesis can only collect data from 22 banks in the observation period from 2012 to 2020 However, the model is good for gathering with many banks With banks of different sizes, the ratio of total assets accounts for a high proportion of the total assets of all banks in Vietnam.
From the above limitations, research direction can be given to increase the number of additional research samples In addition, the time period can be extended to enhance the explanation of the research model Next, most of the previously conducted research in Vietnam focused on the annual change of commercial banks, which is a proxy of total deposit at year t minus total deposit at year t-1 divided by total deposit at year t-1 of each commercial bank So next research can focus on banks' deposit mobilization (measured as a log of total deposit).
The reason for the above limitations is due to limited time and current capacity. Therefore, in future studies, the author wishes to be further studied to provide a more general assessment of deposit growth in the Vietnamese banking system as well as to build a model with better tests and identify many factors affecting deposit growth in the Vietnamese banking system to make a useful reference for research as well as necessary suggestions for banks in making policies to improve deposits.
Chapter 5 has drawn conclusions from the research results in the thesis, and on that basis, the author has made some recommendations and discussed the policy implications for improving the deposit growth of Vietnam's environmental banks For factors that have a positive impact on deposit growth, the author provides policy implications to improve these indicators On the contrary, for factors with negative impacts, the author proposes policy implications to limit and minimize those impacts At the same time, it also points outs the limitations of the study and finds out the causes and solutions by expanding the research sample and adding independent and dependent variables so that future studies can better explain the factors affecting the deposit growth of commercial banks in Vietnam, becoming a useful document and creating a basis for applying measures to increase deposits for Vietnamese banks.
The issue of determining the factors affecting deposit growth at commercial banks is one of the important contents, attracting the attention of many researchers in the world and in Vietnam in the past period In this research topic, the author shows how a group of internal and external factors affects the deposit growth of Vietnamese commercial banks in the period 2012-2020 and has answered two research questions raised in Chapter 1, including:
What factors affect deposit growth of Vietnamese commercial banks ?
To what extent does factors affect deposit growth of Vietnamese commercial banks ? However, the answer to each question posed has limitations In the future, the author will expand the research direction in both space and time as well as find more independent and dependent variables that affect deposit growth of Vietnamese commercial banks.
In summary, as a the research findings discussed above, the author hopes to provide a more complete picture of the variables influencing the growth of commercial banks' deposits in order to assist bank managers in increasing commercial banks' deposits in Vietnam and ensuring that the country's banking industry continues to grow in a safe, sound, efficient, and sustainable manner.
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Appendix 1: List of commercial banks selected for the research
No Name of Banks Abbreviations Indications
1 An Binh Commercial Joint Stock Bank ABBank ABB
2 Asia Commercial Joint Stock Bank ACB ACB
3 Joint Stock Commercial Bank for
Investment and Development of Vietnam BIDV BIDV
4 Vietnam Export Import Commercial Joint
5 Ho Chi Minh city Development Joint
Stock Commercial Bank HDbank HDB
6 Kien Long Commercial Joint Stock Bank Kienlongbank KLB
7 LienViet Commercial Joint Stock Bank LienVietPostBank LPB
8 The Maritime Commercial Joint Stock
9 Military Commercial Joint Stock Bank MB MBB
10 Nam A Commercial Joint Stock Bank NamABank NAB
11 National Citizen Bank NCB NCB
12 Orient Commercial Joint Stock Bank OCB OCB
13 Petrolimex Group Commercial Joint Stock
14 Saigon Thuong Tin Commercial Joint
15 Saigon Bank for Industry & Trade Saigonbank SGB
16 Saigon-Hanoi Commercial Joint Stock
Joint Stock Bank Techcombank TCB
19 Ngân hàng TMCP Việt Á Vietabank VAB
20 Joint Stock Commercial Bank for Foreign
Trade of Vietnam Vietcombank VCB
21 Vietnam Joint Stock Commercial Bank of
Industry and Trade Vietinbank CTG
22 Vietnam Commercial Joint Stock Bank for
Bank Yea r DG ROA CAP SIZE NPL LDR INF GDP M2 growth ABB 201
Appendix 03 Research results Picture 1 Descriptive statistics
summarize dg roa cap size npl ldr inf gdp m2growth
Picture 2 Correlation matrix between variables pwcorr dg roa cap size npl ldr inf gdp m2growth dg ro a cap si ze npl ldr inf g d 1.00 ro 00 a 0.04
pwcorr dg roa cap size npl ldr inf gdp m2growth, star(0.01) dg roa cap size npl ldr inf dg roa 1.0000
0.0402 1.0000 cap size npl ldr inf gdp m2growth
pwcorr dg roa cap size npl ldr inf gdp m2growth, star(0.05) dg roa cap size npl ldr inf dg roa 1.0000
0.0402 1.0000 cap size npl ldr inf gdp m2growth
pwcorr dg roa cap size npl ldr inf gdp m2growth, star(0.1) d g roa cap siz e npl ldr inf dg 1.000 ro 0 a 0.040
Picture 3 Test results for multicollinearity
Picture 4 Pooled OLS output with Deposit growth
reg dg roa cap size npl ldr inf gdp m2growth
Source SS d f MS Number of obs = 198
707 dg Coef Std Err t P>| t| [95% Conf Interval
Picture 5 FEM output with ROE
xtreg dg roa cap size npl ldr inf gdp m2growth, fe
R-sq: within = 0.1768 between = 0.0164 overall = 0.0460 dg Coef Std Err t P>|t| [95% Conf
9.205165 cap size npl ldr inf
14436998 13873295 51990368 (fraction of variance due to u_i)
Picture 6 REM output with ROE
xtreg dg roa cap size npl ldr inf gdp m2growth, re
Random-effects GLS regression Number of obs = 198
Group variable: bank1 Number of groups = 22
Group variable: bank1 corr(u_i, Xb) = -0.7262
Number of Number obs = groups = 198
Obs per group min = 9 avg = 9.0 max = 9
05178327 13873295 12228499 (fracti on of vari ance due to u_i)
Picture 7 Hausman test with ROE
1 b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B)
Prob>chi2 = 0.0000 (V_b-V_B is not positive definite)
Picture 8 Breusch and Pagan Lagrangian test with ROE
Breusch and Pagan Lagrangian multiplier test for random effects dg[bank1,t] = Xb + u[bank1] + e[bank1,t]
Test: Var(u) = 0 chibar2(01) = 12.40 Prob > chibar2 = 0.0002
Picture 9 Heteroskedasticity diagnostics with Deposit growth
Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (22) = 2264.49