A STATISTICAL ANALYSIS OF PROFITABILITY OF COMMERCIAL BANKS IN VIETNAM

 

ABSTRACT

This quantitative research paper focuses on examining the relationship between macroeconomic factors and the profitability of commercial banks in Vietnam. The data used in the research is collected from financial reports of 9 commercial banks listed on the stock market during the period from 2008 to 2019. This study uses the Panel Data Model, which shows that ROA is affected by macroeconomic factors.

АННОТАЦИЯ

Данная статья посвящена изучению взаимосвязи между макроэкономическими факторами и рентабельностью коммерческих банков во Вьетнаме. Данные, использованные в исследовании, собраны из финансовых отчетов 9 коммерческих банков, котирующихся на фондовом рынке, за период с 2008 по 2019 год. В статье используется модель панельных данных, которая показывает, что на рентабельность активов влияют макроэкономические факторы.

 

Keywords: Vietnam, empirical model, macroeconomic factors, commercial banks, panel data.

Ключевые слова: Вьетнам; эмпирическая модель; макроэкономические факторы; коммерческие банки; панельные данные.

 

I. Introduction

The 2008 financial crisis had caused negative consequences for the economy of Vietnam in general, and the national commercial banking system in particular. All commercial banks faced many difficulties such as decline in profitability, increase of bad debts due to high inflation and rapid economic recession. Particularly, listed commercial banks were affected by the sudden decline of the stock market. Currency is an effective tool for macroeconomic management, which determines the development and recession of an entire economy. Currency is strictly managed by the State, but at the same time commercial banks must operate effectively and profitably as this is a two-way impact. Therefore, in order to make sound policies on the operation of Vietnam’s commercial banking system in the coming time, determining the impact level of macro factors on the profitability of banks is essential. In order to examine the importance of improving profitability of Vietnam’s commercial banks, the author conducts empirical research on the impact of macro factors on the profitability of banks listed on the stock market during the period from 2008 to 2019.

II. Theoretical basis

2.1. The groups of indexes to reflect the profitability and performance of banks commonly used are ROA (Return on total assets), ROE (Return on common equity), NIM (Net Interest Margin), and NNIM or NOM (Net Non-Interest Margin). However, within the scope of this research paper, the author uses the ROA index only

ROA is the ratio of net income to assets, which measures the profitability of a bank per dollar of assets.

The higher the ROA is, the better it is as the bank makes more money on less investment. ROA provides investors with information about the profits generated from the amount of assets formed from debt and equity.

2.2. The group of internal factors affecting the profitability of banks considered in the scope of this research includes three factors: bank size (SIZE), financial structure (CA), and Age of Operation (AGE).

2.2.1. Bank size (SIZE) – variable X1

Total asset size is one of the factors affecting the profitability of commercial banks. These are assets formed during the operation of commercial banks.

Economic theories suggest that large organizations will be more efficient and able to provide services at lower prices due to economies of scale, thereby generating greater profits. However, there are also many perspectives that the unreasonable expansion in the scale of operation of a bank will pose many difficulties for management and cause managers to make the wrong decisions, thus reducing bank profitability.

Theoretically, banks with large asset size can achieve high profitability from economies of scale because there are many favorable conditions in the process of expanding products and services distribution, as well as saving transaction costs, thereby enhancing profits. Economies of scale, or increasing returns to scale, are revealed when the long-run average cost falls as output increases.

According to Phan Thi Hang Nga (2013), the main activity of a bank is evidently shown in a bank’s assets. The size, structure, and quality of assets will determine the existence and development of a commercial bank. Assets include earning assets (accounting for 80-90% of total assets) and non-earning assets (accounting for 10-20% of total assets). When it comes to the growth of total assets, the size of credit and investment activities matter. The larger the total assets of a bank, the more likely it is to expand the loan scale as the bank is a borrower in order to lend others. The assets’ size of commercial banks is a general indicator that shows the financial sustainability and management capacity of a credit institution. Most of the risk in currency trading concentrates on assets. It is assumed that there is a positive relationship between the size of banks and profitability because by increasing the bank size, costs can be reduced, and thus profitability can be improved (Berger et al. 1987; Smirlock, 1985).

According to Nguyen Viet Hung (2008), a bank size variable measured by the natural base logarithm of total assets is taken as a representative variable for the size of a commercial bank. Gaganis et al. (2006), Lanine and Vennet (2006) use a similar measure while Chen and Shih (2006) use total assets to represent a bank’s size. However, if total assets are taken to represent a bank’s size, there will be a large disparity between the groups of banks, affecting the regression results. Therefore, the method of calculating bank size using the logarithmic function of total assets is the most commonly used.

2.2.2. Size of equity (Capital-CA) – variable X2

Equity size is considered as a variable to measure the safety and soundness of a bank, assessing the solvency of the bank in case of loss, thereby showing its ability to absorb unexpected losses (Javaid et.al, 2011). Banks with high equity will reduce the cost of capital (Molyneux, 1993) and have a positive effect on bank profitability. Moreover, capital raising can increase expected profits and reduce expected costs due to the financial crisis such as bankruptcy (Berger, 1995) as cited by Sufian (2011). Gul, Irshad and Zaman (2011), Zeitun (2012) and Trujilo- Ponce (2010) find a positive correlation between equity size and the profitability of commercial banks.

Research by Athanasoglou (2005) based on the data of Greek banks in the period from 1985 to 2001 also shows that equity is an important factor explaining the bank profit margin. The larger the equity, the higher the profit margin of a bank.

2.2.3. Age of Operation (AGE) – variable X3

According to Nguyen Thi Dieu Chi (2018), for banks in particular and enterprises in general, age is a factor belonging to the specific characteristics of the organization that can affect its performance. When customers decide to establish a business transactional relationship with a bank, reputation is of utmost importance, especially in terms of the bank’s ability to receive a large deposit and make large transactions, which is reflected in the age of the bank. Most of the established banks will be relatively large in size due to capital accumulation from retained earnings over a long period of time and this will give the bank access to greater business opportunities as well as a larger base of customers compared to smaller ones. Thereby, bringing about high-value contracts that leads to improved profitability and operational efficiency of the bank.

Moreover, over many years of operation, banks will gradually accumulate experience in the management and appraisal of investment projects, and at the same time obtaining long-term and good relationships with customers. This is also a favorable condition to create large and stable profits in the long run, contributing to enhancing the banks' operational efficiency.

2.3. The group of external (macro) factors affecting the performance and profitability of banks is included in the research paper  by the author, involving 2 factors: economic growth (GDP growth rate) and inflation (consumer price index CPI).

2.3.1. Economic growth (GDP) – variable Z1

According to the textbook “Commercial Banking Management” by Tran Huy Hoang (2011), the main business areas of a bank are currency, credit and banking services. These are special areas because they are directly related to all industries and aspects of the socio-economic life. Therefore, fluctuations in the economy will have a significant effect on a bank’s performance, and they are reflected in GDP growth. The impact of GDP growth on the performance of the banking industry is two-way. Banking system plays an important role in increasing the GDP of the economy, and this increase will in turn improve the operational efficiency of the banking system.

In economics, GDP is the monetary value of all goods and services produced within a territory during a given period of time, usually a year. When applied to the whole country, it is also known as national gross domestic product. GDP is one of the basic indicators to assess the economic development of a certain territory. GDP indicates the size of the economy, but it is not an accurate measure of living standards. GDP does not take into account the shadow and non-monetary economies such as the barter economy, volunteer work, free childcare by (non-working) parents, or the production of goods in the family. Therefore, in countries where informal business practices are a major part of GDP, those figures will be less accurate. GDP does not take into account the harmonization of development. For example, a country may have a high GDP growth rate due to overexploitation of natural resources. GDP includes jobs that have no net benefit and does not consider negative effects.

GDP is expected to affect many factors related to the supply and demand of bank deposits and loans. According to the literature on the link between economic growth and financial sector profitability, GDP growth is expected to have a positive relationship with bank profitability (Demirguc-Kunt and Huizinga 1999; Bikker and Hu, 2002).

The relationship between GDP growth and bank performance is two-way, high economic growth will first lead to the demand for loans, deposits, payments and many other services, then the operation of the banking system will play a role in increasing GDP. The high and stable GDP growth rate will create an impetus for business expansion to improve the profitability of commercial banks. An increase in the average income of people will motivate them to spend more, pay more and even save more, thereby helping to expand banking services, mobilize capital, invest and do business to make a profit. For commercial banks, this system helps idle capital flows to form and circulate smoothly in the economy for investment and profit. Moreover, banks are essentially business enterprises with the aim of maximizing profits, so they will have to choose businesses and projects that have the ability to recover debts and are effective for lending. As a result, capital is effectively allocated among sectors and fields; while reducing risks and costs in terms of time, information collection and processing.

2.3.2. Inflation (INF) – variable Z2

Inflation measures the overall increase in the consumer price index (CPI) for all goods and services. Inflation affects the real value of costs and revenues.

In the view of Perry (1992), a high rate of inflation is associated with a high loan interest rate and income will therefore also be high. However, it is asserted that the impact of inflation on bank profitability is highly dependent on the ability to predict expected inflation. If inflation is predicted correctly and interest rates are adjusted accordingly, inflation will have a positive impact on bank profitability. In contrast, an unexpected increase in inflation can make it difficult for customers to forecast cash flows, which can lead to premature termination of loan agreements and cause credit losses. Indeed, if banks are slow in adjusting their interest rates, it could lead to higher costs than revenues. Hoggarth et al. (1998) even point out that high inflation will make it difficult for banks to plan and negotiate loans. Thus, the findings on the relationship between inflation and bank profitability are contradictory.

Although studies in Hong Kong by Jiang et al. (2003) and in Malaysia by Guru et al. (2002) have shown that high inflation rates lead to higher profits, study by Abreu and Mendes (2002) gives negative results on the relationship between inflation and profitability of European banks. In addition, Demirguc-Kunt and Huizinga (1999) find that banks in developing countries tend to be less profitable in an inflationary environment, especially when they have a high capital ratio. In these countries, the expenses of banks actually increase more than their revenue. Thus, inflation will have a positive effect if the bank income increases faster than its costs, otherwise, it will negatively affect the bank profitability.

The relationship between inflation and profitability is predicted to be positive according to the following studies: Molyneux and Thorton (1992), Hassan (2003), Kosmidou (2006).

Towards joint-stock commercial banks, high inflation adversely affected capital mobilization, lending, investment and performance of banking services. Banks will have to raise mobilizing interest rates which increases costs and reduces profits. In addition, the implementation of strict monetary policy by the State Bank of Vietnam reduces the amount of money in circulation, while the demand for loans is high but the bank can only accommodate a small number of customers. High inflation also causes the use of short-term capital for medium and long-term loans to occur frequently, affecting the bank liquidity, leading to forward risk and exchange rate risk.

III. Research Methods

3.1. Research data

Commercial banks listed on the stock market need to follow the rules of information transparency for shareholders, therefore financial statements must be fully-disclosed quarterly, annual and be assured by independent audit firms such as KPMG, Ernst & Young. This is the main database in selecting indicators to assess the profitability of banks such as ROA, ROE as well as internal factors such as asset size, bank size, bad debt, credit risk… In order to fully assess the impacts on banks that are listed on the stock market, the author has selected 9 commercial banks as research samples, including 5 banks listed on HOSE (CTG, EIB, MBB, STB, VCB) and 4 banks listed on HNX (ACB, HBB, NVB, SHB) during the period from 2008 to 2019.

In addition, the group of external factors, mainly macro factors, does impact the profitability of banks as gathered from various reliable sources such as the International Monetary Fund (IMF), World Bank, General Statistics Office of Vietnam (GSO) and State Securities Commission of Vietnam. In order to match the selected research sample in banks, groups of external factors were also collected in the period from 2008 to 2019.

3.2. Research models

The data source is collected from 9 commercial banks at different time points, all of which have data from 2008 to 2019 so this is symmetrical array data.

A basic array data model has the following form:

In which index i refers to individuals, i=1,2,..,n and t is a time index (years, months, ...), t=1,2,…, T.

Components of the model:  is the normal random error, which is assumed to satisfy the normative conditions of the OLS method;  represents unobservable characteristics of individuals. Depending on the characteristics of these unobserved factors, different estimation methods are proposed.

- If does not exist or is not significant, then the POLS pooled estimation method will be used. Observations of the same individual are ordered chronologically and estimated as a conventional one-dimensional model.

- If  is not correlated with the independent variables X, then the estimation method using the random-effects model RE is suitable.

- Nếu  is correlated with the independent variables X, then the estimation method using the fixed effects model FE gives the best results.

Based on the above theoretical model, the author proposes a research model of the factors affecting the profitability of listed commercial banks as follows:

The model with the independent variable ROA

In which index i refers to individuals (banks), i=1,2,..,9 and t is a time index, t=2008, 2009,…, 2019.  The group of independent variables X indicates groups of internal factors and Z indicates groups of external factors.

Descriptive statistics of variables

Table 1.

Description of used variables and research hypotheses

 

IV. Regression results and discussion

4.1. Statistical results on the current status of commercial banks profitability

Table 2.

Descriptive statistics of profitability variables of 09 Vietnamese commercial banks in the period 2008-2019

Source: Author’s calculations, using STATA software and data from financial statements of Vietnamese commercial banks in the period 2008-2019

 

According to the result of Table 2, the average ROA value of 09 commercial banks reached nearly 1.4%/year in the period from 2008 to 2019, with a standard deviation of 0.85%/year. In which, the highest ROA among commercial banks reached 4.49%/year and the lowest ROA was 0.016%/year. This proves that there was a huge difference between the profitability of commercial banks in the period 2008-2019.

4.2. Actual results of the impact of factors on profitability of Vietnamese commercial banks in the period 2008-2019

The study evaluates the impact of factors on the profitability of commercial banks through a linear regression model with the dependent variable ROA and 05 independent variables including Asset Size (SIZE), Equity Size (CA), Age of Operation (AGE), Economic Growth Rate (GGDP), Inflation broadcast (INF).

Table 3.

Description of used variables and research hypothesis

Source: Author’s calculation, using STATA software and data from financial statements of Vietnamese commercial banks in the period 2008-2019

 

4.2.1. Asset Size (SIZE)

The average size of 9 listed commercial banks in the period 2008-2019 was 19.15. Among the 9 listed commercial banks selected, the bank with the largest scale value reached 21.12 and the bank with the smallest scale value was only 16.2. The level of difference in size between banks is the standard deviation, nearly 1.08. It can be observed that, in general, there is no large difference amongst Vietnamese commercial banks in terms of scale.

4.2.2. Size of equity (CA)

In terms of size, with the equity/total assets ratio of 9 Vietnamese listed commercial banks in the period 2008-2019, the average size of equity in total assets was at 7.88%. The bank with the highest proportion of equity in total assets was 26.62%, the lowest was 4.06%. This indicator also reflects the ability to mobilize the working capital of banks besides the available equity, which means that in 100 dong of working capital of commercial banks, the highest bank would have 26.62 dong of equity, and the lowest bank would have 4.06 dong of equity. Through the indicators, it can be seen that, in general, commercial banks are active in using borrowed funds to operate. However, there are potential risks of liquidity, a decline in profit ratio and risk of non-recoverable bad debt since the real capital is too small while the loan ratio is too large.

4.2.3. Age of Operation (AGE)

Vietnam’s commercial banks in the period 2008-2019 have been operating for an average of nearly 29 years, as of 2019 all banks have been operating for more than 13 years, and the one with the longest operating history was up to 62 years. Research shows that, with over 13 years of operation, banks are relatively stable, extremely knowledgeable of its markets and customers. Due to the relatively good understanding of the psychology and needs of the financial market, banks’ ability to mobilize and use capital is also more stable, thereby contributing to improving the operational efficiency and profitability of Vietnam’s commercial banks in general.

 

Figure 1. Average comparison of internal factors: X1-Bank size (SIZE), X2- Financial structure (CA), X3- Age of operation (AGE), from 2008 to 2019

 

Source: Author’s calculation, using STATA software and data from financial statements of Vietnamese commercial banks in the period 2008-2019

4.2.4. Economic growth rate (GGDP)

In the period 2008-2019, the average growth rate of Vietnam’s GDP is 6.18%/year, especially during the period of economic recovery after the 2008 financial crisis. Vietnam’s lowest GDP growth is nearly 5.25%, which is higher than many countries in the region and in the world. In 2018, Vietnam’s GDP reached the highest rate in the past 12 years, up to 7.08%.

4.2.5. Inflation (INF)

However, the consumer price index during this period was quite high compared to the average level of nearly 7.73%, thus showing that the price slippage of the economy exceeded the average growth rate up to 6.44%. The highest level of slippage was in 2008, up to nearly 23.12%, and the lowest level was in 2015, down to 0.63%. This shows a certain degree of influence on the income source of the economy in general and commercial banks in particular. To ensure a steady stream of income, commercial banks had to increase deposit and lending interest rates during this period so that their income would compensate for the devaluation of the currency. However, along with the decisions of the Government and the State Bank of Vietnam, the inflation rate decreased and interest rates at commercial banks also stabilized.

 

Figure 2. Groups of external factors: Z1- GGDP growth rate and Z2- INF inflation, from 2008 to 2019

Source: Author’s calculation, using STATA software and data from financial reports of Vietnamese main commercial banks 2008- 2019 period

 

Table 4.

Descriptive Statistics ROA of listed commercial banks from 2008 to 2019

Source: Author’s calculation, using STATA software and data from financial statements of Vietnamese commercial banks in the period 2008-2019

 

Table 5.

Descriptive statistics of groups of internal factors: X1-Bank size (SIZE), X2- Financial structure (CA), X3- Age of operation (AGE)

Source: Author’s calculation, using STATA software and data from financial statements of Vietnamese commercial banks in the period 2008-2019

 

Table 4.

 Correlation analysis table between variables in the model - TR169

Source: Author’s calculation, using STATA software and data from financial statements of Vietnamese commercial banks in the period 2008- 2019

 

Correlation analysis of results between variables in the model according to Table 4 shows that the Pearson correlation coefficient within the correlation matrix is low (the highest is 0.68), showing that the possibility of multicollinearity among independent variables in the regression model is relatively low. Therefore, the selected variables are suitable to explain the impacts of selected variables on the independent ones, which is the profitability of listed commercial banks in Vietnam in the period from 2008 to 2019.

4.3. The results of testing the fit of the regression model

The initial regression model takes the following form:

The results of Breusch and Pagan test give p-value=0.0003 <, so for the given data set, the fixed-effect method FEM or the random-effect method REM should be used.

Figure 3. Breusch&Pagan test results for the initial model

 

Figure 3. Breusch&Pagan test results for the initial model

Source: Author’s calculations, using STATA software and data from financial statements of Vietnamese commercial banks in the period 2008-2019

 

Hausman test results are used to test whether the model fits FEM or REM. With a p-value probability level of 0.000, it can be concluded that the random-effects model will fit the given data set.

 

Figure 4. Hausman test results for the initial model

Source: Author’s calculations, using STATA software and data from financial statements of Vietnamese commercial banks in the period 2008-2019

 

However, when performing the t-test to see whether the coefficients are statistically significant, the coefficients of the variables SIZE, AGE are not statistically significant while the coefficients of the CA variable are only statistically significant at 5%. Only the coefficients of GGDP and INF are statistically significant at the 1% level. In addition, the coefficient of the GGDP variable produces a negative result, showing that there is a negative relationship between the variable GGDP and ROA, which is an inappropriate point in this result.

 

Figure 5. Estimation results in REM original model

Source: Author’s calculations, using Stata software and data from the financial statements of Vietnamese commercial banks in the period 2008- 2019

 

In the next process, the author performs a nonlinear regression model with SIZE, AGE variables as follows:

The results of the Breusch & Pagan and Hausman tests give the following results: with the 1% significance level, we can use the POLS pooled regression method to estimate but with the significance level of 5% we can use the REM regression method for the above model (p-value=0.0397).

To ensure that the obtained results are consistent and statistically significant, the next step is to regress both POLS and REM models to compare and contrast.

* With the POLS pooled model: The estimated coefficients are all statistically significant at 1% and 5%, but when testing the model, the unequal variance is at 10% and the multicollinearity is very high. (VIF of SIZE2=1041.35 and VIF of SIZE=1039.16).

 

Figure 6. POLS model estimation results

Source: Author’s calculation, using STATA software and data from financial statements of Vietnamese commercial banks in the period 2008-2019

 

Figure 7. Results of defects testing in the POLS model

Source: Author’s calculation, using STATA software and data from financial statements of Vietnamese commercial banks in the period 2008-2019

 

* With REM model: Hausman test results show that the REM model perfectly fits the given data.

 

Figure 8. Hausman test results of the following model

Source: Author’s calculations, using STATA software and data from financial statements of Vietnamese commercial banks in the period 2008-2019

 

Therefore, the author has selected the effects model REM as the final model to analyze the relationship between factors and the dependent variable ROA. 

4.4. Significance of regression results

 

Figure 9. Regression table of random effects model REM

Source: Author’s calculation, using STATA software and data from financial statements of Vietnamese commercial banks in the period 2008- 2019

 

The implemented REM random-effects model is completely suitable (since the p-value of the Wald test = 0.000 <), the explanatory level of the independent variables in this model is about 39.88% for all data (R-square overall=0.3988). The level of explanation between banks (R-squared between = 0.4104) and within each bank (R-squared = 0.4054) over the years is 41.04 and 40.54%, respectively. It proves that the used model can explain the relatively high rate of changes in the ROA dependent variables of 9 commercial banks in the period from 2008 to 2019.

V. Conclusion and Recommendations

5.1. Conclusion

The estimated results of the panel data regression model show that the relationship between groups of internal and external factors do affect the profitability of commercial banks. As follows:

The coefficients in the model are statistically significant at the 1% level and 5% level (specifically, SIZE, SIZE2, CA, INF at the 1% significance level and AGE, AGE2, GDP at the 5% one). The relationship of the independent variables with the dependent ones is also consistent with the given expectations.

* In terms of the variable SIZE (The Bank Size): The slope of the variable SIZE2 of the quadratic form is given less than 0, so it shows that the larger the size of the bank's assets, the more likely the ROA gradually increases to the top, but if the SIZE is too large, the ROA tends to decrease. This is completely consistent with theory and reality of large banks with cumbersome administrative and management apparatus. As a result, it is difficult for these banks to control the quality of loans, leading to bad debt or outstanding debt.

* In terms of the variable CA (The Financial Structure): The positive CA estimated coefficient shows that equity size has a positive correlation with ROA, which means that the large equity will ensure the risk provision basis for large banks. Customers will have more confidence in those banks when depositing money as well as making big loans.

* In terms of the AGE variable (The Operation Time): The form of the function used is also quadratic, but the result of the coefficient of the AGE2 variable is positive, so the relationship of AGE with ROA is shown to be opposite to SIZE. With the increased operation time, ROA will bottom out and then rise again. This shows that newly established banks often do not have much experience in operation so customers do not have enough trust. As a result, their profits are often at a lower level. But over a longer period, when customers have sufficient time to experience a bank’s services and verify its safety, the number of customers can increase. The bank's operations will be better, avoiding risks and leading to better business performance.

* In terms of external factors that are groups of macro factors, the results are shown completely under expected. The variable GDP (growth rate) has a negative correlation while the variable INF (inflation) has a positive correlation with ROA. This can be due to the economic context of the period 2008 - 2019 when the economy was experiencing many fluctuations, crises and economic recessions occurring all over the world. The economic growth with a skyrocketing speed can cast suspicion for customers on the safety of the bank's operations or when the inflation is high, people tend to make more loans. To better understand this, it is necessary to have a more in-depth analysis, such as adding more factors affecting ROA or identifying activities which bring most profits to the banks.

5.2. Policy recommendations

According to the aforementioned research results, it can be seen that first of all, to gain an increase in profitability of commercial banks, the banks themselves should pay attention to the 5 statistically significant factors that have been tested through the model. This means that there must be measures to improve the above factors in turn or synchronously. Firstly, this can only be effectively implemented by the banks themselves, and secondly there is a necessity for the Government and the State Bank to adjust banking policies accordingly.

Through the study of outcomes and the development orientation of commercial banks in the coming time, the author proposes the following solutions to increase the profitability of Vietnam’s commercial banks:

5.2.1.Macro policy

Commercial banks’ profitability depends greatly on the health of the economy and the Government’s economic and banking policies. Therefore, the Government needs to take appropriate measures to promote sustainable economic development and create favourable conditions and policies for commercial banks to develop. In addition, it is necessary to have solutions to consider and forecast inflation more accurately to help banks to quickly adjust to fluctuations of the macro-environment.

5.2.2. Improving the bank’s asset quality

In the context of current international economic integration, the quality of bank's assets is not only influenced by the nature of the assets that banks are holding and the correlation between the structure of assets and liabilities, but also by external factors such as political stability, changes in foreign policies and laws, and fluctuations of national currencies. The increase in the bank’s asset quality means that it is necessary to consider its quality, instead of just expanding and being dispersed in quantity compared to the previous one.

5.2.3. Improving the quality and capacity of bank management

One factor that determines the bank’s safety and its business performance is the capacity and quality of management. It is necessary for a bank’s management to devise concrete policies and action plan/agenda in order to increase coordination amongst officers and employees from all departments to the board of directors, to achieve business goals in each defined period with the least resources spent, specifically: (i) Proposing relevant and effective business policies; (ii) Developing management procedures, operating business processes that are reasonable, realistic and lawful; (iii) Establishing an organizational structure that operates effectively; (iv) Reducing ethical risks in the management system.

5.2.4.Enhancing the bank solvency

To ensure solvency, a bank must maintain a certain percentage of its assets in the form of liquid assets, especially highly liquid assets such as cash, depositing cash in the central bank and other liquidity reserve instruments. In addition, banks must also focus on raising asset quality, building a reasonable portfolio of assets, being able to convert into cash quickly and recover debts on time to meet payment deadlines and fulfill its obligations.

5.2.5. Increasing charter capital

This is the most important factor to ensure the minimum capital adequacy ratio as prescribed by the State Bank of Vietnam and also prudent banking during credit operations. The increased capital will allow the bank to invest in technology development, train human resources and expand its distribution channels. These are also indispensable factors that support Vietnam’s commercial banks to improve their competitiveness.

5.2.6. Continuing to transform to the customer-oriented model

In the context of an increasingly open economy, competition between banks in Vietnam is becoming more fierce, so commercial banks must be more customer-centric. Unlike in the past, customers now have access to information and different types of services, thereby helping customers to have better perception of service quality and make informed decisions. Therefore, it is vital for commercial banks to strongly promote the application of information technology in its operation, products and customer services. Such application will in turn attract customers. It is imperative for banks to always try its best to maintain and improve service/product’s quality. Implementing customer-centric service style will also in the long run help banks to increase brand value.

Further expanding a network of branches is another solution to help banks reach a larger customer base. Besides, branch expansion will also improve quality of service to existing customers.

5.3. Limitations, shortcomings and directions for further research

The results of testing the relationship among variables in the theoretical model show that the influence of the aforementioned factors on the profitability of Vietnam’s commercial banks is completely different. The author acknowledges that there are other factors that can affect profitability of Vietnam’s commercial banks which are not covered in the proposed research model of this paper.

Specifically, the model does not clarify the impact of policy factors on the performance of commercial banks. The reason is that the monetary policies of the State Bank of Vietnam changed greatly throughout the the years, so it is difficult for the author to choose the appropriate variables to include in the model. In order to evaluate such policy factors, it is necessary to observe a shorter timeframe to be able to accurately reflect policy changes in each period. In addition, due to data limitations, some non-financial factors such as brand strength or operating network, etc... have not been analysed specifically in this study.

In the research model, only ROA is used to measure the banks’ profitability, while on the theoretical basis, we can see that in order to measure profitability, there are many criteria as well as different methods, and each indicator also only demonstrates banks’ performance in a certain aspect.

Data sources of Vietnamese commercial banks are limited, many small banks do not fully disclose information which causes difficulties in accessing accurate sources, so the research only collects data from 09 Vietnamese commercial banks, observing changes between 2008 and 2019. However, this sample is representative because it gathers many banks of different sizes, accounting for a large proportion of the total assets of Vietnam’s commercial banks.

From the above limitations, the next research can include a larger number of research samples and the study period can be extended. Moreover, the dependent variables in the model only use the ROA variable, it can be replaced with other variables such as ROE, NIM, or NNIM. This research has not considered the influence of non-financial factors such as brand strength, operating network, etc. as well as a group of policy factors on the profitability of commercial banks. Therefore, further studies can also include these independent variables.

The cause of the above limitations is mainly due to the limited time as well as the limited capacity of the author in quantitative research. Therefore, in the next research paper, the author wishes to provide a general assessment of the profitability of the Vietnam’s banking system as well as to build a research model with a better test method to identify more factors affecting the bank's profitability. In the future, it is the intention of the author to provide suggestions and recommendations for banks to improve their profitability.

 

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