INTRODUCTION
Rationale
The banking industry is essential to the economy, serving as the main link between capital sources and providing diverse financial services to individuals, businesses, and institutions The performance of banks significantly influences a country's financial system, especially in emerging nations By supporting investment projects across various sectors, commercial banks create job opportunities, thereby reducing unemployment rates Additionally, central banks can bolster weak economies during downturns by implementing suitable monetary policies for commercial banks, helping to manage inflation and deflation Ultimately, the stability and health of the banking sector, particularly commercial banks, are critical for the development of a growing economy.
Commercial banks play a crucial role in the financial landscape, with profit generation as their primary objective for sustainability and growth In the face of globalization, deregulation, and rising competition from non-bank entities, maintaining profitability is essential for their survival To mitigate unsystematic risks, financially stable banks often diversify their operations The 2007-2008 Financial Crisis demonstrated that profitable banks were better equipped to endure economic downturns, ultimately contributing to the stability of the broader economy.
To achieve their annual profit targets, banks must have a comprehensive understanding of the factors that influence commercial bank profitability, as emphasized by Ramlall (2009) This concern extends beyond bank management to include numerous academics, financial market experts, and government authorities, all of whom prioritize the importance of profitability in the banking sector.
The Vietnamese banking system has experienced significant growth in recent years, with a total of 49 banks as of November 2021, including 31 joint-stock commercial banks, 4 state-owned banks, 9 wholly foreign-owned banks, and 2 policy banks, alongside numerous international bank branches The total assets of the banking sector have seen remarkable increases, rising from approximately VND 5,600 trillion in 2016 to VND 10,200 trillion by September 2021, reflecting an average annual growth rate of about 12% from 2016 to 2020 (State Bank of Vietnam, 2021).
Research objectives Error! Bookmark not defined
This study investigates how both internal and external factors impact the profitability of 25 commercial banks in Vietnam from 2017 to 2021 Researchers highlight that the performance of these banks is significantly influenced by the national economic environment and their own creditworthiness As the world evolves rapidly, it is crucial for commercial banks to adapt to these changes The findings will provide insights and recommendations aimed at enhancing the operational efficiency of these financial institutions.
Research questions
What factors significantly impact the profitability of commercial banks of Vietnam?
What are the recommendations needed in order to improve the performance of the commercial banks?
Scope of research
This research examines the impact of both internal factors, such as bank size, capital adequacy, non-performing loans, and operating costs, as well as external factors, including GDP growth and inflation rate, on the profitability of 25 Vietnamese commercial banks from 2017 to 2021.
Methodology
This study analyzed internal factors using data from 25 listed commercial banks in Vietnam over a five-year period from 2017 to 2021, sourced from the banks' annual financial statements and available on Vietstock Finance and the banks' websites External data was obtained from the World Bank database.
The qualitative method employs statistical techniques to analyze the factors influencing the profitability of Vietnamese commercial banks This approach provides a comprehensive overview of these determinants' effects, leading to informed discussions and recommendations for improvement.
This study employs a quantitative approach using a regression model for panel data to analyze the factors influencing the profitability of commercial banks Additionally, it evaluates the Random Effects Model (REM), Fixed Effects Model (FEM), and conducts the Hausman test to assess the significance of the relationships between dependent and independent variables, ultimately identifying the most appropriate model for the research.
Dissertation structure
Chapter 1: Introduction – Introducing a brief background of the dissertation, research objectives and research questions, the scope of research and research methodology
Chapter 2: Literature Review – Overviewing the concept, measurements, as well as the determinants of bank profitability From there, summarizing the results of previous research papers about the factors that affect the profitability of banks
Chapter 3: Data and Methodology – Mentioning the sources of data collection and the research method of the dissertation
Chapter 4: Empirical Results – Presenting the estimation results, performing tests to achieve the most accurate results and interpreting it
Chapter 5: Conclusion – Concluding the dissertation and confirming the research objectives
LITERATURE REVIEW Error! Bookmark not defined
Theoretical Framework Error! Bookmark not defined
Bank profitability is a key indicator of a bank's success, defined as the difference between its revenues and expenditures Most of a bank's income comes from service charges and interest on its assets, while its main costs include operational expenses and interest on liabilities To accurately evaluate bank profitability, it's important to look beyond profits per share and consider how effectively the bank utilizes its assets and equity to generate earnings.
Bank profitability can be assessed using financial ratios, as highlighted by Burhonov (2006) Key metrics include returns on assets (ROA), returns on equity (ROE), returns on deposits (ROD), net interest margin (NIM), and profit margin (BTP/TA) Among these, ROA and ROE stand out as the most effective measures ROA evaluates a bank's ability to generate profit from its assets, while ROE indicates the profitability for common stockholders per dollar of equity.
According to Bourke (1989), bank profitability is significantly influenced by internal variables linked to management, including factors such as bank size, capital strength, total assets, deposits, credit risk, operating costs, asset quality, and liquidity.
Financial stability not only serves to protect the interests of banks but also benefits stakeholders, including investors and depositors (Sarwar et al., 2018) Consequently, managing these internal factors is often more straightforward than addressing external challenges.
Research indicates that a bank's size may influence its profitability and operational efficiency, with larger banks potentially benefiting from economies of scale and scope, leading to reduced expenses and enhanced product variety However, findings on efficiency are mixed, showing that both large and small banks can achieve lower costs Similarly, the relationship between bank size and profitability is inconsistent; while some studies suggest a positive correlation, others indicate negative effects or a limited relationship altogether.
The Capital Adequacy Ratio (CAR) is a critical benchmark for the banking industry, assessing a bank's ability to meet creditor obligations and manage credit and operational risks A robust CAR indicates that a financial institution possesses adequate capital to absorb potential losses, thereby minimizing the risk of bankruptcy and protecting depositor funds In response to the 2008 global economic crisis, the Bank for International Settlements (BIS) implemented stricter CAR regulations to enhance depositor security.
Goddard et al (2004) provided the first conclusive evidence of a positive relationship between capital adequacy ratio (CAR) and profitability by analyzing 665 banks across six major European nations from 1992 using a dynamic panel model.
In 1998, a finding emerged that challenges the risk-return expectation theory, which posits that a well-capitalized bank indicates excessive caution and a lack of interest in investment opportunities However, this finding aligns with the signaling theory and the expected bankruptcy costs hypothesis, as discussed by Molyneux and Thornton in 1992.
Recent studies show conflicting results regarding the relationship between bank loans and profitability, despite loans being a primary revenue source for banks Research by Gul, Irshad, and Zaman (2011) analyzed the impact of bank-specific and macroeconomic factors on profitability, using data from 15 commercial banks in Pakistan from 2005 to 2009, revealing that higher lending correlates with increased profitability This finding aligns with Abreu and Mendes' (2002) research, which examined the determinants of bank interest margins and profitability across several European countries from 1986 to 1999.
Banks face various operating costs that significantly affect their profitability, including commission fees, foreign exchange charges, interest payments on savings deposits, loans, and bond issuance Additionally, banks incur other business-related expenses Consequently, both academics and writers agree that these operational expenses negatively impact banks' profits.
Research indicates a significant inverse correlation between operational costs and bank profitability (Nguyen et al., 2018; Minh & Canh, 2015; Le, 2017) Studies reveal that as the cost-to-revenue ratio decreases, profitability tends to increase Therefore, enhancing efficiency in managing operational costs is crucial for banks aiming to boost their earnings Furthermore, strategically reducing operating expenses can enable banks to lower service prices, such as loan rates and fees, thereby attracting more customers Efficient resource utilization and the adoption of new technologies can also lead to reduced operational costs.
External factors influencing bank profitability include the dynamic social, legal, and macroeconomic environment, such as GDP growth, inflation, interest rates, and stock market capitalization relative to GDP, which can be challenging to predict and manage.
A study by Erina and Lace (2013) reveals that operational efficiency and asset portfolio structure negatively impact the profitability indicators of Latvian banks from 2006 to 2011 Additionally, capital and credit risk adversely affect return on assets (ROA) When evaluating return on equity (ROE), a positive linear relationship between capital and profitability is observed, while operational efficiency and credit risk demonstrate negative correlations, supported by empirical data.
Research by Athanasoglou et al (2008) highlights the significant effects of inflation and cyclical production on banking sector performance, revealing a positive correlation between the economic cycle and bank profitability, with notable asymmetric influences during expansion periods Additionally, Demirgỹỗ-Kunt & Huizinga (2000) found that bank performance can be protected during recessions Furthermore, Flamini et al (2009) noted that when inflation is anticipated, bank management can effectively adjust interest rates, allowing banks to mitigate losses from inflation and enhance overall profitability.
Empirical Studies Review
A study by Nguyen, C., Luong, and Nguyen, H (2018) employed a multivariate linear regression model using the ordinary least squares (OLS) method to analyze the internal factors influencing the profitability of nine listed banks on the Vietnam stock market from 2008 to 2016 The findings revealed that capital size and loans positively and significantly affect bank profitability, while asset size, deposits, liquidity risk, and bad debts negatively and significantly impact profitability.
A study conducted on listed commercial banks in Vietnam from 2008 to 2018 utilized IV regression and OLS models to analyze factors affecting their rate of returns The findings revealed that operating efficiency, loan size, retail loans share, state ownership, inflation rate, and GDP positively influence returns Conversely, variables such as credit risk, liquidity risk, capital size, bank size, and revenue diversification showed no significant impact (Phan et al, 2020)
Using panel data collected from 9 Vietnamese commercial banks listed on the stock
A study by Le, T., Pham, and Le, H (2017) analyzed data from 16 exchanges between 2007 and 2013, concluding that bank profitability is influenced by various internal and external factors Key internal factors include bank size, where smaller banks tend to yield higher profits, and the growth rate of total assets, which correlates with increased profitability Additionally, higher interest rates benefit banks more than deposit customers On the macroeconomic front, profitability is also impacted by inflation and GDP growth rates.
A study by Nguyen et al (2018) utilized regression analysis on panel data from 13 Vietnamese commercial banks between 2006 and 2015, revealing that foreign shareholder ownership ratio, cost to income ratio, and credit risk significantly negatively affect bank profitability In contrast, factors such as state ownership, bank size, and external influences like GDP growth rate and inflation do not correlate with profitability Additionally, the study found that capital structure and liquidity risk have a minimal impact on the banks' profitability.
The literature review indicates that research findings on bank profitability are varied Profitability is primarily assessed through returns on assets (ROA) and returns on equity (ROE) Key internal factors influencing bank profitability include bank size, capital adequacy ratio, non-performing loans, and operating costs Additionally, external factors such as GDP growth and inflation rate have also been evaluated in relation to bank profitability.
DATA AND METHODOLOGY
Methodology
This dissertation has two models corresponding to the following two equations:
𝑹𝑶𝑨 𝒊𝒕 : Return on Assets of bank i for period t
𝑹𝑶𝑬 𝒊𝒕 : Return on Equity of bank i for period t
𝑩𝑰𝒁𝑬 𝒊𝒕 : Bank size of bank i for period t
𝑪𝑨𝑹 𝒊𝒕 : Capital adequacy ratioof bank i for period t
𝑵𝑷𝑳 𝒊𝒕 : Non-performing loansof bank i for period t
𝑶𝑪 𝒊𝒕 :Operating costsof bank i for period t
𝑮𝑫𝑷: Gross Domestic Productof bank i for period t
The research model of this dissertation is as below (Figure 1)
This dissertation utilized both qualitative and quantitative research methods Initially, a qualitative approach was applied, employing statistical techniques to analyze previous findings and outline the impact of independent factors on dependent variables over time Subsequently, the quantitative approach involved running regression models on the collected dataset to assess the significance of variances The research method framework is illustrated in Figure 2.
This dissertation utilizes panel data from 25 Vietnamese commercial banks collected between 2017 and 2021, which enhances variability and reduces collinearity among variables By integrating time series with cross-sectional observations, the data analysis is more effective Eviews 14 software is employed for precise testing, utilizing two estimation techniques: the Random Effects Method (REM) and the Fixed Effects Model (FEM) Subsequently, a Hausman test is conducted to determine the most appropriate model for the research.
In the Random Effects Model (REM), each cross-sectional unit is associated with a unique intercept drawn from a distribution centered around the mean intercept This implies that the error term for each observation is independent, as each intercept is randomly selected from a set of possible intercepts (Torres-Rayna, 2007).
23 differences between units have an impact on the dependent variables, then the random effects model would be appropriate for this study
The Fixed Effects Model (FEM) offers a key advantage by effectively removing bias from time-invariant omitted variables, also referred to as unobserved heterogeneity, particularly when this heterogeneity remains consistent over time and is related to explanatory factors However, one limitation of the model is the reduction in degrees of freedom, as it loses one degree for each cross-sectional observation due to time-demeaning Furthermore, the coefficients for significant explanatory variables that do not change over time within each entity cannot be estimated, as they are fully collinear with the fixed effects (Studenmund, n.d.).
This dissertation employs the Hausman test to explore the relationship between AI and its explanatory factors The test analyzes regression coefficients from both fixed effects and random effects models to determine any significant differences If the coefficients differ, the fixed effects model is preferred; conversely, the random effects model is utilized when the coefficients are similar.
Based on the theoretical framework and empirical researches, the dissertation expected results of the internal and external factors are presented in term of Positive (+) and Negative (–) reaction as below:
Table 3: Expected results of variables upon bank profitability
RELATED STUDIES BSIZE Log (Total Assets) + / – Obamuyi (2013); Boyd &
CAR (Tier 1 Capital + Tier 2 Capital) /
(Risk Weighted Assets) + Goddard, et al (2004);
Gul, Irshad, and Zaman (2011); Abreu and Mendes
OC Total expenses / Revenue – Nguyen et al (2018); Minh &
GDP GDP growth rate + Erina and Lace (2013)
INF Inflation growth rate + / – Athanasoglou et al (2008);
This dissertation explores the complex relationships between bank size (BSIZE) and profitability, as well as inflation (INF) and profitability, which may exhibit either positive or negative correlations, as indicated by previous research Additionally, it is anticipated that capital adequacy ratio (CAR) and gross domestic product (GDP) will demonstrate a positive and significant relationship with profitability, whereas non-performing loans (NPL) and operating costs (OC) are expected to have a negative impact The findings will be presented and discussed in the subsequent chapter.
EMPIRICAL RESULTS
Descriptive statistics
The descriptive statistics of 25 Vietnamese commercial banks, represented in Table 4 over five years, reveal 125 observations The standard deviations for the ROA and ROE ratios are notably low at 0.76% and 9.71%, respectively, indicating a high level of reliability in the data, with observed values closely clustered around their means.
The average Return on Assets (ROA) for Vietnamese commercial banks is 0.69%, while the average Return on Equity (ROE) stands at 7.07% However, there are significant disparities in these performance indicators, with Tien Phong Bank reporting the lowest profitability levels in 2017, showing ROA and ROE ratios of -5.51% and -82%, respectively The range for ROA varies from a minimum of -5.512% to a maximum of 2.538%, whereas ROE ranges from -82.002% to 26.823%.
Table 4: Summary of Descriptive statistics
OBSV MEAN MAX MIN STA DEV
Table 5 illustrates the correlation between dependent and independent variables, revealing that the ROA ratio has a strong positive correlation with BSIZE (3.26%), CAR (3.26%), OC (21.93%), GDP (18.42%), and INF (19.97%) In contrast, the ROE ratio shows strong correlations only with BSIZE (34.06%) and OC (23.12%) Notably, NPL is the only variable that negatively correlates with both ROA (-8.36%) and ROE (-11.46%).
The correlation among the explanatory variables is relatively low, with the strongest association between CAR and BSIZE at -74.8%, and a 60.83% correlation between INF and GDP However, since the absolute values of these correlations are below 0.8, the risk of multicollinearity in these models is minimal.
ROA ROE BSIZE CAR NPL OC GDP INF
Regression model results
Using secondary data 25 Vietnamese commercial banks over a five-year period from 2017 to 2021, the findings of the final regression models to ascertain the quantitative correlation between
02 dependent variables (i.e., ROA and ROE) and 06 independent (i.e., BSIZE, CAR, NPL, OC, GDP, and INF) The results of the regression are presented below (Table 6)
Table 6: Summary of Regression model results
NOTES: (***): p-value < 0.01; (**): p-value < 0.05, (*): p-value < 0.1 The relationship between ROA, ROE and independent variables can be considered significant when the confidence level is equal or greater than 0.05
Statistically shown via Hausman test (Chi-Sq Statistic value of 0 and Probability equals 1, larger than 0.05) and the summary of Regression model results, the random effects model of ROA
The analysis indicates that both the Return on Assets (ROA) and Return on Equity (ROE) models are statistically significant, with probability levels of 0.0000, well below the conventional significance threshold of 0.05 Additionally, the R-squared values for the ROA and ROE models are 0.317884 and 0.259963, respectively, suggesting that these models explain 31.78% and 25.99% of the variations in ROA and ROE Therefore, the regression models employed in this research are considered reliable for assessing the determinants of performance in Vietnamese commercial banks.
The estimated functions of the ROA as well as the ROE model are written as follows:
Research indicates a strong positive correlation between Return on Assets (ROA) and Return on Equity (ROE) with bank size, evidenced by p-values of 0.000 This suggests that larger banks tend to be more profitable than their smaller counterparts For instance, Vietcombank, one of Vietnam's largest commercial banks with assets exceeding VND 1.6 million billion, demonstrates significantly higher ROA and ROE ratios compared to NCB, which has total assets of less than VND 100,000 billion.
The relationship between bank performance and capital adequacy ratio is notably significant and positive, particularly in the ROA model, evidenced by a p-value of 0.000 Literature indicates that commercial banks with higher capital ratios can lower funding costs and reduce reliance on external funding, thereby decreasing insolvency risks and boosting profitability Similarly, the ROE model also demonstrates a positive correlation with capital adequacy, showing a likelihood level of CAR at 0.0031, which is significant at the 0.01 level Consequently, the capital adequacy ratio is confirmed as a valid factor in both the ROA and ROE models.
Contrary to expectations, the coefficients for non-performing loans in both the Return on Assets (ROA) and Return on Equity (ROE) models are positive, indicating that higher profitability is linked to increased exposure to high-risk loans These results challenge the theoretical concepts outlined in Chapter 2, with non-performing loan probability values recorded at 0.0019 for both models.
0.0020, respectively As a result, the NPL is positively significant to the evaluation of the bank's performance
The impact of operating costs on Return on Assets (ROA) ratios has been both positive and negative Although the relationship between operational expenses and bank performance remains unclear, the regression analysis yields inconclusive results The operating cost probability thresholds in both models exceed 0.05, indicating a minor effect and resulting in an unreliable conclusion regarding the connection between bank profitability and operating costs.
The analysis of both the ROA and ROE models reveals a positive correlation between GDP growth and bank performance, indicating that as economic growth rates increase, investments and consumption also rise This upward trend allows for higher credit limits, potentially enhancing financial success However, it is important to note that the impact of GDP growth on bank profitability is not conclusively demonstrated in this study, as the GDP growth coefficients in both models are statistically insignificant, with p-values exceeding 0.05.
Inflation demonstrates a positive but statistically insignificant relationship with bank performance, as indicated by p-values of 0.1242 for ROA and 0.2902 for ROE Consequently, these results surpass the established significance levels, suggesting that inflation (INF) does not significantly affect bank profitability, which contradicts findings from earlier studies.
Discussion
The analysis reveals the relationships between dependent variables (internal and external factors) and independent variables (Return on Assets - ROA and Return on Equity - ROE), indicating both positive (+) and negative (–) reactions.
Table 7: Actual results of variables upon bank profitability
The analysis reveals significant relationships among bank size, capital adequacy ratio, non-performing loans, and profitability, with results consistent at a 0.05 significance level The findings align with previous studies regarding bank size and capital adequacy ratio Additionally, non-performing loans demonstrated a significant inverse correlation with bank profitability This outcome corroborates the conclusions of Noman et al (2015) and Kolapo et al (2012), highlighting that a bank's primary revenue source is credit operations Consequently, increased exposure to credit risk adversely affects a bank's gross profit.
The study reveals that operating costs (OC) have a statistically insignificant impact on the profitability indicators of banks, such as Return on Assets (ROA) and Return on Equity (ROE), contrary to expectations Additionally, external factors like GDP growth and inflation also yield surprising results, showing no significant effect on the profitability of Vietnamese commercial banks Supporting this, Klein and Weill (2019) argue that past bank profitability negatively influences economic growth, suggesting that the positive effects of bank profitability on economic growth are only temporary Furthermore, research by Jeevitha et al (2019) indicates that inflation does not affect ROA or ROE when interest rates rise, as higher interest rates provide banks with opportunities to increase earnings, despite the potential rise in funding costs that may reduce profitability.
CONCLUSION
Descriptive statistics
ROA ROE BSIZE CAR NPL OC GDP INF
Correlation maxtrix
ROA ROE BSIZE CAR NPL OC GDP INF
ROA estimation with Random Effects
Dependent Variable: ROA Method: Panel EGLS (Cross-section random effects) Date: 09/21/22 Time: 21:38
Cross-sections included: 25 Total panel (balanced) observations: 125 Swamy and Arora estimator of component variances Variable Coefficient Std Error t-Statistic Prob
Mean dependent var 0.006011 Adjusted R-squared 0.280972 S.D dependent var 0.007311 S.E of regression 0.006200 Sum squared resid 0.004535 F-statistic 9.075857 Durbin-Watson stat 0.942370 Prob(F-statistic) 0.000000
R-squared 0.317884 Mean dependent var 0.006930 Sum squared resid 0.004830 Durbin-Watson stat 0.884798
ROE estimation with Random Effects
Dependent Variable: ROE Method: Panel EGLS (Cross-section random effects) Date: 09/21/22 Time: 21:40
Sample: 2017 2021 Periods included: 5 Cross-sections included: 25 Total panel (balanced) observations: 125 Swamy and Arora estimator of component variances Variable Coefficient Std Error t-Statistic Prob
Mean dependent var 0.068005 Adjusted R-squared 0.219007 S.D dependent var 0.096111 S.E of regression 0.084937 Sum squared resid 0.851290 F-statistic 6.795379 Durbin-Watson stat 1.022534 Prob(F-statistic) 0.000003
R-squared 0.259963 Mean dependent var 0.070667 Sum squared resid 0.865188 Durbin-Watson stat 1.006108
ROA estimation with Fixed Effects
Dependent Variable: ROA Method: Panel Least Squares Date: 09/23/22 Time: 17:49 Sample: 2017 2021
Periods included: 5 Cross-sections included: 25 Total panel (balanced) observations: 125 Variable Coefficient Std Error t-Statistic Prob
Effects Specification Cross-section fixed (dummy variables)
Mean dependent var 0.006930 Adjusted R-squared 0.371645 S.D dependent var 0.007557 S.E of regression 0.005990 Akaike info criterion -7.186360 Sum squared resid 0.003373 Schwarz criterion -6.484938 Log likelihood 480.1475 Hannan-Quinn criter -6.901410 F-statistic 3.444689 Durbin-Watson stat 1.309319 Prob(F-statistic) 0.000003
ROE estimation with Fixed Effects
Dependent Variable: ROE Method: Panel Least Squares Date: 09/23/22 Time: 17:50 Sample: 2017 2021
Periods included: 5 Cross-sections included: 25 Total panel (balanced) observations: 125 Variable Coefficient Std Error t-Statistic Prob
Effects Specification Cross-section fixed (dummy variables)
Mean dependent var 0.070667 Adjusted R-squared 0.299677 S.D dependent var 0.097100 S.E of regression 0.081258 Akaike info criterion -1.971390 Sum squared resid 0.620671 Schwarz criterion -1.269968 Log likelihood 154.2119 Hannan-Quinn criter -1.686440 F-statistic 2.768701 Durbin-Watson stat 1.386302 Prob(F-statistic) 0.000098
Data for the estimation
Bank Year ROA ROE CAR BSIZE NPL OC GDP INF
An Binh 2021 0.14% 1.58% 9.00% 31.7957 1.63% 63.40% 2.58 1.84 BacABank 2017 0.59% 4.69% 12.61% 30.879 0.21% 64.95% 6.81 3.53 BacABank 2018 0.10% 1.10% 9.33% 31.1497 0.51% 70.46% 7.08 3.54 BacABank 2019 0.38% 5.81% 6.58% 31.5492 1.20% 60.78% 7.02 2.79 BacABank 2020 0.48% 6.65% 7.21% 31.6773 0.75% 65.22% 2.91 3.23 BacABank 2021 0.57% 7.19% 7.90% 31.7814 0.41% 67.18% 2.58 1.84 BIDV 2017 0.79% 13.12% 6.01% 33.6368 1.35% 82.90% 6.81 3.53 BIDV 2018 0.53% 9.71% 5.47% 33.8147 0.98% 74.53% 7.08 3.54 BIDV 2019 0.74% 12.64% 5.84% 33.938 1.63% 72.67% 7.02 2.79 BIDV 2020 0.77% 14.84% 5.17% 34.1085 1.54% 69.62% 2.91 3.23 BIDV 2021 0.75% 15.06% 4.98% 34.377 0.93% 71.77% 2.58 1.84 Eximbank 2017 1.66% 18.64% 8.88% 32.8436 0.37% 40.34% 6.81 3.53 Eximbank 2018 1.26% 13.53% 9.29% 32.7677 0.25% 56.12% 7.08 3.54
In recent years, Eximbank has shown fluctuating performance, with a notable decline in 2021, where key metrics included a 0.03% return and a 67.19% loan-to-deposit ratio HDBank's results from 2017 to 2021 indicate a steady growth trend, highlighted by a 6.71% growth rate in 2021 Kien Long Bank's performance demonstrated a gradual decrease in key indicators from 2017 to 2021, culminating in a 0.65% return and a 63.90% loan-to-deposit ratio in 2021 Lien Viet Bank experienced a consistent decline in returns over the same period, with a 0.33% return in 2021 and a 52.26% loan-to-deposit ratio Maritime Bank's data reflects a significant drop in performance metrics, ending with a mere 0.11% return in 2021, despite a 13.05% growth rate Overall, these banks exhibit varying degrees of financial health and trends over the years, highlighting the competitive landscape of the banking sector.
MB 2021 1.14% 10.84% 10.49% 33.0294 1.61% 58.92% 2.58 1.84 Nam A 2017 1.27% 7.63% 16.69% 30.5697 0.31% 36.48% 6.81 3.53 Nam A 2018 1.13% 5.51% 20.47% 30.4041 1.05% 48.06% 7.08 3.54 Nam A 2019 0.47% 4.14% 11.32% 30.9908 0.58% 46.26% 7.02 2.79 Nam A 2020 0.50% 5.62% 8.93% 31.2498 0.38% 55.20% 2.91 3.23 Nam A 2021 0.55% 5.69% 9.63% 31.1997 1.09% 62.80% 2.58 1.84 NCB 2017 0.74% 5.17% 14.30% 30.7444 0.55% 56.70% 6.81 3.53 NCB 2018 0.01% 0.07% 14.76% 30.703 0.68% 60.18% 7.08 3.54 NCB 2019 0.06% 0.58% 11.02% 31.0009 0.18% 47.76% 7.02 2.79 NCB 2020 0.02% 0.25% 8.72% 31.2375 0.26% 51.42% 2.91 3.23 NCB 2021 0.01% 0.20% 6.67% 31.507 0.50% 42.73% 2.58 1.84 OCB 2017 1.19% 8.07% 14.76% 30.8667 0.56% 53.77% 6.81 3.53 OCB 2018 0.84% 6.02% 13.93% 30.9424 1.39% 66.31% 7.08 3.54 OCB 2019 0.74% 6.09% 12.09% 31.1213 1.48% 61.51% 7.02 2.79
In recent years, various banks in Vietnam have shown notable trends in their financial metrics For instance, Saigon Bank recorded a gradual decline in growth from 2017 to 2021, with 2018 peaking at a 2.00% growth rate SeABank's performance remained relatively stable, with a slight decrease in growth from 2017 to 2021, indicating a consistent but modest market presence SHB demonstrated fluctuations in growth, peaking in 2018 at 1.45% before declining to 0.39% in 2021 Techcombank showed a positive trajectory, with growth increasing from 1.75% in 2017 to 0.80% in 2021 Tien Phong Bank experienced a significant setback in 2017 but recovered with a steady growth rate in subsequent years VIB maintained a steady growth pattern, hovering around 0.62% to 0.80% from 2017 to 2021 Lastly, Viet A Bank exhibited a gradual decline in growth, from 1.10% in 2017 to 0.13% in 2020, reflecting broader trends in the banking sector.