Therefore, to fill the literature gaps, this comprehensive study evaluates two dimensions of diversification strategies employed by banks, namely income and funding diversification ID an
INTRODUCTION
Research Objectives
The author analyzes the impact of income and funding diversification on the performance of 30 commercial banks from 2012 to 2022, utilizing the FGLS model based on data from audited consolidated financial statements The findings aim to provide valuable policy implications and recommendations for policymakers and Vietnamese commercial banks to enhance operational efficiency, especially in the wake of the COVID-19 pandemic.
Research Questions
This study was conducted by two following research questions:
• How does income and funding diversification affect the performance of
• What are policy implications and recommendations needed to improve the performance of Vietnamese Commercial banks?
Scope of Study
The research targeted Vietnamese banks that are not publicly traded (UPCOM) and those that are listed in two largest stock exchange markets in Vietnam, namely HNX and HOSE
This research examines the effects of bank diversification—both income and funding—alongside various bank-specific factors such as size, state ownership, capitalization ratio, total loans, total deposits, operating expenses, and non-performing loans Additionally, it considers the macroeconomic variable of the inflation rate and the significant impact of COVID-19 on the performance of 30 Vietnamese commercial banks over an eleven-year period from 2012 to 2022.
Significance of the Study
This study enhances existing literature by providing new empirical evidence on the effects of diversification on bank performance, with a specific focus on Vietnam While numerous studies support bank diversification for its potential benefits, such as increased profit stability through non-interest income, this research offers a more thorough analysis of the subject Notably, previous works by Berger (1995) and Elsas et al (2010) highlight that non-interest revenue tends to be more stable than interest income, thereby lowering risk for diversified banks Additionally, diversification across various products and markets allows banks to mitigate bankruptcy risk, as differing activities present varying levels of risk, ultimately reducing overall exposure.
Diversification of operations in banking can enhance profitability, reduce income fluctuations, and lower bankruptcy risks, as noted by Boot and Schmeits (2000) and supported by Lee et al (2014) and Saghi-Zedek (2016) Additionally, revenue diversification helps mitigate various risks while improving bank efficiency (Rossi et al., 2009) However, some experts argue against this approach, suggesting that reliance on non-interest income may lead to greater income volatility without guaranteed higher returns (Gamra and Plihon, 2011; Stiroh and Rumble, 2006) Furthermore, rising fixed costs can increase financial leverage, exacerbating profitability issues (DeJonghe, 2010; Fiordelisi et al., 2011) Despite these challenges, Baele et al (2007) indicate that diversification remains a necessary strategy for banks seeking to enhance profits and remain competitive in a complex global landscape.
The author conducts a comprehensive study on the effects of diversification on the performance of Vietnamese banks, addressing a gap in existing research that has primarily focused on the US and European markets with established economic histories The rising importance of emerging markets, especially in Southeast Asia, has heightened interest in their banking systems and strategies Vietnam presents a unique context for this discussion, as it is a frontier market with a financial system that significantly differs from those of advanced economies.
In the wake of the Covid-19 pandemic, many countries are beginning to treat the situation as a regular occurrence, prompting a shift towards economic recovery This raises the question of whether diversification strategies have proven effective for banks in preparing for potential future pandemics The author contributes to the discussion surrounding the effects of diversification on bank performance during and after Covid-19, particularly in Vietnam Commercial banks are increasingly adopting income diversification strategies to counteract declining returns from traditional banking operations It is crucial for these banks to ensure capital stability while diversifying their income streams, especially in light of recent liquidity crises Policymakers and banks are focused on securing funding to improve the banking system's efficiency, as funding diversification enhances security by minimizing dependence on a single source Furthermore, banks with diversified funding are better equipped to manage risks and operate safely, particularly during crises when depositors may withdraw funds.
To mitigate the risks associated with bank runs, banks should enhance their funding diversity Despite its importance, funding diversification has been less emphasized in previous studies, which primarily focused on income diversification, as highlighted by researchers like Hiep et al (2019), Ngoc (2019), Quynh (2020), and Cuong et al (2020) This article further investigates the relationship between funding sources and overall financial stability.
6 diversification and profitability of Vietnamese banks, which is particularly significant given current shifts in funding composition of Vietnamese banks, with increasing proportion of non-deposit fundings
The author presents key policy recommendations for governing bodies and financial institutions to effectively implement banking diversification in the future Vietnamese commercial banks can tailor these solutions based on their capacity and diversification strategies, providing a strong foundation for innovative approaches that enhance performance through income and funding diversification.
Dissertation Structure
Chapter 1: Introduction - Introducing a brief background of the dissertation, research objectives, research questions, research scope and significance of the study
Chapter 2: Theoretical background and Literature Review - Reviewing related theories and literature on the relationship between diversification and bank performance
Chapter 3: Data and Methodology - Mentioning sources of data collection, empirical methodology and estimation technique of the dissertation
Chapter 4: Empirical Results and Discussions - Performing descriptive statistics, correlation tests and model selection to achieve the most accurate results and discussing about it
Chapter 5: Conclusion and Implications - Concluding the dissertation, confirming research objectives, giving policy implications and recommendations and pointing out research limitations and direction for future research
THEORETICAL BACKGROUND AND LITERATURE
Theoretical Background
Resource-based theory, introduced by Penrose in 1959, highlights the dual role of internal managerial resources as both enablers and limitations for firm growth It gained prominence in the 1980s through significant contributions from researchers like Wernerfelt, Dierickx, Cool, and Barney While the focus during this period was largely on external factors, exemplified by Porter's five-forces model, there was a notable shift towards emphasizing the importance of internal organizational elements (Hoskisson et al., 1999).
A firm is essentially a collection of resources and routines that significantly influence its growth According to the Resource-Based View (RBV), only resources that are valuable, rare, imperfectly imitable, and non-substitutable can yield a sustainable competitive advantage Therefore, firms should formulate their strategies based on their unique superior resources This approach helps protect firms from competitor imitation and allows them to achieve superior profits through product and service differentiation, ultimately expanding commercial opportunities and enhancing corporate effectiveness Resources can be classified into three categories: tangible assets such as plant and machinery, human resources including managerial and technical personnel, and organizational resources encompassing information and experience.
Possessing unique resources alone does not guarantee a competitive advantage; a firm must also develop capabilities, which are the skills and competencies within its organization, to effectively utilize these resources (Grant, 1991) While resources contribute to a firm's capabilities, it is the capabilities themselves that serve as the primary source of competitive advantage A sustained competitive advantage is achieved when a firm consistently outperforms its competitors through cost reduction or by increasing its market share over time (Porter, 1980).
The Resource-Based View (RBV) suggests that a diversification strategy can enhance firm performance more effectively than a focused strategy, as it allows companies to leverage competencies across various business lines to achieve higher returns and increase overall value By utilizing operational economies of scope, firms can create a portfolio of interrelated businesses that share key resources, leading to the exploitation of synergies through shared functions and competencies This approach fosters sustainable competitive advantages and profitability through cost reduction, indicating that diversification positively influences a firm's financial performance.
Signaling theory addresses the challenge of information asymmetry, aiming to enhance decision-making by bridging the gap between information holders and beneficiaries (Spence, 2002).
In his 1973 study, Spence introduced signaling mechanisms to address information asymmetries in the labor market He argued that candidates with more information can effectively signal their quality to potential employers through educational credentials, as only those who meet rigorous academic standards can succeed in higher education This model highlights education as a key tool for communicating unobservable traits of job candidates, thereby enhancing the reliability of the hiring process (Weiss, 1995).
Management scholars have applied signaling theory to explore the effects of information asymmetries in various research contexts For instance, a recent study on corporate governance revealed that CEOs convey the hidden quality of their firms to potential investors via financial statements (Zhang and Wiersema, 2009) Additionally, financial experts have suggested that indicators of firm quality can be discerned through factors such as firm debt (Ross, 1973) and dividends (Bhattacharya).
Models indicate that only high-quality firms can consistently provide interest and dividend payments, while low-quality firms struggle to do so This ability significantly influences how investors and lenders perceive firm quality Quality is defined as a signaler's unobservable capability to meet the expectations of external observers, encompassing traits like prestige and reputation.
1982) Moreover, Bini et al (2010) posited that high-profitable companies would convey signals to enhance their competitiveness Thus, the signaling theory is
11 based on asymmetric information when the information holder must transmit a signal to the party needing this information to achieve certain goals.
Literature Review about Relationship between Diversification and Bank
There are two strands of literatures on impacts of diversification on bank performance
Diversification in banking can enhance income sources while mitigating risk exposure, leading to increased profitability from non-traditional revenues (Elsas et al., 2006) The Resource-Based View (RBV) indicates that banks benefit from operational efficiency through economies of scope (Meslier et al., 2014) and gain competitive advantages when entering new markets (Bodnar et al., 1997) By leveraging customer information, banks can reduce fixed costs and offer multiple services alongside traditional interest income (Berk and DeMarzo, 2007) For instance, insights gained during the loan process can facilitate financial consultancy, securities underwriting, and asset management services (Stein, 2002) Consequently, this approach significantly enhances overall bank performance.
Baele et al (2007) conducted a study on the impact of diversity in banking operations by analyzing data from 17 European banks between 1989 and 2004, focusing on two key aspects: revenue diversification and asset diversification Their findings revealed that increased diversity positively influences bank performance, particularly noting that banks with a higher ratio of non-interest income to total revenue demonstrate enhanced operational effectiveness.
Asset diversification has a limited impact on banking operations, yet it allows commercial banks to meet the high demand for financial services and encourages customers to utilize a broader range of offerings, ultimately enhancing bank profits (Drucker and Puri, 2009) Research by Rossi et al (2009) on Austrian banks from 1997 to 2003 indicated that while diversification negatively affected cost efficiency, it positively influenced profitability and reduced operational risks Similarly, Elsas et al (2010) found that diversification significantly enhanced bank profitability across various developed countries, even during the financial crisis of 2007-2008.
In terms of risk reduction, the expansion of banks towards non-traditional activities has stabilizing benefits for their overall earnings (Smith et al., 2003)
Engaging in non-interest activities allows banks to diversify risks, leading to more stable income and a reduced likelihood of default (Berger et al., 1999) Research by Sanya and Wolfe (2010) using System GMM on commercial banks from 11 emerging economies between 2000 and 2007 found that increased participation in non-interest activities not only minimizes bankruptcy risk but also enhances profitability and stability within these banks.
This research highlights that medium-risk banks can maximize benefits, offering strategic insights for bank managers and supervisors aiming to enhance bank growth In line with studies from the US and Europe, Lee et al (2014) examined the effects of non-interest income on banking returns and risks across 29 Asia-Pacific countries, involving 2,372 private banks from 1995 to 2009 They discovered that banks can boost operational efficiency through diversification in key areas like financial reform and banking supervision, although this primarily aids in risk reduction rather than profit maximization The findings revealed an increasing share of non-interest and fee income in total income, with diversification positively influencing returns, thereby guiding bankers in formulating effective diversification strategies to enhance value and mitigate risk (Ismail et al., 2015) Stiroh and Rumble (2004) also noted a positive correlation between bank performance and diversification in the US, although non-interest activities were found to be less profitable than traditional credit operations The study suggested that banks should focus on increasing income through maintenance fees to positively impact risk reduction (Trivedi, 2015) Addressing the benefits and challenges of revenue diversification, Nisar et al (2018) analyzed data from 200 banks across South Asia and concluded that revenue diversification positively affects bank profitability and stability.
Various activities generating non-interest income significantly impact bank performance, indicating that banks can strategically balance these activities to enhance revenue A study by Hamdi et al (2017) analyzed panel data from 20 Tunisian banks between 2005 and 2015, highlighting the importance of tailored strategies for optimizing income generation.
A 2015 study highlighted the importance of diversification for enhancing performance and minimizing uncertainty in the banking sector It revealed a negative correlation between non-interest income and the volatility of return on equity Consequently, it is advised that Tunisian banks diversify their activities and optimize their balance sheet denominations (Hamdi, 2017).
To reduce banks' capital requirements, enhancing non-interest income in their revenue portfolios may help mitigate price fluctuations (Shim, 2019) Perold (2001) noted that diversification across core lines can lessen banks' risky capital expenses Over the past 30 years, there has been a notable shift in the income structure of banks in the US, Canada, and Europe, moving from interest income to non-interest income (Allen and Santomero, 2001) Furthermore, Kim et al (2020) revealed a significant nonlinear (inverted U-shaped) relationship between bank diversification and financial stability in OECD member countries.
Loan diversification enhances a bank's financial strength, particularly during crises like the COVID-19 pandemic, where income diversification is essential for resilience (Shim, 2019; Maghyereh and Yamani, 2022) Various studies have highlighted diversification as a key strategy for recovery in the banking sector, emphasizing the importance of non-interest income in improving bank performance.
A study by Kozak and Wierzbowska (2022) examined the relationship between profitability and the COVID-19 pandemic among banks in 40 European countries, revealing that profitability significantly increased due to a higher proportion of non-interest income Similarly, Li et al (2021) found a positive correlation between bank efficiency and non-interest income among US banks during the pandemic, highlighting the importance of technological innovations in enhancing performance through digital services Le et al (2022) focused on the Islamic banking system, suggesting that income diversification via “Sukuk” can mitigate the pandemic's adverse effects on profitability Additionally, research by Simoens and Baele (2022) indicated that European banks with high functional diversification experienced a 10% smaller decline in market value during the pandemic's early months Maghyereh (2022) also explored the impact of income diversification on systemic risk in 42 GCC banks, emphasizing the role of diversification in maintaining stability.
16 diversifying income has a positive effect on reducing systemic risk both prior and after initial wave of COVID-19
Corporate finance theory suggests that banks should prioritize leveraging their specialized knowledge and expertise in specific fields instead of attempting to diversify their income sources While diversification can offer benefits, these must be carefully balanced against potential drawbacks, such as agency problems and inefficient investments resulting from inadequate resource allocation.
Research indicates that aligning the interests of stockholders and agents in complex organizations is challenging, which can lead to a decrease in the market value of conglomerates (Laeven & Levine, 2007) Insiders may pursue business diversification primarily for personal gain Additionally, studies suggest that diversification may increase bank risks rather than reduce them, due to heightened volatility in commission and fee-based earnings (Lepetit et al., 2008), rising operational costs, and insufficient management expertise (Berger et al., 2010) Engaging in multiple business lines can also expose banks to new risks, including operational and market risks, alongside traditional credit risks.
DeYoung and Torna (2013) conducted an analysis of the influence of revenue diversification on the collapse of banks during financial crisis The
Research indicates that the ability of banks to withstand crises is significantly compromised by their shift towards non-traditional activities Excessive investments in innovative financial products can diminish performance and heighten default risks Acharya et al (2006) found that diversification does not ensure better performance, as evidenced by a study of 105 Italian banks from 1993 to 1999, which linked a higher non-interest income ratio to decreased profitability and credit quality This aligns with Delpachitra and Lester's (2013) analysis of nine Australian banks from 2000 to 2009, which showed that diversification in non-interest income does not enhance bank performance and may increase default risk Additionally, Smith et al (2003) highlighted that non-interest income is inherently more volatile than interest income, further complicating banks' financial stability.
A study involving 4,100 banks across 15 European countries highlights a concerning increase in risk exposure Research by Šeho, which analyzed 46 Islamic and 60 conventional banks in Malaysia, Saudi Arabia, and the UAE from 2000 to 2015, suggests that diversification in sectoral loans or financing may reduce returns while heightening risks Similarly, Duho et al (2019) examined the effects of diversification on the financial stability, profitability, and efficiency of 32 Ghanaian banks over the same period, indicating that banks should focus on their core business activities and limit their reliance on non-interest income Efforts to boost non-interest income could lead to greater risks of default rather than enhanced profitability.
18 monitored and periodically assessed to determine whether non-interest income is causing more harm than good
Emerging markets are increasingly significant in the global economy, attracting more research interest A study by Berger et al (2010) found that from 1996 to 2006, Chinese banks faced higher operating costs and reduced profitability due to greater diversification of services and territories Similarly, Hidayat et al (2012) explored the link between product diversification and banking risk in Indonesian banks from 2002 to 2008, revealing that this relationship is largely influenced by bank size, with larger banks more likely to pursue non-interest services compared to their smaller counterparts.
DATA AND METHODOLOGY
Data Collection
This thesis analyzes annual data from 30 commercial banks in Vietnam over the period from 2012 to 2022, encompassing the years impacted by the COVID-19 pandemic in 2020 and 2021 All financial statements utilized in this research are published by the banks and audited by external auditors to ensure data accuracy To provide a comprehensive view of the banks' actual business situations, consolidated financial statements were selected, reflecting their diverse investments across various sectors Data were obtained from reliable sources such as S&P Capital IQ, the State Bank of Vietnam (SBV) website, and the banks under investigation The final sample comprises 330 firm-year observations from these 30 unique banks over the 11-year period.
In 2022, a robust sample size was utilized for regression analysis, although the panel dataset was unbalanced due to certain banks not disclosing their complete financial statements during the study period Appendix B lists the commercial banks involved, which includes four banks with over 50% state ownership (AGRB, BID, CTG, VCB) and 26 joint stock commercial banks After data collection, the information was imported into an Excel file, where the author meticulously cleaned the data to identify errors, fill in missing information, and complete the data matrix To analyze and process the data according to the model, the researcher employed STATA 17 software, with additional inflation data sourced from the IMF and GSO.
Data Analysis
This study builds upon the original model by Lee et al (2014) to investigate the effects of diversification strategy on bank performance The author utilizes a specific regression analysis to assess these impacts effectively.
In which: i represents the number of banks in research sample (i 1,…,30); t represents the time (t = 2012-2022); β represents regression coefficient; λtis unobservable individual effect and ԑi,t is error term
The dependent variable, BPi,t is performance of commercial banks, proxied by return on assets (ROA) and return on equity (ROE) (following literatures of Stiroh, 2004b; Lee, et al., 2014)
This study examines two key aspects of bank diversification strategy: income diversification (ID) and funding diversification (FD) Income diversification is quantified by the ratio of non-interest income to total operating income, while funding diversification assesses banks' dependence on non-deposit funding sources, including the wholesale market and commercial paper issuance A comprehensive discussion on the definition and measurement of bank diversification is provided in Subsection 3.2.2.
There is an important explanatory variable, COVIDi,t It is a dummy variable that takes the value 0 when there is no COVID-19 pandemic and gets the value 1 during the period 2020-2021
The model also incorporates numerous control variables affecting bank performance as in previous literature (Chiorazzo et al., 2008; Luu et al., 2019)
The research model of this dissertation is:
Bank performance can be evaluated from various perspectives, with shareholders primarily focusing on profit generation through revenue maximization and cost minimization Economic theories suggest that in a perfectly competitive market, maximizing profit aligns with minimizing costs However, practical challenges, such as regulatory changes, can hinder optimal performance outcomes Deviations from profit maximization are often attributed to two main factors: misaligned incentives and operational inefficiencies, as noted by Bikker and Bos (2008).
Economic efficiency in banking refers to the ability to minimize costs or maximize profits Profit efficiency compares a bank's profits to those of the best-performing banks using the same inputs (Berger and Mester, 1997) All performance metrics, regardless of their specific objectives, depend on accounting and market data to evaluate financial status at a given time and assess management effectiveness over time (Jianu et al., 2017) Consequently, profitability acts as a key summary index of performance (Liang et al., 2013; De Andres and Vallelado, 2008) To effectively measure bank performance, Laeven and Levine suggest utilizing various analytical approaches.
In 2007, Tobin’s Q was employed to assess bank profitability, calculated by dividing the sum of the market value of common equity and the book value of preferred shares by the book value of total assets Additionally, key financial metrics such as Return on Assets (ROA), Return on Equity (ROE), Net Interest Margin (NIM), returns on deposits, and profit margin have been extensively utilized by researchers, with ROA being the most commonly used indicator (Burhonov, 2006; Kapur and Gualu, 2011; Olweny and Mamba, 2011; Tan and Floros, 2012).
This study analyzes bank performance in Vietnam using two key financial ratios, Return on Assets (ROA) and Return on Equity (ROE), due to the lack of comprehensive market value data Unlike previous methods that incorporate cost efficiency, asset quality, and bank stability indicators, this approach aligns with the methodologies of Chiorazzo et al (2008) and Lee et al (2014) (Beck et al., 2013; Vennet, 2002) Additionally, the research explores the impact of bank diversification on performance metrics.
The study employs two independent variables of income diversification (ID) and funding diversification (FD) to measure bank diversification
Referring to studies of Stiroh and Rumble (2006), Meslier et al (2014), Edirisuriya et al (2015), Ngoc (2019), income diversification index is computed as:
ID= Net Non interest income Total Operating income
The measurement of ID is determined by the ratio of net non-interest income to total operating income, as detailed in audited consolidated financial statements For this study, non-interest income includes fees and commissions, foreign currency and gold trading, held-for-trading securities, investment securities, income from investments in other entities, and non-credit income Total operating income is calculated by summing net interest income and non-interest income.
A higher ID value indicates greater diversification for banks beyond traditional interest income, which is linked to improved performance Research has consistently shown that income diversification benefits banks, enhancing their overall effectiveness and stability (Nguyen, 2017; Sang).
In 2017, it was found that institutional diversification (ID) can lead to both short-term and long-term profits, contributing to bank growth (Rossi et al., 2009; Meslier et al., 2014) However, the effectiveness of ID varies based on banks' market experience (Hiep et al., 2019) Conversely, Berger et al (2010) argued that ID may negatively impact bank performance, as overdiversification can create an imbalance in profit growth due to rising costs Supporting this perspective, Ngoc (2019) conducted a study on Vietnamese commercial banks, revealing similar findings attributed to a lack of experience in implementing diversification, particularly in non-traditional areas.
This dissertation posits that greater income diversification in commercial banks can lead to decreased profitability and stability Supporting this view, Demirgüç-Kunt and Huizinga (2010) argue that an increase in non-interest income correlates with heightened risks for banks.
Hypothesis 1: Income diversification has negative effect of bank performance
The funding of a commercial bank encompasses monetary value generated internally or sourced externally for purposes such as lending and investing In Vietnam, the capital sources of banks consist of liabilities and shareholders' equity, which include amounts owed to the Government and the State Bank of Vietnam (SBV), deposits and borrowings from other credit institutions, customer deposits, derivative financial instruments, entrusted investments and loans, issued securities, and other liabilities This comprehensive structure highlights the diverse funding mechanisms that support a bank's operations.
(2018), the measurement of FD is as below:
FD = Total Liabilities − Deposit from Customers
FD is assessed by the ratio of non-deposit liabilities to total liabilities, with a higher ratio signifying reduced reliance on traditional deposit sources for bank funding Research by Demirgüç-Kunt and Huizinga (2010) indicates that achieving diversification in funding strategies is complex and can hinder the profitability and stability of banks Consequently, establishing a correlation between funding sources and Return on Assets (ROA) poses challenges due to concerns related to endogeneity.
Curi et al (2015) explored the effects of funding diversification on bank performance in Luxembourg during the financial crisis, finding significant variations across different banking models Their research concluded that funding diversification led to operational inefficiencies and negatively impacted banking performance during the 2007-2008 global financial crisis Conversely, Lam Anh (2018) studied diversification in emerging markets, particularly within ASEAN countries like Vietnam, and found that funding diversification enhanced profitability efficiency This suggests that a higher level of funding diversification correlates with improved and sustained profit efficiency for banks.
Hypothesis 2: Funding diversification has positive effect of bank performance c Control Variables
Similar to studies of Berger et al (2010) and Kohler (2014), the author also adds several bank-specific variables and macro factors representing bank diversification into regression model as below:
*) The explanatory variables show characteristics of commercial banks:
SIZEi,t measures bank size as calculated by natural logarithm of total assets
SIZE = Ln (Total Assets) There are two opposing views on the impact of bank size on bank safety and profitability Several studies, namely Matousek and Stewart (2009);
Larger banks can enhance profitability through economies of scale, as noted by Velnampy and Nimalathasan (2010) and others, by reducing capital mobilization costs, increasing lending efficiency, and sharing management fees across various services Rapid asset growth reflects banks’ investment expansion and diversification, with Demsetz and Strahan (1997) highlighting a positive correlation between bank asset size and customer loans, which contributes to improved bank safety and efficiency However, Vallascas and Keasey (2012) argue that larger banks may engage in riskier investments and face greater challenges in managing their offerings, potentially rendering them less effective than smaller banks Additionally, Fu et al (2014) point out that Southeast Asian nations have adopted credit expansion policies to foster long-term economic growth, positioning banks as vital capital conduits for key sectors, although this rapid growth often leads to a decreased emphasis on prudent credit policies, as noted by Abedifar et al.
(2013) believed that total assets grow too fast, leading to moral hazards or adverse selection if banks loosen standards for rating and monitoring loans Suleiman
Research indicates a negative correlation between asset volume and profitability in Jordanian commercial banks (2015) Conversely, studies by Tharu and Shrestha (2019), ệhman and Yazdanfar (2018), and Curi et al (2015) reveal no definitive relationship between bank size and profitability.
30 profitability This relationship is still under debate in the literature In this study, relationship between SIZE and bank performance in Vietnam is expected to be positive
Hypothesis 3: Bank size has positive effect on bank performance
Another control variable is a dummy variable (STATE) which takes value
EMPIRICAL RESULTS AND DISCUSSIONS
Descriptive Statistics
Appendix D - Table 1 provides descriptive statistics for 330 observations in the dataset, revealing that the Return on Assets (ROA) of Vietnamese commercial banks ranges from a low of -1.28% (VBB) to a high of 3.17% (TCB), with an average of 0.76% Similarly, the Return on Equity (ROE) varies from -8.27% (VBB) to 26.39% (VIB), averaging 9.08% Between 2012 and 2022, the positive mean values of these profitability measures suggest that the banking sector's performance was generally strong However, as seen in other emerging markets, there is considerable variability in profitability among banks, indicated by standard deviations of 0.66% for ROA and 6.87% for ROE.
On average, 22.08% of total operating income for Vietnamese Commercial banks is derived from non-interest activities, while 25.63% of liabilities come from non-deposit funding sources Despite this diversification, interest income remains the primary revenue source, and customer deposits continue to dominate financing for these banks The analysis reveals a significant variance in income and funding diversification, with standard deviations of 0.188 and 0.114, respectively, indicating differences across various banks and over the sample period.
The average bank size in Vietnam is 32.63, with the largest bank, BID, having a size of 35.29 and state ownership exceeding 50%, while the smallest bank, SGB, measures at 30.32 Vietnamese commercial banks exhibit a high level of financial leverage, with an average Equity to Total Assets (ETA) ratio of 8.71%, though this varies significantly among banks, ranging from 23.84% to 2.69% Between 2012 and 2022, bank credit and customer deposit sizes showed considerable fluctuations, with standard deviations of 9.97% and 8.75%, respectively Primarily, commercial banks in Vietnam focus on traditional banking activities, reflected in mean values of 84.85% for lending and 77.29% for deposits The average Non-Performing Loan (NPL) ratio stood at 2.14% over an 11-year observation period, indicating a relatively low level of bad debt and good asset quality, with bank risks well-managed Furthermore, a mean management efficiency of 54.43% highlights the need for banks to optimize resource use and adopt emerging technologies, which can reduce operating expenses and enhance both safety and profitability.
For macroeconomics factors, INF fluctuates from 0.63% to 9.09% and reaches mean of 3.74% SD of 2.2% demonstrates that INF fluctuates significantly, especially during COVID-19 pandemic.
Correlation Test
To ensure unbiased and consistent regression results, the author performs autocorrelation test in Appendix D - Table 2 Correlation coefficient measures the strength of association between two variables It shows that correlation
The analysis reveals that 43 coefficients among independent variables do not exceed the standard threshold of 0.8, indicating that multicollinearity is not a significant concern for the regression results (Judge et al., 1985; Cohen, 1988; Hair et al., 2006) However, the correlations between Operating Cost (OC) and Return on Equity (ROE) (0.7573) and OC and Return on Assets (ROA) (0.7158) are notably high Additionally, a strong negative correlation of 0.7434 exists between Debt to Assets (DTA) and Funding Diversification (FD), attributed to FD being derived from non-deposit funding sources Furthermore, the correlation between State Ownership (STATE) and Bank Size (SIZE) at 0.6405 indicates a significant concentration within the Vietnamese banking sector, as the largest banks are predominantly state-owned.
To ensure that multicollinearity did not affect the research variables, the author examined the variance inflation factor (VIF) values, as detailed in Appendix D - Table 3, following the guidelines established by Gujarati.
In 2004, it was established that a Variance Inflation Factor (VIF) value of independent variables should not exceed 5 to avoid multi-collinearity, while values above 10 indicate its presence According to the test results in Appendix D - Table 3, the average VIF is 2.48, with all variables in the model falling below the threshold of 5 Consequently, the research model demonstrates no signs of multi-collinearity among the independent variables.
Model Selection
The author employed model selection procedure to determine appropriate model between Pooled OLS, FEM and REM The testing procedure referred to
In Chapter 3, the author executed both the F-test and LM test, which yielded results rejecting the null hypothesis and indicating the presence of unobserved components in the model As a result, the author utilized the Hausman test to determine the most suitable model for the study, choosing between Fixed Effects Model (FEM) and Random Effects Model (REM).
The OLS regression method is not appropriate for panel data analysis because it does not consider cross-unit differences and time effects This limitation is evidenced by the findings of the F-test and LM test, detailed in Appendix D - Tables 4 and 5.
The Hausman test produced a p-value of 0.224, indicating that at a significance level of α = 5%, the hypothesis suggesting a correlation between individual characteristics and regressors is not rejected Consequently, the Random Effects Model (REM) was selected, as detailed in Appendix D - Table 7 The findings from the REM model reveal that independent variables account for approximately 74% of bank performance, as measured by Return on Assets (ROA); however, the primary explanatory variable, ID, does not demonstrate a statistically significant impact on bank performance.
The author employs heteroskedasticity and autocorrelation tests to identify defects in the REM model, utilizing the GLS approach for correction if necessary Results are detailed in Appendix D - Table 8 The Wooldridge test indicates the presence of autocorrelation, as evidenced by a p-value of 0.0002, which is below the significance level of 5% This leads to the conclusion that the model displays autocorrelation Additionally, the LM test is used to assess heteroskedasticity.
The test result shows that p-value = 0.0000 smaller than significance level α 5%, hence the author concludes that the model has heteroskedasticity
To address heteroskedasticity and autocorrelation, the author employed the Generalized Least Squares (GLS) estimation technique A variable is considered to explain variability in the dependent variable when its p-value is below the predetermined significance levels of α = 1%, 5%, or 10% Conversely, if a variable's p-value exceeds the significance level of α = 10%, it indicates that the variable does not possess statistical significance.
Table 2 Cross-sectional time-series FGLS regression results for ROA
Number of obs = 330 Number of groups = 30 Time periods = 11 Wald chi2(11) = 769.42 Prob > chi2 = 0.0000 ROA Coefficient Std err z P> |z| [95% conf interval]
FD 0.00900*** 0023302 3.86 0.000 0044361 0135701 SIZE 0.00381*** 0003142 12.14 0.000 003197 0044286 STATE -0.0084*** 0008499 -9.92 0.000 -.0100961 -.0067648 ETA 0.0999*** 0080009 12.49 0.000 0842609 115624 LTA 0.00695*** 0020569 3.38 0.001 0029206 0109837
Note: *, **, and *** indicate the statistical significance at 10%, 5%, and 1%, respectively
Table 2 reveals a statistically significant negative relationship between income diversification (ID) and return on assets (ROA), indicating that increased income diversification adversely impacts bank profitability This finding is consistent with the research conducted by Berger et al.
A study conducted in 2010 reveals a significant positive relationship between funding diversification (FD) and return on assets (ROA) in Vietnamese banks This correlation indicates that banks can improve their profitability by diversifying their funding sources The results support both hypotheses H1 and H2, highlighting that financial institutions with diverse revenue streams and limited funding options often demonstrate poorer performance.
The author conducted multiple model testing procedures to identify the optimal model for assessing the relationship between diversification and Return on Equity (ROE) The results of these analyses are detailed in Appendix D, specifically in Tables 9, 10, and 11.
The F-test and LM test results lead to the rejection of the null hypotheses, indicating that both the Fixed Effects Model (FEM) and Random Effects Model (REM) outperform the Ordinary Least Squares (OLS) model Following this, the Hausman test was conducted, revealing a p-value of 0.0585 at a significance level of α = 5%, which suggests that the Random Effects Model is the more appropriate choice for analysis (see Appendix D - Table 12).
To enhance the reliability of estimation results, it is essential to assess their validity The author performed two tests—LM test and Wooldridge test—to evaluate heteroskedasticity and autocorrelation in the ROE model Appendix D - Table 13 outlines the findings from this examination, revealing that the REM model demonstrates both autocorrelation and heteroskedasticity Consequently, the research will continue using the GLS regression technique to analyze the impact of diversification on ROE, as detailed in Table 3.
Table 3 Cross-sectional time-series FGLS regression results for ROE
Number of obs = 330 Number of groups = 30 Time periods = 11 Wald chi2(12) = 541.12 Prob > chi2 = 0.0000 ROE Coefficient Std err z P> |z| [95% conf interval]
OC -0.161*** 0137984 -11.64 0.000 -.1876004 -.1335118 DTA -0.0120 0335819 -0.36 0.721 -.0778262 0538124 NPL -0.156 0921708 -1.69 0.090 -.3368374 0244655 INF 0.111 0688798 1.62 0.106 -.0235603 2464435 COVID -0.00811** 0030334 -2.67 0.008 -.0140515 -.0021609 _cons -0.998*** 1333774 -7.48 0.000 -1.259545 -.7367152
Note: *, **, and *** indicate the statistical significance at 10%, 5%, and 1%, respectively
The impact of diversification on Return on Equity (ROE) is statistically significant and aligns with its influence on Return on Assets (ROA) Specifically, income diversification (ID) has a coefficient of -0.018 at a 10% significance level, while funding diversification shows a coefficient of 0.08 with a 5% significance level These results reinforce the validity of hypotheses H1 and H2, consistent with the outcomes from the ROA model.
Result Discussions
The variable ID demonstrates a consistently negative and statistically significant impact in both the ROA and ROE models Specifically, the coefficient for ID in the ROA model is estimated at -0.00203, indicating a detrimental effect on return on assets Similarly, the ROE model shows a negative coefficient for the ID variable, reinforcing its adverse influence on return on equity.
49 is estimated to be -0.0186, both statistically significant at 10% level Theoretically, diversification helps commercial banks take advantage of economies of scale through sharing and effectively operating available resources
Diversifying services allows banks to capitalize on productive opportunities, enhancing profits and supporting Wernerfelt’s Resource-based Theory (1984) In Vietnam, the positive impact of diversification on commercial banks' Return on Assets (ROA) can be understood through Signaling Theory, as an expansion of banking services signals growth to customers, bolstering the bank's reputation and future prospects However, the Vietnamese banking sector has only recently prioritized non-interest revenue, with an average non-interest income ratio of 22.08% from 2012 to 2022 across 30 banks Consequently, the negative effects of income diversification on these banks are not unexpected, aligning with findings from Berger et al (2010), Laeven and Levine (2007), Rajan et al (2000), and Acharya et al (2006).
The Vietnamese financial market is characterized by a robust bank-based structure, where banks primarily rely on interest income for their growth Approximately 70 to 75% of banks' total income comes from credit, highlighting the significance of this revenue stream While income diversification offers potential benefits, it is often counterbalanced by the rising risks associated with non-interest activities Additionally, trading in foreign exchange, gold, and securities is susceptible to market fluctuations, which can directly impact bank risk levels.
The banking system in Vietnam struggles to develop an effective profit transformation model for non-traditional businesses due to weak corporate governance Revenue diversification is currently passive and slow to embrace innovation, with economic efficiency heavily reliant on credit operations As a result, expanding into non-interest activities could increase overall risks for Vietnamese banks, potentially diminishing their performance.
Moreover, significance placed on diversifying income is apparent in governmental measures such as Decision No 254/QD-TTg in 2012 and Decision
In 2018, the government issued No 986/QD-TTg, outlining strategies for the reorganization and advancement of the banking sector The pressure to implement Basel II standards is prompting banks to shift their operational strategies, reducing reliance on credit operations while focusing on enhancing revenue through non-credit services This shift involves reallocating resources towards developing diverse services like payment solutions, electronic financial services, asset management, and insurance, especially as credit activities face sluggish growth However, rapid growth in these new areas remains challenging, with some leading retail banks experiencing declining revenues from services Despite the potential benefits, the contributions from service activities are not yet sufficient to mitigate the risks associated with net interest income.
Conventional commercial banks are facing challenges from agile, customer-centric fintech firms that are reshaping the banking industry in the digital age To remain competitive, traditional banks must continuously invest in technology, despite the immediate lack of tangible benefits, as highlighted by Kauffman et al (2015) This ongoing investment is crucial to counter the impact of these emerging rivals and to maintain financial performance in a rapidly evolving landscape.
Vietnamese banks are in the early stages of transitioning to offer a diverse array of non-interest services, resulting in limited experience and consistency Not all banks, including larger and retail-focused institutions, generate significant revenue from these services Additionally, the weak stock market in Vietnam poses challenges for banks in acquiring sufficient non-interest-bearing assets, particularly when it comes to diversifying into securities.
Funding diversification is a crucial aspect that remains underexplored in the banking industry across various nations, including Vietnam Research indicates that funding diversification positively influences the performance of Vietnam's banking system, as evidenced by improvements in Return on Assets (ROA) and Return on Equity (ROE).
The correlations of 0.00900 and 0.0729 are statistically significant at the 1% and 5% levels, respectively This finding contradicts the conclusions of Demirgỹỗ-Kunt and Huizing (2010) and Curi et al (2015), suggesting that traditional banks are generally more secure than those heavily involved in diversification Diversifying funding sources, particularly through wholesale markets, can increase liquidity risks, especially during financial stress, as banks compete for scarce funds However, the results of this study align with the expected outcomes and are consistent with previous research by Anh (2018) and Vo (2020).
Banks with greater funding diversification tend to achieve improved funding certainty, which can lead to increased profitability In contrast, a decrease in certainty regarding a bank's capital can result in reduced profitability (Ritz and Walther, 2015).
Utilizing diverse funding sources enhances a bank's ability to manage collateral assets effectively Similar to businesses aiming to maximize profits, banks focus on increasing asset value to fulfill their role as financial intermediaries They generate revenue by selling collateral assets with unique features—balancing liquidity, risk, and return—and using the proceeds to acquire different assets This service allows banks to convert one asset into another for the public, ultimately leading to improved efficiency and economic advantages in funding.
The adoption of funding diversification (FD) strategies enhances overall profitability for banks, as those with greater funding diversification experience improved profit efficiency in both the short and long term This approach not only provides solutions but also creates utilities for customers, fostering mutual benefits.
Over 80% of the operational capital for financial institutions comes from capital mobilization, primarily through customer deposits The similarity of products and intense competition among banks make it easy for customers to switch their deposits, raising concerns about the long-term viability of deposit capital in terms of scale and cost This situation directly impacts the effectiveness of these institutions within the banking system Consequently, flexible funding management through financial derivatives (FD) could enhance bank profitability.
The COVID-19 pandemic has a statistically significant negative impact on the profitability of the banking sector, as demonstrated by a coefficient of -0.0007 in the Return on Assets (ROA) model and -0.008 in the Return on Equity (ROE) model This aligns with existing literature, highlighting the detrimental effects of the outbreak on banking soundness (Elnahass et al., 2021; Kozak, 2021; Katusiime, 2021; Almonifi et al., 2021; Tran et al., 2022) While the magnitude of this impact is relatively small compared to other variables, it underscores the ongoing challenges faced by the banking industry during the pandemic.
54 coefficients This can be explained by implementation of Circular 01/2020/TT- NHNN, Circular 14/2021/TT-NHNN and Circular 03/2021/TT-NHNN by SBV
In response to economic challenges, banks have actively restructured debts, leading to a reduction in the bad debt ratio in the fourth quarter of 2020, despite ongoing risks from non-performing loans (NPLs) The Credit Department reported that by 2020, commercial banks had restructured repayment terms for approximately 200,000 customers, amounting to around 355 trillion VND out of a total of 8.5 million billion VND in outstanding loans Over two years since the pandemic began, NPLs remain under tight control Additionally, banks have diversified into non-traditional activities to adapt to stricter credit standards and reduced demand for traditional lending Research by Li et al (2021) indicates that the performance of Vietnamese banks was largely unaffected due to these diversification efforts during the COVID-19 pandemic.
The findings pertaining to other control variables are generally consistent with prior research
CONCLUSION AND IMPLICATIONS
Concluding Remarks
Vietnam is emerging as a leading economy in Southeast Asia, driven by extensive financial sector liberalization reforms that enhance competition and foster the growth of non-traditional banking activities This research investigates the effects of diversification strategies on the performance of Vietnamese banks While global studies offer varying insights based on bank characteristics and regional contexts, there is a scarcity of quantitative research in Vietnam's banking sector Utilizing data from 30 commercial banks between 2012 and 2022, the author examines income and funding diversification strategies and their impact on bank profitability, measured by ROA and ROE Through quantitative analysis using the FGLS model, the study reveals contrasting effects of these strategies; while diversified funding sources enhance profitability, a shift towards non-traditional activities may decrease profit efficiency These findings reflect recent trends in the Vietnamese banking sector, where transitioning to non-deposit borrowings can lower capital costs and improve funding stability, alongside increased issuance of valuable papers to bolster Tier 2 capital and financing diversification.
The negative impacts of income diversification on the profitability of Vietnamese commercial banks do not necessarily mean that these institutions should avoid non-interest activities, especially in light of the unforeseen challenges posed by the COVID-19 pandemic and subsequent mitigation measures Traditional banking activities faced significant pressure, leading to tightened credit standards and reduced demand, alongside the implementation of Basel II regulations Consequently, non-interest income emerged as a crucial additional source to bolster the resilience of banks during this crisis The study highlights that while operating expenses, non-performing loans, and state ownership negatively affect bank profitability, other control variables show positive effects Notably, the relationship between deferred tax assets and profitability indicators such as return on equity and return on assets is inconsistent, with DTA showing a positive correlation with ROA but a negative one with ROE, and lacking statistical significance across various levels Furthermore, the influence of non-performing loans on profitability may require additional time for confirmation, although its impact on bank performance is evident in the ROA model.
Policy Implications and Recommendations
This article explores the impact of income and funding diversification on the performance of banks in Vietnam from 2012 to 2022, presenting key policy implications and recommendations for enhancing financial stability and growth in the banking sector.
5.2.1 Recommendation for Vietnamese Commercial Banks
First, banks need to implement an appropriate diversification strategy
Vietnamese commercial banks must modernize their banking services, particularly e-banking, to stay competitive in the digitized banking landscape and meet consumer demands To thrive amid international integration, banks need to adopt comprehensive solutions that restructure revenue streams, focusing on non-traditional income sources aligned with their financial capabilities and business goals, while leveraging government and State Bank of Vietnam policies Establishing dedicated research departments for product development is essential to ensure effective diversification Banks should prioritize high-tech products and services with unique features to effectively compete against foreign rivals Additionally, ongoing exploration and implementation of non-cash payment solutions are crucial, as many Vietnamese banks have recently initiated digital transformation efforts.
In 2022, Techcombank introduced two innovative digitization solutions: iDO, a digital platform for branches, and PayLink, a Payment Hub system that integrates with interbank payment networks, along with a suite of personal financial planning tools available on the Techcombank mobile app Additionally, BIDV became the first bank to implement chip-mounted citizen identification for banking transactions, utilizing a biometric authentication solution developed in collaboration with the Ministry of Public Security.
2022) Moreover, commercial banks need to diversify their products in-depth and
To enhance customer benefits, banks should focus on synergizing their products and services to improve added value Additionally, strengthening advertising and marketing efforts is crucial for increasing customer accessibility to banking services Implementing fee exemption and reduction policies during the initial usage period can also attract more customers' attention.
Vietnamese commercial banks need to effectively diversify their funding sources Recently, they have shifted focus towards issuing certificates of deposit and bonds instead of relying solely on traditional deposits This strategy has resulted in a significant growth rate for these financial instruments in recent years, as highlighted in the consolidated financial statements for the first half of the year.
2023, more than 891,000 VND billion is total value of valuable papers issued by
A total of 27 listed banks have seen a 9.2% increase, representing approximately 76% of their total equity The issuance of valuable papers, including certificates of deposit and long-term bonds, is essential for enhancing Tier 2 capital, thereby improving funding stability and reducing short-term liquidity risks However, commercial banks must also focus on raising Tier 1 capital by offering shares to investors or increasing retained earnings to comply with capital adequacy regulations Strengthening Tier 1 capital enables banks to expand lending capabilities and total assets Additionally, banks can adjust interest rates, offer flexible deposit terms in various currencies, and diversify their capital mobilization products, including deposits, bonds, promissory notes, and certificates of deposit.
To enhance their growth, Vietnamese commercial banks must continuously increase their total assets and equity by attracting investment from both domestic and foreign sources This includes improving their reputation and brand development while learning management practices from experienced partners Banks should also reinvest retained earnings instead of distributing dividends or issuing new shares, which can alleviate risks for existing shareholders Additionally, expanding their branch networks beyond major cities and exploring operations in other promising countries is essential For banks with limited customer bases and inefficient operations, mergers and acquisitions should be considered, always prioritizing risk management and the enhancement of human resource quality.
To enhance the operational efficiency of the banking sector, it is essential to bridge the gap between state-owned and joint-stock commercial banks State-owned banks should function within a legal framework that adheres to market principles, minimizing direct government intervention in their management, such as credit limit adjustments and interest rate regulations Meanwhile, Vietnamese commercial banks should accelerate the process of equitization, reducing state ownership to between 65% and 95%, and restructure the banking system to better align with the evolving business environment A viable strategy for raising capital involves selling shares to foreign strategic investors, who bring significant financial resources and managerial expertise.
Further, banks need to optimize operating costs to increase profits and minimize risks, especially during crisis period of COVID-19 pandemic
Implementing digital transformation and leveraging contemporary technology can significantly reduce costs and enhance operational efficiency for banks (Miklaszewska et al., 2021) To fully embrace digitization, banks must reassess their traditional network systems and work towards eliminating administrative paperwork in transactions By standardizing automated processes, Vietnamese banks can lower salary expenses while improving customer service.
Finally, banks should extend credit limit and control credit risk effectively Credit is also a variable that strongly affects business performance of
Vietnamese banks are focusing on income diversification to enhance their performance and support the country's economic growth amidst unstable foreign capital By expanding credit activities, banks can better meet the capital demands of socio-economic initiatives However, effective credit management is crucial, necessitating improved credit appraisal processes that align with international standards, emphasizing transparency and market discipline This requires internal compliance within each bank and strengthened oversight by the State Bank of Vietnam (SBV) Currently, SBV's credit control relies primarily on "room" credit, which, while beneficial for SBV as a monetary policy enforcer, may hinder socio-economic activities Therefore, the State should consider a balanced approach to intervene in the internal management of banks.
To enhance credit regulation and provide optimal conditions for Vietnamese commercial banks, it is essential for these banks to develop robust credit risk early warning systems These integrated data processing systems will automatically assess all debts and identify potential deterioration, enabling effective management of credit portfolio quality This proactive approach will also prepare commercial banks for the implementation of the Basel II Accord.
Policymakers must foster a conducive environment for the growth of a robust financial market that encourages both domestic and international investment It is essential to maintain stable inflation and prevent the rise of excessive monopolies during the restructuring of projects, mergers, and acquisitions Additionally, enhancing institutional frameworks and legal structures is vital for facilitating banking operations, which includes establishing clear guidelines for new services like financial advisory, asset management, and derivatives Lastly, the State Bank of Vietnam (SBV) should improve its efficiency in monitoring and inspecting banks, providing early warnings for potential systemic risks, and ensuring strict adherence to banking laws to mitigate instability.
In conclusion, while these measures may not be exhaustive, they play a crucial role in stabilizing banks in the current climate and provide a temporary foundation for their future sustainable development.
Research limitations and Future research direction
Future research on diversification strategies in the banking sector should extend beyond profitability to examine their effects on market power and efficiency Additionally, incorporating other performance indicators, such as bank risks, would provide a more comprehensive analysis The current study's limited sample of 30 out of 49 Vietnamese banks, due to ongoing restructuring and lack of financial disclosures, suggests a need for broader data inclusion, including foreign and joint-venture banks Investigating how diversification impacts vary among different bank categories, particularly those with foreign investment, could yield valuable insights Furthermore, exploring industry-specific and political factors, as well as comparing findings with countries like Thailand, Malaysia, and the Philippines, would enhance understanding of the effects of income and funding diversification on the performance of commercial banks in Vietnam.
Appendix A: The banking system in Vietnam
Classification Name of Bank Abbreviation
Vietnam Bank for Agriculture and Rural
Ocean Commercial One Member Limited Liability
Global Petro Commercial Joint Stock Bank GPBank Construction Commercial One Member Limited
ANZ Bank Vietnam Limited ANZVL
Hong Leong Bank Vietnam Limited HLBVN
Hongkong-Shanghai Bank Vietnam Limited HSBC
Shinhan Bank Vietnam Limited SHBVN
Standard Chartered Bank (Vietnam) Limited SCBVL
CIMB Bank (Vietnam) Limited CIMB
Woori Bank (Vietnam) Limited Woori
United Overseas Bank (Vietnam) Limited UOB Joint-ventures banks
Vietnam-Russia Joint Venture Bank VRB
Tien Phong Commercial Joint Stock Bank TPBank
An Binh Commercial Joint Stock Bank ABBank
Bao Viet Joint Stock Commercial Bank Bao Viet
Viet Capital Commercial Joint Stock Bank Viet Capital
Bac A Commercial Joint Stock Bank Bac A Bank
Lien Viet Commercial Joint Stock Bank LPB
Dong A Commercial Joint Stock Bank (DongA Bank), Southeast Asia Commercial Joint Stock Bank (SeABank), Vietnam Maritime Commercial Joint Stock Bank (MSB), Kien Long Commercial Joint Stock Bank (Kienlongbank), and Vietnam Technological and Commercial Joint Stock Bank represent key financial institutions in Vietnam, each contributing to the country's banking sector with a range of services tailored to meet the needs of individual and corporate clients.
Nam A Commercial Joint Stock Bank Nam A Bank
Orient Commercial Joint Stock Bank OCB
Military Commercial Joint Stock Bank MBBank
Vietnam International Commercial Joint Stock Bank VIB
Sai Gon Commercial Joint Stock Bank SCB
Saigon Bank for Industry & Trade SGB
Saigon-Hanoi Commercial Joint Stock Bank (SHB), Saigon Thuong Tin Commercial Joint Stock Bank (Sacombank), Viet A Commercial Joint Stock Bank (VietABank), and Vietnam Joint Stock Commercial Bank for Industry and Trade (ViettinBank) are key players in Vietnam's banking sector, providing a range of financial services to support economic growth and development.
Viet Nam Thuong Tin Commercial Joint Stock Bank VietBank Viet Nam Export Import Commercial Joint Stock Eximbank
Ho Chi Minh City Development Joint Stock
Petrolimex Group Commercial Joint Stock Bank PGBank
Vietnam Prosperity Joint Stock Commercial Bank VPBank
Bank for Foreign Trade of Vietnam VCB
Bank for Investment and Development of Vietnam BIDV
Policies banks Vietnam Bank for Social Policies VBSP
Cooperative bank Co-operative bank of VietNam Co-opBank
Source: The State Bank of Vietnam, 2022
Appendix B: List of 30 Vietnamese Commercial banks in the research sample
No Name of Bank Abbreviation Listing status
1 An Binh Joint Stock Commercial Bank ABB UPCOM
2 Asia Joint Stock Commercial Bank ACB HNX
3 Vietnam Bank for Agriculture and Rural
4 Bac A Joint Stock Commercial Bank BAB HNX
5 Joint Stock Commercial Bank for Investment and
Development of Vietnam BID HOSE
6 Viet Capital Joint Stock Commercial Bank BVB UPCOM
7 Joint Stock Commercial Bank for Indutry and Trade of Vietnam CTG HOSE
8 Vietnam Export Import Joint Stock Commercial Bank EIB HOSE
9 Ho Chi Minh City Development Joint Stock
10 Kien Long Joint Stock Commercial Bank KLB UPCOM
11 Lien Viet Post Joint Stock Commercial Bank LPB UPCOM
12 Military Joint Stock Commercial Bank MBB HOSE
13 Vietnam Maritime Joint Stock Commercial Bank MSB HOSE
14 Nam A Joint Stock Commercial Bank NAB UPCOM
15 National Citizen Joint Stock Commercial Bank NVB HNX
16 Orient Joint Stock Commercial Bank OCB HOSE
17 Petrolimex Group Joint Stock Commercial Bank PGB UPCOM
18 Vietnam Public Joint Stock Commercial Bank PVCOM OTC
19 Sai Gon Joint Stock Commercial Bank SCB OTC
20 Saigon Bank for Industry and Trade SGB UPCOM
21 Sai Gon - Ha Noi Joint Stock Commercial Bank SHB HNX
22 Southeast Asia Joint Stock Commercial Bank SSB HOSE
23 Saigon Thuong Tin Joint Stock Commercial Bank STB HOSE
24 Vietnam Technological and Joint Stock Commercial
25 Tien Phong Joint Stock Commercial Bank TPB HOSE
26 Viet A Joint Stock Commercial Bank VAB UPCOM
27 Vietnam Thuong Tin Joint Stock Commercial Bank VBB UPCOM
28 Joint Stock Commercial Bank for Foreign Trade of
29 Viet Nam International Joint Stock Commercial Bank VIB HOSE
30 Vietnam Prosperity Joint Stock Commercial Bank VPB HOSE
Appendix C: Research variables’ definition and expected signs
Source: The author’s own summarization.
Classification Variable Description Sources Expected
Chiorazzo et al (2008); Stiroh (2004a, 2004b); Laliee et al (2014) ROE Returns on equity
ID ID = Net non Interest Income
Total Operating Income Lepetit et al (2008); Chiorazzo et al (2008); Trujillo - Ponce (2013) -
FD FD = Total Liabilities − Deposit from Customers
Total Liabilities Berger et al (2010); Lam Anh (2018); Wu et al (2020) +
SIZE SIZE = Ln (Total Assets) Boyd and Runkle (1993); Berger et al (2010); Sanya and Wolfe
(2011); Vallascas and Keasey (2012); Curi et al (2015) +
STATE Dummy variable takes value 1 for state-owned banks and 0 for others Phong and Tuan (2020) -
Hughes and Mester (1998); Goddard, et al (2004); Pasiouras and Kosmidou, 2007; Demirgỹỗ-Kunt and Huizinga, 2010; Sanya and Wolfe (2011)
LTA LTA = Total Loans to Customers and Other Credit Institutions
Abreu and Mendes (2002); Sanya and Wolfe (2011); Chiorazzo et al
OC OC = Total Operating Expenses
Total Operating Profit Nguyen et al (2018); Minh and Canh (2015); Le (2017) -
DTA DTA = Total Deposits from Customers and Credit Institution
Total Assets Wagner (2007); Viet and Phuc (2020) +
Total Debt (Group 1−5) Abreu and Mendes (2002); Gul et al (2011) -
INF t−1 Athanasoglou et al (2008); Trujillo - Ponce (2013) +
Variables COVID Dummy variable taking a value of 1 if it is the year 2020 and
2021 with the COVID-19 pandemic and 0 if otherwise Almonifi et al (2021); Katusiime (2021); Elnahass et al (2021) -
Appendix D: Empirical results of the research model Table 1: Descriptive Statistics Results
Source: Author’s calculations from financial reporting of 30 Vietnamese
Commercial banks in the period of 2012-2022
Variable Obs Mean Std dev Min Max
ROA ROE ID FD SIZE STATE ETA LTA OC DTA NPL INF COVID
Source: Results calculated by the authors
Table 3 Variance Inflation Factor test result multicollinearity
Table 4 F-test for fixed effects in ROA model
Table 5 Breusch-Pagan LM test for random effects in ROA model
ROA[BANK,t] = Xb + u[BANK] + e[BANK,t]
Table 6 Hausman test for ROA model
(b) (B) (b-B) sqrt (diag (V_b V_B)) femROA RemROA Difference Std err
COVID -.0004758 -.000235 -.0002407 0000936 b = Consistent under H0 and Ha; obtained from xtreg
B = Inconsistent under Ha, efficient under H0; obtained from xtreg
Test of H0: Difference in coefficients not systematic chi2(11) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 12.98
Table 7 REM regression model with ROA
ROA Coefficient Std err z P>|z| [95% conf interval]
FD 0.0119*** 0032029 1.95 0.052 -.0000431 0125649 SIZE 0.00407*** 0005186 9.82 0.000 0040705 0061119 STATE -0.0096*** 0015435 -6.25 0.000 -.0126692 -.0066188 ETA 0.0901*** 0082943 10.87 0.000 0738651 1063783 LTA 0.0129*** 0026133 4.93 0.000 0077693 0180133
OC -0.0175*** 0016625 -10.52 0.000 -.020756 -.0142391 DTA 0.00277 0041561 0.67 0.505 -.0053736 0109182 NPL -0.0172 0109825 -1.57 0.117 -.0387633 0042875 INF 0.0208* 0086888 2.40 0.017 0037932 0378527 COVID -0.000235 0004173 -0.56 0.573 -.0010528 0005828 _cons -0.135*** 0150883 -8.97 0.000 -.1649748 -.1058296 sigma_e 00231576 sigma_u 00256791 rho 44850517 (fraction of variance due to u_i)
Source: Author’s calculation, Stata Note: *, **, and *** indicate the statistical significance at 10%, 5%, and 1%, respectively
Table 8 Test results for heteroskedasticity and autocorrelation in ROA model
Heteroskedasticity Test: Var(u) = 0 chibar2(01) = 203.79 Prob > chibar2 = 0.0000
Autocorrelation H0: no first-order autocorrelation
Table 9.F-test for fixed effects in ROE model
Table 10 Breusch-Pagan LM test for random effects in ROE model
ROE[BANK,t] = Xb + u[BANK] + e[BANK,t]
Table 11 Hausman test for ROE model
(b) (B) (b-B) sqrt(diag(V_b V_B)) femROE remROE Difference Std err
COVID -.0034705 -.0006655 -.002805 0010005 b = Consistent under H0 and Ha; obtained from xtreg
B = Inconsistent under Ha, efficient under H0; obtained from xtreg
Test of H0: Difference in coefficients not systematic chi2(11) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 17.79
Table 12 REM regression model with ROE
ROE Coefficient Std err z P> |z| [95% conf interval]
OC -0.184*** 0191512 -9.60 0.000 -.2213775 -.1463061 DTA -0.299* 0478304 -0.27 0.786 -.1067344 0807573 NPL -0.299* 1261675 -2.37 0.018 -.5467586 -.0521912 INF 0.204* 099987 2.04 0.042 0076259 3995678 COVID -.000666 0047939 -0.14 0.890 -.0100613 0087303 _cons -1.065*** 1745151 -6.10 0.000 -1.406841 -.722755 sigma_u 02764464 sigma_e 02940843 rho 46911465 (fraction of variance due to u_i)
Table 13 Test results for heteroskedasticity and autocorrelation in ROE model
Heteroskedasticity Test: Var(u) = 0 chibar2(01) = 226.65 Prob > chibar2 = 0.0000
Autocorrelation H0: no first-order autocorrelation
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