INTRODUCTION
Reasons for choosing the study
In the banking industry, conventional wisdom suggests that diversification enhances financial performance by minimizing volatility in returns Beginning in the 2000s, commercial banks globally have increasingly diversified their operations, driven by competitive pressures and the allure of profits from financial investments (DeYoung & Roland, 2001) This intense competition has prompted banks to transition from reliance on interest income to seeking non-interest income, aiming to sustain their market position and boost profitability.
The Covid-19 pandemic has significantly impacted the global economy and disrupted business operations across various sectors, including banking In Vietnam, where banks primarily rely on over-the-counter transactions, the pandemic has posed challenges to transaction activities, ultimately leading to a decline in profits for these financial institutions.
Vietnam's banking sector faces challenges due to stringent legal barriers imposed by the State Bank, which has enforced strict regulations to oversee commercial banks' credit activities In response to lending difficulties, banks are increasingly adopting income diversification strategies to lessen their reliance on traditional credit operations and mitigate risks, particularly liquidity risk.
The impact of income diversification on banks' financial performance is debated, with two opposing views Proponents argue that income diversification enhances bank income and reduces cyclical risks, thereby lessening reliance on financial market fluctuations, as supported by studies from Elsas et al (2010), Gurbuz et al (2013), Lee et al (2014), and Moudud-Ul-Huq et al (2018) Conversely, critics, including DeYoung & Rice (2004), Stiroh & Rumble (2006), and Lepetit et al (2008), contend that income diversification can diminish profits and elevate risks due to conversion costs associated with diversifying activities.
In Vietnam, various authors have explored the effects of income diversification on banks' financial performance, leading to two contrasting perspectives Research by Minh & Canh (2015), Vinh & Mai (2015), and Hau indicates one viewpoint on this relationship.
Research by Quynh (2016) indicates that income diversification enhances income and positively influences the financial performance of commercial banks Conversely, studies by Vinh & Mai (2015) and Hau & Quynh (2017) suggest that income diversification can lead to decreased income and negatively impact the financial performance of Vietnamese commercial banks, while also elevating associated risks.
Empirical studies on income diversification in Vietnamese commercial banks reveal a lack of consensus among researchers regarding its impact on financial performance Consequently, there is no definitive conclusion about the advantages of income diversification for banks Additionally, economic fluctuations have led to outdated data and a limited research scope over time and space.
Therefore, the research topic "THE IMPACT OF INCOME
DIVERSIFICATION ON THE FINANCIAL PERFORMANCE OF
The study of "Commercial Banks in Vietnam" is essential for enhancing theoretical understanding and providing empirical evidence on how income diversification influences financial performance in these banks It also aims to identify the factors that affect income diversification within Vietnamese commercial banks This research will equip these institutions with the necessary insights to adopt effective income diversification strategies, ultimately improving their financial performance.
Research objectives
This article explores how income diversification affects the financial performance of Vietnamese commercial banks It aims to provide actionable insights for bank managers to optimize benefits through effective income diversification strategies.
From the above general objectives, the author has given some specific goals:
Firstly, identifying and evaluating the impact of income diversification on financial performance based on quantitative analysis models
The study will identify key solutions and recommendations aimed at improving the financial performance of commercial banks in Vietnam through enhanced income diversification activities.
Research questions
What is the correlation between income diversification and financial performance of Vietnamese commercial banks?
In addition to income diversification, what factors affect the financial performance of Vietnamese commercial banks?
Which are the optimal solutions to improve the financial performance of Vietnamese commercial banks?
The research subject and scope of study
The impact of income diversification on financial performance of commercial banks in Vietnam
The data for the research is collected from 2012 to 2022 The author chose to conduct the survey during this period because the commercial banking system in
Vietnam's commercial banks faced significant fluctuations in financial performance due to the America-China trade war in 2018, followed by a recession, and the impacts of the Covid-19 pandemic in 2020-2021.
The authors analyze the financial performance of 26 out of 31 joint-stock commercial banks in Vietnam, focusing on those listed on the stock market This selection is due to the availability of comprehensive and censored financial data, allowing for a thorough analysis Furthermore, these banks represent a significant portion of the Vietnamese banking industry, making their performance indicative of the overall banking system.
Research methodology
To solve the research objectives, this study combines both research methods, which are qualitative research methods and quantitative research methods
The author will employ qualitative research methods, including aggregation, statistics, description, comparison, and analysis, to systematically explore the theoretical frameworks linking technology investment and financial performance in Vietnamese commercial banks Additionally, a review of previous studies will provide a foundation for developing research models and hypotheses.
The author employs quantitative methods for regression analysis, utilizing Pooled OLS, Fixed Effects Method (FEM), and Random Effects Method (REM) to evaluate various factors An F-test is conducted to determine whether to use OLS or FEM, followed by a Hausman test to distinguish between FEM and REM Finally, the Breusch-Pagan test is applied to choose between OLS and REM, ensuring a robust analysis.
After choosing an appropriate model, it is essential to test for autocorrelation and variance changes If defects are identified, the author will apply Feasible Generalized Least Squares (FGLS) to address these issues Additionally, the study employs the Generalized Method of Moments (GMM) to tackle endogeneity, variance, and autocorrelation, allowing for a comprehensive comparison of the results with the research model that examines factors influencing financial performance.
Contributions of the research
This study presents updated spatial and temporal data, making it more current than previous research Utilizing FGLS and GMM methods, it accurately assesses the impact of income diversification on the financial performance of commercial banks in Vietnam.
The study offers valuable policy recommendations that equip banking policymakers with a comprehensive understanding of how income diversification can enhance the stability of banking and financial performance This information serves as a resource for researchers in the banking sector, encouraging further exploration of related topics.
Disposition of the dissertation
The structure of the research project consists of 5 chapters:
This chapter outlines the thesis content, detailing the rationale behind the topic selection, research objectives, questions, and scope It also discusses the research methods and data utilized, highlights the contributions of the topic, and presents the overall layout of the thesis.
Chapter 2 Theoretical framework and review of previous experimental studies
This chapter explores key concepts related to the research problems, focusing on income diversification and its impact on financial performance It also examines the theoretical framework and relevant empirical studies from both international and Vietnamese contexts to identify research gaps and establish a foundation for the research model presented in the subsequent chapter.
This chapter establishes a research model by outlining the chosen framework It details the selected variables within the model, describes the methods for data collection, and explains the sequence of model implementation.
Chapter 4 Empirical results and discussions
This chapter outlines the quantitative analysis steps applied to a secondary dataset derived from commercial banks, utilizing econometric methods on observed samples The findings from this data analysis will be discussed, including comparisons with results from other pertinent empirical studies.
Chapter 5 will offer recommendations for bank managers aimed at enhancing the financial performance of Vietnamese commercial banks, based on the findings from Chapter 4 Additionally, this chapter will discuss the limitations of the thesis and suggest directions for future research.
In Chapter 1, the author outlines the rationale behind selecting the topic "The Impact of Income Diversification on the Financial Performance of Commercial Banks in Vietnam." The chapter details the research objectives, formulates key research questions, and defines the research scope and methods, including data sources Additionally, it highlights the contributions of the study and presents the overall structure of the topic.
CHAPTER 2 THEORETICAL FRAMEWORK AND REVIEW OF
Review of income diversification
Ansoff (1957) defined "diversification" as a shift in a company's product line or market, distinguishing it from strategies like market penetration, market development, and product development In the banking industry, diversification involves banks expanding their core business, which primarily includes interest income and traditional services, into new product and service markets.
Income diversification in banks, as defined by Rose & Hudgin (2008), involves expanding the range of financial products and services to boost the share of non-interest income in overall bank revenue This approach contrasts with the income concentration strategy, which relies heavily on interest income By shifting focus from traditional credit activities to non-traditional revenue streams such as service fees and commissions, banks can enhance their income diversification strategy.
Research by Vinh (2016) and Minh & Canh (2015) highlights that income diversification in banks involves a rise in non-interest income and a corresponding decline in interest income Consequently, as banks expand their income sources, they experience an increase in non-interest revenue.
Commercial banks are increasingly diversifying their income streams by shifting from traditional activities, such as earning interest from deposits and loans, to generating fees and engaging in non-traditional activities like investments This strategy aims to enhance the share of non-interest income in the overall revenue of the bank (Elsas et al., 2010).
Diversification of a bank's income involves enhancing revenue through non-traditional business activities, thereby decreasing reliance on conventional banking services Today, commercial banks increasingly engage in popular non-traditional segments such as import and export payments, foreign exchange operations, securities investment brokerage, e-banking, and Bancassurance, which combines agency commissions with insurance services.
2.1.2 The benefits of income diversification on financial performance of commercial banks
Since the late 20th century, banks globally have embraced income diversification to mitigate the inherent risks of credit activities This strategy not only stabilizes profits but also enhances customer attraction through a broader range of products and services, leading to increased non-interest income Additionally, the integration of modern, technology-driven offerings allows banks to better meet customer needs, thereby strengthening their reputation, brand, and market position.
In 2022, the significance of non-interest income became evident as rising interest rates impacted banks' interest income Non-interest income sources, such as fees and commissions, insurance fees, foreign currency trading, and trading securities, play a crucial role in banks' revenue streams A higher proportion of non-interest income enables banks to diversify their income, promoting more sustainable and stable long-term growth.
Income diversification in banks is primarily assessed using the ratio of non-interest income and the Herfindahl-Hirschman index, as noted by Asif & Akhter (2019) Their research indicates that 44% of studies utilize the non-interest income ratio, while 29% employ the Herfindahl-Hirschman index for this measurement.
Measure income diversification through net non-interest income ratio
Banks generate income from two primary sources: interest income and non-interest income Interest income comprises earnings from loans and other interest-bearing assets, while non-interest income includes service fees, commission revenue, business income, investment revenue, and other miscellaneous earnings Thus, non-interest income plays a significant role in a bank's overall financial performance, reflecting diverse revenue streams beyond traditional interest earnings.
Bank income, as defined by Decree 93/2017/ND-CP and Circular 16/2018/TT-BTC, encompasses various sources including interest and similar incomes, fee and commission income, gains from foreign currency trading, revenue from securities trading, and income from capital contributions or share transfers The primary components of interest income consist of deposit interest, loan interest, profits from debt securities, and income generated from guarantee operations and financial leasing Additionally, non-interest income arises from service activities, foreign exchange and gold trading, and other related activities, highlighting the diverse revenue streams within the banking sector.
Net interest income = Interest income – Interest expense
Net non-interest income = Non-interest income – Non-interest expense
= Net fee and commission income + Net gain/(loss) from investment securities + Net other income
Total net income = Net interest income + Net non-interest income
Vietnamese commercial banks' financial statements reveal that total net income encompasses various components, including net interest income, net fee and commission income, and net gains or losses from foreign currency and securities trading Additionally, it includes net income from other activities and capital contributions or share purchases Consequently, the study calculates the net non-interest income of these banks by considering these diverse income sources.
Net non-interest income is calculated by summing net fee and commission income, net gains or losses from foreign currency trading, trading securities, and investment securities, along with net income from other activities and capital contributions or share purchases.
Measure income diversification through Herfindahl Hirschman index
Previous studies by Stiroh & Rumble (2006), Chiorazzo et al (2008) calculate the income diversification index (DIV) through the Herfindahl Hirschman index according to the following formula:
INT is the ratio of net interest income to total operating income
NON is the ratio of net non-interest income to total operating income.
Review of financial performance
Financial performance is used in socio-economic studies as well as in the financial management of businesses or individuals Financial performance can be approached from different levels:
Performance, as defined by Farrell (1957), refers to the capacity of an entity to optimize its output relative to the input costs incurred Essentially, it represents the advantages derived from targeted activities.
Economic performance, as defined by Minh (2004), refers to the relationship between limited resources (inputs) and the goods and services produced (outputs) This concept evaluates how effectively markets distribute resources In the banking sector, performance measures the success of banks in efficiently allocating inputs and outputs to achieve specific objectives.
Financial performance in commercial banks, as noted by Berger & Mester (1997), is determined by the balance between revenue and resource costs, highlighting the effectiveness of converting inputs into outputs A bank is deemed efficient when it maximizes output through the optimal utilization of its available resources.
The evaluation of a bank's financial performance varies among researchers and relies on the data sources utilized This study examines the financial performance of commercial banks by analyzing the correlation between their business outcomes and the costs incurred in their operations.
According to Berger & Humphrey (1997), the financial performance analysis of banks typically employs three primary methods: financial ratio analysis, marginal performance analysis, and the CAMELS safety framework.
The method of using financial ratios to measure financial performance
Using financial ratios to assess the performance of commercial banks remains a widely accepted and straightforward approach, as highlighted by studies from Minh & Canh (2015) and Chiorazzo et al This method's simplicity and ease of understanding contribute to its popularity in the financial analysis of banking institutions.
Lee et al (2014) and Quyen et al (2021) highlight the use of Return on Assets (ROA) and Return on Equity (ROE) ratios as effective metrics for assessing the financial performance of banks, primarily due to the accessibility of reliable data from audited financial statements.
Return on asset: ROA = Net income
Return on Assets (ROA) is a key indicator of a bank's management efficiency, reflecting its ability to transform assets into net income (Rose & Hudgin, 2008) A higher ROA signifies greater profitability and adaptability to economic changes; however, it may not always indicate effective asset utilization, as it can result from underinvestment, leading to declining asset values and adversely affecting long-term performance Moody's standards suggest that a ROA of 1% or higher indicates good profitability, while the CAMEL framework identifies a ROA of 1.5% or more as optimal for banking efficiency (Rozzani & Rahman).
Return on equity: ROE = Net income
Return on Equity (ROE) is a key indicator of a bank's profitability relative to its equity, with higher ratios indicating more efficient capital utilization However, an elevated ROE may also result from a bank's strategy of lowering equity proportions while increasing loan capital, which can introduce liquidity and default risks According to Moody's standards, a ROE of 12% to 15% signifies good profitability in the banking sector, while in Vietnam, a ROE of 14% to 17% is deemed favorable (Dat & Tam, 2016) The CAMEL criteria suggest that banks achieve optimal efficiency with a ROE of 22% or higher (Rozzani & Rahman).
Net interest margin: NIM =Interest revenue – Interest expense
Net Interest Margin (NIM) reflects a bank's efficiency in mobilizing and lending by managing profitable assets and securing low-cost capital sources This ratio indicates the capability of bank management to sustain growth in interest income while balancing interest expenses effectively.
Marginal efficiency analysis is a technique used to assess the relative efficiency of banks by comparing their performance to that of the best-performing units on the efficiency frontier This analysis identifies how well a bank operates in relation to its peers The two primary methods for empirically evaluating bank financial performance through frontier efficiency analysis are parametric and non-parametric approaches (Sang, 2014).
The parametric approach to efficiency analysis, as outlined by Berger & Humphrey (1997), encompasses three primary methods: the Stochastic Frontier Approach (SFA), the Thick Frontier Approach (TFA), and the Distribution Free Approach (DFA) In contrast, non-parametric methods include Data Envelopment Analysis (DEA) and Free Disposal Hull (FDH).
The parametric approach necessitates defining a specific relationship or function between inputs and outputs However, accurately identifying the bank's input resources poses a challenge due to the diverse nature of its business activities across various fields.
It is because of this complexity that the parametric method is rarely used in analysis in Vietnam in general and in particular for the banking system (Sang, 2014)
The CAMEL Rating System, developed by the National Credit Union Administration (NCUA) and endorsed by the IMF and World Bank, evaluates the health of financial institutions and the overall financial system In Vietnam, the system was officially recognized following the establishment of the Regulation on Co-Financing by Credit Institutions in 1998 The CAMEL framework assesses six key factors: Capital Adequacy, Asset Quality, Management, Earnings, and Liquidity, providing a comprehensive analysis of an institution's financial stability.
Research by Asif & Akhter (2019) indicates that the financial performance of banks is primarily assessed through metrics such as Return on Equity (ROE), Return on Assets (ROA), or the CAMEL model Given the specific focus of this study and the constraints in data collection, it will utilize the ROE financial ratio to evaluate the performance of Vietnamese commercial banks.
Factors affecting the financial performance of commercial banks
The financial performance of commercial banks is shaped by various factors, which can be categorized into two main groups: micro factors and macroeconomic factors, as highlighted by previous empirical studies.
2.3.1 The internal (bank-specific) factors
Income diversification theories examine the advantages and disadvantages of a bank's growth strategy, highlighting that diversification enhances financial performance, particularly as a bank's operations expand This approach also mitigates cyclical volatility, fostering competitive pressure that compels banks to innovate and improve efficiency in their service offerings (Lepetit et al., 2008) Numerous studies, both domestically and internationally, demonstrate a positive correlation between income diversification and financial performance, as evidenced by research from Chiorazzo et al (2008), Lee et al (2014), and Quyen et al (2021).
Contrary to some findings, several studies indicate that income diversification can negatively impact banks' financial performance DeYoung and Roland (2001) suggest that banks focusing more on fee-generating activities than lending may alienate customers, leading to decreased profits Furthermore, the inherent volatility of certain revenue streams, coupled with lower customer switching costs and higher operating leverage due to necessary investments in fixed assets like technology, can result in increased earnings volatility and heightened risk for banks This perspective is supported by research from DeYoung and Rice (2004), Stiroh (2004), and Mercieca et al (2007).
The Vietnamese banking system is increasingly focusing on the development of non-credit services as a strategic approach to enhance the structure of business outcomes in the industry (Oanh & Thuy, 2022) A key aspect of this strategy is income diversification, which plays a vital role in the restructuring of financial activities for credit institutions, as outlined in the project "Restructuring the Credit Institution System for the 2011-2015 Period" under Decision No 254/QD.
TTg, and continues to be included in the content of the project "Restructuring the credit institution system associated with handling of non-performing loans for the 2021 –
2025 period" according to Decision No 689/QD-TTg by the State Bank
Income diversification is crucial for Vietnamese commercial banks during the challenging economic landscape shaped by the Covid-19 pandemic As banks encounter difficulties in their credit activities, revenue generated from non-credit operations becomes essential for stabilizing income and enhancing overall financial performance.
Increased scale is crucial for banks to enhance their financial potential, competitiveness, and overall performance The theory of economies of scale and scope indicates that larger banks enjoy cost advantages over smaller ones, leading to higher profits and significant improvements in financial performance (Berger et al., 1987; Fu & Heffernan, 2008) Additionally, larger banks tend to exhibit greater stability as individual risks diminish with size, enabling them to invest in modern technology and expand their operations (Meslier et al., 2014) Supporting studies, including those by DeYoung & Rice (2004), Chiorazzo et al (2008), and Vinh (2016), reinforce this perspective Conversely, small banks may leverage a broad range of small mobilizations and economies of scope to mitigate the drawbacks of lacking economies of scale, thereby also enhancing their financial performance (Miller & Noulas).
1997) However, research by Meslier et al (2014) showed a negative relationship between bank size and financial performance
Equity to total assets ratio (CAP)
The financial leverage of a bank is indicated by its capital index, which reflects its strength and market position, particularly in its ability to withstand economic fluctuations A higher capital adequacy ratio correlates with reduced risks and improved financial performance, as banks with substantial capital are less reliant on external financing Research by Stiroh & Rumble (2016) and Hau & Quynh (2017) demonstrates a positive relationship between equity and total assets in relation to financial performance, while Anbar & Alper (2011) found no significant connection.
Deposits to total assets ratio (DTA)
The main source of funding for banks' business is customer deposits because it is more stable and cheaper than other sources of funding Research results of Lee et al
(2014) show that deposits to total assets ratio (DTA) has a positive impact on financial performance of banks In contrast, the empirical research results of Hau & Quynh
(2017) indicate that DTA has an inverse relationship with the financial performance of the bank
Loans to total assets ratio (LTA)
An increase in an illiquid portfolio can heighten a bank's exposure to the risks associated with its customers' businesses However, previous research indicates that income from lending activities provides banks with a stable revenue stream, as customers typically maintain their lending relationships Consequently, banks tend to prioritize financial resources for lending purposes Thus, a higher ratio of loans to total assets correlates with improved financial performance for banks.
Provision for loans to customers to total assets (LLP)
The provision for loans to customers serves as a key indicator of credit risk in banking A lower credit risk correlates with a reduced provision for loans to customers relative to total assets, which in turn enhances the bank's financial performance This relationship is supported by the findings of Bikker & Metzemakers.
(2005), the higher the provision that a bank will use to cover expected losses, the higher the credit risk that will increase costs and reduce the bank's profits Busch et al
(2009), Vinh (2016) argue that credit risk has a negative relationship with financial performance Meanwhile, the study of Lee et al (2014) concluded that credit risk positively affects the financial performance of banks
A challenging economic environment can negatively impact the quality of a bank's loan portfolio, leading to higher credit risk provisions and diminished profitability Conversely, stable economic growth enhances bank profitability Research by Meslier et al (2014) and Trang (2021) demonstrates a positive correlation between economic growth and bank profitability.
Inflation is measured based on the Consumer price indices (CPI) index Revell
The relationship between inflation and profitability is complex, as highlighted by various studies Research from 1979 indicates that the impact of inflation on a bank's profitability is influenced by its effect on wages and operating costs High inflation can adversely affect the economy, leading to increased expenses that outpace income, thereby reducing bank profits Supporting this view, Ongore & Kusa (2013) and Ayaydin & Karakaya (2014) found that inflation negatively impacts financial performance Conversely, Bourke (1989) identified a positive relationship between inflation and financial performance, suggesting that the effects of inflation can vary across different contexts.
An overview of previous studies
Chiorazzo et al (2008) examined the impact of income diversification on profitability using annual data from Italian banks between 1993 and 2003 Their regression analysis, employing both fixed and random effects, revealed that income diversification enhances risk-adjusted returns Furthermore, the study highlights a correlation between income diversification and bank size, indicating that smaller banks benefit more from income diversification This is because non-interest income represents a smaller share of their total income, thus an increase in non-interest income significantly boosts their financial performance.
A study by Busch et al (2009) examines the factors influencing non-interest income and its effects on the financial performance and risk of German banks from 1995 to 2007 The findings indicate that higher fee income activities correlate with improved risk-adjusted returns on equity and total assets for universal banks in Germany Conversely, for commercial banks, an increase in fee-based business is associated with greater volatility in return on equity (ROE) and return on assets (ROA), leading to heightened risk for these institutions.
Lee et al (2014) investigated the effects of income diversification on the financial performance of banks across 22 Asia-Pacific countries, analyzing data from 967 banks between 1995 and 2009 The study found that diversifying beyond interest income enhances bank profitability and reduces risk, as operating income and additional revenue contribute positively to performance However, the findings indicate that revenue generated from commissions and fees does not significantly enhance banking operations.
Moudud-Ul-Huq et al (2018) examine the effects of income and asset diversification on the performance and risks of commercial banks in emerging Southeast Asian nations, including Indonesia, Malaysia, Thailand, the Philippines, and Vietnam, during the period from 2011 to 2015 using the GMM method Their findings reveal that diversification offers varying advantages for each bank, with revenue diversification positively influencing bank performance and stability, while the effects of asset diversification differ by country Overall, the study concludes that diversification strategies can enhance the benefits for commercial banks in the analyzed regions.
Deyong & Rice (2004) determine the relationship between non-interest income and financial performance of 4,712 banks in the US in the period 1989 - 2001
Research results show a negative relationship between non-interest income and financial performance of these banks through regression and General Least Square (GLS) estimation
Stiroh (2004a) conducted research on American community banks in the period
From 1984 to 2000, research indicates that small banks experience a decline in performance when they diversify their income streams, resulting in lower profits and heightened risks In contrast, large banks benefit from income diversification, which enhances their financial performance and mitigates risks The increased risk associated with non-interest income in small banks is attributed to their limited management experience compared to their larger counterparts.
Stiroh (2004b) analyzed the US banking system from 1984 to 2001 and identified a significant correlation between the growth rates of net interest income and non-interest income He noted that the decline in operating income was primarily attributed to the greater volatility of non-interest income compared to interest income Stiroh argued that the stability of income volatility stems from the consistent nature of net interest income rather than the advantages of income diversification, suggesting that non-interest income negatively affects bank profitability Consequently, he concluded that banks derive minimal benefits from income diversification.
A study by Luu et al (2019) examined the effects of income diversification on the financial performance of 39 commercial banks in Vietnam from 2007 to 2017, utilizing a two-step system GMM method The findings indicate that income diversification positively influences the financial performance of the Vietnamese banking sector, although the impact varies by bank type Specifically, state-owned and foreign banks benefit from income diversification, while it negatively affects the financial performance of nonstate-owned domestic banks.
In addition, the authors also found that for banks with more experience in the market, the effect of income diversification on financial performance is stronger
Quyen et al (2021) conducted a study on income diversification and financial performance with data collected from 29 Vietnamese commercial banks in the period
Between 2005 and 2018, a study utilizing GMM regression revealed that income diversification does not significantly affect the financial performance of Vietnamese commercial banks under normal economic conditions However, it does have a notable positive impact during economic crises Furthermore, the findings indicate that larger banks and state-owned banks can effectively leverage income diversification strategies to enhance their profitability.
In a study conducted by Vinh (2016), the impact of income diversification on the profitability and risks of Vietnamese commercial banks was analyzed, utilizing data from 37 banks between 2006 and 2013 The findings indicate that income diversification positively influences the profitability of these banks; however, it also leads to increased risk as the level of diversification rises.
Son et al (2020) investigated the impact of income diversification on the performance of Vietnamese commercial banks by analyzing audited financial statements from 30 banks over the period of 2010 to 2017, resulting in a panel data set comprising 231 observations The findings indicate that income diversification positively influences both Return on Assets (ROA) and Return on Equity (ROE) However, while non-interest income diversification negatively affects risk-adjusted return on assets, it positively impacts risk-adjusted return on equity.
Hau & Quynh (2017) analyzed financial statements from 26 Vietnamese commercial banks between 2006 and 2016 to explore the link between non-interest income and business performance Their findings, derived from fixed effects (FEM) and random effects (REM) models, indicate a positive correlation between non-interest income and both overall business performance and risk-adjusted performance The study suggests that enhancing income from service, business, and investment activities leads to increased profitability for these banks.
A study by Minh & Canh (2015) examined income diversification and its impact on the profitability of Vietnamese commercial banks, analyzing data from the financial statements of 22 banks between 2007 and 2013 using GMM estimation panel data methods The findings revealed that factors such as the income diversification index, the ratio of loans to total assets, the ratio of deposits to total assets, and the inflation rate positively influenced bank profitability Conversely, the non-performing loan ratio, the equity to total assets ratio, and the operating expense ratio negatively affected profitability Notably, the study found no significant evidence linking total asset size and economic growth rate to the profitability of these banks.
Table 2.1 Summary of related studies
Income diversification and Bank performance:
SDROA, SDROE Income diversification, Bank size, Equity to total assets ratio, Ratio of loans to total assets, Asset quality, Growth rate, Non-interest income ratio
Income diversification increases a bank's risk-adjusted returns and operational performance
The smaller the size, the greater the advantage of income diversification
Income diversification in the German banking industry GMM
Return on Equity (ROE) and Return on Assets (ROA) are critical metrics for assessing a bank's profitability, while Standard Deviation of ROA (SDROA) and Standard Deviation of ROE (SDROE) provide insights into financial stability Income diversification plays a vital role in mitigating risks associated with Non-Performing Loans (NPLs) Additionally, factors such as bank size, growth rate, and the capital to asset ratio significantly influence overall performance The ratio of loans to total assets and the provision for loans to customers relative to total assets are essential for understanding asset quality Finally, monitoring the operating expenses ratio is crucial for maintaining efficiency and profitability in banking operations.
Risk-adjusted returns are positively affected by non-interest activities
Non-interest activities create higher risks for banks
Impact of income diversification on the
ROA, ROE, SDROA, SDROE, Zscore, Bank-level ratio of loan loss reserves to non-performing loans, Bank-level ratio of non-performing loans to total loans,
A bank cannot diversify by simply entering commissions
Liberalizing international capital inflow and privatization performance of Asian banks from 1995 to
Bank-level ratio of non-performing loans to equity
Gross interest revenue, Net commission revenue, Net trading revenue, All other net revenue, Div index have a negative impact on revenue diversification and reduce the bank's operational performance
The impact of diversification on bank performance and risk tolerance for Southeast Asian economies
ROA, ROE, NIM, EFF, Z-score
Interest income, commission income, trading income, and other income are crucial components of financial performance Key metrics such as capitalization ratio, the natural logarithm of total assets, and the growth rate of total assets provide insights into a company's stability and growth potential Additionally, the ratio of liquid assets to total assets and the ratio of total debt to total assets are essential indicators of liquidity and financial leverage.
Banks that engage in diversification have high financial performance and reduce risk
Revenue diversification brings more benefits to banks than asset diversification
Different diversification strategies will affect different types of banks
Noninterest income and financial performance at U.S
ROE, Volatility of ROE Lending strategy, Loan quality, Size, Growth rate, Type of ownership, Number of ATMs
Non-interest income has a negative relationship with risk- adjusted financial performance
Large banks diversify more slowly than small banks
The effect of noninterest income in banks
ROA, ROE, SDROA, SDROE, Zscore Non-interest income ratio, Capital adequacy ratio, Size, Ratio of loans to total assets, Growth rate, Number of years of operation
The growth rates of net interest income and non-interest income are correlated in US banks
A bank's greater reliance on non-interest income, especially transaction revenue, will result in higher risk and lower risk- adjusted returns
The benefit from diversification for community banks
ROE, SDROE, Zscore Capital adequacy ratio, Size, Growth rate, Ratio of each type of non-interest income, Number of years of operation
Increasing non-interest activities contributes to reducing the effectiveness of the bank's risk management
Differences in the relationship between risk-adjusted returns and business line focus for community banks
Income diversification GMM ROE, ROA
Income diversification has a and financial performance of commercial banks in
Vietnam: Do experience and onwership structure matter)
Loan Quality, Equity, Asset Growth positive impact on a bank's financial performance, but the extent of the impact depends on the ownership structure and operating experience of the bank
Income diversification and financial performance:
The mediating effect of banks size, Ownership Structure, and the Financial
ROA, ROE Size, Credit risk, Equity, Liquid, Cost, Diversification, Growth
There is no direct effect of income diversification on financial performance, however the indirect effect is clear
A bank's size, credit risk, liquidity, and growth positively affect its profitability
3 Vinh Profit and risk FEM, ROA, ROE, Zscore ROAA, ROEA Income diversification has a
(2016) from income diversification of Vietnamese commercial banks
Income diversification, Ratio of loans to total assets, Size, Asset growth rate, Ratio of deposit to total assets, Loan growth rate positive impact on the financial performance of banks
Diversific ation and operational efficiency of Vietnamese commercial banks
ROA, ROE Asset Quality, Liquidity Ratio, Equity to total asset ratio, Cost Efficiency, Size, GDP, INF
Income diversification has a positive effect on return on assets
(ROA), while asset diversification has a positive effect on return on equity (ROE) of banks
Impact of non- interest income on business performance of Vietnamese commercial banks in the period 2006 -
ROA, ROE Size, Loan, Equity, Cost of Deposit to Total Assets Ratio, Customer Deposits to Total Liabilities, GDP, INF
Increasing non-interest income increases business performance
Increasing the size of equity will benefit the bank's operations
Income diversification and factors affecting profitability of Vietnamese commercial banks
ROA, ROE Asset structure, Asset quality, ratio of equity to total assets, Funding structure, Cost effectiveness, Size, GDP, INF
Diversifying income helps increase profitability of Vietnamese commercial banks
Stable funding structure will help increase ROA and ROE Conversely, declining asset quality, high equity ratio and low operating performance will reduce the bank's profitability
Source: Statistics from the author
Research indicates that both micro and macroeconomic factors significantly influence the financial performance of commercial banks The impact of income diversification and other determinants on banks' financial outcomes varies according to the prevailing economic conditions and the specific economic policies of each country.
RESEARCH MOTHODOLOGY
Implementation process
The study was conducted according to the following steps:
Step 1 Identify the research problem
The author identifies the research problem as the impact of income diversification on the financial performance of Vietnamese commercial banks
Step 2 Overview the theoretical foundations and empirical studies
Based on theoretical frameworks and empirical research, the author examines prior studies and develops an appropriate research model to assess how income diversification affects the financial performance of commercial banks in Vietnam.
Step 3 Analyze the impact of income diversification on bank financial performance
This study examines the effect of income diversification on the financial performance of Vietnamese commercial banks Utilizing a proposed research model, the analysis employs various quantitative methods, including Pooled-OLS, FEM, REM, FGLS, and GMM models, to estimate the relationship between income diversification and financial performance, alongside other independent variables.
To ensure transparent research results, the author conducts related tests such as testing for multicollinearity, autocorrelation, variable variance, and endogeneity
Step 5: Regression analysis and discussion of research results
The thesis presents research results on the impact of income diversification on financial performance of Vietnamese commercial banks, and discusses and compares the results with related empirical studies
Based on the research results, the author makes conclusions and recommendations to improve the level of income diversification and financial performance of Vietnamese commercial banks.
Research model
Research indicates that several key factors influence the financial performance of commercial banks, including income diversification, bank size, equity-to-total-assets ratio, deposits-to-total-assets ratio, loans-to-total-assets ratio, provisions for loans to customers relative to total assets, and the economic growth rate.
Inflation rate Therefore, the model of income diversification affecting financial performance at Vietnamese commercial banks is built as follows:
ROEit: Financial performance of bank i at year t
DIVit: Income diversification of bank i at year t
SIZEit: Bank size i at year t
CAPit: Ratio of equity to total assets i at year t
DTAit: Ratio of deposits to total assets i at year t
LTAit: Ratio of loans to total assets i at year t
LLPit: Provision for loans to customers to total assets i at year t
GDPt: Economic growth rate at year t
INFt: Inflation rate at year t
Research variables metrics
The ratio reflects a bank's performance in using equity and is calculated as profit after tax divided by equity, representing a measure of profitability per dollar of equity
Income diversification is considered based on the bank's income structure including interest income and non-interest income The study uses the HHI index
The Herfindahl-Hirschman Index (HHI) is utilized to assess the diversification of bank income, as highlighted in studies by Elsas et al (2010), Gurbuz et al (2013), and Chiorazzo et al (2008) The degree of diversification is determined using a specific formula that quantifies income sources within banking institutions.
INT: Ratio of net interest income to total income
NON: Ratio of net non-interest income to total income
Therefore, the income diversification formula can be rewritten:
NET: Interest income is measured by net interest income
Net non-interest income (NOI) is derived from various sources, including net fee and commission income, gains or losses from foreign currency trading, securities trading, investment securities, and other activities It also encompasses net income from capital contributions and share purchases.
NETOP: Net income is determined as the sum of net interest income and net non-interest income: NETOP = NET + NOI
If net non-interest income is negative, the study establishes the net non-interest income ratio at zero, indicating that non-interest activities do not contribute to net income (Minh & Canh 2015).
The size of a bank is determined by taking the natural logarithm of its total assets, measured in millions of VND, at the conclusion of the financial year, as noted by Gurbuz et al (2013) and Lee et al (2014).
Equity to total assets ratio (CAP)
Equity to total assets ratio is measured by the ratio of equity to total assets, data collected at the end of the financial year of commercial banks
Deposit to total assets ratio (DTA)
The ratio of deposits to total assets is determined by dividing customer deposits by total assets, using data sourced from the bank's audited financial statements at the close of the financial year.
Loan to total assets ratio (LTA)
The loan to total assets ratio is calculated by comparing outstanding loans to total assets, utilizing data gathered from the bank's audited financial statements at the end of the financial year.
Provision for loans to customers to total assets ratio (LLP)
The credit risk of a bank is assessed by evaluating the ratio of provisions for customer loans to total assets, based on data gathered from the bank's audited financial statements at the end of the financial year.
LLP =Provision for loans to customers
The annual rate of economic growth is measured by the annual growth rate of gross domestic product (GDP) Data were collected from General Statistics Office
The inflation rate is measured by the annual growth rate of the consumer price index (CPI) Data collected from General Statistics Office of Vietnam
Research hypotheses
Income diversification theories examine the advantages and disadvantages of a bank's growth strategy, highlighting that diversification can enhance financial performance and reduce cyclical volatility as banks expand their operations This competitive pressure compels banks to innovate and improve their services (Lepetit et al., 2008) Numerous studies, including those by Chiorazzo et al (2008), Lee et al (2014), and Quyen et al (2021), indicate a positive correlation between income diversification and financial performance Conversely, other research suggests a negative impact, as certain revenue streams may be more volatile, customer switching costs are lower, and higher operating leverage from fixed asset investments can lead to increased earnings volatility and risk for banks (DeYong & Roland, 2001; DeYoung & Rice, 2004; Stiroh, 2004; Mercieca et al.).
2007) Based on the theoretical basis and some previous studies, the author supports the view that income diversification helps to increase the financial performance of banks
H1: Income diversification has a positive impact on financial performance
Large banks are often perceived as more stable due to the reduction of individual risks associated with their size, allowing them to invest in modern technology and expand their operations (Meslier et al., 2014) Supporting this perspective, research by DeYoung & Rice (2004), Chiorazzo et al (2008), and Vinh (2016) also highlights the advantages of larger banking institutions However, Meslier et al (2014) found a negative correlation between bank size and financial performance Recent studies have increasingly incorporated diversification as a variable (Stiroh 2004a, Stiroh 2004b) Overall, drawing from existing theories and research, it is argued that larger banks positively influence financial performance.
H2: Bank size has a positive impact on the financial performance of the bank
The equity to total assets ratio serves as a key indicator of a bank's financial leverage, reflecting its strength and resilience in the financial market, particularly during economic fluctuations Banks with a higher equity to total assets ratio typically experience lower risks and enhanced financial performance, as they are less reliant on external financing Research by Stiroh & Rumble (2016) and Hau & Quynh (2017) supports the positive correlation between this ratio and financial performance, while Anbar & Alper (2011) found no significant relationship Nonetheless, based on theoretical foundations and empirical evidence, it is concluded that a higher equity to total assets ratio correlates with improved financial performance.
H3: Equity to total assets ratio has a positive relationship with financial performance of commercial banks
Customer deposits serve as the primary funding source for banks, offering a more stable and cost-effective alternative compared to other funding methods Consequently, a higher ratio of customer deposits correlates with improved financial performance for banks, as demonstrated by research conducted by Lee et al.
Research from 2014 demonstrates a positive correlation between the customer deposit to total assets ratio and the financial performance of banks However, Hau & Quynh (2017) found an inverse relationship between these two variables Despite these conflicting findings, the author argues that increased customer deposits lead to greater profitability for banks, aligning with both theoretical foundations and empirical evidence.
H4: Deposit to total assets ratio has a positive relationship with the financial performance of the bank
The bank's lending strategy significantly influences its financial performance and highlights variations in its asset portfolio While a rise in illiquid assets may heighten the bank's susceptibility to customer business fluctuations, previous studies indicate that income generated from lending activities provides a stable revenue stream, as customers tend to maintain consistent lending relationships Consequently, banks often prioritize the allocation of financial resources towards lending activities.
Research indicates a positive correlation between the loan-to-total-assets ratio and bank financial performance, as demonstrated by studies from Chiorazzo et al (2008) and Tuan (2022) This suggests that a higher ratio of loans to total assets leads to improved financial outcomes for banks Therefore, it can be concluded that an increased loan-to-total-assets ratio enhances overall financial performance.
H5: Loan to total assets ratio has a positive relationship with the financial performance of the bank
The loan provision to total assets ratio serves as a key indicator of a bank's credit risk and financial performance A lower ratio signifies reduced credit risk, leading to improved financial outcomes for the bank Research by Bikker & Metzemakers (2005) highlights that increased provisions for expected losses correlate with higher credit risk, which in turn elevates costs and diminishes profits Additionally, findings from Musch et al (2009) further support this relationship, emphasizing the importance of managing loan provisions effectively.
Research indicates a complex relationship between credit risk and financial performance While some studies, such as those by Lee et al (2014), suggest that credit risk can positively influence banks' financial performance, the prevailing view, supported by 2016 findings, is that high credit risk adversely impacts bank profitability Consequently, increased credit risk is associated with a decline in the overall financial performance of banks.
H6: Provision for loans to customers to total assets ratio has a negative relationship with the financial performance of the bank
During economic growth, banks typically reduce interest rates, leading to increased loan demand and higher service charges, which can enhance profitability Studies by Meslier et al (2014) and Trang (2021) indicate a positive correlation between GDP growth and bank profitability Conversely, Sanya & Wolfe (2011) found that economic growth negatively impacts banks' financial performance Additionally, research by Anbar & Alper also explores this complex relationship.
In 2011, research indicated that GDP does not significantly influence the financial performance of banks However, drawing on theoretical foundations and empirical studies, the author argues that economic growth positively enhances banks' financial performance.
H7: Economic growth rate has a positive relationship with the financial performance of banks
Inflation, measured by the CPI index, significantly influences bank profitability Revell (1979) highlighted that the effect of inflation on banks hinges on its impact on wages and operating costs High inflation can strain the economy, as banks often struggle to adjust interest rates promptly, leading to rising expenses that outpace income and consequently reducing profits Research by Ongore & Kusa (2013) and Ayaydin & Karakaya (2014) indicates a negative correlation between inflation rates and financial performance Conversely, studies by Bourke (1989) and Minh & Canh (2015) suggest a positive relationship, while Anbar & Alper (2011) found no significant link Ultimately, the consensus supports the notion that increased inflation adversely affects banks' financial performance.
H8: Inflation rate has a negative relationship with the financial performance of banks
Table 3.1 Statistics of expected signs of variables in the research model
Symbol Variable name Measurement method Source Expected sign
Busch et al (2009), Lee et al (2014), Minh
} Chiorazzo et al (2008), Gurbuz et al
SIZE Bank size log(Total assets) Stiroh (2014b), Sanya & Wolfe (2011),
Gurbuz et al (2013), Lee et al (2014),
CAP Equity to total assets ratio
Total assets Chiorazzo et al (2004), Stiroh (2004a),
Deposits to total assets ratio
Total assets Lee et al (2014), Minh & Canh (2015),
LTA Loan to total assets ratio
Loans to customers Total assets
Chiorazzo et al (2004), Meslier et al
Provision for loans to customers to total assets ratio
Provision for loans to customers
GDP t−1 Meslier et al (2014), Minh & Canh (2015),
INF Inflation rate INF =CPI t − CPI t−1
CPI t−1 Pham Gia Quyen et al (2021),
Source: Statistics from the author
The research data
Microeconomic data and macroeconomic data collected by the author are as follows:
Microeconomic data for this study is sourced from the audited consolidated financial statements of 26 Vietnamese commercial banks, which are available on the official websites of each institution A comprehensive list of these Vietnamese joint stock commercial banks can be found in the appendix of the thesis.
For macroeconomic data: the author summarizes data on General Statistics Office Vietnam
The research was conducted between 2012 and 2022, a period marked by economic recovery following the 2008-2009 global financial crisis This decade also witnessed significant global events, including the America-China trade war and the Covid-19 pandemic, which have profoundly impacted the world economy Consequently, the author selected this timeframe for the study to analyze these critical influences on economic development.
The research methodology
According to Gujarati (2004), a correlation coefficient exceeding 0.8 among independent variables may indicate high multicollinearity, potentially altering the signs of regression coefficients and skewing research findings To mitigate this risk, it's essential to assess the correlation coefficients of independent variables prior to running the regression model Additionally, checking for multicollinearity using Variance Inflation Factors (VIF) is crucial; a VIF value below 10 suggests that multicollinearity is not significantly impacting the model's estimation results.
This study utilizes Pooled Ordinary Least Squares (Pooled OLS) in conjunction with Fixed Effects Model (FEM), Random Effects Model (REM), Feasible Generalized Least Squares (FGLS), and Generalized Method of Moments (GMM) to investigate the influence of income diversification on the financial performance of commercial banks.
The Pooled-OLS model is suitable when there are no distinct bank-specific or time-related factors present In contrast, Fixed Effects Model (FEM) and Random Effects Model (REM) estimation methods account for both time and individual-specific factors, making them more appropriate for regression analysis To determine the best-fitting regression model among these three options, various statistical tests are employed.
F-Test: to choose Pooled OLS or FEM model When the P-value ≤5%, the FEM model is selected
Hausman test: to choose between FEM and REM models When the P-value
≤5%, the FEM model is selected and vice versa, P-value ≥5%, the REM model is selected
Breusch & Pagan test: to choose OLS and REM, when P-value ≤5%, choose REM model, otherwise choose OLS model
Upon selecting the appropriate model, if the REM model is chosen, the analysis will proceed based on its results Conversely, if the FEM model is selected, the study will continue with autocorrelation and heteroscedasticity assessments.
𝑯 𝟎 : The model does not have autocorrelation
According to Wooldridge (2002), if the P-value ≤5%, then reject the hypothesis
𝑯 𝟎 , that is the model has autocorrelation phenomenon
𝑯 𝟎 : The model does not have the phenomenon of Heteroscedasticity
𝑯 𝟏 : The model has the phenomenon of Heteroscedasticity
If the P-value is less than or equal to 5% (P-value ≤ 5), then the hypothesis H0 is rejected, meaning that the model has the phenomenon of Heteroscedasticity
The FGLS method effectively addresses autocorrelation and variable variance in models; however, its accuracy diminishes when lagged and endogenous variables are present To tackle these challenges more precisely, the GMM model is recommended as a superior alternative.
A P-value of 5% or less indicates that the null hypothesis (𝑯 𝟎) is supported, suggesting that the variable is endogenous Conversely, if we accept the null hypothesis, we conclude that the variable is exogenous.
GMM method is suitable for panel data, and overcomes the phenomenon of endogenous variables when the following conditions are met:
Firstly, Number of instruments < Number of groups
Secondly, Hansen test and Sagan test ≥ The significance level is that Sagan and
Hansen tests assess the hypothesis that 𝑯 𝟎 serves as an exogenous instrumental variable, ensuring it remains uncorrelated with the error term in the primary model; thus, a higher P-value indicates a more favorable outcome.
Thirdly, AR2 > The significance level of the AR2 test is important because this test overcomes autocorrelation in the model
Chapter 3 presents the research method in order of steps in the process and proceeds to build a panel data regression model applied to the secondary data set collected from the audited financial statements of commercial banks in Vietnam The research model is as follows:
Quantitative research employs various models, including Pooled-OLS, FEM, REM, FGLS, and GMM, to construct a linear regression model that effectively evaluates the impact of income diversification on the financial performance of Vietnamese commercial banks Chapter 3 outlines the author's expectations regarding the income diversification variable and other factors influencing financial performance.
EMPIRICAL RESULTS ANALYSIS
Descriptive statistical analysis
The research analyzed a sample of 26 Vietnamese commercial banks over the period from 2012 to 2022 Data for the study was sourced from the audited financial statements of these banks and macroeconomic statistics provided by the General Statistics Office of Vietnam.
Table 4.1 Statistical results of variables used in the research method
Variable Obs Mean Std.Dev Min Max
Source: Analysis results from STATA 14.0 software
Table 4.1 presents the results for the dependent variable, Return on Equity (ROE), based on 281 observations, with a mean value of 10.58% and a standard deviation of 7.55% The data reveals a significant disparity in ROE among banks, with the lowest recorded at 0.3% and the highest at 30.33%.
The average income diversification index (DIV) for Vietnamese commercial banks in the sample is 0.0168, with a minimum of 0.0066 and a maximum of 0.0329, indicating an average level of diversification during the study period Additionally, the standard deviation of 0.0050 suggests that the degree of diversification among the banks in the sample has remained relatively stable over the years.
The average bank size in Vietnam, represented by the variable SIZE, is 18.7492, with the highest value recorded at 21.4750 for the Joint Stock Commercial Bank for Investment and Development (BIDV) in 2022 and the lowest at 16.5023 for Saigon Bank for Industry and Trade (SGB) in 2013 This indicates that the size differences among the banks in the sample are relatively small, as reflected by a standard deviation of 1.1483.
The average equity ratio (CAP) among banks is 9.34%, with a standard deviation of 6.04%, indicating significant variations in equity levels In 2021, the Joint Stock Commercial Bank for Foreign Trade of Vietnam (VCB) reported the highest equity ratio at 90.77%, while the lowest was recorded at 4.06% by the Joint Stock Commercial Bank for Investment and Development (BIDV) in 2017 To enhance their capital adequacy ratio (CAR) and meet the Basel II requirement of at least 8%, commercial banks are focusing on increasing their equity during 2021-2022.
The deposits to total assets ratio (DTA) for banks averages 66.54%, with a standard deviation of 11.34%, indicating a strong reliance on customer deposits In 2015, Saigon Thuong Tin Commercial Joint Stock Bank (STB) recorded the highest DTA at 89.37%, while Viet A Bank (VAB) reported the lowest ratio of 7.68% in 2012.
The Loan to Total Assets (LTA) ratio for surveyed commercial banks averages 57.70%, with a standard deviation of 11.55%, highlighting that a significant portion of their assets consists of customer loans Notably, BIDV reported the highest LTA ratio at 78.81% in 2020, while LPB recorded the lowest at 10.12% in 2018.
The provision for loans to customers to total assets ratio (LLP) has remained relatively stable over the years, averaging 0.78% Notably, the highest ratio recorded is 2.17%, attributed to Vietnam Prosperity Joint Stock Commercial Bank.
Bank (VPB) in 2022, the lowest value belongs to Nam A Commercial Joint Stock Bank (NAB) with 0.27% in 2013
Vietnam's GDP has an average growth rate of 5.84% with a standard deviation of 1.59% In 2021, the economy experienced its lowest growth due to the severe effects of the fourth Covid-19 outbreak, which resulted in government-imposed distancing and lockdown measures that disrupted production and business activities However, in 2022, the implementation of effective economic recovery policies led to a gradual rebound in the Vietnamese economy, increasing the GDP growth rate to 8.02%.
The inflation rate in Vietnam averaged 3.79% from 2012 to 2022, with a standard deviation of 2.23% During this period, the highest inflation rate occurred in 2012 at 9.1%, while the lowest was recorded in 2015 at just 0.63%.
Correlation matrix analysis
Table 4.2 Correlation coefficients between research variables
ROE DIV SIZE CAP DTA LTA LLP GDP INF
Source: Analysis results from STATA 14.0 software
According to Gujarati (2004), a correlation coefficient exceeding 0.8 among independent variables indicates a high likelihood of multicollinearity, which can distort regression coefficients and skew research findings However, as shown in Table 4.2, the correlation coefficients among all pairs of independent variables in the model are below 0.8, indicating no multicollinearity exists Notably, variables DIV, SIZE, LTA, and LLP exhibit a positive correlation with the dependent variable, ROE, while CAP, DTA, GDP, and INF demonstrate a negative correlation with ROE.
Multicollinearity testing
Table 4.3 Results of multicollinearity test
Source: Analysis results from STATA 14.0 software
The analysis presented in Table 4.3 indicates that the average Variance Inflation Factor (VIF) value is 1.36, which is significantly below the threshold of 2 As noted by Tho (2011), a VIF coefficient exceeding 10 suggests the presence of multicollinearity among independent variables However, the statistical results reveal that the VIF values for the independent variables range from 1.04 to 1.70, all of which are well below 10, confirming the absence of multicollinearity in the model.
The analysis of regression results
Table 4.4 Results of Pooled-OLS, FEM and REM
Model Pooled-OLS FEM REM
Variable Coef P-value Coef P-value Coef P-value DIV 4.0252*** 0.000 2.5991*** 0.007 2.6574*** 0.002
Note: *, **, *** represent statistical significance level at 10%, 5% and 1%, respectively
Source: Analysis results from STATA 14.0 software
The analysis in Table 4.4 reveals that three variables—CAP, GDP, and INF—are not statistically significant in the research model Conversely, the variables DIV, SIZE, DTA, and LTA show significance at the 1% level, while LLP is significant at the 5% level Notably, DIV, SIZE, and LTA exhibit a positive correlation with the return on equity (ROE), whereas DTA and LLP demonstrate a negative correlation with ROE.
According to R-squared = 0.5446 (54.46%) the independent variables interpreting the variation of the dependent variable is 54.46%
The FEM model indicates that while GDP is not statistically significant, the variables DIV, SIZE, DTA, LTA, LLP, and INF are significant at the 1% level, and CAP is significant at the 10% level Among these, DIV, SIZE, CAP, LTA, and INF exhibit a positive correlation with the independent variable ROE, whereas DTA and LLP show a negative correlation with ROE.
From table 4.4, the R-squared of the FEM model is 0.5810, which means the independent variables can clarify 58.10% of the variation in ROE
In the REM model, the CAP and GDP variables lack statistical significance, while the remaining variables demonstrate notable significance Specifically, the DIV, SIZE, and LTA variables exhibit a strong positive correlation with the ROE variable at the 1% significance level Additionally, the INF macroeconomic variable shows a positive correlation with ROE at the 10% significance level Conversely, the DTA and LLP variables both reveal a negative correlation with ROE, maintaining a statistical significance of 1%.
The R-squared coefficient of the REM model is 0.5695 which means that the variation of the ROE variable can be explained by 56.95% by the independent variables.
Selection of estimation method
Table 4.5 Selection of method Pooled OLS and FEM FEM and REM Pooled OLS and REM
Chibar2(01) = 168.37 Prob > chibar2 = 0.0000 Prob>F=0.0000Chi2=0.0011Chi2=0.0000 F = 0.0000 < 0.05, so the hypothesis H0 is rejected and the appropriate research model is FEM
After establishing that the FEM model is preferable to the Pooled-OLS model, the author proceeds to conduct a Hausman test to identify the most suitable model between FEM and REM.
H0: There is no correlation between the independent variables and the residual, so the REM model is more appropriate
H1: There is a correlation between the independent variables and the residual, so the FEM model is more appropriate
The experimental results show that Prob>Chi2=0.0011Chi2=0.0000chi2 = 0.0000 The research model has heteroscedasticity
Source: Analysis results from STATA 14.0 software
The results of the Modified Wald test show that Prob>chi2 = 0.0000 < 0.05, so the author rejects the hypothesis H0, which means that the research model has heteroscedasticity
Then, to test the phenomenon of autocorrelation, the author conducts the Wooldridge test, the hypothesis of the test:
H0: The model does not have autocorrelation
F(1,25) = 54.887 Prob > F = 0.0000 The research model has autocorrelation
Source: Analysis results from STATA 14.0 software
The experimental results of Wooldridge test show that Prob > F = 0.0000 < 0.05 Therefore, hypothesis H0 is rejected and the model has autocorrelation.
The results of overcoming the research model by FGLS method
Table 4.8 FGLS model troubleshooting results Cross-sectional time-series FGLS regression
Coef Std.Err z P > |z| [95% Conf Interval]
Note: *, **, *** represent statistical significance level at 10%, 5% and 1%, respectively
Source: Analysis results from STATA 14.0 software
The FGLS model results indicate that the variables DIV, SIZE, DTA, and LTA are statistically significant at the 10% level, while LLP is significant at the 5% level Specifically, DIV, SIZE, and LTA positively influence the return on equity (ROE), whereas DTA and LLP negatively affect ROE Additionally, macroeconomic variables such as GDP, inflation (INF), and capital (CAP) do not show significant effects on ROE within the FGLS model.
From the above statistical results, the research model on the impact of income diversification on the financial performance of Vietnamese commercial banks in the period 2012 - 2022 is written as follows:
Endogenous variables testing
The author tests the phenomenon of endogenous variables with the hypothesis:
Table 4.9 Endogenous and exogenous variables in the research model Variables P-value Endogenous variables Exogenous variables
Source: Analysis results from STATA 14.0 software
The analysis presented in Table 4.9 identifies key endogenous variables such as Bank Size (SIZE), Deposit to Total Assets (DTA), Economic Growth Rate (GDP), and Inflation Rate (INF) Additionally, the study highlights exogenous variables, which include Income Diversification (DIV), Equity to Total Assets (CAP), Loans to Total Assets (LTA), and Provision for Loans to Customers to Total Assets (LLP).
GMM Regression Model Method
After addressing variable variance and autocorrelation using the FGLS model, the author applies the GMM regression method to tackle the issue of endogenous variables The findings from the GMM regression are summarized in the table below.
Table 4.10 GMM endogenous test results
Arellano-Bond test for AR(2) in first differences 0.250
Sargan test of overid restrictions 0.003
Hansen test of overid restrictions 0.643
ROE Coef Std Err t P > |t| [95% Conf Interval]
Note: *, **, *** represent statistical significance level at 10%, 5% and 1%, respectively
Source: Analysis results from STATA 14.0 software
The GMM model analysis indicates that the number of tools (19) is less than the number of groups (26), with the Arellano-Bond test yielding a P-value of 0.250, suggesting no series autocorrelation in the model Additionally, Sargan's test shows a P-value of 0.003, confirming the suitability of the instrumental variable and the presence of an endogenous phenomenon Meanwhile, Hansen's test presents a P-value of 0.643, indicating that the tools used in the model are appropriate Overall, the GMM regression model meets four essential conditions, demonstrating its suitability, efficiency, and high accuracy.
The GMM regression model provides more comprehensive results compared to the FGLS method, highlighting its effectiveness in controlling for endogenous factors Consequently, this thesis employs the GMM method to analyze the impact of income diversification on the financial performance of Vietnamese commercial banks from 2012 to 2022, as demonstrated in the following equation.
CONCLUSIONS AND RECOMMENDATIONS
Conclusions
This research utilizes empirical evidence and quantitative analysis to examine the impact of income diversification on the financial performance of 26 Vietnamese commercial banks from 2012 to 2022, focusing on the dependent variable Return on Equity (ROE) Additionally, the study assesses the influence of various factors, including bank-specific independent variables such as bank size (SIZE), loan to total asset ratio (LTA), deposit to total assets ratio (DTA), equity to total assets ratio (CAP), and provision for loans to customers to total assets ratio (LLP), along with macroeconomic factors like economic growth rate (GDP) and inflation rate (INF), on the banks' financial performance.
This study analyzes unbalanced data from 281 observations of 26 Vietnamese commercial banks listed on the stock market between 2012 and 2022, utilizing Stata 14 software for data processing The research employs descriptive statistics and correlation analysis, along with estimation methods such as Pooled-OLS, FEM, REM, FGLS, and GMM regression modeling to present the findings comprehensively.
Research on Vietnamese commercial banks highlights several bank-specific factors influencing financial performance, including Income Diversification (DIV), Bank Size (SIZE), Deposit to Total Assets Ratio (DTA), Loan to Total Assets Ratio (LTA), and Provision for Loans to Customers to Total Assets Ratio (LLP) Notably, the Deposit to Total Assets Ratio (DTA) and Provision for Loans to Customers to Total Assets Ratio (LLP) are identified as having a negative impact on the financial performance of these banks.
In conclusion, the research successfully met both the general and specific objectives outlined in Chapter 1, effectively addressing the research questions The findings from the empirical study have clarified the influence of income diversification on the financial performance of Vietnamese commercial banks over an 11-year period.
Suggestions and recommendations
Research on the impact of income diversification on the financial performance of Vietnamese commercial banks from 2012 to 2022 reveals key insights Based on these findings, the author proposes several recommendations for both commercial banks and state management agencies to enhance financial outcomes and stability in the banking sector.
5.2.1 Increasing income diversification to improve financial performance
Empirical research indicates that increasing income diversification enhances the financial performance of banks To boost profits, banks must continually expand into various activities, particularly non-interest operations This trend is essential for Vietnamese commercial banks amid economic challenges and intense industry competition Currently, their revenue primarily stems from credit activities, highlighting the need for a strategic shift towards diversifying products and services By investing in non-interest activities, banks can diversify their revenue sources and enhance overall income The author offers several recommendations to support this strategic transition.
In today's digital age, it is essential to develop information technology-based products and services that meet customer demands swiftly and conveniently As the Internet continues to grow in popularity, Vietnamese consumers are increasingly accustomed to utilizing products and services via computers and mobile devices By enabling customers to easily access and engage with these offerings online, businesses can attract a larger audience and significantly boost their profits.
The bank is dedicated to continuously innovating and enhancing its product offerings to effectively address customer needs By conducting thorough market research, the bank aims to identify and respond to various demands, launching a diverse range of services such as foreign currency transactions, bond underwriting, and enterprise equitization Additionally, the development of accessible and straightforward microfinance products tailored for the general population, particularly in rural areas, is essential for meeting the financial needs of underserved communities.
To enhance customer satisfaction and meet diverse needs, banks should focus on developing a comprehensive value-added chain of products and services Beyond traditional offerings like loans and deposits, customers seek additional support such as consulting, financial management, and investment advice By introducing complementary services, such as real estate valuation, banks can effectively address the evolving requirements of their clients throughout their banking experience.
To enhance product utility and expand their distribution network, banks should actively promote linked products and services through strategic partnerships A notable collaboration is with insurance companies, known as bancassurance, which allows banks to offer comprehensive financial solutions Additionally, forming alliances with real estate companies can facilitate home-buying loan products, further broadening the scope of banking services This approach not only increases the value of banking offerings but also strengthens the overall network of product distribution.
To be able to implement the strategy of diversifying products and services, in addition to creating new products and services, banks need to focus on the following issues:
To enhance brand promotion, banks must expand their marketing strategies for products and services While all banks have established websites to showcase their offerings and participate in sponsorships, they should also increase advertising efforts through mass media channels like television and Facebook Additionally, commercial banks need to identify their target customers, segment the market effectively, and select appropriate marketing methods to introduce their products.
To effectively develop new products and services that align with customer preferences, banks must conduct thorough research, including surveys and market analyses Staying informed about global trends is also crucial, as it helps banks anticipate market demand and innovate by introducing a range of new offerings that meet evolving customer needs.
To enhance financial performance, banks must establish a risk management system that aligns with international standards As they introduce new products and services to diversify income, it is crucial to effectively manage and mitigate associated risks.
Research indicates that larger banks tend to exhibit improved financial performance To enhance their size, the author recommends several strategies for banks to consider.
Merging and acquiring small-scale credit institutions with limited financial capacity into larger commercial banks is a growing trend in the banking sector This strategy enables large banks to expand their market share, enhance financial performance, and reduce investment costs by consolidating resources For smaller banks, mergers and consolidations provide an opportunity to increase their scale, broaden their market presence, and establish a competitive position in the industry.
Increasing charter capital is crucial for expanding the scale of a bank, as it enhances asset growth and mobilizes capital more effectively This expansion enables banks to invest more, broaden their operational scope, and adopt innovative technologies, ultimately improving product and service quality Consequently, charter capital serves as a key criterion for Vietnamese commercial banks aiming to enhance their size and financial performance.
5.2.3 Continuing to maintain loan activities and improving credit quality
Vietnamese commercial banks must enhance their credit quality, as research indicates that higher loan ratios positively impact their financial performance To achieve this improvement, banks should implement effective strategies and practices that focus on risk management and borrower assessment.
To thrive in the new normal, banks must swiftly innovate their business models by enhancing personal financial services and expanding digital banking offerings This approach will enable them to meet diverse customer needs and strengthen connections with individual clients Furthermore, actively promoting these products through media and social networks is essential for attracting a broader customer base.
Small and medium enterprises (SMEs) represent a significant portion of the total credit balance in Vietnamese commercial banks, highlighting the need for tailored credit programs that cater specifically to their industries Banks should offer incentives such as favorable interest rates, collateral support, and debt management aligned with the Credit Policy for Agriculture and Rural Development as outlined in Decree No 55/2015/ND-CP and Decree No 116/2018/ND-CP Additionally, the implementation of a 2% interest rate subsidy from the State budget, as part of the socio-economic recovery and development program under Decree No 31-2022/ND-CP, is crucial Furthermore, banks must enhance their lending processes and loan procedures, improve appraisal capabilities, and expedite loan settlement times to facilitate easier access to capital for businesses while maintaining loan safety.
Limitations of the graduate thesis and direction for future research
Besides the research results achieved, the study still has certain limitations:
The research is confined to the analysis of 26 Vietnamese commercial banks, excluding all other commercial banks in Vietnam, as well as Social Policy Banks, Cooperative Banks, and Joint Venture Banks due to limitations in the available data.
The study exclusively utilizes the Return on Equity (ROE) indicator to assess the financial performance of Vietnamese commercial banks, overlooking other significant financial metrics like Return on Assets (ROA) and Net Interest Margin (NIM).
Thirdly, there are a number of other micro and macroeconomic factors that have not been considered for inclusion in the model: liquidity ratio, capital adequacy ratio, operating costs,
To improve the above disadvantages, the author makes a few suggestions for future research directions:
To enhance the accuracy of research findings, it is essential to broaden the scope of the study by increasing the number of banks included in the analysis This expansion will provide a larger sample size, ultimately leading to more reliable and valid results.
This research article aims to enhance the assessment of financial performance in Vietnamese banks by incorporating additional financial indicators beyond Return on Equity (ROE) Furthermore, the study introduces explanatory variables to better elucidate the relationship between these factors and the overall financial performance of the banks.
Chapter 5 concludes the experimental results obtained and makes some suggestions and recommendations to contribute to improving the impact of income diversification on the financial performance of commercial banks in Vietnam The research results are expected to serve as a source of additional references for research students and commercial bank managers in management and strategic planning At the same time, this chapter also presents some limitations of the research that the author has not done and orientations for future research
The research explores the impact of income diversification on the financial performance of commercial banks in Vietnam, concluding that diversification is essential for stabilizing bank profits and enhancing overall financial performance To achieve this, banks should focus on developing non-credit products, which will not only decrease reliance on interest income but also better address customer needs.
A study analyzing secondary data from 26 Vietnamese commercial banks between 2012 and 2022 reveals key factors influencing their financial performance beyond income diversification Notably, bank size, the ratio of deposits to total assets, and the loan-to-total-assets ratio positively impact performance, while credit risk variables negatively affect it Interestingly, the research found no significant influence of macroeconomic factors or the equity-to-total-assets ratio on financial outcomes Overall, the findings align closely with the author's initial hypotheses.
Anbar, A., & Alper, D (2011) Bank specific and macroeconomic determinants of commercial bank profitability: Empirical evidence from Turkey Business and economics research journal, 2(2), 139-152
Ansoff, H I 1957, “Strategies for diversification”, Harvard business review,
Asif, R., & Akhter, W (2019) Exploring the influence of revenue diversification on financial performance in the banking industry: A systematic literature review Qualitative Research in Financial Markets, 11(3), 305-327
Ayaydin, H., & Karakaya, A (2014) The effect of bank capital on profitability and risk in Turkish banking International Journal of Business and Social
Berger A N., G A Hanweck, and D.B Humphrey (1987) “Competitive Viability in Banking: Scale, Scope, and Product Mix Economies”, Journal of Monetary
Berger, A N., & Humphrey, D B (1997) Efficiency of financial institutions: International survey and directions for future research European journal of operational research, 98(2), 175-212
Bikker, J A., & Metzemakers, P A (2005) Bank provisioning behavior and procyclicality Journal of international financial markets, institutions and money, 15(2), 141-157
Bourke, P (1989) Concentration and other determinants of bank profitability in Europe, North America and Australia Journal of Banking & Finance, 13(1), 65-79
Busch, R., & Kick, T K (2009) Income diversification in the German banking industry
Chiorazzo, V., Milani, C., & Salvini, F (2008) Income diversification and bank performance: Evidence from Italian banks Journal of financial services research, 33(3), 181-203
DeYoung, R., & Roland, K P (2001) Product mix and earnings volatility at commercial banks: Evidence from a degree of total leverage model Journal of
DeYoung, R., & Rice, T (2004) Noninterest income and financial performance at US commercial banks Financial review, 39(1), 101-127
Elsas, R., Hackethal, A., & Holzhọuser, M (2010) The anatomy of bank diversification Journal of Banking & Finance, 34(6), 1274-1287
Gurbuz, A O., Yanik, S., & Aytürk, Y (2013) Income Diversification and Bank Performance: E Evidence From Turkish Banking Sector BDDK Bankacılık ve Finansal
Gujarati, D N 2004, Basic Economics, 4 th edition, McGraw-Hill Inc NewYork, pp.359
Hau, L L., & Quynh, P X (2016) Tác động của đa dạng hóa thu nhập đến hiệu quả kinh doanh của các Ngân hàng thương mại Việt Nam Tạp chí Công nghệ Ngân hàng, 124, 11-22
Hau, L L., & Quynh, P X (2017) Ảnh hưởng của thu nhập ngoài lãi đến hiệu quả kinh doanh của các ngân hàng thương mại Việt Nam giai đoạn 2006 – 2016 Tạp chí ngân hàng, 9, 13-17
Lee, C C., Yang, S J., & Chang, C H (2014) Non-interest income, profitability, and risk in banking industry: A cross-country analysis The North
American Journal of Economics and Finance, 27, 48-67
Lepetit, L., Nys, E., Rous, P., & Tarazi, A (2008) Bank income structure and risk: An empirical analysis of European banks Journal of banking & finance, 32(8), 1452-1467
Luu, H N., Nguyen, L Q T., Vu, Q H., & Tuan, L Q (2020) Income diversification and financial performance of commercial banks in Vietnam: do experience and ownership structure matter? Review of Behavioral Finance, 12(3), 185-199
Mercieca, S., Schaeck, K., & Wolfe, S (2007) Small European banks: Benefits from diversification? Journal of Banking & Finance, 31(7), 1975-1998
Meslier, C., Tacneng, R., & Tarazi, A (2014) Is bank income diversification beneficial? Evidence from an emerging economy Journal of International Financial
Bài viết của Minh và Canh (2015) nghiên cứu về việc đa dạng hóa thu nhập và các yếu tố ảnh hưởng đến khả năng sinh lời của các ngân hàng thương mại tại Việt Nam Nghiên cứu chỉ ra rằng việc đa dạng hóa nguồn thu nhập có thể nâng cao hiệu quả hoạt động của ngân hàng Các yếu tố như quản lý rủi ro, chất lượng dịch vụ và sự cạnh tranh trên thị trường cũng đóng vai trò quan trọng trong việc cải thiện lợi nhuận Kết quả nghiên cứu cung cấp cái nhìn sâu sắc về chiến lược phát triển bền vững cho các ngân hàng thương mại Việt Nam.
Minh, N K (2004) Từ điển Toán kinh tế Thống kê, Kinh tế lượng Anh–Việt, Nhà xuất bản Khoa học kỹ thuật, Hà Nội
Moudud-Ul-Huq, S., Ashraf, B N., Gupta, A D., & Zheng, C (2018) Does bank diversification heterogeneously affect performance and risk-taking in ASEAN emerging economies? Research in International Business and Finance, 46, 342-362
Oanh, K L D., & Thuy, A P (2022) Vai trò phát triển dịch vụ phi tín dụng tại các Ngân hàng thương mại Việt Nam Tạp chí Nghiên cứu & Trao đổi, 6(16), 41-45
Quyen, P G., Ha, N T T., Darsono, S N A C., & Minh, T D T (2021) Income diversification and financial performance: The mediating effect of banks’ size, ownership structure, and the financial crisis in Vietnam Journal of Accounting and Investment, 22(2), 296-309
Revell, J (1979) Inflation and financial institutions, Financial Times, London Rose, P S., & Hudgins, S.C (2008) “Bank management & financial services McGraw-Hill”
Rozzani, N., & Rahman, R A (2013) Camels and performance evaluation of banks in Malaysia: conventional versus Islamic Journal of Islamic Finance and
Sanya, S., & Wolfe, S (2011) Can banks in emerging economies benefit from revenue diversification? Journal of financial services research, 40, 79-101
Sang, N M (2014) Phân tích các nhân tố tác động đến hiệu quả sử dụng nguồn lực của các ngân hàng thương mại Việt Nam Tạp chí Ngân hàng, 4, 23-30
Son, T H., Khuong, N V., & Nhi, N T Y Đa dạng hóa và hiệu quả hoạt động của các ngân hàng thương mại Việt Nam Tạp chí Kinh tế & Phát triển, 281, 44-53
Stiroh, K J (2004a) Diversification in banking: Is noninterest income the answer? Journal of money, Credit and Banking, 853-882
Stiroh, K J (2004b) Do community banks benefit from diversification? Journal of Financial Services Research, 25, 135-160
Stiroh, K J., & Rumble, A (2006) The dark side of diversification: The case of
US financial holding companies Journal of banking & finance, 30(8), 2131-2161
Nghiên cứu của Trang (2020) phân tích các nhân tố ảnh hưởng đến đa dạng hóa thu nhập tại các ngân hàng thương mại Việt Nam Bài viết chỉ ra rằng môi trường kinh doanh, chiến lược quản lý rủi ro, và sự phát triển công nghệ là những yếu tố quan trọng Ngoài ra, nghiên cứu cũng nhấn mạnh vai trò của việc nâng cao chất lượng dịch vụ và sự cạnh tranh trong ngành ngân hàng Kết quả cho thấy, việc đa dạng hóa thu nhập không chỉ giúp ngân hàng tăng trưởng bền vững mà còn cải thiện khả năng chống chịu trước biến động kinh tế.
Tuan (2022) đã tiến hành nghiên cứu về tác động của đa dạng hóa thu nhập đến hiệu quả kinh doanh của các ngân hàng thương mại tại Việt Nam Nghiên cứu này được đăng tải trên Tạp chí Khoa học Trường Đại học Mở Hà Nội, cho thấy rằng việc đa dạng hóa nguồn thu nhập có ảnh hưởng tích cực đến hiệu quả hoạt động của các ngân hàng Các kết quả chỉ ra rằng sự đa dạng hóa không chỉ giúp giảm thiểu rủi ro mà còn nâng cao khả năng cạnh tranh trong ngành ngân hàng.
Vinh, V X (2016) Profitability and risk in relation to income diversification of Vietnamese commercial banking system Journal of Economic Development
Wooldridge, J M (2002) Econometric analysis of cross section and panel data MIT press Cambridge, ma, 108(2), 245-254
APPENDIX APPENDIX 1 26 COMMERICIAL BANKS IN THE THESIS Number Stock code Name of commercial bank
1 ABBANK An Binh Commercial Joint Stock Bank
2 ACB Asia Commercial Joint Stock Bank
3 BAB Bac A Commercial Joint Stock Bank
4 BID Joint Stock Commercial Bank for Investment and
5 BVB Viet Capital Commercial Joint Stock Bank
6 CTG Vietnam Joint Stock Commercial Bank of Industry and
7 EIB Vietnam Export Import Commercial Joint Stock
8 HDB Ho Chi Minh City Development Commercial Joint Stock
9 KLB Kien Long Commercial Joint Stock Bank
10 LPB LienViet Commercial Joint Stock Bank
11 MBB Military Commercial Joint Stock Bank
12 MSB The Maritime Commercial Joint Stock Bank
13 NAB Nam A Commercial Joint Stock Bank
14 NVB National Citizen Commercial Joint Stock Bank
15 OCB Orient Commercial Joint Stock Bank
16 PGB Petrolimex Group Commercial Joint Stock Bank
17 SGB Saigon Bank For Industry And Trade
18 SHB Saigon Hanoi Commercial Joint Stock Bank
19 SSB Southeast Asia Commercial Joint Stock Bank
20 STB Saigon Thuong Tin Commercial Joint Stock Bank
21 TCB Vietnam Technological and Commercial Joint Stock Bank
22 TPB Tien Phong Commercial Joint Stock Bank
23 VAB Vietnam – Asia Commercial Joint Stock Bank
24 VCB Joint Stock Commercial Bank for Foreign Trade of Vietnam
25 VIB Vietnam International Commercial Joint Stock Bank
26 VPB Vietnam Prosperity Joint Stock Commercial Bank
APPENDIX 2: DESCRIPTIVE STATISTIC AND CORRELATION
Table 2.2 Correlation between variables inf -0.1587 0.1825 -0.2414 0.1010 -0.2418 -0.3029 -0.0001 -0.0896 1.0000 gdp -0.0434 0.0451 -0.0417 -0.1265 0.0218 0.0106 -0.0740 1.0000 llp 0.3190 0.0511 0.5217 0.1221 0.1510 0.4049 1.0000 lta 0.3026 0.0826 0.3286 0.0031 0.4337 1.0000 dta -0.1179 -0.0181 0.1572 -0.0831 1.0000 cap -0.0092 0.2483 -0.1901 1.0000 size 0.6270 -0.1915 1.0000 div 0.1467 1.0000 roe 1.0000 roe div size cap dta lta llp gdp inf
corr roe div size cap dta lta llp gdp inf inf 281 0378999 0222837 00631 09095 gdp 281 0584325 0159006 0258 0802 llp 281 0077504 0028986 00266 02167 lta 281 5770103 115515 10115 78806 dta 281 665414 1133727 07676 89372 cap 281 0934306 0603754 04062 90771 size 281 18.7492 1.148252 16.50232 21.47497 div 281 0167656 0049622 00655 03289 roe 281 10581 0755382 0003 3033
Variable Obs Mean Std Dev Min Max
sum roe div size cap dta lta llp gdp inf
APPENDIX 3: RESUTLS OF POOL-OLS, FEM, REM, FGLS, AND GMM Table 3.1 Results of Pool-OLS model
Table 3.2 Results of FEM model
_cons -.7497793 0684964 -10.95 0.000 -.8846298 -.6149289 inf -.1817087 1533618 -1.18 0.237 -.4836358 1202183 gdp -.1331607 1952247 -0.68 0.496 -.5175042 2511828 llp -2.917031 1.369924 -2.13 0.034 -5.614032 -.2200287 lta 1370787 0332597 4.12 0.000 0715994 2025579 dta -.2046164 030181 -6.78 0.000 -.2640345 -.1451984 cap 0620392 0549011 1.13 0.259 -.046046 1701243 size 0467563 0034447 13.57 0.000 0399746 053538 div 4.025209 6620259 6.08 0.000 2.721863 5.328555 roe Coef Std Err t P>|t| [95% Conf Interval]
Total 1.59768393 280 005706014 Root MSE = 05098 Adj R-squared = 0.5446 Residual 70679179 272 002598499 R-squared = 0.5576 Model 890892139 8 111361517 Prob > F = 0.0000 F(8, 272) = 42.86 Source SS df MS Number of obs = 281 reg roe div size cap dta lta llp gdp inf
F test that all u_i=0: F(25, 247) = 10.29 Prob > F = 0.0000 rho 70054303 (fraction of variance due to u_i) sigma_e 03744171 sigma_u 05726719
_cons -1.530832 1383059 -11.07 0.000 -1.803241 -1.258423 inf 4969557 1439333 3.45 0.001 2134625 7804489 gdp -.0144688 1460471 -0.10 0.921 -.3021254 2731878 llp -5.192384 1.154013 -4.50 0.000 -7.465345 -2.919422 lta 1607463 0395626 4.06 0.000 0828232 2386694 dta -.1091448 032634 -3.34 0.001 -.1734211 -.0448685 cap 0745361 0448602 1.66 0.098 -.0138211 1628934 size 0847093 0069449 12.20 0.000 0710306 0983881 div 2.599057 9570744 2.72 0.007 7139893 4.484125 roe Coef Std Err t P>|t| [95% Conf Interval] corr(u_i, Xb) = -0.6818 Prob > F = 0.0000 F(8,247) = 42.82 overall = 0.4842 max = 11 between = 0.5237 avg = 10.8 within = 0.5810 min = 10 R-sq: Obs per group:
Group variable: MACK Number of groups = 26Fixed-effects (within) regression Number of obs = 281 xtreg roe div size cap dta lta llp gdp inf, fe
Table 3.3 Results of REM model
Table 3.4 Results of FGLS model rho 46780349 (fraction of variance due to u_i) sigma_e 03744171 sigma_u 03510358
The analysis reveals significant findings regarding the impact of various financial metrics on corporate performance The variable "llp" shows a strong negative correlation with a coefficient of -4.773588, indicating a significant effect (p < 0.001) Conversely, "lta" positively influences performance with a coefficient of 0.1787016 (p < 0.001) Additionally, "dta" demonstrates a negative relationship with a coefficient of -0.1537226 (p < 0.001), while "div" positively affects performance with a coefficient of 2.657435 (p < 0.01) The overall model exhibits a high Wald chi-squared value of 329.71, suggesting that the variables collectively explain a significant portion of the variance in corporate performance, with an overall R-squared value of 0.5194.
Group variable: MACK Number of groups = 26 Random-effects GLS regression Number of obs = 281 xtreg roe div size cap dta lta llp gdp inf, re
_cons -.8459869 0758815 -11.15 0.000 -.994712 -.6972618 inf 0739406 1024875 0.72 0.471 -.1269312 2748124 gdp 1191054 0932602 1.28 0.202 -.0636811 301892 llp -2.545463 9841267 -2.59 0.010 -4.474316 -.6166098 lta 1032736 0301227 3.43 0.001 0442342 1623131 dta -.1372537 0262801 -5.22 0.000 -.1887618 -.0857456 cap 0289636 026276 1.10 0.270 -.0225365 0804637 size 0498187 0038907 12.80 0.000 0421931 0574442 div 3.405592 6249426 5.45 0.000 2.180727 4.630457 roe Coef Std Err z P>|z| [95% Conf Interval]
Prob > chi2 = 0.0000 Wald chi2(8) = 248.24 max = 11 avg = 10.80769 min = 10 Estimated coefficients = 9 Obs per group:
Estimated autocorrelations = 1 Number of groups = 26 Estimated covariances = 26 Number of obs = 281
Correlation: common AR(1) coefficient for all panels (0.6616)
Cross-sectional time-series FGLS regression
xtgls roe div size cap dta lta llp gdp inf, corr(ar1) panels(h) force
Table 3.5 Results of GMM model
Difference (null H = exogenous): chi2(4) = 2.04 Prob > chi2 = 0.729
Hansen test excluding group: chi2(6) = 5.82 Prob > chi2 = 0.444 iv(div cap lta llp)
Difference (null H = exogenous): chi2(7) = 4.86 Prob > chi2 = 0.677
Hansen test excluding group: chi2(3) = 2.99 Prob > chi2 = 0.393
Difference-in-Hansen tests of exogeneity of instrument subsets:
(Robust, but can be weakened by many instruments.)
Hansen test of overid restrictions: chi2(10) = 7.85 Prob > chi2 = 0.643
(Not robust, but not weakened by many instruments.)
Sargan test of overid restrictions: chi2(10) = 26.77 Prob > chi2 = 0.003
Arellano-Bond test for AR(2) in first differences: z = -1.15 Pr > z = 0.250
Arellano-Bond test for AR(1) in first differences: z = -0.68 Pr > z = 0.494
DL5.(L3.size L3.dta L3.gdp L4.inf)
GMM-type (missing=0, separate instruments for each period unless collapsed) div cap lta llp
GMM-type (missing=0, separate instruments for each period unless collapsed)
Instruments for first differences equation
Warning: Uncorrected two-step standard errors are unreliable.
_cons -.8513466 2955631 -2.88 0.008 -1.46007 -.2426231 inf 4081282 7102556 0.57 0.571 -1.054671 1.870927 gdp -.3340879 293223 -1.14 0.265 -.9379919 269816 llp -4.551648 2.648595 -1.72 0.098 -10.00653 9032353 lta 2995432 1514829 1.98 0.059 -.0124418 6115281 dta -.6069731 1844425 -3.29 0.003 -.9868395 -.2271068 cap 0981266 0606045 1.62 0.118 -.0266907 2229439 size 0626552 013514 4.64 0.000 0348227 0904877 div 3.033187 1.225194 2.48 0.020 5098521 5.556522 roe Coef Std Err t P>|t| [95% Conf Interval]
Number of instruments = 19 Obs per group: min = 10
Time variable : year Number of groups = 26
Group variable: MACK Number of obs = 281
Dynamic panel-data estimation, two-step system GMM
Difference-in-Sargan statistics may be negative.
Using a generalized inverse to calculate optimal weighting matrix for two-step estimation.
Warning: Two-step estimated covariance matrix of moments is singular.
Favoring space over speed To switch, type or click on mata: mata set matafavor speed, perm.
xtabond2 roe div size cap dta lta llp gdp inf, gmm(l3.size l3.dta l3.gdp l4.inf, lag(6 6)) iv( div cap lta llp) sm two
Mean VIF 1.36 gdp 1.04 0.963098 div 1.16 0.859929 cap 1.18 0.844661 inf 1.26 0.794614 dta 1.26 0.792649 lta 1.59 0.628709 size 1.69 0.593171 llp 1.70 0.588579
Test: Ho: difference in coefficients not systematic
B = inconsistent under Ha, efficient under Ho; obtained from xtreg b = consistent under Ho and Ha; obtained from xtreg inf 4969557 228701 2682547 0434522 gdp -.0144688 -.0731661 0586974 llp -5.192384 -4.773588 -.4187953 lta 1607463 1787016 -.0179553 0149584 dta -.1091448 -.1537226 0445778 0100826 cap 0745361 0657859 0087502 size 0847093 0656352 0190741 0044223 div 2.599057 2.657435 -.0583782 4090482 fe re Difference S.E.
Table 4.5 Breusch and Pagan test
H0: sigma(i)^2 = sigma^2 for all i in fixed effect regression model
Modified Wald test for groupwise heteroskedasticity
Wooldridge test for autocorrelation in panel data
xtserial roe div size cap dta lta llp gdp inf
Estimated results: roe[MACK,t] = Xb + u[MACK] + e[MACK,t]
Breusch and Pagan Lagrangian multiplier test for random effects
Instruments: cap dta lta llp gdp inf L.size
_cons -.6213262 0751283 -8.27 0.000 -.768575 -.4740774 inf -.0498193 2304545 -0.22 0.829 -.5015019 4018633 gdp -.1014124 2083411 -0.49 0.626 -.5097534 3069287 llp -2.088621 1.578761 -1.32 0.186 -5.182937 1.005694 lta 1783683 037397 4.77 0.000 1050715 2516651 dta -.2356633 0346979 -6.79 0.000 -.3036699 -.1676566 cap 0937694 0584097 1.61 0.108 -.0207114 2082503 size 0424305 0039255 10.81 0.000 0347366 0501244 roe Coef Std Err z P>|z| [95% Conf Interval]
Root MSE = 05371 R-squared = 0.5115 Prob > chi2 = 0.0000 Wald chi2(7) = 251.70 Instrumental variables (2SLS) regression Number of obs = 251 ivregress 2sls roe (size=l.size) cap dta lta llp gdp inf
Instruments: size cap dta lta llp gdp inf L.div
_cons -.7435837 0719878 -10.33 0.000 -.8846771 -.6024903 inf -.1432983 2160852 -0.66 0.507 -.5668174 2802209 gdp -.2047805 1956156 -1.05 0.295 -.5881799 178619 llp -2.744033 1.470876 -1.87 0.062 -5.626898 1388322 lta 1447331 0354527 4.08 0.000 0752471 2142192 dta -.2241295 0324573 -6.91 0.000 -.2877446 -.1605144 cap 0404483 0557089 0.73 0.468 -.0687391 1496358 size 0469411 0036766 12.77 0.000 0397352 0541471 div 4.145124 8073394 5.13 0.000 2.562768 5.72748 roe Coef Std Err z P>|z| [95% Conf Interval]
Root MSE = 05013 R-squared = 0.5743 Prob > chi2 = 0.0000 Wald chi2(8) = 328.18Instrumental variables (2SLS) regression Number of obs = 251 ivregress 2sls roe (div=l.div) size cap dta lta llp gdp inf
Instruments: dta lta llp gdp inf L.cap
_cons 1575614 05042 3.12 0.002 0587401 2563827 inf -.4004644 290598 -1.38 0.168 -.970026 1690973 gdp -.3504071 2852394 -1.23 0.219 -.9094661 2086519 llp 8.404338 1.859834 4.52 0.000 4.759131 12.04954 lta 2225497 0473048 4.70 0.000 1298341 3152654 dta -.2648227 045328 -5.84 0.000 -.353664 -.1759815 cap -.3612919 280404 -1.29 0.198 -.9108735 1882898 roe Coef Std Err z P>|z| [95% Conf Interval]
Root MSE = 06835 R-squared = 0.2089 Prob > chi2 = 0.0000 Wald chi2(6) = 84.13 Instrumental variables (2SLS) regression Number of obs = 251 ivregress 2sls roe (cap=l.cap) dta lta llp gdp inf
Instruments: lta llp gdp inf L.dta
_cons 0726948 0394507 1.84 0.065 -.0046271 1500167 inf -.3566447 2838691 -1.26 0.209 -.9130178 1997284 gdp -.216994 256962 -0.84 0.398 -.7206302 2866422 llp 7.333706 1.608928 4.56 0.000 4.180265 10.48715 lta 1864542 0480655 3.88 0.000 0922474 2806609 dta -.1577104 0540955 -2.92 0.004 -.2637356 -.0516852 roe Coef Std Err z P>|z| [95% Conf Interval]
Root MSE = 06673 R-squared = 0.2458 Prob > chi2 = 0.0000 Wald chi2(5) = 60.93Instrumental variables (2SLS) regression Number of obs = 251 ivregress 2sls roe (dta=l.dta) lta llp gdp inf
Instruments: llp gdp inf L.lta
_cons -.0207936 0379724 -0.55 0.584 -.0952182 053631 inf -.234311 3045462 -0.77 0.442 -.8312107 3625887 gdp -.232775 2720748 -0.86 0.392 -.7660318 3004818 llp 6.736627 1.785008 3.77 0.000 3.238076 10.23518 lta 1673228 0578549 2.89 0.004 0539292 2807164 roe Coef Std Err z P>|z| [95% Conf Interval]
Root MSE = 07067 R-squared = 0.1542 Prob > chi2 = 0.0000 Wald chi2(4) = 47.82 Instrumental variables (2SLS) regression Number of obs = 251 ivregress 2sls roe (lta=l.lta) llp gdp inf
_cons 0505684 0266267 1.90 0.058 -.0016191 1027558 inf -.4728493 2968909 -1.59 0.111 -1.054745 1090461 gdp -.2014011 2768579 -0.73 0.467 -.7440327 3412305 llp 10.93682 2.102276 5.20 0.000 6.816435 15.05721 roe Coef Std Err z P>|z| [95% Conf Interval]
Root MSE = 07172 R-squared = 0.1288 Prob > chi2 = 0.0000 Wald chi2(3) = 31.84Instrumental variables (2SLS) regression Number of obs = 251 ivregress 2sls roe (llp=l.llp) gdp inf
_cons 5003967 2299602 2.18 0.030 049683 9511105 inf -.4941455 5190394 -0.95 0.341 -1.511444 5231531 gdp -6.414226 3.912532 -1.64 0.101 -14.08265 1.254195 roe Coef Std Err z P>|z| [95% Conf Interval]
Root MSE = 12539 R-squared = Prob > chi2 = 0.1586 Wald chi2(2) = 3.68 Instrumental variables (2SLS) regression Number of obs = 251 ivregress 2sls roe (gdp=l.gdp) inf
_cons 1635026 0166109 9.84 0.000 1309459 1960594 inf -1.713029 4876838 -3.51 0.000 -2.668872 -.7571863 roe Coef Std Err z P>|z| [95% Conf Interval]
Root MSE = 07858 R-squared = Prob > chi2 = 0.0004 Wald chi2(1) = 12.34Instrumental variables (2SLS) regression Number of obs = 251 ivregress 2sls roe (inf=l.inf)
CODE YEAR ROE DIV SIZE CAP DTA LTA LLP GDP INF