Factors Influencing Profitibility Of Joint - Stock Commercial Banks In Viet Nam.pdf

Specifically, bank size SIZE, liquidity LIQ, Equity Capital CAP, and Operational cost OPR demonstrate positive effects on ROA, whereas Credit risk CR has a negative impact.. The key prof

INTRODUCTION

The necessity of the research

In the context of the increasingly volatile global economy, Vietnam is not exempt from facing various crises However, in order to stabilize and develop the economy amidst the world's tense situation, the State consistently makes efforts in macroeconomic regulation, issuing numerous policies to support businesses In this process, the banking sector plays a crucial role in the operation chain of the economy

To optimize the effectiveness of banking activities, profitability is one of the key indicators given top priority

Ho & Saunders (1981) profitability is a criterion related to the profit-generating ability used to assess the effectiveness of a bank's business activities It is not only a financial source needed for expanding production but also a source of finance for the state, contributing to national income growth and encouraging workers to be committed to their jobs Profitability is always considered a key factor in affirming the existence and development of banks A bank with growing profits helps improve the bank's financial strength, reflects the circulation of capital in the economy, and contributes to the financial stability of a nation

Worldwide, there is significant research interest in this issue Specifically, the study

"Factors Influencing the Profitability of Commercial Banks in Ethiopia" focuses on evaluating and examining macroeconomic factors and measuring their impact on the profitability of banks Meanwhile, some studies only utilize internal factors within banks to assess their impact on profitability, as seen in the research "The Impact of Capital Structure on the Efficiency of Financial Institutions in Palestine" by Abbadi

Experimental Evidence from Palestine" by (Abugamea, 2018)

Recognizing the significant role of banks in the economy, especially the importance of profitability in the operation and development of a bank, the author has chosen the topic "Factors Influencing the Profitability of Joint Stock Commercial Banks in Vietnam" for the research, considering it highly necessary The purpose is to investigate which factors have an impact on the profitability of these banks Through this research, the author aims to provide recommendations and suggest relevant policy implications to help banks manage potential risks, identify optimal and effective solutions to enhance profitability.

Research Objectives

According to general objectives, this study aims to identify the factors affecting, the extent of their impact, and the direction of their influence on the profitability of Vietnamese Joint Stock Commercial Banks (JSCBs) Based on the results, recommendations will be provided to enhance the profitability of JSCBs

Building upon the general objectives, the author outlines the specific objectives Firstly, to systematize the theoretical foundation regarding factors influencing the profitability of JSCBs to select an appropriate research model for the system of Vietnamese JSCBs Secondly, to determine the factors, their direction, and the extent of their impact on the profitability of Vietnamese JSCBs Thirdly, to propose managerial implications to improve the profitability of JSCBs in Vietnam.

Research questions

From the specific research objectives above, the author formulates the following research questions: i What factors impact the profitability of JSCBs in Vietnam? ii What is the extent of each factor's impact on the profitability of JSCBs in Vietnam? iii What recommendations can help Vietnamese JSCBs increase their profitability?

In this study, the research focuses on factors influencing the profitability of commercial joint-stock banks in Vietnam

Spatial Scope is 25 commercial joint-stock banks in Vietnam The reason the author chose this timeframe is that it offers a comprehensive dataset that accurately reflects recent economic conditions, ensures data reliability, and aligns with the research objectives, thereby facilitating a thorough analysis of the factors influencing the profitability of commercial banks in Vietnam

Time Frame is Data used for the study is secondary data covering the period 2013–

2022 The data source used in the study is cited and synthesized from the financial statements of the consolidated financial statements audited by the JSCBs in the period

2013 – 2022, the website of the General Statistics Office of Vietnam, and the website of World Bank

The data used is secondary data, collected from historical data published in the media and sampled within the timeframe from 2013 to 2022 The collected data is presented in tabular form, a combination of cross-sectional and time series data on Excel

Integrated and combined to perform synthesis, analysis, comparison, and correlation with the results of variable models from relevant previous studies, thereby drawing conclusions about the factors influencing profitability, as well as proposing recommendations and solutions for the research problem

The thesis employs regression analysis of tabular data along with the Pooled Ordinary Least Squares (Pooled OLS), Fixed-Effects Model (FEM), and Random-Effects Model (REM) Based on the regression results of tabular data, the author conducts tests such as F-test, Hausman test to choose the most suitable model, with the support of Stata software to identify the factors influencing the profitability of JSCBs in Vietnam Additionally, the author will perform descriptive statistics, synthesis, comparison, and analysis to achieve the stated objectives

In theoretical terms, the research proposes a model to assess the factors influencing the profitability of JSCBs in Vietnam, simultaneously examining the extent of the impact of these factors Through a review of previous studies, the author has constructed Pooled OLS, FEM, and REM models based on scientific theory to enhance the practical significance of the model

In practical terms, the author measures the direction and degree of influence of these factors on the profitability of JSCBs in Vietnam at the present time The author will develop the model, analyze the effects, and refine the model From the analysis results, the author will provide some new managerial implications that can be applied to enhance the profitability of JSCBs in Vietnam

Chapter 1 is Introduction, which presents an overview of the topic, discusses its significance, outlines the research objectives, scope, and methodology

Chapter 2 is Theoretical Basis and Literature Review This chapter introduces profitability and its metrics for commercial joint-stock banks It also provides a literature review of relevant studies conducted in Vietnam and globally

Chapter 3 is Research Methodology This section details the steps involved in the research process It encompasses hypothesis formulation, model design and selection, data collection methods, and analysis techniques

Chapter 4 is Research Results and Discussion This chapter will focus on presenting, commenting on, and analyzing the research model introduced in Chapter 3

Chapter 5 is Conclusion and Recommendation This chapter serves to summarize the research findings It proposes suggestions for enhancing the profitability of Vietnamese commercial joint-stock banks, and suggests new avenues for future development

This chapter sets the stage for exploring factors impacting the profitability of joint- stock commercial banks in Vietnam It underscores the research's significance, identifies gaps in understanding, outlines research objectives, formulates questions, defines scope, and discusses methodology and structure.

Research methodology

The data used is secondary data, collected from historical data published in the media and sampled within the timeframe from 2013 to 2022 The collected data is presented in tabular form, a combination of cross-sectional and time series data on Excel

Integrated and combined to perform synthesis, analysis, comparison, and correlation with the results of variable models from relevant previous studies, thereby drawing conclusions about the factors influencing profitability, as well as proposing recommendations and solutions for the research problem

The thesis employs regression analysis of tabular data along with the Pooled Ordinary Least Squares (Pooled OLS), Fixed-Effects Model (FEM), and Random-Effects Model (REM) Based on the regression results of tabular data, the author conducts tests such as F-test, Hausman test to choose the most suitable model, with the support of Stata software to identify the factors influencing the profitability of JSCBs in Vietnam Additionally, the author will perform descriptive statistics, synthesis, comparison, and analysis to achieve the stated objectives

In theoretical terms, the research proposes a model to assess the factors influencing the profitability of JSCBs in Vietnam, simultaneously examining the extent of the impact of these factors Through a review of previous studies, the author has constructed Pooled OLS, FEM, and REM models based on scientific theory to enhance the practical significance of the model

In practical terms, the author measures the direction and degree of influence of these factors on the profitability of JSCBs in Vietnam at the present time The author will develop the model, analyze the effects, and refine the model From the analysis results, the author will provide some new managerial implications that can be applied to enhance the profitability of JSCBs in Vietnam

Structure of the research

THEORETICAL BASIS AND LITERATURE REVIEW

Overview of commercial bank

A bank is a type of credit institution that can carry out all banking activities as regulated by this Law Based on the nature and objectives of their operations, types of banks include joint-stock commercial banks, management-focused banks, and cooperative banks Joint-stock commercial banks are a type of bank that conducts all banking activities and other specified business activities according to this Law with the goal of profitability Joint-stock commercial banks function to primarily mobilize capital through deposit attraction, issuance of certificates of deposit, bonds, and subsequently utilize these funds for business and production loans, as well as consumer loans Additionally, they offer various services such as payment, fund transfers, guarantees, trust services, etc (Guru, 2002)

Allen (2015) defines a commercial bank as organizations engaged in lending and issuing various term deposit certificates Banks possess a variety of assets, different types of liabilities, and may participate in off-balance-sheet activities, including financial guarantees and derivative instruments These financial institutions play a significant role in foreign exchange operations and are consistently subject to rigorous management Commercial banks are considered the earliest form of banks, closely associated with the emergence of banking activities (Dang, 2020) The profit-generating activities of commercial banks are typically diverse and involve a synthesis of various transactions and services Therefore, commercial banks are defined as financial intermediary institutions holding a significant position in the economy and the entire market

In summary, commercial banks are among the oldest existing credit institutions, with close relationships and impacts not only on individuals and other businesses but also on the national economy and the international monetary market Commercial banks also have other economic tasks, such as contributing to the effective implementation of monetary policies, interest rate adjustments, and inflation stabilization.

Theoretical basis about commercial bank profitability

According to Rose (1999), the profitability of a bank is defined as the net income after taxes or the net profit of the bank Financial balance is achieved through monetary efficiency Monetary efficiency is measured by profitability Evaluating profitability must be based on a reference period

According to Grene (2018), profitability is the key factor and serves as a cushion for banks to cope with unexpected risks or losses in emergency situations Profitability enables banks to supplement their own capital with significant amounts of capital at low capital utilization costs High profitability of banks enables them to have proactive resources for investing in modern equipment, improving infrastructure, enhancing customer services, and generating profits from these services Therefore, high profitability provides investors with confidence in investing, as banks can compensate for any losses incurred in the event of risks

According to Clews (2019), the profitability of commercial banks is the measure reflecting the efficiency of their operations in business From a macroeconomic perspective, banks with high operational profits can overcome instability and risks during their operations, thereby contributing to the stability of the overall economy At the microeconomic level, when banks generate good profits, they create retained earnings as a form of self-capitalization, which serves as a secure source of capital at the lowest cost

In summary, the profitability of a commercial bank shares similarities with other businesses as it represents the difference between income and expenses However, for banks, income is interest earned and expenses include interest payments The profitability reflects the entire labor process of the entire banking system throughout the year It demonstrates the capacity and financial scale of commercial banks When considering, evaluating investment opportunities, or making decisions regarding the use of financial services, the profitability indicators become a primary concern for shareholders and customers Therefore, achieving profit growth alongside maintaining a stable and secure system is a consistently prioritized and pursued goal for banks

2.2.2 The indicators reflect the profitability of commercial banks

When evaluating the business performance of a bank, various metrics are of interest, such as Return on Capital Employed (ROCE), Risk-Adjusted Return on Assets, etc However, within the scope of this thesis, the author focuses on commonly used metrics in both domestic and international literature related to bank profitability The key profitability metrics for commercial banks are:

ROA, as defined by (Phuoc & Bui, 2016) measures the efficiency of a bank in utilizing its assets to generate post-tax profits, regardless of whether the assets are created from borrowed funds or equity capital The formula for ROA is:

ROA is a crucial financial metric used to assess the profitability and efficiency of a bank's asset management It is calculated by dividing the net income after taxes by the average total assets of the bank This formula represents how effectively a bank generates profits from its assets

The relationship between ROA and a bank's profitability is pivotal A higher ROA indicates that the bank is generating more profit per unit of assets, suggesting efficient asset utilization Banks with higher ROA tend to have better profitability, as they are maximizing returns from their investments in assets ROA reflects the bank's ability to generate earnings relative to its asset base, thereby serving as a key indicator of its financial health and operational efficiency

ROA is a variable representing bank profitability, commonly used in many studies and calculated by dividing the total after-tax profit for the year by the average total assets of the bank (Grene, 2018) This is a measure of the efficiency of asset utilization by the bank, implying that all assets are investments

ROE is defined as the return on a bank's equity capital According to Phuoc & Bui, (2016) ROE indicates how much profit each unit of equity capital will yield after deducting corporate income tax The formula for ROE is:

ROE reflects the bank's ability to leverage its capital and the influence of financial leverage in generating income A high ROE makes a bank more attractive to investors as it indicates efficient financial management, optimal utilization of shareholders' capital, and satisfactory profitability

NIM is a measure of efficiency and profit-generating ability The NIM is defined as the difference between the average interest rate on deposits and the average interest rate on loans The calculation of NIM is expressed as:

Net interest income is the line item reflecting on the financial statements Interest- earning assets include deposits at the State Bank, deposits at other credit institutions, loans to other credit institutions (excluding risk provisions), loans to customers (excluding risk provisions), purchased debt (excluding risk provisions), and investment securities (excluding discounted provisions) on the balance sheet

A higher NIM corresponds to larger bank income, reflecting the capabilities of the Board of Directors and employees in sustaining income growth primarily from lending and investments compared to the rising cost of paying interest on deposits and borrowed funds in the market

These profitability metrics play a crucial role in assessing the financial health, decision- making processes, and investment attractiveness of commercial banks.

Empirical research overview

Bogale (2019) conducted a study on the factors influencing the profitability of private commercial banks in Ethiopia during the period 2008–2017 The article utilized the Fixed Effects Model (FEM) and collected data from 14 private commercial banks in Ethiopia from 2008 to 2017 The author used ROA as the dependent variable representing profitability, and 9 independent variables including capital adequacy ratio, bank size, liquidity risk, credit risk, operational efficiency, lending interest rate, inflation, GDP growth, and exchange rate The results of the study showed that two variables (capital adequacy ratio, bank size) had a positive impact, while three variables (operational efficiency, lending interest rate, exchange rate) had a negative impact The remaining variables (liquidity risk, credit risk, inflation, GDP growth) did not affect profitability in this study

Abate & Mesfin (2019) analyzed the internal factors of banks and macroeconomic factors affecting the profitability of commercial banks Data were collected from 9 commercial banks in Ethiopia during the period 2007–2016, and were using the FEM and REM The results of the study indicated that capital adequacy, leverage, liquidity, and ownership had a positive and statistically significant relationship with bank profitability On the other hand, GDP, inflation, and interest rates had a negative and statistically significant relationship with bank profitability However, the relationship between bank size and branch network size was found to be statistically insignificant

Abugamea (2018) conducted a research on the impact of internal and external factors on the profitability of commercial banks in Palestine from 1995 to 2015 The experimental results showed that factors such as size, capital, and lending positively influenced both ROA and ROE, while deposit factors had no effect on ROA and ROE Additionally, the study revealed that internal and external factors had no significant impact on Net Interest Margin (NIM) External factors such as inflation and GDP also did not significantly affect the profitability of banks

Noman (2015) collected data from 35 banks in Bangladesh from 2003 to 2013 to explore the factors affecting profitability measures such as ROA, ROE, and NIM The research model was based on independent variables including Non-performing loans to total loans, Equity capital to total assets, Loans to total assets, Cost to income ratio, Asset size, Real interest rate, Inflation, and GDP The study concluded that the ratio of non- performing loans to total loans and inflation had a positive impact on ROA and ROE, while GDP had a contrasting effect Additionally, the two factors of Cost to income ratio and Asset size positively affected ROA and NIM but negatively affected ROE Equity capital to total assets positively influenced ROA and NIM, while Loans to total assets positively affected NIM

Islam (2016) conducted a study on the determinants of Net Interest Margin (NIM) for

230 banks in four South Asian countries: Bangladesh, India, Nepal, and Pakistan, from

1997 to 2012 The report utilized a Fixed Effects Model (FEM) and relied on diverse independent variables including Size, Non-performing loans to total loans ratio, Liquidity, Equity capital to total assets ratio, Loans to deposit ratio, Non-interest income, Statutory reserves to total assets ratio, Operational expenses to total assets ratio, Operational expenses to operational income ratio, Market concentration, Inflation, and GDP The scientific conclusion drawn from the research was that Liquidity, Equity capital to total assets ratio, Statutory reserves, and Operational expenses to total assets ratio had a positive impact on NIM Conversely, Size, Market concentration, and GDP had a negative impact on the independent variable

Vo Phuong Diem (2016) conducted a study titled: Analysis of factors influencing the profitability of Vietnamese commercial banks Data from 22 Vietnamese commercial banks were collected for the period 2008–2015 The dependent variables were Return on Assets (ROA) and Return on Equity (ROE) Independent variables included the capital adequacy ratio, bank size, liquidity ratio, operational expense ratio, provision for credit risk ratio, loan-to-deposit ratio, and bank development level The results revealed that bank size, loan-to-deposit ratio, and liquidity ratio positively impact bank profitability However, the operational expense ratio and bank development level have a negative impact Additionally, the capital adequacy ratio positively affects ROA but negatively affects ROE The provision for credit risk ratio showed no statistically significant impact

Phuoc & Bui (2016) conducted a study on the factors influencing profitability in Vietnamese commercial banks Data were collected from the financial reports of 20 commercial banks during the period 2007–2012 The article employed Ordinary Least Squares (OLS), Fixed Effects Model (FEM), and Random Effects Model (REM) The authors selected Net Interest Margin (NIM) as the dependent variable, and independent variables included capital adequacy ratio, loan-to-deposit ratio, GDP, and inflation rate The results indicated that GDP positively affects NIM, while the capital adequacy ratio and loan-to-deposit ratio exert a negative impact on NIM However, inflation rate showed no significant effect in the model

Nguyen Thi Thu Hien (2017) conducted a study on the characteristic factors affecting the profitability of Vietnamese commercial banks The research utilized regression analysis to identify internal factors influencing the Return on Assets (ROA) and Return on Equity (ROE) of Vietnamese commercial banks during the period 2006 to 2015 The results indicated that lending as a percentage of total assets, provisions for credit risk on lending, interest expense on debt, and non-interest income on assets positively correlate with the profitability of banks Conversely, non-performing loans, operational expenses to income, and the size of the board of directors exhibit a negative correlation with profitability The study did not find statistically significant evidence regarding the impact of variables representing risk management, liquidity, capital structure, cost control, and scale

Le Dong Duy Trung (2020) investigated the factors affecting the profitability of commercial banks in Vietnam: An empirical approach The author utilized data from 30 banks, including 4 state-owned commercial banks, 25 domestic private joint-stock commercial banks, and 1 foreign bank, spanning the period from 2009 to 2017, employing the Generalized Method of Moments (GMM) model The dependent variables chosen were Return on Assets (ROA) and Return on Equity (ROE) The results indicated that asset size and equity capital positively impact profitability However, the ratios of operational expenses to total assets, liquidity risk, credit risk, and operational costs negatively influence profitability

Le Van Hop (2021) conducted a study on the impact of equity capital on the profitability of Vietnamese commercial banks, utilizing data from 24 Vietnamese commercial banks spanning the period from 2008 to September 2020 The study employed the Ordinary Least Squares (OLS) method and Hausman test to select between Fixed Effects Model (FEM) and Random Effects Model (REM) The dependent variables chosen were Return on Assets (ROA) and Return on Equity (ROE) The independent variables included the ratio of equity capital to total assets, the ratio of total loans to total assets, the ratio of provisions for credit risk to total loans, the ratio of deposits to total assets, bank size, state ownership, GDP, and inflation The results indicated that the ratio of equity capital to total assets has a positive impact on ROA but a negative impact on ROE The ratio of total loans to assets negatively affects both ROA and ROE The ratio of provisions for credit risk to total loans negatively affects both ROA and ROE Bank size positively influences profitability State ownership does not have statistically significant effects Regarding macroeconomic factors, inflation has a positive impact on both ROA and ROE However, GDP only positively and significantly affects ROE and seemingly has no impact on ROA.

Factors affecting commercial bank profitability

Abugamea (2018) argued that banks have a better position compared to other businesses in leveraging the advantage of asset size in transactions and are likely to enjoy higher profits Similarly, this was demonstrated in the study by Allen (2015) where the bank size variable positively influenced the competitiveness of the observed units, growth potential, and revenue generation in profit-oriented activities

In another study by Islam (2016), it was found that the asset size variable had an opposite effect on the ability of commercial banks to generate surplus The research illustrated the viewpoint that excessively large scale may result in additional costs and inefficient bank management practices, leading to decreased profitability Therefore, large-scale banks can help enhance profitability, increase lending, expand investment portfolios, diversify financial products and services, better develop new customer segments, and attract high- quality human resources to their systems

However, these large credit institutions must make intelligent management decisions to effectively control asset size to achieve optimal operational results

Some studies have shown the negative impact of liquidity risk on the business efficiency of commercial banks, such as Satria (2018) with Malaysian commercial banks However, this was only true for ROA measured by after-tax profit, with results showing no statistical significance when ROA was measured by pre-tax profit as well as ROE (pre- and post-tax) Similarly, Ngweshemi (2021) also found the negative impact of liquidity risk on both ROA and ROE of commercial banks in the United States

On the other hand, some studies have yielded opposite results, indicating that liquidity risk has a positive impact on business efficiency For example, Shahara (2020) with European commercial banks, or demonstrating that high liquidity capability negatively affects the profitability of commercial banks in the United States (Ho & Saunders, 1981)

In summary, liquidity is a sensitive issue that every bank needs to be cautious about because it reflects the ability to meet the immediate needs of customers such as deposit withdrawals, granting credit loans, repaying debts on time, and covering operational expenses If a bank lacks the ability to meet these demands or loses liquidity, its credibility will significantly decline

According to Abate & Mesfin (2019), they argue that as credit risk increases, profitability tends to decrease Similarly, Satria (2018) also indicates that credit risk has an inverse impact on the profitability of commercial banks Conversely, (Yaffee, 2003) suggest that credit risk and bank profitability have a positive relationship However, Bogale (2019), the results show that credit risk is not correlated with the profitability of commercial banks

In conclusion, credit risk is considered the most significant risk in the banking business Therefore, to mitigate credit risk, managers need to promptly identify, analyze, measure the level of risk, and implement management solutions to minimize that risk

According to the studies conducted by Vo Thi Phuong (2020), Abugamea (2018), Guru (2002), and Kassem (2023), they argue that the scale of equity capital has a positive impact on profitability Conversely, some studies have found a negative impact of equity capital scale, such as Yaffee (2003) with results showing no statistical significance, as seen in (Abbadi, 2022)

In summary, the scale of equity capital plays a crucial and indispensable role in the business activities of every enterprise For commercial banks, it reflects the scale and efficiency of business operations Through equity capital, banks not only support the sustainability and profitability of their operations but also contribute to economic stability and efficient capital distribution in the economy (Hinduru, 2017)

Ngweshemi (2021) argue that banks with low operational cost have a positive impact on bank profitability On the other hand, Ramadan (2019) did a research about an inverse relationship between operational cost and bank profitability The authors suggest that excessive operational cost negatively affects bank profitability

Gross Domestic Product (GDP) is the market value of all final goods and services produced within a specific territory during a certain period GDP is one of the key indicators to assess the economic development of a particular region Economic growth of a country in one year compared to the previous year indicates that the economy of that country is expanding, with many business opportunities being extended, leading to growth across all sectors of the economy, including the banking sector, thereby creating conditions for increased profitability for banks However, when this indicator decreases, the economy weakens, and business activities face many difficulties, then the financial and banking sector will not be immune to the risk of reduced profitability because GDP is one of the macroeconomic factors that can directly impact the operations of banks, specifically affecting profitability

Satria (2018), in their study on the most important factors influencing the profitability of the 10 largest banks in Asia during the period 2012 – 2016, showed a significant correlation between the economic growth rate and bank profitability This is consistent with the findings of two other studies by (Shahara, 2020) On the other hand, the research conducted by Abate & Mesfin (2019) demonstrated that the economic downturn led to a decrease in deposit and lending demand, resulting in a reduction in the banking service activities and subsequently decreasing bank profitability Clews (2019) also suggested that GDP has an inverse relationship with the profit potential of banks According to studies by Ngweshemi (2021), Bogale (2019), and Abugamea (2018) GDP does not have a significant impact on the profitability of non-bank financial institutions

Inflation is the sustained increase in the general price level of goods and services over time The rise in the overall price level of goods reduces the purchasing power of the currency, meaning that with the same amount of money, consumers can buy fewer goods than before When comparing different countries, inflation can also be understood as the depreciation of the domestic currency relative to foreign currencies Inflation is one of the macroeconomic factors that affect bank profitability High inflation leads to higher borrowing interest rates and improves bank profitability Therefore, banks need to predict the level of inflation to benefit from it and increase profitability

According to the study by Allen (2015) inflation has a positive impact on profitability

In cases where key factors affecting banks remain unchanged, banks may include inflation in interest rates to ensure profitability Abate & Mesfin (2019) found that inflation has an inverse relationship with bank profitability The research by Shahara (2020) also yielded similar results, indicating that bank management may fail to predict future inflation and may be slow in adjusting interest rates, affecting customer sentiment and transaction difficulties, thereby reducing the bank's profit potential However, according to Ngweshemi (2021), inflation has no impact on profitability

Table 2.1 Summary of empirical researches

Liquidity Risk did not affect Credit Risk did not affect Operational

Inflation did not affect Abate &

Non- performing loans to total loans

Equity capital to total assets

Loans to total assets Did not affect

ROA Did not affect ROE

FEM 230 banks in four South Asian countries:

Equity capital to total assets ratio

Loans to deposit ratio Did not affect Non-interest income

Statutory reserves to total assets ratio

Operational expenses to total assets ratio

Operational expenses to operational income ratio

Market concentration NIM - Inflation Did not affect

Operational expense ratio ROA- Provision for credit risk ratio

Loan-to- deposit ratio ROA-

Inflation rate Did not affect

2006-2015 ROA Lending as a percentage of total assets

Provisions for credit risk on lending

Non-interest income on assets

Size of the board of directors

Liquidity Did not affect Capital structure Did not affect Cost control Did not affect Scale Did not affect

Generalized Method of Moments (GMM)

Ratios of operational expenses to total assets

ROA Ratio of equity capital to total assets

The ratio of provisions for credit risk to total loans

The Ratio of Deposits to Total Assets

The Ratio of Total Loans to Total Assets

State Ownership Did not affect

These existing research on factors influencing bank profitability has provided valuable insights into the complex dynamics of the banking sector However, several gaps and opportunities for further investigation have been identified

RESEARCH METHODOLOGY

Hypothesis of the research

Bank size refers to the scale or magnitude of a bank's operations, typically measured by its total assets, deposits, loans, or the number of branches It reflects the overall financial capacity, market presence, and scope of activities of a bank within the financial system

According to Abugamea (2018) a larger bank size may indicate greater financial stability, market influence, and ability to serve a broader customer base However, Islam (2016) argued that the increase in the scale of the bank will lead to increased operational costs, reducing profit potential

H1: SIZE has a positive impact on commercial banks' profitability

Liquidity ratios are financial metrics used to assess a company's ability to meet its short- term financial obligations with its liquid assets These ratios measure the company's ability to convert its assets into cash quickly without incurring significant losses in value

Liquidity ratios are important indicators of a company's financial health and its ability to withstand short-term financial challenges, such as unexpected expenses or fluctuations in cash flow According to Abate & Mesfin (2019) a higher liquidity ratio generally indicates a stronger financial position and better ability to meet its obligations However,

Le Dong Duy Trung (2020) believed that excessively high liquidity ratios may suggest underutilization of assets, which could impact profitability

H2: LIQ ratio has a negative impact on commercial banks' profitability

Credit risk ratio, also known as the loan loss reserve ratio or provision for credit losses ratio, is a financial metric used to assess the level of risk associated with a bank's loan portfolio It measures the adequacy of the bank's provision for potential losses from loans that may default

The credit risk ratio is calculated by dividing the total amount of provisions for credit losses by the total amount of loans outstanding It represents the percentage of loans that the bank has set aside as provisions for potential credit losses relative to its total loan portfolio

A higher credit risk ratio indicates that the bank has allocated a larger portion of its earnings to cover potential loan losses, which may suggest a more conservative approach to risk management, (Bogale, 2019) On the other hand, a lower credit risk ratio may indicate that the bank is taking on more risk by not setting aside sufficient provisions for potential loan defaults, (Le Van Hop, 2021)

H3: CR ratio has a negative impact on commercial banks' profitability

The "Scale of Equity Capital ratio" is not a standard financial term or ratio commonly used in finance or accounting However, it seems to refer to a metric that assesses the proportion of equity capital relative to other financial metrics, such as total assets, liabilities, or revenue

In general, equity capital ratio or equity-to-assets ratio is a common financial ratio used to evaluate a company's financial health and risk It measures the proportion of a company's assets that are financed by equity capital rather than debt

A higher equity-to-assets ratio suggests that a larger portion of the company's assets is financed by equity capital, which can indicate a lower financial risk and greater financial stability, (Noman, 2015)

H4: CAP has a positive impact on commercial banks' profitability

The Operational Cost (OPR), also known as the Operational Expense Ratio, is a financial metric used to assess a company's efficiency in managing its operational expenses in relation to its revenue It measures the percentage of revenue that is consumed by operational expenses

A lower Operational Cost ratio indicates that the company is more efficient in managing its operational expenses relative to its revenue, which is generally favorable as it implies higher profitability Conversely, a higher ratio suggests that a larger portion of revenue is being consumed by operational expenses, which may indicate inefficiencies in cost management, (Islam, 2016)

H5: OPR has a negative impact on commercial banks' profitability

Every financial institution is inherently linked to the GDP growth rate of the country's economy In analyzing the operational efficiency of commercial banks, Noman (2015) concluded that GDP has a positive impact on the profitability metrics of ROA and ROE

This demonstrates that a high economic growth rate provides an environment conducive for banks to conduct business more effectively in certain specific aspects

Note: The data were collected from the World Bank and have been pre-calculated

H6: GDP has a positive impact on commercial banks' profitability

The inflation rate is a macroeconomic factor expected to correlate positively with banking activities Research findings demonstrate that the inflation rate has a positive impact on all three profitability metrics: ROA, ROE, and NIM This signifies that transparent inflation control policies enable commercial banks to benefit from actively adjusting credit and deposit interest rates, resulting in profitability Vo Thi Phuong (2020) Conversely, Abugamea (2018), in their study focusing on banks in Palestine, proves that inflation has a negative impact on both ROA and NIM

𝐂𝐏𝐈𝐭– 𝟏 Note: The data were collected from the World Bank and have been pre-calculated

H7: INF has a positive impact on commercial banks' profitability

The research model

In studies concerning the profitability of commercial joint-stock banks, researchers mainly focus on key indicators representing profit variables such as ROA, ROE, and NIM Within the scope of this thesis, the author only utilizes a single profitability indicator for banks, which is the Return on Assets (ROA) Selected research works that have employed ROA as the dependent variable include those by Le Van Hop (2021), Le Dong Duy Trung (2020), Bogale, (2019), Abugamea, (2018) and Nguyen Thi Thu Hien (2017)

Therefore, based on the theoretical foundations as well as the indicators impacting bank profitability outlined in Chapter 2, the thesis introduces 7 independent variables applied in the paper, which are: Bank Size (SIZE), Liquidity (LIQ), Credit Risk (CR), Equity Capital (CAP), Operational cost (OPR), GDP growth (GDP), Inflation (INF)

In terms of the testing method, the thesis will adopt the multivariate regression model approach for the dataset after observing its similarity with the study conducted by Dang Van Dan (2020), Le Van Hop, (2021), Bogale (2019), Abate & Mesfin (2019), Abugamea (2018)

After outlining the foundations in the preceding section, the study will be conducted using the following model:

Where: β0: constant term Β1, , β7: Regression coefficients for the independent variables i: bank; t: year of observation; μi: error term/ residual term

Independent variables (bank-specific factors):

SIZE: Bank Size (billion VND)

The thesis will proceed to compile a summary table of the calculation method as well as the previous studies that have utilized dependent variables and the independent variables that will be constructed in the research model

Table 3.1 Independent variable and dependent variable of the research model

Variable Description Formula Source Expectation

Net Profit after Tax Total Assets

(Bogale, 2019), (Abugamea, 2018), (Islam, 2016), (Vo Phuong Diem, 2016), (Le Van Hop, 2021)

Mesfin, 2019), (Islam, 2016), (Vo Phuong Diem, 2016), (Nguyen, 2017), (Le

(Islam, 2016), (Vo Phuong Diem, 2016), (Nguyen, 2017), (Le

CR Credit Risk Provision for Credit Losses

(Bogale, 2019), (Vo Phuong Diem,, 2016), (Nguyen, 2017), (Le

Mesfin, 2019), (Abugamea, 2018), (Noman, 2015), (Islam, 2016), (Phuoc

& Bui, 2016), (Le Van Hop, 2021), (Le

(Islam, 2016), (Vo Phuong Diem, 2016), (Nguyen, 2017), (Le

Not calculated variable, taken from World Bank

Mesfin, 2019), (Abugamea, 2018), (Noman, 2015), (Islam, 2016), (Phuoc

INF Inflation Not calculated variable, taken from World Bank

Mesfin, 2019), (Islam, 2016), (Vo Phuong Diem, 2016), (Nguyen Thi Thu Hien, 2017), (Le

Data

As of 2024, there are currently 31 joint-stock commercial banks in Vietnam, according to statistics from the State Bank of Vietnam For this thesis, the author will select research based on data collected from 25 joint-stock commercial banks that have fully disclosed their financial reports audited annually within the scope of 10 years from 2013 to 2022 Some banks may not fully disclose financial information during the research period or may be under special control or mandatory restructuring due to poor performance In such cases, the author will conduct selective analysis based on the remaining 25 joint-stock commercial banks, estimated to represent 80.64% of the total number of joint-stock commercial banks in Vietnam

The author has chosen the period from 2013 to 2022 The thesis will utilize data from audited financial reports of the 25 listed joint-stock commercial banks, official bank websites, financial investment tools such as Finance Vietstock and Cafef, to calculate macroeconomic variables used in the model Additionally, macroeconomic data including GDP and inflation will be collected from the World Bank website

Table 3.2 List of 25 commercial banks

1 An Binh Commercial Joint Stock Bank ABB

2 Asia Commercial Joint Stock Bank ACB

3 Bank for Investment and Development of Vietnam BID

4 Vietnam Bank for Social Policies BVB

5 Joint Stock Commercial Bank for Foreign Trade of Vietnam CTG

6 Vietnam Export Import Commercial Joint Stock Bank EIB

7 Ho Chi Minh City Development Joint Stock Commercial Bank HDB

8 Kien Long Commercial Joint Stock Bank KLB

10 Military Commercial Joint Stock Bank MBB

12 Nam A Commercial Joint Stock Bank NAB

14 Orient Commercial Joint Stock Bank OCB

15 Petrolimex Joint Stock Commercial Bank PGB

16 Saigon Commercial Joint Stock Bank for Industry and Trade SGB

17 Saigon - Hanoi Commercial Joint Stock Bank SHB

18 Southeast Asia Commercial Joint Stock Bank SSB

19 Saigon Thuong Tin Commercial Joint Stock Bank STB

20 Vietnam Technological and Commercial Joint Stock Bank TCB

21 Tien Phong Commercial Joint Stock Bank TPB

22 Vietnam Asia Commercial Joint Stock Bank VAB

23 Joint Stock Commercial Bank for Foreign Trade of Vietnam VCB

24 Vietnam International Commercial Joint Stock Bank VIB

25 Vietnam Prosperity Joint Stock Commercial Bank VPB

Research process

This step is used to provide basic information about the variables constructed in the research model, and the indicators presented in the descriptive statistics include the sample size, mean, minimum value, maximum value, and standard deviation of the joint- stock commercial banks in Vietnam from 2013 to 2022

The correlation analysis method is applied to determine the linear relationship between variables in the model The correlation coefficient ranges from (-1) to (+1) Through the correlation coefficient, the direction of correlation between the dependent and explanatory variables can be determined When the independent variables have a higher correlation coefficient (+0.8), it indicates multicollinearity (Wooldridge, 2002)

Testing Pooled OLS, FEM, REM

Based on the results of the analysis using the Pooled OLS, FEM, and REM models, the study will determine how much each independent variable explains the variation in the dependent variable within each model, whether the independent variables are statistically significant, and assess the direction and magnitude of their impact on the dependent variable The regression results are considered quantitative evidence to address the research questions, evaluate the effects, and provide the foundation for selecting the most suitable model for the thesis

The Pooled Ordinary Least Squares (Pooled OLS) model

The Pooled Ordinary Least Squares (Pooled OLS) model is considered the simplest because it fundamentally disregards two factors: space dimension and time dimension in the panel data In the case of using Pooled OLS, it assumes that the effects of factors on profitability are homogeneous, applying uniformly to all banks and remaining constant over time These assumptions of this method are rarely met in reality because each bank exhibits certain differences, and the impact of various factors on surplus generation in each bank is not uniform, always varying over time units Therefore, the results are often less accurate The Pooled OLS model is represented by:

Note: i represents the bank under study, t represents the year of study βo is the intercept of the model, βi is the regression coefficient, μ is the error term of the regression model, representing the residuals and variables not included in the model

The Fixed Effects Model (FEM) assumes that each bank possesses unique characteristics inherent to its organization, and these idiosyncratic features of each unit affect the independent variables included in the model In other words, the independent variables and the error term contain specific characteristics of the bank that are correlated with each other These time-invariant fixed effects are unique to each bank, linked to the bank itself, and are unrelated to characteristics of organizations operating in the same industry Therefore, FEM has the ability to control, isolate the effects of these invariant idiosyncratic characteristics from the independent variables, thereby estimating the impact of the factors studied in the model on profitability However, FEM is not without its shortcomings as multicollinearity often occurs, leading to estimation of the model not meeting expectations

When the observed cross-units are not homogeneous, FEM is used to clarify the impact k of explanatory variables Xk,it on the dependent variable Yi,t Accordingly, FEM assumes that regression coefficients are the same across cross-units, with intercept coefficients distinguished between cross-units The FEM model is expressed as follows: α includes the intercept and omitted variable of each cross-unit, referred to as the characteristic parameter of the object, also known as the fixed effect component, invariant over time Additionally, α indicates the heterogeneity among cross-units due to the effects of unobservable variables, thereby FEM address omitted variable bias β is the common coefficient for all cross-units, reflecting all cross-units having similar growth rates; μ represents the residual term

This model assumes that the variation among units is assumed to be random and uncorrelated with the explanatory variables Essentially, REM is fundamentally an advancement from the Pooled OLS model, with the added feature of controlling for heterogeneity among financial organizations, but here, the correlation between the error term and the independent variables is not present Instead of considering α as fixed, the study assumes it is a random variable with a mean value of α

In the FEM model, each bank carries a distinct fixed intercept, so the number of banks corresponds to the number of intercepts, but for the REM model, this is not the case The entire set of intercepts will be represented by a single average intercept value, and the random deviation between individual intercepts and the mean value will be treated as the error component Although the error component is an unobservable variable, it has the capability to reflect influential factors The REM model is presented as follows: α represents the common intercept coefficient across all cross-sectional units; ε is the composite error term The component of ε reflects the distinctive impact of each cross-sectional unit and is referred to as the random effects component

Y is the uncorrelated disturbance among units (cross-sectional correlation) and is uncorrelated over time within the same unit

The F-test is conducted to choose between Pooled OLS and FEM

The F-test is used to select the appropriate regression model between FEM and Pooled OLS The hypotheses for the F-test are as follows:

Null Hypothesis (H0): The individual characteristics of the entities do not explain the dependent variable (Pooled OLS is more suitable than FEM)

Alternative Hypothesis (H1): The individual characteristics of the entities explain the dependent variable (FEM is more suitable than Pooled OLS)

After running the regression model using the FEM method, if the P-value < 0.05 with a significance level of 5%, then reject the null hypothesis (H0) and choose the FEM model

If the P-value > 0.05, then choose the Pooled OLS model

The Hausman test is conducted to choose FEM or REM

The Hausman test addresses whether there is a correlation between the residuals and the independent variables If there is no such correlation, then the Random Effects Model (REM) may be preferable to the Fixed Effects Model (FEM) If such correlation exists, the REM model will be considered inconsistent, and the FEM model will be chosen (Yaffee, 2003)

Therefore, to select between the FEM and REM regression models, the study will conduct the Hausman test In this test, the comparison between the two estimates from FEM and REM is conducted based on the following hypotheses:

Null hypothesis H0: βFEM = βREM (REM is the appropriate model)

Alternative hypothesis H1: βFEM ≠ βREM (FEM is the appropriate model)

If the P-value is less than 0.05 (at the 5% significance level), the null hypothesis H0 is rejected, and thus the FEM model is chosen Conversely, if the P-value is greater than 0.05, the null hypothesis H0 is accepted, and the REM model is chosen

Testing the Assumptions of the Selected Model (multicollinearity, autocorrelation, heteroskedasticity)

Multicollinearity is the phenomenon where independent variables exhibit linear correlation with each other in the research model To test for multicollinearity, the commonly used index is the Variance Inflation Factor (VIF) Using the correlation matrix of independent variables and VIF, if the VIF coefficient exceeds 10, it indicates a severe multicollinearity issue in the model (Guru, 2002) To address multicollinearity (if it exists), the author will exclude variables with VIF coefficients exceeding 10 from the research model

To examine the phenomenon of autocorrelation in the model, the researcher conducts the Wooldridge method with the hypothesis:

Null Hypothesis (H0): No autocorrelation occurs

If the P-value > 0.05 (at a significance level of 5%), H0 is accepted, rejecting H1 Thus, the model does not exhibit autocorrelation Conversely, if the P-value < 0.05 (at a significance level of 5%), H1 is accepted, rejecting H0, indicating that the model exhibits autocorrelation (Wooldridge, 2002)

The heteroskedasticity test is conducted following the Hausman test and model selection The Breusch-Pagan test is applied with the hypotheses as follows:

Null Hypothesis (H0): The model exhibits heteroskedasticity

Alternative Hypothesis (H1): The model does not exhibit heteroskedasticity

If the P-value is less than 0.05, H1 is rejected, and H0 is accepted Thus, the model shows signs of heteroskedasticity Conversely, if the P-value is greater than 0.05, H0 is rejected, and H1 is accepted Hence, the model does not exhibit heteroskedasticity

Model fix using FGLS (if there are issues related to auto correlation and heteroscedasity)

RESULTS AND DISCUSSION

Descriptive statistics

The results of conducting descriptive statistical analysis for the thesis are presented in the following table:

Table 4.1 Descriptive statistics of variables in the regression model

Variable Obs Mean Std dev Min Max

Referring to Table 4.1, the data for the study consists of 25 commercial banks in Vietnam during the period 2013-2022, comprising a total of 250 observations The descriptive statistics for each variable are as follows:

For the dependent variable ROA representing the profitability of commercial banks in Vietnam, the model shows an average ROA of 0.0086, with a standard deviation of 0.0068 Specifically, the smallest ROA value belongs to SHB bank in 2013, while the largest belongs to TCB bank in 2021 From this, it can be observed that there is significant variation in profitability among commercial banks in Vietnam, stemming from differences in their revenue-generating capabilities Banks with larger market shares find it easier to increase profitability Particularly, in recent years, TCB bank has consistently been ranked with the highest ROA in the banking industry

For the variable Bank Asset Size (SIZE), the average value is 18.8419, with a standard deviation of 1.1368 Additionally, SGB bank has the smallest asset size in 2013, while BIDV bank has the largest asset size in 2022

The variable LIQ represents the liquidity of the bank The average value of this indicator is 0.1702, with a standard deviation of 0.0686 The bank with the lowest liquidity is STB in 2017, while the highest is KLB bank in 2021

Regarding the Credit Risk Provision Ratio (CR), it achieves an average value of 0.0119, with a standard deviation of 0.0094 The highest value belongs to VPB bank in 2022, and the lowest value belongs to SHB bank in 2013

For the variable Capital Adequacy Ratio (CAP), the average value is 0.0879, with a standard deviation of 0.0329 Specifically, the smallest value belongs to BIDV in 2017, while the largest value belongs to SGB in 2013

The Operational Expense Ratio (OPR) has an average value of 0.2200, with a standard deviation of 0.1949 The bank with the lowest operational expense ratio is NVB in 2021, while the highest is SGB in 2022

The annual Economic Growth Rate (GDP) has an average value of 0.0589, with a standard deviation of 0.0173 The year 2022 marks the highest GDP value, while 2021 records the lowest value within the 10-year survey period

The inflation rate in Vietnam (INF) has an average value of 0.0320, with a standard deviation of 0.0146 Specifically, the year with the lowest inflation rate is 2015, and the highest inflation rate occurred in 2013.

Correlation analysis and multicollinearity diagnostics

Table 4.2 Correlation matrix between variables

ROA SIZE LIQ CR CAP OPR GDP INF

Based on Table 4.2 the results of the correlation analysis show that all independent and dependent variables have correlation coefficients with absolute values less than 0.8, indicating no signs of multicollinearity Variables SIZE, CAP, CR, and OPR are positively correlated with ROA Conversely, variables LIQ, INF, and GDP are negatively correlated with ROA Additionally, the variable with a high correlation coefficient with ROA is SIZE (0.4516)

From Table 4.3, it can be observed that the average VIF value is 1.27, and the values of the variance inflation factors range from 1.07 to 1.44, all of which are less than 10 Therefore, it can be concluded that the model does not exhibit multicollinearity.

Analysis of factors affecting ROA

Pooled OLS FEM REM β P_Value β P_Value β P_Value

Regression results using Pooled OLS

Based on Table 4.4, the R-squared coefficient is determined to be 0.5168, indicating that 51.68% of the variance in the dependent variable ROA is explained by the independent variables included in the model Among them, the independent variables INF, LIQ and

CR are not statistically significant in the model Meanwhile, the variables SIZE, CAP, OPR and GDP are statistically significant at the 5% significance level Among these, SIZE, CAP, and OPR have a positive impact on the dependent variable ROA, while the variable GDP has a negative impact on ROA

Results using Fixed Effects Model (FEM)

The results presented in Table 4.4 indicate that the R-squared coefficient is 0.4001, suggesting that the independent variables included in this model explain 40.01% of the variability in the dependent variable ROA Among them, the independent variables LIQ,

OPR, GDP and INF are not statistically significant However, the independent variables SIZE, CAP, and CR, are statistically significant at the 5% significance level Among these, SIZE, CAP have a positive impact on ROA, while CR has a negative impact

Regression results using Random Effects Model (REM)

Based on Table 4.4, the R-squared coefficient is 0.4422, indicating that 44.22% of the variation in the dependent variable ROA is explained by the independent variables included in the model Among them, the independent variables LIQ, OPR, GDP and INF are not statistically significant The remaining independent variables, SIZE, CAP, and

CR, are statistically significant at the 5% significance level Specifically, all independent variables SIZE, CAP have a positive impact on ROA, whereas the variable CR has a negative impact on ROA

In the previous section, three distinct regression models are proposed The author aims to ascertain the superior model among them Various statistical tests will be employed to select the optimal model in three sets of comparisons: Pooled OLS versus FEM, and Pooled OLS versus REM, FEM versus REM

Table 4.5 F-test, Breusch - Pagan and Hausman model selection for variable ROA

Breusch and Pagan Lagrangian test

Based on Table 4.5, the results of the F-test used to choose between the FEM and OLS models show that Prob = 0.0000 < α = 5%, thus rejecting the null hypothesis (H0: OLS model is appropriate) Therefore, the FEM model is deemed more suitable than the OLS model

Table 4.5 also displays the results of the Breusch-Pagan Lagrangian test to aid in choosing between the REM and OLS models The result is Prob.Chi2 = 0.0000 < α 5%, leading to the rejection of the null hypothesis Consequently, the REM model is deemed more suitable than Pooled OLS

Table 4.5 presents the results of the Hausman test for selecting between the REM and FEM models With Prob.Chi-Square = 0.000 < α = 5%, indicating that we should reject the null hypothesis (H0: εi and independent variables are uncorrelated) Therefore, the FEM model is deemed more suitable than the REM model

In summary, the FEM model is identified as the most appropriate model for analyzing the results Additionally, the author conducted tests for violations of assumptions such as autocorrelation and heteroscedasticity in the study The results of testing these violations will be analyzed in subsequent sections

4.3.3 Testing the hypothesis violations of the FEM

Table 4.6 Autocorrelation test and Heteroskedasticity test

In Table 4.6, Autocorrelation test has Prob > F = 0.0068, which is smaller than α = 5%, indicating that the model exhibits autocorrelation

The results also indicate that Prob > chi2 = 0.0000 of Heteroskedasticity test, which is smaller than α = 5%, demonstrating that the model exhibits heteroskedasticity

In this study, the FGLS method is employed to address the issues of autocorrelation and variable variance encountered in the FEM model The regression model yields the subsequent outcomes:

Table 4.7 ROA regression model results after fixing

ROA = -0.0841+ 0.0044SIZE + 0.0028 LIQ - 0.0331 CR + 0.1183CAP + 0.0058 OPR

According to the results of the regression model above, the relationships of the SIZE, CAP, and OPR variables with the dependent variable ROA are consistent with the initial hypothesis Conversely, the GDP and INF variables have a negative impact on ROA, contradicting the author's initial hypothesis Additionally, the two variables LIQ and CR are not statistically significant in the model, indicating that they do not influence ROA.

Discuss the influence of individual factors on ROA

The research findings have revealed that the relationship between bank size (SIZE) and Return on Assets (ROA) in commercial banks in Vietnam is positive, which is consistent with the initial hypothesis of the study As the bank size increases, the ROA also increases Specifically, when the bank size increases by 1 unit, holding other factors constant, the ROA of that bank increases by 0.0044 units, with a significance level of 1% The research results are similar to those of Bogale (2019), Abugamea (2018), Noman, (2015), Vo Phuong Diem (2016), Le Dong Duy Trung (2020) and Le Van Hop (2021)

The analysis does not provide strong evidence for a significant relationship between liquidity (LIQ) and Return on Assets (ROA) in commercial banks in Vietnam The coefficient associated with liquidity suggests a positive impact on ROA, indicating that higher liquidity may be associated with higher returns, but the p-value of 0.368 indicates that this relationship is not statistically significant at conventional levels Further investigation or replication of the study may be needed to ascertain the true nature of this relationship Similar findings were observed in the studies conducted by Bogale (2019) and Nguyen Thi Thu Hien (2017)

Similarly, the analysis does not support a significant relationship between credit rating (CR) and Return on Assets (ROA) in commercial banks in Vietnam While the coefficient for credit rating suggests a negative impact on ROA, indicating that higher credit ratings may be associated with lower returns, the p-value of 0.332 indicates that this relationship is not statistically significant at conventional levels Future research could explore alternative measures of creditworthiness or expand the dataset to confirm these findings The research findings align closely with those of Bogale (2019) and (Vo Phuong Diem (2016)

The research reveals a significant positive relationship between capitalization (CAP) and Return on Assets (ROA) in commercial banks in Vietnam As the level of capitalization increases, the ROA of banks also tends to increase Specifically, a one-unit increase in capitalization corresponds to a substantial 0.1183 unit increase in ROA, holding other factors constant

This result underscores the importance of Equity Capital for achieving higher returns on assets, consistent with the conclusions drawn by Bogale (2019), Abate & Mesfin (2019), Abugamea (2018), Noman (2015), Le Dong Duy Trung (2020), and Le Van Hop (2021)

The analysis demonstrates a significant positive relationship between operational profitability (OPR) and Return on Assets (ROA) in commercial banks in Vietnam As operational profitability increases, the ROA of banks also tends to increase Specifically, a one-unit increase in operational profitability corresponds to a 0.0058 unit increase in ROA, holding other factors constant This finding emphasizes the critical role of efficient operational management in driving higher returns on assets

This research outcome stands in stark contrast to previous studies, as Bogale (2019), Vo Phuong Diem (2016), Nguyen Thi Thu Hien (2017) and Le Dong Duy Trung (2020) all concluded that OPR has an inverse relationship with ROA

The research identifies a significant negative relationship between Gross Domestic Product (GDP) and Return on Assets (ROA) in commercial banks in Vietnam As the GDP of the economy increases, the ROA of banks tends to decrease Specifically, a one- unit increase in GDP corresponds to a 0.0294 unit decrease in ROA, holding other factors constant, with a statistically significant p-value of 0.036 This finding suggests that economic expansion may pose challenges for banks in maintaining their profitability, consistent with insights from (Bui & Ngo, 2009)

The research findings are consistent with those of Abate & Mesfin (2019), and Noman (2015) However, the findings of this study contradict those of (Phuoc & Bui, 2016)

The analysis reveals a significant negative relationship between inflation (INF) and Return on Assets (ROA) in commercial banks in Vietnam As inflation levels rise, the ROA of banks tends to decrease Specifically, a one-unit increase in inflation corresponds to a 0.0319 unit decrease in ROA, holding other factors constant, with a statistically significant p-value of 0.044 This result suggests that inflationary pressures may erode the profitability of banks, echoing findings from (Clews, 2019)

The study by the authorsAbate & Mesfin (2019), also indicates thatINF has an negative relationship with banks' profitability However, according toNoman (2015), and Le Van Hop, (2021) this independent variable has a positive impact on ROA Some studies by Abugamea (2018) and Phuoc & Bui (2016) suggest that INF does not affect ROA

Chapter 4 undertakes statistical analysis to describe the variables in the research model based on criteria such as the largest and smallest values, mean, and standard deviation The author employs correlation analysis to examine the strength and direction of the relationships between independent variables and the dependent variable, ROA, in Vietnamese credit institutions Regression analysis is conducted using Pooled OLS, FEM, and REM models, with FEM identified as the most appropriate model During model checking, issues such as autocorrelation and variable variance are detected, and FGLS is used to address these problems

Out of the seven proposed factors, five factors - Bank size (SIZE), CAP (Equity Capital), OPR (Operational cost), GDP Growth (GDP), Inflation (INF) - are found to have statistically significant impacts on the dependent variable ROA SIZE, CAP, and OPR exhibit positive effects on ROA, whereas GDP and INF have negative impacts Additionally, LIQ and CR are found to have no significant influence on the dependent variable in the model

Based on the research findings, to enhance ROA growth in Vietnamese credit institutions, the next chapter will discuss the results of the research questions and provide recommendations to help these institutions increase ROA by managing the identified influencing factors discussed in this chapter.

CONCLUSION AND RECOMMENDATION

Conclusion

This study examines the factors influencing ROA in commercial banks in Vietnam during the period of 2013-2022 To quantify the ROA of each bank included in the study, the author utilized the annual ROA differentials of 25 selected banks Based on theoretical frameworks and related empirical studies, the research proposed a study model and selected a research method suitable for the nature of the data, objectives, and research questions The explanatory variables considered include bank size, liquidity, credit risk, equity capital, operational costs, GDP growth, and inflation rate Through FGLS regression analysis, the study obtained empirical research results in Chapter 4 and identified the factors influencing ROA in Vietnamese commercial banks The findings from the study can be summarized as follows:

Firstly, micro-level factors originating from banks significantly affect ROA in Vietnamese commercial banks and are statistically significant, including bank size (SIZE), equity capital (CAP), and operational costs (OPR) Among these, CAP has a positive impact and is the most influential factor on the ROA of Vietnamese commercial banks Alongside, GDP and INF also have statistical relationship with ROA

Secondly, although not statistically significant, specific variables such as LIQ and CR still have considerable impacts on the ROA of commercial banks in Vietnam It can be concluded that, despite not being statistically significant, fluctuations in LIQ and CR may affect the profitability performance of banks Therefore, monitoring and evaluating these fluctuations are important and may need to be considered in the risk management strategies and business plans of commercial banks in Vietnam.

Recommendation

Bank profitability is positively correlated with bank size, indicating that increasing asset size can lead to higher profits Expanding bank scale can enhance the ability to attract capital and reduce fixed costs To ensure a solid foundation and safe operations, banks should prioritize efforts to boost equity

Recommended strategies include strengthening own capital through accumulated profits Accumulated profits serve as a sustainable source of capital for banks To accumulate profits, banks should focus on improving service quality, diversifying products, increasing service revenue, cost control, and effective capital utilization by lending to high-margin subjects while managing credit risk and limiting bad debt

Issuing additional shares to existing shareholders or attracting investment from foreign strategic partners presents a straightforward and sustainable method for capital expansion Banks can also gain management expertise and technology from partners However, to attract investors, banks must ensure attractive returns for shareholders and develop a clear plan for capital increase while complying with regulations

Considering bank mergers and consolidations is another strategy to be explored In recent years, regulatory bodies have actively addressed weak and inefficient banks, often employing merger and consolidation strategies to enhance efficiency

Although liquidity does not directly impact the profitability of banks, optimizing liquidity management and developing lending strategies can be an effective way to increase profitability in the current market environment

Recommended actions include stabilizing the liquidity system, enhancing the liquidity defense ratio, and reducing bad rates by improving asset quality Another measure involves focusing on implementing State Bank directives to upgrade risk management systems, debt management, and enhancing electronic banking systems like automatic ATMs and Mobile Banking

Despite the lack of direct impact on bank profitability, the optimization of credit risk management practices holds potential for bolstering financial performance By refining credit risk assessment methodologies, enhancing monitoring mechanisms, and implementing proactive measures to mitigate potential risks, banks can effectively safeguard their assets and maintain stability in lending operations Additionally, focusing on customer development strategies such as personalized financial solutions, targeted marketing initiatives, and superior customer service can foster stronger client relationships and increase customer loyalty This, in turn, can lead to higher retention rates, expanded market share, and ultimately, enhanced profitability for the bank By combining these efforts, banks can navigate the challenges of the current business landscape more effectively and capitalize on opportunities for sustainable growth

Given the positive impact of Equity Capital (CAP) on Return on Assets (ROA) as evidenced by the research findings, joint stock commercial banks in Vietnam should capitalize on this relationship to further enhance their profitability

Firstly, banks should prioritize efforts to strengthen their equity capital base through various means such as retaining earnings, issuing new shares to existing shareholders, or attracting investments from strategic partners This will not only bolster the bank's financial position but also enhance investor confidence and support future growth initiatives

Secondly, prudent capital allocation strategies should be implemented to ensure optimal utilization of equity capital, including directing investments towards high-yielding assets and profitable business segments

Additionally, banks should focus on improving operational efficiency and cost- effectiveness to maximize returns on equity capital By leveraging the positive impact of Equity Capital on ROA, banks can effectively enhance their financial performance and competitiveness in the market

Given the positive impact of Operational Cost (OPR) on Return on Assets (ROA), joint stock commercial banks in Vietnam can leverage this relationship to optimize their profitability

Firstly, banks should focus on implementing cost-saving measures and enhancing operational efficiency to minimize operational expenses without compromising service quality This may involve streamlining internal processes, adopting innovative technologies to automate routine tasks, and renegotiating vendor contracts to reduce costs

Secondly, banks can explore revenue-generating opportunities that align with their core competencies and strategic objectives This may include expanding fee-based services, cross-selling financial products, and diversifying revenue streams to offset operational costs

Additionally, continuous monitoring and management of operational expenses are essential to ensure sustainable profitability over the long term By capitalizing on the positive impact of Operational Cost on ROA, banks can enhance their financial performance and maintain a competitive edge in the market

GDP Growth shows a negative coefficient in the regression model, suggesting a potential adverse impact of GDP growth on ROA

As a recommendation, joint stock commercial banks in Vietnam should closely monitor economic indicators, including GDP growth, as they can provide valuable insights into the overall business environment and potential risks While the statistical significance may be lacking, banks should still consider the implications of negative GDP growth on their profitability and adjust their strategies accordingly This may involve implementing proactive measures to mitigate the adverse effects of economic downturns, such as strengthening risk management practices, diversifying revenue streams, and maintaining adequate capital buffers to withstand periods of economic instability

Additionally, banks should remain vigilant and agile in responding to changes in economic conditions to safeguard their financial performance and long-term sustainability

Inflation affect various aspects of a bank's operations, including loan demand, interest rates, and operational costs Therefore, banks should closely monitor inflation trends and anticipate potential changes in consumer behavior and market conditions to adjust their strategies accordingly This may involve adjusting interest rates on loans and deposits, diversifying revenue streams, managing operational costs, and maintaining capital adequacy

By implementing these measures, banks can mitigate the impact of inflation on their profitability and ensure financial stability amidst changing economic conditions.

Limitation of the study

The research investigates the impact of various factors on the Return on Assets (ROA) of commercial banks in Vietnam, focusing on seven main factors affecting ROA However, there are several other influential factors that the study did not analyze, as bank ROA is not only influenced by banking-specific characteristics but also by macroeconomic factors such as unemployment rates, exchange rate fluctuations, mandatory reserves, government bond yields, and stock market indices Moreover, legal and policy environments set by the government may also impact ROA For instance, changes in mandatory reserve requirements can affect the amount of funds banks have available for lending Additionally, shifts in government policies related to taxation or economic stimulus can influence ROA

The data from Vietnamese commercial banks is limited: although data from 25 commercial banks in Vietnam was collected, it may not be exhaustive Nonetheless, the proposed model is suitable for many banks From these limitations, future research directions could include expanding the sample size and extending the time frame to enhance the explanatory power of the research model

These limitations stem from constraints in time and resources Therefore, in future studies, researchers aim to further investigate and provide a more comprehensive assessment of ROA within the Vietnamese banking system Additionally, building models with better testing capabilities to identify more factors impacting ROA in the banking system aims to serve as a useful reference document for research purposes and provide essential recommendations for banks in formulating policies to enhance mobilization efficiency

Chapter 5 of the thesis draws conclusions based on the research findings and offers recommendations and policy implications aimed at enhancing the profitability of Vietnam's environmental banks Where factors positively impact ROA, the author suggests policies to bolster these indicators Conversely, for factors yielding negative impacts, the author proposes measures to mitigate their effects Additionally, the chapter acknowledges study limitations and suggests remedies, such as expanding the research sample and incorporating additional independent and dependent variables, to better elucidate factors influencing the profitability of commercial banks in Vietnam This endeavor aims to furnish a valuable resource for implementing profit-boosting strategies within Vietnamese banks

The banking sector plays a crucial role in the economic development of every nation A comprehensive understanding of the factors influencing ROA is vital as it can assist banks in making informed decisions about their operations and help policymakers devise effective policies to support the growth of the banking industry Based on empirical findings from 25 commercial banks in Vietnam over a period of 10 years (2013-2022), the study examined and identified internal factors from banks that impact ROA in commercial banks It addressed the specific research objectives posed in Chapter 1, including: i Systematizing the theoretical foundation regarding factors influencing the profitability of JSCBs to select an appropriate research model for the system of Vietnamese JSCBs ii Determining the factors, their direction, and the extent of their impact on the profitability of Vietnamese JSCBs iii Proposing managerial implications to improve the profitability of JSCBs in Vietnam

However, the answers provided for each question still have limitations In subsequent studies, the author will address these limitations, expand and delve deeper into the variables

In summary, as evidenced by the research results above, the author hopes to provide a more comprehensive understanding of the variables influencing the ROA of commercial banks to assist bank managers in increasing lending activities of commercial banks in Vietnam and ensure that the banking sector continues to develop safely, healthily, efficiently, and sustainably

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Picture 6: Model choice between FEM and REM

Picture 7: Model choice between Pooled OLS and FEM

Picture 8: Breusch and Pagan Lagrangian

Tiêu đề	Factors Influencing Profitability Of Joint-Stock Commercial Banks In Vietnam
Tác giả	Vo Anh Thu
Người hướng dẫn	Assoc. Pro. PHD. Le Phan Thi Dieu Thao
Trường học	Banking University of Ho Chi Minh City
Chuyên ngành	Finance – Banking
Thể loại	Graduation Thesis
Năm xuất bản	2024
Thành phố	Ho Chi Minh City

Định dạng
Số trang	82
Dung lượng	1,17 MB