Impact of covid 19 pandemic on factors affecting profitability of commercial banks in vietnam

BANKING FACULTY GRADUATION THESIS Impact of Covid-19 pandemic on factors affecting profitability of commercial banks in Vietnam Hanoi, 2023... of Covid-19 pandemic on factors affecting

Significance of the study

The finance industry in Vietnam, like its global counterparts, is currently navigating significant challenges due to the pressures of integration and rapid globalization in the international economic landscape Ensuring sustainable and efficient development has become a critical focus for financial institutions amidst these evolving dynamics.

Commercial banks are facing increasing pressure from non-bank financial intermediaries, such as Fintech companies and international banks, which intensifies competition in the financial sector The ability of banks to adapt and respond to the rapidly changing economic landscape, particularly during the Fourth Industrial Revolution, will be crucial for their competitiveness This transformation necessitates flexible management systems and the continuous upgrading of processes, products, and services to align with technological advancements and integration trends Banks that fail to compete effectively may be supplanted by those that can operate more efficiently Consequently, operational efficiency is a key determinant of a bank's sustainability and competitiveness in today's economy.

At the end of 2019, the Covid-19 pandemic emerged, severely impacting the global economy and leading to challenges that persisted through 2020 and 2021, including a notable decline in global GDP of -3.1% While global GDP rebounded to 5.5% in 2021, Vietnam's GDP continued to decline This economic downturn has posed significant challenges for Vietnam's banking industry, which is struggling with reduced capital mobilization and credit growth amidst the broader economic difficulties.

In the context of Vietnam's integration and globalization, as well as the significant role of the banking system, this research explores the "Impact of the Covid-19 Pandemic on Factors Affecting the Profitability of Commercial Banks in Vietnam." By addressing the practical demands and urgent needs arising from the pandemic's economic effects, the study aims to offer a comprehensive perspective that contributes to the sustainable development and operational efficiency of Vietnam's banking sector.

Research objectives

- The theoretical basis of operational efficiency will be researched, specifically in terms of profitability, and the factors affecting the profitability of commercial banks will be identified and explained

This article evaluates the profitability of commercial banks before and after the Covid-19 pandemic, focusing on key factors that influence bank profitability It compares the differences in financial performance between these two periods to provide a comprehensive analysis of the pandemic's impact on the banking sector.

- Proposed solutions will be provided to improve and optimize the operational efficiency and profitability of commercial banks in Vietnam.

Research subjects and scope

This study investigates the factors influencing the efficiency and profitability of commercial banks in Vietnam, acknowledging the complex and diverse elements that affect profitability and the challenges in data access To construct the research model, it will utilize findings from previous studies to identify key variables, including return on assets (ROA), equity to assets ratio (EAR), credit risk (ALLL), operating efficiency (OEOI), non-term deposits (CASA), bank size (Size), exchange rates (EXR), and inflation growth rate (INF).

This research examines 15 commercial banks in Vietnam, categorizing them into state-owned, joint-stock, and international banks The analysis will specifically include three state-owned banks and twelve joint-stock banks, spanning an 11-year period from 2012 to 2022.

Overview of empirical researches on banks’ profitability

Numerous domestic and international studies have examined the factors affecting the profitability of commercial banks Key international research includes works by Gazi et al (2022), Ichsan et al (2021), Xiazi & Shabir (2022), X Li et al (2021), Jean Paul (2021), Artha & Mulyana (2018), and F Fuadi et al (2022), among others Notable contributions also come from MR Affandi (2022), Q Ali et al (2018), YR Bhattarai (2016), and R Apriyanti et al., highlighting a diverse range of insights into bank profitability.

(2021) and Jamel & Mansour (2018) In addition, some domestic studies have been conducted by research groups such as Nguyen & Dang (2022); Nga et al (2022) and

Gazi et al (2022) conducted a study on the impact of CAMELS model and macroeconomic factors on the profitability of commercial banks in Bangladesh from

From 2010 to 2021, a study utilizing the Fixed Effects Model (FEM) assessed the profitability impacts of various factors before and during the Covid-19 pandemic Key findings indicated that the non-performing loan ratio (NPLR) and bank size negatively influenced return on assets (ROA), return on equity (ROE), and net interest margin (NIM) in both periods The capital adequacy ratio (CAR) similarly affected ROA and ROE, while the loan to deposit ratio (LDR) only had a negative impact on ROA during the pandemic Additionally, the total equity to total assets ratio (EAR) and inflation rate (INFR) positively influenced ROA in both periods Notably, the liquid asset to total assets ratio (LATAR) negatively impacted ROA and ROE during Covid-19, though this was not significant beforehand The GDP growth rate in Bangladesh negatively affected ROA and ROE pre-pandemic, while its impact became insignificant during the pandemic; however, it positively influenced NIM during this period Lastly, the real interest rate (INTR) had a positive effect on NIM but showed no significant relationship with ROA and ROE.

A study by Ichsan et al (2021) examined the factors influencing the financial performance of Indonesian Sharia banks during the Covid-19 pandemic, analyzing data from multiple Islamic banks between 2011 and 2020 Utilizing Multiple Linear Regression and the Ramsey test for linearity, the research revealed that the capital adequacy ratio (CAR) positively and significantly impacts the return on assets (ROA) Interestingly, the operating costs to operating income ratio (BOPO), typically inversely related to ROA, also demonstrated a positive and significant influence on the financial performance of Sharia banks Conversely, the not performing financing (NPF) was found to have a negative and insignificant effect on ROA.

In October 2022, Xiazi & Shabir conducted a study analyzing the impact of Covid-19 on the performance of 1,575 banks across 85 countries from Q1 2020 to Q4 2021 Utilizing the Fixed Effects Model (REM) as a baseline and the Generalized Method of Moments (GMM) to address potential endogeneity issues, the study revealed that the Covid-19 pandemic significantly and negatively affected bank performance Notably, smaller, undercapitalized, and less diversified banks experienced the most severe impacts, while banks with stronger financial development and a favorable institutional environment were less adversely affected.

X Li et al (2021) investigated the impact of revenue diversification on bank profitability and risk during the Covid-19 pandemic through a regression model Their findings revealed that noninterest income positively influences bank performance, as indicated by Return on Assets (ROA) and Return on Equity (ROE) However, the study also highlighted that noninterest income has a negative and significant effect on bank risk measures Additionally, the research concluded that banks with strong performance prior to the pandemic are likely to maintain their success, while those with higher risk profiles may encounter increased challenges amid the ongoing crisis.

Jean Paul (2021) examined the effectiveness of the CAMEL model in evaluating the performance of commercial banks in Rwanda from 2014 to 2018, utilizing descriptive statistics and a panel regression model The study found that capital adequacy and asset quality positively influence the financial performance of banks, while the management efficiency ratio and earnings management negatively affect it Notably, a decrease in net interest margin was associated with an increase in banks’ return on assets (ROA), and higher liquidity management correlated with lower financial performance Additionally, the study introduced bank size as a mediator variable, but it did not significantly impact the relationship between the independent and dependent variables.

A study by Artha & Mulyana (2018) analyzed the influence of internal and external factors on state-owned banks in Indonesia from 2012 to 2017 using the Fixed Effects Model (FEM) The findings revealed that the Current Account Saving Account (CASA) and Net Interest Margin (NIM) both have a positive and significant impact on the Return on Assets (ROA) of these banks Conversely, while the Capital Adequacy Ratio (CAR) was shown to negatively affect ROA, this effect was not statistically significant Additionally, external factors such as inflation and economic growth were found to have a negative and significant impact on ROA, whereas the Bank Indonesia Reference Interest Rate (BI Rate) positively and significantly influenced ROA.

A study by Fuadi et al (2022) examined the effects of inflation, the Bank Indonesia rate (BI rate), and exchange rates on the profitability of Islamic banks in Indonesia from 2009 to 2019, utilizing Vector Auto-Regressive (VAR) analysis The findings revealed that inflation negatively impacts the Return on Assets (ROA) index, but the effect is minimal at only 0.62%, rendering it insignificant Similarly, the BI rate affects the ROA by just 0.13%, suggesting an indirect influence that is also insignificant These results contrast with Artha & Mulyana (2018), which found a significant impact of inflation and the BI rate on the ROA of state-owned banks in Indonesia The discrepancies may stem from the different types of banks studied, as Fuadi et al focused on Islamic banks while Artha & Mulyana concentrated on state-owned institutions.

A study by Ali et al (2018) examined the influence of macroeconomic factors on the profitability of Islamic banks in Brunei from 2012 to 2016, utilizing a fixed effects panel regression model The research identified that GDP growth rate, inflation rate, exchange rate, oil prices, and money supply positively affected the banks' profitability, with oil prices, GDP growth rate, and inflation rate being the most significant indicators.

A study by Bhattarai (2016) evaluated the impact of credit risk on the performance of 14 commercial banks in Nepal using a pooled data regression model The research found that the capital adequacy ratio had a positive but insignificant effect on return on assets (ROA), while the non-performing loan ratio demonstrated a strong negative relationship with ROA Interestingly, the cost per loan assets significantly and positively influenced bank performance, identifying it as a key credit risk variable Additionally, the cash reserve ratio showed a negative and insignificant association with ROA, whereas bank size had a significant positive relationship with performance, suggesting that larger banks in Nepal enjoy better profitability due to greater growth opportunities and improved loan diversification.

A study by Jamel & Mansour (2018) analyzed the factors influencing bank profitability in Tunisia from 1999 to 2016 using the General Least Squares (GLS) technique The findings revealed that the ratio of owner equity to total assets and bank size positively and significantly affected the Return on Assets (ROA) of Tunisian banks Conversely, credit risk demonstrated a strong negative impact on ROA Furthermore, macroeconomic variables such as the inflation rate and GDP growth rate were determined to have no effect on the performance of banks in Tunisia.

A study by Nguyen & Dang (2022) examined the factors influencing the profitability of commercial banks in Vietnam from 2014 to 2020, utilizing an adjustment model on the FEM framework The research identified a positive relationship between the total equity to total assets ratio and the return on assets (ROA) of these banks While bank size was found to impact profitability, this effect was deemed insignificant Conversely, operating expenses relative to total assets, tax expenses, and credit risks were found to significantly and negatively affect bank performance Additionally, the study revealed that both the total loans to total assets ratio and the total loans to total deposit ratio had a positive yet insignificant correlation with ROA Importantly, macroeconomic variables such as GDP growth and inflation rates showed no statistical relationship with the ROA of Vietnam's commercial banks.

Moreover, Nga et al (2022) conducted a study on the impact of Covid-19 on the business performance of 21 commercial banks in Vietnam during the period 2012-

In 2021, a study employing a Random Effects Model and Feasible Generalized Least Squares (FGLS) method examined the impact of Covid-19 on bank profitability, specifically focusing on Return on Assets (ROA) and Return on Equity (ROE) The independent variable "Covid-19" was coded as 1 for affected banks and 0 for unaffected banks Findings revealed a significant negative impact of Covid-19, with ROE decreasing by up to 4.066 times for affected banks The authors concluded that the pandemic's adverse effects on bank profitability aligned with economic shock theories, particularly in the context of Vietnam, where businesses faced operational challenges and difficulties in meeting financial obligations to banks Additionally, the study identified cost management ability (CIR) and non-performing loan ratio (NPL/TL) as significant negative factors influencing ROE, while the TE/TA variable showed a positive but insignificant effect on ROA.

A study by HT Lam & NNH Anh (2022) analyzed the factors influencing the profitability of listed commercial banks in Vietnam from 2010 to 2020 using the Feasible Generalized Least Squares (FGLS) model The findings revealed that bank size, liquidity risk, economic growth, and inflation positively affect profitability, while the expenses to income ratio, financial leverage, and credit risk negatively impact it The research highlighted that profitability is influenced by both internal bank factors and external macroeconomic variables Notably, bank size correlates positively with profitability across most Vietnamese bank groups, as larger banks benefit from product diversification and enhanced brand competitiveness, gaining greater trust from clients.

Numerous studies, both domestic and international, have explored the factors influencing the profitability of commercial banks, yet most focus on various banking systems across different countries and timeframes In Vietnam, however, the impact of the Covid-19 pandemic on bank profitability has not been thoroughly examined This study aims to fill that gap by offering valuable insights into the current situation of commercial banks in Vietnam.

THEORETICAL BASIS ON THE FACTORS AFFECTING

Overview of Commercial Banks

1.1.1 The concept of Commercial Banks

Commercial banks have played a crucial role in economic development throughout history, evolving from simple banking systems into sophisticated financial conglomerates Despite their transformation and modernization to meet the diverse needs of the market, the fundamental definition of commercial banks remains consistent.

Commercial banks serve as vital financial intermediaries, facilitating the flow of capital between savers and borrowers in the economy Their primary functions include mobilizing deposits from individuals and organizations and utilizing these funds for lending and investment in profitable assets Additionally, commercial banks offer a range of financial and payment services tailored to meet the diverse needs of the market.

Based on the concept of commercial banks, it can be seen that commercial banks play an extremely important role in the movement and regulation of capital in the economy

Commercial banks serve as vital financial intermediaries in the economy, effectively mobilizing idle funds from savers and channeling them to borrowers in need of capital for investments or business activities By offering a diverse array of products tailored to meet varying financial requirements, these banks successfully address the short, medium, and long-term capital demands of the market.

Commercial banks serve as payment intermediaries, facilitating transfer transactions for customers to purchase goods and services They offer various products and services, including check issuance and clearing, salary payments, and electronic payment networks, ensuring smooth and efficient financial transactions.

Third, with the role of providing guarantee services, commercial banks will commit to repaying debts to customers in case customers are insolvent

Commercial banks play a crucial role in executing macroeconomic policies, particularly monetary policy, which is essential for moderating economic growth and achieving national objectives.

Theoretical informations of profitability of commercial banks

1.2.1 The concept of profitability of commercial banks

The profitability of commercial banks is a vital metric that bank management must prioritize, as it serves as a key indicator of operational effectiveness and financial performance It is assessed through a combination of business performance and the efficient utilization of bank resources Essentially, a bank's profitability is determined by the difference between its generated revenue and the total costs incurred.

The profitability of a bank is commonly assessed using key indicators such as return on average assets (ROA), return on average equity (ROE), and net interest margin (NIM) Among these, ROA is frequently utilized as the primary financial ratio, measuring the relationship between a bank's after-tax profit and its average total assets over a specific period This indicator evaluates the bank's efficiency in managing revenues and expenses, highlighting its ability to convert assets into net profit Consequently, ROA serves as a comprehensive measure of a bank's operational effectiveness.

1.2.2 Importance of evaluating profitability of commercial banks

Evaluating the profitability of commercial banks is essential in today's globalized economy and competitive financial market A key indicator of this profitability is their capacity to generate profits Assessing the profitability of commercial banks serves several important functions, including providing insights into their financial health and operational efficiency.

Evaluating profitability is essential for bank managers to assess the effectiveness of the bank's operations comprehensively By focusing on optimizing profitability, managers can formulate strategic business plans and adopt appropriate models to improve the bank's competitive edge in the market.

The capacity to analyze specific factors influencing a bank's profitability provides management with deeper insights into critical issues affecting performance This understanding empowers them to make informed decisions that enhance operations while effectively balancing the associated benefits and risks that impact the bank's profits.

Analyzing and evaluating profitability is crucial for assessing the effectiveness of a bank's operations By measuring these financial metrics, banks can identify areas for improvement and implement strategies to enhance profitability, which is vital for effective bank management.

In Vietnam and globally, key indicators for assessing a bank's profitability include Return on Assets (ROA), Return on Equity (ROE), and Net Interest Margin (NIM) ROA and ROE are the most prevalent metrics used, while NIM is typically applied in studies concentrating on net interest income rather than a comprehensive analysis of overall profitability The formulas for calculating these indicators are essential for accurate evaluation.

The Return on Assets (ROA) ratio measures the relationship between a company's generated profit and its invested capital in assets, assessing the effectiveness of asset management and investment in yielding profits However, ROA can differ widely across industries due to varying business operations and asset types.

Banks exhibit higher asset intensity compared to other industries, resulting in larger amounts of assets like loans, investments, and deposits on their balance sheets This increased asset volume can lead to a lower Return on Assets (ROA), as it often incurs higher costs, reduces profit margins, and slows asset turnover Given that a significant portion of banks' revenue comes from interest income on loans and investments, the ROA ratio effectively reflects the quality of their operating activities.

Return on Equity (ROE) is a key financial metric that assesses a company's profitability in relation to shareholder equity For banks, ROE is crucial as it indicates how efficiently they utilize shareholder capital to generate profits.

Banks typically operate with higher leverage compared to other industries, utilizing more debt to finance their activities This strategy can result in a higher return on equity (ROE) since banks can generate significant income from a relatively small amount of shareholder capital However, this increased leverage also exposes banks to greater risk If loans or investments underperform, banks may struggle to meet their debt obligations, potentially leading to financial distress or bankruptcy To mitigate these risks and maintain customer and investor confidence, banks are mandated to uphold minimum capital levels to absorb potential losses.

Net Interest Margin (NIM) is a crucial performance metric for banks and financial institutions, reflecting the difference between interest income from loans and investments and the interest expenses on deposits and liabilities This difference serves as a primary source of income for banks, highlighting the importance of effective interest rate management.

A higher NIM means that a bank is earning more interest income, which can help it generate higher profits

A higher net interest margin (NIM) can signify that a bank is engaging in riskier investments, such as loans to borrowers with poor credit ratings, which typically offer higher interest rates While this can enhance profitability, it also increases the likelihood of defaults, thereby exposing the bank to greater credit risk.

Monitoring Net Interest Margin (NIM) is crucial for banks to align profitability with risk management Achieving a balance between profit generation and risk mitigation is essential for long-term financial stability A bank with a high NIM that engages in excessive risk-taking may encounter significant financial distress due to potential defaults or losses in its lending activities.

Factors affecting profitability of commercial banks

To effectively assess a bank's operational performance, it is essential to consider various internal and external factors Internal elements include financial capabilities, operational management skills, technological advancements, and workforce quality, as highlighted by Nguyen V.H (2008) Conversely, external factors influencing a bank's profitability encompass economic, political, and social environments, both locally and globally, which lie outside the bank's direct control.

This article aims to identify key factors influencing bank profitability and propose solutions for optimization It will evaluate financial indicators and business efficiency, while also considering macroeconomic factors like inflation rates, GDP growth, and exchange rates to assess their impact on bank performance over time.

1.3.1 Bank-specific factors a Capital adequacy determinants

Capital adequacy is the minimum capital requirement mandated for financial institutions, ensuring they can meet loan obligations when borrowers default (Jean Paul, 2021) This reserve acts as a risk-hedging tool during adverse conditions, making it essential for assessing the financial health of banks and their capacity to manage term debts and other risks Consequently, government regulatory bodies continuously monitor and evaluate the capital adequacy of these institutions.

Capital adequacy is commonly assessed using key financial ratios, with the most prevalent being the Equity to Asset ratio and the Capital Adequacy ratio The Equity to Asset ratio is calculated by dividing Total Equity by Total Assets, while the Capital Adequacy ratio is determined by dividing core capital by risk-weighted assets.

Equity to Asset ratio (EAR) = !"#$% /0*#1

The equity to asset ratio is an important measure of a bank's financial health

The equity to asset ratio reflects the percentage of a bank's assets funded by shareholder equity rather than debt A higher ratio signifies a greater buffer of shareholder equity available to absorb potential losses, enhancing the bank's resilience in times of financial distress.

A higher equity to asset ratio is typically linked to increased profitability, as indicated by return on assets (ROA), since it reduces a bank's dependence on debt financing, thereby lowering borrowing costs and enhancing financial stability However, the impact of the equity to asset ratio on ROA is not universally applicable, as it can fluctuate based on factors such as the bank's business model, risk tolerance, and financial strategy Despite this variability, a higher equity to asset ratio is generally considered a positive sign of a bank's financial health and can contribute to improved performance.

The capital adequacy ratio (CAR) measures a bank's ability to withstand losses and meet its financial obligations, calculated by dividing regulatory capital by risk-weighted assets A higher CAR indicates that a bank has more capital available to absorb losses, enhancing its resilience during financial challenges.

A higher capital adequacy ratio (CAR) is often associated with increased profitability, as indicated by return on assets (ROA) This correlation exists because a robust CAR reduces the likelihood of bank insolvency, leading to lower borrowing costs and enhanced financial stability However, it's important to recognize that the relationship between CAR and ROA can vary among banks, influenced by various other factors.

The quality of assets is a crucial indicator of financial strength within the banking sector, as maintaining high asset quality is fundamental to banking operations Evaluating asset quality primarily focuses on determining the ratio of nonperforming assets to total assets, which is vital for assessing a bank's overall financial health (Altan et al., 2014).

Poor asset quality is a primary factor contributing to bank failures, often resulting from inadequate lending management practices When the market perceives low asset quality, it can trigger pressure on the bank's short-term funding, potentially leading to a liquidity crisis or a bank run Therefore, it is crucial to assess a bank's asset quality comprehensively and implement strategies to enhance it, ultimately boosting the bank's profitability and operational effectiveness.

Non-performing loan ratio (NPL) = !"#$% +"+46(3B"3-*+9 C"$+'

Non-performing loans (NPLs) are loans for which borrowers have not made timely payments or have defaulted, posing a significant challenge for banks and financial institutions The prevalence of NPLs can adversely affect a bank's financial stability and overall performance.

Higher levels of non-performing loans (NPLs) are linked to decreased profitability, as indicated by lower return on assets (ROA), since NPLs can result in bank losses that diminish net income and weaken the capital base Additionally, banks burdened with high NPLs may encounter increased borrowing costs, as lenders perceive them as riskier and demand higher interest rates, further impacting profitability and overall performance Moreover, non-performing loans can drain significant resources, including personnel, time, and legal expenses, diverting attention from other critical operations and potentially harming the bank's overall effectiveness.

Loan to Deposit ratio (LDR) = !"#$% C"$+'

The loan-to-deposit ratio (LDR) is a measure of a bank's loan portfolio relative to its deposit base It is calculated by dividing a bank's total loans by its total deposits

A higher LDR indicates that a bank is lending more of its deposits, while a lower LDR indicates that a bank is holding a higher proportion of its deposits as reserves

The Loan-to-Deposit Ratio (LDR) significantly influences a bank's performance, particularly its return on assets (ROA) Generally, a higher LDR correlates with increased profitability, as it reflects a bank's ability to lend more of its deposits and earn greater interest income However, elevated LDR levels can also heighten a bank's risk, potentially leading to liquidity shortages and increased borrowing costs if funds are needed to fulfill obligations.

A low Loan-to-Deposit Ratio (LDR) may suggest that a bank is maintaining excess reserves, potentially hindering its profitability by limiting interest income from loans On the other hand, this lower ratio can reflect a bank's conservative lending approach, making it less susceptible to liquidity shortages and credit risk.

Management quality in banks encompasses various aspects, including asset, capital, cost, labor, and organizational structure management This study will focus specifically on profit and cost management, as it serves as the most direct and clear indicator of a bank's profitability.

CURRENT STATE

Situation of the Vietnamese economy and banking system before the Covid-19 period (2012-2019)

2.1.1 Situation of the economy before Covid-19

In 2018, Vietnam's economy experienced significant expansion, achieving a GDP growth rate of 7.08%, surpassing the planned target of 6.7% and marking the highest growth since 2011 This growth reflects a successful structural transformation, with productivity from synthetic factors contributing 43.5% to GDP growth, compared to an average of 33.6% from 2011 to 2015 Additionally, Vietnam's labor productivity has shown consistent improvement, establishing the country as a leader in labor productivity growth within the ASEAN region.

In 2018, Vietnam's GDP composition included 14.57% from agriculture, forestry, and fisheries, 34.28% from industry and construction, and 41.17% from the service sector, with product tax excluding subsidies at 9.98% The country achieved a trade surplus of USD 7.2 billion, highlighting its effective integration into global markets and underscoring the vital role of exports in driving GDP growth.

The restructuring of the labor force has been accelerated by the growth of various sectors, with agriculture, forestry, and fisheries now employing 38.5% of the workforce, while industry and construction account for 26%, and the service sector comprises 35.3% The overall unemployment rate stands at 2.2%, a slight decrease from 2017, with urban areas experiencing a rate of 3.09% and rural areas at 1.75%.

Graph 1: Investment structure of Vietnam in 2018 (trillion dong)

In 2018, the investment structure revealed that the non-state sector comprised 42.5%, FDI accounted for 23.9%, and the state sector made up 33.6% The non-state enterprises led the way in new business registrations, with 131,300 companies and a total registered capital of 1,478.1 trillion dong, reflecting a 3.5% increase in the number of businesses and a 14.1% rise in registered capital compared to 2017, resulting in an average registered capital of 11.3 billion dong per enterprise This sector has shown positive long-term growth, as evidenced by large private enterprises adopting investment strategies focused on capital, technology, and high-tech agriculture, while increasingly adapting to a challenging business environment for technological innovation.

In 2018, foreign direct investment (FDI) in Vietnam surpassed 35.46 billion USD, with nearly half stemming from joint ventures and share purchases, reflecting investors' long-term strategies and confidence in the country's future This positive trend is attributed to an improved investment climate and Vietnam's commitment to integration Additionally, the implemented FDI capital reached 19.1 billion USD, marking a 9.1% increase from 2017, significantly contributing to the nation's economic growth.

The processing and manufacturing industry remains the leading sector for foreign direct investment (FDI), attracting newly registered capital of $16.58 billion, which constitutes 46.7% of total FDI The real estate sector follows, with nearly $6.6 billion, accounting for 18.6% of the total Among investment partners, Japan leads with $8.6 billion (36%), followed by South Korea at $7.2 billion (28.9%) and Singapore at $5 billion (18.7%), while China contributes close to $1 billion.

In 2018, despite some concerns regarding inflation risks, the average inflation rate rose only to 3.54%, remaining below the established target This achievement was made possible through a flexible and cautious fiscal policy that was closely aligned with monetary policy, ensuring both macroeconomic stability and the attainment of growth objectives.

Limitations and difficulties of the Vietnamese economy before Covid-19:

The manufacturing sector remains the primary driver of economic growth, particularly in advantageous FDI industries, though the overall growth quality has improved only gradually Structural shifts within the domestic industry are slow, with emerging new industries and 4.0 technology applications not yet robust enough to significantly alter the product and industry landscape While trade activities are expanding, exports have seen limited changes in value, product structure, and market diversification, with over 70% still originating from FDI and primarily consisting of traditional goods Additionally, Vietnam has yet to fully leverage the potential of ASEAN and Chinese markets.

In 2018, the business environment showed significant improvement, driving an increase in new business registrations Over 40% of these new businesses are focused on commercial activities, particularly in wholesale and retail trade, as well as motor vehicle and motorcycle repair The real estate sector experienced remarkable growth, with a 42% rise in registered businesses In contrast, vital sectors like manufacturing and processing saw minimal growth at just 0.6%, while science, technology, and consulting services grew by 6.6%, and education and training by 12% However, some sectors faced declines, notably information and communication, which decreased by 3.7%, and agriculture, forestry, and fisheries, which dropped by 5.5%.

According to information from the World Economic Forum in 2018, among

Despite being one of 140 economies, Vietnam's innovative businesses experience low growth rates due to an insufficiently motivating business environment Key challenges include a poor information infrastructure ranking at 95th, low rankings for trained personnel skills at 128th, difficulty in finding skilled labor at 104th, vocational training quality at 115th, intellectual property rights at 105th, and inadequate regulations on reporting standards and auditing at 128th.

The intrinsic limitations of the economy have significantly affected the strategy and business quality of the domestic business sector, as well as the pace of economic development

2.1.2 Situation of the banking system before the Covid-19 period a The situation of credit growth

In the period from 2014-2019, Vietnam has had outstanding credit growth compared to previous periods This growth rate is many times higher than GDP growth rate in the same period

Graph 2: Credit growth rate and GDP growth rate of Vietnam from 2014-2019 (%)

Source: State Bank of Vietnam

The global economy's recovery following the 2008-2011 financial crisis, coupled with optimistic domestic economic forecasts, has significantly fueled credit growth within the banking system Notably, from 2016 to 2017, commercial banks experienced remarkable credit growth, reaching approximately 18.25%, which is three times the GDP growth rate for that period This surge in credit is attributed to various factors influencing the economy and the anticipated revival of production, business sectors, and individual incomes Although credit growth slowed in 2018-2019, this decline was deemed necessary to reflect an expanding scale Additionally, the introduction of legal measures aimed at enhancing banking safety has moderated rapid credit expansion while promoting sustainable banking practices.

Non-performing loans (NPLs) in Vietnam's commercial banking system began to rise in 2007 due to high credit growth and inadequate loan quality and risk management From 2008 to 2011, NPLs surged by 51%, significantly outpacing the average credit growth rate during that time By the end of 2012, NPLs reached VND 85,000 billion, representing 4.86% of total outstanding loans, before declining to 2.46% at the end of 2016 and slightly increasing to 2.56% by February 2017.

Graph 3: Bad debt ratio in Vietnam from 2007-2016 (% total loans)

The surge in bad debt in Vietnam from 2007 to 2012 can be attributed to several key factors: the country's broad economic development model, a loosening of monetary policy in 2006-2007 that increased credit volume without enhancing credit quality, and the rapid growth of hot credit during 2009-2010 Additionally, the repercussions of the 2008 financial crisis severely impacted export-oriented enterprises, while significant proportions of credit were directed towards real estate and securities lending, exacerbating bad debt during market downturns in 2008-2009 and 2011-2012 Furthermore, deficiencies in the management, appraisal, and supervision of bank loan usage contributed to this issue Overall, the causes of bad debt in Vietnam are intertwined with both objective economic factors and subjective shortcomings within the banking system and regulatory agencies.

The bad debt ratio of the Vietnamese banking system was relatively stable in the period 2016-2019 Here are some key numbers:

By the end of 2016, Vietnam's banking system recorded a non-performing loan (NPL) ratio of 2.5%, a slight decrease from 2.55% in 2015 This positive trend continued into 2017, with the NPL ratio further declining to 1.89%, attributed to the effective measures implemented by the State Bank of Vietnam (SBV) and commercial banks to tackle bad debts.

In 2018, the non-performing loan (NPL) ratio was 1.89%, but it rose to 1.98% in 2019, largely influenced by the economic challenges posed by the COVID-19 pandemic towards the year's end.

Situation of Vietnam's economy and banking system during and after Covid-19 (2020-2022)

2.2.1 Impact of Covid-19 on Vietnam's economy

The Covid-19 pandemic has significantly affected Vietnam's economy and society, causing widespread challenges across various sectors Nevertheless, through the collective efforts of the government, businesses, and citizens, the negative impacts of the pandemic are gradually diminishing.

The tourism industry in Vietnam experienced a significant decline during the pandemic, with international visitor numbers plummeting by 50-60% The ongoing complexities of Covid-19 severely impacted tourism for over two years, causing total revenue to drop from VND 755 trillion in 2019 to VND 312 trillion in 2020, marking a staggering decrease of 58.7% This downward trend continued in 2021, with revenue further declining to VND 180 trillion, a 42.3% reduction compared to the previous year.

After the tourism industry, the aviation industry is the most severely affected sector by the Covid-19 pandemic In the report on the development of enterprises in

Between 2020 and May 2021, the aviation market experienced a significant decline, with air transport demand plummeting by 34.5% to 65.9% Airline revenue also saw a dramatic decrease of 61% compared to 2019 The third Covid-19 outbreak during the Lunar New Year 2021 further exacerbated the situation, leading to an 80% drop in airline revenue compared to the same period the previous year.

The import-export industry has faced significant challenges due to the economic downturn and temporary border closures in China, impacting global trading relationships, including with Vietnam This stagnation has led to a decline in revenue from export taxes, a crucial budget source The Covid-19 pandemic further exacerbated the situation, causing a decrease in export turnover for high-revenue goods such as machinery, equipment, iron and steel, and petroleum Nevertheless, through the proactive efforts of the business community and government-led export promotion initiatives, Vietnam has successfully navigated these obstacles, resulting in a gradual increase in export turnover in 2021.

The Covid-19 pandemic has significantly affected the education and training sector globally, including in Vietnam In response to the outbreak, all educational institutions, whether public, private, or non-public, suspended in-person classes and shifted to online teaching and e-learning to minimize gatherings and curb the virus's spread Despite these efforts, many institutions faced numerous challenges and experienced substantial losses and unforeseen negative consequences due to the pandemic.

2.2.2 Impact of Covid-19 on the banking industry in Vietnam

As of the end of 2021, the State Bank of Vietnam reported a non-performing loan (NPL) ratio of 1.9%, reflecting a rise of 0.21 percentage points from the previous year When factoring in bad debts sold to the Vietnam Asset Management Company (VAMC), the total NPL ratio escalates to 3.9% Furthermore, the gross NPL ratio, which encompasses unresolved VAMC bad debts and potential NPLs from restructuring, surged to 7.31% at the end of 2021, up from 5.1% at the end of 2020, nearly matching the 7.4% recorded at the end of 2017.

42 on piloting the mechanism for handling bad debts of credit institutions took effect

Graph 4: Bad debt ratio in Vietnam from 2016-2021 (%)

The Covid-19 pandemic, particularly the Delta variant's fourth wave in 2021, exacerbated the pre-existing trend of rising bad debts within the credit institution system, leading to considerable losses for businesses and affecting the livelihoods of many Recent financial reports for 2021 reveal a significant increase in bad debts across several banks, including VPBank (up 60% from 2020), Vietinbank (49%), VIB (58%), and HDB (43%) On average, the bad debt balance among 28 listed commercial banks and Agribank rose by 17.3% compared to the previous year.

Graph 5: Bad debt ratio in Vietnam from Q4/2020 to Q4/2022

In 2022, banks faced ongoing challenges with bad debts due to rising inflation and interest rates, which hindered business operations and diminished their capacity to repay loans According to data from the State Bank of Vietnam (SBV), the non-performing loan (NPL) ratio escalated from 1.4% at the end of March 2022 to 1.9% by August.

2022 and is 1.92% by the end of 2022

Although bad debts have increased significantly in the context of the Covid-

In the wake of the 2019 pandemic, banks continue to actively manage and recover bad debts while utilizing provisions to mitigate risks Their efforts focus on enhancing credit quality and minimizing the emergence of new bad debts, ensuring that overall bad debts remain under control.

Profitability decrease, credit management cost and risk mitigation costs increase:

The COVID-19 pandemic has prompted countries worldwide to adopt low-interest rate policies, significantly impacting the profitability of traditional banks This reduction in short-term interest rates decreases banks' interest income and influences medium- and long-term rates due to the term structure of interest rates In countries with low or negative interest rate policies, the adverse effects on net interest income are even more pronounced To mitigate these challenges, banks are diversifying their revenue streams by enhancing income from additional services and expanding their digital banking offerings.

As the global economy declines, banks face increased credit risks for both business and retail customers due to reduced consumer demand, significantly impacting production industries like transportation, tourism, and retail These sectors are forced to cut costs, leading to layoffs and heightened poverty rates Additionally, industries such as oil and automobile production also experience decreased demand In response, banks implement measures like loan extensions and debt restructuring, which compromise credit quality and reduce profits Some smaller banks reported losses in early 2020, while others, in an attempt to boost income, have relaxed loan standards, further escalating credit risks.

The Covid-19 pandemic has significantly impacted countries, prompting them to implement tailored measures based on their unique circumstances Government support packages have provided temporary relief to businesses and individuals, but this assistance will gradually be phased out as operations normalize In this environment, banks are encouraged to continue lending, but must adapt their lending policies and customer selection criteria, emphasizing effective risk management While banks should refrain from lending to businesses unable to meet financial obligations despite receiving government aid, they can support those capable of recovery, ensuring these businesses maintain low financial leverage and are closely monitored In nations with advanced financial markets, banks may also consider selling bad debts to mitigate credit risks.

The pressure of digital banking transformation, increasing competitiveness and cooperation with Fintech companies:

The COVID-19 pandemic has heightened awareness among bank leaders regarding the significance of distance in service delivery, prompting a swift digital transformation through strategic partnerships with Fintech companies To expedite this digitization, banks have made substantial investments in infrastructure and revamped their banking technology.

A recent study by RFi Group reveals that 71% of global consumers now use digital banking channels weekly, marking a 3% increase from last year, with daily usage rising by 6% In the UK, this figure is even higher, with 73% of consumers engaging in weekly digital banking, and monthly mobile banking usage climbing from 52% to 57% between Q2 2019 and Q2 2020 While the COVID-19 pandemic has accelerated this shift, the transition from cash to digital payments has been a gradual process developed over many years, as more individuals integrate digital solutions into their daily lives.

As per UK Finance, cash transactions account for just 23% of total purchases in the UK, while over 70% of the population engaged in online shopping in 2019 Without the influence of COVID-19, these trends are anticipated to grow significantly in the coming years The World Economic Forum forecasts that by 2030, online consumption of consumer goods could reach 50% in many developed markets, and UK Finance predicts that only 9% of payments in the UK will involve physical currency by 2028.

ANALYSIS OF FACTORS AFFECTING THE

Data and research methods

This study analyzed financial report indicators from 15 commercial banks in Vietnam over a decade, from 2012 to 2022 The banks included are An Binh Commercial Joint Stock Bank, Asia Commercial Joint Stock Bank, Vietnam Investment and Development Bank, Vietnam Joint Stock Commercial Bank for Industry and Trade, Vietnam Export-Import Commercial Joint Stock Bank, Ban Viet Joint Stock Commercial Bank, Ho Chi Minh City Development Joint Stock Commercial Bank, LienVietPostBank, Military Commercial Joint Stock Bank, Vietnam Maritime Commercial Joint Stock Bank, Saigon - Hanoi Commercial Joint Stock Bank, Saigon Thuong Tin Commercial Joint Stock Bank, Vietnam Technological and Commercial Joint Stock Bank, Joint Stock Commercial Bank for Foreign Trade of Vietnam, and Vietnam International Commercial Joint Stock Bank.

The data for this analysis was sourced from the annual reports and financial statements of each bank, which are available on their official websites Furthermore, additional information was gathered from SSI iBoard, the platform provided by SSI Securities Company.

This research is structured into two distinct phases: the pre-Covid-19 period from 2012 to 2019 and the Covid-19 period from 2012 to 2022 While some studies have focused on the years 2020 to 2022 or included earlier years to analyze Covid-19's impact on bank performance, this study posits that separating the analysis into two phases will yield more precise and insightful quantitative results regarding the pandemic's effect on bank profitability.

The analysis of the data will be conducted using Stata 14.1 software, following a systematic approach that includes descriptive statistics, regression models (pool, fixed effects, and random effects), model selection tests, and assessments for heteroscedasticity and autocorrelation Additionally, biases in the model will be corrected using the Feasible Generalized Least Squares (FGLS) method to enhance the clarity of the research objectives.

Table 1: Previous results on factors affecting commercial banks’ ROA

+ Nguyen & Dang (2022) + Gazi et al (2022)

Credit risk ALLL ALLL = Loan loss provision/Total loans

OEOI OEOI Operating Expense/Net operating Income

CASA CASA = Total demand deposit/Total deposit

Bank size SIZE SIZE = Ln(Total assets)

USD/VND exchange rate each year

+ Ali et al (2018) + Fuadi et al (2022)

- Last Year's Price Index)/Previous Year's Price Index

+ Gazi et al (2022) + Lam & Anh (2022)

Measuring the affectations of factors affecting profitability of

3.2.1 Descriptive statistics a Pre-pandemic period (2012-2019)

Variable Obs Mean Std Dev Min Max

From 2012 to 2019, the average Return on Assets (ROA) for 15 commercial banks was 0.0076, with a standard deviation of 0.0053, indicating minimal profitability differences among banks The Equity to Assets Ratio (EAR) averaged 0.0832, reflecting that total equity constitutes approximately 8.3% of total assets The Average Loan Loss Provision Ratio (ALLL) stood at 0.014, signifying that total loan loss provisions represent an average of 1.44% of the loan portfolio The Operating Expenses to Operating Income Ratio (OEOI) averaged 1.41, indicating that operating expenses are 141% of operating income Additionally, the Current Account and Savings Account (CASA) ratio averaged 0.17, showing that non-term deposits account for 17% of total bank deposits The average bank size, measured by total assets, was 12.19, with a range from 9.9 to 14.21, highlighting a lack of homogeneity in asset scales among banks The average USD/VND exchange rate over the 12-year period was 21,820.55 VND, with fluctuations between 20,828 and 23,050.24 VND, while the average inflation rate in Vietnam during this period reflects economic conditions.

11 years, which is 4.11% with a standard deviation of 2.4% b Including pandemic period (2012-2022)

Variable Obs Mean Std Dev Min Max

Between 2012 and 2022, the average Return on Assets (ROA) for 15 commercial banks was 0.0093, with a standard deviation of 0.0066, indicating minimal differences in profitability among these institutions The average Equity-to-Assets Ratio (EAR) stood at 0.0838, suggesting that shareholder equity constitutes approximately 8.4% of total assets Additionally, the average Allowance for Loan and Lease Losses (ALLL) was 0.014, reflecting that total loan loss reserves average 1.44% of total loans The Operating Expense to Operating Income Ratio (OEOI) averaged 1.23, indicating operating expenses exceed net operating income by 123% Non-term deposits, represented by the CASA variable, accounted for an average of 18% of total deposits The average bank size, measured by total assets, was 12.39, with variability from 9.9 to 14.57, highlighting inconsistencies in asset scale The average USD/VND exchange rate over the 12-year period was 22,214.3 dong, varying between 20,828 and 23,424.8 dong Finally, the average inflation rate in Vietnam over 11 years was 3.74%, with a standard deviation of 2.2%.

Table 4: Comparison of descriptive statistic results between two periods

2012-2019 (Pre-pandemic period) 2012-2022 (Including pandemic period) Mean Std Dev Min Max Mean Std Dev Min Max ROA 0.0076 0.0053 0.000085 0.0266 0.0093 0.0066 0.000085 0.0323

EAR 0.0831 0.0245 0.041 0.162 0.0838 0.0256 0.0407 0.1697 ALLL 0.014 0.0044 0.0077 0.0276 0.0144 0.0046 0.0077 0.0276 OEOI 1.412 1.044 0.413 7.374 1.233 0.9574 0.2937 7.375 CASA 0.1709 0.0774 0.0294 0.4121 0.1796 0.0882 0.0294 0.4698 Size 12.18792 0.9545 9.9365 14.2144 12.3907 0.9981 9.9365 14.5672

According to table 4, the average ROA index of 15 banks during the period 2012-2019 is 0.76%, much lower than the figure of 0.93% during the period 2012-

2022, while the standard deviation value also increased significantly from 0.53% to 0.66% during the period 2012-2022 The average OEOI index in the period 2012-

2019 is 1.412 compared to 1.233 in the period 2012-2022, while the standard deviation of the period 2012-2022 tends to decrease compared to the period 2012-

2019 The average CASA index in both periods seems to have little change, however, in the period 2012-2022, the highest value and standard deviation have increased significantly compared to the period 2012-2019

Despite the challenges posed by the Covid-19 pandemic, Vietnamese banks have demonstrated resilience, with key indicators like Return on Assets (ROA) and CASA showing growth The rising average ROA indicates that bank profitability has not only remained stable but has also improved during this period Additionally, the increase in the CASA index reflects a significant shift towards non-cash payments in the economy amid the pandemic Conversely, the decline in the Operating Efficiency and Operating Income (OEOI) index suggests enhanced control over operating costs, leading to a notable reduction in the ratio of operating expenses to net operating revenue for banks in Vietnam.

Table 5: Matrix table of correlation coefficients between variables

ROA EAR ALLL OEOI CASA Size EXR INF

The correlation matrix analysis from 2012 to 2022 reveals that Return on Assets (ROA) positively correlates with Earnings at Risk (EAR), Current Account Savings Account (CASA), Size, and Exchange Rate (EXR), indicating a strong influence on ROA from these variables Conversely, ROA shows a negative correlation with Allowance for Loan and Lease Losses (ALLL), Other Operating Income (OEOI), and Inflation (INF), with OEOI notably affecting ROA negatively.

To address potential multicollinearity among the model variables, the author performed a variance inflation factor (VIF) test, as detailed in Table 6 The VIF coefficients obtained indicate that all variables meet the criterion of VIF < 10, confirming the absence of significant multicollinearity issues Consequently, these variables are deemed suitable for inclusion in the regression model.

3.2.2 Reliability validation of the measurement scale

3.2.2.1 Pre-pandemic period (2012-2019) a Pool regression model

Table 7: Pool regression model result (2012-2019)

ROA Coef Std Err t P>|t| [95% Conf Interval]

The Heteroscedasticity test, as shown in Table 1 - Appendix 1, reveals a significance level of 5%, with a p-value of 0.67%, which is below the established threshold Consequently, the null hypothesis (Ho) is rejected, demonstrating that the Pool model displays heteroscedasticity, indicating that the variance of errors varies across different levels of the independent variable(s).

The test results indicate a p-value of 0% for the F-statistics, significantly below the 5% significance level, leading to the rejection of the null hypothesis and confirming the presence of autocorrelation in the Pool model This violation of the assumption of no autocorrelation can compromise the accuracy and reliability of the estimated coefficients and standard errors Consequently, it is essential to explore alternative modeling strategies or employ techniques such as robust or clustered standard errors to mitigate the effects of autocorrelation on the analysis.

The estimation results from the Fixed Effects Model (FEM) indicate a p-value below 0.05 in the F-test, suggesting that FEM is more suitable than the Pool model This implies that the variation in the dependent variable is better explained by the unique characteristics of each unit in the panel data, rather than by a uniform set of coefficients in the Pool model Consequently, FEM is recommended for further analysis.

Table 8: Regression models results in the period of 2012-2019

Pool OLS FEM REM FGLS

Note: *** shows 1% level of significance, ** shows 5% level of significance, and * shows 10% level of significance

The results of the Random Effects Model (REM) indicate statistical significance, with the Wald chi-square test confirming significance at the 0.05 level This suggests that at least one independent variable significantly impacts the dependent variable, as evidenced by a chi-square statistic probability of 0.0000, which is below the significance threshold Consequently, we reject the null hypothesis that the coefficients of the independent variables are jointly zero Thus, the REM model is deemed more suitable than the Pool regression model for this analysis, as it accounts for individual unit effects and unobserved heterogeneity.

After estimating using the FEM and REM models, the results indicate that both models yield favorable outcomes Consequently, the author will conduct a Hausman test to determine the appropriate regression model between FEM and REM.

The Hausman test is a crucial statistical tool in econometrics for assessing the efficiency and consistency of multiple estimators, especially in panel data analysis It helps determine the suitability of either the fixed-effects model (FEM) or the random-effects model (REM) for a specific dataset.

The Hausman test results indicate a chi-square statistic of 3.74 with 6 degrees of freedom and a p-value of 0.7114 Since this p-value exceeds the significance level of 0.05, we cannot reject the null hypothesis, suggesting that the random effects model (REM) is preferred over the fixed effects model (FEM) Consequently, we will proceed with the REM model for our analysis.

The tests for autocorrelation and heteroscedasticity conducted on the Random Effects Model (REM) revealed a Prob>chi2 value of less than 0.05, indicating significant issues with both autocorrelation and heteroscedasticity, which undermined the model's effectiveness To rectify these problems, the author employed the Feasible Generalized Least Squares (FGLS) regression model, which addresses variance differences among observation units and corrects autocorrelation, ensuring unbiased and effective estimation results (Beck & Katz, 1995) Consequently, the FGLS regression model produced the following estimation results.

3.2.2.2 Including pandemic period (2012-2022) a Pool regression model

Table 9: Pool regression model result (2012-2022)

ROA Coef Std Err t P>|t| [95% Conf Interval]

The results from Table 9 show that the Pool model from 2012 to 2022 is statistically significant, with a Prob>F value below 5% Additionally, the R-squared value of 0.645 indicates that the model accounts for approximately 64.5% of the total variance in the dependent variable, demonstrating a good fit for the data and moderate to strong explanatory power, thereby confirming the model's appropriateness.

The statistical test (Table 1 - Appendix 2) conducted at a 5% significance level yielded a p-value of 1.79% for Prob>chi2, which is below the predefined threshold

As a result, the null hypothesis (Ho) is rejected, indicating that the Pool model is heteroscedastic, implying that the errors' variance varies across different levels of the independent variable(s)

The test results reveal a p-value of 0% for the F-statistics probability, significantly below the 5% significance level, allowing us to reject the null hypothesis and confirm the presence of autocorrelation in the Pool model This violation of the assumption of no autocorrelation can result in inaccurate coefficient estimates and standard errors To mitigate these issues, it may be essential to consider alternative modeling strategies, such as robust standard errors or clustered standard errors, to effectively address the effects of autocorrelation in the analysis.

SUGGESTIONS TO IMPROVE THE PROFITABILITY OF

Solutions toward the profitability of commercial banks in Vietnam

Commercial banks should prioritize maintaining a healthy capital adequacy ratio by raising additional capital through equity issuance and retaining earnings to strengthen their capital base Attracting capital contributions from both domestic and foreign investors, particularly through share issuance to foreign shareholders, is essential Additionally, a balanced approach to distributing financial results is crucial, ensuring that dividends to shareholders are managed while retaining sufficient profits to enhance owner's equity for reinvestment, as this serves as a low-cost funding source that protects shareholder interests Furthermore, banks need to increase their merger and acquisition activities and restructure their operations to separate investment and commercial banking functions, thereby mitigating excessive risk accumulation that can lead to failures, as evidenced by cases in various countries.

Secondly, banks need to promote the mobilization of non-term deposits

CASA accounts play a crucial role for banks in accessing abundant capital at low costs, particularly highlighted during the Covid-19 lockdown when non-cash payment services surged in popularity This trend provides a strong foundation for the growth of online banking services, including digital banking applications By leveraging the low-cost nature of CASA, commercial banks can alleviate the impact of declining interest rates on net interest income (NIM) This allows banks to maintain a healthy NIM despite reduced lending rates To further enhance CASA, banks must offer personalized banking services and innovative digital solutions that encourage customers to keep a higher percentage of their funds in CASA accounts.

To enhance the return on assets (ROA) of commercial banks, it is essential to optimize operational efficiency This involves effectively managing costs through regular reviews, identifying inefficiencies, and implementing cost reduction initiatives, which can improve the cost-to-income ratio Robust risk management practices, including credit and operational risk management, are also crucial in mitigating risks that could disrupt operations Additionally, embracing digital transformation by digitizing processes and offering online banking services can significantly reduce costs related to physical branches, further enhancing operational efficiency.

To mitigate the adverse effects of inflation on the return on assets (ROA), commercial banks must develop effective asset-liability management strategies This includes adjusting loan and deposit pricing structures and adopting inflation-linked financial products By incorporating inflationary components into loan interest rates, such as utilizing floating rates that align with inflation indexes, banks can safeguard their interest income and maintain profitability Additionally, employing hedging instruments like inflation swaps or inflation futures contracts allows banks to manage inflation risks, thereby reducing potential volatility in their ROA.

In light of the increasing significance of bank size during the Covid-19 pandemic, commercial banks must prioritize efficient growth strategies alongside prudent risk management To enhance their total assets, banks should focus on expanding lending activities, particularly by targeting underserved segments like small and medium-sized enterprises (SMEs) and rural communities, while developing specialized loan products for various industries Additionally, forming strategic partnerships with fintech companies and non-bank financial institutions can enable banks to access new customer segments, utilize innovative technologies, and diversify revenue streams, ultimately strengthening their product offerings and expanding their customer base.

Recommendations on policies

Governments and regulatory bodies must prioritize financial inclusion by creating a supportive environment that allows underserved populations to access banking services This includes implementing effective policies, promoting digital financial solutions, and partnering with commercial banks to reach unbanked communities By integrating more individuals and businesses into the financial system, we can enhance the growth and stability of the banking sector.

To effectively address the challenges posed by the Covid-19 pandemic, it is essential for government and regulatory bodies to implement timely and targeted support measures for commercial banks These measures should encompass liquidity support, credit guarantee schemes, regulatory forbearance, and fiscal stimulus packages aimed at fostering economic recovery and strengthening the resilience of the banking sector.

To enhance resilience and innovation, governments and regulatory bodies must encourage collaboration among commercial banks, industry associations, and key stakeholders By facilitating information sharing and the exchange of best practices, banks can better adapt to evolving market conditions and improve their risk management capabilities.

Governments and regulatory bodies should actively support research and development in the banking sector By promoting initiatives focused on digital transformation, financial technology, and emerging risks, they can enhance industry advancement and stimulate innovation, leading to greater efficiency and profitability.

The Covid-19 pandemic has triggered a historic crisis for the global economy, profoundly affecting various sectors through lockdowns, restricted movement, reduced production, decreased demand, and trade barriers This has resulted in a notable slowdown in economic growth, particularly impacting the banking sector In Vietnam, while studies have assessed the pandemic's effects on the banking industry, there has been a lack of research differentiating between pre-pandemic and pandemic periods This paper aims to investigate the influence of Covid-19 on the profitability of Vietnamese commercial banks by comparing these two distinct time frames.

This study analyzed a panel dataset from 2012 to 2022, focusing on 15 commercial banks in Vietnam The years 2012-2019 were classified as the pre-pandemic period, while 2012-2022 encompassed the pandemic period To assess the impact of the Covid-19 pandemic on bank profitability, measured by Return on Assets (ROA), various independent variables were utilized, categorized into bank-specific factors and macroeconomic factors Key independent variables included EAR, ALLL, OEOI, CASA, SIZE, along with macroeconomic variables EXR and INF.

Our quantitative analysis reveals that EAR, CASA, SIZE, and INF significantly influence the ROA of commercial banks, all exhibiting positive correlations Notably, EAR and INF demonstrate the most substantial effects, with pronounced changes observed during the two periods studied The Covid-19 pandemic amplified the influence of EAR on ROA, while the significance of INF diminished Additionally, CASA and SIZE gained importance as their coefficients increased during the pandemic Conversely, OEOI emerged as the sole factor negatively affecting ROA, with its impact remaining stable across both periods Lastly, the EXR variable showed an insignificant effect on ROA, with no variation noted during the pandemic.

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Appendix 1: Detailed Stata 14.1 Test Result (2012-2019 period)

Table 1: Heteroscedasticity test on Pool model

White’s test for Ho: homoskedasticity

Table 2: Autocorrelation test on Pool model

Wooldrigde test for autocorrelation in panel data

Ho: no first-order autocorrelation

Table 3: Result of FEM regression model

Number of obs = 120 Number of groups = 15 Obs per group:

ROA Coef Std Err t P>t [95% Conf Interval]

0.55476197 (fraction of variance due to u_i)

Table 4: Result of REM regression model

Min = 8 Avg = 8 Max = 8 Wald chi2(7) = 109.82 Prob > chi2 = 0.0000

ROA Coef Std Err z P>z [95% Conf Interval]

Table 5: Hausman test on FEM and REM models

(b) (B) (b-B) sqrt(diag(V_b-V_B)) fe re Difference S.E

INF 0.0822152 0.0838242 -0.001609 0.0027901 b = consistent under Ho and Ha; obtained from xtreg

B = inconsistent under Ha, efficient under Ho; obtained from xtreg

Test: Ho: difference in coefficients not systematic chi2(6) = (b-B)'[(V_b-V_B)^(-1)](b-B)

Table 6: Autocorrelation test & Heteroscedasticity test for the REM model

Wooldridge test for autocorrelation in panel data

Breusch and Pagan Lagrangian multiplier test for random effects

ROA[bankMH,t] = Xb + u[bankMH] + e[bankMH,t]

Appendix 2: Detailed Stata 14.1 Test Result (2012-2022 period)