INTRODUCTION
Background of study
In 2020, while the Covid-19 pandemic severely impacted the global economy, Vietnam emerged as a standout performer, achieving a 2.91% economic growth rate—the lowest in a decade—yet remaining among the few countries with positive growth The banking sector played a crucial role in this recovery, operating stably and supporting economic growth despite global challenges Consequently, the focus has shifted to the business performance of banks, particularly their profitability ratios One key measure of banking effectiveness is the net interest margin, which serves as an important determinant of operational success.
Net Interest Margin (NIM) is calculated by subtracting total interest payment expenses from total interest income, resulting in net interest income relative to total assets This includes average profitability from deposits at the State Bank of Vietnam, loans to credit institutions, customer loans, and investment securities By analyzing this ratio, banks can effectively manage profit-generating assets and assess which capital sources incur the lowest costs.
A competitive banking system enhances efficiency, evidenced by low net interest margins, which are crucial for domestic financial intermediaries High net interest margins hinder savings due to lower deposit rates and limit banks' investment opportunities with elevated lending rates Consequently, banks must operate as intermediaries at minimal costs to foster economic growth The marginal interest income ratio serves as a key indicator of both efficiency and profitability Efficient banks facilitate robust capital flow, generating financial resources for the state, creating jobs, and fostering collaboration among economic participants Thus, analyzing profit and its influencing factors is essential for evaluating overall performance and business conditions.
This article explores 11 policies and measures aimed at enhancing the performance of banks and the overall economy The focus is on the "Factors Affecting the Net Interest Margin of Vietnamese Joint Stock Commercial Banks," with the author seeking to address key issues related to this topic.
Objectives
Firstly, determining the factors affecting the net interest margin of commercial banks in Vietnam
The primary goal of this study is to identify effective strategies that enable banks to enhance profitability, boost operational efficiency, and strengthen competitiveness This is achieved by analyzing the factors influencing the net interest margin of commercial banks listed on the Vietnamese Stock Exchange from 2009 to 2020.
Firstly, researching on theorical basis of the net interest margin of commercial banks from previous studies in Vietnam as well as all over the world
Secondly, creating an analysis model of factors affecting the net interest income of commercial banks
This study examines the empirical evaluation model of the commercial banking system in Vietnam from 2009 to 2020, focusing on the net interest margin of Vietnamese banks It aims to identify and analyze the factors influencing the net interest margin through quantitative analysis models, providing insights into the dynamics of the banking sector in recent years.
This thesis aims to enhance the understanding of how bank-specific factors influence net interest margins, enabling the board and management of commercial banks to implement strategies for ongoing financial performance improvement Additionally, it provides a framework for managers and shareholders to evaluate their bank's profitability in relation to key determinants.
Research Questions
The study attempts to answer and clarify the following questions based on the aforementioned objectives:
Firstly, which are theorical basis of the net interest margin of commercial banks from
12 previous studies in Vietnam as well as all over the world ?
Secondly, which is the most suitable analysis model of factors affecting the net interest income of commercial banks in this study ?
The net interest margin of joint stock commercial banks in Vietnam is influenced by several key factors These include the cost of funds, the competitive landscape of the banking sector, regulatory policies, and the overall economic environment Understanding the extent to which these factors impact net interest margins is crucial for assessing the financial performance of these banks.
Finally, which solution is suitable for improvement of the net interest margin of joint stock commercial banks in Vietnam ?
Research’s contribution
The Vietnamese banking system faces significant challenges due to global crises, national economic vulnerabilities, and operational shortcomings To navigate the unstable money market, Vietnamese commercial banks must enhance efficiency across their business activities, prioritizing profitable growth and long-term sustainability amid international integration A stable level of bank profitability is crucial for both financial stability and economic growth, as well-capitalized financial institutions ensure access to credit for businesses and consumers, fostering economic development The Covid-19 pandemic has further exacerbated these challenges, directly impacting banks' revenue from credit activities and revealing operational flaws As the situation evolves, bank management must critically evaluate their operations and develop strategies to address the issues highlighted by the pandemic.
The net interest margin (NIM) is a key indicator of a bank's financial health and its ability to manage deposits and loans effectively Extensive research has focused on the factors that enable banks to generate interest income from loans that exceeds their interest expenses In Vietnam, recent banking deregulation has led banks to expand their activities in pursuit of long-term profitability However, Vietnamese commercial banks are currently facing criticism for maintaining high loan interest rates, which pose challenges for businesses seeking capital Despite a gradual reduction in the ceiling interest rate set by the State Bank, lending rates remain unacceptably high for many businesses.
This study significantly enhances previous research by identifying and analyzing the factors influencing the net interest margin (NIM) of commercial banks in Vietnam It examines both internal factors, such as bank size, capitalization, liquidity, and customer deposits, as well as external macroeconomic indicators like inflation and GDP In addition to these established variables, the research introduces the effects of non-interest income and market share on NIM Covering a 12-year period from 2009 to 2020, this study aims to provide a more accurate and comprehensive understanding of the dynamics affecting bank profitability in the Vietnamese market.
This research identifies key factors influencing the net interest margin of Vietnamese commercial banks and assesses their impact It highlights the determinants affecting the net interest income ratio and offers recommendations for the State Bank's interest rate policy The findings suggest that the State Bank could implement more effective tools, beyond administrative measures, to enhance the net interest margin.
Objects and scope of study
Research’s object is the net interest margin and factors affecting the net interest margin of Vietnamesse Joint Stock Commercial Bank
The research focuses on the period from 2009 to 2020, chosen to enhance the reliability of the study This timeframe is significant as it encompasses the recovery phase of Vietnamese banks following the global financial crisis and the Covid-19 pandemic.
Structure of study
Beside table of contents, appendices and bibliography, thesis structure consists of 5 chapters:
Chapter 2: Theoretical framework and literature review
Chapter 4: Empirical result and discussion
Chapter 5: Conclusion and management interpretation
THEORETICAL FRAMEWORK AND LITERATURE REVIEW
Theoretical Framework
There are a variety of definitions about commercial banks depending on different perspectives When it comes to legal perspective, there are several definitions of commercial banks from several countries
Commercial banks are institutions that primarily accept deposits from the public and utilize these funds for their own lending and financial operations, as outlined in the Banking Act of France, 1941.
A commercial bank is a credit institution authorized to perform a wide range of banking and related business activities, as defined by Law No 47/2010/QH12 of the Law on Credit Institutions of Vietnam These activities encompass various monetary services, including capital mobilization, short, medium, and long-term loans, discounting of valuable papers, factoring, financial leasing, overdrafts, consumer loans, and other essential banking services.
A commercial bank is a financial institution that operates within the monetary sector by accepting deposits from individuals and businesses, providing loans, and offering various financial services such as credit and payment processing Additionally, these banks engage in investment activities with other earning assets, acting as intermediaries that connect those with surplus capital to those in need of funds for their business operations.
Commercial banks serve as essential financial institutions that significantly contribute to the economy, often referred to as its lifeblood.
Commercial banks act as intermediaries by channeling deposits and savings into loans for individuals, businesses, and other financial entities, effectively meeting consumer demand and supporting production and commerce.
16 as investment activities Other positions include credit operations, payment, guarantee, and commercial bank agent, which are becoming increasingly significant in our country's process of international economic integration
Commercial banks facilitate payments for clients purchasing goods and services by processing transactions through methods such as checks and electronic payment networks.
Third role is guarantee, as a guarantor, commercial banks commit to pay debts back to customers when customers lose their solvency
Commercial banks serve as agents by managing and underwriting securities issuance for their clients, while also offering various forms of credit, such as consumer and real estate loans, to meet diverse needs Additionally, they play a significant role in the bond market, facilitating funding for public projects initiated by state and municipal governments.
Finally, commercial banks play a vital role in the implementation of government macroeconomic policies, assisting in the regulation of economic growth and the pursuit of social objectives
The net interest margin (NIM) is a crucial indicator of a bank's financial health, reflecting its ability to generate interest income from loans that exceeds interest expenses This ratio has been widely studied in financial literature and allows banks to assess profitability from assets while identifying the most cost-effective sources of capital NIM is calculated by subtracting total interest payment expenses from total interest income, resulting in net interest income, which is then expressed as a percentage of total assets A higher NIM signifies better financial performance for the bank.
Net Interest Margin (NIM) indicates the interest earnings generated from invested capital, reflecting both efficiency and profitability A higher NIM value signifies greater efficiency, as it demonstrates that a company is generating more income from a smaller investment Additionally, it's important to remember that total assets equal the sum of total liabilities and shareholders' equity, in accordance with the balance sheet accounting equation.
Efficient banks play a crucial role in the economy by enhancing capital flow and creating financial resources for the state, generating jobs, and fostering strong connections among economic participants Therefore, analyzing profit and its influencing factors is essential for evaluating overall bank performance and business conditions, which can lead to the development of policies and strategies aimed at improving the banking sector's efficiency and, consequently, the country's economic health.
Research by Ho and Saunder (1981) indicates that commercial banks serve as intermediaries between borrowers and lenders, with the net interest margin (NIM) arising from the disparity between capital mobilization and credit extension, resulting in interest expenses and income This section categorizes the factors contributing to NIM to facilitate a review of previous studies and the selection of appropriate variables for subsequent chapters.
Commercial banks offer various types of credit to meet the diverse needs of their customers, and these credit extensions can be categorized into three groups based on how assets are represented in the transaction (Peter Roses, 2013).
Cash credit groups encompass various financial services, including loans, discounts, and factoring A loan involves a lender providing a specific amount of money to a customer for a designated purpose, with an agreement for full repayment of the principal and interest within a set timeframe Discount services refer to the purchase of negotiable instruments and other valuable papers before their maturity date, often with recourse to the beneficiary Lastly, factoring services enable banks to offer credit facilities to sellers or purchasers by acquiring the rights to claim receivables or payables resulting from the sale of goods and provision of services under contractual agreements.
A sign credit group encompasses bank guarantees and various foreign exchange commitments A bank guarantee serves as a financial assurance where the bank pledges to fulfill the financial obligations of a customer if that customer fails to meet their commitments.
Customers are required to acknowledge their debts and repay them to the Bank as per the agreed terms Additionally, foreign exchange commitments involve agreements to pay, repay, or extend credit between the bank and its customers, which create future exchange rate obligations that are recorded off the balance sheet Since these commitments are not yet executed, they remain documented solely on the off-balance sheet.
Literature review
2.2.1 Some typical studies in Vietnam:
Thu and Huyen (2014) identified key factors influencing the net interest margin of banks from 2008 to 2011 Their quantitative research revealed a significant positive relationship between a bank's risk aversion, credit risk, and implied interest payments with the net interest margin Conversely, management quality was found to have a statistically significant negative relationship with the net interest margin.
Khanh and Tra (2015) conducted an OLS regression analysis using 175 observations from 2008 to 2012, revealing that net interest margin (NIM) is significantly impacted by operating costs, managerial quality, risk aversion, and inflation rate, whereas the concentration ratio adversely affects NIM.
Linh and Huong (2015) conducted a study on the factors influencing the net interest margin of joint stock commercial banks in Vietnam, analyzing data from 27 banks between 2008 and 2013 using various methods, including Pooled OLS, FEM, REM, and GLS The findings revealed that positive contributors to marginal interest income include credit risk, interest rates, equity size, lending activity scale, and bank size Conversely, GDP growth and management efficiency were found to negatively affect marginal interest income.
Dien and Nga (2018) investigated the impact of the Lerner index, the Herfindahl-Hirschman Index (HHI), and the opportunity cost of reserves on the net interest margin (NIM) of commercial banks in Vietnam from 2011 to 2015 Utilizing the adjusted standard error estimation model (PCSE), the study analyzed balance sheet data from 27 joint stock commercial banks during this period to identify factors influencing the marginal interest income ratio The findings revealed that the Lerner index, opportunity cost of reserves, and operating costs positively affect the profit margin of Vietnamese commercial banks.
2.2.2 Some typical studies in the world
Between 1996 and 2009, Hamadi & Awdeh (2012) analyzed the factors influencing commercial bank net interest margins in Lebanon, using a combination of bank-specific, industry-specific, monetary policy, and macroeconomic data Their findings revealed significant differences in net interest margins between domestic and international banks Key determinants negatively affecting interest margins for domestic banks included size, liquidity, efficiency, capitalisation, credit risk, concentration, dollarization, and economic growth.
Net interest margin is affected by factors such as deposit growth, lending activity, inflation, the central bank discount rate, national savings, and domestic investment, with the interbank rate having a lesser influence Additionally, the study found that for foreign banks, factors like size, liquidity, capitalization, and credit risk do not significantly impact net interest margin.
In his 2016 study, Sentürk examined the factors influencing the Net Interest Margin (NIM) in the banking sectors of Russia and Japan from 2005 to 2014 Utilizing multi-way cluster estimation methods to address cross-sectional and time-series dependencies, the research identified key variables affecting NIM In Russia, factors such as capitalization, liquidity risk, inflation, economic growth, and levels of private and public debt play significant roles Conversely, in Japan, the concentration of loan and deposit markets and bank size are the primary determinants Additionally, characteristics like substitution effects, cost efficiency, and profitability are pertinent in both countries.
Angori, G., Aristei, D., and Gallo, M (2019) identified key factors affecting net interest margins in the Euro Area from 2008 to 2014 Their research focused on bank-level drivers, including market power, capitalization, interest risk, and efficiency Additionally, the study examined the impact of regulatory and institutional frameworks on net interest margins.
Cruz-García and Fernández de Guevara (2020) investigated the key factors influencing net interest margins by analyzing a sample of banks from 31 OECD countries, including Australia, Canada, Germany, and the United States, during the period from 2000 to 2014 The researchers employed the Generalized Method of Moments (GMM) technique to effectively address the model's complexities.
According to Ho & Saunders (1981), internal determinants such as implicit payments, efficiency, average operating costs, competition intensity, deposit insurance premiums, and capital stringency significantly influence the Net Interest Margin (NIM).
Yuksel and Zengin (2017) investigated the factors influencing net interest margin (NIM) in the Turkish banking sector from 2003 to 2014, utilizing a model based on multivariate adaptive regression splines Their research revealed that non-interest income, non-performing loans, total assets, and exchange rates have a negative correlation with net interest margin.
RESEARCH METHODS
Research model
This study employs a multi-linear regression model with balanced panel data to achieve its research objectives Previous studies by Demirguc-Kunt (2014), Nassar (2014), and Hanweck and Ryu (2005) have demonstrated the suitability of this panel data method for research in Vietnam Additionally, the advantages of using a regression model with panel data are highlighted, emphasizing its effectiveness in analyzing complex datasets.
Panel data allows for interpretation of heterogeneity or heterogeneity of cross units
Panel data analysis can take into account each feature of each cross unit
The integration of time and cross-sectional units in panel data enhances the volume and richness of observations Additionally, this combination minimizes the issue of multi-collinearity in multivariable research models.
Panel data enables the resolution of problems through various methods by facilitating time dynamic analysis and cross-unit comparisons This dual capability enhances the understanding of differences across components within the data.
Panel data will reflect research results more accurately and optimally when researched from a macro perspective because to minimizing the biases when synthesizing, collect and measure data
To address the limitations of prior research, this study will employ multivariable regression models and evaluate the effectiveness of Pooled OLS, Fixed Effects Model (FEM), and Random Effects Model (REM) to enhance the accuracy and validity of the findings.
Industry and market factors Bank specific factors
Source: Author's own calculation and visualization
The equation relating the net interest margin to the set of explanatory variables including banking factors and macroeconomic factors is therefore:
NIMi,t = β0 + β1SIZEi,t + β2DEPi,t + β3CAPi,t + β4NI + β5NPL + β6CI + β7LIQ + β8IP + β9MS + β10INF + β11GDP + εi
To analyze the impact of bank size on interest rate margins, this study utilizes the natural logarithm of assets (SIZE) to examine how bank-specific characteristics influence bank performance.
This study investigates the factors influencing Net Interest Margin (NIM), focusing on the impact of deposit growth (DEP) and the equity-to-asset ratio (CAP) to assess bank capitalization Additionally, we will analyze how bank liquidity (LIQ), efficiency (measured by the cost-to-income ratio - CI), and non-interest factors such as non-interest income (NI) and implied interest payment (IP) affect NIM Lastly, the effect of non-performing loans (NPL) on interest margins will also be examined.
Secondly, in terms of industry-specific variables, we use the market share (MS)
Finally, we use real GDP growth (GDP) and end-of-period inflation rates (INF) to determine the link between macroeconomic parameters and bank NIM.
Defining and Measuring Variables
3.2.1 Dependence variable: The net interest margin
This research focuses on the net interest margin (NIM) as the dependent variable, which reflects a bank's capacity to maintain higher interest income compared to its interest expenses The study examines the key factors influencing NIM, calculated as the ratio of the difference between financial income and expenses to total assets This methodology aligns with previous studies, including those by Angori & Gallo (2019) and Dien & Nga (2018).
(1999) Concerning to financial income, this proxy used interest income that represents how efficiently the bank converts its interest bearing liabilities (deposits) to interest earning assets (loans)
This research examines the impact of bank size, measured by the natural logarithm of total assets, on net interest margins Larger banks benefit from enhanced business capabilities, including easier access to low-cost capital, which helps meet customer borrowing needs and boosts profitability Additionally, economies of scale play a crucial role, as increased size enhances competitive advantages and operational efficiency, ultimately leading to improved bank performance.
However, in previous studies state that the interest margins of larger banks are much lower than those of smaller banks According to their findings, the former pays higher
Larger banks tend to generate lower interest income compared to smaller banks, primarily due to their focus on fee-based services rather than interest on deposits and loans Additionally, major banks may offer higher deposit rates to leverage cross-selling opportunities and benefit from economies of scale Consequently, the findings of Hamadi & Awdeh (2012) indicate a negative correlation between net interest margin and bank size.
Hypothesis 1: There is a negative correlation between bank size and the net interest margin (H1)
The impact of the financing structure on the net interest margin of commercial banks is calculated by the ratio of customer deposit to total asset
Deposits serve as a vital source of capital mobilization for banks, enabling them to provide credit to economic entities When banks accumulate deposits, they incur a liability to pay interest to customers, which can be viewed as a form of debt The conversion of deposits into loans is essential for generating profits; however, excessive deposits can lead to higher interest expenses, ultimately reducing the net interest margin Thus, while deposits are crucial for bank operations, managing interest rates on these deposits is key to maintaining profitability.
Previous empirical studies by Yuksel & Zengin (2017) and Memmel & Schertler (2011) indicate that this ratio serves as an independent variable affecting the net interest margin of banks, demonstrating a negative impact.
Hypothesis 2: There is a negative correlation between deposit and the net interest margin (H2)
Capitalization is calculated by equity capital to total asset
Equity capital is crucial for banks, as it represents the owner's initial investment and ongoing contributions during operations It serves essential functions, including providing the foundational resources necessary for a bank's establishment, fostering customer trust for transactions, and supporting overall financial stability.
Equity, encompassing initial and additional capital, serves as a crucial indicator of a bank's financial health and risk profile Well-capitalized banks are perceived as less risky, enabling them to raise uninsured funds to offset declines in deposits (Van den Heuvel, 2002) However, existing literature presents conflicting views on the relationship between capitalization and net interest margin (NIM) While Angori & Gallo (2019) found a significant positive impact of capitalization on NIM, Hamadi & Awdeh (2012) reported a negative correlation Nevertheless, prior research supports the notion that higher capitalization positively influences NIM, as well-capitalized banks can command higher loan interest rates and offer lower deposit interest rates due to their reduced bankruptcy risk Thus, we propose Hypothesis 3: a positive correlation exists between capitalization and net interest margin (H3).
NI is calculated by non-interest income to total asset
Non-interest income and expenses arise from business activities outside of credit operations, allowing banks to diversify their revenue streams Key sources of non-interest income include fees from deposits and transactions, insufficient funds, annual and monthly account service charges, inactivity fees, and charges for check and deposit slips, as well as investments from other financial institutions When the net interest margin declines, market volatility also tends to decrease, prompting banks to focus on generating revenue from non-interest sources to maintain profitability.
Therefore, Yuksel & Zengin (2017) concludes that it is projected that NIM and non- interest income will have a negative relationship
Hypothesis 4: There is a negative correlation between non-interest income and the net interest margin (H4)
NPL is measured as Total non-performing loan to Total Loan Balance
Non-performing loans includes sub-standard loan, doubtful loan and loan loss (Regulations on classification of assets, level of deduction, method of making provision
28 for risks and use of provisions to handle risks in operations of credit institutions, foreign bank branches, 2013)
In the CAMELS analysis framework, Asset Quality (represented by the letter A) is a crucial indicator of financial stability, as highlighted by the International Monetary Fund's financial strength indexes Bad debt, a significant reflection of credit risk in banking operations, poses a persistent threat to the credit system's integrity The risk associated with bad debt is measured by the ratio of non-performing loans (NPL) to total outstanding loans; a high ratio can lead to bank insolvency Non-performing loans are defined as those that consumers are unable to repay, which subsequently reduces banks' interest revenue Consequently, a negative correlation is expected between net interest margin and the amount of non-performing loans.
In the study of Yuksel & Zengin (2017), the result show that the bad debt ratio has a negative impact on the net interest margin of commercial banks
Hypothesis 5: There is a negative correlation between Non-performing loan ratio and the net interest margin (H5)
This ratio is measured by interest cost to interest income is used to analyze the impact of the bank expense efficiency on the net interest margin of commercial banks
The cost-to-income ratio is a crucial indicator of a bank's expense efficiency, reflecting the relationship between operational costs, primarily personnel salaries and overhead expenses, and profitability A higher ratio indicates management inefficiency, while a lower score signifies better spending control, leading to enhanced bank profitability This metric highlights the importance of effective management in optimizing operational costs for financial success.
& Awdeh (2012) show the negative impact between cost efficiency and the net interest margin So that this study expect the negative effect between CI and NIM
Hypothesis 6: There is a negative correlation between cost efficiency ratio and the net interest margin (H6)
The liquidity ratio is calculated by comparing high liquidity assets to total assets, where high liquidity assets encompass cash, deposits at the State Bank of Vietnam (SBV), demand deposits at other credit institutions, trading account securities, valuable papers, and government bonds.
The relationship between liquidity and Net Interest Margin (NIM) is assessed through the ratio of cash and cash equivalents to total assets A higher ratio indicates greater liquidity for the bank, which can lead to increased profit revenue However, to attract deposits, banks incur additional costs in the form of interest rates Consequently, it is anticipated that a higher liquidity ratio will negatively impact NIM, aligning with the findings of Hamadi & Awdeh.
Hypothesis 7: There exists a negative correlation between the liquidity and the net interest income (H7)
Implied interest payment calculated by taking non-interest expense minus non-interest income, then divide for total assets
Non-interest income and expenses encompass earnings and costs from business activities outside of credit operations, including services, foreign exchange trading, and securities transactions To attract deposits, banks may offer implied interest through reduced fees or promotional savings offers This study employs a method to assess these implied interest payments, anticipating a positive correlation between implicit interest costs and net interest margin (NIM), as banks are likely to raise NIM to offset the implied interest paid to customers.
Hypothesis 8: There exists a positive correlation between the implied interest payment and the net interest income (H8)
In the study, this factor is calculated by the formula which is the ratio of bank’s total asset to total asset of 28 selected banks
Extensive research has explored the relationship between market share and profitability, highlighting that a firm's market share reflects its competitive position Larger market shares enable firms to better meet customer needs, providing them with a competitive advantage over smaller rivals The relative market power hypothesis suggests that increased market share can enhance interest rate margins Supporting this, McShane and Sharpe (1985) identified a positive correlation between market share and the net interest margin (NIM) ratio Thus, it can be hypothesized that there is a positive correlation between market share and net interest income.
Inflation is a fundamental aspect of the market economy, arising when the principles of commodity economics, particularly the law of money circulation, are not adhered to Higher inflation rates lead to increased inflation expectations, resulting in a higher inflation risk premium on loans Consequently, banks, similar to investors, tend to elevate lending rates in response to rising inflation, aiming to mitigate inflation risk by increasing their Net Interest Margin (NIM) This creates a positive autocorrelation between inflation and NIM, aligning with findings from Hamadi & Awdeh (2012) and López-Espinosa, Moreno & de Gracia (2011).
Hypothesis 10: There is a positive correlation between inflation and the net interest income (H10)
Method of data collection
The thesis research focuses on commercial banks in Vietnam, utilizing secondary data sourced from consolidated financial statements and annual audit reports in line with accounting standards, which are available on the banks' official websites In addition to the micro variables, macroeconomic factors such as GDP growth and inflation rates are analyzed, with data obtained from the Asian Development Bank (ADB) report.
The research is based on the data of 28 commercial banks trade in the period 2009-
Number Bank’s name Stock Symbol
2 Asia Commercial Joint Stock Bank ACB
3 Vietnam Bank for Agricultural and Rural
4 JSC Bank for Investment and Development of Vietnam
5 Vietnam Export Import Commercial Joint
6 Hochiminh City Housing Development Bank HDB
7 Kien Long Commercial Joint Stock Bank KLB
8 Lien Viet Post Joint Stock Commercial Bank LPB
9 Military Commercial Joint Stock Bank MBB
10 Vietnam Maritime Joint Stock Commercial
14 Orient Commercial Joint Stock Bank OCB
15 Joint Stock Commercial Petrolimex Bank PGB
16 Sai Gon Thuong Tin Commercial Joint Stock
17 Sai Gon Commercial Bank SCB
18 Sai Gon Thuong Tin Bank SGB
19 South East Commercial Bank SeABank
20 Sai Gon – Ha Noi Commercial Joint Stock
23 Vietnam International and Commercial Joint
26 JSC Bank for Foreign Trade of Vietnam VCB
27 Vietnam Joint Stock Commercial Bank for
Source: Author's own calculation and visualization
The selection of commercial banks is based on basis presented below:
Joint venture banks, foreign banks will not be counted from the date of score
2020 according to the published financial statements
The independent and dependent variables must be based on publicly available data and the most complete in the period 2009-2020 by each year
Some banks weren’t chosen in the thesis due to a number of integers being considered presented below:
Data from joint venture and foreign banks are typically not fully disclosed, leading to significant differences in their financial statements compared to domestic banks, primarily due to the influence of foreign currency from their parent institutions Additionally, the organizational structure and operational methods of these banks often differ from those of the banks included in the study.
Incomplete data of independent and dependent variables of commercial banks will distort with the results of the thesis.
Method of data analysis
To align with the research objectives, a detailed procedure guides the analytical steps Stata 13 serves as the appropriate software and analysis tool for executing the proposed steps.
Source: Author's own calculation and visualization
The mathematical operations and statements will be used by the author in Stata 13 software to conduct the most descriptive statistical analysis such as: maximum value,
Testing Pooled-OLS, FEM, REM models
Using feasible general least squares (FGLS) to fix disability
The author utilizes the minimum value, mean value, mean position, and standard error of the model's variables to make informed decisions and, if needed, refine the research data based on these statistical criteria.
3.4.2 Testing Pool-OLS, FEM, REM models
Panel data regression employs three primary methods: the Pooled-OLS method, the fixed effects method (FEM), and the random effects method (REM) The Pooled-OLS method analyzes panel data by stacking all observations without considering individual cross-units, making it the simplest and most commonly used approach, akin to standard OLS analysis This method disregards both spatial and temporal dimensions of the data.
𝑦it =∝1+ 𝛽1x1it + 𝛽2x2it + … + βkxkit + uit
Where: 𝑦it is the dependent variable of observation i in period t, 𝑥kit is the independent variable of observation k in period k
The model presents several drawbacks, including inaccurate identification indicated by the Durbin-Watson (DW) statistic and overly stringent constraints on cross units, which do not reflect real-world scenarios To address these issues, the Fixed Effects Model (FEM) and Random Effects Model (REM) are utilized.
The fixed-effects model (FEM) regression method allows for the slope to change across different cross-units while maintaining a constant slope coefficient This approach enables the intercept to vary among cross-units, although it remains unchanged over time, effectively illustrating the specific impact of each cross-unit on the dependent variable.
The Fixed Effects Model (FEM) examines the relationship between the residuals of individual units and explanatory variables, accounting for unique characteristics that may influence these variables By isolating the impact of time-constant individual traits, FEM allows for a clearer estimation of the net effects of explanatory variables on the dependent variable The structure of the FEM model facilitates this analysis effectively.
In the analysis, the dependent variable, denoted as yit, represents the observation for unit i at time t, while xit signifies the independent variable for the same unit and time Each study unit has a unique intercept coefficient, Ci, and the slope coefficient reflects the impact of the factors x on the dependent variable Additionally, the term uit accounts for the residuals in the model.
The key distinction between the random effects model and the fixed effects model lies in how they treat variation between units In the fixed effects model, this variation is associated with the independent variable, or explanatory variable, while the random effects model assumes that the variation between units is random and uncorrelated with the explanatory variables.
If the variation between units influences the dependent variable, the Random Effects Model (REM) is more suitable than the Fixed Effects Model (FEM) In this context, the residuals of each entity, which are not correlated with the explanatory variable, are treated as new explanatory variables The REM model is based on this fundamental concept.
Instead of in the above model, 𝐶i is fixed, in REM it is assumed that it is a random variable with mean C1 and the intercept value is described as follows 𝐶i = 𝐶 + 𝜀i (𝑖 = 1,
Where: 𝜀𝑖 is a random error with mean 0 and variance 𝜎 2 Model becomes:
𝑦it = 𝐶 + 𝛽𝑥it + 𝜀it + 𝑢it hay 𝑦it = 𝐶 + 𝛽𝑥it + 𝑤it và 𝑤it = 𝜀it + 𝑢it
In the context of enterprise analysis, 𝜀it represents the component error associated with various objects, reflecting the unique characteristics of each enterprise Meanwhile, 𝑢it denotes the error arising from a combination of individual characteristics and temporal factors, highlighting the complexities involved in evaluating performance over time.
The REM method addresses the limitations of the FEM method; however, it assumes that the individual characteristics of the 𝜀𝑖 units are uncorrelated with the independent variables This assumption can lead to inaccuracies in the estimated REM if it does not hold true.
To enhance the model's suitability, unnecessary variables will be eliminated through redundancy tests Variables deemed statistically insignificant from the estimation results of the Pooled OLS, FEM, and REM models will be excluded The necessity of the selected variables will be assessed using the Wald test.
After the variables are eliminated, the model will be regressed by the author with the remaining independent variables, then test the parameters The T-test (T-test) will be
38 conducted to check the fit of the regression coefficients Statistical significance levels at 1%, 5%, 10% will be selected to fit the model
Heteroskedasticity refers to the inconsistency in the variances of residuals across different observations, which can lead to biased variance estimates and inefficient ordinary least squares (OLS) estimates This results in ineffective regression coefficient tests To detect heteroskedasticity in Pooled OLS or Fixed Effects Models (FEM), the Breusch-Pagan test is utilized If variable variance is present, the model can be re-estimated using the Generalized Least Squares (GLS) method In cases where the Random Effects model is chosen, the focus shifts to testing for multicollinearity and autocorrelation, as this model does not provide a mechanism for variance testing.
Autocorrelation refers to the correlation between elements in a time series or spatial data, which can lead to various issues in statistical analysis When autocorrelation is present, it can result in inefficient variances and standard errors in predictions, although the Ordinary Least Squares (OLS) estimates remain unbiased This may cause the t-ratio to be overstated and lead to unreliable coefficients of determination, with potential overestimation of results Additionally, the t and F tests may produce unreliable outcomes, and the standard formula for calculating error variance can yield biased estimates, often underestimating the true variance To address these concerns, the study will utilize the Durbin-Watson test to evaluate autocorrelation effects.
If there is an autocorrelation phenomenon, the author decided to choose the remedial variable to estimate based on Durbin - Watson statistics
Multicollinearity occurs when two or more explanatory variables in a regression model exhibit a linear relationship, which can compromise the reliability of estimated coefficients and T-statistics This issue can result in ordinary least squares (OLS) estimates and standard errors becoming overly sensitive to data fluctuations, potentially leading to incorrect signs for regression coefficients Additionally, modifying the model by adding or removing collinear variables can cause significant variability in the coefficients of the remaining variables.
EMPRICAL RESULT AND DISCUSION
Description statistics
This research analyzes data from 28 banks within Vietnam's commercial banking sector, sourced from financial statements, annual reports, and audited management reports published between 2009 and 2020 After data cleaning using Excel, the information was processed with STATA 13 software The findings of the descriptive statistics are presented in Table 4 below.
Table 4: Descriptive statistics of research variables
Variable Number of observations Mean Standard
From chart 1, it can be seen that the average NIM of commercial banks during the study period is approximately 3.07% In addition, the maximum value of NIM is 3.63% in
From 2009 to 2020, the net interest margin (NIM) of commercial banks in Vietnam exhibited a gradual growth trend Notably, Tien Phong Bank recorded the lowest NIM of -0.79% in 2011 During the initial period from 2009 to 2012, NIM rose from 2.78% to a peak of 3.63%, followed by a significant decline until it reached a low of 2.67% between 2012 and 2014 However, from 2015 onwards, the NIM began to recover, reaching 2.92% by 2020.
Chart 2: Yearly average of LIQ
Cash and cash equivalents are highly liquid assets essential for payments, circulation, and storage From 2009 to 2020, Vietnamese commercial banks maintained an average of 4.21% in cash and cash equivalents, indicating a limited reserve of liquid assets and highlighting a pressing need for improvement Notably, while the highest value reached 5.42% in 2009, the lowest dipped to 3.5% in 2016, suggesting that commercial banks prioritize effective capital use and investment over holding large amounts of highly liquid assets.
Chart 3: Yearly average of capitalization
Between 2009 and 2020, the average equity ratio of Vietnamese commercial banks was 9.75%, with a standard deviation of 5.48% This capitalization ratio has shown a consistent decline, dropping from a peak of 16.61% in 2009 to 7.92% by 2020 This trend can be attributed to Vietnam's increasing integration into the global economy, where larger commercial banks have rapidly increased their capital, while smaller banks have struggled to keep pace, resulting in a slight overall decrease in the average equity ratio among commercial banks in recent years.
Chart 4: Yearly Average of size of bank
Between 2009 and 2020, the SIZE ratio of Vietnamese commercial banks showed minimal variation, with the highest average value at 8.323 in 2020 and the lowest at 7.495 in 2009 The mean SIZE value of 7.91, coupled with a high standard deviation of 52.62%, highlights significant disparities in bank sizes This variation has prompted banks to expand their operations, enhance their customer image, and strengthen brand recognition, ultimately boosting competitiveness and operational efficiency.
Chart 5: Yearly average of non-performing loan
The average non-performing loan (NPL) ratio of commercial banks during the study period is relatively low at approximately 1.25%, indicating a generally high level of bad debt among these institutions, with a standard deviation of 1.6% suggesting minimal differences in their approaches to managing bad debt The peak NPL ratio occurred in 2012 at 1.6%, while the lowest recorded value was 1.04% in 2009.
From 2009 to 2020, the non-performing loan (NPL) rate of commercial banks in Vietnam experienced a slight growth, rising from 1.04% in 2009 to a peak of 1.6% in 2012, before declining gradually By 2020, the NPL rate had decreased to 1.15%, indicating a positive trend in the performance of Vietnam's commercial banks.
Chart 6: Yearly average Non-interest income
Chart 6 shows the average NI of commercial banks during the study period is quite low, only approximately 0.17% In addition, the maximum average of NPL is 0.93% in 2019, the smallest value of NPL is 0.55% belongs to 2015 In the period of 2009 - 2011, the non-performing loan of commercial banks in Vietnam has a unstable growth rate Specifically, in the period 2009-2011, NPL increased from 0.59% to 1.6% and then decreased slightly and from 2012-2015, this rate decreased gradually to 1.15% before increase to 0.87% in 2020 This increase illustrated the development of banks services that generate fees and charges nowadays
Chart 7: Yearly average of Cost efficiency
From 2009 to 2020, Vietnam's commercial banks recorded an average cost-to-income ratio of 64.95%, peaking at 74.05% in 2011 and dropping to a low of 57.41% in 2015 Overall, the size of these banks, as measured by total assets, has shown fluctuations around this average ratio.
Chart 8: Yearly average of implied interest payment
During the study period, commercial banks exhibited an average implied-interest payment of around 0.65% Notably, the highest recorded value of implied-interest payment was 0.86% in 2020, while the lowest value, at -0.54%, was attributed to Viet A Bank.
Between 2009 and 2020, the interest margin (NIM) of commercial banks in Vietnam experienced slight fluctuations Initially, from 2009 to 2015, NIM declined from 0.84% to a low of 0.39% However, from 2012 to 2014, this rate saw a significant increase, ultimately reaching a peak of 0.86% in 2020.
Chart 9: Yearly average of deposit
From 2009 to 2020, the average customer deposit rate in Vietnamese commercial banks was 62.87%, peaking at 70.83% in 2016 and dropping to a low of 45.26% in 2011 A noticeable trend is the slight increase in customer deposit ratios over the years, despite a decline during the global financial crisis from 2009 to 2011, where confidence in banks waned and the deposit ratio fell from 57.79% to a low of 48.22%.
2011 However, since 2011, the amount of deposits from customers into the bank increased again and reached 70.24% in 2020
Chart 10: Yearly average of Inflation
During the analyzed period, Vietnam experienced an average inflation rate of 5.91%, with a peak of 18.68% in 2011 and a low of 0.88% in 2015.
Following the global crisis of 2009, our country successfully controlled inflation despite facing significant domestic and global economic challenges, aligning it with national economic growth This achievement marked a pivotal moment in our macroeconomic strategy However, from 2009 to 2011, inflation persisted, peaking at 18.13% in 2011 due to inefficient investments in state-owned enterprises and an overreliance on credit within a fragile financial system In response, the State Bank of Vietnam (SBV) implemented flexible monetary policies and coordinated fiscal measures, resulting in a reduction of inflation to 6.81% in the subsequent year.
Between 2012 and 2015, inflation steadily decreased to 0.63%, marking a significant achievement in maintaining macroeconomic stability and keeping inflation below target levels In 2016, inflation rose to 3.24%, yet it remained lower than the rates observed from 2009 to 2013 and stayed within the target limit of 5% This increase was attributed to Inter-Circular No.
Correlation analysis between variables and multicollinearity test
The correlation relationship over time refers to the connection between fluctuating numerical series, where certain series represent the variations of causal indicators, and others reflect the resulting indicators that depend on these fluctuations The findings of the correlation analysis are presented in Table 5.
Table 5 indicates that the correlation coefficients among the variables are generally low, with the Net Interest Margin (NIM) exhibiting the strongest inverse correlation with the Cost-to-Income ratio (CI) at -0.5994, while the weakest correlation is observed with Non-Performing Loans (NPL) at -0.0058.
The absolute values of the correlation coefficients among the independent variables are all below 0.8, indicating no significant multicollinearity, as defined by Farrar & Glauber (1967) The lowest correlation coefficient is 0.0001, observed between GDP and MS, while the highest, at -0.6242, exists between CAP and SIZE.
Table 5: Matrix of correlation coefficients between variables
NIM LIQ CAP SIZE NPL NI CI IP DEP INF GDP MS
Despite its common use, the correlation coefficient can be misleading, as there are instances where a low correlation still indicates the presence of multicollinearity To enhance the accuracy and robustness of the model, this thesis will conduct further testing by analyzing the variance inflation factor (VIF) using STATA.
Table 6: Test of variance exaggeration factor VIF
In table 6, the results of the VIF indexes are shown all independent variables
The analysis indicates that the model exhibits no signs of multicollinearity issues, suggesting that the independent variables are effective for estimating and assessing the model's predictions.
Regression Analysis
Table 7: Regression result of Pooled-OLS
NIM Coeficent Standard Error Prob
The Pooled-OLS results indicate that three variables—CAP, CI, and INF—exhibit statistical significance at the 1% level, while NPL is significant at the 10% level, and MS at the 5% level Conversely, the variables LIQ, SIZE, NI, IP, and GDP show no statistical significance The model's R² value is 0.4680, indicating that the independent variables account for 46.8% of the variation in the data.
Table 8: Regression result of FEM
NIM Coefficient Standard Error Prob
The FEM analysis identified six statistically significant variables at the 1% level: CAP, SIZE, CI, DEP, INF, and MS, along with one variable, LIQ, that is significant at the 10% level Conversely, the variables NPL, NI, IP, and GDP were found to be statistically insignificant The model's R² value of 0.2876 indicates that the independent variables account for 28.76% of the data variation.
Table 9: Regression result of REM
NIM Coefficient Standard Error Prob
The REM analysis reveals five statistically significant variables at the 1% level: CAP, SIZE, CI, INF, and MS, along with two variables significant at the 10% level: LIQ and DEP Conversely, the variables NPL, NI, IP, MS, and GDP do not show statistical significance The model's R² value is 0.4446, indicating that the independent variables account for 44.46% of the data variation.
Hausman test is conducted to test FEM model or REM model, which is more appropriate for researching factors affecting the net interest margin
In table 10, hausman test concludes that the FEM method is the more optimal for NIM model (test value chi2 has p-value = 0.0092 < 0.05)
Value chi2 = 24.96 p-value Prob>chi2 = 0.0092
Breusch – Pagan test is conducted to test FEM model or Pool-OLS model, which is more appropriate for researching factors affecting the net interest margin
The Breusch-Pagan test in Table 11 indicates that the Pool-OLS method is not optimal for the NIM model due to the presence of heteroscedasticity, as evidenced by a chi-squared test value with a p-value of 0.0000, which is less than the 0.05 threshold Consequently, the Fixed Effects Model (FEM) is identified as the more appropriate model for this analysis.
Table 11: Breusch – Pagan Test result
Value Chibar2 = 117.12 p-value Prob>chi2 = 0.0000
(Note: YES, NO represent for having and without defects respectively)
After conducting the Hausman and Breusch-Pagan tests, the Fixed Effects Model (FEM) was chosen from the three models analyzed: FEM, Random Effects Model (REM), and Pooled Ordinary Least Squares (Pooled-OLS) To identify heteroskedasticity within the FEM model, the Ward test was employed.
In table 12 the heteroscedasticity test result shows that the chi2 test value has p-value < 0.05 ( 0.0000 < 0.05), so it is concluded that the FEM model has heteroscedasticity error
Value Chibar2 = 5806.83 p-value Prob>chi2 = 0.0000
(Note: YES, NO represent for having and without defects respectively)
From table 13, testing the autocorrelation phenomenon of FEM model, the results of the F test have p-value< 0.05, so it is concluded that FEM model has autocorrelation defects
The analysis of the partial test results indicates that the Fixed Effects Model (FEM) does not exhibit multicollinearity issues However, it does present challenges such as autocorrelation and homoskedasticity, which compromise the effectiveness of conventional regression estimates on panel data and render the tests invalid To address these shortcomings and ensure stable and efficient estimates, the Generalized Least Squares (GLS) estimation method is employed (Wooldridge, 2002).
4.3.8 Regression results of research model according to GLS:
Table 14: Regression results of research model NIM
Table 14 presents the regression model outcomes that analyze the factors influencing the net interest margin (NIM) of Vietnamese commercial banks using the Generalized Least Squares (GLS) method The findings indicate that variables such as Non-performing Loans (NPL), Non-interest Income (NI), Implied Interest Payment (IP), and Gross Domestic Product (GDP) do not have statistically significant effects on changes in NIM.
NIMi,t = 0.0355 + 0.0293 LIQ + 0.0364 CAP + 0.0036 SIZE - 0.0785 CI – 0.013DEP + 0.0973 INF - 0.0631 MS+ εi,t
Variables CAP, DEP and INF have positive impact on NIM and have statistical significance at 1% level of significance It means that when the variable capital equity
59 to total asset (CAP), deposit (DEP), Inflation (INF) increase by 1%, NIM will increases by 3.64%, 1.3%, 9.73% respectively
The variables Liquidity (LIQ) and Size (SIZE) positively influence Net Interest Margin (NIM) and are statistically significant at the 5% level Specifically, a 1% increase in the Liquidity Ratio (LR) leads to a 2.93% increase in NIM, while Size contributes an additional 0.36% increase.
Variable CI have the negative effect on NIM and have statistical significance at 1% level of significance It means that, Cost efficiency (CI) increase by 1%, NIM will reduces by 7.85%
An increase in Market Share (MS) by 5% leads to a statistically significant reduction in Net Interest Margin (NIM) by 6.32%, highlighting the negative impact of variable MS on NIM at a 5% significance level.
Discussing the results of the analysis
Table 15: Summary of research results
Independent variables β- coefficient Expectation Result
From the research results, the author discussed the following factors:
The INF variable shows a significant positive correlation with NIM at a 1% significance level, aligning with initial expectations of a positive effect This finding is consistent with the research conducted by Hamadi & Awdeh (2012) Overall, moderate inflation can benefit the economy by expanding credit, enhancing bank profits, and stimulating growth.
Higher interest rates can lead to increased inflation expectations, which in turn raises the inflation risk premium on loans As banks, similar to investors, prioritize rate returns, they often respond to rising inflation by increasing lending rates Consequently, to mitigate inflation risk, banks tend to raise their net interest margin (NIM).
The cost-to-income ratio (CI) negatively impacts net interest margin (NIM), aligning with the findings of Hamadi & Awdeh (2012) In all analyzed models, a strong negative correlation between CI and NIM indicates that more efficient commercial banks can offer lower loan rates and/or higher deposit rates to attract customers A high cost efficiency ratio reflects inefficiencies in expenses and bank management, adversely affecting overall performance and earnings Consequently, banks leverage their efficiency to maintain competitiveness in the market.
The analysis reveals a significant negative impact on the net interest margin (NIM) at a 1% significance level, contrary to initial expectations of a positive effect Specifically, there is an inverse relationship between the market share of total assets among Vietnamese commercial banks and the NIM ratio, indicating that as the market share increases, the NIM ratio tends to decrease.
A high market share offers banks a competitive edge by enabling them to provide products and services at reduced costs McShane and Sharpe (1985) found a positive correlation between market share and bank profitability However, Ugur & Erkus (2010) argue that the effect of market share is limited, primarily benefiting banks only when system costs are minimized Additionally, they caution that if banks fail to align their human resources and management with the demands of scaling and increasing market share, they may face heightened risks, leading to increased operational costs and a decline in service quality.
The analysis reveals a significant positive impact of net interest margin (NIM) at the 1% level, contrary to initial expectations of a negative effect This finding aligns with the research conducted by Angori & Gallo (2019), indicating that capitalization plays a crucial role in influencing net interest margin.
During uncertain times, such as the global financial crisis, risk aversion becomes a prominent human trait Banks with greater capitalization and heightened risk aversion in their financing structures tend to demand higher interest rates to achieve increased profit margins.
In the current financial landscape, commercial banks face significant pressure to raise capital to comply with Basel II standards, as mandated by the National Financial Supervisory Commission (NFNSC) This necessity drives banks to enhance their financial efficiency and service quality through long-term strategies focused on optimizing capital structure and minimizing mobilization costs Increasing capital not only enables banks to expand credit and diversify their offerings but also strengthens their financial capacity and ensures compliance with key financial ratios Moreover, adequate capital serves as a safeguard for depositors against operational risks, thereby enhancing the banks' reputation among customers and investors By effectively improving financial capacity, banks can reduce capital waste and lower mobilization costs, leading to more efficient operations and increased profitability.
The liquidity of commercial banks has a positive impact on the net interest margin This result is opposite with the author's hypothesis as well as the research results of Hamadi
Awdeh (2012) noted that domestic banks may increase interest rates to attract customer deposits, which boosts liquidity but narrows interest margins A high liquidity ratio enhances a bank's reputation and allows it to use part of its collateral to liquidate substantial investments, significantly increasing post-tax profits However, holding excessive cash for large investments can expose commercial banks to higher risks.
The DEP coefficient serves as an independent variable that positively influences the Net Interest Margin (NIM) at a 1% significance level, contrary to the initial negative expectation This finding highlights that deposits, a key banking service associated with interest expenses, can enhance net interest earnings.
In the Vietnamese banking industry, customer deposit choices are influenced by various factors beyond just deposit interest rates Research by Thao & ctg (2020) highlights the importance of brand reputation, financial benefits, promotions, and the influence of acquaintances, alongside the quality of facilities and staff Similarly, Anh (2015) identifies key factors such as tangible means, safety, convenience, service quality, and the impact of social circles in shaping customer preferences.
Capital mobilization is essential for banks as it supports various business activities, with deposits serving as the primary funding source Increased deposits positively influence bank performance, as converting more deposits into loans enhances the interest margin and boosts profitability.
SIZE has a positive relationship with the net interest margin of commercial banks This result is opposite with the author's expectation and the study of Hamadi & Awdeh
In 2012, it was noted that net interest margins (NIM) would rise due to economies of scale, with larger Vietnamese commercial banks benefiting from enhanced capital mobilization, product development, and customer access These banks possess a competitive edge over smaller institutions, leading to increased profitability as their size grows Currently, the total assets of commercial banks are steadily increasing, with larger banks experiencing more rapid asset growth compared to their smaller counterparts This trend highlights the significant expansion of banking services and improved customer reach To optimize the benefits of network expansion and scale, commercial banks must implement targeted strategies to boost capital and enhance the quality of their products and services, ultimately driving profitability.
The net interest margin (NIM) of commercial banks during this period shows no statistical significance, indicating that it was unaffected by non-interest income Yuksel & Zengin (2017) found a negative correlation between these two factors, suggesting that fluctuations in non-interest income do not influence the NIM of commercial banks.
Stiroh (2004) highlighted that engaging in non-banking activities often entails significant risks and does not guarantee increased profitability, suggesting that profitability may not rise due to diseconomies of scope It is crucial to assess whether banks that diversified into non-banking activities during the study period outperformed those that did not or limited their diversification Net Interest Income (NII) is intended to capture the impact of non-banking activities on bank performance However, due to the lack of specific information on the types of non-banking activities undertaken by banks, and without a clear expectation of profitability from these ventures, the precise relationship between NII and profitability measures such as Net Interest Margin (NIM) remains indeterminate.