INTRODUCTION
Problem statements
Bank systems are vital intermediaries in the modern economy, significantly influencing economic development, as evidenced by their impact in countries like India and Hungary during transitional periods The efficiency of financial intermediaries directly affects economic stability and resilience to shocks, particularly in Europe where the banking sector is integral to monetary policy through its deposit and lending channels A well-functioning banking system not only stabilizes the economy but also serves as a crucial bridge connecting various economic sectors and regions Given their role in diverse activities such as deposits, credit, and financial services, banks primarily generate profit through lending and deposits, underscoring their importance as financial intermediaries in both national and global economies.
The traditional banking model revolves around managing loans and deposits, where banks collect customer deposits to fund loans High funding costs can negatively impact bank profitability, making the net interest margin (NIM) a crucial indicator of the relationship between deposits and loans NIM can be calculated in two ways: first, by subtracting deposit rates from loan interest rates, and second, by dividing the difference between interest income and interest expenses by total assets for the relevant period Interest income reflects the bank's earnings after taxes, while interest expenses denote the costs associated with customer deposits Most studies, including those by Ho and Saunders (1981), Angbazo (1997), and Saunders and Schumacher (2000), predominantly utilize the second method for calculating NIM, which is also the approach most banks employ in their income statements.
As for the impact of NIM on banking operation, Demirgỹỗ-Kunt and Huizinga
In 1999, it was found that banks relying heavily on deposits for funding experience reduced profitability due to rising costs of funds Research indicates that stable and efficient banks focus on maintaining balanced interest margins, which should not be excessively large The net interest margin (NIM), representing the difference between interest income and expenses as a percentage of total earning assets, serves as a key indicator of banking efficiency A low NIM reflects effective intermediation costs and the stability of monetary policy Conversely, high costs can diminish economic incentives Raharjo et al (2014) demonstrated that a healthy banking sector can withstand economic shocks and support financial stability Given that the banking sector dominates the financial landscape, any failures can significantly impact a country's economic growth, potentially leading to bank runs and broader financial crises.
This research aims to analyze the determinants of net margin during the financial period, identifying significant factors that can enhance the health of banks through improved net interest margins.
From 2008 to 2012, the global economy faced significant challenges due to the financial crisis originating in the United States, impacting various sectors, including industry, services, and finance The banking sector was particularly hard-hit, with notable bankruptcies such as Lehman Brothers, the fourth-largest American bank in 2008, and Integra Bank Corp in 2011, along with numerous smaller banks This instability within the U.S banking system had a ripple effect on the global banking landscape, including banks in the ASEAN region.
The 2008-2009 economic crisis is regarded as the worst in global history, leading major economies like the United States, Japan, and Europe into recession During this period, GDP experienced significant declines, with unemployment rates soaring and numerous companies facing bankruptcy Notably, countries such as the EU (-0.5%), Germany (-0.8%), the United States (-0.7%), and Japan (-0.2%) reported negative GDP growth, while Russia and China saw reductions in growth rates from 10% to 8% in 2008 Additionally, global industrial exports witnessed a substantial decrease from 2007 to 2009.
Figure 1: GDP growth rate in main regions and countries, 2005 - 2009
World Developed countries European Union United States Japan Developing countries Brazil
Source: International Monetary Fund(IMF) and Author’s calculation
Figure 2: The growth rate of worldwide industrial exports, 2005 – 2009
Source: Source: IMF and Author’s calculation
Grigor and Salikhov (2009) identified key factors contributing to the global crisis, including high economic growth rates since the early 2000s, significant savings imbalances, negative real interest rates in developed nations, and weakened financial sector regulation due to the increased use of new financial instruments Fidrmuc and Korhonen (2010) highlighted the severe impact of the 2008 global crisis on business cycles in Asian developing countries, noting a significant decline in GDP growth rates and low business cycle levels in OECD countries Additionally, Ivashina and Scharfstein (2010) and Aisen and Franken (2010) examined the crisis's detrimental effects on bank credit, which is central to banking operations Although the global economy began to recover from the crisis between 2010 and 2012, its effects continued to be felt.
The crisis developed and spread to other Asian countries, including the countries of the ASEAN region In ASEAN countries, Figure 3 showed that GDP growth rate
Between 2008 and 2012, significant fluctuations were observed, particularly during the GDP slump of 2008-2009 However, the GDP growth rate began to recover from 2010 to 2012 Similarly, the inflation rate experienced a sharp decline in most ASEAN countries during 2008-2009, followed by increased stability from 2010 to 2012.
Figure 3: GDP growth rate from 2008 to 2009 in Asean countries.
Source: Work Bank (WB) and Author’scalculation
Figure 4: Inflation rate from 2008 to 2012 in Asean countries
Source: WB and Author’s calculation
On the other hand, Figure 5 showed the trend of NIM in Asean banks from 2008 to
In 2012, the Net Interest Margin (NIM) emerged as a key indicator of banking efficiency, with fluctuations in NIM directly impacting banks' efficiency and profitability During the crisis period, a noticeable decline in net interest margins was observed across ASEAN countries, as illustrated in Figure 3, which depicts the trend of average NIM among ASEAN banks.
Between 2008 and 2010, the inflation rate exhibited a downward trend during the crisis and subsequent recovery phase Throughout this period, the Net Interest Margin (NIM) experienced fluctuations This study aims to identify the factors influencing the volatility of NIM following the global economic crisis.
Figure 5:Trend of Net Interest Margins in Asean banks from 2008 – 2012
: Source: Bankscope and Author’s calculation
Research objectives
The goal of the study:
This study aims to model and evaluate the key determinants of net interest margins in ASEAN banks, focusing on ten critical factors: Gross Domestic Product (GDP) growth rate, inflation rate, banking market structure (measured by the Herfindahl-Hirschman Index), bank size, liquidity risk, credit risk, capital adequacy, operating costs, implicit interest payments, and managerial efficiency Additionally, the research seeks to provide empirical conclusions and offer policy recommendations for decision-makers in the banking sector.
To meet this goal, specific objectives are set out:
1 Determine the factors, magnitude, sign and significant level of determinants of NIM.
2 Inferring conclusions to suggest recommendations
Research questions
To solve objective of this paper, the relevant questions are answered:
1 What factors influence on the bank interest margins in Asean banks?
2 How those factors impact on the bank interest margins?
3 To recommend general policies for managing bank interest margins of Asean banks Which policy recommendation to manage NIM?
Research scope
This study examines the factors influencing bank interest margins across nine ASEAN countries—Brunei, Cambodia, Malaysia, Philippines, Laos, Vietnam, Singapore, Thailand, and Indonesia—over the period from 2008 to 2012 Although the ASEAN region comprises ten members, including Myanmar, data collection limitations have led to the exclusion of Myanmar from this analysis.
Research structure
This research is structured into several chapters: Chapter 1 discusses the rationale for selecting this theme and outlines the primary objectives of the study Chapter 2 provides a review of relevant literature and establishes the conceptual framework regarding the factors influencing net interest margins Chapter 3 details the research methodology and the data utilized Chapter 4 presents the key findings from the analysis, while Chapter 5 concludes the study and offers policy recommendations.
LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK
Literature review for interest margins
2.1.1 DEFINITION OF NET INTEREST MARGIN:
Net Interest Margin (NIM) is derived from the relationship between deposits and lending at banks, where funds from depositors are mobilized through deposit interest rates The bank then invests these funds by offering loans at higher interest rates, creating a profit margin Analyzing net interest margins serves as a measure of the cost of financial intermediation, reflecting the difference between the interest paid by borrowers and the interest earned by depositors (Brock and Suarez, 2000) Consequently, banks establish their loan and deposit rates accordingly.
RL : the rate on loans
RD : the rate on deposits
R : risk – free interest rate a : fees charged on loans b : fees charged on deposits
And the pure margin is:
The determinants of net interest margins (NIM) can be understood through two primary approaches: the traditional and the modern The traditional approach analyzes bank balance sheets to identify variables affecting NIM, while the modern approach considers demand and supply rates within the bank's microstructure Most prior studies have focused on the modern approach NIM is defined as the ratio of net interest income to total earning assets, typically reported annually by banks Net interest income represents the difference between interest income and interest expenses A seminal study by Ho and Saunders (1981) established a model that positions banks as intermediaries in the flow of funds, highlighting the reinvestment risk banks face if short-term rates decline Their model suggests that optimal fees must be charged to compensate banks for this risk, leading to the determination of the optimal interest margin.
In which: s : the difference between lending and deposit rates
2 ; the instantaneous variance of the interest rate on deposits and loans
R : the bank management’s coefficient of absolute risk aversion
Net Interest Margin (NIM) is defined as the difference between a bank's interest income and interest expenses, expressed as a percentage of average earning assets According to Ho and Saunder (1986), NIM represents the spread between interest revenue on bank assets and interest expenses on bank liabilities Dietrich, Wanzenried, and Cole (2010) further emphasized that NIM is the net interest income relative to interest-earning assets Raharjo et al (2014) measured NIM as the ratio of net interest income to average total earning assets, where net interest income is calculated by subtracting interest expenses from interest income Brock and Suarez (2000) also highlighted NIM as the difference between the interest costs paid to borrowers and the income received from depositors Although the definitions vary in phrasing, they convey a similar concept of NIM In this study, NIM will be measured using the ratio of net interest income to total earning assets, with data sourced from Bankscope, ensuring consistency across international NIM databases.
Ho and Saunders (1981) were pioneers in analyzing the determinants of net interest margins (NIM) Their findings identified key factors influencing NIM, including interest rate volatility, transaction size, risk aversion, and market competition, utilizing a two-step regression approach In the first step, NIM was estimated based on bank-specific characteristics, while the second step focused on macroeconomic and market structural factors Their dealership model provided valuable insights into these relationships.
Since 1981, banks have adopted a risk-averse approach while navigating the costs associated with loans and deposit markets Numerous studies have built upon Ho and Saunders' model to examine net interest margins from various perspectives.
Wong (1997) built upon the model established by Ho and Saunders (1981), demonstrating that credit and interest rate risk significantly influence net interest margin (NIM) within a simple firm-theoretical framework This model reflects the behavior of risk-averse banks, highlighting the relationship between NIM, credit risk, and interest rate risk The findings indicate a positive correlation between NIM and factors such as market power, operating costs, and credit risk, while also revealing that interest rate risk positively impacts bank interest margins.
In their study, the authors analyzed the impacts on Net Interest Margin (NIM) across several countries, including Germany, Italy, Switzerland, the UK, Spain, France, and the US, during the period from 1988 to 1995, focusing on factors such as implicit interest rates, opportunity costs, and credit risk Additionally, Claeys and Vander Vennet (2008) utilized a random effects estimator to compare the determinants of NIM in Central and Eastern Europe versus Western countries, revealing that interest rate volatility and regulatory restrictions, including minimum capital and liquidity reserve requirements, significantly influenced NIM.
In 1997, research based on the dealership model by Ho and Sauders (1981), McShane and Sharpe (1985), and Allen (1988) investigated the impact of interest rate risk, default risk, liquidity risk, and off-balance sheet items on the fluctuations of Net Interest Margin (NIM) from 1989 to 1993, utilizing 1,400 observations from 286 commercial banks' Call Report data In contrast, Lin et al (2012) employed a switching regression model to analyze bank margins in several Asian countries, including China, India, Indonesia, Japan, the Philippines, Singapore, South Korea, Taiwan, and Thailand, during the period from 1997 to 2005 Their findings indicated that NIM is sensitive to various bank risk factors such as liquidity risk, interest rate risk, credit risk, and implicit interest payments, alongside other elements derived from balance sheets and income statements Similar to other studies, Dumičić and Ridzak also contributed to this research area.
The determinants of Net Interest Margin (NIM) in Central and Eastern Europe (CEE) were analyzed using the fixed effect estimator based on the Ho and Saunders (1981) model, covering the period from 2000 to 2010 In the European Union, Maudos and Guevara (2004) expanded this model, finding that NIM is positively correlated with market power and concentration, while negatively affected by interest rate risk, credit risk, and operating costs Additionally, Kasman, Tunc, Vardar, and Okan (2010) highlighted the influence of bank-specific factors, country-specific market characteristics, and macroeconomic conditions on NIM, noting that consolidation impacts NIM in both new and old EU member states The Ho and Saunders model is recognized as a foundational framework for understanding NIM, supported by the works of Claeys and Vander Vennet (2008) and Lin et al (2012) Most research in this area has utilized panel data, with some studies focusing on data from banks within a single country over time, similar to the approach taken by Ho and Saunders (1981) with American banks.
Between 1976 and 1979, various studies have examined the determinants of Net Interest Margin (NIM) across different banking systems Notably, Entrop, Memmel, Ruprecht, and Wilkens (2012) analyzed the Albanian banking system from 2001 to 2007, while Fungacova and Poghosyan (2009) explored factors influencing NIM in Russian banks from 1999 to 2007 Additionally, Williams (2007) utilized panel data from 1989 to 2001 to investigate NIM determinants in Australia Overall, many research papers leverage panel data, which combines time series data, often focusing on multiple countries during similar time frames.
In detail, Saunders and Schumacher (2000) employed data from seven countries to prove the relationship between NIM and implicit interest rate, opportunity cost, credit risk in
Between 1988 and 1995, Claeys and Vander Vennet (2008) analyzed panel data from 36 countries across Western and Eastern Europe, focusing on the years 1994 to 2001 Their study utilized panel data from multiple countries over a five-year period, providing valuable insights into the economic trends of the time.
Between 2008 and 2012, various studies (Claeys & Vander Vennet, 2008; Dumičić & Ridzak, 2012; Kasman et al., 2010) categorized influencing factors into three distinct groups: macroeconomic factors, banking market-specific variables, and bank-specific variables This paper similarly organizes independent variables into these three categories for analysis.
Macroeconomic variables play a crucial role in influencing the Net Interest Margin (NIM) of a national economy Key indicators such as GDP growth and inflation rates serve as essential proxies for assessing economic health and its impact on NIM Numerous empirical studies, including those by Schwaiger & Liebeg (2008) and Ben Naceur & Goaied (2008), have highlighted the significant effects of GDP on NIM Additionally, the relationship between inflation and NIM further emphasizes the importance of these macroeconomic factors in understanding economic dynamics.
Dumičić and Ridzak demonstrated a negative impact of inflation on net interest margins (NIM) in Central and Eastern Europe (CEE) In contrast, Kasman et al (2010) identified a contra-variant effect on NIM Aliaga-Díaz and Olivero (2005) noted that high inflation correlates with increased costs and income for banks, suggesting that bank income rises more significantly than costs during inflationary periods This study analyzes the effects of GDP growth and inflation on interest rate margins, referencing Demirgüç-Kunt and Huizinga (1999), who indicated that while inflation raises bank costs, it also enhances interest margins and profitability, albeit with a low positive coefficient They found no significant impact of GDP growth on NIM and profitability Furthermore, Claeys and Vander Vennet (2008) concluded that higher GDP growth rates in CEE lead to increased margins, alongside a significant positive effect of inflation on those margins.
This study focuses on bank-specific variables that influence banking performance and net interest margin (NIM) The majority of these variables are derived from income statements, balance sheets, and other financial reports However, this paper selectively employs certain variables based on empirical literature to analyze their effects.
The suggested research approach
The concept of Net Interest Margin (NIM) was first explored by Ho and Saunders (1981), who proposed a dealership model where banks act as risk-averse financial intermediaries In this model, banks serve as intermediaries between fund suppliers and demanders, utilizing deposit and loan rates to mobilize funds in the money market They face asymmetric demands for loans and deposits, necessitating a careful balance between the two, as deposit periods often differ from loan periods This disparity requires banks to strategically set interest rates to attract capital while managing risks associated with interest rates, defaults, and credit Angbazo (1997) expanded on Ho and Saunders' work by introducing additional risk factors affecting NIM, highlighting the complexities banks face in maintaining financial stability.
The dealership model of Ho and Saunders (1981) becomes the basic model for last researchers about net interest margins Angbazo(1997), Saunders and Schumacher
The NIM model was developed by Claeys and Vander Vennet (2008) and Maudos and de Guevara (2004), building upon earlier research Additionally, Angbazo (1997) and Maudos and de Guevara (2004) expanded on the model proposed by Ho and Saunders (1981).
?? : The structure of the market for loans and deposits
? (?? ) : The coefficient of absolute risk aversion
? ? 2 : the volatility of money market interest rates
? ?? : the covariance between interest rate risk and credit risk
(? + 2? 0 ): The total volume of credits
(? + ? ) : The average size of the credit and deposit operations undertaken by the bank
This model examines the factors influencing Net Interest Margin (NIM) in relation to the risk associated with the spread between lending and deposit rates in the money market Angbazo (1997) found that riskier loans and elevated interest rates lead to increased bank interest margins in U.S commercial banks from 1989 to 1993 Additionally, NIM is influenced by market power, interest rate risk, credit risk, and operating costs in EU banks between 1993 and 2000.
The NIM model developed by Ho and Saunders (1981) has been widely utilized in empirical research across various dimensions Notably, Kasman et al (2010) explored the link between consolidation and commercial bank net interest margins in both old and new European Union member states and candidate countries Aliaga-Díaz and Olivero (2005) applied this model to examine the cyclical patterns of net interest margins in U.S banks Furthermore, English (2002) identified a correlation between net interest margins and market interest rates Additionally, Fungáčová and Poghosyan (2011) demonstrated that ownership factors significantly influence NIM in Russia Dietrich et al (2010) concluded that net interest margins vary across countries due to specific bank factors and macroeconomic variables.
The concept framework
The framework for understanding interest margins identifies three key determinant groups: bank-specific characteristics, macroeconomic factors, and banking market characteristics Macroeconomic conditions primarily focus on GDP growth and inflation rates The bank-specific group assesses seven performance-related parameters, including bank size, liquidity risk, credit risk, capital adequacy, operating costs, implicit interest payments, and managerial efficiency Lastly, the banking market group utilizes the Herfindahl index to evaluate the market's influence on bank margins.
Macroeconomic factors Banking market specific characteristics Bank-specific characteristics
Chapter 2 provides a comprehensive overview of the theoretical literature on net interest margins and their determinants, which include GDP growth rate, inflation rate, market structure, bank scale, liquidity risk, credit risk, capital adequacy, operating costs, implicit interest payments, and managerial efficiency It details the computation methods for each variable and presents an empirical model tailored for this research, drawing from the theoretical frameworks established by Ho and Saunders (1981) and Angbazo (1997) Additionally, this chapter establishes a conceptual framework based on the reviewed theoretical literature.
RESEARCH METHODOLOGY AND DATA COLLECTION
Identification of variables
Net interest margin (NIM) can be approached in two ways: first, by measuring the difference between the contractual interest rates for deposits and loans, and second, by calculating the difference between interest income and interest expenses over a specific period The first method has its drawbacks due to the varied sources of deposits and loans at banks, which may not align effectively Conversely, the second method also faces challenges, as noted by Demirgüç-Kunt and Huizinga (1999), who highlighted that interest income and expenses often materialize in different periods This method relies on data derived from the banks' financial statements.
This study defines net interest margin (NIM) as the cost of intermediation, represented by the difference between the interest paid by borrowers and the income earned by depositors (Bernake, 1983; Brock & Suarez, 2000) Consistent with numerous empirical studies, NIM is calculated as the difference between interest income and interest expenses relative to total earning assets (Claeys & Vander Vennet, 2008; Dietrich et al., 2010; Entrop et al., 2012) The data for NIM will be sourced from Bankscope.
3.1.2 THE INDEPENDENT VARIABLES AND HYPOTHESIS TESTING:
The GDP growth rate, a key macroeconomic factor measured by GDP per capita, reflects the economic growth of each country and influences prices, costs, and the business cycle due to changes in monetary policies This economic growth is linked to net interest margin (NIM), with numerous studies indicating a positive relationship between GDP and NIM For instance, Claeys and Vander Vennet (2008) found a positive association between the business cycle and NIM in Western European bank markets, suggesting that higher economic growth correlates with increased net interest margins Similarly, Dumičić and Ridzak (2012) noted that rising GDP growth leads to greater credit demand, enhancing bank margins However, contrasting findings by Ben Naceur and Goaied (2008) and Ben-Kediri et al (2005) indicate no relationship between economic growth and NIM in Tunisia This research utilizes GDP growth rate data from ASEAN countries for the period of 2008 to 2012, sourced from the World Bank.
Hypothesis 1: Economic growth (GDP) is expected a positive significant impact on NIM The greater economic growth will have a higher net interest margins
The inflation rate (INF), determined by changes in the Consumer Price Index (CPI), significantly impacts market prices and purchasing power; as inflation rises, purchasing power diminishes, often leading to higher interest rates on deposits and loans This relationship positions inflation as a crucial macroeconomic factor influencing the Net Interest Margin (NIM) Research by Kasman et al (2010) indicates that an increase in inflation correlates with higher costs and incomes, establishing a positive relationship between inflation rate and NIM Similarly, a study in Indonesia covering the period from 2008 to 2012 by Raharjo et al (2014) confirmed that inflation positively and significantly affects NIM Thus, inflation can exhibit both positive and negative impacts within this economic model, with inflation data sourced from the World Bank alongside GDP growth rates.
Hypothesis 2: Inflation rate is expected that there is a positive effect on bank interest margin
The Herfindahl-Hirschman Index (HHI) serves as a key measure of market structure in the banking sector, reflecting the distribution of bank sizes and their positions within the market over time In this study, HHI is utilized as a proxy for market structure, defined as the square of each bank's asset share in the loan market Previous research, including studies by Claeys & Vander Vennet (2008), Fungacova & Poghosyan (2009), and Maudos & Guevara (2004), indicates a significant positive correlation between the Herfindahl index and Net Interest Margin (NIM), suggesting that an increase in HHI is likely associated with a higher NIM.
Hypothesis 3: HHI is expected that there is a positive impact on net interest margins
Bank size, measured by the logarithm of total assets, reflects the operational scale of financial institutions in the market Generally, larger banks tend to have lower margins, while smaller banks charge higher interest rates to borrowers, resulting in elevated margins Research by Fungacova and Poghosyan (2009) supports this notion, indicating that a bank's size negatively impacts its net interest margin (NIM) Additionally, Dumičić and Ridzak (2012) found that in Central and Eastern European banks, larger institutions experience lower income costs and higher NIMs Consequently, the SIZE variable is anticipated to have a positive correlation with NIM.
Hypothesis 4: It is expected that scale of bank will effect on net margins significantly by a negative relationship
Liquidity risk (LIQ) is assessed by the ratio of liquid assets to total liabilities, indicating a bank's ability to meet withdrawal demands from depositors or new loan requests Banks aim to minimize liquidity risk, as it is critical for ensuring that liabilities are adequately supported by liquid assets A significant negative correlation is expected between the liquidity risk coefficient and net interest margin (NIM) This relationship suggests that as a bank's demand liabilities are increasingly backed by liquid assets, its liquidity risk decreases, which positively impacts its margins, as observed in Russian banks from 1999 to 2007 (Fungacova & Poghosyan, 2009).
In 1997, Angbazo expanded upon the dealership model proposed by Ho and Saunders (1981) by incorporating liquidity risks, revealing that increased liquid assets lead to a reduction in bank margins due to heightened liquidity risk Further research by Aliaga-Díaz and Olivero (2005) on the cyclical behavior of net interest margins in the U.S banking sector demonstrated that balance sheet liquidity exhibits counter-cyclicality, with credit risk rising more significantly for illiquid assets compared to their liquid counterparts Consequently, this study anticipates a significantly negative relationship between liquidity risk and net interest margin (NIM).
Hypothesis 5: Author expects that liquidity risk will have a negative effect on net interest margin
Credit risk (CRD) is a crucial determinant of net interest margin (NIM), represented by the ratio of total loans to total assets Research, including Wong (1997), indicates a positive relationship between credit risk and bank interest margins, suggesting that as the percentage of total loans increases, the interest spread tends to rise This correlation highlights that higher loan volumes lead to increased credit risk, subsequently resulting in higher bank interest margins.
Numerous studies, including those by 2000 and Hawtrey and Liang (2008), have established a positive correlation between credit risk and net interest margins (NIM) in OECD countries The majority of empirical research analyzing NIM consistently supports this relationship, indicating that higher credit risk tends to lead to increased bank margins Consequently, this study anticipates a similar positive correlation between credit risk and bank margins.
Hypothesis 6: It is expected that credit risk will have positive impact on bank margins
Capital adequacy (CAP), defined as the ratio of equity to assets, indicates the extent to which a bank's assets are financed by equity; a higher ratio often leads to increased capital costs and a potential reduction in net interest margin (NIM) Empirical studies, such as those by Lin et al (2012), reveal that equity is a more expensive funding source, necessitating higher NIM to offset increased capital costs Additionally, research by Kasman et al (2010) highlights the correlation between capital adequacy and the creditworthiness of banks, demonstrating a positive relationship between capital adequacy and NIM in both old and new EU contexts Furthermore, Claeys and Vander Vennet (2008) emphasize that capital adequacy serves as a risk-limiting measure, ensuring the stability of banking operations.
On the other hand, they explored that capital adequacy influence NIM positive significantly in CEE
Hypothesis 7: It is expected that capital adequacy will effect on NIM positive significantly
Operating cost (OPE), represented as the ratio of overhead to total assets, has been shown to have a positive correlation with net interest margin (NIM) Research by Dietrich, Wanzenried, and Cole (2010) highlights that operating costs significantly influence NIM, particularly in the banking systems of new EU member and candidate countries, as evidenced by Kasman et al (2010) Their findings indicate that banks may need to implement higher interest margins to offset elevated operating costs Additionally, Maudos and de Guevara (2004) identified operating cost as a crucial factor impacting NIM in the EU, suggesting that increased operating expenses compel banks to charge higher margins on credit and deposit rates Therefore, it is anticipated that the coefficient in this study's model will reflect a positive relationship.
Hypothesis 8: Operating cost is expected that there is a positive impact on net interest margins
Implicit interest payments (IIP), defined as the difference between operating expenses and non-interest revenue relative to total assets, are identified as a key factor influencing net interest margin (NIM) Research by Hawtrey and Liang (2008) indicates that IIP is highly variable in its impact on NIM Additionally, Kasman et al (2010) highlight that banks often provide free banking services instead of paying interest on deposits, which can lead to increased bank margins A lower IIP is associated with a decline in NIM, while Angbazo (1997) found a significant positive relationship between IIP and NIM among U.S banks from 1989 to 1993, suggesting that rising implicit interest payments contribute to higher costs and, consequently, larger margins Zhou and Wong (2008) further support this by noting that costs associated with IIP are reflected in bank margins Overall, this research anticipates a positive correlation between IIP and NIM.
Hypothesis 9: It is expected that implicit payment will have positive effect on net margins
Managerial efficiency (MGE), defined as the ratio of operating costs to gross income, reflects the quality of management within a bank Effective management significantly impacts interest margins, with evidence suggesting that banks with poor management experience lower interest margins.
Research indicates a complex relationship between management quality and net interest margins (NIM) Angbazo (1997) suggests that effective management leads to increased revenues and higher NIM, while Kasman et al (2010) argue that market efficiency (ME) negatively impacts NIM in both old and new EU contexts As ME rises, banks may need to offer higher deposit rates and lower credit rates, reflecting a dual perspective on ME's influence on NIM Ultimately, this paper posits that ME is likely to exhibit a negative correlation with NIM, aligning with findings from Angbazo (1997) and Vardar and Okan (2010).
Hypothesis 10: Managerial efficiency is expected that it can effect on NIM negative significant
Data collection and expected results
The study utilizes panel data from nine ASEAN countries—Brunei, Cambodia, Indonesia, Laos, Malaysia, the Philippines, Singapore, Thailand, and Vietnam—covering the period from 2008 to 2012, as detailed in Table 1 Due to insufficient data from Myanmar, banks from that country were excluded from the analysis The research incorporates bank-specific variables sourced from the Bankscope database, while macroeconomic indicators such as GDP and inflation are obtained from the World Bank database Although not all banks provided complete data throughout the survey period, the analysis focuses on 202 banks with full data, resulting in a total of 1,010 observations from 2008 to 2012 across the nine countries.
Table 1: Feature and source of variables
Sign Data source Dependent variable
Net interest margins – the difference between interest income and interest expenses as a proportion of total earning assets (in %)
1.GDP Gross Domestic Product growth rate (in %) + World Bank
2.INF Inflation rate – the annual inflation rate (in %) + World Bank
Herfindahl - Hirchman Index for assets
HHI – the sum of squares of individual bank asset shares in the total banking sector assets for given region.
4 SIZE Bank size – the logarithm of bank total asset + Calculation from
5 LIQ Liquidity risk–liquid assets/total liabilities - Calculation from
6 CRD Credit risk – total loans/ total assets + Calculation from
7 CAP Capital adequacy – total equity/assets + Bankscope
8 OPE Operating cost – the ratio of overhead to total assets + Calculation from
Implicit interest payments - The difference between operating expense and non – interest revenue divided by total assets
10 MGE Managerial efficiency: Operating cost/ Gross
The research methodology
Previous studies predominantly utilized linear models to examine the factors influencing Net Interest Margin (NIM) In alignment with this approach, the authors of this study also employed linear models for their analysis, selecting relevant variables based on findings from prior empirical research.
The panel data equation model as follows:
NIM i,j,t = β 0 +β 1 GDP j,t +β 2 INF j,t + β 3 HHI j,t + β 4 SIZE i,j,t + β 5 LIQ i,j,t + β 6 CRD i,j,t + β 7 CAP i,j,t + β 8 OPE i,j,t + β 9 IIP i,j,t + β 10 MGE i,j,t + ε i,j,t
- i,j,t are bank, country and time, respectively
- NIMi,j,t : net interest margin value of bank i at time t in country j
This study utilizes panel data to evaluate the effects of independent variables on the dependent variable, considering both Fixed Effects Model (FEM) and Random Effects Model (REM) Each model offers distinct advantages and disadvantages for analyzing the relationships within the data.
Finite Element Method (FEM) is employed to assess the impact of various variables over time, allowing for the isolation of stable characteristics from explanatory variables This enables the estimation of the net effect on the dependent variable Within each entity, there exists a relationship between predictor and outcome variables, with individual characteristics that can influence these predictor variables (Oscar, 2007).
The equation for the FE:
Y i,j,t = β 1 GDP j,t +β 2 INF j,t + β 3 HHI j,t + β 4 SIZE i,j,t + β 5 LIQ i,j,t + β 6 CRD i,j,t + β 7 CAP i,j,t + β 8 OPE i,j,t + β 9 IIP i,j,t + β 10 MGE i,j,t + vi+ ε i,j,t
vi: the unknown intercept for each entity ( n entity – specific intercepts) – The component represents the unobservable factors differ between entities but does not change over the vary time.
it : the error term – the unobserved factors differ between entities but changes over the vary time
Yit: the dependent variable where i, = entity (i = 1…n) and t = time
βi : The coefficient for the independent variables
In estimating parameters for the Fixed Effects Model (FEM), two common methods are the Least Squares Dummy Variable (LSDV) and the Fixed Effects Estimator (FE estimator) LSDV is typically preferred when the number of observations is small, as creating dummy variables becomes cumbersome with larger datasets Given that this study involves 1,010 observations, which is relatively large, the author will utilize the FE estimator, provided that FEM is deemed suitable for the analysis.
When the characteristics of an entity are assumed to be random and uncorrelated with the explanatory variables, the Random Effects Model (REM) is appropriate In REM, the residuals of each entity serve as new explanatory variables A key distinction between fixed and random effects lies in whether the unobserved individual effects are correlated with the model's regressors, rather than their stochastic nature (Green, 2008, p.183) One advantage of the random effects approach is its ability to incorporate time-invariant variables into the model, utilizing the Feasible Generalized Least Squares (FGLS) estimator.
The Random Effected model is:
Y i,j,t = β 1 GDP j,t +β 2 INF j,t + β 3 HHI j,t + β 4 SIZE i,j,t + β 5 LIQ i,j,t + β 6 CRD i,j,t + β 7 CAP i,j,t + β 8 OPE i,j,t + β 9 IIP i,j,t + β 10 MGE i,j,t +α+vi+ ε i,j,t
vi: the unknown intercept for each entity ( n entity – specific intercepts) – The component represents the unobservable factors differ between entities but does not change over the vary time.
it : the error term – the unobserved factors differ between entities but changes over the vary time
Yit: the dependent variable where i, = entity (i = 1…n) and t = time
βi : The coefficient for the independent variables
The Hausman test is essential for selecting between Fixed Effects (FE) and Random Effects (RE) models, as outlined by Baltagi (2008, p 320) The null hypothesis (H0) posits no correlation between subjects and the explanatory variables, making RE a valid estimate under H0, but inconsistent with alternative hypotheses Conversely, FE serves as a suitable estimate for both H0 and alternative hypotheses If H0 is rejected, Fixed Effects estimates are preferred over Random Effects estimates However, if H0 is not rejected, indicating a correlation between residuals and explanatory variables, Fixed Effects estimates remain more appropriate In large samples, using Least Squares Dummy Variable (LSDV) is impractical, thus the Fixed Effects Estimator is the recommended method for estimation in FE models.
The outline of estimation method
This chapter clearly defines the variables and calculation methods, along with the source data for each variable Building on the conceptual framework from Chapter 2 and empirical evidence, it presents ten hypotheses linking independent variables to dependent variables The study utilizes panel data from nine ASEAN countries—Brunei, Cambodia, Indonesia, Laos, Malaysia, Philippines, Singapore, Thailand, and Vietnam—over five years (2008-2012), totaling 1,010 observations It employs both fixed effect and random effect models for panel data analysis, with the Hausman test determining the most suitable model.
DATA ANALYSIS AND DISCUSSION
The data description
The observation totaled 1010 observations corresponding 202 banks from 2008 to
2012 The data of this paper is described on as followed:
Variable name Variable measurements Variable label
GDP The annual Gross Domestic Product growth rate
INF The annual inflation rate (in %) Inflation rate
HHI The sum of squares of individual bank asset shares in the total banking sector assets for given region.
SIZE The logarithm of bank total asset Bank size
LIQ The ratio of liquid assets to total liabilities Liquidity risk
CRD The ratio total loans to total assets Credit risk
CAP The ratio of total equity to assets Capital adequacy OPE The ratio of overhead to total assets Operating cost
IIP The difference between operating expense and non
– interest revenue divided by total assets Operating cost MGE The ratio of operating cost to gross income Managerial efficiency id Bank name is put from 1 to 202 Bank name
The summary statistic
Source: Bankscope and Author’s estimation with Stata
Table 2 presents summary statistics for the Net Interest Margin (NIM) over a five-year period from 2008 to 2012, based on 1,010 observations The analysis reveals significant fluctuations in NIM, with a mean value of 5.74%, a minimum of -14.5%, and a maximum of 484.23% The standard deviation is notably high at 19.72%, indicating considerable variability in the data.
Economic growth of the Asean countries (GDP) average for the period 2008 -
2012 reach 4.911736%, the lowest achieving -2.329849%, the highest 14.78079%, 2.94025 standard deviations achieved.
Table 3: Deterministic statistic of main variables
Variable Obs Mean Std Dev Min Max
The inflation rate of the Asean countries (INF) in 5-year study averaged 5.749471%; -0.8538899 % is the lowest value and the highest value is 24.99718%, standard deviation is 4.756319.
The average HHI index value, measuring market concentration, is 14.587%, indicating a high degree of concentration The index ranges from a low of 7.29% to a high of 96.33%, with a standard deviation of 9.41.
Scale of operating (SIZE) in Asean banks average 6.165555, the banks have the lowest scale at 4.032128, large banks clicked with value 8.460281, and standard deviation is 0.8442338.
Between 2008 and 2012, the liquidity of banks (LIQ) was assessed using the ratio of liquid assets to average demand liabilities, yielding an average value of 54.18% The analysis revealed a minimum liquidity value of 0.07% and a maximum of 6,550.50%, with a standard deviation of 2.68%.
Between 2008 and 2012, the credit risk (CRD) of the ASEAN banking system was assessed using the ratio of loans to total assets, yielding an average value of 54.24% The lowest recorded value was -0.96%, while the highest reached 96.59%, with a standard deviation of 21.25% Additionally, the ratio of equity to total assets (CAP) averaged 18.72%, with a minimum of -6.01% and a maximum of 99.20%, accompanied by a standard deviation of 18.05%.
The ratio of overhead to total asset (OPE) as a proxy of operating cost has 4.07586% average value with the lowest value is 0.1034%, 31.19787% is the highest value, standard deviation is 4.58181.
The difference the between operating expense and non - revenue divided by total assets (IIP) task interest value 1.23444% average, the lowest value is -25.83798%, the highest value achieves 23.81817%, 3.20568 standard deviation get.
The ratio of operating cost to gross income (MGE) average 58.81804, the lowest value of 3.93, the highest value was 467.53 with a standard deviation of 30.55281.
Table 2 presents the summary statistics of the net interest margin (NIM) based on 1,010 observations collected over five years from 2008 to 2012 The analysis reveals significant fluctuations in NIM, with a mean value of 5.74% The minimum recorded NIM was -14.5%, while the maximum reached an astonishing 484.23% Additionally, the standard deviation stands at 19.72, indicating considerable variability in the data.
Economic growth of the Asean countries (GDP) average for the period 2008 -
2012 reach 4.91%, the lowest achieving -2.33%, the highest 14.78%, 2.94 standard deviations achieved.
Over a five-year study, the inflation rate in ASEAN countries averaged 5.74%, with a minimum of 0.85% and a maximum of 24.99%, resulting in a standard deviation of 4.75 Additionally, the average Herfindahl-Hirschman Index (HHI), which measures market concentration, was 14.58%, indicating a high degree of market concentration The HHI values ranged from a low of 7.29% to a high of 96.33%, with a standard deviation of 9.41.
Scale of operating (SIZE) in Asean banks average 6.16, the banks have the lowest scale at 4.03, large banks clicked with value 8.46, and standard deviation is 0.84.
Between 2008 and 2012, the liquidity of banks (LIQ) was assessed using the ratio of liquid assets to average demand liabilities, yielding an overall average of 54.17% During this period, the liquidity ratio fluctuated significantly, with a minimum value of 0.07% and a maximum of 6550.49%, resulting in a standard deviation of 2.68.
Between 2008 and 2012, the credit risk (CRD) within the ASEAN banking system was assessed using the ratio of loans to total assets, yielding an average value of 54.24% During this period, the lowest recorded value was -0.95%, while the highest reached 96.58%, with a standard deviation of 21.24%.
The bank's equity to total assets ratio (CAP) averaged 18.72%, with a minimum of -6.01% and a maximum of 99.20%, resulting in a standard deviation of 18.04 Additionally, the overhead to total assets ratio (OPE), which serves as a proxy for operating costs, had an average value of 4.07586%, ranging from a low of 0.10% to a high of 31.19%, with a standard deviation of 4.58.
The difference the between operating expense and non - revenue divided by total assets (IIP) task interest value 1.23% average, the lowest value is -25.83%, the highest value achieves 23.81%, 3.20 standard deviation get.
The ratio of operating cost to gross income (MGE) average 58.81, the lowest value of 3.93, the highest value was 467.53 with a standard deviation of 30.55.
Testing for correlation relationship
Table 4: correlation coefficient of variables
NIM GDP INF HHI SIZE LIQ CRD CAP OPE IIP MGE
Source: Bankscope and Author’s estimation with Stata
The correlation analysis presented in Table 3 indicates that the variables in this study exhibit low levels of correlation, suggesting that multicollinearity is not a significant concern for the model Among the independent variables, LIQ and CAP show the highest correlation at 0.3632, yet this remains relatively low Additionally, the correlation between IIP and NIM is the strongest at 0.4054, followed by OPE and CAP with correlations of 0.2462 and 0.0451, respectively.
Checking for multicollinearity
To check the multicollinearity, the author use Variance Inflation Factor (VIF) to test, compare with VIF>10, the model is considered that there is multicollinearity
However, in this study the mean VIF = 1.32 (in table 5) is much smaller than comparable value so there is no multicollinearity phenomenon.
VIF = 1.32 => multicollinearity does not effect on model
Source: Bankscope and Author’s estimation with Stata
The relationship between independent variables and
GDP growth rate and NIM
The figure 6 gives information about the relationship between GDP and NIM
In 2008 -2009, GDP declined rapidly while there was a slight increase in NIM Similarly, Asean countries experienced a stead decrease in NIM over the period
2009 -2010, GDP increase remarkably And in 2010 – 2010, while GDP fluctuate dramatically NIM changed slightly
Figure 6: The relationship between GDP and NIM
Source: WB and Author’s calculation
Figure 7 illustrates the correlation between inflation and Net Interest Margin (NIM), highlighting a significant decline in GDP alongside a gradual rise in NIM from 2008 to 2009 In the following years, despite rapid fluctuations in inflation, NIM demonstrated a consistent trend.
Figure 7:The relationship between INF and NIM
Source: WB and Author’s calculation
Market structure (HHI) and NIM
In term of the relationship between structure market and NIM, the story was quite different There was a positive correlation between HHI and NIM over the period 2008 to 2012
Figure 8: The relationship between HHI and NIM
Source: Bankscope and Author’s calculation
The scale of bank (SIZE) and NIM
As figure 9 showed, the changing of SIZE and NIM had a same trend from
2008 to 2012 Hence, there was a positive relationship between SIZE and NIM
Figure 9:The relationship between SIZE and NIM
Source: Bankscope and Author’s calculation
Liquidity risk (LIQ) and NIM
Figure 10 illustrates the correlation between liquidity risk and net interest margin (NIM) in the ASEAN region from 2008 to 2012 During this period, both NIM and liquidity (LIQ) exhibited similar trends, indicating a positive relationship between the two variables.
Figure 10:The relationship between LIQ and NIM
Source: Bankscope and Author’s calculation
The credit risk (CRD) and NIM
As is showed in figure 11, there were a negative trend of NIM and CRD However, the fluctuations of NIM and NIM were not rapidly in this period 2008 -
Figure 11: The relationship between CRD and NIM
Source: Bankscope and Author’s calculation
The capital adequacy (CAP) and NIM
The data illustrates the relationship between capital adequacy (CAP) and net interest margin (NIM) from 2008 to 2012 Overall, the analysis reveals that fluctuations in CAP corresponded with changes in NIM during this period Notably, a positive correlation was observed between NIM and CAP throughout the surveyed years.
Figure 12:The relationship between CAP and NIM
Source: Bankscope and Author’s calculation
The operating cost (OPE) and NIM
The relationship between operating costs and net interest margin (NIM) exhibited a negative trend from 2008 to 2012, contrasting with the dynamics observed between capital adequacy and NIM during the same period.
Figure 13:The relationship between OPE and NIM
Source: Bankscope and Author’s calculation
The implicit interest payment (IIP) and NIM
The figure 14 gives information about the relationship between implicit interest payment and NIM As is shown, the NIM had negative trend compared with changing of IIP from 2008 to 2012
Figure 14:The relationship between IIP and NIM
Source: Bankscope and Author’s calculation
Managerial efficiency (MGE) and NIM
As figure 15 showed, the changing of MGE was positive with fluctuation of NIM from 2008 – 2010 However, there was a different trend of NIM and MGE in
Figure 15:The relationship between MGE and NIM
Source: Bankscope and Author’s calculation
4 2 ECONOMETRIC ESTIMATION AND TESTING MODELS:
Table 6 shows the results of regression by Random Effect and Fixed Effect, Hausman test is employed to choose the most appropriate model is that the fixed effect model.
Table 6: Comparison of regression result of FEM and REM
* denote statistical significance at 10% ;**denote statistical significance at 5%;
Source: Bankscope and Author’s estimation with Stata
Whether FEM or REM is more consistent
This paper use Fixed Effects Model and Random Effects Model to regression After that, using Hausman test to choose the appreciate model
Table 7 : Testing for selecting appropriate model
Panel test Test Signal Appropriate model
RE vs FE Hausman test Chi2(10) = 109.99
FE chi2 (202) = 4.9e+07 Prob>chi2 = 0.0000 Yes (5%)
Source: Bankscope and Author’s estimation with Stata
H0: REM is consistent and efficient
H1: FEM is more consistent and efficient than REM
The Chi-square value obtained is 109.99 with a probability value of 0.0000, which is below the significance level of 5% Consequently, the null hypothesis (H0) is rejected, indicating that the Fixed Effects Model (FEM) is more consistent than the Random Effects Model (REM) for this analysis.
Fixed Effects Model
Table 8 : Results of Fixed Effect Estimator
Source: Bankscope and Author’s estimation with Stata
Macroeconomic factors such as GDP and inflation (INF) influence the net interest margin (NIM) in a consistent manner, aligning with the author's expectations and previous studies However, their impact is relatively modest, with GDP affecting NIM at a level of 0.078 and inflation at 0.023 This indicates that fluctuations in economic growth rates lead to corresponding changes in NIM; specifically, an increase in GDP growth results in a higher NIM, and vice versa The findings regarding inflation are consistent with the results of Claeys and Vander Vennet (2008).
According to Dietrich, Wanzenried, and Cole (2010), rising inflation leads to increased costs for deposits and bank loans, causing the net interest margin (NIM) to rise as loan rates outpace deposit rates However, the statistical analysis reveals that GDP and inflation (INF) are not statistically significant, with p-values of 0.634 and 0.852, respectively.
The regression analysis indicates that for every one-unit increase in the Herfindahl-Hirschman Index (HHI), the Net Interest Margin (NIM) increases by 0.224379 units, suggesting that HHI significantly influences NIM However, since the statistical value of HHI at 0.538 exceeds the significance threshold, the hypothesis H3 is rejected based on these findings.
In the group of banking factors, SIZE, LIQ, CRD and OPE were not significant effect on NIM at the significant level Consequently, hypothesis 4, 5,6 and 8 were rejected
Three key banking-specific factors significantly impact net interest margin (NIM): capital adequacy, implicit interest payment, and managerial efficiency Both capital adequacy and implicit interest payments have a positive relationship with NIM, whereas managerial efficiency negatively affects it These insights are valuable for bank managers, enabling them to adjust net interest margins in alignment with their development strategies over time.
Implicit interest payments significantly enhance net interest margins (NIM) by accounting for additional bank expenses beyond deposit interest Furthermore, there is a positive correlation between capital adequacy and NIM, suggesting that increasing capital to support business growth and mitigate potential risks raises the bank's cost of capital Consequently, banks may offset this increased cost by elevating their net interest margins Conversely, high-quality management negatively impacts NIM, indicating that effective management can generate profits with lower costs, leading to the potential for lower interest rates charged by the bank.
Empirical findings
Growth Domestic Product rate ( GDP)
The regression analysis revealed a positive correlation between GDP and NIM, indicated by a coefficient of β 1 = 0.0781859 However, with a p-value of 0.634, which exceeds the 0.01 significance level, the null hypothesis (H1) is rejected at α = 10%, suggesting that GDP does not have a statistically significant relationship with NIM This finding aligns with previous studies by Dietrich, Wanzenried, and Cole (2010) and Claeys and Vander Vennet (2008), emphasizing that more stable economic growth correlates with higher NIM.
The relationship between inflation (INF) and net interest margin (NIM) reveals that INF has a dimensional impact on NIM, with a coefficient of β2 = 0.0236975, as reported by Kasman, Tunc, Vardar, and Okan (2010) and Claeys and Vander Vennet (2008) However, the p-value of 0.852, which exceeds the 0.05 threshold, indicates that the hypothesis (H2) is rejected, suggesting that INF is not statistically significant in influencing NIM.
The analysis indicates a positive relationship between market structure, as measured by the Herfindahl-Hirschman Index (HHI), and Net Interest Margin (NIM) with a coefficient of β3 = 0.2243791 However, the p-value of 0.2243791 exceeds the 0.05 significance threshold, leading to the rejection of hypothesis H3 Consequently, the statistical evidence suggests that HHI does not have a significant impact on NIM.
The regression analysis of market structure aligns with the findings of Claeys and Vander Vennet (2008) as well as Dumicic and Ridzak (2012), showing a coefficient of β4 = -2.659778 This indicates that the total assets of the bank remain stable, reflecting a consistent marginal rate reduction.
= 0424> 0.05, H4 is rejected The scale of operations of the bank has no statistical significance in relation to the NIM.
Liquidity Risk of bank (LIQ) effect negatively on NIM with β 5 =-0.0017152;
However, LIQ do not have statically significant with NIM because of α = 0.415 and reject H5
The regression analysis
Regression equation give coefficient of OPE β 8 -0.2927425 but α = 0.268 so H8 is rejected Therefore, OPE is not statistically significant with NIM
The regression analysis revealed a statistically significant negative impact of Capital Adequacy on Net Interest Margin (NIM), with a coefficient of β7 = 0.2475961 and a p-value of α = 0.007, indicating significance at the 5% level This suggests that, holding other factors constant, an increase in Capital Adequacy leads to a rise in NIM by 0.2475961, and conversely, a decrease in Capital Adequacy results in a lower NIM These findings align with the research conducted by Claeys and Vander Vennet (2008) as well as Dumicic and Ridzak (2012).
IIP have coefficient β 9 = 5.918933 and α = 0.000 < 5%, H9 is accepted This explain that all else equal when IIP increase 1%, the NIM will rise 5.91%, this finding is similar study of Hawtrey, K., & Liang, H (2008).
The regression equation showed that there is the negative relationship between
ME and NIM based on β 10 = -0.209689 In addition, α =0.000 < 5%, H10 is accepted so MGE has statically significant on NIM
This chapter addresses the study's objectives by summarizing the statistical analysis of various variables, focusing on the dependent variable net interest margin (NIM) and independent variables including GDP, inflation (INF), Herfindahl-Hirschman Index (HHI), size, credit risk (CRD), capital adequacy (CAP), liquidity (LIQ), industrial production index (IIP), operational efficiency (OPE), and managerial efficiency (MGE) in ASEAN countries from 2008 to 2012 The Hausman test confirmed that the Fixed Effects Model (FEM) was the appropriate choice for analysis The hypothesis testing revealed that hypotheses 1, 2, 3, 4, 5, 6, and 8 were rejected, while hypotheses 7, 9, and 10 were accepted, indicating that capital adequacy, implicit interest payments, and managerial efficiency significantly impact net interest margins.