INTRODUCTION
Problem statements
Bank systems are pivotal in the modern economy, acting as vital intermediaries that influence economic development, as evidenced by the impact of banking efficiency on India's growth (Sathye, 2003) and Hungary's transition (Hasan & Marton, 2003) In Europe, the banking sector is integral to monetary policy, with customer dependence and bank capability being key factors in establishing monetary stability within the European Monetary Union (Kashyap & Stein, 1997) Efficient banking systems enhance economic resilience against shocks (Athanasoglou, Brissimis, and Delis, 2005; Sbracia & Zaghini, 2003) and serve as a bridge connecting various economic sectors and regions Consequently, the performance of the banking system is crucial for both national and global economies, as banks engage in diverse activities such as deposits, lending, and financial services, with deposits and loans being the primary profit-generating areas Thus, banks are essential financial intermediaries that support economic stability and growth.
The traditional banking model involves collecting customer deposits to fund loans, with profitability impacted by the cost of these funds The net interest margin (NIM) serves as a key indicator of the relationship between deposits and loans, measured in two ways: first, by the difference between loan and deposit interest rates, and second, by the ratio of the difference between interest income and expenses to total assets Interest income reflects the bank's earnings after taxes, while interest expenses represent the costs of deposits The second method is commonly utilized in previous studies on NIM, such as those by Ho and Saunders (1981), Angbazo (1997), and Saunders and Schumacher (2000), and is the basis for most NIM calculations reported by banks.
As for the impact of NIM on banking operation, Demirgỹỗ-Kunt and Huizinga
In 1999, it was noted that banks relying heavily on deposits for funding face reduced profitability due to rising costs Research indicates that stable and efficient banks should maintain balanced interest margins, as net interest margins (NIM) serve as a key indicator of banking efficiency, calculated as the difference between interest income and expenses relative to total earning assets A lower NIM reflects effective intermediation costs and indicates the effectiveness of monetary policy and financial stability (Hadad et al., 2003) Conversely, high costs can diminish economic incentives Raharjo et al (2014) demonstrated that a healthy banking sector, capable of generating profits, can better withstand economic shocks and contribute to overall financial system stability Given that the banking sector dominates the financial landscape in many countries, failures within this sector can severely impact economic growth, potentially leading to bank runs, crises, and broader financial turmoil (Ongore and Kusa).
This research aims to analyze the determinants of net margin during the financial period to identify significant factors that can enhance the health of banks through improved net interest margins.
Between 2008 and 2012, the global economy faced immense pressure 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 bank in the U.S in 2008, and Integra Bank Corp in 2011, alongside numerous smaller banks This instability within the largest banking system had a ripple effect, influencing banking systems worldwide, including those in ASEAN countries.
The 2008-2009 economic crisis is widely regarded as the worst in recent history, severely impacting major economies such as the United States, Japan, and Europe During this period, GDP experienced significant declines, leading to soaring unemployment rates and widespread corporate bankruptcies Notably, several countries reported negative GDP growth, including the EU (-0.5%), Germany (-0.8%), the United States (-0.7%), and Japan (-0.2%) In contrast, Russia's economy contracted by 3.5%, while China saw a decrease from 10% to 8% in 2008 Additionally, global industrial export growth rates plummeted sharply 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
The global crisis was primarily driven by several key factors, 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 (Grigor and Salikhov, 2009) Research by Fidrmuc and Korhonen (2010) highlighted the substantial negative impact of the 2008 crisis on business cycles in Asian developing countries, leading to declines in GDP growth and lower business cycle activity in OECD nations Additionally, studies by Ivashina and Scharfstein (2010) and Aisen and Franken (2010) revealed that the crisis severely affected bank credit, which is a core function of banks Although the global economy began to recover between 2010 and 2012, the effects of the crisis 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 economic fluctuations were observed, particularly during the GDP slump of 2008-2009 However, a recovery in GDP growth began in 2010 and continued through 2012 Additionally, the inflation rate experienced a sharp decline in most ASEAN countries during 2008-2009, followed by a period of 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 impacting banks' profitability and operational effectiveness During the crisis period, net interest margins in ASEAN countries exhibited a declining trend, as illustrated in Figure 3, which highlights the mean NIM across ASEAN banks.
Between 2008 and 2010, the inflation rate exhibited a downward trend amid the economic crisis and subsequent recovery phase During 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
This study aims to model and assess the key determinants of net interest margins in ASEAN banks, focusing on ten critical factors: GDP growth rate, inflation rate, banking market structure (measured by HHI), 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 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 analyzes the factors influencing bank interest margins across nine ASEAN countries—Brunei, Cambodia, Malaysia, Philippines, Laos, Vietnam, Singapore, Thailand, and Indonesia—during the period from 2008 to 2012 While the ASEAN region comprises ten members, including Myanmar, data collection limitations have led to the exclusion of Myanmar from this research.
Research structure
This research is structured into several key chapters: Chapter 1 discusses the rationale for selecting the theme and outlines the primary research objectives Chapter 2 reviews relevant literature and establishes a conceptual framework for understanding the determinants of net interest margins Chapter 3 details the research methodology and the data utilized in the study Chapter 4 presents the main findings from the analysis, while Chapter 5 concludes the research and offers policy recommendations.
LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK
Literature review for interest margins
Net Interest Margin (NIM) reflects the relationship between a bank's deposits and lending activities Banks attract depositor funds by offering interest rates on deposits, which are then used to provide loans to borrowers at higher interest rates Analyzing NIM is a key method for assessing the cost of financial intermediation, highlighting the difference between the interest paid by borrowers and the interest income received by depositors (Brock and Suarez, 2000) Consequently, banks strategically establish their loan and deposit rates to optimize this margin.
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 analyzed through two primary approaches: the traditional and modern approaches The traditional approach examines variables affecting NIM by analyzing bank balance sheets, while the modern approach considers the demand and supply dynamics within the bank's microstructure Most previous studies have utilized the modern approach NIM is defined as the ratio of net interest income to total earning assets, a key indicator typically reported annually by banks Net interest income represents the difference between interest income and the interest costs incurred A seminal study by Ho and Saunders (1981) established a banking model that positions banks as intermediaries between fund recipients and channels Their model highlights the reinvestment risk banks face if short-term interest rates decline, necessitating that fees a and b adequately compensate banks for this risk, thereby determining 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), it represents the spread between interest revenue on bank assets and interest expenses on bank liabilities Dietrich, Wanzenried, and Cole (2010) further clarify that NIM is the net interest income as a percentage of interest-earning assets Raharjo et al (2014) measure NIM as the ratio of net interest income to average total earning assets, where net interest income is calculated by subtracting interest costs from interest income Brock and Suarez (2000) emphasize that NIM reflects the difference between interest paid to borrowers and interest earned from depositors Despite variations in phrasing, these definitions converge on the same concept of NIM In this study, NIM will specifically be measured as the ratio of net interest income to total earning assets, with data sourced from Bankscope, ensuring consistency across international datasets.
2.1.2 DETERMINANTS OF NIM 2.1.2.1 RELATED LITERATURE:
Ho and Saunders (1981) were pioneers in analyzing the determinants of net interest margins (NIM) Their research identified key factors influencing NIM, including interest rate volatility, transaction size, risk aversion, and market competition, utilizing a two-step regression procedure In the initial step, NIM was estimated based on bank-specific characteristics, while the second step focused on macroeconomic and market structure characteristics.
Since 1981, the bank has adopted a risk-averse approach in response to the costs associated with loan and deposit markets Numerous studies have utilized the model proposed by Ho and Saunders (1981) to examine net interest margins from various perspectives.
Wong (1997) expanded upon the model established by Ho and Saunders (1981), revealing that credit and interest rate risks significantly influence net interest margin (NIM) within a theoretical framework focused on risk-averse banks This model demonstrated a positive correlation between NIM and factors such as market power, operating costs, and credit risk, while also highlighting the beneficial effect of interest rate risk on bank interest margins Building on the dealership model by Ho and Saunders, Saunders and Schumacher further contributed to this understanding.
A study conducted in 2000 analyzed the factors influencing Net Interest Margin (NIM) in several countries, including Germany, Italy, Switzerland, the UK, Spain, France, and the US, during the period from 1988 to 1995 This research focused on variables such as implicit interest rates, opportunity costs, and credit risk Additionally, Claeys and Vander Vennet (2008) employed a random effects estimator to compare the determinants of NIM between Central and Eastern Europe and Western countries Their findings highlighted that interest rate volatility and regulatory restrictions, including minimum capital requirements and liquid reserve mandates, significantly impacted NIM.
In 1997, researchers built upon the dealership model established by Ho and Sauders (1981), Mcshane and Sharpe (1985), and Allen (1988) to investigate interest rate risk, default risk, liquidity risk, and off-balance sheet factors, analyzing their correlation with fluctuations in Net Interest Margin (NIM) using 1,400 observations from 286 commercial banks based on Call Report data from 1989 to 1993 In contrast, Lin et al (2012) employed a switching regression model to study bank margins and diversification across several Asian countries, including China, India, Indonesia, Japan, the Philippines, Singapore, South Korea, Taiwan, and Thailand, 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 metrics derived from balance sheets and income statements.
Research on the determinants of net interest margins (NIM) in Central and Eastern Europe (CEE) has evolved from the foundational model by Ho and Saunders (1981), particularly through studies spanning from 2000 to 2010 using fixed effect estimators In the EU context, Maudos and Guevara (2004) expanded on this model, revealing that NIM is positively correlated with market power and concentration, while negatively influenced by interest rate risk, credit risk, and operating costs Furthermore, Kasman et al (2010) highlighted the significance of bank-specific factors, country-specific market characteristics, and macroeconomic conditions in relation to NIM, noting that consolidation affects NIM in both new and established EU markets The Ho and Saunders model remains a cornerstone for analyzing NIM, supported by the works of Claeys and Vander Vennet (2008) and Lin et al (2012) Most prior research has utilized panel data, with some focusing on longitudinal analyses of banks within specific countries, as demonstrated by Ho and Saunders (1981) in their study of American banks.
From 1976 to 1979, various studies have explored the determinants of Net Interest Margin (NIM) across different banking systems Notably, Entrop, Memmel, Ruprecht, and Wilkens (2012) examined the Albanian banking system from 2001 to 2007, while Fungacova and Poghosyan (2009) analyzed 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 employ panel data, which combines time series data, 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 This study utilized panel data to examine trends and patterns over a five-year period in the surveyed countries.
Between 2008 and 2012, several studies (Claeys & Vander Vennet, 2008; Dumičić & Ridzak, 2012; Kasman et al, 2010) categorized various factors into distinct groups, such as macroeconomic factors, banking market specifics, and bank-specific variables This paper similarly classifies independent variables into these three groups: macroeconomic factors, banking market specifics, and bank-specific variables.
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 rate serve as essential proxies for assessing economic health's impact on NIM Numerous empirical studies, including those by Schwaiger & Liebeg (2008) and Ben Naceur & Goaied (2008), have consistently shown a significant relationship between GDP and NIM Additionally, the effects of inflation on NIM warrant further examination, highlighting the interconnectedness of these macroeconomic factors.
Dumičić and Ridzak have demonstrated a negative impact of inflation on net interest margins (NIM) in Central and Eastern Europe (CEE) Conversely, the study by Kasman et al (2010) indicated a contra-variant effect on NIM Aliaga-Dıaz and Olivero (2005) found that high inflation correlates with increased costs and income for banks, suggesting that bank income rises more significantly with inflation than costs This paper examines the effects of GDP growth rates and inflation on interest rate margins, with Demirgỹỗ-Kunt and Huizinga (1999) asserting that inflation raises bank costs, subsequently increasing interest margins and profitability, albeit with a low positive coefficient They also noted that GDP growth rates have no impact on NIM and profitability Additionally, Claeys and Vander Vennet (2008) revealed that higher GDP growth rates in CEE lead to increased margins, further emphasizing the significant positive influence of inflation on margins.
This study focuses on bank-specific variables that influence banking performance, particularly the Net Interest Margin (NIM) Most of these variables are derived from the income statement, balance sheet, 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 facilitate the flow of funds between suppliers and demanders by managing deposit and loan rates They mobilize funds in the money market, attracting deposits with competitive rates while providing loans at varying interest rates based on market conditions Due to the asymmetric nature of loan demands and deposit supplies, banks must carefully balance their operations to mitigate interest rate risk Typically, borrowers require long-term capital, while deposits are often of shorter duration, necessitating effective fund management Additionally, banks face challenges such as interest rate risk, default risk, and credit risk, as
The dealership model of Ho and Saunders (1981) becomes the basic model for last researchers about net interest margins Angbazo(1997), Saunders and Schumacher
In their studies, Claeys and Vander Vennet (2008), along with Maudos and de Guevara (2004), developed the NIM model Additionally, Angbazo (1997) and Maudos and de Guevara (2004) expanded upon the model established 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 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 US commercial banks from 1989 to 1993 Additionally, NIM is impacted by market power, interest rate risk, credit risk, and operating costs in EU banks from 1993 to 2000.
The NIM model developed by Ho and Saunders (1981) has been widely utilized in empirical research across various contexts For instance, Kasman et al (2010) explored the connection between bank consolidation and net interest margins in both old and new European Union member states Similarly, Aliaga-Díaz and Olivero (2005) applied this model to analyze the cyclical patterns of net interest margins in U.S banks English (2002) examined the link between net interest margins and market interest rates, while Fungáčová and Poghosyan (2011) highlighted the influence of ownership factors on NIM in Russia Furthermore, Dietrich et al (2010) found that net interest margins vary across countries due to bank-specific characteristics and macroeconomic variables.
The concept framework
The framework for understanding interest margin determinants identifies three key groups of factors: bank-specific characteristics, macroeconomic conditions, and banking market characteristics Macroeconomic factors primarily include GDP growth and inflation rates Bank-specific characteristics encompass seven performance-related parameters: bank size, liquidity risk, credit risk, capital adequacy, operating costs, implicit interest payments, and managerial efficiency Lastly, the banking market characteristics are analyzed using the Herfindahl index to assess the market's influence on bank margins.
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 computational methods for each variable and presents an empirical model for this research, drawing on the theoretical frameworks established by Ho and Saunders (1981) and Angbazo (1997) Additionally, a conceptual framework is developed based on the insights gained from the theoretical literature review.
RESEARCH METHODOLOGY AND DATA COLLECTION
Identification of variables
Net Interest Margin (NIM) can be approached in two ways: the first method measures it as the difference between the contractual interest rates for deposits and loans, while the second calculates it as the difference between interest income and interest expenses over a specific period The first method has its drawbacks, as the diverse sources of deposits and loans at banks can lead to inconsistencies Conversely, the second method also has limitations, as noted by Demirgüç-Kunt and Huizinga (1999), who observed that interest income and expenses tend to occur in different periods This method relies on the financial statements of the banks for data.
In this study, net interest margin (NIM) is defined as the cost of intermediation, represented by the difference between the interest paid by borrowers and the interest income received by depositors (Bernanke, 1983; Brock & Suarez, 2000) NIM is commonly 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) 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 countries and significantly influences prices, costs, and business cycles due to changes in financial monetary policies Research indicates a positive relationship between GDP growth and net interest margin (NIM), with studies by Claeys and Vander Vennet (2008) highlighting this association in Western European bank markets Higher economic growth correlates with increased demand for credit, thereby enhancing bank margins (Dumičić & Ridzak, 2012) However, contrasting findings by Ben Naceur and Goaied (2008) and Ben-Kediri et al (2005) suggest no relationship between economic growth and NIM in Tunisia This analysis utilizes GDP growth rate data from ASEAN countries sourced from the World Bank, covering the period from 2008 to 2012.
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), derived from changes in the Consumer Price Index (CPI), significantly impacts market prices and purchasing power As inflation rises, it typically leads to higher interest rates on deposits and loans, influencing the Net Interest Margin (NIM) Research by Kasman et al (2010) indicates a positive correlation between inflation and NIM, as increased inflation raises both costs and income Similarly, a study in Indonesia covering 2008-2012 by Raharjo et al (2014) found that inflation positively and significantly affects NIM Therefore, inflation can have both positive and negative implications in economic models, with data sourced from the World Bank.
Hypothesis 2: Inflation rate is expected that there is a positive effect on bank interest margin
The Herfindahl-Hirschman Index (HHI) serves as a measure of market structure in the banking sector, reflecting the size distribution of banks 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 relationship between the Herfindahl index and Net Interest Margin (NIM), suggesting that a higher HHI is associated with an increase in 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, indicates the operational scale of financial institutions in the market Generally, larger banks tend to have lower margins, while smaller banks impose higher margins due to elevated interest rates for borrowers Research by Fungacova and Poghosyan (2009) supports this notion, demonstrating that a bank's operating size negatively impacts its net interest margin (NIM) Additionally, Dumičić and Ridzak (2012) found that in Central and Eastern European banks, larger institutions exhibit lower costs of income, resulting in a higher NIM 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 quantified by the ratio of liquid assets to total liabilities, representing a bank's ability to meet depositor withdrawal demands and new loan requests High liquidity risk is undesirable for banks, as liabilities must be supported by sufficient liquid assets A significant negative correlation is expected between the liquidity risk coefficient and net interest margin (NIM); as the demand liabilities of a bank are increasingly backed by liquid assets, its liquidity risk and margins decrease, as observed in Russian banks from 1999 to 2007 (Fungacova & Poghosyan, 2009).
In 1997, Angbazo expanded the dealership model proposed by Ho and Saunders (1981) by incorporating liquidity risks, revealing that increased liquid assets lead to a decrease in liquidity risk within bank margins Additionally, Aliaga-Díaz and Olivero (2005) demonstrated in their research on the cyclical behavior of net interest margins in the U.S banking sector that the counter-cyclicality of balance sheet liquidity is influenced by the fact that credit risk escalates more for risky and illiquid assets compared to liquid ones Consequently, this study anticipates a significantly negative coefficient for liquidity risk in relation to net interest margins (NIM).
Hypothesis 5: Author expects that liquidity risk will have a negative effect on net interest margin
Credit risk (CRD) is a crucial factor influencing net interest margins (NIM), determined by the ratio of total loans to total assets Research, including Wong's 1997 study, indicates a positive relationship between bank interest margins and credit risk, suggesting that as the percentage of total loans increases relative to total assets, the interest spread also rises This correlation highlights that higher loan volumes lead to increased credit risk, which in turn elevates bank interest margins.
Research indicates a positive correlation between credit risk and net interest margins (NIM), as demonstrated by findings from 2000 and supported by Hawtrey and Liang (2008) in OECD countries Numerous empirical studies consistently highlight this relationship, reinforcing the expectation that credit risk positively influences bank margins.
Hypothesis 6: It is expected that credit risk will have positive impact on bank margins
Capital adequacy (CAP), measured by the ratio of equity to assets, indicates that a higher ratio reflects a greater reliance on equity financing, which can lead to a shrinking net interest margin (NIM) due to increased capital costs Empirical studies, such as those by Lin et al (2012), highlight that rising equity raises the cost of capital, necessitating banks to achieve a higher NIM to offset these expenses Furthermore, capital adequacy serves as an indicator of banks' creditworthiness, as noted by Kasman et al (2010), demonstrating a positive relationship between capital adequacy and NIM in both old and new EU contexts Additionally, Claeys and Vander Vennet (2008) assert that capital adequacy helps banks mitigate risks and ensures 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), defined as the ratio of overhead to total assets, is positively correlated with net interest margin (NIM), as evidenced by studies from Dietrich, Wanzenried, and Cole (2010) Their findings indicate that operating cost is a crucial driver of NIM, especially in the banking systems of new EU member and candidate countries, according to research by Kasman et al (2010) This relationship suggests that banks may need to charge higher interest margins to offset elevated operating costs Additionally, Maudos and de Guevara (2004) highlighted that higher operating costs compel banks in the EU to implement increased margins to cover overhead, thereby influencing credit interest and deposit rates Consequently, the model in this study anticipates a positive coefficient for operating cost.
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 divided by total assets, are a key factor in analyzing net interest margin (NIM), as highlighted by Hawtrey and Liang (2008) Banks often provide free services instead of paying interest on deposits, which can lead to higher bank margins (Kasman et al., 2010) A lower IIP is associated with a decline in NIM, while a positive correlation between IIP and NIM was observed in U.S banks from 1989 to 1993, indicating that increasing implicit interest payments can raise costs and, consequently, margins (Angbazo, 1997) Zhou and Wong (2008) further noted that costs associated with IIP are passed on to bank margins, emphasizing that banks do not truly offer free services Thus, this research anticipates a positive relationship between IIP and the NIM model.
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, serves as a key indicator of management quality Effective management positively influences interest margins, indicating that banks with poor management practices tend to 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 Conversely, Kasman et al (2010) found that market efficiency (ME) negatively impacts NIM in both old and new EU contexts, requiring banks to adjust by offering higher deposit rates and lower credit rates during periods of rising ME This paper will explore both positive and negative correlations of ME with NIM, ultimately expecting a negative relationship, as supported by the findings of 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
This 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 limited data availability, Myanmar banks were excluded from the analysis The bank-specific variables were sourced from the Bankscope database, while macroeconomic indicators such as GDP and inflation were obtained from the World Bank database Table 1 provides a comprehensive overview of the data sources and variables used It is important to note that not all banks had complete data for the entire survey period; therefore, the analysis focused on banks with full datasets, resulting in a total of 1,010 observations across 202 banks from 2008 to 2012 in the selected 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 research predominantly utilized linear models to examine the factors influencing Net Interest Margin (NIM), prompting the authors of this study to adopt a similar approach The variables included in the analysis were carefully selected based on findings from earlier empirical studies, leading to the development of the following panel data equation model.
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, employing both Fixed Effects Model (FEM) and Random Effects Model (REM) for analysis Each model presents unique advantages and disadvantages in interpreting the relationships within the data.
Finite Element Method (FEM) is utilized to assess the impact of various time-dependent variables while isolating the effects of constant characteristics from explanatory variables This approach 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 may 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 the estimation of parameters for the Fixed Effects Model (FEM), two primary methods are commonly employed: Least Squares Dummy Variable (LSDV) and Fixed Effects Estimator (FE estimator) While LSDV is suitable for smaller datasets, it becomes cumbersome with larger datasets due to the complexity of creating numerous dummy variables Given that this study involves 1,010 observations, which is considered relatively large, the author will utilize the FE estimator, provided that the FEM is validated as appropriate for this analysis.
When the characteristics of an entity are assumed to be random and uncorrelated with the explanatory variables, the Random Effects Model (REM) should be utilized In REM, the residuals for each entity serve as new explanatory variables A key difference between fixed and random effects lies in whether the unobserved individual effects are correlated with the model's regressors, rather than whether these effects are stochastic (Green, 2008, p.183) One advantage of the random effects model is its ability to incorporate time-invariant variables The Feasible Generalized Least Squares (FGLS) estimator is employed in REM for analysis.
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
Y it : the dependent variable where i, = entity (i = 1…n) and t = time
βi : The coefficient for the independent variables
The Hausman test is utilized to determine the most suitable model between Fixed Effects (FE) and Random Effects (RE) in panel data analysis (Baltagi, 2008, p.320) The null hypothesis (H0) posits no correlation between subjects and the explanatory variables While RE provides a consistent estimate under H0, it becomes inconsistent under alternative hypotheses Conversely, FE offers reliable estimates for both H0 and alternative hypotheses If H0 is rejected, FE estimates are preferred over RE estimates However, if H0 is not rejected, indicating a correlation between residuals and explanatory variables, FE estimates remain the better choice In large samples, using Least Squares Dummy Variable (LSDV) is impractical, making the FE Estimator the most appropriate method for estimation in the FE model.
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, it presents ten hypotheses linking independent variables to dependent variables The analysis utilizes panel data from nine ASEAN countries—Brunei, Cambodia, Indonesia, Laos, Malaysia, Philippines, Singapore, Thailand, and Vietnam—covering five years from 2008 to 2012, amounting to 1,010 observations The study employs both fixed effect and random effect models for 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 the summary statistics, including standard deviation, mean, minimum, and maximum values The analysis of 1,010 observations from 2008 to 2012 reveals significant fluctuations in Net Interest Margin (NIM), with a mean of 5.74% The NIM experienced a minimum value of -14.5% and a maximum of 484.23%, accompanied by a standard deviation of 19.72%.
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, which measures market concentration, stands at 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% During this period, the lowest recorded liquidity was 0.07%, while the highest reached an impressive 6550.50%, 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, which averaged 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 for the variable, including standard deviation, mean, minimum, and maximum values The analysis, based on 1,010 observations collected over five years from 2008 to 2012, reveals significant fluctuations in the Net Interest Margin (NIM), with a mean value of 5.74% The minimum recorded NIM was -14.5%, while the maximum reached an extraordinary 484.23%, accompanied by a standard deviation of 19.72.
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.
The average inflation rate across ASEAN countries over a five-year study period was 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, stood at 14.58%, indicating a high degree of market concentration; this index 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 average value of 54.17% During this period, the liquidity ratio varied significantly, with a minimum 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) of 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%, while the standard deviation stood at 18.04 Additionally, the overhead to total assets ratio (OPE), reflecting operating costs, had an average value of 4.07586%, with a low of 0.10% and a high of 31.19%, accompanied by 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
HHI -0.0219 0.0603 -0.1521 1 SIZE -0.1029 -0.0594 0.0756 -0.1905 1 LIQ 0.0096 -0.0161 -0.0452 0.0191 -0.1416 1 CRD -0.0253 0.0043 0.0498 -0.1758 0.1187 -0.1704 1 CAP 0.0451 -0.0829 -0.1462 0.0375 -0.4800 0.3632 -0.2887 1 OPE 0.2462 -0.0384 -0.0878 -0.0476 -0.3130 0.0229 -0.0798 0.3871 1 IIP 0.4054 0.0750 0.0338 0.0239 -0.1474 -0.0170 0.2262 -0.2203 0.0133 1 MGE 0.0105 -0.0061 -0.1202 0.0537 -0.2923 0.0043 -0.1352 0.1155 0.2825 0.2980 1
Source: Bankscope and Author’s estimation with Stata
The correlation analysis presented in Table 3 indicates that the variables exhibit low correlation levels, suggesting that multicollinearity does not significantly impact the model of this study Among the independent variables, LIQ and CAP show the highest correlation at 0.3632, yet this remains a low value Additionally, the correlation with IIP demonstrates the strongest explanation of NIM levels, followed by OPE and CAP with correlation values of 0.4054, 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 drop in GDP alongside a gradual rise in NIM from 2008 to 2009 In the following years, despite rapid fluctuations in inflation, NIM exhibited 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, revealing a consistent trend The data indicates a positive relationship between liquidity (LIQ) and NIM during this period.
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, there was a notable fluctuation in CAP that corresponded with changes in NIM during this period This indicates a positive correlation 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
Between 2008 and 2012, a negative correlation was observed between operating costs and net interest margin (NIM), contrasting with the relationship between capital adequacy and NIM.
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 Independent
Source: Bankscope and Author’s estimation with Stata
Macroeconomic factors such as GDP and inflation (INF) influence Net Interest Margin (NIM) in the same direction, aligning with the author's expectations and previous studies However, their impact on NIM is relatively modest, with GDP affecting it by 0.078 and inflation by 0.023 This indicates that fluctuations in economic growth rates will lead to corresponding changes in NIM; specifically, as GDP increases, NIM rises, and vice versa The effect of inflation on NIM corroborates findings from Claeys and Vander Vennet (2008).
According to Dietrich, Wanzenried, and Cole (2010), rising inflation leads to increased costs for deposits and bank loans, with loan rates outpacing deposit rates, thereby widening the gap and increasing the Net Interest Margin (NIM) Additionally, the statistical analysis shows that GDP (p = 0.634) and inflation (p = 0.852) are not statistically significant variables in this context.
The regression analysis reveals that for every one-unit increase in the Herfindahl-Hirschman Index (HHI), the Net Interest Margin (NIM) increases by 0.224379 units, indicating that HHI significantly impacts NIM However, since the statistical value of HHI is 0.538, which exceeds the significance level, 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
Capital adequacy and implicit interest payments positively influence net interest margin (NIM) in banking, while managerial efficiency has a negative effect These findings highlight the importance of these banking-specific factors, providing valuable insights for bank managers to adjust NIM 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, a positive correlation exists between capital adequacy and NIM, indicating that increased capital supports business growth and serves as a risk buffer, leading banks to raise net interest margins to cover higher capital costs Conversely, high-quality management is associated with a negative impact on NIM, as effective management enables banks to achieve greater profitability with lower costs, often resulting in lower interest rates for customers.
Empirical findings
Growth Domestic Product rate ( GDP)
The regression analysis revealed a positive relationship between GDP and Net Interest Margin (NIM), with a coefficient of β 1 = 0.0781859 However, the p-value of 0.634 exceeds the 0.01 significance level, leading to the rejection of hypothesis H1 at α = 10% This indicates that GDP does not have a statistically significant relationship with NIM Supporting this finding, previous research by Dietrich, Wanzenried, Cole (2010), and Claeys and Vander Vennet (2008) suggests that more stable economic growth correlates with higher NIM.
The relationship between inflation (INF) and net interest margin (NIM) shows that INF has a dimensional impact on NIM, with a coefficient of β2 = 0.0236975, according to research by Kasman et al (2010) and Claeys and Vander Vennet (2008) However, since the p-value is 0.852, which exceeds the 0.05 threshold, the hypothesis (H2) is rejected, indicating that INF is not statistically significant in relation to NIM.
The analysis reveals that the Herfindahl-Hirschman Index (HHI) coefficient of β 3 = 0.2243791 indicates a positive effect of market structure on Net Interest Margin (NIM) However, with a p-value of 0.2243791, which is greater than the 0.05 threshold, we reject hypothesis H3, suggesting that HHI is not statistically significant in relation to NIM.
The regression analysis of market structure aligns with the findings of Claeys and Vander Vennet (2008) and Dumicic and Ridzak (2012), indicating a coefficient of β4 = -2.659778 This suggests that the total assets of the bank remain stable in relation to the 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 reveals a coefficient for CRD (β6 = -0.062357), indicating a positive effect on NIM, which contrasts with prior predictions and empirical studies Furthermore, the p-value of 0.280 exceeds the significance level of α = 5%, leading to the rejection of hypothesis H6, thus confirming that CRD is not statistically significant.
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 indicates a statistically significant negative impact of Capital Adequacy on Net Interest Margin (NIM), with a coefficient of β7 = 0.2475961 and a significance level of α = 0.007, which is below the 5% threshold Therefore, accepting hypothesis H7 suggests that, holding other factors constant, an increase in Capital Adequacy will lead to a rise in NIM by 0.2475961, and vice versa These findings align with previous research conducted by Claeys and Vander Vennet (2008) and 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 summarizes the statistical analysis of the dependent variable, Net Interest Margin (NIM), and independent variables including GDP, Inflation (INF), Herfindahl-Hirschman Index (HHI), Size (SIZE), Credit Risk (CRD), Capital Adequacy (CAP), Liquidity (LIQ), Interest Income Proportion (IIP), Operational Efficiency (OPE), and Management Efficiency (MGE) across ASEAN countries from 2008 to 2012 The Hausman test confirmed that the Fixed Effects Model (FEM) was the appropriate model The analysis 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.