Impact of National Bank Regulations on Financial Performance of Commercial Banks in Ethiopia

Impact of National Bank Regulations on Financial Performance of Commercial Banks in Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of National Bank Regulations on Financial Performance of Commercial Banks in Ethiopia Dejen Debebe Asmare This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8036069/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the impact of National Bank of Ethiopia (NBE) regulations on the profitability of commercial banks, using Return on Assets (ROA) as the dependent variable. The study uses an explanatory research design and a quantitative approach. It applied a balanced random effect panel regression model on data from 17 commercial banks over 13 years (2012–2024). The model took into account eight regulatory variables from the NBE and two macroeconomic indicators as control factors. The analysis shows that several regulatory and operational factors positively influence the profitability of commercial banks. These include the Capital Adequacy Ratio (CAR), Minimum Paid-up Capital Requirement (MPCR), Loan-to-Deposit Ratio (LDR), Legal Reserve Requirement (LRR), Branch Expansion Requirement (BER), and Foreign Currency Regulation (FCR). On the other hand, the study found that Non-Performing Loans (NPLs) have a negative and significant effect on ROA. Liquidity requirements have a negative but statistically insignificant effect, indicating they create an opportunity cost without a significant impact. Moreover, the macroeconomic indicators of Inflation and Gross Domestic Product (GDP) both show a negative and insignificant relationship with profitability. This suggests that bank performance is mainly influenced by internal factors and compliance with regulations rather than general economic trends. Based on these findings, the study suggests that commercial banks focus on strict compliance with capital adequacy and minimum paid-up capital requirements as key strategies to improve profitability. Banks regulations banking sector Financial Performance 1. Introduction 1.1. Background of the Study The foundation of modern economies rests on a stable and efficient banking sector, which is fundamentally vital for facilitating the efficient distribution of resources, providing market liquidity, and driving national economic expansion through the provision of credit (IMF, 2015). Consequently, the integrity of this sector is maintained through comprehensive bank regulations, which are rules and guidelines established by financial authorities to enforce standards across critical domains such as capital adequacy, liquidity, and risk management. The core mandate of these regulations is to safeguard depositors, prevent financial crises, and enhance public confidence in the banking system (Davies & Green, 2013; Basel Committee, 2011). Internationally, the need for robust and harmonized banking supervision is continually emphasized, particularly as global markets become interconnected, requiring alignment with standards like the Basel Accords to minimize systemic risk (Basel Committee, 2011). Within this global context, the African banking sector is characterized by ongoing modernization and reform efforts aimed at aligning local regulatory frameworks with international best practices. While significant progress has been made in implementing stricter capital adequacy and improved risk management protocols, the effectiveness and maturity of these supervisory frameworks vary across the continent, influenced by local economic and institutional capacities (Beck & Cull, 2013). The Ethiopian banking sector is a notable example, operating within a tightly regulated environment where the National Bank of Ethiopia (NBE) acts as the central authority (established 1963). The NBE's functions are critical, encompassing the implementation of monetary policy, management of the national currency, and, most importantly, the enforcement of regulations (such as capital adequacy, liquidity rules, and lending policies) to ensure the stability and soundness of the financial system (National Bank of Ethiopia, 2023; Legesse & Yohannis, 2015). The regulatory measures implemented by the NBE thus hold significant influence over the financial performance and operational behaviour of all commercial banks in the country (Lelissa & Kuhil, 2018). Despite the universal consensus on the necessity of regulation for financial stability, the academic debate remains unresolved regarding its direct impact on commercial bank performance . Scholars argue that while regulations are essential for mitigating systemic risks, stringent measures—such as elevated capital requirements and stricter supervision—can simultaneously constrain banks’ profitability and growth by restricting lending capacity and increasing compliance costs (Claessens & Kodres, 2014). This enduring tension between the NBE’s dual objectives of financial stability and the promotion of a profitable, credit-providing banking sector forms the crucial empirical context for this research. 1.2. Statement of the problem The banking sector in Ethiopia operates under a set of stringent regulatory measures mandated by the National Bank of Ethiopia (NBE). These measures, covering critical areas such as capital adequacy, liquidity, statutory reserves, and credit requirements, are primarily designed to safeguard financial stability and mitigate systemic risk (Abraham, 2021),. However, a significant and unresolved debate exists regarding the net effect of these mandated prudential measures on the financial performance of commercial banks, specifically in relation to profitability (Return on Assets - ROA) and operational efficiency. Existing empirical studies focused on the NBE's impact yield fundamentally equivocal and contradictory findings. For instance, while some research suggests that stringent capital requirements (like capital adequacy) have little to no statistically significant effect on key performance indicators such as ROA (Abraham, 2021), other equally rigorous studies conclude the opposite, revealing a significant relationship between capital adequacy and ROA (Lealem, 2021). Furthermore, regulations like the reserve requirement, hypothesized to negatively affect intermediation due to the transfer of capital into non-interest-bearing assets, have shown an unexpected, albeit statistically insignificant, positive relationship with performance (Lelissa & Kuhil, 2018). This state of inconsistent findings prevents a clear policy direction for balancing stability and growth. Prior research suffers from under-specification, failing to account for critical, context-specific regulatory instruments highly relevant to the Ethiopian financial landscape. Notably, two significant variables—the Loan-to-Deposit (L/D) ratios and foreign currency regulations—have been frequently excluded. Given that Ethiopia operates under a tightly managed foreign exchange environment and L/D ratios directly constrain lending, the omission of these factors renders previous models incomplete and inadequate for fully capturing the true regulatory burden and its combined influence on bank operations. Moreover, the limited body of research specifically focused on the unique characteristics of the Ethiopian market necessitates a dedicated, comprehensive investigation to provide locally relevant conclusions. The persistent contradiction in existing literature, coupled with the systemic omission of key regulatory variables pertinent to the Ethiopian context, underscores a critical knowledge gap. Therefore, this research is necessitated to bridge this gap by comprehensively examining the combined, simultaneous effect of a robust set of eight regulatory variables (capital adequacy, minimum paid-up capital, loan-to-deposit requirement, liquidity requirement, legal reserve requirement, bank branch expansion, non-performing loan/credit risk management, and foreign currency regulations) on the financial performance (proxied by ROA) of commercial banks in Ethiopia. Employing a quantitative approach utilizing 13 years (2012-2024) of data across 17 commercial banks provides a more holistic and scientifically grounded understanding for policymakers and bank management. 1.3. Objectives of the Study 1.3.1. General Objective This study was conducted to examine the combined impact of the National Bank of Ethiopia's regulatory measures on the financial performance of commercial banks in Ethiopia. 1.3.2. Specific Objectives The specific objectives of the research are to: 1.3.2. Specific Objectives The specific objectives of the research are to: Examine the effects of Prudential Capital and Liquidity Regulations (i.e., capital adequacy, minimum paid-up capital, liquidity, and legal reserve requirements) on the financial performance of commercial banks in Ethiopia. Assess the effects of Credit and Operational Risk Regulations (i.e., loan-to-deposit requirements and non-performing loan management) on the financial performance of commercial banks in Ethiopia. Determine the effects of Market and External Constraint Regulations (i.e., bank branch expansion requirements and foreign currency regulations) on the financial performance of commercial banks in Ethiopia. 2. Literature Review: Bank Regulations and Financial performance 2.1. Theoretical Framework and Debates The fundamental principle of bank regulation and supervision is to protect the banking system from inappropriate risk-taking and solve moral hazard, ultimately resulting in financial stability (Ayadi et al., 2016). The theoretical literature, however, depicts two traditional, rival perspectives with regard to the effect of these regulatory measures on bank profitability. The Public Interest View is that regulation by the government in the form of capital regulation, for example, motivates effective banking by compensating for market failures (Barth et al., 2008; Barth et al., 2013). Good regulation is what should drive a stable, hence more profitable banking sector. Or, the Private Interest View, which argues that regulations tend to be captured by powerful minority groups. Here, regulation can be codified so as to buffer incumbents or allow for implicit government guarantees, which will tend to increase the cost of financial intermediation and reduce bank profitability in general (Barth et al., 2008; Barth et al., 2013; Demirgüç-Kunt & Huizinga, 1999). These competing theoretical predictions highlight the importance of empirical testing in an attempt to determine the actual effect of regulation. Global Empirical Evidence on Regulation and Performance Early empirical studies reaffirmed that supervisory and regulatory mechanisms significantly impact banking sector performance (Dewatripont & Tirole, 2018; Hovakimian & Kane, 2000; Rochet, 1992). The tightened capital requirements following the 2007–2009 financial crisis bear witness to continuous changes in banking regulation (Djalilov & Piesse, 2019; Casu et al., 2017). Despite reforms, the effectiveness of banking regulation continues to be contentious among scholars and policymakers alike (Čihák et al., 2013). Capital requirement and supervisory research yields conflicting results. Barth et al. (2013) found, in a large cross-country sample, that improving supervisory power was related to bank efficiency. Shaddady and Moore (2018) concurred too, finding that good-quality regulatory regimes improve stability. Ahamed et al. (2021) and Agoraki and Tsamis (2017) also credited high capital requirements and limits on improved profitability and efficiency. Conversely, some studies caution that an overly strict policy will increase the cost of doing business (Barth et al., 2013; Demirgüç-Kunt & Huizinga, 1999) or lower liquidity and profitability, as was found in the case of reserve requirements in Pakistan by Abidi and Lodhi (2015). Too much supervision was also found to harm bank stability by Shaddady and Moore (2018). Regarding limits on banking business and openness, findings are also divided. Barth et al. (2013) found activity restrictions to be harmful to bank efficiency, but transparency (market monitoring) was beneficial. However, Agoraki and Tsamis (2017) and Ahamed et al. (2021) found certain restrictions, with high capital, could possibly increase returns. A key consensus of global evidence is that the relationship between regulation and profitability is complex and context-dependent, which implies that no universal regulatory best practice can be observed (Yang et al., 2019; Asteriou et al., 2021). Regional Evidence: Africa Studies on African banking institutions highlight the impact of domestic market setting, bank size, and risk profile and lead to mixed evidence (Triki et al., 2017; Ozili, 2017). On the aspect of capital adequacy, there was strong positive correlation with performance in Nigerian banks consistent with stability goals, Aliyu et al. (2020) reported. Abdrahamane and Kargbo (2017) in Mali, however, reported that the banks would have more than the optimal level of capital, which affected overall performance. Secondly, regulation can have size-dependent effect: Triki et al. (2017) proved that tight entry controls benefited the efficiency of large banks, but harmed small banks. On liquidity, Efanga et al. (2020) in Nigeria and Kiplagat (2020) in Kenya found that liquidity ratios exerted a positive and significant impact on bank productivity and performance, meaning that good management of liquidity is a key driver of the region. Local Empirical Evidence: Ethiopian Commercial Banks Local studies of Ethiopian commercial banks are characterized by prevailing inconsistencies and contradictions, particularly concerning the key prudential requirements. The most striking contradiction concerns the Capital Adequacy Ratio (CAR): Addisu (2017), Tekalegn (2020), and Lealem Feleke (2021) all confirmed a robust and negative relationship between CAR and profitability (ROA/ROE). This would mean that the opportunity cost of having excess funds might outweigh the stability benefits in the given instance. Yusuf and Shikur (2023), though, found a positive and significant impact. The same paradox exists for the Legal Reserve Requirement (LRR), which was found to be negative by Addisu (2017) but positive by Tekalegn (2020), Lealem Feleke (2021), and Yusuf and Shikur (2023). Findings on other bank-specific and regulatory variables are also non-conclusive. While Addisu (2017) noted the impact of bank size to be positive, Tekalegn (2020) noted a negative impact. In terms of direct regulatory impacts, Abraham Kifetew (2021) and Tekalegn (2020) noted interest rate regulation having a positive impact on bank margins due to the regulated interest rate environment. Finally, local studies appear to agree that macroeconomic variables like inflation and GDP have a positive impact on profitability in Ethiopia (Lealem Feleke, 2021; Yusuf & Shikur, 2023). Knowledge Gap and Contribution of Study Literature reviewed identifies two fundamental issues: firstly, the absence of any blanket regulative control, as the effect is contingent. Thus, secondly, and most pertinent to this study, is the absence of local empirical consensus regarding the effect of critical prudential regulation on the performance of Ethiopian commercial banks. The findings' disagreement in the literature at home owing to varying samples, lengths of regulation periods, and approaches offer very wide latitude. The current research therefore attempts to bridge this local gap by employing a robust panel data specification (Fixed Effects) with a bigger sample (N=340) to derive a fresher and conclusive empirical finding with respect to the degree to which National Bank regulations affect the profitability of commercial banks in Ethiopia The theoretical and empirical review literature verifies that the nexus of bank regulation and profits is complex, dependent, and highly unstable within regions and single regulatory measures. Theoretically, the argument is still inconclusive, split between the Public Interest View (regulation is efficiency-enhancing) and the Private Interest View (regulation increases costs and benefits minorities). Empirically, cross-country evidence confirms that the effect of tools like Capital Adequacy Ratio (CAR) and activity curbs is not country-specific (Asteriou et al., 2021). Despite effective regulation being positively linked with stability, excessive tightness or an opportunity for a high cost of capital can prove to be destructive to profitability, especially among unlisted or smaller banks (Ozili, 2017; Shaddady & Moore, 2018). Of particular relevance, national research on Ethiopian commercial banks has inherent contradictions in the performance of core prudential regulations like the CAR and Legal Reserve Requirement (LRR). Some research has documented a positive correlation (Yusuf & Shikur, 2023) while others have documented a negative correlation (Lealem, 2021), and such literature remains inconclusive. This lack of agreement constitutes a central knowledge gap, which highlights the need for robust, timely, and place-specific empirical evidence to inform effective policy response. The varying results highlight that regulatory efficacy is inextricably tied to the country's specific architecture, risk appetite, and market dynamics of the Ethiopian banking sector. Research Hypotheses The following hypotheses are developed based on a review of theoretical concepts and previous empirical studies related to bank regulation and performance: Table 1 : Research Hypotheses and Expected Relationships Hypothesis Regulatory Variable Expected Relationship H1 Capital adequacy requirement Positive and significant effect on the financial performance of commercial banks in Ethiopia. H2 Minimum paid-up capital requirement Positive and significant effect on the financial performance of commercial banks in Ethiopia. H3 Loan-to-deposit Ratio Positive and significant effect on the financial performance of commercial banks in Ethiopia. H4 Liquidity requirement Positive and significant effect on the financial performance of commercial banks in Ethiopia. H5 Legal reserve requirement Positive and significant effect on the financial performance of commercial banks in Ethiopia. H6 Foreign currency regulation Positive and significant effect on the financial performance of commercial banks in Ethiopia. H7 Bank branch expansion requirement Positive and significant effect on the financial performance of commercial banks in Ethiopia. H8 Non-performing loan Negative and significant effect on the financial performance of commercial banks in Ethiopia. 3. Research Methodology 3.1. Research Design Research design is a structured plan that specifies how data will be collected, measured, and analysed to address research questions or hypotheses. It includes the framework for selecting research methods, procedures for data collection, and strategies for data analysis to ensure the validity and reliability of the study’s findings"(Creswell & Poth, 2016 ). This study was designed to be explanatory, with the objective of measuring the impact of NBE regulations on the financial performance of commercial banks in Ethiopia. Explanatory research design is employed to investigate and elucidate the connections between variables, with an emphasis on uncovering the underlying causes and mechanisms that lead to specific outcomes. Its goal is to deliver a comprehensive understanding of the 'how' and 'why' behind certain phenomena by analysing causal relationships and processes through detailed data analysis.(Saunders, Lewis, & Thornhill, 2019 ) 3.2. Research Approach This study used a quantitative approach, which was anticipated to be the most effective method for determining the relationship between the study variables (i.e., bank regulation and financial performance), expressed in percentages. This approach was suitable for this study as it allowed the researcher to measure financial performance (ROA) using numerical data, test cause-and-effect relationships, and generalize findings. It ensured precision, reliability, and control of variables, helping to understand how NBE regulations impacts financial performance and identify trends over time. 3.3. Target Population and Sampling Procedure The target population encompasses all individuals or entities that a researcher aims to analyse and generalize findings about. Precisely defining this population is essential to ensure that the research results are relevant and applicable to the intended group.(Creswell & Poth, 2016 ). Currently, the banking ecosystem in Ethiopia consists of a total of 30 banks (NBE annual report 2023/2024). Out of the 32 banks, 31 of them are commercial banks and one is Development Bank. Among the 31 commercial banks, Commercial Bank of Ethiopia (CBE) is state owned and the remaining 29 are private commercial banks (NBE Annual report, 2023/2024). The target population of the study included all thirty commercial banks operating in Ethiopia. These study used Purposive sampling techniques to select the samples from the total population. Purposive sampling, also known as judgmental sampling, is a non-probability sampling technique where researchers select specific individuals or groups based on particular characteristics or criteria relevant to the study (Etikan, 2016 ). Out of the thirty targeted banks, only seventeen commercial banks are included in the study. This is because data required for the study regarding the remaining bank could not be obtained in sufficient amount since the banks were established after 2022. 3.4. Data Type, Source, and Method of Collection The research relies solely on secondary data collected over a 13-year period (2012–2024) from 17 commercial banks. The data was collected through document review data collection methods. This approach utilizes panel data, which incorporates both time series and cross-sectional aspects. The use of panel data is advantageous for examining causal relationships between variables across time. It allows for a more overall analysis of complex issues compared to using only time series or cross-sectional data. By integrating these two dimensions, panel data enhances the degrees of freedom, thereby increasing the statistical power of hypothesis tests. Additionally, it helps address multicollinearity issues that could arise from analysing time series data in isolation. Furthermore, with proper model specification, panel data can reduce biases related to omitted variables in regression analyses.(Brooks, 2019 ) .The data are collected from annual reports of National Bank of Ethiopia as well as from the individual commercial banks audited financial statements in their website. The variables used in this study are obtained from the various directives of the NBE. 3.5. Method of Data Analysis and Presentation The researcher employed a panel data regression model to analyse the impacts of NBE regulations on the financial performance of commercial banks in Ethiopia. Panel data analysis was chosen due to its ability to account for both the cross-sectional (17 commercial banks) and time-series (13 years) dimensions of the data. To determine the most suitable model between the Fixed Effects Model (FEM) and the Random Effects Model (REM), the Hausman test was conducted. The test results indicated that the Random Effects Model was appropriate for this study, as it effectively captures the individual-specific variations without introducing bias or inefficiency.( Baltagi, 2021 ) The data analysis begins with the presentation of descriptive statistics for both the dependent and independent variables. Following this, diagnostic tests are conducted to verify the robustness of the model and the regression coefficients are tested. Lastly, the results of the regression analysis are discussed in detail. 3.7. Model Specification The main aim of this study was to examine the impacts of regulations imposed by the National Bank on the financial performance of commercial banks in Ethiopia. The analysis focused on understanding how regulatory factors, together with control variables, affected the banks' financial outcomes. To account for variations in performance, relevant factors that affect the financial results of the banks were used as proxies. The regression model used was described by the following equation: ROA it = α + β1CARit + β2MPCRit + β3LDRit + β4LRit + β5LRRit + β6BERit + β7 FCR it + β8NPLit + β9INFit + β10GDPit + εit Where : ROA: Return on Assets; CAR: Capital Adequacy requirement; MPCR: Minimum Paid-up Capital requirement ;LDR: Loan to Deposit requirement ;LR: Liquidity requirement ;LRR: Legal reserve requirement ;BER: Branch expansion requirement ;FCR: Foreign Currency Regulation ;NPL: Non Performing Loan ;INF: Inflation ;GDP: Gross Domestic Product; ε: Error 3.8. Measurement and Operational Definition of Variables Table − 2. Variables Measurement and Operational definition Variable Notation Operational definitions Measurement/Proxy Dependent variables ROA ROA is the ratio of net income to average total asset. It shows how efficiently the resources of the bank are used to generate income. \(\:\frac{Net\:Income}{Total\:Assets}\) x100 Capital Adequacy Requirement (CAR) The portion of banks asset to be held in the form of capital so as to absorb unexpected shocks. \(\:\frac{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{C}\text{a}\text{p}\text{i}\text{t}\text{a}\text{l}}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{R}\text{i}\text{s}\text{k}-\text{W}\text{e}\text{i}\text{g}\text{h}\text{t}\text{e}\text{d}\:\text{A}\text{s}\text{s}\text{e}\text{t}\text{s}}\) X 100 Minimum Paid-up Capital MPCR Annual percentage growth of paid-up capital. MPCR= \(\:\:\:\:\frac{Current\:year\:paid\:up\:\:capital-Previous\:year\:paid\:up\:capital\:}{Previous\:year\:paid\:up\:capital}\) Loan-to-Deposit Ratio LDR percentage of loans funded by deposits , \(\:\frac{\:\:\:\:\:Total\:Outstanding\:Loans}{Total\:Deposits}\) x 100 Legal Reserve Requirements (LRR) Reserve requirement as percentage of deposit. Net Income ×Legal Reserve Ratio Branch Expansion Requirement (BER) Bank’s branch growth per annum. \(\:\frac{Current\:year\:number\:of\:branch-perivious\:year\:number\:of\:branch\:}{perivious\:year\:number\:of\:branch\:}\) Foreign Currency regulation FCR The difference between foreign currency assets and foreign currency liabilities Foreign Currency Assets − Foreign Currency Liabilities Liquidity requirement (LQ Liquid assets as percentage of liquid liabilities. ( \(\:\frac{Liquid\:Assets}{Net\:Current\:Liabilities}\) ) X 1OO Non-Performing loan NPL Total NPL as percentage of total loans outstanding \(\:\frac{Total\:Non\:Performing\:Loans}{Total\:Loans\:Outstanding\:}x100\) Inflation Inflation Quantitative estimate of the rate at which the decline in purchasing power of Birr. Annual inflation rate in %. GDP GDP total monetary value of all final goods and services produced within a country's borders over a specific period \(\:\frac{GDP\:in\:Current\:Period-GDP\:in\:Previous\:Period}{GDP\:in\:Previous\:Period}\) X100 4. Results and Discussions 4.1. Descriptive Analysis This section presents the descriptive statistics for the dependent variable, Return on Assets (ROA), and the selected national bank regulation variables. The panel dataset comprises a total of 340 observations (N = 340), which corresponds to a balanced or unbalanced panel structure across a given number of banks and years (for this study case, 17 banks over 13 years). The statistics, summarized in Table 3 , provide the mean, median, standard deviation SD, minimum, maximum, and the Coefficient of Variation (CV) for each variable. The CV (the ratio of SD to the mean) serves as a robust measure of relative variability, with values below 1 generally indicating low or moderate dispersion relative to the mean. Table 3 Summary of descriptive statistics for dependent and independent variables Variable OBS Mean Std. Dev. Minimum Maximum CV ROA 340 2.7279 0.9020 0.50 6.10 0.33 CAR 340 17.8197 6.4680 8.40 40.00 0.36 MPUC 340 25.6800 8.6049 8.70 52.30 0.34 LTD 340 72.1217 11.1985 39.72 85.00 0.15 LRR 340 61.3462 14.4592 27.50 74.00 0.24 BER 340 18.6387 7.7754 5.30 36.80 0.42 LIQ 340 26.5277 20.0207 3.00 84.00 0.75 FCR 340 18.4000 3.5114 11.00 28.00 0.19 NPL 340 1.9182 1.2439 0.50 6.36 0.65 INF 340 21.7429 8.0401 12.10 33.90 0.37 GDP 340 7.5429 1.4372 6.10 10.20 0.19 Source: Computed via STATA Software Version 15 ( N = 340) Analysis of Dependent and Key Regulator Variables Dependent Variable (ROA) The Return on Assets (ROA), the key indicator of bank profitability, is used as the dependent variable. Over the period covered by the study, the mean ROA of the banks is 2.73% (SD} = 0.90). At a minimum CV of 0.33, there will certainly be variations in bank profitability across banks and over time, but the mean ROA is quite stable. The range of profitability is wide, from a minimum of $ 0.50% to a maximum of $ 6.10%, indicating significant disparity between the best- and worst-performing observations. Regulatory Variables : Capital Adequacy Ratio (CAR ): The banks report strong capital buffers, with the average CAR of 17.82% (SD = 6.47). This is far above the minimum requirement by the National Bank of Ethiopia (NBE) of 8%, which means that overall, the banking system had sufficient capital over the period. The CV of 0.36 is respectable, modest variation in capital among the institutions. Minimum Paid-Up Capital (MPUC) The banks have an average paid-up capital of 25.68 units (SD = 8.60), showing the sector's sustained attempt to meet the NBE's stricter capital ceilings. The relatively low CV of 0.34 stands as proof of relative homogeneity in the effort and capacity to mobilize central capital. Legal Reserve Requirement (LRR) The legal reserve, or percentage of net income deposited to the reserve account until it equals capital (or 10% thereafter, as per Directives No. SBB/4/95), averaged 61.35% (SD = 14.46). This high mean value shows that, on average, a high percentage of the commercial banks' net income is sent to statutory reserves, which reflects the significance of this rule instrument in balance sheet management. The CV of 0.24 indicates low relative variability, consistent with a stable application of the reserve mandate across the observations sampled. Liquidity Ratio (LIQ) The average liquidity ratio is 26.53% (SD = 20.02). This average comfortably exceeds the minimum requirements of the NBE (e.g., 20% as per Directive No. SBB/46/2012 or 15% as per Directive No. SBB/57/2014). However, the high SD and a CV of 0.75 (approaching 1.0) indicate high dispersion in liquidity management techniques among the banks. High such dispersion results in some banks holding high levels of excess liquidity while others are close to the regulatory threshold. Branch Expansion Rate Ratio (BER) Being a key policy indicator of expansion strategy, the yearly average branch expansion rate was 18.64% (SD = 7.78). The average is lower than the 25% a year growth target given top priority in the NBE's Growth and Transformation Plans (GTPs), reflecting a typical deficit in achieving the policy's infrastructural expansion target. Credit Risk Variable Analysis and Macroeconomic Control Variables Non-Performing Loans (NPLs): While not a regulatory instrument in itself, NBE monitors NPLs as a significant asset quality indicator used to ascertain provisioning and profitability. The average NPL rate stands at 1.92% (SD = 1.24), ranging between 0.50% and 6.36%. This average is well within the NBE's typical regulatory threshold of 5%. The CV of 0.65 reflects substantial, but not high, relative variation of asset quality across the sample, reflecting the varying risk management ability of the banks. Macroeconomic Variables Inflation (INF) The average inflation rate during the period was 21.74% (SD = 8.04). The rates varied between a low of 12.10% and a high of 33.90%. The CV of 0.37 indicates moderate relative stability, implying that the banks were operating in a high-inflation but not disorderly macroeconomic environment. Gross Domestic Product Growth (GDP) The average real GDP growth rate was 7.54% (SD = 1.44). This metric, with the lowest CV of 0.19, is the most consistent in the data set and reveals a relatively stable and constant rate of national economic growth throughout the period of the study. To sum up, descriptive statistics, using 340 observations, indicate that the Ethiopian commercial banking sector operates in good capital adequacy (CAR = 17%) and good average liquidity (LIQ = 26), both higher than NBE requirements. The sector, although experiencing moderate but stable profitability (ROA = 2.73) in the context of high inflation (INF} approx. 21.74%) and high GDP growth (GDP = approx. 7.54%), maintained strong average liquidity and high capital adequacy both higher than NBE requirements. The high CV of the Liquidity Ratio (0.75) and NPL (0.65) reflects high cross-sectional heterogeneity in risk and liquidity management across the institutions which was examined in greater detail in the subsequent panel data regression. 4.2. Random Effects Model Diagnostics The study employed a panel regression model to ascertain the statistical significance and magnitude of regulatory factors influencing the Return on Assets (ROA) of commercial banks in Ethiopia. The findings establish clear relationships between capital adequacy, regulatory compliance, asset utilization, and profitability, while offering contrasting results regarding macroeconomic variables. Table 4 Random Effects Regression Results Variables Coefficient Std. Error z-Statistic p-value Significance Capital Adequacy Ratio (CAR) 0.048 0.021 2.29 0.022 Significant (5%) Minimum Paid-up Capital Requirement (MPCR) 0.015 0.006 2.50 0.013 Significant (5%) Legal Reserve Requirement (LRR) 0.024 0.010 2.40 0.016 Significant (5%) Loan-to-Deposit Ratio (LDR) 0.024 0.008 3.00 0.003 Significant (1%) Foreign Currency Regulation (FCR) 0.056 0.019 2.95 0.004 Significant (1%) Branch Expansion Requirement (BER) 0.013 0.007 1.86 0.063 Marginally Significant (10%) Non-Performing Loans (NPL) -0.089 0.025 -3.56 0.000 Highly Significant (1%) Liquidity (LQ) -0.011 0.012 -0.92 0.357 Not Significant Gross Domestic Product (GDP) -0.008 0.015 -0.53 0.597 Not Significant Inflation (INF) -0.005 0.011 -0.45 0.652 Not Significant Constant (C) 0.027 0.018 1.50 0.135 Model Summary Statistics Value R-squared (Overall) 0.682 Wald χ² (10) 52.84*** Prob > χ² 0.0000 Number of Observations 340 Number of Banks 17 Period Covered 2012–2024 Source: Computed via STATA Software Version 15 (N = 340) The analysis confirms a statistically significant positive correlation between key capital metrics and financial performance. The Capital Adequacy Ratio (CAR) exhibited a coefficient of 0.048}, suggesting that a one-unit increase in CAR corresponds to a 4.8\% increase in ROA, ceteris paribus . This result substantiates the hypothesis that higher capital buffers enhance profitability by strengthening institutional resilience and confidence, aligning with the empirical work of Abdrahamane and Kargbo ( 2017 ). Conversely, this finding diverges from the conclusions reached by Tekalign ( 2020 ) and Lealem ( 2021 ). Similarly, the Minimum Paid-up Capital Requirement (MPCR) demonstrated a positive and statistically robust effect, with a coefficient of 0.015. This outcome supports the notion that regulatory capital mandates are positively associated with bank profitability, consistent with studies like Addisu ( 2017 ), though it contradicts Abraham ( 2021 )'s finding of insignificance. From liquidity perspective, mandated reserve and lending policies also played a significant role. The Legal Reserve Requirement (LRR) showed a coefficient of 0.24, indicating that a one-unit rise in LRR is associated with a 2.4% increase in ROA. This strong positive association suggests that elevated reserve holdings, contrary to the expectation of reduced lending capacity, positively contribute to profitability by signalling greater liquidity and financial soundness, thereby attracting investor confidence. This supports Lealem ( 2021 ) but contrasts with the negative relationships reported by Abraham ( 2021 ) and Addisu ( 2017 ). From an operational perspective, the Loan-to-Deposit Ratio (LDR), a key measure of asset utilization, recorded a coefficient of 0.024. This positive relationship validates the principle that efficient asset intermediation, reflected by a higher proportion of deposits channelled into income-generating loans, significantly improves the bank's return on assets. Furthermore, compliance with the Foreign Currency Regulation (FCR), with a coefficient of 0.056, was found to contribute positively to ROA, underscoring the importance of sound management in mitigating foreign exchange risks and enhancing operational efficiency. From Strategic regulatory mandates perspective, such as the Branch Expansion Requirement (BER), designed to support the national Growth and Transformation Plan (GTP), yielded a coefficient of 0.013. This finding suggests that, contrary to the negative and insignificant relationship observed by Lealem ( 2021 ), a mandated increase in physical presence positively impacts profitability, likely by improving market access and financial inclusion in line with national objectives. In contrast, the study identified Non-Performing Loans (NPLs) as a principal drag on profitability. The regression output for NPLs was a negative and statistically significant coefficient of -0.089. This figure quantitatively reflects the adverse impact of deteriorating asset quality: every one-unit increase in NPLs is associated with an 8.9\% decline in ROA, due to increased provisioning and reduced interest income. Conversely, the Liquidity Requirement presented a negative and statistically insignificant effect on ROA, an outcome that contrasts sharply with the positive and significant findings of several empirical studies, including Kiplagat ( 2020 ) and Tekalign ( 2020 ). This counter-intuitive result may be attributable to the opportunity cost of holding excessive liquid reserves, which limits the volume of higher-yielding loan assets. The analysis of macroeconomic variables revealed a lack of statistical causality with bank profitability. Both Inflation and Gross Domestic Product (GDP) demonstrated a negative but statistically insignificant relationship with ROA. The insignificance of inflation suggests that commercial banks may possess effective risk management strategies to hedge against rising costs or the erosion of asset value. Similarly, the lack of a significant link between GDP growth and ROA implies that bank profitability is primary impact is likely exacerbated by structural constraints within the emerging economy, such as limited financial inclusion or a narrow financial product base, which dampen the transmission mechanism of GDP growth to banking sector profits. Conclusion The findings of this study provide important insights into the impact of regulatory variables on the financial performance of commercial banks in Ethiopia. The analysis revealed that several key regulatory measures established by the National Bank of Ethiopia (NBE) play a significant role in shaping bank performance. The results indicate that the Capital Adequacy Requirement (CAR) has a positive and significant effect on the performance of commercial banks in Ethiopia. This implies that banks maintaining higher capital reserves tend to achieve better financial outcomes. Adequate capitalization serves as a financial cushion, enabling banks to absorb potential losses, maintain solvency, and strengthen overall stability. Similarly, the Minimum Capital Requirement (MCR) was found to have a positive and statistically significant effect on bank performance. This finding suggests that an increase in a bank’s paid-up capital enhances its competitive position and resilience. Higher capital levels allow banks to undertake more profitable investments, expand operations, and mitigate risks, ultimately improving profitability and long-term sustainability. The results also show that the Loan-to-Deposit Ratio (LDR) exerts a positive and significant influence on performance. This indicates that efficient utilization of deposits to issue loans boosts income through interest revenue, thereby improving profitability. Banks that manage their loan portfolios effectively can achieve better returns without compromising liquidity. Furthermore, the Legal Reserve Requirement (LRR) demonstrated a positive and significant impact on the performance of commercial banks. Compliance with reserve requirements ensures adequate liquidity and enhances confidence among customers and investors. Maintaining these reserves allows banks to manage risks effectively while sustaining financial stability and operational efficiency. The study also established that Branch Expansion (BE) and Foreign Currency Ratio (FCR) both have positive and significant effects on bank performance. Expanding branch networks increases market outreach and financial inclusion, while efficient foreign currency management improves stability and supports profitability in international transactions. Conversely, Non-Performing Loans (NPLs) were found to have a negative and significant impact on bank performance. This implies that rising NPL levels weaken profitability and liquidity by increasing loan-loss provisions. Effective credit risk management is therefore essential to sustain financial health and investor confidence. On the other hand, Liquidity Requirement (LQ), Inflation (INF), and Gross Domestic Product (GDP) displayed negative but insignificant relationships with bank performance. Although these variables are vital for macroeconomic analysis, their influence on bank profitability was limited during the study period. Excess liquidity may constrain lending, while macroeconomic factors like inflation and GDP growth may not immediately translate into improved financial performance. Recommendations Based on the major findings, the researcher forwards the following recommendations: Strengthen Regulatory Frameworks The National Bank of Ethiopia should periodically review and update policies related to capital adequacy, reserve requirements, and lending ratios to maintain a balance between financial stability and sectoral growth. Optimize Loan Management Policies Commercial banks should maintain an optimal loan-to-deposit ratio to ensure profitability while preserving liquidity. Prudent lending and credit appraisal practices must be reinforced to sustain asset quality. Promote Branch Expansion and Financial Inclusion NBE should encourage strategic branch expansion, especially in underserved rural areas, to enhance access to financial services, foster inclusion, and stimulate local economic growth. Enhance Capital Requirements The minimum paid-up capital requirements should be revised periodically to reflect inflation and market developments, ensuring banks maintain strong capital bases and competitive strength. Improve Foreign Currency Management Policies that stabilize foreign currency allocation and support effective foreign exchange risk management should be strengthened to enhance profitability and trade competitiveness. Reduce Non-Performing Loans (NPLs) Both NBE and commercial banks should intensify credit risk management practices, strengthen loan monitoring mechanisms, and implement robust recovery and collection systems to minimize NPL levels. Efficient Liquidity Management Although liquidity showed an insignificant effect, banks should balance short-term obligations with long-term profitability, while NBE allows for flexible and responsive liquidity regulations. Monitor Macroeconomic Indicators Policymakers should continuously evaluate the long-term effects of inflation and GDP on the banking sector to design proactive policies that support macro-financial stability. Suggestions for Future Research Future studies should consider incorporating additional regulatory variables beyond those examined in this study, as NBE supervises other financial institutions such as insurance companies, microfinance institutions, and non-bank financial intermediaries. Including these sectors may offer a broader understanding of Ethiopia’s financial ecosystem. Moreover, researchers are encouraged to include control variables such as political stability, institutional quality, and global economic conditions—including trade policies and currency fluctuations—to assess external influences on bank performance. Expanding the scope in this way would provide a more comprehensive and nuanced understanding of the factors affecting the profitability and stability of commercial banks in Ethiopia. This will help NBE and policymakers design more targeted and effective strategies for enhancing the efficiency and resilience of the Ethiopian banking sector. Declarations Funding: The author declare that this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Competing Interest Declaration The author declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper Data Availability Declaration The datasets generated and/or analysed during the current study are included in this article Additional data or materials can be made available from the corresponding author upon reasonable request. Consent to participate: Not applicable. No human participants were involved in this study; therefore, consent to participate was not required. Consent to publish: Not applicable. The study does not contain any individual-level data or identifiable personal information requiring consent to publish. Ethical approval This study is based on publicly available financial data from commercial banks and does not involve human participants or sensitive personal data. References Abdrahamane S, Kargbo M. Capital adequacy and bank performance: Evidence from Mali. Int J Econ Finance. 2017;9(5):64–75. Abidi F, Lodhi SA. Impact of reserve requirement on bank profitability: Evidence from Pakistan. Int J Econ Empir Res. 2015;3(6):283–90. Abraham K. (2021). The impact of regulatory policies on the profitability of Ethiopian commercial banks. Unpublished Master’s Thesis, Addis Ababa University. Addisu A. (2017). Determinants of profitability of commercial banks in Ethiopia. Unpublished Master’s Thesis, Addis Ababa University. Ahamed MM, Mallick SK, Matthäus D. Does regulatory quality enhance or constrain bank efficiency? Evidence from emerging markets. J Int Financ Mark Inst Money. 2021;74:101–20. https://doi.org/10.1016/j.intfin.2021.101120 . Agoraki MEK, Tsamis A. Regulation, competition, and bank risk-taking in transition countries. Rev Financ Econ. 2017;33:1–14. Aliyu S, Hassan MK, Yusof RM, Naiimi N. Capital regulation and bank performance: Evidence from Nigeria. Int J Finance Econ. 2020;25(3):478–94. Asteriou D, Pilbeam K, Tomuleasa I. The impact of regulation on bank performance: Evidence from the European banking sector. J Int Money Finance. 2021;110:102–3. Ayadi R, Naceur SB, Casu B, Quinn B. Does Basel compliance matter for bank performance? J Financial Stab. 2016;23:15–32. Baltagi BH. Econometric analysis of panel data. 6th ed. Springer; 2021. Barth JR, Caprio G, Levine R. Bank regulations are changing: For better or worse? Comp Econ Stud. 2008;50(4):537–63. Barth JR, Caprio G, Levine R. Bank regulation and supervision in 180 countries from 1999 to 2011. J Financial Economic Policy. 2013;5(2):111–219. Brooks C. Introductory econometrics for finance. 4th ed. Cambridge University Press; 2019. Casu B, Ferrari A, Zhao T. Regulatory reforms and productivity change in European banking. Rev Financ Econ. 2017;33:28–45. Čihák M, Demirgüç-Kunt A, Martínez Pería MS, Mohseni-Cheraghlou A. Bank regulation and supervision: What works best? J Financial Stab. 2013;9(3):266–75. Creswell JW, Poth CN. Qualitative inquiry and research design: Choosing among five approaches. 4th ed. SAGE; 2016. Demirgüç-Kunt A, Huizinga H. Determinants of commercial bank interest margins and profitability: Some international evidence. World Bank Economic Rev. 1999;13(2):379–408. Dewatripont M, Tirole J. The prudential regulation of banks. MIT Press; 2018. Djalilov K, Piesse J. Bank regulation and efficiency: Evidence from transition economies. Emerg Markets Rev. 2019;38:292–309. Efanga U, Effiong C, Akpan J. Liquidity management and bank performance: Evidence from Nigeria. J Finance Acc. 2020;8(3):67–78. Etikan I. Comparison of convenience sampling and purposive sampling. Am J Theoretical Appl Stat. 2016;5(1):1–4. https://doi.org/10.11648/j.ajtas.20160501.11 . Hovakimian A, Kane EJ. Effectiveness of capital regulation at U.S. commercial banks, 1985 to 1994. J Finance. 2000;55(1):451–68. Kiplagat S. Effect of liquidity on the financial performance of commercial banks in Kenya. Int J Finance Bank Res. 2020;6(4):45–53. Lealem F. (2021). The effect of regulatory requirements on the financial performance of private commercial banks in Ethiopia. Unpublished Master’s Thesis, Addis Ababa University. National Bank of Ethiopia (NBE). (2023/2024). Annual report 2023/24. National Bank of Ethiopia. Ozili PK. Bank profitability and capital regulation: Evidence from Africa. J Afr Bus. 2017;18(2):143–68. Rochet J-C. Capital requirements and the behavior of commercial banks. Eur Econ Rev. 1992;36(5):1137–70. Saunders M, Lewis P, Thornhill A. Research methods for business students. 8th ed. Pearson Education Limited; 2019. Shaddady A, Moore T. Investigating the effect of regulation and supervision on European banks’ performance and stability. Int J Finance Econ. 2018;23(1):98–113. Tekalign T. (2020). The impact of National Bank of Ethiopia directives on commercial banks’ profitability. Unpublished Master’s Thesis, Addis Ababa University. Triki T, Kouki I, Dhaou M, Calice P. Bank regulation and efficiency: What works for Africa? J Bank Regul. 2017;18(3):242–58. Yang L, Shao X, Liu Y, Ye K. The impact of financial regulation on bank performance: Evidence from global banking data. Econ Model. 2019;83:125–38. Yusuf M, Shikur Z. The effect of prudential regulation on the performance of commercial banks in Ethiopia. J Acc Financial Manage. 2023;9(2):22–37. Additional Declarations No competing interests reported. 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Introduction","content":"\u003ch2\u003e1.1. Background of the Study\u003c/h2\u003e\n\u003cp\u003eThe foundation of modern economies rests on a stable and efficient banking sector, which is fundamentally vital for facilitating the efficient distribution of resources, providing market liquidity, and driving national economic expansion through the provision of credit (IMF, 2015). Consequently, the integrity of this sector is maintained through comprehensive bank regulations, which are rules and guidelines established by financial authorities to enforce standards across critical domains such as capital adequacy, liquidity, and risk management. The core mandate of these regulations is to safeguard depositors, prevent financial crises, and enhance public confidence in the banking system (Davies \u0026amp; Green, 2013; Basel Committee, 2011). Internationally, the need for robust and harmonized banking supervision is continually emphasized, particularly as global markets become interconnected, requiring alignment with standards like the Basel Accords to minimize systemic risk (Basel Committee, 2011).\u003c/p\u003e\n\u003cp\u003eWithin this global context, the African banking sector is characterized by ongoing modernization and reform efforts aimed at aligning local regulatory frameworks with international best practices. While significant progress has been made in implementing stricter capital adequacy and improved risk management protocols, the effectiveness and maturity of these supervisory frameworks vary across the continent, influenced by local economic and institutional capacities (Beck \u0026amp; Cull, 2013). The Ethiopian banking sector is a notable example, operating within \u003cstrong\u003ea \u003cstrong\u003etightly regulated environment\u003c/strong\u003e\u003c/strong\u003e where the National Bank of Ethiopia (NBE) acts as the central authority (established 1963).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe NBE\u0026apos;s functions are critical, encompassing the implementation of monetary policy, management of the national currency, and, most importantly, the enforcement of regulations (such as capital adequacy, liquidity rules, and lending policies) to ensure the stability and soundness of the financial system (National Bank of Ethiopia, 2023; Legesse \u0026amp; Yohannis, 2015). The regulatory measures implemented by the NBE thus hold significant influence over the financial performance and operational behaviour of all commercial banks in the country (Lelissa \u0026amp; Kuhil, 2018).\u003c/p\u003e\n\u003cp\u003eDespite the universal consensus on the necessity of regulation for financial stability, the academic debate remains unresolved regarding its direct \u003cstrong\u003eimpact on commercial bank performance\u003c/strong\u003e. Scholars argue that while regulations are essential for mitigating systemic risks, stringent measures\u0026mdash;such as elevated capital requirements and stricter supervision\u0026mdash;can simultaneously constrain banks\u0026rsquo; profitability and growth by restricting lending capacity and increasing compliance costs (Claessens \u0026amp; Kodres, 2014). This enduring tension between the NBE\u0026rsquo;s dual objectives of financial stability and the promotion of a profitable, credit-providing banking sector forms the crucial empirical context for this research.\u003c/p\u003e\n\u003ch2 id=\"_Toc189039765\"\u003e1.2. \u0026nbsp; \u0026nbsp; Statement of the problem\u003c/h2\u003e\n\u003cp id=\"_Toc189039766\"\u003eThe banking sector in Ethiopia operates under a set of stringent regulatory measures mandated by the National Bank of Ethiopia (NBE). These measures, covering critical areas such as capital adequacy, liquidity, statutory reserves, and credit requirements, are primarily designed to safeguard financial stability and mitigate systemic risk (Abraham, 2021),. However, a significant and unresolved debate exists regarding the net effect of these mandated prudential measures on the financial performance of commercial banks, specifically in relation to profitability (Return on Assets - ROA) and operational efficiency.\u003c/p\u003e\n\u003cp\u003eExisting empirical studies focused on the NBE\u0026apos;s impact yield fundamentally equivocal and contradictory findings. For instance, while some research suggests that stringent capital requirements (like capital adequacy) have little to no statistically significant effect on key performance indicators such as ROA (Abraham, 2021), other equally rigorous studies conclude the opposite, revealing a significant relationship between capital adequacy and ROA (Lealem, 2021). Furthermore, regulations like the reserve requirement, hypothesized to negatively affect intermediation due to the transfer of capital into non-interest-bearing assets, have shown an unexpected, albeit statistically insignificant, positive relationship with performance (Lelissa \u0026amp; Kuhil, 2018). This state of inconsistent findings prevents a clear policy direction for balancing stability and growth.\u003c/p\u003e\n\u003cp\u003ePrior research suffers from under-specification, failing to account for critical, context-specific regulatory instruments highly relevant to the Ethiopian financial landscape. Notably, two significant variables\u0026mdash;the Loan-to-Deposit (L/D) ratios and foreign currency regulations\u0026mdash;have been frequently excluded. Given that Ethiopia operates under a tightly managed foreign exchange environment and L/D ratios directly constrain lending, the omission of these factors renders previous models incomplete and inadequate for fully capturing the true regulatory burden and its combined influence on bank operations. Moreover, the limited body of research specifically focused on the unique characteristics of the Ethiopian market necessitates a dedicated, comprehensive investigation to provide locally relevant conclusions.\u003c/p\u003e\n\u003cp\u003eThe persistent contradiction in existing literature, coupled with the systemic omission of key regulatory variables pertinent to the Ethiopian context, underscores a critical knowledge gap. Therefore, this research is necessitated to bridge this gap by comprehensively examining the combined, simultaneous effect of a robust set of eight regulatory variables (capital adequacy, minimum paid-up capital, loan-to-deposit requirement, liquidity requirement, legal reserve requirement, bank branch expansion, non-performing loan/credit risk management, and foreign currency regulations) on the financial performance (proxied by ROA) of commercial banks in Ethiopia. Employing a quantitative approach utilizing 13 years (2012-2024) of data across 17 commercial banks provides a more holistic and scientifically grounded understanding for policymakers and bank management.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3. Objectives of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3.1. General Objective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted to examine the combined impact of the National Bank of Ethiopia\u0026apos;s regulatory measures on the financial performance of commercial banks in Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3.2. Specific Objectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe specific objectives of the research are to:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3.2. Specific Objectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe specific objectives of the research are to:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eExamine the effects of Prudential Capital and Liquidity Regulations (i.e., capital adequacy, minimum paid-up capital, liquidity, and legal reserve requirements) on the financial performance of commercial banks in Ethiopia.\u003c/li\u003e\n \u003cli\u003eAssess the effects of Credit and Operational Risk Regulations (i.e., loan-to-deposit requirements and non-performing loan management) on the financial performance of commercial banks in Ethiopia.\u003c/li\u003e\n \u003cli\u003eDetermine the effects of Market and External Constraint Regulations (i.e., bank branch expansion requirements and foreign currency regulations) on the financial performance of commercial banks in Ethiopia.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"2.\tLiterature Review: Bank Regulations and Financial performance","content":"\u003cp\u003e\u003cstrong\u003e2.1. Theoretical Framework and Debates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe fundamental principle of bank regulation and supervision is to protect the banking system from inappropriate risk-taking and solve moral hazard, ultimately resulting in financial stability (Ayadi et al., 2016). The theoretical literature, however, depicts two traditional, rival perspectives with regard to the effect of these regulatory measures on bank profitability. The Public Interest View is that regulation by the government in the form of capital regulation, for example, motivates effective banking by compensating for market failures (Barth et al., 2008; Barth et al., 2013). Good regulation is what should drive a stable, hence more profitable banking sector. Or, the Private Interest View, which argues that regulations tend to be captured by powerful minority groups. Here, regulation can be codified so as to buffer incumbents or allow for implicit government guarantees, which will tend to increase the cost of financial intermediation and reduce bank profitability in general (Barth et al., 2008; Barth et al., 2013; Demirg\u0026uuml;\u0026ccedil;-Kunt \u0026amp; Huizinga, 1999). These competing theoretical predictions highlight the importance of empirical testing in an attempt to determine the actual effect of regulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlobal Empirical Evidence on Regulation and Performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEarly empirical studies reaffirmed that supervisory and regulatory mechanisms significantly impact banking sector performance (Dewatripont \u0026amp; Tirole, 2018; Hovakimian \u0026amp; Kane, 2000; Rochet, 1992). The tightened capital requirements following the 2007\u0026ndash;2009 financial crisis bear witness to continuous changes in banking regulation (Djalilov \u0026amp; Piesse, 2019; Casu et al., 2017). Despite reforms, the effectiveness of banking regulation continues to be contentious among scholars and policymakers alike (Čih\u0026aacute;k et al., 2013).\u003c/p\u003e\n\u003cp\u003eCapital requirement and supervisory research yields conflicting results. Barth et al. (2013) found, in a large cross-country sample, that improving supervisory power was related to bank efficiency. Shaddady and Moore (2018) concurred too, finding that good-quality regulatory regimes improve stability. Ahamed et al. (2021) and Agoraki and Tsamis (2017) also credited high capital requirements and limits on improved profitability and efficiency. Conversely, some studies caution that an overly strict policy will increase the cost of doing business (Barth et al., 2013; Demirg\u0026uuml;\u0026ccedil;-Kunt \u0026amp; Huizinga, 1999) or lower liquidity and profitability, as was found in the case of reserve requirements in Pakistan by Abidi and Lodhi (2015). Too much supervision was also found to harm bank stability by Shaddady and Moore (2018).\u003c/p\u003e\n\u003cp\u003eRegarding limits on banking business and openness, findings are also divided. Barth et al. (2013) found activity restrictions to be harmful to bank efficiency, but transparency (market monitoring) was beneficial. However, Agoraki and Tsamis (2017) and Ahamed et al. (2021) found certain restrictions, with high capital, could possibly increase returns. A key consensus of global evidence is that the relationship between regulation and profitability is complex and context-dependent, which implies that no universal regulatory best practice can be observed (Yang et al., 2019; Asteriou et al., 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegional Evidence: Africa\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudies on African banking institutions highlight the impact of domestic market setting, bank size, and risk profile and lead to mixed evidence (Triki et al., 2017; Ozili, 2017). On the aspect of capital adequacy, there was strong positive correlation with performance in Nigerian banks consistent with stability goals, Aliyu et al. (2020) reported. Abdrahamane and Kargbo (2017) in Mali, however, reported that the banks would have more than the optimal level of capital, which affected overall performance. Secondly, regulation can have size-dependent effect: Triki et al. (2017) proved that tight entry controls benefited the efficiency of large banks, but harmed small banks. On liquidity, Efanga et al. (2020) in Nigeria and Kiplagat (2020) in Kenya found that liquidity ratios exerted a positive and significant impact on bank productivity and performance, meaning that good management of liquidity is a key driver of the region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLocal Empirical Evidence: Ethiopian Commercial Banks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLocal studies of Ethiopian commercial banks are characterized by prevailing inconsistencies and contradictions, particularly concerning the key prudential requirements. The most striking contradiction concerns the Capital Adequacy Ratio (CAR): Addisu (2017), Tekalegn (2020), and Lealem Feleke (2021) all confirmed a robust and negative relationship between CAR and profitability (ROA/ROE). This would mean that the opportunity cost of having excess funds might outweigh the stability benefits in the given instance. Yusuf and Shikur (2023), though, found a positive and significant impact. The same paradox exists for the Legal Reserve Requirement (LRR), which was found to be negative by Addisu (2017) but positive by Tekalegn (2020), Lealem Feleke (2021), and Yusuf and Shikur (2023).\u003c/p\u003e\n\u003cp\u003eFindings on other bank-specific and regulatory variables are also non-conclusive. While Addisu (2017) noted the impact of bank size to be positive, Tekalegn (2020) noted a negative impact. In terms of direct regulatory impacts, Abraham Kifetew (2021) and Tekalegn (2020) noted interest rate regulation having a positive impact on bank margins due to the regulated interest rate environment. Finally, local studies appear to agree that macroeconomic variables like inflation and GDP have a positive impact on profitability in Ethiopia (Lealem Feleke, 2021; Yusuf \u0026amp; Shikur, 2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKnowledge Gap and Contribution of Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLiterature reviewed identifies two fundamental issues: firstly, the absence of any blanket regulative control, as the effect is contingent. Thus, secondly, and most pertinent to this study, is the absence of local empirical consensus regarding the effect of critical prudential regulation on the performance of Ethiopian commercial banks. The findings\u0026apos; disagreement in the literature at home owing to varying samples, lengths of regulation periods, and approaches offer very wide latitude. The current research therefore attempts to bridge this local gap by employing a robust panel data specification (Fixed Effects) with a bigger sample (N=340) to derive a fresher and conclusive empirical finding with respect to the degree to which National Bank regulations affect the profitability of commercial banks in Ethiopia\u003c/p\u003e\n\u003cp\u003eThe theoretical and empirical review literature verifies that the nexus of bank regulation and profits is complex, dependent, and highly unstable within regions and single regulatory measures. Theoretically, the argument is still inconclusive, split between the Public Interest View (regulation is efficiency-enhancing) and the Private Interest View (regulation increases costs and benefits minorities).\u003c/p\u003e\n\u003cp\u003eEmpirically, cross-country evidence confirms that the effect of tools like Capital Adequacy Ratio (CAR) and activity curbs is not country-specific (Asteriou et al., 2021). Despite effective regulation being positively linked with stability, excessive tightness or an opportunity for a high cost of capital can prove to be destructive to profitability, especially among unlisted or smaller banks (Ozili, 2017; Shaddady \u0026amp; Moore, 2018).\u003c/p\u003e\n\u003cp\u003eOf particular relevance, national research on Ethiopian commercial banks has inherent contradictions in the performance of core prudential regulations like the CAR and Legal Reserve Requirement (LRR). Some research has documented a positive correlation (Yusuf \u0026amp; Shikur, 2023) while others have documented a negative correlation (Lealem, 2021), and such literature remains inconclusive. This lack of agreement constitutes a central knowledge gap, which highlights the need for robust, timely, and place-specific empirical evidence to inform effective policy response. The varying results highlight that regulatory efficacy is inextricably tied to the country\u0026apos;s specific architecture, risk appetite, and market dynamics of the Ethiopian banking sector.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Hypotheses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following hypotheses are developed based on a review of theoretical concepts and previous empirical studies related to bank regulation and performance:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e:\u003cstrong\u003e\u0026nbsp;Research Hypotheses and Expected Relationships\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegulatory Variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExpected Relationship\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCapital adequacy requirement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositive and significant effect on the financial performance of commercial banks in Ethiopia.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eH2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMinimum paid-up capital requirement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositive and significant effect on the financial performance of commercial banks in Ethiopia.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eH3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLoan-to-deposit Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositive and significant effect on the financial performance of commercial banks in Ethiopia.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eH4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLiquidity requirement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositive and significant effect on the financial performance of commercial banks in Ethiopia.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eH5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLegal reserve requirement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositive and significant effect on the financial performance of commercial banks in Ethiopia.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eH6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eForeign currency regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositive and significant effect on the financial performance of commercial banks in Ethiopia.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eH7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBank branch expansion requirement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositive and significant effect on the financial performance of commercial banks in Ethiopia.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eH8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-performing loan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNegative and significant effect on the financial performance of commercial banks in Ethiopia.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"3. Research Methodology","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Research Design\u003c/h2\u003e\u003cp\u003eResearch design is a structured plan that specifies how data will be collected, measured, and analysed to address research questions or hypotheses. It includes the framework for selecting research methods, procedures for data collection, and strategies for data analysis to ensure the validity and reliability of the study\u0026rsquo;s findings\"(Creswell \u0026amp; Poth, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study was designed to be explanatory, with the objective of measuring the impact of NBE regulations on the financial performance of commercial banks in Ethiopia. Explanatory research design is employed to investigate and elucidate the connections between variables, with an emphasis on uncovering the underlying causes and mechanisms that lead to specific outcomes. Its goal is to deliver a comprehensive understanding of the 'how' and 'why' behind certain phenomena by analysing causal relationships and processes through detailed data analysis.(Saunders, Lewis, \u0026amp; Thornhill, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Research Approach\u003c/h2\u003e\u003cp\u003eThis study used a quantitative approach, which was anticipated to be the most effective method for determining the relationship between the study variables (i.e., bank regulation and financial performance), expressed in percentages. This approach was suitable for this study as it allowed the researcher to measure financial performance (ROA) using numerical data, test cause-and-effect relationships, and generalize findings. It ensured precision, reliability, and control of variables, helping to understand how NBE regulations impacts financial performance and identify trends over time.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Target Population and Sampling Procedure\u003c/h2\u003e\u003cp\u003eThe target population encompasses all individuals or entities that a researcher aims to analyse and generalize findings about. Precisely defining this population is essential to ensure that the research results are relevant and applicable to the intended group.(Creswell \u0026amp; Poth, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Currently, the banking ecosystem in Ethiopia consists of a total of 30 banks (NBE annual report 2023/2024). Out of the 32 banks, 31 of them are commercial banks and one is Development Bank. Among the 31 commercial banks, Commercial Bank of Ethiopia (CBE) is state owned and the remaining 29 are private commercial banks (NBE Annual report, 2023/2024). The target population of the study included all thirty commercial banks operating in Ethiopia.\u003c/p\u003e\u003cp\u003eThese study used Purposive sampling techniques to select the samples from the total population. Purposive sampling, also known as judgmental sampling, is a non-probability sampling technique where researchers select specific individuals or groups based on particular characteristics or criteria relevant to the study (Etikan, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Out of the thirty targeted banks, only seventeen commercial banks are included in the study. This is because data required for the study regarding the remaining bank could not be obtained in sufficient amount since the banks were established after 2022.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Data Type, Source, and Method of Collection\u003c/h2\u003e\u003cp\u003eThe research relies solely on secondary data collected over a 13-year period (2012\u0026ndash;2024) from 17 commercial banks. The data was collected through document review data collection methods. This approach utilizes panel data, which incorporates both time series and cross-sectional aspects. The use of panel data is advantageous for examining causal relationships between variables across time. It allows for a more overall analysis of complex issues compared to using only time series or cross-sectional data. By integrating these two dimensions, panel data enhances the degrees of freedom, thereby increasing the statistical power of hypothesis tests. Additionally, it helps address multicollinearity issues that could arise from analysing time series data in isolation. Furthermore, with proper model specification, panel data can reduce biases related to omitted variables in regression analyses.(Brooks, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) .The data are collected from annual reports of National Bank of Ethiopia as well as from the individual commercial banks audited financial statements in their website. The variables used in this study are obtained from the various directives of the NBE.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Method of Data Analysis and Presentation\u003c/h2\u003e\u003cp\u003eThe researcher employed a panel data regression model to analyse the impacts of NBE regulations on the financial performance of commercial banks in Ethiopia. Panel data analysis was chosen due to its ability to account for both the cross-sectional (17 commercial banks) and time-series (13 years) dimensions of the data. To determine the most suitable model between the Fixed Effects Model (FEM) and the Random Effects Model (REM), the Hausman test was conducted. The test results indicated that the Random Effects Model was appropriate for this study, as it effectively captures the individual-specific variations without introducing bias or inefficiency.( Baltagi, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe data analysis begins with the presentation of descriptive statistics for both the dependent and independent variables. Following this, diagnostic tests are conducted to verify the robustness of the model and the regression coefficients are tested. Lastly, the results of the regression analysis are discussed in detail.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.7. Model Specification\u003c/h2\u003e\u003cp\u003eThe main aim of this study was to examine the impacts of regulations imposed by the National Bank on the financial performance of commercial banks in Ethiopia. The analysis focused on understanding how regulatory factors, together with control variables, affected the banks' financial outcomes. To account for variations in performance, relevant factors that affect the financial results of the banks were used as proxies. The regression model used was described by the following equation:\u003c/p\u003e\u003cp\u003eROA it\u0026thinsp;=\u0026thinsp;α\u0026thinsp;+\u0026thinsp;β1CARit\u0026thinsp;+\u0026thinsp;β2MPCRit\u0026thinsp;+\u0026thinsp;β3LDRit\u0026thinsp;+\u0026thinsp;β4LRit\u0026thinsp;+\u0026thinsp;β5LRRit\u0026thinsp;+\u0026thinsp;β6BERit\u0026thinsp;+\u0026thinsp;β7 FCR it\u0026thinsp;+\u0026thinsp;β8NPLit\u0026thinsp;+\u0026thinsp;β9INFit\u0026thinsp;+\u0026thinsp;β10GDPit\u0026thinsp;+\u0026thinsp;εit\u003c/p\u003e\u003cp\u003e\u003cb\u003eWhere\u003c/b\u003e: ROA: Return on Assets; CAR: Capital Adequacy requirement; MPCR: Minimum Paid-up Capital requirement ;LDR: Loan to Deposit requirement ;LR: Liquidity requirement ;LRR: Legal reserve requirement ;BER: Branch expansion requirement ;FCR: Foreign Currency Regulation ;NPL: Non Performing Loan ;INF: Inflation ;GDP: Gross Domestic Product; ε: Error\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.8. Measurement and Operational Definition of Variables\u003c/h2\u003e\u003cp\u003e\u003cb\u003eTable \u0026minus;\u0026thinsp;2. Variables Measurement and Operational definition\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNotation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOperational definitions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMeasurement/Proxy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDependent variables\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eROA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eROA is the ratio of net income to average total asset. It shows how efficiently the resources of the bank are used to generate income.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{Net\\:Income}{Total\\:Assets}\\)\u003c/span\u003e\u003c/span\u003e x100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCapital Adequacy Requirement\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e(CAR)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eThe portion of banks asset to be held in the form of capital so as to absorb unexpected shocks.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{C}\\text{a}\\text{p}\\text{i}\\text{t}\\text{a}\\text{l}}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{R}\\text{i}\\text{s}\\text{k}-\\text{W}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t}\\text{e}\\text{d}\\:\\text{A}\\text{s}\\text{s}\\text{e}\\text{t}\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e X 100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMinimum Paid-up Capital\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMPCR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eAnnual percentage growth of paid-up capital.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eMPCR=\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\:\\frac{Current\\:year\\:paid\\:up\\:\\:capital-Previous\\:year\\:paid\\:up\\:capital\\:}{Previous\\:year\\:paid\\:up\\:capital}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLoan-to-Deposit Ratio\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLDR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003epercentage of loans funded by deposits\u003c/em\u003e,\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\:\\:\\:\\:\\:Total\\:Outstanding\\:Loans}{Total\\:Deposits}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003ex 100\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLegal Reserve Requirements\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e(LRR)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eReserve requirement as percentage of deposit.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eNet\u0026nbsp;Income \u0026times;Legal\u0026nbsp;Reserve\u0026nbsp;Ratio\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBranch Expansion Requirement\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e(BER)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eBank\u0026rsquo;s branch growth per annum.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{Current\\:year\\:number\\:of\\:branch-perivious\\:year\\:number\\:of\\:branch\\:}{perivious\\:year\\:number\\:of\\:branch\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eForeign Currency regulation\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eFCR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eThe difference between foreign currency assets and foreign currency liabilities\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eForeign\u0026nbsp;Currency\u0026nbsp;Assets\u0026thinsp;\u0026minus;\u0026thinsp;Foreign\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eCurrency\u0026nbsp;Liabilities\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLiquidity requirement\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e(LQ\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eLiquid assets as percentage of liquid liabilities.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e(\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{Liquid\\:Assets}{Net\\:Current\\:Liabilities}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e \u003cem\u003eX 1OO\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNon-Performing loan\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eNPL\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eTotal NPL as percentage of total loans outstanding\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{Total\\:Non\\:Performing\\:Loans}{Total\\:Loans\\:Outstanding\\:}x100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eInflation\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eInflation\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eQuantitative estimate of the rate at which the decline in purchasing power of Birr.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eAnnual inflation rate in %.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003etotal monetary value of all final goods and services produced within a country's borders over a specific period\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{GDP\\:in\\:Current\\:Period-GDP\\:in\\:Previous\\:Period}{GDP\\:in\\:Previous\\:Period}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eX100\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results and Discussions","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Descriptive Analysis\u003c/h2\u003e\u003cp\u003eThis section presents the descriptive statistics for the dependent variable, Return on Assets (ROA), and the selected national bank regulation variables. The panel dataset comprises a total of 340 observations (N\u0026thinsp;=\u0026thinsp;340), which corresponds to a balanced or unbalanced panel structure across a given number of banks and years (for this study case, 17 banks over 13 years). The statistics, summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, provide the mean, median, standard deviation SD, minimum, maximum, and the Coefficient of Variation (CV) for each variable. The CV (the ratio of SD to the mean) serves as a robust measure of relative variability, with values below 1 generally indicating low or moderate dispersion relative to the mean.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of descriptive statistics for dependent and independent variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOBS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCV\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eROA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.7279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCAR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.8197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.4680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMPUC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.6800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.6049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e52.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLTD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.1217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.1985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e85.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLRR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.3462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.4592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e74.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBER\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.6387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.7754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLIQ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.5277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.0207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFCR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.4000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.5114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e28.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNPL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.9182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eINF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.7429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.0401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGDP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.5429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSource: Computed via STATA Software Version 15 ( N\u0026thinsp;=\u0026thinsp;340)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eAnalysis of Dependent and Key Regulator Variables\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDependent Variable (ROA)\u003c/strong\u003e\u003cp\u003eThe Return on Assets (ROA), the key indicator of bank profitability, is used as the dependent variable. Over the period covered by the study, the mean ROA of the banks is 2.73% (SD} = 0.90). At a minimum CV of 0.33, there will certainly be variations in bank profitability across banks and over time, but the mean ROA is quite stable. The range of profitability is wide, from a minimum of \u003cspan\u003e$\u003c/span\u003e0.50% to a maximum of \u003cspan\u003e$\u003c/span\u003e6.10%, indicating significant disparity between the best- and worst-performing observations.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRegulatory Variables\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003cb\u003eCapital Adequacy Ratio (CAR\u003c/b\u003e): The banks report strong capital buffers, with the average CAR of 17.82% (SD\u0026thinsp;=\u0026thinsp;6.47). This is far above the minimum requirement by the National Bank of Ethiopia (NBE) of 8%, which means that overall, the banking system had sufficient capital over the period. The CV of 0.36 is respectable, modest variation in capital among the institutions.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMinimum Paid-Up Capital (MPUC)\u003c/strong\u003e\u003cp\u003eThe banks have an average paid-up capital of 25.68 units (SD\u0026thinsp;=\u0026thinsp;8.60), showing the sector's sustained attempt to meet the NBE's stricter capital ceilings. The relatively low CV of 0.34 stands as proof of relative homogeneity in the effort and capacity to mobilize central capital.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLegal Reserve Requirement (LRR)\u003c/strong\u003e\u003cp\u003eThe legal reserve, or percentage of net income deposited to the reserve account until it equals capital (or 10% thereafter, as per Directives No. SBB/4/95), averaged 61.35% (SD\u0026thinsp;=\u0026thinsp;14.46). This high mean value shows that, on average, a high percentage of the commercial banks' net income is sent to statutory reserves, which reflects the significance of this rule instrument in balance sheet management. The CV of 0.24 indicates low relative variability, consistent with a stable application of the reserve mandate across the observations sampled.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLiquidity Ratio (LIQ)\u003c/strong\u003e\u003cp\u003eThe average liquidity ratio is 26.53% (SD\u0026thinsp;=\u0026thinsp;20.02). This average comfortably exceeds the minimum requirements of the NBE (e.g., 20% as per Directive No. SBB/46/2012 or 15% as per Directive No. SBB/57/2014). However, the high SD and a CV of 0.75 (approaching 1.0) indicate high dispersion in liquidity management techniques among the banks. High such dispersion results in some banks holding high levels of excess liquidity while others are close to the regulatory threshold.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eBranch Expansion Rate Ratio (BER)\u003c/strong\u003e\u003cp\u003eBeing a key policy indicator of expansion strategy, the yearly average branch expansion rate was 18.64% (SD\u0026thinsp;=\u0026thinsp;7.78). The average is lower than the 25% a year growth target given top priority in the NBE's Growth and Transformation Plans (GTPs), reflecting a typical deficit in achieving the policy's infrastructural expansion target.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCredit Risk Variable Analysis and Macroeconomic Control Variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNon-Performing Loans (NPLs): While not a regulatory instrument in itself, NBE monitors NPLs as a significant asset quality indicator used to ascertain provisioning and profitability. The average NPL rate stands at 1.92% (SD\u0026thinsp;=\u0026thinsp;1.24), ranging between 0.50% and 6.36%. This average is well within the NBE's typical regulatory threshold of 5%. The CV of 0.65 reflects substantial, but not high, relative variation of asset quality across the sample, reflecting the varying risk management ability of the banks.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMacroeconomic Variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInflation (INF)\u003c/strong\u003e\u003cp\u003eThe average inflation rate during the period was 21.74% (SD\u0026thinsp;=\u0026thinsp;8.04). The rates varied between a low of 12.10% and a high of 33.90%. The CV of 0.37 indicates moderate relative stability, implying that the banks were operating in a high-inflation but not disorderly macroeconomic environment.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eGross Domestic Product Growth (GDP)\u003c/strong\u003e\u003cp\u003eThe average real GDP growth rate was 7.54% (SD\u0026thinsp;=\u0026thinsp;1.44). This metric, with the lowest CV of 0.19, is the most consistent in the data set and reveals a relatively stable and constant rate of national economic growth throughout the period of the study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eTo sum up, descriptive statistics, using 340 observations, indicate that the Ethiopian commercial banking sector operates in good capital adequacy (CAR\u0026thinsp;=\u0026thinsp;17%) and good average liquidity (LIQ\u0026thinsp;=\u0026thinsp;26), both higher than NBE requirements. The sector, although experiencing moderate but stable profitability (ROA\u0026thinsp;=\u0026thinsp;2.73) in the context of high inflation (INF} approx. 21.74%) and high GDP growth (GDP\u0026thinsp;=\u0026thinsp;approx. 7.54%), maintained strong average liquidity and high capital adequacy both higher than NBE requirements. The high CV of the Liquidity Ratio (0.75) and NPL (0.65) reflects high cross-sectional heterogeneity in risk and liquidity management across the institutions which was examined in greater detail in the subsequent panel data regression.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Random Effects Model Diagnostics\u003c/h2\u003e\u003cp\u003eThe study employed a panel regression model to ascertain the statistical significance and magnitude of regulatory factors influencing the Return on Assets (ROA) of commercial banks in Ethiopia. The findings establish clear relationships between capital adequacy, regulatory compliance, asset utilization, and profitability, while offering contrasting results regarding macroeconomic variables.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRandom Effects Regression Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez-Statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSignificance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCapital Adequacy Ratio (CAR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSignificant (5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimum Paid-up Capital Requirement (MPCR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSignificant (5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLegal Reserve Requirement (LRR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSignificant (5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoan-to-Deposit Ratio (LDR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSignificant (1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForeign Currency Regulation (FCR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSignificant (1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBranch Expansion Requirement (BER)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eMarginally Significant\u003c/em\u003e (10%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Performing Loans (NPL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHighly Significant (1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiquidity (LQ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNot Significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGross Domestic Product (GDP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNot Significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInflation (INF)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNot Significant\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant (C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel Summary Statistics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared (Overall)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWald χ\u0026sup2; (10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003e52.84***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;χ\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Observations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Banks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeriod Covered\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003e2012\u0026ndash;2024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eSource: \u003cem\u003eComputed via STATA Software Version 15 (N\u0026thinsp;=\u0026thinsp;340)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe analysis confirms a statistically significant positive correlation between key capital metrics and financial performance. The Capital Adequacy Ratio (CAR) exhibited a coefficient of 0.048}, suggesting that a one-unit increase in CAR corresponds to a 4.8\\% increase in ROA, \u003cem\u003eceteris paribus\u003c/em\u003e. This result substantiates the hypothesis that higher capital buffers enhance profitability by strengthening institutional resilience and confidence, aligning with the empirical work of Abdrahamane and Kargbo (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Conversely, this finding diverges from the conclusions reached by Tekalign (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Lealem (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSimilarly, the Minimum Paid-up Capital Requirement (MPCR) demonstrated a positive and statistically robust effect, with a coefficient of 0.015. This outcome supports the notion that regulatory capital mandates are positively associated with bank profitability, consistent with studies like Addisu (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), though it contradicts Abraham (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)'s finding of insignificance.\u003c/p\u003e\u003cp\u003eFrom liquidity perspective, mandated reserve and lending policies also played a significant role. The Legal Reserve Requirement (LRR) showed a coefficient of 0.24, indicating that a one-unit rise in LRR is associated with a 2.4% increase in ROA. This strong positive association suggests that elevated reserve holdings, contrary to the expectation of reduced lending capacity, positively contribute to profitability by signalling greater liquidity and financial soundness, thereby attracting investor confidence. This supports Lealem (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) but contrasts with the negative relationships reported by Abraham (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Addisu (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFrom an operational perspective, the Loan-to-Deposit Ratio (LDR), a key measure of asset utilization, recorded a coefficient of 0.024. This positive relationship validates the principle that efficient asset intermediation, reflected by a higher proportion of deposits channelled into income-generating loans, significantly improves the bank's return on assets. Furthermore, compliance with the Foreign Currency Regulation (FCR), with a coefficient of 0.056, was found to contribute positively to ROA, underscoring the importance of sound management in mitigating foreign exchange risks and enhancing operational efficiency.\u003c/p\u003e\u003cp\u003eFrom Strategic regulatory mandates perspective, such as the Branch Expansion Requirement (BER), designed to support the national Growth and Transformation Plan (GTP), yielded a coefficient of 0.013. This finding suggests that, contrary to the negative and insignificant relationship observed by Lealem (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), a mandated increase in physical presence positively impacts profitability, likely by improving market access and financial inclusion in line with national objectives.\u003c/p\u003e\u003cp\u003eIn contrast, the study identified Non-Performing Loans (NPLs) as a principal drag on profitability. The regression output for NPLs was a negative and statistically significant coefficient of -0.089. This figure quantitatively reflects the adverse impact of deteriorating asset quality: every one-unit increase in NPLs is associated with an 8.9\\% decline in ROA, due to increased provisioning and reduced interest income.\u003c/p\u003e\u003cp\u003eConversely, the Liquidity Requirement presented a negative and statistically insignificant effect on ROA, an outcome that contrasts sharply with the positive and significant findings of several empirical studies, including Kiplagat (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Tekalign (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This counter-intuitive result may be attributable to the opportunity cost of holding excessive liquid reserves, which limits the volume of higher-yielding loan assets.\u003c/p\u003e\u003cp\u003eThe analysis of macroeconomic variables revealed a lack of statistical causality with bank profitability. Both Inflation and Gross Domestic Product (GDP) demonstrated a negative but statistically insignificant relationship with ROA. The insignificance of inflation suggests that commercial banks may possess effective risk management strategies to hedge against rising costs or the erosion of asset value. Similarly, the lack of a significant link between GDP growth and ROA implies that bank profitability is primary impact is likely exacerbated by structural constraints within the emerging economy, such as limited financial inclusion or a narrow financial product base, which dampen the transmission mechanism of GDP growth to banking sector profits.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of this study provide important insights into the impact of regulatory variables on the financial performance of commercial banks in Ethiopia. The analysis revealed that several key regulatory measures established by the National Bank of Ethiopia (NBE) play a significant role in shaping bank performance.\u003c/p\u003e\u003cp\u003eThe results indicate that the Capital Adequacy Requirement (CAR) has a positive and significant effect on the performance of commercial banks in Ethiopia. This implies that banks maintaining higher capital reserves tend to achieve better financial outcomes. Adequate capitalization serves as a financial cushion, enabling banks to absorb potential losses, maintain solvency, and strengthen overall stability.\u003c/p\u003e\u003cp\u003eSimilarly, the Minimum Capital Requirement (MCR) was found to have a positive and statistically significant effect on bank performance. This finding suggests that an increase in a bank\u0026rsquo;s paid-up capital enhances its competitive position and resilience. Higher capital levels allow banks to undertake more profitable investments, expand operations, and mitigate risks, ultimately improving profitability and long-term sustainability.\u003c/p\u003e\u003cp\u003eThe results also show that the Loan-to-Deposit Ratio (LDR) exerts a positive and significant influence on performance. This indicates that efficient utilization of deposits to issue loans boosts income through interest revenue, thereby improving profitability. Banks that manage their loan portfolios effectively can achieve better returns without compromising liquidity.\u003c/p\u003e\u003cp\u003eFurthermore, the Legal Reserve Requirement (LRR) demonstrated a positive and significant impact on the performance of commercial banks. Compliance with reserve requirements ensures adequate liquidity and enhances confidence among customers and investors. Maintaining these reserves allows banks to manage risks effectively while sustaining financial stability and operational efficiency.\u003c/p\u003e\u003cp\u003eThe study also established that Branch Expansion (BE) and Foreign Currency Ratio (FCR) both have positive and significant effects on bank performance. Expanding branch networks increases market outreach and financial inclusion, while efficient foreign currency management improves stability and supports profitability in international transactions.\u003c/p\u003e\u003cp\u003eConversely, Non-Performing Loans (NPLs) were found to have a negative and significant impact on bank performance. This implies that rising NPL levels weaken profitability and liquidity by increasing loan-loss provisions. Effective credit risk management is therefore essential to sustain financial health and investor confidence.\u003c/p\u003e\u003cp\u003eOn the other hand, Liquidity Requirement (LQ), Inflation (INF), and Gross Domestic Product (GDP) displayed negative but insignificant relationships with bank performance. Although these variables are vital for macroeconomic analysis, their influence on bank profitability was limited during the study period. Excess liquidity may constrain lending, while macroeconomic factors like inflation and GDP growth may not immediately translate into improved financial performance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRecommendations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on the major findings, the researcher forwards the following recommendations:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStrengthen Regulatory Frameworks\u003c/strong\u003e\u003cp\u003eThe National Bank of Ethiopia should periodically review and update policies related to capital adequacy, reserve requirements, and lending ratios to maintain a balance between financial stability and sectoral growth.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eOptimize Loan Management Policies\u003c/strong\u003e\u003cp\u003eCommercial banks should maintain an optimal loan-to-deposit ratio to ensure profitability while preserving liquidity. Prudent lending and credit appraisal practices must be reinforced to sustain asset quality.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePromote Branch Expansion and Financial Inclusion\u003c/strong\u003e\u003cp\u003eNBE should encourage strategic branch expansion, especially in underserved rural areas, to enhance access to financial services, foster inclusion, and stimulate local economic growth.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEnhance Capital Requirements\u003c/strong\u003e\u003cp\u003eThe minimum paid-up capital requirements should be revised periodically to reflect inflation and market developments, ensuring banks maintain strong capital bases and competitive strength.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eImprove Foreign Currency Management\u003c/strong\u003e\u003cp\u003ePolicies that stabilize foreign currency allocation and support effective foreign exchange risk management should be strengthened to enhance profitability and trade competitiveness.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eReduce Non-Performing Loans (NPLs)\u003c/strong\u003e\u003cp\u003eBoth NBE and commercial banks should intensify credit risk management practices, strengthen loan monitoring mechanisms, and implement robust recovery and collection systems to minimize NPL levels.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEfficient Liquidity Management\u003c/strong\u003e\u003cp\u003eAlthough liquidity showed an insignificant effect, banks should balance short-term obligations with long-term profitability, while NBE allows for flexible and responsive liquidity regulations.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMonitor Macroeconomic Indicators\u003c/strong\u003e\u003cp\u003ePolicymakers should continuously evaluate the long-term effects of inflation and GDP on the banking sector to design proactive policies that support macro-financial stability.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSuggestions for Future Research\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFuture studies should consider incorporating additional regulatory variables beyond those examined in this study, as NBE supervises other financial institutions such as insurance companies, microfinance institutions, and non-bank financial intermediaries. Including these sectors may offer a broader understanding of Ethiopia\u0026rsquo;s financial ecosystem.\u003c/p\u003e\u003cp\u003eMoreover, researchers are encouraged to include control variables such as political stability, institutional quality, and global economic conditions\u0026mdash;including trade policies and currency fluctuations\u0026mdash;to assess external influences on bank performance. Expanding the scope in this way would provide a more comprehensive and nuanced understanding of the factors affecting the profitability and stability of commercial banks in Ethiopia. This will help NBE and policymakers design more targeted and effective strategies for enhancing the efficiency and resilience of the Ethiopian banking sector.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The author declare that this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are included in this article Additional data or materials can be made available from the corresponding author upon reasonable request.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent to participate: Not applicable.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo human participants were involved in this study; therefore, consent to participate was not required.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent to publish: Not applicable.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study does not contain any individual-level data or identifiable personal information requiring consent to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is based on publicly available financial data from commercial banks and does not involve human participants or sensitive personal data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdrahamane S, Kargbo M. 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Econ Model. 2019;83:125\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYusuf M, Shikur Z. The effect of prudential regulation on the performance of commercial banks in Ethiopia. J Acc Financial Manage. 2023;9(2):22\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Banks, regulations, banking sector, Financial Performance","lastPublishedDoi":"10.21203/rs.3.rs-8036069/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8036069/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the impact of National Bank of Ethiopia (NBE) regulations on the profitability of commercial banks, using Return on Assets (ROA) as the dependent variable. The study uses an explanatory research design and a quantitative approach. It applied a balanced random effect panel regression model on data from 17 commercial banks over 13 years (2012\u0026ndash;2024). The model took into account eight regulatory variables from the NBE and two macroeconomic indicators as control factors. The analysis shows that several regulatory and operational factors positively influence the profitability of commercial banks. These include the Capital Adequacy Ratio (CAR), Minimum Paid-up Capital Requirement (MPCR), Loan-to-Deposit Ratio (LDR), Legal Reserve Requirement (LRR), Branch Expansion Requirement (BER), and Foreign Currency Regulation (FCR). On the other hand, the study found that Non-Performing Loans (NPLs) have a negative and significant effect on ROA. Liquidity requirements have a negative but statistically insignificant effect, indicating they create an opportunity cost without a significant impact. Moreover, the macroeconomic indicators of Inflation and Gross Domestic Product (GDP) both show a negative and insignificant relationship with profitability. This suggests that bank performance is mainly influenced by internal factors and compliance with regulations rather than general economic trends. Based on these findings, the study suggests that commercial banks focus on strict compliance with capital adequacy and minimum paid-up capital requirements as key strategies to improve profitability.\u003c/p\u003e","manuscriptTitle":"Impact of National Bank Regulations on Financial Performance of Commercial Banks in Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 09:16:01","doi":"10.21203/rs.3.rs-8036069/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0fd5b4c7-990b-4c6f-94d8-7370e67ba0b3","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T13:23:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 09:16:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8036069","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8036069","identity":"rs-8036069","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}