Financial intermediation is very important for economic growth. According to financial expert, Pakistan requires a secure and well-organized banking system. Despite the fact, that Pakistan has built some progress because deregulation of its banking industry, bank margins remain certainly sky- scraping. Financial institutions have different ways to measure its performance. One of the most basic is **net** **interest** **margin**. It is calculated for a period of time, quarterly or a yearly, and is expressed as a proportion. Bank **interest** **margin** is also known as the **net** yield on **interest** earning assets. It is often confused that the **Net** **interest** **margin** and spread is the same perception while spread is the difference between lending and borrowing rates without earning assets. **Interest** **margin** is used to find out the profitability of a bank by investing and lending activities over a period of time. If generated **interest** income from the investing yield is low it means that the cost charged (**interest** expenses) are greater than **interest** income generated by investing assets. High **net** **interest** **margin** does not a mean high profitability if difference between **interest** income and **interest** expenses is high than people do not invest in banking industry and prefer to keep their saving in their home which shows the inefficiency of banks.

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Table 1 shows descriptive statistics with number of observation, minimum, maximum, mean value and standard deviation for the total period of data. As it can be seen from the table above, **Net** **interest** **margin** ratio has mean value of .034 or 3.4 percent which is ranked as a moderate average. The standard deviation is .016 or 1.6 percent which is not far from mean average and show moderate variability. Capital ratio has the lowest mean value of .072 or 7.2 percent, which shows moderate variability, and the moderate standard deviation of .006 or 0.6 percent. This shows that the data are consistent because the standard deviation value is not much far from the mean value. Liquid ratio has mean value of .237 or 23.7 percent. The minimum and maximum value is .169, respectively .326 (16.9 percent respectively 32.6 percent) which shows that banking system of Kosovo is liquid. Loan to deposit ratio has a mean value .778 or 77.8 percent with standard deviation of .0477 or 4.7 percent. These result shows that banking system in Kosovo is using their funds in a satisfied level for providing loan to their costumer, which reflect with a higher **interest** income. Provision for loan losses to total **net** income has a mean value of .286 or 28.6 percent of total **net** income, with standard deviation of .176 or 17.6%. Non-**interest** income to total income has a mean value of .201 or 20.1% of total income, with standard deviation of 0.176 or 1.7 percent. According to these we can conclude that non-**interest** income contribute to total income in average of 20.1% of total income.

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In this study, there are 10 financial ratios selected namely, profitability ratio, Aset Quality Ratio, Size, **Net** **Interest** **Margin**, Leverage, Liquidity Ratio, Loan Growth Ratio, Market ratio in industry, Inflation rate and also bank **interest** rate. To identify the determinants influencing **net** **interest** **margin** of commercial bank, this study has chosen multiple regression analysis. Secondly, this study also the effects of credit risk on **net** interset **margin**.

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A number of studies, underpinned by the theoretical frameworks discussed, have examined the determinants of bank **net** **interest** margins and profitability across various markets. Hanweck and Ryu (2005:7), in their study to determine the sensitivity of bank **net** **interest** margins and profitability to credit, **interest** rate and term structure shocks across bank product specialisation, observed that the models’ fundamental assumption is that the NIMs should be maximised for the benefit of the shareholders. Almost all models developed by various researchers and authors consider how internal factors (a bank’s specific characteristics) and/or external factors (financial industry and economic environment) impact on the margins and how to maximise them. The most prominent of the empirical models that have been developed over time which form the basis for the study of **net** **interest** margins are the Firm Theoretic model and the Dealer model. The objective of both models is to derive an optimal **net** **interest** **margin** for a bank in a given competitive environment (Hanweck and Ryu, 2005:7).

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Several studies have also been done related to level of **net** **interest** **margin** in Indonesia, including Dermiguc - Kunt and Huizinga (1998) which found that the average banking **margin** Indonesia period 1988-1995 was 3.6%, higher than other ASEAN countries such as Singapore ( 2.2%) and Malaysia (2.7 %). Research conducted Lin et al (2012) also give results that in the period 1997 to 2005 the average value of bank's **net** **interest** **margin** in amount of 6.36% in Indonesia is the highest value among other Asian countries that become the research sample.The high value of the **net** **interest** **margin** becomes an important issue that must be resolved by banks in Indonesia. Research has been proved as well as providing a comparison of **net** **interest** **margin** in Indonesia and other countries. This result requires banks in Indonesia to be able to work more efficient and produces better performance to create better quality in banking industry.

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In this study, we investigate factors affecting **net** **interest** **margin** (NIM) of commercial banks in Turkey. Especially, our results highlight the relation between unconventional monetary policy shocks and bank margins. To this end, first, we conduct an identification analysis about which parameters of asymmetric **interest** corridor framework are important in explaining variations in NIM. Using industry- level data, we show that there exists a pass through from BIST interbank overnight repo/reverse repo market rate and weighted average cost of funding (WACF) to bank loan and deposit rates. As a result of reduced-form Vector Autoregression (VAR) analysis we find the existence of a transmission mechanism from BIST rate and WACF to commercial loan rate, consumer loan rate and deposit rate. Same pass through to loan and deposit rates is also shown in individual bank level with the Panel Vector Autoregression (Panel VAR) analysis in the case of 16 commercial banks in Turkey during the period 2011Q1-2016Q1. After the identification analysis, we examine the relationship between NIM and policy rates through System Generalized Method of Moments (GMM) techniques by controlling bank specific, industry related and macroeconomic factors. We find that a change in the monetary policy rate has significant and positive impact on NIM. Among bank-specific factors, equity ratio and operating expenses are found to be significantly affecting NIM during the sample period. Our empirical findings also stress the significance of lag values of NIM. Estimations conducted with standardized variables indicate that economic significance of lag values and bank specific variables are larger than that of policy.

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The global financial crunch became a source of great turmoil for the banking institutions of developed countries. By making comparison among the collapses of global financial giants of developed economies, there was less number of bank crashes in developing economies. Hence, the emerging economies follow the catching up process of 2007-2008 global financial crises. This paper seeks to presents the first inclusive evaluation of the **Net** Stable Funding Ratio (NSFR) and estimates the ratio for the developed and developing economies by making a sharp comparison. In this research study, various strategies are observed for those financial institutions whose value is less than the threshold level, thus to meet NSFR and assess about impact these changes bring on **Net** **Interest** **Margin** a financial institution. The financial crisis of 2007–2009 witnessed that in various countries the displacement of wholesale funding markets of banks was the core reason for the sufferance of shortages of liquidity. The financial institutions were unable to overturn their debts as they financed long-term assets with debt of short –term thus making these banks most exposed to risk (Acharya and Merrouche, 2013; Huang and Ratnovski, 2011; Afonso et al., 2011; Diamond and Rajan, 2009; Gorton, 2009; Brunnermeier and Markus, 2009). However, NSFR deal with funding risk and is devised to endorse structural amendments developing profiles of risks for banks attend to be more stable by funding in longer-term assets. Banks which do not meet the NSFR require condensing assets entailing stable funding along with an increase in sources of stable funding. This empirical study draws attention to the tradeoffs between liquidity reforms, financial institution risk and profitability by making comparison among developing and developed countries. The NSFR is devised to promote banks for holding more high-quality of liquid assets and use stable sources to boost its funding. Such changes will enhance the buoyancy of banks in the periods of stress.

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It can be seen that CAR and NIM variables (in lag form) have positive effects on the dependent variable ROA and both are significant at 5% significance level, while variable NPL (in lag form) has a significant negative effect on banking profitability. It is shown, by the regression equation obtained, that the variables having the strongest positive effect and being significant on banking profitability (ROA) are **net** **interest** **margin** (NIM) with regression coefficient of 0.202 and non-performing loans (NPL) with regression coefficient of -0.270 while capital adequacy ratio (CAR) with regression coefficient of 0.001 but not significant.

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The 2007-09 financial crisis revealed that the investors in the financial market were more concerned about the future as opposed to the current capital adequacies for banks. Stress testing promises to complement the regulatory capital adequacy regimes, which assess a bank’s current capital adequacy, with the ability to assess its future capital adequacy based on the projected asset-losses and incomes from the forecasting models from regulators and banks. The effectiveness of stress-test rests on its ability to inform the financial market, which depends on whether or not the market has confidence in the model-projected asset-losses and incomes for banks. Post-crisis studies found that the stress-test results are uninformative and receive insignificant market reactions; others question its validity on the grounds of the poor forecast accuracies using linear regression models which forecast the banking-industry incomes measured by Aggregate **Net** **Interest** **Margin**. Instead, our study focuses on NIM forecasting at an individual bank’s level and employs both linear regression and non-linear Machine Learning techniques. First, we present both the linear and non-linear Machine Learning regression techniques used in our study. Then, based on out-of-sample tests and literature-recommended forecasting techniques, we compare the NIM forecast accuracies by 162 models based on 11 different regression techniques, finding that some Machine Learning techniques as well as some linear ones can achieve significantly higher accuracies than the random-walk benchmark, which invalidates the grounds used by the literature to challenge the validity of stress-test. Last, our results from forecast accuracy comparisons are either consistent with or complement those from existing forecasting literature. We believe that the paper is the first systematic study on forecasting bank-specific NIM by Machine Learning Techniques; also, it is a first systematic study on forecast accuracy comparison including both linear and non-linear Machine Learning techniques using financial data for a critical real-world problem; it is a multi-step forecasting example involving iterative forecasting, rolling-origins, recalibration with forecast accuracy measure being scale-independent; robust regression proved to be beneficial for forecasting in presence of outliers. It concludes with policy suggestions and future research directions. Keywords: Machine Learning; time series forecasting; forecast accuracy; bank capital; stress testing; **net** **interest** **margin**; systemic risk.

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regulators in Basel-III. The results show that the **interest** **margin** rate is increasing with the passage of time by implementing conservative capital buffer condition on commercial banks in the short run, other things held constant. These results also reflect that as the banks increase their capital buffer, their funds remain unused, which leads to a decrease in profits. However, the expenses remain constant, which leads to an increase in the **net** **interest** **margin** as suggested in the mean-variance framework. The subgroups dummies are used to inspect the effect of well, adequately, under, significantly under and critically undercapitalized commercial banks. The results show that the influence of capital buffer on the **net** **interest** **margin** is not similar in all subgroups. The influence of critically undercapitalized banks is different to charge **net** **interest** **margin** as compared to other groups in the short run, ceteris paribus. The results show that there is a positive relationship between bank risk and the **net** **interest** **margin**. According to the economic theory, the higher the risk is, the higher the return will be, other things remain. The findings show that the relationship between common equity buffer and **net** **interest** **margin** is positive, which indicates that as the common equity buffer of banks increases, the bank **net** **interest** **margin** also increases, other things remain similar. The impact of common equity buffer is negative in the case of critically undercapitalized banks, which indicates that the banks which have a capital problem cannot charge higher **interest** **margin**, other things remain constant. In other words, the negative association between **net** **interest** **margin** and common equity buffer may be owing to the availability of limited borrowers caused by lack of confidence lead by the bank poor condition in the market. The effect of common equity buffer on the total risk is positive and significant in case of well, adequately, and under capital banks. The findings show that tier one capital has a positive impact on **net** **interest** **margin**, which indicates that by increasing capital buffer, the profitability decrease. The banks want to earn their target profits by making limited loans for which they charge higher **interest** **margin**. The findings provide valuable information for decision makers, regulators and bankers.

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Apart from the credit risk and quality of management, degree of risk aversion and opportunity cost of holding liquid reserve of banks followed by dora and ocbr respectively are also found statistically significant in explaining the variation of **net** **interest** **margin** ratio followed by nim being dependent variable to show the changes in **net** **interest** income of banks as per the output revealed by Fixed effect regression model. The degree of risk aversion measured by capital ratio calculated with dividing equity capital by total assets of bank is found positively related with **net** **interest** **margin** as banks increase the NIM to cover the higher cost of equity financing compared to debt financing. In addition, Opportunity cost of holding bank’s reserve measured by dividing cash due from other banks by total assets is found inversely related with **net** **interest** **margin** ratio as higher liquid reserve increases the opportunity cost for banks and thus reduces the profitability of banks measured by NIM. On the contrary, all other variables are found statistically insignificant in explaining the variation of **net** **interest** **margin** of bank. The overall R square value of 0.6047 divulges that 60.47% variability in **net** **interest** **margin** has been explained by the fitted regression model estimated by Fixed effect method. The F - value of 7.84 is also found statistically significant

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The 2007-09 financial crisis revealed that the investors in the financial market were more con- cerned about the future as opposed to the current capital adequacies for banks. Stress testing promises to complement the regulatory capital adequacy regimes, which assess a bank’s current capital ad- equacy, with the ability to assess its future capital adequacy based on the projected asset-losses and incomes from the forecasting models from regulators and banks. The e ff ectiveness of stress-test rests on its ability to inform the financial market, which depends on whether or not the market has confi- dence in the model-projected asset-losses and incomes for banks. Post-crisis studies found that the stress-test results are uninformative and receive insignificant market reactions; others question its validity on the grounds of the poor forecast accuracies using linear regression models which fore- cast the banking-industry incomes measured by Aggregate **Net** **Interest** **Margin**. Instead, our study focuses on NIM forecasting at an individual bank’s level and employs both linear regression and non-linear Machine Learning techniques. First, we present both the linear and non-linear Machine Learning regression techniques used in our study. Then, based on out-of-sample tests and literature- recommended forecasting techniques, we compare the NIM forecast accuracies by 162 models based on 11 di ff erent regression techniques, finding that some Machine Learning techniques as well as some linear ones can achieve significantly higher accuracies than the random-walk benchmark, which in- validates the grounds used by the literature to challenge the validity of stress-test. Last, our results from forecast accuracy comparisons are either consistent with or complement those from existing forecasting literature. We believe that the paper is the first systematic study on forecasting bank- specific NIM by Machine Learning Techniques; also, it is a first systematic study on forecast accuracy comparison including both linear and non-linear Machine Learning techniques using financial data for a critical real-world problem; it is a multi-step forecasting example involving iterative forecast- ing, rolling-origins, recalibration with forecast accuracy measure being scale-independent; robust regression proved to be beneficial for forecasting in presence of outliers. It concludes with policy suggestions and future research directions. Keywords: Machine Learning; time series forecasting; forecast accuracy; bank capital; stress testing; **net** **interest** **margin**; systemic risk.

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The aim of this study was to analyze the factors that affect the implementation of banking intermediation include Capital, **Net** **Interest** **Margin**, Credit Risk and Profitability. The methods used are descriptive and verificative, with secondary data from financial statements all over 26 Indonesian Regional Development Banks as a research object’s units. Data analysis technique is the multiple linear regression, hypothesis testing while using t - test to examine the effect of partial variables and test - F to examine the effect of variables simultaneously with a significance level of 5 %. Based on the results it is concluded that partial NIM and ROA have positive and significant effects on LDR. NPL has positive effect but no significant effect to LDR. While the CAR has negative effect but no significant effect to LDR. Simultaneously CAR, NIM, NPL and ROA significantly influence the level of influence of LDR with 40.5 % while the remaining 59.5% thought to be influenced by other variables not examined in this study.

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Abstract- This paper analyzes the factors affecting the commercial bank of Ethiopia’s (CBE’s) **net** **interest** margins during 2005 to 2014, a period characterized by increasing the bank’s **net** **interest** **margin**. The pooled ordinary multiple regression models are used to estimate the results without compromising the classical linear regression assumptions. In line with findings in the previous literature, this paper finds that capital adequacy (risk aversion), credit risk, operating costs, degree of competition (Lerner index) and deposit growth rate are the most important drivers of CBE’s **net** **interest** margins. Almost all variables in the model indicates a positive and highly significant association ship with **net** **interest** margins, and are found to be the most important bank specific factors that determine the **net** **interest** **margin** of the bank, CBE. The results of the study also suggests that high concentration led to lower competition, and thereby increase the **net** **interest** margins of banks, especially the dominant bank like CBE in case of Ethiopia. All in all, the results suggests that there has to be a measure to be taken by the sector to reduce the banks concentration ratio, operating costs, risk premium on credits, and increase the level of capital to offer competitive **interest** margins and fairly shared growth rates in deposits among others. In doing so, this paper conclude that further structural reforms and merger or consolidation enter alia may lower CBE’s **net** **interest** margins and share the market potentially fairly to other private banks operating in the industry.

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The aim of this paper is to define the influencing factors of **net** **interest** **margin** in Turkish banking sector. Within this scope, the effects of 14 explanatory variables on **net** **interest** **margin** were analyzed. Moreover, quarterly data for the period between 2003 and 2014 was used in this study. After that, we created a model by using multivariate adaptive regression splines method so as to illustrate the relationship. The major finding in this study is that **net** **interest** **margin** is negatively related with non-**interest** income, non-performing loans, total assets and exchange rates. According to these results, it was determined that banks should focus on the quality of the assets in order to increase **net** **interest** **margin**. In addition to this situation, volatility in exchange rates should also be taken into the consideration by the banks for this situation.

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Accordingly, the size –**net** **interest** **margin** relationship is expected to be non linear ( Ali et al. 2011). On the other hand, Naceur (2003) says that big banks tend to lower margins as a result of economies of scale. Besides, Ho and Saunders (1981), Maudos and Solos (2009) find a positive relationship because the larger the transaction, the larger the potential loss will be. Funcagova and Poghosyan (2009), Maudos and Fernando De Guevara (2004), Angbazo (1997), among others, report a negative association between bank size and **interest** margins, pointing to the cost reduction attributed to economies of scale.

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4 **interest** costs funding with the total cost of loan **interest**. In the banking world it is called **Net** **Interest** **Margin**. This ratio is used to measure the ability of bank management to manage productive assets to generate **net** **interest** income. **Net** **interest** income is derived from **interest** income less **interest** expense. This ratio shows the ability of banks to obtain operating income from funds placed in loans. The higher the NIM shows the more effective the bank is in placing productive assets in the form of credit.

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This study aimed to examine the effect of Capital Adequacy Ratio (CAR), Non- Performing Loan (NPL), Operating Efficiency (BOPO), **Net** **Interest** **Margin** (NIM), and Loan to Deposit Ratio (LDR) on Return on Assets (ROA).The population used in this study was Private Foreign Exchange National Bank in Indonesia that listed on Indonesia Stock Exchange (IDX) in 2013-2016. By using Purposive Sampling method, it could be obtained 44 samples of 15 banking companies which observed in 2013-2016. The analysis technique employed was Panel Data Regression. Research findings indicated that Capital Adequacy Ratio (CAR), Non-Performing Loan (NPL), Operational Efficiency (BOPO), **Net** **Interest** **Margin** (NIM), and Loan to Deposit Ratio (LDR) simultaneously had effect on Return on Assets (ROA). Then, partial test of each variables were CAR and LDR had not significant effect on ROA, while NPL, BOPO, and NIM have significant effect on ROA.

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Log of total asset as a measure of bank size has postive and singinficant effect on **net** **interest** **margin** at 1% as revealed in table 4 (cofficient 0.575930 and p value 0.0048). This indicates that a 1% increase in bank size will increase **net** **interest** marigin by 57.59 centsand vice versa. The standared error of bank size is highest than other variables (0.198476) which shows that the bank size of Ethiopian commercial banks is deviated according to their total assets and positive sign indicates that the banks realizing economies of scale have better **net** **interest** **margin**. This means banks can allocate fixed costs over a greater asset base, thereby reducing their average costs or reduce risk by diversifying operations across product lines, sectors, and regions to promote their profitability. The same result is found by Almarzoqi and Naceur (2015) 10

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The original mandate of MFIs is to address the financing needs of the poor at a lower cost. However, majority of these MFIs rather unfortunately charge very high **interest** rates on their loans and reward suppliers of their deposits with low **interest** rates resulting in high **net** **interest** margins. Previous studies on **interest** spreads and margins in Ghana have mostly concentrated on commercial banks and not so much on microfinance institutions. As a result this study was conducted to investigate into the factors which determine the **net** **interest** margins of microfinance institutions in Ghana. The study covered 20 MFIs over the period 2004 – 2013. For microfinance specific variables, MFI size was found to be positively and significantly related to **interest** margins. The result, however, revealed that a higher percentage of women borrowers is associated with a lower **interest** **margin** due to the variable’s negative and significant effect on NIM. Operating expense was found to have a negative relationship with NIM and it was significant in determining **interest** margins. In this study, it was found to be the next biggest determiner of **interest** margins, next to inflation. We also observed that the ownership variable has a positive significant relationship with **net** **interest** margins, signaling the important role the variable plays in determining **net** **interest** **margin** of microfinance institutions in Ghana. In the case of macroeconomic variables, the study found a negative significant relationship between **interest** margins and inflation. This however deviates from theory, which suggests that MFIs risk making loses during inflationary periods. It also suggests failure on the part of MFIs management to anticipate inflation and duly factor it into the pricing of their lending and deposit rates. This study concludes that the high **interest** margins observed within the Ghanaian microfinance

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