Academic literature on the topic 'The Net NPLs to Total Loans Ratio'

The aims of this study is to be analyzed the comparative position of all the fours banking categories on non-performing loans in Bangladesh and also investigate what are the different causes of nonperforming loans in banking sectors of Bangladesh. This study will cover based on secondary data, which has been collected from the annual reports of the Bangladesh Bank during the 2006-2017 for 12 years and total NPL, NPL to Total Loans ratio and Trends of net NPL to total loans ratio are taken as variables of the study. The data are analyzed by using descriptive Statistics, ANOVA Test and the Test of Homogeneity of Variances. In this paper it is found that there is significant difference at the performances of four categories of banks on the Non-performing loans and there is no Homogeneity of Variances of total NPL, NPL to Total Loans ratio and Trends of net NPL to total loans ratio among all the banking categories. The SCBs and DFIs have continues maintain the high level of NPL and NPLs ratio to total loans then PCBs and FCBs and the NPLs recovery record during the study period has witnessed some sign or signal for improvement, due to number of step are taken with regard to internal restructuring for strengthen the loan recovery mechanism and initiative taken for written off measurement in recent time for all the banking categories. The suggestions and recommendation have been given for improve the present situation of nonperforming loans in the banking sectors in Bangladesh.

The aims of this study is to be analyzed the comparative position of all the fours banking categories on non-performing loans in Bangladesh and also investigate what are the different causes of non-performing loans in banking sectors of Bangladesh. This study will cover based on secondary data, which has been collected from the annual reports of the Bangladesh Bank during the 2006-2017 for 12 years and total NPL, NPL to Total Loans ratio and Trends of net NPL to total loans ratio are taken as variables of the study. The data are analyzed by using descriptive Statistics, ANOVA Test and the Test of Homogeneity of Variances. In this paper it is found that there is significant difference at the performances of four categories of banks on the Non-performing loans and there is no Homogeneity of Variances of total NPL, NPL to Total Loans ratio and Trends of net NPL to total loans ratio among all the banking categories. The SCBs and DFIs have continues maintain the high level of NPL and NPLs ratio to total loans then PCBs and FCBs and the NPLs recovery record during the study period has witnessed some sign or signal for improvement, due to number of step are taken with regard to internal restructuring for strengthen the loan recovery mechanism and initiative taken for written off measurement in recent time for all the banking categories. The suggestions and recommendation have been given for improve the present situation of non-performing loans in the banking sectors in Bangladesh.

This study aims to analyse the effect of BOPO, Loan to Deposit Ratio (LDR), Capital Adequacy Ratio (CAR), Net Interest Margin, Oil Price, inflation, and Interest Rate on Non-performing Loans (NPL) of banks listed on the Indonesia Stock Exchange during 2016-2020. The total population of banks listed on the IDX is 46 banks. In contrast, the sample in this study is part of the population, which is 32 banks. This study is quantitative, so the data analysis method used is a statistical method—the analysis of these data using Econometric Views (Eviews) software version 12.0. The results showed that BOPO has a positive and significant effect on bank NPLs, LDR has a positive but not significant effect on NPLs, CAR has a negative and significant effect on NPLs, NIM has a negative and significant effect on NPLs, Oil Price has a positive and significant effect on NPLs, inflation has a negative but not significant effect on NPLs, and Interest Rate has a positive and significant effect on NPLs. Banks must pay attention to the reference and rules of the regulator, the Financial Services Authority, as a guideline where the maximum limit for the NPL ratio is 5% so that the quality of banking credit can be maintained. Investors it is expected to be able to make considerations in investing by analyzing the company's condition through financial statements published by banks by looking at the ratio of Non-Performing Loans as a crucial consideration.

The banking sectors of G7 countries play a crucial role in supporting their economies, yet they remain fragile and susceptible to shocks that may threaten global financial stability. Non-performing loans (NPLs) represent a significant risk to the overall health of the banking system. This study investigates the factors that influence NPLs in the G7 banking sector during the period from 2019 to 2022. We assess the impact of both macroeconomic variables (economic growth, unemployment, and lending interest rates) and bank-specific variables (total assets, net interest income, Tier 1 capital ratio, and credit growth) on NPLs by employing a panel data regression model. The results indicate that economic growth and lending interest rates have a negative and statistically significant effect on NPLs, while the unemployment rate does not exhibit a significant impact. Among the bank-specific variables, only credit growth demonstrates a negative and significant relationship with NPLs. These findings offer valuable insights into the determinants of NPLs and can serve as a foundation for designing more effective risk mitigation strategies within the G7 banking sector.

Non-Performing Loans (NPL) is a financial ratio that shows the credit risk faced as a result of granting credit and investment funds on different portfolios. This study aimed to analyze the determinants of Non-Performing Loans (NPL) before and during the Covid 19 pandemic at Bank XYZ. NPL can be caused by internal or external factors from Bank XYZ. The analytical method used is the Mixed method which combines quantitative and qualitative analysis. Data analysis used multiple linear regression method using time series data for the 1st quarter of 2013- 4th quarter of 2020. The analysis method used multiple linear regression to see the influence of internal factors are total credit, Return on Equity, Loan to Deposit Ratio, Net Interest Margin, total assets, BOPO, condition dummy before after transformation and external factors are Benchmark Interest Rate, exchange rate (exchange rate), Inflation, Industry Production Index, dummy conditions before and during the Covid 19 pandemic on Bank XYZ's NPL. The estimated regressions are the overall NPL, the Small Medium Enterprise (SME) Business Segment NPL, the Small Medium Enterprise (SME) Business Segment for the wholesales business sector, and the Small Medium Enterprise (SME) Business Segment NPL for the Retailer business sector. Data processing using E-views software version 9.0. The result of this research are factors that affect the overall NPL: Dummy Transformation, Net Interest Margin, and total assets, for the NPL for the Small and Medium Enterprises (SME) segment: total assets, dummy transformation and Net Interest Margin (NIM), NPL for the Small and Medium Enterprises (SME) business sector Wholesales: BOPO NIM, Lending growth, total assets, ROE and Dummy Transformation, for the NPL segment of Small and Medium Enterprises (SMEs) Retailer business sector: Net Interest Margin (NIM) and total assets. The impact of the COVID-19 pandemic on NPLs was most felt by the NPL all, SME business segment credit and the wholesales business sector. Keywords: covid-19 pandemic, NPL, NPL SME, retailer, wholesales.

The aim of this study is to examine the determinants of firm value for listed banks in Pakistan. This study is based on secondary data extracted from the annual reports of listed banks in Pakistan with quarterly data comprising 512 sample observations from 2015 to 2021. Panel data estimation techniques were employed for the analysis. The findings of the study revealed that all determinants influence firm value except liquid assets to total assets (LATA). The stock price of listed banks in Pakistan is continuously falling, which causes the stock's value to change from being overpriced to being undervalued. Nonperforming loans (NPLs) and the cost-to-income ratio (CIR) reduce firm value, while increases in the net interest margin (NIM) and capital adequacy ratio (CAR) enhance firm value. Further, NPLs and the CIR have a negative relationship with firm value. However, CAR, LATA, and NIM have positive associations with the firm value. The study concluded that the average capital adequacy ratio of listed banks in Pakistan is 10.5%, which is higher than the minimum requirement set by the regulator. This indicates that CAR helps to increase firm value in listed banks in Pakistan. The study will be useful to policymakers, regulators, and the banking sector in evaluating the major determinants that affect firm value.

India’s banking sector is facing the toughest challenge in recent times in terms of distressed loans. Higher write offs and provisioning has adversely affected growth in advances.Poor earnings growth by companies, slow pace of investments, risk aversion of banks due to rising bad loans, and availability of alternative funding sources for corporates pulled down credit growth during the year. A high and rising proportion of banks stressed loans, particularly those of public sector banks (PSBs) and the resulting increase in NPAs provisioning has weighed down credit growth. Hence it becomes important to analyse the banking sectors performance through appropriate criteria. This paper is an attempt to do a comparative evaluation of the performance of selected public sector and private sector banks in India using CAMELS framework. The paper also attempts to identify the ratios discriminating private sector and public sector banks. Four factors viz: D/E, Tier I capital ratio, Sensitive sector loans to total loans and net NPA ratio have emerged as significant discriminators between private sector and public sector banks.

This study aims to analyze the effect of Non-Performing Loans (NPL) and Debt to Equity Ratio (DER) on Return on Assets (ROA) at PT. Bank Artha Graha Internasional, Tbk. The research employs an associative approach using quantitative data from the bank's financial statements over a ten-year period from 2013 to 2022. The dataset includes total non-performing loans, total credit, total debt, total equity, net profit, and total assets. The research population spans 32 years, while the sample consists of 10 years selected through a purposive sampling technique. The study utilizes secondary data collected through documentation and literature review. Data analysis includes classical assumption tests, multiple linear regression analysis, correlation coefficients, determination tests, t-tests, and F-tests, conducted using SPSS version 24. These statistical methods are applied to examine the relationship between NPL, DER, and ROA and to determine the significance of their impact. The findings indicate that NPL and DER do not have a significant effect on ROA, either partially or simultaneously, at PT. Bank Artha Graha Internasional, Tbk. Although the relationship between these variables is of moderate strength, it suggests that other financial or external factors may play a more influential role in determining the bank’s profitability. These results highlight the need for further research to explore additional determinants of financial performance in the banking sector, considering aspects such as operational efficiency, risk management, and macroeconomic conditions that may influence profitability.

ABSTRAKDengan memperoleh profit yang berkesinambungan dan terus meningkat, bank dapat terus mengembangkan bisnisnya untuk memberikan kepuasan kepada pihak yang berkepentingan yaitu para pemegang saham, manajemen, karyawan dan nasabah. Di sisi lain, profitabilitas menunjukkan bank telah beroperasi secara efektif dan efisien dalam menggunakan aset yang dimilikinya. Profitabilitas diukur dengan rasio Return On Assets (ROA) menggunakan indikator laba sebelum pajak dan total aset. Beberapa faktor yang dapat berkontribusi terhadap ROA di antaranya yaitu Non Performing Loan (NPL), Net Interest Margin (NIM) dan Capital Adequacy Ratio (CAR). Tujuan penelitian ini adalah untuk mengetahui sejauhmana NPL, NIM dan CAR berpengaruh signifikan secara parsial maupun bersama-sama terhadap ROA. Sampel penelitian ini adalah 8 perusahaan perbankan baik BUMN maupun Swasta yang terdaftar di Bursa Efek Indonesia periode 2018-2022. Metode penelitian yang digunakan adalah deskriptif kuantitatif dengan pengolahan data menggunakan SPSS versi 26. Data penelitian yang digunakan bersumber dari laporan keuangan tahunan berupa laporan kinerja keuangan yang diunduh pada laman website masing-masing bank. Penelitian ini menggunakan metode dokumentasi dan melakukan analisa deskriptif statistik, uji asumsi klasik, uji regresi berganda, uji koefisien determinasi, uji t (parsial) dan uji F (Simultan). Kesimpulan yang diperoleh dari hasil penelitian dan uji hipotesis yang telah dilakukan menunjukkan bahwa NPL, NIM dan CAR secara parsial maupun simultan memiliki pengaruh yang signifikan terhadap ROA pada periode 2018-2022.Kata Kunci: NPL, NIM, CAR, ROAABSTRACTBy obtaining sustainable and increasing profits, banks can continue to develop their business to provide satisfaction to interested parties, namely shareholders, management, employees and customers. On the other hand, profitability shows that the bank has operated effectively and efficiently in using the assets it owns. Profitability is measured by the Return On Assets (ROA) ratio using indicators of profit before tax and total assets. Several factors that can contribute to ROA include Non Performing Loans (NPL), Net Interest Margin (NIM) and Capital Adequacy Ratio (CAR). The aim of this research is to determine the extent to which NPL, NIM and CAR have a significant effect, partially or jointly, on ROA. The sample for this research is 8 banking companies, both state-owned and private, listed on the Indonesia Stock Exchange for the 2018-2022 period. The research method used is quantitative descriptive with data processing using SPSS version 26. The research data used comes from annual financial reports in the form of financial performance reports downloaded on each bank's website. This research uses the documentation method and carries out descriptive statistical analysis, classical assumption test, multiple regression test, coefficient of determination test, t test (partial) and F test (simultaneous). The conclusions obtained from the results of research and hypothesis testing that have been carried out show that NPL, NIM and CAR partially or simultaneously have a significant influence on ROA in the 2018-2022 period.Keywords: NPL, NIM, CAR, ROA

The prime objective of this research is to identify the main determinants of non-performing loans in the commercial banking system of Bangladesh for the period 2011-2016 using panel data modeling. This paper uses balanced panel data method to examine both bank-specific (return on average assets, net loans to deposit ratio, bank size, cost-to-income ratio, and capital adequacy ratio) and macroeconomic (real GDP growth rate and inflation rate) variables. To attain the objectives, the present research analyzed historical data and panel data model using secondary data. To examine panel data modeling, the researcher considers 16 private commercial banks in Bangladesh and executed pooled OLS model, fixed effect model, random effect model and random effect with the robust standard error. The researcher found a negative significant relationship for return on average assets, net loans to deposit ratio and inflation rate in relation to NPLs and results are supporting the previous researcher. Based on the findings, the study offers some valuable strategies to the management to improve return on average assets, net loans to deposit ratio and inflation rate to reduce the NPLs at least under the tolerance level. The study also delineates the limitations of this work and direction for future research.

Indian Banking sector is the backbone of Indian economy. State Bank of India is the largest public sector bank in India holding 25% of market share by its loans and deposits. The main focus of the study is to access the performance of State Bank of India. The study is based on purely secondary data. And the data has been collected for the period from 2016-17 to 2020-21. Some important ratios namely Capital Adequacy Ratio, Net Total Income per Employee, Net NPA to Advances Ratio, Return on Assets, Net Interest Margin, and Return on Equity has used to measure the performance of State Bank of India. After analysis it is found that the performance of State Bank of India is satisfactory.

This research includes all banks in Bosnia and Herzegovina and testing internal and external variables on bank profitability indicators. In addition, the profitability of banks in B&H is also influenced by the financial result of operations, which is determined by price and interest rate risk. The primary goal of this paper is to determine, through correlation and regression analysis, the strength and significance of external and internal variables on bank profitability in Bosnia and Herzegovina. The research period covered from 2008: q1 to 2019: q4 on a quarterly database. Also, in this paper, the STATA 13.0 software package will be used. The following dependents variable were used: return on asset (ROA) and return on equity (ROE). The following independent variables were used: the growth rate of net gross/loss (GRNGL), the growth rate of non-performing loans (GRNPL), GDP growth rate (GRGDP), concentration ratio of loans of the largest banks in the system (CR Loans), concentration ratio of deposits of the largest banks in the system (CR Deposits), capital adequacy ratio (CAR) and loan-to-deposit ratio. The total number of observations was 48. The results showed that the significant influence on the dependent variables were the return on equity (ROE) and return on asset (ROA), which has been achieved by the following independent variables, such as the growth rate of net gross/loss, the growth rate of non-performing loans and concentration ratio of loans and deposit of the largest banks.