Determinants Of Credit Risk Research Articles

DETERMINANTS OF CREDIT RISK: A COMPARATIVE ANALYSIS BETWEEN ISLAMIC AND CONVENTIONAL BANKS IN BANGLADESH

The paper investigates the determinants of credit risk of Islamic and conventional banks in Bangladesh. In so doing, it collects data from 30 private commercial banks comprising of seven Islamic banks and 23 conventional banks for the period 2001–2018. Collected data are analyzed using GMM estimation technique. This method is perceived to be robust because it reduces the endogeneity problem that exists in the panel data set. Analysis of data shows that among the macro-economic variables, GDP growth decreases credit risk, whereas real interest rate and inflation increase credit risk. Bank-specific variables prove that both clusters of banks suffer from adverse selection and moral hazard problems. Results also indicate that competition has a risk-enhancing effect on banks, which supports the competition-fragility nexus. Further analysis shows that board size and board independence affect the credit risk of both clusters of banks. Findings of this study suggest some policy implications from macro, bank and governance perspectives. Specifically, banks should adopt ‘speed limit’ policy to reduce the poor quality loan. Also, competition in the banking industry should be regulated. Finally, central bank should maintain uniform capital adequacy ratio for both clusters of banks. Although this study is limited to private commercial banks in Bangladesh, the results can be generalized for other emerging economies.

Determinants of Credit Risk in the Banking Sector of Ghana: A Panel Co-integration Approach

The economic development of any nation hinges on the health of its financial system. In recent years, the health of the Ghanaian Banking sector has been affected severely as a result of high levels of non-performing loans (NPLs), which has been identified as a major threat to the overall profitability and survival of banks. To minimize the impact of NPLs on the financial sector, key stakeholders such as the government, bank officials and regulators are working hard in that regard. However, any policy response aimed at dealing with the high rate of non-performing loans first requires the understanding of the underlying determinants of NPLs. Against this backdrop, this paper apply panel co-integration techniques to investigate the determinants of credit risk (NPLs) in the banking sector of Ghana. We use NPL as a proxy to measure credit risk and assess how it is influenced by macroeconomic and bank-specific factors. A balanced panel data of 16 universal banks in Ghana from 2010 to 2016 has been analyzed using Panel co-integration techniques such as Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS). Our result shows that growth in the economy, measured by Gross Domestic Product (GDP) has significant influence on the NPLs of banks in the long-run. The results further revealed that capital adequacy, profitability and liquidity of banks are significant predictors of NPLs. However, our results suggest that bank size, inflation and interest rate have statistically insignificant influence on the NPLs of Ghanaian banks. The study recommend, among others, that whereas it is important for government and policymakers to work to improve macroeconomic outcomes, banks should also improve their capital adequacy, profitability, and efficiency position as these bank-specific interventions could significantly improve credit quality and minimize NPLs.

The Determinants of Credit Risk: An Evidence from ASEAN and GCC Islamic Banks

In less than a decade, the Islamic Banking (IB) industry has become an essential part of the global financial system. During the last ten years, the IB industry has witnessed changes in economic conditions and proved to be resilient during the periods of financial crisis. This paper aims to examine the important issues related to credit risk in selected Islamic banks in nine countries from Association of South East Asian Nations (ASEAN) and Gulf Cooperation Council (GCC) regions. It employs the generalized least squares panel data regression, to estimate the ratio of non-performance financing to total financing as dependent variables and bank specific variables (BSV) to determine the credit risk. It uses 12 years of unbalanced panel data from 40 different Islamic banks. The overall findings show that financing quality has a significant positive effect on credit risk. It is observed that the larger IBs owned more assets with lower credit risk compared to smaller banks. The bank’s age is also an important factor influencing the credit risk level. Moreover, regulatory capital significantly reduces the credit risk exposure adherence to the minimum regulatory capital requirements which help IBs to manage their credit risk exposures. It was also observed that IBs were not affected by the global financial crisis due to less credit risk compared to the conventional banks.

Financial determinants of credit risk in the logistics and shipping industries

This study examines factors affecting the credit risk of global logistics and shipping companies using Altman’s Z-score and Ohlson’s O-score models. Panel data multiple regression analysis is conducted to evaluate the impacts of various financial ratios on credit risk for both industries and to assess their unique characteristics. We find that while credit risk is, on average, similar between the shipping and logistics industries, the variability in credit risk in the shipping industry is much higher. We also find that while both equity and current ratios have a significant impact on credit risk in both industries, return on assets and the quick ratio have the most significant impact on the logistics and maritime industries, respectively. There are slight differences in the determinants of credit risk when analyses are further segmented into different regions (i.e., Asia, EU, USA and Africa). This study introduces scientific models and recommends financial indicators for financiers to evaluate the credit risk of both industries, help improve decision-making, and minimize the probability of default by debtors.

Determinants of Credit Risk in Commercial Banks of Kosovo

Purpose: The purpose of this paper is to analyze some of the determinants of credit risk in commercial banks in Kosovo through the use of regression analysis for a dataset covering a time series of 7 years (2012 - 2018). Design/Methodology/Approach: The data have been collected from publications of Central Bank of Kosovo and from Kosovo Agency of Statistics The data have been analyzed on quarterly basis. In order to conduct the empirical part of the study that gives us the answer to the relationship between credit risk and the determinants of this risk, we analyzed 6 variables in the study. To perform the necessary analysis we have used the statistical software SPSS 23. Through the regression analysis, the main findings and results of the study were generated. Findings: After analyzing the necessary data, the paper concludes that, among credit risk determinants, interest rates on loans and profitability of banks (ROA) have the largest and most significant impact on credit risk, namely non-performing loans as the credit risk measure. Practical implications: For researchers and academics, the study provides a useful basis on which further studies on credit risk and the factors that cause this risk can be conducted. Originality/Value: The research paper is based on recent studies that assess credit risk in other countries and use reliable data on the banking sector in Kosovo.

The European Union’s Short Selling Regulation and Its Impact on CDS Spreads

The European Union introduced Regulation 236/2012 in 2012 to address short selling and certain aspects of credit default swaps (CDS). Consequently, a uniform short position disclosure regime was developed, which is used in this paper to examine CDS spreads as a proxy for credit risk around public short sale announcements and evaluate the disclosure policy's relevance from the debtholder's perspective. Existing literature documents short selling regulations’ impacts on the stock market, but no evidence exists from the CDS market. Therefore, we first conduct an event study to examine the effects of different short sale events on corresponding firms’ CDS spreads between 2012 and 2018. Moreover, we use regression analyses to control for several credit risk determinants that may also affect CDS spreads. Our evidence suggests that opening and increasing short positions are perceived as negative information, and in this regard lead to higher CDS spreads. In contrast, CDS spreads tend to be lower if short positions decrease or close. Additionally, we find that negative information ceteris paribus more strongly affects CDS spreads than positive information. Finally, we investigate certain anticipatory reactions when negative news enters the CDS market.

Riesgo de crédito: Evidencia en el sistema bancario ecuatoriano

Utilizando datos del sistema bancario ecuatoriano del periodo 2000-2018, se analizan variables macroeconómicas y efectos propios de las instituciones bancarias como factores determinantes del riesgo de crédito, evaluado a través de un enfoque de datos de panel estático. Los resultados indican que las condiciones macroeconómicas y financieras del país son un medio para explicar la probabilidad de default de los préstamos otorgados por los bancos. En particular, los factores que influyen en el riesgo de crédito del sistema privado bancario ecuatoriano son la tasa del crecimiento del PIB, variables gubernamentales, tasa de crecimiento de los créditos, las provisiones y la rentabilidad. Palabras clave: Riesgo de crédito, sistema bancario, factores macroeconómicos, factores institucionales. URL: http://revistas.uta.edu.ec/erevista/index.php/bcoyu/article/view/842

Determinants of Credit Risk : The Case of Selected Commercial Banks in Ethiopia

Risk assessment is the careful analysis and evaluation of the diverse factors that can bring risks. Risk assessment provides the banks an opportunity to determine the vulnerabilities and risk associated with a banking system. The aim of this study is to assess determinants of credit risk in selected commercial banks in Ethiopia. To this end, secondary data from ten commercial banks over the period 2010 to 2017 were collected from the audited financial statement of the selected banks available in the National Bank of Ethiopia. The assumption tests were made and indicate the fitness of data for further tests and also the post estimation tests recommend pooled ordinary least square for the regression analysis. The result indicated that predictor variables like efficiency ratio, return on equity and bank size have statistically significant positive effects on the dependent variable (credit risk). In addition, the study showed that capital adequacy had statistically significant negative effect in predicting the dependent variable, credit risk. However, predictor variables like return on asset, leverage and loan to deposit ratio didn’t make any statistically significant effect on credit risk. Finally, it was recommended that, commercial banks in Ethiopia are advised to become more effective, in management to use funds contributed by shareholders, in spending sufficient resources on analyzing loan application, introduction of modern risk management technology, that are believed to improve the effectiveness of commercial banks in Ethiopia.

Bankers' perception on the determinants of credit risk in case of Ethiopian commercial banks

Bankers' perception on the determinants of credit risk in case of Ethiopian commercial banks

An Analysis of Sovereign Credit Risk Premia in the Euro Area: Are They Explained by Local or Global Factors?

We study the determinants of sovereign credit risk in the euro area in a time period that includes the financial and sovereign debt crisis, as well as the unconventional monetary policy adopted by the European Central Bank. First, we detect the presence of commonality in sovereign credit spreads of different countries, justifying the search for the common factors that drive CDS prices. Building on the work of Longstaff et al. (2011), we employ the econometric model used in Cecchetti (2017) to decompose sovereign credit default swap spreads into expected default losses and risk premia, finding evidence of a significant contribution of the latter component. We use the model to understand to what extent the variations in CDS spreads and in the two embedded components of selected euro-area countries are more linked to local or euro area economic variables. The results point to the importance of both global and local factors, which have a greater impact on the risk premium component. Finally, we estimate the contribution of the objective probability and risk premium components of redenomination risk (as measured by the ISDA basis) to the related CDS spread components, detecting some differences between countries.

The Determinants of Credit Risk in Tiong Nam Logistics Holdings Berhad

The study examined the impact of internal and external factors towards credit risk in Tiong Nam Logistics Holdings Berhad. Debt to Income was used as dependent variable represented the credit risk. Debt to Income was calculated by total liability divided by total income. This study obtained time series regression analysis of five years from 2014 to 2018. The findings resulted debt to equity is the most significant variable to debt to income which influenced credit risk of the company. Based on the result for Model 1, debt to equity recorded the moderate significant and positively influenced to debt to equity when only the internal factors were tested. Whereas, when external factors were examined in Model 2 there was no variable giving significant influenced to debt to income. Meanwhile, for Model 3, there was only debt to equity has statically significant and positively influenced to debt to income out of all variables when internal and external factors were tested together. Thus, debt to equity is the most significant variable to debt to income which will arise the credit risk in Tiong Nam Logistics Holdings Berhad.

CREDIT RISK MANAGEMENT A COMPARATIVE STUDY BETWEEN ISLAMIC AND CONVENTIONAL BANKS IN TURKEY.

This study aims to identify variables which determine credit risk in Islamic and Conventional banks. Panel data fixed effect model employed to analyze which belongs to three Islamic Banks in Turkey for the period 2008 Q1 to 2017 Q4. While for conventional banks, previous studies that has been conducted in Turkey used to compare Islamic to Conventional banks (CB). Non-performing Loans (NPL) ratio was used as a proxy for credit risk. Result from fixed effect model showed that NPL in Islamic Banks is positively affected by Loan Loss Provision and Proportion of Loans to Deposits, and it is negatively affected by Assets Size. While literature showed that conventional bank’s credit risk is positively affected by Net Interest Margin, Loan Loss Provision, and Capital Adequacy Ratio and it is negatively affected by Proportion of Loan to Deposits, Proportion of Loans to Assets and Size. There were clear differences between both Islamic and Conventional banks related to all variables of study except Loan Loss Provision and Proportion of Loan to Assets ratios.

PERBANDINGAN KINERJA ALGORITMA DATA MINING PREDIKSI PERSETUJUAN KARTU KREDIT

Credit analysis needs to identify and assess factors that can influence customers in credit returns. Accurate measurement and good management capability in dealing with credit risk is an effort to save the economic operations unit and is beneficial for a stable and healthy financial system. Failure to identify credit risk results in loss of income and extends credit risk to a bad type of threat to profitability. Data mining prediction techniques are used to determine credit risk. Using the Cross-Industry Standard Process for data mining CRISP-DM. This study has tested the model using a neural network using PCA feature selection and optimized with the PSO algorithm to predict credit card approval. Several experiments were conducted to see the best results. The results of this study prove the use of a single Neural Net method produces an accuracy of 80.33%. while the use of the hybrid PCA+NN+PSO method has been proven to increase accuracy to 82.67%. Likewise, the AUC NN value of 0.706 increased to 0.749 when the NN was optimized using PSO and using the PCA. This study implements and compares PCA-based SVM, L. Regression and NN algorithms and optimize PSO to improve accuracy in credit card approval predictions.

The Determinants of the Credit Risk in Developing Countries: An Empirical Study on Vietnamese Listed Banking Sector

The research study is focusing on the determinants of credit risk in developing countries, Vietnam is one the most fastest growing economies in the world with the rate of roughtly 7 percent during the period of1986 – 2018. The bank system in the country has widely been expanded both quantity and quality. Using the secondary data in the period of 2009 – 2018 retrieving from Ho Chi Minh City Stock Exchange (HOSE), Hanoi Stock Exchange (HNX), and General Statistics Office with the theoretical framework of bank’s non-performance loans (so-called credit risk), the results demonstrate that that real GDP growth rate is significantly and negatively correlated with the credit risk in the bank system. Loan growth in the current year and a previous year can negatively generate non-performing loans in this year. In addition, a larger bank can perfectly have a lower level of bankruptcy cost and a higher level of growth rate related to a lower level of credit risk. Further, the bad debt, inflation, and net interest margin can positively generate credit risk.

Performance Comparison of Data Mining Algorithm to Predict Approval of Credit Card

Credit analysis needs to identify and assess the factors that can affect customers in returning credit. Accurate measurement and good management ability in dealing with credit risk is an effort to save the economic operations unit and be beneficial for a stable and healthy financial system. Data mining prediction techniques are used to determine credit risk. Using the Cross-Industry Standard Process for Data Mining (CRISP-DM) method which consists of several stages, namely Business Understanding (dataset), Data Processing (Feature Selection PCA & Dimension Reduce), Algorithm Models (NN+PSO, SVM, LR), Evaluation (Validation and Accuracy). This study has tested the model using a neural network using the PCA selection feature and optimized with the Particle Swarm Optimize (PSO) algorithm to predict credit card approval. Several experiments were conducted to see the best results. The results of this study prove that the use of a single Neural Network method produces an accuracy of 80.33%. whereas the use of PCA + Neural Network + PSO hybrid method has been proven to increase accuracy to 82.67%. Likewise, the AUC NN value of 0.706 increased to 0.749 when the Neural Network was optimized using PSO and used feature selection. The purpose of this study is to implement and compare Support Vector Machine, Logistic Regression and Neural Network algorithms based on PCA and optimize PSO (Particle Swarm Optimization) to improve accuracy in predictions of credit card approvals.

An Integrated Analysis of the Fundamental Determinants of Credit Risk Premiums on Corporate Debt

This research investigates the fundamental components of corporate credit spreads through an integrated examination of how well different models, default probability functions, and market factors explain CDS prices. Individual company yield spreads are discovered to be determined by the interrelated risks of cash, income, and valuation insolvency, along with various measures of systematic downside risk. The empirical findings indicate that the priced risk of a jump to and at default is well explained by those fundamentals, including in ex-post tests, although the financial determinants of credit spreads are found to vary significantly across different groups of companies.

Appraisal Accuracy and Automated Valuation Models in Rural Areas

Accurate and unbiased property value estimates are essential to credit risk management. Along with loan amount, they determine a mortgage’s loan-to-value ratio, which captures the degree of homeowner equity and is a key determinant of borrower credit risk. For home purchases, lenders generally require an independent appraisal, which, in addition to a home’s sales price, is used to calculate a value for the underlying collateral. A number of empirical studies have shown that property appraisals tend to be biased upwards, and over 90 percent of the time, either confirm or exceed the associated contract price. Our data suggest that appraisal bias is particularly pervasive in rural areas where over 25 percent of rural properties are appraised at more than five percent above contract price. Given this significant upward bias, we examine a host of alternate valuation techniques to more accurately estimate rural property values.

Determinants of Non-performing Loans

The aim of the empirical study is to investigate credit risk determinants in banking sectors across three kinds of South Asian economies. An accumulated sample of 105 unbalanced panel data of financial firms over the period of 2000-2015, by applying General Method of Moment (GMM) estimation techniques one-step at the difference in order to identify factors influencing credit risk. This study is inspired by two broad categories of explanatory variables which are bank-specific and macroeconomic. Bank-specific factors influencing unsystematic risk, while macroeconomic factors promoting systematic risk. The study uses a proxy of non-performing loans for credit risk in banking sectors of Pakistan, India, and Bangladesh. The empirical results have been found aligned with theoretical arguments and literature as expected. In comparison, NPLs in Pakistan is greater than India and Bangladesh, while India has the lowest ratio of non-performing loans. The study documents that bank-specific factors (inefficiency, profitability, capital ratio and leverage) have a significant contribution towards credit risk. Further, the study also finds a significant impact of macroeconomic variables on non-performing loans. While, the result in the case of Bangladesh predicts contradictions that have no significant effect on non-performing loans at various levels. The overall results indicate that credit risk is not influenced by only external factors but also affect by internal factors like bad management and skimping etc.

Tournament incentives and firm credit risk: Evidence from credit default swap referenced firms

AbstractIn this paper, we evaluate the impact of managerial tournament incentives on firm credit risk in credit default swap (CDS) referenced firms. We find that intra‐firm tournament incentives are negatively related to credit risk. Our results suggest that tournament incentives reduce credit risk by alleviating the potential for underinvestment when managers are concerned about exacting empty creditors. Further, we find that tournament incentives decrease credit risk when internal governance is strong or product market competition is intense. Taken together, our results suggest that creditors perceive senior manager tournament incentives (SMTI) as a critical determinant of a firm's credit risk, particularly in settings where managerial risk aversion is high.

Re-Examining the Impact of Credit Risk on Profitability of Banks:Panel Evidence from Ghana

Credit risk management has become an instrument for the survival and growth of financial institutions. The major cause of banking problems has been identified as ineffective credit risk management. The Ghanaian banking sector is currently undergoing significant reforms which have led to some banks being collapse whiles others consolidated. This study seeks to re-examine the impact of credit risk on the profitability of Banks in Ghana. Panel data covering the period of 2010-2015 was gathered from 20 banks. Three determinants of credit risk were selected. These are asset quality, non-performing loan, and liquidity. Return on Asset (ROA) was employed as a measure of profitability. We found that that while the relationship between asset quality, non-performing loan and profitability were statistically significant, the relationship between liquidity ratio and banks’ profitability was found to be insignificant. This shows that banks with huge non- performing loans are less profitable and prone to a high rate solvency rate. Based on the result of the study, it is recommended that banks should adopt and implement effective credit risk management strategies as it will enhance their profitability. Keywords : Asset Quality Credit risk, Ghana, Non- performing loans. Liquidity Risk. DOI : 10.7176/EJBM/11-5-03