Determinants Of Credit Risk Research Articles

Quenching thirst, easing debt: Improvement in water resource endowment alleviates local government debt risk

Water resources serve as a vital pillar sustaining both economic activities and daily life for residents. However, the uneven distribution of water resources across time and space has made water scarcity a bottleneck factor restricting the economic and social development of some regions. The implementation of the South-to-North Water Diversion project provides an excellent quasi-natural experiment for in-depth research on the effects and influence mechanism of cross-regional productive factor allocation on economic development. This study focuses on whether the water resource endowment improvement is reflected on the capital market. Based on the trading data of urban construction investment bonds from 2011 to 2016, we empirically examine how the South-to-North Water Diversion project affects the local government financing costs. The results indicate that the water diversion project significantly reduces the credit spread of urban construction investment bonds from the water-receiving areas. Further analysis reveals that the water diversion project improves the economic fundamentals and increases the land sales revenue of the water receiving cities, enhancing local governments' implicit guarantee ability for the financing platforms, thereby reducing the credit risks of urban construction investment bonds. This study not only enriches the research on the determinants of credit risk for urban construction investment bonds, but also provides empirical evidence from the capital market that the coordinated allocation of productive factors promotes regional balanced development. The conclusions imply that the government should actively advance the corresponding infrastructure construction and institutional arrangements to promote the flow and complementarity of economic elements between regions.

Cushion hypothesis and credit risk: Islamic versus conventional banks from the MENA region.

Conventional banks are 'indirectly' allowed to take more risk under the shadow of sovereign guarantees. Banks commit moral hazards as any major banking crisis will be 'cushioned' by deposit insurance and bailed out using the taxpayer's money. This study offers an alternative explanation for the determinants of banks' credit risk, particularly those from the Islamic regions. Although conventional banks and Islamic banks may share state and social cushioning systems, Islamic banks are strictly prohibited by moral and religious principles from gambling with depositors' funds, even if there is a cushion available to bail them out. However, banks belonging to collective societies, such as those in the MENA area, may be inclined to take more risks due to the perception of having a larger safety net to protect them in the event of failure. We analyse these theoretical intersections by utilising a dataset consisting of 320 banks from 20 countries, covering the time span from 2006 to 2021. Our analysis employs a combination of Ordinary Least Squares (OLS), Fixed Effects (FE), and 2-step System-GMM methodologies. Our analysis reveals that Islamic banks are less exposed to credit risk compared to conventional banks. We contend that the stricter ethical and moral ground and multi-layer monitoring system amid protracted geopolitical and post-pandemic crises impacting Islamic countries contribute to the lower credit risk. We examine the consequences for credit and liquidity management in Islamic banks and the risk management strategies employed by Islamic banks, which can serve as a valuable reference for other banks.

Comprehensive Analysis of Credit Risk Determinants in the Egyptian Property and Casualty Insurance Sector: Exploring Macroeconomic, Industrial, and Company Specific Factors

Comprehensive Analysis of Credit Risk Determinants in the Egyptian Property and Casualty Insurance Sector: Exploring Macroeconomic, Industrial, and Company Specific Factors

Network centrality and credit risk: A comprehensive analysis of peer-to-peer lending dynamics

This letter analyzes credit risk assessment in the Peer-to-Peer (P2P) lending domain by leveraging a comprehensive dataset from Bondora, a leading European P2P platform. Through combining traditional credit features with network topological features, namely the degree centrality, we showcase the crucial role of a borrower’s position and connectivity within the P2P network in determining loan default probabilities. Our findings are bolstered by robustness checks using shuffled centrality features, which further underscore the significance of integrating both financial and network attributes in credit risk evaluation. Our results shed new light on credit risk determinants in P2P lending and benefit investors in capturing inherent information from P2P loan networks.

CREDIT RISK DETERMINANTS OF COMMERCIAL BANKING

Studies on non-performing loans in banking provide mixed results regarding the bank-specific factors that influence them. This research aims to analyze the determinants of credit risk in commercial banking in Indonesia. Testing was conducted on 32 banking companies listed on the Indonesia Stock Exchange for 2019-2021 with 96 observations using panel data regression. The research results show that increasing bank diversification and external audit quality significantly reduce the risk of credit non-performing loans (NPL). Other findings of this research show that credit growth, operational efficiency, and bank size do not significantly affect credit risk. To overcome the problem of endogeneity and dynamic panel bias, a series of robustness tests were carried out using the 2SLS, LIML, and GMM approaches provided significant results. The results of this research imply that bank risk management plays a role in reducing problem loans, which must align with the business entity's efforts to increase profits.

Determinant of credit risk of Islamic banks in Pakistan

This study aims to investigate the influence of macroeconomic variables and bank-specific factors on the credit risk of Islamic banking in Pakistan, through the panel data regression tools. The statistical tool which is applied to the research is ordinary least square (OLS) regression model. All the assumption to be fulfilled before using OLS. The secondary data have been taken from four (04) full-fledged Islamic banks in Pakistan, from 2007 to 2021. The focus of the research is to find the impact of macroeconomic variables like Gross domestic product, inflation, and growth in the interest rate and bank-specific factors like size, return on assets, loan loss provision, capital Adequacy ratio, and Asset quality to determine the credit risk (non-performing loans) of Islamic banks in Pakistan. The result of the ordinary least square (OLS) regression model is that loan loss provisions (LLP) have a positive and significant impact on credit risk (CR) and size of bank (S), and Capital adequacy ratio (CAR) have a negative and significant impact on credit risk (CR) of Islamic Bank of Pakistan. Inflation (INF) and Gross domestic product (GDP) have a positive and insignificant impact on credit risk (CR), and growth in interest rate (INT), return on assets (ROA), and asset quality (AQ) has a negative and insignificant impact on Credit risk (CR) of Islamic Bank of Pakistan. Therefore, Islamic banks should carefully examine their specific factors, i.e. LLP, S, and CAR to manage their credit risk, particularly in monitoring loans.

Short Communication: Indian Public Sector Commercial Banks: An Empirical Analysis of Bank-Specific Determinants of Credit Risk

The public sector banks dominate India’s financial sector and play a crucial role. Commercial banks are indispensable in the processes of capital circulation, capital integration, capital resource allocation, and overall societal demand and supply adjustment. But performing these vital roles, the commercial banks are associated with different risks out of which credit risk hampers them a lot. This risk attacks the major pillar of the banking industry i.e., solvency on which the industry survives. In this paper, a modest attempt is made to identify the bank-specific determinants of credit risk in the public sector commercial banks in India. Data was collected from the RBI database for the period 2005 to 2020 for 13 banks. Variables pertaining to credit risk, bank size, capital adequacy ratio, profitability, liquidity and diversification were taken. These variables were analysed with the help of different panel data analytical tools. The size of the bank, represented by the log of total assets, and diversification, measured as the ratio of non-interest income to total income, were found to positively and significantly influence credit risk in public sector commercial banks. The Return on Asset, Loan-deposit ratio, and Capital adequacy ratio were found to be negatively correlated with credit risk.

Relationship Banking, Collateral, and the Economic Crisis as Determinants of Credit Risk: An Empirical Investigation of SMEs

Abstract This study examines the impact of relationship banking and collateral on the probability of firm loan default in Kosovo. Using a sample of 2,320 loan-level data from an individual bank credit register, findings indicate that stronger firm-bank relationships reduce the probability of default, and tighter credit policies regarding higher collateral requirements and interest rates have the opposite effect. Re-specifying the model to control for the banking sector concentration Hirschman-Herfindahl Index (HHI) and the Net Interest Margin (NIM), the firm-bank relationship is no longer statistically significant. Results show that the crisis negatively impacts credit risk, while HHI positively affects the probability of loan default. This evidence suggests that banking relationship matters only in competitive markets. To test the potential interaction effect between relationship banking and collateral, Fairlie’s (1999) decomposition technique is deployed. Our results imply that high concentration levels in the banking sector render firm-bank relationships relatively less important. This is of utmost importance for SMEs, banks, and policymakers.

Credit Risk Prediction Based on Psychometric Data

In today’s financial landscape, traditional banking institutions rely extensively on customers’ historical financial data to evaluate their eligibility for loan approvals. While these decision support systems offer predictive accuracy for established customers, they overlook a crucial demographic: individuals without a financial history. To address this gap, our study presents a methodology for a decision support system that is intended to assist in determining credit risk. Rather than solely focusing on past financial records, our methodology assesses customer credibility by generating credit risk scores derived from psychometric test results. Utilizing machine learning algorithms, we model customer credibility through multidimensional metrics such as character traits and attitudes toward money management. Preliminary results from our prototype testing indicate that this innovative approach holds promise for accurate risk assessment.

A comparative analysis of Altman's Z-score and T. Jury's cash-based credit risk models with the application to the production company and the data for the years 2016-2022

Research Questions: In the present paper the author answers the following research questions: 1) What are the potential strengths of the credit risk model based on the cash flow principle? 2) What are the weaknesses of the accrual-based credit risk model? 3) What are the benefits of the combined use of both cash-based and accrual-based credit risk modeling methods when analyzing companies? Motivation: nowadays there are no researches comparing the accrual-based credit risk model to a cash-based credit risk model with the application to a production company trading its shares on Stock Exchange. However, for investors, auditors and financial institutions it is important to know if there is a difference between these two models in the interpretation of analysis results, and determination of prebankruptcy stage of the company and credit risk default. Idea: in this paper, the author has focused on the comparative analysis of the cash-based credit risk model and the accrual-based credit risk model. The author applies it to the case of a manufacturing company and compares the effectiveness of determining the probability of default using a cash-based credit risk model and an accrual-based credit risk model. Data: the data analysed is obtained from the annual reports, managerial reports and auditor’s reports of Linas Agro Group for the years 2016-2022. The company information is taken from Nasdaq Baltic where Linas Agro Group has its shares traded. Tools: mixed research methods were used, combining quantitative calculations with analysis based on qualitative information. The author elaborates on the cash-based credit risk model based on the improved Timothy Jury’s template. The accrual-based model chosen for comparison and analysis is Altman’s Z-Score model. Findings: The results of the study have shown that the cash-based model is more effective in determining credit risk and default probability. The cash-based model indicated a high-risk default for the manufacturing company in four years out of seven years, while Altman’s Z-Score showed the company to be in the moderate risk grey zone in five years out of seven, and the two last years the model indicated the company in the green zone. The author suggests to financial institutions, financial managers, and investors using a cash-based credit risk model or combination of it with the accrual-based model. Contribution: the paper contributes to the knowledge about the comparison of cash-based and accrual-based credit risk models and emphasizes their strengths and weaknesses. It helps investors, auditors, business owners, and finance professionals to make a decision about which credit risk model to use for the analysis to determine the pre-bankruptcy state of the company, avoid bad loans and improve investment decision-making. It also encourages the academic society for further research and comparison on the topics of accrual-based and cash-based credit risk models in the strive to develop the ultimate credit risk model capable to analyse the data as precisely as possible.

Credit Risk Determinants in Selected Ethiopian Commercial Banks: A Panel Data Analysis

The study aims to investigate the factors that contribute to credit risk in Ethiopian commercial banks, considering both macroeconomic and bank-specific factors. The research utilized multiple regression models, a quantitative research approach, and explanatory research designs. A purposive sample technique was used to select 10 commercial banks for the study, and secondary data from audited financial reports were analyzed. The findings of the study reveal a significant positive relationship between credit risk and several variables, including bank size, profitability, efficiency, capital adequacy, and inflation. Conversely, there is an inverse relationship between credit risk and both loan growth and currency rates. Surprisingly, the study found that neither GDP nor interest rates have a significant impact on credit risk. Based on these findings, the study provides recommendations for Ethiopian commercial banks. It suggests maintaining adequate levels of capital, avoiding business in sectors influenced by inflationary pressures, carefully evaluating non-interest income, and adjusting lending policies as necessary. Furthermore, the study advises periodically examining the relationships between GDP growth, interest rates, and credit risk. It also emphasizes the importance of adapting credit risk management practices to changing market conditions and staying vigilant toward emerging trends.

Credit risk and bubble behavior of credit default swaps in the corporate energy sector

This paper analyzes the determinants of credit risk in the energy sector using CDS spreads of energy corporations as well as CDS energy sectorial indexes to assess whether credit risk can be linked to the crude oil price fundamental and to other exogenous financial variables. Applying the multiple bubble methodology proposed by Phillips Shi and Yu (2015) we associate bubble behavior in CDSs with fundamentals via a series regressions applied to time changing autoregressive coefficients. Our results show that there is bubble propagation which should be closely monitored by market participants as an early signal of deteriorating market conditions.

An explained extreme gradient boosting approach for identifying the time-varying determinants of sovereign risk

We use a combination of Extreme Gradient Boosting and SHAP Additive Explanations to uncover the determinants of sovereign risk across a wide range of countries from 2002 to 2021. By considering numerous variables established in existing literature within a single framework, we identify year-by-year determinants of sovereign credit risk. To gage the liquidity and solvency aspects of sovereign risk, we utilize 5- and 10-year yield spreads as proxies. Our findings show that the key variables driving sovereign risk have remained relatively stable over time and exhibit similarities in both liquidity and solvency components. Among the prominent variables, various macroeconomic fundamentals play a crucial role, including the current account, GDP growth, per capita GDP growth, and the real exchange rate. Prior to the Global Financial Crisis, macroeconomic variables, particularly the current account, held the highest importance in explaining sovereign risk. However, following the GFC, the relative importance of these variables diminished, giving way to institutional variables, especially the rule of law.

Credit Risk Determinants: Specific and Macroeconomic Factors of Islamic Banks

The development of Islamic banking credit risk shows a downward trend. However, the existence of Indonesian Islamic banking in the world is not yet among the best ranked, even though it has the largest Muslim population. This study examines the factors that influence credit risk in Islamic banks in Indonesia by using the bank's specific characteristics and macroeconomic factors. This study uses a multiple linear regression method with monthly data on Islamic banking in Indonesia for the period 2020-2022. The dependent variable for credit risk uses Non-performing financing (NPF). Independent variables from bank-specific factors are used Capital Adequacy Ratio (CAR), Financing to Deposit Ratio (FDR), and Operating Expenses to Operating Income (BOPO). While the independent variables from macroeconomic factors use inflation and interest rates. The results showed that the CAR, FDR, and inflation variables affected the credit risk of Islamic banks. FDR has a positive effect, while CAR and inflation have a negative effect. BOPO and interest rates do not affect credit risk.

Determinants of credit risk: Empirical evidence from Indian commercial banks

Credit risk is a significant factor affecting the financial stability of banks. Keeping the credit risk under control is essential to maintain a bank’s cash flow. This paper examines the various profitability, microeconomic and macroeconomic indicators that affect a bank’s credit risk. The study uses the dataset of 31 banks from 2012 to 2021 and employs a panel data modelling approach to account for any variations in risk-taking behavior. The results revealed a statistically significant negative relationship between return on equity and credit risk when nonperforming loans proxy credit risk. This finding was consistent across fixed effect, random effect, and pooled OLS methods, at 1 percent significance (P value < 0.00), indicating that the extent of credit risk decreases as profitability increases. It was further found that bank age and ownership type positively affect a bank’s credit risk, while factors such as bank size and operational efficiency negatively affect credit risk when nonperforming loans proxy credit risk. Further, macroeconomic variables showed that gross domestic product is positively associated with credit risk, while inflation negatively affects credit risk. Overall, the findings of this paper demonstrated that credit risk is affected by both micro and macroeconomic factors. The paper also addresses significant policy implications as it helps various stakeholders to examine the determinants of credit risk, make credit decisions, and ultimately lower their credit risk.

Climate transition risk in determining credit risk: evidence from firms listed on the STOXX Europe 600 index

This paper assesses whether a climate factor is relevant to measure default risk in a sample of main companies listed on the STOXX Europe 600 exchange from 2010 to 2020. The starting point is a factorial panel data model which is subsequently modified to capture the climate impact through different functional forms. We find that relevant differences in default risk exist before and after the Paris Agreement. Our analysis also indicates that this difference cannot be explained by means of traditional financial factors. Finally, we further show that a climate change risk and opportunities label is a significant factor in evaluating credit risk, both prior to and post-Paris agreement. These results are important to the extent that they suggest that companies' market performance itself allows to measure differences in credit risk between companies and to link them with climate risk factors. This approach may be useful as a complement or in combination with the traditional use of exogenous climate factors that have been widely used in the literature in this field.

Islamic Personality Model as Psychometric Tool To Assess Creditworthiness of Micro Financing

This study aims to develop an Islamic personality model as a psychometric tool to assess creditworthiness as an alternative predictive character analysis for micro businesses. The method designed to formulate the proposed model coded in R Studio uses two approaches. First, we modify Moslem Religiosity Personality Inventory and then frame a structural model based on Partial Least Square. Subsequently, we use the random forest technique to see the model's accuracy. The result shows a valid and reliable model and performs with 89.47 % accuracy with an Area Under Curve -Receiver Operating Characteristic of 90.06 %. This model implies a solution to strengthen the assessment of the character of creditworthiness of a potential micro-business and helps Islamic Financial Institutions to assess prospective micro-business to determine credit risk and pricing.JEL Classification: B41, D81, D87, G21, P43How to Cite:Hardiansyah., Amalia, E., & Hamid, A. (2023). Islamic Personality Model as Pychometric Tool To Access Creditworthiness of Micro Financing. Etikonomi, 22(1), 233–246. https://doi.org/10.15408/etk.v22i2.30370.

Determinants of Credit Risk and Operational Risk in Banking Sector Evidence from Pakistani Banking Sector

Risk management is what kind of strategies should be adopted to reduce all kinds of risk. Risk the difference between the actual return and the expected return. And credit risks the potential that borrower will fail to meet the obligation. The banking industry of Pakistan is faced with several challenges among them are determinants of credit risk and operational risk. Determinants of credit risk are defined as the factors that may affect the credit risk and determinants of operational risk are defined as the factors that affect the operation of business. Many banks in Pakistan have created credit risk management departments that are responsible for managing the credit risk associated with banking operation. The objective of this study is to evaluate the determinants of credit risk in Pakistani banking sector. The collected data consists of secondary data. Financial data was collected from three banks of Pakistan listed at Karachi stock exchange (KSE) over the period of 17 years from 2000 to 2016. Panel Regression Model was applied to find the cause and effect relationship for the under-consideration issue. The result has shown that credit risk and operational risk have a significant and positive relationship with NPLs, Gearing Ratio, and Operating Efficiency. And Credit Risk and operational risk have positive but insignificant relationship with Liquid Assets (LA). The recommendation of this study is if we pursue proper bank regulations, then the regulation should be backed up by sound credit analysis, and provision for suitable situation of credit loans.

Macroeconomic Determinants of Credit Risk on the Example of Non-performing Loans

Abstract The primary goal of this article is to examine the principal macroeconomic factors influencing credit risk as assessed by the nonperforming loan ratio (hereinafter NPL ratio). Based on the results, the ratio of domestic credit to the private sector, Organization for Economic Cooperation and Development (OECD) membership with a negative correlation with NPLs while the unemployment rate and the ratio of public debt with a positive relation with NPLs were statistically significant. In addition, the correlation between the inflation rate and the depreciation of the home currency was proven. The research examines the effects of the 2008 credit crunch, which triggered the financial crisis. The sample comprises 106 countries for the period 2009–2019. The real GDP growth, unemployment rate, public debt ratio, domestic credit to private sector ratio, currency depreciation, inflation rate, and interest rate were analysed as macroeconomic factors. A dummy variable representing OECD membership has been included in the analysis. The estimations were performed using the ordinary least squares (OLS) method. This article contributes to the academic discourse on the panel data perspective with regard to non-performing loans, while the practical implications are beneficial for governments and international investors.

Effect of Credit Risk Determinants on Financial Performance of Commercial Banks in Kenya

Effect of Credit Risk Determinants on Financial Performance of Commercial Banks in Kenya