Empirical Examination of Credit Risk Determinant of Commercial Banks in Jordan

ALrfai, Mohammad Motasem; Salleh, Danilah; Waemustafa, Waeibrorheem

doi:10.3390/risks10040085

Cited by 12 publications

(2 citation statements)

References 30 publications

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“…They derived consequences for theory, business practice, and regulation from their analysis. Mwangi, Njuguna & Njoroge (2022) examined the credit risk determinants of commercial banks in Kenya using panel data from 2015 to 2020. They employed the fixed effects model to estimate the relationship between credit risk and bank-specific factors, macroeconomic factors, and regulatory factors.…”

Section: Empirical Reviewmentioning

confidence: 99%

Evolution of Risks Facing Commercial Banks in Kenya and Associated Strategic Responses

Kimani,

Kibera

2023

IJMRM

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Purpose: The main objective of this study was to explore the evolution of risks facing commercial banks in Kenya and their strategic responses Methodology: The study adopted a desktop methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The study found that the commercial banking sector in Kenya is operating within a highly dynamic and ever-evolving risk landscape. The risks faced by these banks are multifaceted and include economic, operational risk, regulatory, technological, systematic, sovereign risk and market-related challenges. It also added that the strategic responses adopted by commercial banks in Kenya are diverse and multifaceted. The conclusions drawn emphasize the need for continual vigilance and adaptation in risk management, the pivotal role of technology and digital transformation, and the significance of regulatory compliance. By taking these conclusions into account, commercial banks in Kenya can better position themselves to thrive in the dynamic and competitive financial landscape of the country. Unique Contribution to Theory, Practice and Policy: The Modern Portfolio theory, liquidity preference and the Liquidity Coverage Ratio (LCR), Agency theory, the term structure of interest rates and duration and Credit Scoring Model and the Resource Based View model may be used to anchor future studies on the evolution of risks facing commercial banks in Kenya and their strategic responses. This study makes significant contributions to theory by developing a risk evolution framework and strategic risk response models. It has practical implications by offering enhanced risk management strategies and guidance for financial decision-making for practitioners. Additionally, the research provides valuable insights for banking regulators policymakers, and investors in Kenya, supporting risk-based supervision and financial stability initiatives.

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Section: Empirical Reviewmentioning

confidence: 99%

Evolution of Risks Facing Commercial Banks in Kenya and Associated Strategic Responses

Kimani,

Kibera

2023

IJMRM

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“…Among the many complex banking behaviours, bank risk exposures (e.g. credit risk and insolvency risk) have been widely studied as important determinant of bank stability (ALrfai et al , 2022; Naili and Lahrichi, 2022; Djebali and Zaghdoudi, 2020; Fratzscher et al , 2016; Ghosh, 2015; Adusei, 2015; Beltratti and Stulz, 2012; Jokipii and Monnin, 2013; Apatachioae, 2015).…”

Section: Introductionmentioning

confidence: 99%

Bank risk exposures and bank stability in Africa: the role of regulations in a non-linear model

Ofori‐Sasu

Mekpor

Adu-Darko

et al. 2023

JFRC

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Purpose This paper aims to examine the interaction effect of regulations (monetary and macro-prudential) in explaining the possible non-linear effect of bank risk exposures (credit risk and insolvency risk) on banking stability in Africa. Design/methodology/approach The study uses a two-step system generalized method of moments (GMM) estimator for a data set of banks across 54 African countries over the period 2006–2020. Findings The authors find that the relationships between bank credit risk–bank stability and bank insolvency risk–bank stability are non-linear and characterized by the presence of optimal thresholds, which are 5.3456 for credit risk and 2.3643 for insolvency. Contrary to their positive effects below these optimal thresholds, credit risk and insolvency risk become negatively linked to bank stability in Africa. The authors find that macro-prudential action and monetary policy both have a positive and significant relationship with bank stability. The authors provide evidence to support that the marginal effect of excessive credit risk and insolvency risk on bank stability is reduced when interacted with monetary and macro-prudential regulations, and the impact is significant in strong institutional environment. Research limitations/implications Future research should extend data to include developing and emerging economies in the world. Also, policymakers, researchers and practitioners should consider different regulatory and institutional frameworks in explaining the relationship between the thresholds of bank risk exposures and bank stability in the world. Practical implications Regulatory authorities should have to deeply reform their financial systems, develop risk-based regulatory framework and effective supervision mechanism relating to appropriate techniques that maintain an optimal and desired level of bank risks and risk-taking behaviours required to ensure a stable banking system. Originality/value To the best of the authors’ knowledge, this is the first study to examine how different regulatory frameworks shape the non-linear impact of bank risk exposures on bank stability in Africa.

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The application of structural and machine learning models to predict the default risk of listed companies in the Iranian capital market

Peykani,

Sargolzaei,

Sanadgol

et al. 2023

PLoS ONE

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Inattention of economic policymakers to default risk and making inappropriate decisions related to this risk in the banking system and financial institutions can have many economic, political and social consequences. In this research, it has been tried to calculate the default risk of companies listed in the capital market of Iran. To achieve this goal, two structural models of Merton and Geske, two machine learning models of Random Forest and Gradient Boosted Decision Tree, as well as financial information of companies listed in the Iranian capital market during the years 2016 to 2021 have been used. Another goal of this research is to measure the predictive power of the four models presented in the calculation of default risk. The results obtained from the calculation of the default rate of the investigated companies show that 50 companies listed in the Iranian capital market (46 different companies) have defaulted during the 5-year research period and are subject to the Bankruptcy Article of the Iranian Trade Law. Also, the results obtained from the ROC curves for the predictive power of the presented models show that the structural models of Merton and Geske have almost equal power, but the predictive power of the Random Forest model is a little more than the Gradient Boosted Decision Tree model.

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Empirical Examination of Credit Risk Determinant of Commercial Banks in Jordan

Cited by 12 publications

References 30 publications

Evolution of Risks Facing Commercial Banks in Kenya and Associated Strategic Responses

Evolution of Risks Facing Commercial Banks in Kenya and Associated Strategic Responses

Bank risk exposures and bank stability in Africa: the role of regulations in a non-linear model

The application of structural and machine learning models to predict the default risk of listed companies in the Iranian capital market

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