The Nexus of Competition, Loan Quality, and Ownership Structure for Risk-Taking Behaviour

Moudud‐Ul‐Huq, Syed; Halim, Md. Abdul; Sobhani, Farid Ahammad; Karim, Ziaul; Nesa, Zinnatun

doi:10.3390/risks11040068

Cited by 8 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…TO, SDG, FD, INF, Tech, LBR, and FOP mean 1347.352, 62.781, 91.469, 7.106, 217.441, 438.508, and 61.249, respectively, with standard deviations of 244.354, 8.523, 11.412, 3.123, 9.347, 49.471 and 5.233. Furthermore, we may conclude that multicollinearity issues do not undermine our findings due to a maximum value of less than 0.80 [ 66 , 67 ].…”

Section: Methodsmentioning

confidence: 88%

Green economic growth in BRIC and CIVETS countries: The effects of trade openness and sustainable development goals

Halim,

Moudud-Ul-Huq

2024

Heliyon

Self Cite

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 88%

Green economic growth in BRIC and CIVETS countries: The effects of trade openness and sustainable development goals

Halim,

Moudud-Ul-Huq

2024

Heliyon

Self Cite

View full text Add to dashboard Cite

“…A loan is an act of trust, esteem and consideration that takes the form of a credit in cash or in kind granted by a person, in return for a promise to repay within a period of time generally agreed in advance. Loan is therefore a financial operation carried out by a bank or other credit institution, which consists in making resources available to a customer, who in exchange undertakes to repay the sum by a specific date, with interest if necessary (Moudud- Ul-Huq et al, 2023). As a means of economic financing, it is used to fund operations on the financial markets, which can be highly profitable thanks to the high leverage provided by bank loans, but also very risky, destabilizing and whose social usefulness is not always well established.…”

Section: Loanmentioning

confidence: 99%

Comparative Machine Learning Models for Predicting Loan Fructification in a Semi-Urban Area

Mpia,

Syasimwa,

Muyisa

2024

AAES

View full text Add to dashboard Cite

The current research proposes a reliable and robust machine learning (ML) model which outperforms among six other models in predicting loan fructification obtained by entrepreneurs in a semi-urban area. The proposed model predicts if an entrepreneur can make grow a loan from a microfinance firm, a bank, a financial company, or an individual. The proposed model uses primary data collected from entrepreneurs residing in Butembo, a semi-urban town located in eastern of the democratic republic of Congo as dataset. This study uses a dataset that contains 5868 records. Seven ML model performances are compared in the loan fructification prediction: support vector machine, random forest, extra-trees, decision tree, naïve bayes, k-nearest neighbors, and logistic regression. Support vector machine reveals to be the best model for predicting loan fructification using features such as age, years of working experience of the entrepreneur, entrepreneur loan repayment conviction, used mean by the lender to recover its loan, entrepreneur opinion on the disadvantage of taking out a loan, capacity of the entrepreneur to invest after obtaining loan, entrepreneur position on the possibility of launching a business without a loan, entrepreneur willingness to apply again for loan in the future, success project after obtaining loan. The study uses accuracy, recall, precision, and f-score as metrics to assess the developed models. The four metrics for support vector machine scored 95%, 95%, 83%, and 83%, respectively. The proposed model confirms the robustness of support vector machine in predicting loan fructification.

show abstract

“…1 Transparency will be critical to the region's economic recovery as it deals with the worldwide coronavirus pandemic and decline in oil prices. While cost estimates for these crises are in flux, the shocks have exacerbated the region's weak economic development, partly due to a lack of data transparency (Moudud-Ul-Huq et al 2023). The MENA region requires more aggressive and comprehensive economic changes.…”

Section: Introductionmentioning

confidence: 99%

The Nexus of Banks’ Competition, Ownership Structure, and Economic Growth on Credit Risk and Financial Stability

et al. 2023

Self Cite

View full text Add to dashboard Cite

The main object of this research is to find out the nexus of banks’ competition, ownership structure, and economic growth on credit risk and financial stability. In addition, it examines the level of financial stability, economic growth, and ownership structure in the Middle East and North African (MENA) economies. The generalized method of moments (GMM) method was used to examine this study. The study used an unbalanced panel dataset from 2011 to 2021 in MENA countries. This research demonstrates a negative relationship between economic growth, credit risk, and financial stability in MENA economies; nevertheless, it proves an insignificant effect among them. It also shows that the lower the level of bank competition, the lower the level of bank risk taking, and the better the level of financial stability. It further found that market competition and bank ownership structure had a homogenous effect on financial stability when looking at the impact of competition and bank ownership structure. In the long-term sense, the square term of competition is highly favorable with financial stability models ((Lerner square × Islamic banks), (Lerner square × commercial banks), (Lerner square × specialized government institutions)). However, financial stability improves with time, as seen by the competition square term bank ownership structure (a square measure of competition) with the Lerner index (LI) and the Herfindahl–Hirschman index total assets (HHIA). This finding of the square measure of competition is supported by competition stability theory. However, this study also proved that Islamic and commercial banks are less vulnerable to credit risk than specialized government institutions (SGI). This study scrutinized how MENA economies can remain stable through banking competition. This study builds a new brand of literature review. As a result, this research gives MENA policymakers better ideas for making policies that help the banking environment.

show abstract

The Nexus of Competition, Loan Quality, and Ownership Structure for Risk-Taking Behaviour

Cited by 8 publications

References 45 publications

Green economic growth in BRIC and CIVETS countries: The effects of trade openness and sustainable development goals

Green economic growth in BRIC and CIVETS countries: The effects of trade openness and sustainable development goals

Comparative Machine Learning Models for Predicting Loan Fructification in a Semi-Urban Area

The Nexus of Banks’ Competition, Ownership Structure, and Economic Growth on Credit Risk and Financial Stability

Contact Info

Product

Resources

About