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Enregistrement W2755234905 · doi:10.17722/ijme.v9i2.932

Determinants of Non Performing Loans: Evidence from Sri Lanka

2017· article· en· W2755234905 sur OpenAlexvenueno aff
Pivithuru Janak Kumarasinghe

Notice bibliographique

RevueInternational Journal of Management Excellence · 2017
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueBanking stability, regulation, efficiency
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésNon-performing loanDefaultRecessionSri lankaLoanFinancial crisisFinancial systemEconomicsBusinessInterest rateQuality (philosophy)Development economicsMonetary economicsFinanceMacroeconomicsSocioeconomics

Résumé

récupéré en direct d'OpenAlex

In the recent past, the global financial crisis and the subsequent recession in many developed countries have increased households’ and firms’ defaults, causing significant losses to the banks. Case of Sri Lanka is no difference. The changes in the economic conditions are believed to have a critical role to play in determining the level of nonperforming loans. Regulators all over the world have started to pay more attention to the credit quality of the Banks and strengthened the regulatory frameworks. This paper attempts to study the macroeconomic determinants of banks’ loan quality in Sri Lanka by analyzing secondary data over the period 1998–2014. The methodology to be adopted for the study was arrived at upon careful review of the literature and following the empirical studies conducted on the determinants of the nonperforming loans. The finding of the analysis is that, out of the six determinants, GDP growth rate and the Export Growth are significant in determining the level of the NPLs in the Sri Lankan banking sector. The relationship of the GDP with the NPL is found to be positive which is not consistent with the majority of the empirical findings Keywords: Nonperforming Loans, Macroeconomic Determinants, In the recent past, the global financial crisis and the subsequent recession in many developed countries have increased households’ and firms’ defaults, causing significant losses to the banks. Case of Sri Lanka is no difference. The changes in the economic conditions are believed to have a critical role to play in determining the level of nonperforming loans. Regulators all over the world have started to pay more attention to the credit quality of the Banks and strengthened the regulatory frameworks. This paper attempts to study the macroeconomic determinants of banks’ loan quality in Sri Lanka by analyzing secondary data over the period 1998–2014. The methodology to be adopted for the study was arrived at upon careful review of the literature and following the empirical studies conducted on the determinants of the nonperforming loans. The finding of the analysis is that, out of the six determinants, GDP growth rate and the Export Growth are significant in determining the level of the NPLs in the Sri Lankan banking sector. The relationship of the GDP with the NPL is found to be positive which is not consistent with the majority of the empirical findings Keywords: Nonperforming Loans, Macroeconomic Determinants, In the recent past, the global financial crisis and the subsequent recession in many developed countries have increased households’ and firms’ defaults, causing significant losses to the banks. Case of Sri Lanka is no difference. The changes in the economic conditions are believed to have a critical role to play in determining the level of nonperforming loans. Regulators all over the world have started to pay more attention to the credit quality of the Banks and strengthened the regulatory frameworks. This paper attempts to study the macroeconomic determinants of banks’ loan quality in Sri Lanka by analyzing secondary data over the period 1998–2014. The methodology to be adopted for the study was arrived at upon careful review of the literature and following the empirical studies conducted on the determinants of the nonperforming loans. The finding of the analysis is that, out of the six determinants, GDP growth rate and the Export Growth are significant in determining the level of the NPLs in the Sri Lankan banking sector. The relationship of the GDP with the NPL is found to be positive which is not consistent with the majority of the empirical findings Keywords: Nonperforming Loans, Macroeconomic Determinants, In the recent past, the global financial crisis and the subsequent recession in many developed countries have increased households’ and firms’ defaults, causing significant losses to the banks. Case of Sri Lanka is no difference. The changes in the economic conditions are believed to have a critical role to play in determining the level of nonperforming loans. Regulators all over the world have started to pay more attention to the credit quality of the Banks and strengthened the regulatory frameworks. This paper attempts to study the macroeconomic determinants of banks’ loan quality in Sri Lanka by analyzing secondary data over the period 1998–2014. The methodology to be adopted for the study was arrived at upon careful review of the literature and following the empirical studies conducted on the determinants of the nonperforming loans. The finding of the analysis is that, out of the six determinants, GDP growth rate and the Export Growth are significant in determining the level of the NPLs in the Sri Lankan banking sector. The relationship of the GDP with the NPL is found to be positive which is not consistent with the majority of the empirical findings Keywords: Nonperforming Loans, Macroeconomic Determinants, In the recent past, the global financial crisis and the subsequent recession in many developed countries have increased households’ and firms’ defaults, causing significant losses to the banks. Case of Sri Lanka is no difference. The changes in the economic conditions are believed to have a critical role to play in determining the level of nonperforming loans. Regulators all over the world have started to pay more attention to the credit quality of the Banks and strengthened the regulatory frameworks. This paper attempts to study the macroeconomic determinants of banks’ loan quality in Sri Lanka by analyzing secondary data over the period 1998–2014. The methodology to be adopted for the study was arrived at upon careful review of the literature and following the empirical studies conducted on the determinants of the nonperforming loans. The finding of the analysis is that, out of the six determinants, GDP growth rate and the Export Growth are significant in determining the level of the NPLs in the Sri Lankan banking sector. The relationship of the GDP with the NPL is found to be positive which is not consistent with the majority of the empirical findings Keywords: Nonperforming Loans, Macroeconomic Determinants, In the recent past, the global financial crisis and the subsequent recession in many developed countries have increased households’ and firms’ defaults, causing significant losses to the banks. Case of Sri Lanka is no difference. The changes in the economic conditions are believed to have a critical role to play in determining the level of nonperforming loans. Regulators all over the world have started to pay more attention to the credit quality of the Banks and strengthened the regulatory frameworks. This paper attempts to study the macroeconomic determinants of banks’ loan quality in Sri Lanka by analyzing secondary data over the period 1998–2014. The methodology to be adopted for the study was arrived at upon careful review of the literature and following the empirical studies conducted on the determinants of the nonperforming loans. The finding of the analysis is that, out of the six determinants, GDP growth rate and the Export Growth are significant in determining the level of the NPLs in the Sri Lankan banking sector. The relationship of the GDP with the NPL is found to be positive which is not consistent with the majority of the empirical findings Keywords: Nonperforming Loans, Macroeconomic Determinants, In the recent past, the global financial crisis and the subsequent recession in many developed countries have increased households’ and firms’ defaults, causing significant losses to the banks. Case of Sri Lanka is no difference. The changes in the economic conditions are believed to have a critical role to play in determining the level of nonperforming loans. Regulators all over the world have started to pay more attention to the credit quality of the Banks and strengthened the regulatory frameworks. This paper attempts to study the macroeconomic determinants of banks’ loan quality in Sri Lanka by analyzing secondary data over the period 1998–2014. The methodology to be adopted for the study was arrived at upon careful review of the literature and following the empirical studies conducted on the determinants of the nonperforming loans. The finding of the analysis is that, out of the six determinants, GDP growth rate and the Export Growth are significant in determining the level of the NPLs in the Sri Lankan banking sector. The relationship of the GDP with the NPL is found to be positive which is not consistent with the majority of the empirical findings

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,043
Score d'incertitude au seuil0,086

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,003
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,038
Tête enseignante GPT0,288
Écart entre enseignants0,250 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations9
Publié2017
Routes d'admission1
Résumé présentoui

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