Income Structure, Profitability and Stability in the Tunisian Banking Sector

Belguith, Houda; Bellouma, Meryem

doi:10.25125/engineering-journal-ijoer-may-2017-14

Cited by 4 publications

(5 citation statements)

References 31 publications

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“…These results support the "bad management II" hypothesis, suggesting that past earnings are negatively connected with increases in problems loan. The findings are consistent with Belaid & Bellouma (2016) and Louzis, Vouldis & Metaxas (2012).…”

Section: Ardl Model Estimationsupporting

confidence: 84%

“…The literature related to credit risk has examined the relationship between profitability and loan portfolio quality of banks and has claimed that bad performance may proxy lower quality of management skills concerning the lending activity, similar reasoning to the "bad management I" hypothesis, cost efficiency ratio being considered as a proxy for management quality. This indicates the existence of a negative association between past earnings and problem loans (Belaid & Bellouma, 2016). Using evidence from the Greek banking sector, Louzis, Vouldis & Metaxas (2012) show a negative relationship between performance (proxied by ROE) and problem loans measured by NPLs.…”

Section: Earnings Quality and Loan Portfolio Qualitymentioning

confidence: 96%

“…Loan portfolio quality is one the issues receiving great attention regarding research carried out in the area of financial institutions. Many investigations conducted on the causes of bank failures have indicated that failing financial institutions show higher non-performing loans ratios, prior to failure and that asset quality is found to be statistically significant predictor of insolvency (Belaid & Bellouma, 2016). Many studies have investigated the factors affecting loan portfolio quality of banks, which are microeconomic or bank specific determinants, representing the variables in CAMEL model.…”

Section: Literature Review and Hypothesesmentioning

confidence: 99%

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The Effect of CAMEL Model on Loan Portfolio Quality of the Haitian Banking Sector

Sifrain

2024

JFRM

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This paper examines the impact of CAMEL approach on Non-Performing Loans (NPLs) of the banking sector in Haiti, using monthly data from October 2019 to April 2023. The statistical analysis is carried out using the Auto-Regressive Distributed Lagged (ARDL)-Error Correction Model (ECM) approach. The results confirm the "moral hazard" hypothesis that low-capitalization of banks implies deterioration of loan portfolio quality. Similarly, the findings support the "bad management II" hypothesis that past earnings are negatively associated with increases in problem loans. Banks' liquidly at times t and t − 1 has a positive and significant effect on NPLs, while at time t − 4 banks' liquidity is negatively and significantly correlated with NPLs. In contrast, management efficiency has no influence on NPLs. Regarding the control variable of the study, the exchange rate, at time t, it is positively and significantly related to NPLs, supporting the "exchange rate" hypothesis that an increase in exchange rate leads to problem loans. Though, the findings show a significant and negative effect of exchange rate on NPLs at time t − 4.In addition, the CUSUM and CUSUMSQ statistics are well within the 5% critical bounds, implying that short and long-term coefficients in the ARDL-ECM are stable. Lastly, our findings highlight the importance of taking into account CAMEL variables and macroeconomic variables like the exchange rate when assessing the loan portfolio quality of Haitian banks from a financial stability perspective.

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Section: Ardl Model Estimationsupporting

confidence: 84%

Section: Earnings Quality and Loan Portfolio Qualitymentioning

confidence: 96%

Section: Literature Review and Hypothesesmentioning

confidence: 99%

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The Effect of CAMEL Model on Loan Portfolio Quality of the Haitian Banking Sector

Sifrain

2024

JFRM

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“…Ojukwu-Ogba [7] discusses the influence of digital currency on the global financial system, and thinks that the growth of digital currency will increase the fragility and uncertainty of the financial system. Belguith et al [8] applied SVM algorithm to the evaluation of financial wind direction, and found that this algorithm can effectively identify the risks of financial markets and give pre-alarm. Rusmanto et al [9] applied SVM algorithm to stock price prediction, and proved the effectiveness and superiority of this algorithm in stock price prediction.…”

Section: Related Workmentioning

confidence: 99%

Analysis of the Impact of Digital Currency on Traditional Banking and Financial Stability

Gao

2023

AEMR

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Digital currency, as a representative product of blockchain technology, such as Bitcoin and Ethereum, has begun to have a far-reaching impact on traditional banking business, but also brought new challenges to financial stability. This article studies the financial risk assessment and pre-alarm model based on support vector machine (SVM) algorithm, aiming at accurately assessing the risks in digital currency market and providing timely pre-alarm. Through comparative experiments, it is found that the algorithm has obvious advantages in dealing with high-dimensional and nonlinear problems, and can accurately classify and warn financial risks. The accuracy of the model is as high as 96.88%, and the average absolute error is reduced by 40.22%, which proves the effectiveness and superiority of the model. Generally speaking, the financial risk assessment and pre-alarm model studied in this article provides a scientific and effective method for digital currency market risk management, which has important application value. Regulators need to invest more resources and energy to monitor and analyze the dynamic changes in the digital currency market and reduce financial risks.

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“…Notre résultat confirme aussi le résultat trouvé par (Belaid et Bellouma 2016) qui ont étudié les déterminants des prêts non performants des banques tunisiennes. Les résultats obtenus avec leurs modèles d'étude montrent clairement que la qualité des prêts dans le secteur bancaire tunisien est affectée positivement par le risque de liquidité.…”

Section: Les Résultats De Causalité De Granger Et Interprétationsunclassified

impact du risque opérationnel sur le risque de crédit et le risque de liquidité

BOUABDALLAH¹,

HENCHIRI²

2020

J.o A.F.

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Cette étude tente de vérifier s’il existe une interaction entre le risque opérationnel et les autres risques financiers. L'interaction entre le risque opérationnel et les autres risques imprègne toute la gamme des événements défavorables qui affectent les résultats bancaires dans les pays en vois de développement. L'interaction résume la dynamique réelle du risque opérationnel dans le secteur bancaire. De cette façon, un risque opérationnel augmente la probabilité que les contreparties soient en défaut de paiement. Une interaction similaire existe entre le risque opérationnel et le risque de liquidité. Nous effectuons des tests de causalité de Granger dans un cadre dynamique d’estimation de panel GMM sur un ensemble de données sur les banques tunisiennes, qui comprend principalement 10 banques cotées de 1998 à 2016. Nous montrons que le risque opérationnel est positivement corrélé avec le risque de crédit. Cependant, il n’a pas d’impact significatif sur le risque de liquidité.

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Income Structure, Profitability and Stability in the Tunisian Banking Sector

Abstract: Abstract-

Cited by 4 publications

References 31 publications

The Effect of CAMEL Model on Loan Portfolio Quality of the Haitian Banking Sector

The Effect of CAMEL Model on Loan Portfolio Quality of the Haitian Banking Sector

Analysis of the Impact of Digital Currency on Traditional Banking and Financial Stability

impact du risque opérationnel sur le risque de crédit et le risque de liquidité

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