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Using partial least square discriminant analysis to distinguish between Islamic and conventional banks in the MENA region
Author(s) -
Sghaier Asma,
Ben Jabeur Sami,
Bannour Boutheina
Publication year - 2018
Publication title -
review of financial economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.347
H-Index - 41
eISSN - 1873-5924
pISSN - 1058-3300
DOI - 10.1002/rfe.1018
Subject(s) - profitability index , linear discriminant analysis , logistic regression , econometrics , stability (learning theory) , islam , financial ratio , investment (military) , partial least squares regression , multiple discriminant analysis , economics , financial system , mathematics , statistics , computer science , finance , machine learning , geography , archaeology , politics , political science , law
The deterioration of bank profitability poses a threat not only to the interests of consumers and internal staff members but also affects investors who may equally suffer from significant financial losses. It is important to establish an effective system which assists investors in their investment choices. In prior literature, traditional models have been developed, but achieved short‐term performances such as logistic regression and discriminant analysis. This paper applies a partial least squares discriminant analysis (PLS‐DA) to distinguish between conventional and Islamic banks in the Middle East and North Africa (MENA) region based on the financial information for the period 2005–2011. This method can successfully identify the non‐linearity and correlations between financial indicators. The results demonstrate superior performance of the proposed method. On one hand, our model can select all financial ratios to distinguish between banks and at the same time identify the most important variables in the distinction process. On the other hand, the proposed model has high levels in terms of accuracy and stability.