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Using partial least square discriminant analysis to distinguish between Islamic and conventional banks in the MENA region

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  • Asma Sghaier
  • Sami Ben Jabeur
  • Boutheina Bannour

Abstract

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.

Suggested Citation

  • Asma Sghaier & Sami Ben Jabeur & Boutheina Bannour, 2018. "Using partial least square discriminant analysis to distinguish between Islamic and conventional banks in the MENA region," Review of Financial Economics, John Wiley & Sons, vol. 36(2), pages 133-148, April.
  • Handle: RePEc:wly:revfec:v:36:y:2018:i:2:p:133-148
    DOI: 10.1002/rfe.1018
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    Cited by:

    1. Sami Ben Jabeur & Nicolae Stef & Pedro Carmona, 2023. "Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 715-741, February.
    2. Shah, Syed Faisal & Albaity, Mohamed, 2022. "The role of trust, investor sentiment, and uncertainty on bank stock return performance: Evidence from the MENA region," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).
    3. Albaity, Mohamed & Shah, Syed Faisal & Al-Tamimi, Hussein A.Hassan & Rahman, Mahfuzur & Thangavelu, Shanmugam, 2023. "Country risk and bank returns: Evidence from MENA countries," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    4. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).

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