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Determinants of Islamic Banks’ Profitability Using Panel Data Analysis and ANFIS Approaches in Saudi Arabia محددات ربحية المصارف الإسلامية باستخدام تحليل البيانات المَقْطعية وأساليب الاستدلال التَّكَيُّفي في المملكة العربية السعودية

Author

Listed:
  • Metin Aktas

    (Professor, Department of Business Faculty of Economics and Administrative Sciences Nigde Omer Halisdemir University, Nigde, Turkey)

  • Osman Taylan

    (Professor, Department of Industrial Engineering, Faculty of Engineering King Abdulaziz University, Jeddah, Saudi Arabia)

Abstract

The purpose of this study is to determine the relationship between profitability and financial ratios of Islamic banks in Saudi Arabia. To accomplish this goal, quarterly data of four Islamic banks from 2009 to 2017 were considered. The Artificial Neural Network (ANN) and Fuzzy System, or ANFIS (Adaptive Neuro-Fuzzy Inference System), was employed to predict the profitability of financial ratios. To determine impacts of financial ratios on the profitability measures, the study includes panel data analysis. The average prediction error for the ANFIS model of return on asset (y1) with four rules was 0.3537%, and the return on equity (y2) with five rules was 0.31829%. The results showed that all the explanatory variables, except stock capital gain ratio, have a significant positive relation with profitability measure of either return on asset or return on equity of Islamic banks in Saudi Arabia. However, only total equity to total asset and earning per share ratios have relation with both the profitability measures. The results of descriptive statistics, multiple regression, and ANFIS models established that successful outcome can be obtained for y1 and y2. Therefore, this study will be beneficial not only for the literature, but also for the investors and executives of Islamic banking. تَهْدفُ الدراسة إلى تحديد العلاقة بين الربحية والنسب المالية للبنوك الإسلامية في المملكة العربية السعودية. ولتحقيق هذا الغرض، تم النظر في البيانات ربع السنوية لأربعة بنوك خلال الفترة 2009م - 2017م. تم استخدام نموذج الاستدلال التَّكَيُّفي (ANFIS) المكون من شبكات عصبية اصطناعية للتنبؤ بربحية النسب المالية. ومن أجل تحديد تأثيرات النسب المالية على مقاييس الربحية تضمنت الدراسة تحليل البيانات المقطعية. كما تم حساب متوسط خطأ التنبؤ لنموذج (ANFIS) لعائد الأصول (y1) بأربعة ضوابط بمقدار (%0.3537) وتم العثور عليه لنموذج (ANFIS) لعائد حقوق الملكية (y2) مع خمسة (5) ضوابط بمقدار (%0.31829). أظهرت نتائج الدراسة أنه في حين أن جميع المتغيرات التفسيرية باستثناء نسبة مكاسب رأس المال للأسهم لها علاقة إيجابية مع مقياس الربحية للعائد على الأصول أو العائد على حقوق الملكية في المصارف الإسلامية في السعودية، إلا أن نسبة إجمالي حقوق الملكية إلى إجمالي الأصول وربح السهم لهما علاقة بمقاييس الربحية. أظهرت نتائج الدراسة المستندة إلى أدوات الإحصاء الوصفي، والانحدار المتعدد، ونماذج (ANFIS) أنه يمكن الحصول على نتائج ناجحة من متغيرات (y1) و (y2) . بناءً عليه يَعتقد مُعدوُ الدراسة أنها ستكون مفيدة ليس من الناحية العلمية فحسب، ولكن من الناحية العميلة بالنسبة للمستثمرين والمدراء التنفيذيين في المصارف الإسلامية.

Suggested Citation

  • Metin Aktas & Osman Taylan, 2021. "Determinants of Islamic Banks’ Profitability Using Panel Data Analysis and ANFIS Approaches in Saudi Arabia محددات ربحية المصارف الإسلامية باستخدام تحليل البيانات المَقْطعية وأساليب الاستدلال التَّكَي," Journal of King Abdulaziz University: Islamic Economics, King Abdulaziz University, Islamic Economics Institute., vol. 34(2), pages 19-40, July.
    • Handle: RePEc:abd:kauiea:v:34:y:2021:i:2:no:2:p:19-40
    DOI: 10.4197/Islec.34-2.2
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    References listed on IDEAS

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    More about this item

    Keywords

    Islamic banking; profitability; financial ratios; panel data analysis; neural-fuzzy prediction. المصرفية الإسلامية، الربحية، النسب المالية، تحليل بيانات القطاع، التنبؤ العصبي.;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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