IDEAS home Printed from https://ideas.repec.org/a/eee/accoun/v43y2008i1p45-65.html
   My bibliography  Save this article

Using accounting ratios to distinguish between Islamic and conventional banks in the GCC region

Author

Listed:
  • Olson, Dennis
  • Zoubi, Taisier A.

Abstract

This study determines whether it is possible to distinguish between conventional and Islamic banks in the Gulf Cooperation Council (GCC) region on the basis of financial characteristics alone. Islamic banks operate under different principles, such as risk sharing and the prohibition of interest, yet both types of banks face similar competitive conditions. The combination of effects makes it unclear whether financial ratios will differ significantly between the two categories of banks. We input 26 financial ratios into logit, neural network, and k-means nearest neighbor classification models to determine whether researchers or regulators could use these ratios to distinguish between the two types of banks. Although the means of several ratios are similar between the two categories of banks, non-linear classification techniques (k-means nearest neighbors and neural networks) are able to correctly distinguish Islamic from conventional banks in out-of-sample tests at about a 92% success rate.

Suggested Citation

  • Olson, Dennis & Zoubi, Taisier A., 2008. "Using accounting ratios to distinguish between Islamic and conventional banks in the GCC region," The International Journal of Accounting, Elsevier, vol. 43(1), pages 45-65, March.
    • Handle: RePEc:eee:accoun:v:43:y:2008:i:1:p:45-65
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0020-7063(08)00004-6
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Philip Molyneux & Munawar Iqbal, 2005. "Banking and Financial Systems in the Arab World," Palgrave Macmillan Studies in Banking and Financial Institutions, Palgrave Macmillan, number 978-0-230-51212-2.
    2. Pesaran, M Hashem & Timmermann, Allan, 1992. "A Simple Nonparametric Test of Predictive Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-565, October.
    3. Pamela K. Coats & L. Franklin Fant, 1993. "Recognizing Financial Distress Patterns Using a Neural Network Tool," Financial Management, Financial Management Association, vol. 22(3), Fall.
    4. Grais, Wafik & Pellegrini, Matteo, 2006. "Corporate governance and Shariah compliance in institutions offering Islamic financial services," Policy Research Working Paper Series 4054, The World Bank.
    5. Aggarwal, Rajesh K & Yousef, Tarik, 2000. "Islamic Banks and Investment Financing," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 32(1), pages 93-120, February.
    6. Olson, Dennis & Mossman, C, 2001. "Cross-Correlations and Predictability of Stock Returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(2), pages 145-160, March.
    7. Waleed Murjan & Cristina Ruza, 2002. "The competitive nature of the Arab Middle Eastern banking markets," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 8(4), pages 267-274, November.
    8. Knez, Peter J & Ready, Mark J, 1996. "Estimating the Profits from Trading Strategies," The Review of Financial Studies, Society for Financial Studies, vol. 9(4), pages 1121-1163.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meslier, Céline & Risfandy, Tastaftiyan & Tarazi, Amine, 2020. "Islamic banks’ equity financing, Shariah supervisory board, and banking environments," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
    2. Raouf, Hajar & Ahmed, Habib, 2022. "Risk governance and financial stability: A comparative study of conventional and Islamic banks in the GCC," Global Finance Journal, Elsevier, vol. 52(C).
    3. Shibani, Osama & De Fuentes, Cristina, 2017. "Differences and similaritites between corporate governance principles in Islamic banks and Conventional banks," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1005-1010.
    4. Tasawar Nawaz, 2019. "Exploring the Nexus Between Human Capital, Corporate Governance and Performance: Evidence from Islamic Banks," Journal of Business Ethics, Springer, vol. 157(2), pages 567-587, June.
    5. Athari, Seyed Alireza & Adaoglu, Cahit & Bektas, Eralp, 2016. "Investor protection and dividend policy: The case of Islamic and conventional banks," Emerging Markets Review, Elsevier, vol. 27(C), pages 100-117.
    6. Joseph Zhi Bin Ling & Albert K. Tsui & Zhaoyong Zhang, 2021. "Trading Macro-Cycles of Foreign Exchange Markets Using Hybrid Models," Sustainability, MDPI, vol. 13(17), pages 1-20, September.
    7. Marcos Álvarez-Díaz & Alberto Álvarez, 2002. "Predicción No-Lineal De Tipos De Cambio: Algoritmos Genéticos, Redes Neuronales Y Fusión De Datos," Working Papers 0205, Universidade de Vigo, Departamento de Economía Aplicada.
    8. Dal Bianco, Marcos & Camacho, Maximo & Perez Quiros, Gabriel, 2012. "Short-run forecasting of the euro-dollar exchange rate with economic fundamentals," Journal of International Money and Finance, Elsevier, vol. 31(2), pages 377-396.
    9. Oznur Ozdamar & Eleftherios Giovanis & Sahizer Samuk, 2020. "State business relations and the dynamics of job flows in Egypt and Turkey," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 10(4), pages 519-558, December.
    10. Samuel Fosu, 2013. "Banking Competition in Africa: Sub-regional Comparative Studies," Discussion Papers in Economics 13/12, Division of Economics, School of Business, University of Leicester, revised Jun 2013.
    11. Michał Dominik Stasiak & Żaneta Staszak, 2024. "Modelling and Forecasting Crude Oil Prices Using Trend Analysis in a Binary-Temporal Representation," Energies, MDPI, vol. 17(14), pages 1-13, July.
    12. Baris Soybilgen & Ege Yazgan, 2017. "An evaluation of inflation expectations in Turkey," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 17(1), pages 1-31–38.
    13. Beck, Thorsten & Demirgüç-Kunt, Asli & Merrouche, Ouarda, 2013. "Islamic vs. conventional banking: Business model, efficiency and stability," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 433-447.
    14. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    15. Yu-Shan Chen & Ke-Chiun Chang, 2009. "Using neural network to analyze the influence of the patent performance upon the market value of the US pharmaceutical companies," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(3), pages 637-655, September.
    16. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    17. Lahiri, Kajal & Peng, Huaming & Zhao, Yongchen, 2015. "Testing the value of probability forecasts for calibrated combining," International Journal of Forecasting, Elsevier, vol. 31(1), pages 113-129.
    18. Bashir, Abdel-Hameed M., 2003. "Determinants Of Profitability In Islamic Banks: Some Evidence From The Middle East," Islamic Economic Studies, The Islamic Research and Training Institute (IRTI), vol. 11, pages 32-57.
    19. Massimiliano Marzo & Paolo Zagaglia, 2010. "Volatility forecasting for crude oil futures," Applied Economics Letters, Taylor & Francis Journals, vol. 17(16), pages 1587-1599.
    20. Giacomo di Tollo & Joseph Andria & Gianni Filograsso, 2023. "The Predictive Power of Social Media Sentiment: Evidence from Cryptocurrencies and Stock Markets Using NLP and Stochastic ANNs," Mathematics, MDPI, vol. 11(16), pages 1-18, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:accoun:v:43:y:2008:i:1:p:45-65. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620179 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.