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ANNs-BASED EARLY WARNING SYSTEM FOR INDONESIAN ISLAMIC BANKS

Author

Listed:
  • Saiful Anwar

    (PT. Bank BRI Syariah)

  • A.M Hasan Ali

    (UIN Syarif Hidayatullah)

Abstract

This research proposes a development of Early Warning System (EWS) model towards the financial performance of Islamic bank using financial ratios and macroeconomic indicators. The result of this paper is ready-to-use algorithm for the issue that needs to be solved shortly using machine learning technique which is not widely applied in Islamic banking. The research was conducted in three stages using Artificial Neural Networks (ANNs) technique: the selection of variables that significantly affect financial performance, developing an algorithm as a predictor and testing the predictor algorithm using out of sample data. Finally, the research concludes that the proposed model results in 100% accuracy for predicting Islamic bank’s financial conditions for the next two consecutive months.

Suggested Citation

  • Saiful Anwar & A.M Hasan Ali, 2018. "ANNs-BASED EARLY WARNING SYSTEM FOR INDONESIAN ISLAMIC BANKS," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 20(3), pages 1-18, January.
    • Handle: RePEc:idn:journl:v:20:y:2018:i:3:p:1-18
    DOI: https://doi.org/10.21098/bemp.v20i3.856
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    References listed on IDEAS

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    1. Takahashi, Kichinosuke & Kurokawa, Yukiharu & Watase, Kazunori, 1984. "Corporate bankruptcy prediction in Japan," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 229-247, June.
    2. Patricia M. West & Patrick L. Brockett & Linda L. Golden, 1997. "A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer Choice," Marketing Science, INFORMS, vol. 16(4), pages 370-391.
    3. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    4. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    5. Maximilian Hall & Dadang Muljawan & Lolita Moorena, 2009. "Using the artificial neural network to assess bank credit risk: a case study of Indonesia," Applied Financial Economics, Taylor & Francis Journals, vol. 19(22), pages 1825-1846.
    6. Gunther, Jeffery W. & Moore, Robert R., 2003. "Early warning models in real time," Journal of Banking & Finance, Elsevier, vol. 27(10), pages 1979-2001, October.
    7. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    8. Arena, Marco, 2008. "Bank failures and bank fundamentals: A comparative analysis of Latin America and East Asia during the nineties using bank-level data," Journal of Banking & Finance, Elsevier, vol. 32(2), pages 299-310, February.
    9. MAHMOOD H. AL-OSAIMY & Ahmed S. Bamakhramah, 2004. "An Early Warning System for Islamic Banks Performance نظام الإنذار المبكر لأداء البنوك الإسلامية," Journal of King Abdulaziz University: Islamic Economics, King Abdulaziz University, Islamic Economics Institute., vol. 17(1), pages 3-14, January.
    10. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    11. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
    12. Pettway, Richard H & Sinkey, Joseph F, Jr, 1980. "Establishing On-Site Bank Examination Priorities: An Early-Warning System Using Accounting and Market Information," Journal of Finance, American Finance Association, vol. 35(1), pages 137-150, March.
    13. Espahbodi, Pouran, 1991. "Identification of problem banks and binary choice models," Journal of Banking & Finance, Elsevier, vol. 15(1), pages 53-71, February.
    14. Altman, Edward I. & Brenner, Menachem, 1981. "Information Effects and Stock Market Response to Signs of Firm Deterioration," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 16(1), pages 35-51, March.
    15. Meyer, Paul A & Pifer, Howard W, 1970. "Prediction of Bank Failures," Journal of Finance, American Finance Association, vol. 25(4), pages 853-868, September.
    16. Mohammad Umer Chapra, 2009. "The Global Financial Crisis: Can Islamic Finance Help? الأزمة المالية العالمية: هل يساعد التمويل الإسلامي؟," Chapters of books published by the Islamic Economics Institute, KAAU or its faculty members., in: Islamic Economics Institute (ed.),Issues in the International Financial Crisis from an Islamic Perspective-05 قضايا في الأزمة المالية العالمية من منظور إسلامي, edition 1, chapter 2, pages 11-18, King Abdulaziz University, Islamic Economics Institute..
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    Cited by:

    1. Ali Namaki & Reza Eyvazloo & Shahin Ramtinnia, 2023. "A systematic review of early warning systems in finance," Papers 2310.00490, arXiv.org.
    2. Juhro, Solikin M. & Narayan, Paresh Kumar & Iyke, Bernard Njindan & Trisnanto, Budi, 2020. "Is there a role for Islamic finance and R&D in endogenous growth models in the case of Indonesia?," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
    3. Berry A. Harahap & Pakasa Bary & Anggita Cinditya M. Kusuma, 2020. "The Determinants of Indonesia’s Business Cycle," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 9(special i), pages 215-235.
    4. Teck-Lee Wong & Wee-Yeap Lau & Tien-Ming Yip, 2020. "Cashless Payments and Economic Growth: Evidence from Selected OECD Countries," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 9(special i), pages 189-213.
    5. Rani Wijayanti & Sagita Rachmanira, 2020. "Early Warning System for Government Debt Crisis in Developing Countries," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 9(special i), pages 103-124.
    6. Salisu, Afees A. & Ndako, Umar B. & Adediran, Idris A. & Swaray, Raymond, 2020. "A fractional cointegration VAR analysis of Islamic stocks: A global perspective," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    7. Rakesh Padhan & K. P. Prabheesh, 2019. "Effectiveness Of Early Warning Models: A Critical Review And New Agenda For Future Direction," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 22(4), pages 457-484.
    8. Mehreen Mehreen & Maran Marimuthu & Samsul Ariffin Abdul Karim & Amin Jan, 2020. "Proposing a Multidimensional Bankruptcy Prediction Model: An Approach for Sustainable Islamic Banking," Sustainability, MDPI, vol. 12(8), pages 1-18, April.
    9. Harun, Cicilia A. & Taruna, Aditya Anta & Ramdani,, 2021. "Capturing the nonlinear impact in distress state: Enhancing scenario design of stress test," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 265-288.
    10. Phan, Dinh Hoang Bach & Narayan, Paresh Kumar & Rahman, R. Eki & Hutabarat, Akhis R., 2020. "Do financial technology firms influence bank performance?," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).

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    More about this item

    Keywords

    Early Warning System; Artificial Neural Networks; Islamic Banks; Financial Distress;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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