ANNs-Based Early Warning System for Indonesian Islamic Banks
AbstractThis 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.
Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 589-609.
Altman, E. I. & Brenner, M. (1981). Information Effects and Stock Market Response to Signs of Firm Deterioration. Journal of Financial & Quantitative Analysis, 16(1), 35-51.
Altman, E. I. (1993). Corporate Financial Distress and Bankruptcy: A Complete Guide to Predicting & Avoiding Distress and Profiting from Bankruptcy (2nd Edition ed.). New York: John Wiley & Sons. Inc.
Anwar, S., & Mikami, Y. (2011). Comparing Accuracy Performance of ANN,
MLR, and GARCH Model in Predicting Time Deposit Return of Islamic Bank.
International Journal of Trade Economics and Finance, 2(1), 44-51.
Anwar, S., & Ismal, R. (2011, May). Robustness Analysis of Artificial Neural
Networks and Support Vector Machine in Making Prediction. Presented at
Parallel and Distributed Processing with Applications (ISPA), 2011 IEEE 9th
International Symposium on (pp. 256-261). IEEE.
Arena, M., (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 and Finance, 32, 299-310.
Beaver, W. H. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4, 71-111.
Bellovary, J. L., Giacomino, D. E., & Akers, M. D. (2007). A review of bankruptcy prediction studies: 1930 to present. Journal of Financial Education, 1-42.
Chapra, M. U. (2009). The Global Financial Crisis: Can Islamic Finance Help
Minimise the Severity and Frequency of Such a Crisis in the Future?. Islam and Civilisational Renewal (ICR), 1(2).
Cybinski, P. (2001). Description, explanation, prediction–the evolution of
bankruptcy studies?. Managerial Finance, 27(4), 29-44.
Espahbodi, P. (1991). Identification of Problem Banks and Binary Choice Models. Journal of Banking & Finance, 15(1), 53-71.
Frydman, H., E. Altman and D. Kao. (1985). Introducing recursive partitioning for Winter 2007 15 financial classifications: The case of financial distress. The Journal of Finance, 40(1): 269-291.
Gunther, J. W., & Moore, R. R. (2003). Early warning models in real time. Journal of banking & finance, 27(10), 1979-2001.
Hall, M. J. B., & Muljawan, D. Suprayogi, & Moorena, L. (2009). Using the artificial neural network to assess bank credit risk: a case study of Indonesia. Applied Financial Economics, 19(22), 1825-1846.
Keuangan, Otoritas Jasa. (2015). Statistik Perbankan Indonesia, from http://www.ojk.go.id/data-statistik-perbankan-indonesia.
Liao, S. H., Chu, P. H., & Hsiao, P. Y. (2012). Data mining techniques and
applications–A decade review from 2000 to 2011. Expert Systems with
Applications, 39(12), 11303-11311.
Martin, D. (1977). Early Warning of Bank Failure: A Logit Regression Approach. Journal of Banking & Finance, 1(3), 249-276.
Meyer, P. A., & Pifer, H. W. (1970). Prediction of bank failures. The Journal of Finance, 25(4), 853-868.
Othman, J. (2012). Analysing Financial Distress in Malaysian Islamic Banks:
Exploring Integrative Predictive Methods. Doctoral dissertation. Durham
Pettway, R. H., & Sinkey, J. F. (1980). Establishing On‐Site Bank Examination Priorities: An Early‐Warning System Using Accounting and Market Information. The Journal of Finance, 35(1), 137-150.
Takahashi, K., Y. Kurokawa and K: Watase. (1984). Corporate bankruptcy prediction in Japan. Journal of Banking and Finance, 8(2): 229-247.
Tsang, E., Yung, P., & Li, J. (2004). EDDIE-Automation, a decision support tool for financial forecasting. Decision Support Systems, 37(4), 559-565.
Wen, W., Chen, Y.H., and Chen, I.C. (2008) “A knowledge-based decision support system for measuring enterprise performance”. Knowledge-Based Systems, Vol 21, p.148–163, 2008.
West, P. M., P. L. Brockett, and L. L. Golden (1997), A comparative analysis of neural networks and statistical methods for predicting consumer choice, Marketing Science, 16(4), 370-391.
Unless otherwise indicated, each paper published in The Bulletin of Monetary Economics and Banking. Authors do not need to contact the journal to obtain rights to reuse their own material. They are automatically granted permission to do the following:
- Reuse the article in print collections of their own writing.
- Present a work orally in its entirety.
- Use an article in a thesis and/or dissertation.
- Reproduce an article for use in the author's courses. (If the author is employed by an academic institution, that institution also may reproduce the article for teaching purposes.)
- Reuse a figure, photo and/or table in future commercial and noncommercial works.
- Post a copy of the paper.
- Link to the journal site containing the final edited PDFs created by the publisher.
Unless otherwise indicated, the authors and the journal grant permission to reproduce and distribute for nonprofit educational uses material published in the journal, provided that: (1) in the case of copies distributed in class, students are charged no more than the cost of duplication; (2) the copied work is well identified with a proper notice of copyright affixed to each copy.
Permission to reproduce and to distribute any work published in The Bulletin of Monetary Economics and Banking should be directed to author(s). All such reproduction must identify the author(s), the Journal, the volume, the number of the first page, and the year of the work’s publication in the Journal.