IJE TRANSACTIONS C: Aspects Vol. 30, No. 12 (December 2017) 1427-1436    Article in Press

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Ali Siahi, A. H. Azadnia and M. Motameni
( Received: May 31, 2017 – Accepted: September 08, 2017 )

Abstract    Nowadays, prediction of corporate bankruptcy is one of the most important issues which have received great attentions among academia and practitioners. Although several studies have been accomplished in the field of bankruptcy prediction, less attention has been devoted for proposing a systematic approach based on fuzzy neural networks. The present study proposes fuzzy neural networks to predict bankruptcy of the listed companies in the Tehran stock exchange. Four input variables including growth, profitability, productivity and asset quality were used for prediction purpose. Moreover, the Altman's Z'score is used as the output variable. The results reveal that the proposed fuzzy neural network model has a high performance for the bankruptcy prediction of the companies.


Keywords    bankruptcy, prediction, fuzzy neural network


چکیده    امروزه، پیش بینی ورشکستگی شرکت های بزرگ یکی از مهم ترین مسائلی است که توجه زیادی را در میان دانشگاهیان و شاغلین از آن خود کرده است. اگر چه مطالعات متعددی در زمینه پیش بینی ورشکستگی انجام شده است، توجه کمتری به ارائه یک رویکرد سیستماتیک بر اساس شبکه های عصبی فازی اختصاص یافته است. مطالعه حاضر برای پیش بینی ورشکستگی شرکت های فهرست شده در بورس اوراق بهادار تهران، استفاده از شبکه‌های عصبی را پیشنهاد می‌دهد. چهار متغیر ورودی از جمله رشد، سودآوری، بهره وری و کیفیت دارایی برای هدف پیش بینی مورد استفاده قرار گرفت. علاوه بر این،نمره Z آلتمن به عنوان متغیر خروجی استفاده می شود. نتایج نشان می دهد که مدل شبکه عصبی فازی پیشنهادی عملکرد بالایی برای پیش بینی ورشکستگی شرکت ها دارد.

References    References 1.             Beaver, W.H., Financial ratios as predictors of failure. Journal of accounting research, 1966: p. 71-111. 2.             Ohlson, J.A., Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, 1980: p. 109-131. 3.             Tseng, F.-M. and Y.-C. Hu, Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks. Expert Systems with Applications, 2010. 37(3): p. 1846-1853. 4.             Pendharkar, P.C., A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem. Computers & Operations Research, 2005. 32(10): p. 2561-2582. 5.             Chauhan, N., V. Ravi, and D.K. Chandra, Differential evolution trained wavelet neural networks: Application to bankruptcy prediction in banks. Expert Systems with Applications, 2009. 36(4): p. 7659-7665. 6.             Cho, S., J. Kim, and J.K. Bae, An integrative model with subject weight based on neural network learning for bankruptcy prediction. Expert Systems with Applications, 2009. 36(1): p. 403-410. 7.             Varetto, F., Genetic algorithms applications in the analysis of insolvency risk. Journal of Banking & Finance, 1998. 22(10): p. 1421-1439. 8.             Min, J.H. and Y.-C. Lee, Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert systems with applications, 2005. 28(4): p. 603-614. 9.             Park, C.-S. and I. Han, A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction. Expert Systems with Applications, 2002. 23(3): p. 255-264. 10.          Haykins, S., A comprehensive foundation on neural networks. 1999, Prentice Hall. 11.          Jo, H., I. Han, and H. Lee, Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis. Expert Systems with Applications, 1997. 13(2): p. 97-108. 12.          Pradeep, J., E. Srinivasan, and S. Himavathi, Neural network based recognition system integrating feature extraction and classification for English handwritten. International Journal of Engineering-Transactions B: Applications, 2012. 25(2): p. 99. 13.          Ghasemi, J. and J. Rasekhi, TRAFFIC SIGNAL PREDICTION USING ELMAN NEURAL NETWORK AND PARTICLE SWARM OPTIMIZATION. International Journal of Engineering-Transactions B: Applications, 2016. 29(11): p. 1558. 14.          Fenjan, S.A., et al., Flow variables prediction using experimental, computational fluid dynamic and artificial neural network models in a sharp bend. International Journal of Engineering-Transactions A: Basics, 2016. 29(1): p. 14. 15.          Ince, H. and T.B. Trafalis, A hybrid model for exchange rate prediction. Decision Support Systems, 2006. 42(2): p. 1054-1062. 16.          Lee, S. and W.S. Choi, A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis. Expert Systems with Applications, 2013. 40(8): p. 2941-2946. 17.          Du Jardin, P., Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy. Neurocomputing, 2010. 73(10): p. 2047-2060. 18.          Zadeh, L.A., Fuzzy sets. Information and control, 1965. 8(3): p. 338-353. 19.          Aliev, R.A., B. Fazlollahi, and R.R. Aliev, Soft computing and its applications in business and economics. Vol. 157. 2012: Springer. 20.          Sugeno, M., Industrial applications of fuzzy control. 1985: Elsevier Science Inc. 21.          Altman, E.I., Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 1968. 23(4): p. 589-609.

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