Abstract




 
   

IJE TRANSACTIONS C: Aspects Vol. 30, No. 12 (December 2017) 1879-1884    Article in Press

PDF URL: http://www.ije.ir/Vol30/No12/C/9.pdf  
downloaded Downloaded: 22   viewed Viewed: 284

  AN ADAPTIVE FUZZY NEURAL NETWORK MODEL FOR BANKRUPTCY PREDICTION OF LISTED COMPANIES ON THE TEHRAN STOCK EXCHANGE
 
A. H. Azadnia, A. Siahi and M. Motameni
 
( Received: May 31, 2017 – Accepted in Revised Form: 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 درمیان دانشگاهیان و شاغلین از آن خودکرده است. اگرچه مطالعات متعددی در زمینه پیشبینی ورشکستگی انجام شده است، توجه کمتری به ارائه یک رویکرد سیستماتیک براساس شبکه های عصبی فازی اختصاص یافته است. مطالعه حاضر برای پیشبینی 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 وکیفیت دارایی برای هدف پیش بینی مورداستفاده قرارگرفت. علاوه براین، نمره Zآلتمن به عنوان متغیرخروجی استفاده می شود. نتایج نشان می دهدکه مدل شبکه عصبی فازی پیشنهادی عملکرد بالایی برای پیش بینی ورشکستگی شرکت ها دارد.

References   

1.      Beaver, W.H., "Financial ratios as predictors of failure", Journal of Accounting Research,  (1966), 71-111.

2.      Ohlson, J.A., "Financial ratios and the probabilistic prediction of bankruptcy", Journal of Accounting Research,  (1980), 109-131.

3.      Tseng, F.-M. and Hu, Y.-C., "Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks", Expert Systems with Applications,  Vol. 37, No. 3, (2010), 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,  Vol. 32, No. 10, (2005), 2561-2582.

5.      Chauhan, N., Ravi, V. and Chandra, D.K., "Differential evolution trained wavelet neural networks: Application to bankruptcy prediction in banks", Expert Systems with Applications,  Vol. 36, No. 4, (2009), 7659-7665.

6.      Cho, S., Kim, J. and Bae, J.K., "An integrative model with subject weight based on neural network learning for bankruptcy prediction", Expert Systems with Applications,  Vol. 36, No. 1, (2009), 403-410.

7.      Varetto, F., "Genetic algorithms applications in the analysis of insolvency risk", Journal of Banking & Finance,  Vol. 22, No. 10, (1998), 1421-1439.

8.      Min, J.H. and Lee, Y.-C., "Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters", Expert Systems with Applications,  Vol. 28, No. 4, (2005), 603-614.

9.      Park, C.-S. and Han, I., "A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction", Expert Systems with Applications,  Vol. 23, No. 3, (2002), 255-264.

10.    Haykins, S., A comprehensive foundation on neural networks. 1999, Prentice Hall.

11.    Jo, H., Han, I. and Lee, H., "Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis", Expert Systems with Applications,  Vol. 13, No. 2, (1997), 97-108.

12.    Pradeep, J., Srinivasan, E. and Himavathi, S., "Neural network based recognition system integrating feature extraction and classification for english handwritten", International Journal of Engineering-Transactions B: Applications,  Vol. 25, No. 2, (2012), 99-108.

13.    Ghasemi, J. and Rasekhi, J., "Traffic signal prediction using elman neural network and particle swarm optimization", International Journal of Engineering-Transactions B: Applications,  Vol. 29, No. 11, (2016), 1558-1564.

14.    Fenjan, S.A., Bonakdari, H., Gholami, A. and Akhtari, A., "Flow variables prediction using experimental, computational fluid dynamic and artificial neural network models in a sharp bend", International Journal of Engineering-Transactions A: Basics,  Vol. 29, No. 1, (2016), 14-22.

15.    Ince, H. and Trafalis, T.B., "A hybrid model for exchange rate prediction", Decision Support Systems,  Vol. 42, No. 2, (2006), 1054-1062.

16.    Lee, S. and Choi, W.S., "A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis", Expert Systems with Applications,  Vol. 40, No. 8, (2013), 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,  Vol. 73, No. 10, (2010), 2047-2060.

18.    Zadeh, L.A., "Fuzzy sets", Information and control,  Vol. 8, No. 3, (1965), 338-353.

19.    Bahramifar, A., Shirkhani, R. and Mohammadi, M., "An anfis-based approach for predicting the manning roughness coefficient in alluvial channels at the bank-full stage", International Journal of Engineering-Transactions B Applications,  Vol. 26, No. 2, (2013), 177-186.

20.    Moghadam-Fard, H. and Samadi, F., "Active suspension system control using adaptive neuro fuzzy (ANFIS) controller", International Journal of Engineering-Transactions C: Aspects,  Vol. 28, No. 3, (2014), 396-401.

21.    Khezri, R., Hosseini, R. and Mazinani, M., "A fuzzy rule-based expert system for the prognosis of the risk of development of the breast cancer", International Journal of Engineering Transactions A: Basics,  Vol. 27, No. 10, (2014), 1557-1564.

22.    Aliev, R.A., Fazlollahi, B. and Aliev, R.R., "Soft computing and its applications in business and economics, Springer,  Vol. 157,  (2012).

23.    Sugeno, M., "Industrial applications of fuzzy control, Elsevier Science Inc.,  (1985).

24.             Altman, E.I., "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy", The journal of finance,  Vol. 23, No. 4, (1968), 589-609.


Download PDF 



International Journal of Engineering
E-mail: office@ije.ir
Web Site: http://www.ije.ir