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

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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.


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