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.

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