Abbas Ramzanzadeh  Zeidi, Seyd Mehdy  Fadakar, Keyvan  Akbarpoor, Maryam  Salimi, 
    Designing Bankruptcy Prediction System Using Artificial Neural Network
Based on Evidence from Iranian Manufacturing Companies,
    International Business Management,
    Volume 10,Issue 26,
    2016,
    Pages 5973-5982,
    ISSN 1993-5250,
    ibm.2016.5973.5982,
    (https://makhillpublications.co/view-article.php?doi=ibm.2016.5973.5982)
    Abstract: Financial distress and bankruptcy result in a lot of costs. The costs will extend to different groups such as creditors, investors, managers, legal institutions and eventually capital owners. Bankruptcy prediction is a way that significantly can avoid financial distress. The purpose of the study is to design a system using artificial neural network to predict bankruptcy of companies listed in Tehran Stock Exchange before occurring bankruptcy, this system should be designed in a way that can predict the financial situation of company within the next three years. The research method is ex-post facto or survey and the statistical population of research including companies listed in Tehran Stock Exchange during 2001-2010. The data of 54 companies (30 bankrupt companies and 24 companies with Tobin Q above one) was tested by two parameters: 0.15 and 03% accuracy (optimism and pessimism).
    Keywords: Designing system;artificial neural network;bankruptcy prediction;Tehran Stock Exchange