The Bankruptcy Prediction by Neural Networks and Logistic Regression
AbstractToday, the intensity of industry competition has led many companies going bankrupt and pulling out of race. The early warning against the possibility of bankruptcy enables the managers and investors to take pre-emptive actions when it is necessary. The bankruptcy prediction models reveal the latent problems in financial structures like a warning bell and provide timely feedback to managers and investors as well as other people who benefit from this. The bankruptcy of manufacturing companies in Tehran Stock Exchange Market has been predicted in this study using artificial neural network in this respect. It has been also used the logistic regression to do compare with neural network as well. All information which has been used here is related to time periods from 2001 to 2011 and the bankrupt groups have been selected on the basis of Article 141 of the Commercial Code of Iran. In the years before bankruptcy, the financial management has the chance to predict the probability of bankruptcy by using this model and take necessary actions in this regard since the results derived from the neural network predictions are very consistent with reality. Moreover, this model is more accurate than that of logistic regression in prediction process.
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Bibliographic InfoArticle provided by Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences in its journal International Journal of Academic Research in Accounting, Finance and Management Sciences.
Volume (Year): 3 (2013)
Issue (Month): 4 (October)
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Web page: http://hrmars.com/index.php/pages/detail/Accounting-Finance-Journal
Bankruptcy Prediction; Artificial Neural Networks; Logistic Regression; Back Propagation Learning Algorithm (BP);
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- Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, 09.
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