Applying Internal Analysis Data and Non-Linear Genetic Algorithm in Developing a Predicting Pattern of Financial Distress
Department of Accounting, Assistant Professor, Central Tehran Banch, Islamic Azad University, Tehran, Iran,
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Master of Accounting, Central Tehran Branch, Islamic Azad University, Tehran, Iran
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Abstract: Bankruptcy is an event with strong impacts on management, shareholders, employees, creditors, customers and other stakeholders, so as bankruptcy challenges the country both socially and economically. The aim of this study is to make a financial distress predicting model for listed companies’ in Tehran stock exchange using financial proportions and artificial intelligent techniques. So financial information relevant to time period 1992 to 2011 is compiled and expected financial proportions’ are extracted and neural network patterns (ANN), principal component analysis combination, and Non-Linear Genetic Algorithm (PCA +NON-LIN) have been compiled to predict the financial distress. Then according to obtained results, these patterns have been compared and the best pattern has been chosen. In accordance with the results, It is distinguished that the neural net work using the information One year before financial distress occurring has more efficiency in predicting the financial distress of the companies rather than the other technique in this research.
[Zahra Poorzamani. Applying Internal Analysis Data and Non-Linear Genetic Algorithm in Developing a Predicting Pattern of Financial Distress. Life Sci J 2013;10(7s):58-63] (ISSN:1097-8135). http://www.lifesciencesite.com.
Key words: Financial Distress, Financial Variables, Non-Linear Genetic Algorithm, Neural Network