The compilation of predicting patterns of financial distress using internal analysis data and artificial intelligent techniques’’

One of external user’s decision making tools such as investors, creditors, trade companies and state organization is decision making about investment, crediting…, and financial statement analysis of the companies. Respecting rapid development of computer technology and techniques, more exact information can be provided for decision maker’s than traditional information in order to be able to make more efficient decisions about probable of return on investment and/or financial distress occurrence before occurring and suffering the high expenses.
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 2001 to 2009 is compiled and expected financial proportions’ are extracted and neural network patterns (ANN), principal component analysis combination, and neural network PCA +ANN have been compiled to predict the financial distress one or more years before the occurring.  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 other techniques in this research and other financial years.