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The Comparison Artificial Neural Networks and Multi Decimal Analysis Models for Forecasting Bankruptcy and Financial Distress

One of the most important issues in the world of financial management is investment assurance for legal and rightful persons. The existence of suitable standards and instruments to evaluate financial conditions and companies situations can help investors to under take their decisions making. Financial corruption and bankruptcy bear extensive costs which cause lasting damages to country’s economy. One of the ways to prevent financial corruption is to forecast financial distress. In this paper which uses the correlation research methodology employing Artificial Neural Networks (ANN), the financial corruption of the companies has been forecasted. The main aim of this research is to compare ANN and Multi Decimal Analysis (MDA) models in bankruptcy forecasting field. In order to study the effect of sampling differences on forecasting, the absolute validation method has been used. Dual Comparison Test was used to test three hypothesises introduced in this research. The results obtained from the models based on the data from 80 accepted companies in Tehran Stock Exchange show that in forecasting financial distress, the ANN model is more accurate than the MDA model.