Comparison of Ant Colony Algorithm (ACA) with MDA & LOGIT Methods in Financial Distress Prediction

In this research, the Ant colony algorithm(ACA) was compared with two parametric models of multiple discriminant analysis (MDA) and LOGIT  for predicting of financial distress, meanwhile, models were applied for data mining directed to superior variables in financial distress prediction. data of 130 companies from 2005 to 2010 in form of two experiments were used.
 
The first experiment was based on distressed companies that fell under article 141, and non-distressed companies that did not fall article 141. This experiment included 130 year company and was done in two training and control samples each consisted 65 companies.
 
By studying the results of this experiment based on 15 variables with using data mining by each of three models, two superior variables of models were obtained including earnings before financial expenses and taxes to total assets and net worth to total assets.
 
Based on superior variables in the first experiment, the second experiment was performed that was based on a sample including all of companies in the first experiment in all years of study that were among domestic listed companies and included 718  year  company.
 
The percent of financial distress prediction's success for ACA was 96.94% (distressed: 95.21%, non-distressed: 97.38%), for MDA was 95.82 (distressed: 82.88%, non-distressed: 99.13%) and for LOGIT was 97.08% (distressed: 88.36%, non-distressed: 99.30%). 
The results have shown that Ant Colony Algorithm in financial distress prediction is significantly superior than MDA (5%) and is  significantly superior than LOGIT (9%).