غلامرضا جندقی - Professor, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran
علیرضا سارنج - Assistant Professor, Department of Finance and Accounting, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran
رضا رجایی - PhD in Financial Management, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran
احمدرضا قاسمی - Assistant Professor, Department of Industrial Management, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran
رضا تهرانی - Professor, Department of Financial Management, Faculty of Management, University of Tehran, Tehran, Iran

Banks and financial institutions strive to develop and improve their credit risk evaluation methods to reduce financial loss resulting from borrowers’ financial default. Although in previous studies, many variables obtained from financial statements – such as financial ratios – have been used as the input to the bankruptcy prediction process, seldom a machine learning method based on computing intelligence has been applied to select the most critical of them. In this research, the data from companies that are were listed in Tehran’s Stock Exchange and OTC market during ۲۶ years since ۱۹۹۲ to ۲۰۱۷ has been investigated, with ۲۱۸ companies selected as the study sample. The ant colony optimization algorithm with k-nearest neighbor has been used to feature the selection and classification of the companies. In this study, the problem of the imbalanced dataset has been solved with the under-sampling technique. The results have shown that variables such as EBIT to total sales, equity ratio, current ratio, cash ratio, and debt ratio are the most effective factors in predicting the health status of companies. The accuracy of final research model is estimated that the bankruptcy prediction ranges between ۷۵.۵% to ۷۸.۷% for the training and testing sample.

credit risk, Probability of default (PD), Bankruptcy prediction (BP), K-nearest neighbor (KNN), Ant colony Algorithm, Imbalanced dataset