Predicting corporate financial distress using data mining techniques An application in Tehran Stock Exchange

Purpose - Financial distress is the most notable distress for companies. During the past four deca des, predicting corporate bankruptcy and financial distress has become a significant concern for the various stakeholders in firms. This paper aims to predict financial distress of Iranian firms, with four techniques: support vector machines, artificial neural networks (ANN), k-nearest neighbor and naive bayesian classifier by using accounting information of the firms for two years prior to financial distress. Design/methodology/approach - The distressed companies in this study are chosen based on Article 141 of Iranian Commercial Codes, i.e. accumulated losses exceeds half of equity, based on which 117 companies qualified for the current study. The research population includes all the companies listed on Tehran Stock Exchange during the financial period from 2011-2012 to 2013-2014, that is, three consecutive periods. Findings - By making a comparison between performances of models, it is concluded that ANN outperforms other techniques. Originality/value - The current study is almost the first study in Iran which used such methods to analyzing the data. So, the results may be helpful in the Iranian condition as well for other developing nations.