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

Abstract

Purpose

Financial distress is the most notable distress for companies. During the past four decades, 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 na

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ve 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.

Citation

Salehi, M., Mousavi Shiri, M. and Bolandraftar Pasikhani, M. (2016), "Predicting corporate financial distress using data mining techniques: An application in Tehran Stock Exchange", International Journal of Law and Management, Vol. 58 No. 2, pp. 216-230. https://doi.org/10.1108/IJLMA-06-2015-0028