Mahmoud Mousavi Shiri; Sadegh Bafandeh Imandoust; Mohammad Bolandraftar Pasikhani
Abstract
Due to the effects of companies’ financial distress on stakeholders, financial distress prediction models have been one of the most attractive scopes in financial research. In recent years, after the global financial crisis, the number of bankrupt companies has risen. Since companies' financial distress ...
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Due to the effects of companies’ financial distress on stakeholders, financial distress prediction models have been one of the most attractive scopes in financial research. In recent years, after the global financial crisis, the number of bankrupt companies has risen. Since companies' financial distress is the first stage of bankruptcy, using financial ratios for predicting financial distress have attracted too much attention of the academics as well as economic and financial institutions.
Although in recent years studies on predicting companies’ financial distress in Iran have been increased, but most efforts have exploited traditional statistical methods; and just a few studies have used nonparametric methods. Recent studies demonstrate machine learning techniques outperform traditional statistical methods.
In the present study k-Nearest Neighbor classification method, derived from the field of data mining, is employed to predict financial distress of Tehran Stock Exchange listed companies during 2005-2008. Experimental results show that k-Nearest Neighbor is able to predict corporate financial distress with high accuracy.
