non-parametric methods

This study aims to investigate the effects of accounting and non-accounting indices on financial distress prediction and also to compare parametric and non-parametric methods. Therefore, the sample consists of 211 distressed firms selected by special distress criteria and 211 healthy firms listed in Tehran Stock Exchange during 2006-2015. This study applies 32 accounting and 20 non-accounting indices and uses 2 parametric methods including Logistic Regression and Multivariate Discriminate Analysis and 7 non-parametric methods including Support Vector Machine, Artificial Neural Network, Decision Tree (with 4 algorithms) and Bayesian Network to predict financial distress. The results show that the models extracted from accounting indices have significantly more predicting accuracy than those from non-accounting indices, and adding non-accounting indices to the models based on accounting indices does not significantly increases their predicting ability. Also, since the average predicting ability of non-parametric methods is more than parametric ones, this difference is not statistically significant.