Empirical models based on features ranking techniques for corporate financial distress prediction

Abstract

Accurate prediction of corporate financial distress is very important for managers, creditors and investors to take correct measures to reduce loss. Many quantitative methods have been employed to develop empirical models for predicting corporate bankruptcy. However, there is so much information disclosed in the companies’ financial statements, what information should be selected for building the empirical models with objective to maximize the predictive accuracy. In this study, more than 20 models based on six features ranking strategies are tested on North American companies and Chinese listed companies. The experimental results are helpful to develop financial models by choosing the proper quantitative methods and features selection strategy.