Exploiting corporate governance and common-size analysis for financial distress detecting models

Abstract

Traditionally, statistical techniques such as multivariate discriminant analysis and logistic regression analysis have been applied for predicting financial distresses by analyzing financial ratios. In addition to statistical methods, recent studies suggest that backpropagation neural networks (BPNs) and support vector machines (SVMs) can be alternative approaches for classification tasks. Hence, we construct two software classifiers, BPNs and SVMs, and then investigate the effects of employing features related to corporate governance and common-size analysis in financial distress model. Experimental results indicate that the proposed features may help SVMs achieve better predication quality when we try to predict financial distresses with more temporally distant data and smaller data set.