Selecting bankruptcy predictors using a Support Vector Machine approach

Abstract

Conventional Neural Network approach has been found useful in predicting corporate distress from financial statements. In this paper, we have adopted a Support Vector Machine approach to the problem. A new way of selecting bankruptcy predictors is shown, using the Euclidean distance based criterion calculated within the SVM kernel. A comparative study is provided using three classical corporate distress models and an alternative model based on the SVM approach.