Selecting bankruptcy predictors using a support vector machine approach

The conventional neural network approach has been found useful in predicting corporate distress from financial statements. We have adapted a support vector machine approach to the problem. A 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