Neural Networks, IEEE - INNS - ENNS International Joint Conference on (2000)
July 24, 2000 to July 27, 2000
Alan Fan , University of Melbourne
Marimuthu Palaniswami , University of Melbourne
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 pro vided using three classical corporate distress models and an alternative model based on the SVM approach.
A. Fan and M. Palaniswami, "Selecting Bankruptcy Predictors Using a Support Vector Machine Approach," Neural Networks, IEEE - INNS - ENNS International Joint Conference on(IJCNN), Como, Italy, 2000, pp. 6354.