Support Vector Machines for Insolvency Prediction of Irish Companies

This study explores experimentally the potential of linear and non-linear support vector machines with three kernels to predict insolvency of Irish firms. The dataset used contains selected financial features based on information collected from 88 companies for a period of six years. Experiments show that non-linear support vector machines (SVM) with polynomial kernel gives highest prediction accuracy and outperforms all other techniques used so far with the same dataset. SVM performance is estimated by various metrics, receiver operating characteristics analysis, and results are validated by the leave-one-out cross-validation technique.