Feature Selection for Support Vector Machine in the Study of Financial Early Warning System

Abstract

Abstract In this article, we introduce the L1 regularized support vector machine (L1‐SVM) as an effective feature selection technique into the modeling of financial early warning system (EWS), for the purpose of establishing compact financial EWSs for Chinese‐listed companies. By introducing LASSO penalty into the SVM framework, the L1‐SVM is a capable methodology to select causative features in classification problem. We evaluate the feature selection performance of L1‐SVM under different circumstances through numerical simulations and find it suitable for selecting features for financial EWS. In the real study, we establish four financial EWSs with features selected by L1‐SVM and compare them with those trained with full features. The empirical result illustrates that our EWSs, with only minority of features, outperform significantly than full ones in the respect of generalization performance, which indicates the feasibility of L1‐SVM in real applications. Copyright © 2014 John Wiley & Sons, Ltd.