Affiliations: [a] Financial Studies of Fudan University, HanDan Road, ShangHai, P.R. China, 200433. E-mail: [email protected], [email protected] | [b] Department of Business, Singapore Management University, 469 Bukit Timah Road, Singapore 259756. E-mail: [email protected]
Note: [1] The research work is funded by National Natural Science Research Fund No. 70501008 and sponsored by Shanghai Pujiang program. E-mail: [email protected].
Abstract: This paper deals with the application of support vector machine (SVM) for bond rating. The three commonly used methods for solving multi-class classification problems in SVM, “one-against-all”, “one-against-one”, and directed acyclic graph SVM (DAGSVM) are used. The performance of SVM is compared with several benchmarks. One real U.S. bond data is collected using the Fixed Investment Securities database (FISD) and the Compustat database. The experiment shows that SVM significantly outperforms the benchmarks. Among the three SVM based methods, there is the best performance in DAGSVM. Furthermore, an analysis of features shows that the generalization performance of SVM can be further improved by performing feature selection.
Keywords: Support vector machine (SVM), multi-class classification, feature selection
