Title

Bond Rating Using Support Vector Machine

Publication Type

Journal Article

Publication Date

5-2007

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.

Discipline

Finance and Financial Management | Portfolio and Security Analysis

Research Areas

Finance

Publication

Intelligent Data Analysis

Volume

10

Issue

3

First Page

285

Last Page

296

ISSN

1088-467X

Publisher

IOS Press

Additional URL

http://dl.acm.org/citation.cfm?id=1165451