Credit Rating Analysis with Support Vector Machines and Artificial Bee Colony Algorithm

Recently, credit rating analysis for financial engineering has attracted many research attentions. In the previous, statistical and artificial intelligent methods for credit rating have been widely investigated. Most of them, they focus on the hybrid models by integrating many artificial intelligent methods have proven outstanding performances. This research proposes a newly hybrid evolution algorithm to integrate artificial bee colony (ABC) with the support vector machine (SVM) to predict the corporate credit rating problems. The experiment dataset are select from 2001 to 2008 of Compustat credit rating database in America. The empirical results show the ABC-SVM model has the highest classification accuracy. Hence, this research presents the ABC-SVM model could be better suited for predicting the credit rating.

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