The study of SVM optimized by Culture Particle Swarm Optimization on predicting financial distress

In the analysis of predicting financial distress based on support vector machine (SVM), the two parameters of SVM, c and sigma, which its value have important effect on the predicting accuracy, must be predetermined carefully. In order to solve this problem, this paper proposed a new culture particle swarm optimization algorithm (CPSO) to optimize the parameters of SVM. Utilizing the colony aptitude of particle swarm and the ability of conserving the evolving knowledge of the culture algorithm, this CPSO algorithm constructed the population space based on particle swarm and the knowledge space. The two spaces evolved independently, at the same time, the population space continuously transferred the evolving knowledge to the knowledge space, and then the knowledge space to achieve global optimization. Additionally, the proposed CPSO-SVM model that can automated to determine the optimal values of SVM parameters was test on the prediction of financial distress of listed companies in China. Then we compared the accuracies of CPSO-SVM with other models (Standard SVM, PSO-SVM and PSO-BPN). Experimental results showed that CPSO-SVM performed the best prediction accuracy and generalization, implying that the hybrid of CPSO with traditional SVM can serve as a promising alternative for predicting financial distress.