Least-square Support Vector Machine for Financial Crisis Forecast Based on Particle Swarm Optimization

Abstract

Whether listed companies run soundly or not has direct impact on development of capital market, therefore, how to forecast financial crisis of listed companies accurately has been a widespread topic. Essentially financial crisis of listed companies is mainly about model pattern classification. Considering that Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) have great performance and strength on classification and regression analysis, this paper puts forward the hybrid forecast thought by combination of the two methods above as research focus. Firstly via building performance indicators, the forecast model based on classification is established and related parameters are optimized by PSO. Then empirical financial crisis analysis will be conducted on this method using financial data of listed companies. The simulation results indicate that the forecast model established in this paper combines the strength of artificial intelligence and statistics, and can avoid phenomenon of over fitting and under fitting compared with traditional models. Moreover, with strong generalization ability, the model is accurate and universal, hence having high application value.