A Hybrid Support Vector Regression Based on Chaotic Particle Swarm Optimization Algorithm in Forecasting Financial Returns

Nowadays there are lots of novel forecasting approaches to improve the forecasting accuracy in the financial markets. Support Vector Machine (SVM) as a modern statistical tool has been successfully used to solve nonlinear regression and time series problem. Unlike most conventional neural network models which are based on the empirical risk minimization principle, SVM applies the structural risk minimization principle to minimize an upper bound of the generalization error rather than minimizing the training error. To build an effective SVM model, SVM parameters must be set carefully. This study proposes a novel approach, know as chaotic particle swarm optimization algorithm (CPSO) support vector regression(SVR), to predict financial returns. A numerical example is employed to compare the performance of the proposed model. Experiment results show that the proposed model outperforms the other approaches in forecasting financial returns.