Yuanhu Cheng

Work place: Soochow University /School of Computer Science & Technology, Suzhou, China

E-mail: lidia421cc@163.com


Research Interests: Computer systems and computational processes, Computational Learning Theory, Data Mining, Data Structures and Algorithms


Yuanhu Cheng, male ,born in April 1986 in Tianmen,  Hubei Province, major in Management Science and  Engineering, the major research interests include  electronic commerce , Machine Learning and data mining 

Author Articles
Application of an integrated support vector regression method in prediction of financial returns

By Yuchen Fu Yuanhu Cheng

DOI: https://doi.org/10.5815/ijieeb.2011.03.06, Pub. Date: 8 Jun. 2011

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, support vector machine method combined with genetic algorithm (GA) for feature selection and chaotic particle swarm optimization(CPSO) for parameter optimization support vector Regression(SVR),to predict financial returns. The advantage of the GA-CPSO-SVR (Support Vector Regression) is that it can deal with feature selection and SVM parameter optimization simultaneously 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.

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