Title :
Application of Support Vector Machine to Capital Flow Risks Prediction
Author_Institution :
Dept. of Econ. & Manage., North China Electr. Power Univ., Baoding, China
Abstract :
Under the opening economic circumstances, forecasting the risks of capital flow has special significance. For effectively early warning the risks associated with capital flow, this study applies support vector machine (SVM) to the domain of capital flow in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, a grid-search technique using 5-fold cross-validation is used to find out the best parameter value of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM, this study compares its performance with that of three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the BPNs.
Keywords :
business data processing; learning (artificial intelligence); pattern classification; risk management; search problems; support vector machines; 5-fold cross-validation; BPN; SVM classification learning; back-propagation neural network; capital flow risk prediction; economic circumstance; grid-search technique; support vector machine; Alarm systems; Artificial neural networks; Economic forecasting; Macroeconomics; Power generation economics; Risk analysis; Signal analysis; Statistical learning; Support vector machine classification; Support vector machines; BPNs; capital flow risk; grid-search; prediction; support vector machine;
Conference_Titel :
Intelligent Ubiquitous Computing and Education, 2009 International Symposium on
Conference_Location :
Chengdu
Print_ISBN :
978-0-7695-3619-4
DOI :
10.1109/IUCE.2009.49
