Title :
Prediction of Stock Trading Signal Based on Support Vector Machine
Author :
Xi Chen;Zhi-Jie He
Author_Institution :
Coll. of Phys. &
fDate :
6/1/2015 12:00:00 AM
Abstract :
The prediction of stock trading signal is studied in this paper. Considering the excellent performance of Support Vector Machine (SVM) in pattern recognition, we apply SVM to construct a prediction model to find the stock trading signal. In addition, Piecewise linear representation (PLR) is good at extracting valuable information from a time sequence. PLR is used for checking of turning points in this study. The experiments on some real stocks show that SVM obtains a better result in prediction accuracy and profitability than traditional Back Propagation neural network does.
Keywords :
"Support vector machines","Time series analysis","Turning","Training","Predictive models","Market research","Security"
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2015 8th International Conference on
DOI :
10.1109/ICICTA.2015.165