Kernel principal component analysis and support vector machines for stock price prediction
Ince, H.
Trafalis, T.B.
Fac. of Bus. Adm., Gebze Inst. of Technol., Kocaeli, Turkey;
This paper appears in: Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
Publication Date: 25-29 July 2004
Volume: 3,
On page(s): 2053- 2058 vol.3
ISSN: 1098-7576
ISBN: 0-7803-8359-1
INSPEC Accession Number: 8231241
Posted online: 2005-01-17 08:53:13.0
Abstract
Financial time series are complex, non-stationary and deterministically chaotic. Technical indicators are used with principal component analysis (PCA) in order to identify the most influential inputs in the context of the forecasting model. Neural networks (NN) and support vector regression (SVR) are used with different inputs. Our assumption is that the future value of a stock price depends on the financial indicators although there is no parametric model to explain this relationship. This relationship comes from technical analysis. Comparison shows that SVR and MLP networks require different inputs. The MLP networks outperform the SVR technique.
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