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Kernel principal component analysis and support vector machines for stock price prediction | IEEE Conference Publication | IEEE Xplore

Kernel principal component analysis and support vector machines for stock price prediction


Abstract:

Financial time series are complex, non-stationary and deterministically chaotic. Technical indicators are used with principal component analysis (PCA) in order to identif...Show More

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.
Date of Conference: 25-29 July 2004
Date Added to IEEE Xplore: 17 January 2005
Print ISBN:0-7803-8359-1
Print ISSN: 1098-7576
Conference Location: Budapest, Hungary

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