Kernel principal component analysis and support vector machines for stock price prediction [2]

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 the technical analysis. Comparison shows that SVR and MLP networks require different inputs. Besides that the MLP networks outperform the SVR technique.