Journal of Signal Processing
Online ISSN : 1880-1013
Print ISSN : 1342-6230
ISSN-L : 1342-6230
Improving Predictive Power and Risk Reduction of Portfolio Models Based on Principal Component Analysis
Kazuki YanagisawaTomoya Suzuki
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2015 Volume 19 Issue 4 Pages 119-122


In our previous study, we enhanced the predictive power of the principal component portfolio (PCP) model by applying a nonlinear prediction model. However, here we point out that this modification destroys the no-correlation relationship among the principal components, and accordingly the portfolio effect of risk reduction is weakened. To solve this problem, we mixed the advantages of the PCP model and our nonlinear portfolio model. To confirm the validity of this, we performed some investment simulations with real stock data and confirmed that our new portfolio model improves the predictive power and risk-reduction power simultaneously, that is, it improves the efficiency and safety of portfolio management.

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© 2015 Research Institute of Signal Processing, Japan
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