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Improving Predictive Power and Risk Reduction of Portfolio Models Based on Principal Component Analysis
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- Yanagisawa Kazuki
- Graduate School of Science and Engineering, Ibaraki University
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- Suzuki Tomoya
- Graduate School of Science and Engineering, Ibaraki University
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Description
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.
Journal
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- Journal of Signal Processing
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Journal of Signal Processing 19 (4), 119-122, 2015
Research Institute of Signal Processing, Japan
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Details 詳細情報について
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- CRID
- 1390282679440120832
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- NII Article ID
- 130005090473
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- ISSN
- 18801013
- 13426230
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- Text Lang
- en
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- Article Type
- journal article
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
- OpenAIRE
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- Abstract License Flag
- Disallowed