Improving Predictive Power and Risk Reduction of Portfolio Models Based on Principal Component Analysis

DOI 参考文献3件 オープンアクセス

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説明

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.

収録刊行物

  • 信号処理

    信号処理 19 (4), 119-122, 2015

    信号処理学会

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詳細情報 詳細情報について

  • CRID
    1390282679440120832
  • NII論文ID
    130005090473
  • DOI
    10.2299/jsp.19.119
  • ISSN
    18801013
    13426230
  • 本文言語コード
    en
  • 資料種別
    journal article
  • データソース種別
    • JaLC
    • Crossref
    • CiNii Articles
    • KAKEN
    • OpenAIRE
  • 抄録ライセンスフラグ
    使用不可

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