ポートフォリオ最適化のための進化アルゴリズム感応度測定。

• Rifki Omar

  九大

• 小野 廣隆

  九大

書誌事項

タイトル別名

• Measuring Sensitivity of Evolutionary Algorithms to Errors in Asset Means for Mean-Variance Portfolio Optimization.

抄録

Mean-Variance Portfolio optimization is highly sensitive to errors in assets means, when solved by Quadratic Programming (QP). We propose a simulation-based evaluation method for the sensitivity, applied to QP and the Evolutionary Algorithms (EA): Genetic Algorithm, Evolution Strategy, Particle Swarm Optimization and Differential Evolution. Comparisons between variants of EAs and QP are made based on assessing the performance, under multiple perturbed runs, of several ‘optimal’ portfolios. Computational experiments show that many individuals of EAs population outperform QP optimal solution.

収録刊行物

• 電気関係学会九州支部連合大会講演論文集

  電気関係学会九州支部連合大会講演論文集 2013 (0), 181-182, 2013

  電気・情報関係学会九州支部連合大会委員会

詳細情報

• CRID: 1390282680641113344
• NII論文ID: 130005487300
• DOI: 10.11527/jceeek.2013.0_181
• データソース種別:
  • JaLC
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可