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Area of Science:

  • Quantitative Finance
  • Computational Finance

Background:

  • Mean-variance portfolio optimization faces challenges due to estimation errors in asset returns and covariances.
  • Traditional resampling methods for robust solutions can lead to unfeasible portfolio compositions in real-world scenarios.

Purpose of the Study:

  • To introduce novel alternatives for combining resampled portfolios when using multiobjective evolutionary algorithms.
  • To enable robust portfolio optimization under real-world constraints, overcoming limitations of standard averaging techniques.

Main Methods:

  • Development and introduction of three alternative methods for portfolio composition averaging.
  • Experimental testing of these methods using 15 years of market data.
  • Application within the framework of multiobjective evolutionary algorithms for efficient frontier identification.

Main Results:

  • The proposed alternatives facilitate the use of resampling with multiobjective evolutionary algorithms under real-world constraints.
  • Demonstrated feasibility and robustness of the new approaches through empirical analysis.
  • Overcame limitations in averaging portfolio compositions when Pareto fronts differ in number or spacing.

Conclusions:

  • The introduced methods offer practical solutions for robust mean-variance portfolio optimization.
  • These alternatives enhance the applicability of advanced computational finance techniques in financial markets.
  • The findings contribute to more reliable and feasible investment strategies in the presence of estimation errors.