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Cardinality-Constrained Higher-Order Moment Portfolios Using Particle Swarm Optimization

In: Applying Particle Swarm Optimization

Author

Listed:
  • Mulazim-Ali Khokhar

    (Vrije Universiteit Brussel
    Sukkur IBA University)

  • Kris Boudt

    (Vrije Universiteit Brussel
    Vrije Universiteit Amsterdam
    Ghent University)

  • Chunlin Wan

    (Sichuan University)

Abstract

Particle swarm optimization (PSO) is often used for solving cardinality-constrained portfolio optimization problems. The system invests in at most k out of N possible assets using a binary mapping that enforces compliance with the cardinality constraint. This may lead to sparse solution vectors driving the velocity in PSO algorithm. This sparse-velocity mapping leads to early stagnation in mean-variance-skewness-kurtosis expected utility optimization when k is small compared to N. A continuous-velocity driver addresses this issue. We propose to combine both the continuous- and the sparse-velocity transformation methods so that it updates local and global best positions based on both the drivers. We document the performance gains when k is small compared to N in the case of mean-variance-skewness-kurtosis expected utility optimization of the portfolio.

Suggested Citation

  • Mulazim-Ali Khokhar & Kris Boudt & Chunlin Wan, 2021. "Cardinality-Constrained Higher-Order Moment Portfolios Using Particle Swarm Optimization," International Series in Operations Research & Management Science, in: Burcu Adıgüzel Mercangöz (ed.), Applying Particle Swarm Optimization, edition 1, chapter 0, pages 169-187, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-70281-6_10
    DOI: 10.1007/978-3-030-70281-6_10
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