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

Mulazim Ali Khokhar; Kris Boudt; Chunlin Wan

doi:10.1007/978-3-030-70281-6_10

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

Mulazim Ali Khokhar, Kris Boudt, Chunlin Wan^*

^*Corresponding author for this work

Econometrics and Data Science

Research output: Chapter in Book / Report / Conference proceeding › Chapter › Academic › peer-review

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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.

Original language	English
Title of host publication	Applying Particle Swarm Optimization
Subtitle of host publication	New Solutions and Cases for Optimized Portfolios
Editors	Burcu Adıgüzel Mercangöz
Publisher	Springer
Pages	169-187
Number of pages	19
ISBN (Electronic)	9783030702816
ISBN (Print)	9783030702809, 9783030702830
DOIs	https://doi.org/10.1007/978-3-030-70281-6_10
Publication status	Published - 2021

Publication series

Name	International Series in Operations Research and Management Science
Publisher	Springer
Volume	306
ISSN (Print)	0884-8289
ISSN (Electronic)	2214-7934

Bibliographical note

Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

Cardinality mapping
Higher-order moment portfolio
Particle swarm optimization

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Khokhar2021_Chapter_Cardinality-ConstrainedHigherOrderMomentPortfoliosFinal published version, 633 KBLicence: Article 25fa Dutch Copyright Act

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Khokhar, M. A., Boudt, K., & Wan, C. (2021). Cardinality-Constrained Higher-Order Moment Portfolios Using Particle Swarm Optimization. In B. A. Mercangöz (Ed.), Applying Particle Swarm Optimization: New Solutions and Cases for Optimized Portfolios (pp. 169-187). (International Series in Operations Research and Management Science; Vol. 306). Springer. https://doi.org/10.1007/978-3-030-70281-6_10

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Khokhar, MA, Boudt, K & Wan, C 2021, Cardinality-Constrained Higher-Order Moment Portfolios Using Particle Swarm Optimization. in BA Mercangöz (ed.), Applying Particle Swarm Optimization: New Solutions and Cases for Optimized Portfolios. International Series in Operations Research and Management Science, vol. 306, Springer, pp. 169-187. https://doi.org/10.1007/978-3-030-70281-6_10

Cardinality-Constrained Higher-Order Moment Portfolios Using Particle Swarm Optimization. / Khokhar, Mulazim Ali; Boudt, Kris; Wan, Chunlin.
Applying Particle Swarm Optimization: New Solutions and Cases for Optimized Portfolios. ed. / Burcu Adıgüzel Mercangöz. Springer, 2021. p. 169-187 (International Series in Operations Research and Management Science; Vol. 306).

Research output: Chapter in Book / Report / Conference proceeding › Chapter › Academic › peer-review

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Khokhar MA, Boudt K, Wan C. Cardinality-Constrained Higher-Order Moment Portfolios Using Particle Swarm Optimization. In Mercangöz BA, editor, Applying Particle Swarm Optimization: New Solutions and Cases for Optimized Portfolios. Springer. 2021. p. 169-187. (International Series in Operations Research and Management Science). Epub 2021 May 13. doi: 10.1007/978-3-030-70281-6_10

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

Abstract

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Keywords

UN SDGs

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