Cardinality-constrained higher order moment portfolios using particle swarm optimization

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)

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 languageEnglish
Title of host publicationApplying Particle Swarm Optimization
Subtitle of host publicationNew Solutions and Cases for Optimized Portfolios
EditorsBurcu Adiguzel Mercang
Place of PublicationSwitzerlan AG
PublisherSpringer International Publishing
Chapter10
Pages169-187
Number of pages19
Volume306
Edition2021
ISBN (Electronic)978-3-030-70281-6
ISBN (Print)978-3-030-70280-9
DOIs
Publication statusPublished - May 2021

Publication series

NameInternational Series in Operations Research and Management Science
Volume306
ISSN (Print)0884-8289
ISSN (Electronic)2214-7934

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