@inbook{fdea4cbd283946b182965bb8befdafee,
title = "Cardinality-constrained higher order moment portfolios using particle swarm optimization",
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",
author = "Kris Boudt and {Mulazim Ali}, Khokhar and Chunlin Wan",
year = "2021",
month = may,
doi = "10.1007/978-3-030-70281-6_10",
language = "English",
isbn = "978-3-030-70280-9",
volume = "306",
series = "International Series in Operations Research and Management Science",
publisher = "Springer International Publishing",
pages = "169--187",
editor = "Mercang, {Burcu Adiguzel}",
booktitle = "Applying Particle Swarm Optimization",
edition = "2021",
}