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A hybrid algorithm for constrained portfolio selection problems

journal contribution
posted on 2023-07-26, 13:58 authored by Khin Lwin
Since Markowitz’s seminal work on the mean-variance model in modern portfolio theory, many studies have been conducted on computational techniques and recently meta-heuristics for portfolio selection problems. In this work, we propose and investigate a new hybrid algorithm integrating the population based incremental learning and differential evolution algorithms for the portfolio selection problem. We consider the extended mean-variance model with practical trading constraints including the cardinality, floor and ceiling constraints. The proposed hybrid algorithm adopts a partially guided mutation and an elitist strategy to promote the quality of solution. The performance of the proposed hybrid algorithm has been evaluated on the extended benchmark datasets in the OR Library. The computational results demonstrate that the proposed hybrid algorithm is not only effective but also efficient in solving the mean-variance model with real world constraints.

History

Refereed

  • Yes

Volume

39

Issue number

2

Page range

251-266

Publication title

Applied Intelligence

ISSN

1573-7497

Publisher

Springer

Language

  • other

Legacy posted date

2016-11-09

Legacy creation date

2016-11-07

Legacy Faculty/School/Department

ARCHIVED Faculty of Science & Technology (until September 2018)

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