eprintid: 1423
rev_number: 6
eprint_status: archive
userid: 17
dir: disk0/00/00/14/23
datestamp: 2012-10-24 09:23:22
lastmod: 2012-10-24 09:23:22
status_changed: 2012-10-24 09:23:22
type: article
metadata_visibility: no_search
creators_name: Dellino, Gabriella
creators_name: Fedele, Mariagrazia
creators_name: Meloni, Carlo
creators_id: gabriella.dellino@imtlucca.it
creators_id: 
creators_id: 
title: Dynamic Objectives Aggregation Methods for Evolutionary Portfolio Optimization. A computational study
ispublished: pub
subjects: HB
subjects: QA
divisions: EIC
full_text_status: none
keywords: multi-objective; portfolio optimisation; dynamic objectives aggregation; bio-inspired optimisation
abstract: This paper proposes a study of different dynamic objectives aggregation methods (DOAMs) in the context of a multi-objective evolutionary approach to portfolio optimisation. Since the incorporation of chaotic rules or behaviour in population-based optimisation algorithms has been shown to possibly enhance their searching ability, this study considers and evaluates also some chaotic rules in the dynamic weights generation process. The ability of the DOAMs to solve the portfolio rebalancing problem is investigated conducting a computational study on a set of instances based on real data. The portfolio model considers a set of realistic constraints and entails the simultaneous optimisation of the risk on portfolio, the expected return and the transaction cost.
date: 2012
date_type: published
publication: International Journal of Bio-Inspired Computation
volume: 4
number: 4
publisher: Inderscience
pagerange: 258-270
id_number: 10.1504/IJBIC.2012.048066
refereed: TRUE
issn: 1758-0366
citation:   Dellino, Gabriella and Fedele, Mariagrazia and Meloni, Carlo  Dynamic Objectives Aggregation Methods for Evolutionary Portfolio Optimization. A computational study.  International Journal of Bio-Inspired Computation, 4 (4).  pp. 258-270.  ISSN 1758-0366      (2012)