Title: Dynamic objectives aggregation methods for evolutionary portfolio optimisation. A computational study
Authors: Gabriella Dellino; Mariagrazia Fedele; Carlo Meloni
Addresses: IMT Institute for Advanced Studies, Research Area in Economics and Institutional Changes, Piazza San Ponziano 6 – 55100 Siena, Italy. ' Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universitá degli Studi di Foggia, Largo Papa Giovanni Paolo II, 1 – 71100 Foggia, Italy. ' Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, Via E. Orabona, 4 70125 Bari, Italy
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.
Keywords: multiobjective optimisation; evolutionary optimisation; portfolio optimisation; dynamic objectives aggregation; bio-inspired computation; chaotic rules; dynamic weights generation; chaos; portfolio rebalancing; portfolio risk; expected return; transaction costs; modelling.
International Journal of Bio-Inspired Computation, 2012 Vol.4 No.4, pp.258 - 270
Published online: 18 Jul 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article