Particle Swarm Optimization with non-smooth penalty reformulation for a complex portfolio selection problem

Marco Corazza (corazza@unive.it), Giovanni Fasano (fasano@unive.it) and Riccardo Gusso

No 2011_10, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

Abstract: In the classical model for portfolio selection the risk is measured by the variance of returns. It is well known that, if returns are not elliptically distributed, this may cause inaccurate investment decisions. To address this issue, several alternative measures of risk have been proposed. In this contribution we focus on a class of measures that uses information contained both in lower and in upper tail of the distribution of the returns. We consider a nonlinear mixed-integer portfolio selection model which takes into account several constraints used in fund management practice. The latter problem is NP-hard in general, and exact algorithms for its minimization, which are both effective and efficient, are still sought at present. Thus, to approximately solve this model we experience the heuristics Particle Swarm Optimization (PSO). Since PSO was originally conceived for unconstrained global optimization problems, we apply it to a novel reformulation of our mixed-integer model, where a standard exact penalty function is introduced.

Keywords: Portfolio selection; coherent risk measure; fund management constraints; NP-hard mathematical programming problem; PSO; exact penalty method; SP100 index's assets. (search for similar items in EconPapers)
JEL-codes: C61 C63 G11 (search for similar items in EconPapers)
Pages: 19
Date: 2011
New Economics Papers: this item is included in nep-cmp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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