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Particle Swarm Optimization with Non-Smooth Penalty Reformulation for a Complex Portfolio Selection Problem

22 Pages Posted: 4 Aug 2011  

Marco Corazza

Ca Foscari University of Venice - Dipartimento di Economia

Giovanni Fasano

affiliation not provided to SSRN

Riccardo Gusso

Independent

Date Written: July 1, 2011

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

JEL Classification: C61, C63, G11

Suggested Citation

Corazza, Marco and Fasano, Giovanni and Gusso, Riccardo, Particle Swarm Optimization with Non-Smooth Penalty Reformulation for a Complex Portfolio Selection Problem (July 1, 2011). Available at SSRN: https://ssrn.com/abstract=1904873 or http://dx.doi.org/10.2139/ssrn.1904873

Marco Corazza (Contact Author)

Ca Foscari University of Venice - Dipartimento di Economia ( email )

Cannaregio 873
Venice, 30121
Italy

Giovanni Fasano

affiliation not provided to SSRN ( email )

Riccardo Gusso

Independent ( email )

No Address Available

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