Active portfolio management under a downside risk framework: Comparison of a hybrid nature - Inspired scheme

Abstract

Hybrid intelligent systems are becoming more and more popular in solving nondeterministic polynomial-time - hard optimization problems. Lately, the focus is on nature - inspired intelligent algorithms, whose main advantage is the exploitation of unique features of natural systems. One type of complex optimization problems is the active portfolio management, where the incorporation of complex, realistic constraints makes it difficult for traditional numerical methods to deal with it. In this paper we perform a computational study of a hybrid Ant Colony Optimization algorithm. The application is a specific formulation of the problem. Our main aim in this paper is to introduce a new framework of study in the field of active portfolio management, where the main interest lies in minimizing the risk of the portfolio return falling below the benchmark. Secondary, we provide some preliminary results regarding the use of a new hybrid nature - inspired scheme in solving this type of problem. © 2009 Springer Berlin Heidelberg.