A reactive greedy randomized adaptive search procedure for a mixed integer portfolio optimization problem

Abstract

Purpose

The purpose of this paper is to present a procedure for finding the efficient frontier, i.e. a non‐decreasing curve representing the set of Pareto‐optimal or non‐dominated portfolios, when the standard Markowitz' classical mean‐variance model is enriched with additional constraints.

Design/methodology/approach

The mean‐variance portfolio optimization model is extended to include integer constraints that limit a portfolio to have a specified number of assets, and to impose limits on the proportion of the portfolio held in a given asset. Optimization‐based procedures run into difficulties in this framework and this motivates the investigation of heuristic algorithms to find acceptable solutions.

Findings

The problem is solved by a greedy randomized adaptive search procedure (GRASP), enhanced by a learning mechanism and a bias function for determining the next element to be introduced in the solution.

Originality/value

This is believed to be the first time, a GRASP for finding the efficient frontier for this class of portfolio selection problems is used.

Citation

Anagnostopoulos, K.P., Chatzoglou, P.D. and Katsavounis, S. (2010), "A reactive greedy randomized adaptive search procedure for a mixed integer portfolio optimization problem", Managerial Finance, Vol. 36 No. 12, pp. 1057-1065. https://doi.org/10.1108/03074351011088450