A reactive greedy randomized adaptive search procedure for a mixed integer portfolio optimization problem
AbstractPurpose – 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.
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Bibliographic InfoArticle provided by Emerald Group Publishing in its journal Managerial Finance.
Volume (Year): 36 (2010)
Issue (Month): 12 (December)
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- N. J. Jobst & M. D. Horniman & C. A. Lucas & G. Mitra, 2001. "Computational aspects of alternative portfolio selection models in the presence of discrete asset choice constraints," Quantitative Finance, Taylor & Francis Journals, vol. 1(5), pages 489-501.
- Mansini, Renata & Speranza, Maria Grazia, 1999. "Heuristic algorithms for the portfolio selection problem with minimum transaction lots," European Journal of Operational Research, Elsevier, vol. 114(2), pages 219-233, April.
- Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
- Crama, Y. & Schyns, M., 2003. "Simulated annealing for complex portfolio selection problems," European Journal of Operational Research, Elsevier, vol. 150(3), pages 546-571, November.
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