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Metaheuristic approaches to realistic portfolio optimisation

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dc.contributor.advisor Potgieter, P. H. Busetti, Franco Raoul 2015-01-23T04:24:21Z 2015-01-23T04:24:21Z 2000-06
dc.identifier.citation Busetti, Franco Raoul (2000) Metaheuristic approaches to realistic portfolio optimisation, University of South Africa, Pretoria, <> en
dc.description.abstract In this thesis we investigate the application of two heuristic methods, genetic algorithms and tabu/scatter search, to the optimisation of realistic portfolios. The model is based on the classical mean-variance approach, but enhanced with floor and ceiling constraints, cardinality constraints and nonlinear transaction costs which include a substantial illiquidity premium, and is then applied to a large I 00-stock portfolio. It is shown that genetic algorithms can optimise such portfolios effectively and within reasonable times, without extensive tailoring or fine-tuning of the algorithm. This approach is also flexible in not relying on any assumed or restrictive properties of the model and can easily cope with extensive modifications such as the addition of complex new constraints, discontinuous variables and changes in the objective function. The results indicate that that both floor and ceiling constraints have a substantial negative impact on portfolio performance and their necessity should be examined critically relative to their associated administration and monitoring costs. Another insight is that nonlinear transaction costs which are comparable in magnitude to forecast returns will tend to diversify portfolios; the effect of these costs on portfolio risk is, however, ambiguous, depending on the degree of diversification required for cost reduction. Generally, the number of assets in a portfolio invariably increases as a result of constraints, costs and their combination. The implementation of cardinality constraints is essential for finding the bestperforming portfolio. The ability of the heuristic method to deal with cardinality constraints is one of its most powerful features. en
dc.format.extent 1 online resource (viii, 93 leaves)
dc.language.iso en en
dc.subject Portfolio optimisation en
dc.subject Efficient frontier en
dc.subject Heuristic en
dc.subject Genetic algorithm en
dc.subject Tabu search en
dc.subject.ddc 332.6015118
dc.subject.lcsh Portfolio management -- Mathematical models en
dc.subject.lcsh Finance -- Mathematical models en
dc.subject.lcsh Heuristic programming en
dc.subject.lcsh Genetic algorithms en
dc.subject.lcsh Combinatorial optimization en
dc.title Metaheuristic approaches to realistic portfolio optimisation en
dc.type Dissertation
dc.description.department Decision Sciences M. Sc. (Operations Research)

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