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Metaheuristic approaches to realistic portfolio optimisation

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dc.contributor.advisor Potgieter PH, Prof en
dc.contributor.author Busetti FR en
dc.date.accessioned 2016-09-22T11:54:13Z
dc.date.available 2016-09-22T11:54:13Z
dc.date.created 2001 en
dc.date.submitted 2002 en
dc.identifier.uri http://hdl.handle.net/20.500.11892/141604
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 100-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 fd|-|in,ding the best-performing portfolio. The ability of the heuristic method to deal with cardinality constraints is one of its most powerful features. en
dc.language English en
dc.subject Mathematics, mathematical statistics and statistics en
dc.subject Mathematical statistics en
dc.title Metaheuristic approaches to realistic portfolio optimisation en
dc.type Masters degree en
dc.description.degree MSc en

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