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dc.contributor.advisorDahal, Keshav P.
dc.contributor.advisorHarnpornchai, Napat
dc.contributor.authorSkolpadungket, Prisadarng*
dc.date.accessioned2014-05-02T16:33:11Z
dc.date.available2014-05-02T16:33:11Z
dc.date.issued2014-05-02
dc.identifier.urihttp://hdl.handle.net/10454/6306
dc.description.abstractPortfolio optimisation has a number of constraints resulting from some practical matters and regulations. The closed-form mathematical solution of portfolio optimisation problems usually cannot include these constraints. Exhaustive search to reach the exact solution can take prohibitive amount of computational time. Portfolio optimisation models are also usually impaired by the estimation error problem caused by lack of ability to predict the future accurately. A number of Multi-Objective Genetic Algorithms are proposed to solve the problem with two objectives subject to cardinality constraints, floor constraints and round-lot constraints. Fuzzy logic is incorporated into the Vector Evaluated Genetic Algorithm (VEGA) to but solutions tend to cluster around a few points. Strength Pareto Evolutionary Algorithm 2 (SPEA2) gives solutions which are evenly distributed portfolio along the effective front while MOGA is more time efficient. An Evolutionary Artificial Neural Network (EANN) is proposed. It automatically evolves the ANN¿s initial values and structures hidden nodes and layers. The EANN gives a better performance in stock return forecasts in comparison with those of Ordinary Least Square Estimation and of Back Propagation and Elman Recurrent ANNs. Adaptation algorithms for selecting a pair of forecasting models, which are based on fuzzy logic-like rules, are proposed to select best models given an economic scenario. Their predictive performances are better than those of the comparing forecasting models. MOGA and SPEA2 are modified to include a third objective to handle model risk and are evaluated and tested for their performances. The result shows that they perform better than those without the third objective.en_US
dc.language.isoenen_US
dc.rights<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.eng
dc.subjectPortfolio optimisationen_US
dc.subjectRealistic constraintsen_US
dc.subjectMulti-objective genetic algorithmen_US
dc.subjectEstimation erroren_US
dc.subjectModel risken_US
dc.subjectFuzzy model selectionen_US
dc.subjectStrength Pareto Evolutionary Algorithm 2en_US
dc.subjectStock return forecastsen_US
dc.subjectForecasting modelsen_US
dc.titlePortfolio management using computational intelligence approaches. Forecasting and Optimising the Stock Returns and Stock Volatilities with Fuzzy Logic, Neural Network and Evolutionary Algorithms.en_US
dc.type.qualificationleveldoctoralen_US
dc.publisher.institutionUniversity of Bradfordeng
dc.publisher.departmentDepartment of Computingen_US
dc.typeThesiseng
dc.type.qualificationnamePhDen_US
dc.date.awarded2013
refterms.dateFOA2018-07-19T12:40:59Z


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