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    Portfolio management using computational intelligence approaches. Forecasting and Optimising the Stock Returns and Stock Volatilities with Fuzzy Logic, Neural Network and Evolutionary Algorithms.

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    Portfolio management using computational intelligence approaches_FinalSubmission.pdf (2.242Mb)
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    Publication date
    2014-05-02
    Author
    Skolpadungket, Prisadarng
    Supervisor
    Dahal, Keshav P.
    Harnpornchai, Napat
    Keyword
    Portfolio optimisation
    Realistic constraints
    Multi-objective genetic algorithm
    Estimation error
    Model risk
    Fuzzy model selection
    Strength Pareto Evolutionary Algorithm 2
    Stock return forecasts
    Forecasting models
    Rights
    Creative Commons License
    The University of Bradford theses are licenced under a Creative Commons Licence.
    Institution
    University of Bradford
    Department
    Department of Computing
    Awarded
    2013
    
    Metadata
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    Abstract
    Portfolio 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.
    URI
    http://hdl.handle.net/10454/6306
    Type
    Thesis
    Qualification name
    PhD
    Collections
    Theses

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