Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/17981
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dc.contributor.authorZhao, Guanglei.-
dc.date.accessioned2009-06-18T06:45:06Z-
dc.date.available2009-06-18T06:45:06Z-
dc.date.copyright2009en_US
dc.date.issued2009-
dc.identifier.urihttp://hdl.handle.net/10356/17981-
dc.description.abstractPortfolio optimization problem calculates the optimal capital weightings for a basket of investments that gives the highest return for the least risk. As we know, modern portfolio theory provides a well-developed paradigm to form a portfolio with the highest expected return for a given level of risk tolerance. However, for making the profit via the limited available capital, allocating the money to construct a portfolio is a challenge to be dealt with. Both the risk and return should be simultaneously considered in practice. Hence, portfolio optimization is a complex multi-objective problem of multistage decision-based. This project deals with the formulation of portfolio optimization problems with the two objectives return and risk. For the first stage, a genetic algorithm for asset ranking developed by Kin Keung Lai and Lean Yu [1] was studied. For the second stage, I used the Genetic Algorithm (GA) Optimization Toolbox (GAOT) for Matlab to implement the portfolio optimization problem and improved the performance of the portfolio by modifying the parameters. Examples are demonstrated with a five-stock portfolio to illustrate the multi-objective portfolio optimization process along with numerical results of the solutions. Experimental results show that the proposed GA approach for portfolio optimization is a useful tool to assist investors in constructing their portfolios. The simulations also reveal that the performance could be significantly improved by using binary representation and increasing the population size accordingly.en_US
dc.format.extent75 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University-
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologiesen_US
dc.titlePortfolio optimization using genetic algorithmen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorWang Lipoen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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