Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/64182
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dc.contributor.authorUpadhyay, Sanjana
dc.date.accessioned2015-05-25T05:26:28Z
dc.date.available2015-05-25T05:26:28Z
dc.date.copyright2015en_US
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/10356/64182
dc.description.abstractThis paper aims to study the efficiency of introducing variations in the Genetic Algorithm (GA) shown by Sefiane and Benbouziane in “Portfolio Selection using Genetic Algorithm” in order to optimize a multi-objective problem, which in this case is portfolio optimization. There can be multiple solutions to an optimal portfolio of a fixed number of stocks depending on the risk appetite of the investor, which are represented on Markowitz’s Efficient Frontier. Higher the required return, greater will be the risk taken. In this paper, results on GA optimization obtained by Sefiane and Benbouziane are further explored using the same data-set, but by changing genetic operator parameters as well as constraints on the portfolio, drawing from the work of Jeffrey Horn and David Goldberg in "A Niched Pareto Genetic Algorithm for Multiobjective Optimization"as well as that of “M. Srinivas and L.M. Patnaik in “Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms”. In this study, a fitness function allocating equal weightage to both return and risk is defined as part of a genetic algorithm, to obtain the weights of each of the components of the optimal portfolio. The performance of the GA is improved as compared to the paper by Sefiane and Benbouziane by varying the parameters of the two genetic operators used in the algorithm, namely crossover and mutation. It can be clearly observed that choice of fitness function, which is different in our case as compared to previous prominent works, does affect the results obtained from the GA, and can be modeled according to the user’s needs. We see that the GA can be used as a powerful tool to help the investor manage his wealth better, in both cases of constrained as well as unconstrained optimization.en_US
dc.format.extent59 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.subjectDRNTU::Business::Finance::Portfolio managementen_US
dc.subjectDRNTU::Business::Finance::Asset allocationen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systemsen_US
dc.titleGenetic algorithm for portfolio optimizationen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorWang Liboen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
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