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|Title:||Genetic algorithm for portfolio optimization||Authors:||Upadhyay, Sanjana||Keywords:||DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
|Issue Date:||2015||Abstract:||This 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.||URI:||http://hdl.handle.net/10356/64182||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
checked on Sep 29, 2020
checked on Sep 29, 2020
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