Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/17981
Title: Portfolio optimization using genetic algorithm
Authors: Zhao, Guanglei.
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies
Issue Date: 2009
Abstract: Portfolio 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.
URI: http://hdl.handle.net/10356/17981
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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