Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88824
Title: Large-scale portfolio optimization using multiobjective evolutionary algorithms and preselection methods
Authors: Qu, B. Y.
Zhou, Q.
Xiao, J. M.
Liang, J. J.
Suganthan, Ponnuthurai Nagaratnam
Keywords: Multiobjective Evolutionary Algorithms
DRNTU::Engineering::Electrical and electronic engineering
Portfolio Optimization
Issue Date: 2017
Source: Qu, B. Y., Zhou, Q., Xiao, J. M., Liang, J. J., & Suganthan, P. N. (2017). Large-scale portfolio optimization using multiobjective evolutionary algorithms and preselection methods. Mathematical Problems in Engineering, 2017, 4197917-. doi:10.1155/2017/4197914
Series/Report no.: Mathematical Problems in Engineering
Abstract: Portfolio optimization problems involve selection of different assets to invest in order to maximize the overall return and minimize the overall risk simultaneously. The complexity of the optimal asset allocation problem increases with an increase in the number of assets available to select from for investing. The optimization problem becomes computationally challenging when there are more than a few hundreds of assets to select from. To reduce the complexity of large-scale portfolio optimization, two asset preselection procedures that consider return and risk of individual asset and pairwise correlation to remove assets that may not potentially be selected into any portfolio are proposed in this paper. With these asset preselection methods, the number of assets considered to be included in a portfolio can be increased to thousands. To test the effectiveness of the proposed methods, a Normalized Multiobjective Evolutionary Algorithm based on Decomposition (NMOEA/D) algorithm and several other commonly used multiobjective evolutionary algorithms are applied and compared. Six experiments with different settings are carried out. The experimental results show that with the proposed methods the simulation time is reduced while return-risk trade-off performances are significantly improved. Meanwhile, the NMOEA/D is able to outperform other compared algorithms on all experiments according to the comparative analysis.
URI: https://hdl.handle.net/10356/88824
http://hdl.handle.net/10220/45926
ISSN: 1024-123X
DOI: 10.1155/2017/4197914
Rights: © 2017 B. Y. Qu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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