Large-Scale Portfolio Optimization Using Multiobjective Evolutionary Algorithms and Preselection Methods

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...

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Veröffentlicht in:	Mathematical problems in engineering Jg. 2017; H. 2017; S. 1 - 14
Hauptverfasser:	Liang, J. J., Xiao, J. M., Zhou, Q., Qu, B. Y., Suganthan, P. N.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cairo, Egypt Hindawi Publishing Corporation 01.01.2017 Hindawi John Wiley & Sons, Inc
Schlagworte:	Algorithms Allocations Complexity Economic models Engineering Evolutionary algorithms Genetic algorithms Investment policy Investments Mathematical problems Methods Multiple objective Multiple objective analysis Optimization Optimization techniques Portfolio management Risk Risk allocation Theory Tradeoffs
ISSN:	1024-123X, 1563-5147
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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.
AbstractList	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.
Author	Xiao, J. M. Suganthan, P. N. Liang, J. J. Zhou, Q. Qu, B. Y.
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Cites_doi	10.1360/crad20070125 10.1109/tpwrs.2003.818693 10.1016/j.ins.2012.02.011 10.1016/j.ejor.2006.12.024 10.1109/CEC.2009.4982949 10.1007/978-3-7091-7535-4_100 10.1002/int.20128 10.1016/j.amc.2007.04.036 10.1287/opre.28.3.754 10.2469/faj.v51.n2.1881 10.1007/s001860000073 10.1016/0305-0548(95)00030-5 10.1109/tevc.2008.925798 10.1109/TEVC.2012.2196800 10.1016/j.jedc.2006.12.004 10.1109/tevc.2007.892759 10.1016/j.cor.2005.06.017 10.1109/CEC.2007.4424514 10.1016/j.econmod.2013.05.006 10.1016/j.ejor.2013.10.060 10.1016/S0305-0548(99)00074-X 10.1016/j.ins.2010.05.013 10.1109/4235.996017 10.1111/j.1540-6261.1952.tb01525.x
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Copyright	Copyright © 2017 B. Y. Qu et al. Copyright © 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.
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SubjectTerms	Algorithms Allocations Complexity Economic models Engineering Evolutionary algorithms Genetic algorithms Investment policy Investments Mathematical problems Methods Multiple objective Multiple objective analysis Optimization Optimization techniques Portfolio management Risk Risk allocation Theory Tradeoffs
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