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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Bibliographic Details
Published in:Mathematical problems in engineering Vol. 2017; no. 2017; pp. 1 - 14
Main Authors: Liang, J. J., Xiao, J. M., Zhou, Q., Qu, B. Y., Suganthan, P. N.
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2017
Hindawi
John Wiley & Sons, Inc
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2017/4197914