Improved particle swarm optimization for mean-variance-Yager entropy-social responsibility portfolio with complex reality constraints
ISSN: 0264-4401
Article publication date: 24 September 2021
Issue publication date: 24 March 2022
Abstract
Purpose
The weighted evaluation function method with normalized objective functions is used to transform the proposed multi-objective model into a single objective one, which reflects the investors' preference for returns, risks and social responsibility by adjusting the weights. Finally, an example is given to illustrate the solution steps of the model and the effectiveness of the algorithm.
Design/methodology/approach
Based on the possibility theory, assuming that the future returns of each asset are trapezoidal fuzzy numbers, a mean-variance-Yager entropy-social responsibility model is constructed including piecewise linear transaction costs and risk-free assets. The model proposed in this paper includes six constraints, the investment proportion sum, the non-negativity proportion, the ceiling and floor, the pre-assignment, the cardinality and the round lot constraints. In addition, considering the special round lot constraint, the proposed model is transformed into an integer programming problem.
Findings
The effects of different constraints and transaction costs on the effective frontier of the portfolio are analyzed, which not only assists investors to make decisions close to their expectations by setting appropriate parameters but also provides constructive suggestions through the overall performance of each asset.
Originality/value
There are two improvements in the improved particle swarm optimization algorithm: one is that the complex constraints are specifically satisfied by using a renewable 0–1 random constraint matrix and random scaling factors instead of fixed ones; the other is eliminating the particles with poor fitness and randomly adding some new particles that satisfy all the constraints to achieve the goal of global search as much as possible.
Keywords
Acknowledgements
This research was supported by the “Humanities and Social Sciences Research and Planning Fund of the Ministry of Education of China, No. 18YJAZH014-x2lxY9180090”, “Natural Science Foundation of Guangdong Province, No. 2019A1515011038”, “Guangdong Province Characteristic Innovation Project of Colleges and Universities, No. 2019GKTSCX023”, “Soft Science of Guangdong Province, No. 2018A070712006, 2019A101002118”. The authors are highly grateful to the referees and editor in-chief for their very helpful comments.
Citation
Deng, X. and Lin, Y. (2022), "Improved particle swarm optimization for mean-variance-Yager entropy-social responsibility portfolio with complex reality constraints", Engineering Computations, Vol. 39 No. 4, pp. 1288-1316. https://doi.org/10.1108/EC-02-2021-0080
Publisher
:Emerald Publishing Limited
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