A Multi-Period Constrained Multi-Objective Evolutionary Algorithm with Orthogonal Learning for Solving the Complex Carbon Neutral Stock Portfolio Optimization Model

Financial market has systemic complexity and uncertainty. For investors, return and risk often coexist. How to rationally allocate funds into different assets and achieve excess returns with effectively controlling risk are main problems to be solved in the field of portfolio optimization (PO). At p...

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Vydáno v:	Journal of systems science and complexity Ročník 36; číslo 2; s. 686 - 715
Hlavní autoři:	Chen, Yinnan, Ye, Lingjuan, Li, Rui, Zhao, Xinchao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2023 Springer Nature B.V
Témata:	Carbon Carbon offsets Complex Systems Complexity Constraint modelling Constraints Control Evolutionary algorithms Genetic algorithms Machine learning Mathematics Mathematics and Statistics Mathematics of Computing Multiple objective analysis Operations Research/Decision Theory Optimization Optimization models Risk management Statistics Systems Theory Constrained multi-objective optimization multi-period constrained multi-objective evolutionary algorithm portfolio optimization carbon-neutral orthogonal learning
ISSN:	1009-6124, 1559-7067
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Abstract	Financial market has systemic complexity and uncertainty. For investors, return and risk often coexist. How to rationally allocate funds into different assets and achieve excess returns with effectively controlling risk are main problems to be solved in the field of portfolio optimization (PO). At present, due to the influence of modeling and algorithm solving, the PO models established by many researchers are still mainly focused on single-stage single-objective models or single-stage multi-objective models. PO is actually considered as a multi-stage multi-objective optimization problem in real investment scenarios. It is more difficult than the previous single-stage PO model for meeting the realistic requirements. In this paper, the authors proposed a mean-improved stable tail adjusted return ratio-maximum drawdown rate (M-ISTARR-MD) PO model which effectively characterizes the real investment scenario. In order to solve the multi-stage multi-objective PO model with complex multi-constraints, the authors designed a multi-stage constrained multi-objective evolutionary algorithm with orthogonal learning (MSCMOEA-OL). Comparing with four well-known intelligence algorithms, the MSCMOEA-OL algorithm has competitive advantages in solving the M-ISTARR-MD model on the proposed constructed carbon neutral stock dataset. This paper provides a new way to construct and solve the complex PO model.
AbstractList	Financial market has systemic complexity and uncertainty. For investors, return and risk often coexist. How to rationally allocate funds into different assets and achieve excess returns with effectively controlling risk are main problems to be solved in the field of portfolio optimization (PO). At present, due to the influence of modeling and algorithm solving, the PO models established by many researchers are still mainly focused on single-stage single-objective models or single-stage multi-objective models. PO is actually considered as a multi-stage multi-objective optimization problem in real investment scenarios. It is more difficult than the previous single-stage PO model for meeting the realistic requirements. In this paper, the authors proposed a mean-improved stable tail adjusted return ratio-maximum drawdown rate (M-ISTARR-MD) PO model which effectively characterizes the real investment scenario. In order to solve the multi-stage multi-objective PO model with complex multi-constraints, the authors designed a multi-stage constrained multi-objective evolutionary algorithm with orthogonal learning (MSCMOEA-OL). Comparing with four well-known intelligence algorithms, the MSCMOEA-OL algorithm has competitive advantages in solving the M-ISTARR-MD model on the proposed constructed carbon neutral stock dataset. This paper provides a new way to construct and solve the complex PO model.
Author	Zhao, Xinchao Chen, Yinnan Ye, Lingjuan Li, Rui
Author_xml	– sequence: 1 givenname: Yinnan surname: Chen fullname: Chen, Yinnan organization: School of Science, Beijing University of Posts and Telecommunications – sequence: 2 givenname: Lingjuan surname: Ye fullname: Ye, Lingjuan organization: School of Science, Beijing University of Posts and Telecommunications – sequence: 3 givenname: Rui surname: Li fullname: Li, Rui organization: Harbin Institute of Technology (Shenzhen) – sequence: 4 givenname: Xinchao surname: Zhao fullname: Zhao, Xinchao email: zhaoxc@bupt.edu.cn organization: School of Science, Beijing University of Posts and Telecommunications
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SubjectTerms	Carbon Carbon offsets Complex Systems Complexity Constraint modelling Constraints Control Evolutionary algorithms Genetic algorithms Machine learning Mathematics Mathematics and Statistics Mathematics of Computing Multiple objective analysis Operations Research/Decision Theory Optimization Optimization models Risk management Statistics Systems Theory
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Title	A Multi-Period Constrained Multi-Objective Evolutionary Algorithm with Orthogonal Learning for Solving the Complex Carbon Neutral Stock Portfolio Optimization Model
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