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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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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.

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Authors and Affiliations

  1. School of Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Yinnan Chen, Lingjuan Ye & Xinchao Zhao

  2. Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China

    Rui Li

Authors
  1. Yinnan Chen
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  2. Lingjuan Ye
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  3. Rui Li
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  4. Xinchao Zhao
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Corresponding author

Correspondence to Xinchao Zhao.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant No. 61973042 and Beijing Natural Science Foundation under Grant No. 1202020.

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Chen, Y., Ye, L., Li, R. et al. A Multi-Period Constrained Multi-Objective Evolutionary Algorithm with Orthogonal Learning for Solving the Complex Carbon Neutral Stock Portfolio Optimization Model. J Syst Sci Complex 36, 686–715 (2023). https://doi.org/10.1007/s11424-023-2406-3

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  • DOI: https://doi.org/10.1007/s11424-023-2406-3

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