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Multi-period portfolio optimization: A parallel NSGA-III algorithm with real-world constraints

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  • Qian, Yihe
  • Wang, Jinpeng

Abstract

This study introduces an enhanced algorithm that integrates the parallel processing capabilities of PGAs with the multi-objective optimization strengths of NSGA-III, designed for multi-period optimization. We extend optimization objectives to T + 1 by minimizing risk over T periods and maximizing the terminal return, with a practical constraint on portfolio loss. It consistently outperforms the standard NSGA-III algorithm in both risk reduction and return optimization, especially when portfolios are adjusted quarterly. We also pinpoint optimal algorithmic parameters: a population size of 70 and 10 % migration rate. Overall, our research offers invaluable insights into real-world investment scenarios, serving both academic and industry interests.

Suggested Citation

  • Qian, Yihe & Wang, Jinpeng, 2024. "Multi-period portfolio optimization: A parallel NSGA-III algorithm with real-world constraints," Finance Research Letters, Elsevier, vol. 60(C).
    • Handle: RePEc:eee:finlet:v:60:y:2024:i:c:s1544612323012400
    DOI: 10.1016/j.frl.2023.104868
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    References listed on IDEAS

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    1. Drenovak, Mikica & Ranković, Vladimir & Urošević, Branko & Jelic, Ranko, 2022. "Mean-Maximum Drawdown Optimization of Buy-and-Hold Portfolios Using a Multi-objective Evolutionary Algorithm," Finance Research Letters, Elsevier, vol. 46(PA).
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