Design of Optimum Portfolio Scheme Based on Improved NSGA-II Algorithm
Yiqian Zhou1, Weinan Chen1, Deqin Lin2
1Faculty of Business, City University of Macau, Macau 999078, China.
Computational Intelligence and Neuroscience
|June 23, 2022
Summary
This study introduces an enhanced multiobjective optimization algorithm for financial portfolio optimization. The novel approach improves investment strategy decision-making efficiency for investors.
Area of Science:
- Finance
- Computational Intelligence
- Operations Research
Background:
- Multiobjective portfolio optimization is crucial for developing sound investment strategies in finance.
- Existing multiobjective evolutionary algorithms require adaptation for real-world portfolio problems.
Purpose of the Study:
- To design an effective financial portfolio scheme using a multiobjective optimization algorithm.
- To enhance the applicability of multiobjective evolutionary algorithms in financial portfolio optimization.
Main Methods:
- The study utilizes the framework of the NSGA-II algorithm for multiobjective optimization.
- A mixed individual coding mechanism incorporating asset information is developed to address portfolio-specific challenges.
- Convergence information is integrated into the algorithm.
Main Results:
- The proposed multiobjective optimization algorithm yields an effective financial portfolio scheme.
- The mixed individual coding mechanism successfully expands the application of multiobjective evolutionary algorithms.
- The approach demonstrates practical effectiveness in portfolio optimization.
Conclusions:
- The developed financial portfolio scheme enhances decision-making efficiency for investors.
- This research enriches the application of modern financial theory through advanced computational methods.
- The integration of NSGA-II with a novel coding mechanism offers a robust solution for multiobjective portfolio optimization.
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