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Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization
Q H Zhai1, T Ye2, M X Huang3,4
1School of Sciences, Hainan University, No. 58 Renmin Avenue, Haikou 570228, China.
Computational Intelligence and Neuroscience
|September 10, 2020
Summary
This study introduces a multiconstraint portfolio optimization model, enhanced by the whale optimization algorithm. It significantly improves investment returns compared to standard strategies, especially under complex constraints.
Area of Science:
- Quantitative Finance
- Computational Economics
Background:
- Balancing investment portfolio returns and fluctuations is a central challenge in asset allocation.
- Existing models often focus solely on maximizing returns, potentially overlooking crucial risk factors.
Purpose of the Study:
- To develop a multiconstraint portfolio optimization model that comprehensively balances returns and risks.
- To evaluate the effectiveness of the whale optimization algorithm in optimizing this model.
Main Methods:
- Utilized Capital Asset Pricing Model, Arbitrage Pricing Theory, and Fama-French three-factor model for asset pricing.
- Constructed a multiconstraint model incorporating second-order stochastic dominance, higher moments, Shannon entropy, and investment constraints.
- Applied the whale optimization algorithm to optimize the model using FTSE100 index data.
Main Results:
- The whale optimization algorithm significantly enhanced portfolio returns compared to buy-and-hold and index strategies.
- Demonstrated the superiority of the whale optimization algorithm over other swarm intelligence algorithms (GWO, FOA, PSO, FA) across various performance indicators.
- The algorithm performed exceptionally well even under stringent investment constraints.
Conclusions:
- The proposed multiconstraint portfolio optimization model effectively balances risk and return.
- The whale optimization algorithm is a powerful tool for optimizing investment portfolios, offering superior performance and robustness, particularly in complex scenarios.
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