Zhai Q H, Ye T, Huang M X, Feng S L, Li H
School of Sciences, Hainan University, No. 58 Renmin Avenue, Haikou 570228, China.
College of Management and Economy, Tianjin University, 92 Weijin Road Nankai District, Tianjin 300072, China.
Comput Intell Neurosci. 2020 Aug 28;2020:8834162. doi: 10.1155/2020/8834162. eCollection 2020.
In the field of asset allocation, how to balance the returns of an investment portfolio and its fluctuations is the core issue. Capital asset pricing model, arbitrage pricing theory, and Fama-French three-factor model were used to quantify the price of individual stocks and portfolios. Based on the second-order stochastic dominance rule, the higher moments of return series, the Shannon entropy, and some other actual investment constraints, we construct a multiconstraint portfolio optimization model, aiming at comprehensively weighting the returns and risk of portfolios rather than blindly maximizing its returns. Furthermore, the whale optimization algorithm based on FTSE100 index data is used to optimize the above multiconstraint portfolio optimization model, which significantly improves the rate of return of the simple diversified buy-and-hold strategy or the FTSE100 index. Furthermore, extensive experiments validate the superiority of the whale optimization algorithm over the other four swarm intelligence optimization algorithms (gray wolf optimizer, fruit fly optimization algorithm, particle swarm optimization, and firefly algorithm) through various indicators of the results, especially under harsh constraints.
在资产配置领域，如何平衡投资组合的回报及其波动是核心问题。资本资产定价模型、套利定价理论和法玛-弗伦奇三因素模型被用于量化个股和投资组合的价格。基于二阶随机占优规则、收益序列的高阶矩、香农熵以及其他一些实际投资约束，我们构建了一个多约束投资组合优化模型，旨在综合权衡投资组合的回报和风险，而非盲目地最大化其回报。此外，基于富时100指数数据的鲸鱼优化算法被用于优化上述多约束投资组合优化模型，这显著提高了简单分散买入并持有策略或富时100指数的回报率。此外，大量实验通过结果的各种指标验证了鲸鱼优化算法相对于其他四种群体智能优化算法（灰狼优化器、果蝇优化算法、粒子群优化和萤火虫算法）的优越性，尤其是在苛刻约束条件下。
