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.

中文翻译：

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用于多约束二阶随机优势投资组合优化的鲸鱼优化算法。


在资产配置领域，如何平衡投资组合的收益及其波动是核心问题。采用资本资产定价模型、套利定价理论和Fama-French三因素模型来量化个股和投资组合的价格。基于二阶随机支配规则、收益级数高矩、香农熵以及其他一些实际投资约束，我们构建了多约束投资组合优化模型，旨在综合加权投资组合的收益和风险，而不是盲目最大化它的回报。此外，采用基于FTSE100指数数据的鲸鱼优化算法来优化上述多约束投资组合优化模型，显着提高了简单多元化买入持有策略或FTSE100指数的收益率。此外，大量实验通过结果的各种指标，特别是在苛刻的约束条件下，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。