Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization

Abstract

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.

An Adaptive Multi-Population Optimization Algorithm for Global Continuous Optimization

“…They are the WOA, LS-cnEPSin and MOFOA. Among them, the excellent performance of WOA in portfolio optimization is also reported by [47]. In addition, referring to [76,77], we select three other competitive algorithms that demonstrate their excellent performance in optimizing the same problem in the literature, namely, Adaptive Particle Swarm Optimization (APSO) [44], Fireworks Algorithm (FA) [45] and Harmony Search Algorithm (HSA) [46].…”

“…For instance, financial portfolio optimization can be with different dimensions given by different sets of assets, which implies an optimization algorithm with a high adaptivity is required for resolving such problems. In spite of some studies [44][45][46][47] applied some metaheuristics to portfolio optimization, the scalability and adaptivity of such approaches are not examined carefully yet. Also, the scale of the datasets the OR-Library [48] frequently used by many researchers is relatively small, i.e.…”

Multiresponse Optimization for a Novel Compliant Z-Stage by a Hybridization of Response Surface Method and Whale Optimization Algorithm

“…Meanwhile, an integration method of TM, RSM [48], and whale optimization algorithm (WOA) integrated the weight factor calculation method based on the signal to noise for optimizing a compliant z-stage applied for the indenter in nanoindentation tester which has not been studied yet. As a result, based on the global convergence quality characteristics of the WOA, a proficient hybrid approach of TM, RSM, and WOA [18,[49][50][51][52] is developed to resolve multicriteria optimization trouble of the z-stage in this article. Additionally, the weight factors based on signal to noise [53] for each response are calculated and assigned for the total weighted objective function in order to enhance the optimal precision results.…”

Research on intelligent algorithms for solving portfolio problems

“…Chen Min et al [4] used the cluster differential evolution algorithm (DEClu algorithm) to solve the mean-VaR model that does not allow short selling. Zhai et al [5] used the whale optimization algorithm to solve the multi-constrained portfolio optimization model, which significantly improves the rate of return of the simple diversified buy-and-hold strategy or the FTSE100 index. S. Almahdi and S.Y.…”