A constrained multi-period portfolio optimization model based on quantum-inspired optimization

Title

A constrained multi-period portfolio optimization model based on quantum-inspired optimization

Creator

Ramaiah K.; Soundarabai P.B.

Description

Multi-period portfolio optimization (MPO) is one of the most important problems to be solved to help investors select optimal portfolios for investment plans. The portfolios are influenced by the risk factors in the market and it is important to select optimal portfolios that can maximize the returns with minimum risk values. Other than the risk factor, there are several other influential factors that reduce the optimality of the portfolios. Therefore, by considering all possible constraints, this study proposes a multi-constraint MPO model that selects the optimal portfolio based on the asset returns. To solve the multi-constrained problem, a novel quantum-inspired whale optimization algorithm (QWOA) is introduced in this paper. The proposed algorithm enhances the traditional optimization model to work in a multi-constrained scenario. Here, quantum entanglement is adapted to reduce the slow convergence issue of whale optimization. Apart from considering only the risk factors, this paper also considers certain higher-order moments (HOM), such as skewness, kurtosis, transaction cost, diversification, boundary and budget constraints. These factors affect the portfolios as the market is dynamic, and timely changes are always seen. Thus, optimizing the mentioned factors aids in attaining an optimal portfolio. Empirical evaluations are performed, and the results suggested that the proposed model provided beneficial outcomes as compared with other algorithms like whale optimization algorithm (WOA), gray wolf optimization (GWO), fruitfly optimization algorithm (FOA), particle swarm optimization (PSO) and fruitfly algorithm (FA). The overall net return rate of the proposed model is always above 0.85% for different values of upper bounds, and the obtained Sharpe ratio, Sortino ratio, STARR ratio, information ratio, Shannon entropy, and downside deviation values of the proposed algorithm are 5.016254, 0.89327, ? 0.01987, 0.103826, 3.04452 and 0.2854. Hence, the proposed approach is highly effective for optimizing the constrained MPO. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

Source

Multimedia Tools and Applications, Vol-83, No. 33, pp. 78769-78796.

Date

2024-01-01

Publisher

Springer

Subject

Higher-order constraints; Market risks; Multi-period portfolio optimization; Net return rate; Quantum entanglement; Quantum whale optimization

Coverage

Ramaiah K., Department of Computer Science, Christ University, Karnataka, Bengaluru, 560029, India; Soundarabai P.B., Department of Computer Science, Christ University, Karnataka, Bengaluru, 560029, India

Rights

Restricted Access

Relation

ISSN: 13807501; CODEN: MTAPF

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1007/s11042-024-18597-y

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186175591&doi=10.1007%2fs11042-024-18597-y&partnerID=40&md5=e98ab5731e02900e13aba5a92ccc72c6