Maximum Entropy Bi-Objective Model and its Evolutionary Algorithm for Portfolio Optimization

Chun-An Liu, Qian Lei () and Huamin Jia ()
Additional contact information
Chun-An Liu: School of Mathematics and Information Science, Baoji University of Arts and Sciences, Baoji, Shaanxi 721013, P. R. China
Qian Lei: Center for Foreign Linguistics and Applied, Linguistics Research, School of English Studies, Xi’an International Studies University, Xi’an, Shaanxi 710128, P. R. China
Huamin Jia: Avionics CNS System Design & ATM, Centre for Aeronautics, Cranfield University, England MK43 0AL, UK

Asia-Pacific Journal of Operational Research (APJOR), 2022, vol. 39, issue 06, 1-26

Abstract: Diversification of investment is a well-established practice for reducing the total risk of investing. Portfolio optimization is an effective way for investors to disperse investment risk and increase portfolio return. Under the assumption of no short selling, a bi-objective minimizing portfolio optimization model, in which the first objective is a semi-absolute deviation mean function used to measure the portfolio risk, and the second objective is a maximum entropy smooth function used to measure the portfolio return, is given in this paper. Also, a maximum entropy multi-objective evolutionary algorithm is designed to solve the bi-objective portfolio optimization model. In order to obtain a sufficient number of uniformly distributed portfolio Pareto optimal solutions located on the true Pareto frontier and fully exploit the useful asset combination modes which can lead the search process toward the frontier direction quickly in the objective space, a subspace multi-parent uniform crossover operator and a subspace decomposition mutation operator are given. Furthermore, a normalization method to deal with the tight constraint and the convergence analysis of the proposed algorithm are also discussed. Finally, the performance of the proposed algorithm is verified by five benchmark investment optimization problems. The performance evaluations and results analyses illustrate that the proposed algorithm is capable of identifying good Pareto solutions and maintaining adequate diversity of the evolution population. Also, the proposed algorithm can obtain faster and better convergence to the true portfolio Pareto frontier compared with the three state-of-the-art multi-objective evolutionary algorithms. The result can also provide optimal portfolio plan and investment strategy for investors to allocate and manage asset effectively.

Keywords: Portfolio optimization; multi-objective optimization; evolutionary algorithm; Pareto optimal solution; assets management (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0217595922500142
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:apjorx:v:39:y:2022:i:06:n:s0217595922500142

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0217595922500142

Access Statistics for this article

Asia-Pacific Journal of Operational Research (APJOR) is currently edited by Gongyun Zhao

More articles in Asia-Pacific Journal of Operational Research (APJOR) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().