Abstract
In financial markets, investors will face not only portfolio risk but also background risk. This paper proposes a credibilistic multi-objective mean-semi-entropy model with background risk for multi-period portfolio selection. In addition, realistic constraints such as liquidity, cardinality constraints, transaction costs, and buy-in thresholds are considered. For solving the proposed multi-objective problem efficiently, a novel hybrid algorithm named Hybrid Dragonfly Algorithm-Genetic Algorithm (HDA-GA) is designed by combining the advantages of the dragonfly algorithm (DA) and non-dominated sorting genetic algorithm II (NSGA II). Moreover, in the hybrid algorithm, parameter optimization, constraints handling, and external archive approaches are used to improve the ability of finding accurate approximations of Pareto optimal solutions with high diversity and coverage. Finally, we provide several empirical studies to show the validity of the proposed approaches.
Highlights
As a research field, portfolio selection is used to accomplish the investments in financial markets by spreading investors’ capital among several different assets considering return and risk.Since the pioneering work of Markowitz [1] in single-period investment problems, the mean–variance portfolio selection problem has attracted much attention and has become a research hotspot.By introducing different risk measures, a large variety of portfolio selection models have been presented, such as the mean–variance–skewness model [2], the mean-conditional value at risk (CVaR) model [3], the mean-value at risk (VaR) model [4], the mean-semi-variance model [5]and the minimax risk model [6]
The multi-objective dragonfly algorithm (MODA) is more stable than the Hybrid Dragonfly Algorithm-Genetic Algorithm (HDA-GA) in terms of standard deviation (SD) index, it is easier for MODA to fall into local optimization
We present four cases to analyze the impact of background risk in the proposed model
Summary
Portfolio selection is used to accomplish the investments in financial markets by spreading investors’ capital among several different assets considering return and risk. Zhang and Liu [45] gave a credibility-based model with a bankruptcy risk control constraint for solving multi-period portfolio selection problems. Numerous studies have been performed for multi-period fuzzy portfolio selections, few studies have considered background risk under the framework of credibility theory. The purpose of this paper is to investigate the multi-period portfolio selection problem with background risk in the framework of credibility theory. The main contributions of this paper are as follows: (1) We formulate a credibility-based mean-semi-entropy multi-period portfolio model, considering background risk and several constraints, namely cardinality, liquidity, and buy-in thresholds; (2) We develop a new meta-heuristic approach, combining the strengths of DA and NSGA.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
