Most of the portfolio optimization problems are devoted to either stochastic model or fuzzy one. However, practical portfolio selection problems often involve the mixture of the stochastic returns with fuzzy information. In this paper, we propose a new mean variance random credibilitic portfolio selection problem with different convex transaction costs, i.e., linear function, non-smooth convex function, smooth convex function. In this proposed model, we assume that the returns of assets obey the trapezoidal-type credibilitic distributions, and the risks obey the stochastic distributions. Based on the random credibilitic theories, these models are transformed into crisp convex programming problems. To find the optimal solution, we, respectively, present a pivoting algorithm, a branch-and-bound algorithm, and a sequence quadratic programming algorithm to solve these models. Furthermore, we offer numerical experiments of different forms of convex transaction costs to illustrate the effectiveness of the proposed model and approach.
Mean variance portfolio optimization model Random credibilitic variable Convex transaction costs A pivoting algorithm Sequence quadratic programming
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This research was supported by the National Natural Science Foundation of China (Nos. 71271161).
Compliance with ethical standards
Conflict of interest
Peng Zhang declares that he/she has no conflict of interest.
This article does not contain any studies with human participants performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
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