Multi-stage stochastic mean-semivariance-CVaR portfolio optimization under transaction costs

被引：47

作者：

Najafi, Amir Abbas ^{[1
]}

Mushakhian, Siamak ^{[1
]}

机构：

[1] KN Toosi Univ Technol, Fac Ind Engn, Tehran, Iran

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2015年 / 256卷

关键词：

Portfolio selection; Multi-period investment; Hybrid algorithm; Transaction costs; Risk measures; GENETIC ALGORITHM; SELECTION; MODEL;

D O I：

10.1016/j.amc.2015.01.050

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, we present a model for portfolio selection, characterized on the basis of three parameters: the expected value, semivariance, and Conditional Value-at-Risk (CVaR) at a specified confidence level. In order to solve the proposed model, we design a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. Because the effectiveness of meta-heuristic algorithms significantly depends on the proper choice of parameters, a Taguchi experimental design method is applied to set the suitable values of parameters to improve the hybrid algorithm performance. Finally, some numerical examples are given to illustrate the effectiveness of the proposed algorithm. (C) 2015 Elsevier Inc. All rights reserved.

引用

页码：445 / 458

页数：14

共 51 条

[1] A GA-based parameter design for single machine turning process with high-volume production [J].