A robust knapsack based constrained portfolio optimization

被引：0

作者：

Vaezi F. ^{[1
]}

Sadjadi S.J. ^{[1
]}

Makui A. ^{[1
]}

机构：

[1] Department of Industrial Engineering, Iran University of Science and Technology, Tehran

来源：

International Journal of Engineering, Transactions B: Applications | 2020年 / 33卷 / 05期

关键词：

Constrained Portfolio Optimization; Genetic Algorithm; Knapsack Problem; Robust Optimization;

D O I：

10.5829/IJE.2020.33.05B.16

中图分类号：

学科分类号：

摘要：

Many portfolio optimization problems deal with allocation of assets which carry a relatively high market price. Therefore, it is necessary to determine the integer value of assets when we deal with portfolio optimization. In addition, one of the main concerns with most portfolio optimization is associated with the type of constraints considered in different models. In many cases, the resulted problem formulations do not yield in practical solutions. Therefore, it is necessary to apply some managerial decisions in order to make the results more practical. This paper presents a portfolio optimization based on an improved knapsack problem with the cardinality, floor and ceiling, budget, class, class limit and pre-assignment constraints for asset allocation. To handle the uncertainty associated with different parameters of the proposed model, we use robust optimization techniques. The model is also applied using some realistic data from US stock market. Genetic algorithm is also provided to solve the problem for some instances. © 2020 Materials and Energy Research Center. All rights reserved.

引用

页码：841 / 851

页数：10

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