Journal
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
Volume 48, Issue 3, Pages 637-648Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207721.2016.1206985
Keywords
Portfolio selection; uncertainty theory; semivariance; genetic algorithm
Categories
Funding
- Humanity and Social Science Foundation of Ministry of Education of China [13YJA630065]
- Hubei Provincial Natural Science Foundation, China [2015CFA144]
- Fundamental Research Funds for the Central Universities, China [31541411222, 31541411209]
Ask authors/readers for more resources
Since the financial markets are complex, sometimes the future security returns are represented mainly based on experts estimations due to lack of historical data. This paper proposes a semivariance method for diversified portfolio selection, in which the security returns are given subjective to experts estimations and depicted as uncertain variables. In the paper, three properties of the semivariance of uncertain variables are verified. Based on the concept of semivariance of uncertain variables, two types of mean-semivariance diversified models for uncertain portfolio selection are proposed. Since the models are complex, a hybrid intelligent algorithm which is based on 99-method and genetic algorithm is designed to solve the models. In this hybrid intelligent algorithm, 99-method is applied to compute the expected value and semivariance of uncertain variables, and genetic algorithm is employed to seek the best allocation plan for portfolio selection. At last, several numerical examples are presented to illustrate the modelling idea and the effectiveness of the algorithm.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available