Mean-risk-skewness models for portfolio optimization based on uncertain measure. (4th May 2018)

• Record Type: Journal Article
• Authors: Zhai, Jia; Bai, Manying; Wu, Hongru
• Is Part Of: Optimization. Volume 67:Number 5(2018)
• Page Start: 701
• Page End: 714
• Publication Date: 2018-05-04
• Subjects: Portfolio optimization -- uncertain variable -- mean-risk-skewness model -- uncertain programming; Mathematical optimization -- Periodicals; 519.7
• DOI: 10.1080/02331934.2018.1426577
• Languages: English
• ISSNs: 0233-1934

Abstract

Abstract: Numerous empirical studies show that portfolio returns are generally asymmetric. In this paper, skewness is considered to measure the asymmetry of portfolio returns and a mean-risk-skewness model for portfolio selection will be proposed in uncertain environment. Here, the returns of the securities are regarded as uncertain variables which are estimated by experienced experts instead of historical data. Furthermore, the corresponding variations and crisp forms of the model are considered. To solve the proposed optimization models, a hybrid intelligent algorithm is designed. Finally, the feasibility and necessity of the hybrid intelligent algorithm and the application of the proposed models are illustrated by two numerical examples.