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Portfolio selection under higher moments using fuzzy multi-objective linear programming

Nguyen, Thanh Thi 2016, Portfolio selection under higher moments using fuzzy multi-objective linear programming, Journal of intelligent and fuzzy systems, vol. 30, no. 4, pp. 2139-2156, doi: 10.3233/IFS-151927.

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Title Portfolio selection under higher moments using fuzzy multi-objective linear programming
Author(s) Nguyen, Thanh Thi
Journal name Journal of intelligent and fuzzy systems
Volume number 30
Issue number 4
Start page 2139
End page 2156
Total pages 18
Publisher IOS Press
Place of publication Amsterdam, The Netherlands
Publication date 2016
ISSN 1064-1246
1875-8967
Keyword(s) portfolio selection
higher moments
fuzzy sets
fuzzy multi-objective linear programming (FMOLP)
marginal impacts
Summary Since asset returns have been recognized as not normally distributed, the avenue of research regarding portfolio higher moments soon emerged. To account for uncertainty and vagueness of portfolio returns as well as of higher moment risks, we proposed a new portfolio selection model employing fuzzy sets in this paper. A fuzzy multi-objective linear programming (MOLP) for portfolio optimization is formulated using marginal impacts of assets on portfolio higher moments, which are modelled by trapezoidal fuzzy numbers. Through a consistent centroid-based ranking of fuzzy numbers, the fuzzy MOLP is transformed into an MOLP that is then solved by the maximin method. By taking portfolio higher moments into account, the approach enables investors to optimize not only the normal risk (variance) but also the asymmetric risk (skewness) and the risk of fat-tails (kurtosis). An illustrative example demonstrates the efficiency of the proposed methodology comparing to previous portfolio optimization models.
Language eng
DOI 10.3233/IFS-151927
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
0801 Artificial Intelligence And Image Processing
1702 Cognitive Science
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, IOS Press and The Author
Persistent URL http://hdl.handle.net/10536/DRO/DU:30084986

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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