Fuzzy multi-period portfolio selection model with time-varying loss aversion
Yong-Jun Liu and
Wei-Guo Zhang
Journal of the Operational Research Society, 2021, vol. 72, issue 4, 935-949
Abstract:
This paper deals with multi-period portfolio selection problem in a fuzzy environment, in which the effects of investors’ time-varying asymmetric attitudes to losses and gains on portfolio selection are taken into consideration. In the paper, a definition of time-varying loss aversion is presented to describe the aforementioned time-varying asymmetric attitudes to losses and gains. A fuzzy multi-period portfolio selection model based on this new definition of time-varying loss aversion is formulated. Then, an improved co-evolutionary particle swarm optimization (ICPSO) algorithm is designed to solve the proposed model. The comparison analysis between the proposed model and the fixed-parameter loss aversion model is provided to demonstrate the fact that the proposed model can significantly affect the performance of portfolio selection. Finally, a numerical example is given to illustrate the application of the proposed model and the efficiency of the designed algorithm.
Date: 2021
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2019.1705191 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:72:y:2021:i:4:p:935-949
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20
DOI: 10.1080/01605682.2019.1705191
Access Statistics for this article
Journal of the Operational Research Society is currently edited by Tom Archibald
More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().