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Fuzzy portfolio allocation models through a new risk measure and fuzzy sharpe ratio

Nguyen, Thanh T., Gordon-Brown, Lee, Khosravi, Abbas, Creighton, Douglas and Nahavandi, Saeid 2015, Fuzzy portfolio allocation models through a new risk measure and fuzzy sharpe ratio, IEEE transactions on fuzzy systems, vol. 23, no. 3, pp. 656-676, doi: 10.1109/TFUZZ.2014.2321614.

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Title Fuzzy portfolio allocation models through a new risk measure and fuzzy sharpe ratio
Author(s) Nguyen, Thanh T.ORCID iD for Nguyen, Thanh T. orcid.org/0000-0001-9709-1663
Gordon-Brown, Lee
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name IEEE transactions on fuzzy systems
Volume number 23
Issue number 3
Start page 656
End page 676
Total pages 18
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015-06-01
ISSN 1063-6706
1941-0034
Keyword(s) Fuzzy return
fuzzy Sharpe ratio
genetic algorithm (GA)
portfolio optimization
return uncertainty
Science & Technology
Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Computer Science
Engineering
VALUE-AT-RISK
RANDOM-VARIABLES
CORRELATION-COEFFICIENT
SELECTION
NUMBERS
VARIANCE
RETURNS
OPTIMIZATION
OPERATIONS
MOMENTS
Summary A new portfolio risk measure that is the uncertainty of portfolio fuzzy return is introduced in this paper. Beyond the well-known Sharpe ratio (i.e., the reward-to-variability ratio) in modern portfolio theory, we initiate the so-called fuzzy Sharpe ratio in the fuzzy modeling context. In addition to the introduction of the new risk measure, we also put forward the reward-to-uncertainty ratio to assess the portfolio performance in fuzzy modeling. Corresponding to two approaches based on TM and TW fuzzy arithmetic, two portfolio optimization models are formulated in which the uncertainty of portfolio fuzzy returns is minimized, while the fuzzy Sharpe ratio is maximized. These models are solved by the fuzzy approach or by the genetic algorithm (GA). Solutions of the two proposed models are shown to be dominant in terms of portfolio return uncertainty compared with those of the conventional mean-variance optimization (MVO) model used prevalently in the financial literature. In terms of portfolio performance evaluated by the fuzzy Sharpe ratio and the reward-to-uncertainty ratio, the model using TW fuzzy arithmetic results in higher performance portfolios than those obtained by both the MVO and the fuzzy model, which employs TM fuzzy arithmetic. We also find that using the fuzzy approach for solving multiobjective problems appears to achieve more optimal solutions than using GA, although GA can offer a series of well-diversified portfolio solutions diagrammed in a Pareto frontier.
Language eng
DOI 10.1109/TFUZZ.2014.2321614
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
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 ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072569

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