Fuzzy Chance-Constrained Multiobjective Portfolio Selection Model

This paper addresses the problem of portfolio selection with fuzzy parameters from a perspective of chance-constrained multiobjective programming. The key financial criteria used here are conventional, namely, return, risk, and liquidity; however, we use short- and long-term variants of return rathe...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems Vol. 22; no. 3; pp. 653 - 671
Main Authors: Mehlawat, Mukesh Kumar, Gupta, Pankaj
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Chance-constrained programming
Computational modeling
Confidence intervals
credibility measure
Fuzzy
Fuzzy logic
fuzzy portfolio selection
Fuzzy set theory
Fuzzy systems
Investment
Linear programming
Markets
Mathematical model
Mathematical models
multiobjective optimization
Numerical models
Portfolios
Programming
real-coded genetic algorithm (RCGA)
ISSN:1063-6706, 1941-0034
Online Access:Get full text
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Summary:This paper addresses the problem of portfolio selection with fuzzy parameters from a perspective of chance-constrained multiobjective programming. The key financial criteria used here are conventional, namely, return, risk, and liquidity; however, we use short- and long-term variants of return rather than a single measure of an investor's expectations in respect thereof. The proposed model aims to achieve the maximal return (short term as well as long term) and liquidity of the portfolio. It does so at a credibility, which is no less than the confidence levels defined by the investor. Further, to capture uncertain behavior of the financial markets more realistically, fuzzy parameters used here are such as those characterized by general functional forms. To solve the problem, we rely on a specially developed algorithm that hybridizes fuzzy simulation and real-coded genetic algorithm. Numerical experiments are included to showcase the applicability and efficiency of the model in a real investment environment.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2013.2272479