Fuzzy Chance-Constrained Integer Programming Models for Portfolio Investment Selection and Optimization Under Uncertainty

Portfolio investment optimization is the process of selecting the best portfolio out of the set of all projects being considered. A high financial return is not the only concern since minimization of associated risk is as important. Its objective should be set to maximize the expected return and min...

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Published in:International journal of knowledge and systems science (Hershey, Pa.) Vol. 11; no. 3; pp. 33 - 58
Main Authors: Chiadamrong, Navee, Srizongkhram, Shayarath, Manitayakul, Kittitath, Suthamanondh, Pisacha
Format: Journal Article
Language:English
Published: Hershey IGI Global 01.07.2020
Subjects:
Algorithms
Credibility
Discounted cash flow
Fuzzy logic
Fuzzy sets
Integer programming
Investment analysis
Linear programming
Literature reviews
Mathematical optimization
Mathematical programming
Optimization algorithms
Optimization models
Portfolio investments
Portfolio management
Present value
Risk
Systems science
Thailand
ISSN:1947-8208, 1947-8216
Online Access:Get full text
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Summary:Portfolio investment optimization is the process of selecting the best portfolio out of the set of all projects being considered. A high financial return is not the only concern since minimization of associated risk is as important. Its objective should be set to maximize the expected return and minimize the risk in the investment as most data need to be justified based on vagueness and future values. Thus, the portfolio investment optimization problem under a fuzzy environment is studied here by incorporating a classical mathematical optimization model with the fuzzy theory. It is solved with the fuzzy chance-constrained integer programming model by linear programming under predetermined conditions and limitations. This study also uses both the credibility index and credibilistic risk index for measuring the investment return and investment risk. A numerical example is illustrated to demonstrate the effectiveness and benefits of the proposed algorithm.
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ISSN:1947-8208
1947-8216
DOI:10.4018/IJKSS.2020070103