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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Veröffentlicht in:International journal of knowledge and systems science (Hershey, Pa.) Jg. 11; H. 3; S. 33 - 58
Hauptverfasser: Chiadamrong, Navee, Srizongkhram, Shayarath, Manitayakul, Kittitath, Suthamanondh, Pisacha
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hershey IGI Global 01.07.2020
Schlagworte:
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
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Abstract 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.
AbstractList 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.
Audience Academic
Author Srizongkhram, Shayarath
Suthamanondh, Pisacha
Chiadamrong, Navee
Manitayakul, Kittitath
AuthorAffiliation Sirindhorn International Institute of Technology, Thammasat University, Thailand
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  surname: Chiadamrong
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  surname: Suthamanondh
  fullname: Suthamanondh, Pisacha
  organization: Sirindhorn International Institute of Technology, Thammasat University, Thailand
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crossref_primary_10_4018_IJKSS_302660
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ContentType Journal Article
Copyright COPYRIGHT 2020 IGI Global
Copyright © 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
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StartPage 33
SubjectTerms 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
Title Fuzzy Chance-Constrained Integer Programming Models for Portfolio Investment Selection and Optimization Under Uncertainty
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