Optimization of Risk and Return Using Fuzzy Multiobjective Linear Programming
Stock selection poses a challenge for both the investor and the finance researcher. In this paper, a hybrid approach is proposed for asset allocation, offering a combination of several methodologies for portfolio selection, such as investor topology, cluster analysis, and the analytical hierarchy pr...
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| Veröffentlicht in: | Advances in Fuzzy Systems Jg. 2018; H. 2018; S. 1 - 9 |
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| Sprache: | Englisch |
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Cairo, Egypt
Hindawi Publishing Corporation
01.01.2018
Hindawi John Wiley & Sons, Inc Wiley |
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| Abstract | Stock selection poses a challenge for both the investor and the finance researcher. In this paper, a hybrid approach is proposed for asset allocation, offering a combination of several methodologies for portfolio selection, such as investor topology, cluster analysis, and the analytical hierarchy process (AHP) to facilitate ranking the assets and fuzzy multiobjective linear programming (FMOLP). This paper considers some important factors of stock, like relative strength index (RSI), coefficient of variation (CV), earnings yield (EY), and price to earnings growth ratio (PEG ratio), apart from the risk and return and stocks which are included within these same factors. Employing fuzzy multiobjective linear programming, optimization is performed using seven objective functions viz., return, risk, relative strength index (RSI), coefficient of variation (CV), earnings yield (EY), price to earnings growth ratio (PEG ratio), and AHP weighted score. The FMOLP transforms the multiobjective problem to a single objective problem using the “weighted adaptive approach” in which the weights are calculated by AHP or choices by the investors. The FMOLP model permits choices in solution. |
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| AbstractList | Stock selection poses a challenge for both the investor and the finance researcher. In this paper, a hybrid approach is proposed for asset allocation, offering a combination of several methodologies for portfolio selection, such as investor topology, cluster analysis, and the analytical hierarchy process (AHP) to facilitate ranking the assets and fuzzy multiobjective linear programming (FMOLP). This paper considers some important factors of stock, like relative strength index (RSI), coefficient of variation (CV), earnings yield (EY), and price to earnings growth ratio (PEG ratio), apart from the risk and return and stocks which are included within these same factors. Employing fuzzy multiobjective linear programming, optimization is performed using seven objective functions viz., return, risk, relative strength index (RSI), coefficient of variation (CV), earnings yield (EY), price to earnings growth ratio (PEG ratio), and AHP weighted score. The FMOLP transforms the multiobjective problem to a single objective problem using the “weighted adaptive approach” in which the weights are calculated by AHP or choices by the investors. The FMOLP model permits choices in solution. |
| Audience | Professional |
| Author | Panwar, Darsha Srivastava, Namita Jha, Manoj |
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| Copyright | Copyright © 2018 Darsha Panwar et al. COPYRIGHT 2018 John Wiley & Sons, Inc. Copyright © 2018 Darsha Panwar et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| DOI | 10.1155/2018/4279236 |
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| SubjectTerms | Algorithms Analytic hierarchy process Behavior Cluster analysis Clustering Coefficient of variation Earnings per share Expected values Investment analysis Investments Linear programming Mathematical analysis Mathematical problems Methods Multiple objective analysis Optimization Optimization techniques Portfolio management Profits Random variables Rankings Risk Social sciences Stock exchanges |
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| Title | Optimization of Risk and Return Using Fuzzy Multiobjective Linear Programming |
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