Abstract
In this study, we propose an improved fuzzy multi-objective portfolio se-lection model (VaR-MOPSM) with distinct risk measurements. The VaR-MOPSM can precisely evaluate the investment and increase the probability of obtaining the expected return. When building the model, fuzzy Value-at-Risk (VaR), which can directly reflect the greatest loss of a selection case under a given confidence level, is used to measure the exact future risk in term of loss. Conversely, variance is utilized to make the selection more stable. In this case, the proposed VaR-MOPSM can provide investors with more significant information for decision-making. To solve this model, we designed a distance based particle swarm optimization algorithm. Finally, the proposed model and algorithm are exemplified by some numerical examples. The experimental results show that the model and algorithm are effective in solving the fuzzy VaR-MOPSM.
| Original language | English |
|---|---|
| Pages (from-to) | 6191-6203 |
| Number of pages | 13 |
| Journal | International Journal of Innovative Computing, Information and Control |
| Volume | 8 |
| Issue number | 9 |
| Publication status | Published - 2012 Sep |
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Keywords
- Fuzzy multi-objective portfolio selec-tion model
- Fuzzy simulation
- Fuzzy Value-at-Risk
- Fuzzy variable
- Improved particle swarm optimization
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Information Systems
- Software
- Theoretical Computer Science
Cite this
A distance-based PSO approach to solve fuzzy MOPSM with distinct risk measurements. / Wang, Bo; Li, You; Watada, Junzo.
In: International Journal of Innovative Computing, Information and Control, Vol. 8, No. 9, 09.2012, p. 6191-6203.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A distance-based PSO approach to solve fuzzy MOPSM with distinct risk measurements
AU - Wang, Bo
AU - Li, You
AU - Watada, Junzo
PY - 2012/9
Y1 - 2012/9
N2 - In this study, we propose an improved fuzzy multi-objective portfolio se-lection model (VaR-MOPSM) with distinct risk measurements. The VaR-MOPSM can precisely evaluate the investment and increase the probability of obtaining the expected return. When building the model, fuzzy Value-at-Risk (VaR), which can directly reflect the greatest loss of a selection case under a given confidence level, is used to measure the exact future risk in term of loss. Conversely, variance is utilized to make the selection more stable. In this case, the proposed VaR-MOPSM can provide investors with more significant information for decision-making. To solve this model, we designed a distance based particle swarm optimization algorithm. Finally, the proposed model and algorithm are exemplified by some numerical examples. The experimental results show that the model and algorithm are effective in solving the fuzzy VaR-MOPSM.
AB - In this study, we propose an improved fuzzy multi-objective portfolio se-lection model (VaR-MOPSM) with distinct risk measurements. The VaR-MOPSM can precisely evaluate the investment and increase the probability of obtaining the expected return. When building the model, fuzzy Value-at-Risk (VaR), which can directly reflect the greatest loss of a selection case under a given confidence level, is used to measure the exact future risk in term of loss. Conversely, variance is utilized to make the selection more stable. In this case, the proposed VaR-MOPSM can provide investors with more significant information for decision-making. To solve this model, we designed a distance based particle swarm optimization algorithm. Finally, the proposed model and algorithm are exemplified by some numerical examples. The experimental results show that the model and algorithm are effective in solving the fuzzy VaR-MOPSM.
KW - Fuzzy multi-objective portfolio selec-tion model
KW - Fuzzy simulation
KW - Fuzzy Value-at-Risk
KW - Fuzzy variable
KW - Improved particle swarm optimization
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UR - http://www.scopus.com/inward/citedby.url?scp=84866041663&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:84866041663
VL - 8
SP - 6191
EP - 6203
JO - International Journal of Innovative Computing, Information and Control
JF - International Journal of Innovative Computing, Information and Control
SN - 1349-4198
IS - 9
ER -