Abstract
On the basis of the portfolio selection theory, this paper proposes a novel fuzzy multiobjective model that can evaluate investment risk properly and increase the probability of obtaining an expected return. In building this model, fuzzy value-at-risk (VaR) is used to evaluate the exact future risk in terms of loss. The VaR can directly reflect the greatest loss of a selection case under a given confidence level. Conversely, variance, the measure of the spread of a distribution around its expected value, is utilized to make the selection more stable. This model can provide investors with more significant information for decision making. To solve this model, an improved Pareto-optimal-set-based multiobjective particle swarm optimization (IMOPSO) algorithm is designed to obtain better solutions in the Pareto front. The proposed model and algorithm are exemplified by specific numerical examples. Furthermore, comparisons are made between IMOPSO and other existing approaches. Experiments show that the model and algorithm are effective in solving the multiobjective portfolio selection problem.
| Original language | English |
|---|---|
| Pages (from-to) | 146-154 |
| Number of pages | 9 |
| Journal | IEEJ Transactions on Electrical and Electronic Engineering |
| Volume | 8 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2013 Mar |
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Keywords
- Algorithm comparisons
- Fuzzy value-at-risk
- Multiobjective particle swarm optimization
- Multiobjective portfolio selection
- Pareto-optimal theory
- Risk measure methods
ASJC Scopus subject areas
- Electrical and Electronic Engineering
Cite this
Multiobjective particle swarm optimization for a novel fuzzy portfolio selection problem. / Wang, Bo; Watada, Junzo.
In: IEEJ Transactions on Electrical and Electronic Engineering, Vol. 8, No. 2, 03.2013, p. 146-154.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Multiobjective particle swarm optimization for a novel fuzzy portfolio selection problem
AU - Wang, Bo
AU - Watada, Junzo
PY - 2013/3
Y1 - 2013/3
N2 - On the basis of the portfolio selection theory, this paper proposes a novel fuzzy multiobjective model that can evaluate investment risk properly and increase the probability of obtaining an expected return. In building this model, fuzzy value-at-risk (VaR) is used to evaluate the exact future risk in terms of loss. The VaR can directly reflect the greatest loss of a selection case under a given confidence level. Conversely, variance, the measure of the spread of a distribution around its expected value, is utilized to make the selection more stable. This model can provide investors with more significant information for decision making. To solve this model, an improved Pareto-optimal-set-based multiobjective particle swarm optimization (IMOPSO) algorithm is designed to obtain better solutions in the Pareto front. The proposed model and algorithm are exemplified by specific numerical examples. Furthermore, comparisons are made between IMOPSO and other existing approaches. Experiments show that the model and algorithm are effective in solving the multiobjective portfolio selection problem.
AB - On the basis of the portfolio selection theory, this paper proposes a novel fuzzy multiobjective model that can evaluate investment risk properly and increase the probability of obtaining an expected return. In building this model, fuzzy value-at-risk (VaR) is used to evaluate the exact future risk in terms of loss. The VaR can directly reflect the greatest loss of a selection case under a given confidence level. Conversely, variance, the measure of the spread of a distribution around its expected value, is utilized to make the selection more stable. This model can provide investors with more significant information for decision making. To solve this model, an improved Pareto-optimal-set-based multiobjective particle swarm optimization (IMOPSO) algorithm is designed to obtain better solutions in the Pareto front. The proposed model and algorithm are exemplified by specific numerical examples. Furthermore, comparisons are made between IMOPSO and other existing approaches. Experiments show that the model and algorithm are effective in solving the multiobjective portfolio selection problem.
KW - Algorithm comparisons
KW - Fuzzy value-at-risk
KW - Multiobjective particle swarm optimization
KW - Multiobjective portfolio selection
KW - Pareto-optimal theory
KW - Risk measure methods
UR - http://www.scopus.com/inward/record.url?scp=84873607254&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84873607254&partnerID=8YFLogxK
U2 - 10.1002/tee.21834
DO - 10.1002/tee.21834
M3 - Article
AN - SCOPUS:84873607254
VL - 8
SP - 146
EP - 154
JO - IEEJ Transactions on Electrical and Electronic Engineering
JF - IEEJ Transactions on Electrical and Electronic Engineering
SN - 1931-4973
IS - 2
ER -