Building a fuzzy multi-objective portfolio selection model with distinct risk measurements

Based on portfolio selection theory, this study proposes an improved fuzzy multi-objective model that can evaluate the invest risk exactly and increase the probability of obtaining the expected return. In building the model, fuzzy Value-at-Risk (VaR) is used to evaluate the exact future risk, in term of loss. The VaR can directly reflect the greatest loss of a selection case under a given confidence level. On the other hand, variance is utilized to make the selection more stable. This model can provide investors with more significant information in decision-making. To better solve this model, an improved particle swarm optimization algorithm is designed to mitigate the conventional local convergence problem. Finally, the proposed model and algorithm are exemplified by some numerical examples. Experiment results show that the model and algorithm are effective in solving the multi-objective portfolio selection problem.