Solving the multiobjective portfolio rebalancing model with fuzzy parameters to develop the expected value by genetic algorithm

The ability to choose the most optimal change in the composition of the portfolio of assets, brings the investor to the highest level of investment in terms of efficiency and effectiveness in the dynamic and changing market. Rebalancing the portfolio occur through a change in the composition of assets weights, remove the assets, bought and sold assets and etc. Therefore, in this study solving the multiobjective portfolio rebalancing model with fuzzy parameters. The return, risk, liquidity and uncertainty as a key financial criteria are considered. Due to its importance as well as transaction costs, the net return of the portfolio are adjusted. the multiobjective portfolio rebalancing model with fuzzy parameters is solved by fuzzy goal programming and a hybrid intelligent algorithm that combines fuzzy simulation with a genetic algorithm. The results demonstrated the effectiveness of the solution approach and effciency of the model in practical applications of rebalancing an existing portfolio.