Expected value multiobjective portfolio rebalancing model with fuzzy parameters

In this paper we develop a multicriteria credibilistic framework for portfolio rebalancing. We use an expected value model with fuzzy parameters considering return, risk and liquidity as key financial criteria. The transaction costs are assumed to be paid on the basis of incremental discounts and are adjusted in the net return of the portfolio. A solution procedure based on fuzzy goal programming and a hybrid intelligent algorithm that combines fuzzy simulation with a real-coded genetic algorithm is presented to solve the portfolio rebalancing problem. The approach adopted here has the advantage of handling the multicriteria portfolio rebalancing problem where the fuzzy parameters are characterized by general functional forms. An empirical study is included to demonstrate the effectiveness of the solution approach and efficiency of the model in practical applications of rebalancing an existing portfolio.

► We propose a new multiobjective portfolio rebalancing model in fuzzy environment. ► Expected value operator is used to model fuzzy portfolio return and liquidity. ► Portfolio risk is calculated using expected value based semivariance measure. ► Solution approach uses fuzzy goal programming and a hybrid intelligent algorithm. ► The approach is useful when the fuzzy parameters take any general functional form.