Abstarct: Numerous empirical studies show that portfolio returns are generally asymmetric, and investor would prefer a portfolio return with larger degree of asymmetry along with risk and return. In this paper, a concept of skewness is de¯ned as the third central moment and studied its mathematical properties. To predict the stock prices, a novel recurrent neural network is preferred. baxsed on these predictions, stock returns, entropic value at risks and skewness are calculated. A mean–EVaR–skewness multi-objective portfolio optimization model is devised to account for market uncertainty. Cardinality, bounding restrictions, and liquidity are considered in addition to risk and return to make the model more e®ective. Uncertain goal programming is used to solve the proposed model. Finally, an example portfolio is presented to display the e±cacy and the feasibility of the model suggested in this paper.