Developing an uncertain mean-chance model for portfolio optimization using forecasted returns

The purpose of this research is to present a portfolio optimization model within the framework of uncertainty theory. To estimate the return on assets, a prospective approach was used based on expert opinions. Also, a different risk-based approach based on uncertainty (chance model) was used to model risk. The theory used to model the uncertainty in model parameters is the uncertainty theory. The team of experts involved in this research was required to complete the required information on the projections used, including 30 managers of the portfolio of active investment funds in the Tehran Stock Exchange. In the end, to demonstrate the applicability, the model was designed in Tehran Stock Exchange and according to the nonlinear nature of the model, the hyper bacterial method of the genetic algorithm was used to solve it. Finally, by generating randomized portfolios and comparing them with the optimal portfolio for solving the model, we conclude that the optimized portfolio achieves a higher level of efficiency while delivering better performance.