mean-avar-entropy ‎o‎ptimization portfolio selection model in uncertain environments

This paper investigates the complexities surrounding uncertain portfolio selection in cases where security returns are not well-represented by historical data. uncertainty in security returns is addressed by treating them as uncertain variables. portfolio selection models are developed using the quadratic-entropy of these uncertain variables, with entropy serving as a standard measure of diversification. additionally, the study underscores the superior risk estimation accuracy of average value-at-risk (avar) compared to variance. the research concentrates on the computational challenges of portfolio optimization in uncertain environments, utilizing the mean-avar-quadratic entropy paradigm to meet investor requirements and assuage concerns. two illustrative examples are provided to show the efficiency of the proposed models in this paper.