Mean-Absolute Deviation-Beta Portfolio Optimization under Ambiguity: A Real-World Case Study

In this study, a new uncertain portfolio optimization model is proposed that is capable to be employed in the presence of fuzzy data and linguistic variables. It should be noted that mean (return), absolute deviation (non-systematic risk measure), and beta (systematic risk measure) as well as investment constraints are considered in the proposed fuzzy portfolio optimization (FPO) model. Also, to deal with uncertainty of financial data, the possibilistic programming (PP) and the chance-constrained programming (CCP) approaches are used. Finally, to show the efficacy and applicability of the proposed approach, the FPO model is applied in a realworld case study from Tehran stock market.

Mean-Absolute Deviation-Beta Portfolio Optimization under Ambiguity: A Real-World Case Study Keywords:

Fuzzy Portfolio Optimization , Possibilistic Programming , Chance-Constrained Programming , Stock Exchange.