Enhanced Index Fund Construction using Robust possiblistic programming

One of the main concerns of modeling is the adaptation of the mathematical model to reality, and in the real world of uncertainty one of the decisive cases ignored in the classical mathematical programming of these uncertainties. robust optimization is one of the methods for uncertainly parameter management. In this research, trying that be studied from robust possiblistic programming to create an enhanced index fund that tracking the index. Enhanced index funds on the one hand trying to have the smallest deviation from the index, On the other hand, will try to achieve target returns than the index. This research model is solved by the real data on the stock exchange. Comparing the results of data analysis and solving the model with a robust feasibility approach points to good performance and high traceability of the improved indicator fund, and the fund has a high positive correlation with the stock index and also return more than index.