A hybrid approach based on multi-criteria decision making and data-driven optimization in solving portfolio selection problem

In this paper, a two-phase approach based on multi-criteria decision making and multi-objective optimization models is proposed to select portfolio optimally. In the first phase, potential companies for investment are selected initially by considering the criteria extracted from the literature review. In the second phase, a multi-objective optimization model is proposed to optimize the investment in selected companies according to risk and return objectives. In order to deal with uncertainty conditions, a data-driven approach is used, which is one of the newest applied methods in this field. According to the obtained results, it is observed that cash adequacy ratio with score 0.1604 is the most important criterion and operating profit with score 0.004 is the least important one. In the alternative prioritization section, it is concluded that Shraz, Shavan, Shenft and Vanft companies have a high priority for investment. In solving the mathematical model under certain conditions, it is observed that the Pareto members 152, 154 and 193 have the smallest distance from the ideal solution (0.0121) and therefore each of them can be used as the final solution. In solving the problem under uncertain conditions, numerical scenarios resulting from changes in the prioritization of companies based on the coefficient v is used in the VIKOR model. Based on the results, it is observed that the impact of different scenarios on corporate investment is not negligible and consequently investors need to pay attention to this fact.