Towards Portfolio Selection in Stock Markets Using Grey Wolf Optimization Approach

In the stock selection problem, maximizing the returns at least possible risks was always the most significant target of investors in the security markets. In recent years, various solutions and recommendations have been proposed concerning the frequency of portfolio optimization problems. Portfolio optimization has been an important investing decisionmaking method over the years. In this work, the Grey Wolf optimization (GWO) algorithm is proposed as one of the most efficient approaches to address the portfolio optimization problem by exploiting the haunting behaviour of the group of wolves. A repair method was used to control the various constraints of the constructed portfolio. The effectiveness of the suggested method is evaluated on well-known benchmark datasets employed in our study. The results are compared with those of alternative solution techniques found in the literature. The overall findings demonstrate that it performs better than other state-of-art meta-heuristics methods.