Comparative performance evaluation of multiobjective optimization algorithms for portfolio management

The objective of portfolio optimization is to find an optimal set of assets to invest on, as well as to determine the optimal investment for each asset. This optimal selection and weighting of assets is a multi-objective problem where total profit of investment has to be maximized and total risk is to be minimized. In this paper the portfolio optimization problem is solved using three different multi-objective algorithms and their performance have been compared in terms of Pareto fronts, the delta, C and S metrics. Exhaustive simulation study of various portfolios clearly demonstrates the superior portfolio management capability of non-dominated sorting genetic algorithm II (NSGA II) based method compared to other two methods.