A Novel Multi-Objective Evolutionary Algorithm for Portfolio Selection

Abstract

Portfolio selection is a problem arising in finance and economics. While its basic formulations can be efficiently solved using linear or quadratic programming, its more practical variants have to be tackled by heuristics in many cases. In this work, both portfolio return and risk factors need to be considered, so it is abstracted as a multi-objective 0/1 knapsack problem and solved by a novel multi-objective evolutionary algorithm based on SPEA2. Experimental results show that the multi-objective optimization to solve the portfolio problem can better reveal the relationship between benefits and risks, to provide investors with a better basis for decision making.