An Approach for Optimizing Group Stock Portfolio Using Multi-Objective Genetic Algorithm

Stock portfolio optimization is always an attractive research topic because of the variety of financial markers. In the past decade, lots of approaches have been proposed to deal with the problem. Previously, an algorithm was also designed for the group stock portfolio optimization problem. In that approach, portfolio satisfaction and group balance are utilized to evaluate the goodness of possible solutions. However, there is trade-off between those factors. In this paper, we thus design an approach for finding group stock group portfolios using the multi-objective genetic algorithm. Two objective functions, Sharpe ratio and group balance, are employed to derive possible Pareto solutions. Experiments on a real dataset were conducted to verity the effectiveness of the proposed approach.