Using grouping genetic algorithm to mine diverse group stock portfolio
In this paper, to increase the diversity of stock portfolios, the diverse group stock portfolio mining algorithm is proposed based on the grouping genetic algorithm. Each chromosome is represented by grouping part, stock part and stock portfolio part. The fitness function that consists of portfolio satisfaction, group balance and diversity factor is designed to evaluate quality of chromosomes. The diversity factor is used to make the numbers of stock categories in groups as similar as possible. The genetic operations are then executed on population to generate offspring for finding a near optimal group stock portfolio. Finally, experiments on a real financial data were made to show the effectiveness of the proposed approach.
2016 IEEE Congress on Evolutionary Computation (CEC)