In this paper, to increase the diversity of stock portfolios, the diverse group stock portfolio mining algorithm is proposed by grouping genetic algorithm. Each chromosome is represented by grouping, stock and stock portfolio parts. 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 to find the near optimal group stock portfolio. Finally, experiments on a real financial data were made to show the proposed approach is effective.
Relation:
2016 IEEE World Congress on Computational Intelligence (IEEE WCCI), pp. 1-5
