An Accelerated Optimization Approach for Finding Diversified Industrial Group Stock Portfolios with Natural Group Detection

Stock portfolio optimization is always an interesting and attractive research problem due to the variety of stock markets. To find a useful portfolio, metaheuristic-based approaches have been presented obtain diverse group portfolios (DGSPs) by considering diversity in past. However, existing DGSP algorithms, two problems remain be solved. The first how set suitable size, second that evolution process time-consuming. solve these problems, this paper, approach using grouping genetic algorithms (GGAs) was proposed for optimizing DGSP. For setting utilized attributes stocks, including return on equity price/earnings ratio. Then, derive better groups, cluster validation factor designed, which used as part fitness function. time-consumption problem, designed temporary chromosome, number need evaluated could reduced speed up process. Finally, experiments real datasets containing 31 50 stocks were conducted show effective efficient. results indicated not only achieve similar returns but also accelerate when compared with algorithms.