An intelligence approach for group stock portfolio optimization with a trading mechanism

Optimizing a stock portfolio from a given financial dataset is always a very attractive task, as various factors should be considered. Hence, many methods based on evolutionary algorithms have been developed in the past decades to deal with the portfolio optimization problem. To provide a more flexible stock portfolio, we propose an algorithm to optimize a group stock portfolio by using a grouping genetic algorithm. In accordance with the optimized group stock portfolio, many stock portfolios can be generated and provided to investors. Each chromosome in the genetic algorithm is composed of a grouping part, a stock part and a stock portfolio part. The grouping and stock parts are used to indicate how to divide stocks into groups. The stock portfolio part is used to represent how many stocks should be selected from groups to form a portfolio and what units should be purchased. Four fitness functions are designed to evaluate each individual. Each of them is composed of the group balance, the unit balance, the stock price balance and the portfolio satisfaction. Genetic operations, including crossover, mutation and inversion, are then executed to obtain new offspring to find the best solution. Furthermore, the proposed approach with a trading mechanism is designed to get a more useful group stock portfolio. Experiments on 31 stocks in accordance with four scenarios were conducted to show the merits and effectiveness of the proposed approach.