An effective stock portfolio trading strategy using genetic algorithms and weighted fuzzy time series

Investments in a stock market may incur risk. To reduce the risk of an investment, many portfolio selection methods have been proposed. By buying several stocks together, a portfolio selection method aims at maximizing the return of an investment given a predefined risk level. To build an optimal stock portfolio, one needs to select stocks and decide the proportion of the capital on each stock. Also, it is very important to decide when to buy or sell a stock portfolio. In this paper, we present a genetic algorithm to build stock portfolios. The proposed method comprises a genetic algorithm and a weighted fuzzy time series. The genetic algorithm is used to construct an optimal portfolio while the weighted fuzzy time series is used to predict the return of the portfolio which in turn is used to formulate the fitness function of the genetic algorithm. Furthermore, we propose the periodically checking and stop-loss point policies to decide selling and buying time points of the stock portfolio. The experiments on the stocks of Taiwan 50 show that the proposed method outperforms the Taiwan 50 index and the TAIEX index in terms of the 7-year average return rate.