摘要：Since the birth of the secondary stock market, the prediction of the stock price trend has become a research direction concerned by many people. Aiming at the problem of non-stationary and non-linear stock price forecasting, this paper builds a computational intelligence model to improve the neural network with genetic algorithm. The results show that, compared with other models, the GA-BP neural network model proposed in this article can effectively improve the prediction of the rise and fall of the HS300 index, and the withdrawal range is small when the market falls. The research of this paper enriches the method of financial time series data analysis, which can not only provide decision-making reference for investors, but also help to enhance the cognition of financial market rules. The model can significantly reduce the forecast error and improve the model fitting ability.
