Abstract
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.
Highlights
Stock market forecasting is a very demanding problem
A BP neural network prediction model optimized on the basis of genetic algorithm is proposed by Cao[4] et al and Wang[5] et al The experimental results achieved by Zou[6] et al show that the GA-BP algorithm is accurate and effective for online identification
GA-BP neural network, BP neural network and GARCH model are used for prediction, and root mean square error (MSE) and mean relative error (MAPE) are used for comparative analysis. where yn is the real value, yn is the simulation predicted value, and n 1, 2,", N is the number of test samples
Summary
Stock market forecasting is a very demanding problem. Many factors influence stock market prices, for example, company news and results, sector performance, consumer sentiment, social media sentiment and financial factors. Econometric method is based on statistical theory, the application of statistical analysis model to predict the stock price. The stock price series is a complex nonlinear time series, so the traditional econometric model can not achieve the best prediction effect. Compared with the econometric model, machine learning method can directly mine valuable information from the data without making assumptions in advance, and machine learning method can better deal with nonlinear data, so it is widely used in stock price forecasting. A BP neural network prediction model optimized on the basis of genetic algorithm is proposed by Cao[4] et al and Wang[5] et al The experimental results achieved by Zou[6] et al show that the GA-BP algorithm is accurate and effective for online identification.
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