Computational intelligence model based on GA-BP neural network

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...

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Bibliographic Details
Published inMATEC Web of Conferences Vol. 355; p. 3038
Main Author Zhang, Chengzhao
Format Journal Article Conference Proceeding
LanguageEnglish
Published Les Ulis EDP Sciences 2022
Subjects
Artificial intelligence
Back propagation networks
bp neural network
Cognition
Data analysis
Decision analysis
Decision making
Economic forecasting
genetic algorithm
Genetic algorithms
Mathematical models
Neural networks
prediction error
stock market
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Summary: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.
ISSN:2261-236X
2274-7214
2261-236X
DOI:10.1051/matecconf/202235503038