Abstract
In recent years, with the rapid development of the economy, more and more people have entered the stock market for investment. Due to the volatility characteristics of the stock market, stock price prediction is often a nonlinear time series prediction. And the fluctuation of stock prices will be affected by many factors, so it is difficult to predict through a simple model. For solving this problem, a hybrid adaptive particle swarm optimization and BP neural network algorithm (APSO-BP) is proposed. The APSO-BP algorithm effectively integrates the global search ability of the PSO algorithm and the local search ability of the BP algorithm and further improves the prediction accuracy. Two sets of real stock data of China's stock market are applied to empirical analysis, and the results show that the algorithm is more effective than the standard BP algorithm in solving this problem and can provide timely risk warning information for investors.
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Funding
This research was funded by the Natural Science Foundation of NingXia Hui Autonomous Region (grant number 2021AAC03185), Research Startup Foundation of North Minzu University (grant number 2020KY QD23), First-Class Disciplines Foundation of NingXia (grant number NXYLXK2017B09) and Major Project of North Minzu University (grant number 2019MS003).
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Sun, Y., He, J., Gao, Y. (2023). Application of APSO-BP Neural Network Algorithm in Stock Price Prediction. In: Tan, Y., Shi, Y., Luo, W. (eds) Advances in Swarm Intelligence. ICSI 2023. Lecture Notes in Computer Science, vol 13969. Springer, Cham. https://doi.org/10.1007/978-3-031-36625-3_38
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DOI: https://doi.org/10.1007/978-3-031-36625-3_38
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