Abstract
In order to improve the stock price prediction effect, based on the improved PSO algorithm, this paper constructs a stock price prediction model through neural network. Based on the idea of avoiding particles falling into the same local solution as much as possible and always keeping the particles with a certain diversity, in order to improve the global search ability of the algorithm in the early stage of evolution and the local search ability in the later stage of the evolution, the adaptive adjustment of inertial weight is proposed, and the algorithm is improved by combining with neural network. In addition, on the basis of the improved algorithm, this paper constructs a stock price prediction system based on neural network. Finally, this paper designs experiments to verify the function of the model from the perspectives of stock data collection and processing, and stock price prediction accuracy, and draw statistical graphs based on the statistical research results. The results of the research show that the system constructed in this paper has a certain practical effect.
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FY presented the concept and wrote the manuscript with the help of all of the authors. JC created the model and carried out the validation and resources. YL performed the experimental analysis and assisted in the review and editing of the manuscript.
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Communicated by Vicente Garcia Diaz.
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Yang, F., Chen, J. & Liu, Y. Improved and optimized recurrent neural network based on PSO and its application in stock price prediction. Soft Comput 27, 3461–3476 (2023). https://doi.org/10.1007/s00500-021-06113-5
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DOI: https://doi.org/10.1007/s00500-021-06113-5