Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China

Research Article

Research on the Application of LSTM Model in Predicting Stocks

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  • @INPROCEEDINGS{10.4108/eai.17-6-2022.2322768,
        author={Jiabo  Li},
        title={Research on the Application of LSTM Model in Predicting Stocks},
        proceedings={Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China},
        publisher={EAI},
        proceedings_a={ICIDC},
        year={2022},
        month={10},
        keywords={lstm model recurrent neural network rnn stock},
        doi={10.4108/eai.17-6-2022.2322768}
    }
    
  • Jiabo Li
    Year: 2022
    Research on the Application of LSTM Model in Predicting Stocks
    ICIDC
    EAI
    DOI: 10.4108/eai.17-6-2022.2322768
Jiabo Li1,*
  • 1: Tianjin University of Science and Technology Beijing
*Contact email: masteralj@outlook.com

Abstract

As we all know, the price of a stock is the focus of investors' attention and even small changes can cause huge changes in the market. The SSE Composite Index reflects the overall performance of companies listed on the Shanghai Stock Exchange. This experiment investigates in using the LSTM model to predict which variable parameter in the SSE index has the most influence on the results predicted by the neural network. The data set was obtained from the Shanghai Stock Exchange and it was concluded that for the same LSTM model of the neural network, the opening price parameter of the SSE index had the greatest impact on the prediction of the model.