IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-142-5_42.html

Prediction of Stock Prices Based on the LSTM Model

In: Proceedings of the 8th International Conference on Financial Innovation and Economic Development (ICFIED 2023)

Author

Listed:
  • Guanze Shao

    (Beijing Foreign Studies University)

Abstract

This paper focuses on improving the structure of the LSTM model and optimizing its parameters to improve its accuracy in predicting stock movements, as well as investigating the effectiveness of the LSTM neural network in predicting weekly and daily data for US stocks. On the one hand, the difference between the two models is analyzed and compared to verify the effect of different data sets on the prediction results; on the other hand, it provides suggestions on the selection of data sets for LSTM stock prediction research to ameliorate the accuracy of stock prediction. This study used a modified LSTM neural network model to predict stock price trends using a multi-series stock prediction method. The experimental results confirmed that the weekly data performed better than the daily data, with an average accuracy of 52.8% for the daily data and 58% for the weekly data, and the stock prediction accuracy was higher when the weekly data was used to train the LSTM model.

Suggested Citation

  • Guanze Shao, 2023. "Prediction of Stock Prices Based on the LSTM Model," Advances in Economics, Business and Management Research, in: Yushi Jiang & Guangming Li & Wilson Xinbao Li (ed.), Proceedings of the 8th International Conference on Financial Innovation and Economic Development (ICFIED 2023), pages 377-387, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-142-5_42
    DOI: 10.2991/978-94-6463-142-5_42
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:advbcp:978-94-6463-142-5_42. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.