IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-052-7_162.html

A Stock Price Foresting Using LSTM Based on Attention Mechanism

In: Proceedings of the 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022)

Author

Listed:
  • Xiaofei Wu

    (Minzu University of China)

Abstract

Stock price prediction has been a hit subject in recent decades. Many researchers find different methods to predict stock price. LSTM is an excellent variant model of RNN, but single LSTM can only process a single form of data and lacks the ability to process multiple mixed forms of data. Considering that stocks represent the financial market, the exchange rate would have a particular impact on the financial market, so rate change affects stock price movement. Therefore, attention mechanism could introduce exchange rate into LSTM, so we produce a hybrid LSTM module based on attention mechanism to predict stock price. We find that the RMSE and MSE of hybrid LSTM are lower than others.

Suggested Citation

  • Xiaofei Wu, 2022. "A Stock Price Foresting Using LSTM Based on Attention Mechanism," Advances in Economics, Business and Management Research, in: Faruk Balli & Au Yong Hui Nee & Sikandar Ali Qalati (ed.), Proceedings of the 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022), pages 1467-1476, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-052-7_162
    DOI: 10.2991/978-94-6463-052-7_162
    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:978-94-6463-052-7_162. 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.