IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-036-7_51.html

Bitcoin Price Prediction Based on Machine Learning and Granger Causality Test

In: Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022)

Author

Listed:
  • Mengyu Hao

    (Shanghai University of International Business and Economics, School of Finance & Management)

  • Feiyang Su

    (Shanghai University, College of Sciences)

  • Kaifei Wang

    (Johns Hopkins University, School of Arts and Sciences)

  • Xiaoqi Zheng

    (Southwestern University of Finance and Economics, School of Computing and Artificial Intelligence)

Abstract

Recently, more and more investors have seen the huge profits that the digital currency market can bring, and Bitcoin price predictions are becoming more valuable both academically and in terms of business value. In this paper, we use the daily price of bitcoin from September 12, 2016, to September 10, 2021. Data pre-processing includes moving average (MA) and BIAS. To find out the causality relationship between two factors, we use Granger causality test. Then we predict bitcoin price with Support Vector Machine (SVM) based on sliding window from machine learning methods and Autoregressive Integrated Moving Average (ARMA) method from statistical methods. The results show that there is causality relationship between gold and bitcoin. Besides, by comparing the Mean Squared errors (MSE) of 7-day-model, 14-day-model and ARMA model, we find that the ARMA model outperform the others, which reminds the investors to focus more on this model.

Suggested Citation

  • Mengyu Hao & Feiyang Su & Kaifei Wang & Xiaoqi Zheng, 2022. "Bitcoin Price Prediction Based on Machine Learning and Granger Causality Test," Advances in Economics, Business and Management Research, in: Yushi Jiang & Yuriy Shvets & Hrushikesh Mallick (ed.), Proceedings of the 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022), pages 342-348, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-036-7_51
    DOI: 10.2991/978-94-6463-036-7_51
    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:978-94-6463-036-7_51. 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.