IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-3-031-15644-1_9.html
   My bibliography  Save this book chapter

Can We Apply Traditional Forecasting Models to Predicting Bitcoin?

In: City, Society, and Digital Transformation

Author

Listed:
  • Matthew Bobea

    (National Chengchi University)

  • Wesley Szuway Shu

    (Xi’an Jiaotong-Liverpool University)

Abstract

As cryptocurrency becomes more accepted as a valid investment tool within financial markets, and as more financial instruments move on to a decentralized finance platform, demand for more advance methods of modeling cryptocurrency have increased. Having a reliable model will improve investor’s confidence in an otherwise high-risk and highly volatized market. Many researchers attempt to create new models and find new variables to forecasting cryptocurrency, however, developing a model that is consistent, accurate, and nondeterministic is still challenging. Nevertheless, traditional models have been proven over time when used for financial market analysis. In this paper, logistic regression and ARIMA will be the key statistical models investigated for use in forecasting Bitcoin. Each model will be tweaked to optimize performance based on current standing research. Furthermore, each model’s result will be scored and compared based on their ability to predict Bitcoin’s performance.

Suggested Citation

  • Matthew Bobea & Wesley Szuway Shu, 2022. "Can We Apply Traditional Forecasting Models to Predicting Bitcoin?," Lecture Notes in Operations Research, in: Robin Qiu & Wai Kin Victor Chan & Weiwei Chen & Youakim Badr & Canrong Zhang (ed.), City, Society, and Digital Transformation, chapter 0, pages 97-112, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-15644-1_9
    DOI: 10.1007/978-3-031-15644-1_9
    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 search for a similarly titled item that would be available.

    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:lnopch:978-3-031-15644-1_9. 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.