• English
    • العربية
  • العربية
  • Login
  • QU
  • QU Library
  •  Home
  • Communities & Collections
  • Help
    • Item Submission
    • Publisher policies
    • User guides
    • FAQs
  • About QSpace
    • Vision & Mission
View Item 
  •   Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Arts & Sciences
  • Mathematics, Statistics & Physics
  • View Item
  • Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Arts & Sciences
  • Mathematics, Statistics & Physics
  • View Item
  •      
  •  
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    A new approach to predicting cryptocurrency returns based on the gold prices with support vector machines during the COVID-19 pandemic using sensor-related data

    Thumbnail
    View/Open
    sensors-21-06319-v3.pdf (1.003Mb)
    Date
    2021
    Author
    Mahdi, Esam
    Leiva, Victor
    Mara'Beh, Saed
    Martin-Barreiro, Carlos
    Metadata
    Show full item record
    Abstract
    In a real-world situation produced under COVID-19 scenarios, predicting cryptocurrency returns accurately can be challenging. Such a prediction may be helpful to the daily economic and financial market. Unlike forecasting the cryptocurrency returns, we propose a new approach to predict whether the return classification would be in the first, second, third quartile, or any quantile of the gold price the next day. In this paper, we employ the support vector machine (SVM) algorithm for exploring the predictability of financial returns for the six major digital currencies selected from the list of top ten cryptocurrencies based on data collected through sensors. These currencies are Binance Coin, Bitcoin, Cardano, Dogecoin, Ethereum, and Ripple. Our study considers the pre-COVID-19 and ongoing COVID-19 periods. An algorithm that allows updated data analysis, based on the use of a sensor in the database, is also proposed. The results show strong evidence that the SVM is a robust technique for devising profitable trading strategies and can provide accurate results before and during the current pandemic. Our findings may be helpful for different stakeholders in understanding the cryptocurrency dynamics and in making better investment decisions, especially under adverse conditions and during times of uncertain environments such as in the COVID-19 pandemic.
    DOI/handle
    http://dx.doi.org/10.3390/s21186319
    http://hdl.handle.net/10576/50169
    Collections
    • COVID-19 Research [‎848‎ items ]
    • Mathematics, Statistics & Physics [‎786‎ items ]

    entitlement


    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Home

    Submit your QU affiliated work

    Browse

    All of Digital Hub
      Communities & Collections Publication Date Author Title Subject Type Language Publisher
    This Collection
      Publication Date Author Title Subject Type Language Publisher

    My Account

    Login

    Statistics

    View Usage Statistics

    About QSpace

    Vision & Mission

    Help

    Item Submission Publisher policiesUser guides FAQs

    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Video