A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices

Divya Aggarwal, Shabana Chandrasekaran and Balamurugan Annamalai

Journal of Behavioral and Experimental Finance, 2020, vol. 27, issue C

Abstract: Bitcoin as an asset class has received phenomenal investor attention and is considered to have similar characteristics like gold. This study aims to analyze the price behavior of bitcoin and apply machine learning algorithm for its prediction. Understanding the nature of Bitcoin price series is a multi-scale problem, and it can be best examined by analyzing its compositional characteristics. This study uses complete empirical ensemble mode decomposition (CEEMD) to analyze the nature of Bitcoin price series. Daily Bitcoin prices from 2012 to 2018 are used to perform CEEMD to identify the short term, medium term, and long-term trend in the Bitcoin price series. The study uses support vector machine (SVM) learning algorithm to find whether it can predict Bitcoin prices and finds that SVM predicts five steps ahead Bitcoin prices for the short term, medium term, long term, and overall Bitcoin price level.

Keywords: Bitcoin; Complete empirical ensemble mode with adaptive noise decomposition (CEEMDAN); Cryptocurrency; Support vector machine; Empirical mode decomposition (EMD); Ensemble empirical mode decomposition (EEMD) (search for similar items in EconPapers)
JEL-codes: C22 G12 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S2214635019302266

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:beexfi:v:27:y:2020:i:c:s2214635019302266

DOI: 10.1016/j.jbef.2020.100335

Access Statistics for this article

Journal of Behavioral and Experimental Finance is currently edited by Michael Dowling and Jürgen Huber

More articles in Journal of Behavioral and Experimental Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().