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Predicting bitcoin returns using high-dimensional technical indicators

Authors :
Jing-Zhi Huang
William Huang
Jun Ni
Source :
Journal of Finance and Data Science, Vol 5, Iss 3, Pp 140-155 (2019)
Publication Year :
2019
Publisher :
KeAi Communications Co., Ltd., 2019.

Abstract

There has been much debate about whether returns on financial assets, such as stock returns or commodity returns, are predictable; however, few studies have investigated cryptocurrency return predictability. In this article we examine whether bitcoin returns are predictable by a large set of bitcoin price-based technical indicators. Specifically, we construct a classification tree-based model for return prediction using 124 technical indicators. We provide evidence that the proposed model has strong out-of-sample predictive power for narrow ranges of daily returns on bitcoin. This finding indicates that using big data and technical analysis can help predict bitcoin returns that are hardly driven by fundamentals. Keywords: Bitcoin return prediction, High-dimensional classification, Decision tree classification, CART, Cryptocurrency, Bitcoin, Technical indicators

Details

Language :
English
ISSN :
24059188
Volume :
5
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Journal of Finance and Data Science
Publication Type :
Academic Journal
Accession number :
edsdoj.3452a54171a34ce998a21f302378147f
Document Type :
article
Full Text :
https://doi.org/10.1016/j.jfds.2018.10.001