Predicting bitcoin returns using high-dimensional technical indicators

Jing Zhi Huang, William Huang, Jun Ni

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)140-155
Number of pages16
JournalJournal of Finance and Data Science
Volume5
Issue number3
DOIs
StatePublished - Sep 2019

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics
  • Finance
  • Statistics and Probability
  • Applied Mathematics
  • Computer Science Applications
  • Business, Management and Accounting (miscellaneous)

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