An Investigation of the Predictability of Uncertainty Indices on Bitcoin Returns

Jinghua Wang (), Geoffrey M. Ngene, Yan Shi and Ann Nduati Mungai
Additional contact information
Jinghua Wang: Martin Tuchman School of Management, New Jersey Institute of Technology, 184-198 Central Ave, Newark, NJ 07103, USA
Geoffrey M. Ngene: Stetson School of Business and Economics, Mercer University, Macon, GA 31201, USA
Yan Shi: Computer Science and Software Engineering Department, College of Engineering, Mathematics and Science, University of Wisconsin-Platteville, Platteville, WI 53181, USA
Ann Nduati Mungai: Cameron School of Business, University of North Carolina Wilmington, 601 South College Street, Wilmington, NC 28403, USA

JRFM, 2023, vol. 16, issue 10, 1-12

Abstract: Policymakers and portfolio managers pay keen attention to sources of uncertainties that drive asset returns and volatility. The influence of uncertainty on Bitcoin has the potential to drive fluctuations in the entire cryptocurrency market. We investigate the predictability of thirteen economic policy uncertainty indices on Bitcoin returns. Using the Random Forest machine learning algorithm, we find that Singapore’s economic policy uncertainty (EPU) has the strongest predictive power on Bitcoin returns, followed by financial crisis (FC) uncertainty and world trade uncertainty (WTU). We further categorize these uncertainties into different groups. Interestingly, the predictability of uncertainty indices on Bitcoin returns within the international trade group is stronger compared to other uncertainty categories. Additionally, we observed that internet-based uncertainty measures have more predictive power of Bitcoin returns than newspaper- and report-based measures. These results are robust using various additional machine learning methods. We believe that these findings could be valuable for policymakers and portfolio managers when making decisions related to uncertainty drivers of cryptocurrency prices and returns.

Keywords: Bitcoin; Singapore economic policy uncertainty; economic policy uncertainty; machine learning methods (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1911-8074/16/10/461/pdf (application/pdf)
https://www.mdpi.com/1911-8074/16/10/461/ (text/html)

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:gam:jjrfmx:v:16:y:2023:i:10:p:461-:d:1265145

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().