An Investigation of the Predictability of Uncertainty Indices on Bitcoin Returns

Jinghua Wang, Geoffrey M. Ngene, Yan Shi, Ann Nduati Mungai

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Article number461
JournalJournal of Risk and Financial Management
Issue number10
StatePublished - Oct 2023

All Science Journal Classification (ASJC) codes

  • Accounting
  • Business, Management and Accounting (miscellaneous)
  • Finance
  • Economics and Econometrics


  • Bitcoin
  • Singapore economic policy uncertainty
  • economic policy uncertainty
  • machine learning methods


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