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Forecasting cryptocurrency returns and volume using search engines

Author

Listed:
  • Muhammad Ali Nasir

    (Leeds Beckett University)

  • Toan Luu Duc Huynh

    (University of Economics Ho Chi Minh City)

  • Sang Phu Nguyen

    (Banking University of Ho Chi Minh City)

  • Duy Duong

    (Banking University of Ho Chi Minh City)

Abstract

In the context of the debate on the role of cryptocurrencies in the economy as well as their dynamics and forecasting, this brief study analyzes the predictability of Bitcoin volume and returns using Google search values. We employed a rich set of established empirical approaches, including a VAR framework, a copulas approach, and non-parametric drawings, to capture a dependence structure. Using a weekly dataset from 2013 to 2017, our key results suggest that the frequency of Google searches leads to positive returns and a surge in Bitcoin trading volume. Shocks to search values have a positive effect, which persisted for at least a week. Our findings contribute to the debate on cryptocurrencies/Bitcoins and have profound implications in terms of understanding their dynamics, which are of special interest to investors and economic policymakers.

Suggested Citation

  • Muhammad Ali Nasir & Toan Luu Duc Huynh & Sang Phu Nguyen & Duy Duong, 2019. "Forecasting cryptocurrency returns and volume using search engines," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-13, December.
    • Handle: RePEc:spr:fininn:v:5:y:2019:i:1:d:10.1186_s40854-018-0119-8
    DOI: 10.1186/s40854-018-0119-8
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    References listed on IDEAS

    as
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