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Can wavelets produce a clearer picture of weak-form market efficiency in Bitcoin?

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  • Andrew Phiri

    (Nelson Mandela University)

Abstract

This study proposes a new approach for testing for random walk behavior in daily Bitcoin returns (19/07/2010–03/03/2022) by contextualizing the Dickey-Fuller test in time-frequency space using continuous complex wavelet transforms. By splitting our full sample into smaller sub-sample periods segregated by Bitcoin halving dates, we find that Bitcoin returns are most predictable or least market efficient (i) at higher frequency or short-run cycles of between 2 and 16 days, (ii) between November-February months, (iii) during ‘bubble’ periods, (iv) across the consecutive halving dates, (v) during the ‘Black Swan event’ caused by financial market turmoil arising from the COVID-19 pandemic, and (vi) subsequent to the announcements of new COVID-19 variants. Altogether, our findings have important policy implications for different stakeholders in Bitcoin markets.

Suggested Citation

  • Andrew Phiri, 2022. "Can wavelets produce a clearer picture of weak-form market efficiency in Bitcoin?," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 373-386, September.
    • Handle: RePEc:spr:eurase:v:12:y:2022:i:3:d:10.1007_s40822-022-00214-8
    DOI: 10.1007/s40822-022-00214-8
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    2. Clement Moyo & Andrew Phiri, 2023. "Re-Examining Bitcoin’s Price–Volume Relationship: A Time-Varying Spectral Analysis," JRFM, MDPI, vol. 16(7), pages 1-16, July.

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