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- W4289780858 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." @default.
- W4289780858 created "2022-08-04" @default.
- W4289780858 creator A5008196047 @default.
- W4289780858 date "2022-08-04" @default.
- W4289780858 modified "2023-10-18" @default.
- W4289780858 title "Can wavelets produce a clearer picture of weak-form market efficiency in Bitcoin?" @default.
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- W4289780858 doi "https://doi.org/10.1007/s40822-022-00214-8" @default.
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