Does transaction activity predict Bitcoin returns? Evidence from quantile-on-quantile analysis

Liya Hau, Huiming Zhu, Muhammad Shahbaz and Wuqin Sun

The North American Journal of Economics and Finance, 2021, vol. 55, issue C

Abstract: This paper uses the quantile-on-quantile regression to examine the predictive power of transaction activity for Bitcoin returns over the period from January 2013 to December 2018. We measure the Bitcoin transaction activity using trading volumes, the number of unique Bitcoin transactions, and the number of unique Bitcoin addresses. Considering the onset of structural breaks, we identify considerable effects of the heterogeneity concerning the quantiles of transaction activity, which cannot be depicted fully by the traditional quantile regression method. The empirical results show that higher transaction activity tends to predict higher/lower Bitcoin returns when the market is in a bullish/bearish state. We find that the nexus is asymmetric across quantiles, depending on the sign and size of the transaction activity, and the predictive relationship intensifies in the upper or lower quantiles of the conditional distribution. In addition, this empirical evidence is in line with the volume-return association in the equity market due to private informative and noninformative trading actions. Overall, our findings suggest that transaction activity-based strategies should be made with respect to Bitcoin market performance, specifically during extreme conditions.

Keywords: Bitcoin; Transaction activity; Return; Quantile-on-quantile regression; Distributional predictability (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:55:y:2021:i:c:s106294082030187x

DOI: 10.1016/j.najef.2020.101297

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