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Price Overreactions in the Cryptocurrency Market

Authors: Guglielmo Maria Caporale; Alex Plastun;

Price Overreactions in the Cryptocurrency Market

Abstract

PurposeThe purpose of this paper is to examine price overreactions in the case of the following cryptocurrencies: bitcoin, litecoin, ripple and dash.Design/methodology/approachA number of parametric (t-test, ANOVA, regression analysis with dummy variables) and non-parametric (Mann–WhitneyU-test) tests confirm the presence of price patterns after overreactions: the next day price changes in both directions are bigger than after “normal” days. A trading robot approach is then used to establish whether these statistical anomalies can be exploited to generate profits.FindingsThe results suggest that a strategy based on counter-movements after overreactions is not profitable, whilst one based on inertia appears to be profitable but produces outcomes not statistically different from the random ones. Therefore, the overreactions detected in the cryptocurrency market do not give rise to exploitable profit opportunities (possibly because of transaction costs) and cannot be seen as evidence against the efficient market hypothesis (EMH).Originality/valueThe overreactions detected in the cryptocurrency market do not give rise to exploitable profit opportunities (possibly because of transaction costs) and cannot be seen as evidence against the EMH.

Country
United Kingdom
Keywords

G17, ddc:330, bitcoin, overreaction, contrarian strategy, momentum, C63, abnormal returns, trading robot, G12, trading strategy, cryptocurrency market, Bitcoin

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
42
Top 10%
Top 10%
Top 10%
bronze