A broad analysis of short-term overreactions in the market for cryptocurrencies

Résumé 0

We analyze market inefficiencies in the market for cryptocurrencies by providing a comprehensive analysis of short-term (over)reactions that follow significant price changes of such currencies. We identify and analyze overreactions and mispricing in markets for crypto currencies by applying a broad set of thresholds that depend on market specific dynamics and volatilities. We analyze the returns on days following abnormal returns and identify significant differences from normal returns using the t-test and the Mann–Whitney-U-test. We further complement the literature by using end-of-the-day returns in addition to high-low returns. Additionally, we consider a broad sample of 50 cryptocurrencies for an expanded time span (2015 to 2020) that includes the big currencies as well as smaller currencies. We detect the existence of overreactions and, thus, market inefficiencies in crypto markets. The findings for different methodological approaches are similar, which underpins the robustness of the findings. By considering a broad sample that includes small and big currencies we can show the existence of a market size effect. By considering a broad set of thresholds, we further found evidence for a magnitude effect, which means that higher initial abnormal returns are related to higher inefficiencies. Our paper has practical implications. We detect market inefficiencies that can be used in practical trading to obtain excess returns. In fact, our methodological approach and its results can be used to derive a strategy for trading in cryptocurrencies that can be easily implemented. Based on our findings, we can expect positive access returns by applying this trading strategy. We complement the literature on market inefficiencies and mispricing in crypto markets by analyzing price patterns after initial abnormal returns. We contribute by applying different methodological approaches in addition to the approaches used so far, by considering a set of different thresholds, and by applying a much broader data set that enables us to analyze additional aspects.