Features of overreactions in the cryptocurrency market

Oliver Borgards and Robert Czudaj

The Quarterly Review of Economics and Finance, 2021, vol. 80, issue C, 31-48

Abstract: This paper examines features of overreactions that are able to enhance the prediction quality for twelve cryptocurrencies compared to the US stock market. For this purpose, we perform random forest classifications on the basis of all feature combinations and a customized performance metric to predict overreactions on interday and various intraday price levels. We find that features describing the price development prior to the overreaction have the highest ability to classify an overreaction for different frequencies, indicating volatility clustering and framing effects. During an overreaction, the duration and the price steadiness are important features describing the overreaction itself. Our findings are largely comparable for cryptocurrencies and the US stock market despite the fact that both markets are fundamentally different. However, the returns of an overreaction trading strategy are superior for cryptocurrencies while those of US stocks are consistently negative due to the different size of their price reversals as the key factor for profitably exploiting our empirical findings. In addition, our results show for all assets and frequencies that the prediction results are slightly higher for positive overreactions compared to negative overreactions.

Keywords: Overreaction; Mean reversion; Cryptocurrency; Random forest; Prediction (search for similar items in EconPapers)
JEL-codes: C53 G14 G17 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:quaeco:v:80:y:2021:i:c:p:31-48

DOI: 10.1016/j.qref.2021.01.010

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