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Features of overreactions in the cryptocurrency market

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  • Borgards, Oliver
  • Czudaj, Robert L.

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.

Suggested Citation

  • Borgards, Oliver & Czudaj, Robert L., 2021. "Features of overreactions in the cryptocurrency market," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 31-48.
    • Handle: RePEc:eee:quaeco:v:80:y:2021:i:c:p:31-48
    DOI: 10.1016/j.qref.2021.01.010
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    Cited by:

    1. Zhou, Xinxing & Gao, Yan & Wang, Ping & Zhu, Bangzhu, 2022. "Examining the overconfidence and overreaction in China’s carbon markets," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 472-489.
    2. Ahmed, Mohamed Shaker & El-Masry, Ahmed A. & Al-Maghyereh, Aktham I. & Kumar, Satish, 2024. "Cryptocurrency volatility: A review, synthesis, and research agenda," Research in International Business and Finance, Elsevier, vol. 71(C).
    3. Almeida, José & Gonçalves, Tiago Cruz, 2023. "A systematic literature review of investor behavior in the cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).

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    More about this item

    Keywords

    Overreaction; Mean reversion; Cryptocurrency; Random forest; Prediction;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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