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Analyzing swings in Bitcoin returns: a comparative study of the LPPL and sentiment-informed random forest models

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Abstract

Forecasting Bitcoin’s returns continues to be a challenging endeavor for both scholars and practitioners. In this paper, we train a random forest model on a variety of features, with the aim of predicting pronounced changes in the returns of Bitcoin. The model that we present in this paper outperforms the baseline model with which we compare it: the LPPL model. Our results have implications for scholars studying financial prediction models, as well as for practitioners interested in Bitcoin investment.

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Data availability

The data sets used for the analysis can be openly downloaded under this DOI: https://doi.org/10.17632/w2s5rf58zd.1.

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Acknowledgements

We are thankful to Dave Brooks from elcs.ch for excellent editorial support.

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Authors and Affiliations

  1. International Institute for Management Development, Lausanne, Switzerland

    José Parra-Moyano

  2. Moonpass, Berlin, Germany

    Daniel Partida

  3. WHU - Otto Beisheim School of Management, Vallendar, Germany

    Moritz Gessl

  4. Copenhagen Business School, Copenhagen, Denmark

    Somnath Mazumdar

Authors
  1. José Parra-Moyano
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  2. Daniel Partida
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  3. Moritz Gessl
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  4. Somnath Mazumdar
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Correspondence to José Parra-Moyano.

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Parra-Moyano, J., Partida, D., Gessl, M. et al. Analyzing swings in Bitcoin returns: a comparative study of the LPPL and sentiment-informed random forest models. Digit Finance (2024). https://doi.org/10.1007/s42521-024-00110-7

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