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

Author

Listed:
  • José Parra-Moyano

    (International Institute for Management Development)

  • Daniel Partida

    (Moonpass)

  • Moritz Gessl

    (WHU - Otto Beisheim School of Management)

  • Somnath Mazumdar

    (Copenhagen Business School)

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.

Suggested Citation

  • José Parra-Moyano & Daniel Partida & Moritz Gessl & Somnath Mazumdar, 2024. "Analyzing swings in Bitcoin returns: a comparative study of the LPPL and sentiment-informed random forest models," Digital Finance, Springer, vol. 6(3), pages 427-439, September.
    • Handle: RePEc:spr:digfin:v:6:y:2024:i:3:d:10.1007_s42521-024-00110-7
    DOI: 10.1007/s42521-024-00110-7
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    References listed on IDEAS

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    More about this item

    Keywords

    Bitcoin; Cryptocurrencies; LPPL; Machine learning; Sentiment analysis;
    All these keywords.

    JEL classification:

    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets

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