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

José Parra-Moyano (), Daniel Partida (), Moritz Gessl () and Somnath Mazumdar ()
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José Parra-Moyano: International Institute for Management Development
Daniel Partida: Moonpass
Moritz Gessl: WHU - Otto Beisheim School of Management
Somnath Mazumdar: Copenhagen Business School

Digital Finance, 2024, vol. 6, issue 3, No 3, 427-439

Abstract: 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.

Keywords: Bitcoin; Cryptocurrencies; LPPL; Machine learning; Sentiment analysis (search for similar items in EconPapers)
JEL-codes: D53 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s42521-024-00110-7

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