Analyzing Swings in Bitcoin Returns: A Comparative Study of the LPPL and Sentiment-informed Random Forest Models

José Parra-Moyano; Daniel Partida; Moritz Gessl; Somnath Mazumdar

doi:10.1007/s42521-024-00110-7

Analyzing Swings in Bitcoin Returns: A Comparative Study of the LPPL and Sentiment-informed Random Forest Models

José Parra-Moyano^*, Daniel Partida, Moritz Gessl, Somnath Mazumdar

^*Corresponding author for this work

Department of Digitalization

Research output: Contribution to journal › Journal article › Research › peer-review

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.

Original language	English
Journal	Digital Finance
Number of pages	13
ISSN	2524-6984
DOIs	https://doi.org/10.1007/s42521-024-00110-7
Publication status	Published - 1 May 2024

Bibliographical note

Epub ahead of print. Published online: 01 May 2024.

Keywords

Bitcoin
Cryptocurrencies
LPPL
Machine learning
Sentiment analysis

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10.1007/s42521-024-00110-7Licence: Unspecified

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Cite this

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title = "Analyzing Swings in Bitcoin Returns: A Comparative Study of the LPPL and Sentiment-informed Random Forest Models",

abstract = "Forecasting Bitcoin{\textquoteright}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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N1 - Epub ahead of print. Published online: 01 May 2024.

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AB - 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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KW - Sentiment analysis

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