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 af dette arbejde

Institut for Digitalisering

International Institute for Management Development (IMD)
Moonpass
WHU - Otto Beisheim School of Management

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › 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.

Originalsprog	Engelsk
Tidsskrift	Digital Finance
Vol/bind	6
Udgave nummer	3
Sider (fra-til)	427-439
Antal sider	13
ISSN	2524-6984
DOI	https://doi.org/10.1007/s42521-024-00110-7
Status	Udgivet - sep. 2024

Bibliografisk note

Published online: 01 May 2024.

Emneord

Bitcoin
Cryptocurrencies
LPPL
Machine learning
Sentiment analysis

Adgang til dokumentet

10.1007/s42521-024-00110-7Licens: Ikke-specificeret

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Citationsformater

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AU - Mazumdar, Somnath

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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 - Cryptocurrencies

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KW - Sentiment analysis

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KW - Machine learning

KW - Sentiment analysis

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