Is It Possible to Forecast the Price of Bitcoin?

Julien Chevallier (), Dominique Guégan () and Stéphane Goutte
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Julien Chevallier: IPAG Business School, LED - Laboratoire d'Economie Dionysien - UP8 - Université Paris 8 Vincennes-Saint-Denis
Dominique Guégan: University of Ca’ Foscari [Venice, Italy], CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

Abstract: This paper focuses on forecasting the price of Bitcoin, motivated by its market growth and the recent interest of market participants and academics. We deploy six machine learning algorithms (e.g., Artificial Neural Network, Support Vector Machine, Random Forest, k-Nearest Neighbours, AdaBoost, Ridge regression), without deciding a priori which one is the ‘best' model. The main contribution is to use these data analytics techniques with great caution in the parameterization, instead of classical parametric modelings (AR), to disentangle the non-stationary behavior of the data. As soon as Bitcoin is also used for diversification in portfolios, we need to investigate its interactions with stocks, bonds, foreign exchange, and commodities. We identify that other cryptocurrencies convey enough information to explain the daily variation of Bitcoin's spot and futures prices. Forecasting results point to the segmentation of Bitcoin concerning alternative assets. Finally, trading strategies are implemented.

Keywords: Forecasting; Bitcoin; Machine learning; Trading strategies (search for similar items in EconPapers)
Date: 2021-06
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-04250269v1
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Published in Forecasting, 2021, 3 (2), pp.377-420. ⟨10.3390/forecast3020024⟩

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Related works:
Journal Article: Is It Possible to Forecast the Price of Bitcoin? (2021)
Working Paper: Is It Possible to Forecast the Price of Bitcoin? (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:cesptp:halshs-04250269

DOI: 10.3390/forecast3020024

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