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Is It Possible to Forecast the Price of Bitcoin?

Author

Listed:
  • 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)

  • Stéphane Goutte

    (VNU - Vietnam National University [Hanoï], Université Paris-Saclay)

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.

Suggested Citation

  • Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021. "Is It Possible to Forecast the Price of Bitcoin?," Post-Print halshs-04250269, HAL.
    • Handle: RePEc:hal:journl:halshs-04250269
    DOI: 10.3390/forecast3020024
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-04250269
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    1. Zeyd Boukhers & Azeddine Bouabdallah & Matthias Lohr & Jan Jurjens, 2022. "Ensemble and Multimodal Approach for Forecasting Cryptocurrency Price," Papers 2202.08967, arXiv.org.
    2. Kate Murray & Andrea Rossi & Diego Carraro & Andrea Visentin, 2023. "On Forecasting Cryptocurrency Prices: A Comparison of Machine Learning, Deep Learning, and Ensembles," Forecasting, MDPI, vol. 5(1), pages 1-14, January.

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