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Does investor sentiment on social media provide robust information for Bitcoin returns predictability?

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  • Dominique Guégan

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, University of Ca’ Foscari [Venice, Italy])

  • Thomas Renault

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

Abstract

We use a dataset of approximately one million messages sent on StockTwits to explore the relationship between investor sentiment on social media and intraday Bitcoin returns. We find a statistically significant relationship between investor sentiment and Bitcoin returns for frequencies of up to 15 minutes. For lower frequencies, the relation disappears. We also find that the impact of sentiment on returns is concentrated on the period around the Bitcoin bubble. However, the magnitude of the effect is rather small making it impossible for a trader to make economic profits by trading on the information published on social media.

Suggested Citation

  • Dominique Guégan & Thomas Renault, 2021. "Does investor sentiment on social media provide robust information for Bitcoin returns predictability?," Post-Print hal-03205154, HAL.
    • Handle: RePEc:hal:journl:hal-03205154
    DOI: 10.1016/j.frl.2020.101494
    Note: View the original document on HAL open archive server: https://hal.science/hal-03205154
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    More about this item

    Keywords

    Cryptocurrency; BitcoinInvestor sentiment; Investor attention; Market efficiency; Social media; Stocktwits;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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