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Whether and when did bitcoin sentiment matter for investors? Before and during the COVID-19 pandemic

Author

Listed:
  • Ahmet Faruk Aysan

    (Hamad Bin Khalifa University)

  • Erhan Muğaloğlu

    (Erciyes University)

  • Ali Yavuz Polat

    (Abdullah Gul University
    Gulf University for Science & Technology)

  • Hasan Tekin

    (Karabuk University)

Abstract
Using a wavelet coherence approach, this study investigates the relationship between Bitcoin return and Bitcoin-specific sentiment from January 1, 2016 to June 30, 2021, covering the COVID-19 pandemic period. The results reveal that before the pandemic, sentiment positively drove prices, especially for relatively higher frequencies (2–18 weeks). During the pandemic, the relationship was still positive, but interestingly, the lead-lag relationship disappeared. Employing partial wavelet tools, we factor out the number of COVID-19 cases and deaths and the Equity Market Volatility Infectious Disease Tracker index to observe the direct relationship between a change in sentiment and return. Our results robustly reveal that, before the pandemic, sentiment had a positive effect on return. Although positive coherence still existed during the pandemic, the lead-lag relationship disappeared again. Thus, the causal relationship that states that sentiment leads to return can only be integrated into short-term trading strategies (up to six weeks frequency).

Suggested Citation

  • Ahmet Faruk Aysan & Erhan Muğaloğlu & Ali Yavuz Polat & Hasan Tekin, 2023. "Whether and when did bitcoin sentiment matter for investors? Before and during the COVID-19 pandemic," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-24, December.
    • Handle: RePEc:spr:fininn:v:9:y:2023:i:1:d:10.1186_s40854-023-00536-9
    DOI: 10.1186/s40854-023-00536-9
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    Cited by:

    1. Hamid Moradi-Kamali & Mohammad-Hossein Rajabi-Ghozlou & Mahdi Ghazavi & Ali Soltani & Amirreza Sattarzadeh & Reza Entezari-Maleki, 2025. "Market-Derived Financial Sentiment Analysis: Context-Aware Language Models for Crypto Forecasting," Papers 2502.14897, arXiv.org, revised Mar 2025.

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    More about this item

    Keywords

    Bitcoin; Return; COVID-19; Sentiment; TRMI;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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