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Influencing cryptocurrency: analyzing celebrity sentiments on X (formerly Twitter) and their impact on bitcoin prices

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Abstract

This paper explores the dynamic relationship between Bitcoin prices and the sentiments expressed by celebrities on X (formerly Twitter), employing a Vector Autoregression (VAR) model. The aim of this study is to understand the impact of a diverse range of sentiments (positive, negative, valence, arousal, dominance) extracted from the posts of 30 influential celebrities in the Bitcoin community on future Bitcoin prices and volumes. The analysis reveals that negative sentiments have a statistically significant impact on Bitcoin price fluctuations. Furthermore, while the shocks to price caused by sentiments converge within approximately 2 weeks, it was found that in the short term, spanning just 2 days, positive sentiments and valence sentiments positively influence price fluctuations, whereas negative sentiments exert a stronger negative effect. This finding underscores the direct influence of celebrity sentiments on the short-term emotions and actions of Bitcoin market participants, supporting psychological researches that indicate a strong influence of negative information on individual cognition and behavior. The significance of this study lies in its broad analysis of Bitcoin price fluctuations using the sentiments of various influencers on social media, not limited to globally recognized figures, and shows one possible way to respond to an immature market. This study offers valuable insights for investors and market analysts in refining investment strategies and risk management by considering market sentiment fluctuations.

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Data availability

We are unable to publish the users’ post data due to the terms of use for the Academic Research product track of X (formerly Twitter).

Notes

  1. https://coinmarketcap.com/currencies/bitcoin/. Accessed: 2023-11-27.

  2. https://companiesmarketcap.com/. Accessed: 2023-11-27.

  3. In July 2023, Twitter was renamed to “X”. This paper will use the name “X” for clarity and consistency.

  4. https://www.coinbase.com. Accessed: 2023-11-27.

  5. https://hive.one/c/Bitcoin. Accessed: 2022-5-31.

  6. https://github.com/cjhutto/vaderSentiment. Accessed: 2023–11-27.

  7. https://github.com/Priya22/EmotionDynamics.

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Authors and Affiliations

  1. Kyushu University, Fukuoka City, Fukuoka, 819-0395, Japan

    Takeshi Inuduka & Shunsuke Managi

  2. University of Electro-Communications, Chofu, Tokyo, 182-8585, Japan

    Akihito Yokose

Authors
  1. Takeshi Inuduka
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  2. Akihito Yokose
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  3. Shunsuke Managi
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Contributions

T.I. wrote the main body of the manuscript. A.Y. wrote part of the manuscript and he prepared all figures and tables. S.M., the supervising professor, was responsible for the supervision and leadership of this study. All authors reviewed the manuscript. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

Corresponding author

Correspondence to Takeshi Inuduka.

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Appendices

Appendix 1. Additional tables for the analysis

See Tables 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38.

Table 14 Augmented Dickey-Fuller test results (Pt, Vt, and Spost)
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Table 15 AIC (Pt, Vt, and Spost)
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Table 16 Estimation results for α, β, and γ in the Pt equation of Pt, Vt, and Spost
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Table 17 Estimation results for α, β, and γ in the Vt equation of Pt, Vt, and Spost
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Table 18 Estimation results for α, β, and γ in the Spost equation of Pt, Vt, and Spost
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Table 19 Augmented Dickey-Fuller test results (Pt, Vt, and Snegt)
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Table 20 AIC (Pt, Vt, and Snegt)
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Table 21 Estimation results for α, β, and γ in the Pt equation of Pt, Vt, and Snegt
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Table 22 Estimation results for α, β, and γ in the Vt equation of Pt, Vt, and Snegt
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Table 23 Estimation results for α, β, and γ in the Snegt equation of Pt, Vt, and Snegt
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Table 24 Augmented Dickey-Fuller test results (Pt, Vt, and Svalt)
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Table 25 AIC (Pt, Vt, and Svalt)
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Table 26 Estimation results for α, β, and γ in the Pt equation of Pt, Vt, and Svalt
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Table 27 Estimation results for α, β, and γ in the Vt equation of Pt, Vt, and Svalt
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Table 28 Estimation results for α, β, and γ in the Svalt equation of Pt, Vt, and Svalt
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Table 29 Augmented Dickey-Fuller test results (Pt, Vt, and Sarot)
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Table 30 AIC (Pt, Vt, and Sarot)
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Table 31 Estimation results for α, β, and γ in the Pt equation of Pt, Vt, and Sarot
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Table 32 Estimation results for α, β, and γ in the Vt equation of Pt, Vt, and Sarot
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Table 33 Estimation results for α, β, and γ in the Sarot equation of Pt, Vt, and Sarot
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Table 34 Augmented Dickey-Fuller test results (Pt, Vt, and Sdomt)
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Table 35 AIC (Pt, Vt, and Sdomt)
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Table 36 Estimation results for α, β, and γ in the Pt equation of Pt, Vt, and Sdomt
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Table 37 Estimation results for α, β, and γ in the Vt equation of Pt, Vt, and Sdomt
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Table 38 Estimation results for α, β, and γ in the Sdomt equation of Pt, Vt, and Sdomt
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Appendix 2. Additional tables for the results of the hypothesis tests

See Tables 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53.

Table 39 Ljung-Box test results (Pt, Vt, and Spost)
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Table 40 ARCH-LM test results (Pt, Vt, and Spost)
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Table 41 Jarque–Bera test results (Pt, Vt, and Spost)
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Table 42 Ljung-Box test results (Pt, Vt, and Snegt)
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Table 43 ARCH-LM test results (Pt, Vt, and Snegt)
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Table 44 Jarque–Bera test results (Pt, Vt, and Snegt)
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Table 45 Ljung-Box test results (Pt, Vt, and Svalt)
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Table 46 ARCH-LM test results (Pt, Vt, and Svalt)
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Table 47 Jarque–Bera test results (Pt, Vt, and Svalt)
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Table 48 Ljung-Box test results (Pt, Vt, and Sarot)
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Table 49 ARCH-LM test results (Pt, Vt, and Sarot)
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Table 50 Jarque–Bera test results (Pt, Vt, and Sarot)
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Table 51 Ljung-Box test results (Pt, Vt, and Sdomt)
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Table 52 ARCH-LM test results (Pt, Vt, and Sdomt)
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Table 53 Jarque–Bera test results (Pt, Vt, and Sdomt)
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Inuduka, T., Yokose, A. & Managi, S. Influencing cryptocurrency: analyzing celebrity sentiments on X (formerly Twitter) and their impact on bitcoin prices. Digit Finance 6, 379–426 (2024). https://doi.org/10.1007/s42521-024-00106-3

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  • DOI: https://doi.org/10.1007/s42521-024-00106-3

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