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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 usersā€™ 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 ā€œXā€. This paper will use the name ā€œXā€ 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: 2023ā€“11-27.

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

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Author information

Authors and Affiliations

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

    Takeshi InudukaĀ &Ā 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.

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Correspondence to Takeshi Inuduka.

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Appendices

Appendix 1. Additional tables for the analysis

See TablesĀ 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.

TableĀ 14 Augmented Dickey-Fuller test results (Pt, Vt, and Spost)
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TableĀ 15 AIC (Pt, Vt, and Spost)
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TableĀ 16 Estimation results for Ī±, Ī², and Ī³ in the Pt equation of Pt, Vt, and Spost
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TableĀ 17 Estimation results for Ī±, Ī², and Ī³ in the Vt equation of Pt, Vt, and Spost
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TableĀ 18 Estimation results for Ī±, Ī², and Ī³ in the Spost equation of Pt, Vt, and Spost
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TableĀ 19 Augmented Dickey-Fuller test results (Pt, Vt, and Snegt)
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TableĀ 20 AIC (Pt, Vt, and Snegt)
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TableĀ 21 Estimation results for Ī±, Ī², and Ī³ in the Pt equation of Pt, Vt, and Snegt
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TableĀ 22 Estimation results for Ī±, Ī², and Ī³ in the Vt equation of Pt, Vt, and Snegt
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TableĀ 23 Estimation results for Ī±, Ī², and Ī³ in the Snegt equation of Pt, Vt, and Snegt
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TableĀ 24 Augmented Dickey-Fuller test results (Pt, Vt, and Svalt)
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TableĀ 25 AIC (Pt, Vt, and Svalt)
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TableĀ 26 Estimation results for Ī±, Ī², and Ī³ in the Pt equation of Pt, Vt, and Svalt
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TableĀ 27 Estimation results for Ī±, Ī², and Ī³ in the Vt equation of Pt, Vt, and Svalt
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TableĀ 28 Estimation results for Ī±, Ī², and Ī³ in the Svalt equation of Pt, Vt, and Svalt
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TableĀ 29 Augmented Dickey-Fuller test results (Pt, Vt, and Sarot)
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TableĀ 30 AIC (Pt, Vt, and Sarot)
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TableĀ 31 Estimation results for Ī±, Ī², and Ī³ in the Pt equation of Pt, Vt, and Sarot
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TableĀ 32 Estimation results for Ī±, Ī², and Ī³ in the Vt equation of Pt, Vt, and Sarot
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TableĀ 33 Estimation results for Ī±, Ī², and Ī³ in the Sarot equation of Pt, Vt, and Sarot
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TableĀ 34 Augmented Dickey-Fuller test results (Pt, Vt, and Sdomt)
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TableĀ 35 AIC (Pt, Vt, and Sdomt)
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TableĀ 36 Estimation results for Ī±, Ī², and Ī³ in the Pt equation of Pt, Vt, and Sdomt
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TableĀ 37 Estimation results for Ī±, Ī², and Ī³ in the Vt equation of Pt, Vt, and Sdomt
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TableĀ 38 Estimation results for Ī±, Ī², and Ī³ 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 TablesĀ 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53.

TableĀ 39 Ljung-Box test results (Pt, Vt, and Spost)
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TableĀ 40 ARCH-LM test results (Pt, Vt, and Spost)
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TableĀ 41 Jarqueā€“Bera test results (Pt, Vt, and Spost)
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TableĀ 42 Ljung-Box test results (Pt, Vt, and Snegt)
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TableĀ 43 ARCH-LM test results (Pt, Vt, and Snegt)
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TableĀ 44 Jarqueā€“Bera test results (Pt, Vt, and Snegt)
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TableĀ 45 Ljung-Box test results (Pt, Vt, and Svalt)
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TableĀ 46 ARCH-LM test results (Pt, Vt, and Svalt)
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TableĀ 47 Jarqueā€“Bera test results (Pt, Vt, and Svalt)
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TableĀ 48 Ljung-Box test results (Pt, Vt, and Sarot)
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TableĀ 49 ARCH-LM test results (Pt, Vt, and Sarot)
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TableĀ 50 Jarqueā€“Bera test results (Pt, Vt, and Sarot)
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TableĀ 51 Ljung-Box test results (Pt, Vt, and Sdomt)
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TableĀ 52 ARCH-LM test results (Pt, Vt, and Sdomt)
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TableĀ 53 Jarqueā€“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, 379ā€“426 (2024). https://doi.org/10.1007/s42521-024-00106-3

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