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Investor attention and bitcoin liquidity: Evidence from bitcoin tweets

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  • Choi, Hyungeun

Abstract

This study employs the number of tweets as a proxy for investor attention. We use high-frequency data to investigate tweets’ real-time effects on Bitcoin liquidity. We find that a 1% increase in tweets leads to about 7% of liquidity improvement in the next five to 10 min. In a precise timeline, we show that the positive impact of tweets decays after approximately an hour. We further find that impacts on liquidity are stronger when tweets draw more attention. This study thus suggests that active investor attention can significantly improve Bitcoin liquidity in real time.

Suggested Citation

  • Choi, Hyungeun, 2021. "Investor attention and bitcoin liquidity: Evidence from bitcoin tweets," Finance Research Letters, Elsevier, vol. 39(C).
    • Handle: RePEc:eee:finlet:v:39:y:2021:i:c:s154461231930902x
    DOI: 10.1016/j.frl.2020.101555
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    Citations

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    Cited by:

    1. Marmora, Paul, 2022. "Does monetary policy fuel bitcoin demand? Event-study evidence from emerging markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    2. Ozdamar, Melisa & Sensoy, Ahmet & Akdeniz, Levent, 2022. "Retail vs institutional investor attention in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    3. Bouteska, Ahmed & Hajek, Petr & Abedin, Mohammad Zoynul & Dong, Yizhe, 2023. "Effect of twitter investor engagement on cryptocurrencies during the COVID-19 pandemic," Research in International Business and Finance, Elsevier, vol. 64(C).
    4. Neto, David, 2022. "Examining interconnectedness between media attention and cryptocurrency markets: A transfer entropy story," Economics Letters, Elsevier, vol. 214(C).
    5. Moser, Stefanie & Brauneis, Alexander, 2023. "Should you listen to crypto YouTubers?," Finance Research Letters, Elsevier, vol. 54(C).
    6. Wang, Fang & Gacesa, Marko, 2023. "Semi-strong efficient market of Bitcoin and Twitter: An analysis of semantic vector spaces of extracted keywords and light gradient boosting machine models," International Review of Financial Analysis, Elsevier, vol. 88(C).
    7. Bouteska, Ahmed & Mefteh-Wali, Salma & Dang, Trung, 2022. "Predictive power of investor sentiment for Bitcoin returns: Evidence from COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    8. Skander Slim & Ibrahim Tabche & Yosra Koubaa & Mohamed Osman & Andreas Karathanasopoulos, 2023. "Forecasting realized volatility of Bitcoin: The informative role of price duration," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1909-1929, November.
    9. Mokni, Khaled & Bouteska, Ahmed & Nakhli, Mohamed Sahbi, 2022. "Investor sentiment and Bitcoin relationship: A quantile-based analysis," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    10. Shahzad, Syed Jawad Hussain & Anas, Muhammad & Bouri, Elie, 2022. "Price explosiveness in cryptocurrencies and Elon Musk's tweets," Finance Research Letters, Elsevier, vol. 47(PB).
    11. Clark, Ephraim & Lahiani, Amine & Mefteh-Wali, Salma, 2023. "Cryptocurrency return predictability: What is the role of the environment?," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    12. Ullah, Subhan & Attah-Boakye, Rexford & Adams, Kweku & Zaefarian, Ghasem, 2022. "Assessing the influence of celebrity and government endorsements on bitcoin’s price volatility," Journal of Business Research, Elsevier, vol. 145(C), pages 228-239.
    13. Ştefan Cristian Gherghina & Liliana Nicoleta Simionescu, 2023. "Exploring the asymmetric effect of COVID-19 pandemic news on the cryptocurrency market: evidence from nonlinear autoregressive distributed lag approach and frequency domain causality," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-58, December.
    14. Oncu, Erdem, 2021. "Investigation of Dogecoin Price Movements: A GSADF Analysis," MPRA Paper 111212, University Library of Munich, Germany.
    15. Wang, Chen & Shen, Dehua & Li, Youwei, 2022. "Aggregate Investor Attention and Bitcoin Return: The Long Short-term Memory Networks Perspective," Finance Research Letters, Elsevier, vol. 49(C).

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    More about this item

    Keywords

    Bitcoin; Tweets; Market liquidity; Investor attention; Market efficiency;
    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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