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Impact of Public Information Arrivals on Cryptocurrency Market: A Case of Twitter Posts on Ripple

Author

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  • Gunay, Samet

    (American University of the Middle East)

Abstract

Public information arrivals and their immediate incorporation in asset price is a key component of semi-strong form of the Efficient Market Hypothesis. In this study, we explore the impact of public information arrivals on cryptocurrency market via Twitter posts. The empirical analysis was conducted through various methods including Kapetanios unit root test, Maki cointegration analysis and Markov regime switching regression analysis. Results indicate that while in bull market positive public information arrivals have a positive influence on Ripple’s value; in bear market, however, even if the company releases good news, it does not divert out the Ripple from downward trend.

Suggested Citation

  • Gunay, Samet, 2019. "Impact of Public Information Arrivals on Cryptocurrency Market: A Case of Twitter Posts on Ripple," East Asian Economic Review, Korea Institute for International Economic Policy, vol. 23(2), pages 149-168, June.
    • Handle: RePEc:ris:eaerev:0359
    DOI: 10.11644/KIEP.EAER.2019.23.2.359
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    Citations

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    Cited by:

    1. Gunay, Samet & Goodell, John W. & Muhammed, Shahnawaz & Kirimhan, Destan, 2023. "Frequency connectedness between FinTech, NFT and DeFi: Considering linkages to investor sentiment," International Review of Financial Analysis, Elsevier, vol. 90(C).
    2. Phumudzo Lloyd Seabe & Claude Rodrigue Bambe Moutsinga & Edson Pindza, 2024. "Optimizing Cryptocurrency Returns: A Quantitative Study on Factor-Based Investing," Mathematics, MDPI, vol. 12(9), pages 1-28, April.
    3. Gunay, Samet & Kaskaloglu, Kerem, 2022. "Does utilizing smart contracts induce a financial connectedness between Ethereum and non-fungible tokens?," Research in International Business and Finance, Elsevier, vol. 63(C).
    4. Nidhal Mgadmi & Azza Béjaoui & Wajdi Moussa, 2023. "Disentangling the Nonlinearity Effect in Cryptocurrency Markets During the Covid-19 Pandemic: Evidence from a Regime-Switching Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 30(3), pages 457-473, September.
    5. Wei Sun & Alisher Tohirovich Dedahanov & Ho Young Shin & Ki Su Kim, 2020. "Switching intention to crypto-currency market: Factors predisposing some individuals to risky investment," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.
    6. Sun, Wei & Dedahanov, Alisher Tohirovich & Shin, Ho Young & Li, Wei Ping, 2021. "Factors affecting institutional investors to add crypto-currency to asset portfolios," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    7. Ardia, David & Bluteau, Keven, 2024. "Twitter and cryptocurrency pump-and-dumps," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    8. Ahmed M. Khedr & Ifra Arif & Pravija Raj P V & Magdi El‐Bannany & Saadat M. Alhashmi & Meenu Sreedharan, 2021. "Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 3-34, January.

    More about this item

    Keywords

    Cryptocurrency; Public Information Arrivals; Semi-Strong Efficiency; Ripple; Twitter Posts;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G40 - Financial Economics - - Behavioral Finance - - - General
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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