IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2402.10481.html

Emoji Driven Crypto Assets Market Reactions

Author

Listed:
  • Xiaorui Zuo
  • Yao-Tsung Chen
  • Wolfgang Karl Hardle

Abstract

In the burgeoning realm of cryptocurrency, social media platforms like Twitter have become pivotal in influencing market trends and investor sentiments. In our study, we leverage GPT-4 and a fine-tuned transformer-based BERT model for a multimodal sentiment analysis, focusing on the impact of emoji sentiment on cryptocurrency markets. By translating emojis into quantifiable sentiment data, we correlate these insights with key market indicators like BTC Price and the VCRIX index. Our architecture's analysis of emoji sentiment demonstrated a distinct advantage over FinBERT's pure text sentiment analysis in such predicting power. This approach may be fed into the development of trading strategies aimed at utilizing social media elements to identify and forecast market trends. Crucially, our findings suggest that strategies based on emoji sentiment can facilitate the avoidance of significant market downturns and contribute to the stabilization of returns. This research underscores the practical benefits of integrating advanced AI-driven analyses into financial strategies, offering a nuanced perspective on the interplay between digital communication and market dynamics in an academic context.

Suggested Citation

  • Xiaorui Zuo & Yao-Tsung Chen & Wolfgang Karl Hardle, 2024. "Emoji Driven Crypto Assets Market Reactions," Papers 2402.10481, arXiv.org, revised May 2024.
  • Handle: RePEc:arx:papers:2402.10481
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2402.10481
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ben Osman, Myriam & Galariotis, Emilios & Guesmi, Khaled & Hamdi, Haykel & Naoui, Kamel, 2024. "Are markets sentiment driving the price bubbles in the virtual?," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 272-285.
    2. Bekaert, Geert & Hoerova, Marie, 2014. "The VIX, the variance premium and stock market volatility," Journal of Econometrics, Elsevier, vol. 183(2), pages 181-192.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zuo Xiaorui & Chen Yao-Tsung & Härdle Wolfgang Karl, 2024. "Emoji driven crypto assets market reactions," Management & Marketing, Sciendo, vol. 19(2), pages 158-178.
    2. Toshiaki Ogawa & Masato Ubukata & Toshiaki Watanabe, 2020. "Stock Return Predictability and Variance Risk Premia around the ZLB," IMES Discussion Paper Series 20-E-09, Institute for Monetary and Economic Studies, Bank of Japan.
    3. Lee, Seung Jung & Liu, Lucy Qian & Stebunovs, Viktors, 2022. "Risk-taking spillovers of U.S. monetary policy in the global market for U.S. dollar corporate loans," Journal of Banking & Finance, Elsevier, vol. 138(C).
    4. Geert Bekaert & Eric Engstrom, 2017. "Asset Return Dynamics under Habits and Bad Environment-Good Environment Fundamentals," Journal of Political Economy, University of Chicago Press, vol. 125(3), pages 713-760.
    5. Dima, Bogdan & Dima, Ştefana Maria & Ioan, Roxana, 2025. "The short-run impact of investor expectations’ past volatility on current predictions: The case of VIX," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 98(C).
    6. Bekaert, Geert & Hoerova, Marie & Xu, Nancy, 2023. "Risk, Monetary Policy and Asset Prices in a Global World," CEPR Discussion Papers 18229, C.E.P.R. Discussion Papers.
    7. Tobias Adrian & Nellie Liang, 2018. "Monetary Policy, Financial Conditions, and Financial Stability," International Journal of Central Banking, International Journal of Central Banking, vol. 14(1), pages 73-131, January.
    8. Xu, Yanyan & Huang, Dengshi & Ma, Feng & Qiao, Gaoxiu, 2019. "Liquidity and realized range-based volatility forecasting: Evidence from China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1102-1113.
    9. Baur, Dirk G. & Smales, Lee A., 2020. "Hedging geopolitical risk with precious metals," Journal of Banking & Finance, Elsevier, vol. 117(C).
    10. Gagnon, Marie-Hélène & Power, Gabriel J. & Toupin, Dominique, 2023. "The sum of all fears: Forecasting international returns using option-implied risk measures," Journal of Banking & Finance, Elsevier, vol. 146(C).
    11. Geert Bekaert & Eric C. Engstrom & Nancy R. Xu, 2022. "The Time Variation in Risk Appetite and Uncertainty," Management Science, INFORMS, vol. 68(6), pages 3975-4004, June.
    12. Lovchikova, Marina & Matschke, Johannes, 2024. "Capital controls and the global financial cycle," European Economic Review, Elsevier, vol. 163(C).
    13. Cesa-Bianchi, Ambrogio & Eguren Martin, Fernando & Thwaites, Gregory, 2019. "Foreign booms, domestic busts: The global dimension of banking crises," Journal of Financial Intermediation, Elsevier, vol. 37(C), pages 58-74.
    14. Wilms, Ines & Rombouts, Jeroen & Croux, Christophe, 2021. "Multivariate volatility forecasts for stock market indices," International Journal of Forecasting, Elsevier, vol. 37(2), pages 484-499.
    15. Tobias Adrian & Richard K. Crump & Erik Vogt, 2019. "Nonlinearity and Flight‐to‐Safety in the Risk‐Return Trade‐Off for Stocks and Bonds," Journal of Finance, American Finance Association, vol. 74(4), pages 1931-1973, August.
    16. Fei Lu & Feng Ma & Elie Bouri, 2024. "Stock market volatility predictability: new evidence from energy consumption," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-17, December.
    17. Jihyun Park & Andrey Sarantsev, 2024. "The VIX as Stochastic Volatility for Corporate Bonds," Papers 2410.22498, arXiv.org, revised Jan 2025.
    18. Guo, Hui & Jiang, Xiaowen, 2021. "Aggregate Distress Risk and Equity Returns," Journal of Banking & Finance, Elsevier, vol. 133(C).
    19. Nida Çakır Melek & Charles W. Calomiris & Harry Mamaysky, 2020. "Big Data Meets the Turbulent Oil Market," Research Working Paper RWP 20-20, Federal Reserve Bank of Kansas City, revised Nov 2022.
    20. Qadan, Mahmoud & Shuval, Kerem, 2022. "Variance risk and the idiosyncratic volatility puzzle," Finance Research Letters, Elsevier, vol. 45(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2402.10481. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.