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Bitcoin price change and trend prediction through twitter sentiment and data volume

Author

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  • Jacques Vella Critien

    (University of Malta)

  • Albert Gatt

    (Utrecht University)

  • Joshua Ellul

    (University of Malta)

Abstract

Twitter sentiment has been shown to be useful in predicting whether Bitcoin’s price will increase or decrease. Yet the state-of-the-art is limited to predicting the price direction and not the magnitude of increase/decrease. In this paper, we seek to build on the state-of-the-art to not only predict the direction yet to also predict the magnitude of increase/decrease. We utilise not only sentiment extracted from tweets, but also the volume of tweets. We present results from experiments exploring the relation between sentiment and future price at different temporal granularities, with the goal of discovering the optimal time interval at which the sentiment expressed becomes a reliable indicator of price change. Two different neural network models are explored and evaluated, one based on recurrent nets and one based on convolutional networks. An additional model is presented to predict the magnitude of change, which is framed as a multi-class classification problem. It is shown that this model yields more reliable predictions when used alongside a price trend prediction model. The main research contribution from this paper is that we demonstrate that not only can price direction prediction be made but the magnitude in price change can be predicted with relative accuracy ( 63%).

Suggested Citation

  • Jacques Vella Critien & Albert Gatt & Joshua Ellul, 2022. "Bitcoin price change and trend prediction through twitter sentiment and data volume," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-20, December.
    • Handle: RePEc:spr:fininn:v:8:y:2022:i:1:d:10.1186_s40854-022-00352-7
    DOI: 10.1186/s40854-022-00352-7
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    References listed on IDEAS

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    1. Shubhankar Mohapatra & Nauman Ahmed & Paulo Alencar, 2020. "KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter Sentiments," Papers 2003.04967, arXiv.org.
    2. Suardi, Sandy & Rasel, Atiqur Rahman & Liu, Bin, 2022. "On the predictive power of tweet sentiments and attention on bitcoin," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 289-301.
    3. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    4. Kraaijeveld, Olivier & De Smedt, Johannes, 2020. "The predictive power of public Twitter sentiment for forecasting cryptocurrency prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
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    Cited by:

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    2. Nakavachara, Voraprapa & Ratanabanchuen, Roongkiat & Saengchote, Kanis & Amonthumniyom, Thitiphong & Parinyavuttichai, Pongsathon & Vinaibodee, Polpatt, 2024. "Do people gamble or invest in the cryptocurrency market? Transactional-level evidence from Thailand," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).
    3. Voraprapa Nakavachara & Roongkiat Ratanabanchuen & Kanis Saengchote & Thitiphong Amonthumniyom & Pongsathon Parinyavuttichai & Polpatt Vinaibodee, 2023. "Do People Gamble or Invest in the Cryptocurrency Market? Transactional-Level Evidence from Thailand," PIER Discussion Papers 206, Puey Ungphakorn Institute for Economic Research, revised Feb 2024.
    4. Mercik, Aleksander & Słoński, Tomasz & Karaś, Marta, 2024. "Understanding crypto-asset exposure: An investigation of its impact on performance and stock sensitivity among listed companies," International Review of Financial Analysis, Elsevier, vol. 92(C).
    5. Wei Xu & Daning Hu & Karl Reiner Lang & J. Leon Zhao, 2022. "Blockchain and digital finance," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-4, December.
    6. Oluwadamilare Omole & David Enke, 2024. "Deep learning for Bitcoin price direction prediction: models and trading strategies empirically compared," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-26, December.
    7. Yihang Fu & Mingyu Zhou & Luyao Zhang, 2024. "DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend Forecasting," Papers 2405.00522, arXiv.org.
    8. Tomas Pečiulis & Nisar Ahmad & Angeliki N. Menegaki & Aqsa Bibi, 2024. "Forecasting of cryptocurrencies: Mapping trends, influential sources, and research themes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1880-1901, September.
    9. Lin Li, 2023. "Investigating risk assessment in post-pandemic household cryptocurrency investments: an explainable machine learning approach," Journal of Asset Management, Palgrave Macmillan, vol. 24(4), pages 255-267, July.

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