IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2405.00522.html
   My bibliography  Save this paper

DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend Forecasting

Author

Listed:
  • Yihang Fu
  • Mingyu Zhou
  • Luyao Zhang

Abstract

In the distributed systems landscape, Blockchain has catalyzed the rise of cryptocurrencies, merging enhanced security and decentralization with significant investment opportunities. Despite their potential, current research on cryptocurrency trend forecasting often falls short by simplistically merging sentiment data without fully considering the nuanced interplay between financial market dynamics and external sentiment influences. This paper presents a novel Dual Attention Mechanism (DAM) for forecasting cryptocurrency trends using multimodal time-series data. Our approach, which integrates critical cryptocurrency metrics with sentiment data from news and social media analyzed through CryptoBERT, addresses the inherent volatility and prediction challenges in cryptocurrency markets. By combining elements of distributed systems, natural language processing, and financial forecasting, our method outperforms conventional models like LSTM and Transformer by up to 20\% in prediction accuracy. This advancement deepens the understanding of distributed systems and has practical implications in financial markets, benefiting stakeholders in cryptocurrency and blockchain technologies. Moreover, our enhanced forecasting approach can significantly support decentralized science (DeSci) by facilitating strategic planning and the efficient adoption of blockchain technologies, improving operational efficiency and financial risk management in the rapidly evolving digital asset domain, thus ensuring optimal resource allocation.

Suggested Citation

  • Yihang Fu & Mingyu Zhou & Luyao Zhang, 2024. "DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend Forecasting," Papers 2405.00522, arXiv.org.
  • Handle: RePEc:arx:papers:2405.00522
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2405.00522
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Koki, Constandina & Leonardos, Stefanos & Piliouras, Georgios, 2022. "Exploring the predictability of cryptocurrencies via Bayesian hidden Markov models," Research in International Business and Finance, Elsevier, vol. 59(C).
    2. Rognone, Lavinia & Hyde, Stuart & Zhang, S. Sarah, 2020. "News sentiment in the cryptocurrency market: An empirical comparison with Forex," International Review of Financial Analysis, Elsevier, vol. 69(C).
    3. Ahmed Ibrahim & Rasha Kashef & Menglu Li & Esteban Valencia & Eric Huang, 2020. "Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables," JRFM, MDPI, vol. 13(9), pages 1-21, August.
    4. 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.
    5. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    6. Xi Zhang & Yunjia Zhang & Senzhang Wang & Yuntao Yao & Binxing Fang & Philip S. Yu, 2018. "Improving Stock Market Prediction via Heterogeneous Information Fusion," Papers 1801.00588, arXiv.org.
    7. 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).
    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. 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).
    2. Fayssal Jamhamed & Franck Martin & Fabien Rondeau & Josué Thélissaint & Stéphane Tufféry, 2024. "Regime-Specific Dynamics and Informational Efficiency in Cryptomarkets: Evidence from Gaussian Mixture Models," Economics Working Paper Archive (University of Rennes & University of Caen) 2024-13, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.
    3. Xian Zhuo & Felix Irresberger & Denefa Bostandzic, 2024. "How are texts analyzed in blockchain research? A systematic literature review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
    4. Yousaf, Imran & Youssef, Manel & Goodell, John W., 2022. "Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index," International Review of Financial Analysis, Elsevier, vol. 83(C).
    5. Zhang, Guangyue & Sannella, Alexander & Brennan, Gerard & Talha Afzal, Muhammad, 2024. "Fair value estimates for illiquid cryptocurrency," International Journal of Accounting Information Systems, Elsevier, vol. 54(C).
    6. 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.
    7. Ivanovski, Kris & Hailemariam, Abebe, 2023. "Forecasting the stock-cryptocurrency relationship: Evidence from a dynamic GAS model," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 97-111.
    8. Kamyr Gomes Souza & Flavio Barboza & Daniel Vitor Tartari Garruti, 2024. "A Discourse Analysis of Tweets and Its Implications for Cryptocurrency Prices and Trade Volumes," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2355-2383, October.
    9. Bouri, Elie & Christou, Christina & Gupta, Rangan, 2022. "Forecasting returns of major cryptocurrencies: Evidence from regime-switching factor models," Finance Research Letters, Elsevier, vol. 49(C).
    10. Suwan (Cheng) Long & Ioannis Chatziantoniou & David Gabauer & Brian Lucey, 2024. "Do social media sentiments drive cryptocurrency intraday price volatility? New evidence from asymmetric TVP-VAR frequency connectedness measures," The European Journal of Finance, Taylor & Francis Journals, vol. 30(13), pages 1470-1489, September.
    11. Hajek, Petr & Hikkerova, Lubica & Sahut, Jean-Michel, 2023. "How well do investor sentiment and ensemble learning predict Bitcoin prices?," Research in International Business and Finance, Elsevier, vol. 64(C).
    12. Wen Long & Man Guo, 2025. "Social media and capital markets: an interdisciplinary bibliometric analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-32, December.
    13. Osman, Myriam Ben & Urom, Christian & Guesmi, Khaled & Benkraiem, Ramzi, 2024. "Economic sentiment and the cryptocurrency market in the post-COVID-19 era," International Review of Financial Analysis, Elsevier, vol. 91(C).
    14. 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.
    15. Erdinc Akyildirim & Ahmet Faruk Aysan & Oguzhan Cepni & Özge Serbest, 2024. "Sentiment matters: the effect of news-media on spillovers among cryptocurrency returns," The European Journal of Finance, Taylor & Francis Journals, vol. 30(14), pages 1577-1613, September.
    16. 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.
    17. A. Hachicha & F. Hachicha, 2021. "Analysis of the bitcoin stock market indexes using comparative study of two models SV with MCMC algorithm," Review of Quantitative Finance and Accounting, Springer, vol. 56(2), pages 647-673, February.
    18. Corbet, Shaen & Goodell, John W. & Günay, Samet, 2022. "What drives DeFi prices? Investigating the effects of investor attention," Finance Research Letters, Elsevier, vol. 48(C).
    19. 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).
    20. Cynthia Weiyi Cai & Rui Xue & Bi Zhou, 2023. "Cryptocurrency puzzles: a comprehensive review and re-introduction," Journal of Accounting Literature, Emerald Group Publishing Limited, vol. 46(1), pages 26-50, June.

    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:2405.00522. 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.