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Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data

Author

Listed:
  • Vincent Gurgul
  • Stefan Lessmann
  • Wolfgang Karl Hardle

Abstract

We introduce novel approaches to cryptocurrency price forecasting, leveraging Machine Learning (ML) and Natural Language Processing (NLP) techniques, with a focus on Bitcoin and Ethereum. By analysing news and social media content, primarily from Twitter and Reddit, we assess the impact of public sentiment on cryptocurrency markets. A distinctive feature of our methodology is the application of the BART MNLI zero-shot classification model to detect bullish and bearish trends, significantly advancing beyond traditional sentiment analysis. Additionally, we systematically compare a range of pre-trained and fine-tuned deep learning NLP models against conventional dictionary-based sentiment analysis methods. Another key contribution of our work is the adoption of local extrema alongside daily price movements as predictive targets, reducing trading frequency and portfolio volatility. Our findings demonstrate that integrating textual data into cryptocurrency price forecasting not only improves forecasting accuracy but also consistently enhances the profitability and Sharpe ratio across various validation scenarios, particularly when applying deep learning NLP techniques. The entire codebase of our experiments is made available via an online repository: https://anonymous.4open.science/r/crypto-forecasting-public

Suggested Citation

  • Vincent Gurgul & Stefan Lessmann & Wolfgang Karl Hardle, 2023. "Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data," Papers 2311.14759, arXiv.org, revised Oct 2024.
  • Handle: RePEc:arx:papers:2311.14759
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    References listed on IDEAS

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    1. Chen, Cathy Yi-Hsuan & Després, Roméo & Guo, Li & Renault, Thomas, 2019. "What makes cryptocurrencies special? Investor sentiment and return predictability during the bubble," IRTG 1792 Discussion Papers 2019-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
    3. Vytautas Karalevicius & Niels Degrande & Jochen De Weerdt, 2018. "Using sentiment analysis to predict interday Bitcoin price movements," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 19(1), pages 56-75, December.
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