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Deep learning-based cryptocurrency sentiment construction

Author

Listed:
  • Sergey Nasekin

    (Deutsche Bank AG)

  • Cathy Yi-Hsuan Chen

    (University of Glasgow)

Abstract

We study investor sentiment on a non-classical asset such as cryptocurrency using machine learning methods. We account for context-specific information and word similarity using efficient language modeling tools such as construction of featurized word representations (embeddings) and recursive neural networks. We apply these tools for sentence-level sentiment classification and sentiment index construction. This analysis is performed on a novel dataset of 1220K messages related to 425 cryptocurrencies posted on a microblogging platform StockTwits during the period between March 2013 and May 2018. Both in- and out-of-sample predictive regressions are run to test significance of the constructed sentiment index variables. We find that the constructed sentiment indices are informative regarding returns and volatility predictability of the cryptocurrency market index.

Suggested Citation

  • Sergey Nasekin & Cathy Yi-Hsuan Chen, 2020. "Deep learning-based cryptocurrency sentiment construction," Digital Finance, Springer, vol. 2(1), pages 39-67, September.
    • Handle: RePEc:spr:digfin:v:2:y:2020:i:1:d:10.1007_s42521-020-00018-y
    DOI: 10.1007/s42521-020-00018-y
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    References listed on IDEAS

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    Cited by:

    1. Kumar Kulbhaskar, Anamika & Subramaniam, Sowmya, 2023. "Breaking news headlines: Impact on trading activity in the cryptocurrency market," Economic Modelling, Elsevier, vol. 126(C).
    2. Jian Mou & Wenting Liu & Chong Guan & J. Christopher Westland & Jongki Kim, 2024. "Predicting the cryptocurrency market using social media metrics and search trends during COVID-19," Electronic Commerce Research, Springer, vol. 24(2), pages 1307-1333, June.
    3. Jingyang Wu & Xinyi Zhang & Fangyixuan Huang & Haochen Zhou & Rohtiash Chandra, 2024. "Review of deep learning models for crypto price prediction: implementation and evaluation," Papers 2405.11431, arXiv.org, revised Jun 2024.
    4. Bowden, James & Gemayel, Roland, 2022. "Sentiment and trading decisions in an ambiguous environment: A study on cryptocurrency traders," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    5. Kyriazis, Nikolaos & Papadamou, Stephanos & Corbet, Shaen, 2020. "A systematic review of the bubble dynamics of cryptocurrency prices," Research in International Business and Finance, Elsevier, vol. 54(C).
    6. Zhang, Jiahang & Zhang, Chi, 2022. "Do cryptocurrency markets react to issuer sentiments? Evidence from Twitter," Research in International Business and Finance, Elsevier, vol. 61(C).
    7. Duygu Ider & Stefan Lessmann, 2022. "Forecasting Cryptocurrency Returns from Sentiment Signals: An Analysis of BERT Classifiers and Weak Supervision," Papers 2204.05781, arXiv.org, revised Mar 2023.
    8. Zuo Xiaorui & Chen Yao-Tsung & Härdle Wolfgang Karl, 2024. "Emoji driven crypto assets market reactions," Management & Marketing, Sciendo, vol. 19(2), pages 158-178.

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    More about this item

    Keywords

    Sentiment analysis; Lexicon; Social media; Word embedding; Deep learning; RNN;
    All these keywords.

    JEL classification:

    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • G4 - Financial Economics - - Behavioral Finance
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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