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Dynamic Topic Modelling for Cryptocurrency Community Forums

Author

Listed:
  • Marco Linton
  • Wolfgang K. Härdle
  • Ernie Gin Swee Teo
  • Elisabeth Bommes
  • Cathy Yi-Hsuan Chen

Abstract

Cryptocurrencies are more and more used in ocial cash ows and exchange of goods. Bitcoin and the underlying blockchain technology have been looked at by big companies that are adopting and investing in this technology. The CRIX Index of cryptocurrencies hu.berlin/CRIX indicates a wider acceptance of cryptos. One reason for its prosperity certainly being a security aspect, since the underlying network of cryptos is decentralized. It is also unregulated and highly volatile, making the risk assessment at any given moment dicult. In message boards one nds a huge source of information in the form of unstructured text written by e.g. Bitcoin developers and investors. We collect from a popular crypto currency message board texts, user information and associated time stamps. We then provide an indicator for fraudulent schemes. This indicator is constructed using dynamic topic modelling, text mining and unsupervised machine learning. We study how opinions and the evolution of topics are connected with big events in the cryptocurrency universe. Furthermore, the predictive power of these techniques are investigated, comparing the results to known events in the cryptocurrency space. We also test hypothesis of self-fulling prophecies and herding behaviour using the results.

Suggested Citation

  • Marco Linton & Wolfgang K. Härdle & Ernie Gin Swee Teo & Elisabeth Bommes & Cathy Yi-Hsuan Chen, 2016. "Dynamic Topic Modelling for Cryptocurrency Community Forums," SFB 649 Discussion Papers SFB649DP2016-051, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    • Handle: RePEc:hum:wpaper:sfb649dp2016-051
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    References listed on IDEAS

    as
    1. Yang Bao & Anindya Datta, 2014. "Simultaneously Discovering and Quantifying Risk Types from Textual Risk Disclosures," Management Science, INFORMS, vol. 60(6), pages 1371-1391, June.
    2. Cheah, Eng-Tuck & Fry, John, 2015. "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin," Economics Letters, Elsevier, vol. 130(C), pages 32-36.
    3. Adrian (Wai-Kong) Cheung & Eduardo Roca & Jen-Je Su, 2015. "Crypto-currency bubbles: an application of the Phillips-Shi-Yu (2013) methodology on Mt. Gox bitcoin prices," Applied Economics, Taylor & Francis Journals, vol. 47(23), pages 2348-2358, May.
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    Cited by:

    1. Young Bin Kim & Jurim Lee & Nuri Park & Jaegul Choo & Jong-Hyun Kim & Chang Hun Kim, 2017. "When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.
    2. Ni, Xinwen & Härdle, Wolfgang Karl & Xie, Taojun, 2020. "A Machine Learning Based Regulatory Risk Index for Cryptocurrencies," IRTG 1792 Discussion Papers 2020-013, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

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    More about this item

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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