Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

SFB649DP2016 051

Dynamic Topic Modelling for Cryptocurrency Community Forums

Marco Linto
Ernie Gin Swee Teo
Elisabeth Bommes
Cathy Yi-Hsuan Chen
Wolfgang K. Härdle

Cryptocurrencies are more and more used in official cash flows 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 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 finds 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.

Dynamic Topic Modelling, Cryptocurrencies, Financial Risk

JEL Classification:
C19, G09, G10