SFB649DP2016 051
Dynamic Topic Modelling for Cryptocurrency Community Forums
Marco Linto
Ernie Gin Swee Teo
Elisabeth Bommes
Cathy Yi-Hsuan Chen
Wolfgang K. Härdle
Abstract:
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
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 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.
Keywords:
Dynamic Topic Modelling, Cryptocurrencies, Financial Risk
JEL Classification:
C19, G09, G10