Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/148887
Authors: 
Linton, Marco
Teo, Ernie Gin Swee
Bommes, Elisabeth
Chen, Cathy Yi-Hsuan
Härdle, Wolfgang
Year of Publication: 
2016
Series/Report no.: 
SFB 649 Discussion Paper 2016-051
Abstract: 
Cryptocurrencies are more and more used in official 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 difficult. 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.
Subjects: 
Dynamic Topic Modelling
Cryptocurrencies
Financial Risk
JEL: 
C19
G09
G10
Document Type: 
Working Paper

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