Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230776
Authors: 
Nasekin, Sergey
Chen, Cathy Yi-Hsuan
Year of Publication: 
2018
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2018-066
Abstract: 
We study investor sentiment on a non-classical asset, cryptocurrencies using a “cryptospecificlexicon” recently proposed in Chen et al. (2018) and statistical learning methods.We account for context-specific information and word similarity by learning word embeddingsvia neural network-based Word2Vec model. On top of pre-trained word vectors, weapply popular machine learning methods such as recursive neural networks for sentencelevelclassification and sentiment index construction. We perform this analysis on a noveldataset of 1220K messages related to 425 cryptocurrencies posted on a microblogging platformStockTwits during the period between March 2013 and May 2018. The constructed sentiment indices are value-relevant in terms of its return and volatility predictability for thecryptocurrency market index.
Subjects: 
sentiment analysis
lexicon
social media
word embedding
deep learning
JEL: 
G41
G4
G12
Document Type: 
Working Paper

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