Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2018 066

Deep learning-based cryptocurrency sentiment construction

Sergey Nasekin
Cathy Yi-Hsuan Chen

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.

sentiment analysis, lexicon, social media, word embedding, deep learning

JEL Classification:
G41, G4, G12