Deep learning-based cryptocurrency sentiment construction

Sergey Nasekin and Cathy Yi-Hsuan Chen

No 2018-066, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

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.

Keywords: sentiment analysis; lexicon; social media; word embedding; deep learning (search for similar items in EconPapers)
JEL-codes: G12 G4 G41 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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