Deep learning-based cryptocurrency sentiment construction

Sergey Nasekin () and Cathy Yi-Hsuan Chen ()
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Sergey Nasekin: Deutsche Bank AG
Cathy Yi-Hsuan Chen: University of Glasgow

Digital Finance, 2020, vol. 2, issue 1, No 3, 39-67

Abstract: Abstract We study investor sentiment on a non-classical asset such as cryptocurrency using machine learning methods. We account for context-specific information and word similarity using efficient language modeling tools such as construction of featurized word representations (embeddings) and recursive neural networks. We apply these tools for sentence-level sentiment classification and sentiment index construction. This analysis is performed on a novel dataset of 1220K messages related to 425 cryptocurrencies posted on a microblogging platform StockTwits during the period between March 2013 and May 2018. Both in- and out-of-sample predictive regressions are run to test significance of the constructed sentiment index variables. We find that the constructed sentiment indices are informative regarding returns and volatility predictability of the cryptocurrency market index.

Keywords: Sentiment analysis; Lexicon; Social media; Word embedding; Deep learning; RNN (search for similar items in EconPapers)
JEL-codes: G12 G4 G41 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s42521-020-00018-y

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