Deep Learning-Based Cryptocurrency Sentiment Construction

29 Pages Posted: 8 Jan 2019 Last revised: 18 Feb 2020

See all articles by Sergey Nasekin

Sergey Nasekin

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Cathy Yi‐Hsuan Chen

University of Glasgow, Adam Smith Business School; Humboldt Universität zu Berlin

Date Written: July 10, 2019

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 by using efficient language modelling tools such as construction of featurized word representations (embeddings) and recursive neural networks (RNNs). 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, LSTM

JEL Classification: G41, G4, G12

Suggested Citation

Nasekin, Sergey and Chen, Cathy Yi‐Hsuan, Deep Learning-Based Cryptocurrency Sentiment Construction (July 10, 2019). Available at SSRN: https://ssrn.com/abstract=3310784 or http://dx.doi.org/10.2139/ssrn.3310784

Sergey Nasekin

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE) ( email )

Spandauer Strasse 1
Berlin, D-10178
Germany

Cathy Yi‐Hsuan Chen (Contact Author)

University of Glasgow, Adam Smith Business School ( email )

University Avenue
Glasgow, G12 8QQ
United Kingdom
01413305065 (Phone)

HOME PAGE: http://https://gla.cathychen.info

Humboldt Universität zu Berlin ( email )

Unter den Linden 6,
Berlin, 10117
Germany
03020935631 (Phone)
10099 (Fax)

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