Deep learning-based cryptocurrency sentiment construction

Nasekin, S. and Chen, C. Y.-H. (2020) Deep learning-based cryptocurrency sentiment construction. Digital Finance, (doi: 10.1007/s42521-020-00018-y) (Early Online Publication)

[img] Text
214034.pdf - Accepted Version
Restricted to Repository staff only until 11 March 2021.



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.

Item Type:Articles
Status:Early Online Publication
Glasgow Author(s) Enlighten ID:Chen, Professor Cathy Yi-Hsuan
Authors: Nasekin, S., and Chen, C. Y.-H.
College/School:College of Social Sciences > Adam Smith Business School > Accounting and Finance
Journal Name:Digital Finance
ISSN (Online):2524-6984
Published Online:11 March 2020
Copyright Holders:Copyright © 2020 Springer Nature Switzerland AG
First Published:First published in Digital Finance 2020
Publisher Policy:Reproduced in accordance with the publisher copyright policy

University Staff: Request a correction | Enlighten Editors: Update this record