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Predicting altcoin returns using social media

Cryptocurrencies have recently received large media interest. Especially the great fluctuations in price have attracted such attention. Behavioral sciences and related scientific literature provide evidence that there is a close relationship between social media and price fluctuations of cryptocurre...

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Detalles Bibliográficos
Autores principales: Steinert, Lars, Herff, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6279012/
https://www.ncbi.nlm.nih.gov/pubmed/30513110
http://dx.doi.org/10.1371/journal.pone.0208119
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author Steinert, Lars
Herff, Christian
author_facet Steinert, Lars
Herff, Christian
author_sort Steinert, Lars
collection PubMed
description Cryptocurrencies have recently received large media interest. Especially the great fluctuations in price have attracted such attention. Behavioral sciences and related scientific literature provide evidence that there is a close relationship between social media and price fluctuations of cryptocurrencies. This particularly applies to smaller currencies, which can be substantially influenced by references on Twitter. Although these so-called “altcoins” often have smaller trading volumes they sometimes attract large attention on social media. Here, we show that fluctuations in altcoins can be predicted from social media. In order to do this, we collected a dataset containing prices and the social media activity of 181 altcoins in the form of 426,520 tweets over a timeframe of 71 days. The containing public mood was then estimated using sentiment analysis. To predict altcoin returns, we carried out linear regression analyses based on 45 days of data. We showed that short-term returns can be predicted from activity and sentiments on Twitter.
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spelling pubmed-62790122018-12-20 Predicting altcoin returns using social media Steinert, Lars Herff, Christian PLoS One Research Article Cryptocurrencies have recently received large media interest. Especially the great fluctuations in price have attracted such attention. Behavioral sciences and related scientific literature provide evidence that there is a close relationship between social media and price fluctuations of cryptocurrencies. This particularly applies to smaller currencies, which can be substantially influenced by references on Twitter. Although these so-called “altcoins” often have smaller trading volumes they sometimes attract large attention on social media. Here, we show that fluctuations in altcoins can be predicted from social media. In order to do this, we collected a dataset containing prices and the social media activity of 181 altcoins in the form of 426,520 tweets over a timeframe of 71 days. The containing public mood was then estimated using sentiment analysis. To predict altcoin returns, we carried out linear regression analyses based on 45 days of data. We showed that short-term returns can be predicted from activity and sentiments on Twitter. Public Library of Science 2018-12-04 /pmc/articles/PMC6279012/ /pubmed/30513110 http://dx.doi.org/10.1371/journal.pone.0208119 Text en © 2018 Steinert, Herff http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Steinert, Lars
Herff, Christian
Predicting altcoin returns using social media
title Predicting altcoin returns using social media
title_full Predicting altcoin returns using social media
title_fullStr Predicting altcoin returns using social media
title_full_unstemmed Predicting altcoin returns using social media
title_short Predicting altcoin returns using social media
title_sort predicting altcoin returns using social media
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6279012/
https://www.ncbi.nlm.nih.gov/pubmed/30513110
http://dx.doi.org/10.1371/journal.pone.0208119
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