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Social media engagement and cryptocurrency performance
Cryptocurrencies are highly speculative assets with large price volatility. If one could forecast their behavior, this would make them more attractive to investors. In this work we study the problem of predicting the future performance of cryptocurrencies using social media data. We propose a new mo...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10174546/ https://www.ncbi.nlm.nih.gov/pubmed/37167281 http://dx.doi.org/10.1371/journal.pone.0284501 |
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author | Qureshi, Khizar Zaman, Tauhid |
author_facet | Qureshi, Khizar Zaman, Tauhid |
author_sort | Qureshi, Khizar |
collection | PubMed |
description | Cryptocurrencies are highly speculative assets with large price volatility. If one could forecast their behavior, this would make them more attractive to investors. In this work we study the problem of predicting the future performance of cryptocurrencies using social media data. We propose a new model to measure the engagement of users with topics discussed on social media based on interactions with social media posts. This model overcomes the limitations of previous volume and sentiment based approaches. We use this model to estimate engagement coefficients for 48 cryptocurrencies created between 2019 and 2021 using data from Twitter from the first month of the cryptocurrencies’ existence. We find that the future returns of the cryptocurrencies are dependent on the engagement coefficients. Cryptocurrencies whose engagement coefficients have extreme values have lower returns. Low engagement coefficients signal a lack of interest, while high engagement coefficients signal artificial activity which is likely from automated accounts known as bots. We measure the amount of bot posts for the cryptocurrencies and find that generally, cryptocurrencies with more bot posts have lower future returns. While future returns are dependent on both the bot activity and engagement coefficient, the dependence is strongest for the engagement coefficient, especially for short-term returns. We show that simple investment strategies which select cryptocurrencies with engagement coefficients exceeding a fixed threshold perform well for holding times of a few months. |
format | Online Article Text |
id | pubmed-10174546 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-101745462023-05-12 Social media engagement and cryptocurrency performance Qureshi, Khizar Zaman, Tauhid PLoS One Research Article Cryptocurrencies are highly speculative assets with large price volatility. If one could forecast their behavior, this would make them more attractive to investors. In this work we study the problem of predicting the future performance of cryptocurrencies using social media data. We propose a new model to measure the engagement of users with topics discussed on social media based on interactions with social media posts. This model overcomes the limitations of previous volume and sentiment based approaches. We use this model to estimate engagement coefficients for 48 cryptocurrencies created between 2019 and 2021 using data from Twitter from the first month of the cryptocurrencies’ existence. We find that the future returns of the cryptocurrencies are dependent on the engagement coefficients. Cryptocurrencies whose engagement coefficients have extreme values have lower returns. Low engagement coefficients signal a lack of interest, while high engagement coefficients signal artificial activity which is likely from automated accounts known as bots. We measure the amount of bot posts for the cryptocurrencies and find that generally, cryptocurrencies with more bot posts have lower future returns. While future returns are dependent on both the bot activity and engagement coefficient, the dependence is strongest for the engagement coefficient, especially for short-term returns. We show that simple investment strategies which select cryptocurrencies with engagement coefficients exceeding a fixed threshold perform well for holding times of a few months. Public Library of Science 2023-05-11 /pmc/articles/PMC10174546/ /pubmed/37167281 http://dx.doi.org/10.1371/journal.pone.0284501 Text en © 2023 Qureshi, Zaman https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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 Qureshi, Khizar Zaman, Tauhid Social media engagement and cryptocurrency performance |
title | Social media engagement and cryptocurrency performance |
title_full | Social media engagement and cryptocurrency performance |
title_fullStr | Social media engagement and cryptocurrency performance |
title_full_unstemmed | Social media engagement and cryptocurrency performance |
title_short | Social media engagement and cryptocurrency performance |
title_sort | social media engagement and cryptocurrency performance |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10174546/ https://www.ncbi.nlm.nih.gov/pubmed/37167281 http://dx.doi.org/10.1371/journal.pone.0284501 |
work_keys_str_mv | AT qureshikhizar socialmediaengagementandcryptocurrencyperformance AT zamantauhid socialmediaengagementandcryptocurrencyperformance |