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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0284501 |
| _version_ | 1827940502842900480 |
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| author | Khizar Qureshi Tauhid Zaman |
| author_facet | Khizar Qureshi Tauhid Zaman |
| author_sort | Khizar Qureshi |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-13T09:10:14Z |
| format | Article |
| id | doaj.art-baee3751dcf44b83aa9d9d114cd510a5 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-13T09:10:14Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-baee3751dcf44b83aa9d9d114cd510a52023-05-27T05:31:26ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01185e028450110.1371/journal.pone.0284501Social media engagement and cryptocurrency performance.Khizar QureshiTauhid ZamanCryptocurrencies 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.https://doi.org/10.1371/journal.pone.0284501 |
| spellingShingle | Khizar Qureshi Tauhid Zaman Social media engagement and cryptocurrency performance. PLoS ONE |
| 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 |
| url | https://doi.org/10.1371/journal.pone.0284501 |
| work_keys_str_mv | AT khizarqureshi socialmediaengagementandcryptocurrencyperformance AT tauhidzaman socialmediaengagementandcryptocurrencyperformance |