Cryptocurrency Day Trading and Framing Prediction in Microblog Discourse

Anna Paula Pawlicka Maule, Kristen Johnson


Abstract
With 56 million people actively trading and investing in cryptocurrency online and globally in 2020, there is an increasing need for automatic social media analysis tools to help understand trading discourse and behavior. In this work, we present a dual natural language modeling pipeline which leverages language and social network behaviors for the prediction of cryptocurrency day trading actions and their associated framing patterns. This pipeline first predicts if tweets can be used to guide day trading behavior, specifically if a cryptocurrency investor should buy, sell, or hold their cryptocurrencies in order to make a profit. Next, tweets are input to an unsupervised deep clustering approach to automatically detect trading framing patterns. Our contributions include the modeling pipeline for this novel task, a new Cryptocurrency Tweets Dataset compiled from influential accounts, and a Historical Price Dataset. Our experiments show that our approach achieves an 88.78% accuracy for day trading behavior prediction and reveals framing fluctuations prior to and during the COVID-19 pandemic that could be used to guide investment actions.
Anthology ID:
2021.econlp-1.11
Volume:
Proceedings of the Third Workshop on Economics and Natural Language Processing
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
ECONLP | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
82–92
Language:
URL:
https://aclanthology.org/2021.econlp-1.11
DOI:
10.18653/v1/2021.econlp-1.11
Bibkey:
Cite (ACL):
Anna Paula Pawlicka Maule and Kristen Johnson. 2021. Cryptocurrency Day Trading and Framing Prediction in Microblog Discourse. In Proceedings of the Third Workshop on Economics and Natural Language Processing, pages 82–92, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Cryptocurrency Day Trading and Framing Prediction in Microblog Discourse (Pawlicka Maule & Johnson, ECONLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.econlp-1.11.pdf
Software:
 2021.econlp-1.11.Software.zip