Pump It: Twitter Sentiment Analysis for Cryptocurrency Price Prediction

Vladyslav Koltun () and Ivan P. Yamshchikov ()
Additional contact information
Vladyslav Koltun: Instituto Superior de Economia e Gestao, University of Lisbon, 1200-781 Lisbon, Portugal
Ivan P. Yamshchikov: THWS, CAIRO, 97082 Würzburg, Germany

Risks, 2023, vol. 11, issue 9, 1-14

Abstract: This study demonstrates the significant impact of market sentiment, derived from social media, on the daily price prediction of cryptocurrencies in both bull and bear markets. Through the analysis of approximately 567 thousand tweets related to twelve specific cryptocurrencies, we incorporate the sentiment extracted from these tweets along with daily price data into our prediction models. We test various algorithms, including ordinary least squares regression, long short-term memory network and neural hierarchical interpolation for time series forecasting (NHITS). All models show better performance once the sentiment is incorporated into the training data. Beyond merely assessing prediction error, we scrutinise the model performances in a practical setting by applying them to a basic trading algorithm managing three distinct portfolios: established tokens, emerging tokens, and meme tokens. While NHITS emerged as the top-performing model in terms of prediction error, its ability to generate returns is not as compelling.

Keywords: cryptocurrency; sentiment analysis; twitter (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-9091/11/9/159/pdf (application/pdf)
https://www.mdpi.com/2227-9091/11/9/159/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:11:y:2023:i:9:p:159-:d:1232611

Access Statistics for this article

Risks is currently edited by Mr. Claude Zhang

More articles in Risks from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().