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Using Tweet Content to Predict Short-Term Cryptocurrency Returns

Article arXiv papers · Author: Vahidin Jeleskovic et al.
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Summary

This study examines whether informative tweets from cryptocurrency foundation channels are associated with price changes over the following 15 minutes. It compares returns and excess returns after publication, considering sentiment polarization as well as features of the messages, including wording and tweet volume. The findings indicate significant return increases concentrated in the first three minutes, while measured sentiment itself showed no discernible relationship with price movements.

The authors report no observed adverse price effects from the messages. A basic trading algorithm based on the analysis produced some benefit over the 15-minute window, but that benefit was not statistically significant. The document presents the algorithm as an initial framework, not evidence of a reliable trading edge; it does not specify transaction costs or other implementation details needed to assess practical profitability.

Key ideas

  • Returns increased significantly after informative tweets, especially during the first three minutes.
  • Measured sentiment had no discernible impact on cryptocurrency price movements.
  • Tweet wording and volume were identified as relevant features of message quality.
  • The study reports no adverse price effects following the messages it examined.
  • The basic algorithm's benefit over the 15-minute window was not statistically significant.

Tags

CryptoSentimentEvent-drivenStatistics
Full text
# Intraday Trading Algorithm for Predicting Cryptocurrency Price Movements Using Twitter Big Data Analysis


# Intraday Trading Algorithm for Predicting Cryptocurrency Price Movements Using Twitter Big Data Analysis









Cryptocurrencies have emerged as a novel financial asset garnering significant attention in recent years. A defining characteristic of these digital currencies is their pronounced short-term market volatility, primarily influenced by widespread sentiment polarization, particularly on social media platforms such as Twitter. Recent research has underscored the correlation between sentiment expressed in various networks and the price dynamics of cryptocurrencies. This study delves into the 15-minute impact of informative tweets disseminated through foundation channels on trader behavior, with a focus on potential outcomes related to sentiment polarization. The primary objective is to identify factors that can predict positive price movements and potentially be leveraged through a trading algorithm. To accomplish this objective, we conduct a conditional examination of return and excess return rates within the 15 minutes following tweet publication. The empirical findings reveal statistically significant increases in return rates, particularly within the initial three minutes following tweet publication. Notably, adverse effects resulting from the messages were not observed. Surprisingly, sentiments were found to have no discerni-ble impact on cryptocurrency price movements. Our analysis further identifies that inves-tors are primarily influenced by the quality of tweet content, as reflected in the choice of words and tweet volume. While the basic trading algorithm presented in this study does yield some benefits within the 15-minute timeframe, these benefits are not statistically significant. Nevertheless, it serves as a foundational framework for potential enhance-ments and further investigations.

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.