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

ArXiv ID: 2401.00603 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Sentiment analysis, Social media, High-frequency trading, Return prediction, Twitter, Cryptocurrencies

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Philosophers
• Why: The paper relies on basic statistical tests (e.g., significance of returns) rather than advanced mathematics, and while it uses real Twitter and price data, the trading algorithm is described as ‘basic’ and its benefits are not statistically significant, indicating low empirical robustness.

  flowchart TD
    A["Research Goal: Predict Cryptocurrency Price<br>Movements via Twitter Data"] --> B["Input: Foundation Channel Tweets<br>15-min Window Analysis"]
    B --> C["Methodology: NLP & Statistical Analysis<br>Return vs Excess Return Rates"]
    C --> D{"Computation & Modeling"}
    D --> E["Trading Algorithm Testing"]
    E --> F["Key Findings"]
    
    F --> G["Significant Returns<br>within 3 mins"]
    F --> H["Tweet Quality & Volume<br>drive price, not Sentiment"]
    F --> I["Algorithm yields benefit<br>but not statistically significant"]