Emoji Driven Crypto Assets Market Reactions

ArXiv ID: 2402.10481 “View on arXiv”

Authors: Unknown

Abstract

In the burgeoning realm of cryptocurrency, social media platforms like Twitter have become pivotal in influencing market trends and investor sentiments. In our study, we leverage GPT-4 and a fine-tuned transformer-based BERT model for a multimodal sentiment analysis, focusing on the impact of emoji sentiment on cryptocurrency markets. By translating emojis into quantifiable sentiment data, we correlate these insights with key market indicators like BTC Price and the VCRIX index. Our architecture’s analysis of emoji sentiment demonstrated a distinct advantage over FinBERT’s pure text sentiment analysis in such predicting power. This approach may be fed into the development of trading strategies aimed at utilizing social media elements to identify and forecast market trends. Crucially, our findings suggest that strategies based on emoji sentiment can facilitate the avoidance of significant market downturns and contribute to the stabilization of returns. This research underscores the practical benefits of integrating advanced AI-driven analyses into financial strategies, offering a nuanced perspective on the interplay between digital communication and market dynamics in an academic context.

Keywords: Sentiment Analysis, Cryptocurrency, Multimodal Analysis, GPT-4, Transformer Models, Cryptocurrencies

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep learning architectures (GPT-4 and fine-tuned transformer models) and statistical correlation with market indices, indicating moderate-to-high mathematical sophistication. It also demonstrates strong empirical rigor with a concrete dataset of social media text, specific market indicators (BTC Price, VCRIX), and a defined backtest-ready trading strategy based on sentiment signals.

  flowchart TD
    A["Research Goal: Analyze Emoji Impact on Crypto Markets"] --> B["Data Inputs: Twitter Data"]
    B --> C["Multimodal Processing: GPT-4 & Fine-tuned BERT"]
    C --> D["Emoji Quantification & Correlation"]
    D --> E["Compare vs. FinBERT Text Analysis"]
    E --> F["Key Findings: Emoji sentiment outperforms text"]
    F --> G["Outcomes: Predict Market Trends & Avoid Downturns"]
    style A fill:#e1f5fe
    style G fill:#e8f5e8