Emoji Driven Crypto Assets Market Reactions
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913340856467456 |
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| author | Zuo, Xiaorui Chen, Yao-Tsung Härdle, Wolfgang Karl |
| author_facet | Zuo, Xiaorui Chen, Yao-Tsung Härdle, Wolfgang Karl |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10481 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Emoji Driven Crypto Assets Market Reactions Zuo, Xiaorui Chen, Yao-Tsung Härdle, Wolfgang Karl Computational Finance Artificial Intelligence Computation and Language Machine Learning Statistical Finance 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. |
| title | Emoji Driven Crypto Assets Market Reactions |
| topic | Computational Finance Artificial Intelligence Computation and Language Machine Learning Statistical Finance |
| url | https://arxiv.org/abs/2402.10481 |