Author
Listed:
- Zuo Xiaorui
(Fudan University, Shanghai, China IDA Institute Digital Assets, Bucharest University of Economic Studies, Bucharest, Romania)
- Chen Yao-Tsung
(Department of Information Management and Finance, College of Management, National Yang Ming Chiao Tung University, Hsinchu, Taiwan)
- Härdle Wolfgang Karl
(BRC Blockchain Research Center, Humboldt Universität zu Berlin, Berlin, Germany Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; Dept Information Management and Finance, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; IDA Institute Digital Assets, Bucharest University of Economic Studies, Bucharest, Romania)
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 such as 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 analyzes into financial strategies, offering a nuanced perspective on the interaction between digital communication and market dynamics in an academic context.
Suggested Citation
Zuo Xiaorui & Chen Yao-Tsung & Härdle Wolfgang Karl, 2024.
"Emoji driven crypto assets market reactions,"
Management & Marketing, Sciendo, vol. 19(2), pages 158-178.
Handle:
RePEc:vrs:manmar:v:19:y:2024:i:2:p:158-178:n:1001
DOI: 10.2478/mmcks-2024-0008
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