Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/323695 
Year of Publication: 
2025
Citation: 
[Journal:] Electronic Markets [ISSN:] 1422-8890 [Volume:] 35 [Issue:] 1 [Article No.:] 64 [Publisher:] Springer Berlin Heidelberg [Place:] Berlin/Heidelberg [Year:] 2025
Publisher: 
Springer Berlin Heidelberg, Berlin/Heidelberg
Abstract: 
Abstract The emergence of cryptocurrencies and decentralized finance (DeFi) applications brings unique challenges, including high volatility, limited fundamental valuation methods, and significant informational reliance on social media. Consequently, traditional trading algorithms and decision support systems (DSS) often fall short in effectively capturing these dynamics, underscoring the need for tailored solutions. Recent research on sentiment analysis in cryptocurrency trading has provided mixed evidence regarding its predictive power, highlighting limitations in generalizability and reliability due to the inherent noise of social media content. Addressing these limitations, this study explores crowd-based trading signals, explicit buy and sell recommendations shared by users on social media platforms including X (formerly Twitter), Reddit, Stocktwits, and Telegram. We apply an event study methodology to analyze over 28,000 trading signals extracted using natural language processing (NLP) techniques based on large language models (LLMs). Our findings demonstrate that these explicit crowd-based signals significantly predict short-term cryptocurrency price movements, particularly for assets with lower market capitalization and recent negative returns. An out-of-sample trading strategy using these signals achieves superior risk-adjusted returns, outperforming both a standard cryptocurrency index (CCI30) and the S&P 500. Additionally, we uncover the role of automated accounts (signal bots) actively disseminating trading recommendations. This research advances literature by introducing a precise alternative to sentiment analysis, contributing to the understanding of social media as a distributed financial information environment, and raising theoretical considerations about algorithmic agency and trust. Practical implications span investors, social media platforms, and regulators.
Subjects: 
Social media signals
Cryptocurrencies
Collective intelligence
Trading signals
Predictive power
Wisdom of crowds
Persistent Identifier of the first edition: 
Additional Information: 
G10;D8;D7;G14;G41
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version
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