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<p>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&amp;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.</p>

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Wisdom of the crowd signals: Predictive power of social media trading signals for cryptocurrencies

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月11日

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  • Frederic Haase,
  • Tom Celig,
  • Oliver Rath,
  • Detlef Schoder

摘要

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.