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加密货币推特信号、新闻报道与价格动态

文章 arXiv papers · 作者: Meysam Alizadeh et al.
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总结

本文研究加密货币影响者和新闻媒体推文中表达的交易信号是否与市场价格相关。大型语言模型识别推文中的买入和非买入信号，再将其与九种主要加密货币的价格进行比较。研究使用向量自回归、格兰杰因果检验和交叉相关分析，考察汇总信号与价格变动之间的时序和关系。

结果因加密货币和时段而异；论文还报告了影响者与新闻媒体在资产报道方面的差异。对于推文中提及最多的三种加密货币，过去24小时的汇总信号按格兰杰因果关系可预测价格波动，滞后至少六小时。这是基于该研究方法得到的时间预测关联证据，并不能证明推文导致价格变动，也不能证明这些信号能够带来盈利交易。摘要未说明交易成本、执行方式或策略回测。

核心观点

  • 大型语言模型将加密货币影响者和新闻媒体的推文分类为买入或非买入信号。
  • 研究使用VAR、格兰杰因果关系和交叉相关分析，将识别出的信号与九种主要加密货币的价格联系起来。
  • 结果因资产和时段而异。
  • 对于提及最频繁的三种加密货币，过去24小时的信号按格兰杰因果关系可预测价格波动，滞后至少六小时。
  • 分析报告了影响者与新闻媒体报道方面的差异，但未证明交易能够盈利。

标签

加密资产市场情绪机器学习统计学
全文
# Exploring Relationships Between Cryptocurrency News Outlets and Influencers' Twitter Activity and Market Prices


# Exploring Relationships Between Cryptocurrency News Outlets and Influencers' Twitter Activity and Market Prices









Academics increasingly acknowledge the predictive power of social media for a wide variety of events and, more specifically, for financial markets. Anecdotal and empirical findings show that cryptocurrencies are among the financial assets that have been affected by news and influencers' activities on Twitter. However, the extent to which Twitter crypto influencer's posts about trading signals and their effect on market prices is mostly unexplored. In this paper, we use LLMs to uncover buy and not-buy signals from influencers and news outlets' Twitter posts and use a VAR analysis with Granger Causality tests and cross-correlation analysis to understand how these trading signals are temporally correlated with the top nine major cryptocurrencies' prices. Overall, the results show a mixed pattern across cryptocurrencies and temporal periods. However, we found that for the top three cryptocurrencies with the highest presence within news and influencer posts, their aggregated LLM-detected trading signal over the preceding 24 hours granger-causes fluctuations in their market prices, exhibiting a lag of at least 6 hours. In addition, the results reveal fundamental differences in how influencers and news outlets cover cryptocurrencies.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。