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利用推文内容预测加密货币短期收益

文章 arXiv papers · 作者: Vahidin Jeleskovic et al.
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总结

本研究考察加密货币基金会频道发布的资讯性推文是否与随后 15 分钟内的价格变化相关。研究比较推文发布后的收益和超额收益，并考察情绪极化程度以及消息特征，包括措辞和推文数量。结果显示，显著的收益增长集中在最初三分钟，而测得的情绪本身与价格走势没有明显关系。

作者称，未观察到这些消息造成不利价格影响。基于分析构建的基础交易算法在 15 分钟窗口内带来了一些收益，但该收益在统计上并不显著。文中将该算法作为初步框架，而非可靠交易优势的证据；文中没有说明交易成本或评估实际盈利能力所需的其他实施细节。

核心观点

  • 资讯性推文发布后，收益显著上升，尤其是在最初三分钟内。
  • 测得的情绪对加密货币价格走势没有明显影响。
  • 推文措辞和数量被确定为反映消息质量的相关特征。
  • 研究报告称，所考察的消息发布后未出现不利价格影响。
  • 基础算法在 15 分钟窗口内带来的收益在统计上并不显著。

标签

加密资产市场情绪事件驱动统计学
全文
# Intraday Trading Algorithm for Predicting Cryptocurrency Price Movements Using Twitter Big Data Analysis


# Intraday Trading Algorithm for Predicting Cryptocurrency Price Movements Using Twitter Big Data Analysis









Cryptocurrencies have emerged as a novel financial asset garnering significant attention in recent years. A defining characteristic of these digital currencies is their pronounced short-term market volatility, primarily influenced by widespread sentiment polarization, particularly on social media platforms such as Twitter. Recent research has underscored the correlation between sentiment expressed in various networks and the price dynamics of cryptocurrencies. This study delves into the 15-minute impact of informative tweets disseminated through foundation channels on trader behavior, with a focus on potential outcomes related to sentiment polarization. The primary objective is to identify factors that can predict positive price movements and potentially be leveraged through a trading algorithm. To accomplish this objective, we conduct a conditional examination of return and excess return rates within the 15 minutes following tweet publication. The empirical findings reveal statistically significant increases in return rates, particularly within the initial three minutes following tweet publication. Notably, adverse effects resulting from the messages were not observed. Surprisingly, sentiments were found to have no discerni-ble impact on cryptocurrency price movements. Our analysis further identifies that inves-tors are primarily influenced by the quality of tweet content, as reflected in the choice of words and tweet volume. While the basic trading algorithm presented in this study does yield some benefits within the 15-minute timeframe, these benefits are not statistically significant. Nevertheless, it serves as a foundational framework for potential enhance-ments and further investigations.

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。