利用社交媒体话题与霍克斯模型研究加密货币收益
文章 arXiv papers · 作者: Ross C. Phillips et al.
总结
本研究考察加密货币社交媒体讨论的时间和主题是否与之后的价格行为相关。研究对社交媒体社区中的交流应用动态主题建模，再使用霍克斯模型分析话题出现与加密货币价格之间的相互作用。该方法旨在识别可能先于特定收益模式出现的话题。
报告的关联包括：有关风险和投资而非交易的讨论先于价格下跌出现，有关大幅价格波动的讨论先于波动率上升出现，以及技术社区讨论加密货币的内在价值先于价格上涨出现。这些发现提示相关话题可能用于交易或预警系统，但文中没有报告样本构建方法、资产范围、预测准确率或样本外测试。所述关系属于相关性，不应仅凭这些结果解读为因果效应，也不能视为扣除执行成本后可盈利信号的证据。
核心观点
- 研究使用动态主题建模追踪加密货币社交媒体中各主题出现的时间。
- 霍克斯模型用于估计话题活动与加密货币价格事件之间的相互作用。
- 据报告，某些话题先于价格下跌、波动率上升或价格上涨出现。
- 观察到的关系可能用于监测或信号系统，但文中未量化预测表现。
- 文中没有证明因果关系或交易盈利能力。
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全文
# Mutual-Excitation of Cryptocurrency Market Returns and Social Media Topics # Mutual-Excitation of Cryptocurrency Market Returns and Social Media Topics Cryptocurrencies have recently experienced a new wave of price volatility and interest; activity within social media communities relating to cryptocurrencies has increased significantly. There is currently limited documented knowledge of factors which could indicate future price movements. This paper aims to decipher relationships between cryptocurrency price changes and topic discussion on social media to provide, among other things, an understanding of which topics are indicative of future price movements. To achieve this a well-known dynamic topic modelling approach is applied to social media communication to retrieve information about the temporal occurrence of various topics. A Hawkes model is then applied to find interactions between topics and cryptocurrency prices. The results show particular topics tend to precede certain types of price movements, for example the discussion of 'risk and investment vs trading' being indicative of price falls, the discussion of 'substantial price movements' being indicative of volatility, and the discussion of 'fundamental cryptocurrency value' by technical communities being indicative of price rises. The knowledge of topic relationships gained here could be built into a real-time system, providing trading or alerting signals.
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。