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结合交易、情绪、搜索热度与哈希率预测比特币波动率

文章 arXiv papers · 作者: Zeyd Boukhers et al.
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总结

本研究考察价格历史以外的信息能否帮助预测比特币兑美元的波动率。提出的 CoMForE 系统结合历史交易数据、相关推文的情绪、衡量公众关注度的搜索量指标，以及区块链哈希率数据。系统采用 AdaBoost–LSTM 集成模型，也旨在预测加密货币价值分布的变化，并将投资决策作为潜在用途。

文档报告的实验显示，结合这些数据源的表现优于仅依赖交易数据，并声称较现有预测方法提升 19.29%。文档没有说明评估期、基准详情、预测期限，也没有说明所报告的提升是否在不同市场状态下通过样本外测试得出。这些信息缺失，使人难以判断结果的适用范围，也难以判断模型在考虑数据可用性和交易成本后是否仍有用。

核心观点

  • 该模型结合市场历史数据、推文情绪、搜索热度和区块链哈希率。
  • CoMForE 使用 AdaBoost–LSTM 集成模型预测加密货币波动率。
  • 该系统还旨在预测加密货币价值分布的波动。
  • 报告的实验显示，多模态输入优于单独使用交易数据。
  • 简要描述未提供足以判断稳健性和实际交易价值的评估详情。

标签

加密资产波动率机器学习市场情绪链上数据
全文
# Beyond Trading Data: The Hidden Influence of Public Awareness and Interest on Cryptocurrency Volatility


# Beyond Trading Data: The Hidden Influence of Public Awareness and Interest on Cryptocurrency Volatility









Since Bitcoin first appeared on the scene in 2009, cryptocurrencies have become a worldwide phenomenon as important decentralized financial assets. Their decentralized nature, however, leads to notable volatility against traditional fiat currencies, making the task of accurately forecasting the crypto-fiat exchange rate complex. This study examines the various independent factors that affect the volatility of the Bitcoin-Dollar exchange rate. To this end, we propose CoMForE, a multimodal AdaBoost-LSTM ensemble model, which not only utilizes historical trading data but also incorporates public sentiments from related tweets, public interest demonstrated by search volumes, and blockchain hash-rate data. Our developed model goes a step further by predicting fluctuations in the overall cryptocurrency value distribution, thus increasing its value for investment decision-making. We have subjected this method to extensive testing via comprehensive experiments, thereby validating the importance of multimodal combination over exclusive reliance on trading data. Further experiments show that our method significantly surpasses existing forecasting tools and methodologies, demonstrating a 19.29% improvement. This result underscores the influence of external independent factors on cryptocurrency volatility.

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。