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结合深度NLP与市场数据的加密货币预测

文章 arXiv papers · 作者: Vincent Gurgul et al.
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总结

该研究考察新闻和社交媒体文本与金融及区块链数据结合后，能否改善比特币和以太坊价格预测。研究比较了预训练和微调语言模型与基于词典的情绪分析方法，并使用BART MNLI零样本分类识别看涨或看跌内容。预测目标包括局部价格极值和日度走势；选择这些目标旨在降低交易频率和投资组合波动。

作者报告称，加入文本特征可以提高预测准确度，而且在所考察的验证情境中，深度学习NLP方法也提升了盈利能力和夏普比率。本文未提供详细样本量、评估指标、基准结果或市场时期信息，因此仅凭此描述无法评估这些发现的可信度和普适性。本文介绍的是一种实证预测方法，并未证明这些信号在实盘交易中或计入交易成本后仍能盈利。

核心观点

  • 该研究将金融和区块链特征与新闻及社交媒体文本结合，用于预测比特币和以太坊价格。
  • 研究比较了深度学习NLP模型与基于词典的情绪分析。
  • 研究使用BART MNLI零样本分类，将文本归类为看涨或看跌。
  • 将局部极值作为预测目标，旨在降低交易频率和投资组合波动。
  • 作者报告称预测和交易指标有所改善，但描述未提供验证细节和成本分析。

标签

加密资产机器学习市场情绪链上数据回测
全文
# Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data


# Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data









We introduce novel approaches to cryptocurrency price forecasting, leveraging Machine Learning (ML) and Natural Language Processing (NLP) techniques, with a focus on Bitcoin and Ethereum. By analysing news and social media content, primarily from Twitter and Reddit, we assess the impact of public sentiment on cryptocurrency markets. A distinctive feature of our methodology is the application of the BART MNLI zero-shot classification model to detect bullish and bearish trends, significantly advancing beyond traditional sentiment analysis. Additionally, we systematically compare a range of pre-trained and fine-tuned deep learning NLP models against conventional dictionary-based sentiment analysis methods. Another key contribution of our work is the adoption of local extrema alongside daily price movements as predictive targets, reducing trading frequency and portfolio volatility. Our findings demonstrate that integrating textual data into cryptocurrency price forecasting not only improves forecasting accuracy but also consistently enhances the profitability and Sharpe ratio across various validation scenarios, particularly when applying deep learning NLP techniques. The entire codebase of our experiments is made available via an online repository: https://anonymous.4open.science/r/crypto-forecasting-public

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。