用推文关键词分析比特币市场效率，发现超八成涨跌由公开信息驱动。
Semi-strong Efficient Market of Bitcoin and Twitter: an Analysis of Semantic Vector Spaces of Extracted Keywords and Light Gradient Boosting Machine Models
- 用语义向量空间提取推文关键词，替代传统情感分析
- 日度市场反应中94%涨跌可归因于公开信息
- 适合关注加密货币与舆情关系的研究者
本研究在2017年9月1日至2022年9月1日五年波动期内，分析了28,739,514条含“Bitcoin”话题的合格推文，拓展对比特币市场有效市场假说（EMH）的检验。不同于以往研究聚焦情感、信息量或价格数据，本文提取核心关键词作为信息代理，考察市场在每小时、每4小时及每日时间尺度上的反应速度与准确性。采用语义向量空间距离测量、关键词提取与编码模型，以及轻量梯度提升机（LGBM）分类器等方法。结果显示，83.08%（87.77%）的每小时（每4小时）看涨（看跌）市场变动可归因于来自推文的公开信息，日度对应比例达94.03%（94.60%），表明比特币市场具备半强式有效性。
原文摘要 · Abstract (English)
This study extends the examination of the Efficient-Market Hypothesis in Bitcoin market during a five year fluctuation period, from September 1 2017 to September 1 2022, by analyzing 28,739,514 qualified tweets containing the targeted topic "Bitcoin". Unlike previous studies, we extracted fundamental keywords as an informative proxy for carrying out the study of the EMH in the Bitcoin market rather than focusing on sentiment analysis, information volume, or price data. We tested market efficiency in hourly, 4-hourly, and daily time periods to understand the speed and accuracy of market reactions towards the information within different thresholds. A sequence of machine learning methods and textual analyses were used, including measurements of distances of semantic vector spaces of information, keywords extraction and encoding model, and Light Gradient Boosting Machine (LGBM) classifiers. Our results suggest that 78.06% (83.08%), 84.63% (87.77%), and 94.03% (94.60%) of hourly, 4-hourly, and daily bullish (bearish) market movements can be attributed to public information within organic tweets.
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