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Semi-strong efficient market of Bitcoin and Twitter: An analysis of semantic vector spaces of extracted keywords and light gradient boosting machine models
International Review of Financial Analysis ( IF 8.235 ) Pub Date : 2023-05-27 , DOI: 10.1016/j.irfa.2023.102692
Fang Wang, Marko Gacesa

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.



中文翻译:

比特币和推特的半强有效市场:提取关键词的语义向量空间分析和光梯度提升机器模型

本研究通过分析包含目标主题“比特币”的 28,739,514 条符合条件的推文,在 2017 年 9 月 1 日至 2022 年 9 月 1 日的五年波动期内扩展了对比特币市场有效市场假说的检验。与之前的研究不同,我们提取了基本关键词作为信息代理,用于对比特币市场的 EMH 进行研究,而不是专注于情绪分析、信息量或价格数据。我们测试了每小时、4 小时和每天时间段的市场效率,以了解市场对不同阈值内信息的反应速度和准确性。使用了一系列机器学习方法和文本分析,包括信息语义向量空间距离的测量、关键词提取和编码模型,和光梯度提升机 (LGBM) 分类器。我们的结果表明,78.06% (83.08%)、84.63% (87.77%) 和 94.03% (94.60%) 的每小时、4 小时和每日看涨(看跌)市场走势可归因于有机推文中的公共信息。

更新日期:2023-05-27
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