Semi-strong efficient market of Bitcoin and Twitter: An analysis of semantic vector spaces of extracted keywords and light gradient boosting machine models

【Author】 Wang, Fang; Gacesa, Marko

【Source】INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS

【影响因子】8.235

【Abstract】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.

【Keywords】Efficient-market hypothesis; Twitter; Bitcoin; LightGBM; GloVe semantic vector spaces

【发表时间】2023 JUL

【收录时间】2023-07-12

【文献类型】实证数据

【主题类别】

区块链治理-市场治理-数字货币