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

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.

Automated summary

This study extends previous research on the Efficient-Market Hypothesis in the Bitcoin market by analyzing 28,739,514 qualified tweets related to Bitcoin over a five-year period. Unlike previous studies, fundamental keywords were used as an informative proxy instead of sentiment analysis, information volume, or price data. Various machine learning methods and textual analyses were employed to examine market efficiency in different time periods. The findings suggest that a significant portion of market movements can be attributed to public information within organic tweets.