Do bitcoin news information flow and return volatility fit the sequential information arrival hypothesis and the mixture of distribution hypothesis?

Abstract

The financial asset return volatility and information field have continued to compare both hypotheses: sequential information arrival hypothesis (SIAH) and the mixture of distribution hypothesis (MDH). However, numerous former studies have not found an appropriate information indicator but just used trading volume as an indirect proxy. The study examines the relationship between Bitcoin return volatility and information flow instead of the trading volume. We apply a text and web mining to get all related 24,316 news items for Bitcoin from 64 news websites. Next, we apply a sentiment analysis of natural language processing (NLP) to generate information flow data to replace the traditional trading volume. Finally, we appropriate vector autoregressive (VAR) models to catch the lead-lag relationship and Spearman Correlation to test contemporaneous nexus. The study results show that Bitcoin return volatility is affected by the negative information flow and parallels SIAH; the positive information flow impacts Bitcoin return volatility and matches MDH. The empirical result benefits investors in making proper investment decisions in Bitcoin, and the gist of the paper fills the gap in academic literature because the aspect of information is still absent in academia.