淡江大學機構典藏:Item 987654321/125192
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    题名: Do bitcoin news information flow and return volatility fit the sequential information arrival hypothesis and the mixture of distribution hypothesis?
    作者: Chou, Ke-hsin;Chiu, Chien-liang
    关键词: Sentiment analysis;Natural language processing (NLP);Sequential information arrival hypothesis (SIAH);Mixture of distribution hypothesis (MDH)
    日期: 2023-07
    上传时间: 2024-03-08 12:05:36 (UTC+8)
    出版者: Elsevier
    摘要: 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.
    關聯: International Review of Economics & Finance 88,　p.365-385
    DOI: 10.1016/j.iref.2023.06.021
    显示于类别:[財務金融學系暨研究所] 期刊論文

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