A comparison of bitcoin futures return and return volatility based on news sentiment contemporaneously or lead-lag

Yu-Sheng Kao, Min-Yuh Day and Ke-Hsin Chou

The North American Journal of Economics and Finance, 2024, vol. 72, issue C

Abstract: This study explores the relationship between sentiment analysis of news articles and the returns and return volatility of the Bitcoin futures index on the Chicago Mercantile Exchange (CME). Utilizing sentiment analysis through Natural Language Processing and collecting a dataset of 41,040 Bitcoin futures news articles from 1,408 public news websites, the study employs a threshold model with GJR-GARCH (1,1) to conduct empirical tests. The findings reveal that negative information flow significantly impacts the Bitcoin futures index returns in the short term, challenging the weak form of the Efficient Market Hypothesis (EMH). Additionally, positive information flow is found to affect return volatility on the contemporaneous trading day, aligning with the Mixture of Distribution Hypothesis (MDH), which suggests that market reactions to new information are efficiently integrated into prices. The analysis also shows that both positive and negative information flows exert significant effects on return volatility in lagged trading days, supporting the Sequential Information Arrival Hypothesis (SIAH). This indicates a nuanced market reaction over time to varying information flows. Moreover, the study highlights the significant role of the COVID-19 pandemic in increasing investor demand for the Bitcoin futures index, driven by a mix of hedging and speculative motives. These findings underscore the complex interplay between information flow and market behavior, elucidating the nuanced responses of the Bitcoin futures market to news information stimuli.

Keywords: Bitcoin futures; Sentiment analysis; Natural language processing; COVID-19; Decentralized finance (search for similar items in EconPapers)
JEL-codes: D53 G10 G14 G17 G40 G41 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:72:y:2024:i:c:s1062940824000846

DOI: 10.1016/j.najef.2024.102159

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