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

Author

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

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.

Suggested Citation

  • Kao, Yu-Sheng & Day, Min-Yuh & Chou, Ke-Hsin, 2024. "A comparison of bitcoin futures return and return volatility based on news sentiment contemporaneously or lead-lag," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    • Handle: RePEc:eee:ecofin:v:72:y:2024:i:c:s1062940824000846
    DOI: 10.1016/j.najef.2024.102159
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    More about this item

    Keywords

    Bitcoin futures; Sentiment analysis; Natural language processing; COVID-19; Decentralized finance;
    All these keywords.

    JEL classification:

    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G40 - Financial Economics - - Behavioral Finance - - - General
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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