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Financial Twitter Sentiment on Bitcoin Return and High-Frequency Volatility

Author

Listed:
  • Xiang Gao

    (Shanghai Business School, Shanghai, China)

  • Weige Huang

    (Zhongnan University of Economics and Law, Wuhan, China)

  • Hua Wang

    (Shenzhen Technology University, Shenzhen, China)

Abstract

This paper studies how sentiment affect Bitcoin pricing by examining, at an hourly frequency, the linkage between sentiment of finance-related Twitter messages and return as well as the volatility of Bitcoin as a financial asset. On the one hand, there was calculated the return from minute-level Bitcoin exchange quotes and use of both rolling variance and high-minus-low price to proxy for Bitcoin volatility per each trading hour. On the other hand, the mood signals from tweets were extracted based on a list of positive, negative, and uncertain words according to the Loughran-McDonald finance-specific dictionary. These signals were translated by categorizing each tweet into one of three sentiments, namely, bullish, bearish, and null. Then the total number of tweets were adopted in each category over one hour and their differences as potential Bitcoin price predictors. The empirical results indicate that after controlling a list of lagged returns and volatilities, stronger bullish sentiment significantly foreshadows higher Bitcoin return and volatility over the time range of 24 hours. While bearish and neutral financial Twitter sentiments have no such consistent performance, the difference between bullish and bearish ratings can improve prediction consistency. Overall, this research results add to the growing Bitcoin literature by demonstrating that the Bitcoin pricing mechanism can be partially revealed by the momentum on sentiment in social media networks, justifying a sentimental appetite for cryptocurrency investment.

Suggested Citation

  • Xiang Gao & Weige Huang & Hua Wang, 2021. "Financial Twitter Sentiment on Bitcoin Return and High-Frequency Volatility," Virtual Economics, The London Academy of Science and Business, vol. 4(1), pages 7-18, January.
    • Handle: RePEc:aid:journl:v:4:y:2021:i:1:p:7-18
    DOI: 10.34021/ve.2021.04.01(1)
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    References listed on IDEAS

    as
    1. Sun, Xiaolei & Liu, Mingxi & Sima, Zeqian, 2020. "A novel cryptocurrency price trend forecasting model based on LightGBM," Finance Research Letters, Elsevier, vol. 32(C).
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