Financial Twitter Sentiment on Bitcoin Return and High-Frequency Volatility


Keywords: Bitcoin, cryptocurrency, sentiment, Twitter, social media, volatility

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.

Downloads

Download data is not yet available.

References

Affuso, E., & Lahtinen, K. (2018). Social Media Sentiment and Market Behavior. Empirical Economics, 57, 105-127. https://doi.org/10.1007/s00181-018-1430-y

Audrino, J., Sigrist, F., & Ballinari, D. (2020). The Impact of Sentiment and Attention Measures on Stock Market Volatility. International Journal of Forecasting, 36, 334-357. https://doi.org/10.1016/j.ijforecast.2019.05.010

Balcilar, M., Bouri, E., Gupta, R., & Roubaud, D. (2017). Can Volume Predict Bitcoin Returns and Volatility? A quantiles-based approach. Economic Modelling, 64, 74-81. https://doi.org/10.1016/j.econmod.2017.03.019

Barber, B.M., Odean, T., & Zhu, N. (2008). Do Retail Trades Move Markets? The Review of Financial Studies, 22(1), 151-186. https://doi.org/10.1093/rfs/hhn035

Bollen, J., Mao, H., & Zheng, X. (2011). Twitter Mood Predicts the Stock Market. Journal of Computational Science, 2, 1-8. https://doi.org/10.1016/j.jocs.2010.12.007

Bouri, E., Molnar, P., Azzi, G., Roubaud, D., & Hagfors, L.I. (2017). On the Hedge and Safe Haven Properties of Bitcoin: Is it Really More Than a Diversifier? Finance Research Letters, 20, 192-198. https://doi.org/10.1016/j.frl.2016.09.025

Broadstock, D., & Zhang, D. (2019). Social-Media and Intraday Stock Returns: The Pricing Power of Sentiment. Finance Research Letters, 30, 116-123. https://doi.org/10.1016/j.frl.2019.03.030

Bukovina, J. (2016). Social Media Big Data and Capital Markets – An Overview. Journal of Behavioral and Experimental Finance, 11, 18-26. https://doi.org/10.1016/j.jbef.2016.06.002

Carrick, J. (2016). Bitcoin as a Complement to Emerging Market Currencies. Emerging Markets Finance and Trade, 52(10), 2321-2334. https://doi.org/10.1080/1540496X.2016.1193002

Catania, L., & Sandholdt, M. (2019). Bitcoin at High Frequency. Journal of Risk and Financial Management, 12(1), 36. https://doi.org/10.3390/jrfm12010036

Chen, H., De, P., Hu, Y.J., & Hwang, B.-H. (2014). Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media. The Review of Financial Studies, 27(5), 1367-1403. https://doi.org/10.1093/rfs/hhu001

Dastgir, S., Demir, E., Downing, G., Gozgor, G., & Lau, C.K.M. (2019). The Causal Relationship between Bitcoin Attention and Bitcoin Returns: Evidence from the Copula-based Granger causality test. Finance Research Letters, 28, 160-164. https://doi.org/10.1016/j.frl.2018.04.019

De Long, J.B., Shleifer, A., Summers, L.H., & Waldmann, R.J. (1990). Noise Trader Risk in Financial Markets. Journal of Political Economy, 98(4), 703-738. https://doi.org/10.1086/261703

Dyhrberg, A.H. (2016). Bitcoin, Gold and the Dollar – A GARCH Volatility Analysis. Finance Research Letters, 16, 85-92. https://doi.org/10.1016/j.frl.2015.10.008

Gandal, N., Hamrick, J.T., Moore, T., & Oberman, T. (2018). Price Manipulation in the Bitcoin Ecosystem. Journal of Monetary Economics, 95, 86-96. https://doi.org/10.1016/j.jmoneco.2017.12.004

Groß-Klußmann, A., König, S., & Ebnera, M. (2019). Buzzwords Build Momentum: Global Financial Twitter Sentiment and the Aggregate Stock Market. Expert Systems with Applications, 136(1), 171-186. https://doi.org/10.1016/j.eswa.2019.06.027

Gu, C., & Kurov, A. (2020) Informational Role of Social Media: Evidence from Twitter Sentiment. Journal of Banking and Finance, 121, 105969. https://doi.org/10.1016/j.jbankfin.2020.105969

Gulacsy, D. (2019). Twitter Investor Sentiment Analysis Dataset, Version 2. Retrieved on April 17, 2020 from https://www.kaggle.com/dominikgulacsy/twitter-investor-sentiment-analysis-dataset.

Hakim das Neves, R. (2020). Bitcoin Pricing: Impact of Attractiveness Variables. Financial Innovation, 6, 21. https://doi.org/10.1186/s40854-020-00176-3

Kristoufek, L. (2015). What are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis. PLoS ONE, 10(4), e0123923. https://doi.org/10.1371/journal.pone.0123923

Loughran, T., & McDonald, B. (2011). When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks. The Journal of Finance, 66(1), 35-65. https://doi.org/10.1111/j.1540-6261.2010.01625.x

Nasir, M.A., Huynh, T.L.D., Nguyen, S.P., & Duong, D. (2019). Forecasting Cryptocurrency Returns and Volume Using Search Engines. Financial Innovation, 5, 2. https://doi.org/10.1186/s40854-018-0119-8

Nuno, O., Cortez, P., & Areal, N. (2016). Stock Market Sentiment Lexicon Acquisition Using Microblogging Data and Statistical Measures. Decision Support Systems, 85, 62-73. https://doi.org/10.1016/j.dss.2016.02.013

Panagiotidis, T., Stengos, T., & Vravosinos, O. (2018). On the Determinants of Bitcoin Returns: A LASSO approach. Finance Research Letters, 27, 235-240. https://doi.org/10.1016/j.frl.2018.03.016

Philippas, D., Rjiba H., Guesmi, K., & Goutte, S. (2019). Media attention and Bitcoin prices. Finance Research Letters, 30, 37-43. https://doi.org/10.1016/j.frl.2019.03.031

Sprenger, T.O., Tumasjan, A., Sandner, P.G., & Welpe, I.M. (2014). Tweets and trades: The Information Content of Stock Microblogs. European Financial Management, 20(5), 926-957. https://doi.org/10.1111/j.1468-036X.2013.12007.x

Sun, X., Liu, M., & Sima, Z. (2020). A Novel Cryptocurrency Price Trend Forecasting Model Based on LightGBM. Finance Research Letters, 32, 101084. https://doi.org/10.1016/j.frl.2018.12.032

Abstract views: 684
PDF Downloads: 422
Published
2021-01-31
How to Cite
Gao, X., Huang, W., & Wang, H. (2021). Financial Twitter Sentiment on Bitcoin Return and High-Frequency Volatility. Virtual Economics, 4(1), 7-18. https://doi.org/10.34021/ve.2021.04.01(1)
Section
Articles