Skip to main content

News Sentiment and Cryptocurrency Volatility

  • Chapter
  • First Online:
Blockchain Economics and Financial Market Innovation

Part of the book series: Contributions to Economics ((CE))

Abstract

The cryptocurrency market has shown remarkable growth in the last decade, resulting in heightened interest in research on several aspects of cryptocurrencies. The drastic price fluctuations have attracted attention from investors, but they have also raised concerns from national regulatory institutions. Several studies are conducted to understand the factors and the dynamics of its value formation. It is becoming more important to be able to value cryptocurrencies as an investor and as part of the process to legitimize them as a financial asset. This study aims to contribute to this field of research by examining the relationship between cryptocurrency’s volatile returns and the effects of different types of news on selected cryptocurrencies. This paper categorizes the news about cryptocurrencies and determines the effect of news from each category on the return structure of each cryptocurrency. By using 1054 news sources, 22 categories are created, and a clustering analysis is used to set these categories into six groups. These groups are modelized in proper ARCH family models, which are created for different cryptocurrencies to analyze the effect on volatility. The results show that different cryptocurrencies react differently to various news categories. News about regulations from national authorities exhibit a significant effect on all selected cryptocurrencies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Amanzholova, B. A., & Teslya, P. N. (2018, October). Threats and opportunities of cryptocurrency technologies. In 2018 XIV International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE) (pp. 335–339). IEEE.

    Google Scholar 

  • Auer, R., & Claessens, S. (2018, September). Regulating cryptocurrencies: Assessing market reactions. BIS Quarterly Review.

    Google Scholar 

  • Bildirici, M. E., Alp, E. A., Ersin, Ö. Ö., & Bozoklu, Ãœ. (2010). Ä°ktisatta kullanılan doÄŸrusal olmayan zaman serisi yöntemleri. Türkmen Kitabevi.

    Google Scholar 

  • Birz, G., & Lott Jr., J. R. (2011). The effect of macroeconomic news on stock returns: New evidence from newspaper coverage. Journal of Banking & Finance, 35(11), 2791–2800.

    Article  Google Scholar 

  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327.

    Article  Google Scholar 

  • Campbell, J. Y., & Hentschel, L. (1992). No news is good news: An asymmetric model of changing volatility in stock returns. Journal of Financial Economics, 31(3), 281–318.

    Article  Google Scholar 

  • Carrera, B. P. (2018). Effect of sentiment on bitcoin price formation (pp. 1–49). North Carolina: Duke University Durham.

    Google Scholar 

  • Chan, W. S. (2003). Stock price reaction to news and no-news: Drift and reversal after headlines. Journal of Financial Economics, 70(2), 223–260.

    Article  Google Scholar 

  • Chu, J., Chan, S., Nadarajah, S., & Osterrieder, J. (2017). GARCH modelling of cryptocurrencies. Journal of Risk and Financial Management, 10(4), 17.

    Article  Google Scholar 

  • Ciaian, P., Rajcaniova, M., & Kancs, D. A. (2016). The economics of BitCoin price formation. Applied Economics, 48(19), 1799–1815.

    Article  Google Scholar 

  • Colianni, S., Rosales, S., & Signorotti, M. (2016). Algorithmic trading of cryptocurrency based on Twitter sentiment analysis. SSRN Electronic Journal.

    Google Scholar 

  • Corbet, S., & Katsiampa, P. (2018). Asymmetric mean reversion of bitcoin price returns. International Review of Financial Analysis.https://doi.org/10.1016/j.irfa.2018.10.004

  • Corbet, S., Larkin, C. J., Lucey, B. M., Meegan, A., & Yarovaya, L. (2018). The volatility generating effects of macroeconomic news on cryptocurrency returns. (March 16, 2018). Retrieved from https://ssrn.com/abstract=3141986 or https://doi.org/10.2139/ssrn.3141986

  • Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar–a GARCH volatility analysis. Finance Research Letters, 16, 85–92.

    Article  Google Scholar 

  • Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 50, 987–1007.

    Article  Google Scholar 

  • Farell, R. (2015). An analysis of the cryptocurrency industry.

    Google Scholar 

  • Gidofalvi, G., & Elkan, C. (2001). Using news articles to predict stock price movements. San Diego: Department of Computer Science and Engineering, University of California.

    Google Scholar 

  • Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779–1801.

    Article  Google Scholar 

  • Hanlon, M., & Slemrod, J. (2009). What does tax aggressiveness signal? Evidence from stock price reactions to news about tax shelter involvement. Journal of Public Economics, 93(1–2), 126–141.

    Article  Google Scholar 

  • Hayes, A. S. (2017). Cryptocurrency value formation: An empirical study leading to a cost of production model for valuing bitcoin. Telematics and Informatics, 34(7), 1308–1321.

    Article  Google Scholar 

  • Hughes, S. J., & Middlebrook, S. T. (2015). Advancing a framework for regulating cryptocurrency payments intermediaries. Yale Journal on Regulation, 32, 495.

    Google Scholar 

  • Katsiampa, P. (2017). Volatility estimation for bitcoin: A comparison of GARCH models. Economics Letters, 158, 3–6.

    Article  Google Scholar 

  • Nelson, D. B. (1991). Conditional Heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347.

    Article  Google Scholar 

  • Vega, C. (2006). Stock price reaction to public and private information. Journal of Financial Economics, 82(1), 103–133.

    Article  Google Scholar 

  • Xie, P., Chen, H., & Hu, Y. J. (2017). Network structure and predictive power of social media for the bitcoin market (Georgia Tech Scheller College of Business Research Paper No. 17-5).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Serkan Cankaya .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Cankaya, S., Aykac Alp, E., Findikci, M. (2019). News Sentiment and Cryptocurrency Volatility. In: Hacioglu, U. (eds) Blockchain Economics and Financial Market Innovation. Contributions to Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-25275-5_7

Download citation

Publish with us

Policies and ethics