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THE INFLUENCE OF THE TONALITY OF NEWS ON THE EXCHANGE RATE OF BITCOIN

https://doi.org/10.26794/2587-5671-2018-22-4-104-113

Abstract

The authors assess the impact of the emotional tonality of bitcoin news on its exchange rate. In particular, we studied the hypothesis of the impact of the readability index of the news text on the volatility of bitcoin. Despite the fact that excessive volatility threatens bitcoin not to become a successful currency, many scientists are interested in the determinants of such volatility. Factors such as speculative investments or the attention of the society are the drivers of the volatility of the exchange rate of bitcoin. In this regard, the question of studying the impact of news on the bitcoin exchange rate is relevant. The purpose of this paper is to assess the impact of the emotional tonality of bitcoin news on its exchange rate. The empirical base of the study was quite extensive since it includes more than 1330 news from the Thomson Reuters information base for the period from 19.08.2011 to 16.08.2016 on the bitcoin market. The research methodology includes the sentiment analysis conducted by using the dictionary MacDonald and Loughran and also the analysis of the interdependence of time series-based causal analysis using the test of Granger causation. We present three hypotheses about the impact of news on the bitcoin exchange rate. During the study, two of them were confirmed. We proved the first hypothesis that the negative news had a more significant impact than positive ones, taking into account the five time-lags. The second hypothesis about the impact of positive tonality in the news on the bitcoin exchange rate, using the Granger test for causation, was not confirmed, since the positive values of this test were obtained in two time-lags out of five. We can confirm that the third hypothesis was proved — the high readability index has an impact on the bitcoin volatility for the entire studied period, taking into account all five time-lags. Thus, the assumption about the impact of the emotional tonality of news on the bitcoin exchange rate can be confirmed.

About the Authors

E. A. Fedorova
Financial University
Russian Federation

Dr. Sci. (Econ.), Professor, Department of Corporate Finance and Corporate Governance



K. Z. Bechvaya
Financial University
Russian Federation

student of the Department of Corporate Finance and Corporate Governance



O. Yu. Rogov
State Research Institute of Aviation Systems
Russian Federation

Junior research fellow, State Research Institute of Aviation Systems, Document processing centre



References

1. Nakamoto S. Bitcoin. A peer-to-peer electronic cash system, 2009. URL: https://bitcoin.org/bitcoin.pdf (accessed 28.04.2018).

2. Georgoula I., Pournarakis D., Bilanakos C., Sotiropoulos D.N., Giaglis G.M. Using time-series and sentiment analysis to detect the determinants of Bitcoin prices. SSRN Electronic Journal. 2015; Oct. DOI: 10.2139/ssrn.2607167

3. Leitch D., Sherif M. Twitter mood, CEO succession announcements and stock returns. Journal of Computational Science. 2017;21:1–10. DOI: 10.1016/j.jocs.2017.04.002

4. Velde F. R. Bitcoin: A primer. Chicago Fed Letter. 2013;(317):1–4. URL: file:///C:/Users/User/Downloads/cfldecember2013–317-pdf.pdf (accessed 23.07.2018).

5. Yermack D. Is bitcoin a real currency? An economic appraisal. NBER Working Paper. 2014;(19747). URL: http://post.nyssa.org/files/is-bitcoin-a-real-currency.pdf (accessed 23.07.2018).

6. Kaplansky G., Levy H. Sentiment and stock prices: The case of aviation disasters. Journal of Financial Economics. 2010;95(2):174–201. DOI: 10.1016/j.jfineco.2009.10.002

7. Ciaian P., Rajcaniova M., Kancs d’A. The digital agenda of virtual currencies. Can BitCoin become a global currency? Information Systems and e-Business Management. 2016;14(4): 883–919. DOI: 10.1007/s10257–016–0304–0

8. Badev A., Chen M. Bitcoin: Technical background and data analysis. FEDS Working Paper. 2014;(104). DOI: 10.2139/ssrn.2544331

9. Pieters G., Vivanco S. Financial regulations and price inconsistencies across Bitcoin markets. Information Economics and Policy. 2017;39(C):1–14. DOI: 10.1016/j.infoecopol.2017.02.002

10. Mai F., Shan Z., Bai Q., Wang X., Chiang R.H.L. How does social media impact bitcoin value? A test of the silent majority hypothesis. Journal of Management Information Systems. 2018;35(1):19–52. DOI: 10.1080/07421222.201 8.1440774

11. Tirunillai S., Tellis G.J. Does chatter really matter? Dynamics of user-generated content and stock performance. Marketing Science. 2012;31(2):198–215. DOI: 10.1287/mksc.1110.0682

12. Luther W.J., Salter A.W. Bitcoin and the bailout. The Quarterly Review of Economics and Finance. 2017;66:50–56. DOI: 10.1016/j.qref.2017.01.009

13. Ojha P.K., Ismail A., Kuppusamy K.S. Perusal of readability with focus on web content understandability. Journal of King Saud University — Computer and Information Sciences. 2018; in press. DOI: 10.1016/j.jksuci.2018.03.007 URL: https://www.sciencedirect.com/science/article/pii/S 1319157817303622 (accessed 23.07.2018).

14. Liu B. Sentiment analysis and opinion mining. San Rafael, CA: Morgan & Claypool Publishers; 2012:1–15. (Synthesis Lectures on Human Language Technologies Series. Book 16). URL: https://www.cs.uic.edu/~liub/FBS/ SentimentAnalysis-and-OpinionMining.pdf (accessed 23.07.2018).

15. Heston S.L., Sinha N.R. News vs. sentiment: Predicting stock returns from news stories. Financial Analysts Journal. 2017;73(3):67–83. DOI: 10.2469/faj.v73.n3.3

16. Jones K.S. A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation. 1972;28(1):11–21. DOI: 10.1108/eb026526

17. Loughran T., McDonald B. When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. The Journal of Finance. 2011;66(1):35–65. DOI: 10.1111/j.1540–6261.2010.01625.x

18. Bodnaruk A., Loughran T., McDonald B. Using 10-K text to gauge financial constraints. Journal of Financial and Quantitative Analysi. 2015;50(4):623–646. DOI: 10.1017/S 0022109015000411

19. Kearney C., Liu S. Textual sentiment in finance: A survey of methods and models. International Review of Financial Analysis. 2014;33:171–185. DOI: 10.1016/j.irfa.2014.02.006

20. Fedorova E.A., Demin I.S., Khrustova L.E., Osetrov R.A., Fedorov F. Yu. The influence of the tone of CEO’s letters on the company’s financial performance. Rossiiskii zhurnal menedzhmenta = Russian Management Journal. 2017;15(4):441–462. DOI: 10.21638/11701/spbu18.2017.403 (In Russ.).


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For citations:


Fedorova E.A., Bechvaya K.Z., Rogov O.Yu. THE INFLUENCE OF THE TONALITY OF NEWS ON THE EXCHANGE RATE OF BITCOIN. Finance: Theory and Practice. 2018;22(4):104-113. (In Russ.) https://doi.org/10.26794/2587-5671-2018-22-4-104-113

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