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Positive and Negative Searches Related to the Bitcoin Ecosystem: Relationship with Bitcoin Price

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Information Systems (EMCIS 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 402))

Abstract

In this study, we investigate whether public awareness of positive or negative possible incidents pertaining to the bitcoin ecosystem are related to bitcoin price and we model bitcoin price volatility taking into consideration public awareness. We take a middle-of-the-road approach, by using a simpler – and thus less data demanding - proxy for public awareness compared to studies that have used complex models that include many parameters to capture the relationships and factors in the ecosystem, but at the same time, a richer approach compared to approaches that simply use the volume of searches for “bitcoin” and its “price” as a proxy in their models. Specifically, we use six different Google Trends queries as proxies in our models: three searches for positive incidents, and three for negative ones. We employ a dataset with monthly price data that covers the time period from September 1st 2011 to December 31st 2019 and we use GARCH and EGARCH models to test whether public awareness of positive or negative possible incidents pertaining to the bitcoin ecosystem is related to bitcoin price and to model price volatility. Results show that majority of our proxies of public awareness are significantly related to price. Moreover, our EGARCH model has detected an asymmetry pertaining to the price volatility’s reaction to price news, specifically an “anti-leverage effect”, that is, the price volatility is more sensitive to good financial news rather to bad news. In addition, we detected a significant effect of both old and novel news.

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Notes

  1. 1.

    Indicatively, [12] examined 1054 news sources, created 22 categories and used cluster analysis to reduce them to six; [21] employ eleven variables in their elaborate sentiment analysis methodology.

  2. 2.

    Google, according to Nielsen, collects more than 60% of online queries, which makes it the leader in search engines. People feel quite comfortable with Google, so they express themselves without hesitation, even for taboo ideas. The Google Trends tool can provide data related to Google search queries and in this way, intrinsic or specific people's interests can be easily retrieved compared to other research methods where researchers may not have such capabilities to achieve similar or same results. Therefore, Google Trends can serve as a research tool due to the reliability and leadership of Google in this field. Google Trends was introduced in 2006 by Google. However, it does contain data from 2004 onwards. It is open access and free to all people [31].

References

  1. Auer, R., Claessens, S.: Regulating cryptocurrencies: assessing market reactions. BIS Quart. Rev. 51–65 (2018)

    Google Scholar 

  2. Balcilar, M., Bouri, E., Gupta, R., Roubaud, D.: Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Econ. Model. 64, 74–81 (2017)

    Article  Google Scholar 

  3. Baur, D.G., Dimpfl, T.: Excess volatility as an impediment for a digital currency (2018). SSRN 2949754

    Google Scholar 

  4. Bitcoin Mining Network, The Report. https://coinshares.com/research/bitcoin-mining-network-june-2019

  5. Bolici, F., Rosa, S.D.: Mt.Gox is dead, long live bitcoin! In: Torre, T., Braccini, A.M., Spinelli, R. (eds.) Empowering Organizations. LNISO, vol. 11, pp. 285–296. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-23784-8_22

    Chapter  Google Scholar 

  6. Bouoiyour, J., Selmi, R.: What does bitcoin look like? Ann. Econ. Financ. 16(2), 449–492 (2014)

    Google Scholar 

  7. Bouoiyour, J., Selmi, R.: The price of political uncertainty: evidence from the 2016 U.S. Presidential Election and the U.S. Stock Markets. arXiv:1612.06200v2 (2017)

  8. Bouoiyour, J., Selmi, R., Tiwari, A., Olayeni, O.: What drives bitcoin price? Econ. Bull. 36(2), 843–850 (2016)

    Google Scholar 

  9. Bouri, E., Molnár, P., Azzi, G., Roubaud, D., Hagfors, L.: On the hedge and safe haven properties of bitcoin: Is it really more than a diversifier? Financ. Res. Lett. 20, 192–198 (2017)

    Article  Google Scholar 

  10. Bouri, E., Molnár, P., Jalkh, N., Roubaud. D.: Bitcoin for energy commodities before and after December 2013 crash: diversifier, hedge or safe haven? Appl. Econ. 49(50), 5063–5073 (2017b)

    Google Scholar 

  11. Brar, H.K.: Bitcoin’s 99 Problems = No Future? Medium (2018). https://medium.com/@orbify.io/bitcoins-99-problems-6c44a360c1d0

  12. Cankaya, S., Alp, E., Findikci, M.: News sentiment and cryptocurrency volatility. In: Hacioglu, U. (ed.) Blockchain Economics and Financial Market Innovation: Financial Innovations in the Digital Age. Springer, Cham (2019)

    Google Scholar 

  13. Cermak, V.: Can bitcoin become a viable alternative to fiat currencies? An empirical analysis of bitcoin’s volatility based on a GARCH model (2017)

    Google Scholar 

  14. Chu, J., Chan, S., Nadarajah, S., Osterrieder, J.: GARCH modelling of cryptocurrencies. J. Risk Financ. Manage. 10(17), 1–15 (2017)

    Google Scholar 

  15. Ciaian, P., Rajcaniova, M., Kancs, D.: The economics of bitcoin price formation. Appl. Econ. 48(19), 1799–1815 (2016)

    Article  Google Scholar 

  16. Corbet, S., Katsiampa, P.: Asymmetric mean reversion of bitcoin price returns. Int. Rev. Financ. Anal. (2018, in Press)

    Google Scholar 

  17. Corbet, S., Larkin, C.J., Lucey, B.M., Meegan, A., Yarovaya, L.: The volatility generating effects of macroeconomic news on cryptocurrency returns. SSRN (2018)

    Google Scholar 

  18. Cusumano, M.A.: The bitcoin ecosystem. Commun. ACM 57(10), 22–24 (2014)

    Article  Google Scholar 

  19. Dyhrberg, A.H.: Bitcoin, gold and the dollar–A GARCH volatility analysis. Financ. Res. Lett. 16, 85–92 (2016)

    Article  Google Scholar 

  20. Garcia, D., Tessone, C., Mavrodiev, P., Perony, N.: The digital traces of bubbles: feedback cycles between socio-economics signals in the bitcoin economy. J. Roy. Soc. Interface 11(99), 1–28 (2014)

    Article  Google Scholar 

  21. Georgoula, I., Pournarakis, D., Bilanakos, C., Sotiropoulos, D., Giaglis, G.: Using time-series and sentiment analysis to detect the determinants of bitcoin prices. In: MCIS 2015 Proceedings, p. 20 (2015)

    Google Scholar 

  22. Hayes, A.: Cryptocurrency value formation: an empirical analysis leading to a cost of production model for valuing bitcoin. In: MCIS 2015 Proceedings, p. 4 (2015)

    Google Scholar 

  23. Hayes, A.: Cryptocurrency value formation: an empirical study leading to a cost of production model for valuing bitcoin. Telematics Inf. 34(7), 1308–1321 (2017)

    Article  Google Scholar 

  24. Johansson, S., Malin, N.T.: The price volatility of bitcoin. a search for the drivers affecting the price volatility of this digital currency. Master thesis. Umeå: Umeå School of Business and Economics (2014)

    Google Scholar 

  25. Kaminski, J., Gloor, P.A.: Nowcasting the bitcoin market with Twitter signals. ArXiv, abs/1406.7577 (2014)

    Google Scholar 

  26. Karalevicius, V., Degrande, N., De Weerdt, J.: Using sentiment analysis to predict interday bitcoin price movements. J. Risk Financ. 19(1), 56–75 (2018)

    Article  Google Scholar 

  27. Katsiampa, P.: Volatility estimation for bitcoin: a comparison of GARCH models. Econ. Lett. 158, 3–6 (2017)

    Article  MathSciNet  Google Scholar 

  28. Kjærland, F., Meland, M., Oust, A., Øyen, V.: How can bitcoin price fluctuations be explained? Int. J. Econ. Financ. Issues 8(3), 323–332 (2018)

    Google Scholar 

  29. Kristoufek, L.: Bitcoin meets Google trends and Wikipedia: quantifying the relationship between phenomena of the Internet era. Sci. Rep. 3(3415), 1–7 (2013)

    Google Scholar 

  30. Kristoufek, L.: What are the main drivers of the bitcoin price? Evidence from wavelet coherence analysis. PLoS ONE 10(4), e0123923 (2015)

    Article  Google Scholar 

  31. Liolios, E.: Google Trends as a predictive tool for the sales of the Apple. Master thesis. International Hellenic University (2015)

    Google Scholar 

  32. Ludvigson, S.C., Ng, S.: The empirical risk–return relation: a factor analysis approach. J. Financ. Econ. 83(1), 171–222 (2007)

    Article  Google Scholar 

  33. Mai, F., Bai, Q., Shan, Z., Wang, X., Chiang, R.: From bitcoin to Big Coin: the impact of social media on bitcoin performance. In: 36th International Conference on Information Systems, 13–16 December, Fort Worth (2015)

    Google Scholar 

  34. Majakivi, A.: Modeling the bitcoin ecosystem. Master of Science in Technology thesis. Aalto University, Espoo Finland (2019)

    Google Scholar 

  35. Matta, M., Lunesu, I., Marchesi, M.: The predictor impact of Web search media on bitcoin trading volumes. In: 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K), vol. 01, pp. 620–626 (2015)

    Google Scholar 

  36. Nelson, D.: Conditional heteroskedasticity in asset returns: a new approach. Econometrica 59(2), 347–370 (1991)

    Article  MathSciNet  Google Scholar 

  37. Newton, E.: International search engines you should know and optimizing for them, brightedge.com (2020)

    Google Scholar 

  38. Polasik, M., Piotrowska, A., Wisniewski, T., Kotkowski, R., Lightfoot, G.: Price fluctuations and the use of bitcoin: an empirical inquiry. Int. J. Electron. Commer. 20(1), 9–49 (2015)

    Article  Google Scholar 

  39. Preis, T., Moat, H.S., Stanley, H.: Quantifying trading behavior in financial markets using Google trends. Sci. Rep. 3, 1684 (2013)

    Article  Google Scholar 

  40. Pryzmont, P.: An empirical study of how bitcoin related incidents impact its price volatility. Thesis in Master of Business Administration. Dublin: National College of Ireland (2016)

    Google Scholar 

  41. Sapuric, S., Kokkinaki, A., Georgiou, I.: The relationship between bitcoin returns, volatility and volume: asymmetric GARCH modeling. J. Enterprise Inf. Manage. (2020)

    Google Scholar 

  42. Statista. https://www.statista.com/statistics/220534/googles-share-of-search-market-in-selected-countries/

  43. Wörner, D., Von Bomhard, T., Schreier, Y.P., Bilgeri, D.: The bitcoin ecosystem: disruption beyond financial services? (2016)

    Google Scholar 

  44. Wu, L.: Reverse return-volatility asymmetry, and short sale constraints: evidence from the Chinese markets. Presented at the EFMA Annual Meeting (2017)

    Google Scholar 

  45. Yermack, D.: Is Bitcoin a real currency? An economic appraisal. In: Handbook of Digital Currency, pp. 31–43. Academic Press (2015)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Business, University of Nicosia, Nicosia, Cyprus

    Ifigenia Georgiou & Svetlana Sapuric

  2. School of Business, University of Nicosia, Cyprus and Hellenic Open University, Patras, Greece

    Athanasia Georgiadi

Authors
  1. Ifigenia Georgiou
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  2. Athanasia Georgiadi
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  3. Svetlana Sapuric
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Correspondence to Ifigenia Georgiou .

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Editors and Affiliations

  1. Department of Digital Innovation, School of Business, University of Nicosia, Nicosia, Cyprus

    Marinos Themistocleous

  2. British University in Dubai, Dubai, United Arab Emirates

    Maria Papadaki

  3. School of Strategy and Leadership, Coventry University, Coventry, UK

    Muhammad Mustafa Kamal

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Georgiou, I., Georgiadi, A., Sapuric, S. (2020). Positive and Negative Searches Related to the Bitcoin Ecosystem: Relationship with Bitcoin Price. In: Themistocleous, M., Papadaki, M., Kamal, M.M. (eds) Information Systems. EMCIS 2020. Lecture Notes in Business Information Processing, vol 402. Springer, Cham. https://doi.org/10.1007/978-3-030-63396-7_10

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  • DOI: https://doi.org/10.1007/978-3-030-63396-7_10

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