Digital currencies have emerged as a new fascinating phenomenon in the financial markets. Recent events on the most popular of the digital currencies--BitCoin--have risen crucial questions about behavior of its exchange rates and they offer a field to study dynamics of the market which consists practically only of speculative traders with no fundamentalists as there is no fundamental value to the currency. In the paper, we connect two phenomena of the latest years--digital currencies, namely BitCoin, and search queries on Google Trends and Wikipedia--and study their relationship. We show that not only are the search queries and the prices connected but there also exists a pronounced asymmetry between the effect of an increased interest in the currency while being above or below its trend value.

] Institute of Economic Studies Faculty of Social Sciences Charles University Prague Opletalova 26 110 00 Prague Czech Republic EU [2] Institute of Information Theory and Automation Academy of Sciences of the Czech Republic Pod Vodarenskou Vezi 4 182 08 Prague Czech Republic EU

Zobrazit více v PubMed

Gordon M. J. The Investment, Financing, and Valuation of the Corporation (Irwin, R. D. & Homewood, I. L. 1962).

Krugman P. & Obsfeld M. International Economics (Pearson Education, Inc., Boston, MA, 2009).

Reinert K. A. & Rajan R. S. (eds.)The Princeton Encyclopedia of the World Economy I (Princeton University Press, Princeton, NJ, 2009).

Levi M. D. International Finance (Routledge, Abingon, 2005).

Feenstra R. C. & Taylor A. M. International Macroeconomics (Worth Publishers, London, 2008).

Mondaria J., Wu T. & Zhang Y. The determinants of international investment and attention allocation: Using internet search query data. J. Int. Econ. 82, 85–95 (2010).

Preis T., Reith D. & Stanley H. E. Complex dynamics of our economic life on different scales: insights from search engine query data. Philos. Trans. R. Soc. A-Math. Phys. Eng. Sci. 368, 5707–5719 (2010). PubMed

Drake M. S., Roulstone D. T. & Thornock J. R. Investor information demand: Evidence from google searches around earnings announcements. J. Account. Res. 50(4), 1001–1040 (2012).

Preis T., Moat H. S., Stanley H. E. & Bishop S. R. Quantifying the advantage of looking forward. Sci. Rep. 2, 350 (2012). PubMed PMC

Preis T., Moat H. S. & Stanley H. E. Quantifying trading behavior in financial markets using Google Trends. Sci. Rep. 3, 1684 (2013). PubMed PMC

Moat H. S. et al. Quantifying wikipedia usage patterns before stock market moves. Sci. Rep. 3, 1801 (2013). PubMed

Kristoufek L. Can Google Trends search queries contribute to risk diversification? Sci. Rep. 3, 2713 (2013). PubMed PMC

Nakamoto S. Bitcoin: A peer-to-peer electronic cash system. http://bitcoin.org/bitcoin.pdf, visited on 11 November 2013.

Kwiatkowski D., Phillips P., Schmidt P. & Shin Y. Testing the null of stationarity against alternative of a unit root: How sure are we that the economic time series have a unit root? J. Econom. 54, 159–178 (1992).

Dickey D. & Fuller W. Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74, 427–431 (1979).

Bahmani-Oskooee M. Export growth and economic growth: an appliation of cointegration and error-correction modeling. J. Dev. Areas 27, 535–542 (1993).

Islam M. Export expansion and economic growth: testing for cointegration and causality. Appl. Econ. 30, 415–425 (1998).

Johansen S. & Juselius K. Maximum likelihood estimation and inference on cointegration – with applications to the demand for money. Oxf. Bull. Econ. Stat. 52, 169–210 (1990).

Miller S. Monetary dynamics: An application of cointegration and error-correction modeling. J. Money Credit Bank. 23, 139–154 (1991).

Hakkio C. & Rush M. Market efficiency and cointegration: an application to the sterling and deutschemark exchange markets. J. Int. Money Finan. 8, 75–88 (1989).

Pedroni P. Purchasing power parity tests in cointegrated panels. Rev. Econ. Stat. 83, 727–731 (2001).

Narayan P. The saving and investment nexus for China: Evidence from cointegration tests. Appl. Econ. 37, 1979–1990 (2005).

Masih A. & Masih R. Energy consumption, real income and temporal causality: results from a multi-country study based on cointegration and error-correction techniques. Energy Econ. 18, 165–183 (1996).

Lee C.-C. Energy consumption and GDP in developing countries: A cointegrated panel analysis. Energy Econ. 27, 415–427 (2005).

Johansen S. Likelihood-Based Inference in Cointegrated Vector Autoregressive Models (Oxford University Press, Oxford, NY, 1995).

Engle R. F. & McFadden D. L. (eds.) Handbook of Econometrics, Vol. IV (Elsevier, Amsterdam, 1994).

Hatanaka M. Time-Series-Based Econometrics: Unit Roots and Co-Integration (Oxford University Press, Oxford, NY, 1996).

Engle R. F. & Granger C. W. J. Co-integration and error correction: Representation, estimation, and testing. Econometrica 55, 251–276 (1987).

Sims C. A. Macroeconomics and reality. Econometrica 48, 1–48 (1980).

Lütkepohl H. Applied Time Series Econometrics (Springer, Berlin, 2005).

Hamilton J. D. Time Series Analysis (Princeton University Press, Princeton, NJ, 1994).

Enders W. Applied Econometric Time Series (John Wiley & Sons, Hoboken, NJ, 2003).

Bitcoin and S&P500: Co-movements of high-order moments in the time-frequency domain

Estimating suicide occurrence statistics using Google Trends

Nowcasting unemployment rates with Google searches: evidence from the Visegrad Group countries

What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis