Abstract
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.
Introduction
Introduction of the Internet has completely changed the way real economy works. By enabling practically all Internet users to interact at once and to exchange and share information almost costfree, more efficient decisions on the markets are possible. Even though the interconnection between digital and real economies has hit several bumps such as the DotCom Bubble of the break of the millennium, the benefits are believed to have overcome the costs.
One of the fascinating phenomena of the Internet era is an emergence of digital currencies such as BitCoin, LiteCoin, NameCoin, PPCoin, Ripple and Ven to name the most popular ones. A digital currency can be defined as an alternative currency which is exclusively electronic and thus has no physical form. It is also not issued by any specific central bank or government of a specific country and it is thus practically detached from the real economy. Note that a digital and a virtual currency are not synonymous since the virtual currencies are trading currencies in virtual worlds (most frequently in the massive multiplayer online games – MMOGs – such as World of Warcraft or Second Life). Even though the digital currencies are almost isolated from the real economies, their prices (exchange rates) have experienced quite an erratic behavior in the recent months. Specifically, the BitCoin currency – the most popular of the digital currencies – started the year of 2013 at levels of $13 per a BitCoin and rocketed to $230 on 9 April 2013 potentially creating an absurd profit of almost 1700% in less than four months. Later the same year, the price soared even higher to $395 on 9 November 2013, which accounts for a profit of approximately 2900% since the beginning of 2013.
Such behavior cannot be explained by standard economic and financial theories – e.g. future cashflows model^{1}, purchasing power parity^{2,3} and uncovered interest rate parity^{4,5} – in a satisfactory manner. In general, currencies can be seen as standard economic goods which are priced by interaction of supply and demand on the market. These are driven by macroeconomic variables of an issuing country or institution (or entity in general) such as GDP, interest rates, inflation, unemployment and others. As there are no macroeconomic fundamentals for the digital currencies, the supply function is either fixed (if the currency amount is fixed) or it evolves according to some publicly known algorithm, which is the case of the BitCoin market. The demand side of the market is not driven by an expected macroeconomic development of the underlying economy (as there is none) but it is driven only by expected profits of holding the currency and selling it later (as there are no profits from simply holding the currency due to no interest rates of the digital currencies). The market is thus dominated by shortterm investors, trend chasers, noise traders and speculators. The fundamentalist segment of the market is completely missing due to the fact that there are no fundamentals allowing for setting of a “fair” price. The digital currency price is thus driven solely by the investors' faith in the perpetual growth. Investors' sentiment then becomes a crucial variable.
However, it is not a trivial task to find a good measure or proxy of investors' sentiment in this matter. Quite recently, search queries provided by Google Trends and Wikipedia have proved to be a useful source of information in financial applications ranging from the home bias and the traded volume explanations through the earnings announcements to the portfolio diversification and trading strategies^{6,7,8,9,10,11,12}. The frequency of searches of terms related to the digital currency can be a good measure of interest in the currency and it can have a good explanatory power.
Here, we study the relationship between prices of the BitCoin currency (for a detailed description of a functioning of the currency, refer to Ref. 13) and related searched terms on Google Trends and Wikipedia. We find a striking positive correlation between a price level of BitCoin and the searched terms as well as a dynamic relationship which is bidirectional. Moreover, we uncover an asymmetry between effects of search queries related to prices above and below a shortterm trend.
Results
Dataset
We analyze the dynamic properties of the BitCoin currency (as the most popular of the digital currencies) and the search queries on Google Trends and Wikipedia as proxies of investors' interest and attention. Time series for the BitCoin currency at the most liquid market (Mt. Gox) are available since 17.7.2010 with the highest reported frequency (a tick) of 1 minute. However, the market remained highly illiquid for approximately the first year of its existence. To separate the period into the illiquid and the liquid one, we investigate a number of ticks with a nonzero return during a specific day. Fig. 1 depicts the evolution of the BitCoin liquidity. As a benchmark, we also show a number of 1minute ticks associated with an 8hour trading day. Even though the BitCoin market is a 24/7 market, we use the 8hour trading day as a simple benchmark of a liquid market. We observe that the number of ticks gets closer to the threshold value approximately in the middle of 2011. Closer inspection uncovers that since the beginning of May 2011, the number of ticks has fluctuated around the 8hour benchmark. Therefore, we analyze the series starting on 1 May 2011 with an ending date of 30 June 2013. For Google Trends, we are working with weekly data and as such, we obtain 113 observations in total; while for Wikipedia, daily data are available so that we have 788 observations.
Evolution of both pairs – Google Trends (weekly) and Wikipedia (daily) with corresponding BitCoin prices – is illustrated in Fig. 2. Obviously, the daily series of Wikipedia entries provides a more detailed picture of the behavior of the Internet users' interest and attention together with a higher potential for a more precise statistical analysis. We observe that the prices of the digital currency are strongly correlated with the search queries of both engines. Specifically, the correlations reach the levels of 0.8786 (with t(111) = 19.3850[<0.01], pvalue is shown in the square brackets) and 0.8271 (with t(786) = 41.2587[<0.01]) for Google Trends and Wikipedia, respectively. The strength of these relationships is nicely illustrated in Fig. 3 where a strong linear correlation between logarithmic prices and logarithmic search frequencies is evident. The fact that such correlation is most apparent for the loglog specification is the first hint for an analysis of the logarithmic transforms rather than the original series. Moreover, the loglog specification also allows for an easy interpretation of the relationship as the elasticity. Such notion is more stressed in the next section where the stationarity and cointegration of the series are discussed.
Stationarity & cointegration
To cover various combinations of relationships, we initially study all standard transformations of the original series, i.e. the logarithmic transformation, the first differences and the first logarithmic differences. For each of the series, we test their stationarity using the KPSS^{14} and ADF^{15} tests. As both tests have opposite null and alternative hypotheses, they form an ideal pair for the stationarity vs. unitroot testing. In Tab. 1, all these results are summarized. For the BitCoin prices (both daily and weekly), we find both the original and the logarithmic series to be nonstationary and to contain the unitroot. Correspondingly, their first differences are stationary. The same results are found for the Wikipedia daily views but for the Google Trends queries, we find the unitroot only for the logarithmic transformation of the searched terms series. For this reason and also for more convenient interpretation, we opt for the logarithmic series.
Turning now to the analysis of the dynamic properties and interconnections between the series, we are firstly interested in a potential cointegration relationship. Cointegration methodology has proved very useful in various economic and financial studies ranging from economic development^{16,17} over monetary economics^{18,19}, international economics^{20,21,22} to energy economics^{23,24} as it enables to study a longterm relationship between series as well as their shortterm dependence via the errorcorrection models (see the Methods section for more details). To test for the cointegration relationships, we utilize two tests of Johansen^{25} – the trace and the likelihood tests. In Tab. 2, we show the results for both pairs and we find that the BitCoin series are not cointegrated with the Google Trends series but the connection to the Wikipedia series can be described as the cointegration. Therefore, for the first pair, we need to turn to the vector autoregression (VAR) methodology applied on the first logarithmic differences (see the Methods section for more details) and for the second pair, we stick to the standard cointegration and vector errorcorrection model (VECM) framework.
General results
Starting with the Google Trends results, we are firstly interested in the dynamic relationship between the search queries on Google – namely “BitCoin” (note that the search query frequency is not case sensitive so that the various versions of the word, such as “BitCoin”, “Bitcoin” and “bitcoin”, are included) – and price of the currency. Based on the Akaike, HannanQuinn and SchwarzBayesian information criteria, we use a single lag in the VAR approach, i.e. VAR(1) is applied on the first logarithmic differences. To control for potential autocorrelation and heteroskedasticity inefficiencies, we opt for heteroskedasticity and autocorrelation robust (HAC) standard errors. The results are summarized in Fig. 4. The charts show the response of a corresponding variable to a shock in the impulse variable. As we are working with logarithmic differences, we can interpret these shocks as a proportional reaction to a 1% shock. A 10% shock in the search queries yields a reaction of approximately 0.8% in the first and 1.2% in the second period, i.e. a total 2% reaction and the effect vanishes for the latter periods. However, the influence also works from the opposite side and it again lasts (remains statistically significant) for two periods. The reaction to a 10% shock in search queries is followed by a total reaction of 0.8% (0.55% and 0.25% for the periods, respectively) of the prices. Putting these two together, we find that the increased interest in the BitCoin currency measured by the searched terms increases its price. As the interest in the currency increases, the demand increases as well causing the prices to increase. However, as the price of BitCoin increases so does also the interest of not only investors but also a general public. Note that it is quite easy to invest into BitCoin as the currency does not need to be traded in large bundles. This evidently forms a potential for a bubble development.
Turning now to the results of the Wikipedia daily views, we are interested in the same relationship as in the previous case but now based on the vector errorcorrection model (VECM) with seven lags (VECM(7)) based on the information criteria. In Fig. 5, we present the response functions which are, however, different from the previous ones as these represent permanent shifts in the response variable compared to the immediate shifts in Fig. 4. In the first 7 days (a trading week), an increase in prices causes an increasing positive reaction of the daily views. After the first week, the effect stabilizes but the interest in BitCoin measured by the daily views does not return back to the initial level. The complete transmission is around 0.05, i.e. a 10% change in prices is connected to a 0.5% permanent shift in the Wikipedia views. From the opposite side, we do not observe any statistically significant effect coming from the daily views to prices. The difference between Wikipedia and Google Trends might be caused by the fact that of course the two engines are different and individuals using these two can have different motives and can be interested in different specifics. Nonetheless, we believe that both engines provide interesting insights into the functioning and relationship between the digital currency and a general interest in the currency. Apart from the standard effects, we are also interested whether the reaction of prices to the searched terms is symmetric, i.e. whether an increasing interest coming in hand with the increasing prices (possibly a bubble forming) has a same effect as an increasing interest connected to the decreasing prices (possibly a bubble burst).
Positive and negative feedback
A crucial disadvantage of measuring interest using the search queries on Google Trends or daily views on Wikipedia is the fact that it is hard to distinguish between interest due to the positive or negative events. Specifically for the BitCoin, there is a big difference between searching for the information during an increasing trend or after the bubble burst. To separate these effects, we introduce a dummy variable equal to one if the price of BitCoin is above its trend level (measured by a moving average of 4 for Google Trends and of 7 for Wikipedia due to different sampling frequency) and zero otherwise. This way, we try to distinguish between a positive feedback defined as a reaction to an increasing interest (measured by search queries) while the price is above its trend value and a negative feedback defined reversely.
For the Google Trends pair, the results are again illustrated in Fig. 4. Here, we can see that practically the whole reaction comes from the positive feedback as there is practically no statistically significant reaction to the negative movements of the prices in a sense of the search queries. Much more interesting results are found for the Wikipedia daily views. In Fig. 5, we find that the positive and negative feedback are practically symmetric around the zero reaction. That is – the reaction of prices to changes in the Wikipedia interest is similar for the prices being both above and below the trend but for the sign of the reaction. The complete transmission is around 0.05 and −0.05 for the positive and negative feedback, respectively. This is a crucial result because without the separation between the positive and negative feedback, we do not find any reaction of the BitCoin prices to the Wikipedia views. However, if the effect is separated, the reaction is statistically significant and of an expected sign. If the prices are going up and the public interest in the matter is growing, the prices will likely continue soaring up. But if the prices decline, the increased interest pushes them even lower.
Discussion
Digital currencies are new economic instruments with special attributes. Probably the most important one of them is the fact that they have no underlying asset, they are not issued by any government or central bank and they bring no interest or dividends. Despite these facts, these currencies and namely the BitCoin currency, have attracted the public attention due to the unprecedented price surges with possible profits of hundreds percent in just several weeks or months. In this paper, we analyzed the dynamic relationship between the BitCoin price and the interest in the currency measured by search queries on Google Trends and frequency of visits on the Wikipedia page on BitCoin. Apart from a very strong correlation between price level of the digital currency and both the Internet engines, we also find a strong causal relationships between the prices and searched terms. Importantly, we find that this relationship is bidirectional, i.e. not only do the search queries influence the prices but also the prices influence the search queries. This is well in hand with the expectations about a financial asset with no underlying fundamentals. Speculation and trend chasing evidently dominate the BitCoin price dynamics.
Specifically, we find that while the prices are high (above trend), the increasing interest pushes the prices further atop. From the opposite side, if the prices are below their trend, the growing interest pushes the prices even deeper. This forms an environment suitable for a quite frequent emergence of a bubble behavior which indeed has been observed for the BitCoin currency. We believe that the paper will serve as a starting point of the research line dealing with statistical properties, dynamics and bubbleburst behavior of the digital currencies as these provide a unique environment for studying a purely speculative financial market.
Methods
Data
Time series have been obtained from http://www.google.com/trends for Google Trends, http://stats.grok.se for Wikipedia and http://www.bitcoincharts.com for BitCoin. Note that the Google Trends series are normalized (so that the maximum value of the series is equal to 100) and rounded whereas the Wikipedia series provide the actual number of visits for the given day. For the BitCoin prices, we focus on the exchange rate with the USD at Mt. Gox platform as this provides the most liquid market. For the fact that Google Trends series are available only at the weekly frequency, we had to reconstruct the weekly series (with a same definition of the week) for the BitCoin prices. The weekly BitCoin prices are taken as an average of the daily closing prices of the specific weeks. The analyzed period ranges between 1.5.2011 and 30.6.2013 due to illiquidity of the market in the period before (see Fig. 1 and the main text).
For the purposes of distinguishing between the positive and negative feedbacks for BitCoin prices, we create a pair of series – and – defined as and where Q_{t} is the search frequency at time t and is an indicator function equal to 1 if the condition in • is met and 0 otherwise and N is a number of periods taken into consideration for the moving average. For the Google Trends series, we use N = 4, i.e. 4 weeks (a trending month) and for the Wikipedia series, we utilize N = 7, i.e. 7 days (a trading week), due to the different frequency sampling. These two variables serve as a proxy for the searchterm activity connected with the positive () and the negative () feedback.
Stationarity tests
For testing stationarity, we utilize the Augmented DickeyFuller test (ADF)^{15} and the KPSS test^{14}. ADF has a null hypothesis of a unit root (d = 1) against the alternative of no unit root (d < 1) whereas KPPS has a null of stationarity (d = 0) against an alternative of a unit root (d = 1). Using the pair of tests, we are able to identify whether the tested series is stationary or not.
If both analyzed series contain a unit root, we can test them for the cointegration. If both series are stationary, we can utilize the vector autoregression (VAR) framework.
Cointegration
We say that two series {x_{t}} and {y_{t}} are cointegrated CI(d, b) if they are both integrated of the same order d and there exists a linear combination of the two series which is integrated of order d − b. The standard cointegration is based on CI(1, 1) relationship, i.e. series {x_{t}} and {y_{t}} contain a unit root (they are both I(1)) and there exists u_{t} = y_{t} − α − βx_{t} which is I(0), i.e. stationary with short memory^{26,27}.
If the series are cointegrated, the longterm equilibrium relationship is characterized by
As long as the series are cointegrated, the parameters can be superconsistently estimated using the simple OLS estimator^{28}. The lagged residual series is called the errorcorrection term and is interpreted as a deviation from the longterm equilibrium.
To test for the cointegration relationship, we use two Johansen tests^{25} – the trace test and the maximum likelihood test. If the series are found to be cointegrated CI(1, 1), the errorcorrection model (ECM) or the vector errorcorrection model (VECM) is standardly applied. If the analyzed series are not cointegrated, we need to proceed with the vector autoregression applied on the first differences of the originally used series.
Vector autoregression
Vector autoregression is a standard procedure for analyzing (ideally causal) relationship between multiple series^{29,30}. In a case of the pair of series {x_{t}} and {y_{t}}, the vector autoregression of order p (VAR(p)) is written as
with possibly correlated disturbances {ε_{1t}} and {ε_{2t}} and lag p selected according to some measure, usually an information criterion, such as the Akaike Information Criterion (AIC), HannanQuinn Information Criterion (HQIC) and Schwarz Information Criterion (SIC). Assuming that series {x_{t}} and {y_{t}} are I(1), their first differences {Δx_{t}} and {Δy_{t}} are I(0) and thus stationary so that the system can be easily estimated using either the ordinary least squares or maximum likelihood procedures. Parameters β_{1}, β_{2}, γ_{1} and γ_{2} are themselves not as important as the statistical inference based on them, for our purposes mainly the ImpulseResponse analysis. ImpulseResponse analysis is based on a vector moving average representation of VAR and it shows what is the reaction of one variable to a unit shock in some other variable and how the effect vanishes in time. For details, see Refs. 29,30,31,32.
Vector errorcorrection model
Vector errorcorrection model (VECM) is a generalization of the vector autoregression which incorporates the longterm corrections so that both shortterm and longterm dynamics can be studied. For cointegrated CI(1, 1) series, we have (VECM(q)) with q lags written as
where parameters θ_{i} and κ_{i} control for the shortterm dynamics and λ_{i} represent the errorcorrections to the longterm cointegration relationship from Eq. 1. VECM(q) framework allows for a similar ImpulseResponse analysis as the VAR framework. The main difference lays in the fact that the ImpulseResponse in the VAR framework illustrates immediate responses whereas in the VECM framework, the permanent shifts in the studied variables are examined^{26,27,32}.
Additional information
Data retrieval: Search volume data were retrieved by accessing the Google Trends website (http://www.google.com/trends) on 5 July 2013 and the Wikipedia article traffic statistics site (http://stats.grok.se) on 21 August 2013. BitCoin series were obtained from http://www.bitcoincharts.com between 5.–8.7.2013.
References
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. AMath. Phys. Eng. Sci. 368, 5707–5719 (2010).
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).
Preis, T., Moat, H. S. & Stanley, H. E. Quantifying trading behavior in financial markets using Google Trends. Sci. Rep. 3, 1684 (2013).
Moat, H. S. et al. Quantifying wikipedia usage patterns before stock market moves. Sci. Rep. 3, 1801 (2013).
Kristoufek, L. Can Google Trends search queries contribute to risk diversification? Sci. Rep. 3, 2713 (2013).
Nakamoto, S. Bitcoin: A peertopeer 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).
BahmaniOskooee, M. Export growth and economic growth: an appliation of cointegration and errorcorrection 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 errorcorrection 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 multicountry study based on cointegration and errorcorrection 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. LikelihoodBased 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. TimeSeriesBased Econometrics: Unit Roots and CoIntegration (Oxford University Press, Oxford, NY, 1996).
Engle, R. F. & Granger, C. W. J. Cointegration 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).
Acknowledgements
The support from the Grant Agency of the Czech Republic (GACR) under projects P402/11/0948 and 402/09/0965 is gratefully acknowledged.
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L.K. solely wrote the main manuscript text, prepared the figures and reviewed the manuscript.
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Kristoufek, L. BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era. Sci Rep 3, 3415 (2013). https://doi.org/10.1038/srep03415
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DOI: https://doi.org/10.1038/srep03415
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