Related Literature

Google search volume has been shown to be a useful predictor in various contexts. The first application is by Ginsberg et al.(2009) who predict influenza epidemics well ahead of the official registration. In economics, Choi and Varian (2012) predict vehicle sales or claims for unemployment benefits. Returns and/or volatility prediction using Google’s SVI has been conducted by Bank et al.(2011),Da et al. (2015), Dimpfl and Jank (2016), Afkhami, Cormack and Ghoddusi (2017), orPerlin et al.(2017) to name but a few. These authors assume that retail investors first satisfy their need for information by means of an internet search which subsequently leads to trading activity. Hence, Google search volume is used as a proxy for retail investor interest in the respective asset (cp. Chen et al. 2014).

While the early literature still made use of Google’s possibility to concatenate daily data, recent research limits itself to weekly or monthly applications.

In our empirical analysis, we use daily time series constructed according to the algorithm presented in the accompanying article (Bleher and Dimpfl 2019), reprinted in Chapter 1, to see whether return and/or volatility prediction is possible in the cryptocurrency market on

a daily basis. Only for the cryptocurrency Bitcoin (when denominated in US dollars) the link to Google search queries is seemingly well-established. We contribute to the literature by extending the analysis to multiple cryptocurrencies traded in Euro. We also improve the data basis of former research as we use accurately constructed time series of Google Trends.¹²

Most closely related to our work is Kristoufek (2013) who analyses the connection between Bitcoin prices, Google’s SVI for Bitcoin and the number of visits to the Wikipedia article of “Bitcoins” on a weekly basis. We overcome the limitation to weekly data imposed by the direct availability of Google’s SVI and also construct daily data using 24 hours trading of the cryptocurrencies instead of a hypothetical 8-hours return which is aggregated to weekly returns. Based on impulse response analysis, Kristoufek(2013) finds that increased interest in Bitcoin leads to higher prices, which again causes higher search volume. He concludes that this forms the potential for a bubble development which might have been observed in December 2017. Recently, Urquhart (2018) investigates the relationship between Bitcoin returns, traded volume, and Google search queries and finds that search queries do not serve as a predictor for volatility. However, he documents that trading activity and volatility draw attention to Bitcoin which manifests in higher search activity.

Similarly, Garcia and Schweitzer (2015) use Google’s SVI for the search-term Bitcoin among other variables (number of tweets or exchanged volume) to device a trading strategy.

Their results suggest that the SVI variable carries no information which is useful as a trading signal, while variables measuring the sentiment of social activity provide robust trading signals. This contradicts the findings of Kristoufek(2013). We therefore revisit the question whether Google search volume indices, if constructed correctly, help to predict returns or volatility of cryptocurrencies.

In principle, every pricing relevant factor qualifies as a potential predictor for returns. The literature on Bitcoin and cryptocurrencies has identified a large number of such factors.

Kristoufek (2013) states that Bitcoin is not comparable to standard currencies, and thus, has its own pricing relevant factors. In general, he classifies Bitcoin as a market without a

“fair” value, driven by the sentiment of investors which suggests that prediction based on variables that are able to capture such sentiment is fruitful. Similarly, Garcia et al. (2014) identify two feedback loops that lead public interest towards Bitcoin pacing from booms to busts. Both loops suggest that individual investors satisfy their information demand using Google or Wikipedia which then leads to trading activity in Bitcoin. Furthermore, they find that search activity responds quicker to negative events (such as hacked Bitcoin exchanges) than prices. Hence, one of these pricing factors may be public attention which can be proxied by Google Trends data.

12 Our results also hold when the entire analysis is conducted using cryptocurrencies traded in US dollars.

Using a LASSO approach, Panagiotidis et al. (2018b) find that gold and search intensity are the most important drivers of Bitcoin returns. However, the authors interpolate weekly data and model the relation to be contemporaneous which leaves the question whether Google Trends data are helpful in forecasting future Bitcoin returns out of sample. Zhang et al. (2018) also investigate contemporaneous cross-correlations between Google searches and Bitcoin based on daily data and document that cross-correlation between Google Trends and the Bitcoin prices is decreasing over time. Related to this finding is the work of Dastgir et al. (2019) who analyze the connection between Bitcoin returns and Google Trends based on a vector autoregressive model coupled with a specified volatility process.

They employ a copula-based test for contemporaneous correlation and find a bi-directional effect in the left and right tail of the distribution.

Still, there might be additional fundamental factors. Hayes(2016) ascribes the determinants of the Bitcoin price to the cost of production, i.e., essentially electricity cost, but leaves demand aside. Ciaian, Rajcaniova and Kancs (2016) (who explicitly omit Google search data since daily time series are not available) find that macroeconomic factors do not influence the Bitcoin price while investor attractiveness does which suggests that the price is mainly determined by the demand side. Kristoufek(2015) identifies technical drivers like money supply, price level and usage in trade which are correlated with the price dynamic of Bitcoin. However, he ultimately concludes that the major driver of the Bitcoin price is only the public interest in the cryptocoin.

Furthermore, Alabi (2017) attributes the value of Bitcoin to network effects. He shows that the price is described well by Metcalfe’s law (see e.g. Shapiro and Varian 1998) which conjectures that the value of a network is proportional to the squared number of people using it. However, whether the network effect is a dominant factor in pricing Bitcoin remains doubtful (see Poon and Dryja 2015).

The possibility to transact value without any middlemen and oversight by a bank or cen-tralized authority constitutes a pricing relevant feature of Bitcoin or other cryptocurrencies.

These features come at the cost that the high volatility of the currencies makes these transactions an unreliable and risky undertaking (cp. Baur and Dimpfl 2021,Baur, Hong and Lee 2018). A reliable forecast of volatility would allow to conduct transaction in low volatility phases and, thus, reduce transaction costs. It should be noted, however, that Bitcoin transactions are not fully anonymous (cp. Reid and Harrigan 2011).

Considering the above literature and the fact that the market is dominated by short-term investors, trend chasers and speculators (cp. Kristoufek 2013, Yelowitz and Wilson 2015), we would expect that the public interest measured with Google’s SVI for a particular cryptocurrency should drive the price. Hence, Google Trends data should, in particular on a high frequency, be a good predictor for Bitcoin returns and volatility. Taking into account the characteristics of the various coins, we expect that some cryptocoins are

driven by the general interest in the cryptocurrencies and others may have coin-specific features that generate interest on their own. Thus, we contribute to the literature by an investigation whether Google’s search volume indices (SVI) can be exploited systematically for the prediction of key characteristics, namely returns and volatility, of Bitcoin and other cryptocurrencies. This is accompanied by a concatenation algorithm which overcomes the limitations and shortcomings in previous studies imposed by the (un-)availability of Google Trends data on high frequencies.