BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era

By Ladislav Kristoufek. Scientific Reports, 2013

826 citations recorded by the source corpus.

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.

Subfields: Information Systems, Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f544743ba489bce65e1a4df0d80d48754f81bcd1

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Citations in the corpus

On the inefficiency of Bitcoin (2017, background): “Kristoufek (2013) studied the relationship between digital currencies, such as Bitcoin, and search queries through Google Trends and Wikipedia.”
How Does Social Media Impact Bitcoin Value? A Test of the Silent Majority Hypothesis (2018, background): “In addition, Kristoufek [33, 34] has shown that bitcoin ’ s price correlates with conventional online behavioral metrics such as Google search.”
Does Sentiment Impact Cryptocurrency? (2021, methodology): “Kyriazis (2019) demonstrated strong interlinkages between Bitcoin, Ethereum, Litecoin, and Ripple.”
An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information (2018, background): “Various studies on statistical or economical properties and characterizations of Bitcoin prices refer to its capabilities as a financial asset; these research focus on statistical properties [4], [5], inefficiency of Bitcoin according to efficient market hypothesis [6], [7], hedging capability [8], ”
Bitcoin as a Complement to Emerging Market Currencies (2016, background): “Similarly, Kristoufek (2013) showed that Bitcoin internet searches are related to the price of Bitcoin; thus, news and information on the internet seems to influence the price of Bitcoin.”
Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies (2016, methodology): “Yet, as with past studies, using data on the Web [52, 53], analyzing social network data [46], and referring to search volumes on Google [10, 12] are conducive to more precise results.”
Statistical properties and multifractality of Bitcoin (2017, methodology): “Bitcoin price properties have also received attention, leading to analysis of various related issues, such as time series analysis using economic models, such as GARCH-type models[4, 5]; statistical properties of Bitcoin prices[6, 7]; inefficiency of Bitcoin[8, 9]; hedging capabilities of Bitcoin[10”
The economics of BitCoin price formation (2014, methodology): “2012; Bouoiyour and Selmi 2015); (ii) BitCoin attractiveness for investors (Kristoufek 2013; Bouoiyour and Selmi 2015); and (iii) global macro-financial development (van Wijk 2013).”
Spillovers between Bitcoin and other assets during bear and bull markets (2018, background): “These empirical findings contrast with those that claim that the Bitcoin market is isolated completely from the rest of the financial system (Ji et al. 2018) or is unaffected by economic and financial factors (Baek and Elbeck 2015; Kristoufek 2013; Ciaian, Rajcaniova, and Kancs 2016).”