Do Google Trends forecast bitcoins? Stylized facts and statistical evidence

Argimiro Arratia1, Albert X. López-Barrantes2
1Department of Computer Sciences, Polytechnical University of Catalonia, Barcelona, Spain
2Department of Mathematics, Autonomous University of Barcelona, Barcelona, Spain

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Tài liệu tham khảo

Arias M, Arratia A, Xuriguera R (2013) Forecasting with Twitter data. ACM Trans Intelligent Syst Technol (TIST) 5(1):1–24

Brandt P (2016). MSBVAR: Markov-Switching, Bayesian, Vector Autoregression Models. R package version 0.9-3. https://CRAN.R-project.org/package=MSBVAR

Chan S, Chu J, Nadarajah S, Osterrieder J (2017) A statistical analysis of cryptocurrencies. J Risk Financial Manag 10(2):12

Chu J, Nadarajah S, Chan S (2015) Statistical analysis of the exchange rate of Bitcoin. PLoS ONE 10:e0133678

Ciulla F, Mocanu D, Baronchelli A, Gonçalves B, Perra N, Vespignani A (2012) Beating the news using social media: the case study of American Idol. EPJ Data Sci 1(1):8

CoinMarketCap https://coinmarketcap.com/

Constantine W, Percival D (2017). fractal: A Fractal Time Series Modeling and Analysis Package. R package version 2.0-4. https://CRAN.R-project.org/package=fractal

Cont R (2001) Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance 1(2):223–236

Fama E (1965) The behavior of stock market prices. J Business 38(1):34–105

Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L (2009) Detecting influenza epidemics using search engine query data. Nature 457(7232):1012

Granger CWJ (1969) Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica 37:424–438

Grcar M, Cherepnalkoski D, Mozetic I, Novak PK (2017) Stance and influence of Twitter users regarding the Brexit referendum. Comput Social Netw 4(1):6

Hencic A, Gourieroux C (2014) Noncausal autoregressive model in application to Bitcoin/USD exchange rate. Econometrics of Risk. Springer, Berlin, pp 17–40

Hyndman RJ, Khandakar Y (2008) Automatic time series forecasting: The forecast package for R. Journal of Statistical Software 26(3)

Kendall M (1953) The analysis of economic time series, part I: prices. J R Stat Society 116:11

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

Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root. J Econometrics. 54(1–3):159–178

Lazer D, Kennedy R, King G, Vespignani A (2014) The parable of Google flu: traps in Big Data analysis. Science 343:1203

Lee TH, White H, Granger CWJ (1993) Testing for neglected nonlinearity in time series models. J Econometrics 56:269–290

Mandelbrot B (1963) The variation of certain speculative prices. J Business 36:394–419

Massicotte P, Eddelbuettel D (2019) gtrendsR: Perform and Display Google Trends Queries. R package version 1.4.4. https://CRAN.R-project.org/package=gtrendsR

Nakamoto S (2008) Bitcoin: a peer-to-peer electronic cash system. Bitcoin. https://bitcoin.org/bitcoin.pdf

Nason G (2013) A test for second order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series. J R Stat Society B 75:879–904

Priestley M. B, Rao T. S (1969) A test for non-stationarity of time-series. J R Stat Society B (Methodological) 31(1):140–149

Sapuric S, Kokkinaki A (2014) Bitcoin is volatile! Isn’t that right? Business Information Systems Workshops. Springer, Lecture Notes in Business Information Processing. Berlin, pp 255–65

Teraesvirta T, Lin CF, Granger CWJ (1993) Power of the neural network linearity test. J Time Series Anal 14:209–220

Trapletti A, Hornik K (2019) tseries: Time Series Analysis and Computational Finance. R package version 0.10-47