Does twitter predict Bitcoin?

By Dehua Shen, Andrew Urquhart, Pengfei Wang. Economics Letters, 2019

329 citations recorded by the source corpus.

This paper adds to the growing literature of Bitcoin by examining the link between investor attention and Bitcoin returns, trading volume and realized volatility. Unlike previous studies, we employ the number of tweets from Twitter as a measure of attention rather than Google trends as we argue this is a better measure of attention from more informed investors. We find that the number of tweets is a significant driver of next day trading volume and realized volatility which is supported by linear and nonlinear Granger causality tests.

Subfields: Economics and Econometrics, Information Systems

View Paper: https://www.semanticscholar.org/paper/e8da6b1ceb20f99d61a341a2485ae2d89f1484ae

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Citations in the corpus

Is Bitcoin a hedge or safe haven for currencies? An intraday analysis (2019, background): “The market efficiency of Bitcoin has also been examined in some detail, with Urquhart (2016) first documenting the inefficiency of Bitcoin returns which has subsequently been supported by Nadarajah and Chu (2017), Bariviera (2017) and Tiwari et al (2017).”
A three-factor pricing model for cryptocurrencies (2020, background): “Also, studies have shown that external variables through Google Trends data (Urquhart, 2018), the number of tweets (Shen et al, 2019) and Economic Policy Uncertainty (Demir et al., 2018) all have predictive power in the Bitcoin market.”