What causes the attention of Bitcoin

By Andrew Urquhart. 2018

339 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Information Systems

View Paper: https://www.semanticscholar.org/paper/ff1b336d2132028bcf88ec2b290b3ee9e2468ce9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Shaen Corbet
2. Brian M. Lucey
3. L. Yarovaya
4. Andrew Urquhart
5. Ender Demir
6. Pengfei Wang
7. Dehua Shen
8. Chi Keung Marco Lau

In Information Systems

1. Shaen Corbet
2. Brian M. Lucey
3. Pengfei Wang
4. Andrew Urquhart
5. Dehua Shen
6. L. Yarovaya
7. Ender Demir
8. Chi Keung Marco Lau

Citations in the corpus

• The asymmetric effect of bitcoin on altcoins: evidence from the nonlinear autoregressive distributed lag (NARDL) model (2020, background): “For instance, several studies explore the relationship between investors’ attention and Bitcoin prices (Urquhart, 2018; Dastgir et al. 2018), the existence of bubbles in the cryptocurrency market (Cheah and Fry, 2015; Corbet et al.”
• Investor attention and global market returns during the COVID-19 crisis (2020, background): “…by GSV, also explains high initial returns to IPOs (Vakrman and Krisoufek, 2015), and market responses in commodity markets (Han et al., 2017a, 2017b), cryptocurrency markets (Urquhart, 2018), FX markets (Smith, 2012; Goddard et al., 2015; Han et al., 2018), and REITs (Yung and Nafar, 2017).”
• A three-factor pricing model for cryptocurrencies (2020, background): “Recently, Wei (2018) examines 456 different cryptocurrencies and shows that the less inefficient the cryptocurrency is, the more illiquid the cryptocurrency.”
• Is Bitcoin a hedge or safe haven for currencies? An intraday analysis (2019, neutral): “Recently, Urquhart (2018) shows that high trading volume and volatility are drivers of the attention of Bitcoin while Shen et al (2019) show that the number of tweets related to Bitcoin significant predict the trading volume and volatility of Bitcoin An area of research which has received some…”
• Cryptocurrencies as a financial asset: A systematic analysis (2019, background): “Urquhart 2018 Google search Product Attention VAR, IRF and Granger Causality Daily bitcoincharts.comVolume >1,200 (continued on next page) 43 Table 2: Key characteristics of included quantitative research Paper Name Authors Year Dep.”
• Does twitter predict Bitcoin? (2019, background): “A recent paper by Urquhart (2018) shows that the attention of Bitcoin, captured by Google Trends, can be explained by the previous days realized volatility and volume, indicating that the high volatility and trading volume experienced by Bitcoin number of times the term ‘Bitcoin’ has been searched…”