Price Fluctuations and the Use of Bitcoin: An Empirical Inquiry

By Michał Polasik, Anna Iwona Piotrowska, Tomasz Piotr Wisniewski, Radosław Kotkowski, Geoffrey Lightfoot. International Journal of Electronic Commerce, 2015

394 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/685a0a1a54cb732c466cbae58ea174211297ac04

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Citations in the corpus

On the Fintech Revolution: Interpreting the Forces of Innovation, Disruption, and Transformation in Financial Services (2018, background): “Yet due to the degree of its volatility over the prior five years [120] and recent meteoric price rise, many observers have questioned the rationality behind its valuation.”
Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach (2018, background): “The Bitcoin literature also provides evidence of an insignificant relation between Bitcoin returns and fluctuations of major global macroeconomic aggregates (Polasik et al. 2015; Ciaian et al. 2016).”
Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero (2018, neutral): “And while there have been plenty of studies examining the future of Bitcoin and its volatility (Polasik et al. 2015; Letra, 2016; Bouoiyour and Selmi, 2016; Katsiampa, 2017; Chiu and Koeppl, 2017; Chu et al. 2017), there have been few that explore the broader cryptocurrency market and how it is…”
Spillovers between Bitcoin and other assets during bear and bull markets (2018, background): “…it often is argued that the Bitcoin market is related very weakly to other global macroeconomic aggregates (Brière, Oosterlinck, and Szafarz 2015; Polasik et al. 2015) and to most conventional asset classes (Baur, Lee, and Hong 2015; Brière, Oosterlinck, and Szafarz 2015; Dyhrberg 2016; Bouri et…”
On the determinants of bitcoin returns: A LASSO approach (2018, background): “Along with its usefulness as a medium of exchange (Kristoufek, 2015; Polasik et al., 2015), bitcoin also has some favourable characteristics as an asset, such as hedging, diversifying and safe haven capabilities (Dyhrberg, 2016a,b; Bouri et al.”
Long- and Short-Term Cryptocurrency Volatility Components: A GARCH-MIDAS Analysis (2018, neutral): “The inclusion of Google trends as some sort of proxy for sentiment or interest is fairly common within the literature (see, for example, Polasik et al. (2015)).”
Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions (2016, result): “Similar results are reported by Glaser et al. (2014) and Polasik et al. (2014).”
Testing for asymmetric nonlinear short- and long-run relationships between bitcoin, aggregate commodity and gold prices (2018, background): “Yermack argues that Bitcoin does not fulfil the functions of money, whereas Polasik et al. (2015) show that it acts as a medium of exchange. Recently, Blau (2017) showed that Bitcoin is a currency rather than a speculative investment. Importantly, Bitcoin combines features from commodities (i.e. gol”
Cryptocurrency Value Formation: An Empirical Analysis Leading to a Cost of Production Model for Valuing Bitcoin (2017, background): “In fact, in an 18-variable multiple regression the R2 value they obtained was only 0.4586, indicating that some other variables must account for over half of bitcoin’s dollar value. Because cryptocurrencies are nascent and still highly speculative and volatile, using time series analysis can be misl”
The predictive power of public Twitter sentiment for forecasting cryptocurrency prices (2020, background): “Sensoy (2019) compared weak form e ciency for the BTC/USD versus the BTC/EUR market and nds that the BTC/USD market is slightly more e cient and that both markets have become more e cient over time.”
Digital Finance and FinTech: current research and future research directions (2017, positive): “Finally, Polasik et al. (2015) empirically investigated the drivers of bitcoin returns and conclude that these returns can be explained primarily by its popularity, the sentiment in newspaper reports, and the total number of transactions.”