The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies

By Ahmet Sensoy. Finance Research Letters, 2019

216 citations recorded by the source corpus.

Abstract We compare the time-varying weak-form efficiency of Bitcoin prices in terms of US dollars (BTCUSD) and euro (BTCEUR) at a high-frequency level by using permutation entropy. We find that BTCUSD and BTCEUR markets have become more informationally efficient at the intraday level since the beginning of 2016, and BTCUSD market is slightly more efficient than BTCEUR market in the sample period. We also find that higher the frequency, lower the pricing efficiency is. Finally, liquidity (volatility) has a significant positive (negative) effect on the informational efficiency of Bitcoin prices.

Subfields: Economics and Econometrics, Information Systems

View Paper: https://www.semanticscholar.org/paper/88bb56c786446e7b8e4f602d897f1c249ec90e6b

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Citations in the corpus

The asymmetric effect of bitcoin on altcoins: evidence from the nonlinear autoregressive distributed lag (NARDL) model (2020, background): “2018), the efficiency of cryptocurrencies (Urquhart, 2016; Sensoy, 2019), hedging capabilities of cryptocurrencies against other assets (Bouri et al.”
The impact of macroeconomic news on Bitcoin returns (2020, unknown): “Recent ﬁndings by Sensoy [2018] also report that Bitcoin prices both in terms US dollar and euros have become more eﬃcient.”
A systematic review of the bubble dynamics of cryptocurrency prices (2020, unknown): “To date, it has been identiﬁed that cryptocurrencies contain a number of pricing ineﬃ-ciencies (Urquhart [2016], Sensoy [2019], Mensi et al. [2019], Corbet et al. [2019]; Ma and Tanizaki [2019]), persistence (Caporale et al. [2018]; Corbet and Katsiampa [2018]), to be correlated or in isolation…”
The predictive power of public Twitter sentiment for forecasting cryptocurrency prices (2020, methodology): “Lastly, Aslan and Sensoy (2019) use several di ering methods to estimate the Hurst exponent and nd that the e ciency in the cryptocurrency market varies per cryptocurrency and per intrahourly sampling frequency.”
The relationship between Bitcoin returns and trade policy uncertainty (2019, background): “…literature shows a rising interest in Bitcoin and other cryptocurrencies and examines the different properties of Bitcoin, including efficiency (Sensoy, 2019; Urquhart, 2016); diversification benefits (Bouri et al., 2017b; Poyser 2018; Guesmi et al., 2019); attention drivers (Dastgir et al.,…”
Effects of the geopolitical risks on Bitcoin returns and volatility (2019, methodology): “Using the Intraday data and the efficiency tests, Sensoy (2018) show that Bitcoin prices in terms of the EUR and the USD have become more informationally efficient since 2016.”
Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios (2019, background): “In the meantime, authors such as Urquhart (2016); Caporale et al. (2018), as well as, Sensoy (2018) emphasize that the Bitcoin market is rather becoming more efficient over time.”
Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting (2019, result): “…of predictability, whereas later studies report contradicting evidence (Urquhart, 2016, Nadarajah & Chu, 2017, Bariviera, 2017, Kristoufek, 2018, Sensoy, 2019), which suggest the possibility to predict Bitcoin volatility.6 Lastly, our work is related to research on the usefulness of…”