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Efficiency in cryptocurrency markets: new evidence

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Abstract

In this paper we carried out a comprehensive study of the efficiency in the cryptocurrency markets. The markets under study are: Bitcoin, Litecoin, Ethereum, Ripple, Stellar and Monero. To study the efficiency of these markets, we use a set of five test which are applied in both a static context and dynamic context. The results obtained depend on both the analysis period and the methodology used to test the predictability of the return. However, some conclusions can be drawn: first, we observe that overall, the efficiency degree tends to increase with the time. Second, although the efficiency market seems to change along the period, the changes in the Bitcoin, Litecoin and Ethereum market show a clear tendency that evolves from less to more efficiency. In the case of Ripple, Stellar and Monero, periods of efficiency alternate with periods of inefficient, which is consistent with the adaptive market hypothesis.

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Fig. 1

Source: CoinMarketCap (http://coinmarketcap.com)

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Notes

  1. As pointed by Vidal-Tomás et al. (2019), the latter is more restrictive since the martingale rejects any dependence of the conditional expectation of price increments while the random walk rejects also dependence involving the higher conditional moments of price (Charles et al., 2011a, b; LeRoy, 1989; Lim and Brooks, 2011).

  2. A cryptocurrency is a digital asset designed to work as a medium of exchange using cryptography to secure the transactions (Katsiampa et al., 2018). The first and largest cryptocurrency in the world by market capitalization is the Bitcoin followed by Ethereum, Ripple, Litecoin, Stellar and Monero. All of them represent over 80% of the total market of cryptocurrencies.

  3. Aslan and Sensoy (2020) use intraday sampling frequency for investigating the weak-form efficiency of the four highest capitalized cryptocurrencies. Applying a battery of long memory tests, they provide evidence of major discrepancies on the predictability of cryptocurrency returns for alternative high frequency intervals. Accordingly, efficiency demonstrates a U-shaped pattern with respect to alternative sampling frequencies, hence they conclude that there exists an optimal intraday sampling frequency that maximizes the market efficiency. Akyildirim et al. (2020) use returns obtained at various intraday frequencies for the most liquid twelve cryptocurrencies in order to test their return predictability via machine learning. The authors refer to the state of the art methodologies used in decision sciences that provide them the potential patterns to be exploited and the resulting gains if the selected strategy is implemented. Also, they use different timescales for prediction that can be easily verified in their ability to generalize in different timescales for different cryptocurrencies. The results find that the direction of returns in cryptocurrency markets can be predicted for the daily or minute level time scales in a consistent manner with classification accuracies reaching as high as 70% success ratio. Their results also indicate the possibility to design trading rules based on the classification algorithms.

  4. Noda (2016) also employs a GSL-based time-varying model to test AMH using Japanese stock market data (TOPIX and TSE2) and also concludes that the degree of market efficiency varies with time. Tran and Leirvik (2019), following Noda's (2016) study, pointed out that sometimes markets work oddly and the time-varying degree of market efficiency measure show that a market is more efficient when the autocorrelation level is high than the autocorrelation is low. To solve this problem, these authors introduced a measure to quantify the level of market inefficiency (MIM) which varies smoothly from zero (very efficient market) to 1 (inefficient market). Their empirical results (based on the same dataset that Noda (2016) and US stock market data) show that in many periods of major economic events, financial markets becomes less efficient. They conclude that markets are often efficient but can be very inefficient over longer periods.

  5. For instance, in the case of the Bitcoin the first sub-sample runs from April 29th, 2013 to December 28th, 2014. The second sub-sample runs from December 29th, 2014 to August 29th, 2016. The third sample goes from August 30th, 2016 to May 1st, 2018 and the fourth sample runs from May 2nd, 2018 to December 31th, 2019.

  6. In order to save space, only the graphs of the estimation of the p-value for Ljung–Box test for five lags have been presented. The plots corresponding to the p-values of 10 and 15 delays as well as for Box–Pierce have also been obtained. They are available for any interested reader upon request to the authors.

  7. Chu et al. (2019) investigated the AMH for Bitcoin and Ethereum to high frequency data, applied Dominguez and Lobato test in a rolling window, and the results were consistent with the AMH. Khuntia and Pattanayak (2018) examine the AMH for Bitcoin and show that market efficiency changes over time and prove presence of AMH.

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Correspondence to Carmen López-Martín.

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López-Martín, C., Benito Muela, S. & Arguedas, R. Efficiency in cryptocurrency markets: new evidence. Eurasian Econ Rev 11, 403–431 (2021). https://doi.org/10.1007/s40822-021-00182-5

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