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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.

References

  • Aggarwal, D. (2019). Do Bitcoins follow a random walk model? Research in Economics, 73(1), 15–22.

    Article  Google Scholar 

  • Akyildirim, E., Goncu, A., & Sensoy, A. (2020). Prediction of cryptocurrency returns using machine learning. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03575-y

    Article  Google Scholar 

  • Alvarez-Ramirez, J., Rodriguez, E., & Ibarra-Valdez, C. (2018). Long-range correlations and asymmetry in the Bitcoin market. Physica A: Statistical Mechanics and Its Applications, 492, 948–955.

    Article  Google Scholar 

  • Aslan, A., & Sensoy, A. (2020). Intraday efficiency-frequency nexus in the cryptocurrency markets. Finance Research Letters, 35, 101298.

    Article  Google Scholar 

  • Bariviera, A. (2017). The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters, 161, 1–4.

    Article  Google Scholar 

  • Bariviera, A. F., Basgall, M. J., Hasperué, W., & Naiouf, M. (2017). Some stylized facts of the Bitcoin market. Physica A: Statistical Mechanics and Its Applications, 484, 82–90.

    Article  Google Scholar 

  • Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81, 637–659. https://doi.org/10.1086/260062

    Article  Google Scholar 

  • Brauneis, A., & Mestel, R. (2018). Price discovery of cryptocurrencies: Bitcoin and beyond. Economics Letters, 165, 58–61.

    Article  Google Scholar 

  • Caporale, G. M., Gil-Alana, L., & Plastun, A. (2018). Persistence in the cryptocurrency market. Research in International Business and Finance, 46, 141–148.

    Article  Google Scholar 

  • Caporale, G. M., & Plastun, A. (2019). The day of the week effect in the cryptocurrency market. Finance Research Letters, 31, 258–269.

    Article  Google Scholar 

  • Charfeddine, L., & Maouchi, Y. (2019). Are shocks on the returns and volatility of cryptocurrencies really persistent? Finance Research Letters, 28, 423–430.

    Article  Google Scholar 

  • Charles, A., & Darné, O. (2009). Variance ratio tests of random walk: An overview. Journal of Economic Surveys, 23(3), 503–527.

    Article  Google Scholar 

  • Charles, A., Darné, O., & Fouilloux, J. (2011a). Testing the martingale difference hypothesis in CO2 emission allowances. Economic Modelling, 28(1–2), 27–35.

    Article  Google Scholar 

  • Charles, A., Darné, O., & Kim, J. H. (2011b). Small sample properties of alternative tests for martingale difference hypothesis. Economics Letters, 110(2), 151–154.

    Article  Google Scholar 

  • Choi, I. (1999). Test the random walk hypothesis for real exchange rates. Journal Applied Econometrics, 14, 293–309.

    Article  Google Scholar 

  • Chu, J., Zhang, Y., & Chan, S. (2019). The adaptive market hypothesis in the high frequency cryptocurrency market. International Review of Financial Analysis, 64, 221–231.

    Article  Google Scholar 

  • Corbet, S., Eraslan, V., Lucey, B., & Sensoy, A. (2019). The effectiveness of technical trading rules in cryptocurrency markets. Finance Research Letters, 31, 32–37.

    Article  Google Scholar 

  • Fama, E. (1965). The behavior of stock market prices. The Journal of Business, 38, 34–105.

    Article  Google Scholar 

  • Fama, E. (1970). Efficient capital markets: a review of theory and empirical work. Journal of Finance, 25, 383–417.

    Article  Google Scholar 

  • Fama, E. (1991). Efficient capital markets: II. The Journal of Finance, 46(5), 1575–1617.

    Article  Google Scholar 

  • Fama, E. (1998). Market efficiency, long-term returns, and behavioral finance. Journal of Financial Economics, 49(3), 283–306.

    Article  Google Scholar 

  • Hu, Y., Valera, H. G. A., & Oxley, L. (2019). Market efficiency of the top market-cap cryptocurrencies: Further evidence from a panel framework. Finance Research Letters, 31, 138–145.

    Article  Google Scholar 

  • Hurst, H. E. (1951). Long-term storage of reservoirs: An experimental study. Transactions of the American Society of Civil Engineers, 116, 770–799.

    Article  Google Scholar 

  • Katsiampa, P., Gkillas, K., & Longin, F. (2018). Cryptocurrency market activity during extremely volatile periods. Available at SSRN: https://ssrn.com/abstract=3220781

  • Khuntia, S., & Pattanayak, J. K. (2018). Adaptive market hypothesis and evolving predictability of Bitcoin. Economics Letters, 167, 26–28.

    Article  Google Scholar 

  • Khursheed, A., Naeem, M., Ahmed, S., & Mustafa, F. (2020). Adaptive market hypothesis: An empirical analysis of time-varying market efficiency of cryptocurrencies. Cogent Economics and Finance, 8(1), 1719574.

    Article  Google Scholar 

  • Kim, J. H. (2009). Automatic variance ratio test under conditional heteroscedasticity. Finance Research Letters, 6(3), 179–185.

    Article  Google Scholar 

  • Köchling, G., Müller, J., & Posch, P. N. (2019). Does the introduction of futures improve the efficiency of Bitcoin? Finance Research Letters, 30, 367–370.

    Article  Google Scholar 

  • Kristoufek, L., & Vosvrda, M. (2019). Cryptocurrencies market efficiency ranking: Not so straightforward. Physica A: Statistical Mechanics and Its Applications, 531, 120853.

    Article  Google Scholar 

  • Kyriazis, N. (2019). A survey on efficiency and profitable trading opportunities in crytocurrency markets. Journal of Risk and Financial Management, 12, 67. https://doi.org/10.3390/jrfm12020067

    Article  Google Scholar 

  • LeRoy, S. F. (1989). Efficient capital markets and martingales. Journal of Economic Literature, 27(4), 1583–1621.

    Google Scholar 

  • Lim, K. P., & Brooks, R. (2011). The evolution of stock market efficiency over time: A survey of the empirical literature. Journal of Economic Surveys, 25(1), 69–108.

    Article  Google Scholar 

  • Lintner, J. (1965). The valuation of risky assets and the selection of risky investments in stock portfolios and capital budgets. Review of Economics and Statistics, 47(1), 13–37.

    Article  Google Scholar 

  • Liu, J., Cheng, C., Yang, X., Yan, L., & Lai, Y. (2019). Analysis of the efficiency of Hong Kong REITs market based on Hurst exponent. Physica A: Statistical Mechanics and Its Applications, 534, 122035.

    Article  Google Scholar 

  • Lo, A. W. (2004). The adaptive markets hypothesis: market efficiency from an evolutionary perspective. Journal of Portfolio Management,, 1, 15–29.

  • Lo, A. W., & MacKinlay, A. C. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. The Review of Financial Studies, 1(1), 41–66.

    Article  Google Scholar 

  • Malkiel, B. G. (1992). Efficient market hypothesis. In M. M. P. Newman (Ed.), New Palgrave Dictionary of Money and Finance. MacMillan.

    Google Scholar 

  • Malkiel, B. G., & McCue, K. (1985). A Random Walk down Wall Street. Norton.

    Google Scholar 

  • Mandelbrot, B. (1963a). New methods in statistical economics. Journal of Political Economy, 71, 421–440.

    Article  Google Scholar 

  • Mandelbrot, B. (1963b). The variation of certain speculative prices. The Journal of Business, 36(4), 394–419.

    Article  Google Scholar 

  • Mandelbrot, B. (1966). Forecasts of future prices, unbiased markets, and “Martingale” models. The Journal of Business, 39(1), 242–255.

    Article  Google Scholar 

  • Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7(1), 77–91.

    Google Scholar 

  • Markowitz, H. M. (1959). Portfolio selection: Efficient diversification of investments. Wiley.

    Google Scholar 

  • Mensi, W., Lee, Y. L., Al-Yahyaee, K. H., Sensoy, A., & Yoon, S. M. (2019). Intraday downward/upward multifractality and long memory in Bitcoin and Ethereum markets: An asymmetric multifractal detrended fluctuation analysis. Finance Research Letters, 31, 19–25.

    Article  Google Scholar 

  • Merton, C. R. (1973). Theory of rational option pricing. The Bell Journal of Economics and Management Science, 4(1), 141–183.

    Article  Google Scholar 

  • Mills, T., & Patterson, K. (2009). Palgrave Handbook of Econometrics. Applied Econometrics (Vol. 2). Palgrave MacMillan.

    Book  Google Scholar 

  • Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4), 768–783.

    Article  Google Scholar 

  • Nadarajah, S., & Chu, J. (2017). On the inefficiency of Bitcoin. Economics Letters, 150, 6–9.

    Article  Google Scholar 

  • Noda, A. (2016). A test of the adaptive market hypothesis using a time-varying AR model in Japan. Finance Research Letters, 17, 66–71.

    Article  Google Scholar 

  • Noda, A. (2020). On the evolution of cryptocurrency market efficiency. Applied Economic Letters. https://doi.org/10.1080/13504851.2020.1758617

    Article  Google Scholar 

  • Ross, S. A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13(2), 341–360.

    Article  Google Scholar 

  • Samuelson, P. (1965). Proof that properly anticipated prices fluctuate randomly. Industrial Management Review, 6(2), 41–49.

    Google Scholar 

  • Sensoy, A. (2019). The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies. Finance Research Letters, 28, 68–73.

    Article  Google Scholar 

  • Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425–442.

    Google Scholar 

  • Tiwari, A. K., Jana, R. K., Das, D., & Roubaud, D. (2018). Informational efficiency of Bitcoin—An extension. Economic Letters, 163, 106–109.

    Article  Google Scholar 

  • Tran, V., & Leirvik, T. (2019). A simple but powerful measure of market efficiency. Finance Research Letters, 29, 141–151.

    Article  Google Scholar 

  • Tran, V. L., & Leirvik, T. (2020). Efficiency in the markets of crypto-currencies. Finance Research Letters, 35, 101382.

    Article  Google Scholar 

  • Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80–82.

    Article  Google Scholar 

  • Vidal-Tomás, D., & Ibáñez, A. (2018). Semi-strong efficiency of Bitcoin. Finance Research Letters, 27, 259–265.

    Article  Google Scholar 

  • Vidal-Tomás, D., Ibáñez, A. M., & Farinós, J. (2019). Weak efficiency of the cryptocurrency market: A market portfolio approach. Applied Economics Letters, 26(19), 1627–1633.

    Article  Google Scholar 

  • Wei, W. C. (2018). Liquidity and market efficiency in cryptocurrencies. Economics Letters, 168, 21–24.

    Article  Google Scholar 

  • Zargar, F. N., & Kumar, D. (2019). Informational inefficiency of Bitcoin: A study based on high-frequency data. Research in International Business and Finance, 47, 344–353.

    Article  Google Scholar 

  • Zhang, W., Pengfei, W., Xiao, L., & Dehua, S. (2018). The inefficiency of cryptocurrency and its cross-correlation with Dow Jones Industrial Average. Physica A: Statistical Mechanics and Its Applications, 510, 658–670.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Business and Accounting, Faculty of Economics and Business Administration, National Distance Education University (UNED), Senda del Rey, 11, 28040, Madrid, Spain

    Carmen López-Martín & Raquel Arguedas

  2. Department of Economic Analysis, Faculty of Economics and Business Administration, National Distance Education University (UNED), Senda del Rey, 11, 28040, Madrid, Spain

    Sonia Benito Muela

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  1. 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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