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Prediction of cryptocurrency returns using machine learning

  • S.I.: Networks and Risk Management
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Abstract

In this study, the predictability of the most liquid twelve cryptocurrencies are analyzed at the daily and minute level frequencies using the machine learning classification algorithms including the support vector machines, logistic regression, artificial neural networks, and random forests with the past price information and technical indicators as model features. The average classification accuracy of four algorithms are consistently all above the 50% threshold for all cryptocurrencies and for all the timescales showing that there exists predictability of trends in prices to a certain degree in the cryptocurrency markets. Machine learning classification algorithms reach about 55–65% predictive accuracy on average at the daily or minute level frequencies, while the support vector machines demonstrate the best and consistent results in terms of predictive accuracy compared to the logistic regression, artificial neural networks and random forest classification algorithms.

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Notes

  1. See Noakes and Rajaratnam (2016) and Avdoulas et al. (2018) for more recent evidence.

  2. Some studies focus on dependency structure between Bitcoin prices and other variables. For example, see El Alaoui et al. (2019) and Bouri et al. (2018a, b, c). For other various aspects of the cryptocurrency markets, see Cretarola and Figà-Talamanca (2019), Giudici and Polinesi (2019) and Koutmos (2019).

  3. These are Bitcoin (BTC): 01/04/2013 to 23/06/2018, Litecoin (LTC): 19/05/2013 to 23/06/2018, Ethereum (ETH): 09/03/2016 to 23/06/2018, Ethereum Classic (ETC): 26/07/2016 to 23/06/2018

  4. We use four different sampling frequencies: 15-min, 30-min, 60-min, and daily.

  5. https://coinmarketcap.com/historical/20180617/.

  6. The logistic regression and other classification algorithms are implemented in Python 3.7 with the scikit-learn package available on the following website: https://scikit-learn.org/.

  7. A recent application of MCS test to cryptocurrency markets to determine the drivers of bitcoin volatility can be found in Walther et al. (2019).

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Authors and Affiliations

  1. Department of Mathematics, ETH, Zurich, Switzerland

    Erdinc Akyildirim

  2. Department of Banking and Finance, University of Zurich, Zurich, Switzerland

    Erdinc Akyildirim

  3. Department of Mathematical Sciences, Xian Jiaotong Liverpool University, Suzhou, 215123, China

    Ahmet Goncu

  4. Hedge Fund Research Center, Shanghai Advanced Institute of Finance, Shanghai Jiaotong University, Shanghai, China

    Ahmet Goncu

  5. Faculty of Business Administration, Bilkent University, 06800, Cankaya, Ankara, Turkey

    Ahmet Sensoy

  6. Department of Banking and Finance, Burdur Mehmet Akif Ersoy University, Burdur, Turkey

    Erdinc Akyildirim

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  1. Erdinc Akyildirim
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  2. Ahmet Goncu
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  3. Ahmet Sensoy
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Correspondence to Ahmet Sensoy.

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Appendix

Appendix

See Tables 16, 17 and 18.

Table 16 t test results for comparison of five classification algorithms, including ARIMA, logistic regression, support vector machines, artificial neural networks, and random forest classifier over different time scales
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Table 17 Wilcoxon signed rank test results for comparison of five classification algorithms, including ARIMA, logistic regression, support vector machines, artificial neural networks, and random forest classifier over different time scales
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Table 18 t test and Wilcoxon signed rank test results for comparison of five classification algorithms, including ARIMA, logistic regression, support vector machines, artificial neural networks, and random forest classifier over equally weighted (EW) and market capitalization weighted (MCW) indices and different time scales
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Akyildirim, E., Goncu, A. & Sensoy, A. Prediction of cryptocurrency returns using machine learning. Ann Oper Res 297, 3–36 (2021). https://doi.org/10.1007/s10479-020-03575-y

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