Statistical Arbitrage in Cryptocurrency Markets

Journal article

Publication Details

Author(s): Fischer T, Krauss C, Deinert A
Journal: Journal of Risk and Financial Management
Publication year: 2019
Volume: 12
Journal issue: 1
ISSN: 1911-8066
eISSN: 1911-8074


Machine learning research has gained momentum-also in finance. Consequently, initial machine-learning-based statistical arbitrage strategies have emerged in the U.S. equities markets in the academic literature, see e.g., Takeuchi and Lee (2013); Moritz and Zimmermann (2014); Krauss et al. (2017). With our paper, we pose the question how such a statistical arbitrage approach would fare in the cryptocurrency space on minute-binned data. Specifically, we train a random forest on lagged returns of 40 cryptocurrency coins, with the objective to predict whether a coin outperforms the cross-sectional median of all 40 coins over the subsequent 120 min. We buy the coins with the top-3 predictions and short-sell the coins with the flop-3 predictions, only to reverse the positions after 120 min. During the out-of-sample period of our backtest, ranging from 18 June 2018 to 17 September 2018, and after more than 100,000 trades, we find statistically and economically significant returns of 7.1 bps per day, after transaction costs of 15 bps per half-turn. While this finding poses a challenge to the semi-strong from of market efficiency, we critically discuss it in light of limits to arbitrage, focusing on total volume constraints of the presented intraday-strategy.

FAU Authors / FAU Editors

Fischer, Thomas
Lehrstuhl für Statistik und Ökonometrie

How to cite

Fischer, T., Krauss, C., & Deinert, A. (2019). Statistical Arbitrage in Cryptocurrency Markets. Journal of Risk and Financial Management, 12(1).

Fischer, Thomas, Christopher Krauss, and Alexander Deinert. "Statistical Arbitrage in Cryptocurrency Markets." Journal of Risk and Financial Management 12.1 (2019).


Last updated on 2019-02-05 at 08:23