Statistical Arbitrage in Cryptocurrency Markets
AbstractMachine 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. View Full-Text
Share & Cite This Article
Fischer, T.G.; Krauss, C.; Deinert, A. Statistical Arbitrage in Cryptocurrency Markets. J. Risk Financial Manag. 2019, 12, 31.
Fischer TG, Krauss C, Deinert A. Statistical Arbitrage in Cryptocurrency Markets. Journal of Risk and Financial Management. 2019; 12(1):31.Chicago/Turabian Style
Fischer, Thomas G.; Krauss, Christopher; Deinert, Alexander. 2019. "Statistical Arbitrage in Cryptocurrency Markets." J. Risk Financial Manag. 12, no. 1: 31.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.