Machine Learning in Futures Markets

Fabian Waldow, Matthias Schnaubelt, Christopher Krauss and Thomas Günter Fischer
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Fabian Waldow: Department of Statistics and Econometrics, University of Erlangen-Nürnberg, 90403 Nürnberg, Germany
Matthias Schnaubelt: Department of Statistics and Econometrics, University of Erlangen-Nürnberg, 90403 Nürnberg, Germany
Christopher Krauss: Department of Statistics and Econometrics, University of Erlangen-Nürnberg, 90403 Nürnberg, Germany
Thomas Günter Fischer: Department of Statistics and Econometrics, University of Erlangen-Nürnberg, 90403 Nürnberg, Germany

JRFM, 2021, vol. 14, issue 3, 1-14

Abstract: In this paper, we demonstrate how a well-established machine learning-based statistical arbitrage strategy can be successfully transferred from equity to futures markets. First, we preprocess futures time series comprised of front months to render them suitable for our returns-based trading framework and compile a data set comprised of 60 futures covering nearly 10 trading years. Next, we train several machine learning models to predict whether the h -day-ahead return of each future out- or underperforms the corresponding cross-sectional median return. Finally, we enter long/short positions for the top/flop- k futures for a duration of h days and assess the financial performance of the resulting portfolio in an out-of-sample testing period. Thereby, we find the machine learning models to yield statistically significant out-of-sample break-even transaction costs of 6.3 bp—a clear challenge to the semi-strong form of market efficiency. Finally, we discuss sources of profitability and the robustness of our findings.

Keywords: statistical arbitrage; futures markets; machine learning (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2021
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