Machine Learning in Futures Markets

Waldow F, Schnaubelt M, Krauss C, Fischer T (2021)


Publication Type: Journal article

Publication year: 2021

Journal

Book Volume: 14

Journal Issue: 3

DOI: 10.3390/jrfm14030119

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.

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How to cite

APA:

Waldow, F., Schnaubelt, M., Krauss, C., & Fischer, T. (2021). Machine Learning in Futures Markets. Journal of Risk and Financial Management, 14(3). https://dx.doi.org/10.3390/jrfm14030119

MLA:

Waldow, Fabian, et al. "Machine Learning in Futures Markets." Journal of Risk and Financial Management 14.3 (2021).

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