Machine Learning in Futures Markets

• Main Authors: Fabian Waldow, Matthias Schnaubelt, Christopher Krauss, Thomas Günter Fischer
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: Journal of Risk and Financial Management
• Subjects: statistical arbitrage; futures markets; machine learning
• Online Access: https://www.mdpi.com/1911-8074/14/3/119

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 <i>h</i>-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-<i>k</i> futures for a duration of <i>h</i> 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.