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.
Highlights
Management 14: 119. https://Futures allow market participants to capitalize on substantial leverage
We find that the size of the traded portfolio is a major determinant of the strategy’s performance, i.e., the strength of the trading signal generated by our machine learning models decreases with the number of futures included in the portfolio
We successfully transfer a well-established statistical arbitrage trading strategy based on machine learning from equities to futures markets
Summary
Futures allow market participants to capitalize on substantial leverage. They represent an appealing asset class for statistical arbitrage trading. The use of financial machine learning for statistical arbitrage attracts academic research, yet most work focuses on equity markets (see Henrique et al (2019) and. Takeuchi and Lee (2013) use Boltzmann machines, which are a form of deep neural networks, to exploit stock momentum effects. Their trading strategy yields average annualized returns of around 46 percent before transaction costs from 1990 to 2009. The ensemble strategy performs best, with an average daily return of
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