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• W3136514097 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." @default.
• W3136514097 created "2021-03-29" @default.
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• W3136514097 date "2021-03-13" @default.
• W3136514097 modified "2023-10-16" @default.
• W3136514097 title "Machine Learning in Futures Markets" @default.
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• W3136514097 doi "https://doi.org/10.3390/jrfm14030119" @default.
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