Machine Learning in Futures Markets

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.

Risk prediction of interest rate futures based on machine learning scenarios

“…The paper written by Li used Convolutional Neural Networks, Support Vector Regression (SVR) and Random Forests [11]. These studies use different machine learning techniques and indicators, but most of them use some basic technical indicators such as moving averages and relative strength indicators [12,13]. The indicators may not fully reflect the characteristics of the interest rate futures market, and there may be correlations between different indicators that may affect the predictive accuracy of the models.…”

Forecasting currency covariances using machine learning tree-based algorithms with low and high prices

“…The applications of the RFR, GBRT, and XGBoost algorithms in the forecasting of financial markets are presented in the works of Ghosh et al (in press), Islam et al (2021), Khaidem et al (2016), Krauss et al (2017), Kumar and Thenmozhi (2006), Waldow et al (2021), Yang (2021) and Yang et al (2021). However, most of those studies are devoted to the forecasting of stock prices or exchange rates.…”