Artikel
Machine learning in futures markets
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.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 14 ; Year: 2021 ; Issue: 3 ; Pages: 1-14 ; Basel: MDPI
- Klassifikation
-
Wirtschaft
- Thema
-
machine learning
futures markets
statistical arbitrage
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Waldow, Fabian
Schnaubelt, Matthias
Krauss, Christopher
Fischer, Thomas G.
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2021
- DOI
-
doi:10.3390/jrfm14030119
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Waldow, Fabian
- Schnaubelt, Matthias
- Krauss, Christopher
- Fischer, Thomas G.
- MDPI
Entstanden
- 2021
Ähnliche Objekte (12)
Expectations for statistical arbitrage in energy futures markets
Volatility and depth in commodity and FX futures markets
Volatility integration in spot, futures and options markets: A regulatory perspective
Machine Learning in Futures Markets
Machine learning for multiple yield curve markets: Fast calibration in the Gaussian affine framework
Hedging performance and multiscale relationships in the German electricity spot and futures markets
Loss reserving models: Granular and machine learning forms
Exchange rate forecasting with advanced machine learning methods
Analysis of Bitcoin price prediction using machine learning
Risks special issue on "granular models and machine learning models"
Time to assess bias in machine learning models for credit decisions
Cryptocurrency trading using machine learning
Expectations for statistical arbitrage in energy futures markets
Volatility and depth in commodity and FX futures markets
Volatility integration in spot, futures and options markets: A regulatory perspective
Machine Learning in Futures Markets
Machine learning for multiple yield curve markets: Fast calibration in the Gaussian affine framework
Hedging performance and multiscale relationships in the German electricity spot and futures markets
Loss reserving models: Granular and machine learning forms
Exchange rate forecasting with advanced machine learning methods
Analysis of Bitcoin price prediction using machine learning
Risks special issue on "granular models and machine learning models"
Time to assess bias in machine learning models for credit decisions
Cryptocurrency trading using machine learning
Expectations for statistical arbitrage in energy futures markets
Volatility and depth in commodity and FX futures markets
Volatility integration in spot, futures and options markets: A regulatory perspective
Machine Learning in Futures Markets
Machine learning for multiple yield curve markets: Fast calibration in the Gaussian affine framework
Hedging performance and multiscale relationships in the German electricity spot and futures markets
Loss reserving models: Granular and machine learning forms
Exchange rate forecasting with advanced machine learning methods
Analysis of Bitcoin price prediction using machine learning
Risks special issue on "granular models and machine learning models"
Time to assess bias in machine learning models for credit decisions