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Supervised Machine Learning Classification for Short Straddles on the S&P500

Author

Listed:
  • Alexander Brunhuemer

    (Institute for Financial Mathematics and Applied Number Theory, Johannes Kepler University Linz, AT-4040 Linz, Austria)

  • Lukas Larcher

    (Institute for Financial Mathematics and Applied Number Theory, Johannes Kepler University Linz, AT-4040 Linz, Austria)

  • Philipp Seidl

    (Institute for Machine Learning, Johannes Kepler University Linz, AT-4040 Linz, Austria)

  • Sascha Desmettre

    (Institute for Financial Mathematics and Applied Number Theory, Johannes Kepler University Linz, AT-4040 Linz, Austria)

  • Johannes Kofler

    (Institute for Machine Learning, Johannes Kepler University Linz, AT-4040 Linz, Austria)

  • Gerhard Larcher

    (Institute for Financial Mathematics and Applied Number Theory, Johannes Kepler University Linz, AT-4040 Linz, Austria)

Abstract

In this paper, we apply machine learning models to execute certain short-option strategies on the S&P500. In particular, we formulate and focus on a supervised classification task which decides if a plain short straddle on the S&P500 should be executed or not on a daily basis. We describe our used framework and present an overview of our evaluation metrics for different classification models. Using standard machine learning techniques and systematic hyperparameter search, we find statistically significant advantages if the gradient tree boosting algorithm is used, compared to a simple “trade always” strategy. On the basis of this work, we have laid the foundations for the application of supervised classification methods to more general derivative trading strategies.

Suggested Citation

  • Alexander Brunhuemer & Lukas Larcher & Philipp Seidl & Sascha Desmettre & Johannes Kofler & Gerhard Larcher, 2022. "Supervised Machine Learning Classification for Short Straddles on the S&P500," Risks, MDPI, vol. 10(12), pages 1-25, December.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:12:p:235-:d:999165
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    References listed on IDEAS

    as
    1. Prilly Oktoviany & Robert Knobloch & Ralf Korn, 2021. "A machine learning-based price state prediction model for agricultural commodities using external factors," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 1063-1085, December.
    2. Thomas C. Chiang, 2020. "Risk and Policy Uncertainty on Stock–Bond Return Correlations: Evidence from the US Markets," Risks, MDPI, vol. 8(2), pages 1-16, June.
    3. Gil Cohen, 2022. "Algorithmic Trading and Financial Forecasting Using Advanced Artificial Intelligence Methodologies," Mathematics, MDPI, vol. 10(18), pages 1-13, September.
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