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Supervised machine learning classification for short straddles on the S&P500

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  • Alexander Brunhuemer
  • Lukas Larcher
  • Philipp Seidl
  • Sascha Desmettre
  • Johannes Kofler
  • Gerhard Larcher

Abstract

In this working paper we present our current progress in the training of machine learning models to execute short option strategies on the S&P500. As a first step, this paper is breaking this problem down to a supervised classification task to decide if a short straddle on the S&P500 should be executed or not on a daily basis. We describe our used framework and present an overview over our evaluation metrics on different classification models. In this preliminary work, using standard machine learning techniques and without hyperparameter search, we find no statistically significant outperformance to a simple "trade always" strategy, but gain additional insights on how we could proceed in further experiments.

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," Papers 2204.13587, arXiv.org.
    • Handle: RePEc:arx:papers:2204.13587
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    File URL: http://arxiv.org/pdf/2204.13587
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    References listed on IDEAS

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    1. Day, Theodore E & Lewis, Craig M, 1997. "Initial Margin Policy and Stochastic Volatility in the Crude Oil Futures Market," The Review of Financial Studies, Society for Financial Studies, vol. 10(2), pages 303-332.
    2. Peter Carr & Liuren Wu & Zhibai Zhang, 2019. "Using Machine Learning to Predict Realized Variance," Papers 1909.10035, arXiv.org.
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    Cited by:

    1. Malvina Marchese & María Dolores Martínez-Miranda & Jens Perch Nielsen & Michael Scholz, 2024. "Robustifying and simplifying high-dimensional regression with applications to yearly stock return and telematics data," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-16, December.

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