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

Alexander Brunhuemer, Lukas Larcher, Philipp Seidl, Sascha Desmettre (), Johannes Kofler and Gerhard Larcher
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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

Risks, 2022, vol. 10, issue 12, 1-25

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.

Keywords: machine learning; supervised classification; gradient tree boosting; option trading strategies; short straddles; S&P500 (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2022
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