Supervised machine learning classification for short straddles on the S&P500
Computational Finance
2022-04-29 v1 Machine Learning
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.
Cite
@article{arxiv.2204.13587,
title = {Supervised machine learning classification for short straddles on the S&P500},
author = {Alexander Brunhuemer and Lukas Larcher and Philipp Seidl and Sascha Desmettre and Johannes Kofler and Gerhard Larcher},
journal= {arXiv preprint arXiv:2204.13587},
year = {2022}
}
Comments
25 pages