Supervised machine learning classification for short straddles on the S&P500
This is an incremental study for financial traders, as it applies existing methods to a new dataset without achieving meaningful gains.
The paper tackles the problem of using supervised machine learning to classify daily execution decisions for short straddle options on the S&P500, finding no statistically significant outperformance compared to a simple 'trade always' strategy.
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.