Predicting the Options Expiration Effect Using Machine Learning Models Trained With Gamma Exposure Data

Abstract: The option expiration effect is a well-studied phenome, however, few studies have implemented machine learning models to predict the effect on the underlying stock market due to options expiration. In this paper four machine learning models, SVM, random forest, AdaBoost, and LSTM, are evaluated on their ability to predict whether the underlying index rises or not on the day of option expiration. The options expiration effect is mainly driven by portfolio rebalancing made by market makers who aim to maintain delta-neutral portfolios. Whether or not market makers need to rebalance their portfolios depend on at least two variables; gamma and open interest. Hence, the machine learning models in this study use gamma exposure (i.e. a combination of gamma and open interest) to predict the options expiration effect. Furthermore, four architectures of LSTM are implemented and evaluated. The study shows that a three-layered many-to-one LSTM model achieves superior results with an F1 score of 62%. However, none of the models achieved better predictions than a model that predicts only positive classes. Some of the problems regarding gamma exposure are discussed and possible improvements for future studies are given.