Machine learning in pair trading

Abstract

This research seeks to develop and compare traditional threshold methods and machine learning models applied in pair trading of stocks. Unsupervised machine learning firstly segments stocks into different clusters based on stocks’ return and volatility profile. Stock pairs would be filtered out according to several criteria. Traditional threshold method, XGBoost, and LSTM models are applied to the pair trading mechanism with performance compared.