Statistical Arbitrage with Momentum Using Machine Learning

Abstract

In this paper machine learning is used to investigate statistical arbitrage in China stock market. We use HS300 index constituent stocks to construct pairs trading. The daily and monthly momentums in these stocks are used as new input factors to forecast the stock price. We develop a trading approach to find that random forest (RF) outperform deep neural net (DNN), XGBoost, support vector machine(SVM) and LSTM from January 2013 to August 2017.