A General Approach for Risk Controlled Trading Based on Machine Learning and Statistical Arbitrage

Nowadays, machine learning usage has gained significant interest in financial time series prediction, hence being a promise land for financial applications such as algorithmic trading. In this setting, this paper proposes a general approach based on an ensemble of regression algorithms and dynamic asset selection applied to the well-known statistical arbitrage trading strategy. Several extremely heterogeneous state-of-the-art machine learning algorithms, exploiting different feature selection processes in input, are used as base components of the ensemble, which is in charge to forecast the return of each of the considered stocks. Before being used as an input to the arbitrage mechanism, the final ranking of the assets takes also into account a quality assurance mechanism that prunes the stocks with poor forecasting accuracy in the previous periods. The approach has a general application for any risk balanced trading strategy aiming to exploit different financial assets. It was evaluated implementing an intra-day trading statistical arbitrage on the stocks of the S&P500 index. Our approach outperforms each single base regressor we adopted, which we considered as baselines. More important, it also outperforms Buy-and-hold of S&P500 Index, both during financial turmoil such as the global financial crisis, and also during the massive market growth in the recent years.