In recent years, machine learning algorithms have been successfully employed to leverage the potential of identifying hidden patterns of financial markets behavior, and, consequently, have become a land of opportunities for financial applications such as algorithmic trading. In this paper, we propose a statistical arbitrage trading strategy with two key elements: an ensemble of regression algorithms for asset return prediction, followed by a dynamic asset selection. More specifically, we construct an extremely heterogeneous ensemble by using state-of-the-art machine learning algorithms and a feature selection process. Then, their predictive results are fed into a quality assurance mechanism that prunes assets with poor forecasting performance in the previous periods. We evaluate the approach on historical data of component stocks of the S&P500 index. Experimental results show that this setup outperforms each considered baseline. Moreover, the approach proved to yield superior results during both financial turmoil and massive market growth periods, and it showed to have general application for any risk balanced trading strategy aiming to exploit different asset classes.

CARTA Salvatore;  CONSOLI Sergio;  PODDA Alessandro Sebastian;  REFORGIATO RECUPERO Diego;  STANCIU Maria Madalina; 

2021-04-27

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

JRC122259

2169-3536 (online),   

https://ieeexplore.ieee.org/document/9353479,    https://publications.jrc.ec.europa.eu/repository/handle/JRC122259,   

10.1109/ACCESS.2021.3059187 (online),