Exécution de notre outil de synthèse sur un nouvel article

Résumé : Artificial Intelligence (AI) and Machine Learning (ML) has seen unprecedented growth over the past decade with applications across healthcare, robotics, data security and automotive industries to name a few. Penetration of AI in finance has been slower; primarily due to the intrinsic nature of markets – non-stationarity of financial processes and lack of sufficient training data. We make a foray into applying ML to finance by training a range of models to trade Momentum – a systematic strategy that benefits from persistence of trends in markets. Our findings suggest that a class of ML algorithms like Random Forests and Neural Nets produce Sharpe ratios close to optimal vanilla time-series momentum (TSMOM). However, correlation between ML and vanilla TSMOM signals is low allowing us to harvest fruits of diversification by creating hybrid vanilla – ML TSMOM strategies. We find such hybrid portfolio Sharpe ratios to be twice as high as vanilla TSMOM.