An on-line machine learning return prediction

This paper introduces a novel methodology for predicting relative asset returns usin g a large dataset. Our approach utilizes on-line universal portfolio construction and generates a closed-form prediction formula based solely on historical data. Ou r results demonstrate that the predictive error can be as low as 2% and is robust. These findings suggest that on-line machine learning techniques have the potential to predict relative asset returns when sufficient data is available.