Residual Factor Prediction Via Time Series-based Machine Learning

Machine Learning algorithms have been widely used and proven effective in financial markets. In this paper, we introduced a Machine Learning model set trained on the residual factors from the Fama-French three-factor model (Fama and French, 1992) to find significant alpha factors. To include more information that the linear factor models did not have, we used time series-based Machine Learning models, like tree-based models and Shallow Neural Networks with time series features. We used the predicted residual factor to construct quintile portfolios and found that it provided a significant alpha return even if style factors were controlled. It helped us find the latent information in the residual term and prove the lack of time series information in the factor models