A Machine Learning Based Asset Pricing Factor Model Extension Comparison On Anomaly Portfolios

We frame linear factor models for asset pricing in a machine learning context and consider a numerical comparison of their performance against ordinary least squares linear regression over a dataset of anomaly portfolios. Specific regression models involved in the comparison include regularized linear, support vector machines, neural networks, and tree based models. Performance metrics are presented on a model and portfolio group basis, and the strongest predictors are recommended as alternative methods for the problem of excess return forecasting