Anomalies as New Hedge Fund Factors : A Machine Learning Approach

We identify factors from a large set of anomalies for explaining hedge fund returns using machine learning methods. Our new model combines anomaly factors with market and macro factors and outperforms existing models both in-sample and out-of-sample. Moreover, the model leads to a significant reduction in hedge fund alphas compared with other models, while revealing substantial cross-sectional performance heterogeneity. Further subsample analysis provides evidence of style shifting in the hedge fund industry. Overall, the anomaly factors help quantify hedge fund strategies and risk exposures and are useful for fund performance evaluation