Alpha Factor Library
Throughout this book, we've described how to engineer features from market, fundamental, and alternative data to build machine learning (ML) models that yield signals for a trading strategy. The smart design of features, including appropriate preprocessing and denoising, is what typically leads to an effective strategy. This appendix synthesizes some of the lessons learned on feature engineering and provides additional information on this important topic.
Chapter 4, Financial Feature Engineering – How to Research Alpha Factors, summarized the long-standing efforts of academics and practitioners to identify information or variables that help reliably predict asset returns. This research led from the single-factor capital asset pricing model to a "zoo of new factors" (Cochrane, 2011). This factor zoo contains hundreds of firm characteristics and security price metrics presented as statistically significant predictors of equity returns in...
