“…Ludvigson and Ng (2007) and Kelly and Pruitt (2013) use principal components regression and partial least squares, respectively, to leverage large predictor sets for market return prediction and achieve shrinkage through dimension reduction. Dong et al (2022) use 100 long-short "anomaly" portfolios to forecast the market return using a variety of forecasting strategies to implement shrinkage (more generally, see the recent survey by Rapach and Zhou (2022)). An emerging literature uses machine learning methods to forecast large panels of individual stock returns or portfolios, including Rapach and Zhou (2020), Kozak, Nagel, andSantosh (2020), Freyberger, Neuhierl, andWeber (2020), Gu, Kelly, andXiu (2020), andChen, Pelger, andZhu (2023) (also see the survey by Kelly and Xiu (2022)).…”