UCLA

We propose multiple advanced learning methods to deal with the "curse of dimensionality"

challenge in the cross-sectional stock returns. Our purpose is to predict the one-month-ahead

stock returns by the rm characteristics which are so-called "anomalies". Compared with

the traditional methods like portfolio sorting and Fama Factor models, we focus on using

all existing machine learning methods to do the prediction rather than the explanation. To

alleviate the concern of excessive data mining, we use several regularization penalties that

can lead to a sparse and robust model. Our method can identify the return predictors with

incremental pricing information and learn the interaction effects by applying to a hierarchical

structure. Our best method can achieve much higher out of sample R2 and portfolio Sharp

Ratios than traditional linear regression method.