Given a lot of market-related features (~100 independent variables such as emerging market, developed market, s&p 500, tech sector returns, etc), I need to select a subset of them that are ideally independent and are the major drivers of the global stock market return during time t=t1 to t=t2.
Specifically, the model has to identify important/non-important variables when: 1) the number of independent variables (p) is large (~100) 2) the number of sample size (n) < the number of independent variables (p) and when n >= p
Are Lasso and PCA good ways to accomplish this? I guess Lasso is a simple, easy method. I think that the problem with PCA is that the interpretation of the result is not going to be easy...
Are there academic literature that deals with this problem (selecting a subset of large independent variables to predict the global stock market return)