I have a financial dataset with 9500 observations with 4000 variables. There are around 2500 variables highly correlated (higher than 0.95). Without removing any variable, I have applied PCA. According to my results, first 100 components explain 78.8%. When I check the PCs in detail, I observed that the highest loadings range between 0.01 - 0.05. On the other hand, the original variables having the highest loadings make sense. That is, same group of variables are appearing on the top (top positive) or on the bottom for PCs (bottom negative).
Besides the approach above, I have removed one variable from the pairs with a correlation higher than 0.95.This reduced my variable set to 1200. Then I again applied PCA. However, there happened no significant change on loadings.
In short, how should I pick the most important components for each PC when such very low loadings present?