I have some experience. Variants of regularization are a must. There are just too few samples and too much noise per sample.

In a related problem, covariance matrix estimation, variants of shrinkage is popular. The most straight forward one being Linear Shrinkage (Ledoit, Wolf).

Excepting neural nets, I think most people doing regression simply use linear regression with above type touches based on the domain.

Particularly in finance you fool yourself too much with more complex models.