I'd love to see linear regression taught by say a quant researcher from Citadel. How do these guys use it? What do they particularly care about? Any theoretical results that meaningfully change the way they view problems? And so on.
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.
Call it 2000 liquid products on the US exchanges. Many years of data. Even if you approximate it down from per tick to 1 minutely, that doesn't feel like you're struggling for a large in sample period?
These may be valid assumptions, but even if they are, "sample size" is always relative to between-sample unit variance, and that variance can be quite large for financial data. In some cases even infinite!
Regarding relativity of sample size, see e.g. this upcoming article: https://two-wrongs.com/sample-unit-engineering
There is no big mystery I'm afraid, there is no big reveal. It's as Jim Simons described in the Numberphile video interview: a slow painstaking accumulation of weak signals, plus crafting and improving various boxes of the system. (the interfaces between them are largely known) The fitting method used does not buy that much in the grand scheme of things - as long as it does not ruin things, that is.
(I've not been at Citadel but been quant R&D&trading last 20yrs)