  We consider correlated \emph{factor} regression models (FRM) and analyze the
performance of classical ridge interpolators. Utilizing powerful \emph{Random
Duality Theory} (RDT) mathematical engine, we obtain \emph{precise} closed form
characterizations of the underlying optimization problems and all associated
optimizing quantities. In particular, we provide \emph{excess prediction risk}
characterizations that clearly show the dependence on all key model parameters,
covariance matrices, loadings, and dimensions. As a function of the
over-parametrization ratio, the generalized least squares (GLS) risk also
exhibits the well known \emph{double-descent} (non-monotonic) behavior.
Similarly to the classical linear regression models (LRM), we demonstrate that
such FRM phenomenon can be smoothened out by the optimally tuned ridge
regularization. The theoretical results are supplemented by numerical
simulations and an excellent agrement between the two is observed. Moreover, we
note that ``ridge smootenhing'' is often of limited effect already for
over-parametrization ratios above $5$ and of virtually no effect for those
above $10$. This solidifies the notion that one of the recently most popular
neural networks paradigms -- \emph{zero-training (interpolating) generalizes
well} -- enjoys wider applicability, including the one within the FRM
estimation/prediction context.
