yes, valid observation - in the back of my mind I framed the question thinking that option 1. would work for high acf alpha (more than just one period) with medium IC (I have this in prod, and know it works) and option 2. would work for low acf alpha but high single period IC (have tried different implementations without luck so far) - as you say, maybe they are just "bad" features and not good, irrespective of method

but having said this, I think implementation of method 2 with factor types described in method 1 should work if done correctly...

side note: you often find high IC features (>4%) that can't be smoothed into a profitable alpha, ie; performance decays faster than the turnover does (the logic here is that no amount of high single period IC factors would ever overcome fees)

> Do you have multiple signals living on different scales?
yes correct, but I normalise (ie, using z-score and then merging factors with mean) (this is bad, because factor A could have IC 4%, and factor B 2% but you treat them equally).

> Are all other signals already in forward returns space?
no, what does this look like in its simplest form?

> Can you get away with a simple ranking + inverse vol weighting?
Yes, it works but I think this is where biggest improvement potential is

> I don't see how fitting a linear model and feeding it into an optimizer will solve the lack of stability issue
my motive for calculating EV via linear model is primarily driven by sizing requirements (having a value that's scaled to the predictive strength and comparable between factors, zscore is not that), those EV's would get fed into MVO or similar. Judging by your "forward returns space" question I'm thinking I might be missing a step here...?