I think the paper is probably correct on this. Noting the referenced study is about classifications models and that it can probably be generalized to regression models. We generally use regression models at P123 and first iteration of AI/ML will be using regression models exclusively if I understand correctly. BTW, I think it was a wise choice to start with regression models and I am not sure that classification models should be a priory… I am not questioning the decision to do that as I think it was a good decision.
I would add to this paper the evidence from classic P123 users. P123 users commonly rebalance weekly. And P123 is kind-of-like a non-parametric (i.e., uses ranks which are ordinal) multivariate regression with manual optimization. Manual optimization rather than using something like gradient descent (internally at P123 anyway). If P123 classic used automated optimization it would clearly be be a machine learning model.
Actually, not “kind-of.” It is, in fact, a non-parametric regression model that is generally optimized manually by members. But a P123 model can be optimized with machine learning. For example, one cloud determine the weights in a ranking system using a regression model. Correct me if this is not factually correct.
But whatever people want to call what they do with classic P123, whether they self-identify as using statistical learning, fundamental analysis or whatever, P123 members’ manual optimizations are not likely to be any different than those resulting from gradient descent optimizations that we will be using with machine learning at P123.
And P123 is moving toward improved automation of P123 classic—considering allowing members to do some of the optimizations in parallel for example. I am not sure at which point everyone agrees that it is at least a little-bit-like machine learning or when P123 finally automates the entire process making it machine learning by definition.
This is just to say P123 classic has been (and is) valuable. Personally, I don’t mind having some of that automated with grading descent algorithms. I am only making the point that I believe much of what has been found to be true with P123 classic will continue to be true with automation.
TL;DR: Most P123 classic members have been using weekly rebalance for a while now and I don’t expect optimization with fully automated machine learning (i.e., gradient descent) to change that in any way. It would be surprising if it did.
Jim