Ranking vs machine-learning algorithms

[yuvaltaylor]

@AlgoMan

Something I would like to see is to use Z-score as ranking option in the classic ranking systems. Like creating a composite that sum all the underlying factor z-score, preferably z-score by dataset as well as by date. That would give traditional ranking a completely new dynamic.

As an example, when using ranking (by date) for volatility factors, in calm markets (tight z-score range) the volatility factor shouldn’t matter as much as in a bear market (wide z-score range), but when always ranking between 0-100, volatility carries the same weight regardless of the volatility range of the underlying factor. If we could use z-score by dataset, the scoring would be a dynamic range and not a static range as now.

But that would introduce look-forward bias. The only way to properly do this would be to limit the dataset to past dates. In that case, the dataset would consist of one date at the beginning of the period and a huge number at the end. And that would, in turn, introduce wild inconsistencies in performance.

This is also a reason not to use the "dataset" option in AI models.

Also--and I'm really not sure about this--but I think the "Normal Distribution (Experimental)" option in ranking systems' ranking method is the Z-score.