Sorry, maybe I wasn’t clear.
Model B has stock market correlation of -1%
Meta model has stock market correlation of -1%
Model B has correlation with meta model of 0%.
These 3 things are completely separate numbers/notions.
You could get a model with the same corr on the stock market as the meta model, but around 0% correlation with the meta model itself. Please do no confuse the 3 correlation types.
I’m not saying that originality should be rewarded when it’s bad.
I’m saying that it gets punished “more” than a meta model clone. Which shouldn’t be the case as this overall diminishes the incentives of MMC, by basically saying that during meta model burns, meta model clones are “safer”.
Perhaps the problem is more like MMC punishes meta model clones less during meta-model burns, than original models.
I have seen actual models during this round that have higher positive corr on target, lower meta-model correlation, but lower MMC than models with lower corr (even negative) on target, but higher meta-model correlation.
As stated, these models would probably have higher MMC during good periods, but why should they have lower MMC during bad periods?
If you run my example code, the two models have the same positive corr on target (around 2.4%) on target, but because the meta model has around - 5% (so really negative) corr on target, the MMC for the original model is negative while the MMC for the 95% meta model correlation model is very high and positive.
Basically, we remove the very bad signal (the meta model in this case) from the meta model clone and we end up with a huge MMC. For the -63% meta model correlation model (so a completely original, even opposite model to the meta model), we get really negative MMC. Why? Because we are basically adding the meta model to the original model (negative times negative equal plus), as per the neutralize_series code from the official forum post.