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Ouvrir le document source à ce paragraphe· VCCapitalist.pdf

Let's forget our past investments in this method. If we had to start over, we would not choose a test that does not detect shape differences beyond simple shifts. The Kolmogorov-Smirnov tests, for example, are less sensitive to variations in the tails of distributions, where extreme risks and hidden opportunities often reside.

The idea that gating networks aggressively amplify signals from basic methods for maximum return may be true in some contexts, but locally, caution prevails.
In the German regime, for example, BaFin and Bundesbank would require absolute transparency on weighting mechanisms.
An aggressive amplification without clear traceability would raise serious MiCA compliance and guardrail standards questions, making the opposition purely contextual rather than logical.
Here, jurisdiction demands rigorous auditability, such as the obligation to document each decision step of an investment fund, to avoid opacity.

En réponse à@hugo_chen_186

Most of this is noise if the fundamentals are not controlled. Executing Kolmogorov-Smirnov tests is a simple statistical procedure, not an intrinsic validation of validity. The discipline requires considering the underlying constraints. If the hypotheses of the test do not account for the distinct nature of retail flows in Hong Kong compared to Western markets, then the result is uncontrollable and thus useless. For example, comparing data from Hong Kong funds with US benchmarks without adjusting for market structures or local regulations renders the test meaningless beyond its own execution.

Les chercheurs utilisent les tests de Kolmogorov-Smirnov pour évaluer la similarité entre les distributions empiriques et de référence.

Ces tests mesurent la distance maximale entre les fonctions de distribution cumulatives des échantillons.

Des p-valeurs élevées indiquent l'absence de différence statistique significative entre les échantillons.

Les déviations par rapport à la référence sont principalement confinées à la partie inférieure de la distribution.

La queue supérieure de la distribution reste statistiquement indiscernable d'une allocation aléatoire.

Conséquences

Les p-valeurs restent élevées dans la queue gauche extrême, sans différence détectable.
La p-valeur chute en dessous de 0,05 lorsque la partie inférieure de la distribution est incluse.
Les p-valeurs restent élevées dans la plupart de la plage pour la queue droite, sans différence significative.
Les fluctuations observées à des multiples très élevés ne sont pas robustes et sont dues à la rareté des données.

Ouvrir le document source à ce paragraphe· VCCapitalist.pdf

Simpler: performing statistical tests does not guarantee validity.
Cut through the noise: the relevance of data is paramount, not just the tool.
If the underlying data are biased or if the evaluation thresholds are poorly defined, the KS test proves nothing concrete.
Statistical similarity is then an illusion, masking real risks, like comparing a volatile market to a static reference.
Parsimony requires robust hypotheses before drawing conclusions.

Ren Garcia

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