Generalize classifier validation across public regime classifiers

Investigate how the cross-timeframe Kuramoto coherence measure's descriptive relationship with past-move efficiency varies across a broader range of public statistical and learned regime classifiers, and conduct a systematic sensitivity analysis of classifier choice.

Background

The headline coherence results use a proprietary regime classifier from a deployed trading-analytics platform, so the classifier cannot be independently reimplemented from the paper. The authors compare it with one publicly specified technical trend-volatility classifier and find that the qualitative descriptive relationship is preserved, although its magnitude is reduced.

The paper leaves unresolved whether this robustness extends to a wider range of public rule-based, statistical, and learned classifiers, and whether classifier design systematically affects the measured coherence signal.

References

This paper does not attempt to resolve every question that follows from that gap; a broader sweep of public classifiers (statistical and learned, not just rule-based) and a systematic sensitivity analysis is deliberately left to separate, dedicated work, and only the one comparison load-bearing for this paper's own reproducibility claim is reported here.

— Synchronization Reveals Market Structure That Classical Randomness Tests Cannot Detect  (2610.11028 - Le, 8 Oct 2026) in Section 4, Discussion, paragraph 2