2026/05/16 by Will Hammond · 1 voice
Economics, Econometrics and Finance · #q-fin.ST
arxiv published 2026/05/16 · arxiv updated 2026/05/27
We extract four geometric observables -- Berry Phase Rate, Spectral Entropy, Reduced State Purity, and Hamiltonian Sensitivity -- from a learned spectral embedding of equity-index returns and evaluate them as regime-shift detectors against 46 classical and machine-learning baselines on 17 historical crises spanning 2000-2024. Under walk-forward nested hyperparameter selection on nine labelled windows, the Berry Phase Rate achieves an unbiased out-of-sample median Cohen's d = 0.72 (95% percentile-bootstrap CI [0.34, 1.18], 10,000 resamples) and produces approximately 67% fewer false alarms per year than a label-supervised Random Forest (1.2 vs. 3.6 per year). Reduced State Purity attains the highest in-sample separability of any method (d = 0.83), tied closely by the Absorption Ratio (d = 0.80); geometric and classical channels are largely uncorrelated (mean |ρ| ≈ 0.22), suggesting they capture distinct risk signals. Score construction is unsupervised; hyperparameter selection is the only supervised step.