Geometric Observables for Financial Regime Detection
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866916052043038720 |
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| author | Hammond, Will |
| author_facet | Hammond, Will |
| contents | 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 $|ρ| \approx 0.22$), suggesting they capture distinct risk signals. Score construction is unsupervised; hyperparameter selection is the only supervised step. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_17117 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Geometric Observables for Financial Regime Detection Hammond, Will Statistical Finance 62M10, 81Q70, 91G70 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 $|ρ| \approx 0.22$), suggesting they capture distinct risk signals. Score construction is unsupervised; hyperparameter selection is the only supervised step. |
| title | Geometric Observables for Financial Regime Detection |
| topic | Statistical Finance 62M10, 81Q70, 91G70 |
| url | https://arxiv.org/abs/2605.17117 |