DisclosureBeta: A Measurement-Channel Theory for Regime-Conditioned Betas from LLM-Read Risk Disclosures

Abstract:The problem is the beta a desk needs when a firm's price history is too short to trust: an S-1 filer, a recent listing, or a name just past a regime break. The state of the art collapses to a comparable-firm peer beta with no error budget, and the recent text-based competitor Breitung (2025) reports strong empirical IPO accuracy but no identification theory, no error budget, and no lower bound. We fill that gap. We model a large language model as a noisy measurement channel on a firm's latent risk characteristics and write its channel noise into the asset-pricing error budget. In a piecewise-stationary Fama-French five-factor model the loadings are a function of latent risk characteristics and an inferred regime. We prove identification and consistency of the regime-conditional loading function under explicit assumptions on the channel, the detector, and within-regime sampling, and give a matching lower bound showing that the disclosure-noise and detector-misclassification terms are unavoidable for any estimator that observes only returns, factors, LLM features, and a regime estimate. A disclosure-incentive corollary makes estimation precision monotone in a firm-level disclosure-incentive measure (DIM). An adaptive convex combination of the text-based and rolling-window estimators is never worse than either component and shifts its weight toward text exactly when price history is short, stale, or straddles a detected regime break. The empirical evaluation on a frozen, pre-registered panel of price-history-thin firms is forthcoming; this preprint records the theory and the pre-registered design so priority is established independently of the empirical outcome.

8 pages, 0 figures. Theory preprint; empirical evaluation forthcoming in a companion paper

Risk Management (q-fin.RM); Computation and Language (cs.CL); General Finance (q-fin.GN)

arXiv:2609.02900 [q-fin.RM]

(or arXiv:2609.02900v1 [q-fin.RM] for this version)

https://doi.org/10.48550/arXiv.2609.02900 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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