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

The paper introduces DisclosureBeta, a theory that treats large language models (LLMs) as noisy measurement channels for a firm’s latent risk characteristics, integrating this noise into the asset‑pricing error budget. It establishes identification and consistency of regime‑conditional beta loadings within a piecewise‑stationary Fama‑French five‑factor framework, provides matching lower bounds, and proposes an adaptive estimator that blends text‑based and rolling‑window approaches, improving precision when price histories are short or regime‑breaks occur. The work also outlines a pre‑registered empirical evaluation on firms with thin price histories.