Disclosure under Noisy Information Processing

Battle Count: 77

This paper is primarily about information economics and voluntary disclosure theory rather than quantitative trading. However, it has indirect relevance: understanding how processing noise affects market pricing of disclosed information could inform event-driven strategies, earnings announcement trading, and the interpretation of disclosure signals. The equilibrium multiplicity results suggest that the same disclosure event could be priced differently depending on the prevailing equilibrium regime, which could affect trading signals derived from corporate disclosures.

This is a purely theoretical paper with no computational implementation required. The model is solved analytically using closed-form expressions for truncated normal distributions and inverse Mills ratio properties. Figures are generated from analytical expressions. There is no algorithm to implement, no training procedure, and no software component. The complexity lies in understanding the mathematical proofs and economic intuition, not in implementation.

The paper is a fully theoretical work with complete mathematical proofs provided in the main text and supplementary appendix. All propositions, lemmas, and corollaries are formally proven. The model is self-contained with explicit distributional assumptions (normal distribution). However, there is no code or numerical implementation provided for replication of figures. The analytical results are fully derivable from the stated assumptions.