Skip to main content
AI × Quant × TradingFoundations US$99 · Signals US$1,299 · AI Live US$5,980 · partner institution employees firstView →
arXiv q-fin· Jeremy Bertomeu, Edwige Cheynel, Peicong Hu·· 5 hr agoAI Score36

Disclosure under Noisy Information Processing

Disclosure under Noisy Information Processing

AI summary

This theoretical study introduces information processing noise as a friction into capital market disclosure models, overturning traditional conclusions about voluntary disclosure costs. The model shows that firms may achieve full disclosure even when facing strictly positive disclosure costs, and when full disclosure is not achieved, two types of equilibria typically exist. This study can explain mixed empirical results regarding proprietary cost assumptions, as well as phenomena such as ESG rating noise and high-cost disclosures that still affect market prices.

Source:arXiv q-fin · arxiv.org

Next steps

Research Column: Backtesting and ValidationWhy backtests look good but fail in live trading, and how to conduct out-of-sample testing.WeChat Community and Contact InformationView official contact details; QR codes will be displayed on the About page once available.Prefer English? Get the research briefing by emailRegister your interest in English research updates on our website.