Informational Feedback and ESG Growth Opportunities: Optimal Public Disclosure

Abstract

We develop a rational expectations model to study the optimal precision of firm's public disclosure when traders have heterogeneous preferences and the firm learns from market prices to guide its ESG investment decisions. In our model, the firm’s payoff combines financial and ESG components, while traders differ in the component they value. The firm's manager can publicly disclose noisy signals about these components and uses stock prices to infer the value of an ESG-linked growth opportunity. We show that when the manager's private information is imprecise, the investment opportunity is large, and prices are more informative about the value of the ESG component that the manager wants to learn about, greater precision of public disclosure may impair firm’s ability to learn from prices and may reduce firm value. In contrast, when manager's information is highly precise, the investment opportunity is small, and prices are more informative about the financial component, full disclosure increases firm value. Thus, the firms' optimal disclosure policy balances the benefits of price efficiency against the costs of reduced market-based learning, generating a non-monotonic relationship between disclosure and firm value. Our findings uncover novel trade-offs in public disclosure design and offer policy implications for firms and policymakers navigating evolving transparency mandates. JEL codes: G11; G20; G23.

Keywords: ESG investors' preferences, Public disclosure, Feedback Effect

JEL Classification: G11, G20, G23