The author in this paper identifies the gap between analytical and empirical studies regarding the relation between disclosure and cost of capital. Distinct from prior reviews, this paper focuses on the various assumptions of theoretical models and the insights and key results derived from those assumptions. The author also reviews how these theoretical papers are “applied” in empirical studies.
The author systematically analyzes both theoretical and empirical papers that investigate disclosure and cost of capital between 2000 and 2020.
The author shows (1) that there is ample room for theorists to move from the pure exchange economy to the production-based economy setting to investigate the real effect of disclosure on the cost of capital; (2) structural estimation, although still nascent, is a promising direction to build the bridge between analytical and empirical studies in disclosure and cost of capital, and (3) besides ordinary least squares (OLS) regressions, researchers are encouraged to think outside the box regarding how to investigate the interplay between disclosure and cost of capital via a Deep Neural Network design.
The author provides a unique perspective and synthesized knowledge in the relations of disclosure and cost of capital.
