Research

[1] Strategic Disclosure when Beliefs Diverge (solo-authored)

I study strategic voluntary disclosure when investors have heterogeneous priors and are uncertain about how precise a public signal is. In this context public information can push beliefs further apart rather than closer together (see Armstrong et al. 2024). Standard intuition would suggest that in such polarized markets firms should disclose less as silence feels safer in a world where any statement is read through partisan priors. My findings overturn this prior and show that it must not generally obtain. Disclosing ex-post, after observing the signal, disagreement depresses the non-disclosure price and makes moderately good news pay off more if revealed. The manager sets a lower threshold than in a no-divergence benchmark. Ex-ante however, when a manager commits to a disclosure policy before the signal is realized, broader non-disclosure reduces announcement-day price dispersion, albeit at the cost of absorbing some favorable states into the non-disclosure price. As such, the optimal commitment threshold is higher than the ex-post threshold even when disclosure is costless. Thus, the same divergence that encourages more disclosure when decisions are taken after observing the signal induces less disclosure under policy commitment. This reversal across margins recasts voluntary disclosure as a tool to manage disagreement-sensitive price risk and yields disciplined empirical predicitons. To speak to data, I pair the theory with descriptive archival tests using earnings calls as a proxy for voluntary disclosure and linking ex-ante disagreement and uncertainty about signal quality to the incidence of voluntary disclosure.

[2] Behavioral Dynamics of Disclosure Timing, with Joaquin Peris and Qian Zhang 

We examine a dynamic disclosure model where a manager interacts with a representative investor who exhibits loss aversion under Prospect Theory. A key focus of our analysis is the strategic timing of disclosure decisions in response to biased capital markets. We find that when facing such bias, the manager will disclose moderately bad news more promptly to dampen the negative price effect resulting from loss aversion, even if that means foregoing a higher non-disclosure price. This contrasts with previous findings of delayed disclosure in models with pending news and suggests that asymmetries in the timeliness of disclosing good versus bad news can arise endogenously as an optimal strategy. Empirically, we investigate how loss aversion influences disclosure timing using analyst forecasts and price target revisions. We find that firms facing higher loss aversion issue management guidance more frequently and release bad news more promptly. Furthermore, we document that preemptive disclosure is associated with a mitigated negative market response to bad news in the presence of prospect-theoretic markets. Our findings offer new insights into capital market effects of reporting asymmetries and the strategic timing of disclosures, adding to the literature on the behavioral foundations of financial reporting.

[3] Strategic Digital Disclosure - A Theory Perspective, with Jeremiah Lewis 

XBRL mandates require firms to tag financial statement items using a standardized taxonomy, promising lower processing costs and enhanced cross-firm comparability. Yet firms retain discretion over tag choice. We provide stylized facts drawn from ESEF and EDGAR filings showing that extension tags are added and dropped at substantially higher rates than standard tags and feature in financial statement areas where the distinction between recurring and nonrecurring items is consequential to equity valuation. Motivated by these patterns we construct a two-firm game in which each firm mandatorily discloses an earnings signal but strategically chooses whether to tag it as standard (signaling persistence) or extension (signaling transience), subject to a private misclassification cost. Tags create value through two channels. A persistence channel, through which tags resolve investor uncertainty about whether current earnings will carry forward, and a comparability channel, through which standard tags allow investors to exploit cross-firm information for relative pricing. We show that truthful tagging strictly improves pricing accuracy, but strategic discretion leads to a negative externality and strategic complementarity in aggressive tagging. The equilibrium truth-tagging region shrinks as the number of strategic firms increases, producing a race to the bottom in tag quality.

[4] Carbon Emissions Disclosure and Green Technology Adoption, with Rishabh Aggarwal 

We examine how disclosure rules shape a manager’s allocation of scarce attention between an incumbent “brown” technology and a risky “green” technology under investor pressure on emissions. Under voluntary disclosure, non-disclosing firms pool and can reallocate attention toward the brown technology, improving brown efficiency but weakening learning on the green frontier. Under mandatory disclosure, stronger penalties shift attention toward the green technology and reduce intensive-margin emissions, but can discourage green adoption on the extensive margin unless sufficiently strong. We also show that proportional carbon taxes can be neutral in this endogenous information environment, whereas additive penalties have bite. Our comparative statics are policy-relevant in clarifying how disclosure and pricing interact when attention itself is a choice variable.