Research

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

When investors hold different priors and are uncertain about signal precision, public information can widen rather than narrow disagreement. This paper studies a firm's strategic disclosure in such markets and how it shapes prices, returns, and trading volume. Standard intuition would suggest that in polarized markets firms should disclose less. My findings overturn this prior and show that the opposite can obtain. When beliefs may diverge and signal precision is uncertain, the firm sets a lower disclosure threshold than in a benchmark with common priors and known precision, so that it discloses more rather than less. The mechanism runs through the pricing of disclosure. When investors are uncertain about precision, the investor who reads a disclosed signal as precise dominates market clearing and bids its price up, so that disclosure becomes worth more under divergence even though silence does too. In equilibrium the firm discloses a credible middle range of signals, withholding unfavorable news and also highly implausible news that the market discounts as too extreme to be credible. At moderate precision uncertainty the upper withholding region carries negligible probability, so disclosure is effectively one sided, while higher precision uncertainty moves the upper threshold into the range of plausible signals.

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

[Draft available upon request]

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 XBRL Tagging, with Jeremiah Lewis 

[Draft available upon request]

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] Audit Quality and Auditing Standards under Artificial Intelligence, with Sebastian Kronenberger

[Draft available upon request]

We study auditing standards when auditors can adopt Artificial Intelligence (AI) as an assisting technology. In our model, a representative auditor chooses a mix of human and AI effort to verify financial statements. AI effort is cheaper and initially frees up resources for higher-value judgment tasks. However, its excessive use leads to deskilling: engagement-specific expertise erodes, which reduces the effectiveness of each unit of audit effort. We compare two auditing standards regimes, (i) a total-effort minimum and (ii) a human-effort minimum. The choice between them generates a regulatory trade-off. Under total-effort standards, a higher minimum inadvertently fuels AI over-adoption because the auditor does not fully internalize the social cost of audit failures. The resulting deskilling causes audit quality to fall below the no-AI benchmark as standards tighten, even as the AI world delivers strictly higher welfare through lower compliance costs. By contrast, human-effort standards preserve a minimum of human work, which avoids deskilling. Thus, audit quality is higher, but the full potential of AI remains underexploited, resulting in smaller welfare gains and audit quality below its feasible maximum. Our analysis suggests that regulators should not merely ask how much audit work to require, but what kind of work.

[5] The Ratchet Effect with a Costly AI Performance Proxy, with Amadeus Bach, Anna Rohlfing-Bastian and Nicolas Rudolf
[Draft available upon request]

[6] The Levelized Cost of AI Compute: A Framework for Auditing GPU Depreciation Policy, with Amadeus Bach
[Draft available upon request]

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

[8] Capital Market Disclosure under Supplier Bargaining Considerations, with Sabrina Popow and Dirk Simons