Research

[1] Strategic Disclosure when Beliefs Diverge (Job Market Paper)

[Draft available upon request]

When investors hold different priors about firm fundamentals 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. A natural presumption is that firms facing polarized markets should disclose less. I find that the opposite can obtain. When beliefs diverge and signal precision is uncertain, the firm sets a lower disclosure threshold relative to a world where investors hold common priors and signal precision is known. The mechanism runs through the pricing of disclosure. When signal precision is uncertain, it is rational to gauge precision by proximity to one’s prior, so the same disclosure is read as precise by some investors and as noise by others. Investors who read a disclosed signal as precise dominate market clearing and bid up price, so disclosure gains more value under divergence than silence does. In equilibrium the firm discloses a middle range of signals, withholding unfavorable news and also news so favorable that the market discounts it as implausible, with uncertainty about precision governing where this range closes. Thus, disclosure in polarized markets is not a casualty of divergent beliefs but a strategic response to them. Consistent with the model's central prediction, patterns in management guidance data suggest that the association between investor disagreement and disclosure becomes more positive as uncertainty about signal precision rises.

[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 (eXtensible Business Reporting Language) mandates require firms to tag financial statement items using a standardized taxonomy, promising lower processing costs and enhanced comparability. Yet, firms retain discretion over tag choice. We provide stylized facts showing that extension tags are added and dropped at higher rates than standard tags and concentrate in areas where the distinction between recurring and nonrecurring items is most consequential to equity valuation. This motivates a model in which two firms tag an earnings signal as standard, implying persistence, or extension, implying transience, at a mistagging cost. Tags create value through a persistence channel, resolving uncertainty about whether earnings carry forward, and a comparability channel, letting investors exploit cross-firm information. Truthful tagging strictly improves pricing accuracy, but discretion generates negative externalities. Discretion therefore coarsens what the investor can infer. Expected pricing accuracy falls at every step across four regimes, from truthful tagging by both firms, through discretion held by one firm, then both, to no tagging.

[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