Knowledge that Transforms

Digitalization is transforming the audit profession. Apprenticeship norms prescribe that knowledge flows down from supervisors to subordinates, but digitalization increasingly positions junior auditors as experts in emerging technology. Consequently, supervisors can learn from subordinates. This reversal challenges long-standing apprenticeship expectations, yet little is known about how it unfolds or the tensions it creates. Against this backdrop, we examine tensions that can arise when subordinate auditors share their knowledge with supervisors. Drawing on 51 semistructured interviews and guided by theory, we identify four recurring roadblocks to upward knowledge sharing: junior auditors' status motives, “same as last year” routines, status insecurity among immediate supervisors, and defensiveness when juniors cross perceived knowledge “lanes.” We also find a rare, countervailing pathway: the emergence of informal champions who legitimize junior auditors' contributions. Our study deepens understanding of how digitalization transforms team learning in audits and shows how historical structures and norms evolve under new conditions. Data Availability: Data were obtained from interviews.

We examine how the 2016 Municipal Advisor Regulatory Reform, which professionalized municipal advisors by imposing standards of conduct and minimum competency requirements, affected advisory firms and issuers. Using a difference-in-differences (DiD) research design, we find that the reform improved the quality of financial advice provided by independent municipal advisory firms relative to dealer firms. Specifically, independent municipal advisors assemble higher-quality financing teams and ensure greater financial disclosure compliance and timeliness in the post-reform period. These improvements provide tangible economic benefits to issuers through lower bond issuance costs and smaller underwriter fees. Finally, we document that independent advisory firms gain market share and charge higher fees relative to dealer firms after the reform. Overall, our study provides novel evidence linking the professionalization of financial intermediaries to improvements in the quality of advice, financial transparency, and issuer borrowing costs. Data Availability: Data are available from the commercial and public sources identified in the paper.

The past two decades have witnessed dramatic growth in passive investing via exchange-traded funds (ETFs). To the extent that ETF flows reflect nonfundamental investor demand, large ETF flows may push the prices of the underlying stocks away from their fundamental values. Consistent with this conjecture, I first find that ETF flows chase past fund performance, suggesting that ETF flows contain a systematic nonfundamental demand component. I then document that ETF flow-induced trading is associated with contemporaneous stock price increases, followed by return reversals. Accounting-based valuation tests show that ETF flow-induced trading is negatively associated with value-to-price (V/P) ratios, consistent with overvaluation. The effect strengthens for specialized ETFs and stocks with high short-selling constraints. Finally, firms with high ETF flow-induced trading behave in ways typically associated with perceived overvaluation. Data Availability: Data are available from the public sources cited in the text.

We show that favoritism biases subjective evaluations and that the presence of a third party can mitigate this bias. Using archival data from professional ski jumping, we find that, controlling for objective performance, judges favor athletes of their own nationality and athletes who have a compatriot on the panel. We predict and provide evidence that in-person observation by an audience is associated with lower levels of favoritism compared with third-party observation via mediated communication. We contribute to the accounting literature by highlighting how in-person observation by a third party can reduce the likelihood that favoritism biases subjective evaluations. Data availability: The data used in this study are publicly available from open-access sources.

Academic freedom is often equated with protection from political and economic interference. Authoritative definitions are broader. They combine protection from external interference, peer-based evaluation, and institutional arrangements for scholarly self-governance. Drawing on Honneth’s (2014) theory of democratic institutions, this paper develops the second and third elements and integrates all three within a recognition-theoretical framework that reconceptualizes academic freedom as social freedom. Using accounting as a case, we reconstruct two transformations that shape the discipline today: the theory revolution of the 1960s, which consolidated peer-based epistemic recognition, and the journal ranking revolution of the 1990s, which reorganized recognition around performance metrics and publication hierarchies. We show that academic freedom can be hollowed out from within without being formally revoked and argue that a discipline weakened from within is less able to resist external threats. Defending academic freedom requires attention to architectures of recognition through which scholarly worth and truth are organized.

This paper provides evidence that the mean and dispersion of manufacturers’ inventory growth, as reflected in accounting disclosures, convey valuable information about subsequent changes in economic output growth. At the same time, I find that although government statistical agencies and professional forecasters incorporate mean inventory growth in their estimates or forecasts, they fail to account for its dispersion. I develop and calibrate a heterogeneous-firm model to show that the leading information embedded in macro-level inventory moments arises from firms’ asymmetric responses to news shocks at the micro level. The model also suggests that the failure to incorporate dispersion is likely due to the noise in the dispersion signal used by statistical agencies and forecasters. Data Availability: Data are available from the public sources cited in the text.

We examine commonly used indicators of aggressive non-GAAP exclusions and find that the majority perform poorly at identifying low-quality exclusions in terms of decision usefulness for investors. We propose a new firm-quarter-specific indicator that identifies instances in which GAAP earnings quality is high (i.e., when firms have less need to provide non-GAAP metrics) but managers disclose non-GAAP earnings anyway. Our new indicator is easy to calculate, requires minimal data, and performs far better at identifying low-quality exclusions than indicators used in prior research. Using our indicator, we find instances in which managers exclude earnings components that are decision useful, consistent with regulators’ concerns about the quality of some non-GAAP earnings disclosures. Our results are robust to a variety of specification checks. Data Availability: Data are derived from a combination of publicly available sources referenced in the article and third-party subscription data bases.

Policymakers claim that combating tax avoidance can help reduce industry concentration by leveling the playing field between industry leaders and their competitors. We test the validity of this claim by using administrative data on industry concentration and exploiting the staggered introduction of anti-tax avoidance regulations across 17 European countries. Although these regulations significantly reduce tax avoidance, we find no statistically or economically meaningful effect on industry concentration. Further tests indicate that our nonresults stem from a genuine lack of effect rather than a lack of statistical power and that our inferences are robust to multiple research designs. The sole exception is in industries with both high levels of leader tax-avoidance advantages and multinational presence, but even in these industries, effect sizes are modest and fall short of regulatory benchmarks. Overall, our findings cast doubt on the idea that broad-based anti-tax avoidance regulations can materially influence industry concentration.

We collect data on ranges of hypothetical asset liquidation values disclosed in U.S. Bankruptcy Court filings. We use this historical information to construct a firm-specific measure, “RecRisk,” which captures asset recovery risk through the uncertainty surrounding asset valuations in liquidation events. We document that higher RecRisk is associated with smaller syndicated loan amounts as a percentage of available collateral, more and tighter performance covenants, and increased loan spreads for borrowers with high credit risk. High RecRisk borrowers also experience lower secondary loan market prices and reduced liquidity for loans with high credit risk. When borrowers become financially distressed, high RecRisk is further associated with declining loan prices and reduced ownership by Collateralized Loan Obligations, the dominant investors in the leveraged loan market. Overall, our results indicate that loan contract terms and prices reflect recovery risk faced by lenders. Data Availability: Data are available from the sources cited in the text. The authors can provide the RecRisk measure at the firm-year level upon request.

This study examines the determinants and consequences of broker in-house ESG research. Using manually collected brokerage ESG research data, we find that the provision of in-house ESG research is positively related to both demand-side factors (client demand, internal demand, and local ESG awareness) and supply-side factors (broker size and coverage of sustainable industries). Regarding the consequences of such research, we find that in-house ESG research is associated with improved earnings forecast accuracy in industries where ESG is more salient. Further, the market reaction to earnings forecast revisions is stronger when equity analysts have access to in-house ESG research. Finally, we observe a consistent pattern suggesting that ESG analysts support equity analysts beyond forecast accuracy, as equity analysts with access to in-house ESG research issue more accurate target prices, more informative recommendation upgrades for ESG-heavy industries, and discuss ESG issues more extensively in their analyst reports. Data Availability Statement: The data used in this study are obtained from publicly available sources identified in the paper, as well as job posting data licensed from Lightcast. Sample construction procedures are described in the paper. Researchers interested in the job posting data should contact Lightcast directly, as access is subject to licensing restrictions.