Knowledge that Transforms

Abstract We propose a new measure of firms' disclosure based on company websites, which are widely available and contain a wealth of information. For a sample of U.S. public firms, we construct our disclosure measure using historical website data, validate it by correlating it with extant measures of disclosure and information asymmetry, and explore its determinants. We then apply our measure to the study of U.S. private firms' disclosure and French firms' compliance with a nonfinancial disclosure mandate. Our applications illustrate that our website‐based measure provides a useful complement to extant measures of disclosure, which are more narrowly focused on investors in public capital markets.

ABSTRACT We investigate the establishment of public audit oversight bodies (POBs) worldwide. We present descriptive evidence on POBs’ characteristics and factors influencing the timing of their establishment, finding that countries with stronger institutions, civil law traditions, and higher media attention to audit quality adopt POBs faster. While countries may choose similar POB design features, these choices do not align with the factors driving adoption speed. We also explore whether the finding of a positive impact on audit quality of the U.S. PCAOB generalizes to other countries. A difference‐in‐differences analysis over 20 years provides some evidence that POB establishment and their characteristics improve audit quality. However, the results appear sensitive to audit quality measures and research design. Our study offers the first broad‐based investigation of POB adoption and provides important nuance on the relation between POBs and audit quality.

ABSTRACT This paper provides early evidence on the integration and impact of generative artificial intelligence (GenAI) in accounting at the accountant and task levels. Using survey data from 277 professional accountants, we document substantial heterogeneity in adoption patterns, perceived benefits, and concerns about GenAI. Using proprietary field data from an AI‐enabled accounting platform serving 79 small‐ and medium‐sized enterprises, we analyze over 200,000 transaction‐level records. We document that GenAI adoption is associated with significant productivity gains and systematic reallocation of effort away from routine data entry toward business communication and quality assurance tasks. GenAI use is also associated with improvements to financial reporting quality, evidenced by more granular ledgers and faster month‐end closing. Examining human–AI interaction, we find that accountants selectively intervene when AI confidence scores are low, consistent with complementarity between professional expertise and AI. A framed field experiment further shows that while AI assistance improves classification accuracy on average, reliance on non‐consensus AI recommendations can increase the risk of error. Overall, our findings highlight both the promise and the risks of GenAI in accounting and suggest that, in practice, AI is most effective as a tool that augments—rather than replaces—professional judgment.

ABSTRACT Recent work within accounting and finance has highlighted that modern AI systems exhibit superhuman performance on a variety of foundational activities within these fields. However, the literature often does not provide economic rationale for why AI models seem to outperform, largely because these models are a black box. Through a series of experiments, I set out to open the black box and provide direct evidence on how and why AI models appear to perform so well on accounting and finance‐related tasks. I show that much of the superior performance of AI models can be attributed to artifacts of the modeling itself, rather than to mechanisms grounded in economics. Focusing on two key components of AI models, which may bias inferences in papers that rely on them, I first show that Large Language Model (LLMs) exhibit extremely poor numerical reasoning and thus application in these settings should proceed with caution. Second, I highlight that commercial LLMs suffer from significant look‐ahead bias, which may explain a large portion of their predictive ability in various settings.

ABSTRACT We examine whether and how common ownership affects Environmental, Social, and Governance (ESG) ratings—an important research question given the increasing use of these ratings in investment decisions and corporate evaluations. We find that companies with major shareholders in common with the rating agency (“sister firms”) tend to receive higher ESG ratings. When a company becomes a sister firm through a change in the rating agency's ownership structure, its rating from that agency is subsequently upgraded, whereas its ESG ratings from other agencies remain unchanged. Sister firms exhibit greater rating disagreements across agencies than other firms. The higher ESG ratings for sister firms are partly attributable to the transfer of immaterial positive ESG information through common owners. The common ownership effect is more pronounced when the owner can exert a greater influence on the rating agency. Moreover, sister firms with initially elevated ratings demonstrate poorer future ESG performance. Overall, our findings suggest that owners can affect ESG ratings of their portfolio companies in a way consistent with their influence and interest.