Knowledge that Transforms

Abstract The PCAOB adopted critical audit matters (CAMs) to meet public demand for informative audit disclosure, but stakeholders are concerned this goal has not been achieved. We explore this disconnect via interviews with 30 highly experienced auditors. We find that audit firms expended considerable resources to implement CAM best practices. However, overwhelming institutional pressure gave rise to informal rules of thumb that prioritize symbolic comfort over substantive change. The first is don’t be an outlier , so auditors defer to the national office to ensure conformity and avoid PCAOB scrutiny. The second is report the “right” number of CAMs by never reporting zero and reporting at least one recurring CAM. The third is avoid surprises by communicating with the client to ensure that CAMs do not contain original information and allowing management to preempt auditor disclosures. Collectively, these rules yield CAMs that comply with PCAOB standards but do not provide new information and instead maintain the status quo.

Abstract We investigate whether green innovators voluntarily provide innovation disclosure to reduce processing costs for stakeholders and gain green-specific disclosure benefits. We observe that green patenting firms provide more innovation disclosure in conference calls than do other innovating peers, controlling for patent value. Using a patent-call unit of analysis, we also provide within-firm evidence that managers highlight their green inventions more on conference calls relative to their other inventions. Green innovators provide more innovation disclosure when the costs of processing patent information are higher and anticipated disclosure benefits are greater. We find some evidence that innovation disclosure is positively associated with green fund ownership and stronger market responses to conference calls as well as proxies for ESG-related reputation. Our findings highlight both capital market and social capital benefits as motivating forces for voluntary innovation disclosure and suggest the nature of a firm’s innovations can impact its information environment.

Abstract We study India’s regulation requiring firms to create board-level CSR committees and spend 2% of profits on CSR initiatives targeting environmental sustainability and local socioeconomic development. We examine whether such a mandate can meaningfully enhance corporate social responsibility. We find significant improvements in environmental and social ratings of Indian firms relative to matched global peers, particularly in community engagement and natural resource stewardship. Outcome-based measures, such as waste and resource use, also indicate real effects. Notably, improvements occur only in firms that substantially increase CSR spending and establish independent, expert committees; firms that merely comply superficially show muted effects. Our findings suggest that, despite criticisms of mandated CSR as paradoxical or potentially rent-seeking, such regulations can effectively promote socially responsible business practices when implemented with sufficient commitment and oversight.

Abstract We examine the role of conference calls in creating information spillover within firms. We find that analyst participation in conference calls is positively associated with subsequent revisions in management forecasts, consistent with analysts’ questions prompting managers to collect additional information. We find that this effect strengthens when analysts pose more questions on new topics and when they ask questions with abnormally positive or negative tones. We also find that analyst participation has greater effects when analysts have more experience and higher forecasting ability. Further analyses demonstrate that analyst participation is associated with higher accuracy in subsequently revised forecasts. Overall, our results illuminate how conference calls contribute to firms’ internal information environment.

Abstract Cryptocurrency has been the subject of heightened regulatory and investor attention in recent years, and regulators and policymakers across the globe are deliberating on how to account for, regulate, tax, and oversee digital assets and cryptocurrency marketplaces. Yet researchers have a limited understanding of key attributes of those who deal in crypto assets, such as whether their financial sophistication differs from that of other investors. Using U.S. administrative data, we provide evidence on (i) the attributes of taxpayers reporting cryptocurrency sales to the IRS, (ii) how these attributes are evolving, and (iii) how investors treat cryptocurrency versus other financial assets in certain settings. The results suggest that average reporting cryptocurrency sellers exhibit demographic attributes generally associated with less financial sophistication and are more likely to trade in meme stocks. Overall, we provide timely evidence that can inform cryptocurrency policy deliberations by highlighting the characteristics of taxpayers who appear to report cryptocurrency sales.

Abstract We examine whether information processing constraints limit managers’ ability to effectively integrate tax planning and core business strategies (i.e., effective tax planning). We propose that artificial intelligence (AI) tools, such as machine learning, can mitigate these constraints by providing enhanced predictive information for key business decisions (e.g., customer demand, supply chain), thereby reducing processing costs. Using a recently developed firm-year measure of investment in AI-related human capital for a broad sample of U.S. nontechnology firms between 2010 and 2018, we find that AI investment is positively associated with tax effectiveness. This effect is concentrated among more complex firms and those where the tax function holds a higher status. Consistent with AI reducing information processing costs, we find that it improves tax effectiveness by enhancing internal information quality and internal capital management. We provide novel evidence that processing constraints hinder effective tax planning and show that AI can mitigate these constraints.

Abstract This paper investigates how institutional investor distraction affects the assimilation of narrative content in the MD&A section of the 10-K filing. We introduce the Aggregate Attribute Index (AAI) and an alternative formulation (AltAAI), which capture linguistic features beyond tone to provide a broader measure of corporate narrative richness. Using machine learning and natural language processing, we analyze U.S. firms that follow a staggered reporting strategy, releasing quantitative results before full narrative disclosures. This design isolates the incremental effects of complex language when other portfolio events distract investors. We find that narrative complexity does not trigger short-term return responses but significantly affects stock prices over longer horizons. Complexity moderates how and when attention-constrained investors adjust prices. These effects are not captured by dictionary-based tone or readability metrics, underscoring the distinct role of multi-dimensional attributes in shaping delayed market reactions and price discovery.