Abstract Form 10-K offers a setting for studying how users process complex, multi-layered disclosures: managerial narratives in the main file alongside separate exhibits, such as contracts and certifications, that provide unfiltered detail. Drawing on rational inattention theory, we investigate how users allocate limited attention across these components. Users typically begin with the main file and selectively access exhibits when the main file appears shorter, less readable, or less confident, indicating higher perceived information loss. This pattern strengthens for exhibits that offer more detail on topics discussed in the main file and among institutional investors and time-constrained users. Exhibit access persists beyond the initial filing window and increases around subsequent firm events, especially when external monitoring strengthens and event-related information asymmetry grows. Collectively, our findings underscore the active, discerning nature of user attention in navigating multi-layered disclosures and reveal the often-overlooked informational value of exhibits in Form 10-K.

In this paper, we use ChatGPT outages to provide early evidence on whether investors rely on generative artificial intelligence (GenAI) to perform professional tasks and the associated impact on stock price informativeness. We document a significant decline in stock trading volume during ChatGPT outages. The effect is stronger for firms with corporate news released immediately before or during the outages and for firms with higher ownership held by transient institutional investors. We then document declines in short-run price impact and return variance during the outage periods, consistent with reduced informed trading. Lastly, we document a positive effect of GenAI-assisted trading on long-run stock price informativeness. Overall, our findings indicate that a significant number of investors use ChatGPT in ways that influence their trading decisions and market outcomes. Future research can investigate the mechanisms underlying these GenAI effects and the potential risks of using GenAI for trading.

We find that the staggered passage of state-level laws that legalize marijuana for medical use increases states' borrowing costs by 7–9 basis points. Consistent with economic theory on substance use suggesting that marijuana legalization increases local consumption of the drug (by expanding its availability and reducing its perceived risks), we predict and find that increased consumption represents an important mechanism that explains the higher state bond spreads. We also show that following such laws’ passage, states incur higher marijuana-consumption-related expenditures, including for police, corrections, and public welfare.