Knowledge that Transforms

We study plaintiff law firms in corporate litigation, focusing on “star” firms that dominate settlement outcomes. Stars are associated with larger settlements; however, much of this effect is predicted by the defendant's litigation insurance coverage, suggesting assortative matching of stars with lawsuits that have ex ante larger expected payoffs. Moreover, stars charge higher fees for a given settlement size. Additional tests suggest that visibility and information asymmetry vis‐à‐vis less sophisticated plaintiffs help sustain the stars’ market share. These findings advance our understanding of corporate litigation and the agency relationship between plaintiff law firms and their clients.

This paper explores U.S. public firms’ cryptocurrency holdings and accounting practices from 2013 to 2022 against the backdrop of the recently enacted crypto accounting rule, ASU 2023‐08. Descriptive analyses suggest exponential growth in corporate crypto holdings and significant variation in crypto accounting practices, underscoring the rule's necessity. Hypothesis tests using the pre‐rule data reveal three insights with direct relevance to the rule. First, firms appear to view crypto assets more akin to investments than intangible assets, consistent with the rule's mandate of the fair value model. Second, Big 4 auditors steer firms toward the impairment model and less detailed presentation choices. This conservative approach is unlikely to meet the new rule's goal of providing the most decision‐useful information. Third, increased liquidity of crypto markets prompts the use of the fair value model and a more detailed presentation, consistent with the rule's focus on more actively traded tokens. However, within our sample, we find some evidence consistent with fair value reporting increasing stock return volatility and no evidence that it enhances earnings informativeness.

I use the setting of generative AI (GenAI) to examine how processing tool interactivity affects investors’ self‐assessments of ability and willingness to invest. Although GenAI can help investors process financial information, I theorize that the interactive nature of GenAI blurs the boundaries between investors’ own abilities and those of GenAI, prompting investors to discount their reliance on GenAI and misattribute its abilities to themselves. I rely on the advantages of a laboratory setting to disentangle the interactive element of GenAI from the mere presence of GenAI assistance. Across three experiments, I find that the interactivity underpinning GenAI heightens investors’ self‐assessments of their own abilities and increases their willingness to invest, despite this interactivity not improving, and in fact hindering, their actual processing of information provided by GenAI. My study thus highlights one potential cost of using GenAI and other highly interactive processing tools.

Recent SEC regulations require that qualified hedge fund advisers provide their investors with narrative disclosures of their business and operations. We find that 40% of these disclosures omit or de‐emphasize information regarding advisers' operational and investment risks when compared to other sources of public information. Funds with such “inconsistencies” are associated with predictably lower fund performance but do not differ in their fund flows, flow‐performance relation, ownership structure, or management fees. These results are consistent with investors being subject to limited strategic thinking, which prevents them from fully unraveling the implications of strategic omissions. This, in turn, contributes to advisers' successful use of discretion to de‐emphasize information with adverse performance implications. Our findings suggest that information processing frictions can facilitate nondisclosure, even in markets with sophisticated investors.

Sell‐side analysts commonly transition to buy‐side money managers. I examine whether and how these career transitions shape sell‐side analysts' behavior, relying on the granular career information of 6,310 analysts in the United States. I identify analysts who transition from the sell‐side to the buy‐side and find that these analysts issue inflated recommendations on stocks of interest to their future buy‐side employers. This favoritism is (1) present only during the year preceding their transition, (2) more pronounced among stocks where a single analyst is more influential in moving the stock price; and (3) present only among transitions likely to be strategically planned. Importantly, stocks receiving inflated recommendations experience a significant price decline following the departure of transitioning analysts from the sell‐side. Overall, these findings suggest that analyst career transitions are an important source of conflicts of interest.

This paper provides descriptive evidence on how stock market participants use Generative Artificial Intelligence (GenAI) to process investment‐related information. Using a data set of 1.7 million stock‐related queries from one of China's largest GenAI platforms during the first half of 2024, we document that user queries address a wide range of topics and tasks and vary systematically with usage intensity and financial sophistication. Query activity increases around corporate disclosure events, but these increases largely track contemporaneous media coverage. We also find evidence consistent with a substitution between the informativeness of voluntary managerial disclosures and investors' reliance on GenAI. Continued platform engagement more likely follows answers that are concise and contain directionally accurate trading signals. Over time, users' subsequent queries increasingly reflect the specificity and financial terminology present in earlier GenAI answers. At the market level, GenAI usage is associated with higher measures of informed trading and lower liquidity, while aggregated sentiment in GenAI‐generated answers correlates with same‐day abnormal returns, particularly when user feedback is positive. Overall, our findings offer insights into early‐stage GenAI adoption by retail investors and inform discussions on how GenAI shapes information processing in financial markets.

Combining hedge funds’ quarterly position information with their access records on the SEC's EDGAR server, we explore whether hedge funds proactively gather and analyze textual information in 10‐K filings related to their stock holdings, and how these behaviors affect their positions. We find that hedge funds adjust their positions according to the textual information in the annual reports they download. Meanwhile, analyzing these reports helps them to generate excess returns. Overall, our evidence suggests that the textual content of annual reports contains crucial company insights, prompting a subset of hedge funds that engage in bulk downloads from the SEC's website to trade based on diligent analysis of 10‐K filings.

Labor unions in the United States are subject to financial reporting mandates. This study examines how these mandates affect unions and their members. Using several regulation‐based empirical designs, we document that more granular reporting requirements adversely affect unions' election outcomes. Supplemental analyses suggest that these findings are consistent with the strategic use of unions' disclosed information by parties such as employers and their consultants. We find mixed evidence on whether the mandates materially improve oversight of unions. Lastly, we find that the mandate reduces employees' average pay without clear benefits for employers, aside from reallocating investment from labor to capital. Collectively, our results suggest that more fine‐grained financial reporting requirements impose costs on unions and weaken their ability to represent employees, resulting in worse employment outcomes.

Prior research has pointed to differences in organizational capital as a reason for the persistent performance discrepancies among otherwise similar firms. In this paper, we develop and validate a new measure of organizational capital. Based on over a million crowd‐sourced employee reviews scraped from Glassdoor, we construct the measure of organizational capital at the firm‐year level using the word embedding model and ChatGPT‐generated synthetic reviews. Our measure varies over time in accordance with macro trends, and differs both across and within firms, reflecting firm heterogeneity and major internal changes. We validate our measure by testing empirical predictions of the properties of organizational capital discussed in prior literature. Our findings suggest that this measure captures a slowly evolving intangible asset that is significantly associated with firm performance and top management's influence, aligning with the conceptualization of organizational capital by Dessein and Prat. We further showcase applications of our measure in accounting, economics, finance, and management literature. Taken together, the paper provides implications for various stakeholders who are interested in assessing and managing firms' organizational capital.

We study the economic consequences of anti‐loss trafficking rules, which disallow the use of loss carryforwards as a tax shield after a substantial ownership change. We use staggered changes to these rules in the EU27 Member States, Norway, and the United Kingdom from 1998 to 2019 and find that limiting the transfer of tax losses is related to the number of mergers and acquisitions (M&A) declining by 18%, driven by loss‐making targets. Turning to broader industry dynamics, we find decreases in survival rates of young companies after tighter regulations. Loosening of regulation is associated with increased firm survival. Tightening (loosening) anti‐loss trafficking rules is related to decreased (increased) industry productivity, especially in R&D‐intensive industries that are more prone to loss‐making. Finally, tighter anti‐loss trafficking rules are associated with lower deal synergies and risk‐taking. All effects concentrate in strict regimes.