ABSTRACT Prior studies predominantly examine fundamental performance‐driven explanations of loan renegotiations. We contrast with this work by investigating the improvement in secondary loan market trading conditions as a non‐fundamental driver of loan renegotiation. Exploiting a regression discontinuity design around the LSTA 100 Index reconstitution, we find that index‐included loans are around five times more likely to receive interest‐rate–reducing amendments than comparable loans just below the index inclusion threshold. Within‐loan‐package tests confirm that these renegotiations are not driven by changes in the borrower's fundamental performance. The threat of refinancing likely drives this effect, as the results are more pronounced when such threats are more credible.
ABSTRACT We examine the spillover effects of local initial public offerings (IPOs) on new business formation. An IPO in a local area is associated with a 1%–4% increase in new business registrations, and this effect is particularly pronounced in counties facing higher economic uncertainty. New business registrations are significantly influenced by the extent of EDGAR downloads related to the IPO firm's public disclosures and the information in the IPO firm's S‐1 disclosure. The findings highlight the role of IPOs in conveying crucial information through signaling of potential success prospects and additional provision of information through disclosures. A field survey of 503 entrepreneurs further supports these conclusions.
We study the use of generative AI for firm-specific financial analysis on the Seeking Alpha platform. After the initial launch of ChatGPT in November 2022, the share of AI-generated articles rose sharply to 13.5% of all articles, then declined in late 2023 after Seeking Alpha equated the use of AI to plagiarism and announced a prohibition on its use. We organize our study around two questions: (1) Does AI use increase author productivity? and (2) does AI use have capital market consequences and ultimately affect the informational landscape? We find that authors who adopt AI become more productive, publishing more articles and covering more new firms than non-adopters. Findings on AI article informativeness are more nuanced. On average, AI articles are less informative than human-written articles, eliciting smaller trading volume and abnormal return responses. However, AI use leads to increased firm coverage and in turn to improved liquidity and faster price discovery. Our findings suggest that, while AI-generated articles are currently perceived as less informative than human-written articles, their comparatively low cost enables increased firm coverage and thereby improves the overall informational landscape.
This study examines the spillover effect of bank financial misconduct on the uninsured deposits of peer banks within local markets. We first validate that misconduct banks experience an increase in deposit spreads and a corresponding outflow of deposits following the misconduct. We then show local peer banks exhibit divergent deposit responses, contingent on how misconduct is perceived by information recipients in different economic contexts. During normal periods, depositors receiving a negative signal about bank misconduct reallocate their funds from misconduct banks to local peers, a local reallocation effect that decreases deposit spreads and increases deposit inflows for peer banks. Cross-sectional analysis further reveals that this local reallocation effect is more pronounced for financially sophisticated depositors, amplified when peer banks have strong fundamentals, but attenuated when misconduct banks are financially sound. During financial crisis periods, however, bank misconduct leads to withdrawals from both misconduct banks and their peer banks, a local contagion effect whereby local peer banks face increased deposit spreads and deposit outflows following the misconduct.
Complex accounting estimates are becoming increasingly important to financial statements. Yet, such estimates create ample opportunities for bias. Although both management and independent auditors are tasked with ensuring these estimates are free from error and bias, evaluating the appropriateness of these measures can be quite difficult. Drawing on unconscious thought theory, we predict and find across three experiments that engaging in unconscious thought can improve accounting practitioners' evaluations of accounting estimates. In Experiment 1, we use practicing auditors as participants and find that unconscious thought improves less-experienced auditors' ability to recognize income-decreasing patterns of bias. In contrast, more-experienced auditors respond similarly to both incoming-increasing and income-decreasing bias, regardless of whether they use conscious or unconscious processing. In Experiments 2 and 3, we provide evidence that prompting managers to engage in unconscious thought also improves their recognition of patterns of bias within estimates. Overall, our findings demonstrate how unconscious processing can be intentionally prompted to improve accounting professionals' ability to recognize subtle patterns of bias that they might otherwise overlook.
We synthesize evidence from six papers presented at the 2025 Journal of Accounting Research Conference on how generative artificial intelligence (GenAI) is reshaping capital-market information flows. Our discussion is organized around an economic framework with three layers: information production by firms and accounting professionals, information dissemination through intermediaries, and information processing by investors. Across these layers, the conference papers show that GenAI can lower preparation costs, improve intermediary productivity, and reduce investors' processing costs. At the same time, they point to a common constraint: whether GenAI improves the information environment depends critically on information verification costs. We also highlight gaps in current evidence and outline future research opportunities within and across the three layers.
We are among the first to investigate how Generative AI (GenAI) shapes investors' trading activities. Using an AI-sentiment measure extracted from earnings-call transcripts to proxy for textual signals, we find notable shifts in trading behaviors around earnings calls. Before the wide deployment of ChatGPT, short selling was aligned with AI-sentiment, whereas retail trading was not. However, following ChatGPT's deployment, the alignment of retail traders with AI-sentiment significantly increases, while the alignment of short sellers weakens, albeit insignificantly. Stocks with higher information processing costs exhibit a more pronounced increase in retail trading alignment, scenarios where retail investors are likely to benefit more from AI. Using retail-AI alignment as a proxy for the extent to which retail investors trade based on AI signals, we show that information asymmetry declines and retail investors' trading profitability improves, whereas short sale profitability declines in high retail-AI alignment stocks. Exogenous outages reduce the alignment between retail trading and AI-sentiment, allowing us to draw causal inferences. Collectively, this study suggests that AI is a promising technology for narrowing the information gap in the trading of complex textual financial disclosures between investor classes with clear disparities in the ability to process public disclosures.
This paper examines investors' perceptions of auditor involvement in non-GAAP reporting as captured by non-GAAP disclosures in 10-K filings. We find that firm-years with auditor involvement in non-GAAP reporting have higher CARs, lower bid-ask spreads, lower stock volatility, and lower abnormal trading volume on 10-K filing dates. To sharpen identification of auditor involvement, we hand-collect non-GAAP measures and reconciliations for S&P 500 firms and identify which exclusions reconcile directly to the audited financial statements. As the percentage of exclusions that reconcile directly to the audited financial statements increases, bid-ask spreads and stock volatility on 10-K filing dates decrease. We find that the results are not driven by strategic reporting or managers' responses to perceived litigation risk. This study provides new insights into non-GAAP disclosures outside of earnings announcements, which have been largely ignored in prior literature. Collectively, our results suggest investors value auditor involvement in non-GAAP reporting and inform policymakers and standard setters considering the usefulness of assurance over non-GAAP measures.
We assemble and describe a sample of 174,782 lawsuits filed against 218,437 public-company lawsuit-defendants in federal district court from 2006 to 2021. These lawsuits involve an array of allegations, including product liability, civil rights discrimination, contract breaches, improper compensation and labor practices, antitrust violations, corruption, securities violations, pollution, and intellectual property infringement. The sample exhibits rich variation across firms, industries, time, suit type, plaintiffs, and outcomes-reflecting not only firm activities but also social, political, and regulatory trends. Although many claims matter very little, some are important individually or in aggregate. We observe 23% of defendants experience a market value decline exceeding 10% of current assets around the lawsuit filing. Consistent with the notion that even low-stakes claims, when numerous or persistent, can introduce frictions or reflect underlying issues, we find that aggregate legal exposure is associated with increased return volatility and decreased profitability. Subsequent tests indicate that materiality, public and private enforcement, and firms' information environments (as well as other firm traits) are associated with managers' decisions to disclose these claims. Collectively, our descriptive evidence establishes a foundation for further research into underexplored types of corporate litigation that represent a broad range of alleged wrongdoing and socially irresponsible behavior.
The engagement quality review is a key component of an audit firm's quality control system. This study leverages unique data on individual engagement quality reviewers (EQRs) to examine how previous shared working experience between EQRs and engagement partners affects audit quality. While prior research suggests that within-firm network ties between predecessor and successor partners facilitate knowledge transfer, we find that prior shared working experience between EQRs and engagement partners is associated with lower audit quality. Mechanism tests indicate that such experience is associated with a higher likelihood of regulatory enforcement actions related to deficiencies in audit procedures, insufficient evidence, and a lack of professional skepticism. We also find that such experience corresponds with higher materiality thresholds, suggesting reduced scrutiny during audit planning and execution. Additional analyses reveal that these adverse effects primarily arise when previous shared working relationships did not produce adverse outcomes, when EQRs are not audit industry leaders, and when they face lower reputational risk. These findings are especially salient given that EQRs' previous shared working experience with engagement partners appears to weigh heavily in EQR assignments. Overall, our study provides important insights into the implications of EQR independence and the determinants of engagement quality review effectiveness.
This paper hypothesizes that information flows from target firms to large shareholders during activist campaigns and that these flows have governance consequences. Focusing on actively managed mutual fund families, we find that informed trading by large-holding fund families increases during activist campaigns relative to smaller-holding fund families invested in the same firms. The effect is stronger for firms that attend more invitation-only investor events, face greater threats from activist campaigns, and are harder to value. Consistent with information flowing from management to large-holding fund families, the effect strengthens when Regulation Fair Disclosure enforcement is lax and when the information is favorable to the firm. Furthermore, the increased information advantage is associated with more management-friendly voting behavior by these investors and a higher likelihood of target firms winning activist campaigns and retaining board seats. Overall, our findings are consistent with a potential quid pro quo in which investors' access to information from management is associated with more pro-management behavior.
We investigate the monitoring quality of accountants with ties to the Mafia in their role as auditors for "clean" firms-those with no known ties to organized crime. Using a proprietary government database, we identify Italian firms with alleged ties to the Mafia through their executives, directors, or shareholders. We define "suspect accountants" as those who serve as auditors for these Mafia-connected firms, acknowledging their potential associations with criminal entities. We predict and find evidence that "clean" clients (treatment group) monitored by suspect accountants are more likely to engage in earnings management practices that reduce taxable income, compared with a control sample of "clean" firms monitored by accountants with no known Mafia ties (control group). Our findings suggest that accountants with ties to the Mafia act as low-quality monitors in the "clean" economy.
This paper explores the influence of two fixed payment arrangements-time-based and output-based wages-on worker behavior and performance in a multidimensional task setting. We examine how these wages affect the time workers spend on individual units of a task and their work quality. We contend that fixed compensation schemes can implicitly communicate standards of acceptable work. Our empirical evidence from MTurk experiments and a laboratory experiment indicates that workers on output-based wages deliver higher quality and spend more time on individual units than their time-based counterparts. These findings are consistent with output-based wages, implying a standard of acceptable quality-without a conflicting standard of speed-to which workers respond. Our results emphasize the power of implicit cues from fixed compensation schemes and offer insights for employers, suggesting the choice between output- and time-based wages should be informed by whether quality or turnaround time is valued more.
Generative artificial intelligence (GAI) will likely alter many aspects of the financial reporting process and spawn a deep stream of academic research. We take an early step by examining the extent to which firms have begun using GAI in one important part of the reporting process: writing disclosures. We begin by evaluating a commercial tool's ability to detect GAI writing in disclosures, and we find that it reliably identifies even very small amounts of GAI usage in realistic samples. We then examine firms' actual earnings press releases, conference call prepared remarks, risk factors, MD&As, and IPO filings through 2024 and find statistically significant GAI usage in all five disclosure types, with up to 4.5% of new text written by GAI in 2024. Usage is predictably higher in the cross-section, and filings with higher GAI have systematically different linguistic properties. Our study provides early insights into the use and effects of GAI in financial reporting, and it motivates future research in this evolving area.
In this study, I explore how accounting rules-in particular the restrictiveness of GAAP-have impacted the labor market for accountants. I find that when the rules become more restrictive, there are fewer students majoring in accounting and fewer accountants and auditors overall. The overall number of accounting positions that firms recruit for does not decrease when the rules become more restrictive; however, the nature of accountants' work changes. There is less focus on tasks such as applying judgment, thinking creatively, and thinking critically and more focus on determining compliance. Despite the decrease in accountants, earnings for accountants do not increase, and the wage distribution becomes more compressed. I supplement these analyses with a survey-based field experiment and find that the salience of restrictiveness heightens students' views of accounting as a profession where they are unable to use creative and critical thinking. Overall, the findings suggest that restrictive regulation can shift the task content of occupations and reduce the pool of individuals interested in the profession.
Women often lack the opportunity to join exclusive social clubs, limiting the benefits they derive from their social networks. We investigate whether, when given the opportunity to interact with the right people in a professional setting, women gain greater advantages from these connections for career performance and advancement compared to men. Using a unique data set that documents when, where, and with whom financial analysts interact at investor conferences, we find that female analysts show greater improvement in earnings forecast accuracy than their male counterparts after interacting with a firm's executives. Further evidence suggests that female analysts overcome homophily in conference interactions with executives and that they sustain their gains, enhancing forecast accuracy for up to three years. In addition, both the capital and labor markets recognize women's superior gains from conference connections. Our findings suggest that women capitalize on professional connections, highlighting the importance of promoting structured networking opportunities for women in professional environments.
This study examines how the subjectivity in measuring fair values of assets without readily observable market prices affects investment efficiency and shareholder value. When fair values are objective measures of asset value, they facilitate efficient investment decisions that align with shareholder interests. In contrast, firms' reliance on subjective valuation inputs causes underinvestment in long-term projects. If fair values are highly subjective, they may lead firms to favor less profitable short-term projects with objectively measurable fair values. When project returns are positively correlated, subjectivity in valuing long-term projects induces overinvestment in short-term projects with objective fair values. Regardless of these distortions, fair value measurement can add shareholder value. Not measuring fair values altogether leads to underinvestment, which moderately subjective fair values can alleviate.
Regulators are contemplating or mandating precise measurement of financial climate-risk exposure to promote sustainable investments. We show that such mandates can be counterproductive in the presence of social funds that catalyze change by subsidizing the adoption of cleaner production technologies. Firms can exploit a social fund's impact motive by measuring their climate-risk exposure imprecisely. This strategic imprecision prevents the fund from distinguishing between firms that require subsidies and those that would switch to clean technologies for financial reasons alone, thereby increasing the ex ante subsidies firms can extract. A by-product of this rent-seeking behavior is that firms adopt clean technologies more frequently than would be jointly efficient under precise measurement. Our analysis suggests that the regulatory push for precise climate-risk measurement can reduce social funds' impact and the frequency of green transitions.
This paper studies how partisan alignment between city leaders and state governors shapes information processing and bond pricing in the municipal bond market. Using a novel data set on 1,045 U.S. cities from 2005 to 2019, we show that cities with the same political affiliation as the state governor face 9 basis points lower borrowing costs than misaligned cities. The effect is stronger for riskier bonds, in states where governors hold greater authority, and for fiscally dependent cities. Aligned cities also receive more aid during fiscal distress. Partisan alignment shapes how investors interpret and respond to financial information: Nondisclosure and adverse audit findings raise borrowing costs primarily for misaligned cities, while penalties for aligned cities are markedly smaller.
We examine whether and, if so, how retail consumers change their shopping in response to firm-specific negative environmental, social, and governance (ESG) news. Using an event study methodology, we do not find significant changes in consumer foot traffic in response to negative ESG news, on average. However, the average consumer reacts negatively when such news is covered by national or global media outlets, which elevates consumer awareness. In addition, we provide evidence of the heterogeneity in responses to negative ESG news across consumer groups. Consumers in more ESG-conscious counties, as measured by county ESG preferences, income, education, and political ideology, reduce store visits in response to negative ESG news. In contrast, consumers in the least ESG-conscious counties increase their visits in response to negative ESG news. These opposing reactions explain the insignificant average consumer response to negative ESG news. Furthermore, the ESG-conscious consumers' negative reaction is, at most, modest, dissipating within six weeks. Overall, our findings suggest that firms face divergent responses to ESG activities from different consumer groups, underscoring the divisive nature of ESG issues.
