Knowledge that Transforms

We study whether sell-side financial analysts’ social ties to firm management help them discern firms’ financial reporting frauds and, upon such detection, how the connected analysts disseminate the information. Using unique data from China, we find that, although unconnected analysts do not manifest a significant change in their likelihood of covering the fraud firms nor in issuing more downgrade stock ratings, connected analysts are significantly more likely to drop coverage right after these firms’ first annual reports containing fraudulent information. Meanwhile, mutual funds with a trading commission relationship to these connected analysts (i.e., client funds) are significantly more likely to unload their holdings of fraud firms than nonclient funds after these firms’ first fraudulent annual reports. Overall, the evidence suggests that analysts with social ties to firm management have early access to bad news and choose to privately communicate the negative information to their clients. Data Availability: All data used in this article are publicly available.

Companies, including public accounting firms, are integrating systems with advanced algorithms into decision-making processes to assist with developing and evaluating complex estimates. However, individuals may hesitate to rely on algorithmic output, particularly under conditions of uncertainty. We conduct two experiments examining whether and how a system’s ability to adapt—an emerging feature of machine learning—interacts with uncertainty to influence accounting professionals’ reliance on algorithmic advice. In Experiment 1, we find that auditors are more willing to rely on advice from learning algorithms than static algorithms when estimation uncertainty is relatively high. Experiment 2 replicates this result in a general accounting context where preparers develop their own estimates. Our findings demonstrate that accounting professionals’ reliance on algorithms is contextually dependent, and highlights algorithm adaptability as an important technological feature that can promote advice utilization, particularly when adaptability is likely important to the judgment context (e.g., when estimation uncertainty is high).

We investigate the link between the expanded audit report and firms’ financial disclosure decisions, focusing on auditors’ mentions of goodwill impairment as a key audit matter (KAM). Drawing from a sample of the United Kingdom Premium Listed companies with goodwill on their balance sheets during 2014–2019, we identify instances where goodwill impairment is flagged as a KAM and contrast firms’ disclosure levels on goodwill impairment using textual measures constructed from information in their annual reports. We find that firm disclosure on goodwill impairment increases (decreases) when auditors start (stop) mentioning goodwill impairment as a KAM. The increase in disclosure is more pronounced in the presence of stronger external information demand and better internal governance. Finally, firms are more likely to impair goodwill in the period following auditors’ mention of goodwill impairment as a KAM. Overall, this paper establishes the role of the expanded audit report for firm disclosure.

This study uses machine learning models to forecast future material misstatements. Using raw financial data, audit variables, qualitative features, and an efficient algorithm, we design a dynamic model that continuously updates with new information. Our model outperforms the benchmarks for both one-year-ahead and two-year-ahead predictions in terms of out-of-sample predictive power and economic impact on net income. Using Explainable Artificial Intelligence, we identify key predictive features, including comprehensive income, foreign firm status, and accrued interest and penalties from unrecognized tax benefits. Results show that investors achieve better outcomes using a proactive investment strategy based on our prediction models than reactive detection models. Furthermore, our prediction model can help managers prevent internal control weaknesses, assist auditors in assessing misstatement risks in advance, and enable regulators to allocate inspection resources proactively. Our study advances the literature by moving beyond the detection of past material misstatements to the forecasting of future misstatements. Data Availability: Publicly available.

The relationships between accounting earnings and stock prices, as well as between unexpected earnings and returns, have received substantial attention in the empirical literature. Several theoretical models predict the shapes of these relationships. However, a comprehensive empirical description that could be used to evaluate these predictions is lacking. By integrating recent advances in statistics and machine learning with findings in the accounting literature, we develop an empirical method to identify the relationships, which is consistent with the firm-specific and nonlinear features of the theoretical models. Our approach provides a clear description of stylized and robust patterns in the relationships that are relevant to distinguish between existing models and to aid future theory development. The findings are consistent with recently proposed dynamic option models for both the earnings-price and unexpected earnings-returns relationships. Data availability: Data are available from the public sources cited in the text. A summary of the R code used in the article is available in Starica and Marton (2024).

We investigate whether meeting Wall Street’s expectations affects rank-and-file employees’ satisfaction. Controlling for firms’ underlying financial performance, we find that those currently working for firms that meet or marginally beat analysts’ forecasts experience increased job satisfaction. This positive effect is concentrated among employees who are less transient, receive more nonexecutive stock options, or are more unionized. Furthermore, the positive effect exists only when employees do not incur higher costs associated with reaching the threshold because they overwork, suffer from labor law violations, or experience layoffs. Lastly, more senior or highly skilled employees respond more strongly when their employer meets Wall Street’s expectations. These results suggest that the effect of meeting earnings targets on employee satisfaction is significant when employees’ incentives align more with those of their employer or when employees are not unduly pressured. Data Availability: Data are commercially available.

Firms commonly employ leaderboards within their peer-to-peer recognition programs. We experimentally investigate how ranking basis—variation in the measure firms use to determine leaderboard rankings—affects employees’ proactive helping behavior. We find that leaderboards ranking employees based on the number of times peer-to-peer recognition is received decrease proactive helping compared with when no leaderboard is provided. Conversely, leaderboards ranking employees based on the number of times peer-to-peer recognition is given increase proactive helping compared with when no leaderboard is provided. These findings underscore the influence of ranking basis on shaping motives linked to proactive helping behavior. Furthermore, these findings highlight for firms the importance of judiciously selecting a ranking basis when utilizing peer-to-peer recognition leaderboards.

We describe the development of public company auditing in the U.S. in the early 20th century to gain perspective on current developments in environmental, social, and governance (ESG) assurance. Using a broad sample of historical annual reports spanning four decades, we document three facts: first, the spread of public company auditing occurred steadily over the span of several decades. Second, audit services were initially heterogeneous but became standardized through the audit profession’s efforts and interactions with private and public actors. Third, the role of regulation in those early developments was seemingly limited to codifying existing practices, as the first federal audit regulation was introduced only late in the development of the profession and did not significantly impact capital markets. Our historical evidence helps us understand how we arrived at today’s widely accepted and highly regulated financial audits. It uncovers parallels to and offers lessons for current developments in ESG assurance. Data Availability: Data are available from the public sources cited in the text.

We examine how managers’ incentives to minimize litigation risk interact with the unique features of environmental information to shape disclosure decisions. We rely on peer firms’ lawsuits to generate variation in environmental disclosure litigation risk, consistent with prior research and anecdotal evidence suggesting that firms perceive an increase in environmental disclosure litigation risk after a peer firm is sued for related disclosures. Although we provide mixed evidence around changes in total environmental disclosure in response to peer lawsuits, we offer robust evidence that firms provide more forward-looking (and less historical) environmental disclosures in their conference calls in response to peers’ environmental disclosure lawsuits. Our evidence is consistent with firms providing less verifiable disclosures to minimize the risk of being sued for misrepresenting their environmental information. Data Availability: Data are available from the public sources cited in the text.

This paper investigates the impact of accounting and auditing enforcement releases (AAERs) on the compensation policies of nonaccused firms. The investigation focuses on releases in which the SEC mentions top executives’ pursuit of wealth through compensation schemes (i.e., compensation mentioned releases (CMRs)). Using a sample of AAERs from 1992 to 2021, I find that peer firms learn from these CMRs and significantly reduce their CEO’s delta and vega following CMRs. Peer firms also decrease their performance share grants and extend the vesting periods for option grants, resulting in a decrease in the convex payoff structure of CEO pay. Furthermore, I find evidence that peer firms reduce their CEO’s risk-taking incentives to circumvent litigation and prevent misreporting and shareholder scrutiny. Overall, the findings indicate that information describing one firm’s misreporting could affect the compensation policies of other firms, suggesting that regulatory enforcement accompanied by public awareness can effectively shape corporate behaviors. Data Availability: Data are available from the sources identified in the text.