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.

Using Census microdata on 28,000 manufacturing plants, we examine how firms manage employee retention concerns. In response to reductions in the local unemployment rate, plants take additional steps beyond increasing compensation. First, plants adjust bonus architecture to ensure bonuses can be paid. Second, plants offer more agency to employees by deploying high-involvement work practices that generate longer-term commitment. Third, plants pull these retention levers less when they have high availability and use of data as this reduces the adverse effects of employee turnover on organizational knowledge. These results are robust to using the fracking revolution as a shock increasing firms’ retention concerns. Additionally, we observe that although compensation increases tend to spill over to other plants within the same firm—aligning with theories of inequity aversion—adjustments to bonus architecture and the provision of employee agency do not, suggesting these may be more cost-effective strategies for multiplant firms.

This study examines whether enterprise resource planning (ERP) implementations are associated with reductions in corporate misconduct. Specifically, we study the relation between staggered facility-level rollouts of ERP systems and facility-level regulatory violations across a large sample of U.S. firms. Our results indicate that facility-level ERP adoptions are associated with substantial reductions in local violations and penalties. Additional analyses suggest that the effects are more pronounced among facilities incorporating advanced analytics into their systems and among workforces that are less resistant to technology change. Overall, our results suggest that ERP systems generate indirect effects that enhance compliance outcomes across a wide range of violations.

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).

In the wake of the financial crisis, policymakers expressed the concern that the incurred loss model delays loan loss recognition to economic stress periods and thereby exacerbates banks’ lending contraction during these periods. Addressing this concern, the FASB issued Accounting Standards Update 2016-13, which requires large public banks to accrue for loan losses using the current expected credit loss (CECL) approach starting in January 2020. We hypothesize and find that banks that adopted CECL prior to the COVID-19 pandemic increased loan loss provisions and reduced loan growth during the accompanying recession more than other banks. The lending contraction is stronger for adopting banks with low regulatory capital and low loan impairment and is primarily driven by commercial loans. Lastly, we find that counties in which CECL-adopting banks have higher market share experience larger increases in unemployment rates during the recession and slower subsequent recoveries. Data Availability: Data are available from the public sources cited in the text.

Many countries have converged their domestic auditing standards with International Standards on Auditing (ISA). This study provides global empirical evidence on first-order determinants of audit quality by examining whether and how convergence affects audit quality through utilizing data on 41 jurisdictions and using a staggered difference-in-differences approach. We find that ISA convergence leads to higher audit quality on average. The positive effect is stronger for clients of domestic audit firms, in jurisdictions with stronger enforcement, and when the ISA convergence level is higher. Insights from textual features suggest that changes in principle-orientation, comparability, readability, and size (or length) of auditing standards are positively related to audit quality. Exploratory analyses of textual content using machine learning reveal that the emphases of ISA on going-concern assessment and legal compliance, fraud risk assessment and internal control evaluation, and related-party transactions and subsequent events contribute to enhanced audit quality.

We use hand-collected data to analyze submission-to-acceptance (STA) times in the top-tier accounting journals relative to other top-tier business journals from 1993 through 2021. We find that, vis-à-vis other business disciplines, STA times at top-tier accounting journals were shorter in the first half of our sample period and significantly longer thereafter. We also observe shorter STA times for articles with authors from more highly ranked institutions; this effect exists only in top-tier accounting journals and has increased over time. In additional analyses, we find that our primary inferences are unchanged when considering maturity of initial journal submissions, journal-level democratization, and review process improvements related to paper quality. Our results should be of interest to researchers, journal editors, reviewers, provosts, deans, and tenure and promotion committees. Data Availability: The data used in this study are available from the sources indicated herein.