We examine how the Big 4 responded to evolving partner qualification requirements in China. By 2017, at least 80 percent of the Big 4 partners were required to hold CICPA qualifications, a requirement that did not apply to local firms. Using data from a 14-year window around the reform, we document that the Big 4 expanded their partnership and primarily complied through internal promotion of locally licensed auditors, often into junior signing roles and serving new clients. Our analyses of audit outcomes yield mixed and time-varying evidence: although we observe relative increases in restatements in selected periods, the triangulated evidence does not suggest a pervasive deterioration in audit quality. However, we find a decline in the Big 4’s market share and audit fee premium. Overall, our findings shed light on how regulatory interventions targeting auditor qualifications reshape audit firms and market competition. Data Availability: Data are available from the public sources cited in the text.

We examine the impact of the SEC’s Office of Minority and Women Inclusion (OMWI) on the role of employee gender in the Division of Corporation Finance’s filing review process. Gender bias theory suggests that women may work harder to compensate for perceived bias and discrimination. Consistent with this theory, we find that women reviewers issue longer comment letters, raise more issues, ask more accounting-specific questions, reference more authoritative guidance, request more filing amendments, follow up on more issues from prior rounds, and take longer to close the comment letter process. We also find that women are less prevalent in higher paygrades and leadership positions. These gender differences attenuate after the establishment of OMWI in 2011, but significant differences remain. Analyses of SEC employee survey data corroborate our comment letter results. Data Availability: All data are publicly available.

We describe variation in the reporting quality (i.e., accuracy and bias of reported net asset values (NAVs)) of private equity (PE) funds across types of external monitors (investors and auditors) and third-party service providers (valuation specialists, marketers, and administrators). In contrast to public markets, we find only limited evidence that reporting quality varies with the composition and types of investors in PE funds. We observe, however, that reporting quality varies with auditor involvement and the use of third-party service providers; these associations often differ across buyout (BO) and venture capital (VC) funds and from those observed in public markets. Our evidence is important to investors and regulators, especially now that PE supersedes public markets as the main vehicle to raise capital and as regulators increase their focus on private markets. Data Availability: Data used in this study are available from public sources listed in the paper.

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.

We investigate (1) how well a machine learning algorithm can predict one-year ahead effective tax rates (ETRs) and (2) which items in the financial statements and notes are most useful for these predictions. We compare our machine-generated ETR predictions with those from ETRs implied by analysts’ earnings forecasts and find the algorithm’s predictions are less biased, more precise, and explain more of the variance in future ETRs. We then use Explainable AI (based on Shapley values) to measure the usefulness of each disclosure item in the algorithm’s predictions. We find that while some tax-related items are useful, others offer minimal value. Using the machine learning algorithm’s use of information as a benchmark, we then further use Shapley values to examine which information is underweighted or overweighted by analysts. Overall, our results help inform standard setters on the relevance of certain tax disclosures in achieving the objective of predicting future ETRs.

We examine the relation between accounting quality and debt concentration in corporate capital structures (i.e., firms' tendency to rely predominantly on only a few types of debt). Motivated by theoretical and empirical research that supports a strong link between debt concentration and creditors' coordination costs and the importance of accounting quality in reducing these costs, we hypothesize that firms with higher accounting quality have less concentrated debt structures. Measuring accounting quality with a comprehensive index based on the occurrence of material internal control weaknesses, accounting restatements, SEC AAERs, and firms' reliance on small auditors, we find that higher accounting quality is indeed associated with less concentrated debt structures. This relation is stronger for firms with higher default risk, as the probability that creditors need to coordinate is higher, and for firms with lower liquidation values, as creditor coordination to avoid liquidation is more important. Data Availability: Data are available from the public sources cited in the text.

We examine whether social ties between engagement auditors and audit committee members shape audit outcomes. Although these social ties can facilitate information transfer and help auditors alleviate management pressure to waive correction of detected misstatements, close interpersonal relations can undermine auditors' monitoring of the financial reporting process. We measure social ties by alma mater connections, professor-student bonding, and employment affiliation, and audit quality by the propensity to render modified audit opinions, financial reporting irregularities, and firm valuation. Our evidence implies that social ties between engagement auditors and audit committee members impair audit quality. In additional results consistent with expectations, we generally find that this relation is concentrated where social ties are more salient, or firm governance is relatively poor and agency conflicts are more severe. Implying reciprocity stemming from social networks, we also report some suggestive evidence that audit fees are higher in the presence of social ties between an engagement auditor and the audit committee. Collectively, our analysis lends support to the narrative that the negative implications—namely, worse audit quality and higher audit fees—of these social ties may outweigh the benefits.