Deciphering the PCAOB Inspection Process: Evidence and Predictive Insights from Public Data
Management Science · 2026 · Daniel Aobdia、Edward Xuejun Li、K. Ramesh、Min Shen
中文摘要
关于美国公众公司会计监督委员会（PCAOB）如何选择审计项目进行检查，人们所知甚少。我们利用可追踪PCAOB对发行人提交美国证券交易委员会（SEC）文件的检索记录以及《美利坚合众国诉David Middendorf案》庭审记录誊本的公开数据，首次提供了关于PCAOB保密监控活动的大样本证据。我们首先验证了PCAOB的检索在实际检查三年一检的审计师事务所期间、以及针对2016年接受检查的一份毕马威（KPMG）审计项目清单而出现激增。重要的是，我们发现PCAOB的检索会随正在进行的审计警示事件、客户公司事件和客户特征而以可预测的方式变化，且这些筛查与检查活动超出了执法行动的范围。利用毕马威的数据，我们引入了一个仅基于公开数据的、针对四大会计师事务所客户的PCAOB检查预测模型，其预测发生率与实际检查情况高度吻合。本研究的一个含义是，或许是为了减轻政治成本，PCAOB过度依赖那些本身已发出低审计质量信号的显眼触发事件（即财务重述、审计师变更、首席财务官更替、破产和SEC意见函），这引发了一个问题：其基于风险的检查项目究竟是如何设计以提升整体审计质量或最小化重大审计失败的。此外，尽管模型推断的PCAOB检查与未来财务重述不相关，但以检索强度为代理的更广泛的PCAOB监控活动则表现出适度的预测能力。总体而言，本研究提供了对PCAOB检查项目及其显露偏好驱动因素的更为细致的理解。本文由Ranjani Krishnan（会计学领域）接收。资助：本研究得到莱斯大学Jones商学院、巴鲁克学院和宾夕法尼亚州立大学Smeal商学院的支持。补充材料：在线附录和数据文件可在 https://doi.org/10.1287/mnsc.2024.08084 获取。
Abstract
Little is known about how the Public Company Accounting Oversight Board (PCAOB) selects audits for inspections. We exploit public data that track PCAOB searches of issuer Securities and Exchange Commission (SEC) filings and the trial transcripts of United States of America v. David Middendorf to provide the first large-sample evidence on confidential PCAOB monitoring activities. We initially validate that PCAOB searches spike during actual inspections of triennially inspected auditors and for a list of KPMG audits inspected in 2016. Importantly, we find that PCAOB searches vary predictably with ongoing audit-flagged events, client corporate events, and client characteristics, and these screening and inspection activities extend beyond enforcement actions. Using KPMG data, we introduce a PCAOB inspection prediction model for Big 4 clients based solely on public data, with predicted incidence closely aligning with actual inspections. One implication of our study is that, perhaps to mitigate political costs, the PCAOB relies overly on conspicuous trigger events that already signal low audit quality (i.e., restatements, auditor changes, chief financial officer turnovers, bankruptcies, and SEC comment letters), raising the question of how its risk-based inspection program is designed to improve overall audit quality or minimize large audit failures. In addition, although model-inferred PCAOB inspections are not associated with future restatements, broader PCAOB monitoring activities, as proxied by search intensity, exhibit modest predictive power. Overall, our study provides a more nuanced understanding of the PCAOB inspection program and the factors driving its revealed preferences. This paper was accepted by Ranjani Krishnan, accounting. Funding: This work was supported by the Jones Graduate School of Business at Rice University, Baruch College, and the Penn State Smeal College of Business. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.08084 .