In this paper, we utilize machine learning techniques to identify the likelihood that a company switches auditors and examine whether increased likelihood of switching is associated with audit quality. Building on research that finds a deterioration in audit quality associated with clients that engage in audit opinion shopping, we predict and find lower audit quality among companies that are more likely to switch auditors but remain with their incumbent auditor. Specifically, we find that companies more likely to switch auditors have a higher likelihood of misstatement and larger abnormal accruals. These results are consistent with auditors sacrificing audit quality to retain clients that might otherwise switch. Our findings are especially concerning because there is no public signal of this behavior, such as an auditor switch. Our methodology is designed such that it could be implemented by investors, audit firms and regulators to identify companies with a higher probability of switching auditors and preemptively address the deterioration in audit quality.

在本文中，我们利用机器学习技术来确定公司更换审计师的可能性，并检查更换可能性的增加是否与审计质量有关。基于发现与参与审计意见购买的客户相关的审计质量下降的研究，我们预测并发现更有可能更换审计师但仍留在现任审计师的公司的审计质量较低。具体而言，我们发现更有可能更换审计师的公司有更高的错报和更大的异常应计费用。这些结果与审计师牺牲审计质量来留住可能会转换的客户是一致的。我们的发现尤其令人担忧，因为没有这种行为的公开信号，例如审计师更换。