Using Sec Edgar Views to Measure Competition Among Big 4 Auditors

Abstract

Prior research provides mixed evidence about whether sufficient audit market competition exists and whether competition impairs or improves audit quality. A major impediment to this stream of research is the unobservable nature of the bidding process by which auditors compete for clients. In this study, we apply a machine learning algorithm to non-incumbent (i.e., competitor) auditor views of public companies’ SEC filings to estimate the probability of bidding at the company-year level. We validate our probability estimates using a proprietary sample where all instances of bidding are known. We then investigate the association between the probability of bidding and previously documented measures of auditor competition (i.e., market concentration), audit quality, and audit pricing. Consistent with concerns that market concentration impedes competition, we find that bidding is less likely in industry-concentrated markets. However, contrary to conclusions in the prior literature, we find no evidence that local market concentration is associated with competitive bidding. We also find that bidding is associated with higher quality auditing but does not constrain audit fees.