Researchers have tested a wide array of theories and developed a broad set of explanatory variables for audit quality. However, little is known about which of these audit-related variables are the most predictive of audit failure (i.e., low-quality audits). We devote this study to provide...

This paper draws on psychological theories and uses archival data to study the error, noise, and bias of auditors' Going Concern Opinion (GCO), which requires auditors' professional judgment. We find that for firms that go bankrupt the following year, GCO is inaccurate 45 percent of the time,...

This paper introduces a semi-supervised machine learning technique, Positive-Unlabeled (PU) Learning, to address the issue of “unlabeled positives” in accounting research. It uses financial statement restatement detection/prediction as a research setting. In this setting, unlabeled positives...

The periodic nature of auditing and monitoring allows for a valuable time-delay between the occurrence of an important business event and the analysis of that event. Advances in technology provide opportunities to reduce the time delay between the occurrence and analysis of a business process...