Detecting and Predicting Accounting Irregularities: A Comparison of Commercial and Academic Risk Measures

Abstract

Although a substantial body of academic research is devoted to developing and testing risk proxies that detect accounting irregularities, the academic literature has paid little attention to commercially developed risk measures. This is surprising given the general consensus that academic risk measures have relatively poor construct validity. We compare the commercially developed Accounting and Governance Risk (AGR) and Accounting Risk (AR) measures with academic risk measures to determine which best detects financial misstatements that result in Securities and Exchange Commission enforcement actions, egregious accounting restatements, and shareholder lawsuits related to accounting improprieties. We find that the commercially developed risk measures outperform the academic risk measures in all head-to-head tests for detecting misstatements. The commercial measures also perform as well as or better than the academic measures in new tests that predict future accounting irregularities using numbers reported one year before the misreporting even begins. Our results suggest commercially developed risk proxies may be useful to practitioners and academics trying to detect or predict accounting irregularities.

Keywords: accounting irregularities, detecting fraud, predicting fraud, risk measures, commercial risk ratings

JEL Classification: M41, G30, K22