Date of Award
Doctor of Philosophy (PhD)
Susan M. Albring
AAER, accounting misstatements, fraud prediction, misstated accounts, misstating industries
This study examines variables that may be useful in predicting accounting misstatements. Using a database of Accounting and Auditing Enforcement Release information and building on recent models and methodology, I separate the observations by industry to determine the firm and financial statement variables that are most useful in predicting the firms within specific industries that may have accounting misstatements. I also extend the previous models to determine the significant variables in predicting not only which firms may have misstatements, but also the account(s) in which a misstatement is likely to have occurred. These models use information that is readily available in the financial statements, making them useful to auditors, regulators, and other users of financial statements. Finally, I examined the consistency of the predictive variables over several time periods.
My findings suggest that several variables that were found to be significant in a generalized model in previous literature lack significance in more specialized models and that some variables that were found to have no significance in a generalized model in previous literature do have significance in more specialized models. Specifically, the variables “soft assets” and “issue” appear to be the most consistent predictors of misstatements across industries, accounts, and time.
Cokeley, Emily, "Predicting Accounting Misstatements within Industries and Accounts" (2018). Dissertations - ALL. 987.