A Review Of Going Concern Prediction Studies: 1976 To Present

Abstract

In 1962, the Securities and Exchange Commission (SEC) was the first to address going concern issues with Accounting Series Release (ASR) No. 90.   Then, in 1963, the AICPA issued Statement on Auditing Procedures (SAP) No. 33, in response to ASR No. 90.  Both ASR No. 90 and SAP No. 33 addressed qualifications for issues that were unresolved and the results of which were indeterminable at the statement date. Soon after the issuance of Statement on Auditing Standards (SAS) No. 2 in 1974, researchers began to conduct studies on going concern issues.  This paper provides a comprehensive review of the literature on going concern studies and updates studies by Mutchler (1983) and Asare (1990) which provide detailed reviews of the evolution of the going concern report and requirements of the standards related to auditors' assessment of going concern.  Since SAS No. 2, the profession has not provided additional guidance on going concern.  Even the Sarbanes-Oxley Act of 2002 (SOX), makes no modifications to the requirements for considering going concern and the Public Company Accounting Oversight Board has not issued guidance addressing going concern. Starting with the first going concern prediction study [McKee, 1976], this paper identifies 27 models developed for predicting the going concern opinion and identifies the primary methods used for model development; multivariate discriminant analysis (MDA), logit analysis, probit analysis, and neural networks are.  This paper also identifies; the most popular type of focused model and identifies three non-U.S. firm models, the number of factors considered in any one study,  and the predictive abilities of the models. The paper also provides an annotated bibliography for the 27 models.