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Research into the usefulness of a neural network model for the prediction of going concern status, in a company, from financial ratios found it compared well with both the probit model and the auditor's findings. It was also noted the neural network could work better with the inclusion of more information, such as the training data set. There has been some criticism over the methods used by auditors to determine the going concern status of a firm. Some observers have suggested the use of statistical methods may help the auditors in their work and make any conclusions seem less subjective.

A study was conducted on 808 financially troubled firms from the years 1984-1991 to investigate the possible influence of auditor incentives in decisions to release going-concern disclosures to financially-distressed client enterprises. A model is designed that will reflect the auditor's going-concern disclosure as a function both of the client business' financial conditions/prospects and variables linked with the auditor's loss function including prospective audit fees, the duration of the auditor-client relationship, recent auditor litigation, client losses and the existence of previously disclosed evidence of going concern difficulties. Results do not seem to support the contention that auditors' going-concern disclosure decisions are methodically influenced by certain incentive factors that were included in the study.

A study was conducted to examine the impact of contrary information and mitigating factors on audit opinion decisions regarding companies on the verge of bankruptcy. Contrary information is defined as information challenging the further existence of the client while mitigating factors serve to offset and therefore lessen the impact of contrary information. Findings revealed a significant correlation between the going-concern opinion decisions of auditors in the face of bankruptcy and the likelihood of bankruptcy as well as an audit-report lag variable. It was also revealed that the going-concern opinion is highly correlated with payment and covenant defaults but not with cured default. The opinion was also correlated with a bankruptcy lag variable and very negative news reports before the audit-report date. Lastly, corporate size was also found to affect auditors' opinions.

A study on the accounting notion that business firms conduct operations in the same manner throughout the whole year was conducted to determine its effect on the capitalization and allocation of costs. The research observed four major Australian bank cases covering the period of 1983-94. Results did not reveal general conclusions regarding other industries and trades. However, the economic behavior of banks observed by the study can be linked with the performance of other business firms. There is proof that these institutions have undergone organizational change.

A game-theoretic model presents how a client can potentially avoid a going-concern opinion and its self-fulfilling prophecy by switching auditors. The model shows that an incumbent auditor who faces the threat of dismissal is less willing to express a going-concern opinion and the probability of auditor switching vary the auditors' reporting judgments. The client's viability also affects the tendency of the auditor to express going-concern opinion.

Less restrictive definitions for both auditor competence and independence and a revision of the conceptual linkage are suggested to provide empirical researchers with a more complete theoretical explanation of auditor behavior. P. Barnes and H.D. Huan studied auditor competence and independence in 1993, in the context of a going concern decision. Barnes and Huan's empirical analysis implied narrow definitions, classifying the two concepts as mutually inclusive characteristics of auditor behavior.

Discrete-Time Survival Analysis is used to examine the effects of auditor's going-concern disclosures on client's financial operations and subsequent continuance.