USING GOOD AUDITING SKILLS TO ASSESS THE QUALITY OF EARNINGS AND THE RISK OF FINANCIAL STATEMENT MANIPULATIONS

Finance and investment professionals have long spoken of ‘quality of earnings’ – a term generally defined in terms of accounting conservatism and replicability of reported earnings (i.e., that profit is not driven by nonrecurring one-time events or accounting gimmicks) – but in practice obtaining valid information by which to assess quality of earnings has not been easy. The reporting entity’s financial statements, including informative disclosures (i.e., footnotes), are the best place, and sometimes the only place, to begin the analysis, but because of the high-level aggregation of information presented, aberrations and anomalies, including financial reporting fraud, may temporarily be hidden from outside observers’ view.

However, even if the obligation to opine runs only to the consolidated financial statements, the auditors, properly applying generally accepted auditing standards (which are quite equivalent under varying regimes worldwide), should be able to detect anomalies and distortions from their analytical and other auditing procedures, if applied to disaggregated accounting data. The odds of detecting manipulations become even more favourable if forensic-type auditing procedures are employed, which can be done even in the context of routine audits.

Although accountants and auditors are renowned as ‘numbers people’, they have often resisted the use of sophisticated quantitative tools, such as statistical methods of audit sampling, models to predict insolvency for going concern assessments, and analytical procedures more complex than the simplistic ‘last year vs. this year’ comparisons. However, the more knowledgeable and adroit auditors have employed such devices, and auditors practicing in the sub-field of forensic accounting are commonly more adept at the use of such tools, including the Altman Z-score model for predicting near-term insolvency, regression analyses to detect monthly or quarterly anomalies possibly caused by channel stuffing or other revenue recognition frauds, and variance analyses to corroborate management assertions regarding the effects of changes in product mix.

Apr-Jun 2014 Issue

Cendrowski Corporate Advisors LLC