Model Predictions and Auditor Assessments of Going Concern Status

This paper compares the predictions of a bankruptcy prediction model and the assessments of auditors on the going concern status of a sample of 165 bankrupt companies and 165 matched non-bankrupt companies. Data from US companies for the period 1978 to 1985 were used. Probit analysis (with the weighted exogenous sampling maximum likelihood procedure) was applied to estimate the model parameters. The Lachenbruch U method hold-out accuracy rates of the model are 85.45% for bankrupt firms, 100.00% for non-bankrupt firms, and 99.91% overall. The corresponding accuracy rates of the auditors based on their audit reports are 54.37% for bankrupt firms, 100.00% for non-bankrupt firms, and 99.73% overall. The sensitivity of optimal cut-off points to misclassification costs of Type I and Type II errors was also considered. Results of the study suggest that bankruptcy prediction models can be useful to auditors in making going concern assessments. Further, such models can serve as analytical tools and defensive devices.     

An investigation of recent changes in going concern reporting decisions among Big N and non-Big N auditors

Corporate accounting failures and regulatory proceedings that led to the enactment of the Sarbanes–Oxley Act of 2002 increased the scrutiny of auditors. We investigate whether these events resulted in a change in auditor behavior with respect to going concern reporting. Generally speaking, we find that non-Big N auditors became more conservative while Big N auditors became more accurate. Specifically, non-Big N auditors issued more going concern opinions to both failing and non-failing clients post-2001, reducing their Type II misclassifications at the expense of increased Type I misclassifications. However, Big N auditors decreased their Type I misclassifications with no corresponding increase in Type II misclassifications. Thus, our findings suggest that increased auditor scrutiny resulted in performance improvements in the area of going concern reporting primarily for larger auditors. For smaller auditors, improved going concern accuracy for subsequently bankrupt clients came at the cost of more going concern opinions being issued to subsequently non-failing clients.     

Predicting Corporate Failure Using a Neural Network Approach

This paper investigates the performance of Artificial Neural Networks for the classification and subsequent prediction of business entities into failed and non-failed classes. Two techniques, back-propagation and Optimal Estimation Theory (OET), are used to train the neural networks to predict bankruptcy filings. The data are drawn from Compustat data tapes representing a cross-section of industries. The results obtained with the neural networks are compared with other well-known bankruptcy prediction techniques such as discriminant analysis, probit and logit, as well as against benchmarks provided by directly applying the bankruptcy prediction models developed by Altman (1968) and Ohlson (1980) to our data set. We control the degree of ‘disproportionate sampling’ by creating ‘training’ and ‘testing’ populations with proportions of bankrupt firms ranging from 1% to 50%. For each population, we apply each technique 50 times to determine stable accuracy rates in terms of Type I, Type II and Total Error. We show that the performance of various classification techniques, in terms of their classification errors, depends on the proportions of bankrupt firms in the training and testing data sets, the variables used in the models, and assumptions about the relative costs of Type I and Type II errors. The neural network solutions do not achieve the ‘magical’ results that literature in this field often promises, although there are notable 'pockets' of superior performance by the neural networks, depending on particular combinations of proportions of bankrupt firms in training and testing data sets and assumptions about the relative costs of Type I and Type II errors. However, since we tested only one architecture for the neural network, it will be necessary to investigate potential improvements in neural network performance through systematic changes in neural network architecture.     

Costs Associated With Going-Concern-Modified Audit Opinions: An Analysis of the Australian Audit Market

This article examines the potential costs to Australian auditors and their clients from the issuance of first-time going-concern-modified audit opinions. We examine the population of Australian companies receiving a first-time going-concern-modified audit opinion during the period 1994–97 and a matched sample of financially distressed firms receiving a clean audit opinion. Results indicate that auditor switching is positively associated with receipt of a going-concern-modified opinion. However, we find no empirical evidence that there is a self-fulfilling prophecy of increased probability of company failure following the issuance of a going-concern-modified opinion for the Australian companies in our study. Our analyses of lost audit fees indicate that auditors issuing first-time going-concern-modified audit opinions lost proportionately more fees by losing clients (through switching or company failure) than firms not issuing a going-concern-modified opinion to financially stressed clients.     

Accruals and firm life cycle: Improving regulatory earnings management detection

Regulators have invested considerable energy into developing analytical tools to better detect earnings management. We propose that firms in similar life cycle stages (LCSs) face similar strategic concerns, managerial pressures, growth prospects, etc., and that the commonality in these factors contribute to the “normal” accruals generating process. Consistent with this prediction, we simulate various earnings management conditions and find that accruals models are misspecified in detecting manipulation within particular LCSs; in particular, introduction, shakeout, and decline firms are over-identified as manipulators, while growth and mature firms are under-identified as manipulators when LCS is not used to estimate accruals. Weighted average performance across life cycle stages reveals that LCS estimation of discretionary accruals substantially improves successful detection and reduces Type I errors relative to other grouping alternatives. The combined improvement across both Type I and Type II errors is over 70% for both the modified Jones and discretionary revenue models of accruals-based earnings management.     

Australian Audit Reports: 1996–2003

In 1996 Australia revised audit reporting standard AUS 702 to align with many of the concepts in the international audit reporting standard ISA 700. These included preventing auditors issuing a "subject to" qualified opinion, and permitting auditors to modify the audit report in specific circumstances by including an emphasis of matter (EoM) paragraph. This research examines the frequency with which different types of opinions are issued and the circumstances giving rise to the inclusion of an EoM paragraph, and compares the types of opinions issued by the major audit firms and for the various industry sectors over the period 1996–2003.     

Linear models for minimizing misclassification costs in bankruptcy prediction

This paper illustrates how a misclassification cost matrix can be incorporated into an evolutionary classification system for bankruptcy prediction. Most classification systems for predicting bankruptcy have attempted to minimize misclassifications. The minimizing misclassification approach assumes that Type I and Type II error costs for misclassifications are equal. There is evidence that these costs are not equal and incorporating costs into the classification systems can lead to better and more desirable results. In this paper, we use the principles of evolution to develop and test a genetic algorithm (GA) based approach that incorporates the asymmetric Type I and Type II error costs. Using simulated and real-life bankruptcy data, we compare the results of our proposed approach with three linear approaches: statistical linear discriminant analysis (LDA), a goal programming approach, and a GA-based classification approach that does not incorporate the asymmetric misclassification costs. Our results indicate that the proposed approach, incorporating Type I and Type II error costs, results in lower misclassification costs when compared to LDA and GA approaches that do not incorporate misclassification costs. Copyright © 2001 John Wiley & Sons, Ltd.     

Management control of public and not-for-profit activities

Traditional approaches to management control usually fail for public and not-for-profit activities.¹ The type of control applicable to such activities depends on four criteria: are objectives unambiguous, outputs measurable, effects of interventions known, and is the activity repetitive? Depending on where activities stand with regard to these criteria, the control applicable corresponds to one of six different types: routine, expert, trial-and-error, intuitive, judgemental, or political control. The first three types can be represented by cybernetic models; the other three ask for more complex and less deterministic models. For these, a “political” and a “garbage-can” model are described. Key elements in the latter models are the values and the culture of the actors. As an example, the topology for management control is applied to the area of budgeting, covering regular budgeting as well as such techniques as PPBS, MBO, and ZBB and distinguishing between investment budgets, operations budgets for input centers, and operations budgets for input-output centers. Coming back to management control in general, the paper discusses the consequences of choosing the wrong model for a given management control situation: it distinguishes between “Type I” and “Type II” errors. It finally relates management control to organizational adaptation and suggests how to avoid control systems which prevent an organizational system from learning.     

False (and Missed) Discoveries in Financial Economics

Multiple testing plagues many important questions in finance such as fund and factor selection. We propose a new way to calibrate both Type I and Type II errors. Next, using a double-bootstrap method, we establish a t-statistic hurdle that is associated with a specific false discovery rate (e.g., 5%). We also establish a hurdle that is associated with a certain acceptable ratio of misses to false discoveries (Type II error scaled by Type I error), which effectively allows for differential costs of the two types of mistakes. Evaluating current methods, we find that they lack power to detect outperforming managers.     

审计报告对预测银行授信失败有用性之实证研究

本研究的主要目的是探讨审计报告是否能帮助债权人评估借款人的违约风险(即借款人在借款之后发生逾期、催收和呆账等情况);亦即,当注册会计师对公司(借款人)的财务报告出具非无保留意见(包括保留意见、否定意见、及无法表示意见)时,该公司后续年度发生违约的机率是否较高﹖本研究结果有助我们了解,对债权人的授信决策及债权监控而言,注册会计师的审计报告是否为具有信息内涵的重要参考信息。实证结果支持本研究的实证假设:受查公司财务报告被注册会计师签具非无保留意见者,该公司后续年度发生违约(即催收、逾期、或呆账)的可能性较高。     

Australian Audit Reports: 1980-89

This paper provides information on auditors' opinions in Australia in the period 1980-1989. Issues which are reviewed include the frequency with which different opinions are issued, the types of opinions which are issued by the different audit firms and the circumstances on which the financial statements have drawn qualification. Between 15% and 20% of the financial statements were found to draw qualification, with very few of these qualifications being in the more serious forms of the adverse opinion or the disclaimer. Most qualifications related to disagreements on balance-sheet values or technical breaches of accounting standards. Divergences were found between the frequency and types of qualifications issued by the different audit firms.     

Off-site monitoring systems for predicting bank underperformance: a comparison of neural networks,discriminant analysis,and professional human judgment

This study compares the ability of discriminant analysis, neural networks, and professional human judgment methodologies in predicting commercial bank underperformance. Experience from the banking crisis of the 1980s and early 1990s suggest that improved prediction models are needed for helping prevent bank failures and promoting economic stability. Our research seeks to address this issue by exploring new prediction model techniques and comparing them to existing approaches. When comparing the predictive ability of all three models, the neural network model shows slightly better predictive ability than that of the regulators. Both the neural network model and regulators significantly outperform the benchmark discriminant analysis model's accuracy. These findings suggest that neural networks show promise as an off-site surveillance methodology. Factoring in the relative costs of the different types of misclassifications from each model also indicates that neural network models are better predictors, particularly when weighting Type I errors more heavily. Further research with neural networks in this field should yield workable models that greatly enhance the ability of regulators and bankers to identify and address weaknesses in banks before they approach failure. Copyright © 2001 John Wiley & Sons, Ltd.     

Audit opinion and earnings management: Evidence from Greece

This study examines the relationship between audit opinions and earnings management, as measured by discretionary accruals, for listed firms on the Athens Stock Exchange (ASE). We divide the qualified audit opinions into two categories: qualified for the going-concern uncertainty and qualified for other reasons. The results indicate that audit opinions are not related to earnings management. Client financial characteristics, such as profitability and size are determinants of the going-concern audit opinion decision. The decision of auditors to issue qualified opinions for other reasons is explained by the type of audit opinion issued in the previous year.     

小额赢利、独立董事和审计鉴证

有关中国资本市场的研究揭示,微利公司更容易被出具非标准无保留意见,本文通过此类非标准意见的说明段,发现其涉及事项和独立董事关注的公司非正常治理结构明显重合。进一步的检验则揭示,独立董事比例的增加能够提升公司机会主义特征激发非标准审计意见的概率,聘入行业专家型的独立董事则有利于公司的营运能力激发标准的审计意见。结论表明独立董事在规避渎职风险的过程中,通过充分披露信息来避免过高的机会主义阻止成本,和审计鉴证发生了协同效应。     

Jurisdictional Differences in Contracting Out GBE Audits: The Impact on Audit Pricing

This paper examines audit costs for government business enterprises in various Australian jurisdictions, focusing on whether contracting private-sector suppliers induces competitive pricing and hence lower-priced audits. One jurisdiction (New South Wales) is shown to have low levels of contracting-out. It is argued that this lowers price competition and therefore, we predict, audit costs in NSW will be higher than elsewhere in Australia. An empirical comparison shows that, controlling for other factors, NSW is statistically significantly more expensive than other Australian jurisdictions. Alternative explanations relating to variation in audit quality, jurisdiction differences and relative cost are also discussed.     

持续经营审计意见、分析师跟踪与融资约束

以2011-2016年我国A股上市公司数据为研究样本,运用现金—现金流敏感性模型,研究持续经营审计意见及分析师跟踪对融资约束的影响。研究发现：持续经营审计意见的出具加剧了上市公司面临的融资约束;分析师跟踪人数越多,上市公司面临的融资约束越小;分析师跟踪能够缓解被出具带强调事项段持续经营无保留审计意见公司的融资约束,加剧被出具其他持续经营审计意见（保留意见、无法表示意见和否定意见）公司的融资约束。     

A re-evaluation of auditors’ opinions versus statistical models in bankruptcy prediction

Existent empirical evidence on the relative performance of auditors’ going concern opinions versus statistical models in predicting bankruptcy is mixed. This study attempts to add new reliable evidence on this important issue by conducting the comparison based upon an improved statistical model. The improved statistical model incorporates some new developments advocated by recent bankruptcy prediction research (e.g., Shumway, 2001). First, the following non-traditional variables are added: a composite measure of financial distress, industry failure rate, abnormal stock returns, and market capitalization. Secondly, a hazard model is employed. The prediction ability of the hazard model with incorporation of non-financial-ratio variables is superior to that of auditors’ going concern opinions in the holdout sample. This suggests that a well-developed statistical model could serve as a decision aid for auditors to better make going-concern judgments. Further analyses reveal some evidence that industry failure rate does not have a significant impact upon auditors’ going concern judgments as it should be; auditors could improve their going concern judgments by considering industry-level information in addition to firm-specific information. Finally, we find that auditors’ opinions do have incremental contribution beyond stock-market information and industry failure rate in predicting bankruptcy.

审计任期与审计质量:来自中国证券市场的经验证据

本文以中国证券市场上2000年至2002年期间获得标准无保留审计意见的上市公司为样本,使用经过一定调整后的截面Jones模型估计出的公司操纵性应计利润的绝对值作为审计质量的衡量指标,考察了会计师事务所审计任期与审计质量之间的关系。研究发现,在控制了事务所变更、事务所特征、行业成长性、公司规模、经营业绩、资产负债率、上市年龄以及样本所在年度后,审计任期与公司操纵性应计利润的绝对值呈正U型关系,即审计任期与审计质量呈倒U型关系。进一步分析发现,当审计任期小于一定年份(约6年)时,审计任期的增加对审计质量具有正面影响,而当审计任期超过一定年份(约6年)时,审计任期的增加对审计质量具有负面影响。     

Address forms in Chinese audit opinions

Although forms of address are widely used in textual and other types of disclosure,empirical evidence of their effects is rare.China provides a unique setting in which to test the economic consequences of the forms of address used in audit reports.From 2003 to 2011,about 60%of auditors surveyed addressed their clients by their real names in audit opinions,while the others used honorifics.Based on a sample of Chinese audit opinions,I report the following findings.First,the announcement of an audit opinion that uses the client’s real name elicits a greater market response than the announcement of an opinion featuring an honorific form of address.Second,the effects of real-name forms of address are stronger in firms with weak board governance.Third,the association between audit fees and audit risk factors,such as loss-making,is stronger in firms that are addressed by their real names in audit reports.I conclude from these findings that the forms of address used in audit opinions may reveal private information on audit quality.The results of this study are consistent with the power-solidarity effect described by sociolinguists.