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.     

Auditors’ going concern reporting accuracy during and after the global financial crisis

Motivated by concerns that one of the reasons for the Global Financial Crisis (GFC) is poor quality auditing, this study examines the accuracy of going concern modifications for a sample of United States (U.S.) companies in the pre-GFC (2005–2006), GFC (2007–2008), and post-GFC (2009–2010) periods. The results show that the type I misclassification is lower during the GFC but not different in the post-GFC period compared with the pre-GFC period. The type II misclassification is not significantly different in the GFC and post-GFC periods compared with the pre-GFC period. Additionally, the results suggest that non-Big 4 auditors, compared with Big 4 auditors, have become more conservative on clients’ going concern problems in the post-GFC period, which reduces their type II misclassification.     

The Garman-Ohlson Structural System

The Garman-Ohlson structural model assumes the evolution of corporate earnings, dividends and book values are generated by a simultaneous equation system which links financial statement information to underlying equity value. However, little is known about the consistency of empirical outcomes with the model's underlying analytical properties. A continuous time interpretation of the model implies that solutions fall into one of three categories: (i) all eigenvalues of the structural model are real and distinct; (ii) some eigenvalues may be complex, and (iii) there are repeated eigenvalues. Maximum likelihood techniques can be used to estimate structural models and likelihood ratio tests can then be used to assess the validity of alternative specifications. We demonstrate both likelihood procedures by applying them to a sample of 214 UK companies covering the twenty one year period ending in 1994.     

Detection of Automobile Insurance Fraud With Discrete Choice Models and Misclassified Claims

The insurance industry is concerned with the detection of fraudulent behavior. The number of automobile claims involving some kind of suspicious circumstance is high and has become a subject of major interest for companies. This article demonstrates the performance of binary choice models for fraud detection and implements models for misclassification in the response variable. A database from the Spanish insurance market that contains honest and fraudulent claims is used. The estimation of the probability of omission provides an estimate of the percentage of fraudulent claims that are not detected by the logistic regression model.     

Predicting a corporate credit analyst's risk estimate by logistic and linear models

The purpose is to predict corporate credit analyst's risk estimate by the weighted logistic (binary response) and linear regression (20-class risk estimate) analyses. The data comprise filed register information from Finska (Suomen Asiakastieto Oy) including 35 variables from 3200 companies. The coefficient of concordance was 95% and the rate of multiple determination 75% for the logistic and linear models, respectively. In a binary classification the differences in performance between the models were insignificant provided that the linear model is rotated. Both of the models give a classification accuracy of 90% in the estimation sample and 96% in the test sample.     

Financial distress and bankruptcy prediction among listed companies using accounting,market and macroeconomic variables

Using a sample of 23,218 company-year observations of listed companies during the period 1980–2011, the paper investigates empirically the utility of combining accounting, market-based and macro-economic data to explain corporate credit risk. The paper develops risk models for listed companies that predict financial distress and bankruptcy. The estimated models use a combination of accounting data, stock market information and proxies for changes in the macro-economic environment. The purpose is to produce models with predictive accuracy, practical value and macro dependent dynamics that have relevance for stress testing. The results show the utility of combining accounting, market and macro-economic data in financial distress prediction models for listed companies. The performance of the estimated models is benchmarked against models built using a neural network (MLP) and against Altman's (1968) original Z-score specification.     

INSOLVENCY PREDICTION IN THE PRESENCE OF DATA INCONSISTENCIES

In this paper we use data inconsistencies as an indicator of financial distress. Traditional models for insolvency prediction normally ignore inconsistent data, either by removing or replacing it. Instead of removing that information, we propose a new variable to capture it; using it together with traditional accounting variables (based on financial ratios) for the purpose of insolvency prediction. Computational tests use three datasets based on the financial results of 2033 Brazilian Health Maintenance Organizations over 7 years (2001 to 2007). Sixteen classification methods were used to evaluate whether or not the new variable impacted solvency prediction. Tests show a statistically significant improvement in classification accuracy – average results improve 1.3 (p = 0.003) and 1.8 (p = 0.006) percentage points, for 10‐fold and leave‐one‐out cross‐validations respectively. In addition, the analysis of false positives and false negatives shows that the new variable reduces the potentially harmful misclassification of false negatives (i.e. financially distressed companies being classified as financially healthy) and also reduces the estimated overall error rate. Regarding the extensibility of the results, even though this work uses data from Brazilian companies only, the calculation of the financial ratios variables, as well as the inconsistencies, could be extended to most companies worldwide subject to governmental accounting regulations aligned with the International Financial Reporting Standards. Copyright © 2014 John Wiley & Sons, Ltd.     

Misclassification of the dependent variable in a debt–repayment behavior context

This paper estimates a model of the household debt–repayment decision that accounts for the possibility of misclassification of self-reported debt–repayment status. It likewise estimates the extent of misclassification in a sample of data from different European countries. The evidence suggests that many households that are in arrears do not report this condition, so that the true level of arrears is, on average, 24% higher than that observed in our data. Furthermore, the effects on the incidence of arrears of adverse income and expense shocks are substantially greater than those predicted by estimators that ignore the possibility of misclassification.     

Some Further Empirical Evidence on Predicting Private Company Failure

A common feature of previous work on failure prediction models of UK companies is that the non-failed samples are restricted to include only sound or healthy companies. This may be considered a major weakness since, as a consequence, the models are biased in statistical design and have unclear relevance to a potential user.

The major purpose of this paper is to develop models which explicitly allow for loss-making companies in the non-failed sample. We novelly experiment with rnultilogit analysis; we also report, as joint products of our analysis, some empirical results on the determinants of the going concern qualification, the time lag in reporting annual accounts and the formal type of legal failure.     

The impact of hedging on the market value of equity

We examine the annual stock performance of firms that disclose the use of derivatives to hedge over the period 1995 to 1999. We find that only 21.6% of publicly traded U.S. corporations in our sample hedged with derivative instruments over this period and their use is concentrated in the larger companies. Similar to other studies we find that when derivatives are used, interest rate and currency securities are used much more frequently than commodity products. Our sample of 1308 companies that hedge outperforms other securities by 4.3% per year on average over our sample period. This result is robust to several alternative methods of estimating abnormal returns. When we segment performance by the type of hedge used, however, we find that the over-performance is due entirely to larger firms that hedge currency. We find no abnormal returns for firms hedging either interest rates or commodities. The abnormal returns in firms hedging currency is robust to alternative models that seek to control for exchange rate fluctuations and global equity returns; however, we find no significant abnormal returns to currency hedgers when using an augmented model that controls for the role of intangible assets.     

Playing to their strengths? Evidence that specialization in the private equity industry confers competitive advantage

The paper examines whether private equity (PE)-backed buyouts have higher post-buyout operating profitability than comparable companies as a result of the alleged superior governance mechanism of private equity (“The Jensen hypothesis”) and whether relative investment specialisation by industry or stage provides the PE firm with a competitive advantage over its peers (“The advantages-to-specialization hypotheses”). A sample of 122 UK buyouts over the period 1995–2002 and a matched sample of non-PE-backed UK companies are constructed to test the three hypotheses. We find that over the first 3 post-buyout years (i) operating profitability of PE-backed companies is greater than those of comparable companies by 4.5%, consistently with the Jensen hypothesis; (ii) industry specialization of PE firms adds 8.5% to this premium, consistently with the industry-specialization hypothesis; (iii) stage (buyout) specialization does not impact profitability but may provide a spur to growth, inconsistently with the stage-specialization hypothesis. Finally, initial profitability of the PE-backed company plays a major role in post-buyout profitability, suggesting that skill in investment selection and financial engineering techniques may be more important than managerial incentives in generating higher PE company performance.     

The going-concern qualification debate: Some UK evidence

Using logistic regression models, derived from financial statement data, this paper provides a novel demonstration that UK quoted companies which were issued with a going-concern qualification in their last accounts before failure were significantly more acutely financially distressed than failing firms whose last accounts were not qualified on this basis.Auditors did not appear to be qualifying the last accounts of failing companies in an arbitrary fashion. More particularly, high gearing, low profitability and low ‘ownership concentration’ were consistently associated with the auditor's decision to issue a going-concern qualification. The analysis is extended to include explanatory variables derived from ‘traditionally’ estimated failure-prediction models. In addition, the properties of multilogit (multi-outcome) models, which attempt to discriminate simultaneously between failing firms with/without going concern qualifications, and a random sample of non-failed companies, are examined. The empirical results offer interesting new evidence relating to the auditor's decision to qualify/not qualify the last accounts of failing firms on the going-concern basis.     

PREDICTING PRIVATE COMPANY FAILURE

Private company failure is a significant problem that is not fully addressed by existing research. This study develops a discriminant model from data on 107 private companies. The model predicts success and failure, based on six ratios obtained from the two immediately prior years' publicly available accounting reports. Based on a hold-out sample of 40 companies a prediction with 85% accuracy was achieved. This prediction was made one year ahead. The model indicates that the retained earnings/total assets, total liabilities/total assets, and shareholders funds/total liabilities ratios are the three major predictors of bankruptcy. Overall the model's coefficients are, as expected, substantially different to those of public company models.     

ZETATM analysis A new model to identify bankruptcy risk of corporations

The paper explores the development of a bankruptcy classification model which incorporates comprehensive inputs with respect to discriminant analysis and utilizes a sample of bankrupt firms essentially covering the period 1969–1975. Financial statement data and market related measures are transformed along guidelines suggested by traditional security analysis to promote comparability of companies and to reflect the most recent reporting standards so as to make the model relevant to future analysis. The results of the study are compared with alternative bankruptcy classification strategies via the explicit introduction of prior probabilities of group membership, observed accuracies, and estimates of costs of errors in misclassification. The latter is based on cost estimates derived from commercial bank lending errors. The results of the study indicate potential significant application to credit worthiness assessment, portfolio management, and to external and internal performance analysis.     

Sampling Practices of Internal Auditors at Corporations on the Standard & Poor's Toronto Stock Exchange Composite Index*

The purpose of this study is to find out how often statistical and nonstatistical audit sampling practices are used by internal auditors in companies listed on the Standard and Poor's (S&P) Toronto Stock Exchange (TSX) Composite Index and how such practices are related to the training and background of the respondents. We adapted the questionnaire used by Hall, Hunton, and Pierce (2002) in their survey of U.S. auditors in public accounting, industry, and government. Although 20 percent of companies responding do not have an internal audit department, the other 80 percent use statistical methods to plan sample sizes 15 percent (+5 percent) of the time, random sample selection methods 23 percent (+5 percent) of the time, but statistical evaluation methods only 10% (+4%) of the time. Despite the low percentage use, almost half of the respondents reported substantial training in statistical sampling and evaluation methods. Moreover, we found statistically significantly higher proportions of respondents with substantial training in audit sampling methods among companies cross‐listed on U.S. exchanges compared with companies listed only on the TSX. Finally, respondents with a chartered accountant designation tend to have a negative impact on the use of statistical methods in audit sampling, and companies cross‐listed on U.S. exchanges tend to have larger internal audit departments than companies listed only on the TSX.     

The Timing and Subsequent Performance of Initial Public Offerings (IPOs) on the Johannesburg Stock Exchange

Companies that have listed on the Johannesburg Stock Exchange by means of a public offering between 1980 and 1991 have subsequently performed poorly. This long run post issue performance is remarkably consistent with the South African evidence for seasoned rights issuing companies and the international evidence for both initial public offerings (IPOs) and seasoned equity offerings (SEOs). Over the four years post issue, the newly listed companies earned an average return of 18.0% as opposed to 81.5% for a size-matched sample of seasoned companies. This study adds to the increasing body of international evidence suggesting the IPO under performance 'puzzle' referred to by Ibbotson (1975), Loughran and Ritter (1995) and Spiess and Affleck-Graves (1995) is not simply sample or country specific.     

Auditors’ going‐concern‐modified opinions after 2001: measuring reporting accuracy

An important change in auditors’ reporting behaviour in the period after the high‐profile corporate collapses in 2001 is that auditors were more likely to issue going‐concern (GC)‐modified audit opinions. Comparing company failure rates subsequent to receiving a first‐time going‐concern (FTGC)‐modified audit opinion in the pre‐ and post‐2001 periods, we find a consistent type 1 error (misclassification) rate (the rate of survival among companies issued an FTGC opinion). Results are indicative of auditors maintaining GC reporting accuracy when comparing the 1995–1996 and 2004–2005 periods. This conclusion is supported after considering the impact of mitigating circumstances surrounding companies that received an FTGC‐modified audit report and survived.     

An Empirical Estimation of Default Risk of the UK Real Estate Companies

Based on the Black and Scholes (Black, F., and M. Scholes. (1973). The Pricing of Options and Corporate Liabilities, Journal of Political Economy 81, 637–659) and Merton (Merton, R. C. (1974). On the Pricing of Corporate Debt: The Risk Structure of Interest Rates, Journal of Finance 29, 449–470) (BSM) contingent claims model, and KMV Corporation framework, we estimate the distance to default and the “risk neutral” default probabilities for a sample of 112 real estate companies over the period 1980 to 2001. Our empirical results classifies failed and non-failed companies into Type I error, cases that the BSM-type model fails to predict default when it did occur, and Type II error where BSM-type model predicts default when it did not occur. We find that none of the companies belong to the category of Type I error. Type II error is observed in 12 out of 112 companies. These results support the theoretical underpinnings of the BSM-type structural model in that the two driving forces of default are high leverage and high asset volatility.     

Are Corporate Restructuring Events Driven by Common Factors? Implications for Takeover Prediction

Abstract:  The paper shows that variables commonly used in takeover prediction models also help to explain the likelihood of several other restructuring events, including divestitures, bankruptcies and significant employee layoffs. This finding helps to explain the larger misclassification errors in binomial takeover prediction models commonly used in prior research. The results show that modelling takeover prediction models in a binomial setting is likely to lead to misspecification in the parameter estimates and, further, result in erroneous conclusions about the determinants of takeover likelihood. The paper shows that controlling for other restructuring events by using a multinomial framework results in consistently lower misclassification errors in out-of-sample prediction tests, when compared to the benchmark of a typical binomial model.