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.     

Using neural networks to predict corporate failure

Predicting corporate failure or bankruptcy is one of the most important problems facing business and government. The recent Savings and Loan crisis is one example, where bankruptcies cost the United States billions of dollars and became a national political issue. This paper provides a ‘meta analysis’ of the use of neural networks to predict corporate failure. Fifteen papers are reviewed and compared in order to investigate ‘what works and what doesn’t work’. The studies are compared for their formulations including aspects such as the impact of using different percentages of bankrupt firms, the software they used, the input variables, the nature of the hidden layer used, the number of nodes in the hidden layer, the output variables, training and testing and statistical analysis of results. Then the findings are compared across a number of dimensions, including, similarity of comparative solutions, number of correct classifications, impact of hidden layers, and the impact of the percentage of bankrupt firms. © 1998 John Wiley & Sons, Ltd.     

Predicting Bankruptcy Resolution

This study examines classification and prediction of the bankruptcy resolution event. Filing of bankruptcy is resolved through one of three alternative resolutions: acquisition, emergence or liquidation. Predicting the final bankruptcy resolution has not been examined in the prior accounting and finance literature. This post-bankruptcy classification and prediction of the final resolution is harder than discriminating between healthy and bankrupt firms because all filing firms are already in financial distress. Motivation for predicting the final resolution is developed and enhanced. A sample of 237 firms filing for bankruptcy is used. Classification and prediction accuracies are determined using a logit model. A ten-variable, three-group resolution logit model, which includes five accounting and five non-accounting variables is developed. The model correctly classifies 62 percent of the firms, significantly better than a random classification. We conclude that non-accounting data add relevant information to financial accounting data for predicting post bankruptcy resolution. Further, public policy implications for investors, researchers, bankruptcy judges, claimants and other stakeholders are discussed.     

A horse race of models and estimation methods for predicting bankruptcy

We use a comprehensive set of performance metrics to analyze the improvement in the classification power and prediction accuracy of various bankruptcy prediction models after adding governance variables and/or varying the estimation method used. In a sample covering bankruptcies of U.S. public firms in the period 2000 to 2015, we find that the addition of governance variables significantly improves the performance of all bankruptcy prediction models. We also find that the additional explanatory power provided by governance measures improves the further the firm is from bankruptcy, which suggests that governance variables may provide earlier and more accurate warning of the firm's bankruptcy potential. Our findings show that the performance of any bankruptcy prediction model is significantly affected by the estimation method used. We find that regardless of the bankruptcy model, hazard analysis provides the best classification and out-of-sample forecast accuracy among the parametric methods. Furthermore, non-parametric methods such as neural networks, data envelopment analysis or classification and regression trees appear to provide comparable and sometimes superior classification accuracy to hazard analysis. Lastly, we use the dynamic panel generalized methods of moments model to address concerns raised in prior studies about the susceptibility of similar studies to endogeneity issues and find that our findings continue to hold.     

Bankruptcy prediction of financially stressed firms: an examination of the predictive accuracy of artificial neural networks

This is an extension of prior studies that have used artificial neural networks to predict bankruptcy. The incremental contribution of this study is threefold. First, we use only financially stressed firms in our control sample. This enables the models to more closely approximate the actual decision processes of auditors and other interested parties. Second, we develop a more parsimonious model using qualitative ‘bad news’ variables that prior research indicates measure financial distress. Past research has focused on the ‘usefulness’ of accounting numbers and therefore often ignored non‐accounting variables that may contribute to the classification accuracy of the distress prediction models. In addition, rather than use multiple financial ratios, we include a single variable of financial distress using the Zmijewski distress score that incorporates ratios measuring profitability, liquidity, and solvency. Finally, we develop and test a genetic algorithm neural network model. We examine its predictive ability to that of a backpropagation neural network and a model using multiple discriminant analysis. The results indicate that the misclassification cost of the genetic algorithm‐based neural network was the lowest among the models. Copyright © 2001 John Wiley & Sons, Ltd.     

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.     

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.     

PROBABILISTIC APPROACHES FOR CREDIT SCREENING AND BANKRUPTCY PREDICTION

Three probabilistic neural network approaches are used for credit screening and bankruptcy prediction: a logistic regression neural network (LRNN), a probabilistic neural network (PNN) and a semi‐supervised expectation maximization‐based neural network. Using real‐world bankruptcy prediction and credit screening datasets, we compare the three probabilistic approaches using various performance criteria of sensitivity, specificity, accuracy, decile lift and area under receiver operating characteristics (ROC) curves. The results of our experiments indicate that the PNN outperforms the other two techniques for decile lift and specificity performance metric. Using the area under ROC curve, we find that for bankruptcy prediction data the PNN outperforms the other two approaches when false positive rates (FPRs) are less than 40 %. LRNN outperforms the other two techniques for FPRs higher than 40 % for bankruptcy data. We observe that the LRNN results are very sensitive to the ratio of examples belonging to two classes in training data and there is a tendency to overfit training data. Copyright © 2012 John Wiley & Sons, Ltd.     

Bankruptcy,absolute priority,and the pricing of risky debt claims

In practice, there are substantial deviations from the doctrine of ‘absolute priority’, which governs the rights of the firm's claimholders in the event of bankruptcy. To determine whether or not the possibility of such deviations is reflected in the prices of the firm's securities, this study examines the risk and return characteristics of financial claims against firms in court-supervised bankruptcy proceedings. Debt claims against bankrupt firms are indeed ‘risky’, exhibiting levels of systematic risk similar to that of common stocks in general. While some of the findings are anomalous, the data are generally consistent with the view that the capital market ‘properly’ prices risky debt claims to reflect both their risk characteristics and the possibility of departures from the doctrine of absolute priority.     

Is Chapter 11 Efficient?

The efficiency of the Chapter 11 bankruptcy process is examined by estimating the impact of Chapter 11 filings on the operating performance of bankrupt firms. We control for firm‐level heterogeneity in prefiling characteristics using matching methods to select benchmark firms comparable to filing firms. We compare bankrupt firms’ operating performances with those of matched nonbankrupt firms. Our results challenge the contention that Chapter 11 is an inefficient, debtor‐friendly mechanism that rehabilitates economically nonviable firms. We demonstrate that firms that file under Chapter 11 perform no worse and, if anything, better than comparable nonfiling firms.     

Bankruptcy prediction: the influence of the year prior to failure selected for model building and the effects in a period of economic decline

Using large amounts of data from small and medium‐sized industrial firms, this study examines two aspects of bankruptcy prediction: the influence of the year prior to failure selected for model building and the effects in a period of economic decline. The results show that especially models generated from the final annual report published prior to bankruptcy were less successful in the timely prediction of failure. Furthermore, it was found that economic decline coincided with the deterioration of a model's performance. With respect to the methods used, we found that neural networks had a somewhat better overall performance than multiple discriminant analysis. Copyright © 2005 John Wiley & Sons, Ltd.     

Beyond bankruptcy: Does the US bankruptcy code provide a fresh start to entrepreneurs?

This paper assesses the extent to which the US bankruptcy system is effective in providing small businesses a “fresh start” after a bankruptcy filing. I use data from the 1993, 1998 and 2003 National Survey of Small Business Finances to explore how firms fare after a bankruptcy filing. On the positive side, previously bankrupt firms are not any more burdened than the average small firm by problems relating to profitability, cash flow, health insurance costs, or taxes. Further, the fact that these firms are surviving several years after the filing is itself a testament to the efficient functioning of the US bankruptcy system. It suggests that the bankruptcy system goes a long way toward helping businesses recover after a bankruptcy filing.     

Predicting the outcome following bankruptcy filing: a three-state classification using neural networks

This paper uses artificial neural networks (ANNs), multi-state ordered logit and nonparametric multiple discriminant analysis (NPDA) for predicting the three-state outcome of bankruptcy filing. The study compares the classification accuracy of these procedures. It differs from previous studies on predicting financial distress by focusing on the firm after the filing of bankruptcy using accounting data, market data, and court-related information. Following the filing and through court approval the bankruptcy is resolved as firms are either acquired by other firms, emerging as independent operating entities, or liquidated. Distinguishing this three-state outcome is more complex than discriminating between healthy and financially distressed firms. Models suggested in previous studies for predicting the two-group financial distress perform poorly for our three-state scenario. Therefore, we develop models which focus on characteristics relevant for the bankruptcy resolution. We use a sample of 237 publicly traded firms which have complete data. For the entire sample and estimation samples, ANNs provide significantly better three-state classification than logit and NPDA. However, for some holdout samples the differences in classification accuracies are statistically insignificant. © 1997 John Wiley & Sons, Ltd.     

Modelling non-linearity in economic classification with neural networks

In this paper results are presented of a study on economic classification with neural networks. Comparison is made between neural networks and linear modelling techniques and, in particular, comments are made on the problem of overfitting and the estimation of prediction errors in cases where the available data sets are relatively small. It is shown that selecting network parameters by k-fold cross-validation and weight decay training are effective remedies for these phenomena. The conclusions are illustrated in two cases: predicting the volume of the mortgage market in the Netherlands and the classification of bond ratings. © 1997 John Wiley & Sons, Ltd.     

Cash-flow shortage as an endogenous bankruptcy reason

This paper develops a simple model for a leveraged firm and endogenizes the firm’s bankruptcy point by assuming that equity issuance is costly. Equity-issuance costs reflect the difficulties in issuing new equity for firms that are close to financial distress. The resulting model captures cash-flow shortage as a reason to go bankrupt, though the equity value is positive. I analyze the optimal bankruptcy point as well as corporate bond prices and yield spreads for various levels of equity-issuance costs in order to study the impact of different liquidity constraints. Finally, I discuss the consequences on optimal capital structure.     

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.     

Performance of Neural Networks in Managerial Forecasting

This paper investigates the effectiveness of a multi-layered neural network as a tool for forecasting in a managerial time-series setting. To handle noisy data of limited length we adopted two different neural network approaches. First, the neural network is used as a pattern classifier to automate the ARMA model-identification process. We tested the performance of multi-layered neural networks with two statistical feature extractors: ACF/PACF and ESACF. We found that ESACF provides better performance, although the noise in ESACF patterns still caused the classification performance to deteriorate. Therefore we adopted the noise-filtering network as a preprocessor to the pattern-classification network, and were able to achieve an average of about 89% classification accuracy. Second, the neural network is used as a tool for function approximation and prediction. To alleviate the overfitting problem we adopted the structure of minimal networks and recurrent networks. The experiment with three real-world time series showed that the prediction by Elman's recurrent network outperformed those by the ARMA model and other structures of multi-layered neural networks, especially when the time series contained significant noise.     

Sources of gains to shareholders from bankruptcy resolution

Using a logistic regression model, we identify the characteristics of firms whose shareholders are likely to benefit from bankruptcy resolution. That is, winners (losers) are firms whose shareholders experience positive (negative) excess returns after bankruptcy filing. We find that winners are relatively smaller firms with higher proportions of convertible debt, tend to file for bankruptcy for strategic reasons, have low share-ownership concentration, and suffer comparatively larger pre-filing stock price declines. Among winners, shareholder returns are greater for firms that have higher levels of private debt and research and development (R&D) expenditures, and operate in more concentrated industries. In addition, our analysis indicates that an ex ante trading strategy of purchasing bankrupt stocks with a greater than 50% probability of being a winner on the day after bankruptcy filing and holding the stocks for a year, on an average, can generate average compounded and excess compounded holding-period returns of +71% and +42%, respectively.     

Bankruptcy prediction using fuzzy convolutional neural networks

We propose a combined method for bankruptcy prediction based on fuzzy set qualitative comparative analysis (fsQCA) and convolutional neural networks (CNN). Currently, CNNs are being applied to various fields, and in some areas are providing higher performance than traditional models. In our proposed method, a CNN uses calibrated variables from fuzzy sets to improve performance accuracy. In addition, there are no published studies on the effect of feature selection at the input level of convolutional neural networks. Therefore, this study compares four well-known feature selection methods used in financial distress prediction, (t-test, stepdisc discriminant analysis, stepwise logistic regression and partial least square discriminant analysis) to investigate their effect on classification performance. The results show that fuzzy convolutional neural networks (FCNN) lead to better performance than when using traditional methods.     

Empirical comparison of hazard models in predicting SMEs failure

This study aims to shed light on the debate concerning the choice between discrete-time and continuous-time hazard models in making bankruptcy or any binary prediction using interval censored data. Building on the theoretical suggestions from various disciplines, we empirically compare widely used discrete-time hazard models (with logit and clog-log links) and the continuous-time Cox Proportional Hazards (CPH) model in predicting bankruptcy and financial distress of the United States Small and Medium-sized Enterprises (SMEs). Consistent with the theoretical arguments, we report that discrete-time hazard models are superior to the continuous-time CPH model in making binary predictions using interval censored data. Moreover, hazard models developed using a failure definition based jointly on bankruptcy laws and firms’ financial health exhibit superior goodness of fit and classification measures, in comparison to models that employ a failure definition based either on bankruptcy laws or firms’ financial health alone.