Estimation risk and auditor conservatism

Estimation risk occurs when individuals form beliefs about parameters that are unknown. We examine how auditors respond to the estimation risk that arises when they form beliefs about the likelihood of client bankruptcy. We argue that auditors are likely to become more conservative when facing higher estimation risk because they are risk-averse. We find that estimation risk is of first-order importance in explaining auditor behavior. In particular, auditors are more likely to issue going-concern opinions, are more likely to resign, and charge higher audit fees when the standard errors surrounding the point estimates of bankruptcy are larger. To our knowledge, this is the first study to quantify estimation risk using the variance-covariance matrix of coefficient estimates taken from a statistical prediction model.     

Bankruptcy prediction for Korean firms after the 1997 financial crisis: using a multiple criteria linear programming data mining approach

The main purpose of this paper is to evaluate the data mining applications, such as classification, which have been used in previous bankruptcy prediction studies and credit rating studies. Our study proposes a multiple criteria linear programming (MCLP) method to predict bankruptcy using Korean bankruptcy data after the 1997 financial crisis. The results, of the MCLP approach in our Korean bankruptcy prediction study, show that our method performs as well as traditional multiple discriminant analysis or logit analysis using only financial data. In addition, our model??s overall prediction accuracy is comparable to those of decision tree or support vector machine approaches. However, our results are not generalizable because our data are from a special situation in Korea.     

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.     

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.     

Consumer Bankruptcies and the Bankruptcy Reform Act: A Time-Series Intervention Analysis, 1960–1997

The Bankruptcy Reform Act of 1978, effective on October 1, 1979, significantly altered the basic rules by which consumers file for bankruptcy. Between 1979 and 1997, the number of nonbusiness bankruptcies filed annually rose from 200,000 to 1.35 million, and the rate of bankruptcies per 100,000 adults increased from 129 to 715. A controversial aspect of bankruptcy is how much of this increase can be attributed to the 1978 act. Early empirical studies provide estimates ranging from a low of 6% to a high of 75% for the immediate post-act period. However, two recent studies using longer data series report that none of the increase was due to the act. Previous studies suffer from several econometric problems, including inadequate attention to stochastic properties and stationarity of the data series, as well as data errors due to reporting changes. This paper uses an ARIMA intervention analysis to estimate the impact of the 1978 act. Using adjusted quarterly data for 1960:3 to 1995:4, the data first are examined for unit roots. The tests reject the presence of seasonal unit roots but confirm the presence of a nonseasonal unit root. The empirical analysis therefore is based on logged first differences of bankruptcy filings and filing rates per capita. An ARIMA model is estimated using the preintervention data for 1960:3 to 1979:3. This model is re-estimated for 1960:3 to 1995:4 with the intervention terms included. The intervention model estimates indicate that the 1978 act increased consumer bankruptcies by 36% in the post-act period relative to the pre-act period, or about 72,400 additional bankruptcies per year. Overall, the net impact of the 1978 act was modest compared to the substantial rise in bankruptcies that has occurred since 1979.     

Bankruptcy Litigation and Relationship Banking

This paper analyzes how bankruptcy litigation affects the value of relationship banking. In our model, bankruptcy courts may make type 1 errors, i.e., they may declare that an insolvent firm is solvent; and they may make type 2 errors, i.e., they may declare that a solvent firm is insolvent. Our model provides four results. First, the cost of bank debt decreases when the probability that bankruptcy courts make type 2 errors increases. Second, the value of relationship banking increases when the probability that bankruptcy courts make type 1 errors increases. Third, the cost of credit intermediation decreases when the probability that bankruptcy courts make type 2 errors increases. Fourth, the diversification mechanism does not fully solve the delegated monitoring problem.     

Personal bankruptcy costs: Their relevance and some estimates

The paper argues that there is a need for the formal treatment of personal bankruptcy costs in the finance literature. The need arises out of the relevance of such costs to both corporate and personal financing decisions. We show that (a) personal bankruptcy costs (like personal taxes) are relevant to the corporate capital structure problem and that (b) differential bankruptcy costs across corporations and individuals can result in a clientele model of individual investment-borrowing decision which could lead to institutional arrangements designed to minimize combined bankruptcy costs. Further, we develop a theory of personal bankruptcy and a set of testable hypothesis with regard to their costs. Some preliminary estimates of personal bankruptcy costs are reported which suggest that they are higher than corporate bankruptcy costs. There is also some evidence of economics of scale in personal bankruptcy costs.     

Earnings Retention as a Specification Mechanism in Logistic Bankruptcy Models: A Test of the Free Cash Flow Theory

This paper explores the applicability of as a specification mechanism to improve forecasting methods in corporate bankruptcy. The study combines lessons from Jensen's Free Cash Flow Theory with a logisitic model of bankruptcy to improve forecasting accuracy. The model uses data from the Indian textile industry to show that data classification based on investment opportunities is yet another way of improving precision. The study also re-examines the Free Cash Flow Theory and concludes that in applying it to a bankruptcy scenario, its initial findings regarding retention policy hold true; that is, low growth firms should retain less of their earnings than their high growth counterparts.     

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.     

Bankruptcy prediction: Application of the Taylor's expansion in logistic regression

The purpose of the present study is to test whether Taylor's series expansion can be used to solve the problem associated with the functional form of bankruptcy prediction models. To avoid the problems associated with the normality of variables, the logistic model to describe the insolvency risk is applied. Taylor's expansion is then used to approximate the exponent of the logistic function, or the logit. The cash to total assets, cash flow to total assets, and shareholder's equity to total assets ratios operationalize the factors affecting the insolvency risk. The usefulness of Taylor's model in bankruptcy prediction is evaluated applying the logistic regression model to the data from the Compustat database. The classification accuracy in the test data for the first and second years before bankruptcy show that the classification accuracy of a simple financial ratio model can be increased using the second-order and interaction terms of these ratios. However, in the third year, for the test data, Taylor's expansion is not able to increase the classification accuracy when compared with the first-order model.     

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.     

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.     

Factors Affecting the Probability of Bankruptcy: A Managerial Decision Based Approach

The majority of classification models developed have used a pool of financial ratios combined with statistical variable selection techniques to maximize the accuracy of the classifier constructed. Rather than follow this approach, this article seeks to provide an explicit economic basis for the selection of variables for inclusion in bankruptcy models. This search to develop an economic theory of bankruptcy augments the existing bankruptcy prediction literature. Variables which occur in bankruptcy probability expressions derived from the solution of a stochastic optimizing model of firm behaviour are 'proxied' by variables constructed from financial statement data. The random nature of the lifetime of a single firm provides the rationale for the use of duration or hazard-based statistical methods in the validation of the derived bankruptcy probability expressions. Results of the validation exercise confirm that the majority of variables included in the empirical hazard formulation behave in a way that is consistent with the model of the firm. The results highlight the need for developments in the measurement of earnings dispersion.     

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.     

Cash Management Behavior and Failure Prediction

The purpose of this study is to evaluate the information contained in static and dynamic inventory cash management models to predict failure in a sample of 41 small and middle-sized Finnish bankrupt firms and their nonbankrupt counterparts. The results indicate that the estimates of the (scale) elasticity of cash balance with respect to the volume of transactions (approximated by net sales) is significantly lower for the failed firms. Furthermore, only the scale elasticity appears to be a statistically significant discriminating variable, and only in the first year before bankruptcy. This estimate remarkably increased the Lachenbruch validated classification accuracy based on traditional financial variables.     

A NOTE ON THE OPTIMAL CUTOFF POINT IN BANKRUPTCY PREDICTION MODELS

A modified Bayesian decision model is derived in the study to systematically estimate an optimal cutoff point for bankruptcy prediction models. In addition, a loss function is implemented in the model so that the total error costs instead of the total error probability is minimized. Any dichotomous classification problem with unequal error costs would find this decision model useful.     

Forum-Shopping and Personal Bankruptcy

The dramatic increase in U.S. personal bankruptcy filings of the last fifteen years has focused attention on the wide disparities between different states' personal bankruptcy exemptions. These differences have been criticized both on the grounds of equity and also because they provide an incentive to move to a state with a higher exemption before declaring bankruptcy, that is to forum-shop. This paper focuses on the latter of these objections. Using data from the Panel Study of Income Dynamics (PSID), we estimate a Nested Logit model of the household migration decision. Our econometric approach specifically avoids the problem of endogenously induced bankruptcy filings by examining the effect of filing propensity, rather than the actual event of filing, on the tendency to migrate to a higher exemption state. We conclude that while there is indeed evidence that considerations of bankruptcy laws do influence interstate migration, the actual effect is relatively modest. We estimate that, in any given year, roughly one percent of moves to higher-exemption states are motivated by considerations of differences in bankruptcy laws; by way of comparison, this is roughly comparable to the magnitude of recent estimates of welfare-induced migration. This suggests that the emphasis on differences in exemptions which has been a feature of recent attempts to reform the bankruptcy code is somewhat exaggerated.     

USING NEURAL NETS TO COMBINE INFORMATION SETS IN CORPORATE BANKRUPTCY PREDICTION

We demonstrate that the use of a neural network (NN) model to combine information from corporate financial statements and equity markets provides improved predictive estimates of the probability of corporate bankruptcy. Using performance measures, based on the receiver operating characteristic curve, the forecast combinations from the NN models are demonstrated to outperform the forecasts derived from a forecast combination generated using a logistic regression approach. This result provides support for the use of forecast combinations generated from NN models in the estimation of corporate bankruptcy probabilities as it outperforms the standard approach of forming a hybrid forecasting model which includes all the explanatory variables. Copyright © 2012 John Wiley & Sons, Ltd.     

The Limitations of Bankruptcy Prediction Models: Some Cautions for the Researcher

The purpose of this study is to demonstrate potential problems associated with the use of bankruptcy prediction models in current research. The tests in this study demonstrate the problems that may arise when bankruptcy prediction models are inappropriately applied. This analysis evaluated the Zmijewski (1984) and Ohlson (1980) models using time periods, industries, and financial distress situations other than those used to originally develop the models. The findings indicated that both models were sensitive to time periods. That is, the accuracy of the models declined when applied to time periods different from those used to develop the models. The findings also suggest that the accuracy of each model continues to decline moving from the 1988–1991 to the 1992–1999 sample period. Additionally, Ohlson's (Zmijewski's) model was (was not) sensitive to industry classifications. The findings of this study also suggest that the Ohlson and Zmijewski models are not sensitive to financial distress situations other than those used to develop the models. Thus, the models appear to be more generally useful for predicting financial distress, not just bankruptcy.In sum, the results of this study suggest that researchers should use bankruptcy prediction models cautiously. Applying the models to time periods and industries other than those used to develop the models may result in a significant decline in the models' accuracies. Additionally, some bankruptcy prediction models may be more appropriate for evaluating various forms of financial distress as opposed to just bankruptcy. To avoid erroneous applications of bankruptcy prediction models in the future, it is necessary for researchers not only to understand the uses of prediction models, but also to understand the limitations of the models.