A Note on Selecting a Response Measure for Financial Distress

Since 1966, researchers have examined financial distress prediction models to determine the usefulness of accounting information to lenders. These researchers primarily used legal bankruptcy as the response variable for economic financial distress, or included legal bankruptcy with other events in dichotomous prediction models. However, theoretical models of financial distress normally define financial distress as an economic event, the inability to pay debts when due (insolvency). This study uses a loan default/accommodation response variable as a proxy for the inability to pay debts when due. The purpose of this note is to empirically test whether or not using the inability of a firm to pay debts when due, loan default/accommodation, as a response measure produces different results than using legal bankruptcy as the response measure. The study's empirical results show that legal bankruptcy and loan default/accommodation financial distress prediction models produce different statistical results, thus suggesting that the responses measure different constructs. A loan default/accommodation model also fits the data better than a bankrupt model. Our results suggest that a loan default/accommodation response may be a more appropriate measure to determine which accounting information is most useful to lenders in evaluating a firm's credit risk.     

Variable selection and corporate bankruptcy forecasts

We investigate the relative importance of various bankruptcy predictors commonly used in the existing literature by applying a variable selection technique, the least absolute shrinkage and selection operator (LASSO), to a comprehensive bankruptcy database. Over the 1980–2009 period, LASSO admits the majority of Campbell et al. (2008) predictive variables into the bankruptcy forecast model. Interestingly, by contrast with recent studies, some financial ratios constructed from only accounting data also contain significant incremental information about future default risk, and their importance relative to that of market-based variables in bankruptcy forecasts increases with prediction horizons. Moreover, LASSO-selected variables have superior out-of-sample predictive power and outperform (1) those advocated by Campbell et al. (2008) and (2) the distance to default from Merton’s (1974) structural model.     

A comparison of alternative bankruptcy prediction models

Early models of bankruptcy prediction employed financial ratios drawn from pre-bankruptcy financial statements and performed well both in-sample and out-of-sample. Since then there has been an ongoing effort in the literature to develop models with even greater predictive performance. A significant innovation in the literature was the introduction into bankruptcy prediction models of capital market data such as excess stock returns and stock return volatility, along with the application of the Black–Scholes–Merton option-pricing model. In this note, we test five key bankruptcy models from the literature using an up-to-date data set and find that they each contain unique information regarding the probability of bankruptcy but that their performance varies over time. We build a new model comprising key variables from each of the five models and add a new variable that proxies for the degree of diversification within the firm. The degree of diversification is shown to be negatively associated with the risk of bankruptcy. This more general model outperforms the existing models in a variety of in-sample and out-of-sample tests.     

Predicting Corporate Financial Distress: A Time-Series CUSUM Methodology

The ability to predict corporate financial distress can be strengthened using models that account for serial correlation in the data, incorporate information from more than one period and include stationary explanatory variables. This paper develops a stationary financial distress model for AMEX and NYSE manufacturing and retailing firms based on the statistical methodology of time-series Cumulative Sums (CUSUM). The model has the ability to distinguish between changes in the financial variables of a firm that are the result of serial correlation and changes that are the result of permanent shifts in the mean structure of the variables due to financial distress. Tests performed show that the model is robust over time and outperforms similar models based on the popular statistical methods of Linear Discriminant Analysis and Logit.     

A MULTIPERIOD MEAN-VARIANCE MODEL OF OPTIMAL CAPITAL STRUCTURE

The continuing interest in the capital structure issue among financial researchers is evidenced by the stream of capital structure models that have appeared in the literature. Much of this research has used a risk-neutral and/or a single-period framework. In this paper, we develop a capital structure model for multiperiod firms and allow for the firm's cash flows to grow over time, for the firm to issue new debt, and for two types of bankruptcy costs to occur. The types of bankruptcy costs that occur are determined by the firm's uncertain operating cash flows and negotiations between the firm and creditors. Risk is priced via the Sharpe-Lintner capital asset pricing model. Multiperiod risk-priced models, we argue, realistically represent actual firms and are thus an important step toward the development of more testable and usable models of capital structure. We execute a demonstration example in which the value of the levered firm achieves a maximum and discuss the steps the firm would take to maximize shareholder wealth within this example. The example illustrates that the value of the firm passes through an interior optimum as the promised debt payment is increased. A simulation of the effect of changes in firm-specific parameters shows that the model exhibits expected and appealing relationships between these parameters and the value of the levered firm.     

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.     

Impaired bank health and default risk

Empirical studies in corporate finance have long been focused on the role of banks in reducing the costs of financial distress. The environment and events in Japan provide a “natural experiment” that allows such empirical studies. The number of bankruptcies steadily increased throughout the 1990s, and peaked in 2000. During this period, Japan's banking sector, in contrast, faced considerable problems regarding the disposal of their bad loans. The purpose of this paper is to investigate how various measures of bank health and how defaults of major trading partners affected the probability of bankruptcy among medium-size firms in Japan. Using probit models, we examine the causes of bankruptcy for unlisted Japanese companies in the late 1990s and early 2000s. We find that several measures of bank-specific financial health have had significant impacts on a borrower's probability of bankruptcy, even when observable characteristics relating to these borrower's financial variables are controlled. In particular, a close bank–firm relationship—which usually reduces the probability of bankruptcy—exacerbates the impacts of a financial crisis, which substantially damages other bank health measures as well.     

Predicting default of Russian SMEs on the basis of financial and non-financial variables

This article develops two models for predicting the default of Russian Small and Medium-sized Enterprises (SMEs). The most general questions that the article attempts to answer are ‘Can the default risk of Russian SMEs be assessed with a statistical model?’ and ‘Would it sufficiently demonstrate high predictive accuracy?’ The article uses a relatively large data set of financial statements and employs discriminant analysis as a statistical methodology. Default is defined as legal bankruptcy. The basic model contains only financial ratios; it is extended by adding size and age variables. Liquidity and profitability turned out to be the key factors in predicting default. The resulting models have high predictive accuracy and have the potential to be of practical use in Russian SME lending.     

AN EXCHANGE RATIO DETERMINATION MODEL FOR MERGERS: A NOTE

One of the most important financial and economic issues involved in corporate mergers is the determination of the exchange ratio between the shares of the acquiring firm and the acquired firm. The model presented in theis paper is based on the dividend growth model. Since growth expectations are crucial in an exchange ratio negotiation, the model derived here, which explicitly incorporates these growth expectations, is relevant. In contrast, Larson and Gonedes' exchange ratio expressions are in terms of price-earnings multiples. The model presented here provides an exchange ratio expression for the buying firm and one for the selling firm. Both expressions are a function of one variable—the post-merger growth rate as perceived by each party. There are two important features of the model: (1) it provides boundary values for negotiation; and (2) it can be used to study the effect of firm characteristics on the bid-and-asked exchange ratios.     

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.     

Proportionate Growth and the Theoretical Foundations of Financial Ratios

The article proposes a theoretical framework for understanding financial ratios, showing that the multiplicative character of the financial variables from which financial ratios are constructed is a necessary condition of valid ratio usage, not just an assumption supported by evidence. Also, by assuming that firm size is a measurable statistical effect, the article offers an informed reappraisal of the limitations of financial ratios, particularly the well–known limitation of proportionality. The article is divided into two parts, one where ratio components are viewed as deterministic vari– ables and the other where they are random. Such an approach allows the characteristics of ratios to be more easily understood before generalizing the relationship between ratio components to encompass randomness. In the second part, when variability introduced by firm size is treated as a random effect, it is shown that if the accounting variables Y and X used to calculate a financial ratio Y/X are exponential Brownian motion, and if continuous growth rates are equal and proportionate to firm size, this may lead to ratios which are asymmetric but which do not necessarily drift.     

Strategic Bankruptcies and Price Reactions to Bankruptcy Filings

This study investigates whether the stock market differentiates between firms that file bankruptcy petitions for strategic reasons and firms that file bankruptcy petitions for financial reasons. We perform both univariate and regression tests on a sample of 245 firms that filed Chapter 11 bankruptcy petitions between 1981 and 1996. After controlling for bankruptcy outcome, probability of bankruptcy, firm financial condition, and firm size, we find that, in the period around bankruptcy filing, firms that file bankruptcy petitions for financial reasons have significantly larger stock price declines than firms that file bankruptcy petitions for strategic reasons.     

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.     

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.

Bankruptcy Prediction with Industry Effects

This paper investigates the forecasting accuracy of bankruptcy hazard rate models for U.S. companies over the time period 1962–1999 using both yearly and monthly observation intervals. The contribution of this paper is multiple-fold. One, using an expanded bankruptcy database we validate the superior forecasting performance of Shumway's (2001) model as opposed to Altman (1968) and Zmijewski (1984). Two, we demonstrate the importance of including industry effects in hazard rate estimation. Industry groupings are shown to significantly affect both the intercept and slope coefficients in the forecasting equations. Three, we extend the hazard rate model to apply to financial firms and monthly observation intervals. Due to data limitations, most of the existing literature employs only yearly observations. We show that bankruptcy prediction is markedly improved using monthly observation intervals. Fourth, consistent with the notion of market efficiency with respect to publicly available information, we demonstrate that accounting variables add little predictive power when market variables are already included in the bankruptcy model.     

On the Efficiency of Bankruptcy Law: Empirical Evidence in Spain

The current economic crisis is showing one of the main problems that many companies in financial distress have to face, namely, the impact of bankruptcy law in relation to companies and firms. This paper aims to analyze the bankruptcy law ex‐ante efficiency when companies are in financial distress. To test it out, two research questions are submitted: (i) Is solvency, the criterion used in the Spanish law, the best one to assess the relative significance of the main indicators, which determine bankrupt firms? (ii) Is the Spanish bankruptcy law efficient according to solvency or are there better criteria? To answer them, a logistic regression model is conducted. The sample embraces 1,387 firms in Spain, the data being obtained from 12 Commercial Justice Courts complemented with financial information. The main conclusion is that the solvency criterion is adequate to classify bankrupt companies although currently Spanish Bankruptcy law is not as efficient as it could be. Additionally, the relevant companies' indicators, which explain the financial distress procedure, are presented. Copyright © 2013 INSOL International and John Wiley & Sons, Ltd     

Modeling conventional residential mortgage refinancings

This article models fixed-rate mortgage refinancings and offers an empirical test of the model. The model relates the probability that a household prepays its residential mortgage to both financial and economic variables. The financial variables included in the model measure the value of the embedded call option present in conventional fixed-rate mortgage loans. The economic variables measure the household's propensity to prepay for housing consumption adjustment reasons. Our main empirical finding is that increased interst-rate volatility significantly decreases prepayment probability. In addition, we find some statistical evidence to support the hypothesis that prepayment rates increase with increases in household income, increases in household size, and vary by age of household head and regionally.     

Features selection,data mining and finacial risk classification: a comparative study

The aim of this paper is to compare several predictive models that combine features selection techniques with data mining classifiers in the context of credit risk assessment in terms of accuracy, sensitivity and specificity statistics. The t‐statistic, Battacharrayia statistic, the area between the receiver operating characteristic, Wilcoxon statistic, relative entropy, and genetic algorithms were used for the features selection task. The selected features are used to train the support vector machine (SVM) classifier, backpropagation neural network, radial basis function neural network, linear discriminant analysis and naive Bayes classifier. Results from three datasets using a 10‐fold cross‐validation technique showed that the SVM provides the best accuracy under all features selections techniques adopted in the study for all three datasets. Therefore, the SVM is an attractive classifier to be used in real applications for bankruptcy prediction in corporate finance and financial risk management in financial institutions. In addition, we found that our best results are superior to earlier studies on the same datasets.     

Bankruptcy risk induced by career concerns of regulators

We introduce a model in which a regulator employs mechanism design to embed her human capital beta signal(s) in a firm’s capital structure. This can enhance her compensation at the firm, and the value of her contract with the firm in the form of an executive stock option. We prove that the agency cost of this revolving door behavior increases the firm’s financial leverage, bankruptcy risk, and affects estimation of firm value at risk (VaR).     

A Monitoring Role for Deviations from Absolute Priority in Bankruptcy Resolution

Firms that have successfully reorganized under Chapter 11 of the bankruptcy laws of the United States frequently award shares of common stock in the reorganized firm to pre‐bankruptcy shareholders, even though pre‐bankruptcy creditors' claims are not fully satisfied. Using a sample of large publicly traded firms, these deviations from absolute priority (DAPR) are found to be positively related to the severity of agency costs within a financially distressed firm. US bankruptcy laws may exacerbate these agency costs by granting exclusivity to management during the reorganization period. Firms in which outside shareholders are more concentrated have a lower occurrence of DAPR indicating that blockholders provide an effective monitoring mechanism for controlling managerial behavior during reorganization. On the other hand, firms without this monitoring mechanism have a higher probability of DAPR indicating that creditors attempt to control managerial behavior by providing them with some sort of financial compensation via their equity holding in the firm. Finally, the evidence indicates that DAPR can be used to mitigate the hold‐up problem resulting from voting rights granted to both junior and senior claimants of the firm by US bankruptcy laws.