An empirical comparison of bankruptcy models

Four types of bankruptcy prediction models based on financial statement ratios, cash flows, stock returns, and return standard deviations are compared. Based on a sample of bankruptcies from 1980 to 1991, results indicate that no existing model of bankruptcy adequately captures the data. During the last fiscal year preceding bankruptcy, none of the individual models may be excluded without a loss in explanatory power. If considered in isolation, the cash flow model discriminates most consistently two to three years before bankruptcy. By comparison, the ratio model is the best single model during the year immediately preceding bankruptcy. Quasi-jack-knifing procedures suggest that none of the models can reliably predict bankruptcy more than two years in advance.     

Alternative bankruptcy prediction models using option-pricing theory

We examine the empirical properties of the theoretical Black–Scholes–Merton (BSM) bankruptcy model. We evaluate the predictive ability of various existing modifications of the BSM model and extend prior studies by estimating volatility directly from market-observable returns on firm value. We show that parsimonious models using our direct market-observable volatility estimate perform better than alternative, more sophisticated, models. Our findings suggest the adoption of simpler modelling approaches relying on market data when implementing the BSM model.     

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.     

Comparing the performance of market-based and accounting-based bankruptcy prediction models

Recently developed corporate bankruptcy prediction models adopt a contingent claims valuation approach. However, despite their theoretical appeal, tests of their performance compared with traditional simple accounting-ratio-based approaches are limited in the literature. We find the two approaches capture different aspects of bankruptcy risk, and while there is little difference in their predictive ability in the UK, the z-score approach leads to significantly greater bank profitability in conditions of differential decision error costs and competitive pricing regime.     

The probability of bankruptcy: A comparison of empirical predictions and theoretical models

The last decade has witnessed the development of many empirical models to predict corporate bankruptcy and of several bankruptcy theories. This paper reviews and integrates these two strands of research and finds a substantial amount of overlap. However, the overlap is not perfect. The paper presents a new theory of bankruptcy that appears to fit the data better. The paper also suggests directions for future empirical and theoretical research.     

Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework

Prediction of corporate failure is one of the major activities in auditing firms risks and uncertainties. The design of reliable models to predict bankruptcy is crucial for many decision making processes. Although a large number of models have been designed to predict bankruptcy, the relative performance evaluation of competing prediction models remains an exercise that is unidimensional in nature, which often leads to reporting conflicting results. In this research, we overcome this methodological issue by proposing an orientation-free super-efficiency data envelopment analysis model as a multi-criteria assessment framework. Furthermore, we perform an exhaustive comparative analysis of the most popular bankruptcy modeling frameworks for UK data including our own models. In addition, we address two important research questions; namely, do some modeling frameworks perform better than others by design? and to what extent the choice and/or the design of explanatory variables and their nature affect the performance of modeling frameworks?, and report on our findings.     

Corporate bankruptcy prediction in Japan

The purpose of this study is to highlight the financial characteristics of failed firms in Japan, and to construct corporate bankruptcy prediction models with greater prediction accuracy. Our principal component analysis indicated that failed firms in Japan could be classified into two groups: a group having negative financial structures and a group having a declining flow of funds. Additionally, they can be classified into two other different categories of groups: one whose financial position during three years before shows a ‘V’ shape and another group that shows a ‘XXX’ shape.Our discriminant analysis indicated that improved prediction accuracy could be obtained by using, as predictor variables, both ratios and absolute amounts based on cash base financial statement data three years before failure. This data was adjusted to properly reflect the exceptions, reservations, and qualifications appearing in the audit reports and those based on accrual base financial statement data.     

Investment opportunities and bankruptcy prediction

A firm's mix of growth options and assets in place is an important determinant of its optimal default strategy. Our simple model shows that shareholders of a firm with valuable investment opportunities would be able/willing to wait longer before defaulting on their contractual debt obligations than shareholders of an otherwise identical firm without such opportunities. More importantly, we show empirically using a dataset of recent corporate bankruptcies that measures of investment opportunities are significantly related to the likelihood of bankruptcy. Augmenting existing bankruptcy prediction models by these measures improves their out-of-sample forecasting ability.     

A note on the use of industry-relative ratios in bankruptcy prediction

The potential for industry-relative financial ratios to improve the prediction of firms in financial distress motivated this comparison of model specifications based on either unadjusted or industry-relative ratios. Both specifications yielded stable parameter estimates over the time periods examined. However, the industry-relative specification appeared to add incremental information not contained in the model based on the unadjusted financial ratios; the converse case did not hold. In addition, with the industry-relative specification, ex post forecast accuracy was slightly improved relative to the ex ante forecast, while with the unadjusted model specification, ex post forecast accuracy declined from that obtained ex ante.     

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.     

Corporate bankruptcy prediction: a high dimensional analysis

Much bankruptcy research has relied on parametric models, such as multiple discriminant analysis and logit, which can only handle a finite number of predictors (Altman in The Journal of Finance 23 (4), 589–609, 1968; Ohlson in Journal of Accounting Research 18 (1), 109–131, 1980). The gradient boosting model is a statistical learning method that overcomes this limitation. The model accommodates very large numbers of predictors which can be rank ordered, from best to worst, based on their overall predictive power (Friedman in The Annals of Statistics 29 (5), 1189–1232, 2001; Hastie et al. 2009). Using a sample of 1115 US bankruptcy filings and 91 predictor variables, the study finds that non-traditional variables, such as ownership structure/concentration and CEO compensation are among the strongest predictors overall. The next best predictors are unscaled market and accounting variables that proxy for size effects. This is followed by market-price measures and financial ratios. The weakest predictors overall included macro-economic variables, analyst recommendations/forecasts and industry variables.     

Estimating corporate bankruptcy forecasting models by maximizing discriminatory power

Review of Quantitative Finance and Accounting - In this paper, we estimate coefficients of bankruptcy forecasting models, such as logistic and neural network models, by maximizing their...     

A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information: A binary quantile regression approach

While using the binary quantile regression (BQR) model, we establish a hybrid bankruptcy prediction model with dynamic loadings for both the accounting-ratio-based and market-based information. Using the proposed model, we conduct an empirical study on a dataset comprising of default events during the period from 1996 to 2006. In this study, those firms experienced bankruptcy/liquidation events as defined by the Compustat database are classified as “defaulted” firms, whereas all other firms listed in the Fortune 500 with over a B-rating during the same time period are identified as “survived” firms. The empirical findings of this study are consistent with the following notions. The distance-to-default (DD) variable derived from the market-based model is statistically significant in explaining the observed default events, particularly of those firms with relatively poor credit quality (i.e., high credit risk). Conversely, the z-score obtained with the accounting-ratio-based approach is statistically significant in predicting bankruptcies of firms of relatively good credit quality (i.e., low credit risk). In-sample and out-of-sample bankruptcy prediction tests demonstrated the superior performance of utilizing dynamic loadings rather than constant loadings derived by the conventional logit model.     

Applying VaR to REITs: A comparison of alternative methods

This study employs five methods to calculate the VaR of twelve REITs portfolios and evaluates the accuracy of these methods. Firstly, we find that the VaR varies among individual portfolios. The Hotel REITs has consistently the largest VaR. The low-leveraging portfolio tends to have the largest VaR measured by the parametric methods, while the high leveraging portfolio has the largest VaR calculated by the non-parametric methods. Secondly, each method performs differently at different confidence levels, and no method dominates the others. At the 95% confidence level, the EWMA method performs relatively well. The EQWMA and the two non-parametric methods perform equivalently and slightly overestimate VaRs. The EQWMA_T method ranks the bottom and significantly overestimates VaRs for all portfolios. At the 99% confidence level, the EQWMA method performs the best. The EQWMA_T and the two non-parametric methods perform equivalently and may overestimate VaR for all portfolios. The EWMA method turns out to be the worst and tends to underestimate the VaR. These findings may provide more insights for institutional real estate investors.     

A comparison of models of long-term inflationary expectations

This paper compares several models of long-term inflationary expectations, including time series models, models drawn from interest rate relationships, and a structural model developed from a portfolio balance framework. Their within-sample performance over 1961–1974 and out-of-sample performance over periods covering 1975–1982 are compared. Evaluation criteria include accuracy of forecasts and ability to capture changes in the trend of long-term inflation.The structural model is judged best. Its forecasts are generally the most accurate and it performs well in periods of rising and falling inflation, an attribute not present in other models.     

Instability of return prediction models

This study examines evidence of instability in models of ex post predictable components in stock returns related to structural breaks in the coefficients of state variables such as the lagged dividend yield, short interest rate, term spread and default premium. We estimate linear models of excess returns for a set of international equity indices and test for stability of the estimated regression parameters. There is evidence of instability for the vast majority of countries. Breaks do not generally appear to be uniform in time: different countries experience breaks at different times. For the majority of international indices, the predictable component in stock returns appears to have diminished following the most recent break. We assess the adequacy of the break tests and model selection procedures in a set of Monte Carlo experiments.     

A comparison of diffusion models of the term structure

A number of different continuous time approaches that have been developed to model the term structure of interest rates are examined. These techniques span the interest rate literature over the last 20 years or so, and are the most commonly used among both academics and practitioners. We view this paper as a reference for the different term structure models, aiming to bring together the three most commonly used approaches, emphasizing their differences, analysing their respective advantages and disadvantages, and with explicit representations where they exist for prices of discount bonds.     

A comparison of some structural models of private information arrival

We show that the PIN and the Duarte and Young (2009) (APIN) models do not match the variability of noise trade in the data and that this limitation has severe implications for how these models identify private information. We examine two alternatives to these models, the Generalized PIN model (GPIN) and the Odders-White and Ready (2008) model (OWR). Our tests indicate that measures of private information based on the OWR and GPIN models are promising alternatives to the APIN’s Adj.PIN and PIN.