Assessing the Probability of Bankruptcy

Hillegeist, Stephen A.; Keating, Elizabeth K.; Cram, Donald P.; Lundstedt, Kyle G.

doi:10.2139/ssrn.307479

Cited by 235 publications

(256 citation statements)

References 48 publications

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“…There exists a large literature developing models of bankruptcy prediction (for example, Altman 1968, andOhlson 1980). This literature is evolving to incorporate a blend of market and accounting information (for example, Hillegeist et al 2004). But very little of this research to date has attempted to calibrate out of sample bankruptcy prediction models to observed credit spreads and future changes in credit spreads.…”

Section: Suggestions For Future Researchmentioning

confidence: 99%

Credit markets and financial information

Lok¹,

Richardson

2011

Rev Account Stud

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The last decade has seen rapid growth in trading of credit instruments on secondary markets. The ensuing availability of a rich set of credit market data has created a novel environment for testing a variety of financial economic theories. In this discussion, we provide a simple framework for linking asset pricing research using equity and credit market data and offer some suggestions for future archival empirical research aiming to establish relations between financial information and credit markets. Credit instruments are intrinsically linked to equity instruments. The strength of this link varies temporally and cross-sectionally in measurable ways that can, and should be, used to guide future empirical research linking information to credit markets.

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Section: Suggestions For Future Researchmentioning

confidence: 99%

Credit markets and financial information

Lok¹,

Richardson

2011

Rev Account Stud

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“…Of course, in the finance literature alternative approaches have been proposed for default prediction, including hazard models (Shumway, 2001) and option-based models (Hillegeist et al, 2004). Such techniques provide a different point of view in assessing default and credit risk.…”

Section: Methodsmentioning

confidence: 99%

Model combination for credit risk assessment: A stacked generalization approach

Doumpos

Zopounidis

2006

Ann Oper Res

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The development of credit risk assessment models is often considered within a classification context. Recent studies on the development of classification models have shown that a combination of methods often provides improved classification results compared to a single-method approach. Within this context, this study explores the combination of different classification methods in developing efficient models for credit risk assessment. A variety of methods are considered in the combination, including machine learning approaches and statistical techniques. The results illustrate that combined models can outperform individual models for credit risk analysis. The analysis also covers important issues such as the impact of using different parameters for the combined models, the effect of attribute selection, as well as the effects of combining strong or weak models.Credit risk arises when a debtor (firm or individual) cannot fulfill its debt obligations towards the creditors. Credit risk assessment is performed through the development of models that are used to estimate the probability of default for a given debtor over a specific time period (usually one year). Such models are used for credit approval and monitoring and are of major importance, mainly for banking institutions. Commercial and industrial firms may also use such models to assess the credit quality of their clients.The development of credit risk assessment models is usually based on a classification approach, in order to distinguish potential defaulters from non-defaulters. Generally, classification refers to the assignment of a finite set of objects into predefined classes. Other well-known problems in finance that fall within this context include, among others, bond rating, country risk assessment, stock evaluation, and corporate mergers/acquisitions (Altman et al., 1981;.

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“…This analysis complements the prediction-oriented tests applied in the previous sections and provides us an "in sample" accuracy measure by comparing error type I and II. Hillegeist et al (2004) argue that prediction-oriented test has several limitations: first, it assume a dichotomous decision context; second, both error types are equally important (for a credit risk manager, it is a lot more serious to classify a bankrupt firm as non-bankrupt than a non-bankrupt firm as bankrupt); third, the classification of firms as bankrupt or as non-bankrupt is somewhat subjective because it implies the definition of a cut-off value.…”

Section: Standard Approachmentioning

confidence: 99%

Expected Default Probabilities in Structural Models: Empirical Evidence

Patel

Pereira

2007

J Real Estate Finan Econ

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We apply a set of structural models (Black and Cox 1976; Collin-Dufresne and Goldstein 2001; Ericsson and Reneby 1998; Leland and Toft 1996; Longstaff and Schwartz 1995; Merton 1974) to estimate expected default probabilities (EDPs) for a sample of failed and non-failed UK real estate companies. Results are generally consistent with models’ predictions and estimates of EDPs for different models are closely clustered. The results of z-scores and synthetic ratings misclassify 33% of the total sample in contrast to 8% misclassification by structural models. Further analysis of EDPs based on logistic regressions suggests the observed misclassification of the companies by structural models is due to special company management and/or regulatory circumstances rather than limitations of these models. Copyright Springer Science+Business Media, LLC 2007Expected default probabilities, Structural models, C30, G13, G33,

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Assessing the Probability of Bankruptcy

Cited by 235 publications

References 48 publications

Credit markets and financial information

Credit markets and financial information

Model combination for credit risk assessment: A stacked generalization approach

Expected Default Probabilities in Structural Models: Empirical Evidence

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