ZETATM analysis A new model to identify bankruptcy risk of corporations

Altman, Edward I.; Haldeman, R. G.; Narayanan, Paul

doi:10.1016/0378-4266(77)90017-6

Cited by 1,449 publications

(846 citation statements)

References 15 publications

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“…The main purpose of another study was to construct a bankruptcy model, which would classify companies up to five years prior to failure of bankruptcy [11]. Used data set covered the period between 1969 and 1975.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing Credit Default using Logistic Regression and Multiple Discriminant Analysis:Empirical Evidence from Bosnia and Herzegovina

Memic¹

2015

: Interdiscip. Descr. Complex Syst.

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This article has an aim to assess credit default prediction on the banking market in Bosnia and Herzegovina nationwide as well as on its constitutional entities (Federation of Bosnia and Herzegovina and Republika Srpska). Ability to classify companies info different predefined groups or finding an appropriate tool which would replace human assessment in classifying companies into good and bad buckets has been one of the main interests on risk management researchers for a long time. We investigated the possibility and accuracy of default prediction using traditional statistical methods logistic regression (logit) and multiple discriminant analysis (MDA) and compared their predictive abilities. The results show that the created models have high predictive ability. For logit models, some variables are more influential on the default prediction than the others. Return on assets (ROA) is statistically significant in all four periods prior to default, having very high regression coefficients, or high impact on the model's ability to predict default. Similar results are obtained for MDA models. It is also found that predictive ability differs between logistic regression and multiple discriminant analysis.

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Section: Literature Reviewmentioning

confidence: 99%

Assessing Credit Default using Logistic Regression and Multiple Discriminant Analysis:Empirical Evidence from Bosnia and Herzegovina

Memic¹

2015

: Interdiscip. Descr. Complex Syst.

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“…However, in the original ZETA model Altman et al (1977) include total tangible assets as the denominator.…”

Section: Variables Of the Zeta Modelmentioning

confidence: 99%

“…Altman et al (1977) describe this measure as a normalized measure of the standard error of estimate around a ten-year trend in ζ 1 . We compute this variable as the coefficient of determination (R-squared) with the trailing twelve-month EBIT as the dependent variable, and time as the independent variable.…”

Section: Variables Of the Zeta Modelmentioning

confidence: 99%

Are Ratings the Worst Form of Credit Assessment Apart from All the Others?

Bloechlinger

Leippold

2012

SSRN Journal

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We present a prediction model to forecast corporate defaults. In a theoretical model, under incomplete information in a market with publicly traded equity, we show that our approach must outperform ratings, Altman's Z-score, and Merton's distance to default. We reconcile the statistical and structural approaches under a common framework, i.e., our approach nests Altman's and Merton's approaches as special cases. Empirically, we cannot reject the superiority of our approach.Furthermore, the numbers of observed defaults align well with the estimated probabilities. Finally, with rank transforms, we obtain cycle-adjusted forecasts that still outperform ratings. AbstractEnding the dependence on rating agencies is a top priority for the Financial Stability Board, which coordinates the G20 financial policies. Rating agencies have been accused of contributing to the recent financial crisis by misjudging the creditworthiness of mortgage-backed securities. Their downgrading practice of sovereigns and corporations is said to amplify procyclicality and to fuel market uncertainty. However, it is one thing to criticize rating agencies, quite another to find an appropriate alternative. For the case of corporate issuer ratings, we show that there is at least one such alternative. From a statistical viewpoint, it outperforms credit ratings. From a practical viewpoint, it is straightforward to implement and depends solely on publicly available data. Therefore, we conclude that for the case of corporate credit risk, appropriate alternatives to credit ratings do exist! JEL Classification Codes: G01, G18, G24

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“…It is for precisely this reason that variables are usually chosen from among those used in previous studies, that is, from among variables that have proven to be reliable failure predictors in the past. Indeed, often chosen are variables that were identified by the very first authors to assess the usefulness of financial ratios in predicting corporate failure and by those who contributed to an understanding of the role played by multivariate statistical methods in the field of bankruptcy prediction, between the 1930's and the 1970's [2,17,18,19,20,21,22].…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy

Jardin

2010

Neurocomputing

112

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We evaluate the prediction accuracy of models designed using different classification methods depending on the technique used to select variables, and we study the relationship between the structure of the models and their ability to correctly predict financial failure. We show that a neural network based model using a set of variables selected with a criterion that it is adapted to the network leads to better results than a set chosen with criteria used in the financial literature. We also show that the way in which a set of variables may represent the financial profiles of healthy companies plays a role in Type I error reduction.

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ZETATM analysis A new model to identify bankruptcy risk of corporations

Cited by 1,449 publications

References 15 publications

Assessing Credit Default using Logistic Regression and Multiple Discriminant Analysis:Empirical Evidence from Bosnia and Herzegovina

Assessing Credit Default using Logistic Regression and Multiple Discriminant Analysis:Empirical Evidence from Bosnia and Herzegovina

Are Ratings the Worst Form of Credit Assessment Apart from All the Others?

Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy

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