Edward I. Altman

ZETATM analysis A new model to identify bankruptcy risk of corporations

Abstract The paper explores the development of a bankruptcy classification model which incorporates comprehensive inputs with respect to discriminant analysis and utilizes a sample of bankrupt firms essentially covering the period 1969–1975. Financial statement data and market related measures are transformed along guidelines suggested by traditional security analysis to promote comparability of companies and to reflect the most recent reporting standards so as to make the model relevant to future analysis. The results of the study are compared with alternative bankruptcy classification strategies via the explicit introduction of prior probabilities of group membership, observed accuracies, and estimates of costs of errors in misclassification. The latter is based on cost estimates derived from commercial bank lending errors. The results of the study indicate potential significant application to credit worthiness assessment, portfolio management, and to external and internal performance analysis.

Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)

Abstract This study analyzes the comparison between traditional statistical methodologies for distress classification and prediction, i.e., linear discriminant (LDA) or logit analyses, with an artificial intelligence algorithm known as neural networks (NN). Analyzing well over 1,000 healthy, vulnerable and unsound industrial Italian firms from 1982–1992, this study was carried out at the Centrale dei Bilanci in Turin, Italy and is now being tested in actual diagnostic situations. The results are part of a larger effort involving separate models for industrial, retailing/trading and construction firms. The results indicate a balanced degree of accuracy and other beneficial characteristics between LDA and NN. We are particularly careful to point out the problems of the ‘black-box’ NN systems, including illogical weightings of the indicators and overfitting in the training stage both of which negatively impacts predictive accuracy. Both types of diagnoslic techniques displayed acceptable, over 90%, classificalion and holdoul sample accuracy and the study concludes that there certainly should be further studies and tests using the two lechniques and suggests a combined approach for predictive reinforcement.

The Link between Default and Recovery Rates: Theory, Empirical Evidence and Implications

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Credit risk measurement: Developments over the last 20 years

Abstract This paper traces developments in the credit risk measurement literature over the last 20 years. The paper is essentially divided into two parts. In the first part the evolution of the literature on the credit-risk measurement of individual loans and portfolios of loans is traced by way of reference to articles appearing in relevant issues of the Journal of Banking and Finance and other publications. In the second part, a new approach built around a mortality risk framework to measuring the risk and returns on loans and bonds is presented. This model is shown to offer some promise in analyzing the risk-return structures of portfolios of credit-risk exposed debt instruments.

The success of business failure prediction models: An international survey

Abstract This paper surveys and discusses numerous studies, both published and unpublished, that have attempted to construct and test business failure models outside the United States. Failure risk models are one of the few types of financial models that have been applied pervasively on an international level. A considerable flow of literature has gone relatively unnoticed due to the fact that the reference source is not well known or the source material is not available in English. It is the purpose of this paper to document these efforts and thereby increase the discourse and knowledge about such studies and others not surveyed here. This survey complements the other company related studies found in this Special Issue.

Modelling Credit Risk for SMEs: Evidence from the U.S. Market

Considering the fundamental role played by small and medium sized enterprises (SMEs) in the economy of many countries and the considerable attention placed on SMEs in the new Basel Capital Accord, we develop a distress prediction model specifically for the SME sector and to analyse its effectiveness compared to a generic corporate model. The behaviour of financial measures for SMEs is analysed and the most significant variables in predicting the entities’ credit worthiness are selected in order to construct a default prediction model. Using a logit regression technique on panel data of over 2,000 U.S. firms (with sales less than

Corporate financial distress and bankruptcy : predict and avoid bankruptcy, analyze and invest in distressed debt

65 million) over the period 1994–2002, we develop a one‐year default prediction model. This model has an out‐of‐sample prediction power which is almost 30 per cent higher than a generic corporate model. An associated objective is to observe our models ability to lower bank capital requirements considering the new Basel Capital Accords rules for SMEs.

Predicting Railroad Bankruptcies in America

A comprehensive look at the enormous growth and evolution of distressed debt, corporate bankruptcy, and credit risk default This Third Edition of the most authoritative finance book on the topic updates and expands its discussion of corporate distress and bankruptcy, as well as the related markets dealing with high-yield and distressed debt, and offers state-of-the-art analysis and research on the costs of bankruptcy, credit default prediction, the post-emergence period performance of bankrupt firms, and more.

Predicting performance in the savings and loan association industry

The purpose of this paper is to discuss the urgent need for an early-warning system covering the historically failure-prone railroad industry and to develop a tool for providing such a system. A multivariate statistical technique called linear discriminant analysis is utilized to identify and quantify those financial measures which are effective indicators of bankruptcies. A model which combined several financial statement ratios proved to be extremely accurate in predicting railroad bankruptcies at one and two annual financial statement dates prior to failure. Subsequent tests on additional railroad samples confirm the validity of the model. Finally, currently existing railroads in America are assessed for their bankruptcy potential by this diagnostic model.

The importance and subtlety of credit rating migration

Abstract The purpose of this study is to develop a system for identifying serious financial problems in savings and loan associations. A ‘serious problem S&L’ is defined as one for which the Federal Savings & Loan Insurance Corporation provides financial assistance or where the S&L is supervisorly merged with a sounder institution. The technique used is to compare operating characteristics of problem S&Ls with those of S&Ls in various degrees of good standing. The results of the study show that a 12-variable econometric system is both accurate and practical for at least three semi-annual periods preceding the serious problem data. The system involves (1) quadratic discriminant analysis, and (2) a composite S&L rating based on three two-group discriminant models.