Wiserfunding

A fifty-year retrospective on credit risk models, the Altman Z-score family of models and their applications to financial markets and managerial strategies

Published: 8th June, 2018
Journal of Credit Risk
By Edward I. Altman
Share:LinkedIn Logo

Abstract

Fifty years ago, in 1967, I completed my PhD dissertation, which involved the first multivariate model for predicting the financial health of US manufacturing firms and whether or not they were likely to file for bankruptcy. That work was followed shortly afterward (in 1968) by the publication of the model’s specifications. Despite its “old age”, the Altman Z-score is still the standard against which most other bankruptcy or default prediction models are measured and is clearly the most used by financial market practitioners and academic scholars for a variety of purposes. The objective of this paper is to reflect upon the evolution of the Altman family of bankruptcy prediction models as well as their extensions and multiple applications in financial markets and managerial decision making.

The Evolution of Corporate Credit-Scoring Systems

Credit scoring systems for identifying the determinants of a firm’s repayment likelihood probably go back to the days of the Crusades, when travelers needed “loans” to finance their travels. They were certainly used much later in the United States as companies and entrepreneurs helped to grow the economy, especially in its westward expansion. Primitive financial information was usually evaluated by lending institutions in the 1800s, with the primary types of information required being subjective or qualitative in nature, revolving around ownership and management variables as well as collateral (see Box 1). It was not until the early 1900s that rating agencies and some more financially oriented corporate entities (eg, the DuPont system of corporate ROE growth) introduced univariate accounting measures and industry peer group comparisons with rating designations (see Figure 1). The key aspect of these “revolutionary” techniques was that they enabled the analyst to compare an individual corporate entity’s financial performance metrics to a reference database of time series (same entity) and cross-section (industry) data. Then, and even more so today, data and databases were the key elements of meaningful diagnostics. There is no doubt that in the credit-scoring field, data is “king” and models for capturing the probability of default (PD) ultimately succeed, or not, based on whether they can be applied to databases of various sizes and relevance.

The original Altman Z-score model (Altman 1968) was based on a sample of sixty-six manufacturing companies in two groups, bankrupt and nonbankrupt firms, and a holdout sample of fifty companies. In those “primitive” days, there were no electronic databases and the researcher/analyst had to construct their own database from primary (annual report) or secondary (Moody’s and Standard & Poor’s (S&P) industrial manuals and reports) sources. To this day, instructors and researchers often times ask me for my original sixty-six-firm database, mainly for instructional or reference exercises. It is not unheard of today for researchers to have access to databases of thousands, even millions, of firms (especially in countries where all firms must file their financial statements in a public database, eg, in the United Kingdom). To illustrate the importance of databases, Moody’s purchased extensive data on 200 million firms and customer access from Bureau van Dijk Electronic Publishing (EQT) for US$3.3 billion in 2017, while S&P purchased SNL Financial’s extensive database, management structure and customer book for US$2.2 billion in 2015. As indicated in Figure 1, the three major rating agencies established a hierarchy of creditworthiness that was descriptive, but not quantified, in its depiction of the likelihood of default. The determination of these ratings was based on a combination of (1) financial statement ratio analytics, usually on a univariate, one-ratioat-a-time basis; (2) industry health discussions; and (3) qualitative factors evaluating the firm’s management plans and capabilities, strategic directions and other, perhaps “inside”, information gleaned from interviews with senior management and experience of the team that was assigned to the rating decision.

Unlocked Padlocked Icon

Unlock the rest of this paper by providing your email address below

You will receive an email with a link to download the paper.

Your request has been registered. Please check your email inbox for a link to the research paper. There is a possibility it may end up in your junk folder so please check there.

Other articles you might enjoy

A fifty-year retrospective on credit risk models, the Altman Z-score family of models and their applications to financial markets and managerial strategies

Abstract Fifty years ago, in 1967, I completed my PhD dissertation…

Read more

Credit Risk Scoring Models

Abstract Credit scoring models play a fundamental role in the risk…

Read more

Assessing the Credit Worthiness of Italian SMEs and Mini-bond Issuers

Abstract A number of innovations have been introduced in the last five…

Read more

Like these pieces? Register for our newsletter to get updates, learn when we publish more, and recieve the latest news from Wiserfunding straight to your inbox.

Thank you for subscribing to our newsletter you should receive an email shortly. Please check you junk folder if you cannot find it.

Join our newsletter and receive quarterly updates about Wiserfunding and our progress.

Wiserfunding logo

Wiserfunding Ltd
Techhub
1-15 Clere Street
EC2A 4UY
London
info@wiserfunding.com

Subscribe

Thank you for subscribing to our newsletter you should receive an email shortly. Please check you junk folder if you cannot find it.
Send Icon

Join our newsletter to stay up to date on features and releases

Linkedin Logo
Linkedin Logo
Twitter logo
Twitter logo
Facebook Logo
Facebook Logo