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Journal ArticleDOI

Financial Ratios As Predictors Of Failure

William H. Beaver
- 01 Jan 1966 - 
- Vol. 4, pp 71-111
TLDR
In this article, the authors focus on the use of ratios as predictors of failure, defined as the inability of a firm to pay its financial obligations as they mature, and demonstrate that a firm is said to have failed when any of the following events have occurred.
Abstract
At the turn of the century, ratio analysis was in its embryonic state. It began with the development of a single ratio, the current ratio,' for a single purpose-the evaluation of credit-worthiness. Today ratio analysis involves the use of several ratios by a variety of users-including credit lenders, credit-rating agencies, investors, and management.2 In spite of the ubiquity of ratios, little effort has been directed toward the formal empirical verification of their usefulness. The usefulness of ratios can only be tested with regard to some particular purpose. The purpose chosen here was the prediction of failure, since ratios are currently in widespread use as predictors of failure. This is not the only possible use of ratios but is a starting point from which to build an empirical verification of ratio analysis. "Failure" is defined as the inability of a firm to pay its financial obligations as they mature. Operationally, a firm is said to have failed when any of the following events have occurred: bankruptcy, bond default, an overdrawn bank account, or nonpayment of a preferred stock dividend.3 A "financial ratio" is a quotient of two numbers, where both num-

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Journal ArticleDOI

Financial ratios and the probabilistic prediction of bankruptcy

TL;DR: In this paper, the authors present some empirical results of a study predicting corporate failure as evidenced by the event of bankruptcy, and the methodology is one of maximum likelihood estimation of the so-called conditional logit model, in which the data set used in this study is from the seventies (1970-76).
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Methodological issues related to the estimation of financial distress prediction models

TL;DR: In this paper, the authors examined conceptually and empirically two estimation biases which can result when financial distress models are estimated on non-random samples and showed that these biases can result in biased parameter and probability estimates if appropriate estimation techniques are not used.
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The robust beauty of improper linear models in decision making.

TL;DR: In this article, Dawes presented evidence that even such improper linear models are superior to clinical intuition when predicting a numerical criterion from numerical predictors, and showed that unit (i.e., equal) weighting is quite robust for making such predictions.
Journal ArticleDOI

In Search of Distress Risk

TL;DR: In this paper, the determinants of corporate failure and the pricing of financially distressed stocks using US data over the period 1963 to 2003 were explored and the most persistent firm characteristics, market capitalization, the market-book ratio, and equity volatility become relatively more significant.
Journal ArticleDOI

In Search of Distress Risk

TL;DR: In this article, the authors explore the determinants of corporate failure and the pricing of financially distressed stocks whose failure probability, estimated from a dynamic logit model using accounting and market variables, is high.
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