财务比率与破产概率预报【外文翻译】
外文翻译
原文
Financial Ratios and the Probabilistic Prediction of Bankruptcy Material Source: Journal of Accounting Rearch V ol. 18 No. 1 Spring 1980 Printed in U.S.A
Author:JAMES A. OHLSON
1.Introduction
This paper prents some empirical results of a study predicting corporate failure as evidenced by the event of bankruptcy. There have been a fair number of previous studies in this field of rearch, the more notable published contributions are Beaver [1966;1968a;1968b],Altman[1968,1973],Altman and Lorries [1976], Altman and McGough [1974],Altman,Haldeman, and Narayanan [1977],Deakin [1972], Libby [1975],Blum [1974],
Edmister [1972], Wilcox [1973], Moyer [1977], and Lev[1971]. Two unpublished papers by White and Turnbull [1975a,1975b] and a paper by Santomero and Vinso [1977] are of particular interest as they appear to be the first studies which logically and systematically develop probabilistic estimates of failure. The prent study is similar to the latter studies, in that the methodology is one of maximum likelihood estimation of the so-called conditional logit model.
The data t ud in this study is from the venties (1970-76).I know of only three corporate failure rearch studies which have examined data from this period. One is a limited study by Altman and McGough[1974] in which only failed firms were drawn from the period 1970-73 and only one type of classification error (misclassification of failed firms)was analyzed.Moyer[1977] considered the period 1965-75,but the sample of bankrupt firms was unusually small (twenty-ven firms).The third study,by Altman,Haldeman,and Narayanan[1977], which "updates"the original Altman[1968] study,basically considers data from the period 1969 to 1975.Their sample was bad on fifty-three failed firms and about the same number of nonfailed firms.In contrast, my study
relies on obrvations from 105 bankrupt firms and 2,058 no bankrupt firms. Although the other three studies differ from the prent one so far as methodology and objectives are concerned, it is, nevertheless, interesting and uful to compare their results with tho prented in
this paper.
However, one might ask a basic and possibly embarrassing question: why forecast bankruptcy? This is a difficult question, and no answer or justification is given here. It could perhaps be argued that we are dealing with a problem of "obvious" practical interest. This is questionable since real-world problems concern themlves with choices which have a richer t of possible outcomes. No decision problem I can think of has a payoff space which is partitioned naturally into the binary status bankruptcy versus non-bankruptcy.(Even in the ca of a "simple" loan decision, the payoff configuration is much more complex.)Existing empirical studies reflect this problem in that there is no connsus on what constitutes "failure" with definitions varying significantly and arbitrarily
across studies. In other words, the dichotomy bankruptcy versus no bankruptcy is, at the most, a very crude approximation of the payoff space of some hypothetical decision problem. It follows that it is esntially a futile exerci to try to establish the relative decision ufulness of alternative predictive systems. Accordingly, I have not concerned mylf with how bankruptcy (and/or failure)"ought" to be defined, I also have refrained from making inferences regarding the relative ufulness of alternative models,ratios,and predictive systems (e.g.univariate versus multivariate).Most of the analysis should simply be viewed as descriptive statistics—which may, to some extent, include estimated prediction error rates—and no "theories" of bankruptcy or ufulness of financial ratios are tested. Even so, there are a large number of difficult statistical and methodological problems which need to be discusd. Many important problems pertaining to the development of data for bankrupt firms have gone mostly unnoticed in the literature.
2.Some Comments Regarding Methodology and Data Collection
The econometric methodology of conditional logit analysis was chon to avoid some fairl
y well known problems associated with Multivariate Discriminant Analysis (MDA,for short).The MDA approach has been the most popular technique for bankruptcy studies using vectors of predictors. Among some of the problems with the studies are:(i)There are certain statistical requirements impod on the distributional properties of the predictors. For example, the variance-covariance matrices of the predictors should be the same for both groups (failed and non-failed firms);moreover, a requirement of normally distributed predictors certainly mitigates against the u of dummy independent variables. A violation of the conditions, it could perhaps be argued, is unimportant(or simply irrelevant)if the only purpo of
the model is to develop a discriminating device. Although this may be a valid point, it is nevertheless clear that this perspective limits the scope of the investigation. Under many circumstances, it is of interest to go through more traditional econometric analysis and test variables for statistical significance, etc.(ii)The output of the application of an MDA model is a score which has little intuitive interpretation, since it is basically an ordinal ranking(discriminatory) device. For decision problems such that a misclassification struct
ure is an inadequate description of the payoff partition, the score is not directly relevant. If, however, prior probabilistic of the two groups are specified, then it is possible to derive posterior probabilities of failure. But, this Bayesian revision process will be invalid or lead to poor approximations unless the assumptions of normality, etc. are satisfied.(iii)There are also certain problems related to the "matching" procedures which have typically been ud in MDA. Failed and non-failed firms are matched according to criteria such as size and industry, and the tend to be somewhat arbitrary. It is by no means obvious what is really gained or lost by different matching procedures, including no matching at all. At the very least, it would em to be more fruitful actually to include variables as predictors rather than to u them for matching purpos.
