外文翻译
原文
Financial Ratios and the Probabilistic Prediction of Bankruptcy
Material Source: Journal of Accounting Research Vol. 18 No. 1 Spring 1980 Printed in U.S.A
Author:JAMES A. OHLSON 1.Introduction
This paper presents 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 research, 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 Two unpublished papers by White and Turnbull [1975a,1975b] and a paper by Santomero and Vinso [1977] are of particular interest as they appear to be the first studies which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 present 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 set used in this study is from the seventies (1970-76).I know of only three corporate failure research 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-seven firms).The third study,by Altman,Haldeman,and Narayanan[1977], which \original Altman[1968] study,basically considers data from the period 1969 to 1975.Their sample was based on fifty-three failed firms and about the same number of nonfailed firms.In contrast, my study relies on observations from 105 bankrupt firms and 2,058 no bankrupt firms. Although the other three studies differ from the present one so far as methodology and objectives are concerned, it is, nevertheless, interesting and useful to compare their results with those presented 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 \themselves with choices which have a richer set 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 case of a \decision, the payoff configuration is much more complex.)Existing empirical studies reflect this problem in that there is no consensus on what constitutes \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 essentially a futile exercise to try to establish the relative decision usefulness of alternative predictive systems. Accordingly, I have not concerned myself with how bankruptcy (and/or failure)\to be defined, I also have refrained from making inferences regarding the relative usefulness 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 %usefulness of financial ratios are tested. Even so, there are a large number of difficult statistical and methodological problems which need to be discussed. 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 chosen to avoid some fairly 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 these studies are:(i)There are certain statistical requirements imposed 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 use of dummy independent variables. A violation of these conditions, it could perhaps be argued, is unimportant(or simply irrelevant)if the only purpose 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 structure 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 \been used in MDA. Failed and non-failed firms are matched according to criteria such as size and industry, and these 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 seem to be more fruitful actually to include variables as predictors rather than to use them for matching purposes.
The use of conditional logit analysis, on the other hand, essentially avoids all of the problems discussed with respect to MDA.The fundamental estimation problem can be reduced simply to the following statement: given that a firm belongs to some prespecified population, what is the probability that the firm fails within some prespecified time period? No assumptions have to be made regarding prior probabilities of bankruptcy and/or the distribution of predictors. These are the major advantages. The statistical significance of the different predictors are obtained from asymptotic (large sample) theory. To be sure, as is the case in any parametric analysis, a model must be specified, so there is always room for misspecification of the basic probability model.
3.Evaluation of Predictive Performance
There is no way one can completely order the predictive power of a set of models used for predictive (decision) purposes. As a minimum, this requires a complete specification of the decision problem, including a preference structure defined over the appropriate state-space. Previous work in the area of bankruptcy prediction has generally been based on two highly specific and restrictive assumptions when predictive performance is evaluated. First, a (mis)classification matrix is assumed to be an adequate partition of the payoff structure. Second, the
two types of classification errors have an additive property, and the \one which minimizes the sums of percentage errors. Both of these assumptions are arbitrary, although it must be admitted that the first assumption is of some value if one is to describe at least one implication of using a model. Much of this discussion will therefore focus on such a (mis)classification description. Nevertheless, the second assumption will also be used at some points, since it would otherwise be impossible to compare the results here with those of previous studies. The comparison cannot be across models because the time periods, predictors, and data sets are different. Rather, the question of interest is one of finding to what extent the results conform with each other.
4．Conclusions
There are two conclusions which should be restated. First, the predictive power of any model depends upon when the information (financial report) is assumed to be available. Some previous studies have not been careful in this regard. Second, the predictive powers of linear transforms of a vector of ratios seem to be robust across (large sample) estimation procedures.Hence,more than anything else, significant improvement probably requires additional predictors.
译文
财务比率与破产概率预报
资料来源:会计研究杂志，18卷第1号，1980年春季美国印刷 作者：詹姆士 A. 欧森 1.说明
本文介绍了通过实证研究来预测公司破产的一些研究结果。在这一领域一直有相当数量的人在研究，值得注意的研究贡献有比弗（1966，1968a；1968b），奥尔特曼（1968；1973），奥尔特曼和洛里斯（1976），奥尔特曼和麦高夫（1974），奥尔特曼，霍尔德曼和纳拉亚南（1977）迪金（1972），利比（1975），布卢姆（1974），埃德米斯特（1972），威尔科克斯（1973），莫耶（1977）和列弗（1971）。瓦特和特恩布尔的两篇未发表的论文（1975a；1975b）和一篇圣美罗和威萨（1977）的文章特别有意义，因为他们是第一次从逻辑和系统上估计失败机率的研究。本研究是类似后者的研究，该方法是对所谓的条件Logit模型的最大似然估计。
用于这项研究的数据集为七十年代（1970—1976）。据我所知，只有三个对企业失败的研究已审查了这一期间的数据。一个是奥特曼和麦高夫（1974）的研究，对来自1970年至1973年期间，且只有一种分类误差（错误分类的失败公司）的失败公司样本进行了分析。莫耶（1977）考虑了1965年至1975年期间的数据，但破产公司的样本是非常小的(27家公司)。第三项研究，奥特曼，霍尔德曼和纳拉亚南（1977），是在奥特曼（1968）研究基础上的进一步探讨，数据来自1969年至1975年期间。他们的样本为五十三家破产企业和相同数目的为破产企业。与此相反，我的研究是基于对105家破产企业和2,058家未破产的公司的研究。尽管这三项研究不同于目前所关注的方法和目标，不过，在本文中，他们的结论是有意义和有用的。
然而，人们可能会问一个可能令人尴尬的问题：为什么预测破产？这是一个很难回答的问题，在这里我也不能给出答案或理由。也许，可以说我们正在处理“明显的”实际利益的问题。在现实世界中，人们可以通过这项研究来及时了解企业情况，并成为人们关注和选择企业的依据。但目前我能想到的解决该
问题的方法没有将盈利空间自然分为非破产与二元状态破产。（即使在“简单的”贷款政策下，回报的配置也是很复杂的。）现有的实证研究与研究跨变极大，且其都任意地定义来研究此问题，对何为“失败”还没有共识。换句话说，没有破产与二分法破产，充其量是近似对盈利空间的一些假设性的回答。因此尝试建立相对决策有用性的替代预测系统基本上是徒劳无益。因此，我并不关心自己如何来对破产（和/或失败）的定义界定。我有
作出替代模式，分析比率的相对有用性，以及预测系统（例如：单变量与多元）。
财务比率与破产概率预报【外文翻译】
