1、 中文 2192 字 外文翻译 原文 Title: Financial Ratios and the Probabilistic Prediction of Bankruptcy Material Source: http:/www.jstor.org/pss/2490395 Author:James Ohlson 1 Introduction This paper presents some empirical results of a study predicting corporate failure as evidenced by the event of bankruptcy. Th
2、ere 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) and so on. Two unpublished papers by White and Turnbuli (1975a； 1975b) and a paper by Santomero and Vinso (1977) are of particular
3、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 us
4、ed 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 er
5、ror (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 updates the original Altman (1968)study, basically considers
6、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 nonbankrupt firms. Although the other three studies differ from the present one so far as
7、 methodology and objectives are concerned, it is, nevertheless, interesting and useful to compare their results with those presented in this paper. Another distinguishing feature of the present study which I should stress is that, contrary to almost all previous studies, the data for failed firms we
8、re not derived from Moodys Manual. The data were obtained instead from 10-K financial statements as reported at the time. This procedure has one important advantage: the reports indicate at what point in time they were released to the public, and one can therefore check whether the company entered b
9、ankruptcy prior to or after the date of release. Previous studies have not explicitly considered this timing issue. 2 Some Comments Regarding Methodology and Data Collection The fundamental estimation problem can be reduced simply to the following statement: given that a firm belongs to some prespec
10、ified 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 predicto
11、rs are obtained from asymptotic (large sample) theory. Clearly, much can be gained by improving the data base. The evaluation of the predictive classification power of a model should be more realistic, and, more important here, the same should apply for standard tests of statistical significance. Th
12、is is not to suggest that it is important to have super accurate data for purposes of developing (as opposed to evaluating) a discriminatory device. It might well be that the predictive quality of any model is reasonably robust across a variety of datagathering and estimating procedures. 3 Collectio
13、n of Financial Statement Data The next phase was one of actually collecting financial data for the bankrupt firms. The objective was to obtain three years of data prior to the date of bankruptcy. Each report had to include the balance sheet, income statement, funds statement, and the accountants rep
14、ort. In case the last available accountants report explicitly stated that the company had filed for bankruptcy, then a fourth report was collected. All reports were retrieved from the Stanford University Business School Library, which has an extensive microfilm file of 10-K reports. The relevant par
15、ts of the 10-K reports were photocopied and subsequently coded. Some firms had to be deleted from the sample because no report whatsoever was available, but these were few. Other firms, again very few, were deleted because they were corporate shells and had no sales. On the other hand, no firm was d
16、eleted because of its young (exchange) age, and some firms had only one set of reports. The final sample was made up of 105 bankrupt firms. I noted that while eighteen of the 105 firms (17 percent) had accountants reports which disclosed that the company had entered bankruptcy, the fiscal year-end w
17、as prior to the date of bankruptcy. These reports were deleted and reports from the previous fiscal year were substituted. 4 A Probabilistic Model of Bankruptcy Let X, denote a vector of predictors for the i th observation； be a vector of unknown parameters, and let P(X, p) denote the probability of
18、 bankruptcy for any given X, and . P is some probability function, 0 P 1. The logarithm of the likelihood of any specific outcomes, as reflected by the binary sample space of bankruptcy versus nonbankruptcy, L( )= log(X, )+ log(1-P(X, ) is then given by: where S1, is the (index) set of bankrupt firm
19、s and S2 is the set of nonbankrupt firms. For any specified function P, the maximum likelihood estimates of 1， 2 , are obtained by solving: max l( ) In the absence of a positive theory of bankruptcy, there is no easy solution to the problem of selecting an appropriate class of functions P.As a pract
20、ical matter, all one can do is to choose on the basis of computational and interpretative simplicity. 5 Ratios and Basic Results For purposes of the present report, no attempt was made to develop any new or exotic ratios. The criterion for choosing among different predictors was simplicity. (1).SIZE
21、 = log(total assets/GNP price-level index). The index assumes a base value of 100 for 1968. Total assets are as reported in dollars. The index year is as of the year prior to the year of the balance sheet date. The procedure assures a real-time implementation of the model. The log transform has an i
22、mportant implication. Suppose two firms, A and B, have a balance sheet date in the same year, then the sign of PA PB is independent of the price-level index. (This will not follow unless the log transform is applied.) The latter is, of course, a desirable property. (2).TLTA = Total liabilities divided by total assets. (3).CLCA = Current liabilities divided by current assets. (4). OENEG = One if total liabilities exceeds total assets, zero otherwise. (5). NITA = Net income divided by total assets. (6). FUTL = Funds provided by operations divided by total liabilities.
