英语翻译Z Score = f(net working capital/total assets; retained e

英语翻译
Z Score = f(net working capital/total assets; retained earnings/total
\x05 assets;EBIT/total assets; market value of equity/book
\x05 value of debt; and sales/ total assets)
\x05The lower the Z score,the greater is the potential of firm bankruptcy.
\x05Altman goes on to test the function on other samples and finds the accuracy of classification ranges from 95% (a secondary sample of bank-rupt firms) to 79% (a group of non-bankrupt firms with negative profits reported).In addition,predictive ability falls below 50% between the second and third year from bankruptcy.
\x05 In discussing possible application of the MDA model,Altman develops a "cut-off" rule for Z scores which can be used as a quick indication of credit-worthiness in business loans.The use of Z scores could also be extended to predictors of "downside movement" when analyzing potential portfolio investment choices.
\x05 Finally,Pinches and Mingo (1973) (PM) use financial ratios in developing a model to predict bond ratings.Both factor analysis and MDA are used.PM find seven factor groups to have significant explanatory power.In applying the MDA technique,PM use five of the factors (years of consecutive dividends; issue size; NI + interest/interest; long term debt/total assets (5 yr avg.); Nl/total assets) and the existence of subordination of the bond issue to develop four discriminant functions The model predicts bond ratings with accuracy ranging from 69.7% (the original sample) to 56.3% (from a stratified random sample).From a multiple range test,PM find that no variables in the model can adequately discriminate between Baa and other adjacent bond rating groups.The authors conclude that the model may require different financial or operating variables to increase its explanatory power or that the quantitative data examined in the model may be more highly related to the actual long term potentialities of rated bonds than the more qualitative assessments of the rating agencies.
Accounting information is strongly interrelated with the various other topics presented here.Accounting and operating information are used in many of the empirical investigations presented in the remainder of this paper.Such papers as

Z分数= f(净营运资本/总资产;留存收益/总
　　资产;息税前利润/总资产;市场股权价值/书
　　债务的价值;销售/总资产)
　　Z分数越低,越有潜在的公司破产.
奥尔特曼继续测试函数对其他样本,发现分类精度范围从95%(一个次要的样本的bank-rupt公司)到79%(一群non-bankrupt负面报道的公司利润).此外,预测能力低于50%至第二年和第三年从破产.
在讨论可能的应用MDA的模型,开发了“切断”奥特曼统治for Z分数,它可以用来作为快速指示在商业贷款的信用价值.使用Z分数还可以被扩展,预测“下行运动”在分析潜在的组合投资的选择.
最后,捏,Mingo(1973)(PM)使用财务比率在发展中一个模型来预测债券评级.两个因素分析和MDA是使用.PM发现七个因素组有显著的解释力.在应用MDA技术、点使用的五因素(年连续的股利,发行规模;镍+利息/兴趣;长期债务/总资产(5年avg.);问/总资产)和存在的下属的债券发行的开发模型预测四判别函数债券评级的准确度范围从69.7%(原样品)到56.3%(从分层随机样本).从多个测试,PM发现没有变量模型能够充分区分Baa和邻近的债券评级组.这个作者得出的结论是,模型可能需要不同的金融或操作变量来增加其解释力,或定量的数据,研究了模型可能更加息息相关,实际的长期潜力的评级债券比定性评估评级机构.
摘要会计信息是强烈相关的各种其他主题这里介绍的.会计和操作信息被用在很多的实证调查提出了本文的其余部分.等证件