统计分析预测财务危机【外文翻译】

1、本科毕业论文外文翻译外文文献译文标题：统计分析预测财务危机资料来源:服务科学与管理出版社作者：nicolas emanuel monti, roberto mariano garcia本文研究的r的是运用推理统计确定如果一个公司很可能成为财务困境的 短期。为此，我们决定收集数据从公司的财务报表。进行的分析是基于一组45 个财务比率观察样本86公司在阿根廷经营。进行的分析是基于一组45个财务 比率观察样本86公司在阿根廷经营。首先，我们采用了主成分分析将信息在 45个原始比例为2新的全局变量命名为风险和返回。这样，我们可以很 容易地代表和比较图中的企业的风险和收益的变化。这样，我们可以很容易地

2、代表和比较图中的企业的风险和收益的变化。通过计算这些新变量可以快速经 济分类一个特定的企业基础上的风险，公司有关于其业务性质和涉及的风险债 务金额，它采取了比较的利润是在过去两年财政年。其次，我们进行了回归分 析的概率估计，一个公司的财务危机的短期。该模型最终选择了成功确定85% 的公司从样品和解释65%的总样本变异。该模型是由以下因素：1）流动负债率, 2）总债务成本，3）操作利润率，和4） 鱼子。在这项研究成果是工具，开 发了基于统计推断，我们可以快速评估的财务状况，企业根据其风险和收益的 变化，以及估计的概率是一个公司的财务危机的短期。不同的方式把这些工具 的工程实践：1）控制和后续的公

3、司财务业绩，2）支持这一决定借钱给公司，3） 支持这一决定投资的决策或合并的公司，4）支持市场分析从财政角度来看，5） 支持行动或决定，有关金融评估一个公司，宣称自己是财政困境。通过本研究，我们设法核实的基础上进行统计分析，财务比率显示不同的 行为之间的这些企业有财务问题和那些没有这样做。虽然并非所有这些比率本 身相同的能力，以便确定这些公司的财务问题，这可能是结合总结所有资料为 2个主要的组成部分，我们命名为风险和a返回。通过计算这些新变量可以 快速经济分类一个特定的企业基础上的风险，公司有关于其业务性质和涉及的风险 债务金额，它采取了比较的利润是在过去两年财政年。最后结果的主成分分析表明，

4、就没有明显的理由，不考虑任何财务比原先 估计的概率，一个公司的实际财务危机的短期。然而，在发展不同的回归模型, 我们发现我们可以获得更好的估计这些概率如果只考虑一些财务比率在一起， 显示出较高的识别能力与公司的财务问题上比较的情况下，收集的数据来自所 有45个比率是用来（在案件的主成分模型）。在这方面，我们制定一个更有 效的模型，我们可以取得更好的成果，减少数据。这种效率可以解释由于事实 的主要成分是一个线性组合的45个比率，这种效率可以解释，由于这一事实, 主要组成部分是一个线性组合的45的比例，这意味着他们中的许多人可能不 会是有益的区分一个健康的财务公司之一，它不是。这一发现显示了如何重

5、要 的是有一个完全的和广泛的数据库开始之前的任何统计分析使更少的限制，介 绍在试图找到近似最优解，即回归模型与现有的比例组合，即回归模型与现有 的比例组合，最好的估计概率的一个公司在短期内成为财政困境同样，我们要强调的 好处，可以得到一个以上的统计分析，结合在一起，更好地理解的性质，过程 进行研究，更有效地实现目标，在我们的情况是确定公司的财务问题。我们已经看到，这些比率有更多的能力来确定公司的财务问题是所有返回 有关方面的公司。事实上，我们己经看到，主成分，更确切的确定财务健康的 企业是回报相对于风险分量。然而，包含的信息在这些比率可以随时补充 信息从其他类型的比例确定公司的财务问题更加准确

6、和有效。然而，包含的信 息在这些比率可以随时补充信息从其他类型的比例确定公司的财务问题更加准 确和有效。我们选择了一个小团体，他们可以解释65%的企业行为。相关模型 由以下比率：1）流动负债率，2）总债务成本，3）经营利润，4） 鱼子。有 趣的是注意到，在大多数的测试log istic回归模型被发现有较高的概率正确分 类公司财务问题，即假设一个公司的财政健康，其实不是。这可能是主要解释 说因为财务比率收集有较高的变异在这些变异在这些公司，财务危机和比，那 些没有任何财务问题。然而，相结合的可能性，回归和主成分分析有助于减少 的概率错的某公司。在这方面，我们注意到，目前的研究并没有包括任何分析

7、有关的费用，参与决策过程，确定公司的财务问题。然而，每当有不明确的定 义决定性的结果，财务状况的公司最保守的决定将承担该公司的财务问题。在这项研究成果是工具的基础上制定的统计推论，我们可以快速评估的财 务状况，企业根据其风险和收益的变化，以及估计的概率是一个公司的财务危 机的短期。有不同的方式把这些工具的工程实践:1）控制和后续的公司财务 业绩，2）支持这一决定借钱给公司，3）支持决策投资或决策与公司合并，4） 支持市场分析，从金融角度来看，和5）支持行动或决定，有关金融评估一个公 司，宣称自己是财政困境。这项研究可以得到进一步的发展，在企业内新的解释变量，而不是财务比 率而是定性测量，可能有

8、助于更准确和有效的估计概率的一个公司在短期内成 为财政困境。另一个选择是将纳入工具，涉及的费用出来的错误决定即假设一 个公司没有财务问题时，它实际上已经或副versa-could最小化。最后，统计 分析进行的这项研究可以被复制的公司，有大量的资产与目标确定的主要特点, 得出在一个坚实的财务结构。我们可以看到，有许多不同的方式继续这一研究 和统计提供有趣的工具。外文文献原文title: a statistical analysis to predict financial distressauthor： nicolas emanuel monti, roberto mariano garciat

9、he aim of this study is to apply the statistical inference to identify if a firm is likely to become financially distressed in the short term. to do this, we decided to collect data from the firmsfinancial statements. the analyses performed were based on a group of 45 financial ratios observed from

10、a sample of 86 firms operating in argentina. first, we used the principal component analysis to turn the information in the 45 original ratios into two new global variables named as arisk and areturn. in this way, we can easily represent and compare in a graph the firms' risk and return variatio

11、ns. by the computation of these new variables it is possible to quickly financially categorize a certain firm based on the risk the company has with regard to the nature of its business and the risk involved in the amount of debt it has taken in comparison to the profits that were generated during t

12、he last two fiscal years. second, we performed a logistic regression analysis to estimate the probability that a firm becomes financially distressed in the short term. the model finally selected managed to successfully identify 85% of the companies from the sample and it explains 65% of the total sa

13、mple variability. the model is represented by the following variables: 1) current debt ratio, 2) total cost of debt, 3) operating profit margin, and 4) aroe. the outcomes from this study are two tools that were developed based on the statistical inference from which we can quickly asses the financia

14、l status of a firm based on its risks and return variation as well as to estimate the probability that a firm becomes financially distressed in the short tcitn. there are different ways of taking these tools into practice such as: 1) to control and follow up the financial performance of a company, 2

15、) to support the decision of lending money to a company, 3) to support the decision of investing money or the decision of merging with a company, 4) to support market analysis from a financial perspective, and 5) to support actions or decisions related to the financial assessment of a company that d

16、eclares itself to be financially distressed.through this study we managed to verify based on the statistical analyses performed that the financial ratios show a different behavior between those firms that have had financial problems and those which did not. al-though not all these ratios have by the

17、mselves the same ability to allow the identification of those firms with financial problems, it is possible to combine and summarize all that information into 2 principal components that we have named as 'risk and /return. by the computation of these new variables it is possible to quickly finan

18、cially categorize a certain firm based on the risk the company has with regard to the nature of its business and the risk involved in the amount of debt it has taken in comparison to the profits that were generated during the last two fiscal years.the conclusive results obtained from the principal c

19、omponent analysis suggest that there would be no apparent reason not to consider any financial ratio originally collected to estimate the probability that a firm be-comes financially distressed in the short teitn. however, after developing different regression models we have seen that we can obtain

20、better estimations of these probabilities if we just consider a few financial ratios that all together show a higher ability to identify a firm with financial problems in comparison to a situation where the data collected fi*om all the 45 ratios is used (as in the case of the principal components mo

21、del). in this way, we managed to develop a more efficient model given that we can obtain better results with less data. this efficiency can be explained due to the fact that the principal components are a linear combination of 45 ratios, which means that many of them might not be useful to distingui

22、sh between a financially healthy firm and one that it is not. this finding shows how important is to have a complete and broad database before starting any statistical analysis so that fewer limitations are introduced when tiying to find a near optimal solution, i.e. the regression model with the av

23、ailable ratios combination that best estimates the probability of a firm of becoming financially distressed in the short term. in the same way, we should emphasis the benefits that can be obtained when combining more than one statistical analysis together to better understand the nature of the proce

24、ss under study and to more effectively achieve the objective proposed.we have seen that those ratios that have more capabilities to identify those firms with financial problems are all related to the return aspects of the companies .in fact, we have seen that the principal component that resulted mo

25、re conclusive to identify financially unhealthy firms was the a return as opposite to the 4 risk component. nevertheless, the information contained in these ratios can always be complemented with information from other type of ratios to identify those firms with financial problems more precisely and

26、 effectively. after performing a logistic regression analysis based on the 45 ratios collected in the sample, we have selected a small group of them that can explain 65% of the firms9 behavior. the related model consists of the following ratios: 1) current debt ratio, 2) total cost of debt, 3) opera

27、ting profit margin, and 4) aroe. it is interesting to notice that in most of the logistic regression models tested it was found that there is higher probability to incorrectly classify a firm with financial problems, i.e. to assume that a company is financially healthy when actually it is not. this

28、could be mainly explained due to the fact that the financial ratios collected have a higher variability in those companies that are financially distressed in comparison to those that do not have any financial problem. nevertheless，the possibility of combining the regression and the principal compone

29、nt analyses helps to reduce the probability of misclassifying a certain finn. with this regard, we should notice that the present study does not include any analysis related to the costs involved in the decision making process of identifying firms with financial problems. nevertheless, whenever ther

30、e are not conclusive results that clear define the financial status of a company then the most conservative decision would be to assume that the firm has financial problems.the outcomes from this study are two tools that were developed based on the statistical inference from which we can quickly ass

31、es the financial status of a firm based on its risks and return variation as well as to estimate the probability that a firm becomes financially distressed in the short teirn. there are different ways of taking these tools into practice such as: 1) to control and follow up the financial performance of a company, 2) to support the decision of lending money to a company, 3) to support the decision of investing money or the decision of merging with a company, 4) to support market analysis fr