公司财务困境预测 外文翻译资料.doc

An Application of Support Vector Machine toCompanis Financial Distress PredictionBecause of the importance of companies financial distress prediction, this paper applies support vector machine(SVM)to the early-warning distress. Taking listed companies three-year data before special treatment(ST)as sample data, adopting cross-validation and grid-search technique to find SVM models good parameters, an empirical study is carried out. By comparing the experiment result of SVM with Fisher analysis, Logistic regression and back propagation neural networks, it is concluded that financial distress early-warning model based on SVM obtains a better balance among fitting ability, generalization ability and model stability than the other models.Stockholders and other interest parts suffer individual economic loss, but also if many enterprises run into bankruptcy the economic development of the whole country will be greatly shocked. In general, most enterprises that ran into bankruptcy had experience condition of financial distress, but they could not detect financial distress at an early stage and timely take effective measures to prevent bankruptcy, in the perspective of management, it is important to explore a more effective financial distress prediction model to signal early-warning for enterprises which will possibly get into financial distress, so that managers can take strategic actions to avoid deterioration of financial state and bankruptcy, from the view of financial institution, an effective financial distress prediction model can help them detect customers with high default risk at an early stage so as to improve their efficiency of commercial credit assignment.Beaver(1966),one of the first researchers to study bankruptcy prediction, the predictability of the 14 financial ratios using 158 samples consisting of ailed and non-failed firms2.Beavers study was followed by Altmans model (1968)based on the MDA to identify the companies into known categories. According to Altman, bankruptcy could be explained quite completely by using a comb of five(selected from an original list of 22)financial ratios. Log model is used to deal with two classes classification problems, and Ohlson was the first apply it to predicting financial distress in 19804.The most widely used machine learning method in the field of financial distress prediction is neural net work(NNS),which has strong capability of identifying and representing nonlinear relationships in the data set. Odom and Sharda(1990)made an early attempt to use NNS for financial distress prediction. He used the same financial ratios as Altmans study and took MDA model as the benchmark5.From then on, many scholars(Fletcher and Goss,1993;Carlos Serrano-Cinca,1996;Parag C.P.,(2005;etc.)were dedicated to compare NNS with MDA and log, which brought a lot of positive support for the conclusion that NNS can predict financial distress more accurate than those benchmarks678.Generally, statistical methods have the advantages of simple model structure and easiness to understand and use, but they have restrictive assumptions such as linearity, normality and independence of input variables, which limits the effectiveness and validity of prediction. In contrary NNS is not constrained by those assumptions and have strong ability of fitting nonlinear relationships between descriptive variables and conclusive variables. But also has the disadvantages such as unfixed structure, over-fitting, needing a lot of samples, and black-box effect.Support vector machine(SVM)is a relatively new machine learning technique, originally developed to resolve local minima and over-fitting problems which are the main sources of trouble to, Shin K.-S.(2005)and Min J.H.(2005) respectively made an attempt to use SVM to predict corporate bankruptcy data and got satisfying results12,13.Other applications of SVM by Kim K. J.(2003)and Tay (2001)also showed that it is a promising classification and prediction method14,15.This paper attempts to apply SVM to predicting the financial distress of Chinese listed companies and compare the result of SVM with the results got by the methods of Fisher disc analysis, Logistic regression. The rest of the paper is divided into five sections. Section 2 is the brief description of SVM theory. Section 3 is about data collection and preprocessing. Section 4 gives the modeling process and experiment results. Section 5 discusses and analyzes the experiment results. Section 6 makes conclusion.SVM, put forward by Vapnik in 1990 a relatively new machine learning technique, which is developed on the basis of statistical learning theory9.Former re- searches have shown that SVM has the following merits according to learning ability and generalization ability.1.SVM is based on the principle of structural risk minimization, not on the principle of empirical risk minimization, so SVM can better avoid the problem of over-fitting.2.SVM algorithm is uneasy to get into local optimization, because it is a convex optimization problem and its local optimal solution is just the global optimal solution.3.In practice, when the number of samples is relatively small, SVM can often.get better result than other classification and prediction techniques. A simple description of the SVM algorithm is provided as follow ,in which N is the number of training samples. In the condition that the training samples are linearly separable, SVM algorithm is to find an optimal separating plane, which can not only separate training samples without error but also make the margin width between the two parallel bounding planes at the opposite side of the separating plane get a biggest value.In the nonlinearly separable case ,SVM firstly uses a nonlinear function(x)to map input space to a high-dimensional feature space. Then a nonlinear optimal separating hyper plane with the biggest margin width can be found by the same technique as linear model. Those data instances which are nearest to the separating hyper plane are called support vectors, and other data instances are irrelevant to the bounding hyper plan. Because most problems are nonlinear separable and line- early separable case is the special situation of nonlinearly separable case only the SVM theory under nonlinearly separable case is stated .So according to original for, the SVM classifier should satisfy the following conditions.An Application of Support Vector Machine to CompaniesFinancial Distress Prediction 277 in which slack variables .Feature space generally can not be linearly separated, if the separating hyper plane is constructed perfectly without even one training error, it is easy to appear over-fitting phenomenon, so slack variables are needed to allow a small part of misclassification. a tuning parameter, weighting the importance of classification errors with the margin width. This problem is transformed into its dual problem because it is easier to interpret the results of the dual problem than those of the primal one.In the optimization problem above, the N-dimension vector of all ones. The three common types of kernel function are polynomial kernel function, basis kernel function and kernel function. Lagrange .A multiplier exits for each training data instance and data instances corresponding to zero support vectors. Do this optimization problem and the ultimate SVM classifier is constructed as following.Source: for the social and behavioral sciences of statistics, the marginal model, Vol.18 (1), 2009 (1): P23-7公司财务困境预测 由于公司财务预警的重要性，本文应用于支持向量危机（SVM）的早期预警金融上市公司的三年数据后，采用交叉验证和网格搜索技术，寻找支持向量危机的合适参数来进行实证研究。通过比较鉴别分析的SVM实验结果，Logistic回归和BP神经网络模型，从而来获取一个相对与三者之间的平衡能力，推广能力和模型稳定性的其他模型。 企业破产不仅使股东、债权人、经理、雇用个人遭受经济损失，而且如果破产的公司过多将会对一国的经济发展造成阻碍。一般来说，大多数企业都遇到过财务困难条件甚至公司破产，很多情况都是由于他们无法检测到早期阶段存在的财务危机，没有及时采取有效措施，从而陷入财务危机。在管理的角度来看，探索有效的财政危机预测模型是十分重要的，具有早期预警的企业，即使陷入财务困境，管理人员也可以采取相应的行动来防止危机进一步恶化。而金融机构认为，一个有效的财务困境预测模型，可以帮助他们发现客户在早期阶段存在的违约风险，从而使得商业信用分配效率大大提高。财务预警是一种量化分析，它有利于清晰、直观地反映上市公司的财务状况，但它难以全面满足揭示上市公司财务危机程度的需要，并不能完全替代传统的定性分析，特别是财务报表的编制质量和审计质量等因素会直接决定模型结果的准确性和实用性。判别盈