基于Lasso 与Cox模型的上市中小企业财务预警分析 Alternative Title Financial Early Warning Analysis of Quoted SMEs 顾云燕 Thesis Advisor 刘卫玮 2016-05-14 Degree Grantor 兰州大学 Place of Conferral 兰州 Degree Name 硕士 Keyword 财务预警 Lasso Cox模型 Abstract 1960年以来，企业财务危机预警得到了快速的研究和推广，纵观国内外的科研成果，财务预警模型方法有：单变量分析法，多元判别分析模型，线性概率模型，Logistic 模型，神经网络模型，生存分析以及基于市场价值的模型等方法。这些方法各有千秋，应用时应该考虑到现实样本数据的统计特征。本文选用的研究方法为生存分析中的Cox 模型，该方法有如下优势：第一，Cox 模型由参数部分和非参数部分构成，并结合了两者的优点，是一个典型的半参数模型，不需要事先知道具体分布，比较符合现实数据的特征。第二，Martin(1977)首次应用Logistic模型预测银行破产概率，由于该模型可以解决解释变量非正态分布的问题且计算简单方便，后来被诸多学者应用在预测财务危机中。美中不足的是，Logistic 模型需要满足自变量间无共线性存在的样本个数大于回归参数个数这一限制条件，而Cox模型没有这样的要求。第三，在观测期内可能会出现不能提供完整信息的数据，Cox模型可以充分利用这些不完整数据，挖掘出更多有用信息，从而使所建立的模型更加精准。 本文通过合适的变量选择方法，Lasso，选择出所需变量，将70% 的样本用于建模，30% 的样本用于预测，结合Cox 这一生存分析方法建立了上市中小企业财务预警模型，模型的准确率为84%，有一定的参考价值。 Other Abstract Since the 1960s, Financial Early Warning Analysis of Small and Medium enterprises has been rapidly researched and promoted. There are some methods about financial warning, for example, Univariate Analysis, Multiple Discriminant Analysis Model, Linear Probability Model, Logistic Model, Neural Network Model, Survival Analysis and the model based on market value an so on. Each approach has advantages, however, we should take the numerical characteristics of sample data into account. In this thesis, Cox is the research method. The Cox model have the following advantages: First, The Cox model composed by the parameters and non-parameters, It combines the advantages of parametrics and non-parametrics model. It is a typical semi-parametric model, It does not require the specific distribution of non-parameters. The Cox model conforms the features of realistic data. Second, Martin(1977) firstly used Logistic models in bankruptcy warning. Because Logistic model can solve the problem of non-normal distribution of independent variables and the calculation is simple, many scholars applied the Logistic model to predicting the financial crisis, However, The logistic model need to satisfy the following condition, that is, the independent variables without the presence of collinearity number of samples is greater than the number of regression parameters, the Cox model does not have such requirement. Lastly, In the observation, some samples are unable to provide complete information, but we would’t like to give up the data, Cox models can take full advantage of these incomplete data, II then, the model will be more accurate. In this thesis, we choose the appropriate variable selection method, Lasso, to select the desired variables. The 70% sample is used for modeling, and the 30% samples are used for prediction, combining with the analysis method of Cox, the financial early warning model of quoted SMEs was established. The accuracy of the model is 84%, there is a certain reference value. URL 查看原文 Language 中文 Document Type 学位论文 Identifier https://ir.lzu.edu.cn/handle/262010/225223 Collection 数学与统计学院 Recommended CitationGB/T 7714 顾云燕. 基于Lasso 与Cox模型的上市中小企业财务预警分析[D]. 兰州. 兰州大学,2016.
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