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Listed Companies' Financial Distress Prediction Models And Applications

Posted on:2005-07-02Degree:MasterType:Thesis
Country:ChinaCandidate:R GuoFull Text:PDF
GTID:2206360122475600Subject:Management Science and Engineering
Abstract/Summary:PDF Full Text Request
Research on financial distress prediction is one of the most important research subjects. It has not only high academic value but also enormous practical value. In Chinese developing but not mature security market, whether the listed companies are healthy or not, will affect related interests of investors and creditors. The research of their financial situation is of grate importance. If we can generalize the research results into the other no-listed companies, it will be more significant to guide the management and risk prevention of the enterprises.Under the above background, this article firstly reviews previous empirical results of the research on the financial predicting models. Secondly, based on the summary of previous research methods and results, twenty-one A Publicly listed firms, which are special treated (ST) in the year of 2002, are selected as samples. And according to the matching criterion of the same industry and size, the equal quantity firms whose financial situation is good are selected as control samples. Then forty-six independent variables are studied with the help of the profile analysis and T test. Furthermore, some financial ratios, which are grate different between the two groups for the years before the financial failure, are picked out to establish multi-variables model to forecast the financial failure in the method of Logistic regression. And the model is proved to have good predictable efficiency and accuracy. At last, the qualitative analysis and research is carried on the base of the above quantitative analysis, which gives the direction of the enterprise operation. And the problems, which should be noticed in the use of the financial predicting model by the firms, are discussed in the end of the article.
Keywords/Search Tags:Financial Distress, Prediction, Model, Logistic Regression
PDF Full Text Request
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