| Summary: | 碩士 === 國立臺北大學 === 企業管理學系 === 102 === As the mainstay nation, Taiwan's economic development has always been a small and medium enterprises. This made Taiwanese industries have a high degree of entrepreneurship with rapid response to environmental change; it’s highly flexible, and has complete industrial network properties. From the impact of a financial tsunami, it is capable of rapid response adjustments to stabilize the employment market as well as the economic development for our country, which is in a pivotal position. Whether operating working capital is sufficient or not, sustainable development is one of the key elements in SMEs. This study uses logistic regression analysis from the perspective of small and medium enterprises in financing of quantitative empirical variables constructed a credit risk early-warning model suitable for SMEs; identify key influential variables, expected as financial institutions credit rating mechanism reference of key parameters, reduce the SMEs defaults caused by information asymmetry risk, and improve financial institutions ' willingness to loan for SMEs. A model construction processes of this study aims to gather small business financial variables; distinguished for viability, growth rates, profitability, cash flow, and solvency for five categories. References of enterprise financial crisis is based on the measured variables, there is 41 variables selected; the t-test and logistic regression identified 9 of the most predictive power of financial variables, to establish a default rate forecasting model. Events in training a year earlier with the results of tests on a model is similar to results of the model, the comparison of the results for the second year and third year shows a considerable amount of difference, it displays the points close to the crisis, the predicted accuracy can be increased by the effect, and prevent the tracery crisis from happening.
Researchers found that when using a 0.5 chance of doing a split point, the overall forecast accuracy for a year before the crisis the Supreme, is decreased. With a 0.3 chance of doing a split point, the average overall probability of accuracy is better than 0.5 as a division point, they are both in the test samples(s) with the same result.
If we want to predict type I error, using a 0.5 chance of split point, which is not recommended compared to 0.3 chance of split point; we can use the previous year’s data as reference pointers to identify whether the defaulting company is classified as a normal company, using the 0.3 chance of split point is the first step when judging.
Keywords: SMEs; Credit risk; Logistic regression; Full delivery unit; OTC companies.
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