Credit Risk Evaluation Of Taiwan’s Listed And Otc Companies

Since the Asian financial crisis of 1997, most of the countries all over the world had spread the tank stock news one after another, collapse happened to many large enterprises in succession, which made the company default more and more serious with each passing day, with the weeding through the old to bring forth the new of the derivatives, with a view to making a living and enhancing the market share, the relative spread of the financial institute in the fierce competition market gradually shrunk, and the bank risk-adjusted asset was seriously eroded. Faced with the situations, such as the business cycle factor, poor corporate governance or internal control and improper financial product manipulation, as long as the financial institute was a little incautious, the overdue loan and bad debt would arise hereby, which caused the poor financial institute constitution, such injury not only caused direct damage to the stockholder and investor of the financial institute but the loss, if taken over by the government thereby, would be settled accounts by all the taxpayers as well. If seriously, the injury formed the internal financial crisis, and the bank and financial institute might generate more default risk; thus, the measurement and management of the enterprise default risk also became important with each passing day.The research motives of the study not only try the Logit model and KMV model of more applicability in practice but also compare three kinds of financial early warning model: whether to incorporate the Logit model of the macroeconomic variables and corporate governance variables and the KMV model. Which has a higher accurate rate to expect the possibility that a financial crisis happened to Taiwan’s enterprises under three kinds of different cut values? In the long run, the article also applies the rigorous performance evaluation model to conduct a test for the credit risk evaluation reference while the bank credits to establish effectively the financial early warning system of an enterprise to further achieve the objective of the enterprise credit risk management. The study uses the internal listed companies and OTC companies to which a financial crisis happened from 2000 to 2009 as the research object. The screening variables are the variables applied by the KMV model. The study also considers the macroeconomic variables and corporate governance variables to construct the logit early warning model and to evaluate the model in light of different performance index. Pursuant to the results of the empirical analysis, the four following conclusions can be acquired: (1) The Logit model which considers the macroeconomic variables and corporate governance variables has the best accurate rate. No matter it is inside or outside the sample, the Logit model which considers the foresaid is the best. The Logit model which does not consider the foresaid is the next. The worst is the KMV model. This indicates that the KMV model only considers the asset, liability and interest rate variable but neglects the business indicator and corporate governance variables, so its predictability reduces. In addition, different company stock prices are over-estimated or under-estimated on account of manmade manipulation. So the market value and fluctuation of the stock price cannot reflect the real value, which makes significance of the credit risk variable and poorer explanatory ability, but part of the companies have the enhanced effect. (2) No matter what kind of model is inside or out the sample, 0.5 is the best cut value of the performance comparison. The cut value of different classifications will influence the identification performance of the model, but it cannot be verified in practice that what kind of cut value is the best. Thus, the study compares three kinds of different cut values, separately 0.3, 0.5 and 0.8. The empirical result shows that no matter what kind of model is inside or outside the sample, 0.5 is the best cut value of the comparison performance. (3) Observing from the type I error, the Logit model which considers the macroeconomic and corporate governance variables is the lowest. However, in consideration of different cut values, 0.3 is better. The performance of the type II error is consistent with the comparison result of the accurate rate. The prediction outside the sample is found to be consistent with the comparison inside the sample. But when the cut value is 0.5, it is the best. (4) As a whole, the performance inside the sample is better than that outside the sample. The empirical result of the study shows that the performance outside the sample of the KMV is superior to that inside the sample. The reason may be that the estimative parameter is appropriate for the data estimation outside the sample. But the estimative parameter outside the sample of the Logit model is significantly lower than that inside the sample. The possible reason is subject to the difference of the credit risk involving extent of the implication of the enterprise per se; moreover, it causes the phenomenon of the judgment ability reduction of the testing sample that the model cannot learn to train the sample information.As for the research of an enterprise’s financial crisis, the relevant literature has accumulated many results, but it cashes in on three kinds of financial early warning model to make a prediction outside the sample: the best default coefficient calculated by the data in sample, the KMV model and the Logit model of whether to incorporate the macroeconomic variables and corporate governance variables. Finally, the model which makes a performance comparison between the data inside and out sample is seldom seen. The analysis of the study obtains that incorporating the macroeconomic variables and corporate governance variables can indeed enhance the financial crisis predictability of the model, which can also serve as a very important reference index in evaluating the credit risk of an enterprise; that is, it can effectively predict the financial crisis of an enterprise to provide an effective investment decision and risk management reference for an enterprise, a creditor, an investor and the government authority to further reduce the investment risk and loss.