From the fourth quarter of 1998 to the fourth quarter of 2001, many listed and over-the-counter corporations in Taiwan have fallen into financial crises, resulting in huge losses to the employees, customers, vendors, debt holders, and stockholders. The purpose of this thesis is to build an effective model which predicts default probability and quantifies credit risk for information needed by the management, stockholders, debt holder, or other related persons in decision use.
This thesis uses logistic discrete hazard model to predict default probability, and evaluates the performance of models with the concept of Accuracy Ratio and Vuong test. The major research findings are as follows:
I.The relative predictive power of the accounting ratios model, the market variables model, and the default distance model is not the same for the electronic industry, construction and cement industry, and the rest of others.
II.With respect to the four types of explaining variables: accounting ratios, market variables, macroeconomic variables, and stockholding variables, it is found that in many cases the types with inferior predictive power contain incremental information relative to the best one.
III.It is found that the accuracy ratios of the combined models including the four types of explaining variables are higher than those of the separate models including only one type of explaining variable, except under my first definition of default in the construction and cement industry, in which case the out-of-sample accuracy ratio of the combined model is slightly lower than that of the model of accounting ratios.