後博達時代上市公司財務危機預警研究

　　In June 2004 Procomp Infomatics Ltd. exposed financial failure that shocked domestic market and authorities. Procomp misstating operation performance and financial condition by financial dealings, however, investors were not aware of Procomp management fraud and suffered huge losses. Focusing on some related issues, this thesis studies the influences that Procomp case brings to the early warning model of financial distress. The main purpose of this thesis is to enhance effectiveness of financial distress predicting models. Using financial variables of past studies, this thesis adds cash-flow variables and non-financial variables – going-concern opinion and auditor size – as predictors to improve explanatory power of model. Besides, this research uses logistic regression models and artificial neural networks to predict financial distress.
　　Using the firm-level data from 2000 to 2004, this research uses a sample of 195 listed companies, 70 in crisis state and 125 in normal state, and establishes five logistic models to examine explanatory power of going-concern opinion and auditor size. Establishing back-propagation networks with the artificial neural network software “NeuralWorks Professional Ⅱ/PLUS”, we also use artificial neural networks to compare with logistic regression model to identify the difference.
　　The empirical results indicate that going-concern opinion has significant incremental explanatory power over financial variables, but auditor size doesn’t have significant interactive effect on going-concern opinion. Artificial neural network has better performance on normal company and overall prediction, but logistic regression model has better hit rate of stressed companies.