Financial Distress Prediction Of The Chinese Listed Firms Based On The Structural Default Risk Models

At present the methods of financial distress research are mainly single-factor / multi-factor analysis and discrete choice models. This dissertation used structural risk analysis methods to forecast the financial distress probability, trying to find the best forecast model for Chinese listed companies.Through analyzing the internal connection of a company's structural default and financial distress, this dissertation firstly pointed out that structural default risk models could be used to forecast financial distress. Secondly, the dissertation compared three classical structural default risk models (BSM Model, BV Model, ER Model) in basic assumptions and critical values, proposed the method of parameter estimation which fit the three models (Transformed-data Based Maximum Likelihood Estimation), and proved the validity of the method.The default risk of the listed companies in SSE (Shanghai Stock Exchanges) was estimated with the Transformed-data Based Maximum Likelihood Estimation method, and then the financial distress of listed companies was analyzed through calculating the default probabilities. Furthermore, the accuracy of the three structural default risk models in forecasting financial distress was compared. The empirical study indicated that BV Model has the best forecasting accuracy. For high risk companies, the other two models fit, either.