Study of Credit Risk for Capital-Intensive Industries Based on Logistic Model

Credit risk assessment of companies has been an important part of the study on risk management for a long time, especially for the default risk of companies with a high liability-to-asset rate. In this paper, we use factor analysis method to establish a logistic regression model and make an empirical analysis on the credit risk of listed companies of Capital-Intensive Industries. The results show that long-term pay ability, cash flow factor, profitability, and sales ability have the greatest influence on credit risk assessment, and the next come the short-term liquidity and asset-liability. By the inspection of testing samples, it is proved that this Logistic model is effective for credit risk assessment because of its high accuracy and reliable recognition, good prediction and generalization ability.