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As the development of financial market and maturity of derivatives's pricing theories, different connotations of credit risk come into being, and many classical credit risk models come up. This paper makes a comparative analysis of current credit risk models and summarizes some of the recent advances in credit risk modeling.
Based on studying the KMV default distance model, the work of this paper is to calculate the default distance using BCC model in DEA methodology to quantify the credit risk. The principle idea is to use DEA value of the company to replace the market value of the company in KMV model, and use average DEA points of ST companies within the industry instead of default point, then get the Default Distance...
The paper combines theory with practice and applies neural network technology to establish a credit risk assessment model based on BP neural network technology. The assessment model, to some extent, improves the traditional credit risk analytical approaches in our country, overcoming the defects that subjectivity exists in credit risk measurement, expanding developing route for credit risk measurement...
In order to investigate the global financial crisis's impact on logistics industry quantitatively, this paper selects 10 logistics listed companies respectively from China and US stock market as samples, calculates the default distances of the companies based on KMV model which is first proposed by US KMV company, analyzes the trend of credit risk change from the third quarter of 2008 to the third...
In this paper, we studied the two most commonly used artificial intelligence methods (Multilayer Perceptron and Radial Basis Function network) to build the credit scoring model of applications, and analyzed the most important restraining factors of the applications of neural network which is the exponential increase in the variables bringing the model over-complex. On this basis, the author combines...
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...
Credit risk management has become a fundamental and crucial work for commercial banks. This paper studies the credit risk measurement of listed corporations by using various types of credit risk models, and analyzes their applicability in China. This paper also makes an empirical analysis to the Chinese listed corporations’ credit risk on the basis of the KMV model. Finally, several proposals on how...
In this paper, by Logit regression technology, we construct a model for recognizing and warning credit risk based on financial and non-financial ratios. The testpsilas results show that the accuracy of the model is up to 86.6%. Obversely, it can offer enterprise some supports for recognizing and warning credit risk in credit sale.
The paper proposes decision tools to be used by commercial banks in order to classify the companies applying for credits in good and bad creditors, based on the number of days of delay in payment. Probit regression and neural networks, applied to a sample of companies which delayed or not their credit reimbursements are used to orient the decision taken by the bank, consistent with its priorities,...
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