Statistical modelling in credit rating

In the present day, investors tend to be more prepared to make investment on exchange market to gain far more outstanding returns. There is a growing demand on credit information of companies. In spite of this, you’ll find not many of Hong Kong businesses have been assessed by credit rating agencies. As a result, U.S. non-finance corporations listed in New York Stock Exchange are adopted in model development. Numerous individual statistical methods, namely multiple discriminant analysis, ordinal logit model, multinomial logit model, ordinal probit model, and neural network, and combining forecast designed by Kamstra, Kennedy and Suan (2001) are followed to forecast S&P’s credit ratings…

In extending the Kamstra, Kennedy and Suan (2001) combined forecast approach to combine the probability forecasts in probability space; it’s found that the modified KK method in probability space outperforms the individual classification methods and the original KK combining prediction methods. Furthermore, an additional well-known credit assessment corporation – KMV Corporation, has evolved one more model to examine the default risk of public company. It’s learned that KMV model has advanced power in predicting default risk than S&P credit rating

Source: City University of Hong Kong

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