The main purpose of this paper was to use the MDA statistical technique in order to build new credit risk models for emerging markets. Today’s emerging markets are too complex to be characterized using simple financial ratios for the analysis of credit worthiness. That is why it is required to use new credit scoring models for the prediction of borrowers default. Modern developments in sophisticated statistical computer applications, as STATISTICA, have helped
and increased the possibility for broader research development and practical implementation of credit risk models.
In this thesis the MDA has proved to be a robust technique in classifying credit risks into “good” and “bad” groups on 83% correct classification on average for all 11 emerging markets. Average accuracy of “bad” companies’ classification (Type I Error) is 60% and average accuracy of “good” companies’ classification (Type II Error) is 91%. Low levels of Type I Error prediction for some emerging markets can be explained by the fact that macroeconomic conditions in the emerging markets are very volatile and some local companies tend to manipulate data for credit applications. This statement can be suggested by the fact that there are a very small number of variables, which are financial ratios, among the most frequent ones in the Top 5 list (Table 21). Only variable SolvRt (Solvency Ratio), financial ratio, has relatively high frequency of 6 in this list.
Possible reasons that the new credit scoring models are not highly accurate for some markets include:
(1)absence of a sufficient number of “good”/ “bad” companies number43, (2)companies for the analysis were not taken from the same period of
time44,
(3)companies were not segmented by size of total assets, (4)incomplete set of discriminant variables.
43 It’s good to have at least 30 companies for each group according to the research done
previously. See Moody’s (2000) p.14
It is sensible to try new sets of discriminant variables to build more accurate credit scoring models. These new variables should not be taken from the company’s books but should be qualitative in nature. For instance, it can be the region where companies are performing business activity inside a particular country or some new variables giving information about the quality of management in a particular company.
The new credit scoring model gives the opportunity to classify new applications by the level of risk more accurately in comparison with the existing FcA’s credit scoring model. This comparison was provided for the Romanian market, but the same approach should be easily implemented for the rest of the markets. This capability of the new model is very important, because it gives FcA the opportunity to decrease significantly the level of Type II Error. The new credit scoring model helps to decrease the number of variables required for the evaluation of credit applications. The existing FcA’s credit scoring model consists of 26 variables. In contrast, we can see that new credit scoring models have from 5 to 10 variables(Table 20).For example, the new credit scoring model for Bulgaria consists of 5 variables and gives the classification accuracy of 95%. The new credit scoring models, obtained in this thesis, help managers to save time required for credit applications evaluation by decreasing a number of variables for the analysis. Surely, it’s not recommended to rely just on the new credit scoring models in situations, when big amounts of credits are provided or new customers are applying for a credit. In this case, it’s better to make an additional thorough analysis of credit application by credit analysts.
Despite the limitations of the MDA, there is no doubt that credit risk models based on this statistical technique will continue to be one of the major tools in predicting credit risk in consumer lending. Altman (2002) provides analysis suggesting that his Z-score model based on the MDA is competitive with probit KMV’s EDF model that is more mathematically sophisticated.
Even though the new model does not have 100% explanatory power the results obtained give support to the MDA. The results in this thesis work showed that
the MDA should be applied to the evaluation of credit applications. We can conclude that the existing FcA’s credit scoring model can be improved utilizing the MDA. But it is also important to remember that it’s necessary to keep the new credit scoring model updated by continuously monitoring its performance to ensure the quality of credit decisions.
It is envisaged that FcA can use the new credit scoring model to gain important strategic advantage and a competitive edge over its rivals.
For any questions regarding the content of this work, you are welcome to contact the author: [email protected]
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